Development of a predictive model for risk stratification of acute kidney injury in patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy
© 2024. The Author(s)..
Acute kidney injury (AKI) following hyperthermic intraperitoneal chemotherapy (HIPEC) is common. Identifying patients at risk could have implications for surgical and anesthetic management. We aimed to develop a predictive model that could predict AKI based on patients' preoperative characteristics and intraperitoneal chemotherapy regimen. We retrospectively gathered data of adult patients undergoing HIPEC at our health system between November 2013 and April 2022. Next, we developed a model predicting postoperative AKI using multivariable logistic regression and calculated the performance of the model (area under the receiver operating characteristics curve [AUC]) via tenfold cross-validation. A total of 412 patients were included, of which 36 (8.7%) developed postoperative AKI. Based on our multivariable logistic regression model, multiple preoperative and intraoperative characteristics were associated with AKI. We included the total intraoperative cisplatin dose, body mass index, male sex, and preoperative hemoglobin level in the final model. The mean area under the receiver operating characteristics curve value was 0.82 (95% confidence interval 0.71-0.93). Our risk model predicted AKI with high accuracy in patients undergoing HIPEC in our institution. The external validity of our model should now be tested in independent and prospective patient cohorts.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Scientific reports - 14(2024), 1 vom: 19. März, Seite 6630 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Krause, Martin [VerfasserIn] |
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| Links: |
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| Themen: |
Acute kidney injury |
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| Anmerkungen: |
Date Completed 21.03.2024 Date Revised 23.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-024-54979-w |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369933796 |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a Acute kidney injury (AKI) following hyperthermic intraperitoneal chemotherapy (HIPEC) is common. Identifying patients at risk could have implications for surgical and anesthetic management. We aimed to develop a predictive model that could predict AKI based on patients' preoperative characteristics and intraperitoneal chemotherapy regimen. We retrospectively gathered data of adult patients undergoing HIPEC at our health system between November 2013 and April 2022. Next, we developed a model predicting postoperative AKI using multivariable logistic regression and calculated the performance of the model (area under the receiver operating characteristics curve [AUC]) via tenfold cross-validation. A total of 412 patients were included, of which 36 (8.7%) developed postoperative AKI. Based on our multivariable logistic regression model, multiple preoperative and intraoperative characteristics were associated with AKI. We included the total intraoperative cisplatin dose, body mass index, male sex, and preoperative hemoglobin level in the final model. The mean area under the receiver operating characteristics curve value was 0.82 (95% confidence interval 0.71-0.93). Our risk model predicted AKI with high accuracy in patients undergoing HIPEC in our institution. The external validity of our model should now be tested in independent and prospective patient cohorts | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Acute kidney injury | |
| 650 | 4 | |a Hyperthermic intraperitoneal chemotherapy | |
| 650 | 4 | |a Intraperitoneal carcinomatosis | |
| 650 | 4 | |a Predictive model | |
| 700 | 1 | |a Mehdipour, Soraya |e verfasserin |4 aut | |
| 700 | 1 | |a Veerapong, Jula |e verfasserin |4 aut | |
| 700 | 1 | |a Baumgartner, Joel M |e verfasserin |4 aut | |
| 700 | 1 | |a Lowy, Andrew M |e verfasserin |4 aut | |
| 700 | 1 | |a Gabriel, Rodney A |e verfasserin |4 aut | |
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