Medical Cannabis Is Not Associated with a Decrease in Activities of Daily Living in Older Adults.

The proportion of older adults using medical cannabis is rising. Therefore, we aimed to assess the effects of herbal medical cannabis on the functional status of older adults. We conducted a prospective observational study of patients aged 65 years or older that initiated cannabis treatment for different indications, mostly chronic non-cancer pain, during 2018-2020 in a specialized geriatric clinic. The outcomes assessed were activities of daily living (ADL), instrumental activities of daily living (IADL), pain intensity, geriatric depression scale, chronic medication use, and adverse events at six months. A cohort of 119 patients began cannabis treatment: the mean age was 79.3 ± 8.5 and 74 (62.2%) were female. Of the cohort, 43 (36.1%) experienced adverse effects due to cannabis use and 2 (1.7%) required medical attention. The mean ADL scores before and after treatment were 4.4 ± 1.8 and 4.5 ± 1.8, respectively (p = 0.27), and the mean IADL scores before and after treatment were 4.1 ± 2.6 and 4.7 ± 3, respectively (p = 0.02). We concluded that medical cannabis in older adults has a number of serious adverse events, but was not associated with a decrease in functional status, as illustrated by ADL and IADL scores after six months of continuous treatment.

Admission of kidney patients to a closed staff nephrology department results in a better short-term survival.

BACKGROUND: The outcome of patients with chronic kidney disease (CKD) and acute kidney injury (AKI) is often dismal and measures to ameliorate their course are scarce. When admitted to the hospital, kidney patients are often hospitalized in general Medicine wards rather than in a specialized Nephrology department. In the current study, we compared the outcome of two cohorts of kidney patients (CKD and AKI) admitted either to general open-staff (with rotating physicians) Medicine wards or to a closed-staff (non-rotating Nephrologists) Nephrology ward. METHODS: In this population-based retrospective cohort study, we enrolled 352 CKD patients and 382 AKI patients admitted to either Nephrology or General Medicine wards. Short-term (< = 90 days) and long-term (>90 days) outcomes were recorded for survival, renal outcomes, cardiovascular outcomes, and dialysis complications. Multivariate analysis was performed using logistic regression and negative binomial regression adjusting to potential sociodemographic confounders as well as to a propensity score based on the association of all medical background variables to the admitted ward, to mitigate the potential admittance bias to each ward. RESULTS: One hundred and seventy-one CKD patients (48.6%) were admitted to the Nephrology ward and 181 (51.4%) were admitted to general Medicine wards. For AKI, 180 (47.1%) and 202 (52.9%) were admitted to Nephrology and general Medicine wards, respectively. Baseline age, comorbidities and the degree of renal dysfunction differed between the groups. Using propensity score analysis, a significantly reduced mortality rate was observed for kidney patients admitted to the Nephrology ward vs. general Medicine in short term mortality (but not long-term mortality) among both CKD patients admitted (OR = 0.28, CI = 0.14-0.58, p = 0.001), and AKI patients (or = 0.25, CI = 0.12-0.48, p< 0.001). Nephrology ward admission resulted in higher rates of renal replacement therapy (RRT), both during the first hospitalization and thereafter. CONCLUSIONS: Thus, a simple measure of admission to a specialized Nephrology department may improve kidney patient outcome, thereby potentially affecting future health care planning.

Stroke mortality prediction using machine learning: systematic review.

BACKGROUND AND AIMS: Accurate prognostication of stroke may help in appropriate therapy and rehabilitation planning. In the past few years, several machine learning (ML) algorithms were applied for prediction of stroke outcomes. We aimed to examine the performance of machine learning-based models for the prediction of mortality after stroke, as well as to identify the most prominent factors for mortality. MATERIALS AND METHODS: We searched MEDLINE/PubMed and Web of Science databases for original publications on machine learning applications in stroke mortality prediction, published between January 1, 2011, and October 27, 2022. Risk of bias and applicability were evaluated using the tailored QUADAS-2 tool. RESULTS: Of the 1015 studies retrieved, 28 studies were included. Twenty-Five studies were retrospective. The ML models demonstrated a favorable range of AUC for mortality prediction (0.67-0.98). In most of the articles, the models were applied for short-term post stroke mortality. The number of explanatory features used in the models to predict mortality ranged from 5 to 200, with substantial overlap in the variables included. Age, high BMI and high NIHSS score were identified as important predictors for mortality. Almost all studies had a high risk of bias in at least one category and concerns regarding applicability. CONCLUSION: Using machine learning, data available at the time of admission may aid in stroke mortality prediction. Notwithstanding, current research is based on few preliminary works with high risk of bias and high heterogeneity. Thus, future prospective, multicenter studies with standardized reports are crucial to firmly establish the usefulness of the algorithms in stroke prognostication.