Spinal anesthesia-induced hypotension (SAIH) occurs frequently, particularly in the elderly and in patients undergoing caesarean section. SAIH is caused by arterial and venous vasodilatation resulting from the sympathetic block along with a paradoxical activation of cardioinhibitory receptors. Bradycardia after spinal anesthesia (SA) must always be treated as a warning sign of an important hemodynamic compromise. Fluid preloading (before initiation of the SA) with colloids such as hydroxyethyl starch (HES) effectively reduces the incidence and severity of arterial hypotension, whereas crystalloid preloading is not indicated. Co-loading with crystalloid or colloid is as equally effective to HES preloading, provided that the speed of administration is adequate (ie, bolus over 5 to 10 minutes). Ephedrine has traditionally been considered the vasoconstrictor of choice, especially for use during SAIH associated with bradycardia. Phenylephrine, a α 1 adrenergic receptor agonist, is increasingly used to treat SAIH and its prophylactic administration (ie, immediately after intrathecal injection of local anesthetics) has been shown to decrease the incidence of arterial hypotension. The role of norepinephrine as a possible alternative to phenylephrine seems promising. Other drugs, such as serotonin receptor antagonists (ondansetron), have been shown to limit the blood pressure drop after SA by inhibiting the Bezold-Jarisch reflex (BJR), but further studies are needed before their widespread use can be recommended.
Summary Interscalene brachial plexus block provides analgesia for shoulder surgery but is associated with hemidiaphragmatic paralysis. Before considering a combined suprascapular and axillary nerve block as an alternative to interscalene brachial plexus block, evaluation of the incidence of diaphragmatic dysfunction according to the approach to the suprascapular nerve is necessary. We randomly allocated 84 patients undergoing arthroscopic shoulder surgery to an anterior or a posterior approach to the suprascapular nerve block combined with an axillary nerve block using 10 ml ropivacaine 0.375% for each nerve. The primary outcome was the incidence of hemidiaphragmatic paralysis diagnosed by ultrasound. Secondary outcomes included: characterisation of the hemidiaphragmatic paralysis over time; numeric rating scale pain scores; oral morphine equivalent consumption; and patient satisfaction. The incidence of hemidiaphragmatic paralysis was 40% (n = 17) vs. 2% (n = 1) in the anterior and posterior groups, respectively (p < 0.001). In one third of patients with hemidiaphragmatic paralysis, it persisted beyond the eighth hour. The median (interquartile range [range]) oral morphine equivalent consumption was significantly higher in the posterior approach when compared with the anterior approach, whether in the recovery area (20 [5–31 (0–60)] mg vs. 7.5 [0–14 (0–52)] mg, respectively; p = 0.004) or during the first 24 h (82 [61–127 (12–360) mg] vs. 58 [30–86 (0–160)] mg, respectively; p = 0.01). Patient satisfaction was comparable between groups (p = 0.6). Compared with the anterior approach, diaphragmatic function is best preserved with the posterior needle approach to the suprascapular nerve block.
Background Due to time limitations, the preanesthetic consultation (PAC) is not the best time for patients to integrate information specific to their perioperative care pathway. Objective The main objectives of this study were to evaluate the effectiveness of a digital companion on patients' knowledge of anesthesia and their satisfaction after real-life implementation. Methods We conducted a prospective, monocentric, comparative study using a before-and-after design. In phase 1, a 9-item self-reported anesthesia knowledge test (Delphi method) was administered to patients before and after their PAC (control group: PAC group). In phase 2, the study was repeated immediately after the implementation of a digital conversational agent, MyAnesth (@+PAC group). Patients’ satisfaction and their representations for anesthesia were also assessed using a Likert scale and the Abric method of hierarchized evocation. Results A total of 600 tests were distributed; 205 patients and 98 patients were included in the PAC group and @+PAC group, respectively. Demographic characteristics and mean scores on the 9-point preinformation test (PAC group: 4.2 points, 95% CI 3.9-4.4; @+PAC: 4.3 points, 95% CI 4-4.7; P=.37) were similar in the two groups. The mean score after receiving information was better in the @+PAC group than in the PAC group (6.1 points, 95% CI 5.8-6.4 points versus 5.2 points, 95% CI 5.0-5.4 points, respectively; P<.001), with an added value of 0.7 points (95% CI 0.3-1.1; P<.001). Among the respondents in the @+PAC group, 82% found the information to be clear and appropriate, and 74% found it easily accessible. Before receiving information, the central core of patients’ representations for anesthesia was focused on the fear of being put to sleep and thereafter on caregiver skills and comfort. Conclusions The implementation of our digital conversational agent in addition to the PAC improved patients' knowledge about their perioperative care pathway. This innovative audiovisual support seemed clear, adapted, easily accessible, and reassuring. Future studies should focus on adapting both the content and delivery of a digital conversational agent for the PAC in order to maximize its benefit to patients.
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.
BACKGROUND The pandemic highlighted the potential of digital health solutions to adapt the organization of care in a crisis context. OBJECTIVE Our aim was to demonstrate the prognostic value of the perioperative ‘MyRISK score’ derived from data collected on a digital conversational agent (chatbot) before the preanesthetic consultation (PAC). METHODS Single-center, prospective, observational study. The 16 items composing the MyRISK score were selected by Delphi method. An algorithm was used to stratify low (‘green’), intermediate (‘orange’) and high (‘red’) risk patients. Postoperative complications occurring in the first 6 months (composite criterion) were numerically collected and verified by phone and consultation of the electronic medical database. A logistic regression was carried out to identify their explanatory variables. A machine learning model was trained to predict the MyRISK score using a dataset of 1823 ‘green’ and ‘red’ patients to re-classify ‘orange’ individuals. User satisfaction and usability were assessed. RESULTS Four hundered and one patients were included. Sixteen of the 389 patients (4.1%) analyzed for the primary endpoint experienced a postoperative complication. An ASA score ≥ 3 and a ‘red’ score were independent predictors of postoperative complications (Odds Ratios of 5.8 [CI95%: 1.7 - 20.2; p=0.006] and 5.9 [CI95%: 1.5 - 22.3; p=0.009] respectively). Once ‘orange’ patients re-classified according to the prediction of the trained model, a ‘red’ score was identified as a strong predictor of postoperative complications with an Odds Ratio of 21.8 [CI95%: 2.8 - 171.5; p=0.003]. Patient satisfaction Numeric Rating Scale and System Usability Scale were 8 [7-9]/10 and 90 [82.5-95]/100 respectively. CONCLUSIONS We demonstrate the good prognostic predictive value of the MyRISK digital perioperative risk score established before the PAC. Its predictive strength was increase using a machine learning model reclassifying intermediate-risk patient. This numerical categorization could be used to guide patients between teleconsultation and face-to-face PAC, or to provide a perioperative personalized care pathway for high-risk patients.
Background Postoperative delirium frequently occurs in the elderly after hip fracture surgery and is associated with poor outcomes. Our aim was to identify a correlation between the atropinic burden (AB) due to drugs with clinical antimuscarinic effect and the occurrence of postoperative delirium . Methods We carried out a prospective, monocentric, observational study including 67 patients over 65 years of age who underwent hip fracture surgery. The addition of the anticholinergic weight of each drug was calculated at different time points to distinguish the prehospital, intra- and postoperative part of the AB. A multivariate analysis was carried out to identify the explanatory variables associated with postoperative delirium . Results Patients were 78 [71–86] years old. The time from admission to surgery was 12 [12–24] hours. The ADL and CIRS scores were 6 [5.5–6] and 6 [4–9], respectively. The total (prehospital plus intraoperative plus postoperative) AB was 5 [3–9]. The incidence of postoperative delirium was 54% (36/67). The demographic characteristics were comparable between delirium and no delirium groups. Univariate analysis showed statistically significant differences between no delirium and delirium groups concerning the number of prehospital atropinic drugs, prehospital AB, the number of postoperative atropinic drugs, postoperative AB, in-hospital AB and the MMSE calculated on postoperative day 5. Using multivariate analysis, postoperative AB, but not pre- and in-hospital ABs, was associated with postoperative delirium with an odds ratio of 1.84 (95% CI: 1.25–2.72; p = 0.002). A postoperative AB > 2 was associated with a postoperative delirium with an area under ROC curve of 0.73 (95% CI: 0.61–0.83; p = 0.0001). Conclusion Contrary to a prior exposure to atropinic drugs, a postoperative atropinic burden >2 was associated with postoperative delirium in elderly patients with hip fracture. Postoperative administration of (new) antimuscarinic drugs is a precipitating factor of delirium that could be avoided.
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