A preliminary prediction model using a deep learning software program for prolonged hospitalization after cardiovascular surgery

• Published in: Surgery Today Vol. 53; no. 3; pp. 393 - 395
• Main Authors: Ryota, Murase, Yasushige, Shingu, Satoru, Wakasa
• Format: Journal Article
• Language: English
• Published: Singapore Springer Science and Business Media LLC 01.03.2023; Springer Nature Singapore
• Subjects: Aging; Deep Learning; Humans; Length of Stay; Medicine; Medicine & Public Health; Risk Factors; Short Communication; Software; Surgery; Surgical Oncology; Deep learning; Cardiovascular surgery; Prolonged hospital stay; Artificial intelligence
• ISSN: 0941-1291; 1436-2813; 1436-2813
• DOI: 10.1007/s00595-022-02565-w

A prolonged length of hospital stay (LOS) has become an important issue among patients undergoing cardiovascular surgery in our aging society. However, there are no established prediction models for a prolonged LOS. We therefore created a prediction model of a prolonged LOS using a deep learning software program (Prediction One; Sony Network Communications Inc., Tokyo, Japan) using preoperative data. Subjects were 157 patients (121 for training data, 36 for validation data). A prolonged LOS was defined as a more than 30-day postoperative stay due to physical inactivity. The area under the receiver operating characteristic curve and the accuracy of the model in the validation data were 0.806 and 67%, respectively. In conclusion, the preliminary model demonstrated acceptable performance for the prediction of a prolonged LOS after cardiovascular surgery.