The physiology of failure: Identifying risk factors for mortality in emergency general surgery patients using a regional health system integrated electronic medical record.
Maria Baimas-George, Samuel W Ross, Timothy Hetherington, Marc Kowalkowski, Huaping Wang, Kyle Thompson, Kyle Cunningham, Brent D Matthews, Addison K May, Caroline E Reinke
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引用次数: 0
Abstract
Background: Emergency general surgery (EGS) patients have increased mortality risk compared with elective counterparts. Recent studies on risk factors have largely used national data sets limited to administrative data. Our aim was to examine risk factors in an integrated regional health system EGS database, including clinical and administrative data, hypothesizing that this novel process would identify clinical variables as important risk factors for mortality.
Methods: Our nine-hospital health system's billing data were queried for EGS International Classification of Disease codes between 2013 and 2018. Codes were grouped by diagnosis, and urgent or emergent encounters were included and merged with electronic medical record clinical data. Outcomes assessed were inpatient and 1-year mortality. Standard and multivariable statistics evaluated factors associated with mortality.
Results: There were 253,331 EGS admissions with 3.6% inpatient mortality rate. Patients who suffered inpatient and 1-year mortality were older, more likely to be underweight, and have neutropenia or elevated lactate. On multivariable analysis for inpatient mortality: age (odds ratio [OR], 1.7-6.7), underweight body mass index (OR, 1.6), transfer admission (OR, 1.8), leukopenia (OR, 2.0), elevated lactate (OR, 1.8), and ventilator requirement (OR, 7.1) remained associated with increased risk. Adjusted analysis for 1-year mortality demonstrated similar findings, with highest risk associated with older age (OR, 2.8-14.6), underweight body mass index (OR, 2.3), neutropenia (OR, 2.0), and tachycardia (OR, 1.7).
Conclusion: After controlling for patient and disease characteristics available in administrative databases, clinical variables remained significantly associated with mortality. This novel yet simple process allows for easy identification of clinical data points imperative to the study of EGS diagnoses that are critical in understanding factors that impact mortality.
Level of evidence: Prognostic and Epidemiologic; Level III.
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