Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review
Abstract Background This systematic review aims to identify predictors of intradialytic hypotension (IDH) in critically ill patients undergoing kidney replacement therapy (KRT) for acute kidney injury (AKI). Methods A comprehensive search of PubMed was conducted from 2002 to April 2024. Studies incl...
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| Format: | Article |
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SpringerOpen
2024-11-01
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| Series: | Intensive Care Medicine Experimental |
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| Online Access: | https://doi.org/10.1186/s40635-024-00695-8 |
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| author | Rafaella Maria C. Lyrio Etienne Macedo Raghavan Murugan Arnaldo A. da Silva Tess M. Calcagno Estevão F. Sampaio Rafael H. Sassi Rogério da Hora Passos |
| author_facet | Rafaella Maria C. Lyrio Etienne Macedo Raghavan Murugan Arnaldo A. da Silva Tess M. Calcagno Estevão F. Sampaio Rafael H. Sassi Rogério da Hora Passos |
| author_sort | Rafaella Maria C. Lyrio |
| collection | DOAJ |
| description | Abstract Background This systematic review aims to identify predictors of intradialytic hypotension (IDH) in critically ill patients undergoing kidney replacement therapy (KRT) for acute kidney injury (AKI). Methods A 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. Results The 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%). Conclusion This 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. |
| format | Article |
| id | doaj-art-2331cd0262744ee8b76a15d4e6b84c9f |
| institution | OA Journals |
| issn | 2197-425X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SpringerOpen |
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| series | Intensive Care Medicine Experimental |
| spelling | doaj-art-2331cd0262744ee8b76a15d4e6b84c9f2025-08-20T02:32:56ZengSpringerOpenIntensive Care Medicine Experimental2197-425X2024-11-0112111110.1186/s40635-024-00695-8Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic reviewRafaella Maria C. Lyrio0Etienne Macedo1Raghavan Murugan2Arnaldo A. da Silva3Tess M. Calcagno4Estevão F. Sampaio5Rafael H. Sassi6Rogério da Hora Passos7Universidade SalvadorDivision of Nephrology, Department of Medicine, University of California San DiegoThe Program for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of MedicineDepartment of Critical Care, Hospital Israelita Albert EinsteinDepartment of Internal Medicine, Cleveland Clinic FoundationDepartment of General Surgery, Hospital Geral Ernesto Simões FilhoDepartment of Hematology, Hospital de Clínicas de Porto AlegreDepartment of Critical Care, Hospital Israelita Albert EinsteinAbstract Background This systematic review aims to identify predictors of intradialytic hypotension (IDH) in critically ill patients undergoing kidney replacement therapy (KRT) for acute kidney injury (AKI). Methods A 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. Results The 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%). Conclusion This 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.https://doi.org/10.1186/s40635-024-00695-8Kidney replacement therapyHypotensionAcute kidney injuryDialysisCritical illness |
| spellingShingle | Rafaella Maria C. Lyrio Etienne Macedo Raghavan Murugan Arnaldo A. da Silva Tess M. Calcagno Estevão F. Sampaio Rafael H. Sassi Rogério da Hora Passos Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review Intensive Care Medicine Experimental Kidney replacement therapy Hypotension Acute kidney injury Dialysis Critical illness |
| title | Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review |
| title_full | Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review |
| title_fullStr | Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review |
| title_full_unstemmed | Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review |
| title_short | Predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy: a systematic review |
| title_sort | predictors of intradialytic hypotension in critically ill patients undergoing kidney replacement therapy a systematic review |
| topic | Kidney replacement therapy Hypotension Acute kidney injury Dialysis Critical illness |
| url | https://doi.org/10.1186/s40635-024-00695-8 |
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