Classification and analysis of outcome predictors in non‐critically ill COVID‐19 patients

Abstract

Background Early detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2)‐infected patients who could develop a severe form of COVID‐19 must be considered of great importance to carry out adequate care and optimise the use of limited resources. Aims To use several machine learning classification models to analyse a series of non‐critically ill COVID‐19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome. Methods We retrospectively analysed non‐critically ill patients with COVID‐19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected. Results In the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64–0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction‐inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors. Conclusions In non‐critically ill COVID‐19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID‐19, such as age or dementia, influence clinical outcomes.

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

“…AI- and ML-based approach can be used as either diagnostic tool or a prognostic model to predict outcome [ 6 ]. Many studies have characterized the association of major risk factors with the COVID mortality such as higher age, cardiovascular disease, chronic respiratory disease, diabetes, hypertension, smoking history, and obesity [ 7 ]. However, they could not be strong individual predictors mainly through using conventional statistical analysis due to high degree of complexity and collinearity among the data.…”

Home management of COVID-19 symptomatic patients: a safety study on COVID committed home medical teams

“…Unfortunately we cannot establish the inverse correlation of chronic renal disease with intra-hospital mortality (identified early as patients at high risk and therefore subjected to more intensive treatment?). What also emerges from the literature is that these patients are probably a particularly frailty population, and the mortality at 30 days could be linked to the basic conditions rather than to the respiratory failure induced by COVID-19 [18]. Whether these patients should be addressed to hospital care early or be managed at home will need to be investigated in future targeted studies.…”

Research on SARS-COV-2 pandemic: a narrative review focused on the Italian contribution

“…In the context of critically ill COVID-19 patients, lymphocytopenia, thrombocytopenia, ferritin, liver enzyme elevation, high D-dimer and lengthening of prothrombin time are strictly correlated with higher ICU and/or hospital mortality [ 40 , 52 , 85 , 86 ].…”

“…In COVID-19 ICU-patients, mid-regional pro-adrenomedullin (MR-proADM) seems to have constantly higher values in non-survivors and predict mortality more precisely than other biomarkers. Repeated MR-proADM measurement may support rapid and effective decision-making [ 86 , 90 ].…”