Hypotension Prediction Index guided Goal Directed therapy and the amount of Hypotension during Major Gynaecologic Oncologic Surgery: a Randomized Controlled clinical Trial.

Intraoperative hypotension (IOH) is associated with increased morbidity and mortality. Hypotension Prediction Index (HPI) is a machine learning derived algorithm that predicts IOH shortly before it occurs. We tested the hypothesis that the application of the HPI in combination with a pre-defined Goal Directed Therapy (GDT) hemodynamic protocol reduces IOH during major gynaecologic oncologic surgery. We enrolled women scheduled for major gynaecologic oncologic surgery under general anesthesia with invasive arterial pressure monitoring. Patients were randomized to a GDT protocol aimed at optimizing stroke volume index (SVI) or hemodynamic management based on HPI guidance in addition to GDT. The primary outcome was the amount of IOH, defined as the timeweighted average (TWA) mean arterial pressure (MAP) < 65 mmHg. Secondary outcome was the TWA-MAP < 65 mmHg during the first 20 min after induction of GA. After exclusion of 10 patients the final analysis included 60 patients (30 in each group). The median (25-75th IQR) TWA-MAP < 65 mmHg was 0.14 (0.04-0.66) mmHg in HPI group versus 0.77 (0.36-1.30) mmHg in Control group, P < 0.001. During the first 20 min after induction of GA, the median TWA-MAP < 65 mmHg was 0.53 (0.06-1.8) mmHg in the HPI group and 2.15 (0.65-4.2) mmHg in the Control group, P = 0.001. Compared to a GDT protocol aimed to SVI optimization, a machine learning-derived algorithm for prediction of IOH combined with a GDT hemodynamic protocol, reduced IOH and hypotension after induction of general anesthesia in patients undergoing major gynaecologic oncologic surgery.Trial registration number: NCT04547491. Date of registration: 10/09/2020.

Personalized Predictive Hemodynamic Management for Gynecologic Oncologic Surgery: Feasibility of Cost-Benefit Derivatives of Digital Medical Devices.

BACKGROUND: Intraoperative hypotension is associated with increased perioperative complications, hospital length of stay (LOS) and healthcare expenditure in gynecologic surgery. We tested the hypothesis that the adoption of a machine learning-based warning algorithm (hypotension prediction index-HPI) might yield an economic advantage, with a reduction in adverse outcomes that outweighs the costs for its implementation as a medical device. METHODS: A retrospective-matched cohort cost-benefit Italian study in gynecologic surgery was conducted. Sixty-six female patients treated with standard goal-directed therapy (GDT) were matched in a 2:1 ratio with thirty-three patients treated with HPI based on ASA status, diagnosis, procedure, surgical duration and age. RESULTS: The most relevant contributor to medical costs was operating room occupation (46%), followed by hospital stay (30%) and medical devices (15%). Patients in the HPI group had EURO 300 greater outlay for medical devices without major differences in total costs (GDT 5425 (3505, 8127), HPI 5227 (4201, 7023) p = 0.697). A pre-specified subgroup analysis of 50% of patients undergoing laparotomic surgery showed similar medical device costs and total costs, with a non-significant saving of EUR 1000 in the HPI group (GDT 8005 (5961, 9679), HPI 7023 (5227, 11,438), p = 0.945). The hospital LOS and intensive care unit stay were similar in the cohorts and subgroups. CONCLUSIONS: Implementation of HPI is associated with a scenario of cost neutrality, with possible economic advantage in high-risk settings.

Achieving Lumbar Epidural Block Competency in Inexperienced Trainees after a Structured Epidural Teaching Model: A Randomized, Single Blind, Prospective Comparison of CUSUM Learning Curves.

Aim: The aim of this randomized, prospective study was to investigate whether the use of the structured epidural teaching model (SETM) may affect the learning curve for lumbar epidural block in novice trainees when compared with a standard teaching module. Introduction: There is a paucity of literature regarding the efficacy of teaching epidural blocks and comparisons between the different educational approaches. Method: Forty-four PGY3 anesthesia trainees were randomized to receive (study group) or to not receive (control group) the SDM (structured didactic model) before the beginning of their 6 months clinical practice rotation in labor and delivery suites. A CUSUM learning curve was built for every trainee. The scores were assigned by the staff instructor, who was unaware of the group to which the trainee belonged. Results: The number of subjects who achieved an improvement in performance was 8 trainees from the control group and 14 from the study group. The probability of achieving an improvement was higher (p < 05) in the study group than in the control group, with an aOR of 3.25 (CI: 1.01; 12.1). The proportion of subjects in the study group who completed the epidural without help was 1.21 (1.05-1.41) times the proportion of subjects who completed the epidural without help in the control group. The probability of completing the epidural block without any assistance was 21% higher in the study group than in the control group (p < 05). Conclusion: We have demonstrated that the use of the structured epidural teaching model (SETM) may improve the learning curve (CUSUM) for lumbar epidural block in novice, entirely inexperienced, anesthesia trainees.

Impact of maternal lateral tilt on cardiac output during caesarean section under spinal anaesthesia: a prospective observational study.

BACKGROUND: Left uterine displacement (LUD) has been questioned as an effective strategy to prevent aortocaval compression after spinal anesthesia (SA) for cesarean delivery (CD). We tested if LUD has a significant impact on cardiac output (CO) in patients undergoing CD under SA during continuous non-invasive hemodynamic monitoring with Clearsight. METHODS: Forty-six patients were included in the final analysis. We considered 4 timepoints of 5 min each: T1 = baseline with LUD; T2 = baseline without LUD; T3 = after SA with LUD; T4 = after SA without LUD. LUD was then repositioned for CD. The primary outcome was to assess if CO decreased from T3 to T4 of at least 1.0 L/min. We also compared CO between T1 and T2 and other hemodynamic variables: mean, systolic and diastolic blood pressure (respectively MAP, SAP and DAP), heart rate (HR), stroke volume (SV), stroke volume variation (SVV), pulse pressure variation (PPV), contractility (dP/dt), dynamic arterial elastance (Eadyn) at the different timepoints. Data on fetal Apgar scores and umbilical arterial and venous pH were collected. RESULTS: CO did not vary from T3 to T4 (CO mean difference -0.02 L/min [95% CI -0.88 to 0.82; P = 1). No significant variation was registered for any variable at any timepoint. CONCLUSIONS: LUD did not show a significant impact on CO during continuous hemodynamic monitoring after SA for CD. TRIAL REGISTRATION: (retrospectively registered on 03/12/2021) NCT05143684 .

Hypotension Prediction Index with non-invasive continuous arterial pressure waveforms (ClearSight): clinical performance in Gynaecologic Oncologic Surgery.

Intraoperative hypotension (IOH) is common during major surgery and is associated with a poor postoperative outcome. Hypotension Prediction Index (HPI) is an algorithm derived from machine learning that uses the arterial waveform to predict IOH. The aim of this study was to assess the diagnostic ability of HPI working with non-invasive ClearSight system in predicting impending hypotension in patients undergoing major gynaecologic oncologic surgery (GOS). In this retrospective analysis hemodynamic data were downloaded from an Edwards Lifesciences HemoSphere platform and analysed. Receiver operating characteristic curves were constructed to evaluate the performance of HPI working on the ClearSight pressure waveform in predicting hypotensive events, defined as mean arterial pressure < 65 mmHg for > 1 min. Sensitivity, specificity, positive predictive value and negative predictive value were computed at a cutpoint (the value which minimizes the difference between sensitivity and specificity). Thirty-one patients undergoing GOS were included in the analysis, 28 of which had complete data set. The HPI predicted hypotensive events with a sensitivity of 0.85 [95% confidence interval (CI) 0.73-0.94] and specificity of 0.85 (95% CI 0.74-0.95) 15 min before the event [area under the curve (AUC) 0.95 (95% CI 0.89-0.99)]; with a sensitivity of 0.82 (95% CI 0.71-0.92) and specificity of 0.83 (95% CI 0.71-0.93) 10 min before the event [AUC 0.9 (95% CI 0.83-0.97)]; and with a sensitivity of 0.86 (95% CI 0.78-0.93) and specificity 0.86 (95% CI 0.77-0.94) 5 min before the event [AUC 0.93 (95% CI 0.89-0.97)]. HPI provides accurate and continuous prediction of impending IOH before its occurrence in patients undergoing GOS in general anesthesia.

Performance of the Hypotension Prediction Index With Noninvasive Arterial Pressure Waveforms in Awake Cesarean Delivery Patients Under Spinal Anesthesia.

BACKGROUND: Arterial hypotension is common after spinal anesthesia (SA) for cesarean delivery (CD), and to date, there is no definitive method to predict it. The hypotension prediction index (HPI) is an algorithm that uses the arterial waveform to predict early phases of intraoperative hypotension. The aims of this study were to assess the diagnostic ability of HPI working with arterial waveforms detected by ClearSight system in predicting impending hypotension in awake patients, and the agreement of pressure values recorded by ClearSight with conventional noninvasive blood pressure (NIBP) monitoring in patients undergoing CD under SA. METHODS: In this retrospective analysis of pregnant patients scheduled for elective CD under SA, continuous hemodynamic data measured with the ClearSight monitor until delivery were downloaded from an Edwards Lifesciences HemoSphere platform and analyzed. Receiver operating characteristic (ROC) curves were constructed to evaluate the performance of HPI algorithm working on the ClearSight pressure waveform in predicting hypotensive events, defined as mean arterial pressure (MAP) <65 mm Hg for >1 minute. The sensitivity, specificity, positive predictive value, and negative predictive value were computed at the optimal cutpoint, selected as the value that minimizes the difference between sensitivity and specificity. ClearSight MAP values were compared to NIBP MAP values by linear regression and Bland-Altman analysis corrected for repeated measurements. RESULTS: Fifty patients undergoing CD were included in the analysis. Hypotension occurred in 23 patients (48%). Among patients experiencing hypotension, the HPI disclosed 71 alerts. The HPI predicted hypotensive events with a sensitivity of 83% (95% confidence interval [CI], 69-97) and specificity of 83% (95% CI, 70-95) at 3 minutes before the event (area under the curve [AUC] 0.913 [95% CI, 0.837-0.99]); with a sensitivity of 97% (95% CI, 92-100) and specificity of 97% (95% CI, 92-100) at 2 minutes before the event (AUC 0.995 [95% CI, 0.979-1.0]); and with a sensitivity of 100% (95% CI, 100-100) and specificity 100% (95% CI, 100-100) 1 minute before the event (AUC 1.0 [95% CI, 1.0-1.0]). A total of 2280 paired NIBP MAP and ClearSight MAP values were assessed. The mean of the differences between the ClearSight and NIBP assessed using Bland-Altman analysis (±standard deviation [SD]; 95% limits of agreement with respective 95% CI) was -0.97 mm Hg (±4.8; -10.5 [-10.8 to -10.1] to 8.5 [8.1-8.8]). CONCLUSIONS: HPI provides an accurate real time and continuous prediction of impending intraoperative hypotension before its occurrence in awake patients under SA. We found acceptable agreement between ClearSight MAP and NIBP MAP.

Anesthesia management of pediatric dentistry patients with cardiofaciocutaneous syndrome: a case report.

Cardiofaciocutaneous syndrome is a rare syndrome characterized by particular craniofacial features, cardiac abnormalities, and multiple organ diseases. Patients present with pulmonary stenosis, hypertrophic cardiomyopathy, short neck, micrognathia, laryngomalacia, and tracheomalacia. These conditions may strongly influence patient perioperative outcomes. We describe a 15-year-old child with cardiofaciocutaneous syndrome presenting for a dentistry procedure. She had an uneventful perioperative and postoperative course except for difficult airway management.

"Please mind the gap": successful use of ultrasound-assisted spinal anesthesia for urgent cesarean section in a patient with implanted spinal cord stimulation system for giant chest wall arteriovenous malformation - a case report.

BACKGROUND: The use of Spinal Cord Stimulation (SCS) system to treat medically refractory neuropathic pain is increasing. Severe neuropathic pain can be found in giant chest wall arteriovenous malformations (AVMs), exceedingly rare and debilitating abnormalities, rarely reported during pregnancy. CASE PRESENTATION: We present a report of a pregnant patient with implanted Spinal Cord Stimulation (SCS) system because of painful thoracic AVM scheduled for an urgent cesarean section in which we used lumbar ultrasound (US) to rule out the possibility to damage SCS electrodes and to find a safe site to perform spinal anesthesia. CONCLUSIONS: The use of lumbar US to find a safe site for a lumbar puncture in presence of SCS system in a patient affected by painful thoracic AVM makes this case a particularly unique operative challenge and offers a new possible use of ultrasound to detect a safe space in patients with SCS implant.