BMC nephrology · 2026
The value of pleth variability index in predicting hypotension during maintenance hemodialysis: a prospective observational multicenter study (PVi-HD study).
СтатьяМногоцентровое исследованиеНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Li Juan, Xu Qiancheng, Li Fangxia, Yang Tingting, Gao Chaoqing, Li Yang, Zhou Jiajun, Cao Yuhan, Tong Changjun, Lu Weihua, Wu Yonggui
Аннотация
BACKGROUND: Intradialytic hypotension (IDH) is a frequent and serious complication of hemodialysis, and existing prediction tools are inadequate. The Pleth Variability Index (PVi), a non-invasive dynamic parameter that reflects cardiorespiratory interactions and fluid responsiveness, has shown promise in acute care settings, but its value in hemodialysis remains unexplored. We prospectively assessed PVi and its dynamic changes as predictors of IDH in a multicenter maintenance hemodialysis cohort.
METHODS: In this prospective, multicenter observational study, we enrolled 200 patients on chronic hemodialysis. PVi was measured at baseline (PVi baseline) and 60 min into the session (PVi 1 h). We calculated the absolute change (ΔPVi) and percentage change (ΔPVi%). The primary outcome was the predictive performance of these parameters for IDH, assessed using receiver operating characteristic (ROC) curve analysis. Multivariate logistic regression was used to identify independent predictors.
RESULTS: IDH occurred in 61 patients (30.5%). While baseline PVi was similar between groups, PVi 1 h, ΔPVi, and ΔPVi% were all significantly higher in patients who developed IDH (all P < 0.001). Dynamic PVi parameters demonstrated excellent predictive value, with ΔPVi showing the highest area under the ROC curve (AUC) at 0.84 (95% CI: 0.78-0.89). Followed closely by ΔPVi% (AUC 0.83), for which the optimal cutoff of 23.53% yielded a sensitivity of 78.69% and a specificity of 74.82% for predicting IDH. These significantly outperformed traditional predictors, including baseline mean arterial pressure (AUC 0.71) and ultrafiltration volume (AUC 0.68) (all P < 0.001 for comparison). In multivariate logistic regression, both ΔPVi% (OR 1.02, 95% CI 1.01-1.03) and ΔPVi (OR 1.22, 95% CI 1.16-1.28) were identified as robust independent predictors for IDH (both P < 0.001). This association was held in a sensitivity analysis using a more stringent definition of IDH based on symptomatic episodes. Using an optimal cutoff, Cox regression analysis revealed that patients with a ΔPVi% > 23.53% had a 7.15-fold higher risk of developing IDH (HR 7.15, 95% CI 3.86-13.22).
CONCLUSIONS: Dynamic changes in PVi during the first hour of hemodialysis are strong, independent predictors of subsequent IDH. Monitoring PVi changes offers a promising and practical non-invasive tool for early risk stratification.
METHODS: In this prospective, multicenter observational study, we enrolled 200 patients on chronic hemodialysis. PVi was measured at baseline (PVi baseline) and 60 min into the session (PVi 1 h). We calculated the absolute change (ΔPVi) and percentage change (ΔPVi%). The primary outcome was the predictive performance of these parameters for IDH, assessed using receiver operating characteristic (ROC) curve analysis. Multivariate logistic regression was used to identify independent predictors.
RESULTS: IDH occurred in 61 patients (30.5%). While baseline PVi was similar between groups, PVi 1 h, ΔPVi, and ΔPVi% were all significantly higher in patients who developed IDH (all P < 0.001). Dynamic PVi parameters demonstrated excellent predictive value, with ΔPVi showing the highest area under the ROC curve (AUC) at 0.84 (95% CI: 0.78-0.89). Followed closely by ΔPVi% (AUC 0.83), for which the optimal cutoff of 23.53% yielded a sensitivity of 78.69% and a specificity of 74.82% for predicting IDH. These significantly outperformed traditional predictors, including baseline mean arterial pressure (AUC 0.71) and ultrafiltration volume (AUC 0.68) (all P < 0.001 for comparison). In multivariate logistic regression, both ΔPVi% (OR 1.02, 95% CI 1.01-1.03) and ΔPVi (OR 1.22, 95% CI 1.16-1.28) were identified as robust independent predictors for IDH (both P < 0.001). This association was held in a sensitivity analysis using a more stringent definition of IDH based on symptomatic episodes. Using an optimal cutoff, Cox regression analysis revealed that patients with a ΔPVi% > 23.53% had a 7.15-fold higher risk of developing IDH (HR 7.15, 95% CI 3.86-13.22).
CONCLUSIONS: Dynamic changes in PVi during the first hour of hemodialysis are strong, independent predictors of subsequent IDH. Monitoring PVi changes offers a promising and practical non-invasive tool for early risk stratification.