Risk Factors and Effect of Acute Kidney Injury on Outcomes Following Total Hip and Knee Arthroplasty

Yayac, Michael; Aman, Zachary S.; Rondon, Alexander J.; Tan, Timothy L.; Courtney, P. Maxwell; Purtill, James J.

doi:10.1016/j.arth.2020.07.072

Cited by 12 publications

(32 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Characteristics Of Eligible Studiesmentioning

confidence: 99%

“…Seventeen studies described the demographic characteristics of their study population, of whom the mean age was 37 to 71 years old and the percentage of males was 16% to 88% 17,[19][20][21][22][23][24][25][26][27][28][29][30][31][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

View full text Add to dashboard Cite

show abstract

“…The included studies were categorized based on the type of the surgery participants received, including cardiothoracic surgery, any inpatient operative procedure, liver transplantation, total knee arthroplasty [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] .…”

Section: Identi Cation Of Relevant Studiesmentioning

confidence: 99%

“…Enrolled studies presented the performance of the AI algorithms with test dataset (internal validation), and there were only four studies 21,26,27,34 that presented the performance of external validation. Nine studies [21][22][23][24][25]28,[32][33][34] established the AI algorithm based on the gradient boosting machine (GBM), three studies 17,19,35 established random forest (RF)-based algorithms, three studies 20,27,29 established two types of arti cial neural network (ANN)based algorithms, one study 26 established Bayesian network (BN)-based algorithm, one study 31 established decision-tree (DT)-based algorithm, one study 30 established an ensemble algorithm, and another study even conducted a novel machine learning risk algorithm 18 called: MySurgeryRisk .…”

Section: Identi Cation Of Relevant Studiesmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Characterization of Risk Prediction Models for Acute Kidney Injury

et al. 2023

View full text Add to dashboard Cite

ImportanceDespite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.ObjectiveTo systematically review published AKI prediction models across all clinical subsettings.Data SourcesMEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.Study SelectionAll studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.Data Extraction and SynthesisTwo authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.Main Outcomes and MeasuresC statistic was used to measure the discrimination of prediction models.ResultsOf the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium–associated AKI: I2 = 99.9%; P &lt; .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.Conclusions and RelevanceIn this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

show abstract

Risk Factors and Effect of Acute Kidney Injury on Outcomes Following Total Hip and Knee Arthroplasty

Cited by 12 publications

References 41 publications

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

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

Artificial Intelligence for the prediction of Acute Kidney Injury during the perioperative period: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

Characterization of Risk Prediction Models for Acute Kidney Injury

Contact Info

Product

Resources

About