Development of clinical screening tool for exocrine pancreatic insufficiency in patients with definite chronic pancreatitis.

BACKGROUND/OBJECTIVES: No simple, accurate diagnostic tests exist for exocrine pancreatic insufficiency (EPI), and EPI remains underdiagnosed in chronic pancreatitis (CP). We sought to develop a digital screening tool to assist clinicians to predict EPI in patients with definite CP. METHODS: This was a retrospective case-control study of patients with definite CP with/without EPI. Overall, 49 candidate predictor variables were utilized to train a Classification and Regression Tree (CART) model to rank all predictors and select a parsimonious set of predictors for EPI status. Five-fold cross-validation was used to assess generalizability, and the full CART model was compared with 4 additional predictive models. EPI misclassification rate (mRate) served as primary endpoint metric. RESULTS: 274 patients with definite CP from 6 pancreatitis centers across the United States were included, of which 58 % had EPI based on predetermined criteria. The optimal CART decision tree included 10 variables. The mRate without/with 5-fold cross-validation of the CART was 0.153 (training error) and 0.314 (prediction error), and the area under the receiver operating characteristic curve was 0.889 and 0.682, respectively. Sensitivity and specificity without/with 5-fold cross-validation was 0.888/0.789 and 0.794/0.535, respectively. A trained second CART without pancreas imaging variables (n = 6), yielded 8 variables. Training error/prediction error was 0.190/0.351; sensitivity was 0.869/0.650, and specificity was 0.728/0.649, each without/with 5-fold cross-validation. CONCLUSION: We developed two CART models that were integrated into one digital screening tool to assess for EPI in patients with definite CP and with two to six input variables needed for predicting EPI status.

Understanding Perspectives on Presbyopia and Use of Pilocarpine HCl 1.25% Twice Daily from Participants of the Phase 3 VIRGO Study.

INTRODUCTION: The phase 3, randomized, vehicle-controlled, 14-day VIRGO study evaluated the efficacy and safety of twice-daily dosing of pilocarpine hydrochloride ophthalmic solution 1.25% (Pilo) in presbyopia. On VIRGO exit, a companion study was conducted to assess the patient experience with presbyopia and satisfaction with Pilo. METHODS: Recruited individuals completed the Presbyopia Patient Satisfaction Questionnaire (PPSQ) plus a three-part exit survey, or a live interview. The PPSQ evaluated respondents' experience with Pilo. Survey parts 1 and 2 evaluated experience managing presbyopia before and during VIRGO, respectively; part 3 assessed future possibilities of using Pilo in real-world situations. The interview further informed the interviewees' experience with presbyopia and Pilo. The primary endpoint was responders (%) in each rating category of the PPSQ items 1-7; the secondary endpoints were summary of categorical (survey) and qualitative (interviews) responses. RESULTS: The PPSQ and survey included 62 participants who received Pilo (N = 28) or vehicle (N = 34) in VIRGO; the interview included ten participants (Pilo, N = 4; vehicle, N = 6). Per the PPSQ, 64.3% of Pilo users reported vision improvement, including 17.9% with complete improvement; ≥ 46.4% were satisfied/very satisfied with their ability to perform daily activities, see up close unaided, and read in dim light. Among vehicle users, these percentages were 35.3%, 0%, and ≤ 23.5%, respectively. In both subgroups, ≥ 67.9% were interested in using Pilo or Pilo and eyeglasses/contact lenses in the future. Per the interview, vehicle users (n = 6/6) found the eyedrop easy to use but none experienced meaningful near-vision improvements, stopped using other correction method(s) part of the day, were satisfied with the eyedrop, preferred it over their previous correction method(s), or would continue using it if prescribed. Conversely, 75% (n = 3/4) of Pilo users responded positively to each of these six criteria. CONCLUSIONS: Findings validate the VIRGO results and improve our understanding of the patient experience, demonstrating improved vision and satisfaction with Pilo (vs. vehicle) when performing daily activities.

Variable selection for latent class analysis in the presence of missing data with application to record linkage.

The Fellegi-Sunter model is a latent class model widely used in probabilistic linkage to identify records that belong to the same entity. Record linkage practitioners typically employ all available matching fields in the model with the premise that more fields convey greater information about the true match status and hence result in improved match performance. In the context of model-based clustering, it is well known that such a premise is incorrect and the inclusion of noisy variables could compromise the clustering. Variable selection procedures have therefore been developed to remove noisy variables. Although these procedures have the potential to improve record matching, they cannot be applied directly due to the ubiquity of the missing data in record linkage applications. In this paper, we modify the stepwise variable selection procedure proposed by Fop, Smart, and Murphy and extend it to account for missing data common in record linkage. Through simulation studies, our proposed method is shown to select the correct set of matching fields across various settings, leading to better-performing algorithms. The improved match performance is also seen in a real-world application. We therefore recommend the use of our proposed selection procedure to identify informative matching fields for probabilistic record linkage algorithms.

Study of the Oxidation Behavior of Fine-Grained Graphite ET-10 by Combining X-ray µCT with Mercury Porosimetry.

By combining X-ray micro-computed tomography with mercury porosimetry, the evolution of the oxygen supply, porous structure, mass loss and oxidized compositions were investigated to characterize the oxidation behavior of fine-grained graphite ET-10, regarding the geometry of the specimen and its oxidation temperature. Here, the porous structure and the gas flows out of and into the porous structure were comprehensively compared for two kinds of specimens-large pure graphite (D = H = 25.4 mm), oxidized at a test facility based on ASTM D7542, and small partially SiC-coated graphite (D ≈ 1 mm and H = 1.95 mm), oxidized in the bottom section of a U-type tube. The fine grains and large geometry resulted in small pores and long flow distances, which exhausted the oxygen in the small stream to the interior of the specimen, making its oxidation deviate from the kinetics-controlled regime. In addition, the well-known three-regime theory was reasonably reinterpreted regarding the oxidation of different compositions, binders and fillers. The kinetics-controlled uniform oxidation mainly oxidizing binders is restricted by their limited contents, while the rate of surface-dominated oxidation increases continuously via the consumption of more fillers. Furthermore, we proposed a new design for the test facility used for the oxidation experiment, wherein a partially shielded millimeter specimen can be oxidized in the long straight bottom section of a U-tube, and this will be discussed further in related future studies.

Application of unsupervised deep learning algorithms for identification of specific clusters of chronic cough patients from EMR data.

BACKGROUND: Chronic cough affects approximately 10% of adults. The lack of ICD codes for chronic cough makes it challenging to apply supervised learning methods to predict the characteristics of chronic cough patients, thereby requiring the identification of chronic cough patients by other mechanisms. We developed a deep clustering algorithm with auto-encoder embedding (DCAE) to identify clusters of chronic cough patients based on data from a large cohort of 264,146 patients from the Electronic Medical Records (EMR) system. We constructed features using the diagnosis within the EMR, then built a clustering-oriented loss function directly on embedded features of the deep autoencoder to jointly perform feature refinement and cluster assignment. Lastly, we performed statistical analysis on the identified clusters to characterize the chronic cough patients compared to the non-chronic cough patients. RESULTS: The experimental results show that the DCAE model generated three chronic cough clusters and one non-chronic cough patient cluster. We found various diagnoses, medications, and lab tests highly associated with chronic cough patients by comparing the chronic cough cluster with the non-chronic cough cluster. Comparison of chronic cough clusters demonstrated that certain combinations of medications and diagnoses characterize some chronic cough clusters. CONCLUSIONS: To the best of our knowledge, this study is the first to test the potential of unsupervised deep learning methods for chronic cough investigation, which also shows a great advantage over existing algorithms for patient data clustering.

Differences in Health-Related Outcomes and Health Care Resource Utilization in Breast Cancer Survivors With and Without Type 2 Diabetes.

PURPOSE: Up to 74% of breast cancer survivors (BCS) have at least one preexisting comorbid condition, with diabetes (type 2) common. The purpose of this study was to examine differences in health-related outcomes (anemia, neutropenia, and infection) and utilization of health care resources (inpatient, outpatient, and emergency visits) in BCS with and without diabetes. METHODS: In this retrospective cohort study, data were leveraged from the electronic health records of a large health network linked to the Indiana State Cancer Registry. BCS diagnosed between January 2007 and December 2017 and who had received chemotherapy were included. Multivariable logistic regression and generalized linear models were used to determine differences in health outcomes and health care resources. RESULTS: The cohort included 6851 BCS, of whom 1121 (16%) had a diagnosis of diabetes. BCS were, on average, 55 (standard deviation: 11.88) years old, the majority self-reported race as White (90%), and 48.8% had stage II breast cancer. BCS with diabetes were significantly older (mean age of 60.6 [SD: 10.34] years) than those without diabetes and were often obese (66% had body mass index of ≥33). BCS with diabetes had higher odds of anemia (odds ratio: 1.43; 95% CI: 1.04, 1.96) and infection (odds ratio: 1.86; 95% CI: 1.35, 2.55) and utilized more outpatient resources (P<0.0001). CONCLUSIONS: Diabetes has a deleterious effect on health-related outcomes and health care resource utilization among BCS. These findings support the need for clinical practice guidelines to help clinicians manage diabetes among BCS throughout the cancer trajectory and for coordinated models of care to reduce high resource utilization.

A Computational Framework to Analyze the Associations Between Symptoms and Cancer Patient Attributes Post Chemotherapy Using EHR Data.

Patients with cancer, such as breast and colorectal cancer, often experience different symptoms post-chemotherapy. The symptoms could be fatigue, gastrointestinal (nausea, vomiting, lack of appetite), psychoneurological symptoms (depressive symptoms, anxiety), or other types. Previous research focused on understanding the symptoms using survey data. In this research, we propose to utilize the data within the Electronic Health Record (EHR). A computational framework is developed to use a natural language processing (NLP) pipeline to extract the clinician-documented symptoms from clinical notes. Then, a patient clustering method is based on the symptom severity levels to group the patient in clusters. The association rule mining is used to analyze the associations between symptoms and patient attributes (smoking history, number of comorbidities, diabetes status, age at diagnosis) in the patient clusters. The results show that the various symptom types and severity levels have different associations between breast and colorectal cancers and different timeframes post-chemotherapy. The results also show that patients with breast or colorectal cancers, who smoke and have severe fatigue, likely have severe gastrointestinal symptoms six months after the chemotherapy. Our framework can be generalized to analyze symptoms or symptom clusters of other chronic diseases where symptom management is critical.

Applying interpretable deep learning models to identify chronic cough patients using EHR data.

BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD code specific for chronic cough makes it challenging to identify such patients from electronic health records (EHRs). For clinical and research purposes, computational methods using EHR data are urgently needed to identify chronic cough cases. This research aims to investigate the data representations and deep learning algorithms for chronic cough prediction. METHODS: Utilizing real-world EHR data from a large academic healthcare system from October 2005 to September 2015, we investigated Natural Language Representation of the EHR data and systematically evaluated deep learning and traditional machine learning models to predict chronic cough patients. We built these machine learning models using structured data (medication and diagnosis) and unstructured data (clinical notes). RESULTS: The sensitivity and specificity of a transformer-based deep learning algorithm, specifically BERT with attention model, was 0.856 and 0.866, respectively, using structured data (medication and diagnosis). Sensitivity and specificity improved to 0.952 and 0.930 when we combined structured data with symptoms extracted from clinical notes. We further found that the attention mechanism of deep learning models can be used to extract important features that drive the prediction decisions. Compared with our previously published rule-based algorithm, the deep learning algorithm can identify more chronic cough patients with structured data. CONCLUSIONS: By applying deep learning models, chronic cough patients can be reliably identified for prospective or retrospective research through medication and diagnosis data, widely available in EHR and electronic claims data, thus improving the generalizability of the patient identification algorithm. Deep learning models can identify chronic cough patients with even higher sensitivity and specificity when structured and unstructured EHR data are utilized. We anticipate language-based data representation and deep learning models developed in this research could also be productively used for other disease prediction and case identification.

Analyzing the symptoms in colorectal and breast cancer patients with or without type 2 diabetes using EHR data.

This research extracted patient-reported symptoms from free-text EHR notes of colorectal and breast cancer patients and studied the correlation of the symptoms with comorbid type 2 diabetes, race, and smoking status. An NLP framework was developed first to use UMLS MetaMap to extract all symptom terms from the 366,398 EHR clinical notes of 1694 colorectal cancer (CRC) patients and 3458 breast cancer (BC) patients. Semantic analysis and clustering algorithms were then developed to categorize all the relevant symptoms into eight symptom clusters defined by seed terms. After all the relevant symptoms were extracted from the EHR clinical notes, the frequency of the symptoms reported from colorectal cancer (CRC) and breast cancer (BC) patients over three time-periods post-chemotherapy was calculated. Logistic regression (LR) was performed with each symptom cluster as the response variable while controlling for diabetes, race, and smoking status. The results show that the CRC and BC patients with Type 2 Diabetes (T2D) were more likely to report symptoms than CRC and BC without T2D over three time-periods in the cancer trajectory. We also found that current smokers were more likely to report anxiety (CRC, BC), neuropathic symptoms (CRC, BC), anxiety (BC), and depression (BC) than non-smokers.

Association of Comorbid Diabetes With Clinical Outcomes and Healthcare Utilization in Colorectal Cancer Survivors.

OBJECTIVES: To compare clinical outcomes and healthcare utilization in colorectal cancer (CRC) survivors with and without diabetes. SAMPLE & SETTING: CRC survivors (N = 3,287) were identified from a statewide electronic health record database using International Classification of Diseases (ICD) codes. Data were extracted on adults aged 21 years or older with an initial diagnosis of stage II or III CRC with diabetes present before CRC diagnosis or no diagnosis of diabetes (control). METHODS & VARIABLES: ICD codes were used to extract diabetes diagnosis and clinical outcome variables. Healthcare utilization was determined by encounter type. Data were analyzed using descriptive statistics, multivariable logistic, and Cox regression. RESULTS: CRC survivors with diabetes were more likely to develop anemia and infection than CRC survivors without diabetes. In addition, CRC survivors with diabetes were more likely to utilize emergency resources sooner than CRC survivors without diabetes. IMPLICATIONS FOR NURSING: Oncology nurses can facilitate the early identification of high-risk survivor groups, reducing negative clinical outcomes and unnecessarily high healthcare resource utilization in CRC survivors with diabetes.

Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer's disease from multi-dimensional genomic data.

The effective and accurate diagnosis of Alzheimer's disease (AD), especially in the early stage (i.e., mild cognitive impairment (MCI)) remains a big challenge in AD research. So far, multiple biomarkers have been associated with AD diagnosis and progression. However, most of the existing research only utilized single modality data for diagnostic biomarker identification, which did not take the advantages of multi-modal data that provide comprehensive and complementary information at multiple levels into consideration. In this paper, we integrate multi-modal genomic data from postmortem AD brains (i.e., mRNA, miRNA and epigenomic data) and propose a hyper-graph based sparse canonical correlation analysis (HGSCCA) method to extract the most correlated multi-modal biomarkers associated with AD and MCI. Specifically, our model utilizes the sparse canonical correlation analysis framework (SCCA), which aims at finding the best linear projections for each input modality so that the strongest correlation within the selected features of multi-dimensional genomic data can be captured. In addition, with the consideration of high-order relationships among different subjects, we also introduce a hyper-graph-based regularization term that will lead to the selection of more discriminative biomarkers. To evaluate the effectiveness of the proposed method, we conduct the experiments on the well-known AD cohort study, The Religious Orders Study and Memory and Aging Project (ROSMAP) dataset, and the results show that our method can not only identify meaningful biomarkers for the diagnosis AD disease, but also achieve superior classification performance than the comparing methods.

Gonorrhea testing, morbidity, and reporting using an integrated sexually transmitted disease registry in Indiana: 2004-2016.

Surveillance of gonorrhea (GC), the second most common notifiable disease in the United States, depends on case reports. Population-level data that contain the number of individuals tested in addition to morbidity are lacking. We performed a cross-sectional analysis of data obtained from individuals tested for GC recorded in a sexually transmitted disease (STD) registry in the state of Indiana. Descriptive statistics were performed, and a Poisson generalized linear model was used to evaluate the number of individuals tested for GC and the positivity rate. GC cases from a subset of the registry were compared to CDC counts to determine the completeness of the registry. A total of 1,870,811 GC tests were linked to 627,870 unique individuals. Individuals tested for GC increased from 54,334 in 2004 to 269,701 in 2016; likewise, GC cases increased from 2,039 to 5,997. However, positivity rate decreased from 3.75% in 2004 to 2.22% in 2016. The difference in the number of GC cases captured by the registry and those reported to the CDC was not statistically significant (P = 0.0665). Population-level data from an STD registry combining electronic medical records and public health case data may inform STD control efforts. In Indiana, increased testing rates appeared to correlate with increased GC morbidity.

Improving Notifiable Disease Case Reporting Through Electronic Information Exchange-Facilitated Decision Support: A Controlled Before-and-After Trial.

OBJECTIVE: Outbreak detection and disease control may be improved by simplified, semi-automated reporting of notifiable diseases to public health authorities. The objective of this study was to determine the effect of an electronic, prepopulated notifiable disease report form on case reporting rates by ambulatory care clinics to public health authorities. METHODS: We conducted a 2-year (2012-2014) controlled before-and-after trial of a health information exchange (HIE) intervention in Indiana designed to prepopulate notifiable disease reporting forms to providers. We analyzed data collected from electronic prepopulated reports and "usual care" (paper, fax) reports submitted to a local health department for 7 conditions by using a difference-in-differences model. Primary outcomes were changes in reporting rates, completeness, and timeliness between intervention and control clinics. RESULTS: Provider reporting rates for chlamydia and gonorrhea in intervention clinics increased significantly from 56.9% and 55.6%, respectively, during the baseline period (2012) to 66.4% and 58.3%, respectively, during the intervention period (2013-2014); they decreased from 28.8% and 27.5%, respectively, to 21.7% and 20.6%, respectively, in control clinics (P < .001). Completeness improved from baseline to intervention for 4 of 15 fields in reports from intervention clinics (P < .001), although mean completeness improved for 11 fields in both intervention and control clinics. Timeliness improved for both intervention and control clinics; however, reports from control clinics were timelier (mean, 7.9 days) than reports from intervention clinics (mean, 9.7 days). CONCLUSIONS: Electronic, prepopulated case reporting forms integrated into providers' workflow, enabled by an HIE network, can be effective in increasing notifiable disease reporting rates and completeness of information. However, it was difficult to assess the effect of using the forms for diseases with low prevalence (eg, salmonellosis, histoplasmosis).

Survival, Chemotherapy Treatments, and Health Care Utilization Among Patients with Advanced Small Cell Lung Cancer: An Observational Study.

INTRODUCTION: Most cases of small cell lung cancer (SCLC) are diagnosed at an advanced stage. The objective of this study was to investigate patient characteristics, survival, chemotherapy treatments, and health care use after a diagnosis of advanced SCLC in subjects enrolled in a health system network. METHODS: This was a retrospective cohort study of patients aged ≥ 18 years who either were diagnosed with stage III/IV SCLC or who progressed to advanced SCLC during the study period (2005-2015). Patients identified from the Indiana State Cancer Registry and the Indiana Network for Patient Care were followed from their advanced diagnosis index date until the earliest date of the last visit, death, or the end of the study period. Patient characteristics, survival, chemotherapy regimens, associated health care visits, and durations of treatment were reported. Time-to-event analyses were performed using the Kaplan-Meier method. RESULTS: A total of 498 patients with advanced SCLC were identified, of whom 429 were newly diagnosed with advanced disease and 69 progressed to advanced disease during the study period. Median survival from the index diagnosis date was 13.2 months. First-line (1L) chemotherapy was received by 464 (93.2%) patients, most commonly carboplatin/etoposide, received by 213 (45.9%) patients, followed by cisplatin/etoposide (20.7%). Ninety-five (20.5%) patients progressed to second-line (2L) chemotherapy, where topotecan monotherapy (20.0%) was the most common regimen, followed by carboplatin/etoposide (14.7%). Median survival was 10.1 months from 1L initiation and 7.7 months from 2L initiation. CONCLUSION: Patients in a regional health system network diagnosed with advanced SCLC were treated with chemotherapy regimens similar to those in earlier reports based on SEER-Medicare data. Survival of patients with advanced SCLC was poor, illustrating the lack of progress over several decades in the treatment of this lethal disease and highlighting the need for improved treatments.

Extending Achilles Heel Data Quality Tool with New Rules Informed by Multi-Site Data Quality Comparison.

Large healthcare datasets of Electronic Health Record data became indispensable in clinical research. Data quality in such datasets recently became a focus of many distributed research networks. Despite the fact that data quality is specific to a given research question, many existing data quality platform prove that general data quality assessment on dataset level (given a spectrum of research questions) is possible and highly requested by researchers. We present comparison of 12 datasets and extension of Achilles Heel data quality software tool with new rules and data characterization measures.

Initial uptake, time to treatment, and real-world effectiveness of all-oral direct-acting antivirals for hepatitis C virus infection in the United States: A retrospective cohort analysis.

BACKGROUND: Data on initiation and utilization of direct-acting antiviral therapies for hepatitis C virus infection in the United States are limited. This study evaluated treatment initiation, time to treatment, and real-world effectiveness of direct-acting antiviral therapy in individuals with hepatitis C virus infection treated during the first 2 years of availability of all-oral direct-acting antiviral therapies. METHODS: A retrospective cohort analysis was undertaken using electronic medical records and chart review abstraction of hepatitis C virus-infected individuals aged >18 years diagnosed with chronic hepatitis C virus infection between January 1, 2014, and December 31, 2015 from the Indiana University Health database. RESULTS: Eight hundred thirty people initiated direct-acting antiviral therapy during the 2-year observation window. The estimated incidence of treatment initiation was 8.8%±0.34% at the end of year 1 and 15.0%±0.5% at the end of year 2. Median time to initiating therapy was 300 days. Using a Cox regression analysis, positive predictors of treatment initiation included age (hazard ratio, 1.008), prior hepatitis C virus treatment (1.74), cirrhosis (2.64), and history of liver transplant (1.5). History of drug abuse (0.43), high baseline alanine aminotransferase levels (0.79), hepatitis B virus infection (0.41), and self-pay (0.39) were negatively associated with treatment initiation. In the evaluable population (n = 423), 83.9% (95% confidence interval, 80.1-87.3%) of people achieved sustained virologic response. CONCLUSION: In the early years of the direct-acting antiviral era, <10% of people diagnosed with chronic hepatitis C virus infection received direct-acting antiviral treatment; median time to treatment initiation was 300 days. Future analyses should evaluate time to treatment initiation among those with less advanced fibrosis.

Predictive modeling of hypoglycemia for clinical decision support in evaluating outpatients with diabetes mellitus.

Objective: Hypoglycemia occurs in 20-60% of patients with diabetes mellitus. Identifying at-risk patients can facilitate interventions to lower risk. We sought to develop a hypoglycemia prediction model. Methods: In this retrospective cohort study, urban adults prescribed a diabetes drug between 2004 and 2013 were identified. Demographic and clinical data were extracted from an electronic medical record (EMR). Laboratory tests, diagnostic codes and natural language processing (NLP) identified hypoglycemia. We compared multiple logistic regression, classification and regression trees (CART), and random forest. Models were evaluated on an independent test set or through cross-validation. Results: The 38,780 patients had mean age 57 years; 56% were female, 40% African-American and 39% uninsured. Hypoglycemia occurred in 8128 (539 identified only by NLP). In logistic regression, factors positively associated with hypoglycemia included infection, non-long-acting insulin, dementia and recent hypoglycemia. Negatively associated factors included long-acting insulin plus sulfonylurea, and age 75 or older. The models' area under curve was similar (logistic regression, 89%; CART, 88%; random forest, 90%, with ten-fold cross-validation). Conclusions: NLP improved identification of hypoglycemia. Non-long-acting insulin was an important risk factor. Decreased risk with age may reflect treatment or diminished awareness of hypoglycemia. More complex models did not improve prediction.

Quantifying Unmet Need in Statin-Treated Hyperlipidemia Patients and the Potential Benefit of Further LDL-C Reduction Through an EHR-Based Retrospective Cohort Study.

BACKGROUND: Statins are effective in helping prevent cardiovascular disease (CVD). However, studies suggest that only 20%-64% of patients taking statins achieve reasonable low-density lipoprotein cholesterol (LDL-C) thresholds. On-treatment levels of LDL-C remain a key predictor of residual CVD event risk. OBJECTIVES: To (a) determine how many patients on statins achieved the therapeutic threshold of LDL-C < 100 mg per dL (general cohort) and < 70 mg per dL (secondary prevention cohort, or subcohort, with preexisting CVD); (b) estimate the number of potentially avoidable CVD events if the threshold were reached; and (c) forecast potential cost savings. METHODS: A retrospective, longitudinal cohort study using electronic health record data from the Indiana Network for Patient Care (INPC) was conducted. The INPC provides comprehensive information about patients in Indiana across health care organizations and care settings. Patients were aged > 45 years and seen between January 1, 2012, and October 31, 2016 (ensuring study of contemporary practice), were statin-naive for 12 months before the index date of initiating statin therapy, and had an LDL-C value recorded 6-18 months after the index date. Subsequent to descriptive cohort analysis, the theoretical CVD risk reduction achievable by reaching the threshold was calculated using Framingham Risk Score and Cholesterol Treatment Trialists' Collaboration formulas. Estimated potential cost savings used published first-year costs of CVD events, adjusted for inflation and discounted to the present day. RESULTS: Of the 89,267 patients initiating statins, 30,083 (33.7%) did not achieve the LDL-C threshold (subcohort: 58.1%). In both groups, not achieving the threshold was associated with patients who were female, black, and those who had reduced medication adherence. Higher levels of preventive aspirin use and antihypertensive treatment were associated with threshold achievement. In both cohorts, approximately 64% of patients above the threshold were within 30 mg per dL of the respective threshold. Adherence to statin therapy regimen, judged by a medication possession ratio of ≥ 80%, was 57.4% in the general cohort and 56.7% in the subcohort. Of the patients who adhered to therapy, 23.7% of the general cohort and 50.5% of the subcohort had LDL-C levels that did not meet the threshold. 10-year CVD event risk in the at-or-above threshold group was 22.78% (SD = 17.24%) in the general cohort and 29.56% (SD = 18.19%) in the subcohort. By reducing LDL-C to the threshold, a potential relative risk reduction of 14.8% in the general cohort could avoid 1,173 CVD events over 10 years (subcohort: 15.7% and 454 events). Given first-year inpatient and follow-up costs of $37,300 per CVD event, this risk reduction could save about $1,455 per patient treated to reach the threshold (subcohort: $1,902; 2017 U.S. dollars) over a 10-year period. CONCLUSIONS: Across multiple health care systems in Indiana, between 34% (general cohort) and 58% (secondary prevention cohort) of patients treated with statins did not achieve therapeutic LDL-C thresholds. Based on current CVD event risk and cost projections, such patients seem to be at increased risk and may represent an important and potentially preventable burden on health care costs. DISCLOSURES: Funding support for this study was provided by Merck (Kenilworth, NJ). Chase and Boggs are employed by Merck. Simpson is a consultant to Merck and Pfizer. The other authors have nothing to disclose.

Applying a Pneumatic Interface to Intervene with Rapid Eating Behaviour.

Higher eating rates are positively correlate with obesity. In this paper, we propose the design of a new eating utensil that can reduce eating rate by interfering with eater's ability to eat quickly. This utensil can change its rigidity and shape by deflating itself to interfere with eating. In this study, a low fidelity proof-of-concept prototype device has been designed to provide physical resistance in order to help people reduce their eating rate. The proposed prototype could be used to demonstrate the feasibility of applying a pneumatically actuated shape-changing interface to embed physical resistance into an eating utensil.

Where Do People Go for Gonorrhea and Chlamydia Tests: A Cross-Sectional View of the Central Indiana Population, 2003-2014.

BACKGROUND: Despite major efforts to control their spread, reported sexually transmitted infections (STI) are increasing. Using data from a mid-sized Midwest metropolitan area, we examined the settings in which individuals are tested for gonorrhea and chlamydia in relation to demographics and test result to determine where interventions may best be focused. METHODS: A deidentified and integrated registry, containing records from all patients tested for an STI from 2003 to 2014, was created by combining data from a large health information exchange and the reporting district's STI Program located in Indianapolis, IN. Individual characteristics and visit settings where gonorrhea and chlamydia testing was performed were analyzed. RESULTS: We identified 298,946 individuals with 1,062,369 visits where testing occurred at least once between the ages of 13 and 44 years. Females were tested significantly more often than males and received testing more often in outpatient clinics whereas males were most often tested in the STI clinic. Individuals who used both STI and non-STI settings were more likely to have a positive test at an STI or emergency department visit (6.4-20.8%) than outpatient or inpatient setting (0.0-11.3%) (P < 0.0001). Test visits increased over the study period particularly in emergency departments, which showed a substantial increase in the number of positive test visits. CONCLUSIONS: The most frequent testing sites remain STI clinics for men and outpatient clinics for women. Yet, emergency departments (ED) are increasingly a source of testing and morbidity. This makes them a valuable target for public health interventions that could improve care and population health.