Meta‐analysed numbers needed to treat of novel antidiabetic drugs for cardiovascular outcomes

Georg Wolff; Yingfeng Lin; Cihan Akbulut; Maximilian Brockmeyer; Claudio Parco; Alexander Hoss; Alexander Sokolowski; Ralf Westenfeld; Malte Kelm; Michael Roden; Sabrina Schlesinger; Oliver Kuss

doi:10.1002/ehf2.14213

. 2022 Nov 7;10(1):552–567. doi: 10.1002/ehf2.14213

Meta‐analysed numbers needed to treat of novel antidiabetic drugs for cardiovascular outcomes

Georg Wolff ^1,^✉, Yingfeng Lin ¹, Cihan Akbulut ^2,^3,⁴, Maximilian Brockmeyer ¹, Claudio Parco ¹, Alexander Hoss ¹, Alexander Sokolowski ¹, Ralf Westenfeld ¹, Malte Kelm ^1,⁵, Michael Roden ^2,^5,^6,⁷, Sabrina Schlesinger ^2,³, Oliver Kuss ^2,^3,⁴

PMCID: PMC9871670 PMID: 36337026

Abstract

Aims

Absolute treatment effects—i.e. numbers needed to treat (NNTs)—of novel antidiabetic drugs for cardiovascular outcomes have not been comprehensively evaluated. We aimed to perform a meta‐analysis of digitalized individual patient outcomes to display and compare absolute treatment effects.

Methods and results

Individual patient time‐to‐event information from Kaplan–Meier plots of cardiovascular mortality (CM) and/or hospitalization for heart failure (HHF) endpoints from cardiovascular outcome trials (CVOTs) evaluating dipeptidyl peptidase‐4 (DPP‐4) inhibitors, glucagon‐like peptide‐1 (GLP‐1) receptor agonists, and sodium glucose transporter 2 (SGLT2) inhibitors vs. placebo were digitalized using WebPlotDigitizer 4.2 and the R code of Guyot et al.; Weibull regression models were generated, validated, and used to estimate NNT for individual trials; random‐effects meta‐analysis generated Meta‐NNT with 95% confidence intervals. Sixteen CVOTs reported time‐to‐event information (14 in primary diabetes and 2 in primary heart failure populations). Thirteen studies including 96 860 patients were meta‐analysed for CM: At the median follow‐up of 30 months, Meta‐NNTs were 178 (64 to ∞ to −223) for DPP‐4 inhibitors, 261 (158 to 745) for GLP‐1 receptor agonists, and 118 (68 to 435) for SGLT2 inhibitors. Ten studies including 96 128 patients were meta‐analysed for HHF: At the median follow‐up of 29 months, estimated Meta‐NNTs were −644 (229 to ∞ to −134) for DPP‐4 inhibitors, 441 (184 to ∞ to −1100) for GLP‐1 receptor agonists, and 126 (91 to 208) for SGLT2 inhibitors. SGLT2 inhibitors were especially effective for HHF in primary heart failure populations [Meta‐NNT 25 (19 to 39)] vs. primary diabetes populations [Meta‐NNT 233 (167 to 385)] at 16 months of follow‐up.

Conclusions

We found only modest treatment benefits of GLP‐1 receptor agonists and SGLT2 inhibitors for CM and HHF in primary type 2 diabetes mellitus populations. In primary heart failure populations, SGLT2 inhibitor benefits were substantial and comparable in efficacy to established heart failure medication.

Keywords: SGLT2 inhibitor, GLP‐1 receptor agonist, Number needed to treat, Absolute treatment effect, Meta‐analysis, Heart failure

Introduction

Cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2DM) are among the leading causes of morbidity and mortality in Europe and worldwide. ¹ , ² , ³ Heart failure is a complex secondary syndrome to both and an emerging cardiovascular epidemic, whose incidence and healthcare‐related cost continue to rise. ⁴ , ⁵

Novel antidiabetic drugs, mainly dipeptidyl peptidase‐4 (DPP‐4) inhibitors, glucagon‐like peptide‐1 (GLP‐1) receptor agonists, and sodium glucose transporter 2 (SGLT2) inhibitors, ⁶ have shown benefits for both diabetes‐related outcomes and cardiovascular endpoints in major cardiovascular outcome trials (CVOTs). ⁷ Current guidelines for the treatment of T2DM have issued respective recommendations, ⁸ , ⁹ especially for SGLT2 inhibitors and GLP‐1 receptor agonists. Recently, updated European guidelines for the treatment of heart failure with reduced ejection fraction (HFrEF) now also recommend SGLT2 inhibitors, dapagliflozin and empagliflozin, as an integral part of pharmacotherapy ¹⁰ because of their benefits for cardiovascular and heart failure endpoints ¹¹ , ¹² —regardless of T2DM. Recent findings even show benefits of SGLT2 inhibitors in heart failure with preserved ejection fraction ¹³ —different from any other tested drug class before.

As the U.S. Food and Drug Administration requires trialists to show non‐inferiority on the hazard ratio (HR) scale, CVOTs almost exclusively report relative effect estimates to describe treatment differences and meta‐analyses of these studies thus resorted to analysis of relative effects. ¹⁴ , ¹⁵ Absolute effect sizes—e.g. numbers needed to treat (NNTs)—are difficult to find in trial reports, even though guidelines recommend their use. ¹⁶ , ¹⁷ , ¹⁸ This is unfortunate, as absolute effects rather than relative measures are key to judging drug efficacy, perform cost/benefit calculations from both medical and health‐economics perspectives, and actually explain benefits to patients. Meta‐analyses of CVOTs should offer the highest class of evidence on the subject but are difficult to perform for absolute effects due to lack of data in trial reports. However, there are validated methods ¹⁹ , ²⁰ , ²¹ , ²² , ²³ to digitalize time‐to‐event information from published reports.

We here report a meta‐analysis of absolute treatment effects expressed as NNTs in the large CVOTs of DPP‐4 inhibitors, GLP‐1 receptor agonists, and SGLT2 inhibitors, focusing on the outcomes of cardiovascular mortality and hospitalization for heart failure.

Methods

Study selection and data extraction

All major randomized controlled CVOTs indicated in fig. 1 of Cefalu et al. ⁶ with comparisons of SGLT2 inhibitors, GLP‐1 receptor agonists, or DPP‐4 inhibitors to placebo were eligible for inclusion. Full texts and online supplements of the CVOTs published until September 2020 were downloaded and searched for original Kaplan–Meier plots reporting time‐to‐event information for outcomes of cardiovascular mortality and hospitalization for heart failure. Outcomes of individual patients were digitalized from Kaplan–Meier plots using WebPlotDigitizer, Version 4.2, ¹⁹ and the R code of Guyot et al. ²⁰ Both methods have been shown to be reliable and valid. ²¹ , ²² , ²³ Additionally, original HRs with 95% confidence intervals (95% CIs) for both outcomes, as well as characteristics of the study and patients, were extracted from the original study reports. All data extractions were double checked; discrepancies were discussed and resolved in the group. CVOTs without placebo group or without reporting of Kaplan–Meier plots for either one of both relevant outcomes were excluded.

Weibull fits and absolute treatment effect estimates

Parametric Weibull regression models were fitted for all studies and both outcomes separately, in order to estimate survival functions that are needed to calculate NNTs. Weibull models are parametric proportional hazards models ²⁴ and thus yield Weibull HRs for treatment effects, which can be compared with the original HRs. From the respective Weibull models, we estimated monthly probability differences (treatment − control) for being free of the event of interest from Month 1 to the respective maximal observation time of each individual trial. These probability differences were then inverted to arrive at estimates for the monthly NNT, which are thus necessarily time dependent. ²⁵

Numbers needed to treat notation

NNTs describe the number of patients that have to be treated for a certain time interval to prevent one additional outcome event in the treatment group: Positive NNT values indicate that the drug is beneficial compared with placebo (or number needed to treat to benefit); negative NNT values indicate placebo advantage (or number needed to treat to harm). Lower NNT values indicate more effective treatment, with the lowest possible value of NNT = 1 (or −1, respectively). A null effect of the treatment, corresponding to an HR of 1, is denoted by a value of infinity (∞) for the NNT. A 95% CI that does not include infinity (∞) [or −infinity (−∞), respectively] corresponds to a statistically significant absolute treatment effect (with a two‐sided α probability of 5%).

Assessment of model validity

To assess the validity of the digitalized data, we compared HRs from the original papers to those from the Weibull models by calculating intra‐class correlation coefficients. In addition, to assess the fit of the Weibull models graphically, we plotted Kaplan–Meier estimates from the digitalized data together with predicted survival curves from these models.

Meta‐analysis

To summarize NNTs overall and in the three drug classes, we used random‐effects inverse‐variance meta‐analysis. Meta‐analyses were performed separately for monthly time points. A sensitivity analysis of SGLT2 inhibitors in studies with primary T2DM vs. primary HFrEF populations for the endpoint of hospitalization for heart failure was performed. All computations were performed on the probability difference scale and only for displaying results in figures and graphs transformed to the NNT scale. We used SAS (SAS Institute Inc., Cary, NC, USA), Version 9.4, for data management and analysis.

Results

Study selection

A total of 16 major CVOTs reported original time‐to‐event information for at least one of the two outcomes and were included in the analysis: four trials on DPP‐4 inhibitors (CARMELINA, EXAMINE, SAVOR‐TIMI 53, and TECOS ²⁶ , ²⁷ , ²⁸ , ²⁹ ), six trials on GLP‐1 receptor agonists (EXSCEL, HARMONY, LEADER, PIONEER 6, REWIND, and SUSTAIN 6 ³⁰ , ³¹ , ³² , ³³ , ³⁴ , ³⁵ ), and six trials on SGLT2 inhibitors (CANVAS, CREDENCE, DAPA‐HF, EMPA‐REG Outcome, EMPEROR‐REDUCED, and VERTIS‐CV ¹¹ , ¹² , ³⁶ , ³⁷ , ³⁸ , ³⁹ ). The CAROLINA trial ⁴⁰ was excluded for not having a placebo control; the results of FREEDOM‐CVO and EMPEROR‐PRESERVED ¹³ were not yet published in full text at the time of analysis. All endpoint definitions of individual trials are listed in Supporting Information, Table S1.

Study characteristics

An overview of all trials and a description of the study populations are given in Table 1 : A total of 131 753 patients with time‐to‐event information were analysed; follow‐up times in the studies ranged between 15.5 and 65.5 months for cardiovascular mortality and between 14.3 and 46.4 months for hospitalization for heart failure. The overall median follow‐up time was 30.6 months for cardiovascular mortality and 29.2 months for hospitalization for heart failure. All trials featured high to very high cardiovascular risk patient populations. ⁴¹ They were conducted in patients with T2DM, with the exception of EMPEROR‐REDUCED ¹² and DAPA‐HF, ¹¹ which primarily included patients with heart failure, about half of them with diabetes mellitus.

Table 1.

Characteristics of included studies

Study	Journal and year	Study drug	Number of patients (n)	Median duration of follow‐up (years)	Mean age (years)	Male sex (%)	Mean BMI (kg/m²)	T2DM (%)	Mean HbA1c (%)	Mean diabetes duration (years)	Atherosclerotic cardiovascular disease (%)	Heart failure (%)	Hypertension (%)	Current smoker (%)	Mean eGFR (mL/min/1.73 m²)
DPP‐4 inhibitors
CARMELINA ²⁷	JAMA 2018	Linagliptin	6991	2.2	65.9	62.9	31.4	100	7.9	14.8	58.5	26.8	91.0	10.2	54.6
EXAMINE ²⁹	NEJM 2013	Alogliptin	5380	1.5	61.0	67.8	28.7 ^a	100	8.0	7.2 ^a	100	27.9	83.1	13.6	71.1 ^a
SAVOR‐TIMI 53 ²⁸	NEJM 2013	Saxagliptin	16 492	2.1	65.1	66.9	31.1	100	8.0	10.7	78.6	12.8	81.8	n.a.	72.6
TECOS ²⁶	NEJM 2015	Sitagliptin	14 671	3.0	65.5	70.7	30.2	100	7.2	11.6	74.0	18.0	n.a.	11.4	74.9
GLP‐1 receptor agonists
EXSCEL ³²	NEJM 2017	Exenatide	14 752	3.2	62.0	62.0	31.8	100	8.0	12.0	73.1	16.2	n.a.	11.7	76.3
HARMONY ³¹	Lancet 2018	Albiglutide	9463	1.6	64.2	69.4	32.3	100	8.7	14.2	100	20.0	86.5	16.0	79.0
LEADER ³⁵	NEJM 2016	Liraglutide	9340	3.8	64.3	64.2	32.5	100	8.7	12.9	81.4	17.9	92.4	n.a.	n.a.
PIONEER 6 ³³	NEJM 2019	Semaglutide	3183	1.3	66.0	68.4	32.3	100	8.2	14.9	84.6	n.a.	95.3	10.9	74.2
REWIND ³⁰	Lancet 2019	Dulaglutide	9901	5.4	66.2	53.7	32.3	100	7.4	10.6	31.5	8.6	93.2	14.2	75.0
SUSTAIN 6 ³⁴	NEJM 2016	Semaglutide	3297	2.1	64.6	60.7	32.6	100	8.7	13.9	60.5	23.6	92.8	n.a.	n.a.
SGLT2 inhibitors
CANVAS ³⁷	NEJM 2017	Canagliflozin	10 142	2.4	63.3	64.2	32.0	100	8.2	13.5	72.2	14.4	90.0	17.8	76.5
CREDENCE ³⁸	NEJM 2019	Canagliflozin	4401	2.6	63.0	66.1	31.3	100	8.3	15.8	50.4	14.8	96.8	14.5	56.2
DAPA‐HF ¹¹	NEJM 2019	Dapagliflozin	4744	1.5	66.3	77.6	28.2	41.8	6.5	n.a.	56.4	100	n.a.	n.a.	65.8
EMPA‐REG ³⁹	NEJM 2015	Empagliflozin	7020	3.1	63.1	71.5	30.7	100	8.1	n.a.	75.6	10.1	95.0	n.a.	74.1
EMPEROR‐REDUCED ¹²	NEJM 2020	Empagliflozin	3730	1.3	66.8	76.0	27.9	49.8	n.a.	n.a.	51.7	100	72.3	n.a.	62.0
VERTIS‐CV ³⁶	NEJM 2020	Ertugliflozin	8246	3.0	64.4	70.0	31.9	100	8.2	13.0	75.9	23.7	n.a.	n.a.	76.0

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BMI, body mass index; DPP‐4, dipeptidyl peptidase‐4; eGFR, estimated glomerular filtration rate; GLP‐1, glucagon‐like peptide‐1; HbA1c, haemoglobin A1c; n.a., not available; SGLT2, sodium glucose transporter 2.

This table shows characteristics of randomized controlled trials included in the meta‐analysis.

^{^a}

Median instead of mean.

Relative and absolute treatment effect estimates of individual trials

Thirteen studies with 4997 digitalized events from 96 860 observed patients gave information on cardiovascular mortality (Table 2 ). Ten studies with 4065 digitalized events from 96 128 observed patients reported data on hospitalization for heart failure (Table 3 ). For both outcomes, original study HRs, extracted HRs, and Weibull HRs with 95% CI are reported as relative effect measures. NNTs at 12, 24, 36, and 48 months are reported as absolute treatment effect estimates for individual trials (Tables 2 and 3 ). Additionally, monthly NNT point estimates with 95% CI for all three study drugs and both outcomes are graphically displayed in Figure 1 .

Table 2.

Effect estimates for the outcome of cardiovascular mortality

Study	Number of digitalized events (n)	Number of patients (n)	Event proportion (%)	Median duration of follow‐up (years)	Original HR [95% CI]	Extracted HR [95% CI]	Weibull HR [95% CI]	NNT [95% CI] 12 months	NNT [95% CI] 24 months	NNT [95% CI] 36 months	NNT [95% CI] 48 months
DPP‐4 inhibitors
CARMELINA ²⁷	518	6991	7.4	2.2	0.96 [0.81 to 1.14]	0.96 [0.81 to 1.14]	0.96 [0.80 to 1.13]	995 [180 to ∞ to −282]	444 [80 to ∞ to −126]	282 [51 to ∞ to −80]	—
EXAMINE ²⁹	237	5380	4.4	1.5	0.85 [0.66 to 1.10]	0.86 [0.66 to 1.11]	0.86 [0.64 to 1.08]	211 [80 to ∞ to −325]	123 [46 to ∞ to −190]	—	—
GLP‐1 receptor agonists
EXSCEL ³²	703	14 752	4.8	3.2	0.88 [0.76 to 1.02]	0.88 [0.76 to 1.02]	0.88 [0.75 to 1.01]	760 [349 to ∞ to −4279]	327 [151 to ∞ to −1886]	202 [93 to ∞ to −1168]	144 [66 to ∞ to −834]
HARMONY ³¹	252	9463	2.7	1.6	0.93 [0.73 to 1.19]	0.90 [0.70 to 1.15]	0.92 [0.69 to 1.15]	754 [192 to ∞ to −391]	358 [91 to ∞ to −186]	—	—
LEADER ³⁵	493	9340	5.3	3.8	0.78 [0.66 to 0.93]	0.77 [0.65 to 0.93]	0.78 [0.64 to 0.92]	396 [230 to 1437]	173 [101 to 602]	107 [63 to 370]	77 [45 to 264]
PIONEER 6 ³³	43	3183	1.4	1.3	0.49 [0.27 to 0.92]	0.48 [0.25 to 0.90]	0.47 [0.17 to 0.76]	138 [76 to 774]	—	—	—
REWIND ³⁰	657	9901	6.6	5.4	0.91 [0.78 to 1.06]	0.91 [0.78 to 1.06]	0.91 [0.77 to 1.05]	1662 [627 to ∞ to −2548]	634 [239 to ∞ to −980]	363 [137 to ∞ to −563]	246 [93 to ∞ to −382]
SUSTAIN 6 ³⁴	70	3297	2.1	2.1	0.98 [0.65 to 1.48]	0.94 [0.59 to 1.51]	0.94 [0.50 to 1.38]	1424 [159 to ∞ to −205]	—	—	—
SGLT2 inhibitors
CANVAS ³⁷	471	10 142	4.6	2.4	0.87 [0.72 to 1.06]	0.88 [0.73 to 1.06]	0.89 [0.73 to 1.06]	806 [309 to −1318]	379 [145 to ∞ to −620]	246 [94 to ∞ to −401]	181 [69 to ∞ to −295]
CREDENCE ³⁸	249	4401	5.7	2.6	0.78 [0.61 to 1.00]	0.77 [0.60 to 0.99]	0.77 [0.58 to 0.96]	262 [133 to 8805]	103 [53 to 2449]	61 [31 to 1395]	—
DAPA‐HF ¹¹	498	4744	10.5	1.5	0.82 [0.69 to 0.98]	0.82 [0.69 to 0.98]	0.82 [0.68 to 0.97]	82 [43 to 826]	39 [21 to 389]	—	—
EMPA‐REG ³⁹	301	7020	4.3	3.1	0.62 [0.49 to 0.77]	0.60 [0.48 to 0.75]	0.60 [0.46 to 0.74]	175 [116 to 353]	74 [50 to 143]	45 [30 to 86]	32 [22 to 61]
VERTIS‐CV ³⁶	505	8246	6.1	3.0	0.92 [0.77 to 1.11]	0.90 [0.75 to 1.09]	0.91 [0.74 to 1.07]	787 [272 to ∞ to −878]	338 [117 to ∞ to −379]	208 [72 to ∞ to −233]	149 [51 to ∞ to −167]

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DPP‐4, dipeptidyl peptidase‐4; GLP‐1, glucagon‐like peptide‐1; SGLT2, sodium glucose transporter 2.

This table shows effect estimates of individual studies for the outcome of cardiovascular mortality; depicted are raw event rates, originally reported hazard ratios (HRs), extracted HR from Kaplan–Meier plots, HR as calculated from the Weibull model, and numbers needed to treat (NNTs) for 12, 24, 36, and 48 months; 95% confidence intervals (CIs) are in brackets.

Table 3.

Effect estimates for the outcome of hospitalization for heart failure

Study	Number of digitalized events (n)	Number of patients (n)	Event proportion (%)	Median duration of follow‐up (years)	Original HR [95% CI]	Extracted HR [95% CI]	Weibull HR [95% CI]	NNT [95% CI] 12 months	NNT [95% CI] 24 months	NNT [95% CI] 36 months	NNT [95% CI] 48 months
DPP‐4 inhibitors
CARMELINA ²⁷	431	6991	6.2	2.2	0.90 [0.74 to 1.08]	0.90 [0.74 to 1.08]	0.90 [0.73 to 1.07]	317 [116 to ∞ to −440]	168 [62 to ∞ to −233]	117 [43 to ∞ to −163]	—
SAVOR‐TIMI 53 ²⁸	517	16 492	3.1	2.1	1.27 [1.07 to 1.51]	1.26 [1.06 to 1.50]	1.25 [1.03 to 1.47]	−267 [−1178 to −150]	−144 [−629 to −81]	—	—
TECOS ²⁶	439	14 671	3.0	3.0	1.00 [0.83 to 1.20]	0.98 [0.81 to 1.18]	0.98 [0.80 to 1.16]	4463 [463 to ∞ to −584]	2257 [234 to ∞ to −296]	1522 [158 to ∞ to −199]	1154 [120 to ∞ to −151]
GLP‐1 receptor agonists
EXSCEL ³²	434	14 752	2.9	3.2	0.94 [0.78 to 1.13]	0.94 [0.78 to 1.14]	0.94 [0.76 to 1.12]	2190 [536 to ∞ to −1052]	983 [241 to ∞ to −473]	619 [152 to ∞ to −297]	447 [110 to ∞ to −215]
LEADER ³⁵	463	9340	5.0	3.8	0.87 [0.73 to 1.05]	0.87 [0.72 to 1.04]	0.87 [0.71 to 1.03]	588 [257 to ∞ to −2006]	285 [125 to ∞ to −988]	188 [82 to ∞ to −654]	140 [61 to ∞ to −489]
SGLT2 inhibitors
CANVAS ³⁷	243	10 142	2.4	2.4	0.67 [0.52 to 0.87]	0.68 [0.53 to 0.88]	0.67 [0.50 to 0.84]	374 [225 to 1101]	185 [112 to 530]	123 [74 to 351]	92 [56 to 264]
DAPA‐HF ¹¹	545	4744	11.5	1.5	0.70 [0.59 to 0.83]	0.71 [0.60 to 0.84]	0.71 [0.59 to 0.83]	38 [26 to 77]	21 [14 to 43]	—	—
EMPA‐REG ³⁹	212	7020	3.0	3.1	0.65 [0.50 to 0.85]	0.63 [0.48 to 0.83]	0.63 [0.46 to 0.81]	223 [135 to 648]	105 [64 to 291]	68 [42 to 186]	50 [31 to 137]
EMPEROR‐REDUCED ¹²	552	3730	14.8	1.3	0.69 [0.59 to 0.81]	0.69 [0.59 to 0.82]	0.69 [0.58 to 0.81]	24 [16 to 44]	15 [10 to 27]	—	—
VERTIS‐CV ³⁶	229	8246	2.8	3.0	0.70 [0.54 to 0.90]	0.68 [0.52 to 0.88]	0.67 [0.50 to 0.85]	305 [178 to 1076]	150 [88 to 502]	99 [58 to 328]	74 [44 to 245]

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DPP‐4, dipeptidyl peptidase‐4; GLP‐1, glucagon‐like peptide‐1; SGLT2, sodium glucose transporter 2.

This table shows effect estimates of individual studies for the outcome of hospitalization for heart failure; depicted are raw event rates, original study hazard ratios (HRs), extracted HR from Kaplan–Meier plots, HR as calculated from the Weibull models, and numbers needed to treat (NNTs) for 12, 24, 36, and 48 months; 95% confidence intervals (CIs) are in brackets.

Numbers needed to treat (NNTs) over study follow‐up time for individual studies, for the outcome of (A) *cardiovascular mortality* and (B) *hospitalization for heart failure*. Blue: dipeptidyl peptidase‐4 inhibitors, yellow: glucagon‐like peptide‐1 receptor agonists, red: sodium glucose transporter 2 inhibitors with their pointwise 95% confidence intervals. Estimates and confidence intervals are truncated from above at 100 000.

Accuracy of data digitalization and Weibull model fits was assessed using scatterplots (Supporting Information, Figure S1) to compare the originally reported HRs to the Weibull HRs from digitalized data: Correspondence was excellent, with an intra‐class correlation of 99.4% (95% CI: 98.7–100%) for cardiovascular mortality and 99.8% (99.5–100%) for hospitalization for heart failure. Supporting Information, Figure S2 depicts Kaplan–Meier estimates of both treatment groups based on the Weibull survival curves. Again, there is no relevant divergence that might compromise computation of NNTs.

Meta‐analysis of absolute treatment effects

Results of the random‐effects inverse‐variance meta‐analysis from monthly pooled survival data from the Weibull models for individual study drugs and for both outcomes are depicted in Figure 2 : At the overall median follow‐up time of 30 months for cardiovascular mortality, estimated Meta‐NNTs were 178 (95% CI: 64 to ∞ to −223) for DPP‐4 inhibitors, 261 (95% CI: 158 to 745) for GLP‐1 receptor agonists, and 118 (95% CI: 68 to 435) for SGLT2 inhibitors. At the overall median follow‐up time of 29 months for hospitalization for heart failure, estimated Meta‐NNTs were −644 (95% CI: 229 to ∞ to −134) for DPP‐4 inhibitors, 441 (95% CI: 184 to ∞ to −1100) for GLP‐1 receptor agonists, and 126 (95% CI: 91 to 208) for SGLT2 inhibitors.

Random‐effects inverse‐variance meta‐analysis of numbers needed to treat (Meta‐NNT, with 95% confidence interval) over study follow‐up time for the outcomes of (A) *cardiovascular mortality* and (B) *hospitalization for heart failure*. Data were pooled from all studies according to study drug. Estimates and confidence intervals are truncated from above at 100 000. DPP‐4, dipeptidyl peptidase‐4; GLP‐1, glucagon‐like peptide‐1; SGLT2, sodium glucose transporter 2.

Accuracy of estimates (smallest 95% CI) was highest for GLP‐1 receptor agonists regarding cardiovascular mortality, with Meta‐NNTs of 351 (95% CI: 214 to 981; CI length 767), 205 (95% CI: 125 to 576; CI length 452), and 141 (95% CI: 86 to 389; CI length 302) after 24/36/48 months of follow‐up. Regarding hospitalization for heart failure, most accurate estimates were found for SGLT2 inhibitors, with Meta‐NNTs of 135 (95% CI: 101 to 205; CI length 105), 102 (95% CI: 73 to 167; CI length 94), and 76 (95% CI: 55 to 125; CI length 70) after 24/36/48 months of follow‐up.

Sensitivity analysis of sodium glucose transporter 2 inhibitors for hospitalization for heart failure

DAPA‐HF ¹¹ and EMPEROR‐REDUCED ¹² were conducted in primary HFrEF patient populations (Table 1 ). Their comparison to primary T2DM populations (Figure 3 ) showed remarkably higher efficacy of SGLT2 inhibitors for hospitalization for heart failure in HFrEF patients [Meta‐NNT 25 (95% CI: 19 to 39) vs. Meta‐NNT 233 (95% CI 167 to 385)] at 16 months of follow‐up.

Sensitivity analysis of numbers needed to treat (Meta‐NNT, with 95% confidence interval) with sodium glucose transporter 2 inhibitors to reduce *hospitalization for heart failure*, stratified for studies with primary type 2 diabetes mellitus (T2DM) or primary heart failure with reduced ejection fraction (HFrEF) populations. Estimates and confidence intervals are truncated from above at 100 000.

Discussion

In this meta‐analysis of absolute treatment effects of novel antidiabetic drugs in all available trials and in HFrEF subpopulations, we found that (i) GLP‐1 receptor agonists and SGLT2 inhibitors were both effective to reduce cardiovascular mortality; (ii) the most effective drug class to reduce hospitalization for heart failure was SGLT2 inhibitors, with the highest efficacy in patients with HFrEF compared with primary T2DM populations; and (iii) DPP‐4 inhibitors were not effective to reduce either endpoint.

GLP‐1 receptor agonists and SGLT2 inhibitors have received Class I recommendations in T2DM ⁸ , ⁹ and heart failure ¹⁰ guidelines for their potential to reduce CVD events and mortality; however, relevant CVOTs predominantly reported relative measures of effect (HRs, odds ratios, and risk ratios). Those normalize treatment effects to the baseline risk of the placebo population. Although this results in relatively stable results across the whole risk spectrum, results can also be misleading: A statistically highly significant relative risk reduction of 50% may turn out to be clinically irrelevant, if the baseline risk is very low for an event in the placebo group. The higher the baseline risk of a population, the more impact on absolute risk reduction can be expected from an intervention such as a study drug. Guidelines thus recommend reporting of both relative and absolute effect measures. ⁴² The latter are key for assessment of treatment efficacy, comparison to other drug classes, cost/benefit calculations, and explanation of benefits to patients.

All trials included in this analysis were rigorously designed and well‐conducted international studies with low risk of bias. ¹⁴ , ¹⁵ All trials included high to very high cardiovascular risk patient populations with long‐term T2DM and a high prevalence of atherosclerotic CVD and chronic kidney disease (Table 1 ); DAPA‐HF and EMPEROR‐REDUCED were conducted in populations with HFrEF, of whom 40–50% had diabetes mellitus. Treatment effects in this high baseline risk population were large—and meta‐analysis of almost 100 000 patients for each endpoint thus provides robust absolute treatment effect estimates.

In contrast, however, no single trial of any of the three drug classes showed a particularly high level of efficacy on cardiovascular mortality (Table 2 ): DPP‐4 inhibitors did not show any effects; GLP‐1 receptor agonists showed the lowest 4 year NNT of 77 in the LEADER trial; and SGLT2 inhibitors reached the best efficacy in DAPA‐HF (24 month NNT of 39) and EMPA‐REG (48 month NNT of 32). In comparison, the Meta‐NNT across all trials at the median follow‐up time was 261 for GLP‐1 receptor agonists and 118 for SGLT2 inhibitors. These effects of all three drug classes on cardiovascular mortality do not seem very impressive when compared on an absolute scale: Metformin as the long‐term standard of care in oral antidiabetic therapy has been estimated with a 10 year NNT of ~10–20 for major endpoints. ⁴³ Typical heart failure medication (beta‐blockers, angiotensin‐converting enzyme inhibitors, mineralocorticoid receptor agonists, sacubitril/valsartan, etc.) showed estimated 5 year NNTs for all‐cause mortality of ~8–24. ⁴⁴ , ⁴⁵ These data put GLP‐1 receptor agonists and SGLT2 inhibitors in similar efficacy categories as PCSK9 inhibitors or statins for cardiovascular mortality. ⁴⁶

Regarding hospitalization for heart failure, GLP‐1 receptor agonists and DPP‐4 inhibitors showed no effects on this endpoint, either in single trials or in meta‐analysis. SGLT2 inhibitors showed considerable effects (Meta‐NNT at median follow‐up of 126), which were especially large in the HFrEF trials DAPA‐HF (24 month NNT of 21) and EMPEROR‐REDUCED (24 month NNT of 15) and thus comparable with other established guideline‐recommended heart failure medication. ⁴⁴ , ⁴⁵ This is a substantial difference between primary HFrEF populations and primary T2DM populations (Figure 3 ), even though population characteristics other than ejection fraction were similar (Table 1 ). SGLT2 inhibitors are thus very effective for the endpoint of hospitalization for heart failure, especially in HFrEF patient populations, with smaller effects in non‐HFrEF patients (Figure 3). This is confirmed by recent findings from EMPEROR‐PRESERVED, ¹³ which found a 26 month NNT of ~31 for hospitalization for heart failure and of ~111 for cardiovascular mortality—again with 50% of the population also suffering from diabetes mellitus. Unfortunately, other trials in HFpEF are not yet available, which rendered further meta‐analysis of HFpEF futile. Further research on the underlying mechanism for these surprisingly strong effects in heart failure is ongoing. ⁴⁷ In addition, these results make SGLT2 inhibitors promising tools to reduce expenditures in heart failure care, where hospitalizations are the main driver of cost. ⁴⁸

Davies et al. previously performed an analysis of NNT for the class of GLP‐1 receptor agonists, ⁴⁹ however, not for the other two drug classes. Ludwig et al. reported NNTs for two drug classes ²⁵ at median follow‐up time points; however, they did not use original data but relied on formulas for summary data. Both groups used other software tools for data digitalization and different statistical models to arrive at NNT estimates as compared with our approach ²⁵ , ⁴⁹ —however with very similar results. Palmer et al. recently published a very large network meta‐analysis ⁵⁰ of GLP‐1 receptor agonists and SGLT2 inhibitors including 764 trials with absolute treatment effect estimates, however did not use digitalization of time‐to‐event data, and thus offered no time‐sensitive analysis. Hence, our work is the first to present absolute treatment effects in all three drug classes and offer time‐sensitive meta‐analysis of individual patient outcomes of cardiovascular mortality and hospitalization for heart failure in the large CVOTs.

Limitations

Trials were selected based on a common definition of CVOTs in diabetes mellitus ⁶ ; however, there was no systematic literature review performed. Trial and population heterogeneity introduces unavoidable bias into this analysis, as in any other meta‐analysis. This work relies on digitalized individual patient outcomes and fitted Weibull models rather than the original patient data, which renders analysis of endpoint associations with patient characteristics or other outcomes [e.g. haemoglobin A1c (HbA1c) and adverse events] impossible. However, this inherent main limitation is unavoidable, as no pooled primary data are available to perform such an analysis in a different way. To account for this and ensure validity of our methods, we performed sensitivity analyses on different levels and found that the full Weibull survival curves (from which the NNTs are directly derived) give excellent fits to the digitalized survival data; they additionally allow computation of HR and thus a comparison to the extracted data. The original studies used Cox models with proportional hazards assumptions, which—of course—might also be wrong. ⁵¹ And a parametric model additionally allows for directly estimating survival probabilities and NNTs.

As the full individual patient data were not available, it was impossible to adjust for the parallel competing risk of all‐cause mortality. Although this would probably be less of a problem with the outcome of cardiovascular mortality (because most deaths in the CVOTs would be cardiovascular deaths), this might be an issue for hospitalization for heart failure: A patient who dies in the course of the trial is no longer at risk for this endpoint. We expect the risks for dying and hospitalization for heart failure to be positively correlated, and thus, we expect treatment effects rather being estimated as too small than overestimated.

Conclusions

In this meta‐analysis of digitalized individual patient outcomes from CVOTs of novel antidiabetic drugs, we report absolute treatment benefits of GLP‐1 receptor agonists and SGLT2 inhibitors on cardiovascular mortality and hospitalization for heart failure. Although the magnitude of effect of both drugs was rather small in primary T2DM populations, SGLT2 inhibitors showed high efficacy to prevent hospitalization for heart failure in patients with reduced ejection fraction, where they were comparable in effect to established heart failure medication.

Conflicts of interest

All authors declare that there was no financial or other support from any industry or third party for the submitted work.

Unrelated to this work, OK received honoraria for biostatistical education from Berlin‐Chemie. MR received personal fees from Allergan, AstraZeneca, Bristol Myers Squibb, Eli Lilly, Fishawack Group, Gilead Sciences, Intercept Pharma, Inventiva, Novartis, Novo Nordisk, Pfizer, ProSciento, Sanofi US, and Target RWE and investigator‐initiated research support from Boehringer‐Ingelheim, Nutricia/Danone, and Sanofi‐Aventis.

Funding

There was no direct funding for this project. GW was personally supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf (No. 2018‐32 for a Clinician Scientist Track).

Supporting information

Table S1. Individual Trial Endpoint Definitions.

Figure S1. Scatterplots to compare the hazard ratios (HRs) from the original publications (x‐axis, with 95% CI) with the hazard ratios from the fitted Weibull model of the extracted data (y‐axis, with 95% CI), both for outcomes of a) Cardiovascular Mortality and b) Hospitalization for Heart Failure.

Figure S2. Kaplan–Meier plots of the Weibull model fit from extracted data, for the placebo groups (red, with 95% CI) in comparison to the treatment groups (blue, with 95% CI), both for outcomes of a) Cardiovascular Mortality and b) Hospitalization for Heart Failure.

Click here for additional data file.^{(391.4KB, docx)}

Acknowledgements

Open Access funding enabled and organized by Projekt DEAL.

Wolff, G. , Lin, Y. , Akbulut, C. , Brockmeyer, M. , Parco, C. , Hoss, A. , Sokolowski, A. , Westenfeld, R. , Kelm, M. , Roden, M. , Schlesinger, S. , and Kuss, O. (2023) Meta‐analysed numbers needed to treat of novel antidiabetic drugs for cardiovascular outcomes. ESC Heart Failure, 10: 552–567. 10.1002/ehf2.14213.

Georg Wolff and Yingfeng Lin contributed equally to this work.

References

1. Timmis A, Townsend N, Gale CP, Torbica A, Lettino M, Petersen SE, Mossialos EA, Maggioni AP, Kazakiewicz D, May HT, de Smedt D, Flather M, Zuhlke L, Beltrame JF, Huculeci R, Tavazzi L, Hindricks G, Bax J, Casadei B, Achenbach S, Wright L, Vardas P, European Society of Cardiology , Mimoza L, Artan G, Aurel D, Chettibi M, Hammoudi N, Sisakian H, Pepoyan S, Metzler B, Siostrzonek P, Weidinger F, Jahangirov T, Aliyev F, Rustamova Y, Manak N, Mrochak A, Lancellotti P, Pasquet A, Claeys M, Kušljugić Z, Dizdarević Hudić L, Smajić E, Tokmakova MP, Gatzov PM, Milicic D, Bergovec M, Christou C, Moustra HH, Christodoulides T, Linhart A, Taborsky M, Hansen HS, Holmvang L, Kristensen SD, Abdelhamid M, Shokry K, Kampus P, Viigimaa M, Ryödi E, Niemelä M, Rissanen TT, le Heuzey JY, Gilard M, Aladashvili A, Gamkrelidze A, Kereselidze M, Zeiher A, Katus H, Bestehorn K, Tsioufis C, Goudevenos J, Csanádi Z, Becker D, Tóth K, Jóna Hrafnkelsdóttir Þ, Crowley J, Kearney P, Dalton B, Zahger D, Wolak A, Gabrielli D, Indolfi C, Urbinati S, Imantayeva G, Berkinbayev S, Bajraktari G, Ahmeti A, Berisha G, Erkin M, Saamay A, Erglis A, Bajare I, Jegere S, Mohammed M, Sarkis A, Saadeh G, Zvirblyte R, Sakalyte G, Slapikas R, Ellafi K, el Ghamari F, Banu C, Beissel J, Felice T, Buttigieg SC, Xuereb RG, Popovici M, Boskovic A, Rabrenovic M, Ztot S, Abir‐Khalil S, van Rossum AC, Mulder BJM, Elsendoorn MW, Srbinovska‐Kostovska E, Kostov J, Marjan B, Steigen T, Mjølstad OC, Ponikowski P, Witkowski A, Jankowski P, Gil VM, Mimoso J, Baptista S, Vinereanu D, Chioncel O, Popescu BA, Shlyakhto E, Oganov R, Foscoli M, Zavatta M, Dikic AD, Beleslin B, Radovanovic MR, Hlivák P, Hatala R, Kaliská G, Kenda M, Fras Z, Anguita M, Cequier Á, Muñiz J, James S, Johansson B, Platonov P, Zellweger MJ, Pedrazzini GB, Carballo D, Shebli HE, Kabbani S, Abid L, Addad F, Bozkurt E, Kayıkçıoğlu M, Erol MK, Kovalenko V, Nesukay E, Wragg A, Ludman P, Ray S, Kurbanov R, Boateng D, Daval G, de Benito Rubio V, Sebastiao D, de Courtelary PT, Bardinet I. European Society of Cardiology: Cardiovascular Disease Statistics 2019. Eur Heart J. 2019; 41: 12–85. [DOI] [PubMed] [Google Scholar]
2. WHO . Cardiovascular diseases—key facts [Online source]. 2021. Available from: https://www.who.int/news‐room/fact‐sheets/detail/cardiovascular‐diseases‐(cvds). Accessed Dec 17, 2021
3. WHO . The top 10 causes of death [Online source]. 2020. Available from: https://www.who.int/news‐room/fact‐sheets/detail/the‐top‐10‐causes‐of‐death. Accessed Dec 17, 2021
4. Braunwald E. Cardiovascular medicine at the turn of the millennium: triumphs, concerns, and opportunities. N Engl J Med. 1997; 337: 1360–1369. [DOI] [PubMed] [Google Scholar]
5. Roger VL. Epidemiology of heart failure. Circ Res. 2021; 128: 1421–1434. [DOI] [PubMed] [Google Scholar]
6. Cefalu WT, Kaul S, Gerstein HC, Holman RR, Zinman B, Skyler JS, Green JB, Buse JB, Inzucchi SE, Leiter LA, Raz I, Rosenstock J, Riddle MC. Cardiovascular outcomes trials in type 2 diabetes: where do we go from here? Reflections from a Diabetes Care editors' expert forum. Diabetes Care. 2018; 41: 14–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. U.S. Food and Drug Administration . Guidance for industry: diabetes mellitus—evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes [Internet]. 2008.
8. ADA . 9. Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes—2021. Diabetes Care. 2021; 44: S111–s24. [DOI] [PubMed] [Google Scholar]
9. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, Federici M, Filippatos G, Grobbee DE, Hansen TB, Huikuri HV, Johansson I, Jüni P, Lettino M, Marx N, Mellbin LG, Östgren CJ, Rocca B, Roffi M, Sattar N, Seferović PM, Sousa‐Uva M, Valensi P, Wheeler DC, ESC Scientific Document Group , Piepoli MF, Birkeland KI, Adamopoulos S, Ajjan R, Avogaro A, Baigent C, Brodmann M, Bueno H, Ceconi C, Chioncel O, Coats A, Collet JP, Collins P, Cosyns B, di Mario C, Fisher M, Fitzsimons D, Halvorsen S, Hansen D, Hoes A, Holt RIG, Home P, Katus HA, Khunti K, Komajda M, Lambrinou E, Landmesser U, Lewis BS, Linde C, Lorusso R, Mach F, Mueller C, Neumann FJ, Persson F, Petersen SE, Petronio AS, Richter DJ, Rosano GMC, Rossing P, Rydén L, Shlyakhto E, Simpson IA, Touyz RM, Wijns W, Wilhelm M, Williams B, Aboyans V, Bailey CJ, Ceriello A, Delgado V, Federici M, Filippatos G, Grobbee DE, Hansen TB, Huikuri HV, Johansson I, Jüni P, Lettino M, Marx N, Mellbin LG, Östgren CJ, Rocca B, Roffi M, Sattar N, Seferović PM, Sousa‐Uva M, Valensi P, Wheeler DC, Windecker S, Aboyans V, Baigent C, Collet JP, Dean V, Delgado V, Fitzsimons D, Gale CP, Grobbee DE, Halvorsen S, Hindricks G, Iung B, Jüni P, Katus HA, Landmesser U, Leclercq C, Lettino M, Lewis BS, Merkely B, Mueller C, Petersen SE, Petronio AS, Richter DJ, Roffi M, Shlyakhto E, Simpson IA, Sousa‐Uva M, Touyz RM, Zelveian PH, Scherr D, Jahangirov T, Lazareva I, Shivalkar B, Naser N, Gruev I, Milicic D, Petrou PM, Linhart A, Hildebrandt P, Hasan‐Ali H, Marandi T, Lehto S, Mansourati J, Kurashvili R, Siasos G, Lengyel C, Thrainsdottir IS, Aronson D, di Lenarda A, Raissova A, Ibrahimi P, Abilova S, Trusinskis K, Saade G, Benlamin H, Petrulioniene Z, Banu C, Magri CJ, David L, Boskovic A, Alami M, Liem AH, Bosevski M, Svingen GFT, Janion M, Gavina C, Vinereanu D, Nedogoda S, Mancini T, Ilic MD, Fabryova L, Fras Z, Jiménez‐Navarro MF, Norhammar A, Lehmann R, Mourali MS, Ural D, Nesukay E, Chowdhury TA. 2019 ESC Guidelines on diabetes, pre‐diabetes, and cardiovascular diseases developed in collaboration with the EASD: the Task Force for diabetes, pre‐diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and the European Association for the Study of Diabetes (EASD). Eur Heart J. 2019; 41: 255–323. [DOI] [PubMed] [Google Scholar]
10. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021; 42: 3599–3726. [DOI] [PubMed] [Google Scholar]
11. McMurray JJV, Solomon SD, Inzucchi SE, Køber L, Kosiborod MN, Martinez FA, et al. Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med. 2019; 381: 1995–2008. [DOI] [PubMed] [Google Scholar]
12. Packer M, Anker SD, Butler J, Filippatos G, Pocock SJ, Carson P, Januzzi J, Verma S, Tsutsui H, Brueckmann M, Jamal W, Kimura K, Schnee J, Zeller C, Cotton D, Bocchi E, Böhm M, Choi DJ, Chopra V, Chuquiure E, Giannetti N, Janssens S, Zhang J, Gonzalez Juanatey JR, Kaul S, Brunner‐la Rocca HP, Merkely B, Nicholls SJ, Perrone S, Pina I, Ponikowski P, Sattar N, Senni M, Seronde MF, Spinar J, Squire I, Taddei S, Wanner C, Zannad F. Cardiovascular and renal outcomes with empagliflozin in heart failure. N Engl J Med. 2020; 383: 1413–1424. [DOI] [PubMed] [Google Scholar]
13. Anker SD, Butler J, Filippatos G, Ferreira JP, Bocchi E, Böhm M, Brunner–la Rocca HP, Choi DJ, Chopra V, Chuquiure‐Valenzuela E, Giannetti N, Gomez‐Mesa JE, Janssens S, Januzzi JL, Gonzalez‐Juanatey JR, Merkely B, Nicholls SJ, Perrone SV, Piña IL, Ponikowski P, Senni M, Sim D, Spinar J, Squire I, Taddei S, Tsutsui H, Verma S, Vinereanu D, Zhang J, Carson P, Lam CSP, Marx N, Zeller C, Sattar N, Jamal W, Schnaidt S, Schnee JM, Brueckmann M, Pocock SJ, Zannad F, Packer M. Empagliflozin in heart failure with a preserved ejection fraction. N Engl J Med. 2021; 385: 1451–1461. [DOI] [PubMed] [Google Scholar]
14. Kristensen SL, Rørth R, Jhund PS, Docherty KF, Sattar N, Preiss D, Køber L, Petrie MC, McMurray JJV. Cardiovascular, mortality, and kidney outcomes with GLP‐1 receptor agonists in patients with type 2 diabetes: a systematic review and meta‐analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol. 2019; 7: 776–785. [DOI] [PubMed] [Google Scholar]
15. Zelniker TA, Wiviott SD, Raz I, Im K, Goodrich EL, Bonaca MP, Mosenzon O, Kato ET, Cahn A, Furtado RHM, Bhatt DL, Leiter LA, McGuire DK, Wilding JPH, Sabatine MS. SGLT2 inhibitors for primary and secondary prevention of cardiovascular and renal outcomes in type 2 diabetes: a systematic review and meta‐analysis of cardiovascular outcome trials. Lancet. 2019; 393: 31–39. [DOI] [PubMed] [Google Scholar]
16. Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010; 340: c332. [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R, Brozek J, Norris S, Meerpohl J, Djulbegovic B, Alonso‐Coello P, Post PN, Busse JW, Glasziou P, Christensen R, Schünemann HJ. GRADE guidelines: 12. Preparing summary of findings tables—binary outcomes. J Clin Epidemiol. 2013; 66: 158–172. [DOI] [PubMed] [Google Scholar]
18. Sciences TAoM . Sources of evidence for assessing the safety, efficacy and effectiveness of medicines [Online source]. 2017. Available from: https://acmedsciacuk/file‐download/86466482. Accessed Oct 10, 2020
19. Rohatgi A. WebPlotDigitizer [Online source]. 2019. Available from: https://automerisio/WebPlotDigitizer/. Accessed Oct 10, 2020
20. Guyot P, Ades AE, Ouwens MJNM, Welton NJ. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan‐Meier survival curves. BMC Med Res Methodol. 2012; 12: 9. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Drevon D, Fursa SR, Malcolm AL. Intercoder reliability and validity of WebPlotDigitizer in extracting graphed data. Behav Modif. 2017; 41: 323–339. [DOI] [PubMed] [Google Scholar]
22. Moeyaert M, Maggin D, Verkuilen J. Reliability, validity, and usability of data extraction programs for single‐case research designs. Behav Modif. 2016; 40: 874–900. [DOI] [PubMed] [Google Scholar]
23. Saluja R, Cheng S, Delos Santos KA, Chan KKW. Estimating hazard ratios from published Kaplan‐Meier survival curves: a methods validation study. Res Synth Methods. 2019; 10: 465–475. [DOI] [PubMed] [Google Scholar]
24. Collett D. Modelling Survival Data in Medical Research, 3rd ed. Chapman and Hall/CRC; 2015. p 548. [Google Scholar]
25. Ludwig L, Darmon P, Guerci B. Computing and interpreting the Number Needed to Treat for Cardiovascular Outcomes Trials: perspective on GLP‐1 RA and SGLT‐2i therapies. Cardiovasc Diabetol. 2020; 19: 65. [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Green JB, Bethel MA, Armstrong PW, Buse JB, Engel SS, Garg J, Josse R, Kaufman KD, Koglin J, Korn S, Lachin JM, McGuire DK, Pencina MJ, Standl E, Stein PP, Suryawanshi S, van de Werf F, Peterson ED, Holman RR. Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2015; 373: 232–242. [DOI] [PubMed] [Google Scholar]
27. Rosenstock J, Perkovic V, Johansen OE, Cooper ME, Kahn SE, Marx N, Alexander JH, Pencina M, Toto RD, Wanner C, Zinman B, Woerle HJ, Baanstra D, Pfarr E, Schnaidt S, Meinicke T, George JT, von Eynatten M, McGuire DK, for the CARMELINA Investigators . Effect of linagliptin vs placebo on major cardiovascular events in adults with type 2 diabetes and high cardiovascular and renal risk: the CARMELINA randomized clinical trial. JAMA. 2019; 321: 69–79. [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Scirica BM, Bhatt DL, Braunwald E, Steg PG, Davidson J, Hirshberg B, Ohman P, Frederich R, Wiviott SD, Hoffman EB, Cavender MA, Udell JA, Desai NR, Mosenzon O, McGuire DK, Ray KK, Leiter LA, Raz I. Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes mellitus. N Engl J Med. 2013; 369: 1317–1326. [DOI] [PubMed] [Google Scholar]
29. White WB, Cannon CP, Heller SR, Nissen SE, Bergenstal RM, Bakris GL, Perez AT, Fleck PR, Mehta CR, Kupfer S, Wilson C, Cushman WC, Zannad F, EXAMINE Investigators . Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N Engl J Med. 2013; 369: 1327–1335. [DOI] [PubMed] [Google Scholar]
30. Gerstein HC, Colhoun HM, Dagenais GR, Diaz R, Lakshmanan M, Pais P, Probstfield J, Riesmeyer JS, Riddle MC, Rydén L, Xavier D, Atisso CM, Dyal L, Hall S, Rao‐Melacini P, Wong G, Avezum A, Basile J, Chung N, Conget I, Cushman WC, Franek E, Hancu N, Hanefeld M, Holt S, Jansky P, Keltai M, Lanas F, Leiter LA, Lopez‐Jaramillo P, Cardona Munoz EG, Pirags V, Pogosova N, Raubenheimer PJ, Shaw JE, Sheu WHH, Temelkova‐Kurktschiev T, Abella M, Alebuena A, Almagro S, Amoroso E, Anadon P, Andreu E, Aristimuño G, Arzadun M, Barbieri M, Barcudi R, Bartolacci I, Bolobanich G, Bordonava A, Bustamante Labarta M, Bustos B, Caccavo A, Camino A, Cantero M, Carignano M, Cartasegna L, Cipullo M, Commendatore V, Conosciuto V, Costamagna O, Crespo C, Cuello J, Cuneo C, Cusimano S, Dean S, Dituro C, Dominguez A, Farah M, Fernandez A, Fernandez F, Ferrari A, Flammia P, Fuentealba J, Gallardo KB, Garcia C, Garcia Duran R, Garrido M, Gavicola R, Gerbaudo C, Gilli G, Giotto AP, Godoy Bolzán P, Gomez Vilamajo O, Guerlloy F, Guridi C, Gutierrez Garrido N, Hasbani E, Hermida S, Hominal M, Hrabar A, Ingaramo A, Izzicupo A, Krynski M, Lagrutta M, Lanchiotti P, Langhe M, Leonard V, Llanos J, Lopez Santi R, Lowenstein J, Luquez C, Mackinnon I, Mana M, Manzur S, Marino J, Martella C, Martinez R, Matias R, Matkovich J, Meritano M, Montaña O, Mulazzi M, Ochoa J, Paterlini G, Pelagagge M, Peralta Lopez ME, Prado A, Pruyas L, Racca M, Ricotti C, Rodriguez C, Romero Vidomlansky M, Ronderos R, Sadowski AL, Sala J, Sánchez A, Santoro A, Schiavi L, Sein M, Sernia V, Serra L, Sicer M, Smith T, Soso L, Sposetti G, Steinacher A, Stival J, Tedesco J, Tonin H, Tortolo M, Ulla M, Vallejos J, Vico M, Virgillito L, Visco V, Vogel D, Waisman F, Zaidman C, Zucchiatti N, Badshah I, Cohen N, Colman P, Colquhoun D, Davis T, Fourlanos S, Fulcher G, Hamlyn J, Haywood C, Hocking S, Huchinson M, Jeffries W, Kyl M, Lo C, Mah PM, Makepeace A, Marope D, Nanayakkar N, Nankervis A, Palmer N, Palolus B, Pillai S, Price S, Price S, Proietto J, Reutens A, Rodrigo N, Sheikh A, Smith G, So M, Soldatos G, Stuckey B, Sumithran P, Teede H, Vora P, Williams L, Abib E, Adão Poço C, Alves ÉF, Andreatta Bernardi Barea J, Avezum Oliveira L, Castro DLC, Correa da Cruz I, Costa M, Cruz I, Cunha S, da Silva MAV, de Carvalho Camara Bona R, de Paula B, Eliaschewitz F, Fazolli G, Ferreira Filho CA, Fortes J, França C, Franco DR, Genestreti PR, Giorgeto F, Gonçalves RM, Grossman ME, Henrique Marcelino AC, Hernandes M, Horta A, Jaeger C, Kaneblai M, Kauffman Rutenberg C, Kerr Saraiva JF, Lemos MA, Maia L, Manenti ER, Marques M, Melissa Valerio C, Moreira R, Mothé F, Mouco OM, Moura P, Moura Jorge JC, Nakashima C, Nakazone M, Napoli T, Nunes C, Nunes Salles JE, Oliveira K, Oliveira M, Pantano GS, Petri F, Piazza L, Pires AC, Pizzato P, Prata S, Precoma D, Rech R, Reis G, Reis H, Resende E, Ribas Fortes J, Rodovalho S, Rossi dos Santos F, Salles JE, Sampaio CR, Santos T, Santos dos Santos V, Silva e Quadros T, Silveira D, Siqueira KN, Teireira M, Uehara M, Valerio C, Vianna H, Vidotti MH, Visconti GL, Zanella MT, Andreeva V, Borisov R, Botushanov N, Dimitrov G, Dimova K, Dragoychev T, Grigorova V, Gushterova V, Ivanov I, Kocelova T, Kurktschiev D, Miletieva M, Nenkova‐Gugusheva N, Pancheva R, Pavlova M, Raev D, Spasova V, Stoikov A, Troev D, Yanev T, Yoncheva‐Mihaylova M, Abitbol A, Ajala B, Alguwaihes A, Ardilouze JL, Aris‐Jilwan N, Arnaout A, Aronson R, Aslam N, Babin S, Bailargeon JP, Bailey A, Bajaj H, Beauchesne C, Beca S, Belanger A, Bell A, Bellabarba D, Berard L, Berenbaum B, Bergeron V, Berlingieri J, Bernier F, Bishara P, Blank D, Blumer I, Brault S, Breton D, Carpentier A, Cha J, Chandra P, Chiasson JL, Conway JR, Couture G, Couture N, Dagenais G, Datta D, D'Ignazio G, Dumas R, Fay D, Frechette A, Frenette L, Fung D, Gagnon N, Galter M, Garon J, Gauthier JS, Geadah C, Gilbert J, Girard R, Goldenberg R, Grossman LD, Gupta N, Halle JP, Hivert MF, Houde G, Houlden R, Hramiak I, Jablonski T, Jain AJ, Khandwala H, Khosla M, Lachance C, Laflamme E, Langlois MF, Larivee L, Liutkus J, Lochnan H, Malik S, McDonald C, Mehta P, Mihailidis J, Milot A, Narula P, Nault P, Nayar A, Nisker W, Ouellet G, Palardy J, Patel M, Paul T, Pedersen S, Perron P, Pesant MH, Poirier P, Poulin MC, Punthakee Z, Rehman W, Ross S, Sagar P, Saliba N, Sandler S, Schiffrin A, Schlosser R, Seth‐Sharma A, Sherman M, Sionit D, Sivakumar T, Soto J, St‐Amour E, Steen O, Sussman J, Telner A, Tobe S, Twum‐Barima DY, van Zanten A, VanRossum N, Vecchiarelli J, Ward R, Wessengel J, Weisnagel S, Wilderman I, Woo V, Yakubovich N, Yale JF, Yared Z, Acevedo M, Aguirre ML, Aizman A, Barroso MS, Cobos L, Danin Vargas A, Descalzi B, Godoy G, Grumberg E, Lahsen R, Larenas G, Ortiz E, Paredes J, Potthoff S, Retamal E, Rojas L, Salgado M, Santibanez C, Solis C, Stokins B, Accini J, Acebedo J, Agudelo Baena LM, Alarcon S, Angel J, Arcos E, Aroca Martinez M, Atuesta L, Balaguera J, Ballestas D, Barrera SI, Barrios Reyes R, Bayona A, Bermudez A, Bernal DZ, Blanquicett M, Bravo V, Bueno W, Burbano Delgado A, Cadena A, Cadena A, Caicedo S, Celemin C, Consuegra R, Contreras Pimienta C, Corredor KJ, Cure C, de la Hoz Rueda LD, Delgado E, Diaz S, Diego M, Donado A, Encinales Sanabria W, Escobar J, Escorcia G, Forero L, Fuentes L, Garcia M, Garcia Lozada H, Garcia Ortiz L, Giraldo A, Gomez Gonzalez L, Granada J, Gutierrez C, Henao N, Hernandez E, Herrera Uejbe OM, Higuera Cobos JD, Ibarra Gómez J, Jaimes EH, Jaramillo M, Jaramillo N, Jaramillo Gomez C, Jaramillo Sanchez M, Jarava Durán I, Lopez Ceballos C, Madrid C, María Amastha E, Mercado J, Molina DI, Molina Soto J, Montoya C, Morales A, Muñoz C, Orozco LA, Osorio O, Palmera Sanchez JM, Peña A, Perez J, Perez Agudelo J, Pérez Amador G, Pertuz C, Posada I, Puerta C, Quintero A, Quiroz D, Rendon C, Reyes A, Reyes A, Ripoll D, Rivera C, Rocha M, Rodriguez JF, Rodriguez Villanueva KA, Rodriguez Zabala JE, Rojas S, Romero M, Rosero R, Rosillo Cardenas AR, Rueda L, Sanchez G, Sanchez T, Sotomayor Herazo A, Suarez M, Torres M, Trujillo F, Urina M, van Strahlen L, Velandia C, Velasquez Guzman C, Velazquez E, Vidal Prada T, Yepez Alvaran JP, Zarate D, Andelova J, Benesova R, Buzova B, Cech V, Chodova I, Choura M, Dufka A, Gamova A, Gorgol J, Hala T, Havlova H, Hlavkova D, Horanska P, Ilcisin‐Valova J, Jenickova P, Jerabek O, Kantorova I, Kolomaznikova K, Kopeckova I, Kopeckova M, Linhart K, Linhart T, Malecha J, Malicherova E, Neubauerova D, Oznerova M, Partys R, Pederzoliova E, Petrusova M, Prymkova V, Racicka E, Reissova I, Roderova E, Stanek L, Striova A, Svarcova D, Svoboda P, Szeghy Malicharova E, Urge J, Vesely L, Wasserburger B, Wasserburgerova H, Zahumensky E, Zamrazil V, Alawi H, Anastasiadis E, Axthelm E, Bieler T, Buhrig C, Degtyareva E, Dellanna F, Derwahl KM, Diessel S, Dogiami B, Dorn‐Weitzel K, Ernst M, Faulmann G, Fetscher B, Forst T, Freyer‐Lahres G, Funke K, Ganz X, Gleixner C, Hanefeld C, Heinrichs S, Helleberg S, Henkel E, Hetzel GR, Hoffmann C, Jacob F, Jacob S, John F, Jonczyk A, Kamke W, Klein C, Kleinhardt M, Kleophas W, Kosch C, Kreutzmann K, Kühn A, Lee‐Barkey YH, Lier A, Maatouk S, Minnich J, Mitry M, Muessig I, Nicula D, Niemann M, Nothroff J, Ott P, Pfuetzner A, Pfützner A, Pistrosch F, Pohl W, Prochazkova Z, Retkowska M, Rosin H, Sachsenheimer D, Samer H, Sanuri M, Schaefer A, Schaper F, Schulze ED, Schulze M, Schumann M, Segiet T, Sowa V, Stahl HD, Steinfeldt F, Teige M, Trieb B, Tschoepe D, Uebel P, Warken B, Weigmann I, Weyland K, Wilhelm K, Balo T, Balsay M, Bende I, Bezzegh K, Birkus Z, Buday B, Csomai M, Deak L, Dezso E, Faludi P, Faluvegi M, Fazekas I, Feher A, Fejer C, Finta E, Fulcz A, Gaal Z, Gurzo M, Hati K, Herczeg G, Jozsef I, Juhasz M, Keltai K, Koranyi L, Kulcsar E, Kun K, Laczko A, Literáti‐Nagy B, Mezo I, Mileder M, Moricz I, Nagy K, Nagybaczoni B, Nemeth C, Oze A, Pauer J, Peterfai E, Polocsanyi B, Poor F, Reiber I, Salamon C, Sebestyen J, Torok I, Tuu M, Varga A, Vass V, Ahn CM, Ahn C, ByungWon P, Chang HJ, Chang K, Choi EY, Choi HS, Chung JW, Hong BK, Hong YJ, Hyon MS, Jeong MH, Kang S, Kim BK, Kim JH, Kim JH, Kim KS, Kim KS, Kim MH, Kim PJ, Kim SK, Kim YS, Kim YK, Koh YS, Kwon HM, Lee BK, Lee BW, Lee JB, Lee MM, Lim YM, Min PK, Park JS, Park J, Park KH, Park S, Pyun WB, Rim SJ, Ryu DR, Seo HS, Seung KB, Shin DH, Sim DS, Yoon YW, Andersone I, Babicka K, Balcere I, Barons R, Capkovska I, Geldnere K, Grigane I, Jegere B, Lagzdina I, Mora L, Pastare S, Ritenberga R, Romanova J, Saknite I, Sidlovska N, Sokolova J, Steina S, Strizko I, Teterovska D, Vizina B, Barsiene L, Belozariene G, Daugintyte‐Petrusiene L, Drungiliene N, Garsviene N, Grigiene A, Grizas V, Jociene V, Kalvaitiene D, Kaupiene J, Kavaliauskiene J, Kozloviene D, Lapteva I, Maneikiene B, Marcinkeviciene J, Markauskiene V, Meiluniene S, Norkus A, Norviliene R, Petrenko V, Radzeviciene R, Sakalyte G, Urbonas G, Urbutiene S, Vasiliauskas D, Velickiene D, Aguilar C, Alcocer M, Avalos‐Ramirez JA, Banda‐Elizondo R, Bricio‐Ramirez R, Cardenas Mejia K, Cavazos F, Chapa J, Cienfuegos E, de la Peña A, de la Peña Topete G, de los Rios Ibarra MO, Elias D, Flores‐Moreno C, Garcia Hernandez P, Gonzalez LG, Guerra Moya RL, Guerra‐Lopez A, Hernandez Baylon R, Herrera Colorado C, Herrera‐Marmolejo M, Islas‐Palacios N, Lopez E, Lopez F, Lopez Alvarado A, Luna Ceballos RI, Morales Villegas E, Moreno‐Virgen G, Parra Perez RL, Pascoe Gonzalez S, Peralta‐Cantu I, Previn R, Ramirez R, Ramirez R, Ramos Zavala MG, Rodriguez M, Salgado‐Sedano R, Sanchez‐Aguilar AC, Santa Rosa Franco E, Sauque‐Reyna L, Suarez Otero R, Torres I, Velarde‐Harnandez E, Villagordoa J, Villeda‐Espinoza E, Vital‐Lopez J, Zavala‐ Bello CJ, Baker J, Barrington‐Ward E, Brownless T, Carroll R, Carson S, Choe M, Corin A, Corley B, Cutfield R, Dalaman N, Dixon P, Drury P, Dyson K, Florkowski C, Ford M, Frengley W, Helm C, Katzen C, Kerr J, Khanolkar M, Kim D, Koops R, Krebs J, Leikis R, Low K, Luckey A, Luke R, Macaulay S, Marks R, McNamara C, Millar‐Coote D, Miller S, Mottershead N, Reid J, Robertson N, Rosen I, Rowe D, Schmiedel O, Scott R, Sebastian J, Sheahan D, Stiebel V, Ternouth I, Tofield C, Venter D, Williams M, Williams M, Wu F, Young S, Arciszewska M, Bochenek A, Borkowski P, Borowy P, Chrzanowski T, Czerwinski E, Dwojak M, Grodzicka A, Janiec I, Jaruga J, Jazwinska‐Tarnawska EK, Jedynasty K, Juzwiak‐Czapiewska D, Karczewicz‐Janowska J, Konieczny J, Konieczny M, Korol M, Kozina M, Krzyzagorska E, Kucharczyk‐Petryka E, Laz R, Majchrzak A, Mrozowska Z, Mularczyk M, Nowacka E, Peczynska J, Petryka R, Pietrzak R, Pisarczyk‐Wiza D, Rozanska A, Ruzga Z, Rzeszotarska E, Sacha M, Sekulska M, Sidorowicz‐Bialynicka A, Stasinska T, Strzelecka‐Sosik A, Swierszcz T, Szymkowiak KM, Turowska O, Wisniewska K, Wiza M, Wozniak I, Zelazowska K, Ziolkowska‐Gawron B, Zytkiewicz‐Jaruga D, Albota A, Alexandru C, Avram R, Bala C, Barbonta D, Barbu R, Braicu D, Calutiu N, Catrinoiu D, Cerghizan A, Ciorba A, Craciun A, Doros R, Duma L, Dumitrache A, Ferariu I, Ferician Moza A, Ghergan A, Ghise G, Graur M, Gribovschi M, Mihai B, Mihalache L, Mihalcea M, Mindrescu N, Morosanu M, Morosoanu A, Mota M, Moza A, Nafornita V, Natea N, Nicodim S, Nita C, Onaca A, Onaca M, Pop C, Pop L, Popa A, Popescu A, Pruna L, Roman G, Rosu M, Sima A, Sipciu D, Sitterli‐Natea CN, Szilagyi I, Tapurica M, Tase A, Tutescu AC, Vanghelie L, Verde I, Vlad A, Zarnescu M, Akhmetov R, Allenova I, Avdeeva I, Baturina O, Biserova I, Bokovin N, Bondar I, Burova N, Chufeneva G, Chumachek E, Demidova M, Demin A, Drobysheva V, Egorova I, Esenyan L, Gelig E, Gilyarevsky S, Golshmid M, Goncharov A, Gorbunova A, Gordeev I, Gorelysheva V, Goryunova T, Grebenshchikova I, Ilchenko R, Ivannikova M, Karabalieva S, Karpeeva J, Khaykina E, Kobalava Z, Kononenko I, Korolik O, Korshunova A, Kostenko V, Krasnopevtseva I, Krylova L, Kulkova P, Kuzmina I, Ledyaeva A, Levashov S, Lokhovinina N, Lvov V, Martirosyan N, Nedogoda S, Nilk R, Osmolovskaya Y, Panov A, Paramonova O, Pavlova E, Pekareva E, Petunina N, Ponamareva S, Reshedko G, Salasyuk A, Sepkhanyan M, Serebrov A, Shabelnikova O, Skvortsov A, Smirnova O, Spiridonova O, Strogova S, Taratukhin E, Tereschenko S, Trukhina L, Tsarkova O, Tsoma V, Tumarov F, Tyan N, Tyurina T, Villevalde S, Yankovaya E, Zarutskaya L, Zenkova E, Badat A, Bester F, Blignaut S, Blom D, Booysen S, Boyd W, Brice B, Brown S, Burgess L, Cawood R, Coetzee K, Conradie H, Cronje T, de Jong D, Ellis G, Emanuel S, Engelbrecht I, Foulkes S, Fourie D, Gibson G, Govender T, Hansa S, Hemus AC, Hendricks F, Heradien M, Holmgren C, Hoosain Z, Horak E, Howard J, Immink I, Janari E, Jivan D, Klusmann K, Labuschagne W, Lai YY, Latiff G, Lombaard J, Lottering H, Meeding R, Middlemost S, Mitha H, Mitha I, Mkhwanazi S, Moodley R, Murray A, Musungaie D, Osman Y, Peacey K, Pillay‐Ramaya L, Pretorius C, Prozesky H, Sarvan M, Scholtz E, Sebesteny A, Skinner B, Skriker M, Smit M, Stapelberg AM, Swanepoel N, Urbach D, van Aswegen D, van Zyl F, van Zyl L, Venter E, Wadvalla S, Wing J, Wolmarans K, Abreu C, Aguilà P, Aguilera E, Alonso N, Alvarez C, Cajas P, Castro JC, Codinachs R, Contreras J, Coves MJ, Fajardo C, Ferrer JC, Font N, Garcia M, Gil MA, Gomez F, Gomez LA, Gonzalbez J, Griera JL, Masmiquel L, Mauricio D, Narejos Perez S, Nicolau JA, Noheda Contreras O, Olivan J, Olivares J, Ortega E, Pellitero S, Pertusa S, Rius F, Rodriguez I, Sánchez‐Juan C, Santos D, Soldevila B, Subias D, Terns M, Trescoli C, Vilaplana J, Villanueva A, Albo J, Antus K, Axelsson M, Bergström L, Binsell‐Gerdin E, Boman K, Botond F, Dotevall A, Graipe A, Jarnet C, Kaminska J, Kempe A, Korhonen M, Linderfalk C, Liu B, Ljungstroem K, Ljungström K, Malmqvist L, Mellbin L, Mooe T, Nicol P, Norrby A, Ohlsson A, Rosengren A, Saaf J, Salmonsson S, Strandberg O, Svensson KA, Tengmark BO, Tsatsaris G, Ulvenstam A, Vasko P, Chang CT, Chang HM, Chen JF, Chen TP, Chung MM, Fu CP, Hsia TL, Hua SC, Kuo MC, Lee CL, Lee IT, Liang KW, Lin SY, Lu CH, Ma WY, Pei D, Shen FC, Su CC, Su SW, Tai TS, Tsai WN, Tsai YT, Tung SC, Wang JS, Yu HI, al‐Qaissi A, Arutchelvam V, Atkin S, Au S, Aye MM, Bain S, Bejnariu C, Bell P, Bhatnagar D, Bilous R, Black N, Brennan U, Brett B, Bujanova J, Chow E, Collier A, Combe A, Courtney C, Courtney H, Crothers J, Eavis P, Elliott J, Febbraro S, Finlayson J, Gandhi R, Gillings S, Hamling J, Harper R, Harris T, Hassan K, Heller S, Jane A, Javed Z, Johnson T, Jones S, Kennedy A, Kerr D, Kilgallon B, Konya J, Lindsay J, Lomova‐Williams L, Looker H, MacFarlane D, Macrury S, Malik I, McCrimmon R, McKeith D, McKnight J, Mishra B, Mukhtar R, Mulligan C, O'Kane M, Olateju T, Orpen I, Richardson T, Rooney D, Ross SB, Sathyapalan T, Siddaramaiah N, Sit LE, Stephens J, Turtle F, Wakil A, Walkinshaw E, Ali A, Anderson R, Arakaki R, Aref O, Ariani MK, Arkin D, Banarer S, Barchini G, Bhan A, Branch K, Brautigam D, Brietzke S, Brinas M, Brito Y, Carter C, Casagni K, Casula S, Chakko S, Charatz S, Childress D, Chow L, Chustecka M, Clarke S, Cohen L, Collins B, Colon Vega G, Comulada‐Rivera A, Cortes‐Maisonet G, Davis M, de Souza J, Desouza C, Dinnan M, Duffy‐Hidalgo B, Dunn B, Dunn J, Elman M, Felicetta J, Finkelstein S, Fitz‐Patrick D, Florez H, Forker A, Fowler W, Fredrickson S, Freedman Z, Gainey Narron B, Gainey‐Ferree K, Gardner M, Gastelum C, Giddings S, Gillespie E, Gimness MP, Goldstein G, Gomes M, Gomez N, Gorman T, Goswami K, Graves A, Hacking S, Hall C, Hanson L, Harman S, Heber D, Henry R, Hiner J, Hirsch I, Hollander P, Hooker T, Horowitz B, Hoste L, Huang L, Huynh M, Hyman D, Idriss S, Iranmanesh A, Karounos D, Kashyap M, Katz L, Kaye W, Khaiton Y, Khardori R, Kitchen T, Klein A, Knffem W, Kosiborod M, Kreglinger N, Kruger D, Kumar A, Laboy I, Larrabee P, Larrick L, Lawson D, Ledet M, Lenhard J, Levy J, Li G, Li Z, Lieb D, Limcolioc A, Lions‐Patterson J, Lorber D, Lorch D, Lorrello M, Lu P, Lucas KJ, Ma SL, MacAdams M, Magee M, Magno A, Mahakala AR, Marks J, McCall A, McClanahan W Jr, McClary C, Melendez L, Melish J, Michaud D, Miller C, Miller N, Mora P, Moten M, Mudaliar S, Myrick G, Narayan P, Nassif M, Neri K, Newton T, Niblack P, Nicol P, Nyenwe E, Odugbesan AO, Okorocha Y, Ortiz Carrasquillo R, Osei K, Palermo C, Patel H, Patel K, Pau C, Perley M, Plevin S, Plummer E, Powell R, Qintar M, Rawls R, Reyes‐Castano J, Reynolds L, Richards R, Rosenstock J, Rowe C, Saleh J, Sam S, Sanchez A, Sander D, Sanderson B, Savin V, Seaquist E, Shah J, Shi S, Shivaswamy V, Shlotzhauer T, Shore D, Skukowski B, Soe K, Solheim V, Soufer J, Steinberg H, Steinsapir J, Tarkington P, Thayer D, Thomson S, Thrasher J, Tibaldi J, Tjaden J, Tores O, Trence D, Trikudanathan S, Ullal J, Uwaifo G, Vo A, Vu K, Walia D, Weiland K, Whitehouse F, Wiegmann T, Wyne K, Wynne A, Yuen K, Zaretzky J, Zebrack J, Zieve F, Zigrang W. Dulaglutide and cardiovascular outcomes in type 2 diabetes (REWIND): a double‐blind, randomised placebo‐controlled trial. The Lancet. 2019; 394: 121–130. [DOI] [PubMed] [Google Scholar]
31. Hernandez AF, Green JB, Janmohamed S, D'Agostino RB Sr, Granger CB, Jones NP, Leiter LA, Rosenberg AE, Sigmon KN, Somerville MC, Thorpe KM, McMurray JJV, del Prato S, del Prato S, McMurray JJV, D'Agostino RB, Granger CB, Hernandez AF, Janmohamed S, Leiter LA, Califf RM, Holman R, DeMets D, Riddle M, Goodman S, McGuire D, Alexander K, Devore A, Melloni C, Patel C, Kong D, Bloomfield G, Roe M, Tricoci P, Harrison R, Lopes R, Mathews R, Mehta R, Schuyler Jones W, Vemulapalli S, Povsic T, Eapen Z, Dombrowski K, Kolls B, Jordan D, Ambrosy A, Greene S, Mandawat A, Shavadia J, Cooper L, Sharma A, Guimaraes P, Friedman D, Wilson M, Endsley P, Gentry T, Collier J, Perez K, James K, Roush J, Pope C, Howell C, Johnson M, Bailey M, Cole J, Akers T, Vandyne B, Thomas B, Rich J, Bartone S, Beaulieu G, Brown K, Chau T, Christian T, Coker R, Greene D, Haddock T, Jenkins W, Haque G, Marquess M, Pesarchick J, Rethaford R, Stone A, al Kawas F, Anderson M, Enns R, Sinay I, Mathieu C, Yordanov V, Hramiak I, Haluzik M, Galatius S, Guerci B, Nauck M, Migdalis I, Tan CBK, Kocsis G, Giaccari A, Lee MK, Muñoz EGC, Cornel J, Birkeland K, Pinto M, Tirador L, Olesinska‐Mader M, Shestakova M, Distiller L, Lopez‐Sendon J, Eliasson B, Chiang CE, Srimahachota S, Mankovsky B, Bethel MA, Dungan K, Kosiborod M, Alvarisqueta A, Baldovino J, Besada D, Calella P, Cantero MC, Castaño P, Chertkoff A, Cuadrado J, de Loredo L, Dominguez A, Español MV, Finkelstein H, Frechtel G, Fretes J, Garrido Santos N, Gonzalez J, Litvak M, Loureyro J, Maffei L, Maldonado N, Mohr Gasparini D, Orio S, Perez Manghi F, Rodriguez Papini N, Sala J, Schygiel P, Sposetti G, Ulla M, Verra F, Zabalua S, Zaidman C, Crenier L, Debroye C, Duyck F, Scheen A, van Gaal L, Vercammen C, Damyanova V, Dimitrov S, Kovacheva S, Lozanov L, Margaritov V, Mihaylova‐Shumkova R, Nikolaeva A, Stoyanova Z, Akhras R, Beaudry Y, Bedard J, Berlingieri J, Chehayeb R, Cheung S, Conway J, Cusson J, Della Siega A, Dumas R, Dzongowski P, Ferguson M, Gaudet D, Grondin F, Gupta A, Gupta M, Halperin F, Houle PA, Jones M, Kouz S, Kovacs C, Landry D, Lonn E, O'Mahony W, Peterson S, Reich D, Rosenbloom A, St‐Maurice F, Tugwell B, Vizel S, Woo V, Brychta T, Cech V, Dvorakova E, Edelsberger T, Halciakova K, Krizova J, Lastuvka J, Piperek M, Prymkova V, Raclavska L, Silhova E, Urbanek R, Vrkoc J, Andersen U, Brønnum‐Schou J, Hove J, Jensen JS, Kober L, Kristiansen OP, Lund P, Melchior T, Nyvad O, Schou M, Boye A, Cadinot D, Gouet D, Henry P, Kessler L, Lalau JD, Petit C, Thuan JF, Voinot C, Vouillarmet J, Axthelm C, Berger D, Bieler T, Birkenfeld A, Bott J, Busch K, Caca K, Chevts J, Donaubauer T, Erlinger R, Funke K, Grosskopf J, Hagenow A, Hamann M, Hartard M, Heymer P, Huppertz W, Illies G, Jacob S, Jung T, Kahrmann G, Kast P, Kellerer M, Kempe HP, Khariouzov A, Klausmann G, Klein C, Kleinecke‐Pohl U, Kleinertz K, Koch T, Kosch C, Lorra B, Luedemann J, Luttermann M, Maxeiner S, Milek K, Moelle A, Neumann G, Nischik R, Oehrig‐Pohl E, Plassmann G, Pohlmeier L, Proepper F, Regner S, Rieker W, Rose L, Samer H, Sauter J, Schaper F, Schiffer C, Schmidt J, Scholz BM, Schulze J, Segner A, Seufert J, Sigal H, Steindorf J, Stockhausen J, Stuebler P, Taeschner H, Tews D, Tschoepe D, Wilhelm K, Zeller‐Stefan H, Avramidis I, Bousboulas S, Bristianou M, Dimitriadis G, Elisaf M, Kotsa K, Melidonis A, Mitrakou A, Pagkalos E, Papanas N, Pappas A, Sampanis C, Tentolouris N, Tsapas A, Tzatzagou G, Ozaki R, Hajdú C, Harcsa E, Konyves L, Mucsi J, Pauker Z, Petró G, Plés Z, Revesz K, Sándor V, Vass V, Avogaro A, Boemi M, Bonadonna R, Consoli A, de Cosmo S, di Bartolo P, Dotta F, Frontoni S, Galetta M, Gambineri A, Gazzaruso C, Giorgino F, Lauro D, Orsi E, Paolisso G, Perriello G, Piatti P, Pontiroli A, Ponzani P, Rivellese AA, Sesti G, Tonolo G, Trevisan R, Ahn CW, Baik SH, Cha BS, Chung CH, Jang HC, Kim CJ, Kim HS, Kim IJ, Lee EY, Lee HW, Lee KW, Moon KW, Namgung J, Park KS, Yoo SJ, Yu J, Llamas EAB, Cervantes‐Escárcega JL, Flota‐Cervera LF, González‐González JG, Pascoe‐Gonzalez S, Pelayo‐Orozco ES, Ramirez‐Diaz SP, Saldana‐Mendoza A, Jerjes‐Díaz CS, Torres‐Colores JJ, Vidrio‐Velázquez M, Villagordoa‐Mesa J, Beijerbacht HP, Groutars RGEJ, Hoek BA, Hoogslag PAM, Kooy A, Kragten JA, Lieverse AG, Swart HP, Viergever EP, Ahlqvist J, Cooper J, Gulseth H, Guttormsen G, Wium C, Arbañil H, Calderon J, Camacho L, Espinoza AD, Garrido E, Luna A, Manrique H, Revoredo FM, Gonzales RV, Rincon LZ, Zubiate C, Ebo G, Morales‐Palomares E, Arciszewska M, Banach M, Bijata‐Bronisz R, Derezinski T, Gadzinski W, Gajek J, Klodawska K, Krzyzagorska E, Madej A, Miekus P, Opiela J, Romanczuk P, Siegel A, Skokowska E, Stankiewicz A, Stasinska T, Trznadel‐Morawska I, Witek R, Aksentyev S, Bondar I, Demidova I, Dreval A, Ershova O, Galstyan G, Garganeeva A, Izmozherova N, Karetnikova V, Kharakhulakh M, Khokhlov A, Kobalava Z, Koshelskaya O, Kosmacheva E, Kostin V, Koziolova N, Kuzin A, Lesnov V, Lysenko T, Markov V, Mayorov A, Moiseev S, Myasoedova S, Petunina N, Rebrov A, Ruyatkina L, Samoylova J, Sazonova O, Shilkina N, Sokolova N, Vasilevskaya O, Verbovaya N, Vishneva E, Vorobyev S, Vorokhobina N, Zanozina O, Zhdanova E, Zykova T, Burgess L, Coetzee K, Dawood S, Lombard L, Makotoko E, Moodley R, Oosthuysen W, Sarvan M, Calvo Gómez C, Cano Rodríguez I, Castro Conde A, Cequier Fillat A, Cuatrecasas Cambra G, de Álvaro Moreno F, de Teresa Parreño L, Delgado Lista J, Domínguez Escribano JR, Durán García S, Elvira González J, Fernández Rodríguez JM, Goday Arno A, Gomez Huelgas R, González Juanatey JR, Hernandez Mijares A, Jiménez Díaz VA, Jodar Gimeno E, Lucas Morante T, Marazuela M, Martell Claros N, Mauricio Puente D, Mena Ribas E, Merino Torres JF, Mezquita Raya P, Nubiola Calonge A, Ordoñez Sánchez X, Pascual Izuel JM, Perea Castilla V, Pérez Pérez A, Perez Soto I, Quesada Charneco M, Quesada Simón A, Redón Mas J, Rego Iraeta A, Rodriguez Alvarez M, Rodríguez Rodríguez I, Sabán Ruiz J, Soto González A, Tinahones Madueno F, Trescoli Serrano C, Ulied Armiñana A, Bachus E, Berndtsson Blom K, Eliasson K, Koskinen P, Larnefeldt H, Lif‐Tiberg C, Linderfalk C, Lund G, Lundman P, Moris L, Olsson Å, Salmonsson S, Sanmartin Berglund J, Sjöberg F, Söderberg S, Torstensson I, Chen JF, Tien KJ, Tseng ST, Tu ST, Wang CY, Wang JH, Phrommintikul A, Yamwong S, Jintapakorn W, Hutayanon P, Sansanayudh N, Bazhan L, Fushtey I, Grachova M, Katerenchuk V, Korpachev V, Kravchun N, Larin O, Mykhalchyshyn G, Myshanych H, Oleksyk O, Orlenko V, Pashkovska N, Pertseva N, Petrosyan O, Smirnov I, Vlasenko M, Zlova T, Aye M, Baksi A, Balasubramani M, Beboso R, Blagden M, Bundy C, Cookson T, Copland A, Emslie‐Smith A, Green F, Gunstone A, Issa B, Jackson‐Voyzey E, Johnson A, Maclean M, McKnight J, Muzulu S, O'Connell I, Oyesile B, Patterson C, Pearson E, Philip S, Smith P, Sukumaran U, Abbas J, Aggarwala G, Akhter F, Andersen J, Anglade M, Argoud G, Ariani M, Ashdji R, Bakhtari L, Banerjee S, Bartlett A, Baum H, Bays H, Beasley R, Belfort de Aguiar R, Benjamin S, Bhagwat R, Bhargava A, Bode B, Bratcher C, Briskin T, Brockmyre A, Broughton R, Brown J, Budhraja M, Cannon K, Carr J, Cathcart H, Cavale A, Chaykin L, Cheung D, Childress R, Cohen A, Condit J, Cooksey E, Cornett GM, Dauber I, Davila W, de Armas L, Dean J, Detweiler R, Diaz E, di Giovanna M, Dor I, Drummond W, Eagerton D, Earl J, Eaton C, Ellison H, Farris N, Fiel T, Firek A, First B, Forgosh L, French W, Gandy W, Garcia R, Gill S, Gordon M, Guice M, Gummadi S, Hackenyos J, Hairston K, Hanson L, Harrison L, Hartman I, Heitner J, Hejeebu S, Hermany P, Hernandez‐Cassis C, Hidalgo H, Higgins A, Ibrahim H, Jacobs S, Johnson D, Joshi P, Kaster S, Kellum D, Kim C, Kim E, Kirby W, Knouse A, Kulback S, Kumar M, Kuruvanka T, Labroo A, Lasswell W, Lentz J, Lenzmeier T, Lewis D, Li Z, Lillestol M, Little R, Lorraine R, McKeown‐Biagas C, McNeill R, Mehta A, Miller A, Moran J, Morawski E, Nadar V, O'Connor T, Odio A, Parker R, Patel R, Phillips L, Raad G, Rahman A, Raikhel M, Raisinghani A, Rajan R, Rasouli N, Rauzi F, Rohr K, Roseman H, Rovner S, Saba F, Sachson R, Schabauer A, Schneider R, Schuchard T, Sensenbrenner J, Shlesinger Y, Singh N, Sivalingam K, Stonesifer L, Storey D, Suh D, Tahir M, Tan A, Tan M, Taylon A, Thakkar M, Tripathy D, Uwaifo G, Vedere A, Venugopal C, Vo A, Welch M, Welker J, White A, Willis J, Wynne A, Yazdani S, Green JB, Rosenberg A, Price L, Sigmon K, Lokhngina Y, Xing W, Overton R, Stewart M, Stead J, Lindsay A, Patel V, Ross J, Soffer J, Daga S, Sowell M, Patel P, Garvey L, Ackert J, Abraham S, Sabol MB, Altobelli D, Ha JY, Kulkarni M, Somerville M, Noronha D, Casson E, Zang E, Sandhu C, Kumar R, Chen D, Taft L, Patel R, Ye J, Shannon J, Wilson T, Babi C, Miller D, Jones NP, Thorpe K, Russell R, Bull G, Hereghty B, Fernandez‐Salazar E, Longley T, Donaldson J, Jarosz M, Murphy K, Adams P, Smith P, James R, Richards J, Sedani S, Althouse D, Watson D, Lorimer J, Lauder S, Schultheis R, Womer T, Wraight E, Li W, Price‐Olsen E, Watson A, Kelly A, McLaughlin P, Fleming J, Schubert J, Schleiden D, Harris T, Prakash R, Breneman J, Deshpande S, Saswadkar A, Kumari A, Shitut A, Raorane A, Karmalkar A, Mhambrey A, Bhosale A, Vaphare A, Patil AP, Khandelwal C, Shaik F, Nadar M, Karka M, Kadgaonkar N, Gupta N, Aher N, Potnis O, Naicker P, Shinde R, Sharma R, Godse R, Solanki S, Sahu S, Dumbre S, Kumar S, Patil S, Mandal T. Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double‐blind, randomised placebo‐controlled trial. The Lancet. 2018; 392: 1519–1529. [DOI] [PubMed] [Google Scholar]
32. Holman RR, Bethel MA, Mentz RJ, Thompson VP, Lokhnygina Y, Buse JB, Chan JC, Choi J, Gustavson SM, Iqbal N, Maggioni AP, Marso SP, Öhman P, Pagidipati NJ, Poulter N, Ramachandran A, Zinman B, Hernandez AF. Effects of once‐weekly exenatide on cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2017; 377: 1228–1239. [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Husain M, Birkenfeld AL, Donsmark M, Dungan K, Eliaschewitz FG, Franco DR, Jeppesen OK, Lingvay I, Mosenzon O, Pedersen SD, Tack CJ, Thomsen M, Vilsbøll T, Warren ML, Bain SC. Oral semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2019; 381: 841–851. [DOI] [PubMed] [Google Scholar]
34. Marso SP, Bain SC, Consoli A, Eliaschewitz FG, Jódar E, Leiter LA, Lingvay I, Rosenstock J, Seufert J, Warren ML, Woo V, Hansen O, Holst AG, Pettersson J, Vilsbøll T. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2016; 375: 1834–1844. [DOI] [PubMed] [Google Scholar]
35. Marso SP, Daniels GH, Brown‐Frandsen K, Kristensen P, Mann JFE, Nauck MA, Nissen SE, Pocock S, Poulter NR, Ravn LS, Steinberg WM, Stockner M, Zinman B, Bergenstal RM, Buse JB, LEADER Steering Committee , LEADER Trial Investigators . Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016; 375: 311–322. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Cannon CP, Pratley R, Dagogo‐Jack S, Mancuso J, Huyck S, Masiukiewicz U, Charbonnel B, Frederich R, Gallo S, Cosentino F, Shih WJ, Gantz I, Terra SG, Cherney DZI, McGuire DK. Cardiovascular outcomes with ertugliflozin in type 2 diabetes. N Engl J Med. 2020; 383: 1425–1435. [DOI] [PubMed] [Google Scholar]
37. Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, Shaw W, Law G, Desai M, Matthews DR. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017; 377: 644–657. [DOI] [PubMed] [Google Scholar]
38. Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJL, Charytan DM, Edwards R, Agarwal R, Bakris G, Bull S, Cannon CP, Capuano G, Chu PL, de Zeeuw D, Greene T, Levin A, Pollock C, Wheeler DC, Yavin Y, Zhang H, Zinman B, Meininger G, Brenner BM, Mahaffey KW. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019; 380: 2295–2306. [DOI] [PubMed] [Google Scholar]
39. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, Mattheus M, Devins T, Johansen OE, Woerle HJ, Broedl UC, Inzucchi SE. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015; 373: 2117–2128. [DOI] [PubMed] [Google Scholar]
40. Rosenstock J, Kahn SE, Johansen OE, Zinman B, Espeland MA, Woerle HJ, Pfarr E, Keller A, Mattheus M, Baanstra D, Meinicke T, George JT, von Eynatten M, McGuire DK, Marx N, for the CAROLINA Investigators . Effect of linagliptin vs glimepiride on major adverse cardiovascular outcomes in patients with type 2 diabetes: the CAROLINA randomized clinical trial. JAMA. 2019; 322: 1155–1166. [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Visseren FLJ, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, Benetos A, Biffi A, Boavida JM, Capodanno D, Cosyns B, Crawford C, Davos CH, Desormais I, di Angelantonio E, Franco OH, Halvorsen S, Hobbs FDR, Hollander M, Jankowska EA, Michal M, Sacco S, Sattar N, Tokgozoglu L, Tonstad S, Tsioufis KP, van Dis I, van Gelder I, Wanner C, Williams B, ESC National Cardiac Societies , ESC Scientific Document Group . 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies with the special contribution of the European Association of Preventive Cardiology (EAPC). Eur Heart J. 2021; 42: 3227–3337.34458905 [Google Scholar]
42. Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010; 340: c869. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Gerstein HC. Metformin reduced diabetes‐related end points and all‐cause mortality in overweight patients with type 2 diabetes. ACP J Club. 1999; 130: 3. [Google Scholar]
44. Srivastava PK, Claggett BL, Solomon SD, McMurray JJV, Packer M, Zile MR, Desai AS, Rouleau JL, Swedberg K, Fonarow GC. Estimated 5‐year number needed to treat to prevent cardiovascular death or heart failure hospitalization with angiotensin receptor‐neprilysin inhibition vs standard therapy for patients with heart failure with reduced ejection fraction: an analysis of data from the PARADIGM‐HF trial. JAMA Cardiol. 2018; 3: 1226–1231. [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Ferreira JP, Docherty KF, Stienen S, Jhund PS, Claggett BL, Solomon SD, Petrie MC, Gregson J, Pocock SJ, Zannad F, McMurray J. Estimating the lifetime benefits of treatments for heart failure. JACC: Heart Failure. 2020; 8: 984–995. [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Lee EY, Yoon K‐H. How to interpret recent CV outcome trials and future: PCSK9 inhibitors. J Lipid Atheroscler. 2018; 7: 1–11. [Google Scholar]
47. Cowie MR, Fisher M. SGLT2 inhibitors: mechanisms of cardiovascular benefit beyond glycaemic control. Nat Rev Cardiol. 2020; 17: 761–772. [DOI] [PubMed] [Google Scholar]
48. Urbich M, Globe G, Pantiri K, Heisen M, Bennison C, Wirtz HS, di Tanna GL. A systematic review of medical costs associated with heart failure in the USA (2014–2020). Pharmacoeconomics. 2020; 38: 1219–1236. [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Davies MJ, Kloecker DE, Webb DR, Khunti K, Zaccardi F. Number needed to treat in cardiovascular outcome trials of glucagon‐like peptide‐1 receptor agonists: a systematic review with temporal analysis. Diabetes Obes Metab. 2020; 22: 1670–1677. [DOI] [PubMed] [Google Scholar]
50. Palmer SC, Tendal B, Mustafa RA, Vandvik PO, Li S, Hao Q, et al. Sodium‐glucose cotransporter protein‐2 (SGLT‐2) inhibitors and glucagon‐like peptide‐1 (GLP‐1) receptor agonists for type 2 diabetes: systematic review and network meta‐analysis of randomised controlled trials. BMJ. 2021; 372: m4573. [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Gregson J, Sharples L, Stone GW, Burman C‐F, Öhrn F, Pocock S. Nonproportional hazards for time‐to‐event outcomes in clinical trials: JACC Review Topic of the Week. J Am Coll Cardiol. 2019; 74: 2102–2112. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Individual Trial Endpoint Definitions.

Click here for additional data file.^{(391.4KB, docx)}

[ehf214213-bib-0002] 2. WHO . Cardiovascular diseases—key facts [Online source]. 2021. Available from: https://www.who.int/news‐room/fact‐sheets/detail/cardiovascular‐diseases‐(cvds). Accessed Dec 17, 2021

[ehf214213-bib-0003] 3. WHO . The top 10 causes of death [Online source]. 2020. Available from: https://www.who.int/news‐room/fact‐sheets/detail/the‐top‐10‐causes‐of‐death. Accessed Dec 17, 2021

[ehf214213-bib-0004] 4. Braunwald E. Cardiovascular medicine at the turn of the millennium: triumphs, concerns, and opportunities. N Engl J Med. 1997; 337: 1360–1369. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0005] 5. Roger VL. Epidemiology of heart failure. Circ Res. 2021; 128: 1421–1434. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0006] 6. Cefalu WT, Kaul S, Gerstein HC, Holman RR, Zinman B, Skyler JS, Green JB, Buse JB, Inzucchi SE, Leiter LA, Raz I, Rosenstock J, Riddle MC. Cardiovascular outcomes trials in type 2 diabetes: where do we go from here? Reflections from a Diabetes Care editors' expert forum. Diabetes Care. 2018; 41: 14–31. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0007] 7. U.S. Food and Drug Administration . Guidance for industry: diabetes mellitus—evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes [Internet]. 2008.

[ehf214213-bib-0008] 8. ADA . 9. Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes—2021. Diabetes Care. 2021; 44: S111–s24. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0010] 10. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021; 42: 3599–3726. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0011] 11. McMurray JJV, Solomon SD, Inzucchi SE, Køber L, Kosiborod MN, Martinez FA, et al. Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med. 2019; 381: 1995–2008. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0012] 12. Packer M, Anker SD, Butler J, Filippatos G, Pocock SJ, Carson P, Januzzi J, Verma S, Tsutsui H, Brueckmann M, Jamal W, Kimura K, Schnee J, Zeller C, Cotton D, Bocchi E, Böhm M, Choi DJ, Chopra V, Chuquiure E, Giannetti N, Janssens S, Zhang J, Gonzalez Juanatey JR, Kaul S, Brunner‐la Rocca HP, Merkely B, Nicholls SJ, Perrone S, Pina I, Ponikowski P, Sattar N, Senni M, Seronde MF, Spinar J, Squire I, Taddei S, Wanner C, Zannad F. Cardiovascular and renal outcomes with empagliflozin in heart failure. N Engl J Med. 2020; 383: 1413–1424. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0013] 13. Anker SD, Butler J, Filippatos G, Ferreira JP, Bocchi E, Böhm M, Brunner–la Rocca HP, Choi DJ, Chopra V, Chuquiure‐Valenzuela E, Giannetti N, Gomez‐Mesa JE, Janssens S, Januzzi JL, Gonzalez‐Juanatey JR, Merkely B, Nicholls SJ, Perrone SV, Piña IL, Ponikowski P, Senni M, Sim D, Spinar J, Squire I, Taddei S, Tsutsui H, Verma S, Vinereanu D, Zhang J, Carson P, Lam CSP, Marx N, Zeller C, Sattar N, Jamal W, Schnaidt S, Schnee JM, Brueckmann M, Pocock SJ, Zannad F, Packer M. Empagliflozin in heart failure with a preserved ejection fraction. N Engl J Med. 2021; 385: 1451–1461. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0014] 14. Kristensen SL, Rørth R, Jhund PS, Docherty KF, Sattar N, Preiss D, Køber L, Petrie MC, McMurray JJV. Cardiovascular, mortality, and kidney outcomes with GLP‐1 receptor agonists in patients with type 2 diabetes: a systematic review and meta‐analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol. 2019; 7: 776–785. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0015] 15. Zelniker TA, Wiviott SD, Raz I, Im K, Goodrich EL, Bonaca MP, Mosenzon O, Kato ET, Cahn A, Furtado RHM, Bhatt DL, Leiter LA, McGuire DK, Wilding JPH, Sabatine MS. SGLT2 inhibitors for primary and secondary prevention of cardiovascular and renal outcomes in type 2 diabetes: a systematic review and meta‐analysis of cardiovascular outcome trials. Lancet. 2019; 393: 31–39. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0016] 16. Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010; 340: c332. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0017] 17. Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R, Brozek J, Norris S, Meerpohl J, Djulbegovic B, Alonso‐Coello P, Post PN, Busse JW, Glasziou P, Christensen R, Schünemann HJ. GRADE guidelines: 12. Preparing summary of findings tables—binary outcomes. J Clin Epidemiol. 2013; 66: 158–172. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0018] 18. Sciences TAoM . Sources of evidence for assessing the safety, efficacy and effectiveness of medicines [Online source]. 2017. Available from: https://acmedsciacuk/file‐download/86466482. Accessed Oct 10, 2020

[ehf214213-bib-0019] 19. Rohatgi A. WebPlotDigitizer [Online source]. 2019. Available from: https://automerisio/WebPlotDigitizer/. Accessed Oct 10, 2020

[ehf214213-bib-0020] 20. Guyot P, Ades AE, Ouwens MJNM, Welton NJ. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan‐Meier survival curves. BMC Med Res Methodol. 2012; 12: 9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0021] 21. Drevon D, Fursa SR, Malcolm AL. Intercoder reliability and validity of WebPlotDigitizer in extracting graphed data. Behav Modif. 2017; 41: 323–339. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0022] 22. Moeyaert M, Maggin D, Verkuilen J. Reliability, validity, and usability of data extraction programs for single‐case research designs. Behav Modif. 2016; 40: 874–900. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0023] 23. Saluja R, Cheng S, Delos Santos KA, Chan KKW. Estimating hazard ratios from published Kaplan‐Meier survival curves: a methods validation study. Res Synth Methods. 2019; 10: 465–475. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0024] 24. Collett D. Modelling Survival Data in Medical Research, 3rd ed. Chapman and Hall/CRC; 2015. p 548. [Google Scholar]

[ehf214213-bib-0025] 25. Ludwig L, Darmon P, Guerci B. Computing and interpreting the Number Needed to Treat for Cardiovascular Outcomes Trials: perspective on GLP‐1 RA and SGLT‐2i therapies. Cardiovasc Diabetol. 2020; 19: 65. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0026] 26. Green JB, Bethel MA, Armstrong PW, Buse JB, Engel SS, Garg J, Josse R, Kaufman KD, Koglin J, Korn S, Lachin JM, McGuire DK, Pencina MJ, Standl E, Stein PP, Suryawanshi S, van de Werf F, Peterson ED, Holman RR. Effect of sitagliptin on cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2015; 373: 232–242. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0027] 27. Rosenstock J, Perkovic V, Johansen OE, Cooper ME, Kahn SE, Marx N, Alexander JH, Pencina M, Toto RD, Wanner C, Zinman B, Woerle HJ, Baanstra D, Pfarr E, Schnaidt S, Meinicke T, George JT, von Eynatten M, McGuire DK, for the CARMELINA Investigators . Effect of linagliptin vs placebo on major cardiovascular events in adults with type 2 diabetes and high cardiovascular and renal risk: the CARMELINA randomized clinical trial. JAMA. 2019; 321: 69–79. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0028] 28. Scirica BM, Bhatt DL, Braunwald E, Steg PG, Davidson J, Hirshberg B, Ohman P, Frederich R, Wiviott SD, Hoffman EB, Cavender MA, Udell JA, Desai NR, Mosenzon O, McGuire DK, Ray KK, Leiter LA, Raz I. Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes mellitus. N Engl J Med. 2013; 369: 1317–1326. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0029] 29. White WB, Cannon CP, Heller SR, Nissen SE, Bergenstal RM, Bakris GL, Perez AT, Fleck PR, Mehta CR, Kupfer S, Wilson C, Cushman WC, Zannad F, EXAMINE Investigators . Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N Engl J Med. 2013; 369: 1327–1335. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0032] 32. Holman RR, Bethel MA, Mentz RJ, Thompson VP, Lokhnygina Y, Buse JB, Chan JC, Choi J, Gustavson SM, Iqbal N, Maggioni AP, Marso SP, Öhman P, Pagidipati NJ, Poulter N, Ramachandran A, Zinman B, Hernandez AF. Effects of once‐weekly exenatide on cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2017; 377: 1228–1239. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0033] 33. Husain M, Birkenfeld AL, Donsmark M, Dungan K, Eliaschewitz FG, Franco DR, Jeppesen OK, Lingvay I, Mosenzon O, Pedersen SD, Tack CJ, Thomsen M, Vilsbøll T, Warren ML, Bain SC. Oral semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2019; 381: 841–851. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0034] 34. Marso SP, Bain SC, Consoli A, Eliaschewitz FG, Jódar E, Leiter LA, Lingvay I, Rosenstock J, Seufert J, Warren ML, Woo V, Hansen O, Holst AG, Pettersson J, Vilsbøll T. Semaglutide and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2016; 375: 1834–1844. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0035] 35. Marso SP, Daniels GH, Brown‐Frandsen K, Kristensen P, Mann JFE, Nauck MA, Nissen SE, Pocock S, Poulter NR, Ravn LS, Steinberg WM, Stockner M, Zinman B, Bergenstal RM, Buse JB, LEADER Steering Committee , LEADER Trial Investigators . Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016; 375: 311–322. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0036] 36. Cannon CP, Pratley R, Dagogo‐Jack S, Mancuso J, Huyck S, Masiukiewicz U, Charbonnel B, Frederich R, Gallo S, Cosentino F, Shih WJ, Gantz I, Terra SG, Cherney DZI, McGuire DK. Cardiovascular outcomes with ertugliflozin in type 2 diabetes. N Engl J Med. 2020; 383: 1425–1435. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0037] 37. Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, Shaw W, Law G, Desai M, Matthews DR. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017; 377: 644–657. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0038] 38. Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJL, Charytan DM, Edwards R, Agarwal R, Bakris G, Bull S, Cannon CP, Capuano G, Chu PL, de Zeeuw D, Greene T, Levin A, Pollock C, Wheeler DC, Yavin Y, Zhang H, Zinman B, Meininger G, Brenner BM, Mahaffey KW. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019; 380: 2295–2306. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0039] 39. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, Mattheus M, Devins T, Johansen OE, Woerle HJ, Broedl UC, Inzucchi SE. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015; 373: 2117–2128. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0040] 40. Rosenstock J, Kahn SE, Johansen OE, Zinman B, Espeland MA, Woerle HJ, Pfarr E, Keller A, Mattheus M, Baanstra D, Meinicke T, George JT, von Eynatten M, McGuire DK, Marx N, for the CAROLINA Investigators . Effect of linagliptin vs glimepiride on major adverse cardiovascular outcomes in patients with type 2 diabetes: the CAROLINA randomized clinical trial. JAMA. 2019; 322: 1155–1166. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0041] 41. Visseren FLJ, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, Benetos A, Biffi A, Boavida JM, Capodanno D, Cosyns B, Crawford C, Davos CH, Desormais I, di Angelantonio E, Franco OH, Halvorsen S, Hobbs FDR, Hollander M, Jankowska EA, Michal M, Sacco S, Sattar N, Tokgozoglu L, Tonstad S, Tsioufis KP, van Dis I, van Gelder I, Wanner C, Williams B, ESC National Cardiac Societies , ESC Scientific Document Group . 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies with the special contribution of the European Association of Preventive Cardiology (EAPC). Eur Heart J. 2021; 42: 3227–3337.34458905 [Google Scholar]

[ehf214213-bib-0042] 42. Moher D, Hopewell S, Schulz KF, Montori V, Gøtzsche PC, Devereaux PJ, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ. 2010; 340: c869. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0043] 43. Gerstein HC. Metformin reduced diabetes‐related end points and all‐cause mortality in overweight patients with type 2 diabetes. ACP J Club. 1999; 130: 3. [Google Scholar]

[ehf214213-bib-0044] 44. Srivastava PK, Claggett BL, Solomon SD, McMurray JJV, Packer M, Zile MR, Desai AS, Rouleau JL, Swedberg K, Fonarow GC. Estimated 5‐year number needed to treat to prevent cardiovascular death or heart failure hospitalization with angiotensin receptor‐neprilysin inhibition vs standard therapy for patients with heart failure with reduced ejection fraction: an analysis of data from the PARADIGM‐HF trial. JAMA Cardiol. 2018; 3: 1226–1231. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0045] 45. Ferreira JP, Docherty KF, Stienen S, Jhund PS, Claggett BL, Solomon SD, Petrie MC, Gregson J, Pocock SJ, Zannad F, McMurray J. Estimating the lifetime benefits of treatments for heart failure. JACC: Heart Failure. 2020; 8: 984–995. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0046] 46. Lee EY, Yoon K‐H. How to interpret recent CV outcome trials and future: PCSK9 inhibitors. J Lipid Atheroscler. 2018; 7: 1–11. [Google Scholar]

[ehf214213-bib-0047] 47. Cowie MR, Fisher M. SGLT2 inhibitors: mechanisms of cardiovascular benefit beyond glycaemic control. Nat Rev Cardiol. 2020; 17: 761–772. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0048] 48. Urbich M, Globe G, Pantiri K, Heisen M, Bennison C, Wirtz HS, di Tanna GL. A systematic review of medical costs associated with heart failure in the USA (2014–2020). Pharmacoeconomics. 2020; 38: 1219–1236. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0049] 49. Davies MJ, Kloecker DE, Webb DR, Khunti K, Zaccardi F. Number needed to treat in cardiovascular outcome trials of glucagon‐like peptide‐1 receptor agonists: a systematic review with temporal analysis. Diabetes Obes Metab. 2020; 22: 1670–1677. [DOI] [PubMed] [Google Scholar]

[ehf214213-bib-0050] 50. Palmer SC, Tendal B, Mustafa RA, Vandvik PO, Li S, Hao Q, et al. Sodium‐glucose cotransporter protein‐2 (SGLT‐2) inhibitors and glucagon‐like peptide‐1 (GLP‐1) receptor agonists for type 2 diabetes: systematic review and network meta‐analysis of randomised controlled trials. BMJ. 2021; 372: m4573. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ehf214213-bib-0051] 51. Gregson J, Sharples L, Stone GW, Burman C‐F, Öhrn F, Pocock S. Nonproportional hazards for time‐to‐event outcomes in clinical trials: JACC Review Topic of the Week. J Am Coll Cardiol. 2019; 74: 2102–2112. [DOI] [PubMed] [Google Scholar]

PERMALINK

Meta‐analysed numbers needed to treat of novel antidiabetic drugs for cardiovascular outcomes

Georg Wolff

Yingfeng Lin

Cihan Akbulut

Maximilian Brockmeyer

Claudio Parco

Alexander Hoss

Alexander Sokolowski

Ralf Westenfeld

Malte Kelm

Michael Roden

Sabrina Schlesinger

Oliver Kuss

Abstract

Aims

Methods and results

Conclusions

Introduction

Methods

Study selection and data extraction

Weibull fits and absolute treatment effect estimates

Numbers needed to treat notation

Assessment of model validity

Meta‐analysis

Results

Study selection

Study characteristics

Table 1.

Relative and absolute treatment effect estimates of individual trials

Table 2.

Table 3.

Figure 1.

Meta‐analysis of absolute treatment effects

Figure 2.

Sensitivity analysis of sodium glucose transporter 2 inhibitors for hospitalization for heart failure

Figure 3.

Discussion

Limitations

Conclusions

Conflicts of interest

Funding

Supporting information

Acknowledgements

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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