title: Severity of disease estimation and risk-adjustment for comparison of outcomes in mechanically ventilated patients using electronic routine care data.
abstract: BACKGROUND Valid comparison between hospitals for benchmarking or pay-for-performance incentives requires accurate correction for underlying disease severity (case-mix). However, existing models are either very simplistic or require extensive manual data collection. OBJECTIVE To develop a disease severity prediction model based solely on data routinely available in electronic health records for risk-adjustment in mechanically ventilated patients. DESIGN Retrospective cohort study. PARTICIPANTS Mechanically ventilated patients from a single tertiary medical center (2006-2012). METHODS Predictors were extracted from electronic data repositories (demographic characteristics, laboratory tests, medications, microbiology results, procedure codes, and comorbidities) and assessed for feasibility and generalizability of data collection. Models for in-hospital mortality of increasing complexity were built using logistic regression. Estimated disease severity from these models was linked to rates of ventilator-associated events. RESULTS A total of 20,028 patients were initiated on mechanical ventilation, of whom 3,027 deceased in hospital. For models of incremental complexity, area under the receiver operating characteristic curve ranged from 0.83 to 0.88. A simple model including demographic characteristics, type of intensive care unit, time to intubation, blood culture sampling, 8 common laboratory tests, and surgical status achieved an area under the receiver operating characteristic curve of 0.87 (95% CI, 0.86-0.88) with adequate calibration. The estimated disease severity was associated with occurrence of ventilator-associated events. CONCLUSIONS Accurate estimation of disease severity in ventilated patients using electronic, routine care data was feasible using simple models. These estimates may be useful for risk-adjustment in ventilated patients. Additional research is necessary to validate and refine these models.
authors.affiliation: 1Department of Population Medicine,Harvard Medical School and Harvard Pilgrim Health Care Institute,Boston,Massachusetts.
authors.forename: Maaike S M
authors.initials: MS
authors.lastname: van Mourik
authors.affiliation: 3Julius Center for Health Sciences and Primary Care,University Medical Center Utrecht,Utrecht,The Netherlands.
authors.forename: Karel G M
authors.initials: KG
authors.lastname: Moons
authors.affiliation: 1Department of Population Medicine,Harvard Medical School and Harvard Pilgrim Health Care Institute,Boston,Massachusetts.
authors.forename: Michael V
authors.initials: MV
authors.lastname: Murphy
authors.affiliation: 2Department of Medical Microbiology,University Medical Center Utrecht,Utrecht,The Netherlands.
authors.forename: Marc J M
authors.initials: MJ
authors.lastname: Bonten
authors.affiliation: 1Department of Population Medicine,Harvard Medical School and Harvard Pilgrim Health Care Institute,Boston,Massachusetts.
authors.forename: Michael
authors.initials: M
authors.lastname: Klompas
mesh_terms: D000368:Aged; D019540:Area Under Curve; D019985:Benchmarking; D057286:Electronic Health Records; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000592:standards / Q000706:statistics & numerical data; D007442:Intubation, Intratracheal / Q000009:adverse effects; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012372:ROC Curve; D012044:Regression Analysis; D012121:Respiration, Artificial / Q000009:adverse effects / Q000401:mortality*; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods*; D012720:Severity of Illness Index; D016896:Treatment Outcome