Search

Objective This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) techniques to identify and classify documentation of preoperative cannabis use status. Materials and Methods We developed and applied a keyword search strategy to identify documentation of preoperative cannabis use status in clinical documentation within 60 days of surgery. We manually reviewed matching notes to classify each documentation into 8 different categories based on context, time, and certainty of cannabis use documentation. We applied 2 conventional ML and 3 deep learning models against manual annotation. We externally validated our model using the MIMIC-III dataset. Results The tested classifiers achieved classification results close to human performance with up to 93% and 94% precision and 95% recall of preoperative cannabis use status documentation. External validation showed consistent results with up to 94% precision and recall. Discussion Our NLP model successfully replicated human annotation of preoperative cannabis use documentation, providing a baseline framework for identifying and classifying documentation of cannabis use. We add to NLP methods applied in healthcare for clinical concept extraction and classification, mainly concerning social determinants of health and substance use. Our systematically developed lexicon provides a comprehensive knowledge-based resource covering a wide range of cannabis-related concepts for future NLP applications. Conclusion We demonstrated that documentation of preoperative cannabis use status could be accurately identified using an NLP algorithm. This approach can be employed to identify comparison groups based on cannabis exposure for growing research efforts aiming to guide cannabis-related clinical practices and policies.

Background Specialized proresolution molecules (SPMs) halt the transition to chronic pathogenic inflammation. We aimed to quantify serum levels of pro- and anti-inflammatory bioactive lipids in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients, and to identify potential relationships with innate responses and clinical outcome. Methods Serum from 50 hospital admitted inpatients (22 female, 28 male) with confirmed symptomatic SARS-CoV-2 infection and 94 age- and sex-matched controls collected prior to the pandemic (SARS-CoV-2 negative), were processed for quantification of bioactive lipids and anti-nucleocapsid and anti-spike quantitative binding assays. Results SARS-CoV-2 serum had significantly higher concentrations of omega-6–derived proinflammatory lipids and omega-6– and omega-3–derived SPMs, compared to the age- and sex-matched SARS-CoV-2–negative group, which were not markedly altered by age or sex. There were significant positive correlations between SPMs, proinflammatory bioactive lipids, and anti-spike antibody binding. Levels of some SPMs were significantly higher in patients with an anti-spike antibody value >0.5. Levels of linoleic acid and 5,6-dihydroxy-8Z,11Z,14Z-eicosatrienoic acid were significantly lower in SARS-CoV-2 patients who died. Conclusions SARS-CoV-2 infection was associated with increased levels of SPMs and other pro- and anti-inflammatory bioactive lipids, supporting the future investigation of the underlying enzymatic pathways, which may inform the development of novel treatments.

The site-selective modification of peptides and proteins facilitates the preparation of targeted therapeutic agents and tools to interrogate fundamental biochemistry. Among the numerous bioconjugation techniques developed to install groups of interest, those that generate C(sp3)-C(sp3) bonds are significantly underrepresented despite affording proteolytically stable, biogenic linkages. Herein we describe a visible-light-mediated reaction that enables the site-selective modification of peptides and proteins via desulfurative C(sp3)-C(sp3) bond formation. The reaction is rapid and high yielding in peptide systems, with comparable translation to proteins. Using this chemistry, we demonstrate the installation of a range of moieties into model systems and successfully integrate an effective PTM-mimic into a recombinantly expressed histone.

Although SARS-CoV-2 vaccines are very safe, we report 4 cases of the bifacial weakness with paresthesias variant of Guillain-Barre syndrome (GBS) occurring within 3 weeks of vaccination with the Oxford-AstraZeneca SARS-CoV-2 vaccine. This rare neurological syndrome has previously been reported in association with SARS-CoV-2 infection itself. Our cases were given either intravenous immunoglobulin, oral steroids, or no treatment. We suggest vigilance for cases of bifacial weakness with paresthesias variant GBS following vaccination for SARS-CoV-2 and that postvaccination surveillance programs ensure robust data capture of this outcome, to assess for causality. ANN NEUROL 2021;90:315-318.

Physicians use perioperative decision-support tools to mitigate risks and maximize benefits to achieve the most successful outcome for patients. Contemporary risk-assessment practices augment surgeon’s judgement and experience with decision-support algorithms driven by big data and machine learning. These algorithms accurately assess risk for a wide range of postoperative complications by parsing large datasets and performing complex calculations that would be cumbersome for busy clinicians. Even with these advancements, large gaps in perioperative risk assessment remain; decision-support algorithms often cannot account for risk-reduction therapies applied during a patient’s perioperative course, and do not quantify tradeoffs between competing goals of care (e.g., balancing postoperative pain control with the risk of respiratory depression or balancing intraoperative volume resuscitation with risk for complications from pulmonary edema). Multi-objective optimization solutions have been applied to similar problems successfully, but have not yet been applied to perioperative decision-support. Given the large volume of data available via electronic medical records, including intraoperative data, it is now feasible to successfully apply multi-objective optimization in perioperative care. Clinical application of multi-objective optimization would require semiautomated pipelines for analytics and reporting model outputs and a careful development and validation process. Under these circumstances, multi-objective optimization has the potential to support personalized, patient-centered, shared decision-making with precision and balance.

We test the hypothesis that for low-acuity surgical patients, postoperative intensive care unit (ICU) admission is associated with lower value of care compared with ward admission.Overtriaging low-acuity patients to ICU consumes valuable resources and may not confer better patient outcomes. Associations among postoperative overtriage, patient outcomes, costs, and value of care have not been previously reported.In this longitudinal cohort study, postoperative ICU admissions were classified as overtriaged or appropriately triaged according to machine learning-based patient acuity assessments and requirements for immediate postoperative mechanical ventilation or vasopressor support. The nearest neighbors algorithm identified risk-matched control ward admissions. The primary outcome was value of care, calculated as inverse observed-to-expected mortality ratios divided by total costs.Acuity assessments had an area under the receiver operating characteristic curve of 0.92 in generating predictions for triage classifications. Of 8592 postoperative ICU admissions, 423 (4.9%) were overtriaged. These were matched with 2155 control ward admissions with similar comorbidities, incidence of emergent surgery, immediate postoperative vital signs, and do not resuscitate order placement and rescindment patterns. Compared with controls, overtraiged admissions did not have a lower incidence of any measured complications. Total costs for admission were $16.4K for overtriage and $15.9K for controls (P=0.03). Value of care was lower for overtriaged admissions [2.9 (2.0-4.0)] compared with controls [24.2 (14.1-34.5), P0.001].Low-acuity postoperative patients who were overtriaged to ICUs had increased total costs, no improvements in outcomes, and received low-value care.

Background: Advantages of digital clock drawing metrics for dementia subtype classification needs examination. Objective: To assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer’s disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer’s disease (AD) versus vascular dementia (VaD). Methods: Healthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer’s disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models. Results: When HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features. Conclusion: The dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes.

BACKGROUND: Evidence suggests that increased early postoperative pain (POP) intensities are associated with increased pain in the weeks following surgery. However, it remains unclear which temporal aspects of this early POP relate to later pain experience. In this prospective cohort study, we used wavelet analysis of clinically captured POP intensity data on postoperative days 1 and 2 to characterize slow/fast dynamics of POP intensities and predict pain outcomes on postoperative day 30. METHODS: The study used clinical POP time series from the first 48 hours following surgery from 218 patients to predict their mean POP on postoperative day 30. We first used wavelet analysis to approximate the POP series and to represent the series at different time scales to characterize the early temporal profile of acute POP in the first two postoperative days. We then used the wavelet coefficients alongside demographic parameters as inputs to a neural network to predict the risk of severe pain 30 days after surgery. RESULTS: Slow dynamic approximation components, but not fast dynamic detailed components, were linked to pain intensity on postoperative day 30. Despite imbalanced outcome rates, using wavelet decomposition along with a neural network for classification, the model achieved an F-score of 0.79 and area under the receiver operating characteristic curve of 0.74 on test-set data for classifying pain intensities on postoperative day 30. The wavelet-based approach outperformed logistic regression (F-score of 0.31) and neural network (F-score of 0.22) classifiers that were restricted to sociodemographic variables and linear trajectories of pain intensities. CONCLUSIONS: These findings identify latent mechanistic information within the temporal domain of clinically documented acute POP intensity ratings, which are accessible via wavelet analyses, and demonstrate that such temporal patterns inform pain outcomes at postoperative day 30. CLINICAL TRIAL NUMBER AND REGISTRY URL: Clinicaltrials.gov, NCT02407743

Background Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. This review assimilates and critically evaluates available evidence and identifies opportunities to improve surgical ward safety. Data sources Fifty-eight articles from Cochrane Library, EMBASE, and PubMed databases were included. Conclusions Only 15–20% of patients suffering ward arrest survive. In most cases, subtle signs of instability often occur prior to critical illness and arrest, and underlying pathology is reversible. Coarse risk assessments lead to under-triage of high-risk patients to wards, where surveillance for complications depends on time-consuming manual review of health records, infrequent patient assessments, prediction models that lack accuracy and autonomy, and biased, error-prone decision-making. Streaming electronic heath record data, wearable continuous monitors, and recent advances in deep learning and reinforcement learning can promote efficient and accurate risk assessments, earlier recognition of instability, and better decisions regarding diagnosis and treatment of reversible underlying pathology.

Dendritic cells (DCs) are professional antigen presenting cells that play a critical role in bridging innate and adaptive immunity. Numerous studies have shown that tobacco constituents present in conventional cigarettes affect the phenotype and function of DCs; however, no studies have examined the effects of vapour from E-cigarettes on human DCs. Here, the effects of E-cigarette vapour extract (ECVE) on the phenotype and function of DCs were investigated by creating an in vitro cell culture model using human monocyte-derived DCs (MoDCs). Immature DCs were generated from peripheral blood monocytes and mature DCs were then produced by treatment with LPS or Poly I:C for 24 h. For LPS-matured DCs, 3% ECVE treatment slightly suppressed HLA-DR and CD86 expression, whereas 1% ECVE treatment enhanced IL-6 production. The overall expression of 29 signalling molecules and other cytoplasmic proteins (mainly associated with DC activation) was significantly upregulated in immature DCs by 1% ECVE, and in LPS-treated DCs by 3% ECVE. In particular, the condition that induced IL-6 production also upregulated MAPK pathway activation. These findings indicate that E-cigarette vapour moderately affects human DCs, but the effects are less pronounced than those reported for tobacco smoke. Electronic supplementary material The online version of this article (10.1007/s00204-020-02757-8) contains supplementary material, which is available to authorized users.

Objective The relationship between preexisting osteoarthritic pain and subsequent post-total knee arthroplasty (TKA) pain is not well defined. This knowledge gap makes diagnosis of post-TKA pain and development of management plans difficult and may impair future investigations on personalized care. Therefore, a set of diagnostic criteria for identification of acute post-TKA pain would inform standardized management and facilitate future research. Methods The Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public–private partnership with the US Food and Drug Administration (FDA), the American Pain Society (APS), and the American Academy of Pain Medicine (AAPM) formed the ACTTION-APS-AAPM Pain Taxonomy (AAAPT) initiative to address this goal. A multidisciplinary work group of pain experts was invited to conceive diagnostic criteria and dimensions of acute post-TKA pain. Results The working group used contemporary literature combined with expert opinion to generate a five-dimensional taxonomical structure based upon the AAAPT framework (i.e., core diagnostic criteria, common features, modulating factors, impact/functional consequences, and putative mechanisms) that characterizes acute post-TKA pain. Conclusions The diagnostic criteria created are proposed to define the nature of acute pain observed in patients following TKA.

Objective Develop unifying definitions and paradigms for data-driven methods to augment postoperative resource intensity decisions. Summary background data Postoperative level-of-care assignments and frequency of vital sign and laboratory measurements (i.e., resource intensity) should align with patient acuity. Effective, data-driven decision-support platforms could improve value of care for millions of patients annually, but their development is hindered by the lack of salient definitions and paradigms. Methods Embase, PubMed, and Web of Science were searched for articles describing patient acuity and resource intensity after inpatient surgery. Study quality was assessed using validated tools. Thirty-five studies were included and assimilated according to PRISMA guidelines. Results Perioperative patient acuity is accurately represented by combinations of demographic, physiologic, and hospital-system variables as input features in models that capture complex, non-linear relationships. Intraoperative physiologic data enriches these representations. Triaging high-acuity patients to low-intensity care is associated with increased risk for mortality; triaging low-acuity patients to ICUs has low value and imparts harm when other, valid requests for ICU admission are denied due to resource limitations, increasing their risk for unrecognized decompensation and failure-to-rescue. Providing high-intensity care for low-acuity patients may also confer harm through unnecessary testing and subsequent treatment of incidental findings, but there is insufficient evidence to evaluate this hypothesis. Compared with data-driven models, clinicians exhibit volatile performance in predicting complications and making postoperative resource intensity decisions. Conclusions To optimize value, postoperative resource intensity decisions should align with precise, data-driven patient acuity assessments augmented by models that accurately represent complex, non-linear relationships among risk factors.

Objective. In 2019, the University of Florida College of Medicine launched the MySurgeryRisk algorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record. Approach. This project was developed in parallel with our Intelligent Critical Care Center and represents a culmination of efforts to build an efficient and accurate model for data processing and predictive analytics. Main Results and Significance. This paper discusses how our model was constructed and improved upon. We highlight the consolidation of the database, processing of fixed and time-series physiologic measurements, development and training of predictive models, and expansion of those models into different aspects of patient assessment and treatment. We end by discussing future directions of the model.

BackgroundSpecialised pro-resolution molecules (SPMs) halt the transition to chronic pathogenic inflammation. We aimed to quantify serum levels of pro- and anti-inflammatory bioactive lipids in SARS-CoV-2 patients, and to identify potential relationships with innate responses and clinical outcome.MethodsSerum from 50 hospital admitted inpatients (22 female, 28 male) with confirmed symptomatic SARS-CoV-2 infection and 94 age and sex matched cohort collected prior to the pandemic, were processed for quantification of bioactive lipids. Anti-nucleocapsid and anti-spike quantitative binding assays were performed.ResultsSARS-CoV-2 serum had significantly higher concentrations of omega-6 derived pro-inflammatory lipids and omega-6 and omega-3 derived SPMs, compared to age and sex matched controls. Levels of SPMs were not markedly altered by age. There were significant positive correlations between SPMs and other bioactive lipids and anti-spike antibody binding. Levels of some SPMs were significantly higher in patients with an anti-spike antibody value >0.5. Levels of linoleic acid (LA) and 5,6-dihydroxy-8Z,11Z,14Z-eicosatrienoic acid (5,6-DHET) were significantly lower in SARS-COV-2 patients who died.DiscussionSARS-COV-2 infection was associated with a robust activation of the pathways that generate the specialised pro-resolution molecules and other anti-inflammatory bioactive lipids, supporting the future investigation of these pathways which may inform the development of novel treatments.

This cross-sectional study investigates the association of undertriage to hospital wards after surgical procedures with mortality, morbidity, and resource use., Key Points Question Is postoperative undertriage associated with increased mortality, morbidity, and resource use? Findings In this cross-sectional study of 14 890 postoperative admissions, undertriage to hospital wards was associated with increased mortality and morbidity compared with admissions that had similar risk profiles and were triaged to intensive care units. Postoperative undertriage was identifiable using automated preoperative and intraoperative data as features for real-time machine-learning models. Meaning These findings suggest that there is a rationale and framework for clinical decision support platforms to augment postoperative triage decisions., Importance Undertriaging patients who are at increased risk for postoperative complications after surgical procedures to low-acuity hospital wards (ie, floors) rather than highly vigilant intensive care units (ICUs) may be associated with risk of unrecognized decompensation and worse patient outcomes, but evidence for these associations is lacking. Objective To test the hypothesis that postoperative undertriage is associated with increased mortality and morbidity compared with risk-matched ICU admission. Design, Setting, and Participants This longitudinal cross-sectional study was conducted using data from the University of Florida Integrated Data Repository on admissions to a university hospital. Included patients were individuals aged 18 years or older who were admitted after a surgical procedure from June 1, 2014, to August 20, 2020. Data were analyzed from April through August 2021. Exposures Ward admissions were considered undertriaged if their estimated risk for hospital mortality or prolonged ICU stay (ie, ≥48 hours) was in the top quartile among all inpatient surgical procedures according to a validated machine-learning model using preoperative and intraoperative electronic health record features available at surgical procedure end time. A nearest neighbors algorithm was used to identify a risk-matched control group of ICU admissions. Main Outcomes and Measures The primary outcomes of hospital mortality and morbidity were compared among appropriately triaged ward admissions, undertriaged wards admissions, and a risk-matched control group of ICU admissions. Results Among 12 348 postoperative ward admissions, 11 042 admissions (89.4%) were appropriately triaged (5927 [53.7%] women; median [IQR] age, 59 [44-70] years) and 1306 admissions (10.6%) were undertriaged and matched with a control group of 2452 ICU admissions. The undertriaged group, compared with the control group, had increased median [IQR] age (64 [54-74] years vs 62 [50-73] years; P = .001) and increased proportions of women (649 [49.7%] women vs 1080 [44.0%] women; P

Gabapentinoids are recommended by guidelines as a component of multimodal analgesia to manage postoperative pain and reduce opioid use. It remains unknown whether perioperative use of gabapentinoids is associated with a reduced or increased risk of postoperative long-term opioid use (LTOU) after total knee or hip arthroplasty (TKA/THA).Using Medicare claims data from 2011 to 2018, we identified fee-for-service beneficiaries aged ≥ 65 years who were hospitalized for a primary TKA/THA and had no LTOU before the surgery. Perioperative use of gabapentinoids was measured from 7 days preadmission through 7 days postdischarge. Patients were required to receive opioids during the perioperative period and were followed from day 7 postdischarge for 180 days to assess postoperative LTOU (ie, ≥90 consecutive days). A modified Poisson regression was used to estimate the relative risk (RR) of postoperative LTOU in patients with versus without perioperative use of gabapentinoids, adjusting for confounders through propensity score weighting.Of 52,788 eligible Medicare older beneficiaries (mean standard deviation [SD] age 72.7 [5.3]; 62.5% females; 89.7% White), 3,967 (7.5%) received gabapentinoids during the perioperative period. Postoperative LTOU was 3.8% in patients with and 4.0% in those without perioperative gabapentinoids. After adjusting for confounders, the risk of postoperative LTOU was similar comparing patients with versus without perioperative gabapentinoids (RR = 1.07; 95% confidence interval [CI] = 0.91-1.26, P = .408). Sensitivity and bias analyses yielded consistent results.Among older Medicare beneficiaries undergoing a primary TKA/THA, perioperative use of gabapentinoids was not associated with a reduced or increased risk for postoperative LTOU.

During the early stages of hospital admission, clinicians use limited information to make decisions as patient acuity evolves. We hypothesized that clustering analysis of vital signs measured within six hours of hospital admission would reveal distinct patient phenotypes with unique pathophysiological signatures and clinical outcomes. We created a longitudinal electronic health record dataset for 75,762 adult patient admissions to a tertiary care center in 2014–2016 lasting six hours or longer. Physiotypes were derived via unsupervised machine learning in a training cohort of 41,502 patients applying consensus k-means clustering to six vital signs measured within six hours of admission. Reproducibility and correlation with clinical biomarkers and outcomes were assessed in validation cohort of 17,415 patients and testing cohort of 16,845 patients. Training, validation, and testing cohorts had similar age (54–55 years) and sex (55% female), distributions. There were four distinct clusters. Physiotype A had physiologic signals consistent with early vasoplegia, hypothermia, and low-grade inflammation and favorable short-and long-term clinical outcomes despite early, severe illness. Physiotype B exhibited early tachycardia, tachypnea, and hypoxemia followed by the highest incidence of prolonged respiratory insufficiency, sepsis, acute kidney injury, and short- and long-term mortality. Physiotype C had minimal early physiological derangement and favorable clinical outcomes. Physiotype D had the greatest prevalence of chronic cardiovascular and kidney disease, presented with severely elevated blood pressure, and had good short-term outcomes but suffered increased 3-year mortality. Comparing sequential organ failure assessment (SOFA) scores across physiotypes demonstrated that clustering did not simply recapitulate previously established acuity assessments. In a heterogeneous cohort of hospitalized patients, unsupervised machine learning techniques applied to routine, early vital sign data identified physiotypes with unique disease categories and distinct clinical outcomes. This approach has the potential to augment understanding of pathophysiology by distilling thousands of disease states into a few physiological signatures.

Understanding the impact of prior infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on the response to vaccination is a priority for responding to the coronavirus disease 2019 (COVID-19) pandemic. In particular, it is necessary to understand how prior infection plus vaccination can modulate immune responses against variants of concern. To address this, we sampled 20 individuals with and 25 individuals without confirmed previous SARS-CoV-2 infection from a large cohort of health care workers followed serologically since April 2020. All 45 individuals had received two doses of the Pfizer-BioNTech BNT162b2 vaccine with a delayed booster at 10 weeks. Absolute and neutralizing antibody titers against wild-type SARS-CoV-2 and variants were measured using enzyme immunoassays and pseudotype neutralization assays. We observed antibody reactivity against lineage A, B.1.351, and P.1 variants with increasing antigenic exposure, through either vaccination or natural infection. This improvement was further confirmed in neutralization assays using fixed dilutions of serum samples. The impact of antigenic exposure was more evident in enzyme immunoassays measuring SARS-CoV-2 spike protein–specific IgG antibody concentrations. Our data show that multiple exposures to SARS-CoV-2 spike protein in the context of a delayed booster expand the neutralizing breadth of the antibody response to neutralization-resistant SARS-CoV-2 variants. This suggests that additional vaccine boosts may be beneficial in improving immune responses against future SARS-CoV-2 variants of concern.

The tumour necrosis factor receptor superfamily (TNFRSF) members contain cysteine-rich domains (CRD) in their extracellular regions, and the membrane-distal CRD1 forms homologous interactions in the absence of ligand. The CRD1 is therefore termed a pre-ligand assembly domain (PLAD). The role of PLAD-PLAD interactions in the induction of signalling as a consequence of TNF-TNFR binding led to the development of soluble PLAD domains as antagonists of TNFR activation. In the present study, we generated recombinant wild-type (WT) PLAD of TNFR1 and mutant forms with single alanine substitutions of amino acid residues thought to be critical for the formation of homologous dimers and/or trimers of PLAD (K19A, T31A, D49A and D52A). These mutated PLADs were compared with WT PLAD as antagonists of TNF-induced apoptosis or the activation of inflammatory signalling pathways. Unlike WT PLAD, the mutated PLADs showed little or no homologous interactions, confirming the importance of particular amino acids as contact residues in the PLAD binding region. However, as with WT PLAD, the mutated PLADs functioned as antagonists of TNF-induced TNFR1 activity leading to induction of cell death or NF-?B signalling. Indeed, some of the mutant PLADs, and K19A PLAD in particular, showed enhanced antagonistic activity compared with WT PLAD. This is consistent with the reduced formation of homologous multimers by these PLAD mutants effectively increasing the concentration of PLAD available to bind and antagonize WT TNFR1 when compared to WT PLAD acting as an antagonist. This may have implications for the development of antagonistic PLADs as therapeutic agents.

BackgroundThe impact of changing social restrictions on the mental health of students during the COVID-19 pandemic warrants exploration.AimsTo prospectively examine changes to university students’ mental health during the pandemic.MethodsStudents completed repeated online surveys at three time points (October 2020 (baseline), February 2021, March 2021) to explore relationships between demographic and psychological factors (loneliness and positive mood) and mental health outcomes (depression, anxiety, and stress).ResultsA total of 893 students participated. Depression and anxiety levels were higher at all timepoints than pre-pandemic normative data (pConclusionDepression and anxiety were significantly higher than pre-pandemic norms, with female students and those with previous mental health difficulties being at greatest risk. Given these elevated rates, universities should ensure adequate support is available to meet potentially increased demand for services.

Aims: Reduced pre-operative cognitive functioning in older adults is a risk factor for postoperative complications, but it is unknown if preoperative digitally-acquired clock drawing test (CDT) cognitive screening variables, which allow for more nuanced examination of patient performance, may predict lengthier hospital stay and greater cost of hospital care. This issue is particularly relevant for older adults undergoing transcatheter aortic valve replacement (TAVR), as this surgical procedure is chosen for intermediate-risk older adults needing aortic replacement. This proof of concept research explored if specific latency and graphomotor variables indicative of planning from digitally-acquired command and copy clock drawing would predict post-TAVR duration and cost of hospitalization, over and above age, education, American Society of Anesthesiologists (ASA) physical status classification score, and frailty. Methods: Form January 2018 to December 2019, 162 out of 190 individuals electing TAVR completed digital clock drawing as part of a hospital wide cognitive screening program. Separate hierarchical regressions were computed for the command and copy conditions of the CDT and assessed how a-priori selected clock drawing metrics (total time to completion, ideal digit placement difference, and hour hand distance from center; included within the same block) incrementally predicted outcome, as measured by R2 change significance values. Results: Above and beyond age, education, ASA physical status classification score, and frailty, only digitally-acquired CDT copy performance explained significant variance for length of hospital stay (9.5%) and cost of care (8.9%). Conclusions: Digital variables from clock copy condition provided predictive value over common demographic and comorbidity variables. We hypothesize this is due to the sensitivity of the copy condition to executive dysfunction, as has been shown in previous studies for subtypes of cognitive impairment. Individuals undergoing TAVR procedures are often frail and executively compromised due to their cerebrovascular disease. We encourage additional research on the value of digitally-acquired clock drawing within different surgery types. Type of cognitive impairment and the value of digitally-acquired CDT command and copy parameters in other surgeries remain unknown.

Existing pain assessment methods in the intensive care unit rely on patient self-report or visual observation by nurses. Patient self-report is subjective and can suffer from poor recall. In the case of non-verbal patients, behavioral pain assessment methods provide limited granularity, are subjective, and put additional burden on already overworked staff. Previous studies have shown the feasibility of autonomous pain expression assessment by detecting Facial Action Units (AUs). However, previous approaches for detecting facial pain AUs are historically limited to controlled environments. In this study, for the first time, we collected and annotated a pain-related AU dataset, Pain-ICU, containing 55,085 images from critically ill adult patients. We evaluated the performance of OpenFace, an open-source facial behavior analysis tool, and the trained AU R-CNN model on our Pain-ICU dataset. Variables such as assisted breathing devices, environmental lighting, and patient orientation with respect to the camera make AU detection harder than with controlled settings. Although OpenFace has shown state-of-the-art results in general purpose AU detection tasks, it could not accurately detect AUs in our Pain-ICU dataset (F1-score 0.42). To address this problem, we trained the AU R-CNN model on our Pain-ICU dataset, resulting in a satisfactory average F1-score 0.77. In this study, we show the feasibility of detecting facial pain AUs in uncontrolled ICU settings.

In a preoperative anesthesia setting with integrated neuropsychology for individuals >64 years, we completed a pilot study examining the association between neurocognitive disorders with frequency of missed colonoscopies and quality of bowel preparation. Gastroenterologists completed the Boston Bowel Preparation Scale (BBPS) for each patient. Of 47 older adults seen in our service, 68% met criteria for neurocognitive disorders. All individuals failing to attend the colonoscopy procedure had met criteria for major neurocognitive disorder. Poor bowel preparation was also identified in 100% of individuals with major neurocognitive disorder and 28% of individuals with mild neurocognitive disorder. Our pilot data suggest that, in high-risk individuals, the presence of neurocognitive disorders are risk factors for missed appointments and inadequate bowel preparation. These pilot data provide reference statistics for future intervention protocols.

Background Clock drawing is a neurocognitive screening tool used in preoperative settings. This study examined hypothesized changes in clock drawing to command and copy test conditions 3 weeks and 3 months after total knee arthroplasty (TKA) with general anesthesia. Methods Participants included 67 surgery and 66 nonsurgery individuals >60 years who completed the digital clock drawing test before TKA (or a pseudosurgery date), and 3 weeks and 3 months postsurgery. Generalized linear mixed models assessed digital clock drawing test latency (ie, total time to completion, seconds between digit placement) and graphomotor output (ie, total number of strokes, clock size). Reliable change analyses examined the percent of participants showing change beyond differences found in nonsurgery peers. Results After adjusting for age, education, and baseline cognition, both digital clock drawing test latency measures were significantly different for surgery and nonsurgery groups, where the surgery group performed slower on both command and copy test conditions. Reliable change analyses 3 weeks after surgery found that total time to completion was slower among 25% of command and 21% of copy constructions in the surgery group. At 3 months, 18% of surgery participants were slower than nonsurgery peers. Neither graphomotor measure significantly changed over time. Conclusions Clock drawing construction slowed for nearly one-quarter of patients after TKA surgery, whereas nonsurgery peers showed the expected practice effect, ie, speed increased from baseline to follow-up time points. Future research should investigate the neurobiological basis for these changes after TKA.

Summary Tumour necrosis factor receptor-associated periodic syndrome (TRAPS) is a hereditary autoinflammatory disorder characterized by recurrent episodes of fever and inflammation. It is associated with autosomal dominant mutations in TNFRSF1A, which encodes tumour necrosis factor receptor 1 (TNF-R1). Our aim was to understand the influence of TRAPS mutations on the response to stimulation of the pattern recognition Toll-like receptor (TLR)-9. Peripheral blood mononuclear cells (PBMCs) and serum were isolated from TRAPS patients and healthy controls: serum levels of 15 proinflammatory cytokines were measured to assess the initial inflammatory status. Interleukin (IL)-1β, IL-6, IL-8, IL-17, IL-22, tumour necrosis factor (TNF)-α, vascular endothelial growth factor (VEGF), interferon (IFN)-γ, monocyte chemoattractant protein 1 (MCP-1) and transforming growth factor (TGF)-β were significantly elevated in TRAPS patients’ sera, consistent with constitutive inflammation. Stimulation of PBMCs with TLR-9 ligand (ODN2006) triggered significantly greater up-regulation of proinflammatory signalling intermediates [TNF receptor-associated factor (TRAF 3), IL-1 receptor-associated kinase-like 2 (IRAK2), Toll interacting protein (TOLLIP), TRAF6, phosphorylated transforming growth factor-β-activated kinase 1 (pTAK), transforming growth factor-β-activated kinase-binding protein 2 (TAB2), phosphorylated TAK 2 (pTAB2), IFN-regulatory factor 7 (IRF7), receptor interacting protein (RIP), nuclear factor kappa B (NF-κB) p65, phosphorylated NF-κB p65 (pNF-κB p65) and mitogen-activated protein kinase kinase (MEK1/2)] in TRAPS patients’ PBMCs. This up-regulation of proinflammatory signalling intermediates and raised serum cytokines occurred despite concurrent anakinra treatment and no overt clinical symptoms at time of sampling. These novel findings further demonstrate the wide-ranging nature of the dysregulation of innate immune responses underlying the pathology of TRAPS and highlights the need for novel pathway-specific therapeutic treatments for this disease.

Background In the 1990s, emergency medicine (EM) physicians were responsible for intubating about half of the patients requiring airway management in emergency rooms. Since then, no studies have characterized the airway management responsibilities in the emergency room. Methods A survey was sent via the Eastern Association for Surgery and Trauma and the Trauma Anesthesiology Society listservs, as well as by direct solicitation. Information was collected on trauma center level, geographical location, department responsible for intubation in the emergency room, department responsible for intubation in the trauma bay, whether these roles differed for pediatrics, whether an anesthesiologist was available "in-house" 24 hours a day, and whether there was a protocol for anesthesiologists to assist as backup during intubations. Responses were collected, reviewed, linked by city, and mapped using Python. Results The majority of the responses came from the Eastern Association for Surgery of Trauma (84.6%). Of the respondents, 72.6% were from level-1 trauma centers, and most were located in the eastern half of the United States. In the emergency room, EM physicians were primarily responsible for intubations at 81% of the surveyed institutions. In trauma bays, EM physicians were primarily responsible for 61.4% of intubations. There did not appear to be a geographical pattern for personnel responsible for managing the airway at the institutions surveyed. Conclusions The majority of institutions have EM physicians managing their airways in both emergency rooms and trauma bays. This may support the observations of an increased percentage of airway management in the emergency room and trauma bay setting by EM physicians compared to 20 years ago.

OBJECTIVES/HYPOTHESIS Head and neck cancer pain is a prevalent problem, and the current opioid crisis has highlighted concerns raised in chronic pain management. This study assessed the characteristics of opioid use in patients undergoing treatment for oropharynx cancer and identified risk factors associated with chronic opioid use. STUDY DESIGN Retrospective cohort study. METHODS A study was conducted of 198 eligible patients who underwent radiotherapy as part of their treatment for oropharynx cancer at a single institution from 2012 to 2017. p16/human papillomavirus (HPV) status was determined by pathology report review. Opioid use was recorded. Statistical analysis was performed to assess risk factors for chronic opioid use and effect on overall survival. RESULTS The average age was 62 years, and the mean follow-up was 38 months. Eighty-three percent of patients had stage III/IV disease, and 73% received chemoradiotherapy. Sixty-nine percent were HPV/p16 positive. Fifty-seven (29%) patients had preexisting chronic pain conditions. Chronic opioid use was observed in 53% of the patients. Age ≤ 62 years (P < .0001), history of depression (P = .0356), p16 negative status (P = .0097), opioid use at pretreatment visit (P = .0021), and presence of a preexisting chronic pain condition at time of diagnosis (P = .0181) were associated with chronic opioid use using univariate analysis. On multivariate analysis, T stage and anxiety/depression were associated with chronic opioid use. Overall survival was worse for patients who had chronic opioid use, but was not significant when recurrence was taken into consideration. CONCLUSIONS More than 50% of the patients treated for oropharynx squamous cell carcinoma in this cohort were chronic opioid users after treatment. Identifying patients at greatest risk for chronic opioid use prior to treatment may help with long-term pain management in this patient population. LEVEL OF EVIDENCE 4 Laryngoscope, 129:2087-2093, 2019.

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

Introduction Concurrent use of prescription opioids with gabapentinoids may pose risks of serious drug interactions. Yet, little is known about the trends in and patient characteristics associated with concurrent opioid–gabapentinoid use among older Medicare opioid users with chronic noncancer pain. Methods A cross-sectional study was conducted among Medicare older beneficiaries (aged ≥65 years) with chronic noncancer pain who filled ≥1 opioid prescription within 3 months after a randomly selected chronic noncancer pain diagnosis (index date) in a calendar year between 2011 and 2018. Patient characteristics were measured in the 6-month baseline before the index date, and concurrent opioid–gabapentinoid use for ≥1 day was measured in the 3-month follow-up after the index date. Multivariable modified Poisson regression hwas used to assess the trends and characteristics of concurrent opioid–gabapentinoid use. Analyses were conducted from January to June 2021. Results Among 464,721 eligible older beneficiaries with chronic noncancer pain and prescription opioids, the prevalence of concurrent opioid–gabapentinoid use increased from 17.0% in 2011 to 23.5% in 2018 (adjusted prevalence ratio=1.48, 95% CI=1.45, 1.53). Concurrent users versus opioid-only users tended to be non-Black, low-income subsidy recipients, and Southern residents. The clinical factors associated with concurrent opioid–gabapentinoid use included having a diagnosis of neuropathic pain, polypharmacy, and risk factors for opioid-related adverse events. Conclusions Concurrent opioid–gabapentinoid use among older Medicare beneficiaries with chronic noncancer pain and prescription opioids has increased significantly between 2011 and 2018. Future studies are warranted to investigate the impact of concurrent use on outcomes in older patients. Interventions that reduce inappropriate concurrent use may target older patients with identified characteristics.

Objective: To understand ethnic differences in SARS-CoV-2 infection risk and symptoms in hospital healthcare workers (HCW). Methods: A Prospective longitudinal observational cohort study. 1364 HCW at five UK hospitals were studied with up to 16 weeks of symptom questionnaires and antibody testing (to both nucleocapsid and spike protein) during the first UK wave in five NHS hospitals. The main outcome measures were SARS-CoV-2 infection (seropositivity at any time-point) and symptoms. Results: 272 of 1364 HCW (mean age 40.7 years, 72% female, 74% white, ≥6 samples per participant) seroconverted, reporting predominantly mild or no symptoms. Seropositivity was lower in ITU workers (OR=0.43 95%CI 0.24, 0.76; p=0.0033). Seropositivity was higher in black (compared to white) participants, independent of age, sex, role and index of multiple deprivation (OR=2.61 95%CI 1.47-4.62 p=0.0009). No association was seen with other ethnic groups. Conclusions: In the UK first wave, black ethnicity (but not other ethnicities) more than doubled HCW infection risk, independent of age, sex, measured socio-economic factors and role. Trial Registration: NCT04318314 Funding Statement: Funding for the PANTHER study was from the UKRI/MRC (Cov-0331 - MR/V027883/1 ), with additional institutional support from the Nottingham NIHR BRC. Funding for COVIDsortium was donated by individuals, charitable Trusts, and corporations including Goldman Sachs, Citadel and Citadel Securities, The Guy Foundation, GW Pharmaceuticals, Kusuma Trust, and Jagclif Charitable Trust, and enabled by Barts Charity with support from UCLH Charity. Wider support is acknowledged on the COVIDsortium website. Institutional support from Barts Health NHS Trust and Royal Free NHS Foundation Trust facilitated study processes, in partnership with University College London and Queen Mary University London. Serology tests (anti-S1 and anti-NP) were funded by Public Health England. JCM, CMa and TAT are directly and indirectly supported by the University College London Hospitals (UCLH) and Barts NIHR Biomedical Research Centres and through the British Heart Foundation (BHF) Accelerator Award (AA/18/6/34223). TAT is funded by a BHF Intermediate Research Fellowship (FS/19/35/34374). MN is supported by the Wellcome Trust (207511/Z/17/Z) and by NIHR Biomedical Research Funding to UCL and UCLH. None of the aforementioned funding bodies have contributed to the design, collection, or analysis of the data. Declaration of Interests: None to declare. Ethics Approval Statement: The study was approved by a UK Research Ethics Committee (South Central - Oxford A Research Ethics Committee, reference 20/SC/0149).

Post marketing data offer rich information and cost-effective resources for physicians and policy-makers to address some critical scientific questions in clinical practice. However, the complex confounding structures (e.g., nonlinear and nonadditive interactions) embedded in these observational data often pose major analytical challenges for proper analysis to draw valid conclusions. Furthermore, often made available as electronic health records (EHRs), these data are usually massive with hundreds of thousands observational records, which introduce additional computational challenges. In this paper, for comparative effectiveness analysis, we propose a statistically robust yet computationally efficient propensity score (PS) approach to adjust for the complex confounding structures. Specifically, we propose a kernel-based machine learning method for flexibly and robustly PS modeling to obtain valid PS estimation from observational data with complex confounding structures. The estimated propensity score is then used in the second stage analysis to obtain the consistent average treatment effect estimate. An empirical variance estimator based on the bootstrap is adopted. A split-and-merge algorithm is further developed to reduce the computational workload of the proposed method for big data, and to obtain a valid variance estimator of the average treatment effect estimate as a by-product. As shown by extensive numerical studies and an application to postoperative pain EHR data comparative effectiveness analysis, the proposed approach consistently outperforms other competing methods, demonstrating its practical utility.

Objective To assess the incidence of symptomatic and asymptomatic SARS-CoV-2 seropositivity in healthcare workers and subsequent transmission to their close contacts within their household. To assess changes in immunoglobulin (Ig) and neutralising antibodies (nAbs) in exposed participants. Setting Two acute National Health Service (NHS) hospitals within the East Midlands region of England. Background The UK has been one of the most severely affected countries during the COVID-19 pandemic. Transmission from healthcare workers to the wider community is a potential major vector for spread of SARS-CoV-2 which is not well described in the current literature. Methods Healthcare workers (HCW) were recruited from two Hospitals within the East Midlands of England and underwent serial blood sampling for anti-SARS-CoV-2 antibodies (both nucleocapsid and spike protein for IgG, IgM and IgA) between 20 April and 30 July 2020, with the presence of neutralising antibodies (nAbs) assessed for positive participants. Cohabitees of the volunteers were invited to attend testing in July -August 2020 and underwent identical serological testing as the HCWs. Results 633 healthcare professionals were recruited. 178 household contacts of 137 professionals volunteered for the study. 18% of healthcare professionals (115 out of 633) tested as seropositive during the study period, compared to an estimated seroprevalence of 7% within the general population. The rate of symptomatic COVID-19 was 27.5% compared to an asymptomatic rate of 15.1%. Rates of positivity declined across the study period for all immunoglobulins (overall positivity from 16.7% to 6.9%). 7.2% of the cohabitees tested as seropositive. 58 cohabitees lived with a serologically positive HCW; this group had a seropositive rate of 15.5%, compared to 2.5% of cohabitees without a seropositive HCW, a six-fold increase in risk (Odds ratio 7.16 95% CI 1.86 to 27.59), p = 0.0025). Given the observed decay rates and data from Public Health England, we estimate that the proportion of seropositive cohabitees living with a seropositive HCW at the height of the first wave could have been as high as 44%. 110 out of 115 (95.7%) HCWs and 12 out of 13 (92.3%) cohabitees who tested positive developed detectable nAbs. 56.5% (65 out of 115) of SARS-CoV-2 positive HCWs developed a neutralising titre with an IC50≥1/300; no cohabitee achieved this level.. Conclusions Transmission of SARS-CoV-2 between healthcare professionals and their home contacts appears to be a significant factor of viral transmission, but, even accounting for the decline in seropositivity over time, less than 44% of adult cohabitees of seropositive healthcare workers became seropositive. Routine screening and priority vaccination of both healthcare professionals and their close contacts should be implemented to reduce viral transmission from hospitals to the community. SUMMARY BOXES Section 1: What is already known on this topic Healthcare workers (HCWs) have increased rates of SARS-CoV-2 infection compared with the general population due, at least in part, to high levels of occupational exposure. IgA, IgM and IgG are detectable for most patients after 11 days post SARS-CoV-2 infection but all decline in the weeks following SAR-CoV-2 exposure. Rates of transmission to healthcare workers, and therefore subsequent transmission to their close contacts, may be reduced with effective PPE. Section 2: What this study adds The amount of neutralising antibodies formed may be dependent on IgG response as it is much lower among seropositive cohabitees than seropositive healthcare workers. NHS Healthcare workers had a far greater seroprevalence of SARS-CoV-2 infection compared to the general population. Cohabitees of positive healthcare workers have a 6-fold increased risk of developing serological evidence of SARS-CoV-2 infection compared to the general population. Despite this increased risk, transmission at home is less than 50% even from highly exposed healthcare workers, but remains an important potential vector of transmission from hospitals to the wider community. Research into context Evidence before this study We searched PubMed for articles published between January 1 2020 and January 27, 2021 with the terms “Covid-19”, “healthcare workers”, and “transmission” “home {NOT nursing} or household”. We did not restrict our search by language or type of publication. We identified 38 studies of which only one assessed the prevalence among HCW households using Canadian national databases. Our PubMed search yielded only one serological study within the German Healthcare system, which suggested very low transmission from healthcare workers to their close cohabitees. Added value of this study To our knowledge, this is the largest longitudinal serological cohort study assessing transmission of SARS-CoV-2 infection from the UK healthcare environment to the home (n = 633 healthcare workers, 178 cohabitees). Our findings showed that serological evidence within the HCW was high with 18% of healthcare professionals (115 out of 633) tested as seropositive during the study period, compared to an estimated seroprevalence of 7% within the general population. A cohabitee of a seropositive HCW had a six-fold increase of being seropositive themselves compared to a baseline rate of 2.5%. Despite this increased risk, transmission at home is less than 50% even from highly exposed healthcare workers, but remains an important potential vector of transmission from hospitals to the wider community. Rates of positivity declined across the study period for all immunoglobulins (overall positivity from 16.7% to 6.9%). Given the observed decay rates and data from Public Health England, we estimate that the proportion of seropositive cohabitees living with a seropositive HCW at the height of the first wave could have been as high as 44%. Implications of all available evidence Understanding the transmission during the first wave from the healthcare setting into the home and the extent of such transmissions is essential to understand containment strategies of novel SARS-CoV-2 variants or to understand viral transmission of future respiratory viruses. NHS workers appeared to be at an increased risk of contracting of SARS-CoV-2 infection compared to the HCWs of other nations; we hypothesise that this may be related to a scarcity of appropriate personal protective equipment during the initial wave of SARS-CoV-2. Healthcare workers (HCWs) have increased rates of SARS-CoV-2 infection compared with the general population. An infected HCW, whether symptomatic or not, appears to be a significant bridge for transmission of SARS-CoV-2 to their close home contacts.

As opportunities for artificial intelligence to augment surgical care expand, the accompanying surge in published literature has generated both substantial enthusiasm and grave concern regarding the safety and efficacy of artificial intelligence in surgery. For surgeons and surgical data scientists, it is increasingly important to understand the state-of-the-art, recognize knowledge and technology gaps, and critically evaluate the deluge of literature accordingly. This article summarizes the experiences and perspectives of a global, multi-disciplinary group of experts who have faced development and implementation challenges, overcome them, and produced incipient evidence thereof. Collectively, evidence suggests that artificial intelligence has the potential to augment surgeons via decision-support, technical skill assessment, and the semi-autonomous performance of tasks ranging from resource allocation to patching foregut defects. Most applications remain in pre-clinical phases. As technologies and their implementations improve and positive evidence accumulates, surgeons will face professional imperatives to lead the safe, effective clinical implementation of artificial intelligence in surgery. Substantial challenges remain; recent progress in using artificial intelligence to achieve performance advantages in surgery suggests that remaining challenges can and will be overcome.