Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings

Abstract

Background: In locations where few people have received coronavirus disease 2019 (COVID-19) vaccines, health systems remain vulnerable to surges in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Tools to identify patients suitable for community-based management are urgently needed.

Methods: We prospectively recruited adults presenting to 2 hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 to develop and validate a clinical prediction model to rule out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 BPM; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex, and SpO2) and 1 of 7 shortlisted biochemical biomarkers measurable using commercially available rapid tests (C-reactive protein [CRP], D-dimer, interleukin 6 [IL-6], neutrophil-to-lymphocyte ratio [NLR], procalcitonin [PCT], soluble triggering receptor expressed on myeloid cell-1 [sTREM-1], or soluble urokinase plasminogen activator receptor [suPAR]), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration, and clinical utility of the models in a held-out temporal external validation cohort.

Results: In total, 426 participants were recruited, of whom 89 (21.0%) met the primary outcome; 257 participants comprised the development cohort, and 166 comprised the validation cohort. The 3 models containing NLR, suPAR, or IL-6 demonstrated promising discrimination (c-statistics: 0.72-0.74) and calibration (calibration slopes: 1.01-1.05) in the validation cohort and provided greater utility than a model containing the clinical parameters alone.

Conclusions: We present 3 clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.

Trial registration: ClinicalTrials.gov NCT04441372.

Keywords: COVID-19; LMIC; low- and middle-income country; prognostic model; triage.

© The Author(s) 2022. Published by Oxford University Press for the Infectious Diseases Society of America.

Figures

Figure 1.

Proportion of individuals fully vaccinated against COVID-19 as of 19 December 2021. Adapted from https://ourworldindata.org/covid-vaccinations#country-by-country-data-on-vaccinations [1]. Abbreviation: COVID-19, coronavirus disease 2019.

Figure 2.

Screening and recruitment of participants into the PRIORITSE study. *Reasons for exclusion: 64 vaccinated, 3 unable to provide consent, and 5 reason not documented. Toward the end of recruitment (March 2021 in AIIMS and May 2021 in CMC) vaccines against COVID-19 began to be rolled out in the study areas and a decision was made to exclude vaccinated participants as the study would not be powered to determine whether the prediction models were valid in this cohort. Abbreviations: AIIMS, All India Institute of Medical Sciences; CMC, Christian Medical College; COVID-19, coronavirus disease 2019.

Figure 3.

Performance measures and calibration plots for each model in the validation cohort. Red line indicates perfect calibration; black dashed line indicates calibration slope for that particular model; blue rug plots indicate distribution of predicted risk for participants who did (top) and did not (bottom) meet the primary outcome; grey shaded rectangle indicates region within which no individual participant’s predicted risk falls for that particular model. C-statistics indicate how well participants who met the primary outcome are differentiated from those who did not; perfect discrimination is indicated by a c-statistic of 1.0. Calibration slopes indicate agreement between predicted probabilities and observed outcomes; perfect calibration is indicated by a slope of 1.0. Abbreviations: CRP, C-reactive protein; IL-6, interleukin 6; NLR, neutrophil-to-lymphocyte ratio; PCT, procalcitonin; sTREM-1, soluble triggering receptor expressed on myeloid cell-1; suPAR, soluble urokinase plasminogen activator receptor.

Figure 4.

Decision curve analysis for each model in the validation cohort. The net benefit for each model is compared to an “admit-all” (red line) and “admit-none” (green line) approach, and each model containing a biochemical biomarker (purple line) is also compared to the model containing only clinical variables (blue line). A threshold probability of 5% indicates a scenario where the value of 1 TP (patient admitted who will subsequently require oxygen) is equivalent to 19 FPs (patients admitted who will not subsequently require oxygen). Abbreviations: CRP, C-reactive protein; FP, false positive; IL-6, interleukin 6; NLR, neutrophil-to-lymphocyte ratio; PCT, procalcitonin; sTREM-1, soluble triggering receptor expressed on myeloid cell-1; suPAR, soluble urokinase plasminogen activator receptor; TP, true positive.