Real-world Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents

Background

The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited.

Objective

To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents.

Design

Comparative effectiveness research accounting for underreported vaccination in three study cohorts: adolescents (12 to 20 years) during the Delta phase, children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase.

Setting

A national collaboration of pediatric health systems (PEDSnet).

Participants

77,392 adolescents (45,007 vaccinated) in the Delta phase, 111,539 children (50,398 vaccinated) and 56,080 adolescents (21,180 vaccinated) in the Omicron period.

Exposures

First dose of the BNT162b2 vaccine vs. no receipt of COVID-19 vaccine.

Measurements

Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100% with confounders balanced via propensity score stratification.

Results

During the Delta period, the estimated effectiveness of BNT162b2 vaccine was 98.4% (95% CI, 98.1 to 98.7) against documented infection among adolescents, with no significant waning after receipt of the first dose. An analysis of cardiac complications did not find an increased risk after vaccination. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (95% CI, 72.2 to 76.2). Higher levels of effectiveness were observed against moderate or severe COVID-19 (75.5%, 95% CI, 69.0 to 81.0) and ICU admission with COVID-19 (84.9%, 95% CI, 64.8 to 93.5). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (95% CI, 83.8 to 87.1), with 84.8% (95% CI, 77.3 to 89.9) against moderate or severe COVID-19, and 91.5% (95% CI, 69.5 to 97.6)) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined after 4 months following the first dose and then stabilized. The analysis revealed a lower risk of cardiac complications in the vaccinated group during the Omicron variant period.

Limitations

Observational study design and potentially undocumented infection.

Conclusions

Our study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time.

Primary Funding Source

National Institutes of Health.

N6-methyladenosine in 7SK small nuclear RNA underlies RNA polymerase II transcription regulation

N6-methyladenosine (m6A) modifications play crucial roles in RNA metabolism. How m6A regulates RNA polymerase II (RNA Pol II) transcription remains unclear. We find that 7SK small nuclear RNA (snRNA), a regulator of RNA Pol II promoter-proximal pausing, is highly m6A-modified in non-small cell lung cancer (NSCLC) cells. In A549 cells, we identified eight m6A sites on 7SK and discovered methyltransferase-like 3 (METTL3) and alkB homolog 5 (ALKBH5) as the responsible writer and eraser. When the m6A-7SK is specifically erased by a dCasRx-ALKBH5 fusion protein, A549 cell growth is attenuated due to reduction of RNA Pol II transcription. Mechanistically, removal of m6A leads to 7SK structural rearrangements that facilitate sequestration of the positive transcription elongation factor b (P-TEFb) complex, which results in reduction of serine 2 phosphorylation (Ser2P) in the RNA Pol II C-terminal domain and accumulation of RNA Pol II in the promoter-proximal region. Taken together, we uncover that m6A modifications of a non-coding RNA regulate RNA Pol II transcription and NSCLC tumorigenesis.

Evaluating the perceptions of pesticide use, safety, and regulation and identifying common pesticide-related topics on Twitter

Synthetic pesticides are important agricultural tools that increase crop yield and help feed the world's growing population. These products are also highly regulated to balance benefits and potential environmental and human risks. Public perception of pesticide use, safety, and regulation is an important topic necessitating discussion across a variety of stakeholders from lay consumers to regulatory agencies since attitudes toward this subject could differ markedly. Individuals and organizations can perceive the same message(s) about pesticides differently due to prior differences in technical knowledge, perceptions, attitudes, and individual or group circumstances. Social media platforms, like Twitter, include both individuals and organizations and function as a townhall where each group promotes their topics of interest, shares their perspectives, and engages in both well-informed and misinformed discussions. We analyzed public Twitter posts about pesticides by user group, time, and location to understand their communication behaviors, including their sentiments and discussion topics, using machine learning-based text analysis methods. We extracted tweets related to pesticides between 2013 and 2021 based on relevant keywords developed through a "snowball" sampling process. Each tweet was grouped into individual versus organizational groups, then further categorized into media, government, industry, academia, and three types of nongovernmental organizations. We compared topic distributions within and between those groups using topic modeling and then applied sentiment analysis to understand the public's attitudes toward pesticide safety and regulation. Individual accounts expressed concerns about health and environmental risks, while industry and government accounts focused on agricultural usage and regulations. Public perceptions are heavily skewed toward negative sentiments, although this varies geographically. Our findings can help managers and decision-makers understand public sentiments, priorities, and perceptions and provide insights into public discourse on pesticides. Integr Environ Assess Manag 2023;19:1581-1599. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Meta-analysis of Association between Newer Glucose-Lowering Drugs and Risk of Parkinson's Disease

Background

The association between newer classes of glucose-lowering drugs (GLDs) and the risk of Parkinson's disease (PD) remains unclear.

Objective

The aim was to examine the effect of newer GLDs on the risk of PD through a meta-analysis of randomized outcome trials.

Methods

The methods included randomized placebo-controlled outcome trials that reported PD events associated with three newer classes of GLDs (ie, dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose co-transporter-2 inhibitors) in participants with or without type 2 diabetes. The pooled odds ratio (OR) and 95% confidence interval (CI) were estimated using Peto's method.

Results

The study included 24 trials involving 33 PD cases among 185,305 participants during a median follow-up of 2.2 years. Newer GLDs were significantly associated with a lower PD risk (OR: 0.50; 95% CI: 0.25-0.98) than placebo.

Conclusion

Newer GLDs may possibly be associated with a decreased risk of PD; however, larger datasets are required to confirm or refute this notion.

Individual and social determinants of adherence to sodium-glucose cotransporter 2 inhibitor therapy: A trajectory analysis

BACKGROUND: Sodium-glucose cotransporter 2 inhibitors (SGLT2is) are known to improve cardiovascular and renal outcomes in patients with type 2 diabetes (T2D). Understanding the longitudinal patterns of adherence and the associated predictors is critical to addressing the suboptimal use of this outcome-improving treatment. OBJECTIVE: To characterize the distinct trajectories of adherence to SGLT2is in patients with T2D and to identify patient characteristics and social determinants of health (SDOHs) associated with SGLT2i adherence. METHODS: In this retrospective cohort study, we identified patients with T2D who initiated and filled at least 1 SGLT2i prescription according to 2012-2016 national Medicare claims data. The monthly proportion of days covered with SGLT2is for each patient was incorporated into group-based trajectory models to identify groups with similar adherence patterns. A multinomial logistic regression model was constructed to examine the association between patient characteristics and group membership. In addition, the association between context-specific SDOHs (eg, neighborhood median income and neighborhood employment rate) and adherence to an SGLT2i regimen was explored in both the overall cohort and the racial and ethnic subgroups. RESULTS: The final sample comprised 6,719 patients with T2D. Four trajectories of SGLT2i adherence were identified: continuously adherent users (49.6%), early discontinuers (27.5%), late discontinuers (14.5%), and intermediately adherent users (8.4%). Patient age, sex, race, diabetes duration, and Medicaid eligibility were significantly associated with trajectory group membership. Areas with a higher unemployment rate, lower income level, lower high school education rate, worse nutrition environment, fewer health care facilities, and greater Area Deprivation Index scores were found to be associated with low adherence to SGLT2is. CONCLUSIONS: Four distinct trajectories of adherence to SGLT2is were identified, with only half of the patients remaining continuously adherent to their treatment regimen during the first year after initiation. Several contextual SDOHs were associated with suboptimal adherence to SGLT2is.

Residential water and energy consumption prediction at hourly resolution based on a hybrid machine learning approach

Predicting water and energy consumption at high resolution over a short-term horizon is critical for water and energy resource management. Water and energy are shown to be closely interlinked in household consumption. However, hourly predictions are often based only on historical consumption data for the resource being predicted, with activity or appliance information and household attribution as additional information. Few studies have used aggregated water and energy consumption for predictions. Within this context, the current study proposed a novel hybrid machine learning model based on the Prophet time-series model, Gated Recurrent Unit network, and self-adaptive weights, called the Prophet-GRU model, which could jointly include historical water and electricity consumption as inputs for hourly water or electricity prediction. Data on hourly water and electricity consumption in six households in Beijing during January-March 2020 were used to train and validate the Prophet-GRU model. The goodness of fit indicator (R2) and prediction accuracy (mean squared error and mean absolute error) for the water and electricity predictions were evaluated. Compared with the single input of water or electricity, with the combined input of data of these two resources, the proposed Prophet-GRU model achieved improvements of 29.2 % and 48.5 % in R2, for water and electricity consumption prediction, respectively. Our results could help better understand water-energy linkages and promote collaborative water and energy management practices.

Disparities Contributing to Late-Stage Diagnosis of Lung, Colorectal, Breast, and Cervical Cancers: Rural and Urban Poverty in Florida

Despite advances in cancer screening, late-stage cancer diagnosis is still a major cause of morbidity and mortality in the United States. In this study, we aim to understand demographic and geographic factors associated with receiving a late-stage diagnosis (LSD) of lung, colorectal, breast, or cervical cancer. (1) Methods: We analyzed data of patients with a cancer diagnosis between 2016 and 2020 from the Florida Cancer Data System (FCDS), a statewide population-based registry. To investigate correlates of LSD, we estimated multi-variable logistic regression models for each cancer while controlling for age, sex, race, insurance, and census tract rurality and poverty. (2) Results: Patients from high-poverty rural areas had higher odds for LSD of lung (OR = 1.23, 95% CI (1.10, 1.37)) and breast cancer (OR = 1.31, 95% CI (1.17,1.47)) than patients from low-poverty urban areas. Patients in high-poverty urban areas saw higher odds of LSD for lung (OR = 1.05 95% CI (1.00, 1.09)), breast (OR = 1.10, 95% CI (1.06, 1.14)), and cervical cancer (OR = 1.19, 95% CI (1.03, 1.37)). (3) Conclusions: Financial barriers contributing to decreased access to care likely drive LSD for cancer in rural and urban communities of Florida.

Erratum: Search for Cosmic-Ray Boosted Sub-GeV Dark Matter Using Recoil Protons at Super-Kamiokande [Phys. Rev. Lett. 130, 031802 (2023)]

This corrects the article DOI: 10.1103/PhysRevLett.130.031802.

Eye Rubbing in Chinese Patients With Keratoconus: A Multicenter Analysis

PURPOSE

To investigate the eye rubbing habits of Chinese patients with keratoconus.

METHODS

This study was carried out from 2018 to June 2022 at Shandong Eye Hospital, Qingdao Eye Hospital, and Henan Eye Hospital. The study compared the number of patients who rubbed their eyes between medical records and second time questionnaires, eye rubbing of patients with myopia and patients with keratoconus, and disease severity between patients with keratoconus. A questionnaire survey of ophthalmologists was conducted to determine their degree of awareness that eye rubbing is a risk factor for keratoconus.

RESULTS

The study assessed 799 patients with keratoconus and 798 control patients, and 97 ophthalmologists. The average proportion of patients with keratoconus who rubbed their eyes was 31.0% in the medical records with an increasing trend related to the increase in ophthalmologists' awareness, 66.6% after the second follow-up, and 25.4% among patients with myopia. After multivariate analysis, the following variables showed significant results: eye rubbing frequency more than 10 times/day (odds ratio [OR], 9.168; P < .001); rubbing with knuckles (OR, 9.804; P = .001); and prone sleep position (OR, 12.427; P < .001). The proportion of patients who rubbed their eyes with stage IV keratoconus was 71.9%, 18.9% higher than those with stage I, 4.8% higher than stage II, and 17.8% higher than stage III.

CONCLUSIONS

The proportion of Chinese patients with keratoconus who rubbed their eyes was relatively high. The main reasons for the low proportions reported were lack of attention. Clinical attention should be paid to eye rubbing in patients with keratoconus who should be educated to avoid it. [J Refract Surg. 2023;39(10):712-718.].

Lung cancer screening adherence among people living with and without HIV: An analysis of an integrated health system in Florida, United States (2012-2021)

Although lung cancer is a leading cause of death among people living with HIV (PLWH), limited research exists characterizing real-world lung cancer screening adherence among PLWH. Our objective was to compare low-dose computed tomography (LDCT) adherence among PLWH to those without HIV treated at one integrated health system. Using the University of Florida's Health Integrated Data Repository (01/01/2012-10/31/2021), we identified PLWH with at least one LDCT procedure, using Current Procedural Terminology codes(S8032/G0297/71271). Lung cancer screening adherence was defined as a second LDCT based on the Lung Imaging Reporting and Data System (Lung-RADS®). Lung-RADS categories were extracted from radiology reports using a natural language processing system. PLWH were matched with 4 randomly selected HIV-negative patients based on (+/- 1 year) age, Lung-RADS category, and calendar year. Seventy-three PLWH and 292 matched HIV-negative adults with at least one LDCT were identified. PLWH were more likely to be male (66% vs.52%,p < 0.04), non-Hispanic Black (53% vs.23%,p < 0.001), and live in an area of high poverty (45% vs.31%,p < 0.001). PLWH were more likely to be diagnosed with lung cancer after first LDCT (8% vs.0%,p < 0.001). Seventeen percent of HIV-negative and 12% of PLWH were adherent to LDCT screenings. Only 25% of PLWH diagnosed with category 4A were adherent compared to 44% of HIV-negative. On multivariable analyses, those with older age (66-80 vs.50-64 years) and with either Medicaid, charity-based, or other government insurance (vs. Medicare) were less likely to be adherent to LDCT screenings. PLWH may have poorer adherence to LDCT compared to their HIV-negative counterparts.

Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium

BACKGROUND

Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach.

METHODS

This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors.

RESULTS

In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium.

CONCLUSIONS

Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.

Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record

OBJECTIVE

To develop and validate an approach that identifies patients eligible for lung cancer screening (LCS) by combining structured and unstructured smoking data from the electronic health record (EHR).

METHODS

We identified patients aged 50-80 years who had at least one encounter in a primary care clinic at Vanderbilt University Medical Center (VUMC) between 2019 and 2022. We fine-tuned an existing natural language processing (NLP) tool to extract quantitative smoking information using clinical notes collected from VUMC. Then, we developed an approach to identify patients who are eligible for LCS by combining smoking information from structured data and clinical narratives. We compared this method with two approaches to identify LCS eligibility only using smoking information from structured EHR. We used 50 patients with a documented history of tobacco use for comparison and validation.

RESULTS

102,475 patients were included. The NLP-based approach achieved an F1-score of 0.909, and accuracy of 0.96. The baseline approach could identify 5,887 patients. Compared to the baseline approach, the number of identified patients using all structured data and the NLP-based algorithm was 7,194 (22.2 %) and 10,231 (73.8 %), respectively. The NLP-based approach identified 589 Black/African Americans, a significant increase of 119 %.

CONCLUSION

We present a feasible NLP-based approach to identify LCS eligible patients. It provides a technical basis for the development of clinical decision support tools to potentially improve the utilization of LCS and diminish healthcare disparities.

Lung Cancer Screening Adherence Among People with HIV Treated at an Integrated Health System in Florida

We evaluated low-dose computed tomography (LDCT) adherence among people with HIV (PWH) treated at University of Florida (UF). From the UF Health Integrated Data Repository, we identified PWH who underwent at least one LDCT procedure (January 1, 2012-October 31, 2021). Lung cancer screening adherence was defined as having a second LDCT within recommended observation window, based on the Lung Imaging Reporting and Data System (Lung-RADS®). We identified 73 PWH with a history of at least one LDCT. PWH were mostly male (66%), non-Hispanic Black (53%), and living in urban (86%), high poverty (45%) areas. Almost 1 in 10 of PWH were diagnosed with lung cancer after their first LDCT. Overall, 48% and 41% of PWH were diagnosed with Lung-RADS categories 1 and 2, respectively. We observed that 12% of PWH were adherent to LDCT. Only 25% of PWH diagnosed with category 4A were adherent. PWH may have poor adherence to lung cancer screening.

Clinical concept and relation extraction using prompt-based machine reading comprehension

OBJECTIVE

To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications.

METHODS

We formulate both clinical concept extraction and relation extraction using a unified prompt-based MRC architecture and explore state-of-the-art transformer models. We compare our MRC models with existing deep learning models for concept extraction and end-to-end relation extraction using 2 benchmark datasets developed by the 2018 National NLP Clinical Challenges (n2c2) challenge (medications and adverse drug events) and the 2022 n2c2 challenge (relations of social determinants of health [SDoH]). We also evaluate the transfer learning ability of the proposed MRC models in a cross-institution setting. We perform error analyses and examine how different prompting strategies affect the performance of MRC models.

RESULTS AND CONCLUSION

The proposed MRC models achieve state-of-the-art performance for clinical concept and relation extraction on the 2 benchmark datasets, outperforming previous non-MRC transformer models. GatorTron-MRC achieves the best strict and lenient F1-scores for concept extraction, outperforming previous deep learning models on the 2 datasets by 1%-3% and 0.7%-1.3%, respectively. For end-to-end relation extraction, GatorTron-MRC and BERT-MIMIC-MRC achieve the best F1-scores, outperforming previous deep learning models by 0.9%-2.4% and 10%-11%, respectively. For cross-institution evaluation, GatorTron-MRC outperforms traditional GatorTron by 6.4% and 16% for the 2 datasets, respectively. The proposed method is better at handling nested/overlapped concepts, extracting relations, and has good portability for cross-institute applications. Our clinical MRC package is publicly available at https://github.com/uf-hobi-informatics-lab/ClinicalTransformerMRC.

Identification of Outcome-Oriented Progression Subtypes from Mild Cognitive Impairment to Alzheimer's Disease Using Electronic Health Records

Alzheimer's disease (AD) is a complex heterogeneous neurodegenerative disease that requires an in-depth understanding of its progression pathways and contributing factors to develop effective risk stratification and prevention strategies. In this study, we proposed an outcome-oriented model to identify progression pathways from mild cognitive impairment (MCI) to AD using electronic health records (EHRs) from the OneFlorida+ Clinical Research Consortium. To achieve this, we employed the long short-term memory (LSTM) network to extract relevant information from the sequential records of each patient. The hierarchical agglomerative clustering was then applied to the learned representation to group patients based on their progression subtypes. Our approach identified multiple progression pathways, each of which represented distinct patterns of disease progression from MCI to AD. These pathways can serve as a valuable resource for researchers to understand the factors influencing AD progression and to develop personalized interventions to delay or prevent the onset of the disease.

Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities

OBJECTIVE

To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions.

METHODS

In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs.

RESULTS

Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions.

CONCLUSIONS

To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.

Optimizing Dynamic Antibiotic Treatment Strategies against Invasive Methicillin-Resistant Staphylococcus Aureus Infections using Causal Survival Forests and G-Formula on Statewide Electronic Health Record Data

Developing models for individualized, time-varying treatment optimization from observational data with large variable spaces, e.g., electronic health records (EHR), is problematic because of inherent, complex bias that can change over time. Traditional methods such as the g-formula are robust, but must identify critical subsets of variables due to combinatorial issues. Machine learning approaches such as causal survival forests have fewer constraints and can provide fine-tuned, individualized counterfactual predictions. In this study, we aimed to optimize time-varying antibiotic treatment -identifying treatment heterogeneity and conditional treatment effects- against invasive methicillin-resistant Staphylococcus Aureus (MRSA) infections, using statewide EHR data collected in Florida, USA. While many previous studies focused on measuring the effects of the first empiric treatment (i.e., usually vancomycin), our study focuses on dynamic sequential treatment changes, comparing possible vancomycin switches with other antibiotics at clinically relevant time points, e.g., after obtaining a bacterial culture and susceptibility testing. Our study population included adult individuals admitted to the hospital with invasive MRSA. We collected demographic, clinical, medication, and laboratory information from the EHR for these patients. Then, we followed three sequential antibiotic choices (i.e., their empiric treatment, subsequent directed treatment, and final sustaining treatment), evaluating 30-day mortality as the outcome. We applied both causal survival forests and g-formula using different clinical intervention policies. We found that switching from vancomycin to another antibiotic improved survival probability, yet there was a benefit from initiating vancomycin compared to not using it at any time point. These findings show consistency with the empiric choice of vancomycin before confirmation of MRSA and shed light on how to manage switches on course. In conclusion, this application of causal machine learning on EHR demonstrates utility in modeling dynamic, heterogeneous treatment effects that cannot be evaluated precisely using randomized clinical trials.

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records

INTRODUCTION

This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs).

METHODS

A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested.

RESULTS

The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified.

DISCUSSION

We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.

Evaluation of a Computable Phenotype for Successful Cognitive Aging

Objective

To establish, apply, and evaluate a computable phenotype for the recruitment of individuals with successful cognitive aging.

Participants and Methods

Interviews with 10 aging experts identified electronic health record (EHR)-available variables representing successful aging among individuals aged 85 years and older. On the basis of the identified variables, we developed a rule-based computable phenotype algorithm composed of 17 eligibility criteria. Starting September 1, 2019, we applied the computable phenotype algorithm to all living persons aged 85 years and older at the University of Florida Health, which identified 24,024 individuals. This sample was comprised of 13,841 (58%) women, 13,906 (58%) Whites, and 16,557 (69%) non-Hispanics. A priori permission to be contacted for research had been obtained for 11,898 individuals, of whom 470 responded to study announcements and 333 consented to evaluation. Then, we contacted those who consented to evaluate whether their cognitive and functional status clinically met out successful cognitive aging criteria of a modified Telephone Interview for Cognitive Status score of more than 27 and Geriatric Depression Scale of less than 6. The study was completed on December 31, 2022.

Results

Of the 45% of living persons aged 85 years and older included in the University of Florida Health EHR database identified by the computable phenotype as successfully aged, approximately 4% of these responded to study announcements and 333 consented, of which 218 (65%) met successful cognitive aging criteria through direct evaluation.

Conclusion

The study evaluated a computable phenotype algorithm for the recruitment of individuals for a successful aging study using large-scale EHRs. Our study provides proof of concept of using big data and informatics as aids for the recruitment of individuals for prospective cohort studies.

A Natural Language Processing Algorithm for Classifying Suicidal Behaviors in Alzheimer's Disease and Related Dementia Patients: Development and Validation Using Electronic Health Records Data

This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) and Deep Learning (DL) techniques to identify and classify documentation of suicidal behaviors in patients with Alzheimer's disease and related dementia (ADRD). We utilized MIMIC-III and MIMIC-IV datasets and identified ADRD patients and subsequently those with suicide ideation using relevant International Classification of Diseases (ICD) codes. We used cosine similarity with ScAN (Suicide Attempt and Ideation Events Dataset) to calculate semantic similarity scores of ScAN with extracted notes from MIMIC for the clinical notes. The notes were sorted based on these scores, and manual review and categorization into eight suicidal behavior categories were performed. The data were further analyzed using conventional ML and DL models, with manual annotation as a reference. The tested classifiers achieved classification results close to human performance with up to 98% precision and 98% recall of suicidal ideation in the ADRD patient population. Our NLP model effectively reproduced human annotation of suicidal ideation within the MIMIC dataset. These results establish a foundation for identifying and categorizing documentation related to suicidal ideation within ADRD population, contributing to the advancement of NLP techniques in healthcare for extracting and classifying clinical concepts, particularly focusing on suicidal ideation among patients with ADRD. Our study showcased the capability of a robust NLP algorithm to accurately identify and classify documentation of suicidal behaviors in ADRD patients.

Newer glucose-lowering drugs and risk of dementia: A systematic review and meta-analysis of observational studies

BACKGROUND

Preclinical studies have suggested potential beneficial effects of newer glucose-lowering drugs (GLDs) including dipeptidyl peptidase (DPP)-4 inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RAs), and sodium glucose co-transporter-2 (SGLT2) inhibitors, in protecting humans against cognitive decline and dementia. However, population studies aiming to demonstrate such cognitive benefits from newer GLDs have produced mixed findings. This meta-analysis aimed to evaluate the association between newer GLDs and risk of dementia in adults with type 2 diabetes (T2D).

METHODS

Electronic databases were searched up to March 11, 2022 to include observational studies that examined the association between DPP-4 inhibitors, GLP-1RAs, and SGLT2 inhibitors and risk of dementia (including all-cause dementia, Alzheimer's disease [AD], and vascular dementia [VD]) in people with T2D. We conducted a random-effects meta-analysis to calculate the relative risk (RR) with 95% confidence interval (CI) for each class of newer GLD.

RESULTS

Ten studies (from nine articles) involving 819,511 individuals with T2D were included. Three studies found that SGLT2 inhibitor users had a lower risk of all-cause dementia than non-SGLT2 inhibitor users (RR, 0.62; 95% CI, 0.39-0.97). Five studies found that users versus nonusers of GLP-1RAs were associated with a significant reduction in the risk of all-cause dementia (RR, 0.72; 95% CI, 0.54-0.97). However, a meta-analysis for AD and VD was unavailable for SGLT2 inhibitors and GLP-1RAs because only one study was included for each drug. In seven studies, users vs. nonusers of DPP-4 inhibitors were significantly associated with a decreased risk of all-cause dementia (RR, 0.84; 95% CI, 0.74-0.94) and VD (RR, 0.59; 95% CI, 0.47-0.75) but not AD (RR, 0.82; 95% CI, 0.63-1.08).

CONCLUSION

Newer GLDs were associated with a decreased risk of all-cause dementia in people with T2D. Because of the observational nature and significant heterogeneity between studies, the results should be interpreted with caution. Further research is warranted to confirm our findings.

Key Technologies of Pure Hydrogen and Hydrogen-Mixed Natural Gas Pipeline Transportation

Thanks to the advantages of cleanliness, high efficiency, extensive sources, and renewable energy, hydrogen energy has gradually become the focus of energy development in the world's major economies. At present, the natural gas transportation pipeline network is relatively complete, while hydrogen transportation technology faces many challenges, such as the lack of technical specifications, high safety risks, and high investment costs, which are the key factors that hinder the development of hydrogen pipeline transportation. This paper provides a comprehensive overview and summary of the current status and development prospects of pure hydrogen and hydrogen-mixed natural gas pipeline transportation. Analysts believe that basic studies and case studies for hydrogen infrastructure transformation and system optimization have received extensive attention, and related technical studies are mainly focused on pipeline transportation processes, pipe evaluation, and safe operation guarantees. There are still technical challenges in hydrogen-mixed natural gas pipelines in terms of the doping ratio and hydrogen separation and purification. To promote the industrial application of hydrogen energy, it is necessary to develop more efficient, low-cost, and low-energy-consumption hydrogen storage materials.

Allostatic load and risk of all-cause, cancer-specific, and cardiovascular mortality in older cancer survivors: an analysis of the National Health and Nutrition Examination Survey 1999-2010

Background

Allostatic load has been linked to an increased risk of death in various populations. However, to date, there is no research specifically investigating the effect of allostatic load on mortality in older cancer survivors.

Aims

To investigate the association between allostatic load (AL) and mortality in older cancer survivors.

Method

A total of 1,291 adults aged 60 years or older who survived for ≥1 year since cancer diagnoses were identified from the 1999-2010 National Health and Nutrition Examination Survey. AL was the exposure of interest incorporating 9 clinical measures/biomarkers; one point was added to AL if any of the measures/biomarkers exceeded the normal level. The sum of points was categorized as an ordinal variable to reflect low, moderate, and high AL. Our outcomes of interest were all-cause, cancer-specific, and cardiovascular disease (CVD)-specific mortality. Death was identified by linkage to the National Death Index. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratio (aHR) and 95% confidence intervals (CI) of mortality by AL category.

Results

Overall, 53.6% of participants were male and 78.4% were white. The mean age of study participants at interview was 72.8 years (SD=7.1). A total of 546 participants died during the follow-up (median follow-up time: 8.0 years). Among them, 158 died of cancer and 106 died of cardiovascular events. Results from multivariable Cox proportional hazards models showed that higher ALS was positively associated with higher all-cause mortality (ALS=4-9 vs. ALS =0-1: aHR=1.52, 95% CI =1.17-1.98, p-trend<0.01) and higher cancer-specific mortality (ALS=4-9 vs. ALS =0-1: aHR=1.80, 95% CI =1.12-2.90, p-trend=0.01). The association between ALS and cardiovascular mortality was positive but non-significant (ALS=4-9 vs. ALS =0-1: aHR=1.59, 95% CI =0.86-2.94, p-trend=0.11).

Conclusions

Our study suggests that older cancer survivors can have a higher risk of death if they have a high burden of AL.

Contextualized medication information extraction using Transformer-based deep learning architectures

OBJECTIVE

To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge.

MATERIALS AND METHODS

We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes. We explored 6 state-of-the-art pretrained transformer models for the three subtasks, including GatorTron, a large language model pretrained using > 90 billion words of text (including > 80 billion words from > 290 million clinical notes identified at the University of Florida Health). We evaluated our NLP systems using annotated data and evaluation scripts provided by the 2022 n2c2 organizers.

RESULTS

Our GatorTron models achieved the best F1-scores of 0.9828 for medication extraction (ranked 3rd), 0.9379 for event classification (ranked 2nd), and the best micro-average accuracy of 0.9126 for context classification. GatorTron outperformed existing transformer models pretrained using smaller general English text and clinical text corpora, indicating the advantage of large language models.

CONCLUSION

This study demonstrated the advantage of using large transformer models for contextual medication information extraction from clinical narratives.