Pancreatic Surgery - Optimal Caseload Thresholds and Predictive Accuracy

Main research questions:

• Can specific intervention case numbers be identified that are suitable as thresholds for annual minimum volumes and are associated with significantly low hospital mortality?
• Almost all previous studies on case number effects have only shown a descriptive association between the number of cases in a given year and the quality of outcomes in the same year. The aim of this study is to investigate whether the correlations described can be demonstrated when using the previous year's procedure volume as a predictor. The study seeks to answer whether the procedure caseload has predictive value, specifically the number of cases in one year and in-hospital mortality in the following year.

Background:

Numerous studies have demonstrated a correlation between the number of cases and the quality of outcomes for various surgical procedures. For instance, patients who underwent surgery in high-volume hospitals (HVH) had lower mortality rates, longer survival rates, lower complication rates, and lower reoperation rates than patients who underwent surgery in low-volume hospitals (LVH). To subdivide into HVHs and LVHs, either concrete case numbers or quartile or quintile limits with an equal number of operations or clinics per group wer used. The aim of the study is to objectively determine these limits using a spline-modeled caseload term, avoiding arbitrary decisions.

One limitation of the previous findings is that they may not be generalisable due to the use of a limited number of cases and outcome quality from the same year. However, it is important to note that the volume from the previous year is crucial in determining the predictive importance of caseload for future outcome quality. A recent study (in press) reported, that there are significant fluctuations in the quality of outcomes among HVHs, even between different years. Therefore, it was hypothesized that using the number of cases as a predictor of high-quality outcomes may lead to overestimation.