Healthcare

BackgroundIntravenous Tissue Plasminogen Activator (IV rt-PA) significantly improves AIS patients functional outcomes within the treatment window, yet the usage of IV rt-PA among AIS patients are substantially lower in China than in developed countries. Healthcare resource utilization manages effective treatment patterns for patients who are adherent to IV rt-PA. This study investigates the association between healthcare resource inputs and IV rt-PA adherence and the impact of Gross Regional Product (GRP) on IV rt-PA. Methods1,456 hospitals from 31 provinces with 158,003 acute ischemic stroke patients who had received IV rt-PA between 2015-2019 were recruited by the Chinese Stroke Center Alliance. The study outcome was the adherence rate of IV rt-PA in each hospital. Healthcare resource input was identified from three aspects: human, material, and economic. Multivariable linear regression was conducted by adjusting healthcare system characteristics and by further adjustment of GRP. ResultsThe median (interquartile range) of IV rt-PA rate was 19.1% (8.6% -34.6%). Physician-nurse ratio ({beta}=0.023, p<0.001), nurse-bed ratio ({beta}=0.0343, p<0.001), and total health expenditure ({beta}=0.00002, p<0.001) were positively associated with the IV rt-PA adherence rate after controlling healthcare system factors. Through additional adjusting of GRP, only health expenditure was significantly positively associated with IV rt-PA adherence rate ({beta}=0.000018, p<0.001). ConclusionsMore health spending and being equipped with equally proportional physician-nurses and nurse-bed combinations in the provincial hospital will increase adherence to IV rt-PA among AIS patients. The difference in GRP among provinces may stimulate hospitals to provide more healthcare input from the workforce, thus indirectly increasing the usage of IV rt-PA.

BackgroundStroke-associated pneumonia (SAP) is a frequent complication of stroke, characterized by its high incidence rate, and it can have a severe impact on the prognosis of patients. The limitations of current clinical treatment measures underscore the critical need to identify high-risk factors promptly to decrease the incidence of SAP. ObjectiveTo analyze the risk factors of SAP in stroke patients, construct a predictive model of SAP based on the SHAP interpretable machine learning method, and explain the important variables. MethodsA total of 763 stroke patients admitted to the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 1, 2023, to May 31, 2024, were selected and randomly divided into the model training set (n=457) and model validation set (n=306) according to the ratio of 6:4. Firstly, the included data were sorted out, and then Lasso regression was used to screen the included characteristic variables. Based on the tidymodels framework, Using decision tree (DT), logistic regression, extreme gradient boosting (XGBoost), support vector machine (SVM), The classification model was constructed by five machine learning methods, including SVM and LightGBM. The grid search and 5-fold cross validation were used to optimize the hyperparameter optimization strategy and the performance index of the model. The predictive performance of the model was evaluated by the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA), and we used Shapley additive explanation (SHAP) to account for the model predictions and provide interpretable insights. ResultsThe incidence of SAP in this study was 31.72% (242/763). Six variables were selected by Lasso regression, including nasogastric tube use, age, ADL score, Alb, Hs-CRP, and Hb. The model with the best performance in the validation set was the XGBoost model, with an AUC of 0.926, an accuracy of 0.914, and an F1 score of 0.889. Its calibration curve and DCA showed good performance. SHAP algorithm showed that ADL score ranked first in importance. ConclusionThe model constructed using XGBoost has good prediction performance and clinical applicability, which is expected to support clinical decision-making and improve the prognosis of patients.

AimEarly enteral nutrition is often recommended for patients with acute stroke who have difficulty with oral intake. This study aimed to develop a predictive model to assess the need for enteral nutrition in older patients with acute cerebrovascular disorders. The model employs a machine learning algorithm using observational parameters related to swallowing ability. MethodsNinety patients experiencing a cerebrovascular accident for the first time were included in this study. Swallowing function was assessed using the Food Intake LEVEL Scale. Nine specific variables were used to create a model for determining the need for enteral nutrition. Initially, variable selection was conducted through correlation analysis. Subsequently, the data were randomly divided into training and test groups. Five machine learning methods were applied to identify the most effective algorithm: logistic regression, decision tree, random forest, support vector machine, and XG Boost. ResultsThrough correlation analysis, we identified the independent variables Functional Independence Measure, motor and cognitive scores and speech intelligibility. The logistic regression model demonstrated high performance (accuracy, 0.82; area under the curve, 0.82). ConclusionWe demonstrated that a predictive model, employing machine learning and integrating Functional Independence Measure motor and cognitive scores and speech intelligibility, exhibits superior predictive efficacy and ascertains the necessity for enteral nutrition. This model can be expediently appraised even by individuals not specialized in dysphagia. Additionally, it is applicable to patients who are incapable of adhering to conventional swallowing assessment protocols owing to compromised consciousness or cognitive impairments, or those with an exceptionally elevated risk of aspiration.

BACKGROUNDCognitive function was significantly associated with post-stroke depression and anxiety in stroke patients. However, no studies have examined whether there is an interaction. this study purposed to investigate whether cognitive function mediates the effect of enrollment in post-acute care (PAC) programs on depression or anxiety in stroke patients and whether the indicators are moderated in the pathway. METHODSThis is a prospective observational cohort study. A group of patients who had received PAC for stroke at one of two medical centers (PAC group, n = 2,087) was compared with a group who had received standard care for stroke at one of four hospitals (three regional hospitals and one district hospital; non-PAC group, n = 1,591) in Taiwan from March, 2015, to March, 2022. The effects of PAC on cognitive function and depression and anxiety at baseline, 12th week, and 1st year after rehabilitation were investigated using structural equation modeling (SEM). The effect of each variable on the moderation of different pathways in the model was analyzed using AMOS 23.0, and The SPSS PROCESS macro also was used to perform mediation analysis. RESULTSThe PAC program had a mediating effect on cognition and depression at week 12 (a*b= -0.098, P<0.05) on cognition and anxiety at week 12 (a*b= -0.107, P<0.05), and the PAC program had a direct effect on depression and anxiety in the first year. It was found that acute lengths of stay had a significant moderation effect in the model (X*W[-&gt;]M=0.204, P=0.002), but the model lost its mediating effect when the moderation variable was added. CONCLUSIONSPatients with stroke should receive post-acute care as soon as possible to improve their cognitive function after rehabilitation, to maximize the effectiveness of treatment for mental disorders, and to reduce the burden of the disease. WHAT IS KNOWNO_LIResearch suggests that cognitive function, including depression and anxiety, significantly improved for patients using post-acute care (PAC). C_LIO_LICognitive function was significantly associated with post-stroke depression and anxiety in patients with stroke. C_LI WHAT THE STUDY ADDSO_LIPAC had a mediating effect on cognition and depression at week 12 on cognition and anxiety at week 12, and it also had a direct effect on depression and anxiety in the first year. C_LIO_LIAcute lengths of stay had a significant moderation effect in the model, but the model lost its mediating effect when the moderation variable was added. C_LIO_LIPatients with stroke should receive post-acute care as soon as possible to improve their cognitive function after rehabilitation, to maximize the effectiveness of treatment for mental disorders, and to reduce the burden of the disease. C_LI

BackgroundPrevious prediction models have predicted a single outcome (e.g. gait) from several patient characteristics at one point (e.g. on admission). However, in clinical practice, it is important to predict an overall patient characteristic by incorporating multiple outcomes. This study aimed to develop a prediction model of overall patient characteristics in acute stroke patients using latent class analysis. MethodsThis retrospective observational study analyzed stroke patients admitted to acute care hospitals (37 hospitals, N=10,270) between January 2005 and March 2016 from the Japan Association of Rehabilitation Database. Overall, 6,881 patients were classified into latent classes based on their outcomes. The prediction model was developed based on patient characteristics and functional ability at admission. We selected the following outcome variables at discharge for classification using latent class analysis: Functional Independence Measure (functional abilities and cognitive functions), subscales of the National Institutes of Health Stroke Scale (upper extremity function), length of hospital stay, and discharge destination. The predictor variables were age, Functional Independence Measure (functional abilities and comprehension), subscales of the National Institutes of Health Stroke Scale (upper extremity function), stroke type, and amount of rehabilitation (physical, occupational, and speech therapies) per day during hospitalization. ResultsPatients (N=6,881) were classified into nine classes based on latent class analysis regarding patient characteristics at discharge (class size: 4-29%). Class 1 was the mildest (shorter stay and highest possibility of home discharge), and Class 2 was the most severe (longer stay and the highest possibility of transfers including deaths). Different gradations characterized Classes 3-9; these patient characteristics were clinically acceptable. Predictor variables at admission that predicted class membership were significant (odds ratio: 0.0- 107.9, P<.001). ConclusionsBased on these findings, the model developed in this study could predict an overall patient characteristic combining multiple outcomes, helping determine the appropriate rehabilitation intensity. In actual clinical practice, internal and external validation is required.

BackgroundOsteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and gender and modifiable factors such as physical activity. PurposeThis study aimed to construct a soft voting ensemble model to predict OA diagnosis using variables related to individual characteristics and physical activity and to identify important variables in constructing the model through permutation importance. MethodUsing the RFECV technique, the variables with the best predictive performance were selected among variables, and an ensemble model combining the RandomForest, XGBoost, and LightGBM algorithms was constructed, and the predictive performance and permutation importance of each variable were evaluated. ResultThe variables selected to construct the model were age, gender, grip strength, and quality of life, and the accuracy of the ensemble model was 0.828. The most important variable in constructing the model was age (0.199), followed by grip strength (0.053), quality of life (0.043), and gender (0.034). ConclusionThe performance of the model for predicting OA was relatively good, and if this model is continuously used and updated, this model could readily be used to predict OA diagnosis and the predictive performance of OA may be further improved.

This study aimed to develop and validate a cardiovascular diseases (CVD) risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using Machine-Learning technique. Three cohorts of Chinese patients with established CVD in Hong Kong were included; Hong Kong Island cohort as the derivation cohort, whilst the Kowloon and New Territories cohorts were validation cohorts. The 10-year CVD outcome was a composite of diagnostic or procedure codes for coronary heart disease, ischaemic or haemorrhagic stroke, peripheral artery disease, and revascularization. We estimated incidence of recurrent CVD events for each cohort with reference to the total person-years of each cohort. Multivariate imputation with chained equations (MICE) and XGBoost were applied for the model development. The comparison with TRS-2{degrees}P and SMART2 used the validation cohorts with 1000 bootstrap replicates. A total 48,799, 119,672 and 140,533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which, eight classes of medications were considered interactive drug use. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C-statistic of 0{middle dot}69. Internal validation showed good discrimination and calibration performance with C-statistic over 0{middle dot}6. P-CARDIAC also showed better performance than TRS-2{degrees}P and SMART2. Compared to other risk scores, P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden. Condensed AbstractA CVD risk prediction model named Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events among Chinese adults using Machine-Learning technique was newly developed. It predicted 10-year CVD outcome including a composite of diagnostic or procedure codes for coronary heart disease, ischaemic or haemorrhagic stroke, peripheral artery disease, and revascularization by incidence of recurrent CVD. Model showed satisfying discrimination and calibration with a C-statistic of 0{middle dot}69. P-CARDIAC also showed better performance than existing risk scores, such as TRS-2{degrees}P and SMART2. P-CARDIAC could help predict recurrent CVD risk and reduce the healthcare burden.

Withdrawal StatementThe authors have withdrawn their manuscript owing to the need to verify the data. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

BackgroundUnmet long-term needs and rehabilitation needs are prevalent among stroke survivors and affect their quality of life. We aimed to identify the long-term unmet needs and unmet rehabilitation needs among stroke survivors in South Korea and evaluate their intercorrelations with health-related quality of life. MethodsStroke survivors who were admitted to four Regional Cardiocerebrovascular Disease Centers between January 1, 2015 and December 31, 2019 were telephonically surveyed using a computer-assisted telephone interview method. With the aim of surveying approximately 1,000 patients, 9,204 people were recruited through random sampling. Unmet needs were evaluated on the basis of Longer-term Unmet Needs after Stroke questionnaire items. Quality of life was evaluated using the EuroQoL 5-dimension, 3-level (EQ-5D-3L) questionnaire and the EQ-5D index. ResultsAmong the participants, 93.6% experienced at least one unmet need and 311 (32.6%) reported unmet rehabilitation needs. The number of unmet needs, age, modified Rankin Scale (mRS) score, and previous stroke showed significant negative correlations with the EQ-5D index (p-value < 0.05). The age-adjusted odds ratio (OR) for reporting unmet rehabilitation needs significantly increased with problems in mobility (OR, 4.96; 95% confidence interval [CI], 3.64-6.76), self-care (OR, 4.46; 95% CI, 3.32-5.98), usual activities (OR, 5.78; 95% CI, 4.21-7.93), pain/discomfort (OR, 3.76; 95% CI, 2.76-5.06), anxiety/depression (OR, 3.67; 95% CI, 2.74-4.91), higher mRS score (OR, 3.13; 95% CI, 2.29-4.28), prior hyperlipidemia (OR, 1.35; 95% CI, 1.00-1.81), and number of unmet needs (OR, 1.30; 95% CI, 1.25-1.36). ConclusionsUnmet needs were prevalent among stroke survivors and were associated with a lower quality of life and increased odds of reporting unmet rehabilitation needs. Further research is needed to investigate strategies for addressing these subjective unmet needs with the aim of improving the long-term quality of life of stroke survivors.

BackgroundPatients and their families often explore the information available online for information about health status. A dialogue-based artificial intelligence (AI) language model (ChatGPT) has been developed for complex question and answer. We sought to assess whether AI model had knowledge of cardiovascular disease (CVD) racial disparities, including disparities associated with CVD risk factors and associated diseases. MethodsTo assess ChatGPTs responses to topic in cardiovascular disease disparities, we created six questions of twenty sets, with each set consisting of three questions with changes to text prompt based on differences in patient demographics. Each question was asked three times to assess variability in responses. ResultsA total of 180 responses were tabulated from ChatGPTs responses to 60 questions asked in triplicate to assess. Despite some variation in wording, all responses to the same prompt were consistent across different sessions. ChatGPTs responses to 63.4% of the questions (38 out of 60 questions) were appropriate, 33.3% (20 out of 60 questions) were inappropriate and 3.3% unreliable (2 out of 60 questions). Of the 180 prompt entries into ChatGPT, 141 (78.3%) were correct, 28 (15.5%) were hedging or indeterminate responses that could not be binarized into a correct or incorrect response, and 11 (6.1%) were incorrect. There were consistent themes in incorrect or hedging responses by ChatGPT, with 91% and 79% of incorrect and hedging responses related to a cardiovascular disease disparity that affects a minority or underserved racial group. ConclusionOur study showed that an online chat-based AI model has a broad knowledge of CVD racial disparities, however persistent gaps in knowledge about minority groups. Given that these models might be used by the general public, caution should be advised in taking responses at face value.

IntroductionThe incidence of stroke mimic symptoms is around 30%. These symptoms impact healthcare costs, often leading mimic stroke patients to undergo unnecessary clinical tests, imaging, and treatments. ObjectivesThe main goal of this study is to describe the types of stroke mimic symptoms and estimate healthcare costs in patients with stroke mimics. Secondary objectives include comparing costs based on Telestroke Mimics Stroke and FABS scores and determining the frequency of thrombolysis. MethodsWe conducted a retrospective observational study. We reviewed medical records of all patients admitted to Fundacion Santa Fe de Bogota with a final diagnosis of ischemic stroke mimic. We characterized the study population and analyzed the costs of interventions in these patients. ResultsA total of 111 patients were included. The average age at mimic presentation was 65 {+/-} 19.4 years, with transient ischemic attack (TIA) being the most common cause of mimics in both sexes, followed by migraine. Tissue plasminogen activator was administered in 0.9% of patients. The direct costs of activating the stroke code averaged US$1,098.72, with a cost of laboratory and imaging at US$773.95. The average cost of total hospitalization was US$2,220.16 per patient. ConclusionsThe most frequent cause of stroke mimic was TIA, and thrombolysis was performed in 0.9% of cases. The direct costs incurred by activating the stroke code, diagnostic tests, treatment with intravenous thrombolysis, and hospitalization of patients with stroke mimic are lower compared to other studies.

This study investigated the mechanism and impact of a policy combination involving centralized drug procurement and national drug price negotiations on health insurance payment management and overall health performance of the population. Relying on a two-dimensional analytical framework of health outcomes and medical expenditures, the entropy value method was applied to construct indicators of residents health portfolios. The year 2019, marked by the large-scale implementation of centralized procurement and national medicine catalog negotiations, was identified as the policy breakpoint for constructing a breakpoint regression model. Based on CFPS data, the model was implemented to evaluate changes in residents health performance and medical expenditure efficiency. Furthermore, the mechanisms underlying policy effects were examined from the perspectives of drug expenditure, pharmaceutical innovation (e.g., R&D inputs and patent output), and drug trade including imports and exports. The results indicated that this policy combination significantly improved population health outcomes and enhanced the efficiency of healthcare spending. The mechanism analysis further confirmed its short-term effects on stimulating innovation, increasing drug accessibility, and promoting expenditure efficiency. In addition, empirical evidence supported the hypothesized synergy between import substitution and export upgrading. Therefore, it is recommended to establish a value-oriented drug classification and payment management mechanism while adapting regional policies to provide a scientific basis for optimizing pharmaceutical policy design and balancing health accessibility with the advancement of innovation in the pharmaceutical industry.Keywords: Volume Procurement; national drug negotiation; health performance; breakpoint regression

BackgroundLow back pain (LBP) is one of the most frequently occurring musculoskeletal disorders, and factors such as lifestyle as well as individual characteristics are associated with LBP. The purpose of this study was to develop and compare efficient low back pain prediction models using easily obtainable demographic and lifestyle factors. MethodsData from adult men and women aged 50 years or older collected from the Korean National Health and Nutrition Examination Survey (KNHANES) were used. The dataset included 22 predictor variables, including demographic, physical activity, occupational, and lifestyle factors. Four machine learning algorithms, including XGBoost, LGBM, CatBoost, and RandomForest, were used to develop predictive models. ResultsAll models achieved an accuracy greater than 0.8, with the LGBM model outperforming the others with an accuracy of 0.830. The CatBoost model had the highest sensitivity (0.804), while the LGBM model showed the highest specificity (0.884) and F1-Score (0.821). Feature importance analysis revealed that EQ-5D was the most critical variable across all models. ConclusionIn this study, an efficient LBP prediction model was developed using easily accessible variables. Using this model, it may be helpful to identify the risk of LBP in advance or establish prevention strategies in subjects who have difficulty accessing medical facilities.

Data Envelopment Analysis (DEA) has been employed as a performance evaluation tool in the evaluation of efficiency and productivity in numerous fields. This includes hospitals in particular as well as the broad healthcare industry. This review examines 89 papers that discuss the use of DEA in hospitals, paying particular attention to approaches for choosing inputs and outputs as well as the most recent developments in DEA studies. English articles with empirical data from year 2014-2022 (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar) were extracted based on PRISMA methodology. DEA Model parameters were specified based on previous studies and approaches were identified narratively. The approaches can be grouped into four: (1) Literature review, (2) Data availability, (3) Systematic method and (4) Expert judgement. The approaches were applied as one strategy either by itself or in combination with others. This reviews emphasis on approaches used in hospital may constrain its conclusions. There might be another strategy or method used to select the input and output for a DEA study in a different area or strategies based on different viewpoints. The trend for DEA application were quite similar to previous studies. There is no evidence that one model fits all DEA model parameters better than another. Based on the reviewed literature, we offer some recommendations and methodological principles for DEA studies.

Physicians "know-do gaps" are a key factor driving the poor quality of healthcare in many developing countries, but there is little guidance on how to address these gaps. We designed a standardised patient audit study in China to evaluate the impact of physician over-service on their investment in learning and disease management decisions. We find that physicians total over-service leads to a 19.2%, 15.6% and 10.8% significant increase in consultation length, adherence to checklists, and patient-centred communication, respectively, but no significant improvement in giving a correct diagnosis, drug prescription or referral. The effects on physicians investment in learning are driven by over-service in drug prescription rather than over-service in medical tests. Moreover, over-service in drug prescription significantly leads to a 28.0 percentage-point increase in the prescription of a correct drug. Our findings imply that physician over-service reduces their "know-do gaps" and improves healthcare quality despite the related inefficient use of medical resources. JEL classificationD82; H75; I10; I11; I18; J45

IntroductionThere is a significant lack of effective pharmaceutical interventions for treating coronary artery calcification (CAC). Severe CAC (sCAC) poses a formidable challenge to interventional surgery and exhibits robust associations with adverse cardiovascular outcomes. Therefore, it is imperative to develop tools capable of early-stage detection and risk assessment for both CAC and sCAC. This study aims to develop and validate nomograms for the accurate prediction of CAC and sCAC. MethodsThis retrospective cross-sectional study was conducted in Taizhou, Jiangsu Province, China. CAC assessment was performed using non-gated thoracic CT scans. Demographic data and clinical information were collected from patients who were then randomly divided into a training set (70%) or a validation set (30%). Least absolute shrinkage and selection operator (LASSO) regression as well as multiple logistic regression analyses were utilized to identify predictive factors for both CAC and sCAC development. Nomograms were developed to predict the occurrence of CAC or sCAC events. The models performance was evaluated through discrimination analysis, calibration analysis, as well as assessment of their clinical utility. ResultsThis study included 666 patients with an average age of 75 years, of whom 56% were male. 391 patients had CAC, with sCAC in 134 cases. Through LASSO and multiple logistic regression analysis, age increase, hypertension, carotid artery calcification, CHD, and CHADS2 score were identified for the CAC risk predictive nomogram with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.845(95%CI 0.809-0.881) in the training set and 0.810(95%CI 0.751-0.870) in the validation set. Serum calcium level, carotid artery calcification, and CHD were identified for the sCAC risk predictive nomogram with an AUC of 0.863(95%CI 0.825-0.901) in the training set and 0.817(95%CI 0.744-0.890) in the validation set. Calibration plots indicated that two models exhibited good calibration ability. According to the decision curve analysis (DCA) results, both models have demonstrated a positive net benefit within a wide range of risks. ConclusionsThe present study has successfully developed and validated two nomograms to accurately predict CAC and sCAC, both of which have demonstrated robust predictive capabilities.

Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians records, it has yet to be widely applied to pharmaceutical care records. In this report, we investigated the feasibility of automatic extraction of patients diseases and symptoms from pharmaceutical care records. The verification was performed using MedNER-J, a Japanese disease-extraction system designed for physicians records. MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Keio University Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F-measure. The F-measure of NER for subjective, objective, assessment, and plan data was 0.46, 0.70, 0.76, and 0.35, respectively. In NER and positive-negative classification, the F-measure was 0.28, 0.39, 0.64, and 0.077, respectively. The F-measure of NER for objective and assessment data (F=0.70, 0.76) was higher than that for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F-measure of NER and positive-negative classification was high for assessment data alone (F=0.64), which was attributed to the similarity of its description format and contents to those of the training data. MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records.

BackgroundCardiovascular (CV) diseases remain the leading cause of mortality worldwide. Various determinants has been indicated contributing in CV mortality, spanning from health policy, universal health coverage (UHC), tobacco and socioeconomic diversity. To date, no quantitative regression model has been established to exhibit the magnitude and prediction of CV mortality. We aimed to formulate a linear regression model of CV determinants involving healthcare insurance coverage and national policy, economic status, smoking prevalence, gender and subregional variety. MethodsA linear regression model was employed, with the percentage of cardiovascular deaths as the dependent variable. Independent variables were Universal Health Coverage (UHC), World Bank income classifications, availability of national policy on CV, gender, availability of national CV guideline, CV risk stratification in primary healthcare, smoking and sub-regions. Data were gathered from World Health Organization and The World Bank dataset. ResultsA total of 2,385 data points from 2015 to 2019 was acquired constituting 183 countries. More extensive UHC ({beta} = -0.052, t = -3.663, p <0.001) and high-income countries (-0.060, t = -2.756, p <0.001) exhibited lower predicted CV mortality. Smoking prevalence was strongly correlated with higher mortality ({beta} = 0.128, t = 8.408, p <0.001). Regional disparities were observed, with Eastern Europe presenting highest mortality rate ({beta} = 0.685, t = 32.686, p <0.001). Compared to male, female showed higher cardiovascular death ({beta} = 0.047, t = 4.017, p <0.001). The availability of national policies in cardiovascular health were associated with lower mortality ({beta} = -0.031, t = -3.211, p = 0.001). CV national guideline was the only non-significant CV determinant. ConclusionsThe development of a quantitative regression model for cardiovascular mortality incorporating multifaceted determinants was expected to promote comprehensive public health strategies, policy reforms, and national health system strengthening, which are essential to reduce the global burden of cardiovascular diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/25326323v1_fig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@14ebbf5org.highwire.dtl.DTLVardef@15e1e1org.highwire.dtl.DTLVardef@a0b130org.highwire.dtl.DTLVardef@1d0e87c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1C_FLOATNO (Graphical Abstract). Summary of the research and quantified determinants of CV death. UHC: universal health coverage, CV: cardiovascular, *[&ge;]50% availability of CV risk stratification program in primary health care. C_FIG

Objectives (Importance)Cerebrovascular accident (Stroke) is a term used in medicine to describe cutting off blood supply to a portion of the brain, which causes tissue damage in the brain. Clots of blood that form in the brains blood vessels and ruptures in the brains blood vessels are the root causes of cerebrovascular accidents. Dizziness, numbness, weakness on one side of the body, and difficulties communicating verbally, writing, or comprehending language are the symptoms of this condition. Smoking, being older and having high blood pressure, diabetes, high cholesterol, heart disease, a history of cerebrovascular accident in the family, atherosclerosis (which is the buildup of fatty material and plaque inside the coronary arteries), or high cholesterol all contribute to an increased risk of having a cerebrovascular accident. (Objective) This paper analyzes available studies on Cerebrovascular accident medication combinations. Evidence acquisition: (Data sources)This systematic review and network meta-analysis analyzed the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI), and Google Scholar databases without a lower time limit and up to July 2022. A network meta-analysis examines the efficacy of this drug combination on genes/proteins that serve as progression targets for cerebrovascular accidents. Results and ConclusionIn scenarios 1 through 3, the p-values for the suggested medication combination and Cerebrovascular accident were 0.036633, 0.007763, and 0.003638, respectively. Scenario I is the combination of medications initially indicated for treating a cerebrovascular accident. The recommended combination of medications for cerebrovascular accidents is ten times more effective. This systematic review and network meta-analysis demonstrate that the recommended medication combination decreases the p-value between cerebrovascular accidents and the genes as potential progression targets, thereby enhancing the treatment for cerebrovascular accidents. The optimal combination of medications improves community health and decreases per-person management costs. HighlightsO_LICombined drugs that make the p-value between Stroke and target genes close to 1 C_LIO_LIUsing Reinforcement Learning to recommend drug combination C_LIO_LIA comprehensive systematic review of recent works C_LIO_LIA Network meta-analysis to measure the comparative efficacy C_LIO_LIConsidered drug interactions C_LI

The article has explored the preferential publication bias of Nepals medical journals. To this end, it has reviewed the frequency and proportion of preferential publishing and editorial involvement in endogenous publication practices in six national medical journals registered in the Scopus database system. For the analysis of the data, social network analysis application - VOSviewer for graphical visualisation has been used. Editorial engagement in self-publishing and preferential publishing is found to be common in all journals. The study suggests that as long as the trend of preferential and sequestered publication continues, the integrity and validity generated and disseminated by the journals risks losing trust by the community concerned and the chances of these non-mainstream journals contributing to mainstream journals being slim. And, by way of recommendatory conclusions, it offers the following four questions and areas for further investigation to arrive at the clarity and understanding of some of the issues that have been flagged in the findings and discussions: (a) Why do editors engage in excess self-promotion using the outlet they are supposed to objectively and transparently manage? (b) What is the motivating factor for authors to rely on a particular journal to get published despite having multiple outlets to pick and choose from? (c) Why should the editors and reviewers of scientific contributions maintain the networking silo to include a few in the loop and exclude others from it?