Detection of Sleep-Disordered Breathing Using Carbon Nanotube Sensors Predicts Complications After Lung Surgery.

OBJECTIVES

Sleep-disordered breathing significantly affects perioperative outcomes; however, it remains frequently undiagnosed. We aimed to evaluate the utility of a novel carbon nanotube sensor system for detecting postoperative breathing abnormalities and investigated its association with postoperative complications following thoracic surgery.

METHODS

In this prospective study, 86 patients who underwent anatomical lung resection without previously diagnosed obstructive sleep apnoea were monitored using carbon nanotube sensors from the immediate postoperative period through the first postoperative day. Abnormal breathing was defined as an ≥30% reduction in the peak sensor signal from baseline lasting more than 10 s, in accordance with standard hypopnea criteria used in polysomnography. Patient characteristics and complications were compared using Fisher's exact and Mann-Whitney U test. Multivariate logistic regression identified predictors of major complications.

RESULTS

Twenty-three patients (26.7%) exhibited abnormal breathing events (sleep-disordered breathing). This group had a higher proportion of males (87% vs 61.9%, P = .035), had more difficult intubation (42.1% vs 13.5%, P = .018), and more frequently received epidural anaesthesia in addition to general anaesthesia (65.2% vs 36.5%, P = .027). Multivariate analysis identified sleep-disordered breathing as an independent predictor of major complications (Clavien-Dindo grade ≥3; odds ratio 4.41, 95% CI 1.14-13.8, P = .011) and prolonged air leakage (odds ratio 15.6, 95% CI 2.39-102, P = .004).

CONCLUSIONS

The carbon nanotube sensor showed potential for detecting undiagnosed sleep-disordered breathing after thoracic surgery, independently associated with increased risk of major complications, particularly prolonged air leakage.

CLINICAL TRIAL REGISTRATION: UMIN-CTR (https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000035066). Trial number: UMIN000031533.