Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy.

Journal: Medicina (Kaunas, Lithuania)
PMID:

Abstract

: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN. : This retrospective study included 785 patients (training set 706, test set 79) from six tertiary referral centers who underwent open or robotic PN. Forty-four perioperative features were used as inputs to train the AI prediction model. XG-Boost and genetic algorithms were used for the final model selection and to determine feature importance. The primary outcome measure was immediate postoperative serum creatinine (Cr) level. The secondary outcome was the incidence of AKI (estimated glomerular filtration rate (eGFR) < 60 mL/h). The average difference between the true and predicted serum Cr levels was considered the mean absolute error (MAE) and was used as a model evaluation parameter. : An AI model for predicting immediate postoperative serum Cr levels was selected from 2000 candidates by providing the lowest MAE (0.03 mg/dL). The model-predicted immediate postoperative serum Cr levels correlated closely with the measured values (R = 0.9669). The sensitivity and specificity of the model for predicting AKI were 85.5% and 99.7% in the training set, and 100.0% and 100.0% in the test set, respectively. The limitations of this study included its retrospective design. : Our AI model successfully predicted accurate serum Cr levels and the likelihood of AKI. The accuracy of our model suggests that personalized guidelines to optimize multidisciplinary plans involving pre- and postoperative care need to be developed.

Authors

  • Tae Young Shin
    Synergy A.I. Co.Ltd., Chuncheon, Korea.
  • Hyunho Han
    Department of Urology, Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul, Korea.
  • Hyun-Seok Min
    1 Department of Cardiology University of Ulsan College of Medicine Asan Medical Center Seoul Korea.
  • Hyungjoo Cho
    Department of Cardiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Seonggyun Kim
    Department of Urology, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea.
  • Sung Yul Park
    Department of Urology, Hanyang University College of Medicine, Seoul, Korea.
  • Hyung Joon Kim
    Department of Urology, Konyang University, College of Medicine, Daejeon, Korea.
  • Jung Hoon Kim
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. jhkim2008@gmail.com.
  • Yong Seong Lee
    Department of Urology, Hallym University Sacred Heart Hospital, Hallym University Collge of Medicine, Anyang, Korea. novavia@hallym.or.kr.