No abstract

OBJECTIVE Rates of diagnosis of prediabetes and uptake of the National Diabetes Prevention Program (NDPP) are low. We evaluated a proactive three-level strategy to identify individuals with prediabetes in a population with employer-sponsored health insurance. RESEARCH DESIGN AND METHODS We studied 64,131 insured employees, dependents, and retirees ≥18 years of age without diagnosed diabetes, 19,397 (30%) of whom were estimated to have prediabetes. Individuals with prediabetes were identified by 1 ) searching claims diagnoses and previously performed HbA 1c test results, 2 ) risk stratifying people 40–64 years of age without diabetes, prediabetes, or documented normal HbA 1c to identify individuals at higher risk and encourage them to be tested, and 3 ) using a media campaign to encourage employees not otherwise targeted to self-screen and, if at higher risk, to be tested. RESULTS Using claims and laboratory data, 11% of the population was identified as having prediabetes. Of those 40–64 years of age, 25% were identified as being at higher risk, and 27% of them were tested or diagnosed within 1 year. Of employees exposed to the media campaign, 14% were tested or diagnosed within 1 year. Individuals with prediabetes were older, heavier, and more likely to have hypertension and dyslipidemia. Testing and diagnosis were associated with receiving medical care and provider outreach. A total of 8,129 individuals, or 42% of those with prediabetes, were identified. CONCLUSIONS Analysis of existing health insurance data facilitated the identification of individuals with prediabetes. Better identification of people with prediabetes is a first step in increasing uptake of the NDPP.

AimsThe risk of HeartMate II (HMII) left ventricular assist device (LVAD) thrombosis has been reported, and serum lactate dehydrogenase (LDH), a biomarker of haemolysis, increases secondary to LVAD thrombosis. This study evaluated longitudinal measurements of LDH post‐LVAD implantation, hypothesizing that LDH trends could timely predict future LVAD thrombosis.Methods and resultsFrom October 2004 to October 2014, 350 HMIIs were implanted in 323 patients at Cleveland Clinic. Of these, patients on 339 HMIIs had at least one post‐implant LDH value (7996 total measurements). A two‐step joint model combining longitudinal biomarker data and pump thrombosis events was generated to assess the effect of changing LDH on thrombosis risk. Device‐specific LDH trends were first smoothed using multivariate boosted trees, and then used as a time‐varying covariate function in a multiphase hazard model to analyse time to thrombosis. Pre‐implant variables associated with time‐varying LDH values post‐implant using boostmtree were also investigated. Standardized variable importance for each variable was estimated as the difference between model‐based prediction error of LDH when the variable was randomly permuted and prediction error without permuting the values. The larger this difference, the more important a variable is for predicting the trajectory of post‐implant LDH. Thirty‐five HMIIs (10%) had either confirmed (18) or suspected (17) thrombosis, with 15 (43%) occurring within 3 months of implant. LDH was associated with thrombosis occurring both early and late after implant (P < 0.0001 for both hazard phases). The model demonstrated increased probability of HMII thrombosis as LDH trended upward, with steep changes in LDH trajectory paralleling trajectories in probability of pump thrombosis. The most important baseline variables predictive of the longitudinal pattern of LDH were higher bilirubin, higher pre‐implant LDH, and older age. The effect of some pre‐implant variables such as sodium on the post‐implant LDH longitudinal pattern differed across time.ConclusionsLongitudinal trends in surveillance LDH for patients on HMII support are useful for dynamic prediction of pump thrombosis, both early after implant and late. Incorporating upward and downward trends in LDH that dynamically update a model of LVAD thrombosis risk provides a useful tool for clinical management and decisions.

We observed different temporal patterns of HeartMate II left ventricular assist device (LVAD) thrombosis regarding clinical manifestations and lactate dehydrogenase (LDH) trends. We propose nomenclature for classification of LVAD thrombosis and compare patient characteristics and outcomes in each pattern of LVAD thrombosis. We reviewed electronic medical records of 362 consecutive HeartMate II devices implanted at Cleveland Clinic from October 2008 to July 2014. We categorized clinical courses of confirmed device thrombosis based on clinical manifestations and LDH patterns. Patients’ characteristics, clinical variables, and outcomes were collected for comparison. From a total of 19 confirmed device thromboses, we categorized the patterns of thrombosis into three distinctive types; abrupt thrombosis (Type 1), subacute thrombosis (Type 2), and asymptomatic hemolysis (Type 3). Abrupt thrombosis (Type 1) tended to be the most morbid clinical course with acute-onset thrombosis at 56.5 (interquartile range: 44–71) days, all New York Heart Association functional class III or IV at presentation. Death and need for surgical intervention were not different in each pattern. Asymptomatic hemolysis had unique comorbidities of preexisting cardiac thrombi and preoperative bacteremia. Confirmed LVAD thrombosis has different patterns of clinical presentation and each pattern of thrombosis has unique characteristics and clinical manifestations.

No abstract