The third paradigm provides an opportunity to look beyond any dichotomy between "standardized" versus "real-life" characteristics of the health care system and study designs. Namely, future research will determine whether the identification of these contextual factors can help to best design randomized controlled trials that provide better estimates of drugs' effectiveness.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

Higher protein intake, and particularly higher leucine intake, is associated with attenuated loss of lean body mass (LBM) over time in older individuals. Dietary leucine is thought to be a key mediator of anabolism. This study aimed to assess this relationship over 6 years among younger and older adult Danes. Dietary leucine intake was assessed at baseline and after 6 years in men and women, aged 35-65 years, participating in the Danish cohort of the WHO-MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) study (n 368). Changes in LBM over the 6 years were measured by bioelectrical impedance using equations developed for this Danish population. The association between leucine and LBM changes was examined using multivariate linear regression and ANCOVA analyses adjusted for potential confounders. After adjustment for baseline LBM, sex, age, energy intake and physical activity, leucine intake was associated with LBM change in those older than 65 years (n 79), with no effect seen in those younger than 65 years. Older participants in the highest quartile of leucine intake (7·1 g/d) experienced LBM maintenance, whereas lower intakes were associated with LBM loss over 6 years (for trend: β = 0·434, P = 0·03). Sensitivity analysis indicated no effect modification of sex or the presence of CVD. Greater leucine intake in conjunction with adequate total protein intake was associated with long-term LBM retention in a healthy older Danish population. This study corroborates findings from laboratory investigations in relation to protein and leucine intakes and LBM change. A more diverse and larger sample is needed for confirmation of these results.

Medication review for older patients with polypharmacy in the emergency department (ED) is crucial to prevent inappropriate prescribing. Our objective was to assess the feasibility of a collaborative medication review in older medical patients (≥65 years) using polypharmacy (≥5 long-term medications). A pharmacist performed the medication review using the tools: Screening Tool of Older Persons’ potentially inappropriate Prescriptions (STOPP) criteria, a drug–drug interaction database (SFINX), and Renbase® (renal dosing database). A geriatrician received the medication review and decided which recommendations should be implemented. The outcomes were: differences in Medication Appropriateness Index (MAI) and Assessment of Underutilization Index (AOU) scores between admission and 30 days after discharge and the percentage of patients for which the intervention was completed before discharge. Sixty patients were included from the ED, the intervention was completed before discharge for 50 patients (83%), and 39 (61.5% male; median age 80 years) completed the follow-up 30 days after discharge. The median MAI score decreased from 14 (IQR 8-20) at admission to 8 (IQR 2-13) 30 days after discharge (p < 0.001). The number of patients with an AOU score ≥1 was reduced from 36% to 10% (p < 0.001). Thirty days after discharge, 83% of the changes were sustained and for 28 patients (72%), 1≥ medication had been deprescribed. In conclusion, a collaborative medication review and deprescribing intervention is feasible to perform in the ED.

Background: Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that Machine Learning (ML) models could be used to predict risks at different stages of management (at diagnosis, hospital admission and ICU admission) and thereby provide insights into drivers and prognostic markers of disease progression and death. Methods: From a cohort of approx. 2.6 million citizens in the two regions of Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. A cohort of SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. Findings: The ML models predicted the risk of death (Receiver Operation Characteristics Area Under the Curve, ROC-AUC) of 0.904 at diagnosis, 0.818, at hospital admission and 0.723 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. We identified some common risk factors, including age, body mass index (BMI) and hypertension as driving factors, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. Interpretation: ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. Prognostic features included age, BMI and hypertension, although markers of shock and organ dysfunction became more important in more severe cases. We provide access to an online risk calculator based on these findings.

No abstract

Within a habitual range, a greater protein intake was associated with BW gain, mostly in FM. This is in contrast to the expectations based on physiological and clinical trials, and calls for a better understanding of how habitual dietary protein influences long-term energy balance, versus how greater changes in dietary proteins may influence short-term energy balance.

Background The prevalence of depression and the exposure to antidepressants are high among women of reproductive age and during pregnancy. Duloxetine is a selective serotonin-norepinephrine reuptake inhibitor (SNRI) approved in the United States and Europe in 2004 for the treatment of depression. Fetal safety of duloxetine is not well established. The present study evaluates the association of exposure to duloxetine during pregnancy and the risk of major and minor congenital malformations and the risk of stillbirths. Methods and findings A population-based observational study was conducted based on data from registers in Sweden and Denmark. All registered births and stillbirths in the medical birth registers between 2004 and 2016 were included. Malformation diagnoses were identified up to 1 year after birth. Logistic regression analyses were used. Potential confounding was addressed through multiple regression, propensity score (PS) matching, and sensitivity analyses. Confounder variables included sociodemographic information (income, education, age, year of birth, and country), comorbidity and comedication, previous psychiatric contacts, and birth-related information (smoking during pregnancy and previous spontaneous abortions and stillbirths). Duloxetine-exposed women were compared with 4 comparators: (1) duloxetine-nonexposed women; (2) selective serotonin reuptake inhibitor (SSRI)-exposed women; (3) venlafaxine-exposed women; and (4) women exposed to duloxetine prior to, but not during, pregnancy. Exposure was defined as redemption of a prescription during the first trimester and throughout pregnancy for the analyses of malformations and stillbirths, respectively. Outcomes were major and minor malformations and stillbirths gathered from the national patient registers. The cohorts consisted of more than 2 million births with 1,512 duloxetine-exposed pregnancies. No increased risk for major malformations, minor malformations, or stillbirth was found across comparison groups in adjusted and PS-matched analyses. Duloxetine-exposed versus duloxetine-nonexposed PS-matched analyses showed odds ratio (OR) 0.98 (95% confidence interval [CI] 0.74 to 1.30, p = 0.909) for major malformations, OR 1.09 (95% CI 0.82 to 1.45, p = 0.570) for minor malformation, and 1.18 (95% CI 0.43 to 3.19, p = 0.749) for stillbirths. For the individual malformation subtypes, some findings were statistically significant but were associated with large statistical uncertainty due to the extremely small number of events. The main limitations for the study were that the indication for duloxetine and a direct measurement of depression severity were not available to include as covariates. Conclusions Based on this observational register-based nationwide study with data from Sweden and Denmark, no increased risk of major or minor congenital malformations or stillbirth was associated with exposure to duloxetine during pregnancy.