Smartband Use During Enhanced Recovery After Surgery Facilitates Inpatient Recuperation Following Minimally Invasive Colorectal Surgery

Application of the quality of recovery-40 questionnaire to evaluate the effectiveness of enhanced recovery after surgery protocols in gastric cancer

Patient reported outcomes is currently considered to be an important supplement to evaluate the effectiveness of enhanced recovery after surgery (ERAS) clinical practice. The Quality of Recovery-40 Questionnaire (QoR-40) is one of the most frequently used and validation tool to assess the subjective feelings of quality of life after surgery. The present study aimed to use the QoR-40 to evaluate the effectiveness of ERAS protocols in gastric cancer from the perspective of patient-reported quality of recovery. The study was designed as a prospective, non-randomized clinical trial, conducted in a single center. Patients in our hospital who were scheduled to undergo radical surgery for gastric cancer were divided into ERAS group and control group (Contr group). The QoR-40 were administered one day before surgery (Baseline) and on postoperative day 1, 3, 6, and 30. The difference in QoR-40 scores between the ERAS and Contr groups was compared by repeated-measures ANOVA. A total of 200 patients completed the study, including 100 patients in the ERAS group and 100 patients in the Contr group. The Baseline time point QoR-40 scores of the ERAS and Contr groups were 179.68 ± 14.46 and 180.12 ± 17.12, respectively, and no significant difference was noted between the two groups (p = 0.845). The postoperative QoR-40 score of the ERAS group was significantly higher than that of the Contr group, and the difference was statistically significant (p = 0.006). This study demonstrated that, in terms of patient-reported quality of recovery, the postoperative recovery effect of ERAS protocols in gastric cancer is significantly better than that of the traditional treatment model.

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Purpose

A large portion of the adult population is thought to suffer from obstructive sleep apnoea syndrome (OSAS), a sleep-related breathing disorder associated with increased morbidity and mortality. International guidelines include the polysomnography and the cardiorespiratory monitoring (CRM) as diagnostic tools for OSAS, but they are unfit for a large-scale screening, given their invasiveness, high cost and lengthy process of scoring. Current screening methods are based on self-reported questionnaires that suffer from lack of objectivity. On the contrary, commercial smartbands are wearable devices capable of collecting accelerometric and photoplethysmographic data in a user-friendly and objective way. We questioned whether machine-learning (ML) classifiers trained on data collected through these wearable devices would help predict OSAS severity.

Patients and Methods

Each of the patients (n = 78, mean age ± SD: 57.2 ± 12.9 years; 30 females) underwent CRM and concurrently wore a commercial wrist smartband. CRM's traces were scored, and OSAS severity was reported as apnoea hypopnoea index (AHI). We trained three pairs of classifiers to make the following prediction: AHI <5 vs AHI ≥5, AHI <15 vs AHI ≥15, and AHI <30 vs AHI ≥30.

Results

According to the Matthews correlation coefficient (MCC), the proposed algorithms reached an overall good correlation with the ground truth (CRM) for AHI <5 vs AHI ≥5 (MCC: 0.4) and AHI <30 vs AHI ≥30 (MCC: 0.3) classifications. AHI <5 vs AHI ≥5 and AHI <30 vs AHI ≥30 classifiers' sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and diagnostic odds ratio (DOR) are comparable with the STOP-Bang questionnaire, an established OSAS screening tool.

Conclusion

Machine learning algorithms showed an overall good performance. Unlike questionnaires, these are based on objectively collected data. Furthermore, these commercial devices are widely distributed in the general population. The aforementioned advantages of machine-learning algorithms applied to smartbands' data over questionnaires lead to the conclusion that they could serve a population-scale screening for OSAS.