Development and Validation of a Nomogram for Preoperative Prediction of Non-Textbook Outcome in Patients Undergoing...

The use of the textbook outcome (TO) as a multidimensional measurement method allows for an accurate assessment of the ideal hospitalization process for surgical patients. This study aims to construct a nomogram for predicting non-TO in patients undergoing hepatectomy for hepatocellular carcinoma (HCC) based on Lasso-Logistic regression.

A retrospective study was conducted to analyze preoperative clinical data from HCC patients who underwent hepatectomy at The University of Hong Kong-Shenzhen Hospital between 2013 and 2021. Lasso regression was employed to identify risk factors and develop a novel nomogram. The performance of the nomogram in terms of discrimination, calibration, and clinical utility was evaluated through internal validation.

Compared to the TO group, the non-TO group exhibited a higher proportion of male patients, fewer patients in the 0/A stage, a greater tumor burden score (TBS), fewer patients with an AFP level of ≤ 400 μg/L, a higher incidence of tumors located in segments 7/8, and a greater number of patients undergoing major hepatectomy. The variables selected through Lasso regression included sex, Charlson comorbidity index, history of abdominal surgery, BCLC staging, TBS, AFP level, tumor location in segments 7/8, and extent of resection. These factors were incorporated into a logistic model to establish the nomogram. The ROC curve demonstrated an area under the curve of 0.755, which was significantly superior to using TBS or BCLC staging alone. The Hosmer-Lemeshow test indicated that the model exhibited good fit (p = 0.582).

This study presents a clinically applicable nomogram that reliably predicts non-TO prior to hepatectomy for HCC. With its favorable performance, the model facilitates informed patient consent and supports strategic resource allocation, ultimately contributing to enhanced healthcare quality and efficiency.