Using a novel 'difference-in-differences' method and syndromic surveillance to estimate the change in local healthcare utilisation during periods of media reporting in the early stages of the COVID-19 pandemic in England.

OBJECTIVES: Syndromic surveillance supplements traditional laboratory reporting for infectious diseases monitoring. Prior to widespread COVID-19 community surveillance, syndromic surveillance was one of several systems providing real-time information on changes in healthcare-seeking behaviour. The study objective was to identify changes in healthcare utilisation during periods of high local media reporting in England using 'difference-in-differences' (DiD). STUDY DESIGN: A retrospective observational study was conducted using five media events in January-February 2020 in England on four routinely monitored syndromic surveillance indicators. METHODS: Dates 'exposed' to a media event were estimated using Google Trends internet search intensity data (terms = 'coronavirus' and local authority [LA]). We constructed a negative-binomial regression model for each indicator and event time period to estimate a direct effect. RESULTS: We estimated a four-fold increase in telehealth 'cough' calls and a 1.4-fold increase in emergency department (ED) attendances for acute respiratory illness in Brighton and Hove, when a so-called 'superspreading event' in this location was reported in local and national media. Significant decreases were observed in the Buxton (telehealth and ED attendance) and Wirral (ED attendance) areas during media reports of a returnee from an outbreak abroad and a quarantine site opening in the area respectively. CONCLUSIONS: We used a novel approach to directly estimate changes in syndromic surveillance reporting during the early phase of the COVID-19 pandemic in England, providing contextual information on the interpretation of changes in health indicators. With careful consideration of event timings, DiD is useful in producing real-time estimates on specific indicators for informing public health action.

The roles of phishing knowledge, cue utilization, and decision styles in phishing email detection.

This study investigated the roles of phishing knowledge, cue utilization, and decision styles in contributing to phishing email detection. Participants (N = 145) completed an online email sorting task, and measures of phishing knowledge, email decision styles, cue utilization, and email security awareness. Cue utilization was the only factor that uniquely predicted the capacity to discriminate phishing from genuine emails. Phishing knowledge was associated with greater phishing detection and a bias towards classifying all emails as phishing. A preference for intuitive decision making predicted lower detection of phishing emails, driven by a greater tendency to classify emails as genuine. These findings support the proposition that cue utilization is a distinct cognitive process that enables expert performance. The outcomes indicate that, in addition to increasing phishing knowledge and developing safe behavioral patterns, anti-phishing training needs to provide opportunities for trainees to develop meaningful cue associations.

Critical updates in neuroendocrine tumors: Version 9 American Joint Committee on Cancer staging system for gastroenteropancreatic neuroendocrine tumors.

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including gastroenteropancreatic neuroendocrine tumors (GEP-NETs), is meant to be dynamic, requiring periodic updates to optimize AJCC staging definitions. This entails the collaboration of experts charged with evaluating new evidence that supports changes to each staging system. GEP-NETs are the second most prevalent neoplasm of gastrointestinal origin after colorectal cancer. Since publication of the AJCC eighth edition, the World Health Organization has updated the classification and separates grade 3 GEP-NETs from poorly differentiated neuroendocrine carcinoma. In addition, because of major advancements in diagnostic and therapeutic technologies for GEP-NETs, AJCC version 9 advocates against the use of serum chromogranin A for the diagnosis and monitoring of GEP-NETs. Furthermore, AJCC version 9 recognizes the increasing role of endoscopy and endoscopic resection in the diagnosis and management of NETs, particularly in the stomach, duodenum, and colorectum. Finally, T1NXM0 has been added to stage I in these disease sites as well as in the appendix.

Structural insights reveal interplay between LAG-3 homodimerization, ligand binding, and function.

Lymphocyte activation gene-3 (LAG-3) is an inhibitory receptor expressed on activated T cells and an emerging immunotherapy target. Domain 1 (D1) of LAG-3, which has been purported to directly interact with major histocompatibility complex class II (MHCII) and fibrinogen-like protein 1 (FGL1), has been the major focus for the development of therapeutic antibodies that inhibit LAG-3 receptor-ligand interactions and restore T cell function. Here, we present a high-resolution structure of glycosylated mouse LAG-3 ectodomain, identifying that cis-homodimerization, mediated through a network of hydrophobic residues within domain 2 (D2), is critically required for LAG-3 function. Additionally, we found a previously unidentified key protein-glycan interaction in the dimer interface that affects the spatial orientation of the neighboring D1 domain. Mutation of LAG-3 D2 residues reduced dimer formation, dramatically abolished LAG-3 binding to both MHCII and FGL1 ligands, and consequentially inhibited the role of LAG-3 in suppressing T cell responses. Intriguingly, we showed that antibodies directed against D1, D2, and D3 domains are all capable of blocking LAG-3 dimer formation and MHCII and FGL-1 ligand binding, suggesting a potential allosteric model of LAG-3 function tightly regulated by dimerization. Furthermore, our work reveals unique epitopes, in addition to D1, that can be targeted for immunotherapy of cancer and other human diseases.

Transmembrane domain-driven PD-1 dimers mediate T cell inhibition.

Programmed cell death-1 (PD-1) is a potent immune checkpoint receptor on T lymphocytes. Upon engagement by its ligands, PD-L1 or PD-L2, PD-1 inhibits T cell activation and can promote immune tolerance. Antagonism of PD-1 signaling has proven effective in cancer immunotherapy, and conversely, agonists of the receptor may have a role in treating autoimmune disease. Some immune receptors function as dimers, but PD-1 has been considered monomeric. Here, we show that PD-1 and its ligands form dimers as a consequence of transmembrane domain interactions and that propensity for dimerization correlates with the ability of PD-1 to inhibit immune responses, antitumor immunity, cytotoxic T cell function, and autoimmune tissue destruction. These observations contribute to our understanding of the PD-1 axis and how it can potentially be manipulated for improved treatment of cancer and autoimmune diseases.

BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study.

BACKGROUND: Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls. OBJECTIVE: This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model. METHODS: A cohort of 4323 adult patients admitted to 3 acute care hospitals in Calgary, Alberta, Canada from 2016 to 2021 were randomly sampled. Trained reviewers determined falls from patient charts, which were linked to electronic medical records and administrative data. The BERT-based language model was pretrained on clinical notes, and a fall detection algorithm was developed based on a neural network binary classification architecture. RESULTS: To address various use scenarios, we developed 3 different Alberta hospital notes-specific BERT models: a high sensitivity model (sensitivity 97.7, IQR 87.7-99.9), a high positive predictive value model (positive predictive value 85.7, IQR 57.2-98.2), and the high F1-score model (F1=64.4). Our proposed method outperformed 3 classical ML algorithms and an International Classification of Diseases code-based algorithm for fall detection, showing its potential for improved performance in diverse clinical settings. CONCLUSIONS: The developed algorithm provides an automated and accurate method for inpatient fall detection using multidisciplinary progress record notes and a pretrained BERT language model. This method could be implemented in clinical practice to improve patient safety and reduce the occurrence of falls in hospitals.

Automated extraction of weight, height, and obesity in electronic medical records are highly valid.

Objective: Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under-reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule-based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs). Methods: In this cross-sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs. A rule-based algorithm was created which looks for patients' height and weight values recorded in EMRs. Clinical notes were split into sentences and searched for height and weight, and BMI was computed. Results: The study cohort consisted of 1904 patients with 50.8% females and 43.3% > 64 years of age. The final model to identify obesity within EMRs resulted in a sensitivity of 92.9%, specificity of 98.4%, positive predictive value of 96.7%, negative predictive value of 96.6%, and F1 score of 94.8%. Conclusions: This study developed a highly valid rule-based EMR algorithm that detects height and weight. This could allow large-scale analyses using obesity that were previously not possible.

Single-Photon Emission Computed Tomography/Computed Tomography Utilization for Extremity Melanomas at a High-Volume Center.

INTRODUCTION: Planar lymphoscintigraphy (PL) is commonly used in mapping before sentinel lymph node biopsy (SLNB) for invasive cutaneous melanoma. Recently, single-photon emission computed tomography (SPECT)/ computed tomography (CT) has been utilized, in addition to PL, for detailed anatomic information and detection of sentinel lymph nodes (SLNs) outside of the primary nodal basin in truncal and head and neck melanoma. Following a protocol change due to COVID-19, our institution began routinely obtaining both PL and SPECT-CT imaging for all melanoma SLN mapping. We hypothesized that SPECT-CT is associated with higher instances of SLNBs from "nontraditional" nodal basins (NTNB) for extremity melanomas. METHODS: Patients with extremity melanoma (2017-2022) who underwent SLNB were grouped into SPECT-CT with PL versus PL alone. Outcomes were total SLNs removed, + or-SLN status, total NTNB sampled, and postoperative complication rate. Poisson regression and logistic regression models were used to assess association of SPECT-CT with patient outcomes. RESULTS: Of 380 patients with extremity melanoma, 42.11% had SPECT-CT. There were no differences between the groups with regards to age at diagnosis or sex. From 2020 to 2022, all patients underwent SPECT-CT. SPECT-CT was associated with increased odds of SLNB from an NTNB, (odds ratio = 2.39 [95% confidence interval: 1.25-4.67]). There was no difference in odds of number of SLNs sampled, SLN positivity rate, or postoperative complication rate with SPECT-CT. CONCLUSIONS: Routine SPECT-CT was associated with higher incidence of SLNB in NTNB but did not increase number of SLNs removed or SLN positivity rate. The added value of routine SPECT-CT in cutaneous melanoma of the extremities remains to be defined.

The Future of Decisions From Experience: Connecting Real-World Decision Problems to Cognitive Processes.

In many important real-world decision domains, such as finance, the environment, and health, behavior is strongly influenced by experience. Renewed interest in studying this influence led to important advancements in the understanding of these decisions from experience (DfE) in the last 20 years. Building on this literature, we suggest ways the standard experimental design should be extended to better approach important real-world DfE. These extensions include, for example, introducing more complex choice situations, delaying feedback, and including social interactions. When acting upon experiences in these richer and more complicated environments, extensive cognitive processes go into making a decision. Therefore, we argue for integrating cognitive processes more explicitly into experimental research in DfE. These cognitive processes include attention to and perception of numeric and nonnumeric experiences, the influence of episodic and semantic memory, and the mental models involved in learning processes. Understanding these basic cognitive processes can advance the modeling, understanding and prediction of DfE in the laboratory and in the real world. We highlight the potential of experimental research in DfE for theory integration across the behavioral, decision, and cognitive sciences. Furthermore, this research could lead to new methodology that better informs decision-making and policy interventions.

Data-driven optimization of version 9 American Joint Committee on Cancer staging system for anal cancer.

INTRODUCTION: The American Joint Committee on Cancer (AJCC) staging system undergoes periodic revisions to maintain contemporary survival outcomes related to stage. Recently, the AJCC has developed a novel, systematic approach incorporating survival data to refine stage groupings. The objective of this study was to demonstrate data-driven optimization of the version 9 AJCC staging system for anal cancer assessed through a defined validation approach. METHODS: The National Cancer Database was queried for patients diagnosed with anal cancer in 2012 through 2017. Kaplan-Meier methods analyzed 5-year survival by individual clinical T category, N category, M category, and overall stage. Cox proportional hazards models validated overall survival of the revised TNM stage groupings. RESULTS: Overall, 24,328 cases of anal cancer were included. Evaluation of the 8th edition AJCC stage groups demonstrated a lack of hierarchical prognostic order. Survival at 5 years for stage I was 84.4%, 77.4% for stage IIA, and 63.7% for stage IIB; however, stage IIIA disease demonstrated a 73.0% survival, followed by 58.4% for stage IIIB, 59.9% for stage IIIC, and 22.5% for stage IV (p <.001). Thus, stage IIB was redefined as T1-2N1M0, whereas Stage IIIA was redefined as T3N0-1M0. Reevaluation of 5-year survival based on data-informed stage groupings now demonstrates hierarchical prognostic order and validated via Cox proportional hazards models. CONCLUSION: The 8th edition AJCC survival data demonstrated a lack of hierarchical prognostic order and informed revised stage groupings in the version 9 AJCC staging system for anal cancer. Thus, a validated data-driven optimization approach can be implemented for staging revisions across all disease sites moving forward.

Exploring the reliability of inpatient EMR algorithms for diabetes identification.

INTRODUCTION: Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms. MATERIALS AND METHODS: A chart review on 3040 individuals was completed, and 583 had diabetes. We linked EMR data on these individuals to the International Classification of Disease (ICD) administrative databases. The following EMR-data-based diabetes algorithms were developed: (1) laboratory data, (2) medication data, (3) laboratory and medications data, (4) diabetes concept keywords and (5) diabetes free-text algorithm. Combined algorithms used or statements between the above algorithms. Algorithm performances were measured using chart review as a gold standard. We determined the best-performing algorithm as the one that showed the high performance of sensitivity (SN), and positive predictive value (PPV). RESULTS: The algorithms tested generally performed well: ICD-coded data, SN 0.84, specificity (SP) 0.98, PPV 0.93 and negative predictive value (NPV) 0.96; medication and laboratory algorithm, SN 0.90, SP 0.95, PPV 0.80 and NPV 0.97; all document types algorithm, SN 0.95, SP 0.98, PPV 0.94 and NPV 0.99. DISCUSSION: Free-text data-based diabetes algorithm can yield comparable or superior performance to a commonly used ICD-coded algorithm and could supplement existing methods. These types of inpatient EMR-based algorithms for case identification may become a key method for timely resource planning and care delivery.

Rare and extreme outcomes in risky choice.

Many real-world decisions involving rare events also involve extreme outcomes. Despite this confluence, decisions-from-experience research has only examined the impact of rarity and extremity in isolation. With rare events, people typically choose as if they underestimate the probability of a rare outcome happening. Separately, people typically overestimate the probability of an extreme outcome happening. Here, for the first time, we examine the confluence of these two biases in decisions-from-experience. In a between-groups behavioural experiment, we examine people's risk preferences for rare extreme outcomes and for rare non-extreme outcomes. When outcomes are both rare and extreme, people's risk preferences shift away from traditional risk patterns for rare events: they show reduced underweighting for events that are both rare and extreme. We simulate these results using a small-sample model of decision-making that accounts for both the underweighting of rare events and the overweighting of extreme events. These separable influences on risk preferences suggest that to understand real-world risk for rare events we must also consider the extremity of the outcomes.

Capnography-An Essential Monitor, Everywhere: A Narrative Review.

Capnography is now recognized as an indispensable patient safety monitor. Evidence suggests that its use improves outcomes in operating rooms, intensive care units, and emergency departments, as well as in sedation suites, in postanesthesia recovery units, and on general postsurgical wards. Capnography can accurately and rapidly detect respiratory, circulatory, and metabolic derangements. In addition to being useful for diagnosing and managing esophageal intubation, capnography provides crucial information when used for monitoring airway patency and hypoventilation in patients without instrumented airways. Despite its ubiquitous use in high-income-country operating rooms, deaths from esophageal intubations continue to occur in these contexts due to incorrect use or interpretation of capnography. National and international society guidelines on airway management mandate capnography's use during intubations across all hospital areas, and recommend it when ventilation may be impaired, such as during procedural sedation. Nevertheless, capnography's use across high-income-country intensive care units, emergency departments, and postanesthesia recovery units remains inconsistent. While capnography is universally used in high-income-country operating rooms, it remains largely unavailable to anesthesia providers in low- and middle-income countries. This lack of access to capnography likely contributes to more frequent and serious airway events and higher rates of perioperative mortality in low- and middle-income countries. New capnography equipment, which overcomes cost and context barriers, has recently been developed. Increasing access to capnography in low- and middle-income countries must occur to improve patient outcomes and expand universal health care. It is time to extend capnography's safety benefits to all patients, everywhere.

Improvements in task performance after practice are associated with scale-free dynamics of brain activity.

Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.

Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing.

BACKGROUND: Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders' abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes. METHODS: CeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients' chart data were linked to administrative discharge abstract database (DAD) and Sunrise™ Clinical Manager (SCM) EMR database records by Personal Health Number (a unique lifetime identifier) and admission date. We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervised machine learning (ML) methods: random forest and XGBoost. Using chart review as the reference standard, we compared the model performances with those of the commonly applied International Classification of Diseases (ICD-10-CA) codes, on the metrics of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULT: Of the study sample (n = 3036), the prevalence of CeVD was 11.8% (n = 360); the median patient age was 63; and females accounted for 50.3% (n = 1528) based on chart data. Among 49 extracted clinical documents from the EMR, four document types were identified as the most influential text sources for identifying CeVD disease ("nursing transfer report," "discharge summary," "nursing notes," and "inpatient consultation."). The best performing NLP model was XGBoost, combining the Unified Medical Language System concepts extracted by cTAKES (e.g., top-ranked concepts, "Cerebrovascular accident" and "Transient ischemic attack"), and the term frequency-inverse document frequency vectorizer. Compared with ICD codes, the model achieved higher validity overall, such as sensitivity (25.0% vs 70.0%), specificity (99.3% vs 99.1%), PPV (82.6 vs. 87.8%), and NPV (90.8% vs 97.1%). CONCLUSION: The NLP algorithm developed in this study performed better than the ICD code algorithm in detecting CeVD. The NLP models could result in an automated EMR tool for identifying CeVD cases and be applied for future studies such as surveillance, and longitudinal studies.

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.

BACKGROUND: Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection. METHODS: This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision-recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated. RESULTS: There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835-0.978), PR AUC of 0.637 (95% CI 0.528-0.746), and F1 score of 0.79 (0.67-0.90). CONCLUSIONS: Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.

The incidence rates of surgical site infections following total hip and knee arthroplasty were 0.5 and 0.52 per 100 surgical procedures. The incidence of SSIs varied significantly between care facilities (ranging from 0.53 to 1.71 per 100 procedures). The optimal machine learning model achieved a ROC AUC of 0.906 (95% CI 0.835­0.978), PR AUC of 0.637 (95% CI 0.528­0.746), and F1 score of 0.79 (0.67­0.90).

CBFA2T3-GLIS2-dependent pediatric acute megakaryoblastic leukemia is driven by GLIS2 and sensitive to navitoclax.

Pediatric acute megakaryoblastic leukemia (AMKL) is an aggressive blood cancer associated with poor therapeutic response and high mortality. Here we describe the development of CBFA2T3-GLIS2-driven mouse models of AMKL that recapitulate the phenotypic and transcriptional signatures of the human disease. We show that an activating Ras mutation that occurs in human AMKL increases the penetrance and decreases the latency of CBF2AT3-GLIS2-driven AMKL. CBFA2T3-GLIS2 and GLIS2 modulate similar transcriptional networks. We identify the dominant oncogenic properties of GLIS2 that trigger AMKL in cooperation with oncogenic Ras. We find that both CBFA2T3-GLIS2 and GLIS2 alter the expression of a number of BH3-only proteins, causing AMKL cell sensitivity to the BCL2 inhibitor navitoclax both in vitro and in vivo, suggesting a potential therapeutic option for pediatric patients suffering from CBFA2T3-GLIS2-driven AMKL.