Clinical predictors of the presence of obstructive sleep apnea in patients with hypertrophic cardiomyopathy

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.

Patients with Obstructive Sleep Apnea and Cardiovascular Diseases: What, When, and Why Is Mandibular Advancement Device Treatment Required? A Short Review

Obstructive sleep apnea is a potentially dangerous condition with significant risks of comorbidities if left untreated. It represents a cardiovascular risk factor in the general population, and a higher prevalence is observed in patients already suffering from cardiovascular diseases. The gold standard treatment, continuous positive airway pressure, is not always accepted or tolerated. The mandibular advancement device represents an alternative treatment that we propose to implement in our study. The objective here is to first present a brief review of the topic. Due to poor evidence in the field, we propose a pilot study to evaluate the effect of a mandibular advancement device in patients with cardiovascular disease who are not treated for their sleep pathology in order to improve their therapeutic management.

Interactions Between Body Mass Index and Glomerular Filtration Rate Increase the Identification Ability of Obstructive Sleep Apnea in Patients with Hypertrophic Cardiomyopathy

PURPOSE

Obstructive sleep apnea (OSA) is common in hypertrophic cardiomyopathy (HCM) patients and is related to worse adverse prognosis in HCM patients. However, there are no acknowledged warning characteristics to help to identify OSA in HCM patients.

METHODS

Seventy-one HCM patients and forty-nine hypertensive (HTN) patients as control group underwent polysomnography (PSG) examination at the Second Affiliated Hospital of Nanchang University from January 2015 to December 2019 patients were consecutively enrolled. The characteristics were analyzed and compared between HCM patients with OSA and without OSA.

RESULTS

A total of 37 (52%) HCM patients and 25 (51%) HTN patients were diagnosed with OSA. High body mass index (BMI) (OR = 1.228, 95% CI: 1.032,1.461, P = 0.020) and low estimated glomerular filtration rate (eGFR) (OR = 0.959, 95% CI: 0.931,0.989, P = 0.007) independently correlated with the occurrence of OSA in HCM patients, respectively. Multiplicative interaction was shown between high BMI and low eGFR on the risk of OSA in HCM patients (OR: 6.050, 95% CI: 1.598, 22.905, P = 0.008). The additive interaction analysis further suggested that 70.1% of HCM patients developed OSA due to the additive interaction between BMI and eGFR. The identification ability of OSA in HCM patients was significantly enhanced by using both BMI and eGFR (area under receiver-operating characteristic analysis curve 0.785; P = 0.000038) as compared with BMI (area under curve 0.683, P = 0.008) or eGFR (area under curve 0.700, P = 0.004), respectively.

CONCLUSION

High BMI or low eGFR independently related to the occurrence of OSA in HCM patients, and the multiplicative and additive interactions between BMI and eGFR increased the identification ability of OSA in HCM patients.