Dataset Information

Use of the Electronic Medical Record to Assess Pancreas Size in Type 1 Diabetes.

ABSTRACT: This study harnessed the electronic medical record to assess pancreas volume in patients with type 1 diabetes (T1D) and matched controls to determine whether pancreas volume is altered in T1D and identify covariates that influence pancreas volume.This study included 25 patients with T1D and 25 age-, sex-, and weight-matched controls from the Vanderbilt University Medical Center enterprise data warehouse. Measurements of pancreas volume were made from medical imaging studies using magnetic resonance imaging (MRI) or computed tomography (CT).Patients with T1D had a pancreas volume 47% smaller than matched controls (41.16 ml vs. 77.77 ml, P < 0.0001) as well as pancreas volume normalized by subject body weight, body mass index, or body surface area (all P < 0.0001). Pancreatic volume was smaller with a longer duration of T1D across the patient population (N = 25, P = 0.04). Additionally, four individual patients receiving multiple imaging scans displayed progressive declines in pancreas volume over time (~ 6% of volume/year), whereas five controls scanned a year apart did not exhibit a decline in pancreas size (P = 0.03). The pancreas was uniformly smaller on the right and left side of the abdomen.Pancreas volume declines with disease duration in patients with T1D, suggesting a protracted pathological process that may include the exocrine pancreas.

SUBMITTER: Virostko J

PROVIDER: S-EPMC4938534 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Use of the Electronic Medical Record to Assess Pancreas Size in Type 1 Diabetes.

Virostko John J Hilmes Melissa M Eitel Kelsey K Moore Daniel J DJ Powers Alvin C AC

PloS one 20160708 7

<h4>Aims</h4>This study harnessed the electronic medical record to assess pancreas volume in patients with type 1 diabetes (T1D) and matched controls to determine whether pancreas volume is altered in T1D and identify covariates that influence pancreas volume.<h4>Methods</h4>This study included 25 patients with T1D and 25 age-, sex-, and weight-matched controls from the Vanderbilt University Medical Center enterprise data warehouse. Measurements of pancreas volume were made from medical imaging ...[more]

PMID: 27391588

Similar Datasets

Project description:BACKGROUND AND AIMS:Screening for substance use disorder (SUD) in general medical settings may be particularly important in patients with comorbid health conditions exacerbated by SUD. This study evaluated whether SUD is associated with type 2 diabetes mellitus (T2DM) complications in patients with co-occurring T2DM and hypertension. DESIGN:Analysis of a limited data set obtained through IBM Watson Health Explorys, a platform integrating data from electronic health records. Matched controls were defined for each of five SUDs: tobacco use disorder (TUD), opioid use disorder (OUD), cocaine use disorder, cannabis use disorder (CUD) and alcohol use disorder (AUD) using Mahalanobis distance within propensity score calipers. SETTING:All patients were seen in the MetroHealth System (Cleveland, OH, USA) and had diagnosis codes for T2DM and hypertension. PARTICIPANTS:SUD group participants had a diagnosis of abuse/dependence for the substance of interest. Controls for each SUD group had no diagnosis code related to the SUD of interest and were selected to match the SUD patients on demographics, residential zip code median income and body mass index. Total sample sizes for each SUD-control comparison ranged from 1160 for CUD to 22?128 for TUD. MEASUREMENTS:Outcome was diagnosis (yes/no) of four T2DM complications (cerebrovascular accident, diabetic neuropathy, diabetic renal disease, myocardial infarction) and all-cause mortality. FINDINGS:Logistic regressions revealed that SUD was significantly associated with greater risk of cerebrovascular accident [TUD odds ratio (OR) = 1.79, OUD-OR = 1.94, cocaine use disorder OR = 2.67], diabetic neuropathy [TUD-adjusted OR (aOR) = 1.47, cocaine use disorder-aOR = 1.35, AUD-aOR = 1.27], diabetic renal disease (TUD-aOR = 1.25, OUD-OR = 1.34), myocardial infarction (TUD-OR = 1.96, OUD-OR = 2.01, cocaine use disorder-OR = 2.68, CUD-OR = 2.48, AUD-OR = 1.42) and mortality (TUD-OR = 1.15, cocaine use disorder-OR = 1.61, CUD-OR = 1.49, AUD-OR = 1.35). CONCLUSIONS:Among patients in Ohio USA with both type 2 diabetes mellitus (T2DM) and hypertension, those with substance use disorders appear to have greater risk for T2DM complications and all-cause mortality.

Project description:BackgroundOne of the main objectives of Electronic Health Records (EHRs) is to enhance collaboration among healthcare professionals. However, our knowledge of how EHRs actually affect collaborative practices is limited. This study examines how an EHR facilitates and constrains collaboration in five outpatient clinics.MethodsWe conducted an embedded case study at five outpatient clinics of a Dutch hospital that had implemented an organization-wide EHR. Data were collected through interviews with representatives of medical specialties, administration, nursing, and management. Documents were analyzed to contextualize these data. We examined the following collaborative affordances of EHRs: (1) portability, (2) co-located access, (3) shared overviews, (4) mutual awareness, (5) messaging, and (6) orchestrating.ResultsOur findings demonstrate how an EHR will both facilitate and constrain collaboration among specialties and disciplines. Affordances that were inscribed in the system for collaboration purposes were not fully actualized in the hospital because: (a) The EHR helps health professionals coordinate patient care on an informed basis at any time and in any place but only allows asynchronous patient record use. (b) The comprehensive patient file affords joint clinical decision-making based on shared data, but specialty- and discipline-specific user-interfaces constrain mutual understanding of that data. Moreover, not all relevant information can be easily shared across specialties and outside the hospital. (c) The reduced necessity for face-to-face communication saves time but is experienced as hindering collective responsibility for a smooth workflow. (d) The EHR affords registration at the source and registration of activities through orders, but the heightened administrative burden for physicians and the strict authorization rules on inputting data constrain the flexible, multidisciplinary collaboration. (e) While the EHR affords a complete overview, information overload occurs due to the parallel generation of individually owned notes and the high frequency of asynchronous communication through messages of varying clinical priority.ConclusionsFor the optimal actualization of EHRs' collaborative affordances in hospitals, coordinated use of these affordances by health professionals is a prerequisite. Such coordinated use requires organizational, technical, and behavioral adaptations. Suggestions for hospital-wide policies to enhance trust in both the EHR and in its coordinated use for effective collaboration are offered.

Project description:ImportanceCardiovascular disease is the leading cause of death in the United States. To improve cardiovascular outcomes, primary care must have valid methods of assessing performance on cardiovascular clinical quality measures, including aspirin use (aspirin measure), blood pressure control (BP measure), and smoking cessation counseling and intervention (smoking measure).ObjectiveTo compare observed performance scores measured using 2 imperfect reference standard data sources (medical record abstraction [MRA] and electronic health record [EHR]-generated reports) with misclassification-adjusted performance scores obtained using bayesian latent class analysis.Design, setting, and participantsThis cross-sectional study used a subset of the 2016 aspirin, BP, and smoking performance data from the Healthy Hearts for Oklahoma Project. Each clinical quality measure was calculated for a subset of a practice's patient population who can benefit from recommended care (ie, the eligible population). A random sample of 380 eligible patients were included for the aspirin measure; 126, for the BP measure; and 115, for the smoking measure. Data were collected from 21 primary care practices belonging to a single large health care system from January 1 to December 31, 2018, and analyzed from February 21 to April 17, 2019.Main outcomes and measuresThe main outcomes include performance scores for the aspirin, BP, and smoking measures using imperfect MRA and EHRs and estimated through bayesian latent class models.ResultsA total of 621 eligible patients were included in the analysis. Based on MRA and EHR data, observed aspirin performance scores were 76.0% (95% bayesian credible interval [BCI], 71.5%-80.1%) and 74.9% (95% BCI, 70.4%-79.1%), respectively; observed BP performance scores, 80.6% (95% BCI, 73.2%-86.9%) and 75.1% (95% BCI, 67.2%-82.1%), respectively; and observed smoking performance scores, 85.7% (95% BCI, 78.6%-91.2%) and 75.4% (95% BCI, 67.0%-82.6%), respectively. Misclassification-adjusted estimates were 74.9% (95% BCI, 70.5%-79.1%) for the aspirin performance score, 75.0% (95% BCI, 66.6%-82.5%) for the BP performance score, and 83.0% (95% BCI, 74.4%-89.8%) for the smoking performance score.Conclusions and relevanceEnsuring valid performance measurement is critical for value-based payment models and quality improvement activities in primary care. This study found that extracting information for the same individuals using different data sources generated different performance score estimates. Further research is required to identify the sources of these differences.

Project description:BackgroundIdentifying cases of disease in primary care electronic medical records (EMRs) is important for surveillance, research, quality improvement and clinical care. We aimed to develop and validate a case definition for type 1 diabetes mellitus using EMRs.MethodsFor this exploratory study, we used EMR data from the Southern Alberta Primary Care Network within the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), for the period 2008 to 2016. For patients identified as having diabetes mellitus according to the existing CPCSSN case definition, we asked family physicians to confirm the diabetes subtype, to create the reference standard. We used 3 decision-tree classification algorithms and least absolute shrinkage and selection operator logistic regression to identify variables that correctly distinguished between type 1 and type 2 diabetes cases.ResultsWe identified a total of 1309 people with type 1 or type 2 diabetes, 110 of whom were confirmed by their physicians as having type 1 diabetes. Two machine learning algorithms were useful in identifying these cases in the EMRs. The first algorithm used "type 1" text words or age less than 22 years at time of initial diabetes diagnosis; this algorithm had sensitivity 42.7% (95% confidence interval [CI] 33.5%-52.5%), specificity 99.3% (95% CI 98.6%-99.7%), positive predictive value 85.5% (95% CI 72.8%-93.1%) and negative predictive value 94.9% (95% CI 93.5%-96.1%). The second algorithm used a combination of free-text terms, insulin prescriptions and age; it had sensitivity 87.3% (95% CI 79.2%-92.6%), specificity 85.4% (95% CI 83.2%-87.3%), positive predictive value 35.6% (95% CI 29.9%-41.6%) and negative predictive value 98.6% (95% CI 97.7%-99.2%).InterpretationWe used machine learning to develop and validate 2 case definitions that achieve different goals in distinguishing between type 1 and type 2 diabetes in CPCSSN data. Further validation and testing with a larger and more diverse sample are recommended.

Dataset Information

Use of the Electronic Medical Record to Assess Pancreas Size in Type 1 Diabetes.

Publications

Use of the Electronic Medical Record to Assess Pancreas Size in Type 1 Diabetes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets