Dynamic Data During Hypotensive Episode Improves Mortality Predictions Among Patients With Sepsis and Hypotension*
- 1 April 2013
- journal article
- research article
- Published by Ovid Technologies (Wolters Kluwer Health) in Critical Care Medicine
- Vol. 41 (4), 954-962
- https://doi.org/10.1097/ccm.0b013e3182772adb
Abstract
Objectives: To determine if a prediction rule for hospital mortality using dynamic variables in response to treatment of hypotension in patients with sepsis performs better than current models. Design: Retrospective cohort study. Setting: All ICUs at a tertiary care hospital. Patients: Adult patients admitted to ICUs between 2001 and 2007 of whom 2,113 met inclusion criteria and had sufficient data. Interventions: None. Measurements and Main Results: We developed a prediction algorithm for hospital mortality in patients with sepsis and hypotension requiring medical intervention using data from the Multiparameter Intelligent Monitoring in Intensive Care II. We extracted 189 candidate variables, including treatments, physiologic variables and laboratory values collected before, during, and after a hypotensive episode. Thirty predictors were identified using a genetic algorithm on a training set (n = 1500) and validated with a logistic regression model on an independent validation set (n = 613). The final prediction algorithm used included dynamic information and had good discrimination (area under the receiver operating curve = 82.0%) and calibration (Hosmer–Lemeshow C statistic = 10.43, p = 0.06). This model was compared with Acute Physiology and Chronic Health Evaluation IV using reclassification indices and was found to be superior with an Net Reclassification Improvement of 0.19 (p < 0.001) and an Integrated Discrimination Improvement of 0.09 (p < 0.001). Conclusions: Hospital mortality predictions based on dynamic variables surrounding a hypotensive event is a new approach to predicting prognosis. A model using these variables has good discrimination and calibration and offers additional predictive prognostic information beyond established ones.Keywords
This publication has 42 references indexed in Scilit:
- Interrogating a clinical database to study treatment of hypotension in the critically illBMJ Open, 2012
- Clinician blood pressure documentation of stable intensive care patients: An intelligent archiving agent has a higher association with future hypotensionCritical Care Medicine, 2011
- Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*Critical Care Medicine, 2011
- Extensions of net reclassification improvement calculations to measure usefulness of new biomarkersStatistics in Medicine, 2010
- Unplanned transfers to the intensive care unit: The role of the shock indexJournal of Hospital Medicine, 2010
- Clinical review: Scoring systems in the critically illCritical Care, 2010
- A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative DataMedical Care, 2009
- SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admissionIntensive Care Medicine, 2005
- Evaluating the Performance of an Institution Using an Intensive Care Unit BenchmarkMayo Clinic Proceedings, 2005
- Outcome prediction for patients with cirrhosis of the liver in a medical ICU: A comparison of the APACHE scores and liver-specific scoringsystemsIntensive Care Medicine, 1996