Reconceptualizing high-quality emergency general surgery care: Non-mortality-based quality metrics enable meaningful and consistent assessment

Abstract

Background: Ongoing efforts to promote quality-improvement in emergency general surgery (EGS) have made substantial strides but lack clear definitions of what constitutes "high-quality" EGS care. To address this concern, we developed a novel set of five non-mortality-based quality metrics broadly applicable to the care of all EGS patients and sought to discern whether (1) they can be used to identify groups of best-performing EGS hospitals, (2) results are similar for simple versus complex EGS severity in both adult (18-64 years) and older adult (≥65 years) populations, and (3) best performance is associated with differences in hospital-level factors.

Methods: Patients hospitalized with 1-of-16 American Association for the Surgery of Trauma-defined EGS conditions were identified in the 2019 Nationwide Readmissions Database. They were stratified by age/severity into four cohorts: simple adults, complex adults, simple older adults, complex older adults. Within each cohort, risk-adjusted hierarchical models were used to calculate condition-specific risk-standardized quality metrics. K-means cluster analysis identified hospitals with similar performance, and multinomial regression identified predictors of resultant "best/average/worst" EGS care.

Results: A total of 1,130,496 admissions from 984 hospitals were included (40.6% simple adults, 13.5% complex adults, 39.5% simple older adults, and 6.4% complex older adults). Within each cohort, K-means cluster analysis identified three groups ("best/average/worst"). Cluster assignment was highly conserved with 95.3% of hospitals assigned to the same cluster in each cohort. It was associated with consistently best/average/worst performance across differences in outcomes (5×) and EGS conditions (16×). When examined for associations with hospital-level factors, best-performing hospitals were those with the largest EGS volume, greatest extent of patient frailty, and most complicated underlying patient case-mix.

Conclusion: Use of non-mortality-based quality metrics appears to offer a needed promising means of evaluating high-quality EGS care. The results underscore the importance of accounting for outcomes applicable to all EGS patients when designing quality-improvement initiatives and suggest that, given the consistency of best-performing hospitals, natural EGS centers-of-excellence could exist.

Level of evidence: Prognostic and Epidemiological; Level III.

Figure 1.

Distributions of condition-specific risk-standardized quality-metrics for simple and complex adults with acute appendicitis

Figure 1.

Distributions of condition-specific risk-standardized quality-metrics for simple and complex adults with acute appendicitis

Figure 1.

Distributions of condition-specific risk-standardized quality-metrics for simple and complex adults with acute appendicitis

Figure 1.

Distributions of condition-specific risk-standardized quality-metrics for simple and complex adults with acute appendicitis

Figure 1.

Distributions of condition-specific risk-standardized quality-metrics for simple and complex adults with acute appendicitis

Figure 2.

Results of K-means cluster analysis for simple adults showing an optimal solution of three clusters based on the elbow method of cluster determination (slowing of meaningful change in the percent of variation explained [reduction in the total within-cluster sum of squared errors] by adding an additional cluster, i.e., the elbow in the graph) (A), minimal overlap along the first principal component when using three clusters (B), and significant separation between centroids (significant difference in resultant cluster centers, two-sided p-value<0.001)

Figure 2.

Results of K-means cluster analysis for simple adults showing an optimal solution of three clusters based on the elbow method of cluster determination (slowing of meaningful change in the percent of variation explained [reduction in the total within-cluster sum of squared errors] by adding an additional cluster, i.e., the elbow in the graph) (A), minimal overlap along the first principal component when using three clusters (B), and significant separation between centroids (significant difference in resultant cluster centers, two-sided p-value<0.001)

Figure 3.

Consistency of K-means cluster performance (best: green triangles, average: red circles, worst: blue squares) for simple adults across differences in “standardized” (mean=0, SD=1) risk-standardized outcomes (5x) and EGS conditions (16x)

Figure 3.

Consistency of K-means cluster performance (best: green triangles, average: red circles, worst: blue squares) for simple adults across differences in “standardized” (mean=0, SD=1) risk-standardized outcomes (5x) and EGS conditions (16x)

Figure 3.

Consistency of K-means cluster performance (best: green triangles, average: red circles, worst: blue squares) for simple adults across differences in “standardized” (mean=0, SD=1) risk-standardized outcomes (5x) and EGS conditions (16x)

Figure 3.

Consistency of K-means cluster performance (best: green triangles, average: red circles, worst: blue squares) for simple adults across differences in “standardized” (mean=0, SD=1) risk-standardized outcomes (5x) and EGS conditions (16x)

Figure 3.

Consistency of K-means cluster performance (best: green triangles, average: red circles, worst: blue squares) for simple adults across differences in “standardized” (mean=0, SD=1) risk-standardized outcomes (5x) and EGS conditions (16x)