Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non‐cardiac surgery-Reference-Cited by-同舟云学术

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Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non‐cardiac surgery

Published:2020-09-15 Issue:5 Volume:91 Page:860-870
ISSN:1445-1433
Container-title:ANZ Journal of Surgery
language:en
Short-container-title:ANZ Journal of Surgery

Author:

Reilly Jennifer R.¹²^ORCID,Gabbe Belinda J.³^ORCID,Brown Wendy A.⁴⁵^ORCID,Hodgson Carol L.³^ORCID,Myles Paul S.¹²^ORCID

Affiliation:

1. Department of Anaesthesiology and Perioperative Medicine Alfred Health Melbourne Victoria Australia

2. Department of Anaesthesia and Perioperative Medicine Monash University Melbourne Victoria Australia

3. School of Public Health and Preventive Medicine Monash University Melbourne Victoria Australia

4. Department of Surgery Alfred Health Melbourne Victoria Australia

5. Department of Surgery Monash University Melbourne Victoria Australia

Funder

Australian Research Council

Australian Society of Anaesthetists

Faculty of Medicine, Nursing and Health Sciences, Monash University

Heart Foundation

Publisher

Wiley

Subject

General Medicine,Surgery

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ans.16255

Reference91 articles.

1. Using predicted 30 day mortality to plan postoperative colorectal surgery care: a cohort study † †This Article is accompanied by Editorial Aew331.

2. Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

3. Risk Stratification Tools for Predicting Morbidity and Mortality in Adult Patients Undergoing Major Surgery

4. Towards better clinical prediction models: seven steps for development and an ABCD for validation

5. Development and validation of the Surgical Outcome Risk Tool (SORT)

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2. Development and validation of the Ex-Care BR model: a multicentre initiative for identifying Brazilian surgical patients at risk of 30-day in-hospital mortality;British Journal of Anaesthesia;2024-07

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4. Development and validation of ‘Patient Optimizer’ (POP) algorithms for predicting surgical risk with machine learning;BMC Medical Informatics and Decision Making;2024-03-11

5. Risk prediction models in emergency surgery: Protocol for a scoping review;Acta Anaesthesiologica Scandinavica;2024-02-06

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