Citation

Celi LA, Galvin S, Davidzon G, Lee J, Scott D, Mark R. J. Pers. Med. 2012; 2(4): 138-148.

Affiliation

Laboratory of Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Avenue, E25-505, Cambridge, MA 02139, USA.

Copyright

(Copyright © 2012, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/jpm2040138

PMID

23766893

PMCID

PMC3678286

Abstract

We hypothesize that local customized modeling will provide more accurate mortality prediction than the current standard approach using existing scoring systems. Mortality prediction models were developed for two subsets of patients in Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC), a public de-identified ICU database, and for the subset of patients ≥80 years old in a cardiac surgical patient registry. Logistic regression (LR), Bayesian network (BN) and artificial neural network (ANN) were employed. The best-fitted models were tested on the remaining unseen data and compared to either the Simplified Acute Physiology Score (SAPS) for the ICU patients, or the EuroSCORE for the cardiac surgery patients. Local customized mortality prediction models performed better as compared to the corresponding current standard severity scoring system for all three subsets of patients: patients with acute kidney injury (AUC = 0.875 for ANN, vs. SAPS, AUC = 0.642), patients with subarachnoid hemorrhage (AUC = 0.958 for BN, vs. SAPS, AUC = 0.84), and elderly patients undergoing open heart surgery (AUC = 0.94 for ANN, vs. EuroSCORE, AUC = 0.648). Rather than developing models with good external validity by including a heterogeneous patient population, an alternative approach would be to build models for specific patient subsets using one's local database.

Language: en