Work

ICU acuity: real-time models versus daily models.

Year: 2009

Type: article

Abstract: OBJECTIVE: To explore the feasibility of real-time mortality risk assessment for ICU patients. DESIGN/METHODS: This study used retrospective analysis of mixed medical/surgical intensive care patients in a university hospital. Logistic regression was applied to 7048 development patients with several hundred candidate variables. Final models were selected by backward elimination on top cross-validated variables and validated on 3018 separate patients. RESULTS: The real-time model demonstrated strong discrimination ability (Day 3 AUC=0.878). All models had circumstances where calibration was poor (Hosmer-Lemeshow goodness of fit test p < 0.1). The final models included variables known to be associated with mortality, but also more computationally intensive variables absent in other severity scores. CONCLUSION: Real-time mortality prediction offers similar discrimination ability to daily models. Moreover, the discrimination of our real-time model performed favorably to a customized SAPS II (Day 3 AUC=0.878 vs AUC=0.849, p < 0.05) but generally had worse calibration.

Source: Europe PMC (PubMed Central)

Authors: Caleb W Hug, Peter Szolovits

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Language: English

Topic: Sepsis Diagnosis and Treatment

Subfield: Critical Care and Intensive Care Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine

Awards: Integrating Data, Models, and Reasoning in Critical Care, Biomedical Informatics and Data Science Research Training Program (BIRT)