Literature DB >> 24704139

Effectiveness of SAPS III to predict hospital mortality for post-cardiac arrest patients.

Magali Bisbal1, Elisabeth Jouve2, Laurent Papazian3, Sophie de Bourmont4, Gilles Perrin5, Beatrice Eon5, Marc Gainnier4.   

Abstract

PURPOSE: The mortality for patients admitted to intensive care unit (ICU) after cardiac arrest (CA) remains high despite advances in resuscitation and post-resuscitation care. The Simplified Acute Physiology Score (SAPS) III is the only score that can predict hospital mortality within an hour of admission to ICU. The objective was to evaluate the performance of SAPS III to predict mortality for post-CA patients.
METHODS: This retrospective single-center observational study included all patients admitted to ICU after CA between August 2010 and March 2013. The calibration (standardized mortality ratio [SMR]) and the discrimination of SAPS III (area under the curve [AUC] for receiver operating characteristic [ROC]) were measured. Univariate logistic regression tested the relationship between death and scores for SAPS III, SAPS II, Sequential Organ Failure Assessment (SOFA) Score and Out-of-Hospital Cardiac Arrests (OHCA) score. Independent factors associated with mortality were determined.
RESULTS: One-hundred twenty-four patients including 97 out-of-hospital CA were included. In-hospital mortality was 69%. The SAPS III was unable to predict mortality (SMRSAPS III: 1.26) and was less discriminating than other scores (AUCSAPSIII: 0.62 [0.51, 0.73] vs. AUCSAPSII: 0.75 [0.66, 0.84], AUCSOFA: 0.72 [0.63, 0.81], AUCOHCA: 0.84 [0.77, 0.91]). An early return of spontaneous circulation, early resuscitation care and initial ventricular arrhythmia were associated with a better prognosis.
CONCLUSIONS: The SAPS III did not predict mortality in patients admitted to ICU after CA. The amount of time before specialized CPR, the low-flow interval and the absence of an initial ventricular arrhythmia appeared to be independently associated with mortality and these factors should be used to predict mortality for these patients.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Cardiac arrest; Mortality; SAPS II; SAPS III

Mesh:

Year:  2014        PMID: 24704139     DOI: 10.1016/j.resuscitation.2014.03.302

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  12 in total

1.  Machine learning applied to a Cardiac Surgery Recovery Unit and to a Coronary Care Unit for mortality prediction.

Authors:  Beatriz Nistal-Nuño
Journal:  J Clin Monit Comput       Date:  2021-04-15       Impact factor: 1.977

2.  Early predictors of poor outcome after out-of-hospital cardiac arrest.

Authors:  Louise Martinell; Niklas Nielsen; Johan Herlitz; Thomas Karlsson; Janneke Horn; Matt P Wise; Johan Undén; Christian Rylander
Journal:  Crit Care       Date:  2017-04-13       Impact factor: 9.097

3.  Predictive modeling in urgent care: a comparative study of machine learning approaches.

Authors:  Fengyi Tang; Cao Xiao; Fei Wang; Jiayu Zhou
Journal:  JAMIA Open       Date:  2018-06-04

4.  Performance of Sequential Organ Failure Assessment and Simplified Acute Physiology Score II for Post-Cardiac Surgery Patients in Intensive Care Unit.

Authors:  Fei Xu; Weina Li; Cheng Zhang; Rong Cao
Journal:  Front Cardiovasc Med       Date:  2021-12-06

Review 5.  Current Utility of Sequential Organ Failure Assessment Score: A Literature Review and Future Directions.

Authors:  Rahul Kashyap; Khalid M Sherani; Taru Dutt; Karthik Gnanapandithan; Malvika Sagar; Saraschandra Vallabhajosyula; Abhay P Vakil; Salim Surani
Journal:  Open Respir Med J       Date:  2021-04-13

6.  Therapeutic hypothermia after cardiac arrest: outcome predictors.

Authors:  Rodrigo Nazário Leão; Paulo Ávila; Raquel Cavaco; Nuno Germano; Luís Bento
Journal:  Rev Bras Ter Intensiva       Date:  2015 Oct-Dec

7.  Performance on the APACHE II, SAPS II, SOFA and the OHCA score of post-cardiac arrest patients treated with therapeutic hypothermia.

Authors:  Jea Yeon Choi; Jae Ho Jang; Yong Su Lim; Jee Yong Jang; Gun Lee; Hyuk Jun Yang; Jin Seong Cho; Sung Youl Hyun
Journal:  PLoS One       Date:  2018-05-03       Impact factor: 3.240

8.  Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care.

Authors:  Jesper Johnsson; Ola Björnsson; Peder Andersson; Andreas Jakobsson; Tobias Cronberg; Gisela Lilja; Hans Friberg; Christian Hassager; Jesper Kjaergard; Matt Wise; Niklas Nielsen; Attila Frigyesi
Journal:  Crit Care       Date:  2020-07-30       Impact factor: 9.097

9.  Combining structured and unstructured data for predictive models: a deep learning approach.

Authors:  Dongdong Zhang; Changchang Yin; Jucheng Zeng; Xiaohui Yuan; Ping Zhang
Journal:  BMC Med Inform Decis Mak       Date:  2020-10-29       Impact factor: 2.796

Review 10.  Use of SOFA score in cardiac arrest research: A scoping review.

Authors:  Anne V Grossestreuer; Tuyen T Yankama; Ari Moskowitz; Long Ngo; Michael W Donnino
Journal:  Resusc Plus       Date:  2020-11-03
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