Identification of risk factors for mortality associated with COVID-19

Abstract

Objectives Coronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA). Methods This study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models. Results A total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and α-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 109/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693–0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859–0.985]) outperformed the linear regression models. Conclusions Our study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.

Impact of liver enzymes on SARS-CoV-2 infection and the severity of clinical course of COVID-19

“…Xu et al ., 26 also found that higher D-dimer concentrations in COVID-19 patients correlated with inflammatory factors and organ function, and the authors proposed that these factors can be used to predict organ injury. Another study by Yu et al ., 27 also found that non-survivors were significantly older than survivors (median 69 (IQR 57–77) years vs . 55 (41–63) years, P < 0.001), and had higher D-dimer concentrations (0.99 (0.44–2.96) mg/L vs .…”

How often and to what extent do admitted COVID-19 patients have signs of cardiac injury?

“…This knowledge may have therapeutic implications, such as need for heart failure medication or revascularisation. Elevated cTn levels, together with other laboratory values and patient characteristics, can also be used in risk stratification models for early triage of high-risk COVID-19 patients and can aid in medical decision-making, for example regarding early medication administration [20,21].…”

Risk profiles for negative and positive COVID-19 hospitalized patients

“…The accurate characterization of a subject's risk profile is critical for the prompt selection of effective treatment for that particular patient. Additionally, it may facilitate effective medical resource allocation and provide critical information to identify and protect the most vulnerable populations [ 5 ]. Numerous studies have been conducted in this risk profiling field.…”