Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke.

BACKGROUND: Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. METHODS: We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. RESULTS: The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. CONCLUSION: This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.

Intention and motivation to pursue higher education among registered nurses and midwives: a cross-sectional study during COVID-19.

OBJECTIVES: To determine the intention and motivation of nurses and midwives to pursue their higher education considering several factors. METHODS: This is a cross-sectional study were a 16-item online survey was distributed using a convenience sampling method to approximately 12,000 nurses and midwives between May - July 2021. RESULTS: A total of 513 eligible nurses and midwives participated in the study. Most of the respondents were females (76.61%), with a bachelor's degree (72.3%) and well experienced (11-15 years) (35.4%). Most nurses and midwives are highly motivated to pursue higher education (79.14%). The most motivating factor for the respondents is their professional goals (85.2%), While the most dissuading factor was the cost. Regarding COVID-19, about 28% of the respondents said that the COVID-19 pandemic has a negative effect on their desire to go for higher education. CONCLUSIONS: The nurses and midwives were highly motivated to pursue higher education regardless of their demographic status, available resources, and barriers. Furthermore, the eagerness of the nurses and midwives to pursue higher education, which is rooted in their desire to grow in their profession.

Nursing privilege: A concept analysis.

AIM: The study aimed to provide a comprehensive concept analysis of nursing privileges by elucidating its meaning and implications within the healthcare context. DESIGN: A concept analysis paper. METHODS: A comprehensive literature review was conducted from nursing and healthcare databases, professional nursing organizations, and regulatory bodies. Documents reviewed include research studies, policy documents and professional guidelines. The study employed Walker and Avant's eight-step method of concept analysis. This involved identifying the uses of the concept, its underlying attributes and referents, and constructing model, borderline, related and contrary cases. The antecedents, consequences and empirical referents of nursing privileges were also determined. RESULTS: The analysis uncovered vital attributes defining nursing privileges, encompassing professional authority, autonomy, access to resources, information, influence, decision-making power, respect and recognition. Additionally, antecedents and consequences of nursing privilege were identified, spanning development and resource access, as well as professional satisfaction and enhanced patient care. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Dreams: The Mind's Minecraft.

Minecraft is a game known for its limitless potential for creation, allowing players to construct elaborate structures, explore vast landscapes, and encounter a variety of creatures and scenarios, all within a controlled, virtual environment. Similarly, our dreams are constructed by the subconscious mind, using the "blocks" of memories, emotions, and sensory experiences accumulated during waking life. This editorial highlights the intricate relationship between the dream worlds created in sleep and the virtual landscapes we explore in Minecraft, highlighting how both territories are constructed from the building blocks of our subconscious mind. It emphasizes the role of dreams as simulators for real-life events, particularly in mitigating potential risks, much like Minecraft allows players to engage in risk-free exploration and problem-solving within its pixelated universe. In addition, this editorial aims to illuminate the functions of dreams in memory consolidation, emotional processing, and brain development while showcasing the importance of creativity and imagination in enhancing our mental health and understanding of reality.

Stroke in Qatar: a decade of insights from a national registry.

BACKGROUND: Stroke is a major cause of death and disability worldwide and presents a significant burden on healthcare systems. This retrospective study aims to analyze the characteristics and outcomes of stroke patients admitted to Hamad General Hospital (HGH) stroke service in Qatar from January 2014 to July 2022. METHODS: The medical records of 15,859 patients admitted during the study period were analyzed. The data collected included patient demographics, stroke types, admission location, procedures performed, mortality rates, and other clinical characteristics. RESULTS: Of the total cohort, 70.9% were diagnosed with a stroke, and 29.1% were diagnosed with stroke mimics. Of the stroke patients, 85.3% had an ischemic stroke, and 14.7% had a hemorrhagic stroke. Male patients below 65 years old (80.2%) and of South Asian ethnicity (44.6%) were the most affected. The mortality rate was 4.6%, significantly higher for hemorrhagic stroke than ischemic stroke (12.6% vs. 3.2%). Female patients had a higher stroke-related mortality rate than male patients (6.8% vs. 4%). The thrombolysis rate was 9.5%, and the thrombectomy rate was 3.4% of the ischemic stroke cohort. The mean door-to-needle time for thrombolysis was 61.2 minutes, and the mean door-to-groin time for thrombectomy was 170 minutes. Stroke outcomes were good, with 59.3% of patients having favorable outcomes upon discharge (mRS ≤2), which improved to 68.2% 90 days after discharge. CONCLUSION: This study provides valuable insights into stroke characteristics and outcomes in Qatar. The findings suggest that stroke mortality rates are low, and favorable long-term disability outcomes are achievable. However, the study identified a higher stroke-related mortality rate among female patients and areas for improvement in thrombolysis and thrombectomy time.

Machine learning-based prognostication of mortality in stroke patients.

Objectives: Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methods: Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. Results: The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. Conclusions: This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Harnessing the Power of Large Language Models (LLMs) for Electronic Health Records (EHRs) Optimization.

This editorial discusses the potential benefits of integrating large language models (LLMs), such as GPT-4, into electronic health records (EHRs) to optimize patient care, improve clinical decision-making, and promote efficient healthcare management. Artificial intelligence (AI)-driven LLMs can revolutionize healthcare practices by streamlining the data input process, expediting information extraction from unstructured narratives, and facilitating personalized patient communication. However, concerns related to patient privacy, data security, and potential biases must be addressed to ensure equitable healthcare for all. Therefore, we encourage healthcare professionals and researchers to explore innovative solutions that leverage AI capabilities while addressing the challenges associated with privacy and equity.

Nursing in the Artificial Intelligence (AI) Era: Optimizing Staffing for Tomorrow.

Amid the global nursing shortage, artificial intelligence (AI) offers a solution to align patient needs with nursing expertise, ensuring equitable workload distribution. Highlighting the transformative potential of artificial intelligence in healthcare, this editorial underscores its revolutionary impact on nursing staffing. By leveraging AI, we can enhance patient outcomes and operational efficiency and reduce staff burnout. However, challenges like data security and job stability arise. It is pivotal to emphasize its optimal integration, engaging nurses in decision-making, rigorous training, and prioritizing data security. This holistic approach ensures that contemporary healthcare practices benefit from AI's capabilities while upholding core values.

Harnessing Large Language Models in Nursing Care Planning: Opportunities, Challenges, and Ethical Considerations.

The rapid progress in artificial intelligence (AI) and the emergence of large language models (LLMs), like GPT-4, create a unique opportunity to transform nursing care planning. In this editorial, we explore the potential applications of AI in the nursing process, with a focus on patient data assessment and interpretation, communication with patients and families, identifying gaps in care plans, and ongoing professional development. We also examine the ethical concerns and challenges associated with AI integration in healthcare, such as data privacy and security, fairness and bias, accountability and responsibility, and the delicate balance between human-AI collaboration. To implement LLMs responsibly and effectively in nursing care planning, we recommend prioritizing robust data security measures, transparent and unbiased algorithms, clear accountability guidelines, and human-AI collaboration. By addressing these issues, we can improve nursing care planning and ensure the best possible care for patients.

Plasma-Lyte-148 Versus Normal Saline 0.9% in Diabetic Ketoacidosis Management: A Review.

Diabetic ketoacidosis (DKA) is a critical complication of diabetes mellitus characterized by hyperglycemia, ketonemia, circulatory collapse, hypokalemia, and metabolic acidosis. The therapeutic management of DKA includes vigilant fluid resuscitation to address dehydration and electrolyte imbalances and restore hemodynamic stability. The choice of fluid, either isotonic saline or a balanced electrolyte solution like Plasma-Lyte 148 (PL), is pivotal in the clinical outcomes of DKA patients. Recent studies have compared the effectiveness of these fluid solutions in DKA management, focusing on different clinical outcomes such as the resolution of metabolic acidosis, electrolyte imbalances, the incidence of acute kidney injury, and length of hospital stay. This review examines the literature comparing isotonic saline and balanced electrolyte solutions for fluid resuscitation in DKA, analyzing the associated clinical outcomes. Through synthesizing research findings, this review aims to elucidate the efficacy and potential advantages of utilizing PL as an alternative to traditional isotonic saline for fluid resuscitation in treating DKA. This would further facilitate evidence-based decision-making among healthcare professionals and contribute to optimizing DKA management strategies. Understanding the intricacies and implications of fluid resuscitation is crucial, given its profound impact on patient outcomes in DKA management.

A Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of ChatGPT Integration in Nursing Education: A Narrative Review.

Amidst evolving healthcare demands, nursing education plays a pivotal role in preparing future nurses for complex challenges. Traditional approaches, however, must be revised to meet modern healthcare needs. The ChatGPT, an AI-based chatbot, has garnered significant attention due to its ability to personalize learning experiences, enhance virtual clinical simulations, and foster collaborative learning in nursing education. This review aims to thoroughly assess the potential impact of integrating ChatGPT into nursing education. The hypothesis is that valuable insights can be provided for stakeholders through a comprehensive SWOT analysis examining the strengths, weaknesses, opportunities, and threats associated with ChatGPT. This will enable informed decisions about its integration, prioritizing improved learning outcomes. A thorough narrative literature review was undertaken to provide a solid foundation for the SWOT analysis. The materials included scholarly articles and reports, which ensure the study's credibility and allow for a holistic and unbiased assessment. The analysis identified accessibility, consistency, adaptability, cost-effectiveness, and staying up-to-date as crucial factors influencing the strengths, weaknesses, opportunities, and threats associated with ChatGPT integration in nursing education. These themes provided a framework to understand the potential risks and benefits of integrating ChatGPT into nursing education. This review highlights the importance of responsible and effective use of ChatGPT in nursing education and the need for collaboration among educators, policymakers, and AI developers. Addressing the identified challenges and leveraging the strengths of ChatGPT can lead to improved learning outcomes and enriched educational experiences for students. The findings emphasize the importance of responsibly integrating ChatGPT in nursing education, balancing technological advancement with careful consideration of associated risks, to achieve optimal outcomes.

Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning.

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar's stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.

Using artificial intelligence to improve body iron quantification: A scoping review.

This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.

Predicting 90-day prognosis for patients with stroke: a machine learning approach.

Background: Stroke is a significant global health burden and ranks as the second leading cause of death worldwide. Objective: This study aims to develop and evaluate a machine learning-based predictive tool for forecasting the 90-day prognosis of stroke patients after discharge as measured by the modified Rankin Score. Methods: The study utilized data from a large national multiethnic stroke registry comprising 15,859 adult patients diagnosed with ischemic or hemorrhagic stroke. Of these, 7,452 patients satisfied the study's inclusion criteria. Feature selection was performed using the correlation and permutation importance methods. Six classifiers, including Random Forest (RF), Classification and Regression Tree, Linear Discriminant Analysis, Support Vector Machine, and k-Nearest Neighbors, were employed for prediction. Results: The RF model demonstrated superior performance, achieving the highest accuracy (0.823) and excellent discrimination power (AUC 0.893). Notably, stroke type, hospital acquired infections, admission location, and hospital length of stay emerged as the top-ranked predictors. Conclusion: The RF model shows promise in predicting stroke prognosis, enabling personalized care plans and enhanced preventive measures for stroke patients. Prospective validation is essential to assess its real-world clinical performance and ensure successful implementation across diverse healthcare settings.

Rhabdomyolysis in Critically Ill Patients With COVID-19: A Retrospective Study.

INTRODUCTION: The total number of ICU admissions for COVID-19 patients has increased steadily. Based on the research team's clinical observations, many patients developed rhabdomyolysis, but few cases were reported in the literature. This study explores the incidence of rhabdomyolysis and its outcomes, like mortality, the need for intubation, acute kidney injury, and the need for renal replacement therapy (RRT). METHODS: We retrospectively reviewed the characteristics and outcomes of patients admitted to the ICU at a COVID-19-designated hospital in Qatar between March and July 2020. Logistic regression analysis was used to determine factors associated with mortality. RESULTS: 1079 patients with COVID-19 were admitted to the ICU, and 146 developed rhabdomyolysis. Overall, 30.1% died (n = 44), and 40.4% developed Acute Kidney Injury (AKI) (n = 59), with only 19 cases (13%) recovering from the AKI. AKI was significantly associated with increased mortality rates among rhabdomyolysis patients. Moreover, significant differences were found between groups regarding the subject's age, calcium level, phosphorus level, and urine output. However, the AKI was the best predictor of mortality for those who got the COVID-19 infection and rhabdomyolysis. CONCLUSION: Rhabdomyolysis increases the risk of death in COVID-19 patients admitted to the ICU. The strongest predictor of a fatal outcome was acute kidney injury. The findings of this study emphasize the importance of early identification and prompt treatment of rhabdomyolysis in patients with severe COVID-19.

Rhabdomyolysis-Induced Acute Kidney Injury (AKI) in a Young Bodybuilder: A Case Report.

Acute kidney injury (AKI) is a serious medical condition that can have many causes, including rhabdomyolysis. Rhabdomyolysis is the breakdown of muscle tissue that can lead to the release of muscle fiber contents into the bloodstream. This can cause serious damage to the kidneys, leading to AKI. In this case, a young bodybuilder was diagnosed with rhabdomyolysis induced by AKI after consuming Ibuprofen for a casual fever. The etiology of AKI in rhabdomyolysis is complex, with multiple factors contributing to the development of the condition. These include muscle trauma, dehydration, infection, and drug toxicity. In this case, Ibuprofen may have contributed to the development of AKI, as it can cause kidney damage when taken in large doses. Additionally, the bodybuilder's physical activity may have contributed to the development of rhabdomyolysis, as intense exercise can cause muscle damage. Treatment for AKI in rhabdomyolysis patients typically involves aggressive fluid resuscitation, electrolyte replacement, and dialysis if necessary. Additionally, the underlying cause of the rhabdomyolysis must be identified and treated. In this case, the patient should be monitored closely for any signs of kidney damage, and the Ibuprofen should be discontinued. In conclusion, this is a case of a relatively common presentation with uncommon circumstances. It is crucial to have a heightened understanding of the likelihood of AKI in patients with rhabdomyolysis and the impact of drug toxicity in exacerbating the condition. Early diagnosis and treatment are essential for the successful management of AKI.