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Background <p>Hemodialysis equipment alarms significantly impact clinical workflow and patient safety. This study aimed to characterize alarm patterns, identify risk factors, and evaluate management effectiveness in hemodialysis facilities.</p> Methods <p>A retrospective cohort analysis was conducted at 1 hemodialysis center from January 2020 to December 2023. Equipment alarm data from hemodialysis stations (Nikkiso, Gambro, and B.Braun) were systematically collected and analyzed. Multivariate regression and machine learning approaches identified risk factors and developed predictive model.</p> Results <p>Among 4231 recorded alarm events over 32&#xa0;months, the monthly alarm rate averaged 132.2 ± 54.4 events. The COVID-19 pandemic period (2020–2021) accounted for 82.2% of alarms, with a significant 78.3% reduction post-pandemic (<i>P</i> = 0.022). B.Braun equipment generated 68.7% of alarms, significantly higher than Gambro (21.2%) and Nikkiso (10.1%) devices (<i>P</i> &lt; 0.001). Engineering analysis revealed B.Braun’s lower pressure thresholds (150 vs. 180&#xa0;mmHg), higher sensor sensitivity (1 vs. 2–5&#xa0;mmHg resolution), and conservative air detection algorithms (0.3 vs. 0.5&#xa0;mL) contributed to increased alarm frequency. Pressure-related alarms predominated (37.4%), while 94.5% occurred during active treatment phases. Vascular access complications contributed to 19.8% of events, with permanent catheters being the primary source (47.3%). Patient-related factors accounted for 83.6% of human factor contributions. Patients with BMI &gt; 30&#xa0;kg/m<sup>2</sup> experienced 28% more alarms (OR 1.28, 95% CI 1.12–1.47, <i>P</i> &lt; 0.001), highlighting the need for individualized alarm strategies. The alarm management protocol achieved a 99.3% immediate resolution rate. Predictive modeling (sensitivity 72.3%, specificity 75.8%, PPV 68.9%, NPV 78.6%) enabled targeted interventions, resulting in a 43% alarm reduction, a 56% false alarm decrease, and 51% fewer treatment interruptions (all <i>P</i> &lt; 0.001).</p> Conclusions <p>Equipment-specific alarm patterns and human factors significantly influence hemodialysis alarm burden. Implementation of predictive analytics and targeted interventions substantially improves alarm management effectiveness and clinical outcomes.</p>

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Hemodialysis equipment malfunction incidence and risk profiling in clinical settings: a retrospective single-center cohort analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月10日

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  • Junjie Wang,
  • Tingting Xu

摘要

Background

Hemodialysis equipment alarms significantly impact clinical workflow and patient safety. This study aimed to characterize alarm patterns, identify risk factors, and evaluate management effectiveness in hemodialysis facilities.

Methods

A retrospective cohort analysis was conducted at 1 hemodialysis center from January 2020 to December 2023. Equipment alarm data from hemodialysis stations (Nikkiso, Gambro, and B.Braun) were systematically collected and analyzed. Multivariate regression and machine learning approaches identified risk factors and developed predictive model.

Results

Among 4231 recorded alarm events over 32 months, the monthly alarm rate averaged 132.2 ± 54.4 events. The COVID-19 pandemic period (2020–2021) accounted for 82.2% of alarms, with a significant 78.3% reduction post-pandemic (P = 0.022). B.Braun equipment generated 68.7% of alarms, significantly higher than Gambro (21.2%) and Nikkiso (10.1%) devices (P < 0.001). Engineering analysis revealed B.Braun’s lower pressure thresholds (150 vs. 180 mmHg), higher sensor sensitivity (1 vs. 2–5 mmHg resolution), and conservative air detection algorithms (0.3 vs. 0.5 mL) contributed to increased alarm frequency. Pressure-related alarms predominated (37.4%), while 94.5% occurred during active treatment phases. Vascular access complications contributed to 19.8% of events, with permanent catheters being the primary source (47.3%). Patient-related factors accounted for 83.6% of human factor contributions. Patients with BMI > 30 kg/m2 experienced 28% more alarms (OR 1.28, 95% CI 1.12–1.47, P < 0.001), highlighting the need for individualized alarm strategies. The alarm management protocol achieved a 99.3% immediate resolution rate. Predictive modeling (sensitivity 72.3%, specificity 75.8%, PPV 68.9%, NPV 78.6%) enabled targeted interventions, resulting in a 43% alarm reduction, a 56% false alarm decrease, and 51% fewer treatment interruptions (all P < 0.001).

Conclusions

Equipment-specific alarm patterns and human factors significantly influence hemodialysis alarm burden. Implementation of predictive analytics and targeted interventions substantially improves alarm management effectiveness and clinical outcomes.