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External validation of the prediction model of intradialytic hypotension: a multicenter prospective cohort study
Yuhe Xiang1, Guoting Ma2, Qin Yang1
1School of Nursing, Chengdu Medical College, Chengdu, China.
Insights
This study improved intradialytic hypotension (IDH) prediction models for maintenance hemodialysis (MHD) patients. The updated model offers better accuracy for identifying patients at risk of IDH during dialysis.
Area of Science:
- Nephrology
- Clinical Prediction Modeling
- Patient Safety
Background:
- Intradialytic hypotension (IDH) is a frequent and severe complication for patients undergoing maintenance hemodialysis (MHD).
- Existing risk prediction models require external validation and optimization for clinical utility.
- Accurate IDH prediction is crucial for preventing adverse events and improving patient outcomes.
Purpose of the Study:
- To externally validate three existing IDH risk prediction models.
- To recalibrate, update, and present an optimal IDH prediction model.
- To enhance the accuracy and clinical applicability of IDH risk assessment.
Main Methods:
- A multicenter prospective cohort study involving 2235 MHD patients across 11 centers in Sichuan Province, China.
- Model performance was evaluated using discrimination (Area Under Curve - AUC) and calibration (Brier score).
- Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), and Decision Curve Analysis (DCA) assessed model update benefits.
Main Results:
- The updated and recalibrated columnar plot model achieved a superior AUC of 0.817 (95% CI: 0.791 to 0.842), outperforming original models (AUCs ranging from 0.709 to 0.746).
- The updated model demonstrated good calibration with a Brier score of 0.081.
- Decision Curve Analysis indicated a significant net benefit for the updated model within a broad threshold probability range (15.2% to 87.1%).
Conclusions:
- Externally validated, recalibrated, and updated IDH prediction models provide a valuable tool for clinical risk assessment.
- The optimized model can assist clinicians in identifying high-risk individuals.
- This facilitates timely and judicious interventions to mitigate IDH complications in MHD patients.
Objective:
Intradialytic hypotension (IDH) is a common and serious complication in patients with Maintenance Hemodialysis (MHD). The purpose of this study is to externally verify three IDH risk prediction models recently developed by Ma et al. and recalibrate, update and present the optimal model to improve the accuracy and applicability of the model in clinical environment.
Methods:
A multicenter prospective cohort study of patients from 11 hemodialysis centers in Sichuan Province, China, was conducted using convenience sampling from March 2022 to July 2022, with a follow-up period of 1 month. Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of updating the model.
Results:
The final cohort comprised 2235 individuals undergoing maintenance hemodialysis, exhibiting a 14.6% occurrence rate of IDH. The externally validated Area Under the Curve (AUC) values for the three original prediction models were 0.746 (95% CI: 0.718 to 0.775), 0.709 (95% CI: 0.679 to 0.739), and 0.735 (95% CI: 0.706 to 0.764) respectively. Conversely, the AUC value for the recalibrated and updated columnar plot model reached 0.817 (95% CI: 0.791 to 0.842), accompanied by a Brier score of 0.081. Furthermore, Decision Curve Analysis (DCA) exhibited a net benefit within the threshold probability range of 15.2% to 87.1%.
Conclusion:
Externally validated, recalibrated, updated, and presented IDH prediction models may serve as a valuable instrument for evaluating IDH risk in clinical practice. Furthermore, they hold the potential to guide clinical providers in discerning individuals at risk and facilitating judicious clinical intervention decisions.
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