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Zhang L, Wang Z, Xu F, Han D, Li S, Yin H, Lyu J. Effects of Stress Hyperglycemia on Short-Term Prognosis of Patients Without Diabetes Mellitus in Coronary Care Unit. Front Cardiovasc Med 2021; 8:683932. [PMID: 34095265 PMCID: PMC8169960 DOI: 10.3389/fcvm.2021.683932] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 04/19/2021] [Indexed: 01/08/2023] Open
Abstract
Background: Diabetes mellitus (DM) has a high morbidity and mortality worldwide, and it is a risk factor for cardiovascular diseases. Non-diabetic stress hyperglycemia is common in severely ill patients, and it could affect prognosis. This study aimed to analyze the influence of different blood glucose levels on prognosis from the perspective of stress hyperglycemia by comparing them with normal blood glucose levels and those of patients with DM. Methods: A retrospective study of 1,401 patients in coronary care unit (CCU) from the critical care database called Medical Information Mart for Intensive Care IV was performed. Patients were assigned to the following groups 1-4 based on their history of DM, random blood glucose, and HbA1c levels: normal blood glucose group, moderate stress hyperglycemia group, severe stress hyperglycemia group and DM group. The main outcome of this study was 30- and 90-day mortality rates. The associations between groups and outcomes were analyzed using Kaplan-Meier survival analysis, Cox proportional hazard regression model and competing risk regression model. Results: A total of 1,401 patients in CCU were enrolled in this study. The Kaplan-Meier survival curve showed that group 1 had a higher survival probability than groups 3 and 4 in terms of 30- and 90-day mortalities. After controlling the potential confounders in Cox regression, groups 3 and 4 had a statistically significant higher risk of both mortalities than group 1, while no difference in mortality risk was found between groups 2 and 1. The hazard ratios [95% confidence interval (CI)] of 30- and 90-day mortality rates for group 3 were 2.77(1.39,5.54) and 2.59(1.31,5.12), respectively, while those for group 4 were 1.92(1.08,3.40) and 1.94(1.11,3.37), respectively. Conclusions: Severe stress hyperglycemia (≥200 mg/dL) in patients without DM in CCU may increase the risk of short-term death, which is greater than the prognostic effect in patients with diabetes. Patients with normal blood glucose levels and moderate stress hyperglycemia (140 mg/dL ≤ RBG <200 mg/dL) had no effect on short-term outcomes in patients with CCU.
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Affiliation(s)
- Luming Zhang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zichen Wang
- Department of Public Health, University of California, Irvine, Irvine, CA, United States
| | - Fengshuo Xu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Didi Han
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Shaojin Li
- Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Haiyan Yin
- Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
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A single arm trial using passive simulated jogging for blunting acute hyperglycemia. Sci Rep 2021; 11:6437. [PMID: 33742027 PMCID: PMC7979828 DOI: 10.1038/s41598-021-85579-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/03/2021] [Indexed: 01/08/2023] Open
Abstract
Glycemic fluctuations increase oxidative stress, promote endothelial dysfunction and cardiovascular disease. Reducing glycemic fluctuations is beneficial. We previously reported that a portable motorized passive simulated jogging device, (JD) reduces 24 h glycemic indices in type 2 and non-diabetic subjects. This study evaluates effectiveness and feasibility of JD in blunting large glycemic fluctuation induced by an oral glucose tolerance test (OGTT). The study was performed in 10 adult participants mean age 41.3 ± 13.5 year using interstitial glucose monitor (IG). Each participant fasted for 8 h. followed by an OGTT (Pre-JD), thereafter JD was used for 90 min per day for 7 days, without change to diet or activities of daily living. A repeat OGTT (Post-JD) was performed after completion. The integrated area under the curve (iAUC2h–4h) was computed for the OGTT Pre-JD and Post-JD. Seven days of JD blunted the glucose fluctuation produced by OGTT. JD decreased AUC2h by 17 ± 4.7% and iAUC4h by 15 ± 5.9% (p < 0.03). In healthy mostly obese participants 7 days of JD blunts the hyperglycemic response produced by an OGTT. JD may be an adjunct to current glycemic management, it can be applied in different postures for those who cannot (due to physical or cognitive limitations) or will not exercise. Trial registration:ClinicalTrials.gov NCT03550105 (08-06-2018).
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Sathish T, Cao Y. What is the role of admission HbA1c in managing COVID-19 patients? J Diabetes 2021; 13:273-275. [PMID: 33270350 PMCID: PMC7754545 DOI: 10.1111/1753-0407.13140] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 01/08/2023] Open
Affiliation(s)
| | - Yingting Cao
- Non Communicable Disease Unit, Melbourne School of Population and Global HealthUniversity of MelbourneParkvilleVictoriaAustralia
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Wu J, Huang J, Zhu G, Liu Y, Xiao H, Zhou Q, Si X, Yi H, Wang C, Yang D, Chen S, Liu X, Liu Z, Wang Q, Lv Q, Huang Y, Yu Y, Guan X, Li Y, Nirantharakumar K, Cheng K, Peng S, Xiao H. Systemic Corticosteroids and Mortality in Severe and Critical COVID-19 Patients in Wuhan, China. J Clin Endocrinol Metab 2020; 105:5900930. [PMID: 32880390 PMCID: PMC7499588 DOI: 10.1210/clinem/dgaa627] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 09/01/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND Systemic corticosteroids are now recommended in many treatment guidelines, although supporting evidence is limited to 1 randomized controlled clinical trial (RECOVERY). OBJECTIVE To identify whether corticosteroids were beneficial to COVID-19 patients. METHODS A total of 1514 severe and 249 critical hospitalized COVID-19 patients from 2 medical centers in Wuhan, China. Multivariable Cox models, Cox model with time-varying exposure and propensity score analysis (inverse-probability-of-treatment-weighting [IPTW] and propensity score matching [PSM]) were used to estimate the association of corticosteroid use with risk of in-hospital mortality in severe and critical cases. RESULTS Corticosteroids were administered in 531 (35.1%) severe and 159 (63.9%) critical patients. Compared to the non-corticosteroid group, systemic corticosteroid use was not associated with beneficial effect in reducing in-hospital mortality in either severe cases (HR = 1.77; 95% CI, 1.08-2.89; P = 0.023), or critical cases (HR = 2.07; 95% CI, 1.08-3.98; P = 0.028). Findings were similar in time-varying Cox analysis. For patients with severe COVID-19 at admission, corticosteroid use was not associated with improved or harmful outcome in either PSM or IPTW analysis. For critical COVID-19 patients at admission, results were consistent with multivariable Cox model analysis. CONCLUSION Corticosteroid use was not associated with beneficial effect in reducing in-hospital mortality for severe or critical cases in Wuhan. Absence of the beneficial effect in our study in contrast to that observed in the RECOVERY clinical trial may be due to biases in observational data, in particular prescription by indication bias, differences in clinical characteristics of patients, choice of corticosteroid used, timing of initiation of treatment, and duration of treatment.
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Affiliation(s)
- Jianfeng Wu
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Jianqiang Huang
- Division of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Guochao Zhu
- Department of Critical Care Medicine, The Affiliated Hospital of Jianghan University (No. Six Hospital of Wuhan), Wuhan, Hubei, People’s Republic of China
| | - Yihao Liu
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Han Xiao
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Qian Zhou
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Xiang Si
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Hui Yi
- Division of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Cuiping Wang
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Daya Yang
- Department of Cardiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Shuling Chen
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Xin Liu
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Zelong Liu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Qiongya Wang
- Department of Critical Care Medicine, Wuhan Hankou Hospital, Wuhan, Hubei, People’s Republic of China
| | - Qingquan Lv
- Department of Gastrointestinal Surgery, Wuhan Hankou Hospital, Wuhan, Hubei, People’s Republic of China
| | - Ying Huang
- Science and Education Section, Wuhan Hankou Hospital, Wuhan, Hubei, People’s Republic of China
| | - Yang Yu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Jianghan University (No. Six Hospital of Wuhan), Wuhan, Hubei, People’s Republic of China
| | - Xiangdong Guan
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Krishnarajah Nirantharakumar
- Institute of Applied Health Research, University of Birmingham, United Kingdom
- Health Data Research UK, United Kingdom
| | - KarKeung Cheng
- Institute of Applied Health Research, University of Birmingham, United Kingdom
| | - Sui Peng
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
- Corresponding author: Sui Peng, MD, Ph.D, Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, No. 58, ZhongShan Road 2, Guangzhou, Guangdong 510080, People’s Republic of China, E-mail:
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China
- Precision Medicine Institute, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People’s Republic of China
- Corresponding author: Haipeng Xiao, MD, Ph.D, Department of Endocrinology & Precision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, No. 58, ZhongShan Road 2, Guangzhou, Guangdong 510080, People’s Republic of China, E-mail:
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Insufficient hyperfibrinolysis in COVID-19: a systematic review of thrombolysis based on meta-analysis and meta-regression. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020. [PMID: 32935113 DOI: 10.1101/2020.09.07.20190165] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background How aberrant fibrinolysis influences the clinical progression of COVID-19 presents a clinicopathological dilemma challenging intensivists. To investigate whether abnormal fibrinolysis is a culprit or protector or both, we associated elevated plasma D-dimer with clinical variables to identify a panoramic view of the derangements of fibrinolysis that contribute to the pathogenesis of COVID-19 based on studies available in the literature. Methods We performed this systematic review based on both meta-analysis and meta-regression to compute the correlation of D-dimer at admission with clinical features of COVID-19 patients in retrospective studies or case series. We searched the databases until Aug 18, 2020, with no limitations by language. The first hits were screened, data extracted, and analyzed in duplicate. We did the random-effects meta-analyses and meta-regressions (both univariate and multivariate). D-dimer associated clinical variables and potential mechanisms were schematically reasoned and graphed. Findings Our search identified 42 observational, or retrospective, or case series from six countries (n=14,862 patients) with all races and ages from 1 to 98-year-old. The weighted mean difference of D-dimer was 0.97 μg/mL (95% CI 0.65, 1.29) between relatively mild (or healthy control) and severely affected groups with significant publication bias. Univariate meta-regression identified 58 of 106 clinical variables were associated with plasma D-dimer levels, including 3 demographics, 5 comorbidies, 22 laboratory tests, 18 organ injury biomarkers, 8 severe complications, and 2 outcomes (discharge and death). Of these, 11 readouts were negatively associated with the level of plasma D-dimer. Further, age and gender were confounding factors for the identified D-dimer associated variables. There were 22 variables independently correlated with the D-dimer level, including respiratory rate, dyspnea plasma K+, glucose, SpO2, BUN, bilirubin, ALT, AST, systolic blood pressure, and CK. We thus propose that "insufficient hyperfibrinolysis (fibrinolysis is accelerated but unable to prevent adverse clinical impact for clinical deterioration COVID-19)" as a peculiar mechanism. Interpretation The findings of this meta-analysis- and meta-regression-based systematic review supports elevated D-dimer as an independent predictor for mortality and severe complications. D-dimer-associated clinical variables draw a landscape integrating the aggregate effects of systemically suppressive and locally (i.e., in the lung) hyperactive derangements of fibrinolysis. D-dimer and associated clinical biomarkers and conceptually parameters could be combined for risk stratification, potentially for tracking thrombolytic therapy or alternative interventions.
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Soares Pinheiro FGDM, Santana Santos E, Barreto ÍDDC, Weiss C, Vaez AC, Oliveira JC, Melo MS, Silva FA. Mortality Predictors and Associated Factors in Patients in the Intensive Care Unit: A Cross-Sectional Study. Crit Care Res Pract 2020; 2020:1483827. [PMID: 32802502 PMCID: PMC7416226 DOI: 10.1155/2020/1483827] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/30/2020] [Accepted: 05/29/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Mortality in the intensive care unit (ICU) has been associated to an array of risk factors. Identification of risk factors potentially contribute to predict and reduce mortality rates in the ICU. The objectives of the study were to determine the prevalence and the factors associated with the mortality and to analyze the survival. METHOD A cross-sectional study conducted in two clinical and surgical ICU in the state of Sergipe, northeastern Brazil. We enrolled 316 patients with at least 48 h of hospitalization, minimum age of 18 years old, sedated or weaned, with RASS ≥ -3, between July 2017 and April 2018. We categorized data in (1) age and gender, (2) clinical condition, and (3) prevalence of delirium. Data from enrolled patients were collected from enrollment until death or ICU discharge. Patients' outcomes were categorized in (1) death and (2) nondeath (discharge). RESULTS Twenty-one percent of participants died. Age (53 ± 17 years vs. 45 ± 18 years, p < 0.01), electrolyte disturbance (30.3% vs 18.1%, p=0.029), glycemic index (33.3% vs 18.2%, p=0.008), tube feeding (83.3% vs 67.1%, p=0.01), mechanical ventilation (50% vs 35.7%, p=0.035), sedation with fentanyl (24.2 vs 13.6, p=0.035), use of insulin (33.8% vs 21.7%, p=0.042), and higher Charlson score (2.61 vs 2.17, p=0.041) were significantly associated with death on the adjusted model. However, the regression model indicated that patients admitted from the emergency (HR = 0.40, p=0.006) and glycemic index alterations (HR = 1.68, p=0.047) were associated with mortality. There was no statistically significant difference (p=0.540) in survival between patients with and without delirium, based on the survival analysis and length of hospitalization. CONCLUSION The prevalence of death was 21%, and age, electrolyte disturbance, glycemic index, tube feeding, mechanical ventilation, sedation with fentanyl, use of insulin, and higher Charlson score were associated with mortality.
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Affiliation(s)
| | | | - Íkaro Daniel de C. Barreto
- Graduate Program of Biometrics and Applied Statistics, Federal Rural University of Pernambuco, Recife, Brazil
| | - Carleara Weiss
- Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Andreia C. Vaez
- Nursing Department, Federal University of Sergipe, São Cristóvão, Sergipe, Brazil
| | - Jussiely C. Oliveira
- Graduate Program in Nursing, Federal University of Sergipe, São Cristóvão, Sergipe, Brazil
| | - Matheus S. Melo
- Graduate Program in Nursing, Federal University of Sergipe, São Cristóvão, Sergipe, Brazil
| | - Francilene A. Silva
- Graduate Program in Health Sciences, Federal University of Sergipe, São Cristóvão, Sergipe, Brazil
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Abstract
Objectives: Poor glycemic control is associated with mortality in critical patients with diabetes. The aim of the study was to assess the predicting value of stress hyperglycemia in patients with diabetes following hospital admission for sepsis. Design: Retrospective observational study. Setting: Adult, emergency department, and critical care in a district hospital. Patients: In a 10-year retrospective analysis of sepsis-related hospitalizations in the emergency department, we carried out a secondary analysis of 915 patients with diabetes (males, 54.0%) in whom both fasting glucose at entry and glycosylated hemoglobin were available. Interventions: None. Measurements and Main Results: Patients’ mean age was 79.0 (sd 11.0), glucose at admission was 174.0 mg/dL (74.3 mg/dL), and glycosylated hemoglobin was 7.7% (1.7%). Stress hyperglycemia was defined by the stress hyperglycemia ratio, that is, fasting glucose concentration at admission divided by the estimated average glucose derived from glycosylated hemoglobin. A total of 305 patients died (33.3%) in hospital. Factors associated with in-hospital case fatality rate were tested by multivariable logistic model. Ten variables predicting outcomes in the general population were confirmed in the presence of diabetes (male sex, older age, number of organ dysfunction diagnoses, in particular cardiovascular dysfunction, infection/parasitic, circulatory, respiratory, digestive diseases diagnosis, and Charlson Comorbidity Index). In addition, also glycemic control (glycosylated hemoglobin: odds ratio, 1.17; 95% CI, 1.15–1.40) and stress hyperglycemia (stress hyperglycemia ratio: 5.25; 3.62–7.63) were significant case fatality rate predictors. High stress hyperglycemia ratio (≥ 1.14) significantly increased the discriminant capacity (area under the receiver operating characteristic curve, 0.864; se, 0.013; p < 0.001). Conclusions: Stress hyperglycemia, even in the presence of diabetes, is predictive of mortality following admission for sepsis. Stress hyperglycemia ratio may be used to refine prediction of an unfavorable outcome.
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Wu J, Huang J, Zhu G, Wang Q, Lv Q, Huang Y, Yu Y, Si X, Yi H, Wang C, Liu Y, Xiao H, Zhou Q, Liu X, Yang D, Guan X, Li Y, Peng S, Sung J, Xiao H. Elevation of blood glucose level predicts worse outcomes in hospitalized patients with COVID-19: a retrospective cohort study. BMJ Open Diabetes Res Care 2020; 8:8/1/e001476. [PMID: 32503812 PMCID: PMC7298690 DOI: 10.1136/bmjdrc-2020-001476] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/15/2020] [Accepted: 05/24/2020] [Indexed: 12/25/2022] Open
Abstract
INTRODUCTION With intense deficiency of medical resources during COVID-19 pandemic, risk stratification is of strategic importance. Blood glucose level is an important risk factor for the prognosis of infection and critically ill patients. We aimed to investigate the prognostic value of blood glucose level in patients with COVID-19. RESEARCH DESIGN AND METHODS We collected clinical and survival information of 2041 consecutive hospitalized patients with COVID-19 from two medical centers in Wuhan. Patients without available blood glucose level were excluded. We performed multivariable Cox regression to calculate HRs of blood glucose-associated indexes for the risk of progression to critical cases/mortality among non-critical cases, as well as in-hospital mortality in critical cases. Sensitivity analysis were conducted in patient without diabetes. RESULTS Elevation of admission blood glucose level was an independent risk factor for progression to critical cases/death among non-critical cases (HR=1.30, 95% CI 1.03 to 1.63, p=0.026). Elevation of initial blood glucose level of critical diagnosis was an independent risk factor for in-hospital mortality in critical cases (HR=1.84, 95% CI 1.14 to 2.98, p=0.013). Higher median glucose level during hospital stay or after critical diagnosis (≥6.1 mmol/L) was independently associated with increased risks of progression to critical cases/death among non-critical cases, as well as in-hospital mortality in critical cases. Above results were consistent in the sensitivity analysis in patients without diabetes. CONCLUSIONS Elevation of blood glucose level predicted worse outcomes in hospitalized patients with COVID-19. Our findings may provide a simple and practical way to risk stratify COVID-19 inpatients for hierarchical management, particularly where medical resources are in severe shortage during the pandemic.
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Affiliation(s)
- Jianfeng Wu
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jianqiang Huang
- Division of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Guochao Zhu
- Department of Critical Care Medicine, The Affiliated Hospital of Jianghan University (No. Six Hospital of Wuhan), Wuhan, China
| | - Qiongya Wang
- Department of Critical Care Medicine, Wuhan Hankou Hospital, Wuhan, China
| | - Qingquan Lv
- Department of Gastrointestinal Surgery, Wuhan Hankou Hospital, Wuhan, China
| | - Ying Huang
- Science and Education Section, Wuhan Hankou Hospital, Wuhan, China
| | - Yang Yu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Jianghan University (No. Six Hospital of Wuhan), Wuhan, China
| | - Xiang Si
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Hui Yi
- Division of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Cuiping Wang
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yihao Liu
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Han Xiao
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Qian Zhou
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xin Liu
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Daya Yang
- Department of Cardiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiangdong Guan
- Department of Critical Care Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Sui Peng
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Joseph Sung
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
- Precision Medicine Institute, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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