The authors argue that postacute SARS-CoV-2 syndrome presents unique challenges in determining risk factors, natural history, and proper management. They call for strategies and actions that are based on rigorous, high-quality evidence.

Advanced imaging technologies play a central role in screening asymptomatic patients. However, the balance between imaging-based screening's potential benefits versus risks is sometimes unclear. Radiologists will have to address ongoing concerns, including high false-positive rates, incidental findings outside the organ of interest, overdiagnosis, and potential risks from radiation exposure. In this article, we provide a brief overview of these recurring controversies and suggest the following as areas that radiologists should focus on in order to tip the balance toward more benefits and less harms for patients undergoing imaging-based screening: interpretive variability, abnormal finding thresholds, and personalized, risk-based screening.

Background

Some patients will experience more or less benefit from treatment than the averages reported from clinical trials; such variation in therapeutic outcome is termed heterogeneity of treatment effects (HTE). Identifying HTE is necessary to individualize treatment. The degree to which heterogeneity is sought and analyzed correctly in the general medical literature is unknown. We undertook this literature sample to track the use of HTE analyses over time, examine the appropriateness of the statistical methods used, and explore the predictors of such analyses.

Methods

Articles were selected through a probability sample of randomized controlled trials (RCTs) published in Annals of Internal Medicine, BMJ, JAMA, The Lancet, and NEJM during odd numbered months of 1994, 1999, and 2004. RCTs were independently reviewed and coded by two abstractors, with adjudication by a third. Studies were classified as reporting: (1) HTE analysis, utilizing a formal test for heterogeneity or treatment-by-covariate interaction, (2) subgroup analysis only, involving no formal test for heterogeneity or interaction; or (3) neither. Chi-square tests and multiple logistic regression were used to identify variables associated with HTE reporting.

Results

319 studies were included. Ninety-two (29%) reported HTE analysis; another 88 (28%) reported subgroup analysis only, without examining HTE formally. Major covariates examined included individual risk factors associated with prognosis, responsiveness to treatment, or vulnerability to adverse effects of treatment (56%); gender (30%); age (29%); study site or center (29%); and race/ethnicity (7%). Journal of publication and sample size were significant independent predictors of HTE analysis (p < 0.05 and p < 0.001, respectively).

Conclusion

HTE is frequently ignored or incorrectly analyzed. An iterative process of exploratory analysis followed by confirmatory HTE analysis will generate the data needed to facilitate an individualized approach to evidence-based medicine.