# Can Telemonitoring Predict Patient-reported Leak Perception in Long Term Home Non-invasive Ventilation for Patients with Chronic Respiratory Failure?

Messaoud Lefouili, Dany Jaffuel, Sandra Journet, Pascal Chauderon, Sébastien Bailly, Jean-Christien Borel

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Messaoud Lefouili, Dany Jaffuel, Sandra Journet, Pascal Chauderon, Sébastien Bailly, et al. Can Telemonitoring Predict Patient-reported Leak Perception in Long Term Home Non-invasive Ventilation for Patients with Chronic Respiratory Failure?. Chest, In press, 10.1016/j.chest.2025.11.050. hal-05427885

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# Can Telemonitoring Predict Patient-reported Leak Perception in Long Term Home Non-invasive Ventilation for Patients with Chronic Respiratory Failure?

Messaoud Lefouili<sup>1</sup>, Dany Jaffuel<sup>2</sup>, Sandra Journet<sup>3</sup>, Pascal Chauderon<sup>3</sup>, Sébastien Bailly<sup>4</sup>, Jean-Christien Borel<sup>5</sup>

<sup>1</sup> HP2 laboratory, INSERM U1300, University Grenoble Alpes, Grenoble, 38000, France; AGIR à dom. Homecare charity, Meylan, 38240, France.

<sup>2</sup> Department of Respiratory Disease, PhyMedExp, University of Montpellier, INSERM U1046, CNRS UMR 9214, CHU Montpellier, Montpellier, France.

<sup>3</sup> AGIR à dom. Homecare charity, Meylan, 38240, France.

<sup>4</sup> HP2 laboratory, INSERM U1300, University Grenoble Alpes, Grenoble, 38000, France.

<sup>5</sup> Centre de Pneumologie Henri Bazire, la Sure en Chartreuse, Isère, France.

## Keywords:

Noninvasive ventilation; Patient reported outcome measures; Respiratory failure; S3-NIV; telemonitoring; unintentional leaks.
# Abstract

**Background:**

Home non-invasive ventilation (NIV) improves outcomes in patients with chronic respiratory failure; however, telemonitoring remains challenging due to undefined appropriate alert thresholds. Current leak thresholds lack clinical validation, potentially leading to inefficient care delivery and alert fatigue among healthcare providers.

**Research question:**

Can telemonitoring leak data predict patient-reported perceptions of leaks in home NIV for patients with chronic respiratory failure?

**Study design and methods:**

Data collected between March 2019 and May 2023 from 571 patients with chronic respiratory failure receiving home NIV were analyzed retrospectively. Inclusion required at least one S3-NIV questionnaire and 28 days of preceding telemonitoring leak data. Twenty-seven leak features were extracted across 7, 14, 21, and 28-day windows. Statistical analysis included ANOVA tests, effect size calculation (Eta-squared), and comparison of predictive performance using Linear Mixed-effects regression and K-nearest neighbors algorithms.

**Results:**

Only S3-NIV Item 8 ("I am disturbed by leaks") showed significant associations with leak features. While no demographic or NIV setting differences were found between patients reporting leak disturbances and those who did not, disturbed patients consistently reported more NIV-related issues and respiratory symptoms. Leak features reflecting central tendency and magnitude showed the highest effect sizes ($\eta^2$ = 0.021-0.075). However, predictive models demonstrated limited ability to accurately predict patient-perceived leaks.

**Interpretation:**

Telemonitoring leak data alone has limitations in predicting patient perception of leaks, suggesting that individual factors beyond objective measurements play a significant role. A proactive patient-centered management approach integrating telemonitoring data and patient-reported outcomes is thus needed.

**Clinical trial registration:**

Registered on Health Data Hub under number N° 23517022; URL: www.health-data-hub.fr (registration details available upon request).
## Take-Home Points

**Research Question:** Can objective leak data from telemonitoring systems accurately predict self-reported leak discomfort for patients with chronic respiratory failure and who are using home NIV?

**Results:** Measured leaks from telemonitoring showed a weak statistical association with patient-reported disturbances and failed to reliably predict patient perception of leaks.

**Interpretation:** Objective leak data alone are insufficient to guide clinical decisions on leaks; effective management requires clinicians to integrate telemonitoring data with patient-reported outcomes.

Home noninvasive ventilation (NIV) is the most frequently used treatment for patients with chronic hypercapnic respiratory failure, 1 with established benefits in improving quality of life, reducing morbidity, and lowering mortality across various respiratory conditions. 2,3 Despite these benefits, continuous optimization of NIV delivery remains essential to maximize its efficacy. 4, the development of information and telecommunication technologies has led to the emergence of telemonitoring as a strategy for enhancing follow-up for patients receiving home NIV therapy. 5,6 Telemonitoring systems can generate alerts based on multiple parameters, including externally measured parameters such as oxygen saturation (SpO<sub>2</sub>), or NIV-derived parameters such as daily NIV usage, residual apnea-hypopnea index, respiratory rate, and leaks. 7 Although some studies suggest telemonitoring may improve PaCO<sub>2</sub> and treatment compliance 8 and reduce hospitalizations, 7-10 successful implementation faces challenges that limit widespread adoption.

11,12

Since 2010

A critical challenge in current telemonitoring systems is the difficulty in determining appropriate thresholds for generating clinical alerts. For example, alerts can be generated when the average NIV use is less than 4 h/d, unintentional leaks are greater than 24 L/min (for ResMed devices), or the apnea-hypopnea index is greater than or equal to 10 events/h. Poorly configured alert systems can overwhelm health care providers with irrelevant notifications, potentially leading to alert fatigue and overuse of medical services, plus increased health care costs. 12,13

Among the various NIV monitored parameters, unintentional leaks represent an important element. Significant leaks are associated with reduced ventilation quality, patient discomfort, and potentially compromised treatment effectiveness. 14,15 However, the leak thresholds currently used by device manufacturers lack clinical validation. These thresholds may fail to correlate with meaningful clinical outcomes or patient experiences. 4 As a result, they do not accurately reflect when intervention is truly necessary, leading to delayed care or unnecessary clinical actions. 12

Patient-reported outcome measures can provide valuable insights into treatment experiences to address this gap between objective measurements and clinical relevance. The S3-NIV questionnaire is a validated patient-reported outcome measure developed to assess patients receiving home
ventilation. It comprises 11 items that evaluate two primary dimensions: “respiratory symptoms” and “sleep and NIV-related side effects.” 16

The objective of this study was to evaluate the relationship between objectively measured leaks obtained through telemonitoring systems and patient-reported perceptions assessed through the S3-NIV score in people with chronic respiratory failure receiving home NIV therapy.

# Study Design and Methods

## Study Design and Participant Selection

This research is a retrospective analysis of data from people with chronic respiratory failure who received home-based nocturnal noninvasive ventilation (NIV) between March 2019 and May 2023. This period corresponds to the time from integration of S3-NIV collection into routine follow-up to the date of data extraction. Participants were identified through the home-care provider database, AGIR à dom, which delivers NIV treatment to more than 3,000 people across the southeast of France (e-Fig 1).

The inclusion criteria for this study were as follows: adults (> 18 years) who had completed at least one S3-NIV questionnaire during a follow-up visit at home, with available telemonitoring data on unintentional leaks for a period of 28 days preceding each questionnaire.

The exclusion criteria were as follows: (1) NIV prescription for more than 12 h/d at the start of the study, to exclude patients requiring diurnal ventilation; (2) use of the volumetric mode, due to its limited representation in the cohort; (3) use of the adaptive servoventilation mode, typically indicated for central or mixed sleep apnea syndromes; (4) participants without consent for personal data use; and (5) patients who were not using a ResMed NIV device, to consider only patients with the main device in use and to obtain homogeneous measurements of unintentional leaks.

NIV or death of the patient. During these visits, data were updated, and a S3-NIV questionnaire was systematically self-reported by the patient.

## Data Processing

The S3-NIV questionnaire evaluates two dimensions: “respiratory symptoms” and “sleep and NIV-related side effects.” It comprises 11 items, scored on a 5point Likert scale. The S3-NIV has good test-retest reliability, with an intraclass correlation coefficient of 0.72 after 4 weeks in the original validation study, 16 and an intraclass correlation coefficient of 0.89 after 1 week in a Dutch validation study. 17 Because of unbalanced response proportions, we recoded the scale into a binary variable: responses of “mostly true” or “always true” indicate reporting a problem, whereas other responses (“sometimes true,” “mostly untrue,” “completely untrue”) indicate not reporting a problem. Although all items were analyzed, item 8 (“I am disturbed by leaks”) was of particular interest for this study because it addresses patient-reported disturbance due to leaks.

Each S3-NIV questionnaire was considered based on the availability of telemonitoring data from the 28 days preceding it. Questionnaires were excluded if the daily 95th percentile of leaks was missing during that period, due to technical issues or the patient not using NIV. In such cases, only the questionnaire was excluded; the patient remained included if other complete telemonitoring data periods before valid questionnaires were available.

The study protocol adhered to the European General Data Protection Regulation and followed the reference methodology (MR004) established by the French Data Protection Authority. It was
submitted to the Health Data Hub. All potential participants received an information letter with the possibility to opt out. This study is reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. ---

## Data Collection

Data were obtained from the homecare provider database. Collected variables included age, sex, anthropometric measurements, type of respiratory condition, time elapsed since NIV initiation, NIV settings, use of supplemental oxygen therapy, daily adherence and daily unintentional leaks (recorded by the built-in software of the NIV device for patients under telemonitoring), type of interface, and S3-NIV scores. --

In France, homecare providers must conduct follow-up visits every 3 to 4 months until either discontinuation of Telemonitoring data were transmitted daily from ResMed devices to home-care provider servers and stored in the patient follow-up database. From this data set, we retrospectively extracted and focused on daily 95th percentile leak values. Twenty-seven features were extracted from these daily leak time series over multiple time windows (7, 14, 21, and 28 days) preceding each questionnaire completion, to capture shortterm and longer-term trends, allowing us to assess which monitoring period has a greater impact on patient perceptions (see e-Appendix 1, e-Table 2.1, in the online article). The extracted features include conventional summary statistics 18 and some signal-processing descriptors defined in established time-series feature libraries. 19 They quantify distinct aspects of leak behavior including the central tendency and magnitude of leaks, variability and dispersion, the temporal dynamics and complexity of the signal, zero-leak episodes, extreme leak events, anomalies, and the most recent leak behavior. Detailed descriptions and calculation methods for all features are provided in the online article (e-Appendix 1, e-Table 2.2 and e-Fig 2). ---

## Statistical Analysis

Multiple one-way analysis of variance (ANOVA) tests were conducted to identify a priori the most associated item among the 11 S3-NIV questionnaire items with leak features calculated over a 28-day time window. Bonferroni multiple testing correction was applied to adjust P values. Item 8 ("I am disturbed by leaks"), were which directly addresses the patient experience with leaks, was considered an item of particular interest. Consequently, we analyzed baseline characteristics of the two subgroups defined by responses to item 8 of the S3-NIV questionnaire at the first visit —those who reported disturbances due to leaks and those who did not. Quantitative variables were summarized as medians and interquartile ranges, and categorical variables as frequencies and percentages. Comparisons between subgroups were performed using the t-test for continuous variables and the χ2 test for categorical variables. --

Next, the effect size (measured as eta squared [η²], which quantifies the proportion of variance in the dependent variable explained by the independent variable) of item 8 responses was calculated for the same 28 features across four time windows (7, 14, 21, and 28 days). η² ranges from 0 to 1; higher values indicate greater explanatory power. Its values were interpreted as small ($0.01$), moderate ($0.06$), or large ($0.14$) effect sizes. 20 ANOVA tests with Bonferroni correction were performed for each time window. A heatmap was then created to show effect sizes, with significant values highlighted. --
For the following analysis, features not significantly associated with responses to item 8 across all time windows were excluded. In addition, to avoid collinearity, when features showed high correlation (absolute Pearson correlation $ 0.8), only the feature with the highest effect size was retained. --

A generalized linear mixed-effects model with a binary outcome and a *k*-nearest neighbors (KNN) algorithm ing patients both applied to classify participants based on the features extracted from device leak data. Both models predicted the binary outcome defined by the recoded item 8 of the S3-NIV questionnaire. Patient answers served as the reference standard. The mixed effects model accounted for repeated measurements, and the KNN algorithm captured potential nonlinear associations. Both models used selected leak features as predictors and were tested across different time windows. Variable selection was applied to reduce multicollinearity, and the final features showed low intercorrelation. Performance metrics, including precision, recall, and F1 score, were used to evaluate model accuracy. The F1 score combines precision (correctly identifying positive cases) and recall (capturing all positive cases). It measures how well the model balances these to predict positive outcomes while limiting false alarms. The specific calculation methods for precision, recall, and F1 score are provided in e-Appendix 1, eTable 3. -----

The ResMed leak threshold (24 L/min) was used as a comparison baseline to assess performance in predict experiencing disturbance due to leaks. 21,22

For each time window, the threshold was applied by labeling the window as positive if at least one daily 95th percentile leak value within the time series exceeded 24 L/min. -

Statistical computations were performed with Python, version 3.10. P values < .05 were considered significant. Figure 1 illustrates the methodologic workflow of this study.

<figure><img src="image_2.png" /></figure>

Figure 1 – Summary diagram of the method. Summary diagram of the methodologic workflow used to evaluate the relationship between noninvasive ventilation (NIV)-recorded leak data and patient-reported leak disturbance. The AGIR à dom database provided S3-NIV questionnaires collected during home visits and telemonitored leak time series from 571 participants receiving long-term home NIV; S3-NIV responses were binarily recoded with a focus on item 8 ("I am disturbed by leaks"); 27 leak features were extracted over 7-, 14-, 21-, and 28-day windows; and these data sets were combined for analysis of variance-based association analyses with effect size estimation for all S3-NIV items across time windows and for prediction models of item 8 compared with the ResMed 24-L/min leak-threshold baseline. ANOVA = analysis of variance.
<figure><img src="image_3.png" /><figcaption>Figure 2 - Flowchart of participants include the study. NIV = noninvasive ventilation.</figcaption></figure>

<table><caption>TABLE 1] Patient Characteristics</caption><thead><tr><th>Characteristic</th><th>Study Population (571)<sup>a</sup></th><th>Participants Who Reported Concerns About Leaks (First Questionnaire) [171<sup>a</sup> (29.9%)]</th><th>Participants Who Did Not Report Concerns About Leaks (First Questionnaire) [400<sup>a</sup> (70.1%)]</th><th>P Value</th></tr></thead><tbody><tr><td><strong>Demographic</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Sex, male</td><td>291 (51.1%)</td><td>87 (50.9%)</td><td>204 (51.0%)</td><td>1</td></tr><tr><td>Age, y</td><td>69.6 (61.7, 76.1)</td><td>69.2 (61.9, 75.8)</td><td>69.7 (61.7, 76.5)</td><td>.74</td></tr><tr><td>BMI, kg/m<sup>2</sup></td><td>33.2 (27.5, 39.1)</td><td>33.3 (28.5, 40.0)</td><td>33.2 (26.6, 39.1)</td><td>.18</td></tr><tr><td><strong>Type of respiratory condition</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>OHS</td><td>242 (42.5%)</td><td>79 (46.5%)</td><td>163 (40.9%)</td><td>.67</td></tr><tr><td>COPD</td><td>183 (32.2%)</td><td>52 (30.6%)</td><td>131 (32.8%)</td><td></td></tr><tr><td>Restrictive</td><td>55 (9.7%)</td><td>13 (7.6%)</td><td>42 (10.5%)</td><td></td></tr><tr><td>Neuromuscular</td><td>34 (6.0%)</td><td>9 (5.3%)</td><td>25 (6.3%)</td><td></td></tr><tr><td>Other</td><td>55 (9.7%)</td><td>17 (10.0%)</td><td>38 (9.5%)</td><td></td></tr><tr><td><strong>NIV settings and adherence</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Interface</td><td></td><td></td><td></td><td>.42</td></tr><tr><td>Face mask</td><td>383 (67.3%)</td><td>119 (70.0%)</td><td>264 (66.2%)</td><td></td></tr><tr><td>Nasal mask or nasal pillows</td><td>186 (32.7%)</td><td>51 (29.8%)</td><td>135 (33.8%)</td><td></td></tr><tr><td>Pressure support, cm H<sub>2</sub>O</td><td>9.2 (8.0, 11.0)</td><td>10.0 (8.0, 12.0)</td><td>9.0 (8.0, 11.0)</td><td>.11</td></tr><tr><td>Positive expiratory pressure, cm H<sub>2</sub>O</td><td>7.0 (6.0, 9.0)</td><td>7.0 (6.0, 9.0)</td><td>7.2 (6.0, 9.0)</td><td>.89</td></tr><tr><td>Back-up rate, cpm</td><td>14.0 (12.0, 15.8)</td><td>14.0 (12.0, 16.0)</td><td>14.0 (12.0, 15.0)</td><td>.63</td></tr><tr><td>Adherence, min</td><td>410 (388, 539)</td><td>453 (374, 534)</td><td>474 (403, 544)</td><td>.195</td></tr><tr><td><strong>Other treatment characteristics</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Supplemental oxygen, yes</td><td>251 (44.1%)</td><td>83 (48.8%)</td><td>168 (42.1%)</td><td>.16</td></tr><tr><td>Time since NIV initiation, mo</td><td>47 (19, 95)</td><td>48 (14, 99)</td><td>46 (20, 94)</td><td>.72</td></tr><tr><td>95th percentile leak</td><td>20.2 (7.3, 37.3)</td><td>27.1 (12.1, 45.0)</td><td>18.9 (5.6, 33.2)</td><td>&lt;.01</td></tr></tbody></table>

Data are presented as No. (%) unless otherwise indicated. Statistics: Frequency (%) if categorical characteristic, median (Q1, Q3) if continuous characteristic. cpm = cycles per minute; NIV = noninvasive ventilation; OHS = obesity hypoventilation syndrome.

<footnote>*Only data from the first questionnaire per participant were included in this analysis.</footnote>
# Results

## Participants

The study flow chart is presented in Figure 2. Most patients had been using NIV for longer than 1 year (1st quartile of time since NIV initiation, 19 months). Table 1 summarizes the baseline characteristics of the study population (n = 571). The population had a median age of 69.6 years (first quartile, third quartile: 61.7, 76.1) and a balanced sex distribution (51.1% male).

The most common underlying respiratory condition was obesity hypoventilation syndrome (42.5%), followed by COPD (32.2%). All patients used a single limb passive leak circuit. After evaluating multiple extracted leak features across all S3-NIV questionnaire items, only item 8 showed significant associations (e-Appendix 2, e-Table 4.2). Table 1 compares participants who reported concerns about leaks in the first completed questionnaire (n = 171; ie, 30%) with those who did not (n = 400; ie, 70%). No significant differences were observed between the two groups concerning demographic variables (sex, age, BMI), types of respiratory conditions, adherence and NIV settings (type of interface used, pressure support, positive expiratory pressure, backup rate), supplemental oxygen use, and duration since NIV initiation, as indicated by the corresponding *P* values (all *P* > .05).

## S3-NIV Item 8 Responses

Our study included 571 participants who answered 1,848 S3-NIV questionnaires. Included patients had a median of three (first quartile, third quartile: 2, 5) completed S3-NIV questionnaires. The median duration between two visits for a patient was 4 months. A total of 510 questionnaires (27.6%) were classified as indicating that patients experienced disturbances due to leaks, reported by 301 patients (52.7%) (who experienced disturbances at least once during the study period).

Figure 3 illustrates the percentage of patients who reported “mostly true” or “always true” on the 10 items of the S3-NIV questionnaire comparing those who reported frequent leak disturbances (item 8: mostly/always true) with those who did not (item 8: sometimes true, mostly/completely untrue). Patients experiencing significant leak disturbances consistently reported higher percentages of problems across all other S3-NIV items. Among the items composing the respiratory symptoms subscore (items 1, 4, 5, 6, 7), item 4 (shortness of breath) showed the largest difference between groups: 58.2% of patients with serious concerns about leaks had shortness of breath, whereas 42.2% of patients who reported concerns less about leaks had shortness of breath. In the “sleep and NIV-related side effects” subscore (items 2, 3, 8, 9, 10, 11), the most pronounced difference appeared in item 9 (mask discomfort: 40.0% vs 9.6%), followed by item 11 (nasal/oral dryness: 34.9% vs 18.2%). Multiple χ2 tests examining the association between each recoded item and the recoded leak-disturbance item demonstrated significance for all items after Bonferroni correction (e-Appendix 2, e-Table 4.2).
<figure><img src="image_4.png" /><figcaption>Figure 3 - Percentage of negative responses to S3-NIV items based on reported leak disturbance. Radar plot displaying the percentage of negative responses (mostly/always true) to S3-NIV questionnaire items, stratified by leak disturbance reported by patients. Orange line: patients who are mostly or always disturbed by leaks. Blue line: patients who are only sometimes disturbed by leaks or for whom disturbance by leaks is mostly or completely untrue.</figcaption></figure>

## Association of Leaks With Item 8

Figure 4 summarizes the ANOVA results assessing the association between leak-related features, extracted across different time windows, and responses to item 8 of the S3-NIV questionnaire. The heatmap displays effect sizes for each test, with significant associations marked by asterisks (Bonferroni-corrected for multiple comparisons across all features and time windows;

Features related to central tendency and magnitude demonstrated the highest effect size on responses to item 8 and statistically significant associations across all time windows. η² values ranged from 0.021 to 0.075 (between 2.1% and 7.5% of the variability in item 8 responses explained), corresponding to moderate effect sizes. Among these features, the maximum leak (Max) over a 7-day window showed the highest effect size *P* < .05). (η² = 0.075).
<figure><img src="image_5.png" /><figcaption>Figure 4 – Effect sizes (eta squared, η²) of leak feature associations with S3-NIV item 8 across 7-, 14-, 21-, and 28-day time windows. Effect sizes (η²) measure the proportion of variance in patient-reported leak perception (S3-NIV item 8) explained by different time series features: higher values indicate stronger predictive relationships. The cells display the effect size of the 27 leak-derived features across four time windows (28, 21, 14, and 7 days). For example, the “Max” feature over a 7-day window explains 7.5% of the variance in patient leak perception responses. The heatmap colors mirror these values to allow comparison, with darker blue indicating higher values. Asterisks (*) denote statistically significant differences in feature values across item 8 response levels at α = 0.05. Features are grouped by domain—central tendency and magnitude, most recent behavior, extreme leak events, variability and dispersion, zero leak episodes, temporal dynamics and complexity, and anomalies—with definitions and formulas provided in e-Appendix 1 (e-Table 2.2). AUC = area under the curve; IQR = interquartile range; Max = maximum; Min = minimum.</figcaption></figure>

In addition to leaks on day -1, extreme leak events (count and longest duration above threshold of 24 L/min) and features representing variability or dispersion, except coefficient of variation, exhibited smaller effect sizes yet statistically significant associations across all tested time windows. Features related to zero-leak episodes (zero count, maximum consecutive zeros) were also associated with the responses to item 8 only for longer time windows (21 days or more) but with very small effect sizes.

Conversely, temporal dynamics and complexity features had very low η² values (< 0.01) and did not reach statistical significance.

Figure 5 illustrates the distribution of the maximum of daily 95th percentile leak values recorded over the seven nights preceding the completion of the S3-NIV questionnaire, categorized by patient-reported disturbance levels due to leaks (item 8 of the questionnaire). Although Figure 5 demonstrates a clear increasing trend in leak values as patient-reported
disturbance severity rises, there remains substantial overlap across all categories. Patients who reported being “sometimes,” “mostly,” or “always” disturbed by leaks showed significant overlap in leak magnitude, indicating that leak values alone may not be sufficiently discriminatory to classify patient perception.

<figure><img src="image_6.png" /></figure>

Figure 5 - Maximum 95th percentile leak values over seven nights by patient-reported leak disturbance. Box plots of the maximum of daily 95th percentile leak values were recorded over the seven nights preceding completion of the S3-NIV questionnaire, stratified by patient responses to item 8 ("I am disturbed by leaks") on the 5-point Likert scale (from "completely untrue" to "always true"). The median leak value increased across categories with higher reported disturbance, but the wide interquartile ranges and overlapping whiskers showed that leak values were highly overlapping between groups.

## Predictive Performance of Different Models

Following feature selection, the retained features for prediction were as follows: 75th percentile, range, longest consecutive duration above 24 L/min, interquartile range, minimum, and zero leak episodes (zero count). Figure 6 displays the performances of the *k*-nearest neighbors (KNN) classifier and generalized linear mixed-effects regression models in predicting patient perception of leaks, as assessed by item 8 of the S3-NIV questionnaire, using selected features extracted from time series data of NIV measured leaks across different window sizes (7, 14, 21, and 28 days) preceding the S3-NIV assessment. The F1 score measures the ability of a model to correctly identify positive cases while minimizing false predictions, with higher scores indicating better overall prediction performance. F1 scores for KNN models across all time windows fall below the ResMed reference threshold (24 L/min) performance in predicting perception of leaks (F1 score = 0.46 across all windows), indicating limited predictive ability. Mixed-effects models consistently outperform the KNN classifier across most window sizes and outperform the ResMed threshold with the highest F1 scores observed over the 14-day window. When checking precision and recall, neither type of model achieves higher recall compared with the ResMed threshold; they demonstrate slightly improved precision. Nonetheless, all models fall short of simultaneously achieving precision and recall above the ResMed leak detection threshold.
<figure><img src="image_7.png" /><figcaption>Figure 6 - Model performance for predicting patient-perceived noninvasive ventilation leaks. F1 scores of the k-nearest neighbors (KNN) classifier and generalized linear mixed-effects models for predicting patient-reported disturbance due to leaks (S3-NIV Item 8) are shown, using selected leak features extracted over 7-, 14-, 21-, and 28-day time windows preceding questionnaire completion. The horizontal dashed line represents the F1 score obtained when using the ResMed leak threshold of 24 L/min as a simple rule-based classifier.</figcaption></figure>

Discussion

This study investigated the relationship between objectively measured unintentional leaks derived from noninvasive ventilation telemonitoring data and patient-reported perceptions appreciated through the S3-NIV questionnaire in people with chronic respiratory failure receiving nocturnal home NIV. Our findings indicate that only item 8 (ie, “I am disturbed by leaks”) had significant associations with features of leaks. We did not identify significant differences between patients disturbed by leaks (as per item 8 of the S3-NIV questionnaire) and those who did not in terms of demographic variables, respiratory conditions, NIV adherence, settings, or interface types; but patients disturbed by leaks consistently reported higher rates of other NIV-related issues and respiratory symptoms. Despite the statistical associations between leaks measured by remote monitoring and leaks perception by patients, predictive models demonstrated limited ability to accurately predict perception of leaks. -

Leak-related discomfort is a common concern among patients undergoing home NIV therapy. 23 In this cohort, about one-half of patients experienced leak disturbances at least once during follow-up. Analysis of the association between the objectively measured leaks and the perceptions of patients provides an understanding of how changes in leaks calculated by NIV devices translate into patient discomfort. The absence of demographic characteristics or NIV setting differences between those who reported leak disturbances and those who did not suggests that leak perception is, rather, related to psychological factors or prior NIV experiences more likely to experience negative perception of leaks. 25 Fresnel and colleagues 26 reported that patients with moderate to severe mask-related side effects (including leaks) showed significantly higher daytime CO2 levels, poorer sleep quality, and reduced quality of life compared with those without such issues, despite similar NIV adherence rates.

The modest association found between telemonitoring leak-extracted features and patient-reported disturbances (Fig 4, maximum explained variability = 7.5%) suggests that although objective leak measurements are related to patient experience, they explain only a small portion of patient discomfort. The stronger associations observed for central tendency and magnitude features suggest that sustained or consistently high leaks levels are more likely to
be perceived by patients than intermittent or highly variable leak patterns. Furthermore, overlap in leak values across perception categories may indicate that patient perception of leaks is influenced by factors beyond the features captured by the ventilator software (Fig 5). This overlap in leak values across perception categories explains the limited discrimination of the different prediction models.

The strong association between reported leak disturbances and other symptoms—particularly mask discomfort and nasal/oral dryness—reveals how NIV-related issues are interconnected. The usual explanation is to consider the vicious cycle caused by leaks: poorly fitting mask increases leaks, exacerbating airway dryness and nasal congestion, and causing patient discomfort. However, our results show that only item 8 (specific to perceived leaks) out of the 11 S3-NIV items correlates with leaks measured by NIV; this suggests a patient profile that may have lower tolerance to NIV adverse effects. -

Patients reporting leak disturbances also experienced more respiratory symptoms, including higher rates of shortness of breath. This relationship is potentially bidirectional: leaks may reduce ventilation effectiveness, 24 leading to poorer control of chronic respiratory symptoms, while respiratory distress might worsen leaks through altered breathing patterns or movement. Moreover, dyspneic patients can exhibit greater sleep disturbances and may be

This study has several limitations. First, our study population primarily consisted of patients with established NIV therapy (time since initiation > 19 months for three quartiles of the population) who had telemonitoring implemented and completed the S3NIV questionnaire, which may have introduced selection bias toward those who tolerated treatment sufficiently to continue and were more closely monitored. Moreover, our analysis was restricted to data from a single ventilator manufacturer (ResMed) to ensure homogeneous leak measurement, limiting generalizability to other systems. In addition, the telemonitoring system provided only daily summary statistics without detailed timing information to differentiate sleep and wake usage periods. Clinical interventions during visits, such as mask or setting adjustments, were not captured in the telemonitoring data and thus not analyzed. Missing telemonitoring data led to the exclusion of some questionnaires, potentially skewing the sample toward more adherent patients. We also lack physiological data (e.g., PaCO2 levels) to correlate leak patterns with objective measures of ventilation effectiveness.

## Interpretation

This work reveals the limitation in using telemonitoring data alone to predict how patients perceive leaks. The lack of predictive capabilities highlights the need to adopt a proactive management approach when using telemonitoring data, one that also incorporates patient perceptions (eg, using a web application). Discrepancies between device-leak data and patient-reported experiences, along with links between NIV issues and symptoms, suggest that individual perception—possibly shaped by individual factors—plays a key role. We recommend future studies to clarify how individual profiles (eg, anxiety, depression, coping) influence leak perception and impact.

The authors have reported to CHEST the following: M. L., S. J., and P. C. are employees of AGIR à dom. J.-C. B. receives fees from AGIR à dom for scientific consulting services. D. J. reports personal fees from Lowenstein, Jazz, Bioprojet, Adene, Bastide, GSK, Astra, ALK,
Boehringer Ingelheim, Sanofi, and ResMed, personal fees and nonfinancial support from Sefam and Nomics, and grants and personal fees from Nova

**Funding/Support**

The authors have reported to CHEST that no funding was received for this study.

**Financial/Non-financial**

Disclosures

**Acknowledgments**

**Author contributions**

**Clinical trial registration:**

Clinical trial registration details are available on request.

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