Individual Risk Score Research Articles

Integrated multi-level omics profiling of disulfidptosis identifis SPAG4 as an innovative immunotherapeutic target in glioblastoma.

To investigate the association between disulfidptosis-related genes (DFRGs) and patient prognosis, while concurrently identifying potential therapeutic targets in glioblastoma (GBM). We retrieved RNA sequencing data and clinical characteristics of GBM patients from the TCGA database. We found there was a total of 6 distinct clusters in GBM, which was identified by the t-SNE and UMAP dimension reduction analysis. Prognostically significant genes in GBM were identified using the limma package, coupled with univariate Cox regression analysis. Machine learning algorithms were then applied to identify central genes. The CIBERSORT algorithm was utilized to assess the immunological landscape across different GBM subtypes. In vitro and in vivo experiments were conducted to investigate the role of SPAG4 in regulating the proliferation, invasion of GBM, and its effects within the immune microenvironment. 23 genes, termed DFRGs, were successfully identified, demonstrating substantial potential for establishing a prognostic model for GBM. Single cell analysis revealed a significant correlation between DFRGs and the progression of GBM. Utilizing individual risk scores derived from this model enabled the stratification of patients into two distinct risk groups, revealing significant variations in immune infiltration patterns and responses to immunotherapy. Utilizing the random survival forests algorithm, SPAG4 was identified as the gene with the highest prognostic significance within our model. In vitro studies have elucidated SPAG4's significant role in GBM pathogenesis, potentially through the regulation of fatty acid metabolism pathways. Our in vivo investigations using a subcutaneous xenograft model have confirmed SPAG4's influence on tumor growth and its capacity to modulate the immune microenvironment. Advanced research hints that SPAG4 might achieve immune evasion by increasing CD47 expression, consequently reducing phagocytosis. These findings highlight SPAG4 as a potential GBM therapeutic target and emphasize the complexity of the immune microenvironment in GBM progression.

Validation of existing risk scores for worsening renal function in patients with newly diagnosed heart failure: a longitudinal population cohort study

Abstract Background Worsening renal function (WRF) is one of the strongest predictors of outcome in patients with heart failure (HF). The presence of WRF often influences the decision to start, up-titrate, or discontinue disease-modifying HF therapies and is one of the key determinants of suboptimal guideline-directed medical therapy. However, there are now potential cardiovascular pharmacotherapies that have favourable cardiorenal outcomes and reduce the risk of renal decline. It is therefore important to identify patients with HF early in their disease trajectory who are at risk of developing WRF. Purpose The aim of this study was to evaluate two existing WRF risk scores (the Forman risk score and the Basel risk score) in a population-based longitudinal cohort diagnosed with incident HF. Methods Data for individuals with incident HF between 2016 to 2021 were extracted and analysed from the NELSON study conducted in Tayside, Scotland. WRF was defined as a composite event of (i) having two consecutive eGFRs declined by 40% or greater; (ii) having an eGFR < 15mL/min/1.73m2; (iii) initiation of sustained dialysis; (iv) development of end-stage kidney disease; or (v) receiving kidney transplantation. The primary outcome was the occurrence of WRF within 180 days of diagnosis of HF. Among patients hospitalised with HF, in-hospital WRF was also assessed. The area under the curve (AUC) was used to assess the discrimination performance of the two risk scores in individuals with incident HF diagnosed as either in-patients or out-patients. Results 4076 individuals (mean age 72.8 ± 13.2; 58% male; 1081 HFrEF, 646 HFmrEF, 1348 HFpEF, and 1001 HF with unknown ejection fraction (EF)) who were WRF-free at incident HF diagnosis were identified. Of these, 2095 (51.4%) were diagnosed as in-patients, and 1981 (48.6%) were diagnosed as out-patients. 285 (7%) patients developed WRF within 180 days post HF diagnosis. Among the 2,095 HF in-patients, 75 (3.6%) developed WRF before discharge. The Forman risk score had an AUC of 0.618 (95% CI: 0.585 – 0.651) in predicting 180-day WRF, and an AUC of 0.650 (0.587 – 0.713) for in-hospital WRF. The Basel risk score showed similar discriminating performance with an AUC of 0.618 (0.585 – 0.651) for 180-day WRF, and 0.656 (0.593 – 0.719) for in hospital WRF. Conclusions In a contemporary population, conventional risk scores such as the Forman or Basel risk scores have limited discrimination in predicting WRF in incident HF. New models with better discrimination for WRF prediction are therefore needed.

Symptoms and ASCVD score fail to identify majority of the patients at risk of first myocardial infarction

Abstract Background Cardiovascular disease screening has long relied on the presence of symptoms like chest pain or on the Atherosclerotic Cardiovascular Disease (ASCVD) risk score, which is typically initiated at age 40. However, current research suggests that the ASCVD risk score may not incorporate a comprehensive range of factors and should be started earlier. Furthermore, the absence of symptoms such as angina in some patients prior to acute coronary syndrome (ACS) casts doubt on the reliability of symptom-based screening methods. Purpose This study aimed to evaluate the effectiveness of the ASCVD risk score and chest pain as predictors for ACS. Methods We retrospectively analyzed the data of all patients of 65 or younger who presented with their first ACS event in a single US center from January 2020 to January 2024. We collected the demographic and clinical data, including age, sex, lipid panel, blood pressure, medical history, onset of symptoms, and calculated their individual ASCVD risk score. We explored whether these patients, if assessed one week before their ACS event, would have been identified as at risk and thus prescribed lipid lowering or recommended for further diagnostic tests. Results Among 166 patients presenting with ACS, 64 (39%) were considered low risk with an ASCVD risk score of <5 or too low to calculate. 20 (12%) patients were assessed as borderline risk with an estimated risk score of 5-7.5%. In the intermediate risk category, 20 patients (12%) had a score of 7.5% -10% and 42 patients (25%) were estimated to have a score of >10 to <20%. 14 patients (8%) fell in the high-risk category, and lastly 6 patients had an LDL that was above 190 mg/dl. Regarding symptoms of chest pain or shortness of breath, 14 patients (8%) did not experience any symptoms prior to their presentation, 98 (59%) patients experienced their first episode of symptoms within 48 hours, 19 patients (11%) within 2 days to a week, 10 patients (6%) had CP leading up to their event from a week to a month, 6 patients (4%) in the range of a month to 3 months and 19 patients (11%) had symptoms longer than 3 months prior to their presentation. To summarize if there were seen 1 week prior to their first ACS events, 51% of them were not recommended for statin therapy based on their ASCVD risk score and 67% of patients either had no chest pain until the event or chest pain within 48hrs of onset of their ACS and consequently would not have been routinely screened for coronary artery disease (CAD) by any anatomical or functional methods. Conclusion(s) ASCVD risk score and symptoms fail to identify majority of patient below 65 years of age with their first MI. Given high prevalence of CAD and associated mortality, improved screening tools, in addition to symptomatology or risk scores, would be more effective in identifying individuals at risk.

Single-cell and bulk transcriptomic datasets enable the development of prognostic models based on dynamic changes in the tumor immune microenvironment in patients with hepatocellular carcinoma and portal vein tumor thrombus.

Hepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses. scRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets. scRNA-seq, clustering, GSVA enrichment, mutational profiling, and predictive immunotherapeutic treatment analyses were conducted using these data with the goal of systematically assessing the heterogeneity of PT and PVTT cells and establishing a model capable of predicting immunotherapeutic and prognostic outcomes in patients with HCC. These analyses revealed that PVTT cells exhibited patterns of tumor proliferation, stromal activation, and low levels of immune cell infiltration, presenting with immune desert and immune rejection-like phenotypes. PT cells, in contrast, were found to exhibit a pattern of immunoinflammatory activity. Core PVTT-associated genes were clustered into three patterns consistent with the tumor immune rejection and immune desert phenotypes. An established clustering model was capable of predicting tumor inflammatory stage, subtype, TME stromal activity, and patient outcomes. PVTT signature genes were further used to establish a risk model, with the risk scores derived from this model providing a tool to evaluate patient clinicopathological features including clinical stage, tumor differentiation, histological subtype, microsatellite instability status, and tumor mutational burden. These risk scores were also able to serve as an independent predictor of patient survival outcomes, responses to adjuvant chemotherapy, and responses to immunotherapy. In vitro experiments were used to partially validate the biological prediction results. These results offer new insight into the biological and immunological landscape of PVTT in HCC patients, By utilizing individual patient risk scores, providing an opportunity to guide more effective immunotherapeutic interventional efforts.

Individualized, cross-validated prediction of future dementia using cognitive assessments in people with mild cognitive symptoms.

We aimed to develop an algorithm to predict the individualized risk of future dementia using brief cognitive tests suitable for primary care. We included 612 participants with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, assessed for at least 4 years or until progression to dementia. A logistic regression model, using cognitive tests as predictors and dementia progression as an outcome, stratified participants into low, intermediate, or high risk. A second model, including 1-year cognitive test changes, was applied to the intermediate group. The models were replicated in 392 SCD/MCI participants from the BioFINDER-1 study. The best two-step model for predicting dementia incorporated Trail Making Test B (attention/executive function), Animal Fluency (verbal fluency), Mini-Mental State Examination (global cognition), and 10-word list recall (memory). The model's positive predictive value in ADNI was 85.8% and negative predictive value was 92.2% versus 62.5% and 95.6%, respectively, in BioFINDER-1. This two-step model accurately predicts individualized dementia risk. To our knowledge, this is the first algorithm for predicting all-cause dementia using a novel two-step model utilizing brief cognitive tests. Applying a validated model including the Trail Making Test B, Animal Fluency, MMSE, Alzheimer's Disease Assessment Scale delayed, and immediate recall can robustly and accurately categorize individuals into low, intermediate, or high risk of dementia progression and can facilitate clinical decision-making and personalized patient care. We created an app that is available for research and educational purposes at https://brainapps.shinyapps.io/PredictAllCauseDementia to provide an individualized risk score for dementia progression.

Prognostic Significance and Immune Landscape of an Efferocytosis-Related Gene Signature in Bladder Cancer.

Bladder cancer poses a significant global health challenge, underscoring the imperative for precise prognostic instruments to advance patient care. Against the backdrop of efferocytosis's increasingly recognized role in cancer, this research endeavors to develop and authenticate a prognostic signature intricately linked to efferocytosis in bladder cancer. LASSO-COX regression analysis crafted an efferocytosis-related genes risk prognostic model, followed by the construction of a column chart. External validation sets confirmed the predictive accuracy of both the model and chart. Clinical, tumor microenvironment, drug sensitivity, and immunotherapy analyses were employed to comprehensively assess efferocytosis-related scores. The expression of TGFB3 key genes was validated via RT-PCR and western blotting. Further validation included Transwell, Wound healing, Colony formation, and EDU assays. We formulated and validated an efferocytosis-related genes risk model in bladder cancer, comprising 13 core genes. The risk model demonstrated autonomous prognostic significance in both univariate and multivariate Cox analyses. Following the multivariate analysis, we devised a nomogram. Moreover, by utilizing individual risk scores derived from this risk model, we successfully stratified patients into two discernible risk cohorts, unveiling noteworthy variances in immune infiltration profiles and responsiveness to immunotherapy. Notably, the model's key gene TGFB3 was validated through comprehensive experimental investigations, including Transwell assays for migration and invasion and Wound healing assays for motility on the T24 and BIU cell lines. This study has furnished innovative perspectives on an efferocytosis-related prognostic signature, elucidating the prognosis and immune milieu intricacies in patients with bladder cancer.

Emergency admission predictive risk stratification models: assessment of implementation consequences (PRISMATIC 2): protocol for a mixed methods study.

Emergency admissions are costly, increasingly numerous, and associated with adverse patient outcomes. Policy responses have included the widespread introduction of emergency admission risk stratification (EARS) tools in primary care. These tools generate scores that predict patients' risk of emergency hospital admission and can be used to support targeted approaches to improve care and reduce admissions. However, the impact of EARS is poorly understood and there may be unintended consequences. To assess effects, mechanisms, costs, and patient and healthcare professionals' views related to the introduction of EARS tools in England. Quasi-experimental mixed methods design using anonymised routine data and qualitative methods. We will apply multiple interrupted time series analysis to data, aggregated at former Clinical Commissioning Group level, to look at changes in emergency admission and other healthcare use following EARS introduction across England. We will investigate GP decision-making at practice level using linked general practice and secondary care data to compare case-mix, demographics, indicators of condition severity and frailty associated with emergency admissions before and after EARS introduction. We will undertake interviews (n~48) with GPs and healthcare staff to understand how patient care may have changed. We will conduct focus groups (n=2) and interviews (n~16) with patients to explore how they perceive that communication of individual risk scores might affect their experiences and health seeking behaviours. Findings will provide policymakers, healthcare professionals, and patients, with a better understanding of the effects, costs and stakeholder perspectives related to the introduction of EARS tools.

Algorithmic fairness in predictive policing

AbstractThe increasing use of algorithms in predictive policing has raised concerns regarding the potential amplification of societal biases. This study adopts a two-phase approach, encompassing a systematic review and the mitigation of age-related biases in predictive policing. Our systematic review identifies a variety of fairness strategies in existing literature, such as domain knowledge, likelihood function penalties, counterfactual reasoning, and demographic segmentation, with a primary focus on racial biases. However, this review also highlights significant gaps in addressing biases related to other protected attributes, including age, gender, and socio-economic status. Additionally, it is observed that police actions are a major contributor to model discrimination in predictive policing. To address these gaps, our empirical study focuses on mitigating age-related biases within the Chicago Police Department's Strategic Subject List (SSL) dataset used in predicting the risk of being involved in a shooting incident, either as a victim or an offender. We introduce Conditional Score Recalibration (CSR), a novel bias mitigation technique, alongside the established Class Balancing method. CSR involves reassessing and adjusting risk scores for individuals initially assigned moderately high-risk scores, categorizing them as low risk if they meet three criteria: no prior arrests for violent offenses, no previous arrests for narcotic offenses, and no involvement in shooting incidents. Our fairness assessment, utilizing metrics like Equality of Opportunity Difference, Average Odds Difference, and Demographic Parity, demonstrates that this approach significantly improves model fairness without sacrificing accuracy.

Multi-factor early monitoring method based on D-dimer for iliac crest flap loss

BackgroundIn recent years, the utilization of autogenous vascularized iliac crest flap for repairing jaw defects has seen a significant rise. However, the visual monitoring of iliac bone flaps present challenges, frequently leading to delayed detection of flap loss. Consequently, there’s a urgent need to develop effective indicators for monitoring postoperative complications in iliac crest flaps.MethodsA retrospective analysis was conducted on 160 patients who underwent vascularized iliac crest flap transplantation for jawbone reconstruction from January 2020 to December 2022. We investigated the changes in D-dimer levels among patients with or without postoperative complications. Additionally, multivariable logistic regression analysis was performed to explore potential individual risk factors, including surgical duration, age, pathology type, absolute and relative D-dimer levels, and gender, culminating in the development of a nomogram.ResultsOn the first day following surgery, patients who experienced thrombosis exhibited a substantial increase in plasma D-dimer levels, reaching 3.75 mg/L, 13.84 times higher than the baseline. This difference was statistically significant (P < 0.05) compared to patients without postoperative complications. Furthermore, the nomogram we have developed and validated effectively predicts venous thrombosis, assigning individual risk scores to patients. This predictive tool was assessed in both training and validation cohorts, achieving areas under the curve (AUC) of 0.630 and 0.600, with the 95% confidence intervals of 0.452–0.807 and 0.243–0.957, respectively.ConclusionsOur study illustrates that postoperative plasma D-dimer levels can serve as a sensitive biomarker for monitoring thrombosis-induced flap loss. Moreover, we have developed a novel prediction model that integrates multiple factors, thereby enhancing the accuracy of early identification of patients at risk of thrombosis-associated flap loss. This advancement contributes to improving the overall management and outcomes of such procedures.

Impact of COVID-19 in the incidence of venous thromboembolism (VTE) and clinical outcomes in cancer patients: a cohort study.

To determine the incidence of VTE and clinical outcomes in a cohort of cancer patients and COVID-19 infection, and to establish possible predictive factors of VTE. A single-center retrospective cohort study was performed to determine the incidence of VTE and mortality in 118 cancer patients with SARS-CoV-2 infection from March to August 2020. We calculated individual Khorana Risk and CATS-MICA scores in order to evaluate their utility to identify risk of VTE or death. Continuous variables were compared using Wilcoxon or Student's T test, and categorical variables were compared using the Chi-Square or Fisher's exact text among patients with and without VTE. A Log-Rank test was performed to detect mortality differences between the groups. A total of 118 patients were included. VTE global incidence was 4.2% (n = 5), and mortality 25.4% (n = 30). Obesity (p = 0.05), recent chemotherapy (p = 0.049) and use of steroids (p = 0.006) were related to higher risk of VTE in the univariate analysis, although they were not confirmed in the multivariate analysis as independent risk factors. Statistically significant differences in all-cause, COVID-19-related and cancer-related mortality according to the Khorana risk score (KRS) were observed. CATS-MICA score (CMS) also showed statistically significant differences in mortality between low- and high-risk patients. Prediction of risk of VTE development with these scores showed a tendency towards significance. In this cohort, VTE incidence was similar to previously reported in the general population with SARS-CoV-2 infection. KRS was associated with overall and specific-cause mortality, and might be a useful prognostic tool in this setting.

Best Practices for Optimizing Patients Undergoing Surgical Procedures to Prevent Postoperative Venous Thromboembolism: A Quality Improvement Project.

Introduction Current studies suggest that both chemical and mechanical venous thromboembolism (VTE) prophylaxis is underused, which is concerning due to the potential lethality of VTEs. The Caprini risk score is a preoperative VTE risk assessment that determines a patient's risk of enduring a VTE. The objective of this study was to examine postoperative cases of VTE to determine if accurate VTE risk stratification was performed and whether appropriateVTE prophylaxis was administered. Methods A retrospective analysis was conducted on 23 reported cases of VTE that occurred at a Central Florida hospitalfrom April 1, 2021, to March 31, 2022. Relevant demographic and medical information was gathered from each patient chart to calculate an individual Caprini risk score and determine the type of chemical VTE prophylaxis that was received. Results Out of 23 reported cases of VTE in surgical patients, 17 were ultimately determined to have suffered VTE associated with their hospitalization and surgery. Thirteen out of 17 (76%) received appropriate perioperative chemical deep vein thrombosis (DVT) prophylaxis based on the calculated Caprini risk score and corresponding recommendations. Four out of 17 (24%) were determined to have received insufficient perioperative chemical DVT prophylaxis. Conclusion Consistent utilization of a DVT/pulmonary embolism (PE) risk stratification tool, such as the Caprini risk score calculator, is essential in the prevention of postoperative VTE. Hospitalscan improve the utilization of such a tool and thereby reduce the number of embolic events by making it more visible and accessible to the overseeing provider in the electronic medical record (EMR).

Alzheimer's disease genetic pathways impact cerebrospinal fluid biomarkers and imaging endophenotypes in non-demented individuals.

Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine. We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity. CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication. This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies. Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.

Cancer risk following onset of type 2 diabetes in New Zealanders with impaired glucose tolerance over 25 years: a matched prospective cohort study

BackgroundIn people with prediabetes, the link between developing type 2 diabetes (T2D) and cancer risk among those with impaired glucose tolerance (IGT) remains uncertain. We examined this association in IGT individuals from primary care in South and West Auckland, New Zealand, spanning 1994–2019, assessing 5- and 10-year cancer risks.MethodsStudy cohorts were extracted from the Diabetes Care Support Service in Auckland, New Zealand, linking it with national registries for death, cancer, hospital admissions, pharmaceutical claims, and socioeconomic status. We compared cancer risks in individuals with IGT newly diagnosed with or without T2D within a 1–5-year exposure window. Employing tapered matching and landmark analysis to address potential confounding effects, we formed comparative IGT cohorts. Weighted Cox regression models were then employed to assess the association between T2D onset and 5- and 10-year cancer risks.ResultsThe study included 26,794 patients with IGT, with 629 newly diagnosed with T2D within 5 years and 13,007 without such a diagnosis. Those progressing to T2D had similar 5-year cancer risk but significantly higher 10-year risk (HR 1.35; 95% CI 1.09–1.68). This association was stronger in older individuals, the socioeconomically deprived, current smokers, those with worse metabolic measures, and lower renal function. Patients with IGT of NZ European ethnicity had lower 10-year cancer risk.ConclusionsT2D diagnosis influences cancer risk in individuals with IGT. Developing risk scores for high-risk IGT individuals and implementing cancer screening and structured diabetes prevention, especially in deprived or minority ethnic populations, is essential.

Personalized starting age of gastric cancer screening based on individuals' risk profiles: a population-based, prospective study.

The current recommended starting age for gastric cancer screening lacks unified guideline and individualized criteria. We aimed to determine the risk-stratified starting age for gastric cancer screening in China based on individuals' risk profiles and to develop an online calculator for clinical application. In this multicenter, population-based, prospective study, we allocated participants enrolled between 2015 and 2017 (N = 59 771, aged 40-69 years) to screened and unscreened groups and observed them for primary endpoints: gastric cancer occurrence as well as all-cause and gastric cancer-specific death. Median follow-up was 6.07 years. To determine the reference starting age, the effectiveness of gastric cancer screening was assessed by age group after propensity score matching. Further, we categorized the calculated individual risk scores (using well-established risk factors) by quantile. Subsequently, we used age-specific, 10-year cumulative risk curves to estimate the risk-stratified starting age-that is, when the individual's risk level matches the reference starting age risk threshold. During follow-up, 475 gastric cancer case patients, 182 gastric cancer-related deaths, and 1860 all-cause deaths occurred. All-cause and gastric cancer-specific mortality decreased among screened individuals 45 years of age and older and 50 to 59 years of age, respectively. Thus, the average population (referent) starting age was set as 50 years. The 10-year cumulative risk of gastric cancer in the average population aged 50 years was 1.147%. We stratified the starting age using 8 risk factors and categorized participants as low-risk, medium-risk, and high-risk individuals whose risk-stratified starting age was 58, 50, and 46 years, respectively. Although high-risk individuals warrant starting gastric cancer screening 3 to 5 years earlier than for the average population (aged 50 years), low-risk individuals can tolerate delayed screening. Our online, personalized starting age calculator will help with risk-adapted gastric cancer screening (https://web.consultech.com.cn/gastric/#/).

One Health Risk and Disease (OHRAD): a tool to prioritise the risks for epidemic-prone diseases from One Health perspective

BackgroundThe rise in epidemic-prone diseases daily poses a serious concern globally. Evidence suggests that many of these diseases are of animal origin and contribute to economic loss. Considering the limited time and other resources available for the animal and human health sectors, selecting the most urgent and significant risk factors and diseases is vital, even though all epidemic-prone diseases and associated risk factors should be addressed. The main aim of developing this tool is to provide a readily accessible instrument for prioritising risk factors and diseases that could lead to disease emergence, outbreak or epidemic.MethodsThis tool uses a quantitative and semi-quantitative multi-criteria decision analysis (MCDA) method that involves five steps: Identifying risk factors and diseases, Weighting the criteria, Risk and disease scoring, Calculating risk impact and disease burden score, and Ranking risks and diseases. It is intended to be implemented through a co-creation workshop and involves individual and group activities. The last two steps are automated in the MS Excel score sheet.ResultsThis One Health Risk and Disease (OHRAD) prioritisation tool starts with an individual activity of identifying the risks and diseases from the more extensive list. This, then, leads to a group activity of weighing the criteria and providing scores for each risk and disease. Finally, the individual risk and disease scores with the rankings are generated in this tool.ConclusionsThe outcome of this OHRAD prioritisation tool is that the top risks and diseases are prioritised for the particular context from One Health perspective. This prioritised list will help experts and officials decide which epidemic-prone diseases to focus on and for which to develop and design prevention and control measures.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BackgroundThe detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.MethodsWe recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan–Meier curve and log-rank tests were used for survival analysis.ResultsOverall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.ConclusionsThe proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Validation of a risk score to differentiate autoimmune and viral encephalitis: a Nationwide Cohort Study in Denmark

BackgroundA score to differentiate autoimmune (AE) and viral encephalitis (VE) early upon admission has recently been developed but needed external validation. The objective of this study was to evaluate the performance of the score in a larger and more diagnostically diverse patient cohort.MethodsWe conducted a retrospective nationwide and population-based cohort study including all adults with encephalitis of definite viral (2015–2022) or autoimmune aetiology (2009–2022) in Denmark. Variables included in the score-model were extracted from patient records and individual risk scores were assessed. The performance of the score was assessed by receiver-operating characteristics (ROC) curve analyses and calculation of the area under the curve (AUC).ResultsA total of 496 patients with encephalitis [AE n = 90, VE n = 287 and presumed infectious encephalitis (PIE) n = 119] were included in the study. The score was highly accurate in predicting cases of AE reaching an AUC of 0.94 (95% CI 0.92–0.97). Having a score ≥ 3 predicted AE with a PPV of 87% and an NPV of 91%. The risk score was found to perform well across aetiological subgroups and applied to the PIE cohort resulted in an AUC of 0.88 (95% CI 0.84–0.93).ConclusionThe excellent performance of the score as reported in the development study was confirmed in this significantly larger and more diverse cohort of patients with encephalitis in Denmark. These results should prompt further prospective testing with wider inclusion criteria.

Comparison of American College of Cardiology/American Heart Association Cardiovascular Risk Score, Framingham Risk Scores, European Society of Cardiology Cardiovascular Disease Risk Calculator, QRISK3-2018 Risk Calculator in Patients Presenting with First Time with Myocardial Infarction: A Step toward the Development of Indian Cardiac Risk Score

Introduction: One of the primary reasons for fatalities and impairments on a global scale is cardiovascular disease (CVD), encompassing a cluster of illnesses impacting the heart and blood vessels. A CVD risk assessment is a screening tool that measures an individual’s risk of developing CVD. Objective: The objective of this study was to compare and evaluate cardiovascular risk using internationally recognized CVD risk scoring systems. Methodology: A prospective, cross-sectional study was carried out for 2 months after the approval of the ethics committee. Naive myocardial infarction (MI) patients presenting to the cardiology department and conforming to the inclusion criteria were enrolled in the study. Patients who are taking statins or any lipid-lowering drugs, as well as patients who are on low-dose aspirin and antidiabetic medications. Using the aforementioned gathered data, the cardiovascular risk scores were computed employing the American College of Cardiology/American Heart Association (ACC/AHA), Framingham risk scores (FRS), European Society of Cardiology (ESC) – CVD risk calculator, and QRISK→3-2018, and subsequently categorized as low risk, intermediate, or high risk based on the individual risk scores. Results: We enrolled 42 patients, with a mean age of 60 years with male dominance. The mean body mass index was 25.5. With a cholesterol/high-density lipoprotein (HDL) ratio of 4.9, the mean systolic blood pressure/diastolic blood pressure was 126/77 mm of Hg. The mean ACC/AHA cardiovascular risk score for 10-year atherosclerotic cardiovascular disease (ASCVD) risk was 11.34%, FRS mean score was 12%, ESC CVD risk calculator mean score was 15.45%, and QRISK3-2018 risk calculator mean score was 20.87% in patients presenting with first time with MI. Conclusion: QRISK3-2018 risk calculator is the most effective out of all the risk calculators with a maximum detection rate of MI in first time presented patients with Naïve MI patients.

Nomogram to Predict Outcomes After Staged Revascularization in ST-Segment Elevation Myocardial Infarction and Multivessel Coronary Artery Disease.

Approximately 50% of ST-segment elevation myocardial infarction (STEMI) patients have multivessel coronary artery disease (MVD). The management strategy for these patients remains controversial. This study aimed to develop predictive models and nomogram of outcomes in STEMI patients with MVD for better identification and classification. The least absolute shrinkage and selection operator (LASSO) method was used to select the features most significantly associated with the outcomes. A Cox regression model was built using the selected variables. One nomogram was computed from each model, and individual risk scores were obtained by applying the nomograms to the cohort. After regrouping patients based on nomogram risk scores into low- and high-risk groups, we used the Kaplan-Meier method to perform survival analysis. The C-index of the major adverse cardiovascular event (MACE)-free survival model was 0·68 (95% CI 0·62-0·74) and 0·65 [0·62-0·68]) at internal validation, and that of the overall survival model was 0·75 (95% CI 0·66-0·84) and (0·73 [0·65-0·81]). The predictions of both models correlated with the observed outcomes. Low-risk patients had significantly lower probabilities of 1-year or 3-year MACEs (4% versus 11%, P= 0.003; 7% versus 15%, P=0.01, respectively) and 1-year or 3-year all-cause death (1% versus 3%, P=0.048; 2% versus 7%, respectively, P=0.001) than high-risk patients. Our nomograms can be used to predict STEMI and MVD outcomes in a simple and practical way for patients who undergo primary PCI for culprit vessels and staged PCI for non-culprit vessels.

A prediction model for lung metastases in patients with indeterminate pulmonary nodules in newly diagnosed colorectal cancer

IntroductionMultidisciplinary teams treating patients with newly diagnosed Colorectal Cancer (CRC) often encounter the appearance of Indeterminate Pulmonary Nodules (IPNs) that warrants follow–up with repetitive medical imaging and anxiety for patients. We determined the incidence of IPNs in patients with newly diagnosed CRC and developed and validated a model for individualized risk prediction of IPNs being lung metastases. Material and methodsNewly diagnosed CRC who underwent surgery between November 2011 to June 2014 were included to create the risk model, developed using both clinical experience and statistical selection. Discrimination and calibration slopes of the risk score were evaluated in an independent temporal validation sample. A nomogram is presented to assist clinicians in estimating an individual risk score. ResultsOut of 2111 CRC patients staged with chest CT, 204 (9.6%) had IPNs and 54/204 (26%) had lung metastases. We identified 4 predictors: “location of primary tumour”, “pathological nodal stage”, “size of the largest nodule” and “extrapulmonary synchronous metastases at diagnosis”. Discrimination of the final model in the validation sample was demonstrated by the difference in mean predicted risk between progressed cases en non-progressed cases (49% versus 21%, p = <0.001). ConclusionA prediction model with 4 clinical risk factors can be used to assist multidisciplinary teams in the prediction of individualized risk of lung metastases and imaging strategy in patients with IPNs and newly diagnosed colorectal cancer. The model performed well in new patients not included in the model development.