Literature DB >> 23770598

Validation of a risk stratification index and risk quantification index for predicting patient outcomes: in-hospital mortality, 30-day mortality, 1-year mortality, and length-of-stay.

Matthew J G Sigakis1, Edward A Bittner, Jonathan P Wanderer.   

Abstract

BACKGROUND: External validation of published risk stratification models is essential to determine their generalizability. This study evaluates the performance of the Risk Stratification Indices (RSIs) and 30-day mortality Risk Quantification Index (RQI).
METHODS: 108,423 adult hospital admissions with anesthetics were identified (2006-2011). RSIs for mortality and length-of-stay endpoints were calculated using published methodology. 91,128 adult, noncardiac inpatient surgeries were identified with administrative data required for RQI calculation.
RESULTS: RSI in-hospital mortality and RQI 30-day mortality Brier scores were 0.308 and 0.017, respectively. RSI discrimination, by area under the receiver operating curves, was excellent at 0.966 (95% CI, 0.963-0.970) for in-hospital mortality, 0.903 (0.896-0.909) for 30-day mortality, 0.866 (0.861-0.870) for 1-yr mortality, and 0.884 (0.882-0.886) for length-of-stay. RSI calibration, however, was poor overall (17% predicted in-hospital mortality vs. 1.5% observed after inclusion of the regression constant) as demonstrated by calibration plots. Removal of self-fulfilling diagnosis and procedure codes (20,001 of 108,423; 20%) yielded similar results. RQIs were calculated for only 62,640 of 91,128 patients (68.7%) due to unmatched procedure codes. Patients with unmatched codes were younger, had higher American Society of Anesthesiologists physical status and 30-day mortality. The area under the receiver operating curve for 30-day mortality RQI was 0.888 (0.879-0.897). The model also demonstrated good calibration. Performance of a restricted index, Procedure Severity Score + American Society of Anesthesiologists physical status, performed as well as the original RQI model (age + American Society of Anesthesiologists + Procedure Severity Score).
CONCLUSION: Although the RSIs demonstrated excellent discrimination, poor calibration limits their generalizability. The 30-day mortality RQI performed well with age providing a limited contribution.

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Year:  2013        PMID: 23770598     DOI: 10.1097/ALN.0b013e31829ce6e6

Source DB:  PubMed          Journal:  Anesthesiology        ISSN: 0003-3022            Impact factor:   7.892


  9 in total

1.  Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Authors:  Christine K Lee; Ira Hofer; Eilon Gabel; Pierre Baldi; Maxime Cannesson
Journal:  Anesthesiology       Date:  2018-10       Impact factor: 7.892

2.  Validation of the All Patient Refined Diagnosis Related Group (APR-DRG) Risk of Mortality and Severity of Illness Modifiers as a Measure of Perioperative Risk.

Authors:  Patrick J McCormick; Hung-Mo Lin; Stacie G Deiner; Matthew A Levin
Journal:  J Med Syst       Date:  2018-03-22       Impact factor: 4.460

3.  Postoperative mortality risk prediction that incorporates intraoperative vital signs: development and internal validation in a historical cohort.

Authors:  Janny Xue Chen Ke; Daniel I McIsaac; Ronald B George; Paula Branco; E Francis Cook; W Scott Beattie; Robin Urquhart; David B MacDonald
Journal:  Can J Anaesth       Date:  2022-08-22       Impact factor: 6.713

4.  Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation.

Authors:  Pei-Fu Chen; Lichin Chen; Yow-Kuan Lin; Guo-Hung Li; Feipei Lai; Cheng-Wei Lu; Chi-Yu Yang; Kuan-Chih Chen; Tzu-Yu Lin
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5.  Tele-transitions of care. A 12-month, parallel-group, superiority randomized controlled trial protocol, evaluating the use of telehealth versus standard transitions of care in the prevention of avoidable hospital readmissions.

Authors:  Kimberly Noel; Shamuel Yagudayev; Catherine Messina; Elinor Schoenfeld; Wei Hou; Gerald Kelly
Journal:  Contemp Clin Trials Commun       Date:  2018-08-17

6.  Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set.

Authors:  Ira S Hofer; Christine Lee; Eilon Gabel; Pierre Baldi; Maxime Cannesson
Journal:  NPJ Digit Med       Date:  2020-04-20

Review 7.  Emerging early diagnostic methods for acute kidney injury.

Authors:  Zuoxiu Xiao; Qiong Huang; Yuqi Yang; Min Liu; Qiaohui Chen; Jia Huang; Yuting Xiang; Xingyu Long; Tianjiao Zhao; Xiaoyuan Wang; Xiaoyu Zhu; Shiqi Tu; Kelong Ai
Journal:  Theranostics       Date:  2022-03-21       Impact factor: 11.600

8.  An Explainable Multimodal Neural Network Architecture for Predicting Epilepsy Comorbidities Based on Administrative Claims Data.

Authors:  Thomas Linden; Johann De Jong; Chao Lu; Victor Kiri; Kathrin Haeffs; Holger Fröhlich
Journal:  Front Artif Intell       Date:  2021-05-21

9.  The influence of the type and design of the anesthesia record on ASA physical status scores in surgical patients: paper records vs. electronic anesthesia records.

Authors:  Anil A Marian; Emine O Bayman; Anita Gillett; Brent Hadder; Michael M Todd
Journal:  BMC Med Inform Decis Mak       Date:  2016-03-02       Impact factor: 2.796

  9 in total

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