Development and internal validation of time-to-event risk prediction models for major medical complications within 30 days after elective colectomy

Patients undergoing colectomy are at risk of numerous major complications. However, existing binary risk stratification models do not predict when a patient may be at highest risks of each complication. Accurate prediction of the timing of complications facilitates targeted, resource-efficient monit...

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Veröffentlicht in:PloS one 2024-12, Vol.19 (12), p.e0314526
Hauptverfasser: Ke, Janny X C, Jen, Tim T H, Gao, Sihaoyu, Ngo, Long, Wu, Lang, Flexman, Alana M, Schwarz, Stephan K W, Brown, Carl J, Görges, Matthias
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container_title PloS one
container_volume 19
creator Ke, Janny X C
Jen, Tim T H
Gao, Sihaoyu
Ngo, Long
Wu, Lang
Flexman, Alana M
Schwarz, Stephan K W
Brown, Carl J
Görges, Matthias
description Patients undergoing colectomy are at risk of numerous major complications. However, existing binary risk stratification models do not predict when a patient may be at highest risks of each complication. Accurate prediction of the timing of complications facilitates targeted, resource-efficient monitoring. We sought to develop and internally validate Cox proportional hazards models to predict time-to-complication of major complications within 30 days after elective colectomy. We studied a retrospective cohort from the multicentered American College of Surgeons National Surgical Quality Improvement Program procedure-targeted colectomy dataset. Patients aged 18 years or above, who underwent elective colectomy between January 1, 2014 and December 31, 2019 were included. A priori candidate predictors were selected based on variable availability, literature review, and multidisciplinary team consensus. Outcomes were mortality, hospital readmission, myocardial infarction, cerebral vascular events, pneumonia, venous thromboembolism, acute renal failure, and sepsis or septic shock within 30 days after surgery. The cohort consisted of 132145 patients (mean ± SD age, 61 ± 15 years; 52% females). Complication rates ranged between 0.3% (n = 383) for cardiac arrest and acute renal failure to 5.3% (n = 6986) for bleeding requiring transfusion, with readmission rate of 8.6% (n = 11415). We observed distinct temporal patterns for each complication: the median [quartiles] postoperative day of complication diagnosis ranged from 1 [0, 2] days for bleeding requiring transfusion to 12 [6, 18] days for venous thromboembolism. Models for mortality, myocardial infarction, pneumonia, and renal failure showed good discrimination with a concordance > 0.8, while models for readmission, venous thromboembolism, and sepsis performed poorly with a concordance of 0.6 to 0.7. Models exhibited good calibration but ranges were limited to low probability areas. We developed and internally validated time-to-event prediction models for complications after elective colectomy. Once further validated, the models can facilitate tailored monitoring of high risk patients during high risk periods. Clinicaltrials.gov (NCT05150548; Principal Investigator: Janny Xue Chen Ke, M.D., M.Sc., F.R.C.P.C.; initial posting: November 25, 2021).
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However, existing binary risk stratification models do not predict when a patient may be at highest risks of each complication. Accurate prediction of the timing of complications facilitates targeted, resource-efficient monitoring. We sought to develop and internally validate Cox proportional hazards models to predict time-to-complication of major complications within 30 days after elective colectomy. We studied a retrospective cohort from the multicentered American College of Surgeons National Surgical Quality Improvement Program procedure-targeted colectomy dataset. Patients aged 18 years or above, who underwent elective colectomy between January 1, 2014 and December 31, 2019 were included. A priori candidate predictors were selected based on variable availability, literature review, and multidisciplinary team consensus. Outcomes were mortality, hospital readmission, myocardial infarction, cerebral vascular events, pneumonia, venous thromboembolism, acute renal failure, and sepsis or septic shock within 30 days after surgery. The cohort consisted of 132145 patients (mean ± SD age, 61 ± 15 years; 52% females). Complication rates ranged between 0.3% (n = 383) for cardiac arrest and acute renal failure to 5.3% (n = 6986) for bleeding requiring transfusion, with readmission rate of 8.6% (n = 11415). We observed distinct temporal patterns for each complication: the median [quartiles] postoperative day of complication diagnosis ranged from 1 [0, 2] days for bleeding requiring transfusion to 12 [6, 18] days for venous thromboembolism. Models for mortality, myocardial infarction, pneumonia, and renal failure showed good discrimination with a concordance &gt; 0.8, while models for readmission, venous thromboembolism, and sepsis performed poorly with a concordance of 0.6 to 0.7. Models exhibited good calibration but ranges were limited to low probability areas. We developed and internally validated time-to-event prediction models for complications after elective colectomy. Once further validated, the models can facilitate tailored monitoring of high risk patients during high risk periods. Clinicaltrials.gov (NCT05150548; Principal Investigator: Janny Xue Chen Ke, M.D., M.Sc., F.R.C.P.C.; initial posting: November 25, 2021).</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0314526</identifier><identifier>PMID: 39621640</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aged ; Aged patients ; Bleeding ; Care and treatment ; Cerebral infarction ; Colectomy ; Colectomy - adverse effects ; Colorectal cancer ; Complications ; Complications and side effects ; Datasets ; Diverticulitis ; Elective Surgical Procedures - adverse effects ; Failure ; Female ; Heart attacks ; Hospitals ; Humans ; Kidney diseases ; Kidneys ; Literature reviews ; Male ; Medical personnel ; Medical prognosis ; Medicine and Health Sciences ; Middle Aged ; Monitoring ; Mortality ; Myocardial infarction ; Observational Studies as Topic ; Patient outcomes ; Patient Readmission - statistics &amp; numerical data ; Patients ; Physical Sciences ; Pneumonia ; Postoperative ; Postoperative Complications - epidemiology ; Postoperative Complications - etiology ; Prediction models ; Proportional Hazards Models ; Quality control ; Renal failure ; Research and Analysis Methods ; Retrospective Studies ; Risk ; Risk Assessment - methods ; Risk Factors ; Risk groups ; Sepsis ; Septic shock ; Statistical models ; Surgery ; Surgical anastomosis ; Surgical outcomes ; Survival analysis ; Telemedicine ; Thromboembolism ; Time Factors ; Transfusion ; Variables</subject><ispartof>PloS one, 2024-12, Vol.19 (12), p.e0314526</ispartof><rights>Copyright: © 2024 Ke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Ke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Ke et al 2024 Ke et al</rights><rights>2024 Ke et al. 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However, existing binary risk stratification models do not predict when a patient may be at highest risks of each complication. Accurate prediction of the timing of complications facilitates targeted, resource-efficient monitoring. We sought to develop and internally validate Cox proportional hazards models to predict time-to-complication of major complications within 30 days after elective colectomy. We studied a retrospective cohort from the multicentered American College of Surgeons National Surgical Quality Improvement Program procedure-targeted colectomy dataset. Patients aged 18 years or above, who underwent elective colectomy between January 1, 2014 and December 31, 2019 were included. A priori candidate predictors were selected based on variable availability, literature review, and multidisciplinary team consensus. Outcomes were mortality, hospital readmission, myocardial infarction, cerebral vascular events, pneumonia, venous thromboembolism, acute renal failure, and sepsis or septic shock within 30 days after surgery. The cohort consisted of 132145 patients (mean ± SD age, 61 ± 15 years; 52% females). Complication rates ranged between 0.3% (n = 383) for cardiac arrest and acute renal failure to 5.3% (n = 6986) for bleeding requiring transfusion, with readmission rate of 8.6% (n = 11415). We observed distinct temporal patterns for each complication: the median [quartiles] postoperative day of complication diagnosis ranged from 1 [0, 2] days for bleeding requiring transfusion to 12 [6, 18] days for venous thromboembolism. Models for mortality, myocardial infarction, pneumonia, and renal failure showed good discrimination with a concordance &gt; 0.8, while models for readmission, venous thromboembolism, and sepsis performed poorly with a concordance of 0.6 to 0.7. Models exhibited good calibration but ranges were limited to low probability areas. We developed and internally validated time-to-event prediction models for complications after elective colectomy. Once further validated, the models can facilitate tailored monitoring of high risk patients during high risk periods. Clinicaltrials.gov (NCT05150548; Principal Investigator: Janny Xue Chen Ke, M.D., M.Sc., F.R.C.P.C.; initial posting: November 25, 2021).</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>39621640</pmid><doi>10.1371/journal.pone.0314526</doi><tpages>e0314526</tpages><orcidid>https://orcid.org/0000-0003-2193-178X</orcidid><orcidid>https://orcid.org/0000-0002-3584-1032</orcidid><orcidid>https://orcid.org/0000-0002-3121-9780</orcidid><orcidid>https://orcid.org/0000-0002-6999-0458</orcidid><orcidid>https://orcid.org/0000-0002-1319-4188</orcidid><oa>free_for_read</oa></addata></record>
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1932-6203
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subjects Aged
Aged patients
Bleeding
Care and treatment
Cerebral infarction
Colectomy
Colectomy - adverse effects
Colorectal cancer
Complications
Complications and side effects
Datasets
Diverticulitis
Elective Surgical Procedures - adverse effects
Failure
Female
Heart attacks
Hospitals
Humans
Kidney diseases
Kidneys
Literature reviews
Male
Medical personnel
Medical prognosis
Medicine and Health Sciences
Middle Aged
Monitoring
Mortality
Myocardial infarction
Observational Studies as Topic
Patient outcomes
Patient Readmission - statistics & numerical data
Patients
Physical Sciences
Pneumonia
Postoperative
Postoperative Complications - epidemiology
Postoperative Complications - etiology
Prediction models
Proportional Hazards Models
Quality control
Renal failure
Research and Analysis Methods
Retrospective Studies
Risk
Risk Assessment - methods
Risk Factors
Risk groups
Sepsis
Septic shock
Statistical models
Surgery
Surgical anastomosis
Surgical outcomes
Survival analysis
Telemedicine
Thromboembolism
Time Factors
Transfusion
Variables
title Development and internal validation of time-to-event risk prediction models for major medical complications within 30 days after elective colectomy
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