Table 1.

Summary of Selected Studies Reflecting the Design Stage of Various AI Tools for Enhancement of Structure, Processes or Outcomes of CRRT Delivery

Study, Year & Country	Stage of Development	Population, Sample & Setting	Intervention	Study Outcomes & Results	Gaps Filled & Gaps Remaining
Zhang et al. 2015 Australia	Design	1 AKI patient with sepsis on CVVH (UF 2L/hr. BFR 200ml/min)	E-monitoring of CRRT pressures and clotting prediction	Monitoring of progressive increases in TMP, filter pressure drop, and negative effluent pressure may predict clotting	Dynamic monitoring and prevention of CRRT complications is possible with machine learning. Validation studies needed to reproduce these results across patient strata, and to test clinical effectiveness; characterization of the data management process needed to maintain accurate dynamic monitoring, as well as implementation protocols and performance metrics.
Guru et al. 2016 United States	Design, Validation	488 ECMO patients, 213 of them on CRRT.	Automated EHR-based algorithm for prediction of CRRT initiation in ECMO patients. Performance of the algorithm was validated against manual searches.	Percent agreement between automated and manual search strategies: 89% or excellent, Kappa agreement statistic 0.99; training and validation cohorts’ agreement 90% or excellent, Kappa 1.0	EHR-based search algorithm can identify CRRT candidates in ECMO patients. External and cross-validation studies needed for reliability across patient strata; clinical validation needed for effectiveness; data management plans for ongoing data curation and monitoring; gap analysis, studies on performance metrics, implementation barriers and protocols.
Keith et al. 2017 United States (Conference abstract)	Design	CRRT patients 2012-2016 EHR data combined with operational data from CRRT machine (sample size and patient characteristics unknown)	Combining EHR flowsheets with CRRT machine data using automated scripts and dynamic data viewing	Multimodal database created successfully with 7,977,878 variables (205,910 EHR and CRRT flowsheet + 7,771,968 CRRT machine variables)	Automation of data pooling and dynamic viewing is possible. Study details, including full methodology not available for review. Further validation needed for accuracy and reliability; characterization of the data management process needed, as well as implementation protocols and performance metrics.
Kang et al. 2019 South Korea	Design, Validation	1571 ICU patients with AKI on CRRT (1094 test, 477 validation cohorts)	ML models (KNN, SVM, MARS, RF, XGB, ANN) for mortality prediction in CRRT, validated against disease severity scores (APACHE II, SOFA, MOSAIC).	Primary outcomes: probability of mortality during hospital/ICU admission. ML models outperformed disease severity scores. AUC values for all models (highest for RF model, 0.784 [0.744–0.825], followed by XGB 0.776 [0.735–0.818])	Machine learning outperforms disease severity scores in predicting mortality in CRRT patients. External validation studies needed for reliability, cross-validation for generalizability; clinical validation studies needed for clinical effectiveness; studies on data management and implementation barriers.
Lee et al. 2021 United States (Conference abstract)	Design	COVID-19 patients with AKI requiring CRRT (sample size and patient characteristics unknown).	Automated CRRT machine allocation scheduling for COVID-19 patients based on need (6-12 h vs. 24 h)	List of needs generated for the upcoming 24 hours on a shared server; machine-specific patient list generated within minutes of server updates, based on demand and machine location	Automated scheduling improves workflow and optimizes use of CRRT resources. Full methodology, including feature selection and model development, needed; validation of the method for accuracy and reliability; data management plan and gap analysis; implementation barriers and performance metrics.
Roy et al. 2021 United Kingdom	Design, Validation	36,498 patients (80% training, 10% validation, 10% test sets) at risk of developing 6 endpoints, including AKI and CRRT.	Comparing ML models (ST, SB multitask, SeqSNR) for CRRT initiation in the ICU using clinical data from the MIMIC-III dataset. Six endpoints were predicted, including AKI, CRRT, vasoactive medications, mortality, mechanical ventilation, and length of stay.	SeqSNR outperformed ST and SB multitask in predicting 4 endpoints (AKI, CRRT, vasoactive drugs, mortality). SeqSNR showed superior label efficiency when reducing training dataset (2.1%, 2.9%, 2.1% additional AUC for tasks using 1%, 5%, and 10% of labels, respectively).	Deep learning SeqSNR has superior capacity to predict clinical endpoints, with less data, than traditional machine learning. Further research needed on clinical validation, identification of implementation barriers, and data curation and maintenance.
Kang et al. 2021 South Korea	Design, Validation	2349 ICU patients with AKI on CRRT (70% training, 30% testing sets)	ML models (XGM, LGBM, DNN, SVM, LR) against disease severity scores (APACHE II, SOFA, MOSAIC) for hypotension prediction after CRRT initiation (MAP reduction ≥ 20 mmHg in 6 hours)	XGM model showed highest AUROC (0.828 [0.796–0.861]); all ML models outperformed disease severity scores	Machine learning outperforms disease severity scores in predicting hypotension during CRRT. External validation studies needed for reliability, cross-validation for generalizability; clinical validation studies needed for clinical effectiveness; studies on data management and implementation barriers.
Chen et al. 2021 China	Design, Validation	Testing: All ICU patients 18+ years receiving RCA for CRRT, obtained from the institutional ICU database. 312 patients for database 1, 81 for database 2; Validation: 314 patients from the MIMIC III database.	Comparing ML algorithms (Adaboost, XGBoost, SVM, SNN) to predict post-filter ionized calcium levels as a measure of citrate overdose. Clinical parameters used included patient age, gender, citrate dose, sodium bicarbonate solvent, replacement fluid solvent, body temperature, and replacement fluid pH.	SNN showed highest F1 score for classifying patients based on ionized calcium levels (90.8%, AUROC 0.86) in the training cohort, Adaboost in the validation cohort (80.5%, AUC 0.81)	Variation In ML-based models’ performances in predicting post-filter ionized calcium. More validation studies needed to ascertain accuracy and reliability of various algorithms; direct comparison of more algorithms and potentially meta-analysis to identify those with consistently higher performance; characterization of the data management process needed to maintain data curation and processing; identification of implementation barriers and validation of quality improvement metrics for CRRT anticoagulation.
Pattharanitima et al. 2021 United States	Design, Validation	684 ICU patients 18+ years with AKI requiring CRRT from MIMIC-III database	Comparing ML models (LR, SVM, AdaBoost, XGB, MLP, MLP+LSTM) for RRTFS prediction in CRRT (alive, not on RRT ≥7 days prior to discharge)	30% had RRTFS; MLP+LSTM model showed the highest AUROC (0.70 [0.67–0.73]).	Deep learning outperforms traditional machine learning for prognostication of RRT-free survival. Internal, external, and cross-validation studies needed for accuracy and reliability ascertainment; clinical validation studies needed to ascertain effectiveness; data curation and maintenance protocols, with gap analysis for performance optimization; studies on identifying implementation barriers.
Yoo et al. 2021 South Korea (Conference abstract)	Design, Validation	784 AKI patients on CRRT (data source unknown)	ML (decision tree) for prediction of early (<60days) vs late (>60 days) mortality in patients with severe AKI undergoing CRRT based on changes in fluid balance. Body Composition Monitoring (BCM) was used as an indicator of sequential changes in total body water and validated against body weight at baseline. Other clinical parameters were included in the prediction models (not listed).	No difference in BCM vs body weight assessment of volume status at baseline; BCM showed marginal benefit from fluid balance in survivor group (p=0.074). In addition to BCM, decision tree model showed platelet count predicts mortality > 60 days; SOFA score, serum sodium, bilirubin, and target clearance for mortality <60 days (AUC = 0.957).	Machine learning highlights the interplay of clinical variables in predicting mortality for CRRT patients. Internal, external, and cross-validation studies needed for accuracy and reliability ascertainment of the prediction model; clinical validation studies needed to ascertain effectiveness of the prediction model; further studies needed on the reliability and clinical utility of BCM.

AKI = Acute Kidney Injury; ANN = artificial neural network; AUC = Area Under receiver operating characteristics Curve; CKD = Chronic Kidney Disease; CRRT = Continuous Renal Replacement Therapy; CVVHD = Continuous Veno-Venous Hemodialysis; DNN = Deep Neural Network; ECMO = Extra-Corporeal Membrane Oxygenation; EHR = Electronic Health Record; APACHE II = Acute Physiology and Chronic Health Evaluation II; ESRD = End Stage Renal Disease; ICU = Intensive Care Unit; KNN = K-nearest neighbor; SVM = support vector machine; LGBM = Light Gradient Boosting Machine; LR = Logistic Regression; LSTM = Long Short-Term Memory; MARS = multivariate adaptive regression splines; MIMIC III = Multiparameter Intelligent Monitoring in Intensive Care III; ML = Machine Learning; MLP = Multilayer Perception; MOSAIC = Mortality Scoring system for AKI with CRRT; MTL = Multitask Learning; NN = Neural Network; PUMCH = Peking Union Medical College Hospital; RCA = Regional Citrate Anticoagulation; RF = random forest; XGB = extreme gradient boost; RRTFS = RRT-Free Survival; SB = Shared Bottom (multitask model with shared parameters); SCUF = Slow Continuous Ultrafiltration; SeqSNR = Sequential Subnetwork Routing; SNN = Shallow Neural Network; SOFA = Sequential Organ Failure Assessment; ST = Single-Task (Recurrent Neural Network); UF = Ultrafiltrate; RBF = Renal Blood Flow.