Establishing risk-adjusted quality indicators in surgery using administrative data-an example from neurosurgery

Area of Science:

• Neurosurgery
• Health Services Research
• Health Informatics

Background:

• German Hospital Structure Law mandates quality indicators for hospital remuneration.
• Neurosurgical quality indicators include readmission, reoperation, mortality, infection rates, and length of stay.
• Benchmarking neurosurgical departments is challenging due to heterogeneous patient populations and lack of risk adjustment.

Purpose of the Study:

• To analyze neurosurgical quality indicators using only administrative data.
• To evaluate the potential of administrative data for risk adjustment in neurosurgery.

Main Methods:

• Analysis of 2623 adult inpatient cases for brain or spinal tumors (2013-2017).
• Utilized DRG-related data including relative weight, patient clinical complexity level (PCCL), ICD-10 categories, secondary diagnoses, age, and sex.
• Calculated age-adjusted Charlson Comorbidity Index (CCI) and performed logistic regression to correlate quality indicators with administrative data.

Main Results:

• Patient Clinical Complexity Level (PCCL) positively correlated with 30-day readmission, reoperation, surgical site infection (SSI), and nosocomial infection rates.
• The age-adjusted Charlson Comorbidity Index (CCI) did not correlate with the studied quality indicators.

Conclusions:

• Quality indicators in neurosurgery are derivable from administrative data.
• Administrative data alone are insufficient for adequate risk adjustment due to uncaptured patient risk and intra-hospital complications.
• Future risk adjustment strategies should be developed using prospectively collected registry data.