Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets
Authors: Reps Jenna M. , Kim Chungsoo , Williams Ross D. , Markus Aniek F. , Yang Cynthia , Salles Talita Duarte , Falconer Thomas , Jonnagaddala Jitendra , Williams Andrew , Fernández-Bertolín Sergio , DuVall Scott L. , Kostka Kristin , Rao Gowtham , Shoaibi Azza , Ostropolets Anna , Spotnitz Matthew E , Zhang Lin , Casajust Paula , Steyerberg Ewout W. , Nyberg Fredrik , Kaas-Hansen Benjamin Skov , Choi Young Hwa , Morales Daniel , Liaw Siaw-Teng , Fernandes Abrahão Maria Tereza , Areia Carlos , Matheny Michael E. , Aragón María , Park Rae Woong , Hripcsak George , Reich Christian G. , Suchard Marc A. , You Seng Chan , Ryan Patrick B. , Prieto-Alhambra Daniel , Rijnbeek Peter R. 
Published: June 2020

Background

SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However, the model is at high risk of bias according to the Prediction model Risk Of Bias ASsessment Tool and has not been externally validated.

Methods

We followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia.

Findings

The internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68.

Interpretation

The results show that the discriminative performance of the C-19 model is low for influenza cohorts, and even worse amongst COVID-19 patients in the US, Spain and South Korea. These results suggest that C-19 should not be used to aid decision making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.