Literature DB >> 27238354

Preoperative assessment of the risk for multiple complications after surgery.

Tezcan Ozrazgat-Baslanti1, Paulette Blanc2, Paul Thottakkara2, Matthew Ruppert2, Parisa Rashidi3, Petar Momcilovic4, Charles Hobson5, Philip A Efron6, Frederick A Moore6, Azra Bihorac2.   

Abstract

BACKGROUND: The association between preoperative patient characteristics and the number of major postoperative complications after a major operation is not well defined.
METHODS: In a retrospective, single-center cohort of 50,314 adult surgical patients, we used readily available preoperative clinical data to model the number of major postoperative complications from none to ≥3. We included acute kidney injury; prolonged stay (>48 hours) in an intensive care unit; need for prolonged (>48 hours) mechanical ventilation; severe sepsis; and cardiovascular, wound, and neurologic complications. Risk probability scores generated from the multinomial logistic models were used to develop an online calculator. We stratified patients based on their risk of having ≥3 postoperative complications.
RESULTS: Patients older than 65 years (odds ratio 1.5, 95% confidence interval, 1.4-1.6), males (odds ratio 1.2, 95% confidence interval, 1.2-1.3), patients with a greater Charlson comorbidity index (odds ratio 3.9, 95% confidence interval, 3.6-4.2), patients requiring emergency operation (odds ratio 3.5, 95% confidence interval, 3.3.-3.7), and patients admitted on a weekend (odds ratio 1.4, 95% confidence interval, 1.3-1.5) were more likely to have ≥3 postoperative complications than they were to have none. Patients in the medium- and high-risk categories were 3.7 and 6.3 times more likely to have ≥3 postoperative complications, respectively. High-risk patients were 5.8 and 4.4 times more likely to die within 30 and 90 days of admission, respectively.
CONCLUSION: Readily available, preoperative clinical and sociodemographic factors are associated with a greater number of postoperative complications and adverse surgical outcomes. We developed an online calculator that predicts probability of developing each number of complications after a major operation.
Copyright © 2016 Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 27238354      PMCID: PMC5114020          DOI: 10.1016/j.surg.2016.04.013

Source DB:  PubMed          Journal:  Surgery        ISSN: 0039-6060            Impact factor:   3.982


  39 in total

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Journal:  Clin Gastroenterol Hepatol       Date:  2011-07-30       Impact factor: 11.382

2.  Acute kidney injury in major gynaecological surgery: an observational study.

Authors:  A J Vaught; T Ozrazgat-Baslanti; A Javed; L Morgan; C E Hobson; A Bihorac
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Authors:  A A Gawande; E J Thomas; M J Zinner; T A Brennan
Journal:  Surgery       Date:  1999-07       Impact factor: 3.982

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Authors:  Lori F Gentile; Alex G Cuenca; Philip A Efron; Darwin Ang; Azra Bihorac; Bruce A McKinley; Lyle L Moldawer; Frederick A Moore
Journal:  J Trauma Acute Care Surg       Date:  2012-06       Impact factor: 3.313

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6.  Adverse events in surgical patients in Australia.

Authors:  A K Kable; R W Gibberd; A D Spigelman
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Authors:  Erin L Vanzant; Cecilia M Lopez; Tezcan Ozrazgat-Baslanti; Ricardo Ungaro; Ruth Davis; Alex G Cuenca; Lori F Gentile; Dina C Nacionales; Angela L Cuenca; Azra Bihorac; Christiaan Leeuwenburgh; Jennifer Lanz; Henry V Baker; Bruce McKinley; Lyle L Moldawer; Frederick A Moore; Philip A Efron
Journal:  J Trauma Acute Care Surg       Date:  2014-01       Impact factor: 3.313

8.  Long-term risk of mortality and acute kidney injury during hospitalization after major surgery.

Authors:  Azra Bihorac; Sinan Yavas; Sophie Subbiah; Charles E Hobson; Jesse D Schold; Andrea Gabrielli; A Joseph Layon; Mark S Segal
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2.  Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.

Authors:  Carolyn D Seib; James P Roose; Alan E Hubbard; Insoo Suh
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Journal:  PLoS One       Date:  2019-04-04       Impact factor: 3.240

4.  Magnitude and modifiers of the weekend effect in hospital admissions: a systematic review and meta-analysis.

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Journal:  BMJ Open       Date:  2019-06-04       Impact factor: 2.692

5.  The new SUMPOT to predict postoperative complications using an Artificial Neural Network.

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6.  Impact of your results: Beyond the relative risk.

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7.  Preoperative Risk Assessment: A Poor Predictor of Outcome in Critically ill Elderly with Sepsis After Abdominal Surgery.

Authors:  Anne C M Cuijpers; Marielle M E Coolsen; Ronny M Schnabel; Susanne van Santen; Steven W M Olde Damink; Marcel C G van de Poll
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  7 in total

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