key: cord-0811732-18f63b5f authors: Bautista, Francisco; Rubio, Alba; Verdú, Jaime; Neven, Anouk; de Rojas, Teresa title: Comment on: Developing a risk assessment score for patients with cancer during the coronavirus disease 2019 pandemic date: 2020-09-06 journal: Eur J Cancer DOI: 10.1016/j.ejca.2020.08.027 sha: 4c6c838e73237daf1227f9ca8c3c1e4cb4c6ee39 doc_id: 811732 cord_uid: 18f63b5f nan To the editor, We have read with interest the article "Developing a risk assessment score for patients with cancer during the coronavirus disease 2019 pandemic" [1] . The authors propose a score to evaluate the risk of patients with cancer of developing complications if infected by COVID-19 and provide patient management recommendations according to the resulting risk stratification. The idea is interesting and could potentially benefit patients. However, we have substantial concerns regarding the methodology used to produce this score and its applicability. Risk models refer to any model that predicts the risk that a condition is present (diagnostic) or will develop in the future (prognostic) based on clinical and/or non-clinical characteristics [2] . These models can help physicians to take decisions that are based on combining information from multiple predictors observed or measured from an individual. A prospective cohort, nested case-control, or case-cohort design is commonly recommended for the development and validation of these models. The correct design of the study (i.e. statistical power and effective sample size consideration), the precise definition of the participants (i.e. eligibility criteria), a clear definition of the outcome that ensures reproducibility and the description of the statistical modeling techniques (including handling of missing data) are critical steps at the very early stage of developing a new model [3] . As part of the model development, the statistical performance, namely the discrimination and the calibration, should also be assessed [4] . Unfortunately, none of these essential premises were followed in the development of the "Milano-Policlinico ONCOVID Score" [1] . The decision to include a certain predictor in the model should be made carefully. In the "Milano-Policlinico ONCOVID Score", a clear definition of the outcome is missing; therefore, the aim of the score remains confusing -whether it predicts the risk of death by SARS-COV2 or of developing COVID-19 infection complications (also not defined). Moreover, in the development of the proposed score, the selection of the variables was not the result of a systematic analysis of the existing evidence, but a "thorough review" of the literature. The methodology of said review is not described by the authors, and hence the variable selection process remains opaque and non-reproducible. For instance, two of the "Patient characteristics" variables (performance status score and use of corticoids) seem to have been arbitrarily added to the ones selected through the initial literature review. Similarly arbitrary is the choice of laboratory parameters, which the authors seem to have selectively chosen from the work by Guan and collaborators et al they reference [5] . The variables were categorized into two or three sub-categories and arbitrarily assigned There have been efforts to produce scoring systems to evaluate the risk of developing severe/critical illness in hospitalized patients with COVID-19 [6] or the risk of death [7] , but none has reached global acceptance yet. Moreover, reputed institutions such as The Centre for Evidence-Based Medicine in Oxford have stated that no reliable, applicable or usable scoring systems is currently available to predict outcomes for patients with COVID-19 [8] . Methodological errors that are made in the development of prediction models [9] can ultimately lead to wrong decisions when applied into clinical practice. It is therefore our collective responsibility to develop well-designed prediction models that follow strict statistical principles [10] . This is of crucial importance in this critical episode of our history. The COVID-19 outbreak has tested national health systems' capacity to unprecedented limits and has demonstrated how fragile they can be. Resources are limited and it is imperative to make an optimal use of them by means of systems that accurately evaluate the need of our patients and allocate the appropriate resources to them. We urge the authors to re-consider the concept and design of this scoring system before using it in daily practice and before starting to collect prospective data for validation. We believe the clinical utility of this scoring system to be severely hampered by its current design. Patients could be equivocally assigned to risk categories, leading to potentially deleterious management decisions such as interruption or modification of cancer treatments, with unforeseeable consequences. J o u r n a l P r e -p r o o f Developing a risk assessment score for patients with cancer during the coronavirus disease 2019 pandemic Prognosis and prognostic research: application and impact of prognostic models in clinical practice Diagnostic and prognostic prediction models Assessing the performance of prediction models: a framework for traditional and novel measures Clinical Characteristics of Coronavirus Disease 2019 in China Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19 Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study What clinical features or scoring system, if any, might best predict a benefit from hospital admission for patients with COVID-19? Cent Evidence-Based Med 2020 Reporting and methods in clinical prediction research: a systematic review Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors