Abstract
Background:
Diabetes Mellitus (DM) and hyperglycaemia (HG) have been identified as risk factors for morbidity and mortality in coronavirus disease 19 (COVID-19) infection. However, a detailed study of various categories of HG and the impacts and characteristics of each of these on COVID-19 was considered important to address this metabolic disorder in COVID-19.
Aims:
This study aimed to describe the patterns of HG and its impact on the clinical outcomes in hospitalised patients with COVID-19 infection.
Methodology:
Data on 1000 consecutive patients with COVID-19 were analysed using Statistical Package for Social Sciences (SPSS) version 20.0 software (SPSS Inc., Chicago, IL, USA).
Results:
A total of 1000 patients were included for analysis The overall mean age of the study group was 52.77 + 19.71 with 636 (63.6%) male patients; 261 had mild, 317 moderate, and 422 severe infections; and 601 had HG (New-onset DM 66, known DM 386, steroid-induced HG 133 and stress HG 16). The HG group has significantly higher levels of inflammatory markers and worse outcomes. Blood glucose levels were higher in patients with known DM. The ROC cut-off of total steroids to predict mortality in the HG group was 84 mg versus 60 mg in the normoglycaemia group. The ROC cut-off of FBS to predict mortality in the overall HG group was 165, with AUC 0.58 (95% CI 0.52, 0.63, P = 0.005), whereas that for pre-existing DM and steroid HG were 232 and 166, which were also significant. There was a wide variation in mean glucose levels against time.
Conclusion:
HG is an independent predictor of mortality, with the highest significance in the steroid-induced category. COVID-19 morbidity and mortality can be minimised by identifying the blood glucose range for best results and instituting appropriate treatment guidelines.
Keywords: COVID-19, hyperglycaemia, new-onset diabetes mellitus, outcomes, steroid-induced hyperglycaemia, stress hyperglycaemia
INTRODUCTION
Diabetes mellitus (DM) is a well-established risk factor for the incidence and severity of Severe Acute Respiratory Syndrome Coronavirus 2 infection (SARS CoV 2) or coronavirus disease 19 (COVID-19) from the beginning of the pandemic.[1] A meta-analysis has reported a strong association between DM and mortality, especially when blood glucose levels are not well controlled.[2] Apart from pre-existing DM, various mechanisms are postulated in the development of hyperglycaemia (HG) in COVID-19.[3] The major aetiologies include new-onset diabetes and stress HG due to insulin resistance from elevated cytokines and counter-regulatory hormones, as in any other severe illness. Beta cell dysfunction mediated through viral effects on ACE 2 receptors in the pancreas may lead to reduced insulin secretion.[4] In addition to these, adipocyte dysfunction with reduced release of adiponectin may add to insulin resistance.[5] Drugs that may derange glucose metabolism include steroids, now routinely used in moderate to severe COVID and catecholamines used in patients needing inotropic support.
Because of the multifactorial aetiology, HG in COVID may be categorised into pre-existing DM, new-onset DM, stress HG and steroid-induced HG. A recent meta-analysis compared the above excluding steroid-induced HG and concluded that new-onset HG carried a higher risk of mortality as compared to the other two.[6] A study on acute and long-term disruption of glycaemic control in COVID patients reported new-onset DM in 11.7% of patients. Furthermore, compared to controls, the acute COVID subgroup had a significantly higher AUC of the duration of glucose levels above 140 mg/dl reiterating higher glucose levels in COVID infection.[7]
Fasting glucose of above 126 mg% was identified as a risk factor for mortality by a group of investigators from China.[8] This study also documented ROC cut-offs of 110 mg/dl for initial and 130 for mid-term fasting blood glucose for predicting the severity of COVID.
Hence, we conducted the current study to assess the characteristics of the four subgroups of HG and their impact on clinical outcomes in hospitalised COVID patients. We also plotted the ROC (Receiver Operating Curve) for HG to define cut-offs to predict the severity and mortality of COVID in each category.
AIMS AND OBJECTIVES
This study aimed to describe the patterns of HG and its impact on the clinical outcomes in hospitalised patients with SARS CoV 2 infection.
METHODOLOGY
We conducted a retrospective study of 1000 laboratory-confirmed COVID-19 patients admitted to St. John’s Medical College Hospital (SJMCH) between April and December 2020. The study was approved with a waiver of consent by the Institutional Ethics Committee (IEC 216/2020). The clinical case records of patients admitted to COVID wards of SJMCH were retrieved from the medical records department. We screened 1000 consecutive medical in-patient charts for 9 months. Demographic data, medical history with co-morbidities, examination findings, laboratory reports, the severity of COVID infection, course in the hospital, treatment and outcome details were recorded.
Laboratory results collected included complete blood count, Neutrophil Lymphocyte Ratio and inflammatory biomarkers, such as C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH) and d- Dimer. Serial bedside monitoring of blood glucose levels using point-of-care meters, as documented in the standard glucose monitoring chart of the institution, done as per the instructions of the treating physician, for the available period were collected. HbA1c, in patients where available, was documented.
The laboratory parameters were assayed in the central laboratory of the institutions accredited by the National Accreditation Board for Testing and Calibration Laboratories (India).
A detailed account of the total dose and duration of corticosteroids was collected and the dosage equivalent of dexamethasone, where patients had received other types of steroids, was computed. Data on outcome measures, such as length of hospital stay, secondary bacterial infections and cardiovascular events (myocardial infarction, cerebrovascular accident, pulmonary embolism, stroke and deep vein thrombosis), were charted. The need for non-invasive ventilation (NIV), mechanical ventilation, ICU care and death were recorded.
Definitions:
The severity of COVID-19 was classified as mild, moderate, or severe as per Ministry of Health and Family guidelines in India.[9]
HG: Any elevation in either Fasting Plasma Glucose/Postprandial Plasma Glucose/random Plasma Glucose beyond its standard reference range of 100, 140 and 200 mg/dl, respectively, as set by the central institutional laboratory, irrespective of the aetiology.
Type 2 DM: As per ADA 2020 guidelines.[10]
Stress-induced HG: Any elevation in RBS ≥200 mg/dl and where available HbA1C <6.5 in a patient with no history of DM and no history of steroid use. As per ADA 2020 guidelines.[10]
New onset (newly diagnosed DM): New-onset diabetes refers to diabetes diagnosed for the first time at admission with COVID-19 (defined as per ADA guidelines with an elevated HbA1c of ≥6.5 in patients with elevated blood glucose values).[10]
Steroid-induced HG: Random blood glucose >140 mg/dl (repeated values) within the first 24–48 h of initiation of steroids for COVID-19 disease, with HbA1c <6.5% at presentation, is considered as “steroid-associated hyperglycemia”.[11]
Sample size estimation and statistical analysis
A sample size of 1000 patients was arbitrarily selected in view of the lack of previously conducted similar studies. Statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 25.0 software (SPSS Inc., Chicago, IL, USA). Descriptive data are represented as percentages and frequencies, mean with standard deviation and median with interquartile range. Prevalence of DM, stress HG and new-onset DM were reported and the impact of HG on clinical outcomes (length of hospital stay, complications, mechanical ventilation and death) was assessed using the Chi-square test, ANOVA and Kruskal–Wallis test as appropriate. Multiple comparisons were performed using the Bonferroni correction test or Mann–Whitney U-test. Student t-test was used to compare the mean difference in outcomes between the overall HG and normoglycaemia groups. ANOVA was performed to compare differences in outcomes (continuous variables) between the various HG groups. Multivariable logistic regression was performed to assess the association of mortality with HG categories adjusted for age and gender. Receiver Operating Characteristics curve was performed to assess the cut-off level for steroid doses and fasting blood sugar levels in predicting mortality in COVID-19 patients. A P value of <0.05 is considered statistically significant.
RESULTS
A total of 1000 patients admitted between April 2020 to December 2020 were included for analysis [Table 1]. The overall mean age of the study group was 52.7 ± 19.7 with 636 (63.6%) male patients. Three hundred and eighty-six (38.6%) patients had type 2 DM, 410 (41%) had hypertension, 95 (9.5%) ischaemic heart disease, 113 (11.3%) chronic kidney disease, 70 (7.1%) hypothyroidism, 24 (2.4%) Chronic obstructive pulmonary disease (COPD), 24 (2.4%) Bronchial asthma, 16 (1.6%) cancer and 5 (0.5%) had human immunodeficiency virus infection as their pre-existing comorbidities. The median duration of pre-existing type 2 DM was 6 (4,10) years with a mean HbA1c of 8.02 (± 2.73).
Table 1.
Baseline characteristics
| Parameters | |
|---|---|
| Age (years) | 52.7±19.7 |
| Male | 636 (63.4%) |
| Oxygen saturation on room air (%) | 91.6±9.39 |
| Mild COVID (%) | 261 (26.1) |
| Moderate COVID (%) | 315 (31.5) |
| Severe COVID (%) | 424 (42.4) |
| Glycemia status | |
| Known DM (%) | 386 (64.2%) |
| Steroid-induced hyperglycaemia (%) | 133 (22.1%) |
| New-onset DM (%) | 66 (10.9%) |
| Stress-induced hyperglycaemia (%) | 16 (2.6%) |
| Normal (%) | 399 (39.9%) |
| Duration of diabetes (years) | 6 (4,10) |
| Duration of hospitalisation (days) | 9 (5,15) |
| Duration dexamethasone (days) | 7 (4,12) |
| Total dose of dexamethasone (mg) | 60 (30, 122.5) |
| Duration of oxygen therapy (days) | 9 (4,18) |
| Duration of HFNO (days) | 5.50 (4,10) |
| BIPAP duration (days) | 5 (2,6) |
| ICU (intensive Care Unit) duration (days) | 7 (3,13) |
| Mechanical ventilation (days) | 7 (4,11) |
| Death (%) | 185 (18.5%) |
| Random Blood sugar (mg/dl) | 170.8±83.8 |
| Hba1C | 8.02±2.73 |
| Peak-d dimer | 516 (232,1167) |
| LDH | 348 (257,507) |
| Ferritin | 376 (157,815) |
| CRP | 5.99 (1.30, 15.80) |
Reported as n (%), mean±SD, median (25th, 75th percentiles)
Among the 1000 patients, 261 had mild, 317 had moderate and 422 had severe COVID infections. Forty-four patients developed diabetic ketoacidosis during admission for COVID infection.
Of the 1000 patients, 601 (60%) had various types of HG and 399 (40%) had normoglycaemia. The median Glycated Hb (HbA1c) was 8.2 (6.9, 10.2) in pre-existing DM as compared to 7.4 (6.7, 9.0) in the new-onset DM and 6 (5.6, 6.4) in both steroid and stress-induced HG groups. The median CRP among patients with pre-existing DM was 9.64 (2.89, 18.5), while it was 7.94 (3.5, 17.2), 4.3 (1, 19.4), 3.94 (0.66, 23) and 2.05 (0.43, 8.65) in steroid-induced HG, new-onset DM, stress HG and normoglycaemia, respectively. There was a statistically significant elevation in CRP among the HG groups as compared to the normoglycaemia group, P < 0.0001. The results were similar for other inflammatory biomarkers [Table 2].
Table 2.
Comparison of clinical characteristics and outcomes between glycaemic categories
| n (%) | P | |||||
|---|---|---|---|---|---|---|
|
| ||||||
| Known DM n=386 | Steroid hyperglycaemia n=133 | New-onset DM n=66 | Stress hyperglycaemia n=16 | Normal n=399 | ||
| Agec, d | 60.7±20.8 | 54.7±15.2 | 56.8±14.9 | 56.3 ± 16.2 | 43.6±16.8 | <0.0001 |
| Male | 269 (69.69%) | 90 (67.67%) | 39 (59.09%) | 8 (50%) | 230 (57.64%) | 0.003 |
| Mild COVID | 53 (13.7) | 5 (3.8) | 10 (15.2) | 5 (31.3) | 188 (47.1) | <0.001 |
| Moderate COVID | 116 (30.1) | 39 (29.3) | 19 (28.8) | 6 (37.5) | 135 (33.8) | |
| Severe COVID | 217 (56.2) | 89 (66.9) | 37 (56.1) | 5 (31.3) | 76 (19.04) | |
| ICU duration (days) | 8 (4, 13) | 6 (3.50, 12.5) | 11 (4, 18.00) | 6 (6, -) | 5 (3, 10.5) | 0.206 |
| Ventilation (days) | 7 (4, 11) | 7 (4, 12.0) | 7 (4.50, 20.0) | 12.5 (6.0, -) | 4.0 (2.50, 9.50) | 0.327 |
| Steroid dosagec (mg) | 72.0 (30.0, 144.0) | 66.0 (36, 148.0) | 72 (34.5, 147) | 54 (28.0, 201) | 34.0 (18.0, 72.0) | <0.0001 |
| HbA1Cc | 8.2 (6.9, 10.2) | 6.3 (5.7, 7.1) | 7.4 (6.7, 9.0) | 6 (5.6, 6.4) | 5.5 (5.2, 6.0) | <0.0001 |
| CRPc | 9.64 (2.89, 18.5) | 7.94 (3.50, 17.2) | 4.30 (0.99, 19.4) | 3.94 (0.66, 23.0) | 2.05 (0.43, 8.65) | <0.0001 |
| Ferritinc | 450 (209, 968) | 520 (172, 895) | 406 (226, 877) | 372 (179, 731) | 255 (110, 644) | <0.0001 |
| D-dimerc | 555 (271, 1215) | 718 (297, 1193) | 752 (306, 1257) | 369 (27, -) | 275 (160, 916) | <0.0001 |
| NLR ratioc | 8.25 (3.31, 15.3) | 7.76 (3.70, 15.0) | 9.17 (4.47, 18.6) | 7.85 (2.76, 14.2) | 3.38 (2.02, 7.72) | <0.0001 |
| Outcomes | ||||||
| Deatha, b | 83 (21.5) | 40 (30.1) | 19 (28.8) | 2 (12.5) | 41 (10.3) | <0.001 |
| NIVa, b | 90 (24.9) | 44 (36.4) | 17 (25.75) | 2 (14.3) | 22 (5.5) | <0.001 |
| Infectiona, b | 109 (28.2) | 54 (40.6) | 16 (24.2) | 4 (25.0) | 43 (10.8) | <0.0001 |
| Cardiovascular eventsa, b | 59 (15.3) | 32 (24.1) | 8 (12.1) | 2 (12.5) | 22 (5.5) | <0.0001 |
aKnown DM, steroid-induced hyperglycaemia, new-onset DM, stress-induced hyperglycaemia are significantly different compared to normal category; bSteroid-induced hyperglycaemia is significantly different known DM and normal category; cKnown DM, steroid-induced hyperglycaemia, new-onset DM significantly different from normal category, with no significant difference between stress-induced and normal category; dSteroid-induced hyperglycaemia is significantly different from known DM and normal category
The cumulative median dose of steroids (equivalent to dexamethasone) received was 72 mg (30,144) in pre-existing DM, 66 (36,148) in steroid-induced HG, 72 (34.5, 147) in new-onset DM and 54 (28, 201) in stress HG. Patients with normoglycaemia received 34 (18,72) mg of steroids. Patients with HG received significantly higher doses of steroids as compared to those with normal sugars, P < 0.0001.
Ninety patients (25%) required non-invasive ventilation (HFNO (High Flow Nasal Oxygen)/BIPAP (bilevel positive airway pressure) among patients with pre-existing DM, 44 (36.4%) among steroid-induced HG, 17 (25.8%) among new-onset DM, 2 (14.3%) among stress HG and 22 (5.5%) patients with normoglycaemia, and statistically significant patients with HG required NIV than those without P < 0.0001. A significantly higher proportion (69.7%) of HG (overall) patients had mortality compared to 30.3% in normoglycaemia category. Adjusted for age and gender, hyperglycaemic patients had higher odds of mortality (Adjusted odds ratio – 1.91, 95% CI. – 1.32, 2.76). The proportion of outcome events such as death, infections and cardiovascular events were significantly higher among pre-existing DM, steroid-induced HG and new-onset DM compared to normoglycaemia category (P < 0.01).
A significantly higher proportion of outcome events was noted in steroid-induced HG and new-onset DM compared to pre-existing DM (P < 0.05). In multivariable logistic regression analysis, adjusted for age and gender, new-onset DM (Adjusted odds ratio – 1.60, 95% CI. – 1.15, 2.23), steroid-induced HG (Adjusted odds ratio – 1.44, 95% CI. – 1.19, 1.73) and pre-existing DM (Adjusted odds ratio – 1.26, 95% CI. – 1.14, 1.39) had higher odds of mortality compared to the normoglycaemia category.
The mean fasting and prandial glucose levels were significantly elevated in patients with various forms of HG, P < 0.0001 [Table 3].
Table 3.
Comparison of mean blood glucose levels across time between glycaemic categories
| n (%) | P | |||||
|---|---|---|---|---|---|---|
|
| ||||||
| Known DM n=386 | Steroid hyperglycaemia n=133 | New-onset DM n=66 | Stress hyperglycaemia n=16 | Normal n=399 | ||
| Fasting glucosea, d | 203.3±59.5 | 177.7±56.9 | 185.7±43.8 | 185.7±43.8 | 119.1±29.5 | <0.0001 |
| 2 h post breakfasta, d | 231.4±63.7 | 207.1±63.4 | 217.8±53.1 | 183.5±57.1 | 134.1±32.5 | <0.0001 |
| Pre-luncha, b, d | 240.9±67.9 | 207.1±61.4 | 233.3±57.7 | 181.3±71.2 | 138.5±43.5 | <0.0001 |
| 2 hr post luncha, d | 240.9±67.9 | 207.1±61.3 | 233.3±57.7 | 181.3±71.0 | 138.6±43.5 | <0.0001 |
| Pre-dinnerc, d | 231.4±63.7 | 207.1±63.4 | 217.8±53.2 | 183.5±57.1 | 134.1±32.5 | <0.0001 |
| 2 h post dinnerc, d | 228.2±62.9 | 207.6±65.1 | 236.0±43.6 | 186.7±34.6 | 133.0±34.6 | <0.0001 |
aKnown DM, steroid-induced hyperglycaemia, new-onset DM, stress-induced hyperglycaemia are significantly different compared to normal category; bSteroid-induced hyperglycaemia is significantly different from other categories; cKnown DM, steroid-induced hyperglycaemia, new-onset DM significantly different from normal category, with no significant difference between stress-induced and normal category; dSteroid-induced hyperglycaemia is significantly different from known DM and normal category
The ROC cut-off of the total dose of steroids received to predict mortality among COVID patients in the overall HG group was 84 mg as compared to 60 mg for patients with normal glucose [Table 4].
Table 4.
Steroid dosage – ROC cut-off for mortality among COVID patients
| Mortality | AUC | 95% C.I. | P | Optimal cut-off point (mg) | Sensitivity | Specificity |
|---|---|---|---|---|---|---|
| Overall HG | 0.63 | 0.58, 0.69 | 0.0001 | 84 | 65 (56, 73) | 62 (56, 67) |
| Pre-existing DM | 0.57 | 0.50, 0.65 | 0.04 | 84 | 61 (48, 72) | 58 (51, 65) |
| New-onset DM | 0.69 | 0.53, 0.85 | 0.02 | 120 | 61 (35, 82) | 81 (64, 92) |
| Steroid-induced HG | 0.69 | 0.59, 0.79 | 0.0001 | 96 | 69 (51, 83) | 71 (60, 80) |
| Normal | 0.70 | 0.58, 0.82 | 0.0007 | 60 | 64 (42, 82) | 77 (69, 83) |
The ROC cut-off of FBS to predict mortality in the overall HG group was 165, with AUC 0.58 (95% CI 0.52, 0.63, P = 0.005), whereas that for pre-existing DM and steroid HG were 232 and 166, which were also significant [Table 5, Figures 1 and 2].
Table 5.
FBS-ROC cut-off for mortality among COVID patients
| Mortality | AUC | 95% C.I. | P | Optimal cut-off point (mg/dl) | Sensitivity | Specificity |
|---|---|---|---|---|---|---|
| Overall HG | 0.58 | 0.52, 0.63 | 0.005 | 165 | 81 (73, 87) | 37 (33, 42) |
| Pre-existing DM | 0.57 | 0.50, 0.6 | 0.05 | 232 | 43 (31, 55) | 75 (69, 79) |
| New-onset of DM | 0.58 | 0.43, 0.74 | 0.27 | 173 | 82 (56, 96) | 47 (32, 63) |
| Steroid-induced hyperglycaemia | 0.62 | 0.52, 0.73 | 0.02 | 166 | 74 (57, 87) | 57 (46, 67) |
| Normal | 0.59 | 0.47, 0.71 | 0.11 | 120 | 59 (36, 79) | 59 (53, 65) |
Figure 1.
ROC cut off for mortality among COVID 19 patients with normoglycemia and overall hyperglycemia
Figure 2.
ROC cut off for mortality among COVID 19 patients with pre-existing diabetes, steroid induced hyperglycemia, new onset diabetes and normoglycemia
Figures 3–5 depict mean glucose variation in pre-existing DM, steroid-induced HG and new-onset DM, respectively, over a period of 2 weeks.
Figure 3.
Mean glucose variation in pre-existing DM
Figure 5.
Mean glucose variation in new-onset DM
Figure 4.
Mean glucose variation in steroid hyperglycaemia
DISCUSSION
Our study details the clinical profile and outcomes of hospitalised patients with COVID-19 infection with and without HG. We studied a total of 1000 consecutive in-patients, with an overall mean age of 53 years and male predominance. Sixty percent of patients had some form of HG and 40% had normal sugar values throughout their stay in the hospital. Out of the 601 patients who had HG, the majority (64%) had pre-existing DM, 22% steroid-induced HG, 11% had new-onset DM and 2.6% had stress HG. Similar proportions and co-morbidities were reported by some previous studies.[12-15]
As reported in previous studies,[16,17] serum levels of C-reactive protein, ferritin, and d-Dimer were significantly higher in patients with HG compared with those without, suggesting that they are more susceptible to an inflammatory storm eventually leading to rapid deterioration in their clinical status.
The clinical outcomes of the need for NIV (HFNO/BIPAP), secondary bacterial infections, diabetic ketoacidosis and any cardiovascular event are significantly different in patients with any form of HG as compared to normoglycaemia. Among the categories of HG, patients with steroid-induced HG had significantly worse outcomes as compared to the other 3 hyperglycaemic categories.[17-20] Similarly, there was a significant difference in death between the HG and normoglycaemia groups. Steroid-induced HG had higher deaths as compared to patients with pre-existing DM.[21-23]
We found that patients received higher doses of steroids than recommended in our study [Table 3]. The significant cut-off of steroid dose, which could predict mortality, overall, among all types of HG was 84 mg, respectively, as compared to normoglycaemia patients which was 60 mg, respectively.
Patients on higher doses of steroids had more severe disease, increased duration of hospital/ICU stay and increased morbidity/mortality. Whether these effects were due to HG per se or compounded by more than required doses of steroids needs to be delineated.
The ROC cut-off of mean fasting glucose levels, for mortality in HG, was 165 mg/dl. The sensitivity and specificity for the cut-off are far from ideal suggesting that there may be wide variability in the spread of glucose values along with other contributory factors such as diet and concomitant medications.[24] The graphs [Figures 1-3] show that the mean glucose levels stayed above 190 in all the groups. This could be explained by intense insulin resistance coupled with insulin secretory deficiency and steroid therapy.[3] However, it is to be noted that the range suggested for the critically ill is between 140 and 180 mg% to have better outcomes.
In a study on critically ill patients (non-COVID) a mean glucose of 146 mg% was the threshold at which mortality started to rise, a value lower than our mean cut-off.[25] In the study by Xiao et al.,[8] fasting glucose cut-off of 110 and 130, respectively, for admission and mid-term blood glucose for severity, which are much lower than our mean readings. Hence, studies with continuous glucose monitoring to assess the patterns and levels of elevated glucose and to recommend targets for fasting and prandial glucose ranges for best results may help in better understanding. The currently recommended insulin increments for high blood glucose levels may not be sufficient to ensure within-range glycaemia and hence guidelines for appropriate and aggressive management of COVID HG will be required to ensure control of blood glucose for improving morbidity and mortality.
Limitations of the study
As data were collected from a real-world setting, few COVID wards were being managed by other specialty doctors other than physicians due to the unprecedented caseload and preparedness for the same, and the total numbers of cases varied widely for various parameters. Even though all doctors deployed for COVID management were trained in the management of HG as well, it was impossible for a physician to supervise the control of sugars.
There is also a referral bias as the patients in this cohort were referred from peripheral centres to the tertiary care hospital as the disease severity was higher. The patients enrolled in this study were not vaccinated and this may have contributed to the severity of the disease.
In conclusion, HG is an independent predictor of mortality, and the risk increases with COVID infection. Better control of sugars in-hospital becomes a ‘modifiable’ risk factor and may help in reducing the severity of disease, complications related to HG and death in those with and without DM. This needs to be confirmed with protocol-based testing and control of sugars, at least within the predicted cut-off values, to begin with.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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