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<p>Vital sign monitoring in palliative care could support symptom management and prediction, though its utility at the end-of-life remains critically discussed. This study aims to test a wireless device for continuous vital sign monitoring at the end-of-life. This prospective observational study included adult terminal care patients in the palliative care unit at a tertiary-care hospital (05/2023–03/2024). Continuous monitoring of heart rate (HR in beats/minute, bpm), respiratory rate, and temperature was conducted using VitalPatches (MediBioSense). Patient demographics, medications, and symptoms were further recorded. Linear mixed-effects models were applied for analyses. 30 patients (median age 70, 53.3% female, 90% with cancer) were included. Median patch duration was 88&#xa0;h (IQR 35–153). Symptom load increased until death (<i>p</i> = 0.004), with weakness and impaired vigilance being prominent. From patch start to end, opioid use rose from 80 to 100% (<i>p</i> = 0.010), and benzodiazepine use from 50 to 80% (<i>p</i> = 0.015). All vital signs increased in daily and hourly intervals. Compared to three days prior, the HR increased as death approached: 3.70&#xa0;bpm (-2 days, <i>p</i> &lt; 0.001), 7.64&#xa0;bpm (-1&#xa0;day, <i>p</i> &lt; 0.001), and 12.26&#xa0;bpm (day of death, <i>p</i> &lt; 0.001). Pain correlated with HR (<i>r</i> = 0.32, <i>p</i> &lt; 0.001), and a 5&#xa0;bpm HR increase over the previous 24&#xa0;h was associated with imminent death within the following 24&#xa0;h. Continuous vital sign monitoring in palliative care using VitalPatches is feasible and depicts vital sign changes at the end-of-life. A positive correlation of HR with pain was detected. These observations provide a rationale for larger studies investigating their relevance for life time prediction as well as symptom detection and management.</p>

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Wireless patches for continuous vital sign monitoring, symptoms and medication at the end-of-life in the palliative care unit: A prospective observational study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月19日

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  • Theresa Tenge,
  • Sebastian Reekers,
  • Oliver Maier,
  • Manuela Schallenburger,
  • Yann-Nicolas Batzler,
  • Marc Stefaniak,
  • Jacqueline Schwartz,
  • Alexandra Stroda,
  • René M´Pembele,
  • Sebastian Roth,
  • Bahne H. Bahners,
  • Guanqing Chen,
  • Maximilian S. Schaefer,
  • Christian Jung,
  • Martin Neukirchen

摘要

Vital sign monitoring in palliative care could support symptom management and prediction, though its utility at the end-of-life remains critically discussed. This study aims to test a wireless device for continuous vital sign monitoring at the end-of-life. This prospective observational study included adult terminal care patients in the palliative care unit at a tertiary-care hospital (05/2023–03/2024). Continuous monitoring of heart rate (HR in beats/minute, bpm), respiratory rate, and temperature was conducted using VitalPatches (MediBioSense). Patient demographics, medications, and symptoms were further recorded. Linear mixed-effects models were applied for analyses. 30 patients (median age 70, 53.3% female, 90% with cancer) were included. Median patch duration was 88 h (IQR 35–153). Symptom load increased until death (p = 0.004), with weakness and impaired vigilance being prominent. From patch start to end, opioid use rose from 80 to 100% (p = 0.010), and benzodiazepine use from 50 to 80% (p = 0.015). All vital signs increased in daily and hourly intervals. Compared to three days prior, the HR increased as death approached: 3.70 bpm (-2 days, p < 0.001), 7.64 bpm (-1 day, p < 0.001), and 12.26 bpm (day of death, p < 0.001). Pain correlated with HR (r = 0.32, p < 0.001), and a 5 bpm HR increase over the previous 24 h was associated with imminent death within the following 24 h. Continuous vital sign monitoring in palliative care using VitalPatches is feasible and depicts vital sign changes at the end-of-life. A positive correlation of HR with pain was detected. These observations provide a rationale for larger studies investigating their relevance for life time prediction as well as symptom detection and management.