Development and Validation of Prediction Models and Risk Calculators for Posthepatectomy Liver Failure and Postoperative Complications Using a Diverse International Cohort of Major Hepatectomies
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved..
OBJECTIVE: The study aim was to develop and validate models to predict clinically significant posthepatectomy liver failure (PHLF) and serious complications [a Comprehensive Complication Index (CCI)>40] using preoperative and intraoperative variables.
BACKGROUND: PHLF is a serious complication after major hepatectomy but does not comprehensively capture a patient's postoperative course. Adding the CCI as an additional metric can account for complications unrelated to liver function.
METHODS: The cohort included adult patients who underwent major hepatectomies at 12 international centers (2010-2020). After splitting the data into training and validation sets (70:30), models for PHLF and a CCI>40 were fit using logistic regression with a lasso penalty on the training cohort. The models were then evaluated on the validation data set.
RESULTS: Among 2192 patients, 185 (8.4%) had clinically significant PHLF and 160 (7.3%) had a CCI>40. The PHLF model had an area under the curve (AUC) of 0.80, calibration slope of 0.95, and calibration-in-the-large of -0.09, while the CCI model had an AUC of 0.76, calibration slope of 0.88, and calibration-in-the-large of 0.02. When the models were provided only preoperative variables to predict PHLF and a CCI>40, this resulted in similar AUCs of 0.78 and 0.71, respectively. Both models were used to build 2 risk calculators with the option to include or exclude intraoperative variables ( PHLF Risk Calculator; CCI>40 Risk Calculator ).
CONCLUSIONS: Using an international cohort of major hepatectomy patients, we used preoperative and intraoperative variables to develop and internally validate multivariable models to predict clinically significant PHLF and a CCI>40 with good discrimination and calibration.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:278 |
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| Enthalten in: |
Annals of surgery - 278(2023), 6 vom: 01. Dez., Seite 976-984 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Jaeyun J [VerfasserIn] |
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| Anmerkungen: |
Date Completed 09.11.2023 Date Revised 22.11.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1097/SLA.0000000000005916 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM357292235 |
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| 041 | |a eng | ||
| 100 | 1 | |a Wang, Jaeyun J |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Development and Validation of Prediction Models and Risk Calculators for Posthepatectomy Liver Failure and Postoperative Complications Using a Diverse International Cohort of Major Hepatectomies |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Completed 09.11.2023 | ||
| 500 | |a Date Revised 22.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved. | ||
| 520 | |a OBJECTIVE: The study aim was to develop and validate models to predict clinically significant posthepatectomy liver failure (PHLF) and serious complications [a Comprehensive Complication Index (CCI)>40] using preoperative and intraoperative variables | ||
| 520 | |a BACKGROUND: PHLF is a serious complication after major hepatectomy but does not comprehensively capture a patient's postoperative course. Adding the CCI as an additional metric can account for complications unrelated to liver function | ||
| 520 | |a METHODS: The cohort included adult patients who underwent major hepatectomies at 12 international centers (2010-2020). After splitting the data into training and validation sets (70:30), models for PHLF and a CCI>40 were fit using logistic regression with a lasso penalty on the training cohort. The models were then evaluated on the validation data set | ||
| 520 | |a RESULTS: Among 2192 patients, 185 (8.4%) had clinically significant PHLF and 160 (7.3%) had a CCI>40. The PHLF model had an area under the curve (AUC) of 0.80, calibration slope of 0.95, and calibration-in-the-large of -0.09, while the CCI model had an AUC of 0.76, calibration slope of 0.88, and calibration-in-the-large of 0.02. When the models were provided only preoperative variables to predict PHLF and a CCI>40, this resulted in similar AUCs of 0.78 and 0.71, respectively. Both models were used to build 2 risk calculators with the option to include or exclude intraoperative variables ( PHLF Risk Calculator; CCI>40 Risk Calculator ) | ||
| 520 | |a CONCLUSIONS: Using an international cohort of major hepatectomy patients, we used preoperative and intraoperative variables to develop and internally validate multivariable models to predict clinically significant PHLF and a CCI>40 with good discrimination and calibration | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Feng, Jean |e verfasserin |4 aut | |
| 700 | 1 | |a Gomes, Camilla |e verfasserin |4 aut | |
| 700 | 1 | |a Calthorpe, Lucia |e verfasserin |4 aut | |
| 700 | 1 | |a Ashraf Ganjouei, Amir |e verfasserin |4 aut | |
| 700 | 1 | |a Romero-Hernandez, Fernanda |e verfasserin |4 aut | |
| 700 | 1 | |a Benedetti Cacciaguerra, Andrea |e verfasserin |4 aut | |
| 700 | 1 | |a Hibi, Taizo |e verfasserin |4 aut | |
| 700 | 1 | |a Adam, Mohamed Abdelgadir |e verfasserin |4 aut | |
| 700 | 1 | |a Alseidi, Adnan |e verfasserin |4 aut | |
| 700 | 1 | |a Abu Hilal, Mohammad |e verfasserin |4 aut | |
| 700 | 1 | |a Rashidian, Nikdokht |e verfasserin |4 aut | |
| 700 | 0 | |a International Post-Hepatectomy Liver Failure Study Group |e verfasserin |4 aut | |
| 700 | 1 | |a Abe, Yuta |e investigator |4 oth | |
| 700 | 1 | |a Armstrong, Thomas |e investigator |4 oth | |
| 700 | 1 | |a Ferrero, Alessandro |e investigator |4 oth | |
| 700 | 1 | |a Corvera, Carlos |e investigator |4 oth | |
| 700 | 1 | |a Hayashi, Koki |e investigator |4 oth | |
| 700 | 1 | |a Imamura, Taisuke |e investigator |4 oth | |
| 700 | 1 | |a Kitago, Minoru |e investigator |4 oth | |
| 700 | 1 | |a Kubo, Shoji |e investigator |4 oth | |
| 700 | 1 | |a Ishii, Masatsugu |e investigator |4 oth | |
| 700 | 1 | |a Mocchegiani, Federico |e investigator |4 oth | |
| 700 | 1 | |a Morise, Zenichi |e investigator |4 oth | |
| 700 | 1 | |a Ogawa, Kosuke |e investigator |4 oth | |
| 700 | 1 | |a Okamura, Yukiyasu |e investigator |4 oth | |
| 700 | 1 | |a Otsuka, Shimpei |e investigator |4 oth | |
| 700 | 1 | |a Primrose, John |e investigator |4 oth | |
| 700 | 1 | |a Rosso, Edoardo |e investigator |4 oth | |
| 700 | 1 | |a Rotellar, Fernando |e investigator |4 oth | |
| 700 | 1 | |a Russolillo, Nadia |e investigator |4 oth | |
| 700 | 1 | |a Syed, Shareef M |e investigator |4 oth | |
| 700 | 1 | |a Tanabe, Minoru |e investigator |4 oth | |
| 700 | 1 | |a Tanaka, Shogo |e investigator |4 oth | |
| 700 | 1 | |a Terasaki, Fumihiro |e investigator |4 oth | |
| 700 | 1 | |a Vivarelli, Marco |e investigator |4 oth | |
| 700 | 1 | |a Zimmitti, Giuseppe |e investigator |4 oth | |
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