Unintended Consequences of the 30-Day Mortality Metric: Fact or Fiction

Ann Surg. 2017 Dec;266(6):962-967. doi: 10.1097/SLA.0000000000002043.

Abstract

Objective: To assess if an incongruous increase in mortality occurs after postoperative day 30.

Background: In the current climate of public reporting and pay-for-performance, 30-day mortality after inpatient surgery has become a key metric to assess performance. Whereas the intent is to improve quality, there has been increasing concern that reporting 30-day mortality may influence providers' timing of treatment withdrawal.

Methods: We used national Medicare data to identify beneficiaries who underwent 1 of 19 major surgical procedures. We performed a survival analysis and calculated an adjusted daily hazard rate using all-cause mortality, accounting for patient comorbidities and case-mix. We ran linear regression models to examine discontinuity points around the 30-day mark, and conducted subgroup analyses for hospitals participating in the National Surgical Quality Improvement Program, which focuses on 30-day mortality reporting.

Results: We identified 872,968 patients who underwent 1 of 19 surgical procedures of interest; 71,583 of these patients (8.2%) died within 60 days of their index operation. We did not observe any statistically significant increases in mortality in the immediate period after day 30 compared with the immediate period before day 30. In fact, in each model, mortality rates tended to fall in the days after day 30, consistent with a general decreasing risk of death over time. These findings were similar among National Surgical Quality Improvement Program hospitals.

Conclusions: We found no evidence of an increase in postoperative mortality after day 30. As payers move towards incorporating 30-day surgical mortality into pay-for-performance programs, these findings serve as a benchmark for measuring potential future unintended consequences of the metric.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Aged
  • Female
  • Hospital Mortality*
  • Hospitals / standards*
  • Humans
  • Linear Models
  • Male
  • Medicare
  • Quality Improvement*
  • Survival Analysis
  • Time Factors
  • United States / epidemiology