Temporal Trends and Factors Associated With the Inclusion of Patient‐Reported Outcomes in Heart Failure Randomized Controlled Trials: A Systematic Review
Bibliographic record
Abstract
Background Patient‐reported outcomes (PROs) are important measures of treatment response in heart failure. We assessed temporal trends in and factors associated with inclusion of PROs in heart failure randomized controlled trials (RCTs). Methods and Results We searched MEDLINE, Embase, and CINAHL for studies published between January 2000 and July 2020 in journals with an impact factor ≥10. We assessed temporal trends using the Jonckheere‐Terpstra test and conducted multivariable logistic regression to explore trial characteristics associated with PRO inclusion. We assessed the quality of PRO reporting using the Consolidated Standards of Reporting Trials (CONSORT) PRO extension. Of 417 RCTs included, PROs were reported in 226 (54.2%; 95% CI, 49.3%–59.1%), with increased reporting between 2000 and 2020 ( P <0.001). The odds of PRO inclusion were greater in RCTs that were published in recent years (adjusted odds ratio [aOR] per year, 1.08; 95% CI, 1.04–1.12; P <0.001), multicenter (aOR, 1.89; 95% CI, 1.03–3.46; P =0.040), medium‐sized (aOR, 2.35; 95% CI, 1.26–4.40; P =0.008), coordinated in Central and South America (aOR, 5.93; 95% CI, 1.14–30.97; P =0.035), and tested health service (aOR, 3.12; 95% CI, 1.49–6.55; P =0.003), device/surgical (aOR, 6.66; 95% CI, 3.15–14.05; P <0.001), or exercise (aOR, 4.66; 95% CI, 1.81–12.00; P =0.001) interventions. RCTs reported a median of 4 (interquartile interval , 3–6) of a possible of 11 CONSORT PRO items. Conclusions Just over half of all heart failure RCTs published in high impact factor journals between 2000 and 2020 included PROs, with increased inclusion of PROs over time. Trials that were large, tested pharmaceutical interventions, and coordinated in North America / Europe had lower adjusted odds of reporting PROs relative to other trials. The quality of PRO reporting was modest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.217 | 0.587 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".