Key result
A predictive model using admission EMR data identified hospitalized cancer patients at risk for 30-day mortality, with 25.32% mortality in high-risk patients versus 4.31% in low-risk patients.
Why the study?
Does a predictive model based on admission criteria via the electronic medical record accurately identify hospitalized cancer patients at risk for 30-day mortality?
Cohort (n=3,062)
Does a predictive model based on admission criteria via the electronic medical record accurately identify hospitalized cancer patients at risk for 30-day mortality?
Absolute Event Rate: 25.32% vs 4.31%
A predictive model using routine electronic medical record data from the first 24 hours of admission can effectively identify hospitalized cancer patients at high risk for 30-day mortality.
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May support EMR-based risk stratification in hospitalized cancer patients; leaves open prospective validation before clinical use.
Ramchandran et al. (2013) conducted a cohort in Hospitalized cancer (n=3,062). Predictive model based on admission criteria via the electronic medical record vs. Low risk score (below -2.09) was evaluated on 30-day mortality. A predictive model using admission EMR data identified hospitalized cancer patients at risk for 30-day mortality, with 25.32% mortality in high-risk patients versus 4.31% in low-risk patients.
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