A preliminary prediction model using a deep learning software program for prolonged hospitalization after cardiovascular surgery
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 sof...
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| Published in | Surgery Today Vol. 53; no. 3; pp. 393 - 395 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Science and Business Media LLC
01.03.2023
Springer Nature Singapore |
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| Online Access | Get full text |
| ISSN | 0941-1291 1436-2813 1436-2813 |
| DOI | 10.1007/s00595-022-02565-w |
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| 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. |
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| AbstractList | 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.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. 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. |
| Author | Satoru, Wakasa Ryota, Murase Yasushige, Shingu |
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| Keywords | Deep learning Cardiovascular surgery Prolonged hospital stay Artificial intelligence |
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| References | Allyn, Allou, Augustin, Philip, Martinet, Belghiti (CR2) 2017; 12 Fernandez, Chen, Anolik, Brdlik, Laub, Anderson (CR1) 1997; 11 Katsuki, Kakizawa, Nishikawa, Yamamoto, Uchiyama (CR3) 2020; 11 J Fernandez (2565_CR1) 1997; 11 J Allyn (2565_CR2) 2017; 12 M Katsuki (2565_CR3) 2020; 11 |
| References_xml | – volume: 11 start-page: 1133 year: 1997 end-page: 1140 ident: CR1 article-title: Perioperative risk factors affecting hospital stay and hospital costs in open heart surgery for patients > or = 65 years old publication-title: Eur J Cardiothorac Surg doi: 10.1016/S1010-7940(97)01216-5 – volume: 12 start-page: e0169772 year: 2017 ident: CR2 article-title: A Comparison of a Machine Learning Model with EuroSCORE II in predicting mortality after elective cardiac surgery: a decision curve analysis publication-title: PLoS ONE doi: 10.1371/journal.pone.0169772 – volume: 11 start-page: 374 year: 2020 ident: CR3 article-title: Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission publication-title: Surg Neurol Int doi: 10.25259/SNI_636_2020 – volume: 11 start-page: 1133 year: 1997 ident: 2565_CR1 publication-title: Eur J Cardiothorac Surg doi: 10.1016/S1010-7940(97)01216-5 – volume: 12 start-page: e0169772 year: 2017 ident: 2565_CR2 publication-title: PLoS ONE doi: 10.1371/journal.pone.0169772 – volume: 11 start-page: 374 year: 2020 ident: 2565_CR3 publication-title: Surg Neurol Int doi: 10.25259/SNI_636_2020 |
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| Title | A preliminary prediction model using a deep learning software program for prolonged hospitalization after cardiovascular surgery |
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