[Operative treatment of diabetics with vascular complications : Secondary data analysis of diagnosis-related groups statistics from 2005 to 2014 in Germany]. / Operative Versorgung von Diabetikern mit vaskulären Komplikationen : Sekundärdatenanalyse der DRG-Statistik von 2005 bis 2014 in Deutschland.

BACKGROUND: In Germany approximately 40,000 amputations per year are performed on patients with diabetes mellitus, often with accompanying vascular complications. OBJECTIVE: The aim of this study was to present the various degrees of severity of the vascular complications and the temporal changes of the treatment options in diabetics with vascular complications in Germany. MATERIAL AND METHODS: The microdata of the diagnosis-related groups (DRG) statistics of the Federal Statistical Office were analyzed over the period from 2005 to 2014. All cases were included in which the main or secondary diagnosis of diabetes mellitus with concurrent vascular complications (diabetic angiopathy and peripheral arterial disease) was encrypted. RESULTS: The median age of the 1,811,422 cases was 73 years and 62% were male. While the total number of amputations remained stable over time, there was a 41% reduction in knee-preserving and a 31% reduction in non-knee preserving major amputations with an 18% increase in minor amputations. Revascularization increased by 33% from 36 procedures in 2005 to 48 procedures per 100,000 inhabitants. The increase in revascularization was evident in the area of endovascular therapy alone where there was an increase of 78%. CONCLUSION: Due to the significant increase in endovascular revascularization measures, there was a significant increase in the proportion of diabetes patients with vascular pathologies in whom revascularization was carried out. As a result, improved limb preservation was achieved despite equally high amputation rates due to increasing minor amputation rates.

Zinc transporter 8 autoantibodies assessment in daily practice.

OBJECTIVES: Zinc transporter 8 (ZnT8) is specifically expressed in the pancreatic ß-cell and is more restricted in its tissue distribution than other auto-antigens as glutamic acid decarboxylase 65 (GAD65) and insulinoma-associated antigen-2 (IA2). ZnT8 autoantibodies (ZnT8A) assessment allows identifying rapid progression to clinical onset of the disease. We evaluated the prevalence of ZnT8A in adults of different ethnic and phenotypic groups and analyzed its potential utility as additional marker of autoimmunity in daily practice. METHODS: ZnT8A, GADA and IA2A were assessed using enzyme-linked immune-sorbent assay (ELISA) in 160 controls and 216 diabetic subjects. 105 were of type 1 diabetes (T1D), 17 had Latent Autoimmune Diabetes of Adults (LADA), 38 were type 2 diabetic (T2D) and 56 had ketosis-prone diabetes (KPD). 82 patients were newly diagnosed cases. RESULTS: ZnT8A were detected in 1% of controls and were not found in any of our 38 T2D subjects or 56 KPD subjects. In contrast, ZnT8A were detected in 18% of LADA subjects and in 38% of T1D subjects. A slight difference of percentage of ZnT8A positivity was found among our T1D ethnic groups. ZnT8A were positive in 41% of patients positive for GADA and 67% of patients positive for IA2A. The percentage of stratification achieved 91% when GADA, IA2A and ZnT8A were assessed simultaneously. CONCLUSIONS: Results obtained for ZnT8A measurement using ELISA were consistent with previous data. Such investigation could improve the risk stratification and would be integrated in our daily practice.

Problem list completeness in electronic health records: A multi-site study and assessment of success factors.

OBJECTIVE: To assess problem list completeness using an objective measure across a range of sites, and to identify success factors for problem list completeness. METHODS: We conducted a retrospective analysis of electronic health record data and interviews at ten healthcare organizations within the United States, United Kingdom, and Argentina who use a variety of electronic health record systems: four self-developed and six commercial. At each site, we assessed the proportion of patients who have diabetes recorded on their problem list out of all patients with a hemoglobin A1c elevation>=7.0%, which is diagnostic of diabetes. We then conducted interviews with informatics leaders at the four highest performing sites to determine factors associated with success. Finally, we surveyed all the sites about common practices implemented at the top performing sites to determine whether there was an association between problem list management practices and problem list completeness. RESULTS: Problem list completeness across the ten sites ranged from 60.2% to 99.4%, with a mean of 78.2%. Financial incentives, problem-oriented charting, gap reporting, shared responsibility, links to billing codes, and organizational culture were identified as success factors at the four hospitals with problem list completeness at or near 90.0%. DISCUSSION: Incomplete problem lists represent a global data integrity problem that could compromise quality of care and put patients at risk. There was a wide range of problem list completeness across the healthcare facilities. Nevertheless, some facilities have achieved high levels of problem list completeness, and it is important to better understand the factors that contribute to success to improve patient safety. CONCLUSION: Problem list completeness varies substantially across healthcare facilities. In our review of EHR systems at ten healthcare facilities, we identified six success factors which may be useful for healthcare organizations seeking to improve the quality of their problem list documentation: financial incentives, problem oriented charting, gap reporting, shared responsibility, links to billing codes, and organizational culture.

Evaluating diabetes mobile applications for health literate designs and functionality, 2014.

INTRODUCTION: The expansion of mobile health technologies, particularly for diabetes-related applications (apps), grew exponentially in the past decade. This study sought to examine the extent to which current mobile apps for diabetes have health literate features recommended by participants in an Institute of Medicine Roundtable and compare the health literate features by app cost (free or not). METHODS: We used diabetes-related keywords to identify diabetes-related apps for iOS devices. A random sample of 110 apps (24% of total number of apps identified) was selected for coding. The coding scheme was adapted from the discussion paper produced by participants in the Institute of Medicine Roundtable. RESULTS: Most diabetes apps in this sample addressed diabetes management and therapeutics, and paid apps were more likely than free apps to use plain language strategies, to label links clearly, and to have at least 1 feature (a "back" button) that helps with the organization. CONCLUSION: Paid apps were more likely than free apps to use strategies that should be more useful and engaging for people with low health literacy. Future work can investigate ways to make free diabetes mobile apps more user-friendly and accessible.

Learning to improve medical decision making from imbalanced data without a priori cost.

BACKGROUND: In a medical data set, data are commonly composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a majority sample is highly expensive. This is the so-called imbalanced classification problem. The traditional classification functions can be seriously affected by the skewed class distribution in the data. To deal with this problem, people often use a priori cost to adjust the learning process in the pursuit of optimal classification function. However, this priori cost is often unknown and hard to estimate in medical decision making. METHODS: In this paper, we propose a new learning method, named RankCost, to classify imbalanced medical data without using a priori cost. Instead of focusing on improving the class-prediction accuracy, RankCost is to maximize the difference between the minority class and the majority class by using a scoring function, which translates the imbalanced classification problem into a partial ranking problem. The scoring function is learned via a non-parametric boosting algorithm. RESULTS: We compare RankCost to several representative approaches on four medical data sets varying in size, imbalanced ratio, and dimension. The experimental results demonstrate that unlike the currently available methods that often perform unevenly with different priori costs, RankCost shows comparable performance in a consistent manner. CONCLUSIONS: It is a challenging task to learn an effective classification model based on imbalanced data in medical data analysis. The traditional approaches often use a priori cost to adjust the learning of the classification function. This work presents a novel approach, namely RankCost, for learning from medical imbalanced data sets without using a priori cost. The experimental results indicate that RankCost performs very well in imbalanced data classification and can be a useful method in real-world applications of medical decision making.

Transformaciones en el abordaje de la diabetes: análisis de las evidencias científicas publicadas por dos sociedades científicas / Transformations in the management of diabetes: an analysis of the scientific evidence published by two scientific societies (1980-2010)

El objetivo de este trabajo es describir y analizar los cambios en la definición de la diabetes como enfermedad y la relación con las transformaciones en su abordaje terapéutico. Se realizó un análisis de contenido sobre los artículos, guías y consensos publicados por la Sociedad Argentina de Diabetes (SAD) y la Asociación Latinoamericana de Diabetes (ALAD) entre los años 1980 y 2010. Se desagregaron las diferentes clasificaciones, los valores considerados normales para definir a una persona como diabética y el tratamiento, analizando críticamente los cambios y modificaciones encontrados con el auxilio de categorías como medicalización, riesgo y estilos de vida. Como resultado del análisis hemos podido observar cómo el creciente proceso de medicalización, la dependencia con respecto al conocimiento científico de países centrales, los intereses de la industria farmacéutica y el lugar central del tratamiento farmacológico se inscriben en el abordaje de la diabetes y se hacen visibles a través de los cambios sucedidos en los últimos 30 años.

The aim of this study was to describe and analyze changes in the definition of diabetes as a disease and the relationship between these changes and subsequent modifications in the therapeutic management of the disease. A content analysis was performed using articles, guidelines, and consensuses published by the Argentina Diabetes Society and the Latin American Diabetes Association between 1980 and 2010. The different classifications, values used to define a person as diabetic, and treatments were assessed and the changes and modifications discovered were critically analyzed using categories such as medicalization, risk and lifestyles. As a result of the analysis we can observe how the growing process of medicalization, the dependence on the scientific knowledge of central countries, the interests of the pharmaceutical industry, and the crucial role played by pharmacological treatments are all inscribed within the management of diabetes, which can be made visible through the changes that have taken place over the last 30 years.

PHIO: a knowledge base for interpretation and calculation of public health indicators.

Existing population health indicators tend to be out-of-date, not fully available at local levels of geography, and not developed in a coherent/consistent manner, which hinders their use in public health. The PopHR platform aims to deliver an electronic repository that contains multiple aggregated clinical, administrative, and environmental data sources to provide a coherent view of the health status of populations in the province of Quebec, Canada. This platform is designed to provide representative information in near-real time with high geographical resolution, thereby assisting public health professionals, analysts, clinicians and the public in decision-making. This paper presents our ongoing efforts to develop an integrated population health indicator ontology (PHIO) that captures the knowledge required for calculation and interpretation of health indicators within a PopHR semantic framework.

Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm.

BACKGROUND: Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date. METHODS: The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard. RESULTS: The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date. CONCLUSIONS: A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management.

Validation of classification algorithms for childhood diabetes identified from administrative data.

OBJECTIVE: Type 1 diabetes is the most common form of diabetes among children; however, the proportion of cases of childhood type 2 diabetes is increasing. In Canada, the National Diabetes Surveillance System (NDSS) uses administrative health data to describe trends in the epidemiology of diabetes, but does not specify diabetes type. The objective of this study was to validate algorithms to classify diabetes type in children <20 yr identified using the NDSS methodology. PATIENTS AND METHODS: We applied the NDSS case definition to children living in British Columbia between 1 April 1996 and 31 March 2007. Through an iterative process, four potential classification algorithms were developed based on demographic characteristics and drug-utilization patterns. Each algorithm was then validated against a gold standard clinical database. RESULTS: Algorithms based primarily on an age rule (i.e., age <10 at diagnosis categorized type 1 diabetes) were most sensitive in the identification of type 1 diabetes; algorithms with restrictions on drug utilization (i.e., no prescriptions for insulin ± glucose monitoring strips categorized type 2 diabetes) were most sensitive for identifying type 2 diabetes. One algorithm was identified as having the optimal balance of sensitivity (Sn) and specificity (Sp) for the identification of both type 1 (Sn: 98.6%; Sp: 78.2%; PPV: 97.8%) and type 2 diabetes (Sn: 83.2%; Sp: 97.5%; PPV: 73.7%). CONCLUSIONS: Demographic characteristics in combination with drug-utilization patterns can be used to differentiate diabetes type among cases of pediatric diabetes identified within administrative health databases. Validation of similar algorithms in other regions is warranted.

Checking coding completeness by mining discharge summaries.

Incomplete coding is a known problem in hospital information systems. In order to detect non-coded secondary diseases we developed a text classification system which scans discharge summaries for drug names. Using a drug knowledge base in which drug names are linked to sets of ICD-10 codes, the system selects those documents in which a drug name occurs that is not justified by any ICD-10 code within the corresponding record in the patient database. Treatment episodes with missing codes for diabetes mellitus, Parkinson's disease, and asthma/COPD were subject to investigation in a large German university hospital. The precision of the method was 79%, 14%, and 45% respectively, roughly estimated recall values amounted to 43%, 70%, and 36%. Based on these data we predict roughly 716 non-coded diabetes cases, 13 non-coded Parkinson cases, and 420 non-coded asthma/COPD cases among 34,865 treatment episodes.

Getting the patients' perspective: a survey of diabetes services on Guam.

The prevention and control of diabetes is a major public health priority for the US Territory of Guam. As part of a strategic planning process, a survey of diabetes patients was conducted to determine patients' perceptions of the availability and adequacy of preventive and clinical services to control diabetes. A total of 125 survey questionnaires were distributed to diabetes patients attending either one of the Guam Department of Public Health and Social Services Community Health Centers or a private Internal Medicine/Endocrinology clinical practice of the only endocrinologist on the island. All 125 questionnaires were returned. Respondents were highly aware of the duration of their diabetes, and almost 75% have had the opportunity to discuss the chronic nature of the illness and the importance of key lifestyle changes to help prevent or retard the progression of the disease. However, almost 40% of patients were not aware of the type of diabetes they had, and one in five have not received diabetes self-management education from their health care providers. Key interventions, such as nutritional counseling, brief tobacco cessation interventions, regular eye and foot examinations and immunization services were not being provided to 30% to 60% of patients, despite clinical practice guidelines that recommend these interventions for all diabetics. While over half of respondents were generally satisfied with the quality of preventive and routine medical care that they receive from their service providers, they identified the need for better quality diabetes self-management education, preventive services, enhanced access to specialists and specialized care, especially for diabetes-related complications, and better financial support to assist them in meeting the costs of chronic care and medications. The feedback from these respondents should provide guidance regarding service gaps and needs as the Department of Public Health and Social Services and its community partners collaboratively develop a strategic plan to better address diabetes prevention and control on Guam. The information should also serve to direct quality improvement efforts to enhance existing diabetes services on the island.

The metabolic syndrome and incident diabetes: Assessment of alternative definitions of the metabolic syndrome in an Iranian urban population.

AIMS: To compare the ability of definitions of Metabolic Syndrome (MetS) in the prediction of type 2 diabetes. METHODS: We examined 4756 subjects in an Iranian population who were non-diabetic at baseline. After 3.6 years, 188 individuals developed diabetes. RESULTS: Impaired glucose tolerance (IGT) and MetS definitions predicted type 2 diabetes with odds ratios ranging from 3.7 to 11.9 (all P<0.05) although IGT had the highest area under the receiver operator characteristic (aROC) curve than all the MetS definitions. Reduction of glucose in the National Cholesterol Education Program (NCEP) definition of the MetS increased diabetes prediction, but adding the family history of diabetes did not change aROC curves. The International Diabetes Federation (IDF) definition had the highest sensitivity and false positive rate (72.2 and 35.1%, respectively) and the WHO definition had the lowest ones (54.8 and 9.2%, respectively) for predicting diabetes. The positive predictive values of all definitions were low (8.6-19.7%) but their negative predictive values were around 98%. CONCLUSIONS: In Iranian population, the MetS was inferior to IGT for predicting type 2 diabetes. The NCEP definition of the MetS with reduced level of glucose (not including the family history of diabetes) and IDF definition predicted type 2 diabetes at least as well as WHO definition.

Lost productive time and costs due to diabetes and diabetic neuropathic pain in the US workforce.

OBJECTIVE: Estimate the impact of diabetes and neuropathic pain on the US workforce. METHODS: Data on lost productive time (LPT) was collected by telephone interview in a random sample of the US population (N=36,634). Of 19,075 occupation-eligible working adults included in the analysis, 1003 reported a physician diagnosis of diabetes; 38% of these reported numbness or tingling in feet or hands due to diabetes (symptom group). We compared diabetes respondents with and without symptoms to other respondents for LPT and related cost. RESULTS: Health-related LPT was 18% higher in the symptom (P<0.05) and 5% higher in the non-symptom (P<0.05) groups versus for those without diabetes. The symptom group lost 1.4 hours of work per week more than the non-symptom group (P<0.05). CONCLUSIONS: Workers who have diabetes with neuropathic symptoms lose the equivalent of $3.65 billion/yr in health-related LPT.

Diabetes research in jeopardy: the extinction of clinical diabetes researchers.

Finding a cure for an autoimmune disease, such as diabetes, must be viewed as a marathon. Although there are occasional quick sprints forward on the road to discovery, the bulk of research is carried out steadily over time. Over the years, researchers have made progressive strides toward a cure, but our field is now facing a crisis. We need youthful researchers with fresh new perspectives to carry on the work started by forerunners in the field. In fact, many young people never get the opportunity to work in a scientific field. Of those who do choose to study science, after receiving a bachelor's degree in that field, only 4% eventually receive their doctorate. There are many reasons for the abrupt end of the academic path for these students. Perhaps they are stymied in their education, frustrated by the lack of grant support and mentorship provided to young scientists. On average, a scientist receives his or her first NIH R01 award at the age of 43 years. Moreover, many young researchers lack the mentorship they seek from more established scientists. This hurdle can be overcome by providing young scientists the ability to work unreservedly with other mature scientists. Ideally, a Diabetes Research Center needs to be geared toward facilitating collaboration and free exchange of ideas to promote the maturation of young scientists. Additionally, this environment, can offer young scientists the resources that would otherwise be unavailable to them. This concept is fundamental to creating more experienced and prolific scientists in the field of diabetes.

Failure of ICD-9-CM codes to identify patients with comorbid chronic kidney disease in diabetes.

OBJECTIVE: To determine prevalence of chronic kidney disease (CKD) in patients with diabetes, and accuracy of International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes to identify such patients. DATA SOURCES/STUDY SETTING: Secondary data from 1999 to 2000. We linked all inpatient and outpatient administrative and clinical records of U.S. veterans with diabetes dually enrolled in Medicare and the Veterans Administration (VA) health care systems. STUDY DESIGN: We used a cross-sectional, observational design to determine the sensitivity and specificity of renal-related ICD-9-CM diagnosis codes in identifying individuals with chronic kidney disease. DATA COLLECTION/EXTRACTION METHODS: We estimated glomerular filtration rate (eGFR) from serum creatinine and defined CKD as Stage 3, 4, or 5 CKD by eGFR criterion according to the Kidney Disease Outcomes Quality Initiative guidelines. Renal-related ICD-9-CM codes were grouped by algorithm. PRINCIPAL FINDINGS: Prevalence of CKD was 31.6 percent in the veteran sample with diabetes. Depending on the detail of the algorithm, only 20.2 to 42.4 percent of individuals with CKD received a renal-related diagnosis code in either VA or Medicare records over 1 year. Specificity of renal codes for CKD ranged from 93.2 to 99.4 percent. Patients hospitalized in VA facilities were slightly more likely to be correctly coded for CKD than patients hospitalized in facilities reimbursed by Medicare (OR 5.4 versus 4.1, p=.0330) CONCLUSIONS: CKD is a common comorbidity for patients with diabetes in the VA system. Diagnosis codes in administrative records from Medicare and VA systems are insensitive, but specific markers for patients with CKD.

Indiana chronic disease management program risk stratification analysis.

OBJECTIVE: The objective of this study was to compare the ability of risk stratification models derived from administrative data to classify groups of patients for enrollment in a tailored chronic disease management program. SUBJECTS: This study included 19,548 Medicaid patients with chronic heart failure or diabetes in the Indiana Medicaid data warehouse during 2001 and 2002. MEASURES: To predict costs (total claims paid) in FY 2002, we considered candidate predictor variables available in FY 2001, including patient characteristics, the number and type of prescription medications, laboratory tests, pharmacy charges, and utilization of primary, specialty, inpatient, emergency department, nursing home, and home health care. METHODS: We built prospective models to identify patients with different levels of expenditure. Model fit was assessed using R statistics, whereas discrimination was assessed using the weighted kappa statistic, predictive ratios, and the area under the receiver operating characteristic curve. RESULTS: We found a simple least-squares regression model in which logged total charges in FY 2002 were regressed on the log of total charges in FY 2001, the number of prescriptions filled in FY 2001, and the FY 2001 eligibility category, performed as well as more complex models. This simple 3-parameter model had an R of 0.30 and, in terms in classification efficiency, had a sensitivity of 0.57, a specificity of 0.90, an area under the receiver operator curve of 0.80, and a weighted kappa statistic of 0.51. CONCLUSION: This simple model based on readily available administrative data stratified Medicaid members according to predicted future utilization as well as more complicated models.