Refine
Document Type
- Article (5)
Language
- English (5)
Has Fulltext
- yes (5)
Is part of the Bibliography
- no (5)
Keywords
- - (5) (remove)
Institute
- Institut für Community Medicine (5)
- Institut für Diagnostische Radiologie und Neuroradiologie (1)
- Institut für Klinische Chemie und Laboratoriumsmedizin (1)
- Klinik für Psychiatrie und Psychotherapie (1)
- Klinik und Poliklinik für Augenheilkunde (1)
- Klinik und Poliklinik für Mund-, Kiefer- und Gesichtschirurgie/Plastische Operationen (1)
- Klinik und Poliklinik für Orthopädie und Orthopädische Chirurgie (1)
- Kliniken und Polikliniken für Innere Medizin (1)
- Poliklinik für Kieferorthopädie, Präventive Zahnmedizin und Kinderzahnheilkunde (1)
- Poliklinik für Zahnerhaltung, Parodontologie und Endodontologie (1)
Publisher
- S. Karger AG (3)
- MDPI (2)
(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.
Variability of Thyroid Measurements from Ultrasound and Laboratory in a Repeated Measurements Study
(2020)
Background: Variability of measurements in medical research can be due to different sources. Quantification of measurement errors facilitates probabilistic sensitivity analyses in future research to minimize potential bias in epidemiological studies. We aimed to investigate the variation of thyroid-related outcomes derived from ultrasound (US) and laboratory analyses in a repeated measurements study. Subjects and Methods: Twenty-five volunteers (13 females, 12 males) aged 22–70 years were examined once a month over 1 year. US measurements included thyroid volume, goiter, and thyroid nodules. Laboratory measurements included urinary iodine concentrations and serum levels of thyroid-stimulating hormone (TSH), free triiodothyronine (fT3), free thyroxine (fT4), and thyroglobulin. Variations in continuous thyroid markers were assessed as coefficient of variation (CV) defined as mean of the individual CVs with bootstrapped confidence intervals and as intraclass correlation coefficients (ICCs). Variations in dichotomous thyroid markers were assessed by Cohen’s kappa. Results: CV was highest for urinary iodine concentrations (56.9%), followed by TSH (27.2%), thyroglobulin (18.2%), thyroid volume (10.5%), fT3 (8.1%), and fT4 (6.3%). The ICC was lowest for urinary iodine concentrations (0.42), followed by fT3 (0.55), TSH (0.64), fT4 (0.72), thyroid volume (0.87), and thyroglobulin (0.90). Cohen’s kappa values for the presence of goiter or thyroid nodules were 0.64 and 0.70, respectively. Conclusion: Our study provides measures of variation for thyroid outcomes, which can be used for probabilistic sensitivity analyses of epidemiological data. The low intraindividual variation of serum thyroglobulin in comparison to urinary iodine concentrations emphasizes the potential of thyroglobulin as marker for the iodine status of populations.
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.
Background: It has not been investigated whether there are associations between urinary iodine (UI) excretion measurements some years apart, nor whether such an association remains after adjustment for nutritional habits. The aim of the present study was to investigate the relation between iodine-creatinine ratio (ICR) at two measuring points 5 years apart. Methods: Data from 2,659 individuals from the Study of Health in Pomerania were analyzed. Analysis of covariance and Poisson regressions were used to associate baseline with follow-up ICR. Results: Baseline ICR was associated with follow-up ICR. Particularly, baseline ICR >300 µg/g was related to an ICR >300 µg/g at follow-up (relative risk, RR: 2.20; p < 0.001). The association was stronger in males (RR: 2.64; p < 0.001) than in females (RR: 1.64; p = 0.007). In contrast, baseline ICR <100 µg/g was only associated with an ICR <100 µg/g at follow-up in males when considering unadjusted ICR. Conclusions: We detected only a weak correlation with respect to low ICR. Studies assessing iodine status in a population should take into account that an individual with a low UI excretion in one measurement is not necessarily permanently iodine deficient. On the other hand, current high ICR could have been predicted by high ICR 5 years ago.