Covid-19 fatality prediction in people with diabetes and prediabetes using a simple score at hospital admission

AIMWe assessed predictors of in-hospital mortality in people with prediabetes and diabetes hospitalized for COVID-19 infection and developed a risk score for identifying those at the highest risk of a fatal outcome. MATERIALS AND METHODSA combined prospective and retrospective multicenter cohort study was conducted in 10 sites in Austria on 247 people with diabetes or newly diagnosed prediabetes, who were hospitalised for COVID-19. The primary outcome was in-hospital mortality and predictor variables at the time of admission included clinical data, comorbidities of diabetes or laboratory data. Logistic regression analyses were performed to identify significant predictors and develop a risk score for in-hospital mortality. RESULTSThe mean age of people hospitalized (n=238) for COVID-19 was 71.1 {+/-} 12.9 years, 63.6% were males, 75.6% had type 2 diabetes, 4.6% had type 1 diabetes, and 19.8% had prediabetes. The mean duration of hospital stay was 18 {+/-} 16 days, 23.9% required ventilation therapy, and 24.4% died in the hospital. Mortality rate in people with diabetes was numerically higher (26.7%) as compared to those with prediabetes (14.9%) but without statistical significance (p=0.128). A score including age, arterial occlusive disease, CRP, eGFR and AST levels at admission predicted in-hospital mortality with a C-statistics of 0.889 (95%CI: 0.837 - 0.941) and calibration of 1.000 (p=0.909). CONCLUSIONSThe in-hospital mortality for COVID-19 was high in people with diabetes and not significantly different to the risk in people with prediabetes. A risk score using five routinely available patient parameters demonstrated excellent predictive performance for assessing in-hospital mortality.

The competing risk between in-hospital mortality and recovery: A pitfall in COVID-19 survival analysis research

BackgroundA plethora of studies on COVID-19 investigating mortality and recovery have used the Cox Proportional Hazards (Cox PH) model without taking into account the presence of competing risks. We investigate, through extensive simulations, the bias in estimating the hazard ratio (HR) and the absolute risk reduction (ARR) of death when competing risks are ignored, and suggest an alternative method. MethodsWe simulated a fictive clinical trial on COVID-19 mimicking studies investigating interventions such as Hydroxychloroquine, Remdesivir, or convalescent plasma. The outcome is time from randomization until death. Six scenarios for the effect of treatment on death and recovery were considered. The HR and the 28-day ARR of death were estimated using the Cox PH and the Fine and Gray (FG) models. Estimates were then compared with the true values, and the magnitude of misestimation was quantified. ResultsThe Cox PH model misestimated the true HR and the 28-day ARR of death in the majority of scenarios. The magnitude of misestimation increased when recovery was faster and/or chance of recovery was higher. In some scenarios, this model has shown harmful treatment effect when it was beneficial. Estimates obtained from FG model were all consistent and showed no misestimation or changes in direction. ConclusionThere is a substantial risk of misleading results in COVID-19 research if recovery and death due to COVID-19 are not considered as competing risk events. We strongly recommend the use of a competing risk approach to re-analyze relevant published data that have used the Cox PH model.