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Association of Sociodemographic Factors With Overtriage, Undertriage, and Value of Care After Major Surgery. Ann Surg Open 2024 Jun;5(2):e429

Date

06/24/2024

Pubmed ID

38911666

Pubmed Central ID

PMC11191932

DOI

10.1097/AS9.0000000000000429

Abstract

OBJECTIVE: To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts.

BACKGROUND: In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally.

METHODS: This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts.

RESULTS: Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P < 0.001) and commercial insurance (6.4% vs 2.5%, P < 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P < 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K-$23.5K) vs $14.1K ($9.1K-$20.7K), P < 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P < 0.001) and low value of care. Unique sociodemographic phenotypes within both overtriage and undertriage cohorts had similar outcomes and value of care, suggesting that triage decisions, rather than patient characteristics, drive outcomes and value of care.

CONCLUSIONS: Postoperative triage decisions should ensure equality across sociodemographic groups by anchoring triage decisions to objective patient acuity assessments, circumventing cognitive shortcuts and mitigating bias.

Author List

Loftus TJ, Ruppert MM, Shickel B, Ozrazgat-Baslanti T, Balch JA, Abbott KL, Hu D, Javed A, Madbak F, Guirgis F, Skarupa D, Efron PA, Tighe PJ, Hogan WR, Rashidi P, Upchurch GR Jr, Bihorac A

Author

William R. Hogan MD Director, Professor in the Data Science Institute department at Medical College of Wisconsin