Abstract
BackgroundNumerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance.ObjectiveWe aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system.Materials & methodsWe developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance.ResultsModels were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85.DiscussionWe were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.
Highlights
Health care providers and policymakers have identified periods of high clinical intensity and cost of care as important opportunities to improve health care value
We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018
We identified past medical history (PMH) and present on admission (POA) variables from administrative ICD-9-CM and ICD-10-CM codes associated with the index encounters
Summary
Health care providers and policymakers have identified periods of high clinical intensity and cost of care as important opportunities to improve health care value. Over 25% of all Medicare dollars are spent in the last year of life [1] and roughly 19.8% of Americans die in hospitals [2]. Another period with high clinical intensity and cost is the 30 days after hospital discharge. Improving care value during these time periods may be based on identification of the highest risk patients. These patients account for a disproportionately high number of adverse outcomes, and are likely to benefit from additional support. Few prediction models have been developed to optimize impact while sustaining sufficient performance.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
