Preoperative nomogram to predict posthepatectomy liver failure

Mashaal Dhir, Kaeli K. Samson, Natesh Yepuri, Ujwal R. Yanala, Lynette M. Smith, Chandrakanth Are

Research output: Contribution to journalArticlepeer-review

Abstract

Background and Objectives: Posthepatectomy liver failure (PHLF) is associated with significant morbidity and mortality. However, it is often difficult to predict the risk of PHLF in an individual patient. We aimed to develop a preoperative nomogram to predict PHLF and allow better risk stratification before surgery. Methods: Data for patients undergoing a partial or major hepatectomy were extracted from the hepatectomy-specific NSQIP database for years 2014–2016. Data set from 2017 was used for validation. Patients with Grade B/C liver failure were compared with patients with no liver failure. Results: A total of 10 808 patients from 2014–2016 data set were included. Of these, 316 patients (2.9%) developed Grade B/C PHLF. In the multivariable model consisting of preoperative variables, the following were predictive of Grade B/C PHLF (all p < 0.05): male gender, biliary stent, neoadjuvant therapy, viral hepatitis B or C, concurrent resections, biliary reconstruction, low sodium, and low albumin (model c statistic-0.78). This model was used to construct a nomogram. In the 2017 validation cohort of 4367 patients the nomogram again demonstrated good c-statistic (0.78). Conclusions: Our nomogram provides patient-specific probabilities for PHLF, and is easy to use. This is a valuable tool that can be utilized for preoperative patient counseling and selection.

Original languageEnglish (US)
Pages (from-to)1750-1756
Number of pages7
JournalJournal of Surgical Oncology
Volume123
Issue number8
DOIs
StatePublished - Jun 15 2021

Keywords

  • hepatectomy
  • liver failure
  • liver resection
  • nomogram

ASJC Scopus subject areas

  • Surgery
  • Oncology

Fingerprint

Dive into the research topics of 'Preoperative nomogram to predict posthepatectomy liver failure'. Together they form a unique fingerprint.

Cite this