Longitudinal Information Research Articles

Coronary Microvascular Disease Registry (CMDR): Study design and rationale

BackgroundCoronary microvascular dysfunction (CMD) is a prevalent condition among patients with cardiovascular risk factors, leading to a reduced quality of life and an increased risk of major adverse cardiovascular events. Novel invasive techniques have emerged to more accurately diagnose CMD. However, CMD's natural history remains poorly understood due to limited data. To address this knowledge gap, the Coronary Microvascular Disease Registry (CMDR) was established with the primary aim of standardizing comprehensive coronary functional testing and understanding of CMD. DesignCMDR is a prospective, multicenter registry enrolling an unlimited number of consecutive subjects who undergo comprehensive invasive hemodynamic assessment of the entire coronary arterial vasculature. Patients undergoing acetylcholine provocation test for coronary vasospasm will also be included. Follow-up assessments will be conducted at 30 days and annually for up to 5 years. The primary endpoint is Canadian Cardiovascular Society angina grade over time. Secondary endpoints, including all-cause mortality, cardiovascular death, acute myocardial infarction, stroke, hospitalizations, medication changes, and subsequent coronary interventions, will be analyzed to establish long-term safety and clinical outcomes in patients undergoing invasive CMD assessment. SummaryCMDR aims to characterize the clinical and physiologic profile of patients undergoing comprehensive invasive coronary functional testing, simultaneously providing crucial longitudinal information on the natural history and outcomes of these patients. This will shed light on CMD's course and clinical implications, which, in turn, holds the potential to significantly improve diagnostic and treatment strategies for CMD patients, ultimately leading to the enhancement of their overall prognosis and quality of life. Clinical trial registrationclinicaltrials.gov, NCT05960474

Trajectories among recipients of social assistance in Norway: A local approach

AbstractBackground and PurposeLittle scholarly attention has been paid to variations in local welfare provisions and how they frame beneficiaries’ welfare‐work trajectories. This is unfortunate since increasingly a varied menu of services is provided locally and welfare‐work trajectories unfold in a local context. The purpose of this paper is to contribute to this debate by examining the welfare‐work trajectories among young social assistance recipients in Norwegian municipalities and how they are related to the local setting and in particular the organization of the local welfare office.Data and MethodsNational registers provide longitudinal information on all young social assistance recipients (N = 4654) in 2012. Information on municipalities (N = 373) and the local labour and welfare offices (N = 399) is collected from official government sources. Welfare‐work trajectories spanning seven years are created by sequence analysis forming eight clusters. The relationship between organizational features of the labour and welfare office and trajectory clusters is modelled by means of multinomial logistic regression, after controlling for variables on the individual, the municipal, and the regional level.ResultsThe analysis shows that: (a) among the individual variables, those reflecting poor health consistently predict trajectories characterized by benefit receipt and loose attachment to the labour market; (b) variables related to the municipal level are of limited importance, and (c) variables pertaining to the organization of the labour and welfare offices play a minor role.ConclusionOnce individual, municipal, and regional labour market factors are considered, characteristics of the welfare office are of marginal significance. [Correction added on 17 September 2024, after first online publication: The Abstract and Keywords have been added in this version.]

The positionality of goods and the positional concern’s origin

This paper investigates what goods are positional, the degree of individuals’ positional concern, and their possible drivers for a sample of Uruguayan younger. The participants’ degree of positional concern is generated using a choice experimental approach. The study combines longitudinal information about participants’ perceptions of the visibility of the goods and their reference groups and randomized information treatments to prime participants into competing narratives regarding (i) the goods, (ii) gender, and (iii) sources of inequality in society. The main findings are: (1) the visibility of the goods would not be a necessary condition for their position: jewelry, cars, and health insurance are positional goods; (2) relative income matters, but less than relative consumption of these goods; (3) the positional concern is heterogeneous at the individuals level with a bimodal distribution: one group of individuals has a high prevalence of relative concern, while the other is positional-neutral; (4) there are no differences by gender, visibility perceptions and declared reference group; and (5) individuals are less likely to report positional concerns (and inequality aversion) when differences in income come from effort and inheritance.

Sexual orientation and gender identity measures and viral suppression for people living with HIV: a protocol for a population-based cohort study

IntroductionThe measure of sexual orientation and gender identity (SOGI) data in electronic health records (EHR) has been critical for addressing health disparities and inequalities, especially for HIV care. Given that...

Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This studyaimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals. In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set. Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission. These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection. Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

Switching of brain networks across different cerebral perfusion states: insights from EEG dynamic microstate analyses.

The alteration of neural interactions across different cerebral perfusion states remains unclear. This study aimed to fulfill this gap by examining the longitudinal brain dynamic information interactions before and after cerebral reperfusion. Electroencephalogram in eyes-closed state at baseline and postoperative 7-d and 3-month follow-ups (moyamoya disease: 20, health controls: 23) were recorded. Dynamic network analyses were focused on the features and networks of electroencephalogram microstates across different microstates and perfusion states. Considering the microstate features, the parameters were disturbed of microstate B, C, and D but preserved of microstate A. The transition probabilities of microstates A-B and B-D were increased to play a complementary role across different perfusion states. Moreover, the microstate variability was decreased, but was significantly improved after cerebral reperfusion. Regarding microstate networks, the functional connectivity strengths were declined, mainly within frontal, parietal, and occipital lobes and between parietal and occipital lobes in different perfusion states, but were ameliorated after cerebral reperfusion. This study elucidates how dynamic interaction patterns of brain neurons change after cerebral reperfusion, which allows for the observation of brain network transitions across various perfusion states in a live clinical setting through direct intervention.

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator.

About 15-40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation.

Climate risk and commercial mortgage delinquency

AbstractNatural disasters such as hurricanes, floods, heatwaves, and wildfires are projected to become more prevalent in the foreseeable future. Climate risk is, therefore, increasingly recognized as an important factor by policy makers, the investment community, and financial markets. Due to the immobility of assets, the commercial real estate industry is especially vulnerable to climate risk, and there is an increasing interest to understand the impact of climate risk on the value of commercial real estate. For commercial real estate lenders, changes in collateral value are only of partial importance. The ability of borrowers to meet their payment obligations is equally, if not more important. By combining historical data on two major climate‐related disasters—Hurricanes Harvey and Sandy—with longitudinal information on commercial mortgage performance, this paper identifies the impact of climate risks on mortgage delinquency rates for commercial real estate mortgages. The results show that both Harvey and Sandy led to elevated levels of commercial mortgage delinquency, with significant heterogeneity based on the extent of damage in the Census block group. Information provided through FEMA 100‐year floodplain maps partially mitigates the effects, an indication that lenders incorporate flood risk information in the underwriting process. An analysis of potential mechanisms indicates a decrease in property income during the 2‐year period following the event for Hurricane Harvey, but no evidence of income effects for Sandy.

Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.

Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a 'relaxed' landmarking approach where only validation data is landmarked and a 'strict' landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.

Changes in adult well-being and economic inequalities: An exploratory observational longitudinal study (2002–2010) of micro-level trends among Tsimane’, a small-scale rural society of Indigenous People in the Bolivian Amazon

Knowing what happens over time to the lifeways of people in contemporary small-scale non-industrial societies of the rural Global South matters because it helps assess changes in the quality of life of underrepresented groups. It has been hard to answer the question because longitudinal information is rarely collected in such settings. A longitudinal dataset of nine years (2002–2010) from a horticultural-foraging society of Indigenous People in the Bolivian Amazon (Tsimane’) is used for an exploratory analysis of micro-level trends in indicators of well-being and economic inequalities. We selected 13 Tsimane’ villages (from ∼ 100) that varied in proximity to town and surveyed all households in each village. ∼ 240 households were followed yearly to estimate trends of 21 outcomes (e.g., income, sociality, macronutrients). For each economic outcome, annual and all-years-combined Gini coefficients were estimated for the entire sample across the 13 villages. We show a rise in total asset wealth, a change in asset composition (less traditional wealth, more commercial wealth), higher monetary value of foods eaten, and better-perceived health, but a decline in caloric and protein consumption and no marked gender differences in objective or hedonic measures of well-being. Economic inequalities were non-trivial and showed no marked trend but varied between years; asset inequality varied less than income inequality. We document the value of longitudinal, locally grounded indexes of well-being to obtain a granular view of micro-level changes in well-being and the possible use of inequality in the consumption of calories and macronutrients as a valid proxy for income inequality in rural areas of the Global South with tenuous links to the market economy.

Predicting Four-Year's Alzheimer's Disease Onset Using Longitudinal Neurocognitive Tests and MRI Data Using Explainable Deep Convolutional Neural Networks.

Prognosis of future risk of dementia from neuroimaging and cognitive data is important for optimizing clinical management for patients at early stage of Alzheimer's disease (AD). However, existing studies lack an efficient way to integrate longitudinal information from both modalities to improve prognosis performance. In this study, we aim to develop and evaluate an explainable deep learning-based framework to predict mild cognitive impairment (MCI) to AD conversion within four years using longitudinal whole-brain 3D MRI and neurocognitive tests. We proposed a two-stage framework that first uses a 3D convolutional neural network to extract single-timepoint MRI-based AD-related latent features, followed by multi-modal longitudinal feature concatenation and a 1D convolutional neural network to predict the risk of future dementia onset in four years. The proposed deep learning framework showed promising to predict MCI to AD conversion within 4 years using longitudinal whole-brain 3D MRI and cognitive data without extracting regional brain volumes or cortical thickness, reaching a balanced accuracy of 0.834, significantly improved from models trained from single timepoint or single modality. The post hoc model explainability revealed heatmap indicating regions that are important for predicting future risk of AD. The proposed framework sets the stage for future studies for using multi-modal longitudinal data to achieve optimal prediction for prognosis of AD onset, leading to better management of the diseases, thereby improving the quality of life.

Trends in the environment profession in Australia: a twenty-first century report

ABSTRACT Over many decades the environment profession in Australia has evolved into a substantial and diverse workforce. There are many indications that environment professionals provide valued services to Australian society, and their involvement will be increasingly sought. To gain an understanding of characteristics of these professionals and their roles leading into the mid-twenty-first century, a survey was undertaken in 2022. This is built on earlier surveys of the environment profession, so that collectively the results provide some longitudinal information and an indication of trends. This article reports these results, covering participants’ general and educational background, current position and overall career situation, generic and specific skills used and thoughts about the environment/sustainability sector. Broadly, the analysis indicates an increasing breadth of the profession, the skills needed and the challenges, with related opportunities, foreseen. Overall, participants continued to express positivity about their profession and potential to build on community and governmental support for issues associated with environment/sustainability.

Employment Status Following Heart Transplantation: Data From the Danish Nationwide Social Service Payment Register During 20years.

Most studies on vocational rehabilitation after heart transplantation (HTX) are based on self-reported data. Danish registries include weekly longitudinal information on all public transfer payments. We intended to describe 20-year trends in employment status for the Danish heart-transplant recipients, and examine the influence of multimorbidity and socioeconomic position (SEP). Linking registry and Scandiatransplant data (1994-2018), we conducted a study in recipients of working age (19-63years). The cohort contained 492 recipients (79% males) and the median (IQR) age was 52years (43-57years). Five years after HTX, 30% of the survived recipients participated on the labor market; 9% were in a flexible job with reduced health-related working capacity. Moreover, 60% were retired and 10% eligible for labor market participation were unemployed. Recipients with multimorbidity had a higher age and a lower prevalence of employment. Five years after HTX, characteristics of recipients with labor market participation were: living alone (27%) versus cohabitation (73%); low (36%) versus medium-high (64%) educational level; low (13%) or medium-high (87%) income group. Heart-transplant recipients with multimorbidity have a higher age and a lower prevalence of employment. Socioeconomically disadvantaged recipients had a lower prevalence of labor market participation, despite being younger compared with the socioeconomically advantaged.

Discovery of High-Risk Clinical Factors That Accelerate Brain Aging in Adults: A Population-Based Machine Learning Study.

Introduction: Brain age prediction using neuroimaging data and machine learning algorithms holds significant promise for gaining insights into the development of neurodegenerative diseases. The estimation of brain age may be influenced not only by the imaging modality but also by multidomain clinical factors. However, the degree to which various clinical factors in individuals are associated with brain structure, as well as the comprehensive relationship between these factors and brain aging, is not yet clear. Methods: In this study, multimodal brain magnetic resonance imaging data and longitudinal clinical information were collected from 964 participants in a population-based cohort with 16 years of follow-up in northern China. We developed a machine learning-based algorithm to predict multimodal brain age and compared the estimated brain age gap (BAG) differences among the 5 groups characterized by varying exposures to these high-risk clinical factors. We then estimated modality-specific brain age in the hypertension group based on hypertension-related regional imaging metrics. Results: The results revealed a significantly larger BAG estimated from multimodal neuroimaging in subjects with 4 or 5 risk factors compared to other groups, suggesting an acceleration of brain aging under cumulative exposure to multiple risk factors. The estimated T1-based BAG exhibited a significantly higher level in the hypertensive subjects compared to the normotensive individuals. Conclusion: Our study provides valuable insights into a range of health factors across lifestyle, metabolism, and social context that are reflective of brain aging and also contributes to the advancement of interventions and public health initiatives targeted at the general population aimed at promoting brain health.

A neural tract-inspired conduit for facile, on-demand biopsy of glioblastoma.

A major hurdle to effectively treating glioblastoma (GBM) patients is the lack of longitudinal information about tumor progression, evolution, and treatment response. In this study, we report the use of a neural tract-inspired conduit containing aligned polymeric nanofibers (i.e., an aligned nanofiber device) to enable on-demand access to GBM tumors in 2 rodent models. Depending on the experiment, a humanized U87MG xenograft and/or F98-GFP+ syngeneic rat tumor model was chosen to test the safety and functionality of the device in providing continuous sampling access to the tumor and its microenvironment. The aligned nanofiber device was safe and provided a high quantity of quality genomic materials suitable for omics analyses and yielded a sufficient number of live cells for in vitro expansion and screening. Transcriptomic and genomic analyses demonstrated continuity between material extracted from the device and that of the primary, intracortical tumor (in the in vivo model). The results establish the potential of this neural tract-inspired, aligned nanofiber device as an on-demand, safe, and minimally invasive access point,thus enabling rapid, high-throughput, longitudinal assessment of tumor and its microenvironment, ultimatelyleading to more informed clinical treatment strategies.

Nonsuicidal self-injury as the gateway and consequence of suicidal ideation among adolescents: a cross-lagged regression analysis.

There is a consensus that both nonsuicidal self-injury (NSSI) and suicidal ideation as risk factors for suicidal behavior have a strong connection. However, a lack of longitudinal information has limited the clarification of the concrete relationship between them. This study aimed to examine the specific mechanism between NSSI and suicidal ideation over time, during adolescence. A longitudinal study was conducted with 193 Chinese adolescents. NSSI and suicidal ideations were examined over the course of a 1year followed-up, and three waves of data were collected. The NSSI at time T1 significantly positively predicted suicidal ideation at time T2; Suicidal ideation at time T2 also significantly positively predicted NSSI at time T3. Given that the small number of participants with suicidal ideation and NSSI, the findings of the study should be interpreted with caution and a lager sample is needed in the future. It was suggested that NSSI may occur before suicidal ideation, which in turn would strengthen NSSI, so interventions should be carried out from two aspects (behaviors and thoughts) to improve adolescents' mental health.

Longitudinal Associations of Diet, Food Insecurity, and Supplemental Nutrition Assistance Program Use with Global Cognitive Performance in Middle-Aged and Older Adults

BackgroundDiet quality, food access, and food assistance policies may be key modifiable factors related to cognitive decline. ObjectiveWe aimed to evaluate whether diet quality, food insecurity, and Supplemental Nutrition Assistance Program (SNAP) use are associated with longitudinal changes in cognition among older adults in the United States. MethodsFood intake data from the Health Care and Nutrition Study were linked with longitudinal health information from 5 waves of the Health and Retirement Study (2012–2020). The analytic sample (n = 6968) included community-dwelling United States adults aged ≥51 y without cognitive impairment. Global cognition was measured using a telephone-based cognitive status interview (range: 0–27). Diet quality was measured with the Healthy Eating Index, using participants’ average intake of 13 dietary components. Questions regarding food access and affordability were used to determine food insecurity and use of SNAP benefits. Linear mixed-effects regression models were used to estimate longitudinal associations between diet-related factors and cognitive score changes. ResultsPoorer diets [β: −0.24; 95% confidence interval (CI): −0.33, −0.15], food insecurity (β: −1.08; 95% CI: −1.31, −0.85), and SNAP use (β: −0.57; 95% CI: −0.82, −0.32) were associated with lower baseline cognitive scores. Poorer diets (β: −0.17; 95% CI: −0.29, −0.05) and food insecurity (β: −0.23; 95% CI: −0.47, −0.01) were associated with significantly steeper declines in cognitive scores over time, after 8 and 2 y of follow-up, respectively; however, SNAP use was not significantly associated with the rate of cognitive decline over time. Estimates were qualitatively similar when restricting the sample to participants aged ≥65 y. ConclusionsResults suggest that food access and adherence to healthy diet recommendations may be important elements to maintain cognitive health in aging. SNAP benefits may be insufficient to prevent negative cognitive effects of poor diet and limited access to nutritious foods.

Enhancing the cancer metastasis diagnosis: Ultrasensitive nano sensors exploiting Cancer stem cell associated DNA methylation as a liquid biopsy marker

Cancer metastasis presents a significant clinical challenge due to its impact on patient survival rates. Early detection of metastasis is pivotal for effective intervention and improved patient outcomes. Liquid biopsy, a non-invasive diagnostic approach, holds promise for cancer detection, yet its utility in metastasis detection remains underexplored. Stable and accurate, DNA methylation reflects early tumor stages and enables molecular characterization, providing valuable longitudinal information. Despite its potential, clinical translation of DNA methylation biomarkers is limited, partially due to technology constraints and tumor heterogeneity. To enable clinical translation, an assay is needed that 1) quantifies DNA methylation, 2) integrates seamlessly into clinical workflows, and 3) ensures accuracy and sensitivity. In this study, we introduce an ultrasensitive nanosensor, Methylprobe, for DNA methylation-based liquid biopsy of metastasis. Methylprobe, synthesized via ultrashort pulsed ionization, provides signal amplification, enabling direct detection of methylated DNA in plasma. The technology is scalable and adaptable for clinical settings. The assay comprises the Methylprobe, metastasis-specific biomarkers, and a machine-learning algorithm. Tumor heterogeneity is addressed by integrating DNA-methylation signatures from distinct cancer stem cell phenotypes, enhancing assay reliability. The Methylprobe assay is validated on breast, lung, and colorectal cancer samples, demonstrating 99.8% accuracy in metastasis detection. This innovative approach offers potential as a reliable, sensitive, and accurate platform for assessing metastasis-associated DNA methylation patterns in blood plasma. Integrating Methylprobe into clinical practice could revolutionize metastasis detection, providing comprehensive insights into metastatic phenotypes and informing tailored therapeutic strategies.

ACDNet: Attention-guided Collaborative Decision Network for effective medication recommendation

Medication recommendation using Electronic Health Records (EHR) is challenging due to complex medical data. Current approaches extract longitudinal information from patient EHR to personalize recommendations. However, existing models often lack sufficient patient representation and overlook the importance of considering the similarity between a patient’s medication records and specific medicines. Therefore, an Attention-guided Collaborative Decision Network (ACDNet) for medication recommendation is proposed in this paper. Specifically, ACDNet utilizes attention mechanism and Transformer to effectively capture patient health conditions and medication records by modeling their historical visits at both global and local levels. ACDNet also employs a collaborative decision framework, utilizing the similarity between medication records and medicine representation to facilitate the recommendation process. The experimental results on two extensive medical datasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms state-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming its superiority. Moreover, the ablation experiments provide solid evidence of the effectiveness of each module in ACDNet, validating their contribution to the overall performance. Furthermore, a detailed case study reinforces the effectiveness of ACDNet in medication recommendation based on EHR data, showcasing its practical value in real-world healthcare scenarios.

Linking Trauma Registry Patients With Insurance Claims: Creating a Longitudinal Patient Record

IntroductionTrauma registries and their quality improvement programs only collect data from the acute hospital admission, and no additional information is captured once the patient is discharged. This lack of long-term data limits these programs’ ability to affect change. The goal of this study was to create a longitudinal patient record by linking trauma registry data with third party payer claims data to allow the tracking of these patients after discharge. MethodsTrauma quality collaborative data (2018-2019) was utilized. Inclusion criteria were patients age ≥18, ISS ≥5 and a length of stay ≥1 d. In-hospital deaths were excluded. A deterministic match was performed with insurance claims records based on the hospital name, date of birth, sex, and dates of service (±1 d). The effect of payer type, ZIP code, International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis specificity and exact dates of service on the match rate was analyzed. ResultsThe overall match rate between these two patient record sources was 27.5%. There was a significantly higher match rate (42.8% versus 6.1%, P < 0.001) for patients with a payer that was contained in the insurance collaborative. In a subanalysis, exact dates of service did not substantially affect this match rate; however, specific International Classification of Diseases, Tenth Revision, Clinical Modification codes (i.e., all 7 characters) reduced this rate by almost half. ConclusionsWe demonstrated the successful linkage of patient records in a trauma registry with their insurance claims. This will allow us to the collect longitudinal information so that we can follow these patients’ long-term outcomes and subsequently improve their care.