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Background: Evaluation of automated attenuation-based tube potential selection and its impact on image quality and radiation dose in CT (computed tomography) examinations for cancer staging.
Methods: A total of 110 (59 men, 51 women) patients underwent chest-abdomen-pelvis CT examinations; 55 using a fixed tube potential of 120 kV/current of 210 Reference mAs (using CareDose4D), and 55 using automated attenuation-based tube potential selection (CAREkV) also using a current of 210 Reference mAs. This evaluation was performed as a single-centre, observer-blinded retrospective analysis. Image quality was assessed by two readers in consensus. Attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured or calculated for objective image evaluation. For the evaluation of radiation exposure, dose-length-product (DLP) values were compared and Size-specific dose estimates (SSDE) values were calculated.
Results: Diagnostic image quality was obtained from all patients. The median DLP (703.5 mGy · cm, range 390–2203 mGy · cm) was 7.9% lower when using the algorithm compared with the standard 120 kV protocol (median 756 mGy · cm, range 345–2267 mGy · cm). A reduction in potential to 100 kV occurred in 32 cases; therefore, these patients received significantly lower radiation exposure compared with the 120 kV protocol.
Conclusion: Automated attenuation-based tube potential selection produces good diagnostic image quality in chest-abdomen-pelvis CT and reduces the patient’s overall radiation dose by 7.9% compared to the standard 120 kV protocol.
Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.
Einleitung : Eine sinnvolle Einbindung von Pflegefachpersonen mit Hochschulabschluss in die Versorgungsabläufe wird international häufig mit besseren Behandlungsergebnissen bei den Patient*innen assoziiert. In Deutschland fehlt es derzeit noch an verlässlichen Zahlen über Absolvent*innen und deren Aufgabenfeldern. Ziel dieser Erhebung war daher, durch Wiederholung einer früheren Erhebung erneut den Anteil von Pflegefachpersonen mit Bachelor- oder Masterabschlüssen in der direkten Patient*innenversorgung zu ermitteln.
Methode: In einer Querschnittserhebung wurden die Pflegedirektor*innen der Universitätskliniken und Medizinischen Hochschulen (UK) Deutschlands mittels einer standardisierten Befragung nach der Anzahl der Pflegefachpersonen mit Hochschulabschlüssen (Bachelor, Master und Doktor) gefragt. Weitere Fragen betrafen deren Aufgabengebiete und Integrationsmaßnahmen. Die Daten wurden mittels deskriptiver Statistik ausgewertet.
Ergebnisse: Insgesamt konnten n = 29 gültige Fragebögen aus 35 UK in die Analyse eingeschlossen werden, daraus ergibt sich eine Rücklaufquote von 82,85%. Für insgesamt 18 UK konnte eine Steigerung der hochschulisch qualifizierten Pflegefachpersonen um n = 786, von 2015 (n = 593) auf 2018 (n = 1379) erreicht werden. Der Anteil an Pflegefachpersonen mit Hochschulabschluss in den teilnehmenden UK liegt bei 3,16% (SD = 1,66; Min - Max = 1,09 - 6,69; Q1 - Q3 = 1,49 - 4,04; 95% KI = 2,30 – 3,95). In der direkten Versorgung beträgt der Anteil 2,11% (SD = 1,40; Min – Max = 0,47 - 5,42; Q1 – Q3 = 0,87 – 3,16; 95% KI 1,36 - 2,76). Die Aufgabenschwerpunkte liegen im Bereich der Regelversorgung und Patient*innenedukation (Bachelorabsolvent*innen), der evidenzbasierten Pflegepraxisentwicklung (Masterabsolvent*innen) und Forschung (promovierte Absolvent*innen).
Diskussion: Im Vergleich zu 2015 ist der Anteil hochschulisch qualifizierter Pflegefachpersonen zwar angestiegen, doch er liegt immer noch auf einem sehr niedrigen Niveau. Die Hochschulabsolvent*innen nehmen versorgungs- und entwicklungsrelevante Aufgaben wahr, doch besteht hinsichtlich ihrer Aufgabengebiete Bedarf an kompetenzorientierter Differenzierung.
Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.