Background-Current guidelines for the diagnosis of acute myocardial infarction (AMI), among other criteria, also require a rise and/or fall in cardiac troponin (cTn) levels. It is unknown whether absolute or relative changes in cTn have higher diagnostic accuracy and should therefore be preferred. Methods and Results-In a prospective, observational, multicenter study, we analyzed the diagnostic accuracy of absolute (⌬) and relative (⌬%) changes in cTn in 836 patients presenting to the emergency department with symptoms suggestive of AMI. Blood samples for the determination of high-sensitive cTn T and cTn I ultra were collected at presentation and after 1 and 2 hours in a blinded fashion. The final diagnosis was adjudicated by 2 independent cardiologists. The area under the receiver operating characteristic curve for diagnosing AMI was significantly higher for 2-hour absolute 001).The receiver operating characteristic curve-derived cutoff value for 2-hour absolute (⌬) change was 0.007 g/L for high-sensitivity cTn T and 0.020 g/L for cTn I ultra (both cutoff levels are half of the 99th percentile of the respective cTn assay). Absolute changes were superior to relative changes in patients with both low and elevated baseline cTn levels. Conclusions-Absolute changes of cTn levels have a significantly higher diagnostic accuracy for AMI than relative changes, and seem therefore to be the preferred criteria to distinguish AMI from other causes of cTn elevations. Clinical Trial Registration-URL: http://www.clinicaltrials.gov. Unique identifier: NCT00470587.

SummaryAlthough medication nonadherence (MNA) is a major risk factor for poor outcomes, the evolution of MNA from pre-to 3 years post-transplant among the four major organ transplant groups remains unknown. Therefore, this study described this evolution and investigated whether pretransplant MNA predicts post-transplant immunosuppressive medication nonadherence (IMNA). Adult participants (single transplant, pretransplant and ≤1 post-transplant assessment, using medications pretransplant) in the Swiss Transplant Cohort Study (a prospective nation-wide cohort study) were included. Nonadherence, defined as any deviation from dosing schedule, was assessed using two self-report questions pretransplant and at 6, 12, 24 and 36 months post-transplant. Nonadherence patterns were modelled using generalized estimating equations. The sample included 1505 patients (average age: 52.5 years (SD: 13.1); 36.3% females; 924 renal, 274 liver, 181 lung, 126 heart). The magnitude and variability of self-reported MNA decreased significantly from pretransplant to 6 months post-transplant (OR = 0.21; 95% CI: 0.16-0.27). Post-transplant IMNA increased continuously from 6 months to 3 years post-transplant (OR = 2.75; 95% CI: 1.97-3.85). Pretransplant MNA was associated with threefold higher odds of post-transplant IMNA (OR = 3.10; 95% CI: 2.29-4.21). As pretransplant MNA predicted posttransplant IMNA and a continuous increase in post-transplant IMNA was observed, early adherence-supporting interventions are indispensible.

The primary aim of this study was to fit and test the hypothesized three-factor model of the Pittsburgh Sleep Quality Index (PSQI) reported by Cole (2006) in renal transplant (RTx) recipients. We conducted a cross-sectional descriptive study using a convenience sample of home-dwelling RTx recipients, transplanted 6 months to 5 years prior to initiation of the study. Of the 135 RTx patients meeting the inclusion criteria, 29% were women with a mean age of 52 years (SD: 12; range: 21 to 76). The PSQI and a structured demographic questionnaire were mailed to the patients' homes. We conducted a confirmatory factor analysis to fit and test a single-factor model proposed by Buysse (1989) as well as the Cole (2006) three-factor model. Confirmatory factor analysis provided weak empirical support for the three-factor model (c 2 = 16.555, d.f. = 8, P < 0.0351; RMSEA = 0.089; WRMR = 0.492; CFI = 0.983). Post hoc exploration of the three-factor model indicated the inclusion of an additional path from sleep-medication items to the factor of sleep efficiency, which demonstrated an improved fit (c 2 = 11.850, d.f. = 8, P = 0.408; RMSEA = 0.060; WRMR = 0.384; CFI = 0.992).Confirmatory factor analysis suggests that the three-factor model of the PSQI has a better fit than the original one-factor model, and the additional pathway may improve its fit. The three-factor model with the additional path should be tested in a new sample before use in RTx recipients.

Posttransplant smoking is associated with poor outcomes. Our results might help clinicians to understand which patients are more likely to smoke posttransplant, guide interventional approaches, and provide recommendations for future research.