Time requirements for perioperative glucose management using fully closed-loop versus standard insulin therapy: A proof-of-concept time-motion study.

AIMS: To compare the time required for perioperative glucose management using fully automated closed-loop versus standard insulin therapy. METHODS: We performed a time-motion study to quantify the time requirements for perioperative glucose management with fully closed-loop (FCL) and standard insulin therapy applied to theoretical scenarios. Following an analysis of workflows in different periods of perioperative care in elective surgery patients receiving FCL or standard insulin therapy upon hospital admission (pre- and intra-operatively, at the intermediate care unit and general wards), the time of process-specific tasks was measured by shadowing hospital staff. Each task was measured 20 times and its average duration in combination with its frequency according to guidelines was used to calculate the cumulative staff time required for blood glucose management. Cumulative time was calculated for theoretical scenarios consisting of elective minor and major abdominal surgeries (pancreatic surgery and sleeve gastrectomy, respectively) to account for the different care settings and lengths of stay. RESULTS: The FCL insulin therapy reduced the time required for perioperative glucose management compared to standard insulin therapy, across all assessed care periods and for both perioperative pathways (range 2.1-4.5). For a major abdominal surgery, total time required was 248.5 min using FCL versus 753.9 min using standard insulin therapy. For a minor abdominal surgery, total time required was 68.6 min and 133.2 min for FCL and standard insulin therapy, respectively. CONCLUSIONS: The use of fully automated closed-loop insulin delivery for inpatient glucose management has the potential to alleviate the workload of diabetes management in an environment with adequately trained staff.

Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes.

AIM: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. MATERIALS AND METHODS: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1 ). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1 ). RESULTS: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). CONCLUSIONS: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.

Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data.

OBJECTIVE: To develop a noninvasive hypoglycemia detection approach using smartwatch data. RESEARCH DESIGN AND METHODS: We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data. RESULTS: Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA1c 6.9 ± 0.6%, 16 males). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76 ± 0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia. CONCLUSIONS: Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.

[Perioperative hypnosis: What analgesic impact†?] / Hypnose périopératoire†: quel impact antalgique†?

Perioperative pain is a burden that often is insufficiently addressed. Considering the limitations of pharmacological approaches in this context, hypnosis is a promising technique as part of a multimodal management plan for acute surgical pain. There is a growing interest for hypnosis from patients and the medical community. It can be practiced in the pre- or post-operative setting for acute symptom management (pain and anxiety), as well as per-operatively as a substitute to anesthetic care, or as a complement (hypnosedation). This article aims to clarify these different uses of hypnosis for perioperative analgesia, as well as the benefits that can be expected.

Les douleurs périopératoires restent problématiques et souvent insuffisamment traitées. Au vu des limitations des approches médicamenteuses, l'hypnose est prometteuse comme adjonction à une prise en charge multimodale de la douleur aiguë chirurgicale. En effet, l'hypnose bénéficie d'un intérêt croissant tant des patients que de la communauté médicale. Elle peut se pratiquer en phase pré- ou postopératoire, pour aider à la gestion de symptômes momentanés (douleur et anxiété), mais aussi en peropératoire, soit en remplacement d'une technique anesthésique ou en complément de celle-ci (hypnosédation). Cet article vise à clarifier les différentes utilisations possibles de l'hypnose antalgique en périopératoire, ainsi que les bénéfices qui peuv ent en être attendus.