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The Study of Health in Pomerania (SHIP), a population-based study from a rural state in northeastern Germany with a relatively poor life expectancy, supplemented its comprehensive examination program in 2008 with whole-body MR imaging at 1.5 T (SHIP-MR). We reviewed more than 100 publications that used the SHIP-MR data and analyzed which sequences already produced fruitful scientific outputs and which manuscripts have been referenced frequently. Upon reviewing the publications about imaging sequences, those that used T1-weighted structured imaging of the brain and a gradient-echo sequence for R2* mapping obtained the highest scientific output; regarding specific body parts examined, most scientific publications focused on MR sequences involving the brain and the (upper) abdomen. We conclude that population-based MR imaging in cohort studies should define more precise goals when allocating imaging time. In addition, quality control measures might include recording the number and impact of published work, preferably on a bi-annual basis and starting 2 years after initiation of the study. Structured teaching courses may enhance the desired output in areas that appear underrepresented.
(1) Background: Predicting chronic low back pain (LBP) is of clinical and economic interest as LBP leads to disabilities and health service utilization. This study aims to build a competitive and interpretable prediction model; (2) Methods: We used clinical and claims data of 3837 participants of a population-based cohort study to predict future LBP consultations (ICD-10: M40.XX-M54.XX). Best subset selection (BSS) was applied in repeated random samples of training data (75% of data); scoring rules were used to identify the best subset of predictors. The rediction accuracy of BSS was compared to randomforest and support vector machines (SVM) in the validation data (25% of data); (3) Results: The best subset comprised 16 out of 32 predictors. Previous occurrence of LBP increased the odds for future LBP consultations (odds ratio (OR) 6.91 [5.05; 9.45]), while concomitant diseases reduced the odds (1 vs. 0, OR: 0.74 [0.57; 0.98], >1 vs. 0: 0.37 [0.21; 0.67]). The area-under-curve (AUC) of BSS was acceptable (0.78 [0.74; 0.82]) and comparable with SVM (0.78 [0.74; 0.82]) and randomforest (0.79 [0.75; 0.83]); (4) Conclusions: Regarding prediction accuracy, BSS has been considered competitive with established machine-learning approaches. Nonetheless, considerable misclassification is inherent and further refinements are required to improve predictions.
Quality of life (QoL) is a core patient-reported outcome in healthcare research, alongside primary clinical outcomes. A conceptual, operational, and psychometric elaboration of QoL in the context of TM is needed, because standardized instruments to assess QoL do not sufficiently represent essential aspects of intended outcomes of telemedical applications (TM). The overall aim is to develop an instrument that can adequately capture QoL in TM. For that purpose, an extended working model of QoL will be derived. Subsequently, an instrument will be developed and validated that captures those aspects of QoL that are influenced by TM. The initial exploratory study section includes (a) a systematic literature review, (b) a qualitative survey for concept elicitation, and (c) pre-testings using cognitive debriefings with patients and an expert workshop. The second quantitative section consists of an online expert survey and two patient surveys for piloting and validation of the newly developed instrument. The resulting questionnaire will assess central experiences of patients regarding telemedical applications and its impact on QoL more sensitively. Its use as adjunct instrument will lead to a more appropriate evaluation of TM and contribute to the improvement of care tailored to patients’ individual needs.
CFTR encodes for a chloride and bicarbonate channel expressed at the apical membrane of polarized epithelial cells. Transepithelial sodium transport mediated by the amiloride-sensitive sodium channel ENaC is thought to contribute to the manifestation of CF disease. Thus, ENaC is a therapeutic target in CF and a valid cystic fibrosis modifier gene. We have characterized SCNN1B as a genetic modifier in the three independent patient cohorts of F508del-CFTR homozygotes. We could identify a regulatory element at SCNN1B to the genomic segment rs168748-rs2303153-rs4968000 by fine-mapping (Pbest = 0.0177), consistently observing the risk allele rs2303153-C and the contrasting benign allele rs2303153-G in all three patient cohorts. Furthermore, our results show that expression levels of SCNN1B are associated with rs2303153 genotype in intestinal epithelia (P = 0.003). Our data confirm that the well-established biological role of SCNN1B can be recognized by an association study on informative endophenotypes in the rare disease cystic fibrosis and calls attention to reproducible results in association studies obtained from small, albeit carefully characterized patient populations.