ReviewIntensive care medicine experimental2024
Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review.
Review in Intensive care medicine experimental, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- A Narrative Summary of Strategies to Mitigate Intradialytic Hypotension in Poisoned Patients Requiring Extracorporeal Toxin Elimination.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2026Review
- A Clinical Prediction Score for Intradialytic Hypotension Among Hospitalized Patients with Acute Kidney Injury.Medical sciences (Basel, Switzerland) · 2026Article
- Article
- Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: a SHAP-interpretable approach.Frontiers in medicine · 2026Article
- Predictive performance of central venous oxygen saturation, lactate, base excess, and alactic base excess for intradialytic hypotension.BMC nephrology · 2025Observational
- Reply: the ups and downs of passive leg raising.Intensive care medicine experimental · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis systematic review aims to identify predictors of intradialytic hypotension (IDH) in critically ill patients undergoing kidney replacement therapy (KRT) for acute kidney injury (AKI).
methodsA comprehensive search of PubMed was conducted from 2002 to April 2024. Studies included critically ill adults undergoing KRT for AKI, excluding pediatric patients, non-critically ill individuals, those with chronic kidney disease, and those not undergoing KRT. The primary outcome was identifying predictive tools for hypotensive episodes during KRT sessions.
resultsThe review analyzed data from 8 studies involving 2873 patients. Various machine learning models were assessed for their predictive accuracy. The Extreme Gradient Boosting Machine (XGB) model was the top performer with an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI 0.796-0.861), closely followed by the deep neural network (DNN) with an AUROC of 0.822 (95% CI 0.789-0.856). All machine learning models outperformed other predictors. The SOCRATE score, which includes cardiovascular SOFA score, index capillary refill, and lactate level, had an AUROC of 0.79 (95% CI 0.69-0.89, p < 0.0001). Peripheral perfusion index (PPI) and heart rate variability (HRV) showed AUROCs of 0.721 (95% CI 0.547-0.857) and 0.761 (95% CI 0.59-0.887), respectively. Pulmonary vascular permeability index (PVPI) and mechanical ventilation also demonstrated significant diagnostic performance. A PVPI ≥ 1.6 at the onset of intermittent hemodialysis (IHD) sessions predicted IDH associated with preload dependence with a sensitivity of 91% (95% CI 59-100%) and specificity of 53% (95% CI 42-63%).
conclusionThis systematic review shows how combining predictive models with clinical indicators can forecast IDH in critically ill AKI patients undergoing KRT, with validation in diverse settings needed to improve accuracy and patient care strategies.
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