Towards a National Clinical Minimum Data Set for General Surgery

Background: Measurement and comparison of surgical performance is accepted as necessary and inevitable. Risk-stratified (case-mix adjusted) models of clinical outcomes form a metric with which to assess performance, but require accurate data. Collecting such data in the clinical environment is time consuming and difficult. This study aimed to construct effective models, for operative and non-operative admissions, from routine clinical data residing in hospital computers, so minimizing data collection and quality problems, and facilitating national implementation.

Methods: Data for 3181 non-operative emergency, 5039 elective and 3043 emergency operative admissions for the 2 years beginning 1 August 1997 were used to generate logistic regression equations for risk of death, which were applied prospectively to the following 3 years' data.

Results: The models use urea, haemoglobin, white blood cell count, sodium, potassium, age on admission, sex, British United Provident Association (BUPA) Operative Severity Score (for operative admissions) and, implicitly, mode of admission and mortality at discharge. All three models successfully stratified risk into five or more bands.

Conclusion: Effective models of mortality, applicable to all general surgical admissions, can be constructed from existing routine clinical data, largely obtained from a single venesection. The data set is a candidate national clinical minimum data set.