Vol 21: Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score ALaRMS.Report as inadecuate



 Vol 21: Using electronic health record data to develop inpatient mortality predictive model: Acute Laboratory Risk of Mortality Score ALaRMS.


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This article is from Journal of the American Medical Informatics Association : JAMIA, volume 21.AbstractObjective: Using numeric laboratory data and administrative data from hospital electronic health record EHR systems, to develop an inpatient mortality predictive model. Methods: Using EHR data of 1 428 824 adult discharges from 70 hospitals in 2006–2007, we developed the Acute Laboratory Risk of Mortality Score ALaRMS using age, gender, and initial laboratory values on admission as candidate variables. We then added administrative variables using the Agency for Healthcare Research and Quality AHRQs clinical classification software CCS and comorbidity software CS as disease classification tools. We validated the model using 770 523 discharges in 2008. Results: Mortality predictors with ORs 2.00 included age, deranged albumin, arterial pH, bands, blood urea nitrogen, oxygen partial pressure, platelets, pro-brain natriuretic peptide, troponin I, and white blood cell counts. The ALaRMS model c-statistic was 0.87. Adding the CCS and CS variables increased the c-statistic to 0.91. The relative contributions were 69% ALaRMS, 25% CCS, and 6% CS. Furthermore, the integrated discrimination improvement statistic demonstrated a 127% 95% CI 122% to 133% overall improvement when ALaRMS was added to CCS and CS variables. In contrast, only a 22% CI 19% to 25% improvement was seen when CCS and CS variables were added to ALaRMS. Conclusions: EHR data can generate clinically plausible mortality predictive models with excellent discrimination. ALaRMS uses automated laboratory data widely available on admission, providing opportunities to aid real-time decision support. Models that incorporate laboratory and AHRQs CCS and CS variables have utility for risk adjustment in retrospective outcome studies.



Author: Tabak, Ying P; Sun, Xiaowu; Nunez, Carlos M; Johannes, Richard S

Source: https://archive.org/







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