To evaluate whether a focused, expert medication management intervention is feasible and potentially effective in preventing anticoagulation-related adverse events for patients transitioning from hospital to home.

Randomised, parallel design.

Medical wards at six hospital sites in southern Ontario, Canada.

Adults 18 years of age or older being discharged to home on an oral anticoagulant (OAC) to be taken for at least 4 weeks.

Clinical pharmacologist-led intervention, including a detailed discharge medication management plan, a circle of care handover and early postdischarge virtual check-up visits to 1 month with 3-month follow-up. The control group received the usual care.

Primary outcomes were study feasibility outcomes (recruitment, retention and cost per patient). Secondary outcomes included adverse anticoagulant safety events composite, quality of transitional care, quality of life, anticoagulant knowledge, satisfaction with care, problems with medications and health resource utilisation.

Extensive periods of restriction of recruitment plus difficulties accessing patients at the time of discharge negatively impacted feasibility, especially cost per patient recruited. Of 845 patients screened, 167 were eligible and 56 were randomised. The mean age (±SD) was 71.2±12.5 years, 42.9% females, with two lost to follow-up. Intervention patients were more likely to rate their ability to manage their OAC as improved (17/27 (63.0%) vs 7/22 (31.8%), OR 3.6 (95% CI 1.1 to 12.0)) and their continuity of care as improved (21/27 (77.8%) vs 2/22 (9.1%), OR 35.0 (95% CI 6.3 to 194.2)). Fewer intervention patients were taking one or more inappropriate medications (7 (22.5%) vs 15 (60%), OR 0.19 (95% CI 0.06 to 0.62)).

This pilot randomised controlled trial suggests that a transitional care intervention at hospital discharge for older adults taking OACs was well received and potentially effective for some surrogate outcomes, but overly costly to proceed to a definitive large trial.

NCT02777047.

Patients with acute abdomen often experience reduced voluntary intake and a hypermetabolic process, leading to a high occurrence of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria have rapidly developed into a principal methodological tool for nutritional diagnosis. Additionally, machine learning is emerging to establish artificial intelligent-enabled diagnostic models, but the accuracy and robustness need to be verified. We aimed to establish an intelligence-enabled malnutrition diagnosis model based on GLIM for patients with acute abdomen.

This study is a single-centre, cross-sectional observational investigation into the prevalence of malnutrition in patients with acute abdomen using the GLIM criteria. Data collection occurs on the day of admission, at 3 and 7 days post-admission, including biochemical analysis, body composition indicators, disease severity scoring, nutritional risk screening, malnutrition diagnosis and nutritional support information. The occurrence rate of malnutrition in patients with acute abdomen is analysed with the GLIM criteria based on the Nutritional Risk Screening 2002 and the Mini Nutritional Assessment Short-Form to investigate the sensitivity and accuracy of the GLIM criteria. After data cleansing and preprocessing, a machine learning approach is employed to establish a predictive model for malnutrition diagnosis in patients with acute abdomen based on the GLIM criteria.

This study has obtained ethical approval from the Ethics Committee of the Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital on 28 November 2022 (Yan-2022–442). The results of this study will be disseminated in peer-reviewed journals, at scientific conferences and directly to study participants.

ChiCTR2200067044.