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March 1, 2002Journal of the American College of Surgeons263 citations

Identifying Patient Preoperative Risk Factors and Postoperative Adverse Events in Administrative Databases: Results From The Department of Veterans Affairs National Surgical Quality Improvement Program

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WBWilliam R. BestSKShukri F. KhuriMPMaureen Phelan

Key Points

  • The aim is to assess the effectiveness of ICD-9-CM codes in predicting preoperative risks and postoperative adverse events compared to NSQIP data.
  • ICD-9-CM codes for 61 preoperative characteristics and 21 adverse events were analyzed for sensitivity and positive predictive value.

Structured PICO

Does administrative data (ICD-9-CM codes) accurately identify preoperative risk factors and postoperative adverse events compared to prospectively gathered clinical data (NSQIP)?

P
Population
Patients undergoing major operations in 123 Department of Veterans Affairs (DVA) hospitals
I
Intervention
ICD-9-CM hospital discharge diagnosis codes in the Patient Treatment File (PTF)
C
Comparator
Prospectively gathered data by trained nurse data collectors (NSQIP)
O
Outcome
Sensitivity and positive predictive value of ICD-9-CM codes for preoperative patient characteristics and postoperative adverse events

Administrative data (ICD-9-CM codes) have poor sensitivity and positive predictive value compared to prospectively collected clinical data for identifying surgical risk factors and adverse events.

Abstract

BACKGROUND: The Department of Veterans Affairs (DVA) National Surgical Quality Improvement Program (NSQIP) employs trained nurse data collectors to prospectively gather preoperative patient characteristics and 30-day postoperative outcomes for most major operations in 123 DVA hospitals to provide risk-adjusted outcomes to centers as quality indicators. It has been suggested that routine hospital discharge abstracts contain the same information and would provide accurate and complete data at much lower cost. STUDY DESIGN: With preoperative risks and 30-day outcomes recorded by trained data collectors as criteria standards, ICD-9-CM hospital discharge diagnosis codes in the Patient Treatment File (PTF) were tested for sensitivity and positive predictive value. ICD-9-CM codes for 61 preoperative patient characteristics and 21 postoperative adverse events were identified. RESULTS: Moderately good ICD-9-CM matches of descriptions were found for 37 NSQIP preoperative patient characteristics (61%); good data were available from other automated sources for another 15 (25%). ICD-9-CM coding was available for only 13 (45%) of the top 29 predictor variables. In only three (23%) was sensitivity and in only four (31%) was positive predictive value greater than 0.500. There were ICD-9-CM matches for all 21 NSQIP postoperative adverse events; multiple matches were appropriate for most. Postoperative occurrence was implied in only 41%; same breadth of clinical description in only 23%. In only four (7%) was sensitivity and only two (4%) was positive predictive value greater than 0.500. CONCLUSION: Sensitivity and positive predictive value of administrative data in comparison to NSQIP data were poor. We cannot recommend substitution of administrative data for NSQIP data methods.

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Cite This Study

Best et al. (2002) studied this question.

synapsesocial.com/papers/6a1c6d2c94dbf6307b2fc297https://doi.org/10.1016/s1072-7515(01)01183-8
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