Bianquis Clara, El Husseini Kinan, Razakamanantsoa Léa, Kerfourn Adrien, Fresnel Emeline, Borel Jean-Christian, Cuvelier Antoine, Dupuis Johan, Gagnadoux Frédéric, Morélot-Panzini Capucine, Gonzalez-Bermejo Jesus, Muir Jean-François, Prigent Arnaud, Rabec Claudio, Trzepizur Wojciech, Winck Joao, Murphy Patrick Brian, Patout Maxime
AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Service des Pathologies du Sommeil (Département R3S (Respiration, Réanimation, Réhabilitation, Sommeil)), Paris, France.
Sorbonne Université, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, Paris, France.
ERJ Open Res. 2025 Mar 3;11(2). doi: 10.1183/23120541.00509-2024. eCollection 2025 Mar.
The increasing number of patients requiring home noninvasive ventilation (HNIV) is a challenge for our healthcare system. Telemonitoring may be used to facilitate the management of HNIV patients. We aimed to assess the ability of telemonitoring algorithms to identify patients not adequately ventilated. Our secondary aim was to assess the consequences related to these algorithms, including costs.
11 HNIV experts each provided an algorithm to identify patients with suboptimal ventilation. Each algorithm was tested using real-life data from a cohort of patients over a 90-day period. Inadequate HNIV was defined as the presence of at least one criterion amongst the following: uncontrolled hypoventilation, daily adherence <4 h·day, HNIV-related severe side-effect, or a residual event index >10·h.
100 patients were included in the cohort. According to our criteria, HNIV was considered as inadequate in 66 (66%) patients, without difference between underlying respiratory disease. Telemonitoring algorithms correctly classified patients in 65% (52-66) of cases. They had a global sensitivity of 78% (95% CI 37-95%), a specificity of 40% (95% CI 19-78%), a positive predictive value of 72% (95% CI 65-77%) and a negative predictive value of 45% (95% CI 37-51%). Applying telemonitoring algorithms resulted in median (interquartile range) 127 (84-238) alerts across the study population with a median cost increase of EUR 2064 (952-6262).
Telemonitoring algorithms have poor diagnostic performances in identifying inadequately ventilated patients. They increase workload for healthcare workers and costs.
需要家庭无创通气（HNIV）的患者数量不断增加，这对我们的医疗系统构成了挑战。远程监测可用于促进HNIV患者的管理。我们旨在评估远程监测算法识别通气不足患者的能力。我们的次要目的是评估与这些算法相关的后果，包括成本。
11名HNIV专家每人提供一种识别通气不足患者的算法。每种算法都使用一组患者90天的实际数据进行测试。HNIV不足定义为存在以下至少一项标准：通气不足未得到控制、每日依从性<4小时/天、与HNIV相关的严重副作用或残余事件指数>10小时。
该队列纳入了100名患者。根据我们的标准，66名（66%）患者的HNIV被认为不足，基础呼吸系统疾病之间无差异。远程监测算法在65%（52 - 66）的病例中正确分类了患者。它们的总体敏感性为78%（95%CI 37 - 95%），特异性为40%（95%CI 19 - 78%），阳性预测值为72%（95%CI 65 - 77%），阴性预测值为45%（95%CI 37 - 51%）。应用远程监测算法导致研究人群中中位数（四分位间距）为127（84 - 238）次警报，成本中位数增加2064欧元（952 - 6262）。
远程监测算法在识别通气不足患者方面诊断性能较差。它们增加了医护人员的工作量和成本。
