Background And Objective: 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.
Methods: 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.
Results: 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).
Conclusion: Telemonitoring algorithms have poor diagnostic performances in identifying inadequately ventilated patients. They increase workload for healthcare workers and costs.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874130 | PMC |
| http://dx.doi.org/10.1183/23120541.00509-2024 | DOI Listing |
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