Daily disease severity in patients with COVID-19 admitted to the hospital: The SCODA (severity of coronavirus disease assessment) score

External validation of two clinical prediction models for mortality in COVID-19 patients (4C and NEWS2), in three centers in Medellín, Colombia: Assessing the impact of vaccination over time

OBJECTIVES

External validation of the 4C and NEWS2 scores for the prediction of in-hospital mortality in COVID-19 patients, and evaluation of its operational performance in two time periods: before and after the start of the vaccination program in Colombia.

METHODS

Retrospective cohort in three high complexity hospitals in the city of Medellín, Colombia, between June 2020 and April 2022.

RESULTS

The areas under the ROC curve (AUC) for the 4C mortality risk score and the NEWS2 were 0.75 (95% CI 0.73-0.78) and 0.68 (95% CI 0.66-0.71), respectively. For the 4C score, the AUC for the first and second periods was 0.77 (95% CI 0.74-0.80) and 0.75 (95% CI 0.71-0.78); whilst for the NEWS2 score, it was 0.68 (95% CI 0.65-0.71) and 0.69 (95% CI 0.64-0.73). The calibration for both scores was adequate, albeit with reduced performance during the second period.

CONCLUSIONS

The 4C mortality risk score proved to be the more adequate predictor of in-hospital mortality in COVID-19 patients in this Latin American population. The operational performance during both time periods remained similar, which shows its utility notwithstanding major changes, including vaccination, as the pandemic evolved.

Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19

Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.