Improve Patient Outcomes Research Articles

Improving Patient Outcomes Using Measures to Increase Discharge Rates to Home

Improving Patient Outcomes Using Measures to Increase Discharge Rates to Home

Predictive Factors of Cloxacillin Susceptibility in Primary Bacterial Spinal Infection.

Prognostic study. The objective of this study is to identify predictive factors for cloxacillin susceptibility in spinal infections. A retrospective analysis was conducted using data from January 1, 1997, to December 31, 2021. The study included patients presenting with back pain and either a positive bacterial culture from the spine or radiological evidence of spinal infection (spondylodiscitis and/or epidural abscess) along with positive bacterial blood culture. Among 171 patients (127 males, 44 females), 53.2% had Staphylococcus isolates, with 40.4% showing cloxacillin resistance. Lower globulin levels (<33.5g/L), recent hospitalization within 90days, and residence in an old age home predicted gram-positive bacteria with cloxacillin resistance and gram-negative bacteria as causative organisms (P<.05). The 30-day and 1-year all-cause mortality rates were 0% and 8.2%, respectively. Higher red cell distribution width (RDW >16.1%) and Charlson comorbidity index (CCI) scores predicted 1-year all-cause mortality (P<.05). Intensive care unit admission was required for 9.9% of patients. This study identified predictive factors for spinal infection by gram-positive bacteria with cloxacillin resistance and gram-negative bacteria. Patients with lower globulin levels (<33.5g/L), recent hospitalization within 90days, or residency in an old age home upon admission should avoid standalone cloxacillin therapy and consider antibiotics with gram-negative coverage. Higher RDW (>16.1%) and CCI scores were associated with increased 1-year all-cause mortality. These findings contribute to treatment decision-making and improving patient outcomes in spinal infections.

Flow cytometry for meningeal infiltration in B acute lymphoblastic leukemia in a low middle income country.

Meningeal infiltration in children with B acute lymphoblastic leukemia is one of the most serious complications. Timely diagnosis not only significantly enhances treatment efficacy but also leads to improve patient outcome and reduce risk of relapse. This is particularly crucial in low to middle income countries facing health constraints, where optimizing resources is essential. Conventional cytology (CC) study of cerebrospinal fluid (CSF) is considered in different countries to be the Gold-standard despite its low sensitivity (< 50%). The study of CSF by multiparametric flow cytometry (MFC) appears to be an alternative. The aim of our study was to assess MFC analytical performance compared with CC. Our cross sectional study was conducted over a six-month period in the biological hematology department. CSF samples underwent analysis for the presence of blasts using both CC and MFC. Cytological slides of the CSF were prepared by cytocentrifugation in a Shandon Cytospin 4™. Flow cytometric analysis was performed on the BD FACSLyric™ flow cytometer. All statistical analyses were performed using SPSS version 21.0 (SPSS Inc.). Agreement between the two methods was made using the Kappa index and χ2 test. This study was approved by the local ethics committee. Sixty CSF samples from 39 children with B acute lymphoblastic leukemia were analyzed. Meningeal infiltration was detected respectively in 20% of cases by MFC and 5% of cases by CC, with a significant difference p = 0.006. Comparing the two methods, the Kappa coefficient was 0.35, indicating weak agreement between the two methods. Moreover, MFC positivity was higher even for hypocellular samples. Of the 51 hypocellular samples, eight were positive by MFC while they were negative by CC. MFC shows better sensitivity while retaining good specificity for the detection of meningeal involvement. MFC could therefore be a complementary method to CC for detecting blast cells in the central nervous system.

Use of implementation strategies to promote the uptake of knee osteoarthritis practice guidelines and improve patient outcomes: A Systematic Review.

Translation of knee osteoarthritis (KOA) clinical practice guidelines (CPGs) to practice remains suboptimal. The primary purpose of this systematic review was to describe the use of implementation strategies to promote KOA CPG recommended care. MEDLINE (via PubMed), Embase, CINAHL, and Web of Science were searched from inception to February 23, 2023, and subsequently updated and expanded on January 16, 2024. Implementation strategies were mapped per the Expert Recommendations for Implementing Change taxonomy. Risk of bias (RoB) was assessed using the Cochrane Effective Practice and Organization of Care criteria. The review was registered prospectively (CRD42023402383). Nineteen studies were included in the final review. All (100% (n=4) studies that included the domains of provide interactive assistance, train and educate stakeholders (89%(n=18)), engage consumers (87%(n=15)) and support clinicians (79%(n=14)) reported change to provider adherence. Studies that reported a change to disability included train and educate stakeholders, engage consumers and adapt and tailor to context. Studies that used train and educate stakeholders, engage consumers, and support clinicians reported a change in pain and quality of life. Most studies had a low to moderate RoB. Implementation strategies have the potential to impact clinician uptake of CPG's and patient reported outcomes (PROs). The implementation context, using an active learning strategy with a patient partner, restructuring funding models, and integrating taxonomies to tailor multifaceted strategies should be prioritized. Further experimental research is recommended to determine which implementation strategies are most effective.

Developing predictive models for HIV Drug resistance: A genomic and AI approach

 This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With the increasing prevalence of drug-resistant strains of HIV, there is a critical need for innovative strategies to predict and manage resistance mutations, thereby optimizing treatment outcomes and prolonging the efficacy of antiretroviral therapy (ART). Drawing on advances in genomics and AI, this study outlines a conceptual framework for the development of predictive models that can identify potential drug-resistance mutations in HIV genomes and inform clinical decision-making. The proposed framework integrates genomic data from HIV-infected individuals with AI algorithms capable of learning complex patterns within the data. By analyzing genomic sequences obtained from HIV-positive patients, the models aim to identify genetic variations associated with drug resistance, predict the likelihood of resistance development, and guide the selection of appropriate treatment regimens. This approach holds promise for personalized medicine in HIV care, enabling clinicians to tailor therapy based on an individual's genetic profile and risk of resistance. Key components of the conceptual framework include data preprocessing to extract relevant genomic features, model training using machine learning techniques such as deep learning and ensemble methods, and validation of predictive performance through cross-validation and independent testing. Furthermore, the integration of clinical data, such as treatment history and viral load measurements, enhances the predictive accuracy of the models and provides valuable insights into treatment response dynamics.The development of predictive models for HIV drug resistance represents a paradigm shift in HIV care, offering a proactive approach to treatment management and surveillance. By leveraging genomic and AI technologies, healthcare providers can anticipate and address emerging resistance mutations before they compromise treatment efficacy. Ultimately, the implementation of predictive models holds the potential to improve patient outcomes, reduce the transmission of drug-resistant HIV strains, and advance the global fight against HIV/AIDS. Keywords:  Developing, Predictive Models, HIV Drug Resistance, Genomic, AI Approach.

Multidimensional biomarker approach integrating tumor markers, inflammatory indicators, and disease activity indicators may improve prediction of rheumatoid arthritis-associated interstitial lung disease.

Rheumatoid arthritis (RA) often leads to interstitial lung disease (ILD), significantly affecting patient outcomes. This study explored the diagnostic accuracy of a multi-biomarker approach to offer a more efficient and accessible diagnostic strategy for RA-associated ILD (RA-ILD). Patients diagnosed with RA, with or without ILD, at Beijing Tiantan Hospital from October 2019 to October 2023 were analyzed. A total of 125 RA patients were included, with 76 diagnosed with RA-ILD. The study focused on three categories of indicators: tumor markers, inflammatory indicators, and disease activity measures. The heatmap correlation analysis was employed to analyze the correlation among these indicators. Logistic regression was used to determine odds ratios (OR) for indicators linked to RA-ILD risk. Receiver-operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic potential of these indicators for RA-ILD. The results of logistic regression analysis showed that tumor markers (carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen 125 (CA125), and cytokeratin 19 fragment (CYFRA21-1)), as well as inflammatory indicators (neutrophil, neutrophil-to-lymphocyte ratio (NLR), platelet, C-reactive protein (CRP)) and disease activity measures (disease activity score-28-CRP (DAS28-CRP), rheumatoid factor (RF), and anti-cyclic peptide containing citrulline (anti-CCP)), were significantly associated with RA-ILD. The correlation coefficients among these indicators were relatively low. Notably, the combination indicator 4, which integrated the aforementioned three categories of biomarkers, demonstrated improved diagnostic accuracy with an AUC of 0.857. The study demonstrated that combining tumor markers, inflammatory indicators, and disease activity measures significantly enhanced the prediction of RA-ILD. Key Points • Multidimensional strategy: Integrated tumor markers, inflammatory indicators, and disease activity measures to enhance early detection of rheumatoid arthritis-associated interstitial lung disease (RA-ILD). • Diagnostic accuracy: Employed heatmap correlation and logistic regression, identifying significant associations and improving diagnostic accuracy with a multidimensional biomarker combination. • Superior performance: The combined multidimensional biomarker strategy demonstrated higher diagnostic precision compared to individual or dual-category indicators. • Clinical relevance: Offers a promising, accessible approach for early detection of RA-ILD in clinical settings, potentially improving patient outcomes.

Factors Associated With Job Fatigue In Outpatient Nurses In Hospital X

Work fatigue is a prevalent issue among public service workers, particularly nurses, impacting their performance and patient care. This study focuses on outpatient nurses at Hospital X in South Jakarta, aiming to identify factors associated with job fatigue. Using a quantitative cross-sectional design, data was collected from 100 outpatient nurses in 2023. Univariate and bivariate analyses, including the chi-square test, were conducted. Results revealed that 68.9% of nurses reported moderate fatigue, with significant associations found between high fatigue complaints and workload (p-value 0.000) and age (p-value 0.026). These findings underscore the need for Hospital X to address workload issues by considering additional personnel to mitigate fatigue among nurses. Such measures can enhance service quality and potentially improve nurse well-being, thereby benefiting patient care. Furthermore, implications suggest the importance of empathy training and holistic awareness of biopsychosocial components in addressing nurse fatigue and improving patient outcomes.

Postpartum length of hospital stay among obstetric patients in Ibadan, Nigeria

BackgroundPostpartum Length of hospital stay (PLOHS) is an essential indicator of the quality of maternal and perinatal healthcare services. Identifying the factors associated with PLOHS will inform targeted interventions to reduce unnecessary hospitalisations and improve patient outcomes after childbirth. Therefore, we assessed the length of hospital stay after birth and the associated factors in Ibadan, Nigeria.MethodsWe used the Ibadan Pregnancy Cohort Study (IbPCS) data, and examined the 1057 women who had information on PLOHS the mode of delivery [spontaneous vagina delivery (SVD) or caesarean section (C/S)]. The outcome variable was PLOHS, which was described as the time interval between the delivery of the infant and discharge from the health facility. PLOHS was prolonged if > 24 h for SVD and > 96 h for C/S, but normal if otherwise. Data were analysed using descriptive statistics, a chi-square test, and modified Poisson regression. The prevalence-risk ratio (PR) and 95% confidence interval (CI) are presented at the 5% significance level.ResultsThe mean maternal age was (30.0 ± 5.2) years. Overall, the mean PLOHS for the study population was 2.6 (95% CI: 2.4–2.7) days. The average PLOHS for women who had vaginal deliveries was 1.7 (95%CI: 1.5–1.9) days, whereas those who had caesarean deliveries had an average LOHS of 4.4 (95%CI: 4.1–4.6) days. About a third had prolonged PLOHS: SVD 229 (32.1%) and C/S 108 (31.5%). Factors associated with prolonged PLOHS with SVD, were high income (aPR = 1.77; CI: 1.13, 2.79), frequent ANC visits (> 4) (aPR = 2.26; CI: 1.32, 3.87), and antenatal admission: (aPR = 1.88; CI: 1.15, 3.07). For C/S: maternal age > 35 years (aPR = 1.59; CI: 1.02, 2.47) and hypertensive disease in pregnancy (aPR = 0.61 ; CI: 0.38, 0.99) were associated with prolonged PLOHS.ConclusionThe prolonged postpartum length of hospital stay was common among our study participants occurring in about a third of the women irrespective of the mode of delivery. Maternal income, advanced maternal age, ANC related issues were predisposing factors for prolonged LOHS. Further research is required to examine providers’ perspectives on PLOHS among obstetric patients in our setting.

Evaluating Deep Learning Techniques for Detecting Aneurysmal Subarachnoid Hemorrhage: A Comparative Analysis of Convolutional Neural Network and Transfer Learning Models.

Machine learning (ML) and deep learning (DL) techniques offer a promising multidisciplinary solution for subarachnoid hemorrhage (SAH) detection. The novel transfer learning approach mitigates the time constraints associated with the traditional techniques and demonstrates a superior performance. This study aims to evaluate the effectiveness of convolutional neural networks (CNNs) and CNN-based transfer learning models in differentiating between aneurysmal subarachnoid hemorrhage (aSAH) and nonaneurysmal subarachnoid hemorrhage (naSAH). Data from Istanbul Ümraniye Training and Research Hospital, which included 15,600 DICOM images from 123 patients with aSAH and 7,793 images from 80 patients with naSAH, were used. The study employed four models: Inception-V3, EfficientNetB4, single-layer CNN, and three-layer CNN. Transfer learning models were customized by modifying the last three layers and using the Adam optimizer. The models were trained on Google Collaboratory and evaluated based on metrics such as F-score, precision, recall, and accuracy. EfficientNetB4 demonstrated the highest accuracy (99.92%), with a better F-score (99.82%), recall (99.92%), and precision (99.90%) than the other methods. The single- and three-layer CNNs and the transfer learning models produced comparable results. No overfitting was observed, and robust models were developed. CNN-based transfer learning models can accurately diagnose the etiology of SAH from CT images and is a valuable tool for clinicians. This approach could reduce the need for invasive procedures such as digital subtraction angiography, leading to more efficient medical resource utilization and improved patient outcomes.

Living with a Smart Knee

After years of wear and tear, many individuals may experience chronic knee pain and require total knee replacement (TKR) to maintain quality of life. However, this approach is limited by a risk of chronic pain after knee replacement. The Persona IQ Smart Knee, an advanced option for all ages designed to improve recovery, may help to overcome this challenge.  Despite proven safety and efficacy, factors such as neighbourhood and physical environment or access to healthcare may limit accessibility. Contraindications and other limitations associated with the failure of electronic components may further affect use. However, emerging research is focused on optimizing storage and device size, helping to address potential limitations and promote improved patient outcomes. 

The Approach to the Management of a Child with Chronic Abdominal Pain

Abstract Purpose of Review Abdominal pain occurs frequently in children; chronic abdominal pain affects about 15% of children. There is always an initial concern for serious organic medical conditions; these can be eliminated with careful history and physical examination, especially looking for “red flags.” These children are often subjected to numerous invasive and noninvasive tests; excessive testing should be avoided. There is no evidence extensive testing improves patient outcome or satisfaction. Recent guidelines recommend celiac serology and fecal calprotectin/lactoferrin as the optimal screening tests. Anxiety is often a co-morbidity; the continued testing and lack of explanation exacerbate the symptoms and cause more dysfunction. Recent Findings Ongoing research suggests chronic abdominal pain is a complex interaction of genetics, environmental factors including diet, changes in the microbiome, previous life events, and stresses. The gut-brain axis is now more accurately described as the microbiome-gut-brain axis. Many disturbances have been reported but it remains unclear which are causative versus reactive. Therapeutic interventions have targeted one or more of the components but rarely in a coordinated manner. A positive diagnosis and explanation of pathophysiology are crucial first steps. A holistic approach that focuses on restoration of functioning and well-being is the best approach. A non-pharmacologic approach is the favored initial therapy; many children improve with counseling and assurance that there are no serious organic disorders. A trusting relationship with child and family is an integral part of the treatment plan. Summary Pediatric chronic abdominal pain is commonly encountered in practice. Serious conditions can be eliminated by determining whether any of the so-called red flags are present and judicious testing. High quality evidence is lacking for many proposed treatments. Data interpretation is confounded by a high placebo response rate, even when the placebo is unblinded. The current best evidence is for non-pharmacologic treatments including cognitive behavioral therapy and hypnosis. Neuromodulation is a new, promising intervention.

Antibody-drug conjugates in solid tumors; new strategy for cancer therapy.

Antibody-drug conjugates (ADCs) have emerged as a novel class of anticancer treatment. ADCs are composed of three parts: a monoclonal antibody, a linker and a payload. A monoclonal antibody binds to the specific antigen present at the cancer cells, allowing selective delivery of the cytotoxic agents to the tumor site. Several ADCs are approved by the US Food and Drug Administration for the treatment of hematologic cancers and solid tumors with clinically meaningful survival benefit. However, the development of ADCs faces a lot of challenges and there is a need to get better understanding of ADCs in order to improve patient outcomes. Here, we briefly discuss the structure and mechanism of ADCs, as well as the clinical data of current approved ADCs in solid tumors.

Redox-Modified Nanostructured Electrochemical Surfaces for Continuous Glucose Monitoring in Complex Biological Fluids

Continuous glucose monitoring is valuable for people with diabetes but faces limitations due to enzyme–electrode interactions and biofouling from biological samples that reduce sensor sensitivity and the monitoring performance. We created an enzyme-based electrochemical system with a unique nanocomposite coating that incorporates the redox molecule, aminoferrocene (NH2-Fc). This coating enhances stability via electroactivity and reduces nonspecific binding, as demonstrated through cyclic voltammetry. Our approach enables real-time glucose detection via chronoamperometry with a calculated linear range of 0.5 to 20 mM and a 1 mM detection limit. Validated with plasma and saliva, this platform shows promise for robust metabolite detection in clinical and research contexts. This versatile platform can be applied to accurately monitor a wide range of metabolites in various biological matrices, improving patient outcomes.

Integrating artificial intelligence techniques for advancements in colorectal cancer management: navigating past and predicting future direction.

Artificial Intelligence (AI) in the last few years has emerged as a valuable tool in managing colorectal cancer, revolutionizing its management at different stages. In early detection and diagnosis, AI leverages its prowess in imaging analysis, scrutinizing CT scans, MRI, and colonoscopy views to identify polyps and tumors. This ability enables timely and accurate diagnoses, initiating treatment at earlier stages. AI has helped in personalized treatment planning because of its ability to integrate diverse patient data, including tumor characteristics, medical history, and genetic information. Integrating AI into clinical decision support systems guarantees evidence-based treatment strategy suggestions in multidisciplinary clinical settings, thus improving patient outcomes. This narrative review explores the multifaceted role of AI, spanning early detection of colorectal cancer, personalized treatment planning, polyp detection, lymph node evaluation, cancer staging, robotic colorectal surgery, and training of colorectal surgeons.

A Review on Pharmacological Advancement of Ellagic Acid

Background: In recent years, ellagic acid has emerged as a focal point in pharmacological research, showcasing promising developments and potential therapeutic applications as a competitive inhibitor of Src homology phosphotyrosyl phosphatase 2 for the treatment of cancer, as a shielding impact that stimulates sirtuin 6 (SIRT 6), against nephrotoxicity induced by cisplatin, as adjuvant treatment in sickle cell anemia, as antiaging and to avoid mild cognitive impairment. Purpose: This review provides a concise overview of the latest advancements in ellagic acid research, highlighting novel pharmacological findings and emerging trends. Methods: A comprehensive search of relevant literature was conducted to gather information on the pharmacological properties and therapeutic applications of ellagic acid. Studies investigating its antioxidant properties, anti-inflammatory effects, molecular interactions, and therapeutic implications were included in the analysis. Results: The review summarizes the multifaceted pharmacological landscape of ellagic acid, encompassing its antioxidant properties, anti-inflammatory effects, and potential therapeutic applications. It sheds light on its evolving role in modern medicine and underscores its significance as a promising avenue for future pharmaceutical exploration. Conclusion: The recent strides in ellagic acid development highlight its potential as a valuable therapeutic agent in various health conditions. Further research into its molecular interactions and clinical applications is warranted to fully harness its therapeutic potential and improve patient outcomes.

Opportunities for Continuing Education by Hematology Societies Available to International Trainees.

Continuing education in hematology is a key for stimulating the development around the world and improving patient outcomes. However, access to training and education is not equally distributed worldwide, and disparities in hematology exist for under-represented groups such as trainees living in low- and middle-income countries (LMICs). To identify and review the different educational and career development opportunities offered by hematology-focused international academic societies directed at healthcare professionals in this field. We conducted an online search to screen the official websites of international hematology societies and extracted data regarding continuing education opportunities in hematology. Twenty hematology societies were identified with 850 continuing medical education opportunities extracted and reviewed. We recorded 55 grants and funding opportunities from 13 societies. More than half required a membership to apply, 9.1% were available globally, and 12.7% were designed for persons living in LMICs. The current state of continuing education in hematology offers numerous opportunities for healthcare trainees. However, disparities persist for LMICs.

A Systematic Review of Herbal Interventions for the Management of Cardiovascular Diseases.

Cardiovascular diseases represent a significant global health burden, necessitating diverse approaches for effective management. Herbal interventions have gained attention as potential adjuncts or alternatives to conventional therapies due to their perceived safety and therapeutic potential. This structured abstract provides a comprehensive review of herbal interventions for the management of CVDs, summarising key findings, mechanisms of action, and clinical implications. This systematic review aims to evaluate the impact of various herbal interventions employed for managing cardiovascular diseases. We conducted an extensive literature search across electronic databases, including PubMed, Scopus, and Web of Science, from inception to 2022. Studies were included if they investigated the use of herbal remedies for preventing or treating CVDs. Data extraction and synthesis focused on botanical sources, active compounds, mechanisms of action, and clinical outcomes. Numerous herbal interventions have demonstrated promising cardiovascular benefits. A number of medicinal herbs well identified to treat CVD are Moringaoleifera, Ginseng, Ginkgo biloba, Celosia argentea, Gongronematrifolium, Gynostemmapentaphyllum, Bombaxceiba, Gentianalutea, Allium sativum, Crataegusspp, Curcuma longa, Camellia sinensis, and Zingiberofficinale. Mechanistic insights reveal that herbal interventions often target multiple pathways involved in CVD pathogenesis. These mechanisms encompass anti-inflammatory, antioxidant, anti-thrombotic, anti-hypertensive, and lipid-lowering effects. Additionally, some herbs enhance endothelial function, promote nitric oxide production, and exert vasodilatory effects, contributing to improved cardiovascular health. Clinical studies have provided evidence of the efficacy of certain herbal interventions in reducing CVD risk factors and improving patient outcomes. However, more rigorous, large-scale clinical trials are needed to establish their long-term safety and effectiveness. It is crucial to consider potential herb-drug interactions and standardise dosages for reliable therapeutic outcomes. This comprehensive review highlights the potential of herbal interventions as valuable adjuncts or alternatives for managing cardiovascular diseases. Herbal remedies offer diverse mechanisms of action, targeting key CVD risk factors and pathways. While promising, their clinical utility warrants further investigation through well-designed trials to establish their safety and efficacy, paving the way for integrated approaches to cardiovascular disease management. Healthcare providers and patients should engage in informed discussions about the use of herbal interventions alongside conventional therapies in the context of CVD prevention and treatment.

Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review.

This narrative review explores the transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological Complete Response (pCR) in Breast Cancer (BC) patients undergoing Neo-Adjuvant Chemotherapy (NACT). Summarizing recent research findings underscores the significant strides made in the accurate assessment of pCR using AI, including deep learning and radiomics. Such AI-driven models offer promise in optimizing clinical decisions, personalizing treatment strategies, and potentially reducing the burden of unnecessary treatments, thereby improving patient outcomes. Furthermore, the review acknowledges the potential of AI to address healthcare disparities in Low- and Middle-Income Countries (LMICs), where accessible and scalable AI solutions may enhance BC management. Collaboration and international efforts are essential to fully unlock the potential of AI in BC care, offering hope for a more equitable and effective approach to treatment worldwide.

Severe morbidity and hospital-based mortality from Rift Valley fever disease between November 2017 and March 2020 among humans in Uganda

BackgroundRift Valley fever (RVF) is a zoonotic viral disease of increasing intensity among humans in Africa and the Arabian Peninsula. In Uganda, cases reported prior to 2016 were mild or not fully documented. We report in this paper on the severe morbidity and hospital-based mortality of human cases in Uganda.MethodsBetween November 2017 and March 2020 human cases reported to the Uganda Virus Research Institute (UVRI) were confirmed by polymerase chain reaction (PCR). Ethical and regulatory approvals were obtained to enrol survivors into a one-year follow-up study. Data were collected on socio-demographics, medical history, laboratory tests, potential risk factors, and analysed using Stata software.ResultsOverall, 40 cases were confirmed with acute RVF during this period. Cases were not geographically clustered and nearly all were male (39/40; 98%), median age 32 (range 11–63). The median definitive diagnosis time was 7 days and a delay of three days between presumptive and definitive diagnosis. Most patients (31/40; 78%) presented with fever and bleeding at case detection. Twenty-eight (70%) cases were hospitalised, out of whom 18 (64%) died. Mortality was highest among admissions in regional referral (11/16; 69%) and district (4/5; 80%) hospitals, hospitalized patients with bleeding at case detection (17/27; 63%), and patients older than 44 years (9/9; 100%). Survivors mostly manifested a mild gastro-intestinal syndrome with nausea (83%), anorexia (75%), vomiting (75%), abdominal pain (50%), and diarrhoea (42%), and prolonged symptoms of severe disease including jaundice (67%), visual difficulties (67%), epistaxis (50%), haemoptysis (42%), and dysentery (25%). Symptom duration varied between two to 120 days.ConclusionRVF is associated with high hospital-based mortality, severe and prolonged morbidity among humans that present to the health care system and are confirmed by PCR. One-health composite interventions should be developed to improve environmental and livestock surveillance, prevent infections, promptly detect outbreaks, and improve patient outcomes.

Estimates and Predictors of Mortality, Stroke Recurrence, and Functional Dependency 1-Year after Ischemic Stroke: A Prospective Multicenter Longitudinal Cohort Study in Central Norway

Background. Stroke incidence and mortality have drastically decreased in high-income countries in the past twenty years. In this study, we provide updated estimates on mortality, recurrent stroke, and functional dependency among patients with first-ever ischemic stroke and assess predictors associated with poor outcomes with a focus on age, vascular factors, stroke severity, function, and comorbidity burden. Methods. MIDNOR STROKE is a multicenter prospective longitudinal study including patients with first-ever ischemic stroke admitted to stroke units in Central Norway during 2015-2017. Data on survival, stroke recurrence, and functional dependency were collected during hospital stay and follow-up. Multivariable Cox proportional hazard models and logistic regression models were used to analyze predictors of mortality, stroke recurrence, and functional dependency. Results. A total of 794 participants were included in the study. After a year, 7.6% of the participants had died, 5.8% had a recurrent stroke, and 13.6% experienced functional deterioration to dependency. Multivariable analysis revealed that age (HR: 1.07, 96% CI: 1.03, 1.10), stroke severity (HR: 1.10, 95% CI: 1.07, 1.13), comorbidity burden (low: HR: 4.05, 95% CI: 1.48, 11.10; moderate: HR: 5.44, 95% CI: 2.06, 14.40; and high: 7.72, 95% CI: 2.85, 21.00), and coronary artery disease (HR: 2.40, 95% CI: 1.32, 4.38) predicted all-cause death. Statin therapy predicted improved survival (HR: 0.39, 95% CI: 0.21, 0.75). High age (HR: 1.09, 95% CI: 1.05, 1.14) and increased stroke severity (OR: 1.26, 95% CI: 1.17, 1.38) predicted elevated risk of functional dependency at one year. Conclusions. In this study, we have demonstrated that 1-year survival following first-ever ischemic stroke was high compared to previous reports and that statin therapy predicted improved survival. The risk of recurrent stroke after one year was found to be low compared to previous studies. Approximately 14% of stroke survivors who were initially functionally independent experienced deterioration to functional dependency. In addition to older age and stroke severity, increased comorbidity burden and a history of coronary artery disease predicted poor stroke prognosis. Interventions aimed at reducing stroke severity may improve patient outcomes. Furthermore, prevention efforts targeting conditions such as CAD and reducing overall comorbidity burden in stroke patients may favorably improve survival. This trial is registered with NCT03962127.