BACKGROUND:Preprocedural fasting guidelines have been in place in the US since 1999. Many studies show that patients actually fast longer than the guidelines recommend. OBJECTIVE:To perform a detailed review based on the reference lists from and citations to articles known to be relevant to the topic. A meta-analysis of the observed preprocedural fasting literature and the interventions intended to reduce fasting times was performed. METHODS:A literature search was performed on multiple databases using the search term preoperative fasting combined with other terms such as aspiration. Reference lists from articles reporting results of observed fasting times were reviewed and all relevant articles titles were entered into Google Scholar to find articles that referenced them. Meta-analyses of the results were reported as mean (±SD) differences along with the degree of heterogeneity between studies. The final search was completed on July 31, 2024. RESULTS:There were 79 articles of which 24 (44, 073 patients) reported pediatric and 31 (16, 039 patients) reported adult observed fasting times. There were 25 (76, 359 patients) articles reporting outcomes from interventions intended to reduce preprocedural fasting times. Because observed fasting times are measured in a relatively short period of time during which the patient is being managed by anesthesia, there was little risk for bias, missing data or problems of data integrity. Fasting duration from all the studies was considered high quality and all the quality assurance intervention studies were considered low quality. Mean observed pediatric fasting times for liquids was 7.1 h (95% CI: 5.7 to 8.5 h) and for solids it was 12.2 h (95% CI: 11.1 h, 13.2 h). Preprocedural liquid fasting times for adults were 10.3 h (95%CI: 9.3 h, 11.4 h) and 13.5 h (95% CI: 12.6 h, 14.3 h) for solids. Fasting times were reduced by QA interventions but not by patient education, change in hospital policy, staff education and text messaging. CONCLUSION:Despite 25 years of guidelines recommending limited preprocedural fasting, observed fasting durations remain very long and are refractory to most efforts intended to reduce them. REGISTRATION:OSF Tue Jun 14 2022 07:19:36 GMT-0700 https://osf.io/pdju4.

BACKGROUND:Perioperative neurocognitive disorders (PNDs) are among the most common complications in older adults after surgery. Awareness of PNDs is important as they often can be prevented, and early recognition can improve recovery. We sought to understand what older adults know about PNDs, the information provided, and the counseling provided to guide recovery when PND symptoms occur. We hypothesized that older adults rarely receive information regarding PND before surgery or counseling after experiencing symptoms. METHODS:We conducted a mixed methods study by (1) employing a survey to better understand the information patients received before surgery and (2) conducting semi-structured interviews in patients who subjectively experienced PNDs to better understand what counseling they received after experiencing symptoms. Surveys were distributed preoperatively to older adults undergoing elective surgery. Semi-structured interviews were conducted with older adults who had undergone surgery and experienced symptoms of PND. The quantitative data were summarized using descriptive statistics, and qualitative data were analyzed using a hybrid inductive and deductive approach. RESULTS:The response rate for survey participants was approximately 19%. Among survey participants (n = 312), 58% of participants were between 65 and 69 years of age, 24% were between 70 and 79 years of age, and 18% were ≥ 80 years of age. Before their scheduled elective surgery, 7% (n = 22) indicated a healthcare provider discussed the risk of PNDs during their preoperative visit. None of the patients received educational material regarding PNDs. Ten older adults participated in the semi-structured interviews, which revealed that 9 (90%) participants attempted to discuss symptoms after they occurred with a healthcare provider, but none received counseling or information on what to do next, and all were instructed to wait to see if symptoms persisted. CONCLUSION:Our findings underscore that discussions about PND risk and symptom management do not occur as part of routine perioperative care, leaving patients without important information and guidance.

Importance:High body mass index (BMI) has been associated with increased postoperative complications including mortality in the general population, leading many perioperative clinicians to recommend preoperative lifestyle modifications aimed at achieving normal body weight. However, aging introduces physiological changes associated with frailty, such as altered body composition, fat redistribution, and stature reduction due to height loss, all of which may modify the association between BMI and surgical outcomes in older adults. Objective:To determine if a higher BMI in older adults who are undergoing major elective surgery is associated with rates of all-cause mortality. Design, Setting, and Participants:Cohort study of adults aged 65 years or older presenting for surgery from February 2019 to January 2022 at a preoperative clinic before planned major elective surgery at a large academic Center in Southern California. Exposure:Body mass index. Main Outcomes and Measures:Postoperative outcomes included all-cause 30-day and 1-year mortality, postoperative delirium, discharge disposition, and complications classified using the Clavien-Dindo system. Results:The study included 414 older adults undergoing major elective surgery with a mean (SD) age of 75.9 (7.2) years; 54.8% (95% CI, 50.2%-60.4%) of the cohort were female. The prevalence of frailty was 24.2% (95% CI, 20.3%-28.5%), and 37.0% (95% CI, 32.6%-41.8%) of the cohort was prefrail. The overall 30-day all-cause mortality rate was 11.0% (95% CI, 8.5%-14.5%). Patients categorized as overweight (BMI, 25.0-29.9; calculated as weight in kilograms divided by height in meters squared) had the lowest 30-day all-cause mortality rate, with a significant risk reduction compared with patients with a normal BMI (18.5-24.9) (1 of 128 patients [0.8%] vs 25 of 133 patients [18.8%]; odds ratio [OR], 0.03; 95% CI, 0.01-0.26; P = .001). This association remained significant in the multivariable logistic regression model after adjusting for potential confounders (OR, 0.14; 95% CI, 0.06-0.34; P < .001). Patients categorized as underweight (BMI <18.5) had the highest 30-day all-cause mortality rate (15 of 20 patients [75.0%]; 95% CI, 55.0%-90.0%). Conclusions and Relevance:In this observational cohort study of older adults undergoing major elective surgery, being overweight was associated with lower odds of 30-day all-cause mortality. These findings suggest that traditional weight loss recommendations based on achieving normal BMI may need to be reevaluated for this population.

Background: Intraoperative hypotension has been associated with postoperative complications, but its relationship with postoperative delirium remains debated. Methods: This single-centre retrospective cohort study included adults (≥60 yr) with ASA physical status score of 3 or 4 undergoing major noncardiac surgery, with documented Confusion Assessment Method assessments. Patients with a history of neurosurgery, stroke, dementia, or neurocognitive disorders were excluded. The primary exposure was the cumulative duration of a mean arterial pressure <65 mm Hg (minutes). The primary outcome was postoperative delirium within 7 days, diagnosed via Confusion Assessment Method. Multivariable logistic regression was used to assess the association between intraoperative hypotension and delirium, adjusting for confounders. Results: Among 5171 patients included from 2013–2024, 632 (11.8%) developed delirium. The median (Q1–Q3) duration of surgery and time with mean arterial pressure <65 mm Hg were 281 (199–430) min and 28 (9–61) min, respectively. In models adjusted for patient characteristics and perioperative factors, intraoperative hypotension was associated with increased odds of delirium (odds ratio per 60 min, 1.12; 95% confidence interval, 1.01–1.24; P=0.038). However, after adjusting for year of surgery, the association was attenuated and no longer statistically significant (odds ratio, 1.06; 95% confidence interval, 0.95–1.18; P=0.320). Both intraoperative hypotension exposure and delirium incidence declined significantly over the study period. Conclusions: Although intraoperative hypotension initially appeared to be associated with postoperative delirium, this association was no longer significant when accounting for temporal improvements in perioperative care. Intraoperative hypotension may represent a marker of historical practice patterns rather than an independent causal driver of delirium.

BACKGROUND: Implementation of goal-directed fluid therapy (GDFT) protocols remains low. Protocol compliance among anesthesiologists tends to be suboptimal owing to the high workload and the attention required for implementation. The assisted fluid management (AFM) system is a novel decision support tool designed to help clinicians apply GDFT protocols. This system predicts fluid responsiveness better than anesthesia practitioners do and achieves higher stroke volume (SV) and cardiac index values during surgery. We tested the hypothesis that an AFM-guided GDFT strategy would also be associated with better sublingual microvascular flow compared to a standard GDFT strategy. METHODS: This bicenter, parallel, 2-arm, prospective, randomized controlled, patient and assessor-blinded, superiority study considered for inclusion all consecutive patients undergoing high-risk abdominal surgery who required an arterial catheter and uncalibrated SV monitoring. Patients having standard GDFT received manual titration of fluid challenges to optimize SV while patients having an AFM-guided GDFT strategy received fluid challenges based on recommendations from the AFM software. In all patients, fluid challenges were standardized and titrated per 250 mL and vasopressors were administered to maintain a mean arterial pressure >70 mm Hg. The primary outcome (average of each patient’s intraoperative microvascular flow index (MFI) across 4 intraoperative time points) was analyzed using a Mann-Whitney U test and the treatment effect was estimated with a median difference between groups with a 95% confidence interval estimated using the bootstrap percentile method (with 1000 replications). Secondary outcomes included SV, cardiac index, total amount of fluid, other microcirculatory variables, and postoperative lactate. RESULTS: A total of 86 patients were enrolled over a 7-month period. The primary outcome was significantly higher in patients with AFM (median [Q1–Q3]: 2.89 [2.84–2.94]) versus those having standard GDFT (2.59 [2.38–2.78] points, median difference 0.30; 95% confidence interval [CI], 0.19–0.49; P < .001). Cardiac index and SVI were higher (3.2 ± 0.5 vs 2.7 ± 0.7 l.min –1 .m –2 ; P = .001 and 42 [35–47] vs 36 [32–43] mL.m –2 ; P = .018) and arterial lactate concentration was lower at the end of the surgery in patients having AFM-guided GDFT (2.1 [1.5–3.1] vs 2.9 [2.1–3.9] mmol.L –1 ; P = .026) than patients having standard GDFT strategy. Patients having AFM received a higher fluid volume but 3 times less norepinephrine than those receiving standard GDFT ( P < .001). CONCLUSIONS: Use of an AFM-guided GDFT strategy resulted in higher sublingual microvascular flow during surgery compared to use of a standard GDFT strategy. Future trials are necessary to make conclusive recommendations that will change clinical practice.

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tool's ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tool's real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tool's detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 %), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.

Background Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aimed to identify and independently validate endotypes of hypotension in big datasets of surgical and critically ill patients using unsupervised deep learning. Methods We developed an unsupervised deep learning algorithm, specifically a deep learning autoencoder model combined with a Gaussian mixture model, to identify endotypes of hypotension based on stroke volume index, heart rate, systemic vascular resistance index, and stroke volume variation observed during episodes of hypotension. The algorithm was developed with data from 871 surgical patients who had 6962 hypotensive events and validated in two independent datasets, one including 1000 surgical patients who had 7904 hypotensive events and another including 1000 critically ill patients who had 53 821 hypotensive events. We defined hypotension as a mean arterial pressure <65 mm Hg for at least 1 min. Results In the development dataset, we identified four hypotension endotypes. Based on their physiological and clinical characteristics, we labelled them as: vasodilation, hypovolaemia, myocardial depression, and bradycardia. The same four hypotension endotypes were identified in the two independent validation datasets of surgical and critically ill patients. Conclusions Unsupervised deep learning identified four endotypes of hypotension in surgical and critically ill patients: vasodilation, hypovolaemia, myocardial depression, and bradycardia. The algorithm provides the probability of each endotype for each hypotensive data point. Identifying hypotensive endotypes could guide clinicians to causal treatments for hypotension.

Intravenous fluid is administered during high-risk surgery to optimize stroke volume (SV). To assess ongoing need for fluids, the hemodynamic response to a fluid bolus is evaluated using a fluid challenge technique. The Acumen Assisted Fluid Management (AFM) system is a decision support tool designed to ease the application of fluid challenges and thus improve fluid administration during high-risk surgery. In this post hoc analysis of data from a randomized controlled trial, we compared the rates of fluid responsiveness (defined as an increase in SV of ≥ 10

Introduction:Although opioids are commonly used to relieve pain associated with surgery, they are not consequence free. Moreover, the USA and many western countries are currently experiencing a significant health crisis because of opioid addiction and its related overdose potential. There have been no studies that have evaluated patient preference regarding opioid use and its potential impact on the quality of recovery. The aim of this study is to compare the effect of patient preference on intraoperative opioid use on early postoperative quality of recovery after moderate risk laparoscopic/robotic abdominal surgery. Methods:This trial is an interventional, pragmatic, partially randomised factorial trial. Adults (N=240) scheduled for moderate-risk abdominal surgery under laparoscopic/robotic assistance (colorectal, urologic, and gynaecologic) will be allocated into four groups, according to their preference (choice of opioid-free vs opioid-based anaesthesia vs no choice and, if no choice, then the patient is randomised to opioid-based vs opioid-free anaesthesia). Anaesthesia providers and patients who choose their anaesthesia type will be unblinded of the allocation group. The primary endpoint will be the Quality of Recovery-15 score at postoperative day 1. Secondary endpoints will include patient satisfaction, postoperative nausea and vomiting, intraoperative bradycardia, postoperative opioid consumption, postoperative hypoxemia, and health-related quality of life using the EuroQoL 5-Dimension 5-Level (EQ-5D-5L). Conclusions:This trial will provide evidence on whether patient preference on intraoperative opioid use can improve patient quality of recovery after moderate-risk abdominal surgery. Clinical trial registration:NCT06855641. Protocol version number and date:2.0, 24 February 2025.

Cardiac output is a key cardiovascular variable quantifying global blood flow. The measurement performance of cardiac output monitoring methods is investigated in validation studies, which are method comparison studies determining the agreement between cardiac output values measured with a test method and those measured with a reference method. The StatistiCal analysis and repOrting of cardiac output Method comPARison studiEs (COMPARE) statement provides a framework for designing, performing, and reporting cardiac output method comparison studies and includes a checklist of 29 items that are essential for reporting of those studies. Considering and reporting the items specified in the COMPARE checklist will help standardize cardiac output method comparison studies and increase the external validity of the results.

The imbalance in anesthesia workforce supply and demand has been exacerbated post-COVID due to a surge in demand for anesthesia care, especially in non–operating room anesthetizing sites, at a faster rate than the increase in anesthesia clinicians. The consequences of this imbalance or labor shortage compromise healthcare facilities, adversely affect the cost of care, worsen anesthesia workforce burnout, disrupt procedural and surgical schedules, and threaten academic missions and the ability to educate future anesthesiologists. In developing possible solutions, one must examine emerging trends that are affecting the anesthesia workforce, new technologies that will transform anesthesia care and the workforce, and financial considerations, including governmental payment policies. Possible practice solutions to this imbalance will require both short- and long-term multifactorial approaches that include increasing training positions and retention policies, improving capacity through innovations, leveraging technology, and addressing financial constraints.

Brain injury patients require precise blood pressure (BP) management to maintain cerebral perfusion pressure (CPP) and avoid intracranial hypertension. Nurses have many tasks and norepinephrine titration has been shown to be suboptimal. This can lead to limited BP control in patients that are in critical need of cerebral perfusion optimization. We have designed a closed-loop vasopressor (CLV) system capable of maintaining mean arterial pressure (MAP) in a narrow range and we aimed to assess its performance when treating severe brain injury patients. Within the first 48 h of intensive care unit (ICU) admission, 18 patients with a severe brain injury underwent either CLV or manual norepinephrine titration. In both groups, the objective was to maintain MAP in target (within ± 5 mmHg of a predefined target MAP) to achieve optimal CPP. Fluid administration was standardized in the two groups. The primary objective was the percentage of time patients were in target. Secondary outcomes included time spent over and under target. Over the four-hour study period, the mean percentage of time with MAP in target was greater in the CLV group than in the control group (95.8 ± 2.2% vs. 42.5 ± 27.0%, p < 0.001). Severe undershooting, defined as MAP < 10 mmHg of target value was lower in the CLV group (0.2 ± 0.3% vs. 7.4 ± 14.2%, p < 0.001) as was severe overshooting defined as MAP > 10 mmHg of target (0.0 ± 0.0% vs. 22.0 ± 29.0%, p < 0.001). The CLV system can maintain MAP in target better than nurses caring for severe brain injury patients.

Background and objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. Methods: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm against marked DN detection, while box plots and Bland-Altman plots were used to compare its performance with both marked DN detection and an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy Python package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R-2(87,343) =0.99, p<.001) and PPG (R-2(86,764) =0.98, p<.001) waveforms. The algorithm had a lower mean error of DN detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high rate of detectability of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. Conclusion: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.