Literature DB >> 29337744

Defining the Intrinsic Cardiac Risks of Operations to Improve Preoperative Cardiac Risk Assessments.

Jason B Liu1, Yaoming Liu, Mark E Cohen, Clifford Y Ko, Bobbie J Sweitzer.   

Abstract

BACKGROUND: Current preoperative cardiac risk stratification practices group operations into broad categories, which might inadequately consider the intrinsic cardiac risks of individual operations. We sought to define the intrinsic cardiac risks of individual operations and to demonstrate how grouping operations might lead to imprecise estimates of perioperative cardiac risk.
METHODS: Elective operations (based on Common Procedural Terminology codes) performed from January 1, 2010 to December 31, 2015 at hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program were studied. A composite measure of perioperative adverse cardiac events was defined as either cardiac arrest requiring cardiopulmonary resuscitation or acute myocardial infarction. Operations' intrinsic cardiac risks were derived from mixed-effects models while controlling for patient mix. Resultant risks were sorted into low-, intermediate-, and high-risk categories, and the most commonly performed operations within each category were identified. Intrinsic operative risks were also examined using a representative grouping of operations to portray within-group variation.
RESULTS: Sixty-six low, 30 intermediate, and 106 high intrinsic cardiac risk operations were identified. Excisional breast biopsy had the lowest intrinsic cardiac risk (overall rate, 0.01%; odds ratio, 0.11; 95% CI, 0.02 to 0.25) relative to the average, whereas aorto-bifemoral bypass grafting had the highest (overall rate, 4.1%; odds ratio, 6.61; 95% CI, 5.54 to 7.90). There was wide variation in the intrinsic cardiac risks of operations within the representative grouping (median odds ratio, 1.40; interquartile range, 0.88 to 2.17).
CONCLUSIONS: A continuum of intrinsic cardiac risk exists among operations. Grouping operations into broad categories inadequately accounts for the intrinsic cardiac risk of individual operations.

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Year:  2018        PMID: 29337744     DOI: 10.1097/ALN.0000000000002024

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


  4 in total

1.  Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning.

Authors:  Michael L Burns; Michael R Mathis; John Vandervest; Xinyu Tan; Bo Lu; Douglas A Colquhoun; Nirav Shah; Sachin Kheterpal; Leif Saager
Journal:  Anesthesiology       Date:  2020-04       Impact factor: 7.892

2.  Variation in preoperative stress testing by patient, physician and surgical type: a cohort study.

Authors:  Matthew A Pappas; Daniel I Sessler; Andrew D Auerbach; Michael W Kattan; Alex Milinovich; Eugene H Blackstone; Michael B Rothberg
Journal:  BMJ Open       Date:  2021-09-27       Impact factor: 3.006

Review 3.  Multimorbidity and Critical Care Neurosurgery: Minimizing Major Perioperative Cardiopulmonary Complications.

Authors:  Rami Algahtani; Amedeo Merenda
Journal:  Neurocrit Care       Date:  2020-08-13       Impact factor: 3.210

4.  Modified paediatric preoperative risk prediction score to predict postoperative ICU admission in children: a retrospective cohort study.

Authors:  Chunwei Lian; Pei Wang; Qingxia Fu; Xudong Du; Junzheng Wu; Qingquan Lian; Wangning ShangGuan
Journal:  BMJ Open       Date:  2020-03-18       Impact factor: 2.692

  4 in total

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