Ontology highlight
ABSTRACT: Background
We aimed to assess the ability of comprehensive complication index (CCI) and Clavien-Dindo complication (CDC) scale to predict excessive length of hospital stay (e-LOS) in patients undergoing liver resection for hepatocellular carcinoma.Methods
Patients were identified from an Italian multi-institutional database and randomly selected to be included in either a derivation or validation set. Multivariate logistic regression models and ROC curve analysis including either CCI or CDC as predictors of e-LOS were fitted to compare predictive performance. E-LOS was defined as a LOS longer than the 75th percentile among patients with at least one complication.Results
A total of 2669 patients were analyzed (1345 for derivation and 1324 for validation). The odds ratio (OR) was 5.590 (95%CI 4.201; 7.438) for CCI and 5.507 (4.152; 7.304) for CDC. The AUC was 0.964 for CCI and 0.893 for CDC in the derivation set and 0.962 vs. 0.890 in the validation set, respectively. In patients with at least two complications, the OR was 2.793 (1.896; 4.115) for CCI and 2.439 (1.666; 3.570) for CDC with an AUC of 0.850 and 0.673, respectively in the derivation cohort. The AUC was 0.806 for CCI and 0.658 for CDC in the validation set.Conclusions
When reporting postoperative morbidity in liver surgery, CCI is a preferable scale.
SUBMITTER: Giani A
PROVIDER: S-EPMC7767420 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Cancers 20201221 12
<h4>Background</h4>We aimed to assess the ability of comprehensive complication index (CCI) and Clavien-Dindo complication (CDC) scale to predict excessive length of hospital stay (e-LOS) in patients undergoing liver resection for hepatocellular carcinoma.<h4>Methods</h4>Patients were identified from an Italian multi-institutional database and randomly selected to be included in either a derivation or validation set. Multivariate logistic regression models and ROC curve analysis including either ...[more]