| 11. |
- Hageman, Isabel C., et al.
(författare)
-
A Quality Assessment of the ARM-Net Registry Design and Data Collection
- 2023
-
Ingår i: Journal of Pediatric Surgery. - : Elsevier BV. - 0022-3468. ; 58:10, s. 1921-1928
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Registries are important in rare disease research. The Anorectal Malformation Network (ARM-Net) registry is a well-established European patient registry collecting demographic, clinical, and functional outcome data. We assessed the quality of this registry through review of the structure, data elements, collected data, and user experience. Material and methods: Design and data elements were assessed for completeness, consistency, usefulness, accuracy, validity, and comparability. An intra- and inter-user variability study was conducted through monitoring and re-registration of patients. User experience was assessed via a questionnaire on registration, design of registry, and satisfaction. Results: We evaluated 119 data elements, of which 107 were utilized and comprised 42 string and 65 numeric elements. A minority (37.0%) of the 2278 included records had complete data, though this improved to 83.5% when follow-up elements were excluded. Intra-observer variability demonstrated 11.7% incongruence, while inter-observer variability was 14.7%. Users were predominantly pediatric surgeons and typically registered patients within 11–30 min. Users did not experience any significant difficulties with data entry and were generally satisfied with the registry, but preferred more longitudinal data and patient-reported outcomes. Conclusions: The ARM-Net registry presents one of the largest ARM cohorts. Although its collected data are valuable, they are susceptible to error and user variability. Continuous evaluations are required to maintain relevant and high-quality data and to achieve long-term sustainability. With the recommendations resulting from this study, we call for rare disease patient registries to take example and aim to continuously improve their data quality to enhance the small, but impactful, field of rare disease research. Level of Evidence: V.
|
|
| 12. |
- Tønnesen, Siren, et al.
(författare)
-
Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder : A Multisample Diffusion Tensor Imaging Study
- 2020
-
Ingår i: Biological Psychiatry. - : Elsevier BV. - 2451-9022 .- 2451-9030. ; 5:12, s. 1095-1103
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts.METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics.CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
|
|
