Bias and Reporting Quality of Clinical Prognostic Models for Idiopathic Pulmonary Fibrosis: A Cross-Sectional Study

Abstract

Objective: This study aims to evaluate the risk of bias (ROB) and reporting quality of idiopathic pulmonary fibrosis (IPF) prediction models by assessing characteristics of these models. Methods: The development and/or validation of IPF prognostic models were identified via an electronic search of PubMed, Embase, and Web of Science (from inception to 12 August, 2021). Two researchers independently assessed the risk of bias (ROB) and reporting quality of IPF prediction models based on the Prediction model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariable prognostic model for Individual Prognosis or Diagnosis (TRIPOD) checklist. Results: Twenty prognostic model studies for IPF were included, including 7 (35%) model development and external validation studies, 8 (40%) development studies, and 5 (25%) external validation studies. According to PROBAST, all studies were appraised with high ROB, because of deficient reporting in the domains of participants (45.0%) and analysis (67.3%), and at least 55% studies were susceptible to 4 of 20 sources of bias. For the reporting quality, none of them completely adhered to the TRIPOD checklist, with the lowest mean reporting score for the methods and results domains (46.6% and 44.7%). For specific items, eight sub-items had a reporting rate ≥80% and adhered to the TRIPOD checklist, and nine sub-items had a very poor reporting rate, less than 30%. Conclusion: Studies adhering to PROBAST and TRIPOD checklists are recommended in the future. The reproducibility and transparency can be improved when studies completely adhere to PROBAST and TRIPOD checklists.