A moderately higher time-in-range threshold improves the prognosis of type-2 diabetes patients complicated with COVID-19

A moderately higher time-in-range threshold improves the prognosis of type-2 diabetes patients complicated with COVID-19 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A moderately higher time-in-range threshold improves the prognosis of type-2 diabetes patients complicated with COVID-19 Riping Cong, Jianbo Zhang, Lujia Xu, Yujian Zhang, Hao Wang, Jing Wang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3859033/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract After fully lifting coronavirus disease 2019 (COVID-19) pandemic control measures in mainland China in 12/2022, the incidence of COVID-19 has increased markedly, making it difficult to meet the general time-in-range (TIR) requirement. We investigated a more clinically practical TIR threshold and examined its association with the prognosis of COVID-19 patients with type-2 diabetes. Sixty-three type-2 diabetes patients complicated with COVID-19 were evaluated. Patient information included epidemiological and laboratory characteristics, treatment options and outcomes. The percentages of time-above-range (TAR), time-below-range (TBR) and TIR were calculated from intermittently scanned continuous glucose monitoring. The composite end point included a >20-day length of stay, intensive care unit admission, mechanical ventilation use, or death. TIR with thresholds of 80 to 190 mg/dL was significantly associated with favorable outcomes. An increase of 1% in TIR is connected with a reduction of 3.70% in the risk of adverse outcomes. The Youden index was highest when the TIR was 54.73%, and the sensitivity and specificity were 58.30% and 77.80%, respectively. After accounting for confounding variables, our analysis revealed that threshold target ranges (TARs) ranging from 200 mg/dL to 230 mg/dL significantly augmented the likelihood of adverse outcomes.The TIR threshold of 80 to 190 mg/dL has a comparatively high predictive value of the prognosis of COVID-19. TIR >54.73% was associated with a decreased risk of adverse outcomes. These findings provide clinically critical insights into possible avenues to improve outcomes for COVID-19 patients with type-2 diabetes. Health sciences/Diseases/Infectious diseases/Viral infection Health sciences/Diseases/Respiratory tract diseases Health sciences/Health care/Geriatrics Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes mellitus Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The Chinese Center for Disease Control and Prevention has reported that since the pandemic control measures of COVID-19 were fully lifted in mainland China in 12/2022, the peak number of COVID-19 nucleic acid-positive cases had reached 6.94 million, admissions to hospitals had reached a peak of 1.625 million, of which the highest number of severe cases had reached 128 thousand, and the cumulative number of deaths had reached 4273 by January 2023. Diabetes has already become the second most common comorbidity of COVID-19 due to the coinciding of two global pandemics 1,2 . A meta-analysis including 7 studies with 1,576 patients showed the prevalence of diabetes of approximately 9.7% (95% CI: 7.2-12.2%) 3 . Another meta-analysis was a comprehensive systematic search including data from 76,993 patients 4 . According to this study, the prevalence of diabetes was estimated to be 7.87% (95% CI: 6.57-9.28%). Poor glycemic control increased the risk of mortality, morbidity, and secondary infections 5,6 . These associations between diabetes and worse outcomes in COVID-19 patients were incontrovertible, as blood glucose fluctuation was not conducive to the improvement of disease, and inflammation caused by hyperglycemia led to increased mortality 7,8 . However, excessively tight glycemic control may increase the risk of hypoglycemia, which also increased mortality 9 . A study showed that the mortality rate was found to be significantly lower when aiming for a blood glucose target of 180 mg or less per deciliter, compared to targeting a range of 81 to 108 mg per deciliter. 10 . American Diabetes Association guidelines recommend targeting blood glucose < 180 mg/dL in critically ill patients 11,12 . Clinicians face a significant challenge in improving outcomes for individuals with COVID-19 and type-2 diabetes due to uncertainty surrounding the optimal degree of glycemic management and its potential impact on treatment benefits and risks. The definition of optimal blood glucose control remains controversial 13 . The wide application of hormonal and nutritional support treatment has led to significant fluctuations in blood glucose levels in clinical practice, making it challenging to maintain the general range. Consequently, our study aimed to analyze glycemic profiles using intermittently scanned continuous glucose monitoring (isCGM) to determine a more clinically practical threshold for TIR and investigate its correlation with prognosis. Methods Study design and participants In our observational study, data of patients admitted to Qilu Hospital from Dec 2022 to Apr 2023 were analyzed. The patients all had moderate or severe cases and were diagnosed according to the guidelines issued by the World Health Organization (WHO) 14 , meeting at least the following criteria: positive COVID-19 RNA PCR and characteristic imaging manifestations of novel coronavirus pneumonia. Exclusion criteria included patients who were intubated on admission and those younger than 18 years of age. The present study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human participants were approved by the Ethics Committee for the Qilu Hospital of Shandong University (Ethical Approval Number: KYLL-202307-047). Given that the medical records or biological specimens utilized in this study were acquired during prior clinical consultations and presented no more than minimal risk to the participants, an application has been submitted for exemption from obtaining informed consent documentation. Trial Registration: clinicaltrials.gov Identifier: NCT06156137 (Registered November 24, 2023). Patient information that we collected through electronic medical records include gender, age, vital signs, symptoms on admission, duration of diabetes, comorbidities, fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), total cholesterol (TC), triglycerides (TG), serum creatinine, uric acid, eGFR 15 , inflammatory biomarkers, brain natriuretic peptide (BNP), CK-MB and medication, including oral hypoglycemic agent (OHA), insulin, anticoagulant drugs and glucocorticoids. CGM was initiated on admission. Diabetic meals were ordered for all patients during hospitalization. All type-2 diabetes patients were equipped with isCGM sensors (FreeStyle Libre Flash glucose monitoring system; Abbott Diabetes Care Ltd, UK) on admission, and the nurse retrieved the probe when the patient was discharged or when the composite endpoint was reached. Measures of glycemic variability, such as TIR, TAR, TBR, mean sensor glucose level and associated CVs, were calculated from isCGM records. TIR was defined as the percentage of time within the following ranges: 70-180 mg/dL, 80-190 mg/dL, 90-200 mg/dL, 100-210 mg/dL, 110-220 mg/dL, and 120-230 mg/dL. A composite adverse outcome included a hospital stay of more than 20 days, admission to the intensive care unit, the need for mechanical ventilation, and death. Statistical analysis All data were analyzed using the SPSS software v.25(IBM Corporation, Armonk, NY). The normal distribution of continuous variables was checked by the Shapiro-Wilk test. Nonnormally distributed variables are presented as the median (IQR), and the Mann‒Whitney U test and Kruskal‒Wallis ANOVA were used for comparisons between groups. Categorical variables were expressed as numbers (percentages) and were compared using the χ2 test or Fisher’s exact test. To identify the covariates for inclusion in the multivariate analysis, a univariate logistic regression was initially performed. Candidate covariates were selected based on a significance level of P < 0.05 in the univariate analysis. Subsequently, multivariable-adjusted logistic regression models were employed to evaluate the association between TIR using isCGM and composite adverse outcomes. All analyses were adjusted for age, sex, CK-MB, symptoms on admission, LDH, use of OHA and anticoagulant. A receiver-operating characteristic (ROC) curve was constructed with TIR as the independent variable and prognosis as the dependent variable, and the diagnostic value of TIR was assessed based on the area under the curve (AUC). The optimal cutoff value was determined using the Youden index. All statistical tests were two-sided, and a significance level of P < 0.05 was considered statistically significant. Odds ratios (ORs) with 95% CIs are presented. Results Clinical Characteristics of Patients With COVID-19 and Type-2 Diabetes Upon Admission This study included a total of 63 patients who met the inclusion criteria (Figure 1). Among them, the mean age was 71.59±12.24 years, including 42.90% female and 57.10% male. Twenty-seven of the 63 patients experienced composite adverse outcomes. The characteristics of these patients are presented in Table 1. Comparison of TIR Between the Adverse and Favorable Outcome Groups Patients with composite adverse outcomes exhibited significantly lower TIR values compared to those with favorable outcomes(P < 0.05)(Figure. 2). Univariate and multivariable logistic regression models were used to analyze data from all 63 patients. Univariate regression analysis showed that TIR variables (0.977 [0.957-0.996], 0.968 [0.947-0.990], 0.960 [0.935-0.985], 0.957 [0.930-0.984], 0.958 [0.931-0.986], 0.963 [0.936-0.991]) were associated with a decreased risk of the composite outcome (Figure 3). Univariate logistic regression analysis of composite outcomes is shown in Table 2. After adjustment for multiple covariates, TIRs (0.975 [0.948-1.002], 0.963 [0.932-0.995], 0.951 [0.916-0.988], 0.952 [0.915-0.990], 0.960 [0.926-0.995], 0.967 [0.934-1.001]) exhibited a significant association with reduced odds of composite adverse outcomes (Table 3). Thus, a TIR of 80–190 mg/dL was significantly associated with favorable outcomes. TIR Predicted the Prognosis of Type-2 Diabetes Patients With COVID-19 The multivariate logistic regression analysis revealed that the TIRs of 80-190, 90-200, and 100-210 mg/dL remained as independent predictors of composite adverse outcomes even after adjusting the multiple covariates. ROC analysis was employed to evaluate the prognostic value of TIR for COVID-19 patients with diabetes. The test variables were defined as TIRs within the ranges of 80–190,90–200,and100–210mg/dL while the state variable was represented by composite adverse outcomes in patients (Figure. 4A). The area under the ROC curve was 0.713 (95% CI: 0.585–0.841, P = 0 .004), 0.739 (95% CI: 0.614–0.863, P = 0 .0013), and 0.748 (95% CI: 0.624–0.872, P < 0 .001). The area under the curve is maximized when TIR exhibits high predictive value for COVID-19 patient prognosis. Although the TIR (100-210 mg/dL) had the largest area under the ROC curve, it was not significantly different from the other two ROC curves. This does not indicate that the TIR (100-210 mg/dL) has higher prognostic value than the TIR (80-190 mg/dL) and the TIR (90-200 mg/dL). In all patients, the TIR of 80-190 mg/dL corresponds to 54.73% and maximizes the Youden index, with a sensitivity and specificity of 58.3% and 77.8%, respectively (Figure. 4B). Discussion Data from this cross-sectional study showed that optimal glycemic control during hospitalization was associated with a lower risk of severe illness and death in patients with COVID-19. After adjusting for covariates, maintaining TIR within the thresholds of 80 to 190 mg/dL significantly relates to favorable outcomes. In our study, the patient population was divided into two cohorts based on the occurrence of composite adverse events. The proportion of severe COVID-19 cases at admission was higher in the population with composite adverse events than in the second cohort (63% vs 33.3%, P = 0.002). Although patients with composite adverse outcomes were more likely to be male and older than 65 years with comorbidities and higher levels of inflammatory, endothelial, and coagulopathy markers on admission, there was no significant difference between the two groups. Patients achieving composite adverse outcomes had significantly higher CK-MB and LDH levels on admission. When analyzing TIR as a factor influencing outcome, all of the above confounding variables were adjusted for to reach the following conclusion: TIR values with thresholds of 80 to 190 mg/dL were significantly associated with a lower risk of the composite adverse outcomes. Previous studies have shown that variability is a potential risk predictor of death and other complications 4,23 . The presence of COVID-19 has been shown to play a significant role in impairing blood glucose control within the range of 70-150 mg/dL 13 . Zhu et al. showed improved outcomes in well-controlled type-2 diabetes patients with COVID-19 infection 17 . A small-sample study suggested that maintaining TIR (70-160 mg/dL) >70% could improve outcomes. In clinical practice, we found that only 15.87% of patients achieved that target, and the average TIR in our study was 39.36% during the pandemic. Inpatient medication (corticosteroids) and enteral and parenteral nutrition contribute to hyperglycemia 18 . The widespread use of glucocorticoids caused patients to experience wide fluctuations in blood glucose levels, which may have more adverse effects than sustained hyperglycemia. In our study, more than 70% of the patients were received hormone therapy, and 75% were treated with enteral or parenteral nutrition, which resulted in a high mean sensor glucose level [203.57 mg/dL (162.7-235.88)] and a wide CV of glucose values [33.29% (27.88 to 37.62)]. This also explained why the TIR threshold of COVID-19 patients with type-2 diabetes was higher. Moreover, the elevation of cortisol levels resulting from COVID-19 infection, stress, and similar factors can contribute to excessive hepatic gluconeogenesis, impaired glucose utilization, and insulin deficiency 19-21 . There is a suggested direct impact of SARS-CoV-2 on pancreatic β-cell function and survival, exacerbating rapid and severe metabolic deterioration in individuals with preexisting diabetes 22,23 . Angiotensin-converting enzyme 2 (ACE 2) potentially serves as a crucial molecular link between COVID-19 severity and insulin resistance 24-26 . Our findings support this hypothesis, as the patients who achieved the composite adverse outcomes had a significantly lower TIR (80-190 mg/dL) and a higher TAR >190 mg/dL. Furthermore, they used a higher maximum insulin dose during hospitalization [34(18-47) vs. 19(0-40), P = 0.046]. In this study, we found that poor glycemic control was associated with a worse outcome that included a higher need for medical intervention, hospitalization, and mortality. The insights gained here provide direct suggestions for the clinical management of type-2 diabetes during the COVID-19 pandemic. Excessive glycemic control leading to severe hypoglycemia has been associated with increased mortality rates 9 . The international consensus on TIR 27 indicated that although evidence regarding TIR for older or high-risk individuals is limited, several studies have demonstrated an elevated risk for hypoglycemia. Therefore, they reduced the TIR target from 70% to 50%. In our study, the age of enrolled patients was relatively high, the mean age was 71.59 ± 12.24 years old, and the TIR (80 to 190 mg/dL) corresponded to 54.73% and had a maximum Youden index. This cutoff value had good clinical significance. The major advantage of our study lies in the utilization of the isCGM system for type-2 diabetes patients complicated with COVID-19, enabling comprehensive assessment of hyperglycemia, hypoglycemia, and glycemic variability. Our study has presented the appropriate threshold and cutoff point for TIR in patients with COVID-19 and type-2 diabetes, which is more relevant to clinical practice. However, several limitations need to be acknowledged. Firstly, it was a retrospective study, which may introduce patient selection bias. Secondly, the sample size was relatively modest and might not fully capture the complexity of the general population. Therefore, large-scale prospective cohort studies involving ethnically diverse cohorts from different geographical regions are warranted to gain a better understanding of the association between glycemic control and COVID-19 progression. Finally, it should be noted that our analysis excluded individuals with type 1 diabetes, but glycemic control could also influence their outcomes. Conclusions In conclusion, maintaining a TIR (80–190 mg/dL) above 54.73% independently correlates with a significant reduction in composite adverse outcomes associated with COVID-19 infection among patients with type-2 diabetes. These findings provide valuable insights into the clinical characteristics of glycemic variability in individuals affected by both COVID-19 and type-2 diabetes while offering potential avenues for improving disease outcomes. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Riping Cong and Kuanxiao Tang signed the study. Hao Wang and Jing Wang provided the CGM data of ICU. Riping Cong, Lujia Xu, Yingli Diao, Yujian Zhang and Wei Wang collected the other data. Jianbo Zhang, Haijiao Liu and Jing Zhang cleaned the data. Riping Cong, Lujia Xu and Jianbo Zhang performed the statistical analysis and wrote the draft of the manuscript. Kuanxiao Tang rescanned and edited the manuscript. All authors read and approved the final manuscript. Kuanxiao Tang was the guarantor of this work, as such, had full access to all the data in the study. Funding This research received no external funding. Acknowledgments The authors acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors. Data Availability Statement The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request. References Zhou F, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395 , 1054-1062. doi: 10.1016/S0140-6736(20)30566-3 (2020) Papazafiropoulou AK, Antonopoulos S. The COVID-19 pandemic and diabetes mellitus. Archives of Medical Science. Atherosclerotic Diseases 5 , 200-205. doi: 10.5114/amsad.2020.97435 (2020) Yang J, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis 94 , 91-95. doi: 10.1016/j.ijid.2020.03.017 (2020) Emami A, Javanmardi F, Pirbonyeh N, Akbari A. Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis. Arch Acad Emerg Med 8 , e35. doi: 10.1016/j.jcv.2020.104371(2020) Nathella PK, Babu S. Influence of diabetes mellitus on immunity to human tuberculosis. Immunology 152 , 13-24. doi: 10.1111/imm.12762 (2017) Katulanda P, et al. Prevention and management of COVID-19 among patients with diabetes: an appraisal of the literature. Diabetologia 63 , 1440-1452. doi: 10.1007/s00125-020-05164-x (2020) Forbes A, Murrells T, Mulnier H, Sinclair AJ. Mean HbA1c, HbA1c variability, and mortality in people with diabetes aged 70 years and older: a retrospective cohort study. Lancet Diabetes Endocrinol 6 , 476-486. doi: 10.1016/S2213-8587(18)30048-2 (2018) Zhu L, et al. Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes. Cell Metab 31 , 1068-1077.e3. doi: 10.1016/j.cmet.2020.04.021(2020) Rodriguez-Gutierrez R, Gonzalez-Gonzalez JG, Zuñiga-Hernandez JA, McCoy RG. Benefits and harms of intensive glycemic control in patients with type 2 diabetes. BMJ 367 , l5887. doi: 10.1136/bmj.l5887 (2019) Hemmingsen B, et al. Intensive glycaemic control for patients with type 2 diabetes: systematic review with meta-analysis and trial sequential analysis of randomised clinical trials. BMJ 343 , d6898. doi: 10.1136/bmj.d6898 (2011) Jacobi J, et al. Guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients. Crit Care Med 40 , 3251-3276. doi: 10.1097/CCM.0b013e3182653269 (2012) American Diabetes Association Professional Practice Committee. 16. Diabetes Care in the Hospital: Standards of Medical Care in Diabetes-2022. Diabetes Care 45 , S244–S253. doi: 10.2337/dc22-S016 (2022) Kapoor R, Timsina LR, Gupta N, Kaur H, Vidger AJ, Pollander AM, et al. Maintaining blood glucose levels in range (70-150 mg/dl) is difficult in COVID-19 compared to Non-COVID-19 ICU patients-A retrospective analysis. J Clin Med 9 , 3635. doi: 10.3390/jcm9113635 (2022) Agarwal A, Hunt B, Stegemann M, Rochwerg B, Lamontagne F, Siemieniuk RA, et al. A living WHO guideline on drugs for covid-19. BMJ 370 , m3379. doi: 10.1136/bmj.m3379 (2020) Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 150 , 604-612. doi: 10.7326/0003-4819-150-9-200905050-00006 (2009) Shen Y, Fan X, Zhang L, Wang Y, Li C, Lu J, et al. Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A retrospective exploratory study using continuous glucose monitoring. Diabetes Care 44 , 976-982. doi: 10.2337/dc20-1448 (2021) Lanspa MJ, Krinsley JS, Hersh AM, Wilson EL, Holmen JR, Orme JF, et al. Percentage of time in range 70 to 139 mg/dl is associated with reduced mortality among critically ill patients receiving IV insulin infusion. Chest 156 , 878-886. doi: 10.1016/j.chest.2019.05.016 (2019) Dungan KM, Braithwaite SS, Preiser JC. Stress hyperglycaemia. Lancet 373 , 1798-1807. doi: 10.1016/S0140-6736(09)60553-5 (2009) He X, et al. COVID-19 induces new-onset insulin resistance and lipid metabolic dysregulation via regulation of secreted metabolic factors. Signal Transduct Target Ther 6 , 427. doi: 10.1038/s41392-021-00822-x (2021) Apicella M, et al. COVID-19 in people with diabetes: understanding the reasons for worse outcomes. Lancet Diabetes Endocrinol 8 , 782-792. doi: 10.1016/S2213-8587(20)30238-2 (2022) Drucker DJ. Diabetes, obesity, metabolism, and SARS-CoV-2 infection: the end of the beginning. Cell Metab 33 , 479-498. doi: 10.1016/j.cmet.2021.01.016 (2021) Hamming I, et al. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. J Pathol 203 , 631-7. doi: 10.1002/path.1570 (2004) Hoffmann M, et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell 181 , 271-280. doi: 10.1016/j.cell.2020.02.052 (2020) Kuba K, et al. A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus-induced lung injury. Nat Med 11 , 875-9. doi: 10.1038/nm1267 (2005) Yang JK, Lin SS, Ji XJ, Guo LM. Binding of SARS coronavirus to its receptor damages islets and causes acute diabetes. Acta Diabetol 47 , 193-199. doi: 10.1007/s00592-009-0109-4 (2010) Lim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: from pathophysiology to clinical management. Nat Rev Endocrinol 17 , 11-30. doi: 10.1038/s41574-020-00435-4 (2020) Bellido V, et al. Expert recommendations for using time-in-range and other continuous glucose monitoring metrics to achieve patient-centered glycemic control in people with diabetes. J Diabetes Sci Technol 17 , 1326-1336. doi: 10.1177/19322968221088601 (2023) Tables Table 1—Characteristics and isCGM data of patients with COVID-19 and type 2 diabetes Parameters Presence of the composite adverse outcome No ( n = 36 ) Yes ( n = 27 ) Clinical Characteristics on Admission Age (years) 72 (62, 82) 75 (65,80) Male gender 21 (58.30) 15 (55.56) Heart rate (bpm) 80 (71,72) 84 (80,99) Respiratory rate (bpm) 18 (18,20) 20 (18,21) SBP (mmHg) 130 (118,142) 133 (120,147) DBP (mmHg) 76 (68,80) 77 (70,81) Fatigue 18 (50) 13 (48) Dyspnea 20 (56) 14 (52) Comorbidities on Admission Hypertension 24 (67) 19 (70) Coronary heart disease 20 (56) 12 (70) Chronic renal diseases 8 (22) 5 (19) Laboratory Examination on Admission Leukocyte count (10 9 /L) 7.37 (5.68,10.05) 7.54 (5.84,10.19) Neutrophil count (10 9 /L) 5.63 (3.54,8.51) 6.60 (4.60,8.05) Lymphocyte count (10 9 /L) 1.27 (0.73,1.54) 0.84 (0.57,1.30) C-reactive protein (mg/L) 41.72 (7.51,95.99) 67.29 (20.84,127.00) Procalcitonin level (ng/mL) 0.12 (0.07,0.32) 0.28 (0.14,0.94) ALT (U/L) 18 (10,25) 16 (13,30) AST (U/L) 20 (15,28) 26 (16,33) Creatinine (μmol/L) 68.50 (53.25,103) 91 (55,144) eGFR (mL/min/1.73 m 2 ) 86.69 (57.76,100.65) 55.40 (36.83,98.58) CK (U/L) 44.00 (25.50,67) 65.00 (26.5,121.25) CK-MB (ng/mL) 1.50 (0.78,2.10) * 2.20 (1.30,4.20) * LDH (U/L) 242(194.25,286) * 280(234,342.75) * Triglycerides (mmol/L) 1.35 (0.96,1.64) 1.43 (0.94,1.9) LDL cholesterol (mmol/L) 2.25 (1.67,3.28) 1.95 (1.55,2.51) HDL cholesterol (mmol/L) 1.08 (0.83,1.33) * 0.94 (0.72,1.09) * D-dimer (μg/mL) 0.97 (0.56,1.81) 1.76 (0.96,2.85) FPG (mg/dL) 7.53 (6.54,16.15) 13.07 (9.43,16.46) HbA 1c (%) 7.6 (6.8,9.33) 8.15 (6.78,10.13) Sensor glucose (mg/dL) 177.84 (153.70,217.95) * 222.84 (183.33,283.49) * Coefficient of variation (%) 32.95 (28.95,37.15) 34.35 (27.23,37.93) Treatment Antibiotic therapy 35 (97) 25 (93) Glucocorticoids 24 (67) 23 (85) Anticoagulant Therapy 14 (39) * 19 (70) * Non-insulin Hypoglycemic Agents 25 (69) * 12 (44) * insulin Hypoglycemic Agents 21 (58) 21 (78) Data were presented as n (%) or median (IQR). * P < 0.05 ALT, alanine aminotransferase; AST, aspartate aminotransferase; eGFR, estimated glomerular filtration rate; CK, creatine kinase; CK-MB, creatine kinase-myocardial isoenzyme. Table 2—Univariate logistic regression analysis of composite outcomes of COVID-19 Odds ratios ( 95% confidence interval ) P Clinical Characteristics on Admission Age (years) 1.015(0.973,1.058) 0.489 Male gender 1.120(0.409,3.068) 0.826 Heart rate (bpm) 1.014(0.850,1.045) 0.345 Respiratory rate (bpm) 0.998(0.944,1.033) 0.584 SBP(mmHg) 1.009(0.988,1.031) 0.410 DBP(mmHg) 1.005(0.969,1.041) 0.803 Fatigue 0.929(0.342,2.520) 0.929 Dyspnea 0.862(0.317,2.344) 0.770 Gastrointestinal symptoms 2.125(0.33,13.704) 0.428 Comorbidities on Admission Hypertension 1.187(0.404,3.490) 0.755 Coronary heart disease 0.640(0.234,1.747) 0.640 Chronic renal diseases 0.795(0.228,2.774) 0.720 Laboratory Examination on Admission Leukocyte count (10 9 /L) 1.073(0.958,1.203) 0.224 Neutrophil count (10 9 /L) 1.107(0.973,1.260) 0.123 Lymphocyte count (10 9 /L) 0.617(0.257,1.480) 0.279 C-reactive protein (mg/L) 1.006(0.998,1.015) 0.122 Procalcitonin level (ng/mL) 0.950(0.831,1.085) 0.447 ALT (U/L) 1.004(0.962,1.049) 0.844 AST (U/L) 1.038(0.985,1.095) 0.160 Creatinine (μmol/L) 0.999(0.996,1.003) 0.801 eGFR (mL/min/1.73 m 2 ) 0.991(0.976,1.006) 0.244 CK (U/L) 1.004(0.998,1.010) 0.175 CK-MB (ng/ml) 1.543(1.070,2.224) 0.020 LDH(U/L) 1.009(1.001,1.017) 0.021 Triglycerides (mmol/L) 1.299(0.775,2.180) 0.321 LDL cholesterol (mmol/L) 0.593(0.322,1.094) 0.094 HDL cholesterol (mmol/L) 0.184(0.032,1.040) 0.055 D-dimer (μg/mL) 1.129(0.966,1.319) 0.128 FPG (mg/dL) 1.000(0.965,1.037) 0.986 HbA 1c (%) 1.129(0.900,1.578) 0.221 Sensor glucose (mg/dL) 1.010(1.001,1.019) 0.031 Coefficient of variation (%) 1.007(0.945,1.073) 0.831 Treatment Antibiotic therapy 0.357(0.031,4.158) 0.411 Glucocorticoids 0.348(0.098,1.236) 0.103 Anticoagulant Therapy 3.732(1.288,10.812) 0.015 Non-insulin Hypoglycemic Agents 0.352(0.125,0.995) 0.049 Insulin Hypoglycemic Agents 2.500(0.813,7.689) 0.110 Table 3—Multivariate analysis for predicting composite adverse outcomes by glycemic metrics derived from isCGM Odds ratios (95% confidence interval) Sensor glucose levels (mg/dL) TIR TIR1(70-180) 0.975 (0.948-1.002) TIR2(80-190) 0.963 (0.932-0.995) TIR3(90-200) 0.951 (0.916-0.988) TIR4(100-210) 0.950 (0.914-0.987) TIR5(110-220) 0.960 (0.926-0.995) TIR6(120-230) 0.967 (0.934-1.001) Data are adjusted for age, sex, CK-MB, symptoms on admission, LDH, Use of OHA and anticoagulant. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3859033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268806147,"identity":"e7f3fd79-e014-400b-833e-c262bb46dcf3","order_by":0,"name":"Riping Cong","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Riping","middleName":"","lastName":"Cong","suffix":""},{"id":268806148,"identity":"93eb4710-3f99-4f17-85bc-b30720c28b5d","order_by":1,"name":"Jianbo Zhang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Zhang","suffix":""},{"id":268806149,"identity":"b575c16a-3d15-4408-b485-131eb0e17cfb","order_by":2,"name":"Lujia Xu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Lujia","middleName":"","lastName":"Xu","suffix":""},{"id":268806150,"identity":"7a549e2e-59e8-4ad3-83bc-27d7b95c0141","order_by":3,"name":"Yujian Zhang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yujian","middleName":"","lastName":"Zhang","suffix":""},{"id":268806151,"identity":"84a47ad0-8c27-4ecd-94c8-8d04969e25f9","order_by":4,"name":"Hao Wang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""},{"id":268806152,"identity":"d4553ceb-08fa-487b-b137-0af207240369","order_by":5,"name":"Jing Wang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":268806153,"identity":"fdefcb98-2b9a-4717-8d3e-761b57006256","order_by":6,"name":"Wei Wang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":268806154,"identity":"ea282b4f-045e-40b6-b2e0-8e1db9997070","order_by":7,"name":"Yingli Diao","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yingli","middleName":"","lastName":"Diao","suffix":""},{"id":268806155,"identity":"9539c363-ce61-4d84-9789-b3899c1de17c","order_by":8,"name":"Haijiao Liu","email":"","orcid":"","institution":"Jinan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haijiao","middleName":"","lastName":"Liu","suffix":""},{"id":268806156,"identity":"4fe383ad-665a-4cfa-b053-99085ab6dd37","order_by":9,"name":"Jing Zhang","email":"","orcid":"","institution":"Lanling County Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":268806157,"identity":"d26012d7-c45c-4f53-bfc0-3b8261f087a0","order_by":10,"name":"Kuanxiao Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYJACxgYgwcfeA+bw8BFSzgPTwsZzhoHhAFCAjXgtEjlgLQwEtdiznz38cmYbg2yb5NuDjz/m2MmwMTA/fHQDny08eWmWG84wGLdJ5yUbHNyWDHQYm7FxDl6H5ZgZPqhgSGyTzjGTOLiNGaiFh00arxb+N0AtBkAtkmdAWuqJ0CKRY/xwA8gWCR6QlsNEaLnxxoxxBsgvPDnGBme3HedhYybgF/b+HOOPvcAQ62c/Y/igclu1PT9788PH+LQwgGKEgeE/OG4ggBm/crCSDwzQ6BwFo2AUjIJRgBUAAEW+Qjj866TXAAAAAElFTkSuQmCC","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Kuanxiao","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-01-13 04:46:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3859033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3859033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50113385,"identity":"0568d2c4-ad96-4a52-9f58-86ade48552bd","added_by":"auto","created_at":"2024-01-24 18:03:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11080,"visible":true,"origin":"","legend":"\u003cp\u003eTrial profile\u003c/p\u003e\n\u003cp\u003e*Meeting the following criteria: 1. Inpatients; 2. Patients diagnosed with type 2 diabetes; 3. Patients receiving CGM during hospitalization; 4. Positive COVID-19 RNA PCR and characteristic imaging manifestations of novel coronavirus pneumonia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3859033/v1/10813fb554ea7ae7411a64ea.png"},{"id":50113386,"identity":"92ac0bd0-fd49-4ef6-9b05-48db464e605a","added_by":"auto","created_at":"2024-01-24 18:03:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161347,"visible":true,"origin":"","legend":"\u003cp\u003eTIRs in favorable outcomes and adverse outcomes groups during hospitalization\u003c/p\u003e\n\u003cp\u003eAdverse outcomes showed significantly lower TIR1 (70-180 mg/dL) than favorable outcomes (\u003cstrong\u003eA\u003c/strong\u003e), TIR2 (80-190 mg/dL) (\u003cstrong\u003eB\u003c/strong\u003e), TIR3 (90-200 mg/dL) (\u003cstrong\u003eC\u003c/strong\u003e), TIR4 (100-210 mg/dL) (\u003cstrong\u003eD), \u003c/strong\u003eTIR5 (110-220 mg/dL) (\u003cstrong\u003eE\u003c/strong\u003e) but TIR6 (120-230 mg/dL) had a weakly negative correlation with the outcomes (\u003cstrong\u003eF\u003c/strong\u003e). *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3859033/v1/b70960b06532f1f7a161ca53.png"},{"id":50113384,"identity":"15652fb7-84b0-451b-950b-d9e10a8292db","added_by":"auto","created_at":"2024-01-24 18:03:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46905,"visible":true,"origin":"","legend":"\u003cp\u003eTIRs variables was associated with decreased risk of the adverse outcome.\u003c/p\u003e\n\u003cp\u003eTIR1:70-180 mg/dL; TIR2:80-190 mg/dL; TIR3:90-200 mg/dL; TIR4:100-210 mg/dL; TIR5:110-220 mg/dL; TIR6:120-230 mg/dL.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3859033/v1/864d5fe27aac43c529ebdcbe.png"},{"id":50113383,"identity":"436acf0f-78bb-4116-89fd-6e5c8cadbec1","added_by":"auto","created_at":"2024-01-24 18:03:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71983,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagnostic value of TIR was evaluated by the receiver operating characteristic (ROC) analysis\u003c/p\u003e\n\u003cp\u003eA. The receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic value of TIRs.\u003c/p\u003e\n\u003cp\u003eTIR1:70-180 mg/dL; TIR2:80-190 mg/dL; TIR3:90-200 mg/dL; TIR4:100-210 mg/dL; TIR5:110-220 mg/dL; TIR6:120-230 mg/dL; AUC, area under curve.\u003c/p\u003e\n\u003cp\u003eB. The receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic value of TIR of 80-190 mg/dL and estimate the optimal cutoff value.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3859033/v1/379dc45cc71892f0aaccdbb1.png"},{"id":51908602,"identity":"41fefba0-1081-4e2a-b417-9d122a1dc5fd","added_by":"auto","created_at":"2024-03-03 05:45:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":533088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3859033/v1/2f3fe583-9b3e-4ed7-b273-0802d1ecfed0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A moderately higher time-in-range threshold improves the prognosis of type-2 diabetes patients complicated with COVID-19","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Chinese Center for Disease Control and Prevention has reported that since the pandemic control measures of COVID-19 were fully lifted in mainland China in 12/2022, the peak number of COVID-19 nucleic acid-positive cases had reached 6.94 million, admissions to hospitals had reached a peak of 1.625 million, of which the highest number of severe cases had reached 128 thousand, and the cumulative number of deaths had reached 4273 by January 2023.\u003c/p\u003e\n\u003cp\u003eDiabetes\u0026nbsp;has\u0026nbsp;already\u0026nbsp;become\u0026nbsp;the second most common comorbidity of COVID-19 due to the coinciding of two global pandemics\u003csup\u003e1,2\u003c/sup\u003e. A meta-analysis including 7 studies with 1,576 patients\u0026nbsp;showed\u0026nbsp;the\u0026nbsp;prevalence of diabetes of approximately 9.7% (95% CI: 7.2-12.2%)\u003csup\u003e3\u003c/sup\u003e. Another meta-analysis was a\u0026nbsp;comprehensive systematic search including data from 76,993 patients\u003csup\u003e4\u003c/sup\u003e. According to this study, the prevalence of diabetes was estimated to be 7.87% (95% CI: 6.57-9.28%). Poor glycemic control increased\u0026nbsp;the risk of\u0026nbsp;mortality, morbidity, and secondary infections\u003csup\u003e5,6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThese associations between diabetes and worse outcomes in COVID-19 patients were incontrovertible, as\u0026nbsp;blood glucose fluctuation\u0026nbsp;was\u0026nbsp;not conducive to the improvement of disease, and inflammation caused by hyperglycemia led\u0026nbsp;to increased mortality\u003csup\u003e7,8\u003c/sup\u003e. However, excessively tight glycemic control may increase the risk of hypoglycemia, which also increased\u0026nbsp;mortality\u003csup\u003e9\u003c/sup\u003e. A\u0026nbsp;study showed that the mortality rate was found to be significantly lower when aiming for a blood glucose target of 180 mg or less per deciliter, compared to targeting a range of 81 to 108 mg per deciliter.\u003csup\u003e10\u003c/sup\u003e. American Diabetes Association guidelines recommend targeting blood glucose \u0026lt; 180 mg/dL in critically ill patients\u003csup\u003e11,12\u003c/sup\u003e. Clinicians face a significant challenge in improving outcomes for individuals with COVID-19 and type-2 diabetes due to uncertainty surrounding the optimal degree of glycemic management and its potential impact on treatment benefits and risks. The definition of optimal blood glucose control remains controversial\u003csup\u003e13\u003c/sup\u003e. The wide application of hormonal and nutritional support treatment has led to significant fluctuations in blood glucose levels in clinical practice, making it challenging to maintain the general range. Consequently, our study aimed to analyze glycemic profiles using intermittently scanned continuous glucose monitoring (isCGM) to determine a more clinically practical threshold for TIR and investigate its correlation with prognosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our observational study,\u0026nbsp;data\u0026nbsp;of patients admitted to Qilu Hospital from Dec 2022 to Apr 2023\u0026nbsp;were analyzed. The patients all had moderate or severe cases and were diagnosed according to the guidelines issued by the World Health Organization (WHO)\u003csup\u003e14\u003c/sup\u003e, meeting at least the following criteria: positive COVID-19 RNA PCR and characteristic imaging manifestations of novel coronavirus pneumonia. Exclusion criteria included patients who were intubated on admission and those younger than 18 years of age. The present study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human participants were approved by the Ethics Committee for the\u0026nbsp;Qilu Hospital of Shandong University (Ethical Approval Number: KYLL-202307-047). Given that the medical records or biological specimens utilized in this study were acquired during prior clinical consultations and presented no more than minimal risk to the participants, an application has been submitted for exemption from obtaining informed consent documentation. Trial Registration: clinicaltrials.gov Identifier: NCT06156137 (Registered November 24, 2023).\u003c/p\u003e\n\u003cp\u003ePatient information that we collected through electronic medical records include gender, age, vital signs, symptoms on admission, duration of diabetes, comorbidities, fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein (TP), total cholesterol (TC), triglycerides (TG), serum creatinine, uric acid,\u0026nbsp;eGFR\u003csup\u003e15\u003c/sup\u003e,\u0026nbsp;inflammatory biomarkers, brain natriuretic peptide (BNP), CK-MB and medication, including oral hypoglycemic agent (OHA), insulin, anticoagulant drugs and glucocorticoids. CGM was initiated on admission. Diabetic meals were ordered for all patients during hospitalization.\u003c/p\u003e\n\u003cp\u003eAll type-2 diabetes patients were equipped with isCGM sensors (FreeStyle Libre Flash glucose monitoring system; Abbott Diabetes Care Ltd, UK) on admission, and the nurse retrieved the probe when the patient was discharged or when the composite endpoint was reached. Measures of glycemic variability, such as TIR,\u0026nbsp;TAR, TBR,\u0026nbsp;mean sensor glucose level and associated CVs, were calculated from isCGM records.\u0026nbsp;TIR was defined as the percentage of time within the following ranges: 70-180 mg/dL, 80-190 mg/dL, 90-200 mg/dL, 100-210 mg/dL, 110-220 mg/dL, and 120-230 mg/dL.\u003c/p\u003e\n\u003cp\u003eA composite adverse outcome included a hospital stay of more than 20 days, admission to\u0026nbsp;the\u0026nbsp;intensive care unit, the need for mechanical ventilation, and death.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eAll data were analyzed using the SPSS software v.25(IBM Corporation, Armonk, NY). The normal distribution of continuous variables was checked by the Shapiro-Wilk test. Nonnormally distributed variables are presented as the median (IQR), and the Mann‒Whitney U test and Kruskal‒Wallis ANOVA were used for comparisons between groups. Categorical variables were expressed as numbers (percentages) and were compared using the\u0026nbsp;\u0026chi;2 test or Fisher\u0026rsquo;s exact test. To identify the covariates for inclusion in the multivariate analysis, a univariate logistic regression was initially performed. Candidate covariates were selected based on a significance level of\u0026nbsp;\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 in the univariate analysis. Subsequently, multivariable-adjusted logistic regression models were employed to evaluate the association between TIR using isCGM and composite adverse outcomes. All analyses were adjusted for age, sex, CK-MB, symptoms on admission, LDH, use of OHA and anticoagulant. A receiver-operating characteristic (ROC) curve was constructed with TIR as the independent variable and prognosis as the dependent variable, and the diagnostic value of TIR was assessed based on the area under the curve (AUC). The optimal cutoff value was determined using the Youden index. All statistical tests were two-sided, and a significance level of P \u0026lt; 0.05 was considered statistically significant. Odds ratios (ORs) with 95% CIs are presented.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eClinical Characteristics of Patients With COVID-19 and Type-2 Diabetes Upon Admission\u003c/h2\u003e\n\u003cp\u003eThis study included a total of 63 patients who met the inclusion criteria (Figure 1). Among them, the mean age was 71.59\u0026plusmn;12.24 years, including 42.90% female and 57.10% male. Twenty-seven of the 63 patients experienced composite adverse outcomes. The characteristics of these patients are presented in Table 1.\u003c/p\u003e\n\u003ch2\u003eComparison of TIR Between the Adverse and Favorable Outcome Groups\u003c/h2\u003e\n\u003cp\u003ePatients with composite adverse outcomes exhibited significantly lower TIR values compared to those with favorable outcomes(P \u0026lt; 0.05)(Figure. 2). Univariate and multivariable logistic regression models were used to analyze data from all 63 patients. Univariate regression analysis showed that TIR variables (0.977 [0.957-0.996], 0.968 [0.947-0.990], 0.960 [0.935-0.985], 0.957 [0.930-0.984], 0.958 [0.931-0.986], 0.963 [0.936-0.991]) were associated with a decreased risk of the composite outcome (Figure 3). Univariate logistic regression analysis of composite outcomes is shown in Table 2. After adjustment for multiple covariates, TIRs (0.975 [0.948-1.002], 0.963 [0.932-0.995], 0.951 [0.916-0.988], 0.952 [0.915-0.990], 0.960 [0.926-0.995], 0.967 [0.934-1.001]) exhibited a significant association with reduced odds of composite adverse outcomes (Table 3). Thus, a TIR of 80\u0026ndash;190 mg/dL was significantly associated with favorable outcomes.\u003c/p\u003e\n\u003ch2\u003eTIR Predicted the Prognosis of Type-2 Diabetes Patients With COVID-19\u003c/h2\u003e\n\u003cp\u003eThe multivariate logistic regression analysis revealed that the TIRs of 80-190, 90-200, and 100-210 mg/dL remained as independent predictors of composite adverse outcomes even after adjusting the multiple covariates. ROC analysis was employed to evaluate the prognostic value of TIR for COVID-19 patients with diabetes. The test variables were defined as TIRs within the ranges of 80\u0026ndash;190,90\u0026ndash;200,and100\u0026ndash;210mg/dL while the state variable was represented by composite adverse outcomes in patients (Figure. 4A). The area under the ROC curve was 0.713 (95% CI: 0.585\u0026ndash;0.841, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0 .004), 0.739 (95% CI: 0.614\u0026ndash;0.863, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0 .0013), and 0.748 (95% CI: 0.624\u0026ndash;0.872, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0 .001). The area under the curve is maximized when TIR exhibits high predictive value for COVID-19 patient prognosis. Although the TIR (100-210 mg/dL) had the largest area under the ROC curve, it was not significantly different from the other two ROC curves. This does not indicate that the TIR (100-210 mg/dL) has higher prognostic value than the TIR (80-190 mg/dL) and the TIR (90-200 mg/dL). In all patients, the TIR of 80-190 mg/dL corresponds to 54.73% and maximizes the Youden index, with a sensitivity and specificity of 58.3% and 77.8%, respectively (Figure. 4B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eData from this cross-sectional study showed that optimal glycemic control during hospitalization was associated with a lower risk of severe illness and death in patients with COVID-19. After adjusting for covariates, maintaining TIR within the thresholds of 80 to 190 mg/dL significantly relates to favorable outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, the patient population was divided into two cohorts based on the occurrence of composite adverse events. The proportion of severe COVID-19 cases at admission was higher in the population with composite adverse events than in the second cohort (63% vs 33.3%, P = 0.002). Although patients with composite adverse outcomes were more likely to be male and older than 65 years with comorbidities and higher levels of inflammatory, endothelial, and coagulopathy markers on admission, there was no significant difference between the two groups. Patients achieving composite adverse outcomes had significantly higher CK-MB and LDH levels on admission. When analyzing TIR as a factor influencing outcome, all of the above confounding variables were adjusted for to reach the following conclusion: TIR values with thresholds of 80 to 190 mg/dL were significantly associated with a lower risk of the composite adverse outcomes.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that variability is a potential risk predictor of death and other complications\u003csup\u003e4,23\u003c/sup\u003e. The presence of COVID-19 has been shown to play a significant role in impairing blood glucose control within the range of 70-150 mg/dL\u003csup\u003e13\u003c/sup\u003e. Zhu et al. showed improved outcomes in well-controlled type-2 diabetes patients with COVID-19 infection\u003csup\u003e17\u003c/sup\u003e. A small-sample study suggested that maintaining TIR (70-160 mg/dL) \u0026gt;70% could improve outcomes. In clinical practice, we found that only 15.87% of patients achieved that target, and the average TIR in our study was 39.36% during the pandemic. Inpatient medication (corticosteroids) and enteral and parenteral nutrition contribute to hyperglycemia\u003csup\u003e18\u003c/sup\u003e. The widespread use of glucocorticoids caused patients to experience wide fluctuations in blood glucose levels, which may have more adverse effects than sustained hyperglycemia. In our study, more than 70% of the patients were received hormone therapy, and 75% were treated with enteral or parenteral nutrition, which resulted in a high mean sensor glucose level [203.57 mg/dL (162.7-235.88)] and a wide CV of glucose values [33.29% (27.88 to 37.62)]. This also explained why the TIR threshold of COVID-19 patients with type-2 diabetes was higher.\u003c/p\u003e\n\u003cp\u003eMoreover, the elevation of cortisol levels resulting from COVID-19 infection, stress, and similar factors can contribute to excessive hepatic gluconeogenesis, impaired glucose utilization, and insulin deficiency\u003csup\u003e19-21\u003c/sup\u003e. There is a suggested direct impact of SARS-CoV-2 on pancreatic\u0026nbsp;\u0026beta;-cell function and survival, exacerbating rapid and severe metabolic deterioration in individuals with preexisting diabetes\u003csup\u003e22,23\u003c/sup\u003e. Angiotensin-converting enzyme 2 (ACE 2) potentially serves as a crucial molecular link between COVID-19 severity and insulin resistance\u003csup\u003e24-26\u003c/sup\u003e. Our findings support this hypothesis, as the patients who achieved the composite adverse outcomes had a significantly lower TIR (80-190 mg/dL) and a higher TAR \u0026gt;190 mg/dL. Furthermore, they used a higher maximum insulin dose during hospitalization [34(18-47) vs. 19(0-40), P = 0.046]. In this study, we found that poor glycemic control was associated with a worse outcome that included a higher need for medical intervention, hospitalization, and mortality. The insights gained here provide direct suggestions for the clinical management of type-2 diabetes during the COVID-19 pandemic.\u003c/p\u003e\n\u003cp\u003eExcessive glycemic control leading to severe hypoglycemia has been associated with increased mortality rates\u003csup\u003e9\u003c/sup\u003e. The international consensus on TIR\u003csup\u003e27\u003c/sup\u003eindicated that although evidence regarding TIR for older or high-risk individuals is limited, several studies have demonstrated an elevated risk for hypoglycemia. Therefore, they reduced the TIR target from 70% to 50%. In our study, the age of enrolled patients was relatively high, the mean age was 71.59\u0026nbsp;\u0026plusmn;\u0026nbsp;12.24 years old, and the TIR (80 to 190 mg/dL) corresponded to 54.73% and had a maximum Youden index. This cutoff value had good clinical significance.\u003c/p\u003e\n\u003cp\u003eThe major advantage of our study lies in the utilization of the isCGM system for type-2 diabetes patients complicated with COVID-19, enabling comprehensive assessment of hyperglycemia, hypoglycemia, and glycemic variability. Our study has presented the appropriate threshold and cutoff point for TIR in patients with COVID-19 and type-2 diabetes, which is more relevant to clinical practice. However, several limitations need to be acknowledged. Firstly, it was a retrospective study, which may introduce patient selection bias. Secondly, the sample size was relatively modest and might not fully capture the complexity of the general population. Therefore, large-scale prospective cohort studies involving ethnically diverse cohorts from different geographical regions are warranted to gain a better understanding of the association between glycemic control and COVID-19 progression. Finally, it should be noted that our analysis excluded individuals with type 1 diabetes, but glycemic control could also influence their outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, maintaining a TIR (80\u0026ndash;190 mg/dL) above 54.73% independently correlates with a significant reduction in composite adverse outcomes associated with COVID-19 infection among patients with type-2 diabetes. These findings provide valuable insights into the clinical characteristics of glycemic variability in individuals affected by both COVID-19 and type-2 diabetes while offering potential avenues for improving disease outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the\u0026nbsp;absence of any commercial or financial relationships that could be\u0026nbsp;construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eRiping Cong and Kuanxiao Tang signed the study. Hao Wang and Jing Wang provided the CGM data of ICU. Riping Cong, Lujia Xu, Yingli Diao, Yujian Zhang and Wei Wang collected the other data. Jianbo Zhang, Haijiao Liu and Jing Zhang cleaned the data. Riping Cong, Lujia Xu and Jianbo Zhang performed the statistical analysis and wrote the draft of the manuscript. Kuanxiao Tang rescanned and edited the manuscript. All authors read and approved the final manuscript. Kuanxiao Tang was the guarantor of this work, as such, had full access to all the data in the study.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhou F, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. \u003cem\u003eLancet\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e395\u003c/strong\u003e, 1054-1062. doi: 10.1016/S0140-6736(20)30566-3 (2020)\u003c/li\u003e\n \u003cli\u003ePapazafiropoulou AK, Antonopoulos S. The COVID-19 pandemic and diabetes mellitus. Archives of Medical Science. \u003cem\u003eAtherosclerotic Diseases\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e, 200-205. doi: 10.5114/amsad.2020.97435 (2020)\u003c/li\u003e\n \u003cli\u003eYang J, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. \u003cem\u003eInt J Infect Dis\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, 91-95. doi: 10.1016/j.ijid.2020.03.017 (2020)\u003c/li\u003e\n \u003cli\u003eEmami A, Javanmardi F, Pirbonyeh N, Akbari A. Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis. \u003cem\u003eArch Acad Emerg Med\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e35. doi: 10.1016/j.jcv.2020.104371(2020)\u003c/li\u003e\n \u003cli\u003eNathella PK, Babu S. Influence of diabetes mellitus on immunity to human tuberculosis. \u003cem\u003eImmunology\u003c/em\u003e \u003cstrong\u003e152\u003c/strong\u003e, 13-24. doi: 10.1111/imm.12762 (2017)\u003c/li\u003e\n \u003cli\u003eKatulanda P, et al. Prevention and management of COVID-19 among patients with diabetes: an appraisal of the literature. \u003cem\u003eDiabetologia\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 1440-1452. doi: 10.1007/s00125-020-05164-x (2020)\u003c/li\u003e\n \u003cli\u003eForbes A, Murrells T, Mulnier H, Sinclair AJ. Mean HbA1c, HbA1c variability, and mortality in people with diabetes aged 70 years and older: a retrospective cohort study. \u003cem\u003eLancet Diabetes Endocrinol\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 476-486. doi: 10.1016/S2213-8587(18)30048-2\u003cem\u003e\u0026nbsp;\u003c/em\u003e(2018)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003eZhu L, et al. Association of Blood Glucose Control and Outcomes in Patients with COVID-19 and Pre-existing Type 2 Diabetes. \u003cem\u003eCell Metab\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1068-1077.e3. doi: 10.1016/j.cmet.2020.04.021(2020)\u003c/li\u003e\n \u003cli\u003eRodriguez-Gutierrez R, Gonzalez-Gonzalez JG, Zu\u0026ntilde;iga-Hernandez JA, McCoy RG. Benefits and harms of intensive glycemic control in patients with type 2 diabetes. \u003cem\u003eBMJ\u003c/em\u003e \u003cstrong\u003e367\u003c/strong\u003e, l5887. doi: 10.1136/bmj.l5887 (2019)\u003c/li\u003e\n \u003cli\u003eHemmingsen B, et al. Intensive glycaemic control for patients with type 2 diabetes: systematic review with meta-analysis and trial sequential analysis of randomised clinical trials. \u003cem\u003eBMJ\u003c/em\u003e \u003cstrong\u003e343\u003c/strong\u003e, d6898. doi: 10.1136/bmj.d6898 (2011)\u003c/li\u003e\n \u003cli\u003eJacobi J, et al. Guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients. \u003cem\u003eCrit Care Med\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 3251-3276. doi: 10.1097/CCM.0b013e3182653269 (2012)\u003c/li\u003e\n \u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 16. Diabetes Care in the Hospital: Standards of Medical Care in Diabetes-2022. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, S244\u0026ndash;S253. doi: 10.2337/dc22-S016 (2022)\u003c/li\u003e\n \u003cli\u003eKapoor R, Timsina LR, Gupta N, Kaur H, Vidger AJ, Pollander AM, et al. Maintaining blood glucose levels in range (70-150 mg/dl) is difficult in COVID-19 compared to Non-COVID-19 ICU patients-A retrospective analysis.\u003cem\u003e\u0026nbsp;J Clin Med\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 3635. doi: 10.3390/jcm9113635 (2022)\u003c/li\u003e\n \u003cli\u003eAgarwal A, Hunt B, Stegemann M, Rochwerg B, Lamontagne F, Siemieniuk RA, et al. A living WHO guideline on drugs for covid-19. \u003cem\u003eBMJ\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e370\u003c/strong\u003e, m3379. doi: 10.1136/bmj.m3379 (2020)\u003c/li\u003e\n \u003cli\u003eLevey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. \u003cem\u003eAnn Intern Med\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e150\u003c/strong\u003e, 604-612. doi: 10.7326/0003-4819-150-9-200905050-00006 (2009)\u003c/li\u003e\n \u003cli\u003eShen Y, Fan X, Zhang L, Wang Y, Li C, Lu J, et al. Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A retrospective exploratory study using continuous glucose monitoring. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 976-982. doi: 10.2337/dc20-1448 (2021)\u003c/li\u003e\n \u003cli\u003eLanspa MJ, Krinsley JS, Hersh AM, Wilson EL, Holmen JR, Orme JF, et al. Percentage of time in range 70 to 139 mg/dl is associated with reduced mortality among critically ill patients receiving IV insulin infusion. \u003cem\u003eChest\u003c/em\u003e \u003cstrong\u003e156\u003c/strong\u003e, 878-886. doi: 10.1016/j.chest.2019.05.016 (2019)\u003c/li\u003e\n \u003cli\u003eDungan KM, Braithwaite SS, Preiser JC. Stress hyperglycaemia. \u003cem\u003eLancet\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e373\u003c/strong\u003e, 1798-1807. doi: 10.1016/S0140-6736(09)60553-5 (2009)\u003c/li\u003e\n \u003cli\u003eHe X, et al. COVID-19 induces new-onset insulin resistance and lipid metabolic dysregulation via regulation of secreted metabolic factors. \u003cem\u003eSignal Transduct Target Ther\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 427. doi: 10.1038/s41392-021-00822-x (2021)\u003c/li\u003e\n \u003cli\u003eApicella M, et al. COVID-19 in people with diabetes: understanding the reasons for worse outcomes. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 782-792. doi: 10.1016/S2213-8587(20)30238-2 (2022)\u003c/li\u003e\n \u003cli\u003eDrucker DJ. Diabetes, obesity, metabolism, and SARS-CoV-2 infection: the end of the beginning. \u003cem\u003eCell Metab\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e33\u003c/strong\u003e, 479-498. doi: 10.1016/j.cmet.2021.01.016 (2021)\u003c/li\u003e\n \u003cli\u003eHamming I, et al. Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. \u003cem\u003eJ Pathol\u003c/em\u003e \u003cstrong\u003e203\u003c/strong\u003e, 631-7. doi: 10.1002/path.1570 (2004)\u003c/li\u003e\n \u003cli\u003eHoffmann M, et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 271-280. doi: 10.1016/j.cell.2020.02.052 (2020)\u003c/li\u003e\n \u003cli\u003eKuba K, et al. A crucial role of angiotensin converting enzyme 2 (ACE2) in SARS coronavirus-induced lung injury. \u003cem\u003eNat Med\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 875-9. doi: 10.1038/nm1267 (2005)\u003c/li\u003e\n \u003cli\u003eYang JK, Lin SS, Ji XJ, Guo LM. Binding of SARS coronavirus to its receptor damages islets and causes acute diabetes. \u003cem\u003eActa Diabetol\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 193-199. doi: 10.1007/s00592-009-0109-4 (2010)\u003c/li\u003e\n \u003cli\u003eLim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: from pathophysiology to clinical management. \u003cem\u003eNat Rev Endocrinol\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e, 11-30. doi: 10.1038/s41574-020-00435-4 (2020)\u003c/li\u003e\n \u003cli\u003eBellido V, et al. Expert recommendations for using time-in-range and other continuous glucose monitoring metrics to achieve patient-centered glycemic control in people with diabetes. \u003cem\u003eJ Diabetes Sci Technol\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e, 1326-1336. doi: 10.1177/19322968221088601 (2023)\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eTable 1\u0026mdash;Characteristics and isCGM data of patients with COVID-19 and type 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003ePresence of the composite adverse outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo ( n = 36 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes ( n = 27 )\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eClinical Characteristics on Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e72 (62, 82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 (65,80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (58.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15 (55.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeart rate (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80 (71,72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84 (80,99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRespiratory rate (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (18,20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (18,21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e130 (118,142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e133 (120,147)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76 (68,80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77 (70,81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities on Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic renal diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLaboratory Examination on Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLeukocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.37 (5.68,10.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.54 (5.84,10.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.63 (3.54,8.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.60 (4.60,8.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.27 (0.73,1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84 (0.57,1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC-reactive protein (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.72 (7.51,95.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.29 (20.84,127.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eProcalcitonin level (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12 (0.07,0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28 (0.14,0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (10,25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (13,30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (15,28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26 (16,33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCreatinine (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.50 (53.25,103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91 (55,144)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.69 (57.76,100.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.40 (36.83,98.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCK (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.00 (25.50,67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.00 (26.5,121.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCK-MB (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50 (0.78,2.10)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.20 (1.30,4.20)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e242(194.25,286)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e280(234,342.75)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.35 (0.96,1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43 (0.94,1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.25 (1.67,3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.95 (1.55,2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08 (0.83,1.33)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94 (0.72,1.09)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD-dimer (\u0026mu;g/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.56,1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.76 (0.96,2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFPG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.53 (6.54,16.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.07 (9.43,16.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA\u003csub\u003e1c\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.6 (6.8,9.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.15 (6.78,10.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensor glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e177.84 (153.70,217.95)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e222.84 (183.33,283.49)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.95 (28.95,37.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.35 (27.23,37.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAntibiotic therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlucocorticoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnticoagulant Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (39)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (70)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-insulin Hypoglycemic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (69)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (44)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003einsulin Hypoglycemic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eData were presented as n (%) or median (IQR). *\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eALT, alanine aminotransferase; AST, aspartate aminotransferase; eGFR, estimated glomerular filtration rate; CK, creatine kinase; CK-MB, creatine kinase-myocardial isoenzyme.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"726\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTable 2\u0026mdash;Univariate logistic regression analysis of composite outcomes of COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003eOdds ratios ( 95% confidence interval )\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eClinical Characteristics on Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.015(0.973,1.058)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eMale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.120(0.409,3.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eHeart rate (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.014(0.850,1.045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory rate (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.998(0.944,1.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eSBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.009(0.988,1.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eDBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.005(0.969,1.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.929(0.342,2.520)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.862(0.317,2.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eGastrointestinal symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e2.125(0.33,13.704)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities on Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.187(0.404,3.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.640(0.234,1.747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eChronic renal diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.795(0.228,2.774)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLaboratory Examination on Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eLeukocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.073(0.958,1.203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eNeutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.107(0.973,1.260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eLymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.617(0.257,1.480)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eC-reactive protein (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.006(0.998,1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eProcalcitonin level (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.950(0.831,1.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.004(0.962,1.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.038(0.985,1.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.999(0.996,1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.991(0.976,1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eCK (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.004(0.998,1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eCK-MB (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.543(1.070,2.224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eLDH(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.009(1.001,1.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.299(0.775,2.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eLDL cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.593(0.322,1.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eHDL cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.184(0.032,1.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eD-dimer (\u0026mu;g/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.129(0.966,1.319)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eFPG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.000(0.965,1.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eHbA\u003csub\u003e1c\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.129(0.900,1.578)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eSensor glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.010(1.001,1.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e1.007(0.945,1.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\"\u003e\n \u003cp\u003eAntibiotic therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.357(0.031,4.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\"\u003e\n \u003cp\u003eGlucocorticoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.348(0.098,1.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eAnticoagulant Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e3.732(1.288,10.812)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eNon-insulin Hypoglycemic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e0.352(0.125,0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.32231404958678%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin Hypoglycemic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003e2.500(0.813,7.689)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.28374655647383%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"775\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTable 3\u0026mdash;Multivariate analysis for predicting composite adverse outcomes by glycemic metrics derived from isCGM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.935483870967744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.064516129032256%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOdds ratios (95% confidence interval)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSensor glucose levels (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.12903225806452%\" valign=\"top\"\u003e\n \u003cp\u003eTIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.225806451612904%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.645161290322584%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.935483870967744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;TIR1(70-180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.064516129032256%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.975 (0.948-1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.935483870967744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;TIR2(80-190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.064516129032256%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.963 (0.932-0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.935483870967744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;TIR3(90-200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.064516129032256%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.951 (0.916-0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.935483870967744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; TIR4(100-210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.064516129032256%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.950 (0.914-0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.935483870967744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; TIR5(110-220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.064516129032256%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.960 (0.926-0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.935483870967744%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; TIR6(120-230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.064516129032256%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.967 (0.934-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eData are adjusted for age, sex, CK-MB, symptoms on admission, LDH, Use of OHA and anticoagulant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3859033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3859033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"After fully lifting coronavirus disease 2019 (COVID-19) pandemic control measures in mainland China in 12/2022, the incidence of COVID-19 has increased markedly, making it difficult to meet the general time-in-range (TIR) requirement. We investigated a more clinically practical TIR threshold and examined its association with the prognosis of COVID-19 patients with type-2 diabetes. Sixty-three type-2 diabetes patients complicated with COVID-19 were evaluated. Patient information included epidemiological and laboratory characteristics, treatment options and outcomes. The percentages of time-above-range (TAR), time-below-range (TBR) and TIR were calculated from intermittently scanned continuous glucose monitoring. The composite end point included a \u003e20-day length of stay, intensive care unit admission, mechanical ventilation use, or death. TIR with thresholds of 80 to 190 mg/dL was significantly associated with favorable outcomes. An increase of 1% in TIR is connected with a reduction of 3.70% in the risk of adverse outcomes. The Youden index was highest when the TIR was 54.73%, and the sensitivity and specificity were 58.30% and 77.80%, respectively. After accounting for confounding variables, our analysis revealed that threshold target ranges (TARs) ranging from 200 mg/dL to 230 mg/dL significantly augmented the likelihood of adverse outcomes.The TIR threshold of 80 to 190 mg/dL has a comparatively high predictive value of the prognosis of COVID-19. TIR \u003e54.73% was associated with a decreased risk of adverse outcomes. These findings provide clinically critical insights into possible avenues to improve outcomes for COVID-19 patients with type-2 diabetes.","manuscriptTitle":"A moderately higher time-in-range threshold improves the prognosis of type-2 diabetes patients complicated with COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-24 18:02:57","doi":"10.21203/rs.3.rs-3859033/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b43346bb-f085-4600-9179-95d2d5620c1a","owner":[],"postedDate":"January 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28318655,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"},{"id":28318656,"name":"Health sciences/Diseases/Respiratory tract diseases"},{"id":28318657,"name":"Health sciences/Health care/Geriatrics"},{"id":28318658,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes mellitus"}],"tags":[],"updatedAt":"2024-03-03T05:37:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-24 18:02:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3859033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3859033","identity":"rs-3859033","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}