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

OBJECTIVES: To assess improvement in the completeness of reporting coronavirus (COVID-19) prediction models after the peer review process. STUDY DESIGN AND SETTING: Studies included in a living systematic review of COVID-19 prediction models, with both preprint and peer-reviewed published versions available, were assessed. The primary outcome was the change in percentage adherence to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) reporting guidelines between pre-print and published manuscripts. RESULTS: Nineteen studies were identified including seven (37%) model development studies, two external validations of existing models (11%), and 10 (53%) papers reporting on both development and external validation of the same model. Median percentage adherence among preprint versions was 33% (min-max: 10 to 68%). The percentage adherence of TRIPOD components increased from preprint to publication in 11/19 studies (58%), with adherence unchanged in the remaining eight studies. The median change in adherence was just 3 percentage points (pp, min-max: 0-14 pp) across all studies. No association was observed between the change in percentage adherence and preprint score, journal impact factor, or time between journal submission and acceptance. CONCLUSIONS: The preprint reporting quality of COVID-19 prediction modeling studies is poor and did not improve much after peer review, suggesting peer review had a trivial effect on the completeness of reporting during the pandemic.

Epidemiology of diabetes in Iran: A scoping review of studies published during 2015-2019.

Background: Proper synthesis of existing epidemiologic studies on diabetes in Iran can guide future research efforts. We aimed to conduct a comprehensive scoping review on all research articles that investigated any aspect of diabetes epidemiology in Iran during 2015-2019. Methods: This work was conducted as a part of the Iran Diabetes Research Roadmap and completed under Arksey and O'Malley's framework for scoping reviews. The Scopus and PubMed databases were searched on Feb 15th, 2020. Eligible document types on diabetes epidemiology in the Iranian population, in Persian or English, that published during the 2015-2019 period underwent eligibility assessment. A total of 315 relevant articles were included and further analysis was performed on the original studies (n = 268). Through classifying them into six domains: Diabetes incidence; the prevalence of diabetes and associated factors; the incidence/prevalence of complications/comorbid conditions; mortality/survival; burden; and prediction modeling. Results: In total, 64 (20.3%) papers were published in Q1 journals, and 40 (12.6%) were international collaborations. No clear annual trend was present in the number of published primary or secondary articles, the portion of papers published in Q1 journals, international collaborations or relative domain proportions. Few review articles were found on prediction modeling, mortality or burden (excluding global studies). Conclusions: Our findings show a minor portion of works on diabetic epidemiology in Iran meets the quality standards of Q1 journals. Researchers have neglected some critical subjects and have occasionally fallen for common pitfalls of epidemiologic research. In particular, adhering to established guidelines can help authors implement rigorous methods to develop, validate, and deploy practical clinical prediction models. Researchers should prioritize investigating longitudinally collected data that aid in measuring disease incidence and enable casual inference. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-022-01094-0.

Improving risk prediction for pulmonary embolism in COVID-19 patients using echocardiography.

SARS-CoV-2 infection is associated with increased risk for pulmonary embolism (PE), a fatal complication that can cause right ventricular (RV) dysfunction. Serum D-dimer levels are a sensitive test to suggest PE, however lacks specificity in COVID-19 patients. The goal of this study was to identify a model that better predicts PE diagnosis in hospitalized COVID-19 patients using clinical, laboratory, and echocardiographic imaging predictors. We performed a cross-sectional study of 302 adult patients admitted to the Johns Hopkins Hospital (March 2020-February 2021) for COVID-19 infection who underwent transthoracic echocardiography and D-dimer testing; 204 patients had CT angiography. Clinical, laboratory and imaging predictors including, but not limited to, D-dimer and RV dysfunction were used to build prediction models for PE using logistic regression. Model discrimination was assessed using area under the receiver operator curve (AUC) and calibration using Hosmer-Lemeshow χ 2 statistic. Internal validation was performed. The prevalence of PE was 7.6%. The model with positive D-dimer above 5 mg/L, RV dysfunction on echocardiography, and troponin had an AUC of 0.77, and cross-validated AUC of 0.74. D-dimer (>5 mg/L) had a positive association with PE (adj odds ratio = 4.40; 95% confidence interval: [1.80, 10.78]). We identified a model including clinical, imaging and laboratory variables that predicted PE in hospitalized COVID-19 patients. Positive D-dimer >5, RV dysfunction on echocardiography, and troponin were important predictors for calculating likelihood of PE diagnosis. This approach may be useful to aid in clinical decision-making related to diagnostic imaging and treatment. Prospective studies are needed to evaluate impact on patient outcomes.