Abstract
Background
Heart rate control during the vulnerable period (1–3 months post-discharge) is critical for improving outcomes in heart failure (HF). Prognostic implications for sinus rhythm (SR) versus atrial fibrillation (AF) patients remain unestablished.
Methods
We conducted an observational study of 438 heart failure patients to evaluate heart rate associations with HF readmission and all-cause mortality, alongside analysis of MIMIC-III database records assessing vulnerable period mortality relationships. This primary investigation was supplemented by systematic review and meta-analysis of cohort studies and randomized trials from PubMed, Embase, and Cochrane Library databases through February 2025 examining heart rate-prognosis correlations during the vulnerable period.
Results
The observational study demonstrated that, in SR patients, heart rates < 76 bpm at 1 month reduced mortality (P = 0.008), while 77–129 bpm increased HF readmission (P < 0.05) and composite all-cause mortality and/or HF readmission risk. AF patients with rates < 71 bpm at 3 months reduced HF readmission and or mortality (P < 0.05). MIMIC-III analysis confirmed that the mortality risk for SR increased with rates > 94 bpm (P = 0.037). Meta-analysis (6 studies) indicated elevated mortality (HR = 1.20, 95%: CI 0.98–1.46) and readmission risk (HR = 1.25, 95% CI: 0.88–1.79) at higher rates.
Conclusions
Maintaining heart rates < 77 bpm (SR) and < 71 bpm (AF) during the vulnerable period reduces readmission and mortality. These thresholds provide clinically actionable guidance for rhythm-stratified heart rate management in HF.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s40001-025-03651-8.
Keywords: Heart failure, Vulnerable period, Heart rate, Prognosis
Introduction
Globally, the aging population is contributing to a rising incidence of heart failure (HF), the end-stage manifestation of numerous cardiovascular diseases [1]. HF now affects over 3% of patients under age 40, necessitating intensified research efforts across all aspects of HF management to mitigate the growing societal burden [2–4].
Extensive clinical studies, such as CHARM [5], SHIFT [6], EVEREST [7], and OPTIMIZE-HF [8], revealed that patients with HF had a higher risk of readmission and death shortly after discharge (usually considered within 90 days). The early post-discharge phase after hospitalization for HF carries a particularly high risk of poor outcomes. It has been termed the “vulnerable period” [9]. Based on this concept, rational management during this period is extremely important for the prognosis of patients with HF [10].
Studies revealed that patients who experienced readmission and death during this period shared a common feature: an increased heart rate, which during the vulnerable period could adversely affect long-term outcomes [11, 12]. Heart rate, positioned centrally in this chain of events, influences many factors; an acceleration of heart rate will undoubtedly hasten the progression towards the terminal stage of HF in various ways [13].
Many clinical cohort studies involving heart rate-reducing drugs in patients with HF after discharge have shown that lowering heart rate during the vulnerable period significantly improves patient prognosis [14]. The extent of heart rate reduction is directly proportional to the benefits of the treatment. Research indicates that heart rate during the vulnerable period is significantly associated with both short- and long-term prognoses in patients with sinus rhythm (SR) and HF. Collectively, these studies suggest that an optimal heart rate of 70 bpm is associated with improved outcomes [15]. However, in clinical practice, a significant number of patients with atrial fibrillation (AF) and HF experience readmissions shortly after discharge, often accompanied by recurring AF [16]. These findings suggest that a vulnerable period persists for these patients. According to established guidelines for patients with AF and HF, the data concerning rate control remain inconclusive [1]. In the RACE II study and a pooled analysis of the RACE and AFFIRM trials [16, 17], a lenient rate control strategy, characterized by a resting heart rate of less than 110 bpm, was compared to a strict rate control strategy, defined by a resting heart rate of less than 80 bpm and a heart rate of less than 110 bpm during moderate exercise. The findings indicated no significant differences in outcomes between the two strategies. According to the 2021 ESC Heart Failure Guidelines and 2024 ESC Atrial Fibrillation Guidelines, a lenient rate control strategy (< 110 bpm at rest) remains the standard recommendation for most AF patients, as evidence for stricter rate control in HF remains inconclusive. Nevertheless, heart rate management in patients with HF during the vulnerable period remains suboptimal. Consequently, investigating the extent to which heart rate control can effectively enhance prognosis during the vulnerable period holds substantial clinical significance.
In this observational clinical study, we aimed to further validate the association between heart rate and the composite outcome of HF readmission and all-cause mortality. Additionally, leveraging the Medical Information Mart for Intensive Care III (MIMIC-III) database, we examined the relationship between heart rate during the vulnerable period and all-cause mortality. To comprehensively synthesize existing evidence, we systematically searched the PubMed, Embase, and Cochrane Library databases, identifying four cohort studies and two randomized controlled trials that investigated heart rate during the vulnerable period and its impact on HF prognosis. Subsequently, we performed a meta-analysis to quantify the correlation between heart rate and prognosis. Ultimately, this study seeks to identify the optimal target heart rate range for patients with HF during the vulnerable period, thereby improving patient outcomes and providing evidence-based guidance for clinical heart rate management.
Methods
Study setting and participants
We included clinical patients with HF between May 2021 and December 2023 by the Cardiology Follow-Up Center at the Second Affiliated Hospital of Xi’an Jiaotong University. The diagnostic criteria for HF were based on the ESC HF guideline [1]. The Biomedical Ethics Committee at Xi’an Jiaotong University Second Affiliated Hospital approved the study, with ethics approval number 2020064.
In the MIMIC-Ⅲ database, we retrieved all data from an openly available critical care database named the MIMIC-Ⅲ (Version 1.4), which includes demographic information, diagnoses, vital signs, and other essential data for patients admitted to the intensive care unit (ICU) (53423 distinct admissions) from June 2001 to October 2012 at Beth Israel Deaconess Medical Center in Boston. The Protecting Human Research Participants exam was passed to gain access to the MIMIC-Ⅲ database, and our certificate number is 9253690. The protocol was approved by the Massachusetts Institute of Technology and the Institutional Review Boards.
For the meta-analysis, we searched the PubMed, Embase, Web of Science, and Cochrane Library databases from inception to February 2025. The main subject words used were “heart failure”, “heart rate”, and “patient discharge”. The retrieval was based on these subject words in addition to free words. The retrieved documents were imported into EndNote X9, and duplicate documents were excluded both automatically and manually. Following this, an initial screening was conducted by reviewing the titles and abstracts. Studies that met the criteria were downloaded, and their full texts were read for rescreening. The original studies that ultimately qualified for the meta-analysis were selected. The literature screening process was conducted independently by the two authors, and cross-checking was performed to ensure accuracy. The systematic review followed PRISMA 2020 guidelines (Supplementary Material 1), and the protocol has been registered in PROSPERO (ID CRD420251180963). Full search strings are provided in Supplementary Material 2.
Eligibility criteria
For the observational study of clinical patients, the inclusion criteria were as follows: (1) patients with HF who were older than 18 y and (2) patients who were able to complete HF follow-up on time. The exclusion criteria were as follows: (1) in-hospital death; (2) loss to follow-up after discharge; (3) lack of basic information, follow-up information, or medication information; and (4) refusal to participate in follow-up.
The inclusion criteria for the MIMIC-Ⅲ database were as follows: (1) patients with HF who were older than 18 y; (2) patients admitted to the ICU for the first time; (3) patients with a heart rate during the vulnerable period after discharge (specifically referred to patients with readmission within 90 days after discharge, as outpatient heart rate is unavailable in this database); and (4) patients in SR or with AF. The exclusion criteria were as follows: (1) in-hospital death; (2) heart rate without vulnerable period (specifically referred to patients without readmission within 90 days after discharge); and (3) ectopic rhythm, except for patients in SR and those in AF.
In the meta-analysis, literature that met all of the following conditions were included: (1) study subjects: adult patients diagnosed with HF according to clinical diagnostic criteria; (2) exposure: reported heart rate levels within 3 months after discharge (including at discharge) and their impact on prognosis, with clear cut-off values; (3) at least one reported heart rate level related to any of the following outcome measures: all-cause mortality or readmission rate; (4) study type: cohort study or randomized controlled trial. Studies meeting any of the following conditions were excluded: (1) duplicate studies using the same population or overlapping databases; (2) meta-analyses, systematic reviews, reviews, letters, replies, conference abstracts, case reports, guidelines, or consensus statements; and (3) clinical trial protocols only.
Data collection
We collected data on clinical patients, including gender, body mass index (BMI), diagnosis, primary cause of HF, and cardiac function classification. We also obtained information on medical history and complications, as well as vital signs upon admission and laboratory test results. We conducted a series of landmark analyses to assess the association between heart rate at different post-discharge timepoints and one-year mortality. Three distinct analyses were performed, each with its own time zero and exposure window: 1) Analysis 1 (heart rate at discharge): time zero was defined as the date of discharge, and the exposure window was on the day of discharge; 2) Analysis 2 (heart rate at 1 month): time zero was set at 30 days after the date of discharge, and the exposure window was defined as 30 days after discharge; 3) Analysis 3 (heart rate at 3 months): time zero was set at 90 days after the date of discharge, and the exposure window was defined as 90 days after discharge.
We obtained data from the MIMIC-Ⅲ database using Structured Query Language (SQL), the PostgreSQL database, and the statistical software Stata 16.0 (StataCorp, College Station, TX, USA). The variables extracted included sex, age, ICU admission category, hospitalization category, medication in hospital, time to death, and heart rate and rhythm after discharge. Heart rate and rhythm measured at hospital readmission within 90 days post-discharge were defined as representing the vulnerable period status.
Quality assessment
Two authors independently evaluated the methodological quality of each included cohort study using eight items from the three major modules of the Newcastle–Ottawa Scale (NOS). The evaluation content primarily consists of three main parts: the selection of the study population (0–4), the comparability between groups (0–2), and the outcome measurement (0–3). A total evaluation score of 6 or more is considered indicative of a high-quality study. For randomized controlled trials, Version 2 of the Cochrane tool for assessing risk of bias (RoB 2) in randomized trials was used to evaluate the quality of the included studies. The review included five evaluation domains: bias in the randomization process, bias from established interventions, bias from missing outcome data, bias from outcome measures, and bias from selective reporting of results. Each included study was evaluated item by item using the criteria mentioned above, and the study was classified as “low risk”, “some concerns”, or “high risk”.
Outcomes
The outcomes of the observational study were defined as HF readmission and all-cause mortality within one year. The composite endpoint was HF readmission or all-cause mortality. Risk set of analysis 1 (heart rate at discharge) included all patients who survived the hospitalization and were discharged. Risk set of analysis 2 (heart rate at 1 month) and analysis 3 (heart rate at 3 months) included only patients who were alive, under follow-up, and had their heart rate measured within the exposure window. For each analysis, follow up for the outcome began the day after its respective time zero to ensure the exposure preceded the outcome.
The outcome of the MIMIC-Ⅲ database was defined as all-cause mortality within one year.
Loss to Follow-up was defined as that patients were administratively censored at the date of their last recorded interaction with our Cardiology Follow-Up Center. HF readmission was recorded within our institutional hospital information system and the provincial medical-insurance claims platform.
Statistical analysis
Data from clinical patients and the MIMIC-Ⅲ database were analysed using SPSS 20.0 for Windows (SPSS, Chicago, IL). A t-test was used for two independent samples with normally distributed measurement data. In contrast, a nonparametric test was used for two independent samples that did not have a normal distribution. The Pearson chi-square test was used for categorical data. The association between heart rate and outcomes was evaluated using Cox proportional hazards regression models. Survival analysis was performed within one year after discharge to compare overall survival between groups using the log-rank method in Kaplan–Meier analysis, and survival curves were plotted using GraphPad Prism 10.1.2. Statistical significance was considered at P < 0.05.
The meta-analysis was performed using Stata 16.0. The HR and 95%CI for heart rate levels for prognostic measures were extracted directly from the included articles. If multiple estimates were reported in the same article, we selected the multivariate analysis results adjusted for confounders. I2 was used to assess heterogeneity between the included studies. The fixed-effects model was applied for I2 < 50% (indicating low heterogeneity). In contrast, the random-effects model was used when I2 was 50% or higher (indicating high heterogeneity). The stability of the study results was examined through a sensitivity analysis, which assessed the effect of a single study on overall outcomes by deleting each study in turn. Publication bias was evaluated using Begg’s and Egger’s tests. If bias was detected, it was corrected using the trim-and-fill method. All P values were two-sided, and statistical significance was set at P < 0.05.
Results
Study characteristics
Table S1 summarizes the baseline characteristics of the observational cohort (n = 438, HF patients). The median age was 66 years (IQR, 58–75 y), with 61.0% of participants being male. SR patients exhibited significantly lower discharge heart rate (77 bpm: 67–87 bpm) compared to AF patients (84 bpm: 68–92 bpm; P < 0.05). This rhythm-dependent difference persisted at 1 month and 3 months post-discharge (both P < 0.05). Table S2 details laboratory profiles and comorbidities. Participants were enrolled from the Cardiology Follow-Up Center (SR: n = 287; AF: n = 151; Figure S1a).
Table S3 summarizes MIMIC-III cohort baseline characteristics (n = 512). The median age was 73 y (IQR, 64–83 y), with 54.1% of participants being male. Most patients presented through emergency departments (89.1%) and were managed in medical intensive care unit (MICU) (45.5%). The median 3 months post-discharge heart rate was 85 bpm (75–95 bpm), with significant rhythm-based differences: SR patients had a heart rate of 83 bpm (73–93 bpm) versus AF patients, 91 bpm (82–102 bpm; P < 0.001). Cohort distribution: SR group n = 391, AF group n = 121 (Figure S1b).
Table S4 summarizes characteristics of 6 meta-analysis studies (n = 9253 subjects; 62.3% male). The literature screening and selection process adhered to the PRISMA 2020 guidelines, with the complete PRISMA flow diagram presented in Figure S1a. Studies originated predominantly from Asia (n = 4), with single European/North American reports (2016–2025). Heart rate thresholds ranged from 55 to 90 bpm. All cohort studies (NOS > 6) demonstrated high quality (Table S5). Randomized controlled trial (RCT) included one open-label and one double-blind design, despite potential measurement bias in the former; however, both met the inclusion criteria following a RoB 2 assessment (Figure S2). The systematic search identified 7240 records, with 6 studies ultimately included after duplicate removal and screening (Figure S1a).
Heart rate during the vulnerable period and survival in patients with HF
In this observational cohort, heart rates measured at discharge, 1 month, and 3 months post-discharge were stratified into quartiles. Kaplan–Meier analysis demonstrated a significantly reduced all-cause mortality in SR patients with heart rate of 40–76 bpm at 1 month post-discharge (P < 0.0001). In contrast, no significant association was observed in SR patients at discharge (P = 0.207) or 3 months post-discharge (P = 0.948; Fig. 1a–c). Among AF patients, the heart rate had no significant survival association at discharge (P = 0.841), 1 month post-discharge (P = 0.590), and 3 months post-discharge (P = 0.545; Fig. 1d–f). These findings demonstrate rhythm- and time-specific relationships between heart rate modulation and mortality risk in HF.
Fig. 1.
Kaplan–Meier curves for all-cause mortality stratified by heart rate quartiles in the observational cohort. a–c Kaplan–Meier curve of the patients in the SR group at discharge, 1 month after discharge, and 3 months after discharge. d–f Kaplan–Meier curve of the patients in the AF group at discharge, 1 month after discharge, and 3 months after discharge. SR, sinus rhythm; AF, atrial fibrillation
Vulnerable period heart rate and outcomes: Cox regression analysis in HF
In HF patients with SR, the adjusted Cox regression model demonstrated that elevated heart rate (77–129 bpm) at 1 month post-discharge significantly increased the risk of both HF readmission and the composite outcome of all-cause mortality (Table 1, Figure S3a-c). Supplemental analyses revealed no significant associations between heart rate and either HF readmission or composite endpoint at discharge or 3 months post-discharge in SR patients (Table S6). Furthermore, discharge heart rate showed no association with all-cause mortality. In contrast, lower heart rates at 3 months were more protective against all-cause mortality.
Table 1.
Cox regression analysis of heart rate at 1 month after discharge with outcomes in the SR group
| Outcomes | Exposure | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | ||
| HF readmission | Heart rate (continuous) | 1.02 (1.00 ~ 1.03) | 0.028 | 1.02 (1.00 ~ 1.03) | 0.015 | 1.02 (1.00 ~ 1.03) | 0.051 | 1.02 (1.00 ~ 1.03) | 0.057 | 1.02 (1.00 ~ 1.03) | 0.062 |
| 71–76 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 40–70 bpm | 1.18 (0.63 ~ 2.20) | 0.598 | 1.20 (0.64 ~ 2.24) | 0.575 | 1.30 (0.69 ~ 2.48) | 0.417 | 1.35 (0.70 ~ 2.57) | 0.369 | 1.34 (0.71 ~ 2.59) | 0.358 | |
| 77–83 bpm | 2.58 (1.43 ~ 4.64) | 0.002 | 2.75 (1.52 ~ 4.96) | < 0.001 | 2.70 (1.48 ~ 4.95) | 0.001 | 2.70 (1.46 ~ 4.99) | 0.002 | 2.60 (1.40 ~ 4.86) | 0.003 | |
| 84–129 bpm | 2.03 (1.13 ~ 3.63) | 0.018 | 2.24 (1.24 ~ 4.05) | 0.008 | 2.24 (1.22 ~ 4.10) | 0.009 | 2.28 (1.23 ~ 4.22) | 0.009 | 2.28 (1.23 ~ 4.24) | 0.009 | |
| All-cause mortality | Heart rate (continuous) | 1.00 (0.98 ~ 1.03) | 0.842 | 1.01 (0.98 ~ 1.04) | 0.635 | 1.01 (0.98 ~ 1.04) | 0.527 | 1.01 (0.98 ~ 1.04) | 0.522 | 1.01 (0.98 ~ 1.04) | 0.530 |
| 71–76 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 40–70 bpm | 1.09 (0.40 ~ 3.01) | 0.868 | 1.13 (0.41 ~ 3.13) | 0.810 | 1.03 (0.37 ~ 2.89) | 0.961 | 1.00 (0.35 ~ 2.83) | 1.000 | 0.97 (0.34 ~ 2.77) | 0.959 | |
| 77–83 bpm | 2.33 (0.92 ~ 5.93) | 0.075 | 2.46 (0.97 ~ 6.25) | 0.059 | 2.37 (0.92 ~ 6.13) | 0.075 | 2.35 (0.91 ~ 6.12) | 0.079 | 2.15 (0.83 ~ 5.61) | 0.116 | |
| 84–129 bpm | 0.76 (0.24 ~ 2.38) | 0.632 | 0.89 (0.28 ~ 2.84) | 0.846 | 0.85 (0.26 ~ 2.75) | 0.785 | 0.85 (0.26 ~ 2.78) | 0.782 | 0.78 (0.23 ~ 2.61) | 0.686 | |
| All-cause mortality and/or HF readmission | Heart rate (continuous) | 1.01 (1.01 ~ 1.03) | 0.045 | 1.02 (1.00 ~ 1.03) | 0.021 | 1.01 (1.00 ~ 1.03) | 0.066 | 1.01 (1.00 ~ 1.03) | 0.080 | 1.01 (1.00 ~ 1.03) | 0.080 |
| 71–76 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 40–70 bpm | 1.13 (0.66 ~ 1.95) | 0.656 | 1.16 (0.67 ~ 2.01) | 0.591 | 1.19 (0.68 ~ 2.08) | 0.548 | 1.21 (0.69 ~ 2.13) | 0.499 | 1.21 (0.69 ~ 2.12) | 0.516 | |
| 77–83 bpm | 2.95 (1.77 ~ 4.91) | < 0.001 | 3.23 (1.94 ~ 5.40) | < 0.001 | 3.15 (1.87 ~ 5.32) | < 0.001 | 3.13 (1.85 ~ 5.30) | < 0.001 | 2.96 (1.74 ~ 5.05) | < 0.001 | |
| 84–129 bpm | 1.69 (1.01 ~ 2.85) | 0.049 | 1.92 (1.13 ~ 3.27) | 0.016 | 1.80 (1.05 ~ 3.09) | 0.033 | 1.80 (1.04 ~ 3.11) | 0.036 | 1.79 (1.03 ~ 3.12) | 0.038 | |
Model 1: Crude
Model 2: Adjust: Age, Sex, Body mass index
Model 3: Adjust: Age, Sex, Body mass index, Smoking, Diabetes, Hypertension, Coronary artery disease, Renal dysfunction, Low density lipoprotein, Serum creatinine
Model 4: Adjust: Age, Sex, Body mass index, Smoking, Diabetes, Hypertension, Coronary artery disease, Renal dysfunction, Low density lipoprotein, Serum creatinine, B-type natriuretic peptide, Left ventricular ejection fraction
Model 5: Adjust: Age, Sex, Body mass index, Smoking, Diabetes, Hypertension, Coronary artery disease, Renal dysfunction, Low density lipoprotein, Serum creatinine, B-type natriuretic peptide, Left ventricular ejection fraction, β-blockers, Angiotensin converting enzyme inhibitor/Angiotensin receptor blocker, Mineralocorticoid receptor antagonists
In AF patients, adjusted Cox regression demonstrated that lower rate (48–71 bpm) at 3 months post-discharge were associated with reduced all-cause mortality and mortality/readmission risk (P < 0.05; Table 2, Figure S3D-F). No significant associations emerged between discharge heart rate and clinical outcomes (Table S7).
Table 2.
Cox regression analysis of heart rate at 1 month and 3 months after discharge with outcomes in the AF group
| Outcomes | Exposure | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | ||
| HF readmission | Heart rate at 1 month after discharge (continuous) | 1.01 (0.99 ~ 1.03) | 0.273 | 1.01 (0.99 ~ 1.03) | 0.247 | 1.01 (0.99 ~ 1.03) | 0.260 | 1.01 (0.99 ~ 1.03) | 0.274 | 1.01 (0.99 ~ 1.03) | 0.369 |
| 73–80 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 40–72 bpm | 0.56 (0.27 ~ 1.14) | 0.110 | 0.54 (0.26 ~ 1.11) | 0.094 | 0.48 (0.23 ~ 1.01) | 0.053 | 0.48 (0.23 ~ 1.01) | 0.054 | 0.51 (0.24 ~ 1.09) | 0.081 | |
| 81–88 bpm | 0.70 (0.34 ~ 1.47) | 0.352 | 0.69 (0.33 ~ 1.45) | 0.332 | 0.60 (0.28 ~ 1.27) | 0.178 | 0.51 (0.23 ~ 1.12) | 0.091 | 0.50 (0.23 ~ 1.09) | 0.081 | |
| 89–150 bpm | 0.63 (0.30 ~ 1.31) | 0.213 | 0.62 (0.30 ~ 1.31) | 0.211 | 0.53 (0.25 ~ 1.14) | 0.103 | 0.54 (0.25 ~ 1.17) | 0.117 | 0.55 (0.25 ~ 1.22) | 0.139 | |
| Heart rate at 3 months after discharge (continuous) | 1.01 (0.99 ~ 1.03) | 0.212 | 1.01 (0.99 ~ 1.03) | 0.205 | 1.01 (0.99 ~ 1.03) | 0.258 | 1.01 (0.99 ~ 1.03) | 0.302 | 1.01 (0.99 ~ 1.03) | 0.274 | |
| 72–78 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 48–71 bpm | 0.56 (0.27 ~ 1.17) | 0.123 | 0.56 (0.27 ~ 1.18) | 0.126 | 0.56 (0.26 ~ 1.24) | 0.153 | 0.57 (0.26 ~ 1.27) | 0.170 | 0.66 (0.29 ~ 1.48) | 0.310 | |
| 79–83 bpm | 0.96 (0.44 ~ 2.10) | 0.910 | 0.94 (0.43 ~ 2.09) | 0.881 | 1.11 (0.47 ~ 2.58) | 0.817 | 1.18 (0.50 ~ 2.77) | 0.712 | 1.24 (0.53 ~ 2.89) | 0.625 | |
| 84–134 bpm | 0.95 (0.48 ~ 1.89) | 0.893 | 0.96 (0.48 ~ 1.89) | 0.894 | 0.93 (0.45 ~ 1.89) | 0.833 | 0.85 (0.40 ~ 1.78) | 0.659 | 0.96 (0.45 ~ 2.05) | 0.907 | |
| All-cause mortality | Heart rate at 1 month after discharge (continuous) | 1.00 (0.97 ~ 1.04) | 0.790 | 1.01 (0.97 ~ 1.04) | 0.724 | 1.01 (0.98 ~ 1.04) | 0.733 | 1.01 (0.98 ~ 1.04) | 0.711 | 1.02 (0.98 ~ 1.05) | 0.339 |
| 73–80 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 40–72 bpm | 2.16 (0.40 ~ 11.81) | 0.373 | 2.04 (0.37 ~ 11.17) | 0.409 | 1.69 (0.30 ~ 9.41) | 0.551 | 1.70 (0.30 ~ 9.55) | 0.544 | 1.67 (0.29 ~ 9.79) | 0.568 | |
| 81–88 bpm | 4.73 (0.96 ~ 23.45) | 0.057 | 5.04 (1.01 ~ 25.17) | 0.049 | 4.45 (0.86 ~ 22.95) | 0.074 | 4.33 (0.83 ~ 22.57) | 0.082 | 5.01 (0.92 ~ 27.42) | 0.063 | |
| 89–150 bpm | 3.06 (0.59 ~ 15.76) | 0.182 | 2.65 (0.51 ~ 13.75) | 0.247 | 2.32 (0.44 ~ 12.15) | 0.320 | 2.40 (0.45 ~ 12.74) | 0.303 | 3.10 (0.55 ~ 17.58) | 0.202 | |
| Heart rate at 3 months after discharge (continuous) | 1.02 (0.99 ~ 1.05) | 0.198 | 1.02 (0.99 ~ 1.05) | 0.213 | 1.02 (0.99 ~ 1.05) | 0.296 | 1.02 (0.99 ~ 1.05) | 0.292 | 1.02 (0.99 ~ 1.05) | 0.187 | |
| 72–78 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 48–71 bpm | 0.10 (0.01 ~ 0.76) | 0.026 | 0.10 (0.01 ~ 0.79) | 0.029 | 0.08 (0.01 ~ 0.62) | 0.016 | 0.08 (0.10 ~ 0.63) | 0.017 | 0.03 (0.00 ~ 0.34) | 0.004 | |
| 79–83 bpm | 0.19 (0.02 ~ 1.45) | 0.109 | 0.19 (0.02 ~ 1.46) | 0.110 | 0.17 (0.02 ~ 1.42) | 0.102 | 0.18 (0.02 ~ 1.45) | 0.106 | 0.14 (0.02 ~ 1.22) | 0.074 | |
| 84–134 bpm | 0.47 (0.15 ~ 1.47) | 0.192 | 0.46 (0.15 ~ 1.44) | 0.181 | 0.36 (0.11 ~ 1.18) | 0.092 | 0.36 (0.11 ~ 1.21) | 0.099 | 0.21 (0.05 ~ 0.88) | 0.033 | |
| All-cause mortality and/or HF readmission | Heart rate at 1 month after discharge (continuous) | 1.01 (0.99 ~ 1.03) | 0.236 | 1.01 (0.10 ~ 1.03) | 0.195 | 1.01 (1.0 ~ 1.03) | 0.196 | 1.01 (1.00 ~ 1.03) | 0.204 | 1.01 (1.00 ~ 1.03) | 0.154 |
| 73–80 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 40–72 bpm | 0.64 (0.33 ~ 1.23) | 0.179 | 0.61 (0.31 ~ 1.18) | 0.144 | 0.52 (0.26 ~ 1.03) | 0.060 | 0.53 (0.27 ~ 1.06) | 0.073 | 0.57 (0.28 ~ 1.14) | 0.112 | |
| 81–88 bpm | 1.11 (0.58 ~ 2.12) | 0.748 | 1.12 (0.59 ~ 2.14) | 0.735 | 0.94 (0.48 ~ 1.83) | 0.856 | 0.86 (0.43 ~ 1.72) | 0.677 | 0.86 (0.43 ~ 1.72) | 0.674 | |
| 89–150 bpm | 0.80 (0.41 ~ 1.54) | 0.499 | 0.76 (0.39 ~ 1.47) | 0.407 | 0.66 (0.33 ~ 1.30) | 0.225 | 0.68 (0.34 ~ 1.35) | 0.273 | 0.73 (0.36 ~ 1.49) | 0.393 | |
| Heart rate at 3 months after discharge (continuous) | 1.02 (1.00 ~ 1.03) | 0.057 | 1.02 (1.00 ~ 1.03) | 0.057 | 1.02 (1.00 ~ 1.03) | 0.070 | 1.02 (1.00 ~ 1.03) | 0.091 | 1.02 (1.00 ~ 1.03) | 0.070 | |
| 72–78 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 48–71 bpm | 0.36 (0.18 ~ 0.70) | 0.003 | 0.36 (0.18 ~ 0.71) | 0.003 | 0.33 (0.16 ~ 0.68) | 0.003 | 0.34 (0.17 ~ 0.70) | 0.003 | 0.34 (0.16 ~ 0.74) | 0.006 | |
| 79–83 bpm | 0.63 (0.31 ~ 1.29) | 0.208 | 0.62 (0.30 ~ 1.28) | 0.195 | 0.68 (0.32 ~ 1.47) | 0.329 | 0.71 (0.33 ~ 1.55) | 0.391 | 0.71 (0.33 ~ 1.54) | 0.383 | |
| 84–134 bpm | 0.71 (0.39 ~ 1.29) | 0.258 | 0.71 (0.39 ~ 1.29) | 0.256 | 0.66 (0.35 ~ 1.23) | 0.186 | 0.61 (0.32 ~ 1.16) | 0.133 | 0.61 (0.31 ~ 1.20) | 0.156 | |
Model 1: Crude
Model 2: Adjust: Age, Sex, Body mass index
Model 3: Adjust: Age, Sex, Body mass index, Smoking, Diabetes, Hypertension, Coronary artery disease, Renal dysfunction, Low density lipoprotein, Serum creatinine
Model 4:Adjust: Age, Sex, Body mass index, Smoking, Diabetes, Hypertension, Coronary artery disease, Renal dysfunction, Low density lipoprotein, Serum creatinine, B-type natriuretic peptide, Left ventricular ejection fraction
Model 5: Adjust: Age, Sex, Body mass index, Smoking, Diabetes, Hypertension, Coronary artery disease, Renal dysfunction, Low density lipoprotein, Serum creatinine, B-type natriuretic peptide, Left ventricular ejection fraction, β-blockers, Angiotensin converting enzyme inhibitor/Angiotensin receptor blocker, Mineralocorticoid receptor antagonists
Heart rate and all-cause mortality: Kaplan–Meier analysis in MIMIC-III
In the MIMIC-III cohort, heart rate at 3 months post-discharge were stratified into quartiles. Kaplan–Meier analysis revealed that both SR and AF patients showed no significant association with survival (P = 0.124; P = 0.193; Fig. 2). Adjusted Cox regression confirmed elevated mortality risk in SR patients with higher heart rate (94–121 bpm; HR = 1.52, 95%CI: 1.03 ~ 2.25, P = 0.037, Table 3).
Fig. 2.
Kaplan–Meier survival curves for all-cause mortality stratified by heart rate quartiles in the MIMIC-III cohort. Kaplan–Meier curve using heart rate data at 3-month after discharge from patients in the SR group (a) and the AF group (b). SR, sinus rhythm; AF, atrial fibrillation
Table 3.
Associations of heart rate at 3 months after discharge with all-cause mortality in the MIMIC-III cohort
| Group | Exposure | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | HR (95%CI) | P | ||
| SR | Heart rate (continuous) | 1.02 (1.01 ~ 1.03) | < 0.001 | 1.02 (1.01 ~ 1.03) | < 0.001 | 1.02 (1.01 ~ 1.03) | < 0.001 | 1.02 (1.01 ~ 1.03) | < 0.001 | 1.02 (1.01 ~ 1.03) | < 0.001 |
| 74–83 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 44–73 bpm | 0.79 (0.52 ~ 1.20) | 0.274 | 0.74 (0.49 ~ 1.13) | 0.168 | 0.74 (0.48 ~ 1.13) | 0.165 | 0.74 (0.48 ~ 1.13) | 0.157 | 0.75 (0.49 ~ 1.14) | 0.179 | |
| 84–93 bpm | 1.25 (0.85 ~ 1.85) | 0.262 | 1.25 (0.84 ~ 1.84) | 0.274 | 1.25 (0.84 ~ 1.85) | 0.276 | 1.19 (0.80 ~ 1.78) | 0.382 | 1.19 (0.80 ~ 1.78) | 0.383 | |
| 94–121 bpm | 1.46 (0.99 ~ 2.16) | 0.056 | 1.53 (1.04 ~ 2.26) | 0.033 | 1.50 (1.01 ~ 2.22) | 0.043 | 1.53 (1.03 ~ 2.26) | 0.035 | 1.52 (1.03 ~ 2.25) | 0.037 | |
| AF | Heart rate (continuous) | 1.01 (0.99 ~ 1.02) | 0.263 | 1.01 (1.00 ~ 1.03) | 0.134 | 1.01 (0.99 ~ 1.03) | 0.256 | 1.01 (0.99 ~ 1.03) | 0.211 | 1.01 (0.99 ~ 1.03) | 0.213 |
| 83–91 bpm | Ref | P | Ref | P | Ref | P | Ref | P | Ref | P | |
| 48–82 bpm | 0.71 (0.38 ~ 1.31) | 0.274 | 0.67 (0.36 ~ 1.26) | 0.215 | 0.67 (0.36 ~ 1.25) | 0.210 | 0.61 (0.32 ~ 1.16) | 0.129 | 0.61 (0.32 ~ 1.17) | 0.135 | |
| 92–102 bpm | 1.28 (0.73 ~ 2.24) | 0.393 | 1.27 (0.72 ~ 2.23) | 0.411 | 1.25 (0.70 ~ 2.21) | 0.450 | 1.18 (0.66 ~ 2.12) | 0.585 | 1.17 (0.65 ~ 2.11) | 0.592 | |
| 103–121 bpm | 0.71 (0.37 ~ 1.39) | 0.323 | 0.73 (0.38 ~ 1.43) | 0.365 | 0.66 (0.34 ~ 1.30) | 0.232 | 0.62 (0.31 ~ 1.24) | 0.176 | 0.62 (0.31 ~ 1.25) | 0.181 | |
Model 1: Crude
Model 2: Adjust: Age, Sex
Model 3: Adjust: Age, Sex, Hypertension, Cardiovascular disease
Model 4: Adjust: Age, Sex, Hypertension, Cardiovascular disease, β-blockers, Angiotensin converting enzyme inhibitor/Angiotensin receptor blocker, Mineralocorticoid receptor antagonists
Model 5: Adjust: Age, Sex, Hypertension, Cardiovascular disease, β-blockers, Angiotensin converting enzyme inhibitor/Angiotensin receptor blocker, Mineralocorticoid receptor antagonists, Digoxin
Heart rate and clinical outcomes: meta-analysis of mortality and readmission
Pooled analysis of 6 studies (n = 9253) suggested heart rate association with all-cause mortality (HR = 1.20, 95%CI: 0.98–1.46; random-effects, I2 = 72.1%; Fig. 3a). Four studies indicated a potential association between heart rate and readmission (HR = 1.25, 95%CI: 0.88–1.79; I2 = 76.5%; Fig. 3b). Neither association reached statistical significance, likely due to the limited number of studies and substantial heterogeneity. Begg’s (P = 0.368) and Egger’s tests (P = 0.080) did not indicate statistically significant asymmetry (Fig. 3c, d). We further conducted sensitivity and influence analyses. The leave-one-out approach demonstrated that no single study disproportionately influenced the overall hazard ratio estimates, confirming the stability of the results (Fig. 3e).
Fig. 3.
Forest plots illustrating the associations between high-level heart rate and all-cause mortality (a), readmission rate (b). Begg’s (c) and Egger’s tests (d) for publication bias. e Sensitivity and influence analysis
Discussion
This comprehensive investigation integrating prospective observational data, MIMIC-III database analyses, and meta-analytical evidence demonstrates that heart rate modulation during the vulnerable period post-discharge constitutes a critical determinant of prognosis in HF, with rhythm-specific patterns emerging as fundamental to risk stratification. The observational cohort analysis provides granular insights into rhythm-stratified relationships, establishing that heart rate during the vulnerable period holds significant prognostic value for HF patients.
By conducting retrospective analyses of the MIMIC-III database focused on HF readmission stratified by rhythm status, we demonstrated that among SR patients, there was a substantially elevated mortality risk manifested at higher rates (94–121 bpm) (Table 3, P = 0.037). Notably, our observational data indicate that for SR patients, 1-month post-discharge heart rate carry particular prognostic significance: maintaining rates above 77 bpm significantly increases the risk of all-cause mortality and/or HF readmission (Table 1). Integrating database and observation findings, we posit that for SR patients, sustaining heart rates below 77 bpm throughout the vulnerable period optimizes prognosis. This corroborates landmark trials including SHIFT [6] and EVEREST [7] which collectively establish 70–75 bpm as the optimal prognostic range, while mechanistic studies by Faragli et al. confirm that discharge heart rate ≥ 90 bpm independently predicts cardiovascular mortality (P = 0.016), with β-blocker therapy mitigating composite endpoint risk through heart rate modulation [18]. Contemporary studies corroborate this threshold: Xin et al. [19] in HFmrEF patients with SR and Shaikh et al. [20] in a post-discharge cohort collectively demonstrate that maintaining heart rate < 70 bpm significantly reduces clinical events. Our study extends this paradigm by defining not only the optimal heart rate control targets (≤ 73 bpm for SR) but also identifying the critical 1–3 month post-discharge window as the most prognostically informative period for intervention, thereby providing actionable clinical guidance for heart rate management goals during high-risk transitions of care.
For AF patients, our analyses reveal distinct prognostic relationships. While the MIMIC-III Kaplan–Meier curves exhibited no significant association between heart rate and survival, prospective data demonstrate that heart rate measured at 1 and 3 months post-discharge significantly correlate with outcomes. Specifically, AF patients exhibiting heart rates < 71 bpm at 3 months showed significantly reduced all-cause mortality and all-cause mortality/composite endpoint risks. Previous evidence indicates that maintaining SR offers a greater survival advantage compared to rate control alone [21], aligning with the understanding that AF is a significant predictor of mortality in critically ill patients [22]. Our findings further affirm that for patients with persistent AF, targeted rate control remains an essential therapeutic strategy. Current European guidelines advocate resting heart rates < 110 bpm (Class IIa) in AF patients but lack HF-specific recommendations [23]. In a large Chinese AF-HF cohort (n = 1760), optimal outcomes at 65–85 bpm [24] corroborate our threshold < 71 bpm [24], indicating a U-shaped risk relationship where excessive rate restriction may compromise outcomes.
The meta-analysis examining heart rate-prognosis relationships during this vulnerable window suggested potential associations whereby elevated heart rate levels may confer increased risks for both all-cause mortality and hospital readmission (Fig. 3). However, these associations failed to reach statistical significance in the pooled estimates (all-cause mortality: HR = 1.20, 95%CI: 0.98–1.46; readmission: HR = 1.25, 95%CI: 0.88–1.79), likely attributable to the limited number of qualifying studies and substantial heterogeneity (I2 > 72%) that constrained analytical power despite rigorous methodology and absence of detectable publication bias (Begg’s P = 0.368, Egger’s P = 0.080). Contemporary studies confirm the prognostic role of post-discharge heart rate: EVEREST trial analysis linked rates ≥ 70 bpm at 4 weeks to increased mortality [25], and Awan et al. associated heart rate increments within 60 days with readmission risk [26]. These findings substantiate early heart rate trajectory as an independent predictor of outcomes, supporting its use in risk stratification.
Clinical implications
Our findings provide important rhythm-stratified reference values for heart rate management during the vulnerable period. However, we wish to emphasize that these thresholds should serve as guiding parameters rather than rigid therapeutic targets. In clinical practice, optimal rate control must be individualized based on comprehensive assessment of each patient’s ejection fraction, hemodynamic status, comorbidity profile, and medication tolerance. For SR patients, targeting rates below 77 bpm may warrant careful β-blocker titration where clinically appropriate, while for AF patients, achieving rates below 71 bpm may require combination therapy including digoxin or dose-adjusted β-blockers. Ultimately, these rhythm-specific thresholds should be integrated with other clinical considerations to optimize patient outcomes.
Limitation
This study has several limitations. Our observational analyses, which identify rhythm-specific heart rate thresholds, may be influenced by unmeasured confounders, including β-blocker titration protocols, arrhythmia burden, and autonomic dysfunction. The meta-analysis was constrained by the limited number of eligible studies (n = 6) and substantial heterogeneity (I2 > 72%), precluding subgroup analyses and definitive conclusions despite the presence of directional trends. Given the exploratory nature of this study and multiple comparisons across timepoints and rhythm strata, all p-values were interpreted descriptively, and estimation with 95% confidence intervals was emphasized. Collectively, these factors underscore the need for future multicenter studies and meta analysis with standardized heart rate assessments, continuous monitoring technologies, and randomized trials testing rhythm-stratified targets during the critical vulnerable period. Nevertheless, convergent evidence confirms vigilant heart rate management represents a modifiable prognostic determinant in HF.
Conclusions
This integrated analysis demonstrates that heart rate during the vulnerable period independently predicts prognosis in HF, with rhythm-specific optimal thresholds: patients with SR exhibit reduced mortality at heart rates below 77 bpm but an increased risk above this range, while patients with AF show improved outcomes at heart rates below 71 bpm. Although these thresholds should be considered exploratory and require validation in randomized trials, they provide clinically actionable guidance for rhythm-stratified management. Meta-analytical trends supported directional associations, although they were limited by heterogeneity. Vigilant heart rate management within this post-discharge window represents a modifiable prognostic determinant, necessitating randomized trials of rhythm-stratified targets to optimize clinical outcomes.
Supplementary Information
Abbreviations
- AF
Atrial fibrillation
- BMI
Body mass index
- CCU
Coronary care unit
- HF
Heart failure
- ICU
Intensive care unit
- IQR
Interquartile range
- MICU
Medical intensive care unit
- MIMIC-III
Medical Information Mart for Intensive Care III
- NOS
Newcastle–Ottawa Scale
- RCT
Randomized controlled trial
- SICU
Surgical intensive care unit
- SR
Sinus rhythm
- SQL
Structured query language
- RCU
Respiratory care unit
- TICU
Transplant intensive care unit
Author contributions
All authors contributed to the study conception and design. Conceptualization, M.X., P.S., and D.G.; methodology, M.X., P.S.; software, Y.F., W.Z.; validation, M.X., P.S., and W.Z.; formal analysis, M.X., P.S.; investigation, M.X., P.S., Y.F., W.Z.; resources, M.X., P.S.; data curation, P.S., Y.F.; writing—original draft preparation, M.X., P.S.; writing—review and editing, D.G.; visualization, M.X., P.S., Y.F., and W.Z.; supervision, D.G.; project administration, D.G.; funding acquisition, Y.F., W.Z.,and D.G.. All authors read and approved the final manuscript.
Funding
This work was supported by financial support from the National Natural Science Foundation of China (82200424), the National Natural Science Foundation of China (82400368), and the Key R&D Plan of Shaanxi Province (2022SF-078).
Data availability
All data generated or analyzed during this study are included in this published article and its supplementary information files.
Declarations
Ethics approval and consent to participate
This study was approved by the Biomedical Ethics Committee, Xi’an Jiaotong University Second Affiliated Hospital (approval number: 2020064).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Min Xu and Peifei Shi contribute equally to this work.
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Data Availability Statement
All data generated or analyzed during this study are included in this published article and its supplementary information files.