Predictive model for survival at the conclusion of a damage control laparotomy

Noriaki Aoki, Matthew J. Wall, Janez Demsar, Blaz Zupan, Thomas Granchi, Martin A. Schreiber, John B. Holcomb, Mike Byrne, Kathleen R. Liscum, Grady Goodwin, J. Robert Beck, Kenneth L. Mattox

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

BACKGROUND: We employed modern statistical and data mining methods to model survival based on preoperative and intraoperative parameters for patients undergoing damage control surgery. METHODS: One hundred seventy-four parameters were collected from 68 damage control patients in prehospital, emergency center, operating room, and intensive care unit (ICU) settings. Data were analyzed with logistic regression and data mining. Outcomes were survival and death after the initial operation. RESULTS: Overall mortality was 66.2%. Logistic regression identified pH at initial ICU admission (odds ratio: 4.4) and worst partial thromboplastin time from hospital admission to ICU admission (odds ratio: 9.4) as significant. Data mining selected the same factors, and generated a simple algorithm for patient classification. Model accuracy was 83%. CONCLUSIONS: Inability to correct pH at the conclusion of initial damage-control laparotomy and the worst PTT can be predictive of death. These factors may be useful to identify patients with a high risk of mortality.

Original languageEnglish (US)
Pages (from-to)540-545
Number of pages6
JournalAmerican journal of surgery
Volume180
Issue number6
DOIs
StatePublished - Dec 1 2000
Externally publishedYes

ASJC Scopus subject areas

  • Surgery

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