Travel Distance Between Participants in US Telemedicine Sessions With Estimates of Emissions Savings: Observational Study.

BACKGROUND: Digital health and telemedicine are potentially important strategies to decrease health care's environmental impact and contribution to climate change by reducing transportation-related air pollution and greenhouse gas emissions. However, we currently lack robust national estimates of emissions savings attributable to telemedicine. OBJECTIVE: This study aimed to (1) determine the travel distance between participants in US telemedicine sessions and (2) estimate the net reduction in carbon dioxide (CO2) emissions attributable to telemedicine in the United States, based on national observational data describing the geographical characteristics of telemedicine session participants. METHODS: We conducted a retrospective observational study of telemedicine sessions in the United States between January 1, 2022, and February 21, 2023, on the doxy.me platform. Using Google Distance Matrix, we determined the median travel distance between participating providers and patients for a proportional sample of sessions. Further, based on the best available public data, we estimated the total annual emissions costs and savings attributable to telemedicine in the United States. RESULTS: The median round trip travel distance between patients and providers was 49 (IQR 21-145) miles. The median CO2 emissions savings per telemedicine session was 20 (IQR 8-59) kg CO2). Accounting for the energy costs of telemedicine and US transportation patterns, among other factors, we estimate that the use of telemedicine in the United States during the years 2021-2022 resulted in approximate annual CO2 emissions savings of 1,443,800 metric tons. CONCLUSIONS: These estimates of travel distance and telemedicine-associated CO2 emissions costs and savings, based on national data, indicate that telemedicine may be an important strategy in reducing the health care sector's carbon footprint.

Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology.

Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.

Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individuals.

Infections are implicated in the etiology of type 1 diabetes mellitus (T1DM); however, conflicting epidemiologic evidence makes designing effective strategies for presymptomatic screening and disease prevention difficult. Considering the temporality and combination in which infections occur may provide valuable insights into understanding T1DM etiology but is rarely studied due to limited longitudinal datasets and insufficient analytical techniques. The objective of this work was to demonstrate a computational approach to classify the temporality and combination of infections in presymptomatic T1DM. We present a sequential data mining pipeline that leverages routinely collected infectious disease data from a prospective cohort study, the Environmental Determinants of Diabetes in the Young (TEDDY) study, to extract, interpret, and compare infection sequences. We then utilize this pipeline to assess risk for developing presymptomatic biomarkers of islet autoimmunity and clinical onset of T1DM. Overall, we identified 229 significant sequential rules that increased the risk for developing presymptomatic biomarkers of islet autoimmunity or clinical onset of T1DM. Multiple significant sequential rules involving varicella increased the risk for all presymptomatic biomarker-specific outcomes, while a single significant sequential rule involving parasites significantly increased risk for T1DM. Significant sequential rules involving respiratory illnesses were differentially represented among the presymptomatic biomarkers of islet autoimmunity and clinical onset of T1DM. Risk for T1DM was significantly increased by a single episode of sixth disease at 12 months, representing the only single-event sequence that increased disease risk. Together, these findings provide the first insights into the timing and combination of infections in T1DM etiology, which may ultimately lead to personalized disease screening and prevention strategies. The sequential data mining pipeline developed in this work demonstrates how temporal data mining can be used to address clinically meaningful questions. This method can be adapted to other presymptomatic factors and clinical conditions.

Detecting hypoglycemia-induced electrocardiogram changes in a rodent model of type 1 diabetes using shape-based clustering.

Sudden death related to hypoglycemia is thought to be due to cardiac arrhythmias. A clearer understanding of the cardiac changes associated with hypoglycemia is needed to reduce mortality. The objective of this work was to identify distinct patterns of electrocardiogram heartbeat changes that correlated with glycemic level, diabetes status, and mortality using a rodent model. Electrocardiogram and glucose measurements were collected from 54 diabetic and 37 non-diabetic rats undergoing insulin-induced hypoglycemic clamps. Shape-based unsupervised clustering was performed to identify distinct clusters of electrocardiogram heartbeats, and clustering performance was assessed using internal evaluation metrics. Clusters were evaluated by experimental conditions of diabetes status, glycemic level, and death status. Overall, shape-based unsupervised clustering identified 10 clusters of ECG heartbeats across multiple internal evaluation metrics. Several clusters demonstrating normal ECG morphology were specific to hypoglycemia conditions (Clusters 3, 5, and 8), non-diabetic rats (Cluster 4), or were generalized among all experimental conditions (Cluster 1). In contrast, clusters demonstrating QT prolongation alone or a combination of QT, PR, and QRS prolongation were specific to severe hypoglycemia experimental conditions and were stratified heartbeats by non-diabetic (Clusters 2 and 6) or diabetic status (Clusters 9 and 10). One cluster demonstrated an arrthymogenic waveform with premature ventricular contractions and was specific to heartbeats from severe hypoglycemia conditions (Cluster 7). Overall, this study provides the first data-driven characterization of ECG heartbeats in a rodent model of diabetes during hypoglycemia.

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.

BACKGROUND: Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed. OBJECTIVE: We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction. METHODS: We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes. RESULTS: We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes. DISCUSSION: The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.

Stratifying risk for onset of type 1 diabetes using islet autoantibody trajectory clustering.

AIMS/HYPOTHESIS: Islet autoantibodies can be detected prior to the onset of type 1 diabetes and are important tools for aetiologic studies, prevention trials and disease screening. Current risk stratification models rely on the positivity status of islet autoantibodies alone, but additional autoantibody characteristics may be important for understanding disease onset. This work aimed to determine if a data-driven model incorporating characteristics of islet autoantibody development, including timing, type and titre, could stratify risk for type 1 diabetes onset. METHODS: Data on autoantibodies against GAD (GADA), tyrosine phosphatase islet antigen-2 (IA-2A) and insulin (IAA) were obtained for 1,415 children enrolled in The Environmental Determinants of Diabetes in the Young study with at least one positive autoantibody measurement from years 1 to 12 of life. Unsupervised machine learning algorithms were trained to identify clusters of autoantibody development based on islet autoantibody timing, type and titre. Risk for type 1 diabetes across each identified cluster was evaluated using time-to-event analysis. RESULTS: We identified 2-4 clusters in each year cohort that differed by autoantibody timing, titre and type. During the first 3 years of life, risk for type 1 diabetes onset was driven by membership in clusters with high titres of all three autoantibodies (1-year risk: 20.87-56.25%, 5-year risk: 67.73-69.19%). Type 1 diabetes risk transitioned to type-specific titres during ages 4 to 8, as clusters with high titres of IA-2A (1-year risk: 20.88-28.93%, 5-year risk: 62.73-78.78%) showed faster progression to diabetes compared with high titres of GADA (1-year risk: 4.38-6.11%, 5-year risk: 25.06-31.44%). The importance of high GADA titres decreased during ages 9 to 12, with clusters containing high titres of IA-2A alone (1-year risk: 14.82-30.93%) or both GADA and IA-2A (1-year risk: 8.27-25.00%) demonstrating increased risk. CONCLUSIONS/INTERPRETATION: This unsupervised machine learning approach provides a novel tool for stratifying risk for type 1 diabetes onset using multiple autoantibody characteristics. These findings suggest that age-dependent changes in IA-2A titres modulate risk for type 1 diabetes onset across 12 years of life. Overall, this work supports incorporation of islet autoantibody timing, type and titre in risk stratification models for aetiologic studies, prevention trials and disease screening.

K-means cluster analysis of cooperative effects of CO, NO2, O3, PM2.5, PM10, and SO2 on incidence of type 2 diabetes mellitus in the US.

Air pollution (AP) has been shown to increase the risk of type 2 diabetes mellitus, as well as other cardiometabolic diseases. AP is characterized by a complex mixture of components for which the composition depends on sources and metrological factors. The US Environmental Protection Agency (EPA) monitors and regulates certain components of air pollution known to have negative consequences for human health. Research assessing the health effects of these components of AP often uses traditional regression models, which might not capture more complex and interdependent relationships. Machine learning has the capability to simultaneously assess multiple components and find complex, non-linear patterns that may not be apparent and could not be modeled by other techniques. Here we use k-means clustering to assess the patterns associating PM2.5, PM10, CO, NO2, O3, and SO2 measurements and changes in annual diabetes incidence at a US county level. The average age adjusted annual decrease in diabetes incidence for the entire US populations is -0.25 per 1000 but the change shows a significant geographic variation (range: -17.2 to 5.30 per 1000). In this paper these variations were compared with the local daily AP concentrations of the pollutants listed above from 2005 to 2015, which were matched to the annual change in diabetes incidence for the following year. A total of 134,925 daily air quality observations were included in the cluster analysis, representing 125 US counties and the District of Columbia. K-means successfully clustered AP components and indicated an association between exposure to certain AP mixtures with lower decreases on T2D incidence.

Increased Incidence of Pseudotumor Cerebri Syndrome Among Users of Tetracycline Antibiotics.

BACKGROUND: To determine whether the use of a tetracycline-class antibiotic is associated with an increased risk of developing pseudotumor cerebri syndrome (PTCS). METHODS: We identified patients in the University of Utah Health system who were prescribed a tetracycline-class antibiotic and determined what percentage of those individuals were subsequently diagnosed with PTCS secondary to tetracycline use. We compared this calculation to the number of patients with PTCS unrelated to tetracycline use. RESULTS: Between 2007 and 2014, a total of 960 patients in the University system between the ages of 12 and 50 were prescribed a tetracycline antibiotic. Among those, 45 were diagnosed with tetracycline-induced PTCS. We estimate the incidence of tetracycline-induced PTCS to be 63.9 per 100,000 person-years. By comparison, the incidence of idiopathic intracranial hypertension (IIH) is estimated to be less than one per 100,000 person-years (Calculated Risk Ratio = 178). CONCLUSIONS: Although a causative link between tetracycline use and pseudotumor cerebri has yet to be firmly established, our study suggests that the incidence of pseudotumor cerebri among tetracycline users is significantly higher than the incidence of IIH in the general population.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

EHR-based cohort assessment for multicenter RCTs: a fast and flexible model for identifying potential study sites.

OBJECTIVE: The Recruitment Innovation Center (RIC), partnering with the Trial Innovation Network and institutions in the National Institutes of Health-sponsored Clinical and Translational Science Awards (CTSA) Program, aimed to develop a service line to retrieve study population estimates from electronic health record (EHR) systems for use in selecting enrollment sites for multicenter clinical trials. Our goal was to create and field-test a low burden, low tech, and high-yield method. MATERIALS AND METHODS: In building this service line, the RIC strove to complement, rather than replace, CTSA hubs' existing cohort assessment tools. For each new EHR cohort request, we work with the investigator to develop a computable phenotype algorithm that targets the desired population. CTSA hubs run the phenotype query and return results using a standardized survey. We provide a comprehensive report to the investigator to assist in study site selection. RESULTS: From 2017 to 2020, the RIC developed and socialized 36 phenotype-dependent cohort requests on behalf of investigators. The average response rate to these requests was 73%. DISCUSSION: Achieving enrollment goals in a multicenter clinical trial requires that researchers identify study sites that will provide sufficient enrollment. The fast and flexible method the RIC has developed, with CTSA feedback, allows hubs to query their EHR using a generalizable, vetted phenotype algorithm to produce reliable counts of potentially eligible study participants. CONCLUSION: The RIC's EHR cohort assessment process for evaluating sites for multicenter trials has been shown to be efficient and helpful. The model may be replicated for use by other programs.

Towards a Newborn Screening Common Data Model: The Utah Newborn Screening Data Model.

As newborn screening programs transition from paper-based data exchange toward automated, electronic methods, significant data exchange challenges must be overcome. This article outlines a data model that maps newborn screening data elements associated with patient demographic information, birthing facilities, laboratories, result reporting, and follow-up care to the LOINC, SNOMED CT, ICD-10-CM, and HL7 healthcare standards. The described framework lays the foundation for the implementation of standardized electronic data exchange across newborn screening programs, leading to greater data interoperability. The use of this model can accelerate the implementation of electronic data exchange between healthcare providers and newborn screening programs, which would ultimately improve health outcomes for all newborns and standardize data exchange across programs.

Erratum to: Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering.

[This corrects the article DOI: 10.1093/jamiaopen/ooab063.].

Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering.

OBJECTIVE: Hyperglycemia has emerged as an important clinical manifestation of coronavirus disease 2019 (COVID-19) in diabetic and nondiabetic patients. Whether these glycemic changes are specific to a subgroup of patients and persist following COVID-19 resolution remains to be elucidated. This work aimed to characterize longitudinal random blood glucose in a large cohort of nondiabetic patients diagnosed with COVID-19. MATERIALS AND METHODS: De-identified electronic medical records of 7502 patients diagnosed with COVID-19 without prior diagnosis of diabetes between January 1, 2020, and November 18, 2020, were accessed through the TriNetX Research Network. Glucose measurements, diagnostic codes, medication codes, laboratory values, vital signs, and demographics were extracted before, during, and after COVID-19 diagnosis. Unsupervised time-series clustering algorithms were trained to identify distinct clusters of glucose trajectories. Cluster associations were tested for demographic variables, COVID-19 severity, glucose-altering medications, glucose values, and new-onset diabetes diagnoses. RESULTS: Time-series clustering identified a low-complexity model with 3 clusters and a high-complexity model with 19 clusters as the best-performing models. In both models, cluster membership differed significantly by death status, COVID-19 severity, and glucose levels. Clusters membership in the 19 cluster model also differed significantly by age, sex, and new-onset diabetes mellitus. DISCUSSION AND CONCLUSION: This work identified distinct longitudinal blood glucose changes associated with subclinical glucose dysfunction in the low-complexity model and increased new-onset diabetes incidence in the high-complexity model. Together, these findings highlight the utility of data-driven techniques to elucidate longitudinal glycemic dysfunction in patients with COVID-19 and provide clinical evidence for further evaluation of the role of COVID-19 in diabetes pathogenesis.

Human activity pattern implications for modeling SARS-CoV-2 transmission.

BACKGROUND AND OBJECTIVES: SARS-CoV-2 emerged in December 2019 and rapidly spread into a global pandemic. Designing optimal community responses (social distancing, vaccination) is dependent on the stage of the disease progression, discovery of asymptomatic individuals, changes in virulence of the pathogen, and current levels of herd immunity. Community strategies may have severe and undesirable social and economic side effects. Modeling is the only available scientific approach to develop effective strategies that can minimize these unwanted side effects while retaining the effectiveness of the interventions. METHODS: We extended the agent-based model, SpatioTemporal Human Activity Model (STHAM), for simulating SARS-CoV-2 transmission dynamics. RESULTS: Here we present preliminary STHAM simulation results that reproduce the overall trends observed in the Wasatch Front (Utah, United States of America) for the general population. The results presented here clearly indicate that human activity patterns are important in predicting the rate of infection for different demographic groups in the population. CONCLUSIONS: Future work in pandemic simulations should use empirical human activity data for agent-based techniques.

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations.

Human activity encompasses a series of complex spatiotemporal processes that are difficult to model but represent an essential component of human exposure assessment. A significant empirical data source, like the American Time Use Survey (ATUS), can be leveraged to model human activity. However, tractable models require a better stratification of activity data to inform about different, but classifiable groups of individuals, that exhibit similar activity sequences and mobility patterns. Using machine learning algorithms, we developed an unsupervised classification and sequence generation method that is capable of generating coherent and stochastic sequences of activity from the ATUS data. This classification, when combined with any spatiotemporal exposure profile, allows the development of stochastic models of exposure patterns and records for groups of individuals exhibiting similar activity behaviors.

Associations Between the Time in Hypoglycemia and Hypoglycemia Awareness Status in Type 1 Diabetes Patients Using Continuous Glucose Monitoring Systems.

Background: Continuous glucose monitoring (CGM) systems help reduce hypoglycemia in patients with type 1 diabetes (T1D). It remains unclear whether T1D patients with impaired awareness of hypoglycemia (IAH) continue to develop more hypoglycemia than those with normal hypoglycemia awareness (NA) despite CGM use. Materials and Methods: For this cross-sectional observational study, 99 T1D patients using real-time CGMs for ≥86% of time were recruited. Fifty and 49 patients were found to have NA and IAH (based on the Clarke questionnaire), respectively. Two-week CGM hypoglycemia data were collected. Results: IAH was associated with greater percentages of CGM values <70 and <54 mg/dL (P = 0.012, P = 0.004) compared to NA. Clarke scores correlated positively with the percentage of CGM values <70 and <54 mg/dL (P = 0.013, P = 0.004). IAH was also related to more events with glucose <70 and <54 mg/dL determined either with at ≥1 time point (P = 0.048, P = 0.003) or lasting ≥20 min (P = 0.016, P = 0.004). IAH patients presented with more day-time events with glucose <54 mg/dL (P = 0.015), nocturnal events with glucose levels <70 and <54 mg/dL (P = 0.009, P = 0.007) and longer day-time event duration with glucose levels <70 and <54 mg/dL (P < 0.001, P = 0.006), respectively. Conclusions: T1D patients with IAH continue to experience more hypoglycemia despite dedicated CGM use.

STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains.

Human exposure to particulate matter and other environmental species is difficult to estimate in large populations. Individuals can encounter significant and acute variations in exposure over small spatiotemporal scales. Exposure is strongly tied to both the environmental and activity contexts that individuals experience. Here we present the development of an agent-based model to simulate human exposure at high spatiotemporal resolutions. The model is based on simulated activity and location trajectories on a per-person basis for large geographical areas. We demonstrate that the model can successfully estimate trajectories and that activity patterns have been validated against traffic patterns and that can be integrated with exposure-agent geographical distributions to estimate total human exposure.

Alarm Settings of Continuous Glucose Monitoring Systems and Associations to Glucose Outcomes in Type 1 Diabetes.

CONTEXT: Little evidence exists regarding the positive and negative impacts of continuous glucose monitor system (CGM) alarm settings for diabetes control in patients with type 1 diabetes (T1D). OBJECTIVE: Evaluate the associations between CGM alarm settings and glucose outcomes. DESIGN AND SETTING: A cross-sectional observational study in a single academic institution. PATIENTS AND MAIN OUTCOME MEASURES: CGM alarm settings and 2-week CGM glucose information were collected from 95 T1D patients with > 3 months of CGM use and ≥ 86% active usage time. The associations between CGM alarm settings and glucose outcomes were analyzed. RESULTS: Higher glucose thresholds for hypoglycemia alarms (ie, ≥ 73 mg/dL vs < 73 mg/dL) were related to 51% and 65% less time with glucose < 70 and < 54 mg/dL, respectively (P = 0.005; P = 0.016), higher average glucose levels (P = 0.002) and less time-in-range (P = 0.005), but not more hypoglycemia alarms. The optimal alarm threshold for < 1% of time in hypoglycemia was 75 mg/dL.Lower glucose thresholds for hyperglycemia alarms (ie, ≤ 205 mg/dL vs > 205 mg/dL) were related to lower average glucose levels and 42% and 61% less time with glucose > 250 and > 320 mg/dL (P = 0.020, P = 0.016, P = 0.007, respectively), without more hypoglycemia. Lower alarm thresholds were also associated with more alarms (P < 0.0001). The optimal alarm threshold for < 5% of time in hyperglycemia and hemoglobin A1c ≤ 7% was 170 mg/dL. CONCLUSIONS: Different CGM glucose thresholds for hypo/hyperglycemia alarms are associated with various hypo/hyperglycemic outcomes. Configurations to the hypo/hyperglycemia alarm thresholds could be considered as an intervention to achieve therapeutic goals.

Evaluation of a D-Dimer Protocol for Detection of Venous Thromboembolism.

BACKGROUND: The use of venous duplex ultrasonography (VDU) for confirmation of deep venous thrombosis in neurosurgical patients is costly and requires experienced personnel. We evaluated a protocol using D-dimer levels to screen for venous thromboembolism (VTE), defined as deep venous thrombosis and asymptomatic pulmonary embolism. METHODS: We used a retrospective bioinformatics analysis to identify neurosurgical inpatients who had undergone a protocol assessing the serum D-dimer levels and had undergone a VDU study to evaluate for the presence of VTE from March 2008 through July 2017. The clinical risk factors and D-dimer levels were evaluated for the prediction of VTE. RESULTS: In the 1918 patient encounters identified, the overall VTE detection rate was 28.7%. Using a receiver operating characteristic curve, an area under the curve of 0.58 was identified for all D-dimer values (P = 0.0001). A D-dimer level of ≥2.5 µg/mL on admission conferred a 30% greater relative risk of VTE (sensitivity, 0.43; specificity, 0.67; positive predictive value, 0.27; negative predictive value, 0.8). A D-dimer value of ≥3.5 µg/mL during hospitalization yielded a 28% greater relative risk of VTE (sensitivity, 0.73; specificity, 0.32; positive predictive value, 0.24; negative predictive value, 0.81). Multivariable logistic regression showed that age, male sex, length of stay, tumor or other neurological disease diagnosis, and D-dimer level ≥3.5 µg/mL during hospitalization were independent predictors of VTE. CONCLUSIONS: The D-dimer protocol was beneficial in identifying VTE in a heterogeneous group of neurosurgical patients by prompting VDU evaluation for patients with a D-dimer values of ≥3.5 µg/mL during hospitalization. Refinement of this screening model is necessary to improve the identification of VTE in a practical and cost-effective manner.

Towards a content agnostic computable knowledge repository for data quality assessment.

BACKGROUND AND OBJECTIVE: In recent years, several data quality conceptual frameworks have been proposed across the Data Quality and Information Quality domains towards assessment of quality of data. These frameworks are diverse, varying from simple lists of concepts to complex ontological and taxonomical representations of data quality concepts. The goal of this study is to design, develop and implement a platform agnostic computable data quality knowledge repository for data quality assessments. METHODS: We identified computable data quality concepts by performing a comprehensive literature review of articles indexed in three major bibliographic data sources. From this corpus, we extracted data quality concepts, their definitions, applicable measures, their computability and identified conceptual relationships. We used these relationships to design and develop a data quality meta-model and implemented it in a quality knowledge repository. RESULTS: We identified three primitives for programmatically performing data quality assessments: data quality concept, its definition, its measure or rule for data quality assessment, and their associations. We modeled a computable data quality meta-data repository and extended this framework to adapt, store, retrieve and automate assessment of other existing data quality assessment models. CONCLUSION: We identified research gaps in data quality literature towards automating data quality assessments methods. In this process, we designed, developed and implemented a computable data quality knowledge repository for assessing quality and characterizing data in health data repositories. We leverage this knowledge repository in a service-oriented architecture to perform scalable and reproducible framework for data quality assessments in disparate biomedical data sources.