Rothman Index As a Real-Time Predictor of Intensive Care Unit Admissions and Mortality in Acute Myeloid Leukemia Patients Undergoing Induction Chemotherapy

Abstract Introduction: In-hospital mortality is higher for patients (Pts) with Acute Myeloid Leukemia(AML) requiring intensive care unit(ICU) admissions (43.1% Vs 9.3%) as described by Halpren,A.B., et al (JAMA Oncol. 2017. 3(3): p. 374-381). Various demographic factors identify patients at risk but may not fully provide primary prevention strategies. The Rothman Index (RI) is a dynamic acuity metric that incorporates 26 variables of physiological measures, clinical assessments, and laboratory results into a composite index that is trended over time. In our study, we hypothesize that RI at admission for induction chemotherapy (IRx) and the number of unstable days defined as a drop in RI by 15 points in a 24-hour period; can predict ICU admissions. This is a score that is trended in real time providing potential opportunities for Pt stabilization and preventing ICU admissions. Methods: This is a single center retrospective observational study of AML pts admitted between 8/24/12 to 4/22/17. Data collection included age at diagnosis, RI at admission, unstable days, days of stability before first unstable day, total length of hospital stay (LOS) for IRx, days to first readmission, total number of unstable days for readmission and mortality. The primary end point was ICU admission. Continuous variables were reported as means and standard deviations or means with confidence intervals or median with inter quartile range(IQR). Dichotomous variables were reported as proportions. A logistic regression model was used to predict probability of ICU admission. Results: A total of 83 pts (54.21% males) were studied with a median age at diagnosis of 66 (IQR 19) years(y) and mean LOS of 21.79±16.19 days. Twenty (24.09%) pts had ICU stay in their index admission and the age at admission was very similar to the non-ICU group [Median 66y (IQR 21) Vs 67y (IQR 19)]. Nineteen pts died with a mortality rate of 22.89%. The RI at admission for the ICU group [Mean 73.34 (CI 63.14-83.54) Vs 82.74 (CI 79.72-85.76)] and days of stability before the first unstable day [Mean 2.89 (CI 1.05-4.73) Vs 5.63 (CI 4.23-7.03)] were lower. Number of unstable days [Mean 8.57 (CI 6.20-10.95) Vs 3.36 (CI 2.32-4.40)] and LOS for the IRx admission [Mean 26.10 (CI 20.23-31.97) Vs 20.38 (CI 16.05-24.70)] was higher in the ICU group. Number of unstable days was the best predictor of ICU admissions [odds ratio 1.25 (CI 1.10-1.41 p 0.00)] as well as days of stability before first unstable day [odds ratio 0.83 (CI 0.70-0.98 p 0.03)] and RI at admission [odds ratio 0.96 (CI 0.93-0.99 p 0.04)]. However, as mortality predictors, number of unstable days [odds ratio 1.08 (CI 0.96-1.21 p 0.18)], days of stability before first unstable day [odds ratio 1.06 (CI 0.95- 1.18 p 0.28)] and RI at admission [odds ratio (CI 0.96-1.04 p 0.74)] did not meet statistical significance and this could be due to the smaller sample size. Conclusions: Real time trends in RI can potentially predict AML pts undergoing IRx that may need critical care. Higher number of unstable RI days along with fewer stable days before first unstable day predict higher likelihood for ICU stay. Finally, RI also can predict the longer LOS in patients with ICU admissions. The Clinical implications of using RI can be far reaching as it provides a real-time opportunity for utilizing clinical resources to stabilize pts on the floor, prevent ICU admissions and reduce LOS. Disclosures Iyer: Genentech: Research Funding; Takeda: Research Funding.