As of Sept 2, 2020, more than 25 million cases of COVID-19 have been reported, with more than 850 000 associated deaths worldwide. Patients infected with severe acute respiratory syndrome coronavirus 2, the virus that causes COVID-19, could require treatment in the intensive care unit for up to 4 weeks. As such, this disease is a major burden on health-care systems, leading to difficult decisions about who to treat and who not to. 1 Prediction models that combine patient and disease characteristics to estimate the risk of a poor outcome from COVID-19 can provide helpful assistance in clinical decision making. 2 In a living systematic review by Wynants and colleagues, 3 145 models were reviewed, of which 50 were for prognosis of patients with COVID-19, including 23 predicting mortality. Critical appraisal of these models showed a high risk of bias for all models (eg, because of a high risk of model overfitting and unclear reporting on intended use of the models, or because of no reporting of the models’ calibration performance). Moreover, external validation of these models, deemed essential before application can even be considered, was rarely done. Therefore, use of any of these reported prediction models was not recommended in current practice. In The Lancet Digital Health, Arjun S Yadaw and colleagues present two models to predict mortality in patients with COVID-19 admitted to the Mount Sinai Health System in the New York city area. 4 These researchers have addressed many of the issues encountered by Wynants and colleagues3 and provide extensive information about the modelling in the appendix. The dataset used for model development (n= 3841) is larger than in most currently published models, and the accompanying number of patients who died (n= 313) seems appropriate according to the prediction model risk of bias assessment tool (PROBAST) 5 and guidance on sample size requirements for prediction model development. 6 The calibration performance of the models is reported, which (although essential) is often missing, particularly in studies reporting on machine-learning algorithms, 7 and external validations of the models was done. Yadaw and colleagues acknowledge that additional external validation will be necessary4 because external validation was done in a random subset of the initial patient population and another set of recent patients from the same health system, and because the number of events in the validation sets were below the 100 suggested for reliable external validity assessment. 8 For other researchers to apply and externally validate models, adherence to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) criteria9 is advised to present the full models, accompanied by code in case of complex machine-learning models. Yadaw and colleagues reported many items in TRIPOD, however, the models themselves are not reported in the Article or appendix (item 15a of TRIPOD) so it is not possible for a reader to make predictions for new individuals (eg, to validate the developed models in their own data or investigate the contribution of the individual predictors). The moment for risk estimation defines which values of predictors will be available and is especially important for time-varying predictors (eg, temperature). The models reported by Yadaw and colleagues predict risk using measurements collected throughout the entire encounter of the patient with the health system, with no specific moment of prediction defined. 4 This raises questions about the actual prognostic value of the timevarying predictors (eg, the minimum oxygen saturation) and, hence …