Outcomes of out-of-hospital cardiac arrest in adult congenital heart disease: a Danish nationwide study.

AIMS: The risk, characteristics, and outcome of out-of-hospital cardiac arrest (OHCA) in patients with congenital heart disease (CHD) remain scarcely investigated. METHODS AND RESULTS: An epidemiological registry-based study was conducted. Using time-dependent Cox regression models fitted with a nested case-control design, hazard ratios (HRs) with 95% confidence intervals of OHCA of presumed cardiac cause (2001-19) associated with simple, moderate, and severe CHD were calculated. Moreover, using multiple logistic regression, we investigated the association between pre-hospital OHCA characteristics and 30-day survival and compared 30-day survival in OHCA patients with and without CHD. Overall, 43 967 cases (105 with simple, 144 with moderate, and 53 with severe CHD) and 219 772 controls (median age 72 years, 68.2% male) were identified. Any type of CHD was found to be associated with higher rates of OHCA compared with the background population [simple CHD: HR 1.37 (1.08-1.70); moderate CHD: HR 1.64 (1.36-1.99); and severe CHD: HR 4.36 (3.01-6.30)]. Pre-hospital cardiopulmonary resuscitation and defibrillation were both associated with improved 30-day survival in patients with CHD, regardless of CHD severity. Among patients with OHCA, simple, moderate, and severe CHD had a similar likelihood of 30-day survival compared with no CHD [odds ratio 0.95 (0.53-1.69), 0.70 (0.43-1.14), and 0.68 (0.33-1.57), respectively]. CONCLUSION: A higher risk of OHCA was found throughout the spectrum of CHD. Patients with and without CHD showed the same 30-day survival, which relies on the pre-hospital chain of survival, namely cardiopulmonary resuscitation and defibrillation.

Prescribing practices in opioid agonist treatment and changes in compliance to clinical dosing guidelines in British Columbia, Canada.

BACKGROUND AND AIM: In British Columbia, Canada, clinical guidelines for the treatment of opioid use disorders (OUD) were updated in 2017, during a period in which the potency and composition of the illicit drug supply changed rapidly. We aimed to describe changes in opioid agonist treatment (OAT) prescribing practices at the population level in a setting in which fentanyl and its analogs have become the primary illicit opioid of use. DESIGN, SETTING AND PARTICIPANTS: This was a population-based retrospective cohort study using three linked health administrative databases in British Columbia (BC), Canada. All individuals with at least one OAT dispensation in BC between 1 January 2014 and 31 August 2021 took part. MEASUREMENTS: To assess changes in OAT prescribing practices over time, we calculated initiation doses, dose titration intervals, maintenance doses and take-home dosing intervals stratified by medication [methadone, buprenorphine-naloxone and slow-release oral morphine (SROM)] according to recommended guidelines. FINDINGS: A total of 265 410 OAT episodes (57.5% on methadone, 34.5% on buprenorphine-naloxone and 8.0% on SROM) were initiated during the study period. Compared with the guideline recommendation, observed initiation doses were higher among all medications from 2014 (2017 for SROM) to 2021 (buprenorphine-naloxone: 14-29%; methadone: 53-66%; SROM: 26-55%). Titration intervals were shorter for all medications, consistent with guidelines for buprenorphine-naloxone (26-49%), but shorter than recommended for methadone or SROM (28-51% and 12-41%, respectively). Higher maintenance dosing was observed for methadone (68-78%) and SROM (3-21%). Take-home allowances extending beyond the recommended guideline length increased across medications (buprenorphine-naloxone: 18-35%; methadone: 50-64%; SROM: 34-39%). Changes in prescribing patterns were similar for first-time OAT initiators. CONCLUSION: In British Columbia, Canada, from 2014 to 2021, prescribers of opioid agonist treatment (OAT) appeared to initiate both new and experienced OAT clients at higher doses than guideline recommendations, titrate them more rapidly and maintain clients at higher doses. Take-home dose allowances also gradually increased.

Learning how a tree branches out: A statistical modeling approach.

The increasingly large size of the graphical and numerical data sets collected with modern technologies requires constant update and upgrade of the statistical models, methods and procedures to be used for their analysis in order to optimize learning and maximize knowledge and understanding. This is the case for plant CT scanning (CT: computed tomography), including applications aimed at studying leaf canopies and the structural complexity of the branching patterns that support them in trees. Therefore, we first show after a brief review, how the CT scanning data can be leveraged by constructing an analytical representation of a tree branching structure where each branch is represented by a line segment in 3D and classified in a level of a hierarchy, starting with the trunk (level 1). Each segment, or branch, is characterized by four variables: (i) the position on its parent, (ii) its orientation, a unit vector in 3D, (iii) its length, and (iv) the number of offspring that it bears. The branching structure of a tree can then be investigated by calculating descriptive statistics on these four variables. A deeper analysis, based on statistical models aiming to explain how the characteristics of a branch are associated with those of its parents, is also presented. The branching patterns of three miniature trees that were CT scanned are used to showcase the statistical modeling framework, and the differences in their structural complexity are reflected in the results. Overall, the most important determinant of a tree structure appears to be the length of the branches attached to the trunk. This variable impacts the characteristics of all the other branches of the tree.