OBJECTIVE To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of becoming infected with covid-19 or being admitted to hospital with the disease. DESIGNLiving systematic review and critical appraisal. DATA SOURCESPubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 7 April 2020.Cite this as: BMJ 2020;369:m1328 http://dx.

In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a 'predictimand' framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference.

Preeclampsia (PE) is a hypertensive pregnancy disorder complicating up to 1-5% of pregnancies, and a major cause of maternal and fetal morbidity and mortality. In recent years, observational studies have consistently shown that PE carries an increased risk for the mother to develop cardiovascular and renal disease later in life. Women with a history of PE experience a 2-fold increased risk of long-term cardiovascular disease (CVD) and an approximate 5-12-fold increased risk of end-stage renal disease (ESRD). Recognition of PE as a risk factor for renal disease and CVD allows identification of a young population of women at high risk of developing of cardiovascular and renal disease. For this reason, current guidelines recommend cardiovascular screening and treatment for formerly preeclamptic women. However, these recommendations are based on low levels of evidence due to a lack of studies on screening and prevention in formerly preeclamptic women. This review lists the incidence of premature CVD and ESRD observed after PE and outlines observed abnormalities that might contribute to the increased CVD risk with a focus on kidney-related disturbances. We discuss gaps in current knowledge to guide optimal screening and prevention strategies. We emphasize the need for research on mechanisms of late disease manifestations, and on effective screening and therapeutic strategies aimed at reducing the late disease burden in formerly preeclamptic women.

It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out‐of‐sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. Although such heterogeneity in predictor measurement between derivation and validation data is common, the impact on the out‐of‐sample performance is not well studied. Using analytical and simulation approaches, we examined out‐of‐sample performance of prediction models under various scenarios of heterogeneous predictor measurement. These scenarios were defined and clarified using an established taxonomy of measurement error models. The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.