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February 12, 2026Medical Sciences0 citationsOpen Access

A Clinical Prediction Score for Intradialytic Hypotension Among Hospitalized Patients with Acute Kidney Injury

PSPiyanet SuwaninPPPattharawin PattharanitimaATAdis Tasanarong

Key Points

  • Identify risk factors for intradialytic hypotension (IDH) and develop a predictive model in hospitalized AKI patients.
  • Conducted a retrospective cohort study on hospitalized patients requiring dialysis due to AKI.
  • Performed univariable and multivariable analyses using generalized estimating equations (GEE).
  • Defined IDH as systolic blood pressure < 90 mmHg during dialysis.
  • Conducted internal validation of the predictive model using 10-fold cross-validation.
  • Calculated model performance using the area under the receiver operating characteristic curve (AUROC).
  • 423 hemodialysis sessions from 85 patients analyzed; median age was 61 years, with 35.9% IDH incidence.
  • Significant predictors included residual urine output <100 mL/day, vasopressor use, prior IDH, and lower pre-dialysis mean arterial pressure.
  • Developed a weighted risk score stratifying patients into low-, intermediate-, and high-risk groups for IDH incidences of 11.6%, 33.9%, and 56.7%.
  • The model demonstrated good discrimination with an AUROC of 0.760 (95% CI, 0.714–0.807).

Abstract

Background: Intradialytic hypotension (IDH) in hospitalized patients with acute kidney injury (AKI) is associated with increased morbidity and mortality. Early identification of high-risk patients may enable preventive strategies. This study aimed to identify risk factors for IDH and develop a prediction model in this setting. Method: We conducted a retrospective cohort study of hospitalized patients with dialysis-requiring AKI who underwent conventional renal replacement therapy (RRT). Univariable and multivariable analyses were performed using generalized estimating equations (GEE) to account for repeated dialysis sessions within patients. IDH was defined as systolic blood pressure < 90 mmHg during dialysis. Although external validation was not performed, internal validation of the predictive model was conducted using 10-fold cross-validation. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC). Result: A total of 423 hemodialysis sessions from 85 patients were analyzed; the median age was 61 years, and the incidence of IDH session was 35.9%. Multivariable GEE analysis identified residual urine output <100 mL/day (OR 1.78, p = 0.007), vasopressor use (OR 3.36, p < 0.001), prior IDH (OR 2.25, p = 0.002), and lower pre-dialysis mean arterial pressure (MAP 80–89 mmHg: OR 2.43, p = 0.002; MAP < 80 mmHg: OR 2.95, p < 0.001) as significant predictors. Serum albumin < 2.5 g/dL was retained in the final model due to its clinical relevance and contribution to model performance despite borderline significance (OR 1.44, p = 0.08). A weighted integer-based risk score was derived directly from the coefficients of the final multivariable GEE model, stratifying patients into low-, intermediate-, and high-risk groups with IDH incidences of 11.6%, 33.9%, and 56.7%. The model demonstrated good discrimination, with an AUROC of 0.760 (95% CI, 0.714–0.807). Conclusions: The predictive score for IDH demonstrated good performance and highlights the importance of raising awareness to guide interventions aimed at improving the outcomes of hospitalized AKI patients requiring conventional RRT.

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Cite This Study

Suwanin et al. (2026) studied this question.

synapsesocial.com/papers/698d6eca5be6419ac0d54ab0https://doi.org/10.3390/medsci14010080
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