Dataset Information

Early temporal characteristics of elderly patient cognitive impairment in electronic health records.

ABSTRACT: BACKGROUND:The aging population has led to an increase in cognitive impairment (CI) resulting in significant costs to patients, their families, and society. A research endeavor on a large cohort to better understand the frequency and severity of CI is urgent to respond to the health needs of this population. However, little is known about temporal trends of patient health functions (i.e., activity of daily living [ADL]) and how these trends are associated with the onset of CI in elderly patients. Also, the use of a rich source of clinical free text in electronic health records (EHRs) to facilitate CI research has not been well explored. The aim of this study is to characterize and better understand early signals of elderly patient CI by examining temporal trends of patient ADL and analyzing topics of patient medical conditions in clinical free text using topic models. METHODS:The study cohort consists of physician-diagnosed CI patients (n?=?1,435) and cognitively unimpaired (CU) patients (n?=?1,435) matched by age and sex, selected from patients 65?years of age or older at the time of enrollment in the Mayo Clinic Biobank. A corpus analysis was performed to examine the basic statistics of event types and practice settings where the physician first diagnosed CI. We analyzed the distribution of ADL in three different age groups over time before the development of CI. Furthermore, we applied three different topic modeling approaches on clinical free text to examine how patients' medical conditions change over time when they were close to CI diagnosis. RESULTS:The trajectories of ADL deterioration became steeper in CI patients than CU patients approximately 1 to 1.5?year(s) before the actual physician diagnosis of CI. The topic modeling showed that the topic terms were mostly correlated and captured the underlying semantics relevant to CI when approaching to CI diagnosis. CONCLUSIONS:There exist notable differences in temporal trends of basic and instrumental ADL between CI and CU patients. The trajectories of certain individual ADL, such as bathing and responsibility of own medication, were closely associated with CI development. The topic terms obtained by topic modeling methods from clinical free text have a potential to show how CI patients' conditions evolve and reveal overlooked conditions when they close to CI diagnosis.

SUBMITTER: Goudarzvand S

PROVIDER: S-EPMC6686236 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Early temporal characteristics of elderly patient cognitive impairment in electronic health records.

Goudarzvand Somaieh S St Sauver Jennifer J Mielke Michelle M MM Takahashi Paul Y PY Lee Yugyung Y Sohn Sunghwan S

BMC medical informatics and decision making 20190808 Suppl 4

<h4>Background</h4>The aging population has led to an increase in cognitive impairment (CI) resulting in significant costs to patients, their families, and society. A research endeavor on a large cohort to better understand the frequency and severity of CI is urgent to respond to the health needs of this population. However, little is known about temporal trends of patient health functions (i.e., activity of daily living [ADL]) and how these trends are associated with the onset of CI in elderly ...[more]

PMID: 31391041

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Project description:Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse. EHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility. EHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface.Availability: https://github.com/hms-dbmi/EHRtemporalVariability/Reproducible vignette: https://cran.r-project.org/web/packages/EHRtemporalVariability/vignettes/EHRtemporalVariability.htmlOnline demo: http://ehrtemporalvariability.upv.es/.

Project description:BackgroundElectronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown.ObjectiveThe objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care.Research designA retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care.SubjectsA total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source.MeasuresPercentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source.ResultsAttrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients.ConclusionThis study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

Project description:BackgroundEHR phenotyping offers the ability to rapidly assemble a precisely defined cohort of patients prescreened for eligibility to participate in health-related research. Even so, stakeholders in the process must still contend with the practical and ethical challenges associated with research recruitment. Patient perspectives on these matters are particularly important given that the success of research recruitment depends on patients' willingness to participate.MethodsWe conducted 15 focus groups (n =?110 participants) in four counties in diverse regions of the southeastern US: Appalachia, the Mississippi Delta, and the Piedmont area of North Carolina. Based on a hypothetical study of a behavioral intervention for type 2 diabetes, we asked about the acceptability and appropriateness of direct investigator versus physician-mediated contact with patients for research recruitment, and whether patients should be asked to opt in or opt out of further contact in response to recruitment letters.ResultsFor initial contact, nearly all participants said it would be acceptable for researchers to contact patients directly and three-fourths said that it would be acceptable for researchers to contact patients through their physicians. When we asked which would be most appropriate, a substantial majority chose direct contact. Themes that arose in the discussion included trust and transparency, decision-making power, the effect on research, and the effect on patient care. For response expectations, the vast majority of participants said both opt-in and opt-out would be acceptable-typically finding neither especially problematic and noting that both afford patients the opportunity to make their own decisions.ConclusionsExternal validity relies heavily on researchers' success enrolling eligible patients and failure to reach accrual targets is a costly and common barrier to advancing scientific knowledge. Our results suggest that patients recognize multiple advantages and disadvantages of different research recruitment strategies and place value on the implications not just for themselves, but also for researchers and healthcare providers. Our findings, including rich qualitative detail, contribute to the body of empirical and ethical literature on improving research recruitment and suggest specific ways forward as well as important areas for future research.

Project description:BackgroundHepatorenal syndrome (HRS) is among the leading causes of hospitalization and mortality in patients with chronic liver disease.ObjectiveTo assess the HRS patient journey from preadmission to postdischarge to understand patient characteristics, disease progression, treatment patterns, and outcomes.MethodsWe conducted a retrospective study using real-world data from a nationwide electronic health record database (Cerner Health Facts, Kansas City, Missouri). We used ICD-9/10 diagnosis codes to identify patients hospitalized with HRS between January 1, 2009, and January 31, 2018. We assessed patient characteristics and history, clinical presentation, treatment, and outcomes. Regression analysis was conducted to assess the association between patient characteristics and survival while adjusting for demographic and clinical covariates.ResultsThe study included 3563 patients (62% men). Precipitants of HRS included gastrointestinal bleeding (18%), diuretics and infections (30%), and paracentesis (26%). Although 21% of patients had liver injury exclusively associated with alcohol use, 20% had hepatitis C, 8% had nonalcoholic steatohepatitis, and the etiology of the remainder (51%) was either some combination of conditions or unknown. A total of 42% of patients received vasopressors, including octreotide and midodrine (10%), other combinations of vasopressors (11%), or another single vasopressor (21%). In-hospital mortality was 34%, and 14% of patients were discharged to hospice. Regression analysis showed patients with acute-on-chronic liver failure had higher mortality in acute-on-chronic liver failure grades 1 (odds ratio = 1.59), 2 (odds ratio = 2.49), and 3 (odds ratio = 4.53) versus no acute-on-chronic liver failure. Among survivor patients, 38% were readmitted within 90 days of discharge; 23% of readmissions were HRS-related.ConclusionsThe HRS patient journey presented in this study highlights inconsistencies in, and provides insight into, associated hospital-based treatment strategies. A mortality rate of 34% along with a readmission rate of 23% associated with HRS-related complications warrant more disease awareness and effective treatment. Further research is needed to examine the interactions between the etiology of cirrhosis, precipitants, treatment, and outcomes. (Curr Ther Res Clin Exp. 2022; 82:XXX-XXX).

Project description:ImportanceSecondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.ObjectiveTo develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.DesignCohort study with patients enrolled between 2003-2016 with follow-up through 2022.SettingPrimary care practices affiliated with two academic medical centers.ParticipantsIndividuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).ExposuresPredictors of PSCI were ascertained from the electronic health record.Main outcomeThe outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.ResultsThe analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.Conclusions and relevanceFive-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.

Project description:BackgroundThe exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish.MethodsWe propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances.ResultsAs we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients.ConclusionOur algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool.

Dataset Information

Early temporal characteristics of elderly patient cognitive impairment in electronic health records.

Publications

Early temporal characteristics of elderly patient cognitive impairment in electronic health records.

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