BACKGROUND: Time and money are limited resources to pursue quality improvement (QI) goals. Computer simulation using Monte Carlo methods may help focus resources towards the most efficacious interventions to pursue. METHODS: This observational, cross-sectional study analysed the length of stay (LOS) for adult American Society of Anesthesiologists (ASA) 1-3 patients in the postanaesthesia care unit (PACU) at a major academic medical centre. Data were collected retrospectively from 1 April 2023 to 31 March 2024. Statistical analysis with Monte Carlo methods simulated the per cent reduction in PACU LOS following the elimination of postoperative nausea and vomiting (PONV), hypothermia (initial temperature<36°C), severe pain (pain score≥7) or moderate opioid use (≥ 50 mcg fentanyl or≥0.4 mg hydromorphone). RESULTS: The PACU LOS of 7345 patients were included in this study. PONV was experienced by 10.29% of patients and was associated with a mean PACU LOS of 96.64 min (±33.98 min). Hypothermia was the least frequent complication, experienced by 8.93% of patients and was associated with a mean PACU LOS of 83.55 min (±35.99 min). Severe pain and moderate opioid use were seen in 34.05% and 40.83% of patients, respectively and were associated with PACU LOS that were shorter than those experienced by patients with PONV. Monte Carlo simulations demonstrated that the greatest impact on PACU LOS (12.5% (95% CI 12.0% to 13.0%)) would result from the elimination of moderate opioid use. DISCUSSION: Although PONV was associated with the longest PACU LOS, statistical simulation with Monte Carlo methods demonstrated the greatest per cent reduction in PACU LOS would result from the elimination of moderate opioid use, thus indicating the most efficacious project to pursue. CONCLUSION: Statistical simulation with Monte Carlo methods can help guide QI teams to the most efficacious project or intervention to pursue.

The Anesthesia Quality Institute (AQI) promotes improvements in clinical care outcomes by managing data entered in the National Anesthesia Clinical Outcomes Registry (NACOR). Each case included in NACOR is classified as "performance met" or "performance not met" and expressed as a percentage for a length of time. The clarity associated with this binary classification is associated with limitations on data analysis and presentations that may not be optimal guides to evaluate the quality of care. High compliance benchmarks present another obstacle for evaluating quality. Traditional approaches for interpreting statistical process control (SPC) charts depend on data points above and below a center line, which may not provide adequate characterizations of a QI process with a low failure rate, or few possible data points below the center line. This article demonstrates the limitations associated with the use of binary datasets to evaluate the quality of care at an individual organization with QI measures, describes a method for characterizing binary data with continuous variables and presents a solution to analyze rare QI events using g charts.

The Hypotension Prediction Index is a machine learning derived algorithm that predicts intraoperative hypotension. The algorithm provides two additional hemodynamic parameters, EaDyn and dP/dt, that characterize the physiological changes and may provide guidance regarding the most efficacious treatments. dP/dt correlates with ventricular contractility from peripheral artery waveforms. EaDyn (dynamic arterial elastance) is the ratio of pulse pressure variation and stroke volume variation (PPV/SVV), correlating with peripheral vascular resistance.

Continuous BP monitoring is essential to intraoperative care, as hypotensive events can significantly increase the risk of AKI, MI, and mortality post-op1,2 . The Hypotension Prediction Index (HPI) is a novel algorithm derived from machine learning that gives anesthesiologists the ability to predictive hypotensive events. The HPI derived from intra-arterial catheter monitoring has been shown to predict hypotensive events with sensitivity and specificity >80%3 . However, the utility and accuracy of the HPI when derived from non-invasive monitoring techniques, such the ClearSight finger cuff, have yet to be examined. This study seeks to compare the intraarterial catheter-derived HPI vs the ClearSight finger cuff-derived HPI, to see if it is viable tool for anesthesiologists to use when non-invasive monitoring is not indicated.

Cardiac output (CO) monitoring is an important tool for hemodynamic optimization.[1] Bolus thermodilution (BTD) with a pulmonary artery catheter (PAC) remains the gold standard for CO measurement, but is invasive and has been associated with complications. [2] This study evaluates the level of agreement of CO values measured from multiple minimally-invasive CO monitor systems before and after cardiopulmonary bypass (CPB).

SVV is the change in stroke volume during inspiration and expiration due to changes in intrathoracic pressure and consequent variations in venous return. SVV to assess fluid status and guide intraoperative fluid management. SVV utility was established using TV of 8 ml/kg while ARDSNet recommendations suggest 6 ml/kg is preferable.

Maintaining accurate measurements ofsedation in the ICU are essential forpatient comfort, safety, and clinicaloutcomes. Current clinical sedation scales, such asthe Richmond Agitation-Sedation Scale(RASS) suffer from subjectivity and poorinter-rater reliability. Processed EEGs (pEEGs) may providea more objective and reliable alternativeto assess depth of sedation in ICUpatients. In this study, we conducted a systematicliterature review on 3 major pEEGs(Masimo Sedline, Bispectral Index, andNarcotrend) and their correlation withgold standard clinical sedation scales.

BACKGROUND: To enhance the utility of functional hemodynamic monitoring, the variables systolic slope (dP/dt) and dynamic arterial elastance (Eadyn) are calculated by the Hypotension Prediction Index (HPI) Acumen® Software. This study was designed to characterize the effects of phenylephrine and ephedrine on dP/dt and Eadyn. METHODS: This was a retrospective, non-randomized analysis of data collected during two clinical studies. All patients required intra-operative controlled mechanical ventilation and had an indwelling radial artery catheter connected to an Acumen IQ sensor. Raw arterial pressure waveform data was downloaded from the patient monitor and all hemodynamic measurements were calculated off-line. The anesthetic record was reviewed for bolus administrations of either phenylephrine or ephedrine. Cardiovascular variables prior to drug administration were compared to those following vasopressor administrations. The primary outcome was the difference for dP/dt and Eadyn at baseline compared with the average after the bolus administration. All data sets demonstrated non-normal distributions so statistical analysis of paired and unpaired data followed the Wilcoxon matched pairs signed-rank test or Mann-Whitney U test, respectively. RESULTS: 201 doses of phenylephrine and 100 doses of ephedrine were analyzed. All data sets are reported as median [95% CI]. Mean arterial pressure (MAP) increased from 62 [54,68] to 78 [76,80] mmHg following phenylephrine and from 59 [55,62] to 80 [77,83] mmHg following ephedrine. Stroke volume and cardiac output both increased. Stroke volume variation and pulse pressure variation decreased. Both drugs produced significant increases in dP/dt, from 571 [531, 645] to 767 [733, 811] mmHg/sec for phenylephrine and from 537 [509, 596] to 848 [779, 930] mmHg/sec for ephedrine. No significant changes in Eadyn were observed. CONCLUSION: Bolus administration of phenylephrine or ephedrine increases dP/dt but does not change Eadyn. dP/dt demonstrates potential for predicting the inotropic response to phenylephrine or ephedrine, providing guidance for the most efficacious vasopressor when treating hypotension. TRIAL REGISTRATION: Data was collected from two protocols. The first was deemed to not require written, informed consent by the Institutional Review Board (IRB). The second was IRB-approved (Effect of Diastolic Dysfunction on Dynamic Cardiac Monitors) and registered on ClinicalTrials.gov (NCT04177225).