A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty

Ko, Sunggeon; Jo, Changwung; Chang, Chong Bum; Lee, Yong Seuk; Moon, Young-Wan; Youm, Jae woo; Han, Hyuck; Lee, Hajeong; Ro, Du Hyun

doi:10.1007/s00167-020-06258-0

Cited by 42 publications

(56 citation statements)

References 29 publications

(38 reference statements)

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“…In clinical situations in which several variables need to be comprehensively assessed, as in the diagnosis of septic arthritis of the knee, machine-learning algorithms have promise as a new approach. Several studies have revealed machine-learning algorithms to be superior to logistic regression at predicting perioperative risks of arthroplasty or patient-reported outcome measures after arthroscopy [ 10 , 13 , 17 ]. The authors of the present study obtained higher prediction accuracy using the XGBoost algorithm than synovial WBC count.…”

Section: Discussionmentioning

confidence: 99%

“…Importance scores indicate how valuable a feature is in the construction of boosted trees within the model. In recent studies applying the gradient boost algorithm, the authors tried to infer the decision-making process by referring to the feature importance applied to the tree decision [ 9 , 10 ]. Synovial WBC count and serum uric acid level, which were significant in the multivariate analysis (Table 1 ), also showed high importance in the XGBoost algorithm (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning has been applied to construct prediction models of complicated problems in which many factors are involved, while their respective relevance remains unclear. Hence, machine-learning algorithms have been applied in the field of knee surgery to predict events, such as postoperative acute kidney injury after total knee arthroplasty and the risk of hospital admission following anterior cruciate ligament reconstruction [ 10 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

Choi

Sim

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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Purpose Prompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy. Methods Patients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared. Results Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751–0.923) than synovial WBC count (0.740, 95% confidence interval 0.684–0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application (www.septicknee.com). Conclusion The diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. Level of evidence Diagnostic study Level III (Case–control study).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

Choi

Sim

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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“…The total number of subjects tested in the included studies was 304,076, with the sample size ranged from 109 to 96,653 [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] .…”

Section: Characteristics Of Eligible Studiesmentioning

confidence: 99%

Artificial Intelligence for the Prediction of Acute Kidney Injury During the Perioperative Period: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

Zhang

Wang

et al. 2022

Preprint

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Background: Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods.Objective: To estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period.Methods: Pubmed, Embase, and Cochrane Library were searched to 2nd October 2021. Studies presenting diagnostic performance of artificial intelligence in the early detection of perioperative acute kidney injury were included. Two independent evaluators extracted data. The risk of bias of eligible studies was assessed using the PROBAST tool.Results: Nineteen studies involving 304,076 patients were included. Quantitative random-effects meta-analysis using the Rutter and Gatsonis hierarchical summary receiver operating characteristics (HSROC) model revealed pooled sensitivity, specificity, and diagnostic odds ratio of 0.77 (95% CI: 0.73 to 0.81),0.75 (95% CI: 0.71 to 0.80), and 10.7 (95% CI 8.5 to 13.5), respectively. Threshold effect was found to be the only source of heterogeneity, and there was no evidence of publication bias.Conclusions: Our review demonstrates the promising performance of artificial intelligence for early prediction of perioperative AKI. Further studies should focus on the improvement of existing models, novel biomarkers, and clinical effectiveness.

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“…Classification models predicting high-risk groups and survival regression models such as random survival forest was mainly used in prognostic models using supervised machine-learning algorithm. [11][12][13] The classification model facilitates the interpretation of the result clearly. Risk status, sensitivity, and specificity data provide clear insight .249 0 (0) Age at onset of pain, y 28.9 ± 9.4 20.9 ± 9.6 22.9 ± 6.…”

Section: Modeling Strengthsmentioning

confidence: 99%

Automated Risk Stratification of Hip Osteoarthritis Development in Patients With Femoroacetabular Impingement Using an Unsupervised Clustering Algorithm: A Study From the Rochester Epidemiology Project

Pareek

et al. 2021

Orthopaedic Journal of Sports Medicine

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Background: Studies evaluating the natural history of femoroacetabular impingement (FAI) are limited. Purpose: To stratify the risk of progression to osteoarthritis (OA) in patients with FAI using an unsupervised machine-learning algorithm, compare the characteristics of each subgroup, and validate the reproducibility of staging. Study Design: Cohort study (prognosis); Level of evidence, 2. Methods: A geographic database from the Rochester Epidemiology Project was used to identify patients with hip pain between 2000 and 2016. Medical charts were reviewed to obtain characteristic information, physical examination findings, and imaging details. The patient data were randomly split into 2 mutually exclusive sets: train set (70%) for model development and test set (30%) for validation. The data were transformed via Uniform Manifold Approximation and Projection and were clustered using Hierarchical Density-based Spatial Clustering of Applications with Noise. Results: The study included 1071 patients with a mean follow-up period of 24.7 ± 12.5 years. The patients were clustered into 5 subgroups based on train set results: patients in cluster 1 were in their early 20s (20.9 ± 9.6 years), female dominant (84%), with low body mass index (<19 ); patients in cluster 2 were in their early 20s (22.9 ± 6.7 years), female dominant (95%), and pincer-type FAI (100%) dominant; patients in cluster 3 were in their mid 20s (26.4 ± 9.7) and were mixed-type FAI dominant (92%); patients in cluster 4 were in their early 30s (32.7 ± 7.8), with high body mass index (≥29 ), and diabetes (17%); and patients in cluster 5 were in their early 30s (30.0 ± 9.1), with a higher percentage of males (43%) compared with the other clusters and with limited internal rotation (14%). Mean survival for clusters 1 to 5 was 17.9 ± 0.6, 18.7 ± 0.3, 17.1 ± 0.4, 15.0 ± 0.5, and 15.6 ± 0.5 years, respectively, in the train set. The survival difference was significant between clusters 1 and 4 ( P = .02), 2 and 4 ( P < .005), 2 and 5 ( P = .01), and 3 and 4 ( P < .005) in the train set and between clusters 2 and 5 ( P = .03) and 3 and 4 ( P = .01) in the test set. Cluster characteristics and prognosis was well reproduced in the test set. Conclusion: Using the clustering algorithm, it was possible to determine the prognosis for OA progression in patients with FAI in the presence of conflicting risk factors acting in combination.

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A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty

Cited by 42 publications

References 29 publications

Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

Artificial Intelligence for the Prediction of Acute Kidney Injury During the Perioperative Period: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

Automated Risk Stratification of Hip Osteoarthritis Development in Patients With Femoroacetabular Impingement Using an Unsupervised Clustering Algorithm: A Study From the Rochester Epidemiology Project

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