The reliability of medical illness reporting in a randomized clinical trial

PLoS One. 2025 Apr 24;20(4):e0320759. doi: 10.1371/journal.pone.0320759. eCollection 2025.

ABSTRACT

BACKGROUND/OBJECTIVE: Reported medical disorders from population surveys, medical records, and clinical trials, may not be accurate and methods are needed to improve confirmation. We report the accuracy of reported prevalence of medical disorders in a clinical trial and comparison with potential verification methods.

METHODS: We report the prevalence of 11 medical disorders, utilizing prospectively collected data from 729 participants in an eight-country multicenter clinical treatment trial on non-arteritic anterior ischemic optic neuropathy (NAION). We chose disorders where the medical history was potentially verifiable. We determined the prevalence using four methods: Method (M)1: Participant and medical health record reporting; M2: Physical examination, clinical tests; M3: Medication indications; M4: Combining M2 and M3. We estimated concordance between M1 and the other methods using Cohen’s kappa (K) statistic.

RESULTS: Prevalence of the medical disorders based on M1 were lower than for either M2 or M3, depending on the disorder, and consistentlly lower for M4. For M1 and M4, moderate concordance (K ≥ 0.50) was observed only for psychiatric disorders (K = 0.52) and prior NAION (K=0.67). The prevalence and concordance for M1 and M4 for anemia, hypertension, diabetes and psychiatric disease were the only disorders that differed between females and males. For all methods, the prevalence varied widely across countries. Concordance for M1 and M4 varied and moderate concordance occurred for psychiatric disorders and prior NAION.

CONCLUSION: Even with prospective, rigorously collected data, medical histories do not reliably identify all medical disorders. Adding the results of physical examination, laboratory tests, and medications increases the accuracy of reporting. This strategy could be adapted for clinical trials and electronic medical record disease-prevalence data mining.

PMID:40273139 | DOI:10.1371/journal.pone.0320759