ArticlePerioperative medicine (London, England)2024
A new risk calculation model for complications of hepatectomy in adults over 75.
Article in Perioperative medicine (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 3 citations in OpenAlex.
- Development and internal validation of a machine learning-based model for predicting postoperative complications after primary liver cancer resection.BMC surgery · 2026Article
- A comparative study of argon plasma coagulation and electrosurgical knife on postoperative complications after curative resection for hepatocellular carcinoma.Langenbeck's archives of surgery · 2025Article
- Epidemiology and risk of pulmonary complications following hepatobiliary surgical procedures: A retrospective study.World journal of gastrointestinal surgery · 2025Article
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3 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundOwing to poor organ function reserve, older adults have a high risk of postoperative complications. However, there is no well-established system for assessing the risk of complications after hepatectomy in older adults.
methodsThis study aimed to design a risk assessment tool to predict the risk of complications after hepatectomy in adults older than 75 years. A total of 326 patients were identified. A logistic regression equation was used to create the Risk Assessment System of Hepatectomy in Adults (RASHA) for the prediction of complications (Clavien‒Dindo classification ≥ II).
resultsMultivariate correlation analysis revealed that comorbidity (≥ 5 kinds of disease or < 5 kinds of disease, odds ratio [OR] = 5.552, P < 0.001), fatigue (yes or no, OR = 4.630, P = 0.009), Child‒Pugh (B or A, OR = 4.211, P = 0.004), number of liver segments to be removed (≥ 3 or ≤ 2, OR = 4.101, P = 0.001), and adjacent organ resection (yes or no, OR = 1.523, P = 0.010) were independent risk factors for postoperative complications after hepatectomy in older persons (aged ≥ 75 years). A binomial logistic regression model was established to evaluate the RASHA score (including the RASHA scale and RASHA formula). The area under the curve (AUC) for the RASHA scale was 0.916, and the cut-off value was 12.5. The AUC for the RASHA formula was 0.801, and the cut-off value was 0.2106.
conclusionRASHA can be used to effectively predict the postoperative complications of hepatectomy through perioperative variables in adults older than 75 years.
trial registrationThe Research Registry: researchregistry8531. https://www.researchregistry.com/browse-the-registry#home/registrationdetails/63901824ae49230021a5a0cf/ .
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