Developing a supervised machine learning model for predicting perioperative acute kidney injury in arthroplasty patients

Nikkinen, Okke; Kolehmainen, Timo; Aaltonen, Toni; Jämsä, Elias; Alahuhta, Seppo; Vakkala, Merja

doi:10.1016/j.compbiomed.2022.105351

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…There is an urgent need to improve reporting standards [ 4 ] and data collection quality [ 5 , 74 ] so that higher quality outputs can be more easily and consistently achieved. Larger (ideally prospective) studies are needed [ 48 ] with larger training datasets [ 1 ] to allow optimized algorithm refinement before extension to “real life” applications.…”

Section: Discussionmentioning

confidence: 99%

“…The widespread evolution from paper-based to electronic medical records (EMRs) [ 47 ] has often made this a less onerous step. Published reports have demonstrated the value of AI in predicting AKI [ 48 ], the risk of perioperative blood transfusion [ 33 , 46 , 49 ], the development of postoperative delirium [ 47 ] or ischemic stroke [ 45 ], and even the likelihood of persistent or prolonged opioid analgesic requirement [ 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

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A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

Kurmis

2023

Arthroplasty

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Background Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value. Methods Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps. Results A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as “demonstration of concepts” rather than “proof of concepts”. There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited. Conclusion While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

Kurmis

2023

Arthroplasty

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“…However, most studies for predicting AKI focus solely on cardiac surgery 15 – 18 and cannot be directly applied to patients undergoing noncardiac surgery. Additionally, prior machine learning-based AKI prediction models had several limitations, such as a narrow focus on specific procedures and small sample sizes 19 – 21 , as well as the absence of key variables 10 , 11 . Furthermore, previous models included numerous features 10 , 14 , which increased their vulnerability to missing data.…”

Section: Introductionmentioning

confidence: 99%

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study

Sun,

Li,

Wei

et al. 2024

International Journal of Surgery

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Background: Early identification of patients at high risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. We also evaluated the predictive performance of models that included only preoperative variables or only important predictors. Materials and methods: Adult patients undergoing noncardiac surgery were retrospectively included in the study (76,457 patients in the discovery cohort and 11,910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees (GBDT). The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. Results: The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 y), and 1179 patients (1.5%) developed AKI after surgery. The GBDT algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI, 0.835-0.863) and 0.828 (95% CI, 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, we observed a similar pattern regarding the models’ predictive performance. Conclusions: The machine learning models we developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Further, we found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

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Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty

Mickley,

Kaji,

Khosravi

et al. 2024

Arthroplasty Today

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Developing a supervised machine learning model for predicting perioperative acute kidney injury in arthroplasty patients

Cited by 8 publications

References 27 publications

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study

Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty

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