Literature DB >> 30522696

ACS-NSQIP risk calculator predicts cohort but not individual risk of complication following colorectal resection.

Laura Z Hyde1, Neda Valizadeh2, Ahmed M Al-Mazrou2, Ravi P Kiran3.   

Abstract

OBJECTIVE: Compare the ACS-NSQIP risk calculator with institutional risk for colorectal surgery.
METHODS: Actual and predicted outcomes were compared for both cohort and individuals.
RESULTS: For the cohort, the risk calculator was accurate for 7/8 outcomes; there were more serious complications than predicted (19.4 vs 14.7%, p < 0.05). Risk calculator Brier scores and null Brier scores were comparable. PATIENTS: with better outcomes than predicted were current smokers (OR 4.3 95% CI 1.2-15.4), ASA ≥ 3 (OR 10.4, 95% CI 2.8-39.2), underwent total/subtotal colectomy (OR 3.5, 95% CI 1.1-12.2) or operated by Surgeon 2 (OR 2.9, 95% CI 1.4-11.6). Patients with serious complications who had low predicted risk had low ASA (OR 10.5, 95% CI 1.3-82.6), and underwent operation by Surgeon 2 (OR 11.8, 95% CI 2.5, 55.2). LIMITATIONS: Single center study, sample size may bias subgroup analyses.
CONCLUSIONS: The ACS NSQIP calculator did not predict outcome better than sample risk.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Clinical decision support; Colectomy; Colorectal surgery; Forecasting; Postoperative complications

Mesh:

Year:  2018        PMID: 30522696     DOI: 10.1016/j.amjsurg.2018.11.017

Source DB:  PubMed          Journal:  Am J Surg        ISSN: 0002-9610            Impact factor:   2.565


  6 in total

1.  Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery.

Authors:  Kevin A Chen; Chinmaya U Joisa; Karyn B Stitzenberg; Jonathan Stem; Jose G Guillem; Shawn M Gomez; Muneera R Kapadia
Journal:  J Gastrointest Surg       Date:  2022-09-07       Impact factor: 3.267

2.  Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform.

Authors:  Yuanfang Ren; Tyler J Loftus; Shounak Datta; Matthew M Ruppert; Ziyuan Guan; Shunshun Miao; Benjamin Shickel; Zheng Feng; Chris Giordano; Gilbert R Upchurch; Parisa Rashidi; Tezcan Ozrazgat-Baslanti; Azra Bihorac
Journal:  JAMA Netw Open       Date:  2022-05-02

Review 3.  Artificial Intelligence and Surgical Decision-making.

Authors:  Tyler J Loftus; Patrick J Tighe; Amanda C Filiberto; Philip A Efron; Scott C Brakenridge; Alicia M Mohr; Parisa Rashidi; Gilbert R Upchurch; Azra Bihorac
Journal:  JAMA Surg       Date:  2020-02-01       Impact factor: 14.766

4.  Risk assessment in aortic aneurysm repair by medical specialists versus the American College of Surgeons National Surgical Quality Improvement Program risk calculator outcomes.

Authors:  Jan van Schaik; Tessa M Hers; Carla Sp van Rijswijk; Maaike S Schooneveldt; Hein Putter; Daniël Eefting; Joost R van der Vorst
Journal:  JRSM Cardiovasc Dis       Date:  2021-04-08

5.  Prediction of intensive care unit admission (>24h) after surgery in elective noncardiac surgical patients using machine learning algorithms.

Authors:  Lan Lan; Fangwei Chen; Jiawei Luo; Mengjiao Li; Xuechao Hao; Yao Hu; Jin Yin; Tao Zhu; Xiaobo Zhou
Journal:  Digit Health       Date:  2022-07-25

Review 6.  Aligning Patient Acuity With Resource Intensity After Major Surgery: A Scoping Review.

Authors:  Tyler J Loftus; Jeremy A Balch; Matthew M Ruppert; Patrick J Tighe; William R Hogan; Parisa Rashidi; Gilbert R Upchurch; Azra Bihorac
Journal:  Ann Surg       Date:  2022-02-01       Impact factor: 13.787

  6 in total

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