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.
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?
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.
Absolute Event Rate: 25.32% vs 4.31%
BACKGROUND: This study sought to develop a predictive model for 30-day mortality in hospitalized cancer patients, by using admission information available through the electronic medical record. METHODS: Observational cohort study of 3062 patients admitted to the oncology service from August 1, 2008, to July 31, 2009. Matched numbers of patients were in the derivation and validation cohorts (1531 patients). Data were obtained on day 1 of admission and included demographic information, vital signs, and laboratory data. Survival data were obtained from the Social Security Death Index. RESULTS: The 30-day mortality rate of the derivation and validation samples were 9.5% and 9.7% respectively. Significant predictive variables in the multivariate analysis included age (P < .0001), assistance with activities of daily living (ADLs; P = .022), admission type (elective/emergency) (P = .059), oxygen use (P < .0001), and vital signs abnormalities including pulse oximetry (P = .0004), temperature (P = .017), and heart rate (P = .0002). A logistic regression model was developed to predict death within 30 days: Score = 18.2897 + 0.6013*(admit type) + 0.4518*(ADL) + 0.0325*(admit age) - 0.1458*(temperature) + 0.019*(heart rate) - 0.0983*(pulse oximetry) - 0.0123 (systolic blood pressure) + 0.8615*(O2 use). The largest sum of sensitivity (63%) and specificity (78%) was at -2.09 (area under the curve = -0.789). A total of 25.32% (100 of 395) of patients with a score above -2.09 died, whereas 4.31% (49 of 1136) of patients below -2.09 died. Sensitivity and positive predictive value in the derivation and validation samples compared favorably. CONCLUSIONS: Clinical factors available via the electronic medical record within 24 hours of hospital admission can be used to identify cancer patients at risk for 30-day mortality. These patients would benefit from discussion of preferences for care at the end of life.
Ramchandran et al. (Fri,) 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.