High resolution data modifies intensive care unit dialysis outcome predictions as compared with low resolution administrative data set.

High resolution clinical databases from electronic health records are increasingly being used in the field of health data science. Compared to traditional administrative databases and disease registries, these newer highly granular clinical datasets offer several advantages, including availability of detailed clinical information for machine learning and the ability to adjust for potential confounders in statistical models. The purpose of this study is to compare the analysis of the same clinical research question using an administrative database and an electronic health record database. The Nationwide Inpatient Sample (NIS) was used for the low-resolution model, and the eICU Collaborative Research Database (eICU) was used for the high-resolution model. A parallel cohort of patients admitted to the intensive care unit (ICU) with sepsis and requiring mechanical ventilation was extracted from each database. The primary outcome was mortality and the exposure of interest was the use of dialysis. In the low resolution model, after controlling for the covariates that are available, dialysis use was associated with an increased mortality (eICU: OR 2.07, 95% CI 1.75-2.44, p<0.01; NIS: OR 1.40, 95% CI 1.36-1.45, p<0.01). In the high-resolution model, after the addition of the clinical covariates, the harmful effect of dialysis on mortality was no longer significant (OR 1.04, 95% 0.85-1.28, p = 0.64). The results of this experiment show that the addition of high resolution clinical variables to statistical models significantly improves the ability to control for important confounders that are not available in administrative datasets. This suggests that the results from prior studies using low resolution data may be inaccurate and may need to be repeated using detailed clinical data.

Temporal Trends in Critical Care Outcomes in U.S. Minority-Serving Hospitals.

Rationale: Whether critical care improvements over the last 10 years extend to all hospitals has not been described.Objectives: To examine the temporal trends of critical care outcomes in minority and non-minority-serving hospitals using an inception cohort of critically ill patients.Measurements and Main Results: Using the Philips Health Care electronic ICU Research Institute Database, we identified minority-serving hospitals as those with an African American or Hispanic ICU census more than twice its regional mean. We examined almost 1.1 million critical illness admissions among 208 ICUs from across the United States admitted between 2006 and 2016. Adjusted hospital mortality (primary) and length of hospitalization (secondary) were the main outcomes. Large pluralities of African American (25%, n = 27,242) and Hispanic individuals (48%, n = 26,743) were cared for in minority-serving hospitals, compared with only 5.2% (n = 42,941) of white individuals. Over the last 10 years, although the risk of critical illness mortality steadily decreased by 2% per year (95% confidence interval [CI], 0.97-0.98) in non-minority-serving hospitals, outcomes within minority-serving hospitals did not improve comparably. This disparity in temporal trends was particularly noticeable among African American individuals, where each additional calendar year was associated with a 3% (95% CI, 0.96-0.97) lower adjusted critical illness mortality within a non-minority-serving hospital, but no change within minority-serving hospitals (hazard ratio, 0.99; 95% CI, 0.97-1.01). Similarly, although ICU and hospital lengths of stay decreased by 0.08 (95% CI, -0.08 to -0.07) and 0.16 (95% CI, -0.16 to -0.15) days per additional calendar year, respectively, in non-minority-serving hospitals, there was little temporal change for African American individuals in minority-serving hospitals.Conclusions: Critically ill African American individuals are disproportionately cared for in minority-serving hospitals, which have shown significantly less improvement than non-minority-serving hospitals over the last 10 years.

Risk Factors for Early Dislocation Following Primary Elective Total Hip Arthroplasty.

BACKGROUND: Dislocation following total hip arthroplasty (THA) continues to be one of the most common reasons for revision THA. The purpose of this study is to measure the current rate of dislocation following THA in the United States. A secondary goal is to identify patients at highest risk of instability after THA. METHODS: The Nationwide Readmissions Database was used to identify cases of elective primary THA between 2012 and 2014. All readmissions associated with dislocations were identified. Kaplan-Meier curves were used to assess the time to dislocation in the study population. A multivariate logistic regression was modeled to assess risk factors associated with readmission for dislocation. RESULTS: A total of 207,285 THAs were identified between 2012 and 2014. Of the total, 2842 dislocation-associated readmissions (1.4%) were identified, at a median of 40 days post-THA. A history of spinal fusion was the strongest independent predictor of dislocation (odds ratio [OR], 2.45; 95% confidence interval [CI], 1.97-3.04; P < .0001). Parkinson's disease was also significantly associated with dislocation (OR, 1.63; 95% CI, 1.05-2.51; P = .03), as well as dementia (OR, 1.96; 95% CI, 1.13-3.39; P = .02), depression (OR, 1.28; 95% CI, 1.13-1.43; P < .0001), and chronic lung disease (OR, 1.2; 95% CI, 1.07-1.33; P = .001). Inflammatory arthritis and avascular necrosis were independent risk factors for dislocation (OR, 1.56; 95% CI, 1.25-1.97; P < .0001; OR, 1.67; 95% CI, 1.45-1.93; P < .0001). CONCLUSION: THA is a highly effective procedure with a low overall rate of instability. A history of spinal fusion was the most significant independent risk factor for dislocation within the first 6 months following THA.