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Erschienen in: Journal of Clinical Monitoring and Computing 4/2021

20.05.2020 | Original Research

Renal resistive index as a predictor of postoperative complications in liver resection surgery. Observational study

verfasst von: Enrico Giustiniano, Fabio Procopio, Emanuela Morenghi, Yari Gollo, Laura Rocchi, Nadia Ruggieri, Valeria Lascari, Guido Torzilli, Maurizio Cecconi

Erschienen in: Journal of Clinical Monitoring and Computing | Ausgabe 4/2021

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Abstract

Mortality after liver surgery reduced during the last three decades to less than 2%, but post-operative morbidity occurs in 20–50% of cases. Patients are often considered eligible for post-operative intensive-care unit (ICU) admission. Predicting which patients that are at higher risk could lead to a more precise perioperative management. We investigated whether renal resistive index (RRI), alone or along with other items, can predict post-operative complication after hepatic resection. All consecutive patients undergoing hepatectomy for primary or metastatic neoplasm at our Institution between February 2015 and March 2017 were enrolled. They received RRI measurement before entering in operative room and after awakening from general anesthesia. 183 Patients were enrolled. High surgical invasiveness, surgery time > 360 min, pre-operative RRI and postoperative serum lactate clearance <  − 6%, showed to be associated with postoperative complications. Pre-operative RRI, complex liver resection, long-lasting surgery and poor lactate clearance (cLac) close to awakening from general anesthesia, all together may permit to classify the risk of post-operative adverse outcome after hepatic resection surgery.
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Metadaten
Titel
Renal resistive index as a predictor of postoperative complications in liver resection surgery. Observational study
verfasst von
Enrico Giustiniano
Fabio Procopio
Emanuela Morenghi
Yari Gollo
Laura Rocchi
Nadia Ruggieri
Valeria Lascari
Guido Torzilli
Maurizio Cecconi
Publikationsdatum
20.05.2020
Verlag
Springer Netherlands
Erschienen in
Journal of Clinical Monitoring and Computing / Ausgabe 4/2021
Print ISSN: 1387-1307
Elektronische ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-020-00529-4

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