Characterising dysfunctional breathing seen in post-acute sequelae of SARS-CoV-2 using approximate entropy, 2025, Eschbach, Natelson+

[SNT Gatchaman]

Characterising dysfunctional breathing seen in post-acute sequelae of SARS-CoV-2 using approximate entropy
Erin Eschbach; Benjamin H. Natelson; Donna M. Mancini; Dane B. Cook; Kevin Rurak; Melissa Miranda; Beno W. Oppenheimer; David M. Rapoport; Ankit Parekh

RATIONALE
Dysfunctional breathing (DB) is a commonly identified abnormality in post-acute sequelae of SARS-CoV-2 (PASC) patients undergoing cardiopulmonary exercise testing (CPET), and is potentially a contributor to ongoing symptoms. Currently, this oscillating, irregular breathing pattern is identified by visual observation of CPET data by an experienced interpreter, which is subjective. We hypothesise that approximate entropy (ApEn), a regularity statistic that quantifies the unpredictability of time-series data can reliably distinguish DB from normal breathing states.

METHODS
Breath-by-breath CPET data were obtained for 82 PASC subjects and 25 controls. CPETs were visually analysed for DB prior to analysis. Minute ventilation (V′E), tidal volume (VT ) and breathing frequency (BF) over time data were normalised with 100% considered as the ventilation at anaerobic threshold (AT) and detrended before ApEn was calculated. Analysis was initiated at 25 W and ceased at AT.

RESULTS
The ApEn V′E of PASC subjects with visualised DB was 0.286±0.128 (mean±SD), which was significantly different from control subjects (0.143±0.081) and PASC subjects without visualised DB (0.183±0.104); p<0.05. Receiver operating characteristic curve analysis produced an optimal cut-off value of 0.17 for distinguishing DB, which resulted in a sensitivity of 81% and specificity of 72%. ApEn V T and ApEn BF were similar among all PASC patients despite visually recognised DB, but significantly greater than controls.

CONCLUSIONS
Identifying DB on CPET requires visual recognition, which has limitations. ApEn V′E is an objective metric that can reliably differentiate DB from normal breathing patterns on CPET. This can be a valuable addition to keen visual scrutiny of CPET data.

Link | PDF (ERJ Open Research) [Open Access]

 

[Hutan]

I've only skimmed the methodology and results:

47 PASC subjects were determined to have DB based on visual analysis of their CPET data (57%). The remaining 35 patients did not have visually identified DB.

57% of the PASC people were found to have 'disordered breathing'.

82 PASC subjects completed the study. PASC subjects presenting to the outpatient cardiology clinic from September 2020 to May 2022 were offered referral to the study. 67 subjects were recruited prospectively. 15 subjects were included after completing a CPET for clinical reasons. One patient was excluded as she was unable to complete the CPET.

15 of the 82 PASC people were recruited after completing a CPET - presumably they would be more likely to be included if disordered breathing was suspected.

The symptoms of the PASC subjects don't sound like ME/CFS:

18% of our PASC cohort was hospitalised with varying degrees of severity, but no subject required mechanical ventilation. The predominant symptom complaint for subjects with PASC was dyspnoea (37.8%), followed by chest pain (20.7%), fatigue (19.5%) and palpitations (13.4%). Neurological symptoms, such as brain fog, dizziness and headache were less commonly identified.

People with 'disordered breathing' were more likely to have breathlessness as a symptom and to have been hospitalised, have a higher BMI and have a lower Vo2 (probably less fit).

Of note, five of the 25 healthy controls had a visually identified DB pattern. These subjects were asymptomatic.

A significant proportion of the healthy controls had 'disordered breathing'.