Asian journal of endoscopic surgery · 2026
Machine Learning-Assisted Identification of Patients Likely to Require Laparoscopic Subtotal Cholecystectomy in Acute Cholecystitis.
СтатьяНазвание и аннотация: Перевод в очереди
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Shinozuka Miyu, Shinozuka Takahiro, Sasahara Masahiro, Suzuki Yunosuke, Morimoto Daishi, Murai Toshifumi, Sakai Mitsuru
Аннотация
PURPOSE: Laparoscopic subtotal cholecystectomy (LSTC) serves as a bailout procedure when the critical view of safety cannot be achieved during laparoscopic cholecystectomy (LC) for acute cholecystitis (AC). Previous prediction models rely solely on baseline variables without considering temporal changes. We aimed to develop prediction models incorporating both static clinical factors and dynamic laboratory changes using machine learning (ML).
METHODS: We retrospectively analyzed 104 patients who underwent LC for AC between January 2020 and October 2022. Three models were developed: Model 1 using logistic regression with baseline variables, Model 2 incorporating temporal laboratory changes, and Model 3 employing ML algorithms. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).
RESULTS: LSTC was performed in 19 patients (18.3%). In Model 1, severity grade II-III was the strongest predictor with an AUROC of 0.735. Model 2 improved performance to an AUROC of 0.782 by incorporating the ALT change rate. Among seven ML algorithms, elastic net achieved the highest AUROC of 0.821.
CONCLUSIONS: The ML-based model enabled preoperative identification of patients more likely to require LSTC. The model may support preoperative discussions in settings where operative strategies vary.
METHODS: We retrospectively analyzed 104 patients who underwent LC for AC between January 2020 and October 2022. Three models were developed: Model 1 using logistic regression with baseline variables, Model 2 incorporating temporal laboratory changes, and Model 3 employing ML algorithms. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).
RESULTS: LSTC was performed in 19 patients (18.3%). In Model 1, severity grade II-III was the strongest predictor with an AUROC of 0.735. Model 2 improved performance to an AUROC of 0.782 by incorporating the ALT change rate. Among seven ML algorithms, elastic net achieved the highest AUROC of 0.821.
CONCLUSIONS: The ML-based model enabled preoperative identification of patients more likely to require LSTC. The model may support preoperative discussions in settings where operative strategies vary.