[Show abstract][Hide abstract] ABSTRACT: Background Previous data suggest that the response of chronic myeloid leukemia cells to imatinib is dose-dependent. The potential benefit of initial dose intensification of imatinib in pre-treated patients with chronic phase chronic myeloid leukemia remains unknown. DESIGN AND METHODS: Two hundred and twenty-seven pre-treated patients with chronic myeloid leukemia in chronic phase were randomly assigned to continuous treatment with a standard dose of imatinib (400 mg/day; n=113) or to 6 months of high-dose induction with imatinib (800 mg/day) followed by a standard dose of imatinib as maintenance therapy (n=114). RESULTS: The rates of major and complete cytogenetic responses were significantly higher in the high-dose arm than in the standard-dose arm at both 3 and 6 months (major cytogenetic responses: 36.8% versus 21.2%, P=0.01 and 50.0% versus 34.5%, P=0.018; complete cytogenetic responses: 22.8% versus 6.2%, P<0.001 and 40.4% versus 16.8%, P<0.001) on the basis of an intention-to-treat analysis. At 12 months, the difference between treatment arms remained statistically significant for complete cytogenetic responses (40.4% versus 24.8%, P=0.012) but not for major cytogenetic responses (49.1% versus 44.2%, P=0.462). The rate of major molecular responses was also significantly better at 3 and 6 months in the high-dose arm (month 3: 14.9% versus 3.5%, P=0.003; month 6: 32.5% versus 8.8%, P<0.001). Overall and progression-free survival rates were comparable between arms, but event-free survival was significantly worse in the high-dose arm (P=0.014). Conclusions Standard-dose imatinib remains the standard of care for pre-treated patients with chronic phase chronic myeloid leukemia (Clinicaltrials.gov identifier: NCT00327262).
[Show abstract][Hide abstract] ABSTRACT: Current theoretical and clinical approaches conceive the avoidance and acceptance of emotions as critical factors in the maintenance and alleviation of psychological problems. This study investigates the role of mindfulness, experiential avoidance (EA), and positive and negative meta-emotions (emotional reactions towards the emotional self) on the symptoms and psychological well-being of inpatients.
Changes of mindfulness measured during a 6-week stay at a psychosomatic clinic were explored in a sample of 293 inpatients with diverse psychological problems. Multivariate analyses were performed to determine the predictive power of mindfulness and acceptance on symptoms and psychological well-being.
Staying on an inpatient ward was associated with reductions in EA and negative meta-emotions as well as improvements in mindful awareness and positive meta-emotions, i.e., participants reported greater acceptance of their own emotional reactions. These aspects were associated with a reduction in symptom severity and greater psychological well-being. A differentiation of meta-emotions allowed the meaningful identification of possible processes of change.
Anger and contempt seem to have distinctive functions in self-regulation. Reducing the amount of contempt/shame for one's own emotions and generating greater interest were associated with symptom reduction and greater psychological well-being. Self-compassion was negatively associated with symptoms, though it had no association with psychological well-being. The theoretical implications are discussed.
[Show abstract][Hide abstract] ABSTRACT: Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. This paper describes the "evtree" package, which implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. Computationally intensive tasks are fully computed in C++ while the "partykit" (Hothorn and Zeileis 2011) package is leveraged for representing the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. "evtree" is compared to "rpart" (Therneau and Atkinson 1997), the open-source CART implementation, and conditional inference trees ("ctree", Hothorn, Hornik, and Zeileis 2006). The usefulness of "evtree" is illustrated in a textbook customer classification task and a benchmark study of predictive accuracy in which "evtree" achieved at least similar and most of the time better results compared to the recursive algorithms "rpart" and "ctree".
[Show abstract][Hide abstract] ABSTRACT: Prospective clinical, x-ray, and magnetic resonance imaging investigation following total lumbar disc replacement (TDR) with ProDisc II (Synthes, Paoli, PA).
To examine the progression of adjacent level degeneration (ALD), facet joint degeneration (FJD) as well as associated risk factors following TDR.
Fusion procedures have been associated with adjacent level morbidities and facet joint pathologies in a considerable number of patients. Whether the incidence of these negative side effects can be reduced with TDR remains unestablished.
Clinical outcome scores Visual Analogue Scale (VAS), Oswestry Disability Index (ODI) and patient satisfaction rates were acquired within the framework of an ongoing prospective study with ProDisc II. The mean index-level ROM was established for every patient over the entire postoperative period from multiple flexion/extension x-ray images. The progression of ALD and FJD was evaluated from pre- and postoperative magnetic resonance images by 2 independent radiologists.
Results from 93 patients with an average follow-up of 53.4 months (range, 24.1-98.7 months) were included in this study. The overall results revealed a significant improvement from preoperative VAS and ODI levels (P < 0.0001).The incidence of ALD was 10.2% (n = 11/108 levels). The degenerative changes were mild and occurred late after surgery (mean, 65.2 months; range, 37.9-85.6 months). There was no significant correlation between index-level ROM and the occurrence of ALD (P > 0.05).Progression of FJD was observed in 20.0% of all facet joints (n = 44/220). FJD occurred significantly more often following TDR at the lumbosacral junction in comparison to the level above the lumbosacral junction (P < 0.02) and was observed more frequently at index-levels than at nonindex levels (P < 0.001).The degenerative changes were associated with a negative influence on postoperative outcome parameters VAS and ODI (P < 0.03) that were already detected early after surgery. The mean postoperative ROM was significantly lower in patients with progression of FJD in comparison to the remaining cohort (P < 0.0001).
TDR proved to have a beneficial effect with respect to adjacent level disc preservation. The degenerative changes were mild, occurred late after surgery and did not reveal a negative effect on postoperative clinical outcome. There was no significant correlation between index-level ROM and the occurrence of ALD (P > 0.05).TDR was, however, associated with a progression of index-level FJD in a considerable number of patients, particularly at the lumbosacral junction. Lower segmental mobility and less favorable clinical results point to the fact that a particular cohort of patients may predominantly be affected in which TDR shows inferior compatibility with the index-segment's biomechanics.
[Show abstract][Hide abstract] ABSTRACT: DRG-systems are used to allocate resources fairly to hospitals based on their performance. Statistically, this allocation is based on simple rules that can be modeled with regression trees. However, the resulting models often have to be adjusted manually to be medically reasonable and ethical.
Despite the possibility of manual, performance degenerating adaptations of the original model, alternative trees are systematically searched. The bootstrap-based method bumping is used to build diverse and accurate regression tree models for DRG-systems. A two-step model selection approach is proposed. First, a reasonable model complexity is chosen, based on statistical, medical and economical considerations. Second, a medically meaningful and accurate model is selected. An analysis of 8 data-sets from Austrian DRG-data is conducted and evaluated based on the possibility to produce diverse and accurate models for predefined tree complexities.
The best bootstrap-based trees offer increased predictive accuracy compared to the trees built by the CART algorithm. The analysis demonstrates that even for very small tree sizes, diverse models can be constructed being equally or even more accurate than the single model built by the standard CART algorithm.
Bumping is a powerful tool to construct diverse and accurate regression trees, to be used as candidate models for DRG-systems. Furthermore, Bumping and the proposed model selection approach are also applicable to other medical decision and prognosis tasks.
BMC Medical Informatics and Decision Making 02/2010; 10(1):9. DOI:10.1186/1472-6947-10-9 · 1.83 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We sought to assess the relation of N-terminal brain natriuretic peptide (NT-pro BNP) determined on day 3 after onset of acute myocardial infarction (AMI) symptoms with acute and chronic infarct size and functional parameters assessed by cardiac magnetic resonance (CMR) imaging. Furthermore, we wanted to investigate its predictive value for recovery of myocardial function.
CMR was performed in 49 consecutive patients within 6 days and in a subgroup 4 (n = 27) and 12 (n = 22) months after first acute ST-elevation AMI and successful primary angioplasty. NT-pro BNP was measured in the subacute phase at 66 ± 8 h after onset of symptoms.
Log-transformed NT-pro BNP (lgNT-pro BNP) significantly correlated with infarct size in % of left ventricular myocardial mass (r = 0.59 to 0.64; p < 0.004), with ejection fraction (EF) (r = -0.49 to -0.55; p < 0.004) as well as with segmental wall thickening (SWT, mm) (r = 0.41 to -0.52; p < 0.04) at any time of assessment. Multiple linear regression analysis revealed baseline EF and lgNT-pro BNP to predict global functional recovery. Patients with NT-pro BNP concentrations <the mean level of 1115 pg/ml significantly improved in EF and SWT (all p < 0.02) during the study period, whereas patients with NT-pro BNP >1115 pg/ml did not show significant functional recovery (all p = NS).
NT-pro BNP on day 3 after admission correlates with acute and chronic infarct size and myocardial function after AMI. Global and regional myocardial function did not recover in patients with higher NT-pro BNP (>1115 pg/ml) during subacute phase of AMI.
International journal of cardiology 11/2009; 147(1):118-23. DOI:10.1016/j.ijcard.2009.09.537 · 4.04 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In recent years more data is logged from the electronic control units on-board in commercial vehicles. Typically, the data is transferred from the vehicle at the workshop to a centralized storage for future analysis. This vast amount of data is used for debugging, as a knowledgebase for the design engineer and as a tool for service planning. Manual analysis of this data is often time consuming, due to the rich amount of information contained. However, there is an opportunity to automatically assist in the process based on knowledge discovery techniques, even directly when the trucks data is first offloaded at the workshop. One typical example of how this technique could be helpful is when two groups of trucks behave differently, e.g. one well-functioning group and one faulty group, when the two groups have the same specification. The desired information is the specific difference in the logged data, e.g. what particular sensors or signals are different. An evaluation cycle is proposed and applied to extract knowledge from three different large real-world data-sets measured on Volvo long haulage trucks. Information in the logged data that describes the vehicle's operating environment, allows the detection of trucks that are operated differently from their intended use. Experiments to find such vehicles were conducted and recommendations for an automated application are given.