Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review

Prediction model protocols indicate better adherence to recommended guidelines for study conduct and reporting

BACKGROUND AND OBJECTIVE

Protocols are invaluable documents for any research study, especially for prediction model studies. However, the mere existence of a protocol is insufficient if key details are omitted. We reviewed the reporting content and details of the proposed design and methods reported in published protocols for prediction model research.

METHODS

We searched MEDLINE, Embase, and the Web of Science Core Collection for protocols for studies developing or validating a diagnostic or prognostic model using any modeling approach in any clinical area. We screened protocols published between Jan 1, 2022 and June 30, 2022. We used the abstract, introduction, methods, and discussion sections of The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement to inform data extraction.

RESULTS

We identified 30 protocols, of which 28 were describing plans for model development and six for model validation. All protocols were open access, including a preprint. 15 protocols reported prospectively collecting data. 21 protocols planned to use clustered data, of which one-third planned methods to account for it. A planned sample size was reported for 93% development and 67% validation analyses. 16 protocols reported details of study registration, but all protocols reported a statement on ethics approval. Plans for data sharing were reported in 13 protocols.

CONCLUSION

Protocols for prediction model studies are uncommon, and few are made publicly available. Those that are available were reasonably well-reported and often described their methods following current prediction model research recommendations, likely leading to better reporting and methods in the actual study.

Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

Scholars and experts argue that future pandemics and/or epidemics are inevitable events, and the problem is not whether they will occur, but when a new health emergency will emerge. In this uncertain scenario, one of the most important questions is an accurate prevention, preparedness and prediction for the next pandemic. The main goal of this study is twofold: first, the clarification of sources and factors that may trigger pandemic threats; second, the examination of prediction models of on-going pandemics, showing pros and cons. Results, based on in-depth systematic review, show the vital role of environmental factors in the spread of Coronavirus Disease 2019 (COVID-19), and many limitations of the epidemiologic models of prediction because of the complex interactions between the new viral agent SARS-CoV-2, environment and society that have generated variants and sub-variants with rapid transmission. The insights here are, whenever possible, to clarify these aspects associated with public health in order to provide lessons learned of health policy that may reduce risks of emergence and diffusion of new pandemics having negative societal impact.