Machine learning models including preoperative and postoperative albumin-bilirubin score: short-term outcomes among patients with hepatocellular carcinoma.
HPB (Oxford)
; 2024 Jul 25.
Article
em En
| MEDLINE
| ID: mdl-39098450
ABSTRACT
BACKGROUND:
We sought to assess the impact of various perioperative factors on the risk of severe complications and post-surgical mortality using a novel maching learning technique.METHODS:
Data on patients undergoing resection for HCC were obtained from an international, multi-institutional database between 2000 and 2020. Gradient boosted trees were utilized to construct predictive models.RESULTS:
Among 962 patients who underwent HCC resection, the incidence of severe postoperative complications was 12.7% (n = 122); in-hospital mortality was 2.9% (n = 28). Models that exclusively used preoperative data achieved AUC values of 0.89 (95%CI 0.85 to 0.92) and 0.90 (95%CI 0.84 to 0.96) to predict severe complications and mortality, respectively. Models that combined preoperative and postoperative data achieved AUC values of 0.93 (95%CI 0.91 to 0.96) and 0.92 (95%CI 0.86 to 0.97) for severe morbidity and mortality, respectively. The SHAP algorithm demonstrated that the factor most strongly predictive of severe morbidity and mortality was postoperative day 1 and 3 albumin-bilirubin (ALBI) scores.CONCLUSION:
Incorporation of perioperative data including ALBI scores using ML techniques can help risk-stratify patients undergoing resection of HCC.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
HPB (Oxford)
Assunto da revista:
GASTROENTEROLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos
País de publicação:
Reino Unido