ABSTRACT
BACKGROUND: No study has yet systematically evaluated the effect of antidiabetic therapy on clinical outcomes of COVID-19 patients with type 2 diabetes (T2D). OBJECTIVE: We aimed to evaluate the effect of different antidiabetic therapy on clinical outcomes of COVID-19 patients with T2D. METHODS: We comprehensively retrieved the published research which examined the effect of antidiabetic therapy on clinical outcomes of COVID-19 patients with T2D. The odds ratio (OR) and its 95% confidence interval (95% CI) for clinical outcomes were calculated using the random-effects model, and meta-regression was adopted to evaluate the potential sources of heterogeneity between studies. RESULTS: A total of 54 studies were included in this study. We found that the use of metformin (OR = 0.66, 95% CI: 0.58-0.75), SGLT-2i (OR = 0.80, 95% CI: 0.73-0.88), and GLP-1ra (OR = 0.83, 95% CI: 0.70-0.98) were significantly associated with lower mortality risk in COVID-19 patients with T2D, while insulin use might unexpectedly increase the ICU admission rate (OR = 2.32, 95% CI: 1.34-4.01) and risk of death (OR = 1.52, 95% CI: 1.32-1.75). No statistically significant associations were identified for DPP-4i, SUs, AGIs, and TZDs. CONCLUSION AND RELEVANCE: We demonstrated that the usage of metformin, SGLT-2i, and GLP-1ra could significantly decrease mortality in COVID-19 patients with T2D. The heterogeneity across the studies, baseline characteristics of the included patients, shortage of dosage and the duration of antidiabetic drugs and autonomy of drug selection might limit the objectivity and accuracy of results. Further adequately powered and high-quality randomized controlled trials are warranted for conclusive findings.
ABSTRACT
BACKGROUND: To systematically evaluate the prevalence of post-sequelae and chronic obstructive pulmonary disease assessment test (CAT) scoring one year after hospital discharge among older COVID-19 patients, as well as potential risk factors. METHODS: A multi-center prospective cohort study involving 1,233 eligible older COVID-19 patients was conducted. All patients were followed-up between Mar 1, 2021 and Mar 20, 2021. CAT scoring was adopted to measure symptom burden in COVID-19 patients. RESULTS: Of the 1233 eligible cases, 630 (51.1%) reported at least one sequelae. The top six post-sequelae included fatigue (32.4%), sweating (20.0%), chest tightness (15.8%), anxiety (11.4%), myalgia (9.0%), and cough (5.8%). Severe patients had significantly higher percentage of fatigue, sweating, chest tightness, myalgia, and cough (P<0.05), while anxiety was universal in all subjects. Sweating, anxiety, palpitation, edema of lower limbs, smell reduction, and taste change were emerging sequelae. Disease severity during hospitalization (OR: 1.46, 95% CI: 1.15-1.84, Pâ¯=â¯0.002), and follow-up time (OR: 0.71, 95% CI: 0.50-0.99, Pâ¯=â¯0.043) were independently associated with risk of post-sequelae, while disease severity during hospitalization was significantly associated with increased risk of emerging sequelae (OR: 1.33, 95% CI: 1.03-1.71, Pâ¯=â¯0.029). The median of CAT score was 2 (0-5) in all patients, and a total of 120 patients (9.7%) had CAT scores ≥10. Disease severity during hospitalization (OR: 1.81, 95% CI: 1.23-2.67, Pâ¯=â¯0.003) and age (OR: 1.07, 95% CI: 1.04-1.09, P<0.001) were significantly associated with increased risk of CAT scores ≥10. CONCLUSIONS: While the dramatic decline in the prevalence rate of persistent symptoms is reassuring, new sequelae among older COVID-19 patients cannot be ignored. Disease severity during hospitalization, age, and follow-up time contributed to the risk of post-sequelae and CAT scoring one year after hospital discharge among older COVID-19 patients. Our study provides valuable clues for long-term post-sequelae of the older COVID-19 patients, as well as their risk factors.
Subject(s)
COVID-19 , Patient Discharge , Hospitalization , Hospitals , Humans , Prospective Studies , SARS-CoV-2ABSTRACT
To establish an effective nomogram for predicting in-hospital mortality of COVID-19, a retrospective cohort study was conducted in two hospitals in Wuhan, China, with a total of 4,086 hospitalized COVID-19 cases. All patients have reached therapeutic endpoint (death or discharge). First, a total of 3,022 COVID-19 cases in Wuhan Huoshenshan hospital were divided chronologically into two sets, one (1,780 cases, including 47 died) for nomogram modeling and the other (1,242 cases, including 22 died) for internal validation. We then enrolled 1,064 COVID-19 cases (29 died) in Wuhan Taikang-Tongji hospital for external validation. Independent factors included age (HR for per year increment: 1.05), severity at admission (HR for per rank increment: 2.91), dyspnea (HR: 2.18), cardiovascular disease (HR: 3.25), and levels of lactate dehydrogenase (HR: 4.53), total bilirubin (HR: 2.56), blood glucose (HR: 2.56), and urea (HR: 2.14), which were finally selected into the nomogram. The C-index for the internal resampling (0.97, 95% CI: 0.95-0.98), the internal validation (0.96, 95% CI: 0.94-0.98), and the external validation (0.92, 95% CI: 0.86-0.98) demonstrated the fair discrimination ability. The calibration plots showed optimal agreement between nomogram prediction and actual observation. We established and validated a novel prognostic nomogram that could predict in-hospital mortality of COVID-19 patients.
Subject(s)
COVID-19 , Hospital Mortality , Nomograms , Age Factors , Aged , Blood Chemical Analysis/methods , Blood Chemical Analysis/statistics & numerical data , COVID-19/blood , COVID-19/diagnosis , COVID-19/mortality , COVID-19/physiopathology , Cardiovascular Diseases/epidemiology , China/epidemiology , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Assessment/methods , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Survival Analysis , Symptom Assessment/methods , Symptom Assessment/statistics & numerical dataABSTRACT
A systematic review and meta-analysis was conducted in an attempt to systematically collect and evaluate the associations of epidemiological, comorbidity factors with the severity and prognosis of coronavirus disease 2019 (COVID-19). The systematic review and meta-analysis was conducted according to the guidelines proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Sixty nine publications met our study criteria, and 61 studies with more than 10,000 COVID-19 cases were eligible for the quantitative synthesis. We found that the males had significantly higher disease severity (RR: 1.20, 95% CI: 1.13-1.27, P <0.001) and more prognostic endpoints. Older age was found to be significantly associated with the disease severity and six prognostic endpoints. Chronic kidney disease contributed mostly for death (RR: 7.10, 95% CI: 3.14-16.02), chronic obstructive pulmonary disease (COPD) for disease severity (RR: 4.20, 95% CI: 2.82-6.25), admission to intensive care unit (ICU) (RR: 5.61, 95% CI: 2.68-11.76), the composite endpoint (RR: 8.52, 95% CI: 4.36-16.65,), invasive ventilation (RR: 6.53, 95% CI: 2.70-15.84), and disease progression (RR: 7.48, 95% CI: 1.60-35.05), cerebrovascular disease for acute respiratory distress syndrome (ARDS) (RR: 3.15, 95% CI: 1.23-8.04), coronary heart disease for cardiac abnormality (RR: 5.37, 95% CI: 1.74-16.54). Our study highlighted that the male gender, older age and comorbidities owned strong epidemiological evidence of associations with the severity and prognosis of COVID-19.