Causal inference for time-to-event data with a cured subpopulation

When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.

Substance use treatment completion and criminal justice system contact in Chile: A retrospective, linked data cohort study

BACKGROUND AND AIMS

Substance use treatment (SUT) has shown to be effective in reducing self-reported offending; however, the association between SUT completion and criminal justice system (CJS) contact has been underexplored, especially in Latin America. This study aimed to estimate the association between SUT completion status and (1) any subsequent CJS contact and (2) CJS contact leading to imprisonment, at 1, 3 and 5 years post-discharge, in Chile.

DESIGN

Retrospective cohort study using multivariable survival analysis based on linked administrative data from 2010 to 2019.

SETTING

This study took place in Chile, where SUT is available at no cost through Chile's publicly funded health-care, and is provided in outpatient and inpatient modalities in public and private centres.

PARTICIPANTS

A total of 70 854 individuals received their first SUT from 2010 to 2019. They were mainly males (76.3%), and their main substance used at admission was cocaine paste (39.2%).

MEASUREMENTS

SUT completion status included completion, late dropout (≥ 3 months) and early dropout (< 3 months). Outcomes were (1) any CJS contact and (2) CJS contact leading to imprisonment after baseline treatment. We estimated the association between treatment completion and CJS contact through flexible parametric Royston-Parmar models while adjusting for several covariates.

FINDINGS

Those who completed SUT (27.2%) were less likely to have any CJS contact at 5 years post-SUT compared with those who dropped out late [with a gap of -9.5%, 95% confidence interval (CI) = -8.7, -10.3] and early (-11.2%, 95% CI = -10.1, -12.3). Also, those who completed SUT were less likely to have CJS contact leading to imprisonment at 5 years post-SUT compared with those who dropped out late (-2.6%, 95% CI = -2.2, -3.1) and early (-4.0%, 95% CI = -3.3, -4.6). These differences were also observed at 1 and 3 years post-SUT for each outcome.

CONCLUSIONS

In Chile, completion of substance use treatment appears to be associated with lower probabilities of both any criminal justice system contact and contact leading to imprisonment.

Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy

Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. We developed approaches to categorize solid tumors into 'desert', 'excluded', and 'inflamed' types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach, we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. We show that categorization based on 'manual' observation is predictive for clinical benefit from anti-programmed death ligand 1 therapy in two large cohorts of patients with non-small cell lung cancer or triple-negative breast cancer. For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based readouts and the patient's response to therapy. Our findings suggest that tumor immunophenotype generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. © 2024 The Pathological Society of Great Britain and Ireland.

Risk prediction for severe COVID-19 progressing to critical illness and death in the ICU and efficacy analysis of using traditional Chinese medicine

To reveal the key factors influencing the progression of severe COVID-19 to critical illness and death in the intensive care unit (ICU) and to accurately predict the risk, as well as to validate the efficacy of treatment using traditional Chinese medicine (TCM), thus providing valuable recommendations for the clinical management of patients. A total of 189 patients with COVID-19 in 25 ICUs in Chongqing, China, were enrolled, and 16 eventually died. Statistical models shown that factors influencing the progression of COVID-19 to critical illness include the severity of illness at diagnosis, the mode of respiratory support, and the use of TCM. Risk factors for death include a history of metabolic disease, the use of antiviral drugs and TCM, and invasive endotracheal intubation. The area under curve of the noncollinearity model predicted the risk of progression to critical illness and the risk of death reached 0.847 and 0.876, respectively. The use of TCM is an independent protective factor for the prevention of the progression of severe COVID-19, while uncorrectable hypoxemia and invasive respiratory support are independent risk factors, and antiviral drugs can help reduce mortality. The multifactorial prediction model can assess the risk of critical illness and death in ICU COVID-19 patients, and inform clinicians in choosing the treatment options and medications.

Tumor treating fields in glioblastoma: long-term treatment and high compliance as favorable prognostic factors

Introduction

Tumor treating fields (TTFields) have earned substantial attention in recent years as a novel therapeutic approach with the potential to improve the prognosis of glioblastoma (GBM) patients. However, the impact of TTFields remains a subject of ongoing debate. This study aimed to offer real-world evidence on TTFields therapy for GBM, and to investigate the clinical determinants affecting its efficacy.

Methods

We have reported a retrospective analysis of 81 newly diagnosed Chinese GBM patients who received TTFields/Stupp treatment in the Second Affiliated Hospital of Zhejiang University. Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan-Meier method. Cox regression models with time-dependent covariates were utilized to address non-proportional hazards and to assess the influence of clinical variables on PFS and OS.

Results

The median PFS and OS following TTFields/STUPP treatment was 12.6 months (95% CI 11.0-14.1) and 21.3 months (95% CI 10.0-32.6) respectively. Long-term TTFields treatment (>2 months) exhibits significant improvements in PFS and OS compared to the short-term treatment group (≤2 months). Time-dependent covariate COX analysis revealed that longer TTFields treatment was correlated with enhanced PFS and OS for up to 12 and 13 months, respectively. Higher compliance to TTFields (≥ 0.8) significantly reduced the death risk (HR=0.297, 95%CI 0.108-0.819). Complete surgical resection and MGMT promoter methylation were associated with significantly lower risk of progression (HR=0.337, 95% CI 0.176-0.643; HR=0.156, 95% CI 0.065-0.378) and death (HR=0.276, 95% CI 0.105-0.727; HR=0.249, 95% CI 0.087-0.710).

Conclusion

The TTFields/Stupp treatment may prolong median OS and PFS in GBM patients, with long-term TTFields treatment, higher TTFields compliance, complete surgical resection, and MGMT promoter methylation significantly improving prognosis.

Sociodemographic inequalities in the incidence of COVID-19 in National Household Sample Survey cohort, Brazil, 2020

OBJECTIVE

To verify the association between sociodemographic factors and the time until the occurrence of new cases of COVID-19 and positive tests for SARS-CoV-2 in Brazil, during the period from May to November 2020, based on a cohort of Brazilians participating in the COVID-19 National Household Sample Survey.

METHODS

A concurrent and closed cohort was created using monthly data from the PNAD COVID-19, carried out via telephone survey. A new case was defined based on the report of the occurrence of a flu-like syndrome, associated with loss of smell or taste; and positivity was defined based on the report of a positive test, among those who reported having been tested. Cox regression models were applied to verify associations. The analyzes took into account sample weighting, calibrated for age, gender and education distribution.

RESULTS

The cumulative incidence of cases in the overall fixed cohort was 2.4%, while that of positive tests in the fixed tested cohort was 27.1%. Higher incidences were observed in the North region, in females, in residents of urban areas and in individuals with black skin color. New positive tests occurred more frequently in individuals with less education and healthcare workers.

CONCLUSION

The importance of prospective national surveys is highlighted, contributing to detailed analyzes of social inequalities in reports focused on public health policies.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer

Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimen to prevent antagonistic effects. Thus, this work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

MASH: MEDIATION ANALYSIS OF SURVIVAL OUTCOME AND HIGH-DIMENSIONAL OMICS MEDIATORS WITH APPLICATION TO COMPLEX DISEASES

Environmental exposures such as cigarette smoking influence health outcomes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for investigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. However, little work has been done on mediation analysis when the mediators are high-dimensional and the outcome is a survival endpoint, and none of it has provided a robust measure of total mediation effect. To this end, we propose an estimation procedure for Mediation Analysis of Survival outcome and High-dimensional omics mediators (MASH) based on sure independence screening for putative mediator variable selection and a second-moment-based measure of total mediation effect for survival data analogous to the R 2 measure in a linear model. Extensive simulations showed good performance of MASH in estimating the total mediation effect and identifying true mediators. By applying MASH to the metabolomics data of 1919 subjects in the Framingham Heart Study, we identified five metabolites as mediators of the effect of cigarette smoking on coronary heart disease risk (total mediation effect, 51.1%) and two metabolites as mediators between smoking and risk of cancer (total mediation effect, 50.7%). Application of MASH to a diffuse large B-cell lymphoma genomics data set identified copy-number variations for eight genes as mediators between the baseline International Prognostic Index score and overall survival.

Wetness severity increases abrupt shifts in ecosystem functioning in arid savannas

The accelerating pace of climate change has led to unprecedented shifts in surface temperature and precipitation patterns worldwide, with African savannas being among the most vulnerable regions. Understanding the impacts of these extreme changes on ecosystem health, functioning and stability is crucial. This paper focuses on the detection of breakpoints, indicative of shifts in ecosystem functioning, while also determining relevant ecosystem characteristics and climatic drivers that increase susceptibility to these shifts within the semi-arid to arid savanna biome. Utilising a remote sensing change detection approach and rain use efficiency (RaUE) as a proxy for ecosystem functioning, spatial and temporal patterns of breakpoints in the savanna biome were identified. We then employed a novel combination of survival analysis and remote sensing time series analysis to compare ecosystem characteristics and climatic drivers in areas experiencing breakpoints versus areas with stable ecosystem functioning. Key ecosystem factors increasing savanna breakpoint susceptibility were identified, namely higher soil sand content, flatter terrain and a cooler long-term mean temperature during the wet summer season. Moreover, the primary driver of changes in ecosystem functioning in arid savannas, as opposed to wetter tropical savannas, was found to be the increased frequency and severity of rainfall events, rather than drought pressures. This research highlights the importance of incorporating wetness severity metrics alongside drought metrics to comprehensively understand climate-ecosystem interactions leading to abrupt shifts in ecosystem functioning in arid biomes. The findings also emphasise the need to consider the underlying ecosystem characteristics, including soil, topography and vegetation composition, in assessing ecosystem responses to climate change. While this research primarily concentrated on the southern African savanna as a case study, the methodological robustness of this approach enables its application to diverse arid and semi-arid biomes for the assessment of climate-ecosystem interactions that contribute to abrupt shifts.

Alcohol initiation before age 15 predicts earlier hazardous drinking: A survival analysis of a 7-year prospective longitudinal cohort of Australian adolescents

BACKGROUND AND AIMS

Early alcohol use may predict later alcohol problems, but the magnitude of this effect and impact of delayed onset remain uncertain. This study measured age-based differences in progression from first full alcoholic drink to hazardous drinking in one of the largest and most recent prospective cohorts of Australian adolescents.

DESIGN, SETTINGS, PARTICIPANTS AND MEASUREMENT

A 7-year (2012-19) prospective longitudinal cohort of 2082 Australian adolescents was established from the Climate and Preventure (cohort 1) and Climate Schools Combined (cohort 2) studies. Participants completed surveys annually from ages 13 to 20 years. Interval censored survival analyses were conducted with first episode of hazardous drinking [three or more on proxy Alcohol Use Disorders Identification Test (AUDIT-C)] as the survival end-point, controlling for age, sex and mental health symptomatology. Onset of hazardous drinking was expressed as hazard ratios (HRs), and median survival time (years) was used to model first onset of hazardous alcohol use in survival curves.

FINDINGS

Compared with those aged 15 or older, those who had their first full drink at 12 or younger had significantly elevated risk of hazardous drinking onset during the study period [log (HR): 9.3; 95% confidence interval (CI) = 7.0-12.0, P < 0.001]. Compared with those who had their first full drink at ages 13-14, those who delayed until 15 or older had significantly later onset of hazardous drinking; 1.63 years for males (95% CI = 1.31-1.92, P < 0.001) and 1.50 for females (95% CI = 1.15-1.81, P < 0.001), resulting in a median age of onset of hazardous drinking of > 19 for both sexes (male: 19.05 years, 95% CI = 18.74-19.38; female: 19.47 years, 95% CI = 19.19-19.75). First drink at ages 13-14 was associated with the earliest onset of hazardous drinking (males: 17.43 years; females: 17.98 years).

CONCLUSIONS

In Australia, alcohol initiation prior to age 15 appears to be associated with an earlier onset of hazardous drinking than initiation after age 15.

Partly linear single-index cure models with a nonparametric incidence link function

In cancer studies, it is commonplace that a fraction of patients participating in the study are cured, such that not all of them will experience a recurrence, or death due to cancer. Also, it is plausible that some covariates, such as the treatment assigned to the patients or demographic characteristics, could affect both the patients' survival rates and cure/incidence rates. A common approach to accommodate these features in survival analysis is to consider a mixture cure survival model with the incidence rate modeled by a logistic regression model and latency part modeled by the Cox proportional hazards model. These modeling assumptions, though typical, restrict the structure of covariate effects on both the incidence and latency components. As a plausible recourse to attain flexibility, we study a class of semiparametric mixture cure models in this article, which incorporates two single-index functions for modeling the two regression components. A hybrid nonparametric maximum likelihood estimation method is proposed, where the cumulative baseline hazard function for uncured subjects is estimated nonparametrically, and the two single-index functions are estimated via Bernstein polynomials. Parameter estimation is carried out via a curated expectation-maximization algorithm. We also conducted a large-scale simulation study to assess the finite-sample performance of the estimator. The proposed methodology is illustrated via application to two cancer datasets.

GS-TCGA: Gene Set-Based Analysis of The Cancer Genome Atlas

Most tools for analyzing large gene expression datasets, including The Cancer Genome Atlas (TCGA), have focused on analyzing the expression of individual genes or inference of the abundance of specific cell types from whole transcriptome information. While these methods provide useful insights, they can overlook crucial process-based information that may enhance our understanding of cancer biology. In this study, we describe three novel tools incorporated into an online resource; gene set-based analysis of The Cancer Genome Atlas (GS-TCGA). GS-TCGA is designed to enable user-friendly exploration of TCGA data using gene set-based analysis, leveraging gene sets from the Molecular Signatures Database. GS-TCGA includes three unique tools: GS-Surv determines the association between the expression of gene sets and survival in human cancers. Co-correlative gene set enrichment analysis (CC-GSEA) utilizes interpatient heterogeneity in cancer gene expression to infer functions of specific genes based on GSEA of coregulated genes in TCGA. GS-Corr utilizes interpatient heterogeneity in cancer gene expression profiles to identify genes coregulated with the expression of specific gene sets in TCGA. Users are also able to upload custom gene sets for analysis with each tool. These tools empower researchers to perform survival analysis linked to gene set expression, explore the functional implications of gene coexpression, and identify potential gene regulatory mechanisms.

A comparison of the outcomes of pulmonary versus extrapulmonary extensive-stage small cell carcinoma

BACKGROUND

Extrapulmonary small cell carcinomas (EPSCCs) are rare cancers, comprising 0.1-0.4% of all cancers. The scarcity of EPSCC studies has led current treatment strategies to be extrapolated from small cell lung cancer (SCLC), justified by analogous histological and clinical features.

AIMS

We conducted a retrospective cohort study comparing the outcomes of extensive-stage (ES) SCLC and EPSCC.

METHODS

Patients diagnosed with ES SCLC or EPSCC between 2010 and 2020 from four hospitals in Sydney were identified. Patients who received active treatment and best supportive care were included. The primary endpoint was overall survival (OS), and secondary endpoints were progression-free survival (PFS) and overall response rates (ORRs).

RESULTS

Three hundred and eighty-four patients were included (43 EPSCC vs. 340 SCLC). EPSCC were of genitourinary (n = 15), unknown primary (n = 13) and gastrointestinal (n = 12) origin. Treatment modalities for EPSCC compared to SCLC included palliative chemotherapy (56% vs 73%), palliative radiotherapy (47% vs 59%) and consolidation chest radiotherapy (10% of SCLC). Overall, median OS was 6.4 versus 7 months for EPSCC versus SCLC respectively, but highest in prostate EPSCC (25.6 months). Of those who received chemotherapy (22 EPSCC vs 233 SCLC), median OS was 10.4 versus 8.4 months (HR OS 0.81, 95% confidence interval (CI): 0.5-1.31, P = 0.38); PFS was 5.4 versus 5.5 months (HR PFS 0.93, 95% CI: 0.58-1.46, P = 0.74) and ORR were 73% versus 68%.

CONCLUSIONS

EPSCC and SCLC appeared to have comparable OS and treatment outcomes. However, the wide range of OS in EPSCC highlights the need for an improved understanding of its genomics to explore alternative therapeutics.

Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects

PURPOSE

To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer.

METHODS

A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography-CT (PET-CT) scans prior to receiving any treatment. From these images, high throughput and quantitative radiomic features, tumor features, and various body composition features were automatically extracted. Causal relationships among these image features, patient demographics, and other clinicopathological variables were analyzed and visualized using a novel score-based directed graph called "Grouped Greedy Equivalence Search" (GGES) while taking prior knowledge into consideration. After supplementing and screening the causal variables, the intervention do-calculus adjustment (IDA) scores were calculated to determine the degree of impact of each variable on survival. Based on this IDA score, a GGES prediction formula was generated. Ten-fold cross-validation was used to assess the performance of the models. The prediction results were evaluated using the R-Squared Score (R2 score).

RESULTS

The final causal graphical model was formed by two PET-based image variables, ten body composition variables, four pathological variables, four demographic variables, two tumor variables, and one radiological variable (Percentile 10). Intramuscular fat mass was found to have the most impact on overall survival month. Percentile 10 and overall TNM (T: tumor, N: nodes, M: metastasis) stage were identified as direct causes of overall survival (month). The GGES casual model outperformed GES in regression prediction (R2  = 0.251) (p < 0.05) and was able to avoid unreasonable causality that may contradict common sense.

CONCLUSION

The GGES causal model can provide a reliable and straightforward representation of the intricate causal relationships among the variables that impact the postoperative survival of patients with esophageal cancer.

Expression and clinical role of PRDX6 in lung adenocarcinoma

OBJECTIVE

To explore the levels of expression and clinical role of peroxiredoxin 6 (PRDX6) in lung adenocarcinoma.

METHODS

This retrospective study used a series of bioinformatics methods to detect the levels of expression of and mutations in the PRDX6 gene in a range of cancers and lung adenocarcinoma. Immunohistochemistry was used to verify the levels of expression of PRDX6 protein in samples of lung adenocarcinoma compared with normal adjacent tissue. The effect of PRDX6 gene knockdown on the in vitro proliferation of a lung adenocarcinoma cell line was measured. Bioinformatics methods were used to determine the diagnostic value and impact on survival of the PRDX6 gene in patients with lung adenocarcinoma.

RESULTS

The results showed that the PRDX6 gene was highly expressed in lung adenocarcinoma and there were five mutations at different sites on the gene. PRDX6 promoted the proliferation of the lung adenocarcinoma cell line. The survival duration of lung adenocarcinoma patients with high levels of PRDX6 gene expression was significantly shorter than that of patients with low PRDX6 gene expression.

CONCLUSION

PRDX6 is highly expressed in lung adenocarcinoma and higher levels of expression of the PRDX6 gene were associated with a poorer prognosis.

Investigating the Association Between Dynamic Driving Pressure and Mortality in COVID-19-Related Acute Respiratory Distress Syndrome: A Joint Modeling Approach Using Real-Time Continuously-Monitored Ventilation Data

IMPORTANCE AND OBJECTIVES

COVID-19-related acute respiratory distress syndrome (ARDS) is associated with high mortality and often necessitates invasive mechanical ventilation (IMV). Previous studies on non-COVID-19 ARDS have shown driving pressure to be robustly associated with ICU mortality; however, those studies relied on "static" driving pressure measured periodically and manually. As "continuous" automatically monitored driving pressure is becoming increasingly available and reliable with more advanced mechanical ventilators, we aimed to examine the effect of this "dynamic" driving pressure in COVID-19 ARDS throughout the entire ventilation period.

DESIGN SETTING AND PARTICIPANTS

This retrospective, observational study cohort study evaluates the association between driving pressure and ICU mortality in patients with concurrent COVID-19 and ARDS using multivariate joint modeling. The study cohort (n = 544) included all adult patients (≥ 18 yr) with COVID-19 ARDS between March 1, 2020, and April 30, 2021, on volume-control mode IMV for 12 hours or more in a Mass General Brigham, Boston, MA ICU.

MEASUREMENTS AND MAIN RESULTS

Of 544 included patients, 171 (31.4%) died in the ICU. Increased dynamic ΔP was associated with increased risk in the hazard of ICU mortality (hazard ratio [HR] 1.035; 95% credible interval, 1.004-1.069) after adjusting for other relevant dynamic respiratory biomarkers. A significant increase in risk in the hazard of death was found for every hour of exposure to high intensities of driving pressure (≥ 15 cm H2O) (HR 1.002; 95% credible interval 1.001-1.003).

CONCLUSIONS

Limiting patients' exposure to high intensities of driving pressure even while under lung-protective ventilation may represent a critical step in improving ICU survival in patients with COVID-19 ARDS. Time-series IMV data could be leveraged to enhance real-time monitoring and decision support to optimize ventilation strategies at the bedside.

Oncotype DX in Breast Cancer Management: Insights and Outcomes From the United Arab Emirates

Introduction Breast cancer remains the most significant cancer affecting women worldwide, with an increasing incidence, especially in developing regions. The introduction of genomic tests like Oncotype DX has revolutionized personalized treatment, allowing for more tailored approaches to therapy. This study focuses on the United Arab Emirates (UAE), where breast cancer is the leading cause of cancer-related deaths among women, aiming to assess the predictive accuracy of the Oncotype DX test in categorizing patients based on recurrence risk. Materials and methods A retrospective cohort study was conducted on 95 breast cancer patients diagnosed at Tawam Hospital between 2013 and 2017 who underwent Oncotype DX testing. Data on patient demographics, tumor characteristics, treatment details, and Oncotype DX scores were collected. Survival analysis was performed using the Kaplan-Meier method, with the chi-square goodness of fit test assessing the model's adequacy. Results The cohort's age range was 27-71 years, with a mean age of 50, indicating a significant concentration of cases in the early post-menopausal period. The Oncotype DX analysis classified 55 patients (57.9%) as low risk, 29 (30.5%) as medium risk, and 11 (11.6%) as high risk of recurrence. The majority, 73 patients (76.8%), did not receive chemotherapy, highlighting the test's impact on treatment decisions. The survival analysis revealed no statistically significant difference in recurrence rates across the Oncotype DX risk categories (p = 0.268231). Conclusion The Oncotype DX test provides a valuable genomic approach to categorizing breast cancer patients by recurrence risk in the UAE. While the test influences treatment decisions, particularly the use of chemotherapy, this study did not find a significant correlation between Oncotype DX risk categories and actual recurrence events. These findings underscore the need for further research to optimize the use of genomic testing in the UAE's diverse patient population and enhance personalized treatment strategies in breast cancer management.

Assessment of risk factors of lymph node metastasis and prognosis of Siewert II/III adenocarcinoma of esophagogastric junction: A retrospective study

Adenocarcinoma of the esophagogastric junction (AEG) has a high incidence, and the extent of lymph node dissection (LND) and its impact on prognosis remain controversial. This study aimed to explore the risk factors for lymph node metastasis (LNM) and prognosis in Siewert II/III AEG patients. A retrospective review of 239 Siewert II/III AEG patients surgically treated at Beijing Friendship Hospital from July 2013 to December 2022 was conducted. Preoperative staging was conducted via endoscopy, ultrasound gastroscopy, CT, and biopsy. Depending on the stage, patients received radical gastrectomy with LND and chemotherapy. Clinicopathological data were collected, and survival was monitored semiannually until November 2023. Utilizing logistic regression for data analysis and Cox regression for survival studies, multivariate analysis identified infiltration depth (OR = 0.038, 95% CI: 0.011-0.139, P < .001), tumor deposit (OR = 0.101, 95% CI: 0.011-0.904, P = .040), and intravascular cancer embolus (OR = 0.234, 95% CI: 0.108-0.507, P < .001) as independent predictors of LNM. Lymph nodes No. 1, 2, 3, 4, 7, 10, and 11 were more prone to metastasis in the abdominal cavity. Notably, Siewert III AEG patients showed a higher metastatic rate in nodes No. 5 and No. 6 compared to Siewert II. Mediastinal LNM was predominantly found in nodes No. 110 and No. 111 for Siewert II AEG, with rates of 5.45% and 3.64%, respectively. A 3-year survival analysis underscored LNM as a significant prognostic factor (P = .001). Siewert II AEG patients should undergo removal of both celiac and mediastinal lymph nodes, specifically nodes No. 1, 2, 3, 4, 7, 10, 11, 110, and 111. Dissection of nodes No. 5 and No. 6 is not indicated for these patients. In contrast, Siewert III AEG patients do not require mediastinal LND, but pyloric lymphadenectomy for nodes No. 5 and No. 6 is essential. The presence of LNM is associated with poorer long-term prognosis. Perioperative chemotherapy may offer a survival advantage for AEG patients.

A review on statistical and machine learning competing risks methods

When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high-dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility.

Predictive value of systemic inflammatory response-related indices for survival in tongue cancer

OBJECTIVES

We assessed the potential prognostic significance of the preoperative systemic inflammation index, platelet-to-lymphocyte ratio, and neutrophil-to-lymphocyte ratio in patients who underwent surgical resection.

SUBJECTS AND METHODS

This retrospective study included 224 patients with clinicopathologically confirmed squamous carcinoma of the tongue who underwent surgery between August 2009 and December 2017. The optimal cut-off values for the indices were determined by receiver operating characteristic curves. Correlations between the indices and clinicopathological variables were determined by Pearson chi-square or Fisher exact tests. The Kaplan-Meier test was used to compare overall survival between groups (high and low values); the log-rank or Breslow test was used to assess differences in survival. Univariate and multivariate Cox regression models were used to analyze predictive values of the indices as independent indicators of overall survival. Bilateral p values of <0.05 were considered statistically significant.

RESULTS

Significant association was found between the indices and sex, tissue grade, tumor location, and lymph nodes metastases (p < 0.05). On Kaplan-Meier analysis, patients with lower values of the indices had longer overall survival (p < 0.05). Univariate and multivariate Cox regression models identified age, lymph node metastases, and neutrophil-to-lymphocyte ratio as independent predictors of overall survival.

CONCLUSION

The studied indices have potential prognostic significance in patients with squamous tongue cancer.

NME6 as a potential biomarker and therapeutic target involved in immune infiltration for lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD), a prevalent form of lung cancer, is characterized by its high global mortality rate. Previous studies have demonstrated the significance of Nucleoside diphosphate kinase (NME) in various cancers; however, the specific role of NME6 in LUAD remains inadequately understood.

OBJECTIVE

This research aims to enhance our understanding of LUAD by investigating the expression level, epigenetic mechanism, signaling activities, and immune infiltrating characteristic immune cells of NME6 in patients.

METHODS

The NME6 expression was explored between LUAD and normal tissue samples using GEPIA, UALCAN and HPA databases. The survival analysis was performed by Kaplan-Meier plotter. The Shiny Methylation Analysis Resource Tool was employed to examine the methylation characteristics of NME6. The Tumor Immune Single-cell Hub (TISCH) and CIBERSORT algorithm were utilized to analyze immune infiltrating characteristic immune cells between NME6 high- and low-expression group in LUAD.

RESULTS

According to GEPIA, UALCAN, and HPA databases, NME6 is highly expressed in LUAD compared to normal tissues. At the same time, elevated levels of NME6 were found to be significantly correlated with inferior overall survival outcomes in LUAD patients. Subsequently, the top 10 genes interacted with NME6 were mainly involved in seven pathways, such as p53 signaling pathway, glutathione metabolism, thiamine metabolism, metabolic pathways, and drug metabolism. Notably, NME6 methylation in LUAD samples was lower than in normal samples. The methylation of cg04625862 has a significant impact on the regulation of NME6 expression in LUAD. Furthermore, high NME6 expression in LUAD was associated with tumor stages and relative abundance of tumor infiltrating immune cells, such as Macrophage M2, activated mast cell, and neutrophil. Moreover, NME6 regulated the expression of m6A modification of genes related to LUAD, including METTL3, WTAP, RBM15B, METTL14, RBMX, VIRMA, YTHDC1, RBM15, ZC3H13, YTHDF1, YTHDC2, IGF2BP2, YTHDF3, HNRNPA2B1, YTHDF2, HNRNPC, FTO, and ALKBH5.

CONCLUSION

The analysis showed that NME6 is a crucial prognostic factor for LUAD patients. NME6 regulates genes related to m6A modification and immune cells infiltration. Furthermore, NME6 could sever as a potential therapeutic target for LUAD.

Challenges with Estimating Long-Term Overall Survival in Extensive Stage Small-Cell Lung Cancer: A Validation-Based Case Study

Objective

The study aimed to explore methods and highlight the challenges of extrapolating the overall survival (OS) of immunotherapy-based treatment in first-line extensive stage small-cell lung cancer (ES-SCLC).

Methods

Standard parametric survival models, spline models, landmark models, mixture and non-mixture cure models, and Markov models were fitted to 2-year data of the CASPIAN Phase 3 randomised trial of PD-L1 inhibitor durvalumab added to platinum-based chemotherapy (NCT03043872). Extrapolations were compared with updated 3-year data from the same trial and the plausibility of long-term estimates assessed.

Results

All models used provided a reasonable fit to the observed Kaplan-Meier (K-M) survival data. The model which provided the best fit to the updated CASPIAN data was the mixture cure model. In contrast, the landmark analysis provided the least accurate fit to model survival. Estimated mean OS differed substantially across models and ranged from (in years) 1.41 (landmark model) to 4.81 (mixture cure model) for durvalumab plus etoposide and platinum and from 1.01 (landmark model) to 2.00 (mixture cure model) for etoposide and platinum.

Conclusion

While most models may provide a good fit to K-M data, it is crucial to assess beyond the statistical goodness-of-fit and consider the clinical plausibility of the long-term predictions. The more complex cure models demonstrated the best predictive ability at 3 years, potentially providing a better representation of the underlying method of action of immunotherapy; however, consideration of the models' clinical plausibility and cure assumptions need further research and validation. Our findings underscore the significance of adopting a clinical perspective when selecting the most appropriate approach to model long-term survival, particularly when considering the use of more complex models.

Modeling multiple correlated end-organ disease trajectories: A tutorial for multistate and joint models with applications in diabetes complications

State-of-the-art biostatistics methods allow for the simultaneous modeling of several correlated non-fatal disease processes over time, but there is no clear guidance on the optimal analysis in most settings. An example occurs in diabetes, where it is not known with certainty how microvascular complications of the eyes, kidneys, and nerves co-develop over time. In this article, we propose and contrast two general model frameworks for studying complications (sequential state and parallel trajectory frameworks) and review multivariate methods for their analysis, focusing on multistate and joint modeling. We illustrate these methods in a tutorial format using the long-term follow-up from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications study public data repository. A formal comparison of prediction error and discrimination is included. Multistate models are particularly advantageous for determining the order and timing of complications, but require discretization of the longitudinal outcomes and possibly a very complex state space process. Intermittent observation of the states must be accounted for, and discretization is a probable disadvantage in this setting. In contrast, joint models can account for variations of continuous biomarkers over time and are particularly designed for modeling complex association structures between the complications and for performing dynamic predictions of an outcome of interest to inform clinical decisions (eg, a late-stage complication). We found that both models have helpful features that can better-inform our understanding of the complex trajectories that complications may take and can therefore help with decision making for patients presenting with diabetes complications.

Post-transplant survival after normothermic regional perfusion versus direct procurement and perfusion in donation after circulatory determination of death in heart transplantation

BACKGROUND

Since 2019, the annual transplantation rate of hearts donated following circulatory death (DCD) has increased significantly in the United States. The 2 major heart procurement techniques following circulatory death are direct procurement and perfusion (DPP) and normothermic regional perfusion (NRP). Post-transplant survival for heart recipients has not been compared between these 2 techniques.

METHODS

This observational study uses data on adult heart transplants from donors after circulatory death from January 1, 2019 to December 31, 2021 in the Scientific Registry of Transplant Recipients. We identified comparable transplant cases across procurement types using propensity-score matching and measured the association between procurement technique and 1-year post-transplant survival using Kaplan-Meier and Cox proportional hazards model stratefied by matching pairs.

RESULTS

Among 318 DCD heart transplants, 216 (68%) were procured via DPP, and 102 (32%) via NRP. Among 22 transplant centers that accepted circulatory-death donors, 3 used NRP exclusively, and 5 used both procurement techniques. After propensity-score matching on recipient and donor factors, there was no significant difference in 1-year post-transplant survival (93.1% for NRP vs 91.1% for DPP, p = 0.79) between procurement techniques.

CONCLUSIONS

NRP and DPP procurements are associated with similar 1-year post-transplant survival. If NRP is ethically permissible and improves outcomes for abdominal organs, it should be the preferred procurement technique for DCD hearts.

Prevalence and predictors of breastfeeding in the MINA-Brazil cohort

OBJECTIVE

To describe the prevalence and factors associated with exclusive (EBF) and continued breastfeeding (BF) practices among Amazonian children.

METHODS

Data from 1,143 mother-child pairs recorded on the Maternal and Child Health and Nutrition in Acre (MINA-Brazil) birth cohort were used. Information on EBF and BF was collected after childbirth (July 2015-June 2016) and during the follow-up visits at 1 and 6 months postpartum, 1, 2, and 5 years of age. For longitudinal analysis, the outcomes were EBF and BF duration. Probability of breastfeeding practices were estimated by Kaplan-Meier survival analysis. Associations between baseline predictors variables and outcomes among children born at term were assessed by extended Cox regression models.

RESULTS

EBF frequencies (95% confidence interval [95%CI]) at 3 and 6 months of age were 33% (95%CI: 30.2-36.0) and 10.8% (95%CI: 8.9-12.9), respectively. Adjusted hazard ratio for predictors of early EBF cessation were: being a first-time mother = 1.47 (95%CI: 1.19-1.80), feeding newborns with prelacteals = 1.70 (95%CI: 1.23-2.36), pacifier use in the first week of life = 1.79 (95%CI: 1.44-2.23) or diarrhea in the first two weeks of life = 1.70 (95%CI: 1.15-2.52). Continued BF frequency was 67.9% (95%CI: 64.9-70.8), 29.3% (95%CI: 26.4-32.4), and 1.7% (95%CI: 0.9-2.8) at 1, 2 and 5 years of age, respectively. Adjusted hazard ratio for predictors of early BF cessation were: male sex = 1.23 (95%CI: 1.01-1.49), pacifier use in the first week of life = 4.66 (95%CI: 2.99-7.26), and EBF less than 3 months = 2.76 (95%CI: 1.64-4.66).

CONCLUSIONS

EBF and continued BF duration among Amazonian children is considerably shorter than recommendations from the World Health Organization. Significant predictors of breastfeeding practices should be considered for evaluating local strategies to achieve optimal breastfeeding practices.

Health Disparities in Time to Diagnosis and Survival Post-Diagnosis of Cirrhosis in Individuals with Alcohol Use Disorder: A Retrospective Cohort Study

OBJECTIVE

This study investigates the impact of race, gender, and ethnicity on the progression from diagnosis to cirrhosis and subsequent survival in patients with alcohol use disorder, with a specific focus on identifying potential disparities in health outcomes.

METHOD

Employing a STROBE-compliant, retrospective cohort design, we analyzed data from patients diagnosed with alcohol use disorder from January 2000 to December 2022, using the University of California Health Data Warehouse. We estimated survival functions using the Kaplan-Meier method and assessed demographic associations using both bivariate and multivariate Cox proportional hazards models.

RESULTS

The analysis highlighted a significant association between Hispanic ethnicity and an accelerated timeline for both the diagnosis of alcohol-related cirrhosis following diagnosis of alcohol use disorder and mortality post-cirrhosis diagnosis. The former was evident only in bivariate analysis, but the latter association persisted in multivariate analysis. Gender did not demonstrate a significant correlation with the time to these outcomes, though multiracial identification emerged as a protective factor.

CONCLUSIONS

The study reveals critical health disparities in the progression and outcomes of cirrhosis, particularly between Hispanic and non-Hispanic patients. These findings underscore the urgent need for targeted healthcare interventions and policies that address these disparities. Future research should delve deeper into the multifaceted factors influencing these outcomes, facilitating the development of more nuanced and effective prevention and treatment strategies for alcohol use disorder and its severe complications.

Reappraisal of prognostic factors used in the European Pediatric Soft Tissue Sarcoma Study Group RMS 2005 study for localized rhabdomyosarcoma to optimize risk stratification and generate a prognostic nomogram

BACKGROUND

The objective of this study was to investigate the role of clinical factors together with FOXO1 fusion status in patients with nonmetastatic rhabdomyosarcoma (RMS) to develop a predictive model for event-free survival and provide a rationale for risk stratification in future trials.

METHODS

The authors used data from patients enrolled in the European Pediatric Soft Tissue Sarcoma Study Group (EpSSG) RMS 2005 study (EpSSG RMS 2005; EudraCT number 2005-000217-35). The following baseline variables were considered for the multivariable model: age at diagnosis, sex, histology, primary tumor site, Intergroup Rhabdomyosarcoma Studies group, tumor size, nodal status, and FOXO1 fusion status. Main effects and significant second-order interactions of candidate predictors were included in a multiple Cox proportional hazards regression model. A nomogram was generated for predicting 5-year event-free survival (EFS) probabilities.

RESULTS

The EFS and overall survival rates at 5 years were 70.9% (95% confidence interval, 68.6%-73.1%) and 81.0% (95% confidence interval, 78.9%-82.8%), respectively. The multivariable model retained five prognostic factors, including age at diagnosis interacting with tumor size, tumor primary site, Intergroup Rhabdomyosarcoma Studies clinical group, and FOXO1 fusion status. Based on each patient's total score in the nomogram, patients were stratified into four groups. The 5-year EFS rates were 94.1%, 78.4%, 65.2%, and 52.1% in the low-risk, intermediate-risk, high-risk, and very-high-risk groups, respectively, and the corresponding 5-year overall survival rates were 97.2%, 91.5%, 74.3%, and 60.8%, respectively.

CONCLUSIONS

The results presented here provide the rationale to modify the EpSSG stratification, with the most significant change represented by the replacement of histology with fusion status. This classification was adopted in the new international trial launched by the EpSSG.

Return to work, work productivity loss and activity impairment in Chinese breast cancer survivors 12-month post-surgery: a longitudinal study

Introduction

Existing evidence of returning-to-work (RTW) after cancer comes predominately from Western settings, with none prospectively examined since the initial diagnostic phase. This study prospectively documents RTW-rate, time-to-RTW, work productivity loss, and activity impairment, within the first-year post-surgery among Chinese women with breast cancer (BCW) and identify potential causal co-variants.

Methods

This observational longitudinal study followed 371 Chinese BCW who were employed/self-employed at the time of diagnosis at 4-week post-surgery (baseline). RTW-status and time-to-RTW were assessed at baseline (T1), 4-month (T2), 6-month (T3), and 12-month (T4) post-baseline. WPAI work productivity loss and activity impairment were assessed at T4. Baseline covariates included demographics, medical-related factors, work satisfaction, perceived work demand, work condition, RTW self-efficacy, B-IPQ illness perception, COST financial well-being, EORTC QLQ-C30 and QLQ-BR23 physical and psychosocial functioning, and HADS psychological distress.

Results

A 68.2% RTW-rate (at 12-month post-surgery), prolonged delay in RTW (median = 183 days), and significant proportions of T4 work productivity loss (20%), and activity impairment (26%), were seen. BCW who were blue-collar workers with lower household income, poorer financial well-being, lower RTW self-efficacy, poorer job satisfaction, poorer illness perception, greater physical symptom distress, impaired physical functioning, and unfavorable work conditions were more likely to experience undesired work-related outcomes.

Discussion

Using a multifactorial approach, effective RTW interventions should focus on not only symptom management, but also to address psychosocial and work-environmental concerns. An organizational or policy level intervention involving a multidisciplinary team comprising nurses, psychologists, occupational health professionals, and relevant stakeholders in the workplace might be helpful in developing a tailored organizational policy promoting work-related outcomes in BCW.

Development and validation of a nomogram for predicting the impact of tumor size on cancer-specific survival of locally advanced renal cell carcinoma: a SEER-based study

This study was aimed to integrate tumor size with other prognostic factors into a prognostic nomogram to predict cancer-specific survival (CSS) in locally advanced (≥pT3a Nany M0) renal cell carcinoma (RCC) patients. Based on the Surveillance, Epidemiology, and End Results (SEER) database, 10,800 patients diagnosed with locally advanced RCC were collected. They were randomly divided into a training cohort (n = 7,056) and a validation cohort (n = 3,024). X-tile program was used to identify the optimal cut-off value of tumor size and age. The cut-off of age at diagnosis was 65 years old and 75 years old. The cut-off of tumor size was 54 mm and 119 mm. Univariate and multivariate Cox regression analyses were performed in the training cohort to identify independent prognostic factors for construction of nomogram. Then, the nomogram was used to predict the 1-, 3- and 5-year CSS. The performance of nomogram was evaluated by using concordance index (C-index), area under the Subject operating curve (AUC) and decision curve analysis (DCA). Moreover, the nomogram and tumor node metastasis (TNM) staging system (AJCC 8th edition) were compared. 10 variables were screened to develop the nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) indicated satisfactory ability of the nomogram. Compared with the AJCC 8th edition of TNM stage, DCA showed that the nomogram had improved performance. We developed and validated a nomogram for predicting the CSS of patients with locally advanced RCC, which was more precise than the AJCC 8th edition of TNM staging system.

Causal mediation for uncausally related mediators in the context of survival analysis

Objective

The study of the potential intermediate effect of several variables on the association between an exposure and a time-to-event outcome is a question of interest in epidemiologic research. However, to our knowledge, no tools have been developed for the evaluation of multiple correlated mediators in a survival setting.

Methods

In this work, we extended the multimediate algorithm, which conducts mediation analysis in the context of multiple uncausally correlated mediators, to a time-to-event setting using the semiparametric additive hazards model. We theoretically demonstrated that, under certain assumptions, indirect, direct and total effects can be calculated using the counterfactual framework with collapsible survival models. We also adapted the algorithm to accommodate exposure-mediator interactions.

Results and conclusions

Using simulations, we demonstrated that our algorithm performs better than the product of coefficients method, even for uncorrelated mediators. The additive hazards model quantifies the effects as rate differences, which constitute a measure of impact, with applications that can be highly informative for public health. Our algorithm can be found in the R package multimediate, which is available in Github.

An Innovative and Efficient Diagnostic Prediction Flow for Head and Neck Cancer: A Deep Learning Approach for Multi-Modal Survival Analysis Prediction Based on Text and Multi-Center PET/CT Images

Objective: To comprehensively capture intra-tumor heterogeneity in head and neck cancer (HNC) and maximize the use of valid information collected in the clinical field, we propose a novel multi-modal image-text fusion strategy aimed at improving prognosis. Method: We have developed a tailored diagnostic algorithm for HNC, leveraging a deep learning-based model that integrates both image and clinical text information. For the image fusion part, we used the cross-attention mechanism to fuse the image information between PET and CT, and for the fusion of text and image, we used the Q-former architecture to fuse the text and image information. We also improved the traditional prognostic model by introducing time as a variable in the construction of the model, and finally obtained the corresponding prognostic results. Result: We assessed the efficacy of our methodology through the compilation of a multicenter dataset, achieving commendable outcomes in multicenter validations. Notably, our results for metastasis-free survival (MFS), recurrence-free survival (RFS), overall survival (OS), and progression-free survival (PFS) were as follows: 0.796, 0.626, 0.641, and 0.691. Our results demonstrate a notable superiority over the utilization of CT and PET independently, and exceed the result derived without the clinical textual information. Conclusions: Our model not only validates the effectiveness of multi-modal fusion in aiding diagnosis, but also provides insights for optimizing survival analysis. The study underscores the potential of our approach in enhancing prognosis and contributing to the advancement of personalized medicine in HNC.

Lifestyle Influence on Mild Cognitive Impairment Progression: A Decision Tree Prediction Model Study

Purpose

This study assessed the influences of different lifestyle on mild cognitive impairment (MCI) progression and established a decision tree prediction model to analyse their predictive significance on MCI progression incidence.

Patients and Methods

From October 2015 to February 2020,330 patients with MCI were recruited, and demographic and lifestyle information collected. They were followed up for 19.04 ± 10.227 months. Cognitive function was assessed using the Mini-Mental State Examination Scale every 6 months, and they were divided into MCI stable group and MCI progression group.

Results

The Kaplan Meier survival analysis showed an overall cohort survival rate of 33.2%; the annual conversion rate of MCI progression was 20%. Physical exercise, social engagement, high-fat diet, age, napping, and tea drinking were decision tree prediction model nodes. Hobbies were the most important factor for predicting MCI progression. The MCI progression probability rates were: with hobbies 26.829% (44 cases), without hobbies 57.831% (96 cases); for those withot hobbies, with physical exercise 43.077% (28 cases) without physical exercise 72.340% (68 cases); for those without hobbies with physical exercise and social engagement 20.000% (4 cases), without social engagement 53.333% (24 cases); for those without hobbies, physical exercises and social engagement and with nap habits 48.485% (16 cases), without nap habits 66.667% (8 cases). The decision tree prediction model AUC for predicting the MCI progression receiver operating characteristic curve was 0.737 (95% confidence interval: 0.685-0.785) (75.71% sensitivity, 71.75% specificity, P < 0.001.

Conclusion

Hobbies, physical exercise, social engagement, napping, and drinking tea can help prevent MCI progression, while a high-fat diet may exacerbate MCI progression. In this study the rule with the lowest MCI progress probability for those who had hobbies, high-fat diet, and social engagement. And the decision tree model had good prediction efficiency.

Normal triglyceride concentration and the risk of diabetes mellitus type 2 in the general population of China

Introduction

Hypertriglyceridemia and its derivatives are independent predictors of diabetes mellitus type 2 (T2DM). However, the relationship between triglyceride concentrations within the normal range and the incidence of T2DM remains to be clarified. This study investigated the potential relationship between variations in plasma triglyceride levels within the normal range and T2DM onset using data from a longitudinal study of health and retirement in China.

Methods

Between, 2010 and, 2016, we conducted a retrospective cohort study involving 36,441 individuals with normal triglyceride levels. Using a Cox proportional hazards regression model, we examined the connection between normal triglyceride levels and T2DM incidence. We employed this method with smooth curve fitting to investigate potential nonlinear associations. Subgroup analyses were performed based on age, sex, body mass index, smoking and drinking status, hypertension, and family history of diabetes.

Results

A significant linear relationship was observed between normal triglyceride levels and the incidence of T2DM. The hazard ratio for T2DM in individuals with normal triglycerides was 1.81 (95% confidence interval: 1.39, 2.36); P<0.001). Kaplan-Meier analysis further demonstrated a prospective association between the higher tertiles of normal triglyceride levels and the development of T2DM (P<0.001). Subgroup analysis revealed a stronger positive correlation between normal triglyceride levels in females and the risk of T2DM.

Discussion

An increase in triglyceride levels within the normal range is related to a continuous increase in the incidence of T2DM in the general population. These findings show that almost everyone can benefit from reducing triglyceride levels, further emphasizing the importance of lifestyle changes in the general population.

PALLIATIVE GASTRECTOMY VERSUS GASTRIC BYPASS FOR SYMPTOMATIC CLINICAL STAGE IV GASTRIC CANCER: A PROPENSITY SCORE MATCHING ANALYSIS

BACKGROUND

Patients with clinical stage IV gastric cancer may require palliative procedures to manage complications such as obstruction. However, there is no consensus on whether performing palliative gastrectomy compared to gastric bypass brings benefits in terms of survival.

AIMS

To compare the overall survival of patients with distal obstructive gastric cancer undergoing palliative surgical treatment, using propensity score matching analysis.

METHODS

Patients who underwent palliative bypass surgery (gastrojejunostomy or partitioning) and resection between the years 2009 and 2023 were retrospectively selected. Initial and postoperative clinicopathological variables were collected.

RESULTS

150 patients were initially included. The derived group (n=91) presented more locally invasive disease (p<0.01), greater degree of obstruction (p<0.01), and worse clinical status (p<0.01), while the resected ones (n= 59) presented more distant metastasis (p<0.01). After matching, 35 patients remained in each group. There was no difference in the incidence of postoperative complications, but the derived group had higher 90-day mortality (p<0.01). Overall survival was 16.9 and 4.5 months for the resected and derived groups, respectively (p<0.01). After multivariate analysis, hypoalbuminemia (hazard ratio - HR=2.02, 95% confidence interval - 95%CI 1.17-3.48; p=0.01), absence of adjuvant chemotherapy (HR=5.97; 95%CI 3.03-11.7; p<0.01), and gastric bypass (HR=3,28; 95%CI 1.8-5.95; p<0.01) were associated with worse survival.

CONCLUSIONS

Palliative gastrectomy was associated with greater survival and lower postoperative morbidity compared to gastric bypass. This may be due to better local control of the disease, with lower risks of complications and better effectiveness of chemotherapy.

Sport Type and Risk of Subsequent Injury in Collegiate Athletes Following Concussion: a LIMBIC MATARS Consortium Investigation

OBJECTIVE

To investigate the association between sport type (collision, contact, non-contact) and subsequent injury risk following concussion in collegiate athletes.

MATERIALS AND METHODS

This retrospective chart review of 248 collegiate athletes with diagnosed concussions (age: 20.0 ± 1.4 years; height: 179.6 ± 10.9 cm; mass: 79.0 ± 13.6 kg, 63% male) from NCAA athletic programs (n = 11) occurred between the 2015-2020 athletic seasons. Acute injuries that occurred within six months following concussion were evaluated. Subsequent injuries were grouped by lower extremity, upper extremity, trunk, or concussion. The independent variable was sport type: collision, contact, non-contact. A Cox proportional hazard model was used to assess the risk of subsequent injury between sport types.

RESULTS

Approximately 28% (70/248) of athletes sustained a subsequent acute injury within six months post-concussion. Collision sport athletes had a significantly higher risk of sustaining any injury (HR: 0.41, p < 0.001, 95% CI: 0.28, 0.62), lower extremity (HR: 0.55, p = 0.04, 95% CI: 0.32, 0.97), and upper extremity (HR: 0.41, p = 0.01, 95% CI: 0.20, 0.81) injuries following concussion. No differences between sport types were observed for other injuries.

CONCLUSION

Collision sport athletes had a higher rate of any subsequent injury, lower, and upper extremity injuries following concussion. Future research should focus on sport-specific secondary injury prevention efforts.

Distant organ metastasis patterns and prognosis of lung adenosquamous carcinoma: a SEER database analysis

OBJECTIVES

Our purpose is to evaluate the patterns of organ metastasis and the prognosis in lung adenosquamous carcinoma patients with organ metastasis.

METHODS

We collected the data from the surveillance epidemiology and end results database, covering the period of 2000-2018. Cox regression, Kaplan-Meier and log-rank analyses were performed.

RESULTS

Totally, 2698 patients were enrolled, comprising 851 (31.54%) patients diagnosed with organ metastasis and 2017 (68.46%) patients without organ metastasis. Patients with distant organ metastasis show a significant decrease in median overall survival. In addition to the aforementioned factors, age over 70 years, male, main bronchus, advanced T stage, larger tumour size, absence of primary tumour surgery and lack of radiotherapy have all been identified as prognostic indicators associated with a poorer outcome. In terms of treatment options, patients with organ metastasis can benefit from chemotherapy and primary tumour surgery. Moreover, in patients with organ metastasis, those who received a combination treatment of surgery, chemotherapy and radiotherapy displayed the most favourable prognosis, with a median overall survival of 17 months.

CONCLUSIONS

We identified the prognostic indicators for organ metastasis in patients with lung adenosquamous carcinoma. Highly selected patients who undergo a combination treatment of surgery, chemotherapy and radiotherapy may experience the greatest survival benefit.

Is inadequate risk stratification diluting hazard ratio estimates in randomized clinical trials?

In randomized clinical trials, analyses of time-to-event data without risk stratification, or with stratification based on pre-selected factors revealed at the end of the trial to be at most weakly associated with risk, are quite common. We caution that such analyses are likely delivering hazard ratio estimates that unwittingly dilute the evidence of benefit for the test relative to the control treatment. To make our case, first, we use a hypothetical scenario to contrast risk-unstratified and risk-stratified hazard ratios. Thereafter, we draw attention to the previously published 5-step stratified testing and amalgamation routine (5-STAR) approach in which a pre-specified treatment-blinded algorithm is applied to survival times from the trial to partition patients into well-separated risk strata using baseline covariates determined to be jointly strongly prognostic for event risk. After treatment unblinding, a treatment comparison is done within each risk stratum and stratum-level results are averaged for overall inference. For illustration, we use 5-STAR to reanalyze data for the primary and key secondary time-to-event endpoints from three published cardiovascular outcomes trials. The results show that the 5-STAR estimate is typically smaller (i.e. more in favor of the test treatment) than the originally reported (traditional) estimate. This is not surprising because 5-STAR mitigates the presumed dilution bias in the traditional hazard ratio estimate caused by no or inadequate risk stratification, as evidenced by two detailed examples. Pre-selection of stratification factors at the trial design stage to achieve adequate risk stratification for the analysis will often be challenging. In such settings, an objective risk stratification approach such as 5-STAR, which is partly aligned with guidance from the US Food and Drug Administration on covariate-adjustment in clinical trials, is worthy of consideration.

Survival analysis of comprehensive treatment in Chinese patients with metastatic melanoma: A retrospective analysis

BACKGROUND

Most of the current progression of immune checkpoint inhibitors for malignant melanoma is based on data from Caucasians in Western countries, but the benefit of Chinese patients is limited, mainly due to different pathological subtypes. The patients in western countries are mainly skin melanoma (about 90%), while the acral and mucosal types are dominant in China, accounting for 41.8% and 22.6% respectively. Acral and mucosal melanoma have lower response rates to immunotherapy and chemotherapy.

OBJECTIVE

Whether immune checkpoint inhibitors can improve the survival of Chinese patients with malignant melanoma, therefore, we conducted a retrospective analysis.

METHODS

We analyzed 53 patients with metastatic melanoma treated in our hospital to evaluate the efficacy and safety of PD-1 mAb in Chinese patients with metastatic melanoma, and performed univariate and multivariate analyses of prognostic factors that may affect overall survival (OS).

RESULTS

In a study of 125 patients with advanced malignant melanoma, 53 patients participated, with a median follow-up of 16 months. Among these, 69.8% died, and 30.2% remained on treatment. Median progression-free survival (PFS) was 6 months, and median OS was 19 months. Patients treated with immune checkpoint inhibitors had improved outcomes, with a median PFS of 7 months and a median OS of 24 months. Patients with bone metastasis and aberrant Lactate dehydrogenase (LDH) post-treatment had worse prognoses, while immunotherapy was a protective factor. Subgroup analysis showed the benefits of immunotherapy across various patient characteristics. No unexpected toxicities were observed.

CONCLUSION

The study highlights the efficacy of immune checkpoint inhibitors, particularly PD-1 mAb, in improving survival outcomes for Chinese patients with metastatic melanoma.

Investigating Causal Genetic Effects on Overall Survival of Glioblastoma Patients using Normalizing Flow and Structural Causal Model

Glioblastoma (GBM) is the most common and aggressive brain tumor with short overall survival (OS) of about 15 months. Understanding the causal factors affecting the patient survival is crucial for disease prognosis and treatment planning. Although previous efforts on survival prediction using multi-omics data has yielded useful predictive models, the causation of the correlated genetic risk factors has not been addressed. Recent advances in causal deep learning models enable the study of causality from complex dataset. In this paper, we leverage the recently proposed structural causal model (SCM) with normalizing flows parameterized by deep networks to perform the counterfactual query to investigate the causal relationship between gene mutation and OS with the presence of other confounders including sex, age and radiomics features. The query amounts to the question that what the survival days will be if the gene mutation status has been changed, i.e., from mutant to non-mutant and vice versa. The trained causal model will infer the counterfactual outcome given the intervention on specific gene mutation. We apply multivariate Cox-PH model to find the genes associated with survival, and investigate the causal genetic effect by comparing the original and counterfactual survival days in a bi-directional fashion. Particularly, the following two scenarios are considered: (1) intervention on a specific gene with non-mutant status to generate the counterfactual survival days as if the gene is mutant, with which the original survival days of the subjects with that mutant gene will be compared; (2) intervention on the gene with mutant status and perform the comparison with survival days of subjects with that non-mutant gene. Our experimental results show that no causation of two correlated genes (NF1, RB1) was revealed in the cohort (n=181), while their genetic effects on OS in terms of prolonging or shortening are generally in accordance with clinical findings.

Glasgow prognostic score as an outcome predictor for patients initiating hemodialysis

INTRODUCTION

This retrospective study examined the relationship between the Glasgow Prognostic Score (GPS) at hemodialysis (HD) initiation and overall/cardiovascular mortality.

METHODS

A total of 264 patients starting HD between 2014 and 2015 at a single center were studied. Follow-up persisted until therapy change, death, or study end (December 31, 2021), with a median of 6.8 years.

RESULTS

Patients with a higher GPS more frequently had emergent HD initiation and showed increased eGFR at initiation. During follow-up, 60% of patients died, with cardiovascular disease being the leading cause. Univariate analysis revealed a significant difference in median survival time across GPS classes. Cox proportional hazard models confirmed a significant association between GPS and mortality.

CONCLUSIONS

We report a significant association between GPS at HD initiation and mortality. GPS may prove useful as a prognostic tool for identifying high-risk patients, underscoring the need for future research to validate these findings and explore the potential of GPS-based interventions.

Advanced considerations in survival analysis

Investigation of survival during the follow-up period is common in cardiovascular research and has intrinsic issues that require precise knowledge, such as survival or censoring. Besides, as the follow-up period lengthens and events other than mortality are studied, the analysis becomes more complex, so Kaplan-Meier analyses or Cox models are not always sufficient. In this primer, we provide the reader with detailed information on the interpretation of the most common survival analyses and delve into methods to analyse competing risks or alternatives to the conventional methods when the proportional hazards assumption is not met.

Regression analysis of multivariate recurrent event data allowing time-varying dependence with application to stroke registry data

In multivariate recurrent event data, each patient may repeatedly experience more than one type of event. Analysis of such data gets further complicated by the time-varying dependence structure among different types of recurrent events. The available literature regarding the joint modeling of multivariate recurrent events assumes a constant dependency over time, which is strict and often violated in practice. To close the knowledge gap, we propose a class of flexible shared random effects models for multivariate recurrent event data that allow for time-varying dependence to adequately capture complex correlation structures among different types of recurrent events. We developed an expectation-maximization algorithm for stable and efficient model fitting. Extensive simulation studies demonstrated that the estimators of the proposed approach have satisfactory finite sample performance. We applied the proposed model and the estimating method to data from a cohort of stroke patients identified in the University of Texas Houston Stroke Registry and evaluated the effects of risk factors and the dependence structure of different types of post-stroke readmission events.

Applying Trial-Derived Treatment Effects to Real-World Populations: Generalizing Cost-Effectiveness Estimates When Modeling Complex Hazards

OBJECTIVES

Generalizability of trial-based cost-effectiveness estimates to real-world target populations is important for decision making. In the context of independent aggregate time-to-event baseline and relative effects data, complex hazards can make modeling of data for use in economic evaluation challenging. Our article provides an overview of methods that can be used to apply trial-derived relative treatment effects to external real-world baselines when faced with complex hazards and follows with a motivating example.

METHODS

Approaches for applying trial-derived relative effects to real-world baselines are presented in the context of complex hazards. Appropriate methods are applied in a cost-effectiveness analysis using data from a previously published study assessing the real-world cost-effectiveness of a treatment for carcinoma of the head and neck as a motivating example.

RESULTS

Lack of common hazards between the trial and target real-world population, a complex baseline hazard function, and nonproportional relative effects made the use of flexible models necessary to adequately estimate survival. Assuming common distributions between trial and real-world reference survival substantially affected survival and cost-effectiveness estimates. Modeling time-dependent vs proportional relative effects affected estimates to a lesser extent, dependent on assumptions used in cost-effectiveness modeling.

CONCLUSIONS

Appropriately capturing reference treatment survival when attempting to generalize trial-derived relative treatment effects to real-world target populations can have important impacts on cost-effectiveness estimates. A balance between model complexity and adequacy for decision making should be considered where multiple data sources with complex hazards are being evaluated.

Intradialytic Hypotension and Mortality in Adolescents and Young Adults With Kidney Failure Receiving Maintenance Hemodialysis

Rationale & Objective

Intradialytic hypotension (IDH) is associated with mortality in adults with kidney failure requiring hemodialysis (HD); however, large-scale pediatric studies are lacking. Moreover, there is no evidence-based consensus definition of IDH in pediatric literature. We aimed to examine the association of commonly used definitions of IDH with mortality in adolescents and young adults.

Study Design

This was a retrospective observational cohort study.

Setting & Participants

In total, 1,199 adolescents and young adults (N = 320, aged 10-18 years and N = 879, aged 19-21 years) who initiated HD in a large dialysis organization were included.

Exposures

This study used different definitions of IDH.

Outcome

The study outcome was 2-year all-cause mortality.

Analytical Approach

Several definitions of IDH were selected a priori based on a literature review. Patients were classified as having IDH if it was present in at least 30% of HD treatments during the first 90 days after dialysis initiation. Cox proportional hazards regression was used to test whether IDH associated with 2-year all-cause mortality.

Results

Over a 2-year follow-up period, 54 (4.5%) patients died. Dependent on its definition, IDH was present in 2.9%-61.1% of patients. After the multivariable adjustment for sociodemographic and clinical characteristics, we found no association of IDH with mortality. Results were consistent across subgroups stratified by age (aged <18 and 19-21 years) and predialysis systolic blood pressure (<120, 120-150, and >150 mm Hg). We also examined IDH as occurring in <5%, 5%-29%, 30%-50%, and >50% of baseline treatments, and did not find a dose-response association with mortality (P > 0.05).

Limitations

Owing to low event rates, our current sample size may have been too small to detect a difference in mortality.

Conclusions

Our study found that IDH was not associated with mortality in adolescents and young adults.

Efficacy of airway stenting and nasogastric tube insertion in airway-esophageal fistula patients with airways compromised by advanced malignancy

INTRODUCTION

Whether airway-compromised airway-esophageal fistula (AEF) patients should undergo combined airway and esophageal stenting is controversial. This study was designed to evaluate the survival prognosis and poststent interventions in AEF patients with airways compromised by advanced malignancy with or without airway stents.

METHODS

A retrospective analysis of the medical records, survival times, and poststent interventions of 17 patients with or without airway stents was performed.

RESULTS

The causes of AEF were esophageal cancer (11/17, 64.7%), lung cancer (6/17, 29.4%), and thyroid cancer (1/17, 5.9%). All patients received a nasogastric tube (n = 12) or underwent gastrostomy (n = 5) to resume enteral nutrition. Thirteen patients underwent airway stent insertion (13/17, 76.5%), whereas four patients did not. Four patients with a high risk of stent migration received external stent fixation to the trachea. Three of the patients with stents suffered severe granulation tissue formation and needed repeated bronchoscopy interventions. In the stented group, none of the patients developed stent migration, and the overall median survival time was 9 months, compared with 1.25 months in the nonstented group (P = 0.04). Cox proportional hazards regression revealed that stent insertion, nasogastric tube insertion, and transcatheter bronchial artery chemoembolization were protective factors against death, whereas surgery-related fistula, fistula larger than 2 cm, continued chemotherapy, and age were risk factors for poor survival (P < 0.05).

CONCLUSION

In airway-compromised AEF patients, airway stents and nasogastric tubes are probably the preferred treatments. Airway stenting is tolerable, and routine weekly poststent bronchoscopy is needed in the first month and depending on respiratory symptoms thereafter.

Statistics in the Operating Room: A Cardiovascular Surgeon's Guide to Numbers That Matter

Pediatric cardiac surgery demands meticulous technique, but optimal outcomes hinge on translating data into actionable insights. This editorial bridges the gap between scalpel and statistical jargon, empowering surgeons to decipher common tests. Descriptive statistics paint portraits of patient cohorts, while hypothesis testing discerns real differences from chance. Regression analysis unveils hidden relationships, predicting outcomes based on complex interplays of variables. Survival analysis tracks the delicate dance of time and survival, informing therapeutic strategies. By embracing statistical fluency, surgeons become architects of personalized care, tailoring interventions to mitigate risks and maximize the precious gift of a beating heart.

Prognostic Factors and Nomogram for Choroid Plexus Tumors: A Population-Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis

Background: Choroid plexus tumors (CPTs) are rare neoplasms found in the central nervous system, comprising 1% of all brain tumors. These tumors include choroid plexus papilloma (CPP), atypical choroid plexus papilloma (aCPP), and choroid plexus carcinoma (CPC). Although gross total resection for choroid plexus papillomas (CPPs) is associated with long-term survival, there is a scarcity of prospective data concerning the role and sequence of neoadjuvant therapy in treating aCPP and CPC. Methods: From the years 2000 to 2019, 679 patients with CPT were identified from the Surveillance, Epidemiology, and End Result (SEER) database. Among these patients, 456 patients had CPP, 75 patients had aCPP, and 142 patients had CPC. Univariate and multivariable Cox proportional hazard models were run to identify variables that had a significant impact on the primary endpoint of overall survival (OS). A predictive nomogram was built for patients with CPC to predict 5-year and 10-year survival probability. Results: Histology was a significant predictor of OS, with 5-year OS rates of 90, 79, and 61% for CPP, aCPP, and CPC, respectively. Older age and African American race were prognostic for worse OS for patients with CPP. Older age was also associated with reduced OS for patients with aCPP. American Indian/Alaskan Native race was linked to poorer OS for patients with CPC. Overall, treatment with gross total resection or subtotal resection had no difference in OS in patients with CPP or aCPP. Meanwhile, in patients with CPC, gross total resection (GTR) was associated with significantly better OS than subtotal resection (STR) only. However, there is no difference in OS between patients that receive GTR and patients that receive STR with adjuvant therapy. The nomogram for CPC considers types of treatments received. It demonstrates acceptable accuracy in estimating survival probability at 5-year and 10-year intervals, with a C-index of 0.608 (95% CI of 0.446 to 0.77). Conclusions: This is the largest study on CPT to date and highlights the optimal treatment strategies for these rare tumors. Overall, there is no difference in OS with GTR vs. STR in CPP or aCPP. Furthermore, OS is equivalent for CPC with GTR and STR plus adjuvant therapy.

Principal stratification analysis of noncompliance with time-to-event outcomes

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. The existing literature on principal stratification lacks generally applicable and accessible methods for time-to-event outcomes. In this paper, we focus on the noncompliance setting. We specify 2 causal estimands for time-to-event outcomes in principal stratification and provide a nonparametric identification formula. For estimation, we adopt the latent mixture modeling approach and illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional hazards model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters. We provide analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial to evaluate the causal effect of taking 81 versus 325 mg aspirin on the risk of major adverse cardiovascular events. We develop the corresponding R package PStrata.

A Bayesian survival treed hazards model using latent Gaussian processes

Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazard assumptions are not always appropriate. Non-parametric models are more flexible but often lack a clear inferential framework. We propose a Bayesian treed hazards partition model that is both flexible and inferential. Inference is obtained through the posterior tree structure and flexibility is preserved by modeling the log-hazard function in each partition using a latent Gaussian process. An efficient reversible jump Markov chain Monte Carlo algorithm is accomplished by marginalizing the parameters in each partition element via a Laplace approximation. Consistency properties for the estimator are established. The method can be used to help determine subgroups as well as prognostic and/or predictive biomarkers in time-to-event data. The method is compared with some existing methods on simulated data and a liver cirrhosis dataset.