Toward the Measure of Credibility of Hospital Administrative Datasets in the Context of DRG Classification

Poor quality of coded clinical data in hospital administrative databases may negatively affect decision making, clinical and health care services research and billing. In this paper, we assessed the level of credibility of a nationwide Portuguese inpatient database concerning the codification of pneumonia, with a special emphasis on identifying suspicious cases of upcoding affecting proper APR-DRG (All-Patient Refined Diagnosis-Related Groups) classification and hospital funding. Using data on pneumonia-related hospitalizations from 2015, we compared six hospitals with similar complexity regarding the frequency of all pneumonia-related diagnosis codes in order to identify codes that were significantly overreported in a given facility relatively to its peers. To verify whether the discrepant codes could be related to upcoding, we built Support Vector Machine (SVM) models to simulate the APR-DRG system and assess its response to each discrepant code. Findings demonstrate that hospitals significantly differed in coding six pneumonia conditions, with five of them playing a major role in increasing APR-DRG complexity, being thus suspicious cases of upcoding. However, those comprised a minority of cases and the overall credibility concerning upcoding of pneumonia was above 99% for all evaluated hospitals. Our findings can not only be relevant for planning future audit processes by signalizing errors impacting APR-DRG classification, but also for discussing credibility of administrative data, keeping in mind their impact on hospital financing. Hence, the main contribution of this paper is a reproducible method that can be employed to monitor the credibility and to promote data quality management in administrative databases

Weekends affect mortality risk and chance of discharge in critically ill patients: a retrospective study in the Austrian registry for intensive care.

BACKGROUND: In this study, we primarily investigated whether ICU admission or ICU stay at weekends (Saturday and Sunday) is associated with a different risk of ICU mortality or chance of ICU discharge than ICU admission or ICU stay on weekdays (Monday to Friday). Secondarily, we analysed whether weekend ICU admission or ICU stay influences risk of hospital mortality or chance of hospital discharge. METHODS: A retrospective study was performed for all adult patients admitted to 119 ICUs participating in the benchmarking project of the Austrian Centre for Documentation and Quality Assurance in Intensive Care (ASDI) between 2012 and 2015. Readmissions to the ICU during the same hospital stay were excluded. RESULTS: In a multivariable competing risk analysis, a strong weekend effect was observed. Patients admitted to ICUs on Saturday or Sunday had a higher mortality risk after adjustment for severity of illness by Simplified Acute Physiology Score (SAPS) 3, year, month of the year, type of admission, ICU, and weekday of death or discharge. Hazard ratios (95% confidence interval) for death in the ICU following admission on a Saturday or Sunday compared with Wednesday were 1.15 (1.08-1.23) and 1.11 (1.03-1.18), respectively. Lower hazard ratios were observed for dying on a Saturday (0.93 (0.87-1.00)) or Sunday (0.85 (0.80-0.91)) compared with Wednesday. This is probably related to the reduced chance of being discharged from the ICU at the weekend (0.63 (0.62-064) for Saturday and 0.56 (0.55-0.57) for Sunday). Similar results were found for hospital mortality and hospital discharge following ICU admission. CONCLUSIONS: Patients admitted to ICUs at weekends are at increased risk of death in both the ICU and the hospital even after rigorous adjustment for severity of illness. Conversely, death in the ICU and discharge from the ICU are significantly less likely at weekends

Sample size implications of mortality definitions in sepsis: a retrospective cohort study

Abstract Background Many randomized controlled trials (RCTs) employ mortality at a given time as a primary outcome. There are at least three common ways to measure 90-day mortality: first, all-location mortality, that is, all-cause mortality within 90 days of randomization at any location. Second, ARDSnet mortality is death in a healthcare facility of greater intensity than the patient was in prior to the hospitalization during which they were randomized. Finally, in-hospital mortality is death prior to discharge from the primary hospitalization of randomization. Data comparing the impact of these different measurements on sample size are lacking. We evaluated the extent to which event rates vary by mortality definition. Methods This was a retrospective cohort study of 30,691 patients hospitalized at Veterans Affairs (VA) hospitals for sepsis during 2009. 12,727 (41.5%) received care in an ICU setting. For each patient, we measured event rates for three different 90-day mortality outcomes: all-location mortality, ARDSnet mortality, and in-hospital mortality. We also calculated sample sizes necessary to power an example RCT given those event rates. Results At 90 days, all-location mortality was 26.4% (95% CI 25.9–26.9%), ARDSnet mortality was 19.2% (95% CI 18.8–19.7%), and in-hospital mortality was 13.4% (95% CI 13.0–13.8%) (p < 0.01 all comparisons). These respective event rates result in different required sample sizes to achieve a 20% relative reduction in mortality with 80% power and a 5% false positive rate. Such a trial of VA sepsis patients would require 2080 patients for all-location mortality, 3080 for ARDSnet mortality, and 4796 for in-hospital mortality. Among sepsis patients mechanically ventilated in an ICU, 2438 experienced all-location mortality (46.2% [95% CI 44.8–47.5%]), 2181 experienced ARDSnet mortality (41.3% [95% CI 40.0–42.6%]), and 1894 experienced in-hospital mortality (36.0% [95% CI 34.7–37.3%]). Conclusions Event rates vary substantially in sepsis patients based on the chosen 90-day mortality definition. This could have important implications for RCT design trade-offs