Development and Validation of a Methodology to Reduce Mortality Using the Veterans Administration Surgical Quality Improvement Program Risk Calculator.

Development and Validation of a Methodology to Reduce Mortality Using the Veterans Administration Surgical Quality Improvement Program Risk Calculator.

J Am Coll Surg. 2017 Jan 11;:

Authors: Keller DS, Kroll D, Papaconstantinou HT, Ellis CN

Abstract
BACKGROUND: To identify patients with a high risk of 30 day mortality after elective surgery that may benefit from referral for tertiary care, an institution-specific process using the Veterans Administration Surgical Quality Improvement Program (VASQIP) Risk Calculator was developed. The goal was to develop and validate the methodology. Our hypothesis was the process could optimize referrals and reduce mortality.
STUDY DESIGN: A VASQIP risk score was calculated for all patients undergoing elective non-cardiac surgery at a single Veteran's Administration (VA) facility. After statistical analysis, a VASQIP risk score of 3.3% predicted mortality was selected as the institutional threshold for referral to a tertiary-care center. The model predicted 16 percent of patients would require referral and 30 day mortality would be reduced by 73 percent at the referring institution. The main outcome measures were the actual versus predicted referrals and mortality rates at the referring and receiving facilities.
RESULTS: The validation included 565 patients; 90 (16 percent) had VASQIP risk scores greater than 3.3 percent and were identified for referral; 60 consented. In these patients, there were 16 (27 percent) predicted mortalities, but only 4 actual deaths (p=0.007) at the receiving institution. Where referral was not indicated, the model predicted 4 mortalities (1%), but no actual deaths (p=0.1241).
CONCLUSIONS: These data validate this methodology to identify patients for referral to a higher level of care, reducing mortality at the referring institutions and significantly improving patient outcomes. This methodology can help guide decisions on referrals and optimize patient care. Further application and studies are warranted.

PMID: 28088600 [PubMed - as supplied by publisher]

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