Background Telemedicine may help improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied. Methods This was a single-centre randomised clinical trial of unselected adult surgical patients. Patients were randomised to receive usual care or decision support from a telemedicine service, which provided real-time recommendations to intraoperative anaesthesia clinicians based on case reviews and physiological alerts. ORs were randomised 1:1. The co-primary outcomes were 30-day all-cause mortality, respiratory failure, acute kidney injury, and delirium in the intensive care unit, analysed by intention to treat. Results Between July 1, 2019, and January 31, 2023, a total of 35,302 patients were randomised to receive telemedicine support, with 36,625 receiving usual care. Telemedicine clinicians provided review in 11,812/35,302 cases, with alerts delivered to 2044/35,302 patients. Telemedicine support had no effect on any of the co-primary outcomes. Within 30 days, 630/35,302 (1.8%) patients randomised to telemedicine died within 30 days, compared with 649/36,625 (1.8%) receiving usual care (relative risk [RR]1.01, 95% confidence interval [CI] 0.87–1.16, P=0.98). Telemedicine support did not alter postoperative respiratory failure [telemedicine 1071/33,996 (3.2%) vs usual care 1130/35,236 (3.2%), RR 0.98, 95% CI 0.88–1.09, P=0.98], acute kidney injury [telemedicine 2316/33 251 (7.0%) vs usual care 2432/34,441 (7.1%); RR 0.99, 95% CI 0.92–1.06, P=0.98], or delirium [telemedicine 1264/3873 (32.6%) vs usual care 1298/4044 (32.1%), RR 1.02, 95% CI 0.94–1.10, P=0.98]. Conclusions In this large randomised clinical trial, intraoperative telemedicine decision support using real-time alerts and case reviews had no impact on adverse postoperative outcomes. Clinical trial registration NCT03923699.

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.

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.

Background: Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. Methods: This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Results: Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Conclusions: Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. Trial Registration: ClinicalTrials.govNCT05042804

IMPORTANCE Telemedicine for clinical decision support has been adopted in many health care settings, but its utility in improving intraoperative care has not been assessed. OBJECTIVE To pilot the implementation of a real-time intraoperative telemedicine decision support program and evaluate whether it reduces postoperative hypothermia and hyperglycemia as well as other quality of care measures.DESIGN, SETTING, AND PARTICIPANTS This single-center pilot randomized clinical trial (Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments [ACTFAST-3]) was conducted from April 3, 2017, to June 30, 2019, at a large academic medical center in the US. A total of 26 254 adult surgical patients were randomized to receive either usual intraoperative care (control group; n = 12 980) or usual care augmented by telemedicine decision support (intervention group; n = 13 274). Data were initially analyzed from April 22 to May 19, 2021, with updates in November 2022 and February 2023.INTERVENTION Patients received either usual care (medical direction from the anesthesia care team) or intraoperative anesthesia care monitored and augmented by decision support from the Anesthesiology Control Tower (ACT), a real-time, live telemedicine intervention. The ACT incorporated remote monitoring of operating rooms by a team of anesthesia clinicians with customized analysis software. The ACT reviewed alerts and electronic health record data to inform recommendations to operating room clinicians. MAIN OUTCOMES AND MEASURES The primary outcomes were avoidance of postoperative hypothermia (defined as the proportion of patients with a final recorded intraoperative core temperature >36 degrees C) and hyperglycemia (defined as the proportion of patients with diabetes who had a blood glucose level <= 180 mg/dL on arrival to the postanesthesia recovery area). Secondary outcomes included intraoperative hypotension, temperature monitoring, timely antibiotic redosing, intraoperative glucose evaluation and management, neuromuscular blockade documentation, ventilator management, and volatile anesthetic overuse. RESULTS Among 26 254 participants, 13 393 (51.0%) were female and 20169 (76.8%) were White, with a median (IQR) age of 60 (47-69) years. There was no treatment effect on avoidance of hyperglycemia (7445 of 8676 patients [85.8%] in the intervention group vs 7559 of 8815 [85.8%] in the control group; rate ratio [RR], 1.00; 95% CI, 0.99-1.01) or hypothermia (7602 of 11447 patients [66.4%] in the intervention group vs 7783 of 11 672 [66.7.%] in the control group; RR, 1.00; 95% CI, 0.97-1.02). Intraoperative glucose measurement was more common among patients with diabetes in the intervention group (RR, 1.07; 95% CI, 1.01-1.15), but other secondary outcomes were not significantly different.CONCLUSIONS AND RELEVANCE In this randomized clinical trial, anesthesia care quality measures did not differ between groups, with high confidence in the findings. These results suggest that the intervention did not affect the targeted care practices. Further streamlining of clinical decision support and workflows may help the intraoperative telemedicine program achieve improvement in targeted clinical measures.

Introduction: Models predicting intensive care unit (ICU) length of stay (LOS) typically use vital signs from the first several hours of ICU admission. However, LOS prediction before ICU arrival can enhance triage and resource allocation. Training such a model requires a large dataset of pre-ICU physiologic parameters, but no such public data are available. To overcome this limitation, we applied a meta-learning approach, training a neural network in a small pre-ICU dataset by leveraging information from related prediction tasks in large ICU datasets. Methods: The pre-ICU dataset included patients admitted to the ICU from the floor at Barnes-Jewish Hospital (BJH) in 2018-21. The ICU datasets included patients from the EICU Collaborative Research Database, MIMIC-IV database, and BJH. For in-ICU deaths, LOS was set to 100d. Model inputs included demographics, lab results, and vital signs during 4 hours before (pre-ICU dataset) or after (ICU datasets) ICU admission. First, these inputs were used to predict diagnoses related to LOS (e.g., respiratory failure, renal failure, sepsis, shock) in the EICU dataset. Next, parameters from those models were used to initialize a neural network predicting ICU LOS in the pooled ICU datasets. Finally, the model was fine-tuned using 70% of the pre-ICU dataset, and performance was measured in the remaining 30%. Performance was compared to three baseline models. Results: In the pooled ICU dataset, ICU LOS < 1d and 1-2d occurred in 58,414 (25% of 234,800) and 67,566 (29%) respectively. In the pre-ICU dataset, ICU LOS < 1d and 1-2d occurred in 676 (13% of 5037) and 1164 (23%). In the held-out pre-ICU testing sample, our meta-learning model achieved area under receiver operating characteristic curve (AUROC) 0.743 and area under precision recall curve (AUPRC) 0.194 for LOS < 1d and AUROC 0.759, AUPRC 0.204 for LOS 1-2d. Comparator model performance ranged AUROC 0.682-0.721, AUPRC 0.104-0.126 (LOS < 1d); AUROC 0.691-0.728, AUPRC 0.112-0.143 (LOS 1-2d). Conclusions: The meta-learning approach improved model performance relative to multiple comparators. We envision using model output during evaluation of a deteriorating floor patient to help determine whether to manage the patient in place using a critical care consult (if brief time of need is anticipated) or to transfer to the ICU.

Background: More than four million people die each year in the month following surgery, and many more experience complications such as acute kidney injury. Some of these outcomes may be prevented through early identification of at-risk patients and through intraoperative risk mitigation. Telemedicine has revolutionized the way at-risk patients are identified in critical care, but intraoperative telemedicine services are not widely used in anesthesiology. Clinicians in telemedicine settings may assist with risk stratification and brainstorm risk mitigation strategies while clinicians in the operating room are busy performing other patient care tasks. Machine learning tools may help clinicians in telemedicine settings leverage the abundant electronic health data available in the perioperative period. The primary hypothesis for this study is that anesthesiology clinicians can predict postoperative complications more accurately with machine learning assistance than without machine learning assistance. Methods: This investigation is a sub-study nested within the TECTONICS randomized clinical trial (NCT03923699). As part of TECTONICS, study team members who are anesthesiology clinicians working in a telemedicine setting are currently reviewing ongoing surgical cases and documenting how likely they feel the patient is to experience 30-day in-hospital death or acute kidney injury. For patients who are included in this sub-study, these case reviews will be randomized to be performed with access to a display showing machine learning predictions for the postoperative complications or without access to the display. The accuracy of the predictions will be compared across these two groups. Conclusion: Successful completion of this study will help define the role of machine learning not only for intraoperative telemedicine, but for other risk assessment tasks before, during, and after surgery. Registration: ORACLE is registered on ClinicalTrials.gov: NCT05042804; registered September 13, 2021.

Introduction: Quality improvement (QI) in healthcare results in better patient outcomes, healthcare system performance, and professional development. One target of QI initiatives in the perioperative period is surgical site infections (SSI), for which several risk factors have been identified. Reliable administration of indicated surgical antibiotic prophylaxis is a modifiable factor of particular relevance. We hypothesize that a novel telemedicine-augmented quality improvement program will improve administration of surgical antibiotic prophylaxis. Objectives: The objective of this QI study is to evaluate the utility of a telemedicine-augmented QI initiative on administration of timely surgical antibiotic prophylaxis. The incidence of SSI will also be reported for multiple surgical services. Methods: This will be a multi-center prospective before-and-after proof-of-concept study. Patients undergoing a surgical procedure across seven operating room facilities at four hospitals in the BJC Healthcare System will be included. Approximately 40,000 patients over an eight-month period will be enrolled. This eight-month period will include a baseline observational phase, an education intervention phase, an intervention phase employing real-time event detection with associated guidance from a remote telemedicine center, and a subsequent observational phase. The primary outcome will be administration of on-time surgical antibiotic prophylaxis throughout the trial. Other outcomes will include incidence of SSIs. Registration Information: This trial is registered on clinicaltrials.gov, NCT04983329 (30th July 2021).

Importance:Falls after elective inpatient surgical procedures are common and have physical, emotional, and financial consequences. Close interactions between patients and health care teams before and after surgical procedures may offer opportunities to address modifiable risk factors associated with falls. Objective:To assess whether a multicomponent intervention that incorporates education, home medication review, and home safety assessment is associated with reductions in the incidence of falls after elective inpatient surgical procedures. Design, Setting, and Participants:This prospective propensity score-matched cohort study was a prespecified secondary analysis of data from the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES) randomized clinical trial, which was conducted at a single academic medical center between January 16, 2015, and May 7, 2018. Patients in the intervention group of the present study were enrolled in either arm of the ENGAGES clinical trial. Patients in the control group were selected from the Systematic Assessment and Targeted Improvement of Services Following Yearly Surgical Outcomes Surveys prospective observational cohort study, which created a registry of patient-reported postoperative outcomes at the same single center. The propensity score-matched cohort in the present study included 1396 patients (698 pairs) selected from a pool of 2013 eligible patients. All patients underwent elective surgical procedures with general anesthesia and had a hospital stay of 2 or more days. Data were analyzed from January 2, 2020, to January 11, 2022. Interventions:The multicomponent safety intervention (offered to all patients in the ENGAGES clinical trial) included patient education on fall prevention techniques, home medication review by a geriatric psychiatrist (with communication of recommended changes to the surgeon), a self-administered home safety assessment, and targeted occupational therapy home visits with home hazard removal (offered to patients with a preoperative history of falls). Main Outcomes and Measures:The primary outcome was patient-reported falls within 1 year after an elective inpatient surgical procedure. The secondary outcome was quality of life 1 year after an elective surgical procedure, which was measured using the physical and mental composite summary scores on the Veterans RAND 12-item health survey (score range, 0-100 points, with 0 indicating lowest quality of life and 100 indicating highest quality of life). Results:Among 1396 patients, the median age was 69 years (IQR, 64-75 years), and 739 patients (52.9%) were male. With regard to race, 5 patients (0.4%) were Asian, 97 (6.9%) were Black or African American, 2 (0.1%) were Native Hawaiian or Pacific Islander, 1237 (88.6%) were White, 3 (0.2%) were of other race, and 52 (3.7%) were of unknown race; with regard to ethnicity, 12 patients (0.9%) were Hispanic or Latino, 1335 (95.6%) were non-Hispanic or non-Latino, and 49 (3.5%) were of unknown ethnicity. Adherence to individual intervention components was modest (from 22.9% for completion of the self-administered home safety assessment to 28.2% for implementation of the geriatric psychiatrist's recommended medication changes). Falls within 1 year after surgical procedures were reported by 228 of 698 patients (32.7%) in the intervention group and 225 of 698 patients (32.2%) in the control group. No significant difference was found in falls between the 2 groups (standardized risk difference, 0.4%; 95% CI, -4.5% to 5.3%). After adjusting for preoperative quality of life, patients in the intervention group had higher physical composite summary scores (3.8 points; 95% CI, 2.4-5.1 points) and higher mental composite summary scores (5.7 points; 95% CI, 4.7-6.7 points) at 1 year compared with patients in the control group. Conclusions and Relevance:In this cohort study, a multicomponent safety intervention was not associated with reductions in falls within the first year after an elective surgical procedure; however, an increase in quality of life at 1 year was observed. These results suggest a need for other interventions, such as those designed to increase adherence, to lower the incidence of falls after surgical procedures.

Background: Intraoperative EEG suppression duration has been associated with postoperative delirium and mortality. In a clinical trial testing anaesthesia titration to avoid EEG suppression, the intervention did not decrease the incidence of postoperative delirium, but was associated with reduced 30-day mortality. The present study evaluated whether the EEG-guided anaesthesia intervention was also associated with reduced 1-yr mortality. Methods: This manuscript reports 1 yr follow-up of subjects from a single-centre RCT, including a post hoc secondary outcome (1-yr mortality) in addition to pre-specified secondary outcomes. The trial included subjects aged 60 yr or older undergoing surgery with general anaesthesia between January 2015 and May 2018. Patients were randomised to receive EEG-guided anaesthesia or usual care. The previously reported primary outcome was postoperative delirium. The outcome of the current study was all-cause 1-yr mortality. Results: Of the 1232 subjects enrolled, 614 subjects were randomised to EEG-guided anaesthesia and 618 subjects to usual care. One-year mortality was 57/591 (9.6%) in the guided group and 62/601 (10.3%) in the usual-care group. No significant difference in mortality was observed (adjusted absolute risk difference, -0.7%; 99.5% confidence interval, -5.8% to 4.3%; P=0.68). Conclusions: An EEG-guided anaesthesia intervention aiming to decrease duration of EEG suppression during surgery did not significantly decrease 1-yr mortality. These findings, in the context of other studies, do not provide supportive evidence for EEG-guided anaesthesia to prevent intermediate term postoperative death.

Background: Preoperative cognitive dysfunction has been associated with adverse postoperative outcomes. There are limited data characterising the epidemiology of preoperative cognitive dysfunction in older surgical patients. Methods: This retrospective cohort included all patients >= 65 yr old seen at the Washington University preoperative clinic between January 2013 and June 2018. Cognitive screening was performed using the Short-Blessed Test (SBT) and Eight-Item Interview to Differentiate Aging and Dementia (AD8) screen. The primary outcome of abnormal cognitive screening was defined as SBT score >= 5 or AD8 score >= 2. Multivariable logistic regression was used to identify associated factors. Results: Overall, 21 666 patients >= 65 yr old completed screening during the study period; 23.5% (n=5099) of cognitive screens were abnormal. Abnormal cognitive screening was associated with increasing age, decreasing BMI, male sex, non-Caucasian race, decreased functional independence, and decreased metabolic functional capacity. Patients with a history of stroke or transient ischaemic attack, chronic obstructive pulmonary disease, diabetes mellitus, hepatic cirrhosis, and heavy alcohol use were also more likely to have an abnormal cognitive screen. Predictive modelling showed no combination of patient factors was able to reliably identify patients who had a <10% probability of abnormal cognitive screening. Conclusions: Routine preoperative cognitive screening of unselected aged surgical patients often revealed deficits consistent with cognitive impairment or dementia. Such deficits were associated with increased age, decreased function, decreased BMI, and several common medical comorbidities. Further research is necessary to characterise the clinical implications of preoperative cognitive dysfunction and identify interventions that may reduce related postoperative complications.

IMPORTANCE Intraoperative electroencephalogram (EEG) waveform suppression, often suggesting excessive general anesthesia, has been associated with postoperative delirium. OBJECTIVE To assess whether EEG-guided anesthetic administration decreases the incidence of postoperative delirium. DESIGN, SETTING, AND PARTICIPANTS Randomized clinical trial of 1232 adults aged 60 years and older undergoing major surgery and receiving general anesthesia at Barnes-Jewish Hospital in St Louis. Recruitment was from January 2015 to May 2018, with follow-up until July 2018. INTERVENTIONS Patients were randomized 1:1 (stratified by cardiac vs noncardiac surgery and positive vs negative recent fall history) to receive EEG-guided anesthetic administration (n = 614) or usual anesthetic care (n = 618). MAIN OUTCOMES AND MEASURES The primary outcome was incident delirium during postoperative days 1 through 5. Intraoperative measures included anesthetic concentration, EEG suppression, and hypotension. Adverse events included undesirable intraoperative movement, intraoperative awareness with recall, postoperative nausea and vomiting, medical complications, and death. RESULTS Of the 1232 randomized patients (median age, 69 years [range, 60 to 95]; 563 women [45.7%]), 1213 (98.5%) were assessed for the primary outcome. Delirium during postoperative days 1 to 5 occurred in 157 of 604 patients (26.0%) in the guided group and 140 of 609 patients (23.0%) in the usual care group (difference, 3.0%[95% CI, -2.0% to 8.0%]; P = .22). Median end-tidal volatile anesthetic concentration was significantly lower in the guided group than the usual care group (0.69 vs 0.80 minimum alveolar concentration; difference, -0.11 [95% CI, -0.13 to -0.10), and median cumulative time with EEG suppression was significantly less (7 vs 13 minutes; difference, -6.0[95% CI, -9.9 to -2.1]). There was no significant difference between groups in the median cumulative time with mean arterial pressure below 60 mmHg (7 vs 7 minutes; difference, 0.0[95% CI, -1.7 to 1.7]). Undesirable movement occurred in 137 patients (22.3%) in the guided and 95 (15.4%) in the usual care group. No patients reported intraoperative awareness. Postoperative nausea and vomiting was reported in 48 patients (7.8%) in the guided and 55 patients (8.9%) in the usual care group. Serious adverse events were reported in 124 patients (20.2%) in the guided and 130 (21.0%) in the usual care group. Within 30 days of surgery, 4 patients (0.65%) in the guided group and 19 (3.07%) in the usual care group died. CONCLUSIONS AND RELEVANCE Among older adults undergoing major surgery, EEG-guided anesthetic administration, compared with usual care, did not decrease the incidence of postoperative delirium. This finding does not support the use of EEG-guided anesthetic administration for this indication.

BACKGROUND:Post-surgical pain that lingers beyond the initial few-week period of tissue healing is a major predictor of pain chronification, which leads to substantial disability and new persistent opioid analgesic use. We investigated whether postoperative medical complications increase the risk of lingering post-surgical pain.METHODS:The study population consisted of patients undergoing diverse elective surgical procedures in an academic referral centre in the USA, between September 2013 and May 2017. Multivariable logistic regression, adjusting for confounding variables and patient-specific risk factors, was used to test for an independent association between any major postoperative complication and functionally limiting lingering pain 1-3 months after surgery, as obtained from patient self-reports.RESULTS:The cohort included 11 986 adult surgical patients; 10 562 with complete data. At least one complication (cardiovascular, respiratory, renal/gastrointestinal, wound, thrombotic, or neural) was reported by 13.3% (95% confidence interval: 12.7-14.0) of patients, and 19.7% (19.0-20.5%) reported functionally limiting lingering post-surgical pain. After adjusting for known risk factors, the patients were twice as likely (odds ratio: 2.04; 1.78-2.35) to report lingering post-surgical pain if they also self-reported a postoperative complication. Experiencing a complication was also independently predictive of lingering post-surgical pain (odds ratio: 1.95; 1.26-3.04) when complication data were extracted from the National Surgical Quality Improvement Program registry, instead of being obtained from patient self-report.CONCLUSIONS:Medical complications were associated with a two-fold increase in functionally limiting pain 1-3 months after surgery. Understanding the mechanisms that link complications to pathological persistence of pain could help develop future approaches to prevent persistent post-surgical pain.

Introduction Mortality and morbidity following surgery are pressing public health concerns in the USA. Traditional prediction models for postoperative adverse outcomes demonstrate good discrimination at the population level, but the ability to forecast an individual patient’s trajectory in real time remains poor. We propose to apply machine learning techniques to perioperative time-series data to develop algorithms for predicting adverse perioperative outcomes. Methods and analysis This study will include all adult patients who had surgery at our tertiary care hospital over a 4-year period. Patient history, laboratory values, minute-by-minute intraoperative vital signs and medications administered will be extracted from the electronic medical record. Outcomes will include in-hospital mortality, postoperative acute kidney injury and postoperative respiratory failure. Forecasting algorithms for each of these outcomes will be constructed using density-based logistic regression after employing a Nadaraya-Watson kernel density estimator. Time-series variables will be analysed using first and second-order feature extraction, shapelet methods and convolutional neural networks. The algorithms will be validated through measurement of precision and recall. Ethics and dissemination This study has been approved by the Human Research Protection Office at Washington University in St Louis. The successful development of these forecasting algorithms will allow perioperative healthcare clinicians to predict more accurately an individual patient’s risk for specific adverse perioperative outcomes in real time. Knowledge of a patient’s dynamic risk profile may allow clinicians to make targeted changes in the care plan that will alter the patient’s outcome trajectory. This hypothesis will be tested in a future randomised controlled trial.

Background: Intraoperative electroencephalogram suppression, suggesting excessive general anaesthesia, predicts postoperative delirium. The primary purpose of this trial was to assess the effectiveness of electroencephalography-guided minimisation of anaesthetic administration and electroencephalogram suppression to prevent postoperative delirium in older adults.