Developing a risk assessment tool for cancer-related venous thrombosis in China: a modified Delphi-analytic hierarchy process study.

OBJECTIVE: To develop a Risk Assessment Tool for Cancer-related Venous Thrombosis in China. METHODS: A modified two-round Delphi method was employed to establish consensus within a field to reach an agreement via a questionnaire or by interviewing a multidisciplinary panel of experts by collecting their feedback to inform the next round, exchanging their knowledge, experience, and opinions anonymously, and resolving uncertainties. Furthermore, The AHP (Analytic Hierarchy Process) was used to determine the final quality indicators' relative importance. RESULTS: The expert's positive coefficient was 85.19% in the first round and 82.61% in the second round, with authoritative coefficients of 0.89 and 0.92 in the respective surveys. The P-value of Kendall's W test was all less than 0.001 for each round, and the W-value for concordance at the end of the two rounds was 0.115. The final Risk Assessment Tool for Cancer-related Venous Thrombosis consisted of three domains, ten subdomains, and 39 indicators, with patient factors weighing 0.1976, disease factors weighing 0.4905, and therapeutic factors weighing 0.3119. CONCLUSION: The tool is significantly valid and reliable with a strong authority and coordination degree, and it can be used to assess the risk of cancer-related VTE and initiate appropriate thrombophylactic interventions in China.

Development of a Brief Assessment Tool to Evaluate Early Low Nutrition Risk in Community Elderly. Creation of the tool and examination of its reliability and criterion-related validity.

BACKGROUND: To date simple assessment tool to evaluate early low nutrition risk in general older population has not been available. This study aimed to create such tool and examined its reliability and criterion-related validity. METHODS: 1,192 community elderly with a mean age of 74.7(5.8) years responded to a questionnaire consisting of 48 (Hatoyama) or 34 items (Kusatsu), which have been reported to be associated with nutritional state in older people. Item analysis was conducted on the 34 common items, and items were selected based on the following criteria: adequate pass rates and discriminative power, no gender and regional differences, and a certain level of commonality based on factor analysis. Next, the factor structure of the candidate items was examined through exploratory factor analysis, and confirmatory factor analysis was conducted as the final scale structure. Furthermore, Spearman's partial rank correlation coefficients (sex- and age-adjusted) between the created index and important health indicators were examined to determine the criterion-related validity. RESULTS: Finally, we obtained a semantic coherence of 4 factors (named health beliefs, dietary status, physical activity, and food-related quality of life) totaling 13 items; confirmatory factor analysis of the 4-factor solution yielded good model fit values, χ2 (59) =275.4 (p<0.001), CFI=0.930, and RMSEA=0.056. The factor loadings for each factor ranged from 0.43 to 0.82, indicating adequate loadings. The reliability of the index was shown to be high by Good-Poor analysis and Cronbach's α. The index showed statistically significant correlations with all health indicators. CONCLUSIONS: We have developed a simple assessment tool to evaluate early low nutrition risk in general older population.

Efficient organized colorectal cancer screening in Shenzhen: a microsimulation modelling study.

BACKGROUND: Colorectal cancer (CRC) is a global health issue with noticeably high incidence and mortality. Microsimulation models offer a time-efficient method to dynamically analyze multiple screening strategies. The study aimed to identify the efficient organized CRC screening strategies for Shenzhen City. METHODS: A microsimulation model named CMOST was employed to simulate CRC screening among 1 million people without migration in Shenzhen, with two CRC developing pathways and real-world participation rates. Initial screening included the National Colorectal Polyp Care score (NCPCS), fecal immunochemical test (FIT), and risk-stratification model (RS model), followed by diagnostic colonoscopy for positive results. Several start-ages (40, 45, 50 years), stop-ages (70, 75, 80 years), and screening intervals (annual, biennial, triennial) were assessed for each strategy. The efficiency of CRC screening was assessed by number of colonoscopies versus life-years gained (LYG). RESULTS: The screening strategies reduced CRC lifetime incidence by 14-27 cases (30.9-59.0%) and mortality by 7-12 deaths (41.5-71.3%), yielded 83-155 LYG, while requiring 920 to 5901 colonoscopies per 1000 individuals. Out of 81 screening, 23 strategies were estimated efficient. Most of the efficient screening strategies started at age 40 (17 out of 23 strategies) and stopped at age 70 (13 out of 23 strategies). Predominant screening intervals identified were annual for NCPCS, biennial for FIT, and triennial for RS models. The incremental colonoscopies to LYG ratios of efficient screening increased with shorter intervals within the same test category. Compared with no screening, when screening at the same start-to-stop age and interval, the additional colonoscopies per LYG increased progressively for FIT, NCPCS and RS model. CONCLUSION: This study identifies efficient CRC screening strategies for the average-risk population in Shenzhen. Most efficient screening strategies indeed start at age 40, but the optimal starting age depends on the chosen willingness-to-pay threshold. Within insufficient colonoscopy resources, efficient FIT and NCPCS screening strategies might be CRC initial screening strategies. We acknowledged the age-dependency bias of the results with NCPCS and RS.

A new risk calculation model for complications of hepatectomy in adults over 75.

BACKGROUND: Owing to poor organ function reserve, older adults have a high risk of postoperative complications. However, there is no well-established system for assessing the risk of complications after hepatectomy in older adults. METHODS: This study aimed to design a risk assessment tool to predict the risk of complications after hepatectomy in adults older than 75 years. A total of 326 patients were identified. A logistic regression equation was used to create the Risk Assessment System of Hepatectomy in Adults (RASHA) for the prediction of complications (Clavien‒Dindo classification ≥ II). RESULTS: Multivariate correlation analysis revealed that comorbidity (≥ 5 kinds of disease or < 5 kinds of disease, odds ratio [OR] = 5.552, P < 0.001), fatigue (yes or no, OR = 4.630, P = 0.009), Child‒Pugh (B or A, OR = 4.211, P = 0.004), number of liver segments to be removed (≥ 3 or ≤ 2, OR = 4.101, P = 0.001), and adjacent organ resection (yes or no, OR = 1.523, P = 0.010) were independent risk factors for postoperative complications after hepatectomy in older persons (aged ≥ 75 years). A binomial logistic regression model was established to evaluate the RASHA score (including the RASHA scale and RASHA formula). The area under the curve (AUC) for the RASHA scale was 0.916, and the cut-off value was 12.5. The AUC for the RASHA formula was 0.801, and the cut-off value was 0.2106. CONCLUSION: RASHA can be used to effectively predict the postoperative complications of hepatectomy through perioperative variables in adults older than 75 years. TRIAL REGISTRATION: The Research Registry: researchregistry8531. https://www.researchregistry.com/browse-the-registry#home/registrationdetails/63901824ae49230021a5a0cf/ .

Development of type 2 diabetes risk assessment model for Turkish society.

Purpose: The study was conducted to develop a risk assessment tool to determine the Turkish population's risk of undiagnosed type 2 diabetes. Methods: The study was carried out in a methodological design. A total of 610 individuals, including those diagnosed with diabetes (321) and not diagnosed with diabetes (289), who applied to the internal medicine and diabetes outpatient clinics of a public hospital, were included in the study. The sample of patients with diabetes was created with the individuals who applied to diabetes outpatient clinics, were 40 years of age and older, and had the values of FPG ≥ 126 mg/dl and HbA1C ≥ 6.5%. The sample of healthy individuals consisted of people over the age of 40 who were not diagnosed with diabetes or prediabetes. Logistic regression and random forest algorithms were used to evaluate the diabetes risk of individuals. The performance of the models was evaluated with sensitivity, specificity, accuracy, and area under the ROC (AUC). Result: In the study, the variables of exercise in daily routines, presence of prediabetes, getting angry, feeling hungry frequently, and excessive thirst formed the diabetes risk assessment model with Sensitivity 0.983 and Specificity 0.984 according to the logistic regression model obtained. Body mass index, physical activity, age, gender, and family history of diabetes were not found to be significant in differentiating cases with diabetes (0.05 < p). Conclusion: This diabetes risk assessment tool is a reliable tool for Turkish society to identify individuals at high risk for diabetes and to raise awareness of the importance of modifiable risk factors.

Decomposing and simplifying the Fracture Risk Assessment Tool-a module from the Taiwan-specific calculator.

The Fracture Risk Assessment Tool (FRAX®) is a widely utilized country-specific calculator for identifying individuals with high fracture risk; its score is calculated from 12 variables, but its formulation is not publicly disclosed. We aimed to decompose and simplify the FRAX® by utilizing a nationwide community survey database as a reference module for creating a local assessment tool for osteoporotic fracture community screening in any country. Participants (n = 16384; predominantly women (75%); mean age = 64.8 years) were enrolled from the Taiwan OsteoPorosis Survey, a nationwide cross-sectional community survey collected from 2008 to 2011. We identified 11 clinical risk factors from the health questionnaires. BMD was assessed via dual-energy X-ray absorptiometry in a mobile DXA vehicle, and 10-year fracture risk scores, including major osteoporotic fracture (MOF) and hip fracture (HF) risk scores, were calculated using the FRAX®. The mean femoral neck BMD was 0.7 ± 0.1 g/cm2, the T-score was -1.9 ± 1.2, the MOF was 8.9 ± 7.1%, and the HF was 3.2 ± 4.7%. Following FRAX® decomposition with multiple linear regression, the adjusted R2 values were 0.9206 for MOF and 0.9376 for HF when BMD was included and 0.9538 for MOF and 0.9554 for HF when BMD was excluded. The FRAX® demonstrated better prediction for women and younger individuals than for men and elderly individuals after sex and age stratification analysis. Excluding femoral neck BMD, age, sex, and previous fractures emerged as 3 primary clinical risk factors for simplified FRAX® according to the decision tree analysis in this study population. The adjusted R2 values for the simplified country-specific FRAX® incorporating 3 premier clinical risk factors were 0.8210 for MOF and 0.8528 for HF. After decomposition, the newly simplified module provides a straightforward formulation for estimating 10-year fracture risk, even without femoral neck BMD, making it suitable for community or clinical osteoporotic fracture risk screening.

Guidelines for fracture risk assessment and management of osteoporosis in postmenopausal women and men above the age of 50 in Qatar.

We present comprehensive guidelines for osteoporosis management in Qatar. Formulated by the Qatar Osteoporosis Association, the guidelines recommend the age-dependent Qatar fracture risk assessment tool for screening, emphasizing risk-based treatment strategies and discouraging routine dual-energy X-ray scans. They offer a vital resource for physicians managing osteoporosis and fragility fractures nationwide. PURPOSE: Osteoporosis and related fragility fractures are a growing public health issue with an impact on individuals and the healthcare system. We aimed to present guidelines providing unified guidance to all healthcare professionals in Qatar regarding the management of osteoporosis. METHODS: The Qatar Osteoporosis Association formulated guidelines for the diagnosis and management of osteoporosis in postmenopausal women and men above the age of 50. A panel of six local rheumatologists who are experts in the field of osteoporosis met together and conducted an extensive review of published articles and local and international guidelines to formulate guidance for the screening and management of postmenopausal women and men older than 50 years in Qatar. RESULTS: The guidelines emphasize the use of the age-dependent hybrid model of the Qatar fracture risk assessment tool for screening osteoporosis and risk categorization. The guidelines include screening, risk stratification, investigations, treatment, and monitoring of patients with osteoporosis. The use of a dual-energy X-ray absorptiometry scan without any risk factors is discouraged. Treatment options are recommended based on risk stratification. CONCLUSION: Guidance is provided to all physicians across the country who are involved in the care of patients with osteoporosis and fragility fractures.

Use of Fracture Risk Assessment Tool in clinical practice and Fracture Risk Assessment Tool future directions.

Fracture Risk Assessment Tool is a free, online fracture risk calculator which can be used to predict 10-year fracture risk for women and men over age 50 years. It incorporates seven clinical risk factors and bone density to give a 10-year risk of major osteoporotic fracture and hip fracture. This dynamic tool can be used with patients at the bedside to help guide treatment decisions. There are some limitations to Fracture Risk Assessment Tool, with the most central limitation being the fact that inputs are binary. Much research has been done to try to refine Fracture Risk Assessment Tool to allow for more accurate risk prediction, and this article describes the data for adjusting Fracture Risk Assessment Tool depending on the clinical scenario such as the dose of glucocorticoid use, presence of diabetes and others. Recently, the new FRAXplus tool has been developed to address many of these concerns and will likely replace the old Fracture Risk Assessment Tool in the future. At the current time, it is available in beta form.

Methods for Refining the FRAX® Tool in Patients with Low Bone Density to Help Improve the Accuracy of Osteoporotic Fracture Risk PredictionMany patients who have low bone density develop fragility fractures, even those whose bone density is not yet within the osteoporosis range. Thus, in patients with low bone density, the health care team should estimate the risk of fracture to decide which patients should take medications to prevent fractures. Factors such as age, body mass index, steroid use, family history and other clinical factors can influence the fracture risk, in addition to bone density. There is an online calculator called the Fracture Risk Assessment Tool (FRAX®) which allows patients and doctors to integrate these risk factors with bone density in order to estimate the 10 year risk of osteoporotic fractures. FRAX® asks a series of yes/no questions about the patient's risks for fracture, and also takes into account the patient's country of residence, age, gender, race and bone density at the femur neck. However, there are some important limitations of this calculator. For example, we think that steroid medications increase the risk of fractures, and the higher the dose, the higher the risk of fractures. However, FRAX® only allows a "yes" or "no" input to the steroid use question. This paper aims to descibe methods for refining the FRAX® calculation to make the fracture risk prediction more accurate. For example, it describes a mathematical adjustment to FRAX® to account for the dose of steroids used. It also reviews methods for FRAX® adjustment for diabetes type 1 and 2, and severity of rheumatoid arthritis, among other considerations. Importantly, there is a new FRAX® tool that is currently in beta testing which will also further refine the accuracy of fracture risk prediction.

Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence-Based Risk Assessment Tool.

Background: We have previously developed an artificial intelligence-based risk assessment tool to identify the individual risk of HIV and sexually transmitted infections (STIs) in a sexual health clinical setting. Based on this tool, this study aims to determine the optimal risk score thresholds to identify individuals at high risk for HIV/STIs. Methods: Using 2008-2022 data from 216 252 HIV, 227 995 syphilis, 262 599 gonorrhea, and 320 355 chlamydia consultations at a sexual health center, we applied MySTIRisk machine learning models to estimate infection risk scores. Optimal cutoffs for determining high-risk individuals were determined using Youden's index. Results: The HIV risk score cutoff for high risk was 0.56, with 86.0% sensitivity (95% CI, 82.9%-88.7%) and 65.6% specificity (95% CI, 65.4%-65.8%). Thirty-five percent of participants were classified as high risk, which accounted for 86% of HIV cases. The corresponding cutoffs were 0.49 for syphilis (sensitivity, 77.6%; 95% CI, 76.2%-78.9%; specificity, 78.1%; 95% CI, 77.9%-78.3%), 0.52 for gonorrhea (sensitivity, 78.3%; 95% CI, 77.6%-78.9%; specificity, 71.9%; 95% CI, 71.7%-72.0%), and 0.47 for chlamydia (sensitivity, 68.8%; 95% CI, 68.3%-69.4%; specificity, 63.7%; 95% CI, 63.5%-63.8%). High-risk groups identified using these thresholds accounted for 78% of syphilis, 78% of gonorrhea, and 69% of chlamydia cases. The odds of positivity were significantly higher in the high-risk group than otherwise across all infections: 11.4 (95% CI, 9.3-14.8) times for HIV, 12.3 (95% CI, 11.4-13.3) for syphilis, 9.2 (95% CI, 8.8-9.6) for gonorrhea, and 3.9 (95% CI, 3.8-4.0) for chlamydia. Conclusions: Risk scores generated by the AI-based risk assessment tool MySTIRisk, together with Youden's index, are effective in determining high-risk subgroups for HIV/STIs. The thresholds can aid targeted HIV/STI screening and prevention.

Ten-year fracture risk in Japanese patients with myasthenia gravis: A comprehensive assessment using the fracture risk assessment tool.

BACKGROUND: Myasthenia gravis (MG) is an immune disorder that causes muscle weakness with an increasing prevalence, particularly among the elderly in Japan. Glucocorticoid treatment for MG is problematic for bone health because of reduced bone density and increased fracture risk. The fracture risk assessment tool (FRAX®) can estimate fracture risk, but its applicability in patients with MG remains uncertain. METHODS: A prospective cohort study was conducted on 54 patients with MG between April and July 2012. Bone mineral density (BMD) was measured, and FRAX® scores were calculated with and without BMD. We also adjusted FRAX® scores based on glucocorticoid dosage. Patients were monitored for major osteoporotic fractures (MOF) until June 2022. Statistical analyses included Kaplan-Meier curves and Cox proportional hazards models. RESULTS: The study group included 12 men and 42 women with a mean age of 62 years. Higher FRAX® scores correlated with increased fracture risk, particularly in the hip and lumbar regions. The 10-year fracture-free rate was significantly lower in the high-FRAX® score group. The FRAX® score using BMD is a significant predictor of MOF risk. The hazard ratio for FRAX® scores was 1.17 (95% CI 1.10-1.26). CONCLUSION: We demonstrated the effectiveness of the FRAX® tool in assessing fracture risk among patients with MG. High FRAX® scores correlated with increased fracture risk, emphasizing its importance. These findings support the incorporation of FRAX® assessment into clinical management to enhance patient care and outcomes. However, the small sample size and observational nature suggest a need for further research.

The American Society for Bone and Mineral Research Task Force on clinical algorithms for fracture risk report.

Using race and ethnicity in clinical algorithms potentially contributes to health inequities. The American Society for Bone and Mineral Research (ASBMR) Professional Practice Committee convened the ASBMR Task Force on Clinical Algorithms for Fracture Risk to determine the impact of race and ethnicity adjustment in the US Fracture Risk Assessment Tool (US-FRAX). The Task Force engaged the University of Minnesota Evidence-based Practice Core to conduct a systematic review investigating the performance of US-FRAX for predicting incident fractures over 10 years in Asian, Black, Hispanic, and White individuals. Six studies from the Women's Health Initiative (WHI) and Study of Osteoporotic Fractures (SOF) were eligible; cohorts only included women and were predominantly White (WHI > 80% and SOF > 99%), data were not consistently stratified by race and ethnicity, and when stratified there were far fewer fractures in Black and Hispanic women vs White women rendering area under the curve (AUC) estimates less stable. In the younger WHI cohort (n = 64 739), US-FRAX without bone mineral density (BMD) had limited discrimination for major osteoporotic fracture (MOF) (AUC 0.53 (Black), 0.57 (Hispanic), and 0.57 (White)); somewhat better discrimination for hip fracture in White women only (AUC 0.54 (Black), 0.53 (Hispanic), and 0.66 (White)). In a subset of the older WHI cohort (n = 23 918), US-FRAX without BMD overestimated MOF. The Task Force concluded that there is little justification for estimating fracture risk while incorporating race and ethnicity adjustments and recommends that fracture prediction models not include race or ethnicity adjustment but instead be population-based and reflective of US demographics, and inclusive of key clinical, behavioral, and social determinants (where applicable). Research cohorts should be representative vis-à-vis race, ethnicity, gender, and age. There should be standardized collection of race and ethnicity; collection of social determinants of health to investigate impact on fracture risk; and measurement of fracture rates and BMD in cohorts inclusive of those historically underrepresented in osteoporosis research.

Using race or ethnicity when calculating disease risk may contribute to health disparities. The ASBMR Task Force on Clinical Algorithms for Fracture Risk was created to understand the impact of the US Fracture Risk Assessment Tool (US-FRAX) race and ethnicity adjustments. The Task Force reviewed the historical development of FRAX, including the assumptions underlying selection of race and ethnicity adjustment factors. Furthermore, a systematic review of literature was conducted, which revealed an overall paucity of data evaluating the performance of US-FRAX in racially and ethnically diverse groups. While acknowledging the existence of racial and ethnic differences in fracture epidemiology, the Task Force determined that currently there is limited evidence to support the use of race and ethnicity­specific adjustments in US-FRAX. The Task Force also concluded that research is needed to create generalizable fracture risk calculators broadly applicable to current US demographics, which do not include race and ethnicity adjustments. Until such population­based fracture calculators are available, clinicians should consider providing fracture risk ranges for Asian, Black, and/or Hispanic patients and should engage in shared decision-making with patients about fracture risk interpretation. Future studies are required to evaluate fracture risk tools in populations inclusive of those historically underrepresented in research.

The Gail Model and Its Use in Preventive Screening: A Comparison of the Corbelli Study.

Background This study aims to determine the usage of the Gail model in screening for breast cancer during physical examinations of women by sampling primary care physicians in two regions of Texas - Hidalgo County and Johnson County. A Gail score of 1.66% or higher indicates increased breast cancer risk. Three specialties are surveyed: internal medicine (IM), family medicine (FM), and gynecology (GYN). The null hypothesis for this study is that primary care physicians do not use the Gail model in screening for breast cancer during physical examinations of women. Methods A survey was distributed to 100 physicians with specialties in IM, FM, and GYN from May 2022 to July 2022. The survey assessed the physician's frequency of use of the Gail model and chemoprevention. Data were collected by distributing survey questionnaires to physicians in person. Descriptive statistics were used for response distributions. Fisher's exact probability test was used for comparisons across specialties. Results The response rate was 34% (34/100). Thirty-eight percent of the physicians surveyed reported using the Gail model in their practice (IM 46%, FM 23%, and GYN 31%). All 13 of the physicians using the Gail model were open to using chemoprevention. Conclusions Only 38% of the physicians surveyed responded that they use the Gail model in their practice. The study concluded that a minority of primary care physicians used the Gail model to decrease breast cancer risk. Further research would help to define better the Gail model and its use in preventing breast cancer in women. The Gail model appears to be beneficial to breast cancer risk reduction; however, risk reduction medication side effects need to be minimized.

Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study.

Background: Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study. Methods: A total of 349 subjects were included in this study. After 1∶1 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis. Results: The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (r = 0.416, p < 0.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (r = 0.307, p < 0.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (r = -0.416, p < 0.001, and r = -0.431, p < 0.001, respectively) and hip fractures (r = -0.357, p < 0.001, and r = -0.311, p < 0.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771-0.897, p < 0.001). Conclusion: SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.

Thrombo-inflammatory prognostic score can predict the outcome of stroke: a retrospective cohort study.

Introduction: Inflammatory and thrombotic biomarkers are simple prognostic indicators of adverse clinical outcomes in patients with ischemic stroke (IS). However, isolated assessment of inflammatory or thrombus biomarkers in patients with IS is limited in clinical practice. Methods: This study aimed to evaluate the predictive value of a novel, simplified thrombo-inflammatory prognostic score (TIPS) that combines both inflammatory and thrombus biomarkers in the early phase of IS and to identify high-risk patients at the time of admission. The study population comprised 915 patients with a primary diagnosis of IS in the emergency departments of five grade A tertiary hospitals in China. Results: Patients were divided into two groups based on the modified Rankin Scale (mRS): <3 and ≥3. TIPS with a value of "2" indicates biomarkers for high inflammation and thrombosis, "1" represents a biomarker, and "0" signals the absence of a biomarker. Multivariate logistic regression analysis was employed to identify the association between TIPS and clinical outcomes. TIPS was an independent predictor of unfavorable functional outcomes and mortality. It had a superior predictive value for clinical outcomes compared to the National Institutes of Health Stroke Scale (NIHSS) (effect ratio, 37.5%), D-dimer (effect ratio, 12.5%), and neutrophil-to-lymphocyte ratio (effect ratio, 25%). Conclusion: The survival probability of TIPS with a score of 0 is twice as high as that of TIPS with a score of 2. The survival rate for TIPS with a score of 1 is one time higher than that for TIPS with a score of 2. The predictive value of TIPS for unfavorable functional outcomes is represented by an AUC of 0.653. TIPS is associated with an increased risk of death and unfavorable functional outcomes in patients with IS and may be a useful tool for identifying high-risk patients at the time of admission.

Geriatric nutritional risk index as a predictor for fragility fracture risk in elderly with type 2 diabetes mellitus: A 9-year ambispective longitudinal cohort study.

BACKGROUND & AIMS: The elderly are prone to fragility fractures, especially those suffering from type 2 diabetes mellitus (T2DM) combined with osteoporosis. Although studies have confirmed the association between GNRI and the prevalence of osteoporosis, the relationship between GNRI and fragility fracture risk and the individualized 10-year probability of osteoporotic fragility fractures estimated by FRAX remains unclear. This study aims to delve into the association between the GNRI and a fragility fracture and the 10-year probability of hip fracture (HF) and major osteoporotic fracture (MOF) evaluated by FRAX in elderly with T2DM. METHODS: A total of 580 patients with T2DM aged ≥60 were recruited in the study from 2014 to 2023. This research is an ambispective longitudinal cohort study. All participants were followed up every 6 months for 9 years with a median of 3.8 years through outpatient services, medical records, and home fixed-line telephone interviews. According to the tertiles of GNRI, all subjects were divided into three groups: low-level (59.72-94.56, n = 194), moderate-level (94.56-100.22, n = 193), and high-level (100.22-116.45, n = 193). The relationship between GNRI and a fragility fracture and the 10-year probability of HF and MOF calculated by FRAX was assessed by receiver operating characteristic (ROC) analysis, Spearman correlation analyses, restricted cubic spline (RCS) analyses, multivariable Cox regression analyses, stratified analyses, and Kaplan-Meier survival analysis. RESULTS: Of 580 participants, 102 experienced fragile fracture events (17.59%). ROC analysis demonstrated that the optimal GNRI cut-off value was 98.58 with a sensitivity of 75.49% and a specificity of 47.49%, respectively. Spearman partial correlation analyses revealed that GNRI was positively related to 25-hydroxy vitamin D [25-(OH) D] (r = 0.165, P < 0.001) and bone mineral density (BMD) [lumbar spine (LS), r = 0.088, P = 0.034; femoral neck (FN), r = 0.167, P < 0.001; total hip (TH), r = 0.171, P < 0.001]; negatively correlated with MOF (r = -0.105, P = 0.012) and HF (r = -0.154, P < 0.001). RCS analyses showed that GNRI was inversely S-shaped dose-dependent with a fragility fracture event (P < 0.001) and was Z-shaped with the 10-year MOF (P = 0.03) and HF (P = 0.01) risk assessed by FRAX, respectively. Multivariate Cox regression analysis demonstrated that compared with high-level GNRI, moderate-level [hazard ratio (HR) = 1.950; 95% confidence interval (CI) = 1.076-3.535; P = 0.028] and low-level (HR = 2.538; 95% CI = 1.378-4.672; P = 0.003) had an increased risk of fragility fracture. Stratified analysis exhibited that GNRI was negatively correlated with the risk of fragility fracture, which the stratification factors presented in the forest plot were not confounding factors and did not affect the prediction effect of GNRI on the fragility fracture events in this overall cohort population (P for interaction > 0.05), despite elderly females aged ≥70, with body mass index (BMI) ≥24, hypertension, and with or without anemia (all P < 0.05). Kaplan-Meier survival analysis identified that the lower-level GNRI group had a higher cumulative incidence of fragility fractures (log-rank, all P < 0.001). CONCLUSION: This study confirms for the first time that GNRI is negatively related to a fragility fracture and the 10-year probability of osteoporotic fragility fractures assessed by FRAX in an inverse S-shaped and Z-shaped dose-dependent pattern in elderly with T2DM, respectively. GNRI may serve as a valuable predictor for fragility fracture risk in elderly with T2DM. Therefore, in routine clinical practice, paying attention to the nutritional status of the elderly with T2DM and giving appropriate dietary guidance may help prevent a fragility fracture event.

Drug-related emergency department visits: external validation of an assessment tool in a general emergency department population.

BACKGROUND: The process of identifying drug-related hospitalisations is subjective and time-consuming. Assessment tool for identifying hospital admissions related to medications (AT-HARM10) was developed to simplify and objectify this process. AT-HARM10 has not previously been externally validated, thus the predictive precision of the tool is uncertain. AIM: To externally validate AT-HARM10 in adult patients admitted to the emergency department (ED). METHOD: This retrospective cross-sectional study investigated 402 patients admitted to the ED, Diakonhjemmet Hospital, Oslo, Norway. A trained 5th-year pharmacy student used AT-HARM10 to assess all patients and to classify their ED visits as possibly or unlikely drug-related. Assessment of the same patients by an interdisciplinary expert panel acted as the gold standard. The external validation was conducted by comparing AT-HARM10 classifications with the gold standard. RESULTS: According to AT-HARM10 assessments, 169 (42%) patients had a possible drug-related ED visit. Calculated sensitivity and specificity values were 95% and 71%, respectively. Further, positive and negative predictive values were 46% and 98%, respectively. Adverse effects/over-treatment and suboptimal treatment were the issues most frequently overestimated by AT-HARM10 compared with the gold standard. CONCLUSION: AT-HARM10 identifies drug-related ED visits with high sensitivity. However, the low positive predictive value indicates that further review of ED visits classified as possible drug-related by AT-HARM10 is necessary. AT-HARM10 can serve as a useful first-step screening that efficiently identifies unlikely drug-related ED visits, thus only a smaller proportion of the patients need to be reviewed by an interdisciplinary expert panel.

Real world fracture prediction of fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OSTA) and one-minute osteoporosis risk test: An 11-year longitudinal study.

Introduction: Fractures affect people's quality of life especially in the elders. One of the most important risk factors is osteoporosis. There are many screening tools to predict osteoporosis and fractures. We aimed to compare the predictive validity of three commonly used screening tools: fracture risk assessment tool (FRAX), osteoporosis self-assessment tool for Asians (OSTA) and one-minute osteoporosis risk test. Among them, OSTA and one-minute osteoporosis risk test were originally developed to predict osteoporosis risks and FRAX was to predict fracture risks. Methods: This is an 11-year longitudinal study. We enrolled 708 senior people from health examinees in Taiwan in 2010. A standardized questionnaire and blood tests were provided. Annual telephone interview was conducted to assess the real fracture status. We calculated risk scores of FRAX, OSTA, and one-minute osteoporosis risk test and compared with real-world fracture records. Results: The mean age of the participants were 74.9 (SD 6.4). There were 356 (50.3 %) men. From 2010 to 2020, a total of 105 (14.8 %) persons suffered from fractures. Compared to people without fractures, people with fractures had higher FRAX major osteoporotic fracture risk scores (14.0 % ± 7.6 % vs.11.3 % ± 5.7 %), higher hip fracture risk scores, and higher OSTA risk (5.9 % ± 1.4 % vs. 5.3 % ± 1.3 %). Cox regression analysis showed that hazard ratios for fracture of high FRAX risk was 1.53 (95 % confidence interval (CI) 1.05-2.21), and for high OSTA risk was 1.37 (95 % CI 1.04-1.82). Conclusions: Only OSTA and FRAX scores were satisfactory in predicting 10-year fractures.

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment.

BACKGROUND: Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML). OBJECTIVE: This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system's outputs to analyze usability aspects and obtain insights related to future implementation. METHODS: A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients' scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients' data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems. RESULTS: The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024. CONCLUSIONS: This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52744.

Multicenter Study to Validate a Hospitalization Risk Assessment Tool in Hemodialysis Patients.

Introduction Protein-energy wasting is a prevalent condition in patients with chronic kidney disease. Our goal was to validate the risk assessment tool (Hashmi's tool) in multiple centers, developed in 2018, as it was easily applicable and cost-effective. Methods The following variables were scored as 0, 1, 2, or 3 as per severity: body mass index, HD vintage in years, functional capacity, serum albumin, serum ferritin, and the number of co-morbid conditions (diabetes mellitus, hypertension, ischemic heart disease, and cerebrovascular disease). This scoring system was applied to maintenance hemodialysis patients in six different centers. The patient's record was evaluated for two years. Patients were divided into low-risk (score <6) and high-risk (score ≥6). We compared the two groups using the chi-square test for the difference in hospitalization and mortality. Results A total of 868 patients' records were analyzed, and the maximum score was 13 with the application of Hashmi's tool. Four hundred twenty-nine patients were in the low-risk group, and 439 patients fell into the high-risk group. Four hundred sixty-seven patients were male, and 401 were females; 84% had hypertension, and 54% had diabetes mellitus. In the high-risk group, we identified more females. Patients' likelihood of being in the high-risk group was higher if they had diabetes mellitus, hypertension, or ischemic heart disease. Hospitalization due to vascular or non-vascular etiologies was more common in the high-risk group (p=0.036 and p<0.001, respectively). A total of 123 patients died during the study period, 92 from the high-risk group as compared to 31 from the low-risk group. This was three times higher and statistically significant (p<0.001). Conclusion Using a simple and cost-effective tool, we have identified malnourished patients who are at risk of hospitalization and mortality. This study has validated the previous work at a single center, which has now been reflected in six dialysis units across Saudi Arabia.