Hormonal basis of sex differences in anesthetic sensitivity.

General anesthesia-a pharmacologically induced reversible state of unconsciousness-enables millions of life-saving procedures. Anesthetics induce unconsciousness in part by impinging upon sexually dimorphic and hormonally sensitive hypothalamic circuits regulating sleep and wakefulness. Thus, we hypothesized that anesthetic sensitivity should be sex-dependent and modulated by sex hormones. Using distinct behavioral measures, we show that at identical brain anesthetic concentrations, female mice are more resistant to volatile anesthetics than males. Anesthetic sensitivity is bidirectionally modulated by testosterone. Castration increases anesthetic resistance. Conversely, testosterone administration acutely increases anesthetic sensitivity. Conversion of testosterone to estradiol by aromatase is partially responsible for this effect. In contrast, oophorectomy has no effect. To identify the neuronal circuits underlying sex differences, we performed whole brain c-Fos activity mapping under anesthesia in male and female mice. Consistent with a key role of the hypothalamus, we found fewer active neurons in the ventral hypothalamic sleep-promoting regions in females than in males. In humans, we demonstrate that females regain consciousness and recover cognition faster than males after identical anesthetic exposures. Remarkably, while behavioral and neurocognitive measures in mice and humans point to increased anesthetic resistance in females, cortical activity fails to show sex differences under anesthesia in either species. Cumulatively, we demonstrate that sex differences in anesthetic sensitivity are evolutionarily conserved and not reflected in conventional electroencephalographic-based measures of anesthetic depth. This covert resistance to anesthesia may explain the higher incidence of unintended awareness under general anesthesia in females.

A Culturally Adapted Perioperative Mental Health Intervention for Older Black Surgical Patients.

OBJECTIVES: Perioperative mental health of older Black surgical patients is associated with poor surgical outcomes; however, evidence-based perioperative interventions are lacking. Our two study objectives included: first, examine factors affecting perioperative care experiences of older Black surgical patients with mental health problems, and second, ascertain design and implementation requirements for a culturally-adapted perioperative mental health intervention. DESIGN SETTING AND PARTICIPANTS: We conducted six focus groups with older Black patients (n = 15; ≥50 years; surgery within the past 5 years and/or interest in mental health research; history of distress, anxiety, or depression coping with surgery/hospitalization/) from a large academic medical center. We engaged study partners, including interventionists and community members, to gather insights on intervention and implementation needs. We followed a hybrid inductive-deductive thematic approach using open coding and the National Institute on Minority Health and Health Disparities Research Framework. RESULTS: Patients reported that their psychological well-being and long-term mental health outcomes were not appropriately considered during perioperative care. Perceived stressors included interpersonal and structural barriers to using mental healthcare services, clinician treatment biases and ageism in care, and lack of healthcare professional connections/resources. Patients utilized various coping strategies, including talk therapy, faith/spirituality, and family and friends. CONCLUSION: This study offers valuable insights into the experiences of older Black surgical patients and the critical elements for developing a personalized perioperative mental health intervention to support their well-being before, during, and after surgery. Our findings demonstrated a need for a patient-centered and culturally adapted intervention targeting the individual/behavioral and interpersonal levels. Informed by the cultural adaptation framework, we propose a multi-component intervention that integrates psychological and pharmacological components.

A Perioperative Mental Health Intervention for Depressed and Anxious Older Surgical Patients: Results From a Feasibility Study.

OBJECTIVES: The perioperative period is challenging and stressful for older adults. Those with depression and/or anxiety have an increased risk of adverse surgical outcomes. We assessed the feasibility of a perioperative mental health intervention composed of medication optimization and a wellness program following principles of behavioral activation and care coordination for older surgical patients. METHODS: We included orthopedic, oncologic, and cardiac surgical patients aged 60 and older. Feasibility outcomes included study reach, the number of patients who agreed to participate out of the total eligible; and intervention reach, the number of patients who completed the intervention out of patients who agreed to participate. Intervention efficacy was assessed using the Patient Health Questionnaire for Anxiety and Depression (PHQ-ADS). Implementation potential and experiences were collected using patient surveys and qualitative interviews. Complementary caregiver feedback was also collected. RESULTS: Twenty-three out of 28 eligible older adults participated in this study (mean age 68.0 years, 65% women), achieving study reach of 82% and intervention reach of 83%. In qualitative interviews, patients (n = 15) and caregivers (complementary data, n = 5) described overwhelmingly positive experiences with both the intervention components and the interventionist, and reported improvement in managing depression and/or anxiety. Preliminary efficacy analysis indicated improvement in PHQ-ADS scores (F = 12.13, p <0.001). CONCLUSIONS: The study procedures were reported by participants as feasible and the perioperative mental health intervention to reduce anxiety and depression in older surgical patients showed strong implementation potential. Preliminary data suggest its efficacy for improving depression and/or anxiety symptoms. A randomized controlled trial assessing the intervention and implementation effectiveness is currently ongoing.

Multi-view representation learning for tabular data integration using inter-feature relationships.

OBJECTIVE: An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, especially in healthcare. This integrating is usually resolved using meta-data such as feature names, which may be unavailable or ambiguous. Our goal is to design methods that create a mapping between structured tabular datasets derived from electronic health records independent of meta-data. METHODS: We evaluate methods in the challenging case of numeric features without reliable and distinctive univariate summaries, such as nearly Gaussian and binary features. We assume that a small set of features are a priori mapped between two datasets, which share unknown identical features and possibly many unrelated features. Inter-feature relationships are the main source of identification which we expect. We compare the performance of contrastive learning methods for feature representations, novel partial auto-encoders, mutual-information graph optimizers, and simple statistical baselines on simulated data, public datasets, the MIMIC-III medical-record changeover, and perioperative records from before and after a medical-record system change. Performance was evaluated using both mapping of identical features and reconstruction accuracy of examples in the format of the other dataset. RESULTS: Contrastive learning-based methods overall performed the best, often substantially beating the literature baseline in matching and reconstruction, especially in the more challenging real data experiments. Partial auto-encoder methods showed on-par matching with contrastive methods in all synthetic and some real datasets, along with good reconstruction. However, the statistical method we created performed reasonably well in many cases, with much less dependence on hyperparameter tuning. When validating feature match output in the EHR dataset we found that some mistakes were actually a surrogate or related feature as reviewed by two subject matter experts. CONCLUSION: In simulation studies and real-world examples, we find that inter-feature relationships are effective at identifying matching or closely related features across tabular datasets when meta-data is not available. Decoder architectures are also reasonably effective at imputing features without an exact match.

User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications.

BACKGROUND: Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. METHODS: Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. RESULTS: During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). CONCLUSIONS: Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.

Brain-Hazardous Medications and Potential Subadequate Antidepressant Dosing in Older Surgical Patients Receiving Home Antidepressants: An Observational Study of a Large US Health System.

BACKGROUND: Older surgical patients with depression often experience poor postoperative outcomes. Poor outcomes may stem from brain-hazardous medications and subadequate antidepressant dosing. METHODS: This was a retrospective, observational cohort study covering the period between January 1, 2021 and December 31, 2021. Patients ≥60 years of age who underwent inpatient surgery and had an overnight stay at an integrated academic health care system comprising 14 hospitals were eligible. We analyzed the prevalence of home central nervous system (CNS)-active potentially inappropriate medication (PIM) and potential subadequate antidepressant dosing in older surgical patients receiving home antidepressants. Univariable and multivariable regression models were used to identify factors associated with home CNS-active PIM prescribing and potential subadequate antidepressant dosing. Additionally, outcomes were compared among patients receiving and not receiving CNS-active PIMs and patients receiving and not receiving subadequate antidepressant dosing. RESULTS: A total of 8031 patients were included in this study (47% female, mean age = 70 years) of whom 2087 (26%) were prescribed antidepressants. Roughly one-half (49%, 95% confidence interval [CI], 46.5-50.1) of patients receiving home antidepressants were also receiving ≥1 CNS-active PIM and 29% (95% CI, 27.0-29.3) were receiving a potential subadequate dose. Factors associated with an increased likelihood of receiving a home CNS-active PIM included female sex (adjusted odds ratio [aOR], 1.46), anxiety (aOR, 2.43), asthma or chronic obstructive pulmonary disease (aOR, 1.39), and serotonin-norepinephrine reuptake inhibitor use (aOR, 1.54). Patients aged ≥75 years (aOR, 1.57), black race (aOR, 1.48) and those with congestive heart failure (aOR, 1.33) were more likely to be prescribed a potential subadequate antidepressant dose. Patients receiving potential subadequate antidepressant doses were discharged home less often (64% vs 73%), had a longer hospital length of stay (9 days vs 7 days), and a higher mortality rate (18% vs 10%) compared to patients receiving adequate home antidepressant doses (P-value for all <0.01). No differences in these outcomes were found among patients receiving home antidepressants with or without CNS-active PIMs. CONCLUSIONS: Older surgical patients receiving antidepressants are frequently prescribed brain-hazardous medications and potentially subadequate antidepressant doses. Those receiving subadequate antidepressant doses may be at risk for worse postoperative outcomes compared to patients receiving adequate doses. The role of preoperative medication optimization to improve outcomes for older surgical patients should be evaluated.

Old vaccines for new infections: Exploiting innate immunity to control COVID-19 and prevent future pandemics.

The COVID-19 pandemic triggered an unparalleled pursuit of vaccines to induce specific adaptive immunity, based on virus-neutralizing antibodies and T cell responses. Although several vaccines have been developed just a year after SARS-CoV-2 emerged in late 2019, global deployment will take months or even years. Meanwhile, the virus continues to take a severe toll on human life and exact substantial economic costs. Innate immunity is fundamental to mammalian host defense capacity to combat infections. Innate immune responses, triggered by a family of pattern recognition receptors, induce interferons and other cytokines and activate both myeloid and lymphoid immune cells to provide protection against a wide range of pathogens. Epidemiological and biological evidence suggests that the live-attenuated vaccines (LAV) targeting tuberculosis, measles, and polio induce protective innate immunity by a newly described form of immunological memory termed "trained immunity." An LAV designed to induce adaptive immunity targeting a particular pathogen may also induce innate immunity that mitigates other infectious diseases, including COVID-19, as well as future pandemic threats. Deployment of existing LAVs early in pandemics could complement the development of specific vaccines, bridging the protection gap until specific vaccines arrive. The broad protection induced by LAVs would not be compromised by potential antigenic drift (immune escape) that can render viruses resistant to specific vaccines. LAVs might offer an essential tool to "bend the pandemic curve," averting the exhaustion of public health resources and preventing needless deaths and may also have therapeutic benefits if used for postexposure prophylaxis of disease.

Electroencephalography-Guided Anesthesia and Delirium in Older Adults After Cardiac Surgery: The ENGAGES-Canada Randomized Clinical Trial.

Importance: Intraoperative electroencephalogram (EEG) waveform suppression, suggesting excessive general anesthesia, has been associated with postoperative delirium. Objective: To assess whether EEG-guided anesthesia decreases the incidence of delirium after cardiac surgery. Design, Setting, and Participants: Randomized, parallel-group clinical trial of 1140 adults 60 years or older undergoing cardiac surgery at 4 Canadian hospitals. Recruitment was from December 2016 to February 2022, with follow-up until February 2023. Interventions: Patients were randomized in a 1:1 ratio (stratified by hospital) to receive EEG-guided anesthesia (n = 567) or usual care (n = 573). Patients and those assessing outcomes were blinded to group assignment. Main Outcomes and Measures: The primary outcome was delirium during postoperative days 1 through 5. Intraoperative measures included anesthetic concentration and EEG suppression time. Secondary outcomes included intensive care and hospital length of stay. Serious adverse events included intraoperative awareness, medical complications, and 30-day mortality. Results: Of 1140 randomized patients (median [IQR] age, 70 [65-75] years; 282 [24.7%] women), 1131 (99.2%) were assessed for the primary outcome. Delirium during postoperative days 1 to 5 occurred in 102 of 562 patients (18.15%) in the EEG-guided group and 103 of 569 patients (18.10%) in the usual care group (difference, 0.05% [95% CI, -4.57% to 4.67%]). In the EEG-guided group compared with the usual care group, the median volatile anesthetic minimum alveolar concentration was 0.14 (95% CI, 0.15 to 0.13) lower (0.66 vs 0.80) and there was a 7.7-minute (95% CI, 10.6 to 4.7) decrease in the median total time spent with EEG suppression (4.0 vs 11.7 min). There were no significant differences between groups in median length of intensive care unit (difference, 0 days [95% CI, -0.31 to 0.31]) or hospital stay (difference, 0 days [95% CI, -0.94 to 0.94]). No patients reported intraoperative awareness. Medical complications occurred in 64 of 567 patients (11.3%) in the EEG-guided group and 73 of 573 (12.7%) in the usual care group. Thirty-day mortality occurred in 8 of 567 patients (1.4%) in the EEG-guided group and 13 of 573 (2.3%) in the usual care group. Conclusions and Relevance: Among older adults undergoing cardiac surgery, EEG-guided anesthetic administration to minimize EEG suppression, compared with usual care, did not decrease the incidence of postoperative delirium. This finding does not support EEG-guided anesthesia for this indication. Trial Registration: ClinicalTrials.gov Identifier: NCT02692300.

Perioperative Anxiety and Depression in Older Adults: Epidemiology and Treatment.

The intervals before and after major surgery is a high-risk period for older adults; in this setting, anxiety and depression are common and serious problems. We comprehensively reviewed current evidence on perioperative anxiety and depression in older adults, focusing on epidemiology, impact, correlates, medication risks, and treatment. Principles of perioperative mental healthcare are proposed based on the findings. Prevalence estimates of clinically significant anxiety and depression range from 5% to 45% for anxiety and 6% to 52% for depression, depending on surgical populations and measurement tools. Anxiety and depression may increase risk for surgical complications and reduce patient participation during rehabilitation. Medical comorbidities, pain, insomnia, cognitive impairment, and delirium are common co-occurring problems. Concomitant uses of central nervous system acting medications (benzodiazepines, anticholinergics, and opioids) amplify the risks of delirium and falls. Based on these findings, we propose that anxiety and depression care should be part of perioperative management in older adults; components include education, psychological support, opioid-sparing pain management, sleep management, deprescribing central nervous system active medications, and continuation and optimization of existing antidepressants. More research is needed to test and improve these care strategies.

Integrating machine learning predictions for perioperative risk management: Towards an empirical design of a flexible-standardized risk assessment tool.

BACKGROUND: Surgical patients are complex, vulnerable, and prone to postoperative complications that can potentially be mitigated with quality perioperative risk assessment and management. Several institutions have incorporated machine learning (ML) into their patient care to improve awareness and support clinician decision-making along the perioperative spectrum. Recent research suggests that ML risk prediction can support perioperative patient risk monitoring and management across several situations, including the operating room (OR) to intensive care unit (ICU) handoffs. OBJECTIVES: Our study objectives were threefold: (1) evaluate whether ML-generated postoperative predictions are concordant with clinician-generated risk rankings for acute kidney injury, delirium, pneumonia, deep vein thrombosis, and pulmonary embolism, and establish their associated risk factors; (2) ascertain clinician end-user suggestions to improve adoption of ML-generated risks and their integration into the perioperative workflow; and (3) develop a user-friendly visualization format for a tool to display ML-generated risks and risk factors to support postoperative care planning, for example, within the context of OR-ICU handoffs. METHODS: Graphical user interfaces for postoperative risk prediction models were assessed for end-user usability through cognitive walkthroughs and interviews with anesthesiologists, surgeons, certified registered nurse anesthetists, registered nurses, and critical care physicians. Thematic analysis relying on an explanation design framework was used to identify feedback and suggestions for improvement. RESULTS: 17 clinicians participated in the evaluation. ML estimates of complication risks aligned with clinicians' independent rankings, and related displays were perceived as valuable for decision-making and care planning for postoperative care. During OR-ICU handoffs, the tool could speed up report preparation and remind clinicians to address patient-specific complications, thus providing more tailored care information. Suggestions for improvement centered on electronic tool delivery; methods to build trust in ML models; modifiable risks and risk mitigation strategies; and additional patient information based on individual preferences (e.g., surgical procedure). CONCLUSIONS: ML estimates of postoperative complication risks can provide anticipatory guidance, potentially increasing the efficiency of care planning. We have offered an ML visualization framework for designing future ML-augmented tools and anticipate the development of tools that recommend specific actions to the user based on ML model output.

Breathe-squeeze: pharmacodynamics of a stimulus-free behavioural paradigm to track conscious states during sedation☆.

BACKGROUND: Conscious states are typically inferred through responses to auditory tasks and noxious stimulation. We report the use of a stimulus-free behavioural paradigm to track state transitions in responsiveness during dexmedetomidine sedation. We hypothesised that estimated dexmedetomidine effect-site (Ce) concentrations would be higher at loss of responsiveness (LOR) compared with return of responsiveness (ROR), and both would be lower than comparable studies that used stimulus-based assessments. METHODS: Closed-Loop Acoustic Stimulation during Sedation with Dexmedetomidine data were analysed for secondary analysis. Fourteen healthy volunteers were asked to perform the breathe-squeeze task of gripping a dynamometer when inspiring and releasing it when expiring. LOR was defined as five inspirations without accompanied squeezes; ROR was defined as the return of five inspirations accompanied by squeezes. Brain states were monitored using 64-channel EEG. Dexmedetomidine was administered as a target-controlled infusion, with Ce estimated from a pharmacokinetic model. RESULTS: Counter to our hypothesis, mean estimated dexmedetomidine Ce was lower at LOR (0.92 ng ml-1; 95% confidence interval: 0.69-1.15) than at ROR (1.43 ng ml-1; 95% confidence interval: 1.27-1.58) (paired t-test; P=0.002). LOR was characterised by progressively increasing fronto-occipital EEG power in the 0.5-8 Hz band and loss of occipital alpha (8-12 Hz) and global beta (16-30 Hz) power. These EEG changes reverted at ROR. CONCLUSIONS: The breathe-squeeze task can effectively track changes in responsiveness during sedation without external stimuli and might be more sensitive to state changes than stimulus-based tasks. It should be considered when perturbation of brain states is undesirable. CLINICAL TRIAL REGISTRATION: NCT04206059.

The posterior dominant rhythm: an electroencephalographic biomarker for cognitive recovery after general anaesthesia.

BACKGROUND: The posterior dominant rhythm (PDR) was the first oscillatory pattern noted in the EEG. Evoked by wakeful eyelid closure, these oscillations dissipate over seconds during loss of arousal. The peak frequency of the PDR maintains stability over years, suggesting utility as a state biomarker in the surveillance of acute cognitive impairments. This EEG signature has not been systematically investigated for tracking cognitive dysfunction after anaesthetic-induced loss of consciousness. METHODS: This substudy of Reconstructing Consciousness and Cognition (NCT01911195) investigated the PDR and cognitive function in 60 adult volunteers randomised to either 3 h of isoflurane general anaesthesia or resting wakefulness. Serial measurements of EEG power and cognitive task performance were assessed relative to pre-intervention baseline. Mixed-effects models allowed quantification of PDR and neurocognitive trajectories after return of responsiveness (ROR). RESULTS: Individuals in the control group showed stability in the PDR peak frequency over several hours (median difference/inter-quartile range [IQR] of 0.02/0.20 Hz, P=0.39). After isoflurane general anaesthesia, the PDR peak frequency was initially reduced at ROR (median difference/IQR of 0.88/0.65 Hz, P<0.001). PDR peak frequency recovered at a rate of 0.20 Hz h-1. After ROR, the PDR peak frequency correlated with reaction time and accuracy on multiple cognitive tasks (P<0.001). CONCLUSION: The temporal trajectory of the PDR peak frequency could be a useful perioperative marker for tracking cognitive dysfunction on the order of hours after surgery, particularly for cognitive domains of working memory, visuomotor speed, and executive function. CLINICAL TRIAL REGISTRATION: NCT01911195.

Postoperative Delirium Severity and Recovery Correlate With Electroencephalogram Spectral Features.

BACKGROUND: Delirium is an acute syndrome characterized by inattention, disorganized thinking, and an altered level of consciousness. A reliable biomarker for tracking delirium does not exist, but oscillations in the electroencephalogram (EEG) could address this need. We evaluated whether the frequencies of EEG oscillations are associated with delirium onset, severity, and recovery in the postoperative period. METHODS: Twenty-six adults enrolled in the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES; ClinicalTrials.gov NCT02241655) study underwent major surgery requiring general anesthesia, and provided longitudinal postoperative EEG recordings for this prespecified substudy. The presence and severity of delirium were evaluated with the confusion assessment method (CAM) or the CAM-intensive care unit. EEG data obtained during awake eyes-open and eyes-closed states yielded relative power in the delta (1-4 Hz), theta (4-8 Hz), and alpha (8-13 Hz) bands. Discriminability for delirium presence was evaluated with c-statistics. To account for correlation among repeated measures within patients, mixed-effects models were generated to assess relationships between: (1) delirium severity and EEG relative power (ordinal), and (2) EEG relative power and time (linear). Slopes of ordinal and linear mixed-effects models are reported as the change in delirium severity score/change in EEG relative power, and the change in EEG relative power/time (days), respectively. Bonferroni correction was applied to confidence intervals (CIs) to account for multiple comparisons. RESULTS: Occipital alpha relative power during eyes-closed states offered moderate discriminability (c-statistic, 0.75; 98% CI, 0.58-0.87), varying inversely with delirium severity (slope, -0.67; 98% CI, -1.36 to -0.01; P = .01) and with severity of inattention (slope, -1.44; 98% CI, -2.30 to -0.58; P = .002). Occipital theta relative power during eyes-open states correlated directly with severity of delirium (slope, 1.28; 98% CI, 0.12-2.44; P = .007), inattention (slope, 2.00; 98% CI, 0.48-3.54; P = .01), and disorganized thinking (slope, 3.15; 98% CI, 0.66-5.65; P = .01). Corresponding frontal EEG measures recapitulated these relationships to varying degrees. Severity of altered level of consciousness correlated with frontal theta relative power during eyes-open states (slope, 11.52; 98% CI, 6.33-16.71; P < .001). Frontal theta relative power during eyes-open states correlated inversely with time (slope, -0.05; 98% CI, -0.12 to -0.04; P = .002). CONCLUSIONS: Presence, severity, and core features of postoperative delirium covary with spectral features of the EEG. The cost and accessibility of EEG facilitate the translation of these findings to future mechanistic and interventional trials.

A survey of surgical patients' perspectives and preferences towards general anesthesia techniques and shared-decision making.

BACKGROUND: The decision about which type of general anesthetic to administer is typically made by the clinical team without patient engagement. This study examined patients' preferences, experiences, attitudes, beliefs, perceptions, and perceived social norms about anesthesia and about engaging in the decision regarding general anesthetic choice with their clinician. METHODS: We conducted a survey in the United States, sent to a panel of surgical patients through Qualtrics (Qualtrics, Provo, UT) from March 2022 through May 2022. Questions were developed based on the Theory of Planned Behavior and validated measures were used when available. A patient partner who had experienced both intravenous and inhaled anesthesia contributed to the development and refinement of the questions. RESULTS: A total of 806 patients who received general anesthesia for an elective procedure in the last five years completed the survey. 43% of respondents preferred a patient-led decision making role and 28% preferred to share decision making with their clinical team, yet only 7.8% reported being engaged in full shared decision making about the anesthesia they received. Intraoperative awareness, pain, nausea, vomiting and quickly returning to work and usual household activities were important to respondents. Waking up in the middle of surgery was the most commonly reported concern, despite this experience being reported only 8% of the time. Most patients (65%) who searched for information about general anesthesia noted that it took a lot of effort to find the information, and 53% agreed to feeling frustrated during the search. CONCLUSIONS: Most patients prefer a patient-led or shared decision making process when it comes to their anesthetic care and want to be engaged in the decision. However, only a small percentage of patients reported being fully engaged in the decision. Further studies should inform future shared decision-making tools, informed consent materials, educational materials and framing of anesthetic choices for patients so that they are able to make a choice regarding the anesthetic they receive.

A multi- and mixed-method adaptation study of a patient-centered perioperative mental health intervention bundle.

BACKGROUND: Anxiety and depression are common among older adults and can intensify during perioperative periods, but few mental health interventions are designed for older surgical patients' unique needs. As part of the feasibility trial, we developed and adapted a perioperative mental health (PMH) bundle for older patients comprised of behavioral activation (BA) and medication optimization (MO) to ameliorate anxiety and depressive symptoms before, during, and after cardiac, orthopedic, and oncologic surgery. METHODS: We used mixed-methods including workshop studios with patients, caregivers, clinicians, researchers, and interventionists; intervention refinement and reflection meetings; patient case review meetings; intervention session audio-recordings and documentation forms; and patient and caregiver semi-structured interviews. We used the results to refine our PMH bundle. We used multiple analytical approaches to report the nature of adaptations, including hybrid thematic analysis and content analysis informed by the Framework for Reporting Adaptations and Modifications - Expanded. RESULTS: Adaptations were categorized by content (intervention components), context (how the intervention is delivered, based on the study, target population, intervention format, intervention delivery mode, study setting, study personnel), training, and evaluation. Of 51 adaptations, 43.1% involved content, 41.2% involved context, and 15.7% involved training and evaluation. Several key adaptations were noted: (1) Intervention content was tailored to patient preferences and needs (e.g., rewording elements to prevent stigmatization of mental health needs; adjusting BA techniques and documentation forms to improve patient buy-in and motivation). (2) Cohort-specific adaptations were recommended based on differing patient needs. (3) Compassion was identified by patients as the most important element. CONCLUSIONS: We identified evidence-based mental health intervention components from other settings and adapted them to the perioperative setting for older adults. Informed by mixed-methods, we created an innovative and pragmatic patient-centered intervention bundle that is acceptable, feasible, and responsive to the needs of older surgical populations. This approach allowed us to identify implementation strategies to improve the reach, scalability, and sustainability of our bundle, and can guide future patient-centered intervention adaptations. CLINICAL TRIALS REGISTRATION: NCT05110690 (11/08/2021).

Awareness With Paralysis Among Critically Ill Emergency Department Patients: A Prospective Cohort Study.

OBJECTIVES: In mechanically ventilated patients, awareness with paralysis (AWP) can have devastating consequences, including post-traumatic stress disorder (PTSD), depression, and thoughts of suicide. Single-center data from the emergency department (ED) demonstrate an event rate for AWP factors higher than that reported from the operating room. However, there remains a lack of data on AWP among critically ill, mechanically ventilated patients. The objective was to assess the proportion of ED patients experiencing AWP and investigate modifiable variables associated with its occurrence. DESIGN: An a priori planned secondary analysis of a multicenter, prospective, before-and-after clinical trial. SETTING: The ED of three academic medical centers. PATIENTS: Mechanically ventilated adult patients that received neuromuscular blockers. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: All data related to sedation and analgesia were collected. AWP was the primary outcome, assessed with the modified Brice questionnaire, and was independently adjudicated by three expert reviewers. Perceived threat, in the causal pathway for PTSD, was the secondary outcome. A total of 388 patients were studied. The proportion of patients experiencing AWP was 3.4% ( n = 13), the majority of whom received rocuronium ( n = 12/13; 92.3%). Among patients who received rocuronium, 5.5% ( n = 12/230) experienced AWP, compared with 0.6% ( n = 1/158) among patients who did not receive rocuronium in the ED (odds ratio, 8.64; 95% CI, 1.11-67.15). Patients experiencing AWP had a higher mean ( sd ) threat perception scale score, compared with patients without AWP (15.6 [5.8] vs 7.7 [6.0]; p < 0.01). CONCLUSIONS: AWP was present in a concerning proportion of mechanically ventilated ED patients, was associated with rocuronium exposure in the ED, and led to increased levels of perceived threat, placing patients at greater risk for PTSD. Studies that aim to further quantify AWP in this vulnerable population and eliminate its occurrence are urgently needed.

The Feasibility of Implementing Targeted SEDation in Mechanically Ventilated Emergency Department Patients: The ED-SED Pilot Trial.

OBJECTIVES: Deep sedation in the emergency department (ED) is common, increases deep sedation in the ICU, and is negatively associated with outcome. Limiting ED deep sedation may, therefore, be a high-yield intervention to improve outcome. However, the feasibility of conducting an adequately powered ED-based clinical sedation trial is unknown. Our objectives were to assess trial feasibility in terms of: 1) recruitment, 2) protocol implementation and practice change, and 3) safety. Patient-centered clinical outcomes were assessed to better plan for a future large-scale clinical trial. DESIGN: Pragmatic, multicenter ( n = 3), prospective before-after pilot and feasibility trial. SETTING: The ED and ICUs at three medical centers. PATIENTS: Consecutive, adult mechanically ventilation ED patients. INTERVENTIONS: An educational initiative aimed at reliable ED sedation depth documentation and reducing the proportion of deeply sedated patients (primary outcome). MEASUREMENTS AND MAIN RESULTS: Sedation-related data in the ED and the first 48 ICU hours were recorded. Deep sedation was defined as a Richmond Agitation-Sedation Scale of -3 to -5 or a Sedation-Agitation Scale of 1-3. One thousand three hundred fifty-six patients were screened; 415 comprised the final population. Lighter ED sedation was achieved in the intervention group, and the proportion of deeply sedated patients was reduced from 60.2% to 38.8% ( p < 0.01). There were no concerning trends in adverse events (i.e., inadvertent extubation, device removal, and awareness with paralysis). Mortality was 10.0% in the intervention group and 20.4% in the preintervention group ( p < 0.01). Compared with preintervention, the intervention group experienced more ventilator-free days [22.0 (9.0) vs 19.9 (10.6)] and ICU-free days [20.8 (8.7) vs 18.1 (10.4)], p < 0.05 for both. CONCLUSIONS: This pilot trial confirmed the feasibility of targeting the ED in order to improve sedation practices and reduce deep sedation. These findings justify an appropriately powered clinical trial regarding ED-based sedation to improve clinical outcomes.

Trials with 'non-significant' results are not insignificant trials: a common significance threshold distorts reporting and interpretation of trial results.

We discuss a newly published study examining how phrases are used in clinical trials to describe results when the estimated P-value is close to (slightly above or slightly below) 0.05, which has been arbitrarily designated by convention as the boundary for 'statistical significance'. Terms such as 'marginally significant', 'trending towards significant', and 'nominally significant' are well represented in biomedical literature, but are not actually scientifically meaningful. Acknowledging that 'statistical significance' remains a major determinant of publication, we propose that scientific journals de-emphasise the use of P-values for null hypothesis significance testing, a purpose for which they were never intended, and avoid the use of these ambiguous and confusing terms in scientific articles. Instead, investigators could simply report their findings: effect sizes, P-values, and confidence intervals (or their Bayesian equivalents), and leave it to the discerning reader to infer the clinical applicability and importance. Our goal should be to move away from describing studies (or trials) as positive or negative based on an arbitrary P-value threshold, and rather to judge whether the scientific evidence provided is informative or uninformative.

Continuous real-time prediction of surgical case duration using a modular artificial neural network.

BACKGROUND: Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration. METHODS: Preoperative (e.g. procedure name) and intraoperative (e.g. medications and vital signs) variables were retrieved from anaesthetic records of surgeries performed between March 1, 2019 and October 31, 2019. A modular artificial neural network was developed and compared with a Bayesian approach and the scheduled surgical duration. Continuous ranked probability score (CRPS) was used as a measure of time error to assess model accuracy. For evaluating clinical performance, accuracy for each approach was assessed in identifying cases that ran beyond 15:00 (commonly scheduled end of shift), thus identifying opportunities to avoid overtime labour costs. RESULTS: The analysis included 70 826 cases performed at eight hospitals. The modular artificial neural network had the lowest time error (CRPS: mean=13.8; standard deviation=35.4 min), which was significantly better (mean difference=6.4 min [95% confidence interval: 6.3-6.5]; P<0.001) than the Bayesian approach. The modular artificial neural network also had the highest accuracy in identifying operating theatres that would overrun 15:00 (accuracy at 1 h prior=89%) compared with the Bayesian approach (80%) and a naïve approach using the scheduled duration (78%). CONCLUSIONS: A real-time neural network model using preoperative and intraoperative data had significantly better performance than a Bayesian approach or scheduled duration, offering opportunities to avoid overtime labour costs and reduce the cost of surgery by providing superior real-time information for perioperative decision support.

On the horns of a dilemma: choosing total intravenous anaesthesia or volatile anaesthesia.

There are two established techniques of delivering general anaesthesia: propofol-based total intravenous anaesthesia (TIVA) and volatile agent-based inhaled anaesthesia. Both techniques are offered as standard of care and have an established safety track record lasting more than 30 years. However, it is not currently known whether the choice of anaesthetic technique results in a fundamentally different patient experience or affects early, intermediate-term, and longer-term postoperative outcomes. This editorial comments on a recently published study that suggests that inhaled volatile anaesthesia might be associated with fewer postoperative surgical complications than propofol-based TIVA for patients undergoing colorectal cancer surgery. We consider the strengths and limitations of the study, place these findings in the context of the broader evidence, and discuss how the current controversies regarding anaesthetic technique can be resolved, thereby helping to bring precision medicine into the modern practice of perioperative care.