Optimal Stratification Research Articles

AB-HFPEF score: A new and improved HFpEF prediction tool for the Indian patient

Abstract Background/Introduction Heart failure with preserved ejection fraction (HFpEF) remains underdiagnosed in low-middle income countries including India. The simple and validated H2FPEF score has largely replaced the diagnostic need for invasive right heart catheterization. The score has therapeutic implications by stratifying patients into low, intermediate and high-risk groups. The relevance of this score is yet to be ascertained in the Indian population. Purpose We hypothesized that the H2FPEF score may fall short in accurately predicting HFpEF in our population, thus warranting a new score. Methods We conducted a prospective observational study over one year at the out-patient department of a tertiary-care hospital in India. Patients presenting with clinical features of heart failure were screened and enrolled in the study. Demographic, clinical, laboratory and echocardiographic details were recorded by the principal investigator using an online data collection tool. Patients were stratified into ‘low risk’, ‘intermediate risk’ and ‘high risk’ probabilities, with respective cut-off values of 0-3, 4-6, and 7-9 using the point-based score, and <30%, 30-59%, and >60% via the continuous model of H2FPEF score. Pearson’s chi-square test identified significant risk factors that were purposed into a novel AB-HFPEF score. Cohen’s kappa correlation determined the degree of agreement in the risk stratifications of the H2FPEF and the AB-HFPEF score, using the continuous scoring model as the reference. Results Among the 189 patients screened, 137 were enrolled in the study. The baseline characteristics are recorded in Table 1. Pedal edema and uncontrolled hypertension were noteworthy risk factors. A novel AB-HFPEF score was subsequently designed and scored as follows: Age (> 60 years) - 1, BMI (> 30 kg/m2) - 2, uncontrolled Hypertension (>140/90 mmHg) - 1, Filling pressure (E/e' > 9) - 1, Pulmonary Artery Systolic Pressure (> 35 mmHg) - 1, pedal Edema - 1, atrial Fibrillation - 3. Receiver operating characteristics (ROC) curve analysis demonstrated an area under the ROC curve (AUC) of 0.85 (95%CI, 0.78 to 0.91) for ‘low risk’ strata and 0.87 (95%CI, 0.81 to 0.93) for ‘high risk’ strata. The optimal stratification was 0-2, 3-4, and 5-10 points for the ‘low risk’, ‘intermediate risk’, and ‘high risk’ respectively. There was a moderate agreement of the AB-HFPEF score with the reference score (κ = 0.48 (95% CI, 0.36 to 0.60), p < 0.001), while the point-based H2FPEF score had only a slight agreement (κ = 0.15 (95% CI, 0.05 to 0.25), p < 0.001), as illustrated in Figure 1. Conclusion(s) The novel AB-HFPEF score performs better risk stratification in Indian patients with HFpEF as compared to the point-based H2FPEF score. We hence propose the AB-HFPEF score as an instrument for tailored cardiology care in the Indian demographic, laying the cornerstone for larger validation and prognostication studies.Table 1:Baseline characteristicsFigure 1:Heat Map Illustration

Strata Design for Variance Reduction in Stochastic Simulation

Stratified sampling is one of the powerful variance reduction methods for analyzing system performance, such as reliability, with stochastic simulation. It divides the input space into disjoint subsets, called strata, to draw samples from each stratum. Partitioning the input space properly and allocating greater computational effort to crucial strata can help accurately estimate system performance with a limited computational budget. How to create strata, however, has yet to be thoroughly examined. Strata design faces the curse of dimensionality and data scarcity as the input dimension increases. We analytically derive the optimal stratification structure that minimizes the estimation variance for univariate problems. Further, reconciling the optimal stratification into decision trees, we devise a robust algorithm for multi-dimensional problems. Numerical experiments and a wind turbine case study demonstrate the superiority of the proposed method in terms of variance reduction, leading to computational efficiency and scalability.

Regional Spatial Mean of Ionospheric Irregularities Based on K-Means Clustering of ROTI Maps

In this paper, we investigate and propose the application of an unsupervised machine learning clustering method to characterize the spatial and temporal distribution of ionospheric plasma irregularities over the Western African equatorial region. The ordinary Kriging algorithm was used to interpolate the rate of change of the total electron content (TEC) index (ROTI) over gridded 0.5° by 0.5° latitude and longitude regional maps in order to simulate the level of ionospheric plasma irregularities in a quasi-real-time scenario. K-means was used to obtain a spatial mean index through an optimal stratification of regional post-processed ROTI maps. The results obtained could be adapted by appropriate K-means algorithms to a real-time scenario, as has been performed for other applications. This method could allow us to monitor plasma irregularities in real time over the African region and, therefore, lead to the possibility of mitigating their effects on satellite-based location systems in the said region.

Developing an optimal stratification model for colorectal cancer screening and reducing racial disparities in multi-center population-based studies

BackgroundEarly detection of colorectal neoplasms can reduce the colorectal cancer (CRC) burden by timely intervention for high-risk individuals. However, effective risk prediction models are lacking for personalized CRC early screening in East Asian (EAS) population. We aimed to develop, validate, and optimize a comprehensive risk prediction model across all stages of the dynamic adenoma-carcinoma sequence in EAS population.MethodsTo develop precision risk-stratification and intervention strategies, we developed three trans-ancestry PRSs targeting colorectal neoplasms: (1) using 148 previously identified CRC risk loci (PRS148); (2) SNPs selection from large-scale meta-analysis data by clumping and thresholding (PRS183); (3) PRS-CSx, a Bayesian approach for genome-wide risk prediction (PRSGenomewide). Then, the performance of each PRS was assessed and validated in two independent cross-sectional screening sets, including 4600 patients with advanced colorectal neoplasm, 4495 patients with non-advanced adenoma, and 21,199 normal individuals from the ZJCRC (Zhejiang colorectal cancer set; EAS) and PLCO (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; European, EUR) studies. The optimal PRS was further incorporated with lifestyle factors to stratify individual risk and ultimately tested in the PLCO and UK Biobank prospective cohorts, totaling 350,013 participants.ResultsThree trans-ancestry PRSs achieved moderately improved predictive performance in EAS compared to EUR populations. Remarkably, the PRSs effectively facilitated a thorough risk assessment across all stages of the dynamic adenoma-carcinoma sequence. Among these models, PRS183 demonstrated the optimal discriminatory ability in both EAS and EUR validation datasets, particularly for individuals at risk of colorectal neoplasms. Using two large-scale and independent prospective cohorts, we further confirmed a significant dose–response effect of PRS183 on incident colorectal neoplasms. Incorporating PRS183 with lifestyle factors into a comprehensive strategy improves risk stratification and discriminatory accuracy compared to using PRS or lifestyle factors separately. This comprehensive risk-stratified model shows potential in addressing missed diagnoses in screening tests (best NPV = 0.93), while moderately reducing unnecessary screening (best PPV = 0.32).ConclusionsOur comprehensive risk-stratified model in population-based CRC screening trials represents a promising advancement in personalized risk assessment, facilitating tailored CRC screening in the EAS population. This approach enhances the transferability of PRSs across ancestries and thereby helps address health disparity.Graphical

In Silico TRials guide optimal stratification of ATrIal FIbrillation patients to Catheter Ablation and pharmacological medicaTION: the i-STRATIFICATION study.

Patients with persistent atrial fibrillation (AF) experience 50% recurrence despite pulmonary vein isolation (PVI), and no consensus is established for secondary treatments. The aim of our i-STRATIFICATION study is to provide evidence for stratifying patients with AF recurrence after PVI to optimal pharmacological and ablation therapies, through in silico trials. A cohort of 800 virtual patients, with variability in atrial anatomy, electrophysiology, and tissue structure (low-voltage areas, LVAs), was developed and validated against clinical data from ionic currents to electrocardiogram. Virtual patients presenting AF post-PVI underwent 12 secondary treatments. Sustained AF developed in 522 virtual patients after PVI. Second ablation procedures involving left atrial ablation alone showed 55% efficacy, only succeeding in the small right atria (<60 mL). When additional cavo-tricuspid isthmus ablation was considered, Marshall-PLAN sufficed (66% efficacy) for the small left atria (<90 mL). For the bigger left atria, a more aggressive ablation approach was required, such as anterior mitral line (75% efficacy) or posterior wall isolation plus mitral isthmus ablation (77% efficacy). Virtual patients with LVAs greatly benefited from LVA ablation in the left and right atria (100% efficacy). Conversely, in the absence of LVAs, synergistic ablation and pharmacotherapy could terminate AF. In the absence of ablation, the patient's ionic current substrate modulated the response to antiarrhythmic drugs, being the inward currents critical for optimal stratification to amiodarone or vernakalant. In silico trials identify optimal strategies for AF treatment based on virtual patient characteristics, evidencing the power of human modelling and simulation as a clinical assisting tool.

Cluster Analysis and Ablation Success Rate in Atrial Fibrillation Patients Undergoing Catheter Ablation

Atrial fibrillation (AF) is a disease of high heterogeneity, and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear. Conventional classification of AF (e.g. according to duration, atrial size, and thromboembolism risk) fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan. In recent years, research on machine learning has found that cluster analysis, an unsupervised data-driven approach, can uncover the intrinsic structure of data and identify clusters of patients with pathophysiological similarity. It has been demonstrated that cluster analysis helps improve the characterization of AF phenotypes and provide valuable prognostic information. In our cohort of AF inpatients undergoing radiofrequency catheter ablation, we used unsupervised cluster analysis to identify patient subgroups, to compare them with previous studies, and to evaluate their association with different suitable ablation patterns and outcomes. The participants were AF patients undergoing radiofrequency catheter ablation at West China Hospital between October 2015 and December 2017. All participants were aged 18 years or older. They underwent radiofrequency catheter ablation during their hospitalization. They completed the follow-up process under explicit informed consent. Patients with AF of a reversible cause, severe mitral stenosis or prosthetic heart valve, congenital heart disease, new-onset acute coronary syndrome within three months prior to the surgery, or a life expectancy less than 12 months were excluded according to the exclusion criteria. The cohort consisted of 1102 participants with paroxysmal or persistent/long-standing persistent AF. Data on 59 variables representing demographics, AF type, comorbidities, therapeutic history, vital signs, electrocardiographic and echocardiographic findings, and laboratory findings were collected. Overall, data for the variables were rarely missing (<5%), and multiple imputation was used for correction of missing data. Follow-up surveys were conducted through outpatient clinic visits or by telephone. Patients were scheduled for follow-up with 12-lead resting electrocardiography and 24-hours Holter monitoring at 3 months and 6 months after the ablation procedure. Early ablation success was defined as the absence of documented AF, atrial flutter, or atrial tachycardia >30 seconds at 6-month follow-up. Hierarchical clustering was performed on the 59 baseline variables. All characteristic variables were standardized to have a mean of zero and a standard deviation of one. Initially, each patient was regarded as a separate cluster, and the distance between these clusters was calculated. Then, the Ward minimum variance method of clustering was used to merge the pair of clusters with the minimum total variance. This process continued until all patients formed one whole cluster. The "NbClust" package in R software, capable of calculating various statistical indices, including pseudo t2 index, cubic clustering criterion, silhouette index etc, was applied to determine the optimal number of clusters. The most frequently chosen number of clusters by these indices was selected. A heatmap was generated to illustrate the clinical features of clusters, while a tree diagram was used to depict the clustering process and the heterogeneity among clusters. Ablation strategies were compared within each cluster regarding ablation efficacy. Five statistically driven clusters were identified: 1) the younger age cluster (n=404), characterized by the lowest prevalence of cardiovascular and cerebrovascular comorbidities but the highest prevalence of obstructive sleep apnea syndrome (14.4%); 2) a cluster of elderly adults with chronic diseases (n=438), the largest cluster, showing relatively higher rates of hypertension, diabetes, stroke, and chronic obstructive pulmonary disease; 3) a cluster with high prevalence of sinus node dysfunction (n=160), with patients showing the highest prevalence of sick sinus syndrome and pacemaker implantation; 4) the heart failure cluster (n=80), with the highest prevalence of heart failure (58.8%) and persistent/long-standing persistent AF (73.7%); 5) prior coronary artery revascularization cluster (n=20), with patients of the most advanced age (median: 69.0 years old) and predominantly male patients, all of whom had prior myocardial infarction and coronary artery revascularization. Patients in cluster 2 achieved higher early ablation success with pulmonary veins isolation alone compared to extensive ablation strategies (79.6% vs. 66.5%; odds ratio [OR]=1.97, 95% confidence interval [CI]: 1.28-3.03). Although extensive ablation strategies had a slightly higher success rate in the heart failure group, the difference was not statistically significant. This study provided a unique classification of AF patients undergoing catheter ablation by cluster analysis. Age, chronic disease, sinus node dysfunction, heart failure and history of coronary artery revascularization contributed to the formation of the five clinically relevant subtypes. These subtypes showed differences in ablation success rates, highlighting the potential of cluster analysis in guiding individualized risk stratification and treatment decisions for AF patients.

Evaluating German PISA stratification designs: a simulation study

Stratification is an important design feature of many studies using complex sampling designs and it is often used in large-scale assessment (LSA) studies, such as the Programme for International Student Assessment (PISA), for two main reasons. First, stratification variables that achieve a high between and low within strata variance can improve the efficiency of a survey design. Second, stratification allows one to, explicitly or implicitly, control for sample sizes across subpopulations. It ensures that some parts of a population are in the sample in predetermined proportions. In this study, we determine through simulation which stratification scheme is best for PISA in Germany. For this, we consider the constraints imposed by the international sampling design, the available information about schools, and specific national characteristics of the German educational system. We examine seven different stratification designs selected based on scenarios used in past LSAs in Germany and theoretical considerations for future implementations. The chosen scenarios were compared with two reference scenarios: (1) an unstratified design and (2) a synthetic optimal stratification design. The simulation study reveals that the stratification design currently applied in PISA produces satisfactory results regarding sampling precision. The present stratification design is based on Germany's federal states and school types. However, this approach leads to small strata, which has been problematic for estimating sampling variance in previous cycles. Therefore, alternative stratification scenarios were considered and, in addition to overcoming the small-strata problem, also led to smaller standard errors for estimates of student mean performance in mathematics, science, and reading. As a result of this study, we recommend considering three different stratification designs for Germany in future cycles of PISA. These recommendations aim to: (1) improve the sampling efficiency while keeping the sample size constant, (2) follow a sound methodological approach, and (3) make conservative and cautious changes while maintaining a reflection of the structure of the German federal school system with different school types. These suggestions include a reinvented stratification of grouped German federal states and designs with school types as explicit stratifiers and federal states as implicit stratifiers.

Abstract PO1-11-11: A Novel Body Composition Risk Score (B-Score) and Overall Survival among Patients with Early-Stage Breast Cancer

Abstract Background: Measurements (amount, distribution, and radiodensity) of muscle and adipose tissue were reported to be individually associated with overall survival in breast cancer survivors, but they were not typically combined to develop an overall risk score. Such a score can identify patients at high risk of death and prioritize patients in need of lifestyle interventions. Objectives: We developed a novel body composition risk score (B-Score) by combining multiple tissue measurements. Methods: We included 3105 patients with stage II or III breast cancer (diagnosed from 2000 to 2013) at Kaiser Permanente Northern California and Dana Farber Cancer Institute. From CT scans at diagnosis, we used the third lumber vertebrae as the landmark to assess areas (cm2) and radiodensity (HU) of muscle and adipose tissue. We divided the area by height2 (m2) to derive the index and measured the average HU of the tissue area as the radiodensity, which acts as a marker of tissue 'quality' to be indicative of lipid content, inflammation, and angiogenesis. Out of all tissue measurements, we considered skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI) and SAT radiodensity as they were independent predictors for overall survival. Each measurement was dichotomized using optimal stratification, with low SMI (&amp;lt; 40.1cm2/m2), high SATI (≥75.7cm2/m2), and high SAT radiodensity (≥ -97.2HU) considered risk factors. We calculated B-Score as the sum of these factors and estimated its association with overall survival using Cox proportional hazards regression with adjustment for clinicopathologic factors. Results: Mean (standard deviation) age at diagnosis was 53.9 (11.8) years, 2161 (70.1%) were Non-Hispanic White, 1880 (60.5%) were stage II, and 919 (29.6%) died over a median follow-up of 10.7 years. Most patients (60.6%) had only one body composition risk factor (B-Score = 1). Compared to those with no body composition risk factors (B-Score = 0), the risk of death increased with more body composition risk factors: the adjusted hazard ratios were 1.10 (95% CI: 0.85, 1.42), 1.47 (95% CI: 1.12, 1.92), and 2.11 (95% CI: 1.26, 3.53) for B-Scores of 1, 2, and 3, respectively (Ptrend &amp;lt; 0.001). Conclusions: More unfavorable body composition characteristics were associated with increased risks of overall mortality in a dose-response manner. Considering body composition measurements together as a composite score (B-Score) may prove a useful tool to identify patients at high risk of death following breast cancer diagnosis. Table. Adjusted Associations of the Body Composition Risk Score (B-Score) with Overall Mortality Among Patients with Early-Stage Breast Cancer Abbreviations: HR, hazard ratio. a Areas (cm2) and radiodensity (HU) of skeletal muscle and subcutaneous adipose tissue (SAT) were extracted from the single axial CT slice at the third lumbar vertebra. The body composition risk score was defined as the sum of three risk factors: low skeletal muscle index (SMI; &amp;lt;40.1cm2/m2), high SAT index (SATI; ≥75.7cm2/m2), and high SAT radiodensity (≥ -97.2HU). b Adjusted for age (years), race and ethnicity (Asian, Black, Hispanic, Non-Hispanic White, and Others), stage (II, III), ER (ER+, ER-), PR (PR+, PR-), HER2 (HER2+, HER2-, unknown), smoking (current, former, never), Charlson comorbidity index (0, 1-2, ≥3), and BMI (&amp;lt;18.5, 28.5-24.9, 25-29.9, and ≥30 kg/m2). For the trend test, B-Score was considered as a continuous variable with adjustment for the above covariates. Citation Format: En Cheng, Bette Caan, Wendy Chen, Carla Prado, Elizabeth Cespedes Feliciano. A Novel Body Composition Risk Score (B-Score) and Overall Survival among Patients with Early-Stage Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-11-11.

Racial differences in body composition and survival among older adults with gastrointestinal malignancies

IntroductionMuscle and adipose tissue measures can be quantified from routinely obtained computed tomography (CT) images and are predictors of chemotherapy-related toxicities and survival among patients with gastrointestinal (GI) malignancies. Most studies to date have consisted of predominantly White patients, and the role of body composition among minoritized racial groups is unknown. We examined racial differences in body composition and survival among patients with GI malignancies. Materials and MethodsThis was a prospective cohort study of patients with GI malignancies. Single slices of axial CT images from L3 segments were analyzed using Slice-O-Matic software. The skeletal muscle area (cm2) was divided by height to obtain the skeletal muscle index (SMI, cm2/m2). Skeletal muscle radiodensity (SMD) in Hounsfield units (HU) was used for muscle composition. We compared body composition parameters between non-Hispanic (NH)-White and NH-Black participants. Cox models were used to examine the impact of body composition on survival. We proposed new race-specific cutoffs for body composition using optimal stratification. ResultsFive hundred forty patients were included, of which 24% were NH-Black. In Cox models stratified by race, each 5 cm2/m2 decrease in SMI was associated with increase in risk of all-cause mortality in NH-Black patients (hazard ratio [HR] 1.25; 95% confidence interval [CI] 1.04–1.49 p = 0.02). With the existing cut points, neither sarcopenia nor myosteatosis was associated with worse survival. Using a new cutoff for sarcopenia in NH-Black patients, NH-Black patients with sarcopenia (HR 2.31 95%CI 1.10–4.88 p = 0.03) and myosteatosis (HR 2.63 95% CI 1.25–5.53 p = 0.01) had worse survival. DiscussionNH-Black older patients with GI cancers and sarcopenia or myosteatosis have worse overall survival.

A novel body composition risk score (B-Score) and overall survival among patients with nonmetastatic breast cancer

Measurements (amount, distribution, and radiodensity) of muscle and adipose tissue were reported to be individually associated with overall survival in patients with breast cancer. However, they were not typically combined to develop an overall risk score, which can identify patients at high risk of death and prioritize patients in need of dietary and lifestyle interventions. Thus, we aimed to develop a novel composite body composition risk score (B-Score). We included 3105 patients with stage II or III breast cancer at Kaiser Permanente Northern California and Dana Farber Cancer Institute. From CT scans at diagnosis, we assessed areas and radiodensity of muscle and adipose tissue at the third lumber vertebrae. We considered skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI) and SAT radiodensity as they were independent prognostic factors for overall survival. Each measurement was dichotomized using optimal stratification, with low SMI (<40.1cm2/m2), high SATI (≥75.7cm2/m2), and high SAT radiodensity (≥-97.2HU) considered risk factors. We calculated B-Score as the sum of these factors and estimated its association with overall survival using Cox proportional hazards regression with adjustment for clinicopathologic factors. Mean (standard deviation) age was 53.9 (11.8) years, 70.3% were Non-Hispanic White, and 60.5% were stage II. Most patients (60.6%) had only one body composition risk factor (B-Score=1). Compared to those with no risk factors (B-Score=0), the risk of death increased with more body composition risk factors: the adjusted hazard ratios were 1.10 (95% CI: 0.85, 1.42), 1.47 (95% CI: 1.12, 1.92), and 2.11 (95% CI: 1.26, 3.53) for B-Scores of 1, 2, and 3, respectively (Ptrend < 0.001). More unfavorable body composition characteristics were associated with increased risks of overall mortality in a dose-response manner. Considering body composition measurements together as a composite score (B-Score) may improve risk stratification and inform dietary and lifestyle interventions following breast cancer diagnosis.

Quantifying the severity of sarcopenia in patients with cancer of the head and neck

Background & AimsExisting skeletal muscle index (SMI) thresholds for sarcopenia are inconsistent, and do not reflect severity of depletion. In this study we aimed to define criterion values for moderate and severe skeletal muscle depletion based on the risk of mortality in a population of patients with head and neck cancer (HNC). Additionally, we aimed to identify clinical and demographic predictors of skeletal muscle depletion, evaluate the survival impact of skeletal muscle depletion in patients with minimal nutritional risk or good performance status, and finally, benchmarking SMI values of patients with HNC against healthy young adults. MethodsPopulation cohort of 1231 consecutive patients and external validation cohorts with HNC had lumbar SMI measured by cross-sectional imaging. Optimal stratification determined sex-specific thresholds for 2-levels of SMI depletion (Class I and II) based on overall survival (OS). Adjusted multivariable regression analyses (tumor site, stage, performance status, age, sex, dietary intake, weight loss) determined relationships between 2-levels of SMI depletion and OS. ResultsMean SMI (cm2/m2) was 51.7±9.9 (males) and 39.8±7.1 (females). The overall and sex-specific population demonstrated an increased risk of mortality associated with decreasing SMI. Sex-specific SMI (cm2/m2) depletion thresholds for 2-levels of muscle depletion determined by optimal stratification for males and females, respectively (male: 45.2-37.5, and <37.5; female: 40.9-34.2, and <34.2). In the overall population, Normal SMI, Class I and II SMI depletion occurred in 65.0%, 24.0%, and 11.0%, respectively. Median OS was: Normal SMI (114 months, 95% CI, 97.1-130.8); Class I SMI Depletion (42 months, 95% CI, 28.5-55.4), and Class II SMI Depletion (15 months, 95% CI, 9.8-20.1). Adjusted multivariable analysis compared with Normal SMI (reference), Class I SMI Depletion (HR, 1.49; 95% CI, 1.18-1.88; P <.001), Class II SMI Depletion (HR, 1.91; 95% CI, 1.42-2.58; P <.001). ConclusionsModerate and severe SMI depletion demonstrate discrimination in OS in patients with HNC. Moderate and severe SMI depletion is prevalent in patients with minimal nutrition risk and good performance status. Benchmarking SMI values against healthy young adults exemplifies the magnitude of SMI depletion in patients with HNC and may be a useful method in standardizing SMI assessment.

Integrated neutrophil-to-lymphocyte ratio and handgrip strength better predict survival in patients with cancer cachexia

ObjectivesSystemic inflammation and skeletal muscle strength play crucial roles in the development and progression of cancer cachexia. In this study we aimed to evaluate the combined prognostic value of neutrophil-to-lymphocyte ratio (NLR) and handgrip strength (HGS) for survival in patients with cancer cachexia. MethodsThis multicenter cohort study involved 1826 patients with cancer cachexia. The NLR-HGS (NH) index was defined as the ratio of neutrophil-to-lymphocyte ratio to handgrip strength. Harrell's C index and receiver operating characteristic (ROC) curve analysis were used to assess the prognosis of NH. Kaplan–Meier analysis and Cox regression models were used to evaluate the association of NH with all-cause mortality. ResultsBased on the optimal stratification, 380 women (NH > 0.14) and 249 men (NH > 0.19) were classified as having high NH. NH has shown greater predictive value compared to other indicators in predicting the survival of patients with cancer cachexia according to the 1-, 3-, and 5-y ROC analysis and Harrell's C index calculation. Multivariate survival analysis showed that higher NH was independently associated with an increased risk of death (hazard ratio = 1.654, 95% confidence interval = 1.389–1.969). ConclusionThis study demonstrates that the NH index, in combination with NLR and HGS, is an effective predictor of the prognosis of patients with cancer cachexia. It can offer effective prognosis stratification and guidance for their treatment.

Heuristic Algorithm for Obtaining Approximate Optimum Stratification With Mixture of Ratio and Product Estimators

Heuristic Algorithm for Obtaining Approximate Optimum Stratification With Mixture of Ratio and Product Estimators

A novel prognostic predictor of immune microenvironment and therapeutic response in clear cell renal cell carcinoma based on angiogenesis–immune-related gene signature

BackgroundClear cell renal cell carcinoma (ccRCC), the most common type of RCC, typically produces no symptoms initially. Patients with ccRCC are at increased risk of developing advanced metastatic disease due to the absence of dependable and effective prognostic biomarkers. Therefore, it is particularly urgent to find optimal stratification of patients with ccRCC to distinguish the clinical benefits of different malignant degrees. Angiogenesis has a profound impact on the malignant behavior of renal cancer cells, and anti-angiogenic drugs have been applied to metastatic renal cancer patients. Moreover, immune function dysregulation is also a significant factor in tumorigenesis. We aim to construct a predictive model that combines angiogenesis and immune-related genes (AIRGs) to aid clinicians in predicting ccRCC prognosis. MethodsWe gathered transcriptome and clinicopathology data from two datasets, the E-MTAB-1980 dataset and the Cancer Genome Atlas (TCGA). We utilized consensus clustering to find new molecular subgroups. A predictive model for the prognosis of angiogenesis-immune-associated genes (AIRGs) was conducted by the lasso and multivariate Cox regression analysis. The signature's predictive ability was then tested in different datasets. Meticulous scrutiny and comprehensive assessment were undertaken, both internally and externally, to establish the prognostic model. Analyses of immunogenomics were carried out to examine the relationship between risk scores and clinical/immune features, including immune cell infiltration, genomic alterations, and response to targeted and immunotherapy therapy. ResultsOur prognostic signature, comprising 4 AIRGs, stood as an independent prognostic factor for ccRCC, while risk scores emerged as a novel indicator for forecasting overall survival. Risk scores exhibited significant associations with various immunophenotypic factors, such as oncogenic pathways, antitumor response, different immune cell infiltration, antitumor immunity, and response to targeted and immunotherapy therapy. ConclusionsAIRGs-based prognostic prediction model could effectively predict immunotherapy responses and survival outcomes of ccRCC.

DNA mismatch repair deficient cancer - Emerging biomarkers of resistance to immune checkpoint inhibition.

The DNA mismatch repair pathway is involved in the identification, excision, and repair of base-base mismatches and indel loops in the genome. Mismatch repair deficiency occurs in approximately 20% of all cancers and results in a type of DNA damage called microsatellite instability. In 2017, the immune checkpoint inhibitor, Pembrolizumab, an anti-PD-1 therapy, was approved for use in all unresectable or metastatic tumours that were mismatch repair deficient or had high microsatellite instability regardless of tissue origin. This landmark approval was the first time a drug had been approved in a site agnostic way, but accumulating data has revealed that up to 50% of mismatch repair deficient tumours are refractory to treatment and there is a huge amount of variability in the therapeutic benefit amongst responders. Several mechanisms of resistance to immune checkpoint blockade for mismatch repair deficient cancers have been identified but our understanding of what is driving resistance in a proportion of patients remains lacking. In this review article, we discuss the emerging mechanisms of resistance which may enable optimal stratification of patients for treatment with immune checkpoint inhibitors in the future.

Prognostic Significance of Serum Lipids in Patients with Non-Small Cell Lung Cancer Treated with Radiotherapy: A Multicenter Prospective Study

Although lipids have been assessed for their possible roles in cancer survival prediction, studies on the association between serum lipids levels and the prognosis of non-small cell lung cancer (NSCLC) patients are limited. This study aimed to evaluate whether serum lipids are associated with outcomes in patients with NSCLC treated with radiotherapy. We conducted a multicenter prospective study on patients diagnosed with NSCLC between January 2018 and February 2021. Participants received thoracic radiotherapy of 60ཞ80 Gy to the primary lung tumor and positive lymph node metastases. We measured patients' serum lipids levels (serum triglyceride, TGs; total cholesterol, TC, high density lipoprotein cholesterol, HDL-C; low density lipoprotein cholesterol, LDL-C) before radiotherapy. The association between serum lipids levels and overall survival (OS) was evaluated using hazard ratios. We sought to determine a threshold point using optimal stratification. Survival analysis was performed using Kaplan-Meier curves. Of the 300 participants diagnosed with NSCLC treated with radiotherapy, 165 (55.0%) were men. Median follow-up time was 24.4 months (range 1.0- 101.9 months). Using univariate and multivariate Cox proportional hazard analysis, among those serum lipids, only serum TG was shown to be independent prognostic factors for OS (hazard ratio: 1.203, 95% confidence interval: 1.038 - 1.393, p = 0.014). The cut-off for TG associated with OS was 2.04 mmol/L. Based on the TG cut-off value, 55 NSCLC patients were categorized into the high TG group (>2.04 mmol/L) and 245 in the low TG group (<2.04 mmol/L). The NSCLC patients in the low TG group exhibited higher OS than the high group (median OS, not reach vs 41.4 months, p = 0.025). TG levels were found to be a significant negative prognostic biomarker for OS in NSCLC patients treated with radiotherapy.

A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients

Rheumatoid arthritis (RA) is an autoimmune disease that exhibits a high degree of heterogeneity, marked by unpredictable disease flares and significant variations in the response to available treatments. The lack of optimal stratification for RA patients may be a contributing factor to the poor efficacy of current treatment options. The objective of this study is to elucidate the molecular characteristics of RA through the utilization of mitochondrial genes and subsequently construct and authenticate a diagnostic framework for RA. Mitochondrial proteins were obtained from the MitoCarta database, and the R package limma was employed to filter for differentially expressed mitochondrial genes (MDEGs). Metascape was utilized to perform enrichment analysis, followed by an unsupervised clustering algorithm using the ConsensuClusterPlus package to identify distinct subtypes based on MDEGs. The immune microenvironment, biological pathways, and drug response were further explored in these subtypes. Finally, a multi-biomarker-based diagnostic model was constructed using machine learning algorithms. Utilizing 88 MDEGs present in transcript profiles, it was possible to classify RA patients into three distinct subtypes, each characterized by unique molecular and cellular signatures. Subtype A exhibited a marked activation of inflammatory cells and pathways, while subtype C was characterized by the presence of specific innate lymphocytes. Inflammatory and immune cells in subtype B displayed a more modest level of activation (Wilcoxon test P < 0.05). Notably, subtype C demonstrated a stronger correlation with a superior response to biologics such as infliximab, anti-TNF, rituximab, and methotrexate/abatacept (P = 0.001) using the fisher test. Furthermore, the mitochondrial diagnosis SVM model demonstrated a high degree of discriminatory ability in distinguishing RA in both training (AUC = 100%) and validation sets (AUC = 80.1%). This study presents a pioneering analysis of mitochondrial modifications in RA, offering a novel framework for patient stratification and potentially enhancing therapeutic decision-making.

What determines the optimal pharmacological treatment of atrial fibrillation? Insights from in silico trials in 800 virtual atria.

The best pharmacological treatment for each atrial fibrillation (AF) patient is unclear. We aim to exploit AF simulations in 800 virtual atria to identify key patient characteristics that guide the optimal selection of anti-arrhythmic drugs. The virtual cohort considered variability in electrophysiology and low voltage areas (LVA) and was developed and validated against experimental and clinical data from ionic currents to ECG. AF sustained in 494 (62%) atria, with large inward rectifier K+ current (IK1 ) and Na+ /K+ pump (INaK ) densities (IK1 0.11±0.03 vs. 0.07±0.03 SmF-1 ; INaK 0.68±0.15 vs. 0.38±26 SmF-1 ; sustained vs. un-sustained AF). In severely remodelled left atrium, with LVA extensions of more than 40% in the posterior wall, higher IK1 (median density 0.12±0.02 SmF-1 ) was required for AF maintenance, and rotors localized in healthy right atrium. For lower LVA extensions, rotors could also anchor to LVA, in atria presenting short refractoriness (median L-type Ca2+ current, ICaL , density 0.08±0.03 SmF-1 ). This atrial refractoriness, modulated by ICaL and fast Na+ current (INa ), determined pharmacological treatment success for both small and large LVA. Vernakalant was effective in atria presenting long refractoriness (median ICaL density 0.13±0.05 SmF-1 ). For short refractoriness, atria with high INa (median density 8.92±2.59 SmF-1 ) responded more favourably to amiodarone than flecainide, and the opposite was found in atria with low INa (median density 5.33±1.41 SmF-1 ). In silico drug trials in 800human atria identify inward currents as critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics. KEY POINTS: Atrial fibrillation (AF) maintenance is facilitated by small L-type Ca2+ current (ICaL ) and large inward rectifier K+ current (IK1 ) and Na+ /K+ pump. In severely remodelled left atrium, with low voltage areas (LVA) covering more than 40% of the posterior wall, sustained AF requires higher IK1 and rotors localize in healthy right atrium. For lower LVA extensions, rotors can also anchor to LVA, if the atria present short refractoriness (low ICaL ) Vernakalant is effective in atria presenting long refractoriness (high ICaL ). For short refractoriness, atria with fast Na+ current (INa ) up-regulation respond more favourably to amiodarone than flecainide, and the opposite is found in atria with low INa . The inward currents (ICaL and INa ) are critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics.

Association Between Visceral Fat Area and Cancer Prognosis: A Population-Based Multicenter Prospective Study

BackgroundDiverse indicators have been used to represent adipose tissue, while the relationship between body adipose mass and the prognosis of patients with cancer remains controversial. ObjectiveThis study aimed to explore the indicators of optimal body composition that represent body fat mass to predict risk of cancer-related mortality. MethodsWe conducted a population-based multicenter prospective cohort study of patients with initial cancer between February 2012 and September 2020. Clinical information, body composition indicators, hematologic test results, and follow-up data were collected. Body composition indicators were analyzed using principal component analysis to select the most representative indicators, and the cutoff value was set according to the optimal stratification method. The hazard ratio (HR) for mortality was calculated using Cox proportional hazards regression models. ResultsAmong 14,018 patients with complete body composition data, visceral fat area (VFA) is a more optimal indicator for body fat content (principal component index: 0.961) than body mass index (principal component index: 0.850). The cutoff points for VFA in terms of time to mortality were 66 cm2 and 102 cm2 for gastric/esophageal cancer and other cancers, respectively. Among the 2788 patients treated systemically, multivariate analyses demonstrated that a lower VFA was associated with a higher risk of death in patients with cancer of diverse types (HR: 1.33; 95% CI: 1.08, 1.64; P = 0.007), especially gastric cancer (HR: 2.13; 95% CI: 1.3, 3.49; P = 0.003), colorectal cancer HR: 1.81; 95% CI: 1.06, 3.08; P = 0.030) and nonsmall-cell lung cancer (HR: 1.27; 95% CI: 1.01, 1.59; P = 0.040). ConclusionVFA is an independent prognostic indicator of muscle mass in patients with diverse types of cancer, particularly gastric, colorectal, and nonsmall-cell lung cancers. Trial registration numberChiCTR1800020329.

#5181 MYOSTEATOSIS: A NEW MARKER OF MUSCLE QUALITY IN CHRONIC KIDNEY DISEASE STRONGLY ASSOCIATED WITH MORTALITY

Abstract Background and Aims Skeletal muscle composition disturbances, like sarcopenia and myosteatosis, are common in non-dialysis chronic kidney disease (ND-CKD) patients and seem to be associated with adverse clinical outcomes. Sarcopenia and myosteatosis can be evaluated by computed tomography (CT) by measuring skeletal muscle area (SMA) and muscular attenuation (MA) in Hounsfield units (HU) at the third lumbar vertebra, respectively, but the optimal cutoff points for diagnosis and outcome prediction are not established in chronic kidney disease (CKD) patients. We aimed to evaluate the prevalence of sarcopenia and myosteatosis in ND-CKD patients and to define the optimal cutoff values of SMA and MA to predict mortality. Method We conducted a retrospective cohort study including non-dialysis CKD patients referred to an outpatient clinic during a two-year period, who underwent a CT as part of clinical workup and with an available serum creatinine evaluation within a 90-days timeframe. Patients with a follow-up under 26 weeks after the CT were excluded. Area under the receiver operating characteristic curve (AuROC) analysis was used to evaluate the ability of SMA and MA to predict mortality and the Youden's index was used to determine the optimal cutoff point. Cox-regression analysis was employed to identify independent predictors of mortality. Results 167 patients (94% Caucasian, 50.9% male, 32.3% diabetics) with a mean age of 68.3 ± 16.4 years were included, most with CKD stage 3 and 4 (53.9%; mean estimated GFR 57.6 ± 33,1 ml/min/1.73m2 at baseline). During a median follow-up of 4.9 (4.2) years, 39 patients (23.4%) died. Median SMA was 127.7 (45.8) cm2 and there was a trend to increased mortality across lower SMA quartiles (1st quartile 32.6%, p = 0.026; 2nd quartile 26.8%, p = 0.095; 3rd quartile 21.4%, p = 0.261; 4th quartile 21.2% - reference). SMA showed a modest ability to predict mortality (AuROC 0.623) and the best cutoff found was 140.7 cm2. Median MA was 28.4 (13.8) HU and there was a statistically significant higher mortality across lower MA quartiles (1st quartile 42.9%, p&amp;lt;0.001; 2nd quartile 31.0%, p = 0.001; 3rd quartile 16.7%, p = 0.028; 4th quartile 2.4% - reference). MA showed a good ability to predict mortality (AuROC 0.733) and the best cutoff was 30 HU. Using the identified cutoff points, sarcopenia (SMA &amp;lt; 140.7 cm2) was present in 67.1% (n = 112) and myosteatosis (MA &amp;lt; 30 HU) in 56.3% (n = 94) of patients. In univariate Cox-regression both sarcopenia and myosteatosis were associated with increased mortality – Hazard ratio (HR) 4.34 (95% CI 1.68-11.19, p = 0.002) and 5.32 (95% CI 2.22-12.73, p&amp;lt;0.001), respectively. In multivariate Cox-regression models (adjusted for age, baseline estimated GFR and presence of diabetes) only myosteatosis kept its association with mortality – HR 2.87 (95% CI 1.15-7.16, p = 0.024). This association was also present when the model was adjusted for the presence of sarcopenia. Patients with myosteatosis were older (median age 77.3 [10.8] vs 62.7 [30.1], p&amp;lt;0.001) and had higher frequency of diabetes (42.6% vs 19.2%, p = 0.001), arterial hypertension (87.2% vs 57.5%, p&amp;lt;0,001), and heart failure (24.5% vs 6.8%, p&amp;lt;0.003). They had also higher BMI (29.3 [7.4] vs 25.1 [6.0] kg/m2, p&amp;lt;0,001), visceral obesity (77.7% vs 43.8%, p&amp;lt;0.001) and frequency of sarcopenia (75.5% vs 56.2%, p = 0.008). Myosteatosis was more frequent in CKD stage 3 to 5 patients, compared to CKD stage 1 or 2 (66.3% vs 42%, p = 0.002). Conclusion Sarcopenia and myosteatosis are prevalent in CKD patients, especially in advanced stages. However, reference values for this population are lacking. We found cutoff values for these muscle parameters using CT analysis in CKD patients, based on optimal stratification for mortality. Additionally, our study highlights that muscle quality (i.e., myosteatosis) may be more closely associated with mortality than muscle quantity (i.e., sarcopenia). Identifying patients at risk for these muscle abnormalities and early diagnosis are paramount for the subsequent implementation of therapeutic interventions.