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BACKGROUND: Residual deep sedation during anesthesia recovery may predict postoperative complications. We examined the incidence and risk factors for deep sedation after general anesthesia. METHODS: We retrospectively reviewed health records of adults who underwent procedures with general anesthesia and were admitted to the postanesthesia care unit from May 2018 to December 2020. Patients were dichotomized by Richmond Agitation-Sedation Scale (RASS) score: ≤−4 (deeply sedated/unarousable) or ≥−3 (not deeply sedated). Anesthesia risk factors for deep sedation were assessed with multivariable logistic regression. RESULTS: Of the 56,275 patients included, 2003 had a RASS ≤−4 (35.6 [95% CI, 34.1–37.2] cases per 1000 anesthetics administered). On adjusted analyses, the likelihood of a RASS ≤−4 increased when more soluble halogenated anesthetics were used. Compared with desflurane without propofol, the odds ratio (OR [95% CI]) for a RASS ≤−4 was higher with sevoflurane (1.85 [1.45–2.37]) and isoflurane (4.21 [3.29–5.38]) without propofol. Compared with desflurane without propofol, the odds of a RASS ≤−4 further increased with use of desflurane-propofol (2.61 [1.99–3.42]), sevoflurane-propofol (4.20 [3.28–5.39]), isoflurane-propofol (6.39 [4.90–8.34]), and total intravenous anesthesia (2.98 [2.22–3.98]). A RASS ≤−4 was also more likely with the use of dexmedetomidine (2.47 [2.10–2.89]), gabapentinoids (2.17 [1.90–2.48]), and midazolam (1.34 [1.21–1.49]). Deeply sedated patients discharged to general care wards had higher odds of opioid-induced respiratory complications (2.59 [1.32–5.10]) and higher odds of naloxone administration (2.93 [1.42–6.03]). CONCLUSIONS: Likelihood of deep sedation after recovery increased with intraoperative use of halogenated agents with higher solubility and increased further when propofol was concomitantly used. Patients who experience deep sedation during anesthesia recovery have an increased risk of opioid-induced respiratory complications on general care wards. These findings may be useful for tailoring anesthetic management to reduce postoperative oversedation.

Background Diagnostic error (DE) is a common problem in clinical practice, particularly in the emergency department (ED) setting. Among ED patients presenting with cardiovascular or cerebrovascular/neurological symptoms, a delay in diagnosis or failure to hospitalise may be most impactful in terms of adverse outcomes. Minorities and other vulnerable populations may be at higher risk of DE. We aimed to systematically review studies reporting the frequency and causes of DE in under-resourced patients presenting to the ED with cardiovascular or cerebrovascular/neurological symptoms. Methods We searched EBM Reviews, Embase, Medline, Scopus and Web of Science from 2000 through 14 August 2022. Data were abstracted by two independent reviewers using a standardised form. The risk of bias (ROB) was assessed using the Newcastle-Ottawa Scale, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. Results Of the 7342 studies screened, we included 20 studies evaluating 7436,737 patients. Most studies were conducted in the USA, and one study was multicountry. 11 studies evaluated DE in patients with cerebrovascular/neurological symptoms, 8 studies with cardiovascular symptoms and 1 study examined both types of symptoms. 13 studies investigated missed diagnoses and 7 studies explored delayed diagnoses. There was significant clinical and methodological variability, including heterogeneity of DE definitions and predictor variable definitions as well as methods of DE assessment, study design and reporting. Among the studies evaluating cardiovascular symptoms, black race was significantly associated with higher odds of DE in 4/6 studies evaluating missed acute myocardial infarction (AMI)/acute coronary syndrome (ACS) diagnosis compared with white race (OR from 1.18 (1.12–1.24) to 4.5 (1.8–11.8)). The association between other analysed factors (ethnicity, insurance and limited English proficiency) and DE in this domain varied from study to study and was inconclusive. Among the studies evaluating DE in patients with cerebrovascular/neurological symptoms, no consistent association was found indicating higher or lower odds of DE. Although some studies showed significant differences, these were not consistently in the same direction. The overall ROB was low for most included studies; however, the certainty of evidence was very low, mostly due to serious inconsistency in definitions and measurement approaches across studies. Conclusions This systematic review demonstrated consistent increased odds of missed AMI/ACS diagnosis among black patients presenting to the ED compared with white patients in most studies. No consistent associations between demographic groups and DE related to cerebrovascular/neurological diagnoses were identified. More standardised approaches to study design, measurement of DE and outcomes assessment are needed to understand this problem among vulnerable populations. Trial registration number The study protocol was registered in the International Prospective Register of Systematic Reviews PROSPERO 2020 CRD42020178885 and is available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178885.

Diagnostic error or delay (DEOD) is common in the acute care setting and results in poor patient outcomes. Many factors contribute to DEOD, but little is known about how contributors may differ across acute care areas and professional roles. As part of a sequential exploratory mixed methods research study, we surveyed acute care clinical stakeholders about the frequency with which different factors contribute to DEOD. Survey respondents could also propose solutions in open text fields. N = 220 clinical stakeholders completed the survey. Care Team Interactions, Systems and Process, Patient, Provider, and Cognitive factors were perceived to contribute to DEOD with similar frequency. Organization and Infrastructure factors were perceived to contribute to DEOD significantly less often. Responses did not vary across acute care setting. Physicians perceived Cognitive factors to contribute to DEOD more frequently compared to those in other roles. Commonly proposed solutions included: technological solutions, organization level fixes, ensuring staff know and are encouraged to work to the full scope of their role, and cultivating a culture of collaboration and respect. Multiple factors contribute to DEOD with similar frequency across acute care areas, suggesting the need for a multi-pronged approach that can be applied across acute care areas.

Objectives: The objective of this derivation and validation study was to develop and validate a search strategy algorithm to detect patients who used professional interpreter services. Methods: We identified all adults who had at least one intensive care unit admission during their hospital stay across the Mayo Clinic Enterprise between 1 January 2015 and 30 June 2020. Three random subsets of 100 patients were extracted from 60,268 patients to develop the search strategy algorithm. Two physician reviewers conducted gold standard manual chart review and any discrepancies were resolved by a third reviewer. These results were compared with the search strategy algorithm each time it was refined. Sensitivity and specificity were calculated during each phase by comparing the search strategy results to the reference gold standard for both derivation cohorts and the final validation cohort. Results: The first search strategy resulted in a sensitivity of 100% and a specificity of 89%. The second revised search strategy achieved a sensitivity of 100% and a specificity of 87%. The final version of the search strategy was applied to the validation subset and sensitivity and specificity were 100% and 89%, respectively. Conclusion: We derived and validated a search strategy algorithm to assess interpreter use among hospitalized patients. Using a search strategy algorithm with high sensitivity and specificity can reduce the time required to abstract data from the electronic medical records compared with manual data abstraction.

Background Non-depolarizing neuromuscular blockade can be reversed with neostigmine/glycopyrrolate or sugammadex. We test the hypothesis that sugammadex is associated with earlier postoperative recovery of bowel function (first bowel movement, BM). Methods In adult patients undergoing craniotomy from 2016 to 2019, we identified time of first postoperative BM after receiving neostigmine/glycopyrrolate or sugammadex to reverse neuromuscular blockade. Logistic and proportional hazard regression, with and without inverse probability of treatment weighting (IPTW), were used to assess whether sugammadex is associated with earlier recovery of bowel function. Results Seven hundred and thirty-one patients underwent craniotomy, 323 (44.2%) received neostigmine/glycopyrrolate, and 408 (55.8%) sugammadex. From logistic regression analysis, the proportion of patients having a BM within the first 24 and 48 hours was higher in sugammadex group (unadjusted OR [95% CI]) 1.79 [1.16 to 2.77] P = .009; and 1.45 [1.08 to 1.94] P = .014; IPTW adjusted OR [95% CI]) 1.58 [.95, 2.61] P = .078; and 1.38 [.95 to 2.02] P = .095 for 24 and 48 h, respectively). From proportional hazards regression, sugammadex was associated with improved bowel function recovery (unadjusted hazard ratio (HR) [95% CI] 1.35 [1.08, 1.68], P = .008; IPTW adjusted HR 1.29 [.97 to 1.71], P = .076). Conclusion Patients undergoing craniotomy who had neuromuscular blockade reversed with sugammadex may have earlier recovered bowel function compared to patients reversed with neostigmine/glycopyrrolate.

Objectives Diagnostic error and delay is a prevalent and impactful problem. This study was part of a mixed-methods approach to understand the organizational, clinician, and patient factors contributing to diagnostic error and delay among acutely ill patients within a health system, as well as recommendations for the development of tailored, targeted, feasible, and effective interventions. Methods We did a multisite qualitative study using focus group methodology to explore the perspectives of key clinician stakeholders. We used a conceptual framework that characterized diagnostic error and delay as occurring within 1 of 3 stages of the patient’s diagnostic journey—critical information gathering, synthesis of key information, and decision making and communication. We developed our moderator guide based on the sociotechnical frameworks previously described by Holden and Singh for understanding noncognitive factors that lead to diagnostic error and delay. Deidentified focus group transcripts were coded in triplicate and to consensus over a series of meetings. A final coded data set was then uploaded into NVivo software. The data were then analyzed to generate overarching themes and categories. Results We recruited a total of 64 participants across 4 sites from emergency departments, hospital floor, and intensive care unit settings into 11 focus groups. Clinicians perceive that diverse organizational, communication and coordination, individual clinician, and patient factors interact to impede the process of making timely and accurate diagnoses. Conclusions This study highlights the complex sociotechnical system within which individual clinicians operate and the contributions of systems, processes, and institutional factors to diagnostic error and delay.

Objectives This study aimed to explore clinicians’ perceptions of the occurrence of and factors associated with diagnostic errors in patients evaluated during a rapid response team (RRT) activation or unplanned admission to the intensive care unit (ICU). Methods A multicenter prospective survey study was conducted among multiprofessional clinicians involved in the care of patients with RRT activations and/or unplanned ICU admissions (UIAs) at 2 academic hospitals and 1 community-based hospital between April 2019 and March 2020. A study investigator screened eligible patients every day. Within 24 hours of the event, a research coordinator administered the survey to clinicians, who were asked the following: whether diagnostic errors contributed to the reason for RRT/UIA, whether any new diagnosis was made after RRT/UIA, if there were any failures to communicate the diagnosis, and if involvement of specialists earlier would have benefited that patient. Patient clinical data were extracted from the electronic health record. Results A total of 1815 patients experienced RRT activations, and 1024 patients experienced UIA. Clinicians reported that 18.2% (95/522) of patients experienced diagnostic errors, 8.0% (42/522) experienced a failure of communication, and 16.7% (87/522) may have benefitted from earlier involvement of specialists. Compared with academic settings, clinicians in the community hospital were less likely to report diagnostic errors (7.0% versus 22.8%, P = 0.002). Conclusions Clinicians report a high rate of diagnostic errors in patients they evaluate during RRT or UIAs.

Abstract Background: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for Palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care. Methods: To that Aim we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist, and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary’s Hospital & Methodist Hospital, Mayo Clinic, Rochester, Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance.Discussion: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; Clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice whilst retaining scientific rigor.Trial registration NCT03976297 (ClinicalTrials.gov), registered on June 6, 2019 prior to trial start.

Abstract Lifestyle interventions are pivotal for successful management of type 2 diabetes (T2D), however, the proportion of people with T2D adhering to physical activity advice has not been thoroughly studied. The purpose of this systematic review was to summarise the evidence on adherence to exercise or physical activity components in lifestyle interventions in those with T2D. We searched MEDLINE EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Scopus on 12 November 2019. Eligible studies enrolled adults with T2D and reported the proportion of adherence to lifestyle interventions as a primary or secondary outcome. We included 11 studies (nine randomised controlled trials (RCTs) enrolling 1717 patients and two nonrandomised studies enrolling 62 patients). Only one of the studies had low risk of bias. The proportion of participants adhering to physical activity varied from 32% to 100% with a median of 58%. Adherence was higher in interventions using supervised training and lowest in interventions using remote coaching and the adherence rate in observational studies was higher compared to RCTs (92% vs. 55%; p < 0.01). Study duration, risk of bias, or participants' sex, were not associated with adherence to physical activity. The proportion of those with T2D adhering to physical activity interventions for T2D varies widely and most of the included studies had a high risk of bias. These findings have important implications for planning and power analysis of future trials and when counselling patients about lifestyle interventions including physical activity or exercise components.

Abstract Objective Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. Materials and Methods Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient’s corresponding care team. Results Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an “in-production” AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. Conclusions A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.