Importance Publicly available datasets hold much potential, but their unique design may require specific analytic approaches. Objective To determine adherence to appropriate research practices for a frequently used large public database, the National Inpatient Sample (NIS) of the Agency for Healthcare Research and Quality (AHRQ). Design, Setting and Participants In this observational study, of the 1082 studies published using the NIS from January 2015 – December 2016, a representative sample of 120 studies was systematically evaluated for adherence to practices required by AHRQ for design and conduct of research using the NIS. Exposure None Main Outcomes All studies were evaluated on 7 required research practices based on AHRQ’s recommendations, compiled under 3 domains: (A) data interpretation (interpreting data as hospitalization records rather than unique patients); (B) research design (avoiding use in performing state-, hospital-, and physician-level assessments where inappropriate; not using non-specific administrative secondary diagnosis codes to study in-hospital events), and (C) data analysis (accounting for complex survey design of the NIS and changes in data structure over time). Results Of 120 published studies, 85% (n=102) did not adhere to ≥1 required practices and 62% (n=74) did not adhere to ≥2 required practices. An estimated 925 (95% CI 852–998) and 696 (95% CI 596–796) NIS publications had violations of ≥1 and ≥2 required practices, respectively. A total of 79 sampled studies, representing 68.3% (95% CI 59.3–77.3) of the 1082 NIS studies, did not account for the effects of sampling error, clustering, and stratification; 62 (54.4%, 95% CI 44.7–64.0) extrapolated non-specific secondary diagnoses to infer in-hospital events; 45 (40.4%, 95% CI 30.9–50.0) miscategorized hospitalizations as individual patients; 10 (7.1%, 95% CI 2.1–12.1) performed state-level analyses; and 3 (2.9%, 95% CI 0.0–6.2) reported physician-level volume estimates. Of 27 studies (weighted: 218 studies, 95% CI 134–303) spanning periods of major changes in the data structure of the NIS, 21 (79.7%, 95% CI 62.5–97.0) did not account for the changes. Among the 24 studies published in journals with an impact factor ≥10, 16 (67%) and 9 (38%) did not adhere to ≥1 and ≥2 practices, respectively. Conclusions and Relevance In this study of 120 recent publications that used data from the NIS, the majority did not adhere to required practices. Further research is needed to identify strategies to improve the quality of research using the NIS and assess whether there are similar problems with use of other publicly available data sets.

Background and Objectives Preclinical studies generated the hypothesis that older stored red blood cells (RBCs) can increase transfusion risks. To examine the most updated and complete clinical evidence and compare results between two trial designs, we assessed both observational studies and randomized controlled trials (RCTs) studying the effect of RBC storage age on mortality. Materials and Methods Five databases were searched through December 2014 for studies comparing mortality using transfused RBCs having longer and shorter storage times. Results Analysis of six RCTs found no significant differences in survival comparing current practice (average storage age of 2 to 3 weeks) to transfusion of 1- to 10-day-old RBCs (OR 0·91, 95% CI 0·77–1·07). RBC storage age was lower in RCTs vs. observational studies (P = 0·01). The 31 observational studies found an increased risk of death (OR 1·13, 95% CI 1·03–1·24) (P = 0·01) with increasing age of RBCs, a different mortality effect than RCTs (P = 0·02). Conclusion RCTs established that transfusion of 1- to 10-day-old stored RBCs is not superior to current practice. The apparent discrepancy in mortality between analyses of RCTs and observational studies may in part relate to differences in hypotheses tested and ages of stored RBCs studied. Further trials investigating 1-to 10-day-old stored RBC benefits would seem of lower priority than studies to determine whether 4- to 6-week stored units have safety and efficacy equivalent to the 2- to 3-week-old stored RBCs commonly transfused today.

Background: Noninferiority trials are increasingly being performed. However, little is known about their methodological quality. We sought to characterize noninferiority cardiovascular trials published in the highest-impact journals, features that may bias results toward noninferiority, features related to reporting of noninferiority trials, and the time trends. Methods: We identified cardiovascular noninferiority trials published in JAMA , Lancet , or New England Journal of Medicine from 1990 to 2016. Two independent reviewers extracted the data. Data elements included the noninferiority margin and the success of studies in achieving noninferiority. The proportion of trials showing major or minor features that may have affected the noninferiority inference was determined. Major factors included the lack of presenting the results in both intention-to-treat and per-protocol/as-treated cohorts, α>0.05, the new intervention not being compared with the best alternative, not justifying the noninferiority margin, and exclusion or loss of ≥10% of the cohort. Minor factors included suboptimal blinding, allocation concealment, and others. Results: From 2544 screened studies, we identified 111 noninferiority cardiovascular trials. Noninferiority margins varied widely: risk differences of 0.4% to 25%, hazard ratios of 1.05 to 2.85, odds ratios of 1.1 to 2.0, and relative risks of 1.1 to 1.8. Eighty-six trials claimed noninferiority, of which 20 showed superiority, whereas 23 (21.1%) did not show noninferiority, of which 8 also demonstrated inferiority. Only 7 (6.3%) trials were considered low risk for all the major and minor biasing factors. Among common major factors for bias, 41 (37%) did not confirm the findings in both intention-to-treat and per-protocol/as-treated cohorts and 4 (3.6%) reported discrepant results between intention-to-treat and per-protocol analyses. Forty-three (38.7%) did not justify the noninferiority margin. Overall, 27 (24.3%) underenrolled or had >10% exclusions. Sixty trials (54.0%) were open label. Allocation concealment was not maintained or unclear in 11 (9.9%). Publication of noninferiority trials increased over time ( P <0.001). Fifty-two (46.8%) were published after 2010 and had a lower risk of methodological or reporting limitations for major ( P =0.03) and minor factors ( P =0.002). Conclusions: Noninferiority trials in highest-impact journals commonly conclude noninferiority of the tested intervention, but vary markedly in the selected noninferiority margin, and frequently have limitations that may impact the inference related to noninferiority.

Importance As medical knowledge and clinical practice rapidly evolve over time, there is an imperative to publish results of clinical trials in a timely way and reduce unnecessary delays. Objectives To characterize the age of clinical trial data at the time of publication in journals with a high impact factor and highlight the time from final data collection to publication. Design and Setting A cross-sectional analysis was conducted of all randomized clinical trials published from January 1 through December 31, 2015, in the Annals of Internal Medicine, BMJ, JAMA, JAMA Internal Medicine, Lancet, and New England Journal of Medicine . Multivariable linear regression analyses were conducted to assess whether data age (adjusted for follow-up duration) and publication time were associated with trial characteristics. Main Outcomes and Measures The outcome measures were the midpoint of data collection until publication (data age), the time from first participant enrollment to last participant enrollment (enrollment time), and the time from final data collection to publication (publication time). Results There were 341 clinical trials published in 2015 by the 6 journals. For assessment of the primary end point, 37 trials (10.9%) had a follow-up period of less than 1 month, 172 trials (50.4%) had a follow-up period of 1 month to 1 year, and 132 trials (38.7%) had a follow-up period of more than 1 year. For all trials, the median data age at publication was 33.9 months (interquartile range, 23.5-46.3 months). Among trials with a follow-up period of 1 month or less, the median data age was 30.6 months (interquartile range, 18.6-39.0 months). A total of 68 trials (19.9%) required more than 4 years to complete enrollment. The median time from the completion of data collection to publication was 14.8 months (interquartile range, 7.4-22.2 months); publication time was 2 or more years in 63 trials (18.5%). In multivariable regression analyses adjusted for follow-up time, inconclusive or unfavorable trial results were significantly associated with older data age (>235 days). Compared with trials funded only by private industry, trials funded by government were associated with a significantly longer time to publication (>180 days). Conclusions and Relevance Clinical trials in journals with a high impact factor were published with a median data age of nearly 3 years. For a substantial proportion of studies, time for enrollment and time from completion of data collection to publication were quite long, indicating marked opportunities for improvement in clinical trials to reduce data age.

BackgroundThe risk of rehospitalization is elevated in the immediate post-discharge period and declines over time. It is not known if the extent and timing of risk vary across readmission diagnoses, suggesting that recovery and vulnerability after discharge differ by physiologic system.ObjectiveWe compared risk trajectories for major readmission diagnoses in the year after discharge among all Medicare fee-for-service beneficiaries hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia from 2008–2010.MethodsWe estimated the daily risk of rehospitalization for 12 major readmission diagnostic categories after accounting for the competing risk of death after discharge. For each diagnostic category, we identified (1) the time required for readmission risk to peak and then decline 50% from maximum values after discharge; (2) the time required for readmission risk to approach plateau periods of minimal day-to-day change; and (3) the extent to which hospitalization risks are higher among patients recently discharged from the hospital compared with the general elderly population.ResultsAmong >3,000,000 hospitalizations, the yearly rate of rehospitalization was 67.0%, 49.5%, and 55.3% after hospitalization for HF, AMI, and pneumonia, respectively. The extent and timing of risk varied by readmission diagnosis and initial admitting condition. Risk of readmission for gastrointestinal bleeding/anemia peaked particularly late after hospital discharge, occurring 10, 6, and 7 days after hospitalization for HF, AMI, and pneumonia, respectively. Risk of readmission for trauma/injury declined particularly slowly, requiring 38, 20, and 38 days to decline by 50% after hospitalization for HF, AMI, and pneumonia, respectively.ConclusionsPatterns of vulnerability to different conditions that cause rehospitalization vary by time after hospital discharge. This finding suggests that recovery of various physiologic systems occurs at different rates and that post-discharge interventions to minimize vulnerability to specific conditions should be tailored to their underlying risks.