Perioperative Risk Assessment of Patients Using the MyRISK Digital Score Completed Before the Preanesthetic Consultation: Prospective Observational Study.

BACKGROUND: The ongoing COVID-19 pandemic has highlighted the potential of digital health solutions to adapt the organization of care in a crisis context. OBJECTIVE: Our aim was to describe the relationship between the MyRISK score, derived from self-reported data collected by a chatbot before the preanesthetic consultation, and the occurrence of postoperative complications. METHODS: This was a single-center prospective observational study that included 401 patients. The 16 items composing the MyRISK score were selected using the Delphi method. An algorithm was used to stratify patients with low (green), intermediate (orange), and high (red) risk. The primary end point concerned postoperative complications occurring in the first 6 months after surgery (composite criterion), collected by telephone and by consulting the electronic medical database. A logistic regression analysis was carried out to identify the explanatory variables associated with the complications. A machine learning model was trained to predict the MyRISK score using a larger data set of 1823 patients classified as green or red to reclassify individuals classified as orange as either modified green or modified red. User satisfaction and usability were assessed. RESULTS: Of the 389 patients analyzed for the primary end point, 16 (4.1%) experienced a postoperative complication. A red score was independently associated with postoperative complications (odds ratio 5.9, 95% CI 1.5-22.3; P=.009). A modified red score was strongly correlated with postoperative complications (odds ratio 21.8, 95% CI 2.8-171.5; P=.003) and predicted postoperative complications with high sensitivity (94%) and high negative predictive value (99%) but with low specificity (49%) and very low positive predictive value (7%; area under the receiver operating characteristic curve=0.71). Patient satisfaction numeric rating scale and system usability scale median scores were 8.0 (IQR 7.0-9.0) out of 10 and 90.0 (IQR 82.5-95.0) out of 100, respectively. CONCLUSIONS: The MyRISK digital perioperative risk score established before the preanesthetic consultation was independently associated with the occurrence of postoperative complications. Its negative predictive strength was increased using a machine learning model to reclassify patients identified as being at intermediate risk. This reliable numerical categorization could be used to objectively refer patients with low risk to teleconsultation.

Assessment of lung ultrasound for early detection of respiratory complications in thoracic surgery.

BACKGROUND AND OBJECTIVES: To assess lung ultrasound for the diagnosis and monitoring of respiratory complications in thoracic surgery. METHODS: Prospective observational study in a University hospital, single institution. Adult patients scheduled for pulmonary resection surgery excluding pneumonectomy. An ultrasound follow-up was performed from the day before the surgery to the third day after surgery with calculation of B-line and lung score (reaeration and loss of aeration scores). Respiratory complications were collected throughout the hospitalization period. RESULTS: Fifty-six patients were included. Eighteen patients presented a respiratory complication (32%), and they presented significantly higher BMI and ASA scores. Patients operated by videothoracoscopy were less at risk of complications. At day 3, a reaeration score ≤ 2 on the ventilated side or ≤ -2 on the operated side, and a B-line score>6 on the operated side were in favor of a complication. CONCLUSION: Lung ultrasound can help in the diagnosis of respiratory complications following pulmonary resection surgery.