Sleep Disordered Breathing in Hypertrophic Cardiomyopathy-Current State and Future Directions

Machine learning-based detection of sleep-disordered breathing in hypertrophic cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is often concomitant with sleep-disordered breathing (SDB), which can cause adverse cardiovascular events. Although an appropriate approach to SDB prevents cardiac remodelling, detection of concomitant SDB in patients with HCM remains suboptimal. Thus, we aimed to develop a machine learning-based discriminant model for SDB in HCM.

METHODS

In the present multicentre study, we consecutively registered patients with HCM and performed nocturnal oximetry. The outcome was a high Oxygen Desaturation Index (ODI), defined as 3% ODI >10, which significantly correlated with the presence of moderate or severe SDB. We randomly divided the whole participants into a training set (80%) and a test set (20%). With data from the training set, we developed a random forest discriminant model for high ODI based on clinical parameters. We tested the ability of the discriminant model on the test set and compared it with a previous logistic regression model for distinguishing SDB in patients with HCM.

RESULTS

Among 369 patients with HCM, 228 (61.8%) had high ODI. In the test set, the area under the receiver operating characteristic curve of the discriminant model was 0.86 (95% CI 0.77 to 0.94). The sensitivity was 0.91 (95% CI 0.79 to 0.98) and specificity was 0.68 (95% CI 0.48 to 0.84). When the test set was divided into low-probability and high-probability groups, the high-probability group had a higher prevalence of high ODI than the low-probability group (82.4% vs 17.4%, OR 20.9 (95% CI 5.3 to 105.8), Fisher's exact test p<0.001). The discriminant model significantly outperformed the previous logistic regression model (DeLong test p=0.03).

CONCLUSIONS

Our study serves as the first to develop a machine learning-based discriminant model for the concomitance of SDB in patients with HCM. The discriminant model may facilitate cost-effective screening tests and treatments for SDB in the population with HCM.

Effect of obstructive sleep apnea on right ventricular ejection fraction in patients with hypertrophic obstructive cardiomyopathy

Background

Obstructive sleep apnea (OSA) is a common disease associated with worse structural and functional impairment of the heart in patients with hypertrophic obstructive cardiomyopathy (HOCM).

Hypothesis

The presence and severity of OSA can decrease the right ventricular ejection fraction (RVEF) in patients with HOCM.

Methods

In total, 151 consecutive patients with a confirmed diagnosis of HOCM at Fuwai Hospital between September 2017 and September 2018 were included. Polysomnography and cardiac magnetic resonance imaging were performed in all patients.

Results

Overall, 84 (55.6%) patients were diagnosed with OSA. The RVEF significantly decreased with the severity of OSA (none, mild, moderate‐severe: 46.1 ± 8.2 vs 42.9 ± 7.5 vs 41.4 ± 7.4, P = .009). The apnea‐hypopnea index (AHI) was significantly high in patients with RVEF<40% among the different OSA groups (mild, moderate:7.7 ± 2.4 vs 9.6 ± 2.9, P = .03; 24.4 ± 9.0 vs 36.3 ± 18.0, P = .01). In the multiple linear regression model, the right ventricular end‐systolic volume (β = −0.28, P < .001), AHI (β = −0.09, P = .02), and oxygen desaturation index (β = −0.11, P = .04) were independently associated with a decrease in RVEF (adjusted R² = 0.347, P < .001). Furthermore, the prevalence of RVEF<40% was high in patients with OSA. Compared with RVEF>40%, RVEF<40% was associated with more symptoms, mainly chest pain, chest distress, NYHA class III or IV, pulmonary hypertension, and moderate or severe mitral regurgitation.

Conclusion

In patients with HOCM, the presence and severity of OSA is independently associated with a lower RVEF. In addition, compared with patients with RVEF>40%, those with RVEF<40% had more symptoms, including chest pain, chest distress, and NYHA class III or IV.