The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

Abstract

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

Risk Stratification Model for Severe COVID-19 Disease: A Retrospective Cohort Study

“…We identified two protective factors against serious illness: vaccination status and prior history of COVID-19 infection. Many of the risk factors that were identified here, such as age, gender, socioeconomic status, and comorbidities, have also been identified in other models that have been developed [ 8 , 9 , 10 , 11 , 14 , 15 , 16 ]. In another HMO-based Israeli study, the number of doses was found to have a protective effect against hospitalization and mortality.…”

The impact of COVID-19 severity on pregnancy outcomes among Iraqi women: a retrospective observational study

“…After adjusting for other signs and symptoms, fever and shortness of breath were the most predictable indicators of severity using binary logistic regression. Although Lian et al, in 2021, stated similar results regarding fever but argued by considering cough as a predictor of developing severe illness, 23 the reason behind this argument may be due to pregnancy itself, as the shortness of breath may occur in normal pregnancy by excluding the pathological causes. It is a challenge for clinicians to differentiate.…”

“…In addition, Lian et al, in 2021, stated that patients with advanced age, H.T., and hypertension tend to have more severe COVID-19 infection. 23 Because COVID-19 affects both the mother and the fetus and negatively impacts pregnancy outcomes, pregnant women with the infection need particular care and attention. 46 This study explored the association between the severity of COVID-19 and adverse pregnancy outcomes.…”

A Calculator for COVID-19 Severity Prediction Based on Patient Risk Factors and Number of Vaccines Received

“…The models can also prioritize which populations to vaccinate or to urge to receive booster doses to maximize lives saved and reduce the load on hospitalization facilities. Several attempts have already been made to build predictive models for COVID-19 severity, notably [4,9,19,[35][36][37][38]. Among the five models cited in the preceding sentence, only the models of Iannou and colleagues [4] and of Experton and colleagues [27] predict at least one of risk of hospitalization or risk of death in a newly infected individual.…”

“…The variations in vaccination uptake [8] provide an opportunity to assess the beneficial effects of different vaccination doses after accounting for patient risk factors. Among the factors known to affect COVID-19 severity are advanced age [9][10][11][12], type II diabetes [10,[13][14][15][16][17], kidney disease [10,[17][18][19], chronic obstructive pulmonary disease (COPD) [19][20][21][22][23], obesity [10,14,15,24,25], hypertension [26][27][28], and malignancy [29].…”