Background The number of medication related hospital admissions and readmissions are increasing over the years due to the ageing population. Medication related hospital admissions and readmissions lead to decreased quality of life and high healthcare costs. Aim of the review To assess what is currently known about medication related hospital admissions, medication related hospital readmissions, their risk factors, and possible interventions which reduce medication related hospital readmissions. Method We searched PubMed for articles about the topic medication related hospital admissions and readmissions. Overall 54 studies were selected for the overview of literature. Results Between the different selected studies there was much heterogeneity in definitions for medication related admission and readmissions, in study population and the way studies were performed. Multiple risk factors are found in the studies for example: polypharmacy, comorbidities, therapy non adherence, cognitive impairment, depending living situation, high risk medications and higher age. Different interventions are studied to reduce the number of medication related readmission, some of these interventions may reduce the readmissions like the participation of a pharmacist, education programmes and transition-of-care interventions and the use of digital assistance in the form of Clinical Decision Support Systems. However the methods and the results of these interventions show heterogeneity in the different researches. Conclusion There is much heterogeneity in incidence and definitions for both medication related hospital admissions and readmissions. Some risk factors are known for medication related admissions and readmissions such as polypharmacy, older age and additional diseases. Known interventions that could possibly lead to a decrease in medication related hospital readmissions are spare being the involvement of a pharmacist, education programs and transition-care interventions the most mentioned ones although controversial results have been reported. More research is needed to gather more information on this topic.
Background/Objectives Nursing home (NH) residents are a vulnerable population, susceptible to respiratory disease outbreaks such as coronavirus disease 2019 (COVID‐19). Poor outcome in COVID‐19 is at least partly attributed to hypercoagulability, resulting in a high incidence of thromboembolic complications. It is unknown whether commonly used antithrombotic therapies may protect the vulnerable NH population with COVID‐19 against mortality. This study aimed to investigate whether the use of oral antithrombotic therapy (OAT) was associated with a lower mortality in NH residents with COVID‐19. Design A retrospective case‐series Setting 14 NH facilities from the NH organization Envida, Maastricht, the Netherlands Participants 101 NH residents with COVID‐19 were enrolled. Measurements The primary outcome was all‐cause mortality. The association between age, sex, comorbidity, OAT, and mortality was assessed using logistic regression analysis. Results Overall mortality was 47.5% in NH residents from 14 NH facilities. Age, comorbidity and medication use were comparable among NH residents who survived and who died. OAT was associated with a lower mortality in NH residents with COVID‐19 in the univariable analysis (OR 0.89 95%CI 0.41‐1.95). However, additional adjustments for sex, age and comorbidity, attenuated this difference. Mortality in males was higher compared with female residents (OR 3.96 (95%CI 1.62‐9.65)). Male residents who died were younger compared to female residents (82.2 (SD 6.3) vs. 89.1 years (SD 6.8), p<.001). Conclusion NH residents in the 14 facilities we studied were severely affected by the COVID‐19 pandemic, with a mortality of 47.5%. Male NH residents with COVID‐19 had worse outcomes than females. We did not find evidence for any protection against mortality by OAT, necessitating further research into strategies to mitigate poor outcome of COVID‐19 in vulnerable NH populations. This article is protected by copyright. All rights reserved.
Introduction Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED). Methods In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Results We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models. Conclusion The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources.
Background: Hip fractures are a major cause of mortality and disability in frail older adults. Therefore, orthogeriatrics has been embraced to improve patient outcomes. With the optimal template of orthogeriatric care still unknown, and to curtail rising healthcare expenditure we implemented a nurse practitioner-led orthogeriatric care program (NPOCP). The objective was to evaluate NPOCP by measuring 3-month and 1-year mortality, compared to usual care (UC). In addition, length of stay (LOS) and location of hospital discharge were reported. Methods: An anonymised data set, of hip fracture patients (n = 300) who presented to Maastricht University Medical Centre, the Netherlands, a level-1 trauma centre, was used. NPOCP was implemented on one of two surgical wards, while the other ward received UC. Patient allocation to these wards was random. Results: In total, 144 patients received NPOCP and 156 received UC. In the NPOCP, 3-month and 1-year mortality rates were 9.0% and 13.9%, compared to 24.4% and 34.0% in the UC group (P < 0.001). The adjusted hazard ratio (aHR) for 3-month (aHR 0.50 [95%CI: 0.26–0.97]) and 1-year mortality (aHR 0.50 [95%CI: 0.29–0.85]) remained lower in NPOCP compared to UC. Median LOS was 9 days [IQR 5–13] in patients receiving UC and 7 days [IQR 5–13] in patients receiving NPOCP (P = 0.08). Thirty-eight (27.5%) patients receiving UC and fifty-seven (40.4%) patients receiving NPOCP were discharged home (P = 0.023). Conclusion: Implementation of NPOCP was associated with significantly reduced mortality in hip fracture patients and may contribute positively to high-quality care and improve outcomes in the frail orthogeriatric population.
IntroductionCoronavirus disease 2019 (COVID-19) has a high burden on the healthcare system and demands information on the outcome early after admission to the emergency department (ED). Previously developed prediction models may assist in triaging patients when allocating healthcare resources. We aimed to assess the value of several prediction models when applied to COVID-19 patients in the ED.MethodsAll consecutive COVID-19 patients who visited the ED of a combined secondary/tertiary care center were included. Prediction models were selected based on their feasibility. The primary outcome was 30-day mortality, secondary outcomes were 14-day mortality, and a composite outcome of 30-day mortality and admission to the medium care unit (MCU) or the intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC).ResultsA total of 403 ED patients were diagnosed with COVID-19. Within 30 days, 95 patients died (23.6%), 14-day mortality was 19.1%. Forty-eight patients (11.9%) were admitted to the MCU, 66 patients (16.4%) to the ICU and 152 patients (37.7%) met the composite endpoint. Eleven models were included: RISE UP score, 4C mortality score, CURB-65, MEWS, REMS, abbMEDS, SOFA, APACHE II, CALL score, ACP index and Host risk factor score. The RISE UP score and 4C mortality score showed a very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84 respectively, 95% CI 0.79-0.88 for both), for 14-day mortality (AUC 0.83, 95% CI: 0.79-0.88, for both) and for the composite outcome (AUC 0.79 and 0.77 respectively, 95% CI 0.75-0.84). The discriminatory performance of the RISE UP score and 4C mortality score was significantly higher compared to that of the other models.ConclusionThe RISE UP score and 4C mortality score have good discriminatory performance in predicting adverse outcome in ED patients with COVID-19. These prediction models can be used to recognize patients at high risk for short-term poor outcome and may assist in guiding clinical decision-making and allocating healthcare resources.
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