Autonomic function effects on postoperative sleep disorder: a prospective cohort study

Abstract

Background Early non-invasive identification of patients at risk of developing postoperative sleep disorder (PSD), which is common after surgery, is an essential step in reducing surgery stress and an important part of enhanced recovery after surgery. Objective We used smart HRV patches to (1) explore different HRV parameters as potential PSD biomarkers and (2) develop and validate a prognostic model for the early prediction of PSD including change of autonomic function in early postoperative period. Methods This is a prospective cohort study where we assessed autonomic function in a separate sample of 51 patients who underwent DaVinci robotic/laparoscopic radical surgery for gastrointestinal cancer with and without insomnia. Results In this study, 22(43.137%) of 51 patients experienced PSD. Multivariate logistic regression analysis showed that ICU, POD3 nocturnal LF/HF and SD daytime pNN50 were risk predictors of postoperative sleep quality. The risk factor prediction model was established using ICU (P = 0.013, OR = 0.030), 120h SDNN (P = 0.072, OR = 0.954), POD3 daytime LF/HF (P = 0.096, OR = 3.894), POD3 nocturnal LF/HF (P = 0.025, OR = 1.235), POD2 24h LF/HF (P = 0.256, OR = 4.370), and SD daytime pNN50 (P = 0.039, OR = 0.828). The AUC was 0.969. Conclusion Circadian rhythm and activity of ANS was involved in PSD. HRV based on remote measurement technology and long-range monitor have potential as digital biomarkers for helping predict PSD.