Purpose: During the first waves of the coronavirus pandemic, evidence on potential effective treatments was urgently needed. Results from observational studies on the effectiveness of... Show morePurpose: During the first waves of the coronavirus pandemic, evidence on potential effective treatments was urgently needed. Results from observational studies on the effectiveness of hydroxychloroquine (HCQ) were conflicting, potentially due to biases. We aimed to assess the quality of observational studies on HCQ and its relation to effect sizes.Methods: PubMed was searched on 15 March 2021 for observational studies on the effectiveness of in-hospital use of HCQ in COVID-19 patients, published between 01/01/2020 and 01/03/2021 on. Study quality was assessed using the ROBINS-I tool. Association between study quality and study characteristics (journal ranking, publication date, and time between submission and publication) and differences between effects sizes found in observational studies compared to those found in RCTs, were assessed using Spearman's correlation.Results: Eighteen of the 33 (55%) included observational studies were scored as critical risk of bias, eleven (33%) as serious risk and only four (12%) as moderate risk of bias. Biases were most often scored as critical in the domains related to selection of participants (n = 13, 39%) and bias due to confounding (n = 8, 24%). There were no significant associations found between the study quality and the characteristics nor between the study quality and the effect estimates.Discussion: Overall, the quality of observational HCQ studies was heterogeneous. Synthesis of evidence of effectiveness of HCQ in COVID-19 should focus on RCTs and carefully consider the added value and quality of observational evidence. Show less
Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who... Show moreBackground: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from 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" criteria, and it has not been externally validated.Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the 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 data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, 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. Show less
