Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data

Abstract

Rationale: Prognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease. The study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission. Methods Hierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of acute respiratory distress syndrome, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models’ predictions of risk. Main Results: Test performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, C-reactive protein, lactate dehydrogenase, and D-dimer were often found to be important in the assignment of risk. Conclusions Risk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.

Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques

“…Up until now, the emphasis of our analysis lay on the prediction of the features, both discrete and continuous, most relevant for triaging patients by Severity and Mortality. As a way to confirm the validity of our results for feature selection, we note that age [ 8 , 11 , 12 , 13 ], CHD [ 4 , 11 , 12 ], CRP [ 12 , 13 ], neutrophil [ 4 , 13 ] and LDH [ 7 ] were also proven to be statistically significant features to predict the Mortality outcome, while age [ 14 ], CRP and LDH [ 3 , 5 , 9 , 14 ] (among many others) were found to be statistically significant for the Severity outcome. Furthermore, in [ 15 ], a variety of results from other studies are summarized, which possess a notable overlap with the results presented above for the statistically relevant features to predict both outcomes.…”

“…Although diagnostic tests, with variable sensitivity and specificity, have been widely available since 2020, it is still problematic to predict when a new peak of infection will present itself in a population and what measures should be taken to contain the spread while furnishing appropriate medical care. For these reasons, researchers have tried to identify specific features or test results that may be reasonably used as a predictor of the Severity of respiratory distress for COVID-19 positive patients as well as their risk of death [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] (see also [ 15 ] and reference therein).…”

Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study

“…LDH, CRP, and D-dimer have been identified by the majority of machine learning models as important risk laboratory parameters linked to COVID-19 disease severity [53,54]. Interestingly, CRP, another nonspecific inflammatory biomarker, was not associated in our logistic regression composite models with severe disease, probably due to the reduced capability of our statistical model to detect complex interactions among attributes.…”

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China

“…Machine learning models based on clinical features have been used in many applications in cancer and tumor prognosis prediction, such as in lung cancer and breast cancer [ 19 , 20 ]. The application of death prediction in infectious diseases is also becoming a trend, typically regarding the prediction of mortality risk and prognosis of COVID-19 patients [ 21 – 23 ]. Similarly, assessing dengue severity risk factors has been reported [ 24 ].…”