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

Journal: Surgery today
Published Date:

Abstract

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.

Authors

  • Ryota Murase
    Department of Cardiovascular and Thoracic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kitaku, Sapporo, 060-8638, Japan.
  • Yasushige Shingu
    Department of Cardiovascular and Thoracic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kitaku, Sapporo, 060-8638, Japan. shingu@huhp.hokudai.ac.jp.
  • Satoru Wakasa
    Department of Cardiovascular and Thoracic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kitaku, Sapporo, 060-8638, Japan.