Pancreatic Surgery - Optimal Caseload Thresholds and Predictive Accuracy (PaSuT)

The main objective of the study is to identify the optimal annual number of cases in a hospital with regard to minimising hospital mortality in pancreatic surgery. In particular, the prognostic value of such case numbers will be analysed.

Study Overview

• Status: Completed
• Conditions: Volume-Outcome Relationship in Pancreatic Surgery
• Intervention / Treatment: Procedure: Pancreatic resection procedure

Detailed Description

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.

Methods:

The nationwide hospital billing data for Germany (DRG statistics) for the period 2010 to 2019 will be analysed. The risk-adjusted mortality rates are determined. For this purpose, logistic regression models are calculated that adjust the mortality risk for the following variables Sex, age, emergency of admission, year of resection, diagnosis (malign neoplasm vs. benign neoplasm vs. neoplasm of unclear dignity vs. acute pancreatitis vs. chronic pancreatitis vs. other pancreatic diseases), additional procedures (venous resections/ multivisceral resections/ arterial resections/ splenectomy/ cholecystectomy/ biliary drainage/ dialysis procedures) and selected comorbidities. To classify additional procedures in order to reflect extent of surgery and technical difficulty, a slight modification of the classification system as described in Mihaljevic et al, 2021 will be used (PMID: 33386130). The Elixhauser definitions are used for the comorbidities as described in Quan et al, 2005 (PMID: 16224307). The selection of comorbidities to be considered is based on the publication by Hunger et al, 2022 (PMID: 35525416).

The case number effect is modelled using natural cubic splines. The 10th, 20th, 40th, 60th, 80th and 90th case number percentiles are used as node points. The adjusted hospital mortality as a function of the number of cases is determined using Estimated Marginal Means. Local extremes (maxima and minima) in the splines are determined using 1st and 2nd graph derivate.

Various regression models are calculated using either the number of cases from the current year of operation or the previous year. The predictive accuracy of the models is determined using the established measures from signal detection theory (AUC, sensitivity, specificity, positive predictive value, negative predictive value). Subgroup analyses for individual resection procedures will be performed.

• Study Type: Observational
• Enrollment (Actual): 80000

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population encompasses all patients that underwent any pancreatic resection procedure in any German hospital (full survey of the German population).

Description

Inclusion Criteria:

• at least 18 years old
• any pancreatic resection procedure
• operated at any German hospital

Exclusion Criteria:

• any transplantation procedure
• Inpatient admission for organ removal
• no information on sex
• no information on age

Study Plan

How is the study designed?

Number of groups / cohorts

6

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
All patients undergoing pancreatic surgery; All patients with at least one pancreatic surgery procedure code	Procedure: Pancreatic resection procedure; Pancreatic resection procedure
Subgroup: Total pancreatectomy; All patients with at least one of the following pancreatic procedure codes (OPS-codes): '55250', '55251', '55252', '5525x', '5525y'	Procedure: Pancreatic resection procedure; Pancreatic resection procedure
Subgroup: Pancreaticoduodenectomy; All patients with at least one of the following pancreatic procedure codes (OPS-codes): '55241', '55242', '55243'	Procedure: Pancreatic resection procedure; Pancreatic resection procedure
Subgroup: Segmental resection; All patients with at least one of the following pancreatic procedure code (OPS-codes): '55244'	Procedure: Pancreatic resection procedure; Pancreatic resection procedure
Subgroup: Distal pancreatectomy; All patients with at least one of the following pancreatic procedure codes (OPS-codes): '55240', '552400', '552401', '552402'	Procedure: Pancreatic resection procedure; Pancreatic resection procedure
Subgroup: Other partial resections; All patients with at least one of the following pancreatic procedure codes (OPS-codes): '5524x', '5524y'	Procedure: Pancreatic resection procedure; Pancreatic resection procedure

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
In-hospital mortality	Patient died during or after surgery	within 30 days

Collaborators and Investigators

• Sponsor: Richard Hunger
• Collaborators: Medizinische Hochschule Brandenburg Theodor Fontane
• Investigators: Study Director: Rene Mantke, MD, Head of Surgery at University Hospital Brandenburg an der Havel

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2010
• Primary Completion (Actual): December 31, 2019
• Study Completion (Actual): December 31, 2019

Study Registration Dates

• First Submitted: March 15, 2024
• First Submitted That Met QC Criteria: April 25, 2024
• First Posted (Actual): April 29, 2024

Study Record Updates

• Last Update Posted (Actual): April 29, 2024
• Last Update Submitted That Met QC Criteria: April 25, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Keywords: General Surgery; Pancreatic Surgery; Public Health; Outcome Assessment; Hospital Planning; Centralized Hospital Services
• Other Study ID Numbers: PaSuTE

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Data will be analyzed by controlled remote data analysis. The data is held exclusively by the Federal Statistical Office for scientific analyses. Direct access to or data sharing of individual patient data is prohibited by German law.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No