Coronavirus disease 2019 (COVID-19) is a devastating disease for the elderly population. The case fatality rate increases with age and is extremely high in individuals aged 80 years and older.^1,2 Nursing home residents are a high-risk population because of their advanced age, complex medical problems, and disabilities. Nursing homes are at risk for outbreaks of respiratory infections because of the gathering of a large number of residents, health care professionals, and visitors in the facility and the high prevalence of cognitive and behavioral symptoms among residents, making it difficult to appropriately apply barriers to infection.³ In addition, most nursing homes are poorly prepared to implement infection control policies owing to shortage of staff and equipment and relatively low level of staff training. All these factors help to explain why COVID-19 is a major threat to nursing homes.⁴

Background: Dementia is a major issue worldwide, and considerable efforts were made to design therapeutic mediation tools and evaluate their benefits on the health of patients. Methods: Design: Multi-center cluster-controlled pilot trial. Settings and participants: Four nursing homes that offered separated access to one conventional sensory garden (CSG) and one enriched garden (EG). The participants were residents with dementia, independent for walking and with no severe dementia or behavioural troubles. Eligible residents were divided into three groups according to the proximity of their room: close to the CSG or EG gardens for the first two groups and further from the gardens for the third (control) group. Interventions: We asked staff members to frequently invite residents to visit the EG or the CSG depending on their group allocation. No invitation to gardens was made to the control group. We installed 12 enrichment modules in the EG that stimulated cognitive, independence and walking/balance functions. Measures: Cognitive function (MMSE), independence for activities of daily living (ADL) and risk of falls (unipodal stance and timed up and go – (TUG)) were assessed at baseline and after 6 months. Results: The 120 participants were 81·0 ± 3·5 years old and comprised of 83 women. Their MMSE score was 17·5 ± 2·9. Patients’ characteristics were not significantly different between the three groups. Among the participants invited to visit the EG group, 6-month changes in MMSE showed improvement compared to other groups (+ 0·93 ± 0·65 vs −0·25 ± 0·71 and −0·24 ± 0·73 in the EG vs CSG and control groups, respectively, P < 0·0001). Changes in ADL, TUG and unipodal stance were significantly improved in the group visiting the EG as compared to other groups, which indicates better functioning. Conclusions: EGs offer a new approach to therapeutic mediation for residents of nursing homes with dementia.

Background and objectives: Cholinesterase inhibitors (ChEIs) have cardiovascular effects in addition to their neurological activity and might alter mortality. We wanted to know if treatment with ChEIs modifies mortality in patients with dementia. Methods: We searched PubMed, EMBASE, Cochrane CENTRAL, ClinicalTrials.gov and ICRTP, from their inception to November 2021, and screened bibliographies of reviews, guidelines and included studies. We included randomized controlled trials (RCTs) and non-randomized controlled studies at lower risk of bias comparing ChEI treatment with placebo or usual treatment, for 6 months or longer, in patients with dementia of any type. Two investigators independently assessed studies for inclusion, assessed their risk of bias and extracted data, using predefined forms. Any discordance between investigators was solved by discussion and consensus. Data on all-cause and cardiovascular mortality, measured as either crude death rates or multivariate adjusted hazard ratios (HR), was pooled using a random-effect model. Information size achieved was assessed using trial sequential analysis (TSA). We followed PRISMA guidelines. Results: 24 studies (12 RCTs, 12 cohorts, mean follow-up 6 to 120 months), cumulating 79 153 patients with Alzheimer’s (13 studies), Parkinson’s (1), vascular (1) or any type (9) dementia, fulfilled inclusion criteria. Pooled all-cause mortality in control patients was 15.1 per 100 person-years. Treatment with ChEIs was associated with lower all-cause mortality (unadjusted RR 0.74, 95%CI 0.66 – 0.84; adjusted HR 0.77, 95%CI 0.70 – 0.84, moderate to high quality evidence). This result was consistent between randomized and non-randomized studies and in several sensitivity analyses. No difference appeared between subgroups by type of dementia, age, individual drug or dementia severity. Less data was available for cardiovascular mortality (3 RCTs, 2 cohorts, 9 182 patients, low to moderate quality evidence), which was also lower in patients treated with ChEIs (unadjusted RR 0.61, 95%CI 0.40 – 0.93, adjusted HR 0.47, 95%CI 0.32 – 0.68). In TSA analysis, results for all-cause mortality were conclusive, but not those for cardiovascular mortality. Discussion: There is moderate to high quality evidence of a consistent association between long-term treatment with ChEIs and a reduction in all-cause mortality in patients with dementia. These findings may influence decisions to prescribe ChEIs in those patients. Trial Registration Information: This systematic review was registered in the PROSPERO international prospective register of systematic reviews with the number CRD42021254458 (11/06/2021).

Background: Frail older people use emergency services extensively, and digital systems that monitor health remotely could be useful in reducing these visits by earlier detection of worsening health conditions. Objective: We aimed to implement a system that produces alerts when the machine learning algorithm identifies a short-term risk for an emergency department (ED) visit and examine health interventions delivered after these alerts and users’ experience. This study highlights the feasibility of the general system and its performance in reducing ED visits. It also evaluates the accuracy of alerts’ prediction. Methods: An uncontrolled multicenter trial was conducted in community-dwelling older adults receiving assistance from home aides (HAs). We implemented an eHealth system that produces an alert for a high risk of ED visits. After each home visit, the HAs completed a questionnaire on participants’ functional status, using a smartphone app, and the information was processed in real time by a previously developed machine learning algorithm that identifies patients at risk of an ED visit within 14 days. In case of risk, the eHealth system alerted a coordinating nurse who could then inform the family carer and the patient’s nurses or general practitioner. The primary outcomes were the rate of ED visits and the number of deaths after alert-triggered health interventions (ATHIs) and users’ experience with the eHealth system; the secondary outcome was the accuracy of the eHealth system in predicting ED visits. Results: We included 206 patients (mean age 85, SD 8 years; 161/206, 78% women) who received aid from 109 HAs, and the mean follow-up period was 10 months. The HAs monitored 2656 visits, which resulted in 405 alerts. Two ED visits were recorded following 131 alerts with an ATHI (2/131, 1.5%), whereas 36 ED visits were recorded following 274 alerts that did not result in an ATHI (36/274, 13.4%), corresponding to an odds ratio of 0.10 (95% IC 0.02-0.43; P<.001). Five patients died during the study. All had alerts, 4 did not have an ATHI and were hospitalized, and 1 had an ATHI (P=.04). In terms of overall usability, the digital system was easy to use for 90% (98/109) of HAs, and response time was acceptable for 89% (98/109) of them. Conclusions: The eHealth system has been successfully implemented, was appreciated by users, and produced relevant alerts. ATHIs were associated with a lower rate of ED visits, suggesting that the eHealth system might be effective in lowering the number of ED visits in this population. Trial Registration: clinicaltrials.gov NCT05221697; https://clinicaltrials.gov/ct2/show/NCT05221697.

To summarize the research literature describing the outcomes of computerized decision support systems (CDSSs) implemented in nursing homes (NHs). Scoping review. Search of relevant articles published in the English language between January 1, 2000, and February 29, 2020, in the Medline database. The quality of the selected studies was assessed according to PRISMA guidelines and the Mixed Method Appraisal Tool. From 1828 articles retrieved, 24 studies were selected for review, among which only 6 were randomized controlled trials. Although clinical outcomes are seldom studied, some studies show that CDSSs have the potential to decrease pressure ulcer incidence and malnutrition prevalence. Improvement of process outcomes such as increased compliance with practice guidelines, better documentation of nursing assessment, improved teamwork and communication, and cost saving, also are reported. Overall, the use of CDSSs in NHs may be effective to improve patient clinical outcomes and health care delivery; however, most of the retrieved studies were observational studies, which significantly weakens the evidence. High-quality studies are needed to investigate CDSS effects and limitations in NHs.