Ten Rules with regard to Performing Retrospective Pharmacoepidemiological Analyses Example COVID19 Research

It remains to be elucidated whether or not DXA provides more accurate assessment of whole body fat mass than ADP in preterm infants.Trial registration NTR5311 What is Known • Diverse methods are used to assess fat mass in preterm infants. What is New • This study showed that there is poor agreement between dual-energy X-ray absorptiometry, air displacement plethysmography, and skinfold thickness measurements. • Our results affirm the need for consensus guidelines on how to measure fat mass in preterm infants, to improve the assimilation of data from different studies and the implementation of the findings from those studies.The clinical presentation of idiopathic dilated cardiomyopathy (IDCM) heart failure (HF) patients who will respond to medical therapy (responders) and those who will not (non-responders) is often similar. A machine learning (ML)-based clinical tool to identify responders would prevent unnecessary surgery, while targeting non-responders for early intervention. We used regional left ventricular (LV) contractile injury patterns in ML models to identify IDCM HF non-responders. MRI-based multiparametric strain analysis was performed in 178 test subjects (140 normal subjects and 38 IDCM patients), calculating longitudinal, circumferential, and radial strain over 18 LV sub-regions for inclusion in ML analyses. Patients were identified as responders based upon symptomatic and contractile improvement on medical therapy. We tested the predictive accuracy of support vector machines (SVM), logistic regression (LR), random forest (RF), and deep neural networks (DNN). The DNN model outperformed other models, predicting response to medical therapy with an area under the receiver operating characteristic curve (AUC) of 0.94. The top features were longitudinal strain in (1) basal anterior, posterolateral and (2) mid posterior, anterolateral, and anteroseptal sub-regions. Regional contractile injury patterns predict response to medical therapy in IDCM HF patients, and have potential application in ML-based HF patient care.Parkinson's disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients' quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. mTOR inhibition Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.Inadequate sleep and excessive exposure to media screens have both been linked to poorer mental health in youth. However, the ways in which these interact to predict behaviour problems have yet to be examined using objective sleep measurement. The lack of objective evidence for these relationships in young children has recently been defined by the World Health Organization (2019) as a gap in the field. We thus aimed to test the interacting effects of screen exposure and objectively measured sleep on behaviour problems in the preschool age. A total of 145 children aged 3-to-6-years participated in this cross-sectional study. Sleep was assessed objectively using actigraphy for 1-week, and subjectively using parent-reported daily sleep diaries. Parents reported the child's daily duration of screen exposure, and completed the Strengths and Difficulties Questionnaire. Results showed that actigraphic sleep duration, timing and efficiency were associated with screen exposure. The link between screen time and behaviour problems was moderated by sleep duration, as it was significant only for children with sleep duration of 9.88 h or less per night. Sleep duration also moderated the relation between screen time and externalizing-but not internalizing-problems. Hence, the combination of increased screen exposure and decreased sleep duration may be particularly adverse for child mental health. While these key relationships should be further examined in longitudinal and experimental investigations, our findings shed light on their complexity, underscoring the importance of the moderating role of sleep.As mental health systems move towards person-centred care, outcome measurement in clinical research and practice should track changes that matter to young people and their families. This study mapped the types of change described by three key stakeholder groups following psychotherapy for depression, and compared the salience of these outcomes with the frequency of their measurement in recent quantitative treatment effectiveness studies for adolescent depression.Using qualitative content analysis, this study identified and categorized outcomes across 102 semi-structured interviews that were conducted with depressed adolescents, their parents, and therapists, as part of a randomized superiority trial. Adolescents had been allocated to Cognitive-Behavioral Therapy, Short-Term Psychoanalytic Psychotherapy, or a Brief Psychosocial Intervention.The study mapped seven high-level outcome domains and 29 outcome categories. On average, participants discussed change in four domains and six outcome categories. The most frequently discussed outcome was an improvement in mood and affect (i.e., core depressive symptoms), but close to half of the participants also described changes in family functioning, coping and resilience, academic functioning, or social functioning. Coping had specific importance for adolescents, while parents and therapists showed particular interest in academic functioning. There was some variation in the outcomes discussed beyond these core themes, across stakeholder groups and treatment arms.Of the outcomes that were frequently discussed in stakeholder narratives, only symptomatic change has been commonly reported in recent treatment studies for adolescent depression. A shift towards considering multiple outcome domains and perspectives is needed to reflect stakeholder priorities and enable more nuanced insights into change processes.