Using Continuous Glucose Monitoring Values for Bolus Size Calculation in Smart Multiple Daily Injection Systems: No Negative Impact on Post-bolus Glycemic Outcomes Found in Real-World Data.

BACKGROUND: Recent evidence shows that it may be safe to estimate bolus sizes based on continuous glucose monitoring (CGM) rather than blood glucose (BG) values using glycemic trend-adjusted bolus calculators. Users may already be doing this in the real world, though it is unclear whether this is safe or effective for calculators not employing trend adjustment. METHODS: We assessed real-world data from a smart multiple daily injections (MDIs) device users with a CGM system, hypothesizing that four-hour post-bolus outcomes using CGM values are not inferior to those using BG values. Our data set included 184 users and spanned 18 months with 79 000 bolus observations. We tested differences using logistic regression predicting CGM or BG value usage based on outcomes and confirmed initial results using a mixed model regression accounting for within-subject correlations. RESULTS: Comparing four-hour outcomes for bolus events using CGM and BG values revealed no differences using our initial approach (P > .183). This finding was confirmed by our mixed model regression approach in all cases (P > .199), except for times below range outcomes. Higher times below range were predictive of lower odds of CGM-based bolus calculations (OR = 0.987, P < .0001 and OR = 0.987, P = .0276, for time below 70 and 54 mg/dL, respectively). CONCLUSIONS: We found no differences in four-hour post-bolus glycemic outcomes when using CGM or BG except for time below range, which showed evidence of being lower for CGM. Though preliminary, our results confirm prior findings showing non-inferiority of using CGM values for bolus calculation compared with BG usage in the real world.

Looking Back on Graduate BME Admissions Data: Lessons Learned and Implications for Holistic Review and Diversity.

Graduate school applications in Biomedical Engineering (BME) are steadily rising, making competition stiffer, applications more complex, and reviews more resource intensive. Holistic reviews are being increasingly adopted to support increased diversity, equity, and inclusion in graduate student BME admissions, but which application metrics are the strongest predictors of admission and enrollment into BME programs remains unclear. In this perspectives article, we aim to shed light on some of the key predictors of student acceptance in graduate school. We share data from a three-year retrospective review of our own institution's graduate BME applications and admission rates and review the influence of grade point averages (GPA), standardized test scores (e.g., GRE), and prior research experience on graduate school admission rates. We also examine how the waiver of GRE requirements has changed the landscape of BME graduate applications in recent years. Finally, we discuss efforts taken by our institution and others to develop and implement holistic reviews of graduate applications that encourage students from underrepresented backgrounds to apply and successfully gain admission to graduate school. We share five key lessons we learned by performing the retrospective review and encourage other institutions to "self-reflect" and examine their historical graduate admissions data and past practices. Efforts aimed at engaging faculty to overcome their own implicit biases, engaging with underrepresented students in hands-on, research-intensive programs, and networking with diverse student populations have strong potential to enhance the diversity of BME graduate programs and our STEM workforce. Supplementary Information: The online version contains supplementary material available at 10.1007/s43683-022-00080-5.

Development of the Self-Injury Risk Assessment Protocol for Corrections (SIRAP-C).

OBJECTIVE: We developed the Self-Injury Risk Assessment Protocol for Corrections (SIRAP-C) to meet legal mandates for self-directed violence (SDV) risk assessment standards in correctional settings. We focused on two empirical aims: (1) factor structure and internal consistency and (2) subscale associations with SDV and intervention recommendation outcomes. HYPOTHESES: We expected a multifactorial SIRAP-C structure with acceptable internal consistency. We further expected SIRAP-C subscales would distinguish history of SDV events while incarcerated, current SDV event category, and treatment recommendation. METHOD: We drew electronic health record data for adult incarcerated persons (N = 3,929) from state Division of Prisons records from 2016 to 2020. Clinical records included demographic and correctional institutional information, as well as SIRAP-C records. Factor analyses assessed Aim 1. Regression models tested Aim 2. RESULTS: Factor analyses supported a seven-factor SIRAP-C structure (27 items) comprising Depressive Symptoms, Reasons for Living, History of Self-Directed Violence, Current Suicidal Thinking, Family History of Self-Directed Violence, Coping Skills, and Social Connectedness. Subscales displayed acceptable internal consistency, with the exception of social connectedness in the confirmatory factor analysis subsample. Lower depressive symptoms and coping skills, as well as higher history of SDV, were associated with increased risk for a prior SDV assessment event while incarcerated. Lower depressive symptoms, current suicidal thinking, and coping skills and higher history of SDV marked worse risk for self-injurious behavior. Higher depressive symptoms and current suicidal thinking, as well as lower reasons for living, demarcated suicidal acts from self-injury. Higher history of SDV and lower coping skills indicated outpatient/residential treatment. Elevated depressive symptoms and history of SDV, as well as lower reasons for living and coping skills, were associated with inpatient hospitalization. CONCLUSIONS: The SIRAP-C represents a promising clinical approach advancing correctional SDV risk assessment. We offer future research, policy, and implementation recommendations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Younger age of stroke in low-middle income countries is related to healthcare access and quality.

Stroke is the second leading cause of mortality globally with higher burden and younger age in low-middle income countries (LMICs) than high-income countries (HICs). However, it is unclear to what extent differences in healthcare access and quality (HAQ) and prevalence of risk factors between LMICs and HICs contribute to younger age of stroke in LMICs. In this systematic review, we conducted meta-analysis of 67 articles and compared the mean age of stroke between LMICs and HICs, before and after adjusting for HAQ index. We also compared the prevalence of main stroke risk factors between HICs and LMICs. The unadjusted mean age of stroke in LMICs was significantly lower than HICs (63.1 vs. 68.6), regardless of gender (63.9 vs. 66.6 among men, and 65.6 vs. 70.7 among women) and whether data were collected in population- (64.7 vs. 69.5) or hospital-based (62.6 vs. 65.9) studies (all p < 0.01). However, after adjusting for HAQ index, the difference in the mean age of stroke between LMICs and HICs was not significant (p ≥ 0.10), except among women (p = 0.048). In addition, while the median prevalence of hypertension in LMICs was 23.4% higher than HICs, the prevalence of all other risk factors was lower in LMICs than HICs. Our findings suggest a much larger contribution of HAQ to the younger mean age of stroke in LMICs, as compared with other potential factors. Additional studies on stroke care quality and accessibility are needed in LMICs.

An analysis of suicidal thoughts and behaviors among transgender and gender diverse adults.

PURPOSE: Suicidal thoughts and behaviors (STBs) remain a pressing public health problem for transgender and gender diverse (TGD) persons. The goal of this study was to apply social-ecological and minority stress frameworks to identify individual and interpersonal-level TGD-specific STB risk and protective factors. METHODS: This is a secondary analysis of the 2015 United States Transgender Health Survey, a comprehensive cross-sectional health assessment of a national sample of TGD adults (N = 27,658). Chi-square and Analysis of Variance (ANOVA) were used to identify bivariate correlates of 12-month and lifetime suicidal ideation (SI) and suicide attempt (SA). Logistic regression was employed to identify the strongest STB risk and protective factors across levels. RESULTS: Sexual minority identification, racial minority identification, and having a disability were lifetime STB risk factors. TGD identity, sexual minority identification, racial minority identification (SA only), lower education, lower income, military experience, having a disability, and being uninsured were 12-month STB risk factors. Psychological distress was the most robust STB risk factor. Workplace discrimination, family rejection, healthcare discrimination, and childhood bias-based victimization were lifetime STB risk factors. All forms of discrimination and victimization (with the exception of family rejection for SI) were 12-month STB risk factors. Family and coworker support were protective factors for lifetime SA (but not SI) and all 12-month STBs. Being less out about TGD identity was a protective factor for STBs (except for 12-month SI). CONCLUSION: Findings support social-ecological and minority stress STB risk frameworks. Recommendations are provided for a comprehensive approach to TGD suicide prevention.

Exploring the Hazards of Scaling Up Clinical Data Analyses: A Drug Side Effect Discovery Case Report.

We assessed the scalability of pharmacological signal detection use case from a single-site CDW to a large aggregated clinical data warehouse (single-site database with 754,214 distinct patient IDs vs. multisite database with 49.8M). We aimed to explore whether a larger clinical dataset would provide clearer signals for secondary analyses such as detecting the known relationship between prednisone and weight. We found significant weight gain rate using the single-site data but not from using aggregated data (0.0104 kg/day, p<0.0001 vs. -0.050 kg/day, p<.0001). This rate was also found more consistently across 30 age and gender subgroups using the single-site data than in the aggregated data (26 vs. 18 significant weight gain findings). Contrary to our expectations, analyses of much larger aggregated clinical datasets did not yield stronger signals. Researchers must check the underlying model assumptions and account for greater heterogeneity when analyzing aggregated multisite data to ensure reliable findings.

What Oncologists Want: Identifying Challenges and Preferences on Diagnosis Data Entry to Reduce EHR-Induced Burden and Improve Clinical Data Quality.

PURPOSE: Accurate recording of diagnosis (DX) data in electronic health records (EHRs) is important for clinical practice and learning health care. Previous studies show statistically stable patterns of data entry in EHRs that contribute to inaccurate DX, likely because of a lack of data entry support. We conducted qualitative research to characterize the preferences of oncological care providers on cancer DX data entry in EHRs during clinical practice. METHODS: We conducted semistructured interviews and focus groups to uncover common themes on DX data entry preferences and barriers to accurate DX recording. Then, we developed a survey questionnaire sent to a cohort of oncologists to verify the generalizability of our initial findings. We constrained our participants to a single specialty and institution to ensure similar clinical backgrounds and clinical experience with a single EHR system. RESULTS: A total of 12 neuro-oncologists and thoracic oncologists were involved in the interviews and focus groups. The survey developed from these two initial thrusts was distributed to 19 participants yielding a 94.7% survey response rate. Clinicians reported similar user interface experiences, barriers, and dissatisfaction with current DX entry systems including repetitive entry operations, difficulty in finding specific DX options, time-consuming interactions, and the need for workarounds to maintain efficiency. The survey revealed inefficient DX search interfaces and challenging entry processes as core barriers. CONCLUSION: Oncologists seem to be divided between specific DX data entry and time efficiency because of current interfaces and feel hindered by the burdensome and repetitive nature of EHR data entry. Oncologists' top concern for adopting data entry support interventions is ensuring that it provides significant time-saving benefits and increasing workflow efficiency. Future interventions should account for time efficiency, beyond ensuring data entry effectiveness.

Vascular mild cognitive impairment and its relationship to hemoglobin A1c levels and apolipoprotein E genotypes in the Dominican Republic.

Dementia and vascular mild cognitive impairment (VaMCI) currently impose a tremendous human and economic burden on patients from aging populations and their families worldwide. Understanding the interplay of cardiometabolic risk factors and apolipoprotein E (APOE) may direct us to a more personalized medicine and preventative care in MCI and dementia. OBJECTIVE: To evaluate the relationship of cardiometabolic risk factors with MCI and assess the APOE genotype's role in an elderly cohort in the Dominican Republic. METHODS: We studied a cohort of 180 participants 65 years of age and older using a combined assessment of cardiometabolic risk factors, neuropsychological battery tests, and APOE genotyping. We used the number of failed tests as a proxy to predict MCI. RESULTS: We found that patients with the ε3-ε4 APOE genotype had 2.91 higher number of failed cognitive tests (p=0.027) compared to patients with the ε3-ε3 genotyped. The rate of test failures increased 10% (p=0.025) per unit increase in HbA1c percentage. CONCLUSIONS: Increased Hemoglobin A1c levels and ε3-ε4 APOE genotypes seem to have an association with the development of VaMCI.

A demência e o comprometimento cognitivo leve vascular (VaMCI) atualmente impõem uma enorme carga humana e econômica aos pacientes de populações envelhecidas e suas famílias em todo o mundo. Compreender a interação dos fatores de risco cardiometabólicos e apolipoproteína E (APOE) pode nos direcionar para uma medicina mais personalizada e de cuidados preventivos em MCI e demência. OBJETIVO: Avaliar a relação dos fatores de risco cardiometabólicos com o MCI e o papel do genótipo APOE em uma coorte de idosos na República Dominicana. MÉTODOS: Estudamos uma coorte de 180 participantes com 65 anos de idade ou mais, utilizando uma avaliação combinada de fatores de risco cardiometabólicos, uma bateria de testes neuropsicológicos e genotipagem APOE. Adotou-se o número de testes com mau desempenho para o diagnóstico de MCI. RESULTADOS: Verificou-se que os pacientes com o genótipo ε3-ε4 do APOE apresentaram 2,91 vezes mais testes cognitivos com mau desempenho (p=0,027) em comparação com os pacientes com o genótipo ε3-ε3. A taxa de falhas de teste aumentou 10% (p=0,025) por aumento de unidade na porcentagem de HbA1c. CONCLUSÕES: Níveis mais altos de HbA1c e os genótipos ε3-ε4 do APOE parecem estar associados ao desenvolvimento de VaMCI.

Vascular mild cognitive impairment and its relationship to hemoglobin A1c levels and apolipoprotein E genotypes in the Dominican Republic / Comprometimento cognitivo leve vascular e sua relação com os níveis de hemoglobina a1c e genótipos apolipoproteína e na república dominicana

ABSTRACT. Dementia and vascular mild cognitive impairment (VaMCI) currently impose a tremendous human and economic burden on patients from aging populations and their families worldwide. Understanding the interplay of cardiometabolic risk factors and apolipoprotein E (APOE) may direct us to a more personalized medicine and preventative care in MCI and dementia. Objective: To evaluate the relationship of cardiometabolic risk factors with MCI and assess the APOE genotype's role in an elderly cohort in the Dominican Republic. Methods: We studied a cohort of 180 participants 65 years of age and older using a combined assessment of cardiometabolic risk factors, neuropsychological battery tests, and APOE genotyping. We used the number of failed tests as a proxy to predict MCI. Results: We found that patients with the ε3-ε4 APOE genotype had 2.91 higher number of failed cognitive tests (p=0.027) compared to patients with the ε3-ε3 genotyped. The rate of test failures increased 10% (p=0.025) per unit increase in HbA1c percentage. Conclusions: Increased Hemoglobin A1c levels and ε3-ε4 APOE genotypes seem to have an association with the development of VaMCI.

RESUMO. A demência e o comprometimento cognitivo leve vascular (VaMCI) atualmente impõem uma enorme carga humana e econômica aos pacientes de populações envelhecidas e suas famílias em todo o mundo. Compreender a interação dos fatores de risco cardiometabólicos e apolipoproteína E (APOE) pode nos direcionar para uma medicina mais personalizada e de cuidados preventivos em MCI e demência. Objetivo: Avaliar a relação dos fatores de risco cardiometabólicos com o MCI e o papel do genótipo APOE em uma coorte de idosos na República Dominicana. Métodos: Estudamos uma coorte de 180 participantes com 65 anos de idade ou mais, utilizando uma avaliação combinada de fatores de risco cardiometabólicos, uma bateria de testes neuropsicológicos e genotipagem APOE. Adotou-se o número de testes com mau desempenho para o diagnóstico de MCI. Resultados: Verificou-se que os pacientes com o genótipo ε3-ε4 do APOE apresentaram 2,91 vezes mais testes cognitivos com mau desempenho (p=0,027) em comparação com os pacientes com o genótipo ε3-ε3. A taxa de falhas de teste aumentou 10% (p=0,025) por aumento de unidade na porcentagem de HbA1c. Conclusões: Níveis mais altos de HbA1c e os genótipos ε3-ε4 do APOE parecem estar associados ao desenvolvimento de VaMCI.

Developing a Data Quality Standard Primer for Cardiovascular Risk Assessment from Electronic Health Record Data Using the DataGauge Process.

The learning health systems aim to support the needs of patients with chronic diseases, which require methods that account for electronic health recorded (EHR) data limitations. EHR data is often used to calculate cardiovascular risk scores. However, it is unclear whether EHR data presents high enough quality to provide accurate estimates. Still, there is currently no open standard available to assess data quality for such applications. We applied the DataGauge process to develop a data quality standard based on expert clinical, analytical and informatics knowledge by conducting four interviews and one focus group that produced 61 individual data quality requirements. These requirements covered all standard data quality dimensions and uncovered 705 quality issues in EHR data for 456 patients. These requirements will be expanded and further validated in future work. Our work initiates the development of open and explicit data quality standards for specific secondary uses of clinical data.

Undertriage Despite Use of Geriatric-Specific Trauma Team Activation Guidelines : Who Are We Missing?

BACKGROUND: Elderly trauma patients are at risk for undertriage, resulting in substantial morbidity and mortality. The objective of this study was to determine whether implementation of geriatric-specific trauma team activation (TTA) protocols appropriately identified severely-injured elderly patients. METHODS: This single-center retrospective study evaluated all severely injured (injury severity score [ISS] >15), geriatric (≥65 years) patients admitted to our Level 1 tertiary-care hospital between January 2014 and September 2017. Undertriage was defined as the lack of TTA despite presence of severe injuries. The primary outcome was all-cause in-hospital mortality; secondary outcomes were mortality within 48 hours of admission and urgent hemorrhage control. A multivariable logistic regression analysis was performed to identify predictors of appropriate triage in this study. RESULTS: Out of 1039 severely injured geriatric patients, 628 (61%) did not undergo TTA. Undertriaged patients were significantly older and had more comorbidities. In-hospital mortality was 5% and 31% in the undertriaged and appropriately triaged groups, respectively (P < .0001). One percent of undertriaged patients needed urgent hemorrhage control, compared to 6% of the appropriately triaged group (P < .0001). One percent of undertriaged patients died within 48 hours compared to 19% in the appropriately triaged group (P < .0001). Predictors of appropriate triage include GCS, heart rate, systolic blood pressure, lactic acid, ISS, shock, and absence of dementia, stroke, or alcoholism. DISCUSSION: Geriatric-specific TTA guidelines continue to undertriage elderly trauma patients when using ISS as a metric to measure undertriage. However, undertriaged patients have much lower morbidity and mortality, suggesting the geriatric-specific TTA guidelines identify those patients at highest risk for poor outcomes.

Workflow Differences Affect Data Accuracy in Oncologic EHRs: A First Step Toward Detangling the Diagnosis Data Babel.

PURPOSE: Diagnosis (DX) information is key to clinical data reuse, yet accessible structured DX data often lack accuracy. Previous research hints at workflow differences in cancer DX entry, but their link to clinical data quality is unclear. We hypothesized that there is a statistically significant relationship between workflow-describing variables and DX data quality. METHODS: We extracted DX data from encounter and order tables within our electronic health records (EHRs) for a cohort of patients with confirmed brain neoplasms. We built and optimized logistic regressions to predict the odds of fully accurate (ie, correct neoplasm type and anatomic site), inaccurate, and suboptimal (ie, vague) DX entry across clinical workflows. We selected our variables based on correlation strength of each outcome variable. RESULTS: Both workflow and personnel variables were predictive of DX data quality. For example, a DX entered in departments other than oncology had up to 2.89 times higher odds of being accurate (P < .0001) compared with an oncology department; an outpatient care location had up to 98% fewer odds of being inaccurate (P < .0001), but had 458 times higher odds of being suboptimal (P < .0001) compared with main campus, including the cancer center; and a DX recoded by a physician assistant had 85% fewer odds of being suboptimal (P = .005) compared with those entered by physicians. CONCLUSION: These results suggest that differences across clinical workflows and the clinical personnel producing EHR data affect clinical data quality. They also suggest that the need for specific structured DX data recording varies across clinical workflows and may be dependent on clinical information needs. Clinicians and researchers reusing oncologic data should consider such heterogeneity when conducting secondary analyses of EHR data.

Building Cancer Diagnosis Text to OncoTree Mapping Pipelines for Clinical Sequencing Data Integration and Curation.

Precision oncology research seeks to derive knowledge from existing data. Current work seeks to integrate clinical and genomic data across cancer centers to enable impactful secondary use. However, integrated data reliability depends on the data curation method used and its systematicity. In practice, data integration and mapping are often done manually even though crucial data such as oncological diagnoses (DX) show varying accuracy and specificity levels. We hypothesized that mapping of text-form cancer DX to a standardized terminology (OncoTree) could be automated using existing methods (e.g. natural language processing (NLP) modules and application programming interfaces [APIs]). We found that our best-performing pipeline prototype was effective but limited by API development limitations (accurately mapped 96.2% of textual DX dataset to NCI Thesaurus (NCIt), 44.2% through NCIt to OncoTree). These results suggest the pipeline model could be viable to automate data curation. Such techniques may become increasingly more reliable with further development.

Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.

Our previous research shows that structured cancer DX description data accuracy varied across electronic health record (EHR) segments (e.g. encounter DX, problem list, etc.). We provide initial evidence corroborating these findings in EHRs from patients with diabetes. We hypothesized that the odds of recording an "uncontrolled diabetes" DX increased after a hemoglobin A1c result above 9% and that this rate would vary across EHR segments. Our statistical models revealed that each DX indicating uncontrolled diabetes was 2.6% more likely to occur post-A1c>9% overall (adj-p=.0005) and 3.9% after controlling for EHR segment (adj-p<.0001). However, odds ratios varied across segments (1.021