Development of a data-driven case-mix adjustment model for comparison of hospital performance in hip fracture care.

To compare hospitals' hip fracture patient mortality in a quality of care registry, correction for patient characteristics is needed. This study evaluates in 39,374 patients which characteristics are associated with 30 and 90-day mortality, and showed how using these characteristics in a case mix-model changes hospital comparisons within the Netherlands. PURPOSE: Mortality rates after hip fracture surgery are considerable and may be influenced by patient characteristics. This study aims to evaluate hospital variation regarding patient demographics and disease burden, to develop a case-mix adjustment model to analyse differences in hip fracture patients' mortality to calculate case-mix adjusted hospital-specific mortality rates. METHODS: Data were derived from 64 hospitals participating in the Dutch Hip Fracture Audit (DHFA). Adult hip fracture patients registered in 2017-2019 were included. Variation of case-mix factors between hospitals was analysed, and the association between case-mix factors and mortality at 30 and 90 days was determined through regression models. RESULTS: There were 39,374 patients included. Significant variation in case-mix factors amongst hospitals was found for age ≥ 80 (range 25.8-72.1% p < 0.001), male gender (12.0-52.9% p < 0.001), nursing home residents (42.0-57.9% p < 0.001), pre-fracture mobility aid use (9.9-86.7% p < 0,001), daily living dependency (27.5-96.5% p < 0,001), ASA-class ≥ 3 (25.8-83.3% p < 0.001), dementia (3.6-28.6% p < 0.001), osteoporosis (0.0-57.1% p < 0.001), risk of malnutrition (0.0-29.2% p < 0.001) and fracture types (all p < 0.001). All factors were associated with 30- and 90-day mortality. Eight hospitals showed higher and six showed lower 30-day mortality than expected based on their case-mix. Six hospitals showed higher and seven lower 90-day mortality than expected. The specific outlier hospitals changed when correcting for case-mix factors. CONCLUSIONS: Dutch hospitals show significant case-mix variation regarding hip fracture patients. Case-mix adjustment is a prerequisite when comparing hospitals' 30-day and 90-day hip fracture patients' mortality. Adjusted mortality may serve as a starting point for improving hip fracture care.

Surgical outcomes of laparoscopic hysterectomy with concomitant endometriosis without bowel or bladder dissection: a cohort analysis to define a case-mix variable.

BACKGROUND: Pelvic endometriosis is often mentioned as one of the variables influencing surgical outcomes of laparoscopic hysterectomy (LH). However, its additional surgical risks have not been well established. The aim of this study was to analyze to what extent concomitant endometriosis influences surgical outcomes of LH and to determine if it should be considered as case-mix variable. RESULTS: A total of 2655 LH's were analyzed, of which 397 (15.0%) with concomitant endometriosis. For blood loss and operative time, no measurable association was found for stages I (n = 106) and II (n = 103) endometriosis compared to LH without endometriosis. LH with stages III (n = 93) and IV (n = 95) endometriosis were associated with more intra-operative blood loss (p = < .001) and a prolonged operative time (p = < .001) compared to LH without endometriosis. No significant association was found between endometriosis (all stages) and complications (p = .62). CONCLUSIONS: The findings of our study have provided numeric support for the influence of concomitant endometriosis on surgical outcomes of LH, without bowel or bladder dissection. Only stages III and IV were associated with a longer operative time and more blood loss and should thus be considered as case-mix variables in future quality measurement tools.

Guidelines on constructing funnel plots for quality indicators: A case study on mortality in intensive care unit patients.

Funnel plots are graphical tools to assess and compare clinical performance of a group of care professionals or care institutions on a quality indicator against a benchmark. Incorrect construction of funnel plots may lead to erroneous assessment and incorrect decisions potentially with severe consequences. We provide workflow-based guidance for data analysts on constructing funnel plots for the evaluation of binary quality indicators, expressed as proportions, risk-adjusted rates or standardised rates. Our guidelines assume the following steps: (1) defining policy level input; (2) checking the quality of models used for case-mix correction; (3) examining whether the number of observations per hospital is sufficient; (4) testing for overdispersion of the values of the quality indicator; (5) testing whether the values of quality indicators are associated with institutional characteristics; and (6) specifying how the funnel plot should be constructed. We illustrate our guidelines using data from the Dutch National Intensive Care Evaluation registry. We expect that our guidelines will be useful to data analysts preparing funnel plots and to registries, or other organisations publishing quality indicators. This is particularly true if these people and organisations wish to use standard operating procedures when constructing funnel plots, perhaps to comply with the demands of certification.