Format

Send to:

Choose Destination
See comment in PubMed Commons below
J Healthc Eng. 2011 Mar;2(1):97-110. Epub 2011 Apr 12.

A Clinical Database-Driven Approach to Decision Support: Predicting Mortality Among Patients with Acute Kidney Injury.

Author information

  • 1Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA USA.

Abstract

In exploring an approach to decision support based on information extracted from a clinical database, we developed mortality prediction models of intensive care unit (ICU) patients who had acute kidney injury (AKI) and compared them against the Simplified Acute Physiology Score (SAPS). We used MIMIC, a public de-identified database of ICU patients admitted to Beth Israel Deaconess Medical Center, and identified 1400 patients with an ICD9 diagnosis of AKI and who had an ICU stay > 3 days. Multivariate regression models were built using the SAPS variables from the first 72 hours of ICU admission. All the models developed on the training set performed better than SAPS (AUC = 0.64, Hosmer-Lemeshow p < 0.001) on an unseen test set; the best model had an AUC = 0.74 and Hosmer-Lemeshow p = 0.53. These findings suggest that local customized modeling might provide more accurate predictions. This could be the first step towards an envisioned individualized point-of-care probabilistic modeling using one's clinical database.

PMID:
22844575
[PubMed]
PMCID:
PMC3404157
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for MetaPress.com Icon for PubMed Central
    Loading ...
    Write to the Help Desk