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PLoS Med. 2025;22(4):e1004566.

A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study.

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제1저자 박세훈, 정수민 · 교신저자 김광수, 이하정

PMID 40299885 PMC12040160 DOI 10.1371/journal.pmed.1004566 IF 9.9 신장내과
소속 (12)
  1. Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  2. Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea.
  3. Department of Radiology, Seoul National University Hospital, Seoul, Korea.
  4. Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Korea.
  5. Department of Internal Medicine, Chung Ang University Hospital, Seoul, Korea.
  6. Department of Internal Medicine, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong, Korea.
  7. KAIST: Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  8. Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea.
  9. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
  10. Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Korea.
  11. Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.
  12. Department of Medicine, Seoul National University, Seoul, Korea.

Abstract

BACKGROUND Postoperative acute kidney injury (PO-AKI) prediction models for non-cardiac major surgeries typically rely solely on preoperative clinical characteristics.

METHODS AND FINDINGS In this study, we developed and externally validated a deep-learning-based model that integrates preoperative data with minute-scale intraoperative vital signs to predict PO-AKI. Using data from three hospitals, we constructed a convolutional neural network-based EfficientNet framework to analyze intraoperative data and created an ensemble model incorporating 103 baseline variables of demographics, medication use, comorbidities, and surgery-related characteristics. Model performance was compared with the conventional SPARK model from a previous study. Among 110,696 patients, 51,345 were included in the development cohort, and 59,351 in the external validation cohorts. The median age of the cohorts was 60, 61, and 66 years, respectively, with males comprising 54.9%, 50.8%, and 42.7% of each cohort. The intraoperative vital sign-based model demonstrated comparable predictive power (AUROC (Area Under the Receiver Operating Characteristic Curve): discovery cohort 0.707, validation cohort 0.637 and 0.607) to preoperative-only models (AUROC: discovery cohort 0.724, validation cohort 0.697 and 0.745). Adding 11 key clinical variables (e.g., age, sex, estimated glomerular filtration rate (eGFR), albuminuria, hyponatremia, hypoalbuminemia, anemia, diabetes, renin-angiotensin-aldosterone inhibitors, emergency surgery, and the estimated surgery time) improved the model's performance (AUROC: discovery cohort 0.765, validation cohort 0.716 and 0.761). The ensembled deep-learning model integrating both preoperative and intraoperative data achieved the highest predictive accuracy (AUROC: discovery cohort 0.795, validation cohort 0.762 and 0.786), outperforming the conventional SPARK model. The retrospective design in a single-nation cohort with non-inclusion of some potential AKI-associated variables is the main limitation of this study.

CONCLUSIONS This deep-learning-based PO-AKI risk prediction model provides a comprehensive approach to evaluating PO-AKI risk prediction by combining preoperative clinical data with real-time intraoperative vital sign information, offering enhanced predictive performance for better clinical decision-making.

Copyright: © 2025 Park et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Figures

Fig 1
Fig 1 Study design and deep-learning model architecture. a. Design of the deep-learning model to construct the prediction models. b. Overall study design for prediction of postoperative AKI or Critical AKI. Conv, Convolutional layer; SepConv, Separable convolutional layer; MBConv, Mobile inverted bottleneck convolutional layer (numbers after MBConv indicate layer depth); k3/k5, kernel size 3 or 5; GAP, Global average pooling; FC, Fully connected layer; Swish, Swish activation function; DBP, Diastolic blood pressure, SBP, Systolic blood pressure; HR, Heart rate; DL-IVSS, A deep-learning algorithm leveraging time-series intraoperative vital sign signals; preOp ML, A machine learning model with 103 baseline characteristics.
Fig 2
Fig 2 Study flow diagram. Flow chart of dataset construction. SNUH, Seoul National University Hospital; SNUBH, Seoul National University Bundang Hospital; SNU-BMC, Seoul National University Boramae Medical Center; Cr, Creatinine; ESRD, End stage renal disease; AKI, Acute kidney injury; EHR, Electronic Health Records; DBP, Diastolic blood pressure; SBP, Systolic blood pressure; HR, Heart rate.
Fig 3
Fig 3 Feature importance of the deep-learning-based models using the intraoperative vital sign signals. a. Results towards PO-AKI outcome. b. Results towards critical AKI outcome. IntraOp Signal, Intraoperative vital sign signal; UALB, Dipstick urine albumin; Op EST Dur, estimated surgery time; EM Op, emergent operation; DM, diabetes mellitus; RASB, Preoperative use of renin-angiotensin-aldosterone inhibitors; Alb, serum albumin; eGFR, Estimated glomerular filtration rate.
Fig 4
Fig 4 Calibration plot for the Ensemble_PCFs 11. The solid black line represents the logistic calibration curve, while the dashed line shows the nonparametric calibration curve that directly connects observed data points, with the grey line showing ideal calibration. The Brier score indicates the mean squared difference between predicted and actual probabilities, where values closer to 0 represent better calibration. The histogram shows the distribution of predicted probabilities.
Fig 5
Fig 5 Discriminative performances for postoperative AKI and critical AKI risk by intraoperative vital sign signals at preset thresholds. a. Model performance in each study data when sensitivity 95% threshold was applied. b. Model performance in each study data when specificity 95% threshold was applied. DL-IVSS_PCFs 11, A deep-learning algorithm leveraging time-series intraoperative vital sign signals and preoperative clinical features 11; Ensemble_PCFs 11, A ensemble model combining preOp_ML and DL-IVSS_PCFs 11; PO-AKI, Postoperative acute kidney injury; SNUH, Seoul National University Hospital; SNUBH, Seoul National University Bundang Hospital; SNU-BMC, Seoul National University Boramae Medical Center.

그림 출처: PubMed Central 공개 논문 (PMC12040160)

Conflict of interest statement

The authors have declared that no competing interests exist.

그 밖의 정보
출판 형태
Journal Article, Multicenter Study, Observational Study, Research Support, Non-U.S. Gov't
저널
PLoS medicine · eISSN 1549-1676
전자 출판
2025-04-29
MeSH
Aged · Female · Humans · Male · Middle Aged · Acute Kidney Injury* / etiology / diagnosis · Algorithms* · Deep Learning* · Postoperative Complications* / diagnosis / etiology · Predictive Value of Tests · Risk Assessment · Risk Factors · Vital Signs*
참고문헌
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