Artificial intelligence approaches for early detection of neurocognitive disorders among older adults.

Introduction: Dementia is one of the major global health issues among the aging population, characterized clinically by a progressive decline in higher cognitive functions. This paper aims to apply various artificial intelligence (AI) approaches to detect patients with mild cognitive impairment (MCI) or dementia accurately. Methods: Quantitative research was conducted to address the objective of this study using randomly selected 343 Saudi patients. The Chi-square test was conducted to determine the association of the patient's cognitive function with various features, including demographical and medical history. Two widely used AI algorithms, logistic regression and support vector machine (SVM), were used for detecting cognitive decline. This study also assessed patients' cognitive function based on gender and developed the predicting models for males and females separately. Results: Fifty four percent of patients have normal cognitive function, 34% have MCI, and 12% have dementia. The prediction accuracies for all the developed models are greater than 71%, indicating good prediction capability. However, the developed SVM models performed the best, with an accuracy of 93.3% for all patients, 94.4% for males only, and 95.5% for females only. The top 10 significant predictors based on the developed SVM model are education, bedtime, taking pills for chronic pain, diabetes, stroke, gender, chronic pains, coronary artery diseases, and wake-up time. Conclusion: The results of this study emphasize the higher accuracy and reliability of the proposed methods in cognitive decline prediction that health practitioners can use for the early detection of dementia. This research can also stipulate substantial direction and supportive intuitions for scholars to enhance their understanding of crucial research, emerging trends, and new developments in future cognitive decline studies.

Clinical and cost-effectiveness analysis of carbapenems versus ciprofloxacin in the treatment of urinary tract infections due to extended spectrum ß-lactamase-producing Enterobacterales.

BACKGROUND: Urinary tract infections (UTIs) are the most common infectious diseases in both hospital and community settings. The management of UTIs caused by extended spectrum ß-lactamase-producing Enterobacterales (ESBL-PE) has become more complicated given the limited options of effective antibiotic agents besides the amplification of total healthcare costs. METHODS: This was a retrospective cohort study conducted among hospitalized patients between January 2018 and March 2020. Adults diagnosed with UTI due to ESBL-PE with at least 2 days of admission were included. Excluded were patients with concomitant infection, polymicrobial UTI, and pregnant women. The primary endpoints were clinical cure and incremental cost-effectiveness ratio (ICER). Clinical cure, hospitalization, and antibiotics costs were considered to evaluate ICER. The secondary endpoints included microbiological eradication, length of stay (LOS), and 30-day readmission. RESULTS: Of 102 patients, 89 received a carbapenem and 13 received ciprofloxacin. The patients had similar baseline characteristics, including history of hospitalization and UTI within 3 months. No difference was observed in clinical cure rates (86.5% vs. 100%, P = 0.159), microbiological eradication (93.1% vs. 100%, P = 0.639), median LOS (6 days in both groups, P = 0.773), and 30-day readmission rates (41.6% vs. 46.2%, P = 0.755). The ICER of carbapenem to ciprofloxacin was - 7,626.05, indicating that ciprofloxacin was more cost-effective compared with carbapenems. CONCLUSION: Ciprofloxacin had comparable cure rates with carbapenems, lower risk of 30-day readmission, and was more cost-effective for the treatment of UTI due to ESBL-PE. Therefore, it should be considered as a valuable option if ESBL-PE showed susceptibility to it.

The impact of pharmacists-led medicines reconciliation on healthcare outcomes in secondary care: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Adverse drug events (ADEs) impose a major clinical and cost burden on acute hospital services. It has been reported that medicines reconciliation provided by pharmacists is effective in minimizing the chances of hospital admissions related to adverse drug events. OBJECTIVE: To update the previous assessment of pharmacist-led medication reconciliation by restricting the review to randomized controlled trials (RCTs) only. METHODS: Six major online databases were sifted up to 30 December 2016, without inception date (Embase, Medline Ovid, PubMed, BioMed Central, Web of Science and Scopus) to assess the effect of pharmacist-led interventions on medication discrepancies, preventable adverse drug events, potential adverse drug events and healthcare utilization. The Cochrane tool was applied to evaluate the chances of bias. Meta-analysis was carried out using a random effects model. RESULTS: From 720 articles identified on initial searching, 18 RCTs (6,038 patients) were included. The quality of the included studies was variable. Pharmacists-led interventions led to an important decrease in favour of the intervention group, with a pooled risk ratio of 42% RR 0.58 (95% CI 0.49 to 0.67) P<0.00001 in medication discrepancy. Reductions in healthcare utilization by 22% RR 0.78 (95% CI 0.61 to 1.00) P = 0.05, potential ADEs by10% RR 0.90 (95% CI 0.78 to 1.03) P = 0.65 and preventable ADEs by 27% RR 0.73 (0.22 to 2.40) P = 0.60 were not considerable. CONCLUSION: Pharmacists-led interventions were effective in reducing medication discrepancies. However, these interventions did not lead to a significant reduction in potential and preventable ADEs and healthcare utilization.