Survival analysis part II: multivariate data analysis--an introduction to concepts and methods

An exploratory review on the empirical evaluation of the quality of reporting and analyzing labor duration

INTRODUCTION

This exploratory review aimed to provide empirical evidence on the definitions of labor, the statistical approaches and measures reported in randomized controlled trials (RCTs) and observational studies measuring the duration of labor.

METHODS

A systematic electronic literature search was conducted using different databases. An extraction form was designed and used to extract relevant data. English, French, and German studies published between 1999 and 2019 have been included. Only RCTs and observational studies analyzing labor duration (or a phase of labor duration) as a primary outcome have been included.

RESULTS

Ninety-two RCTs and 126 observational studies were eligible. No definition of the onset of labor was provided in 21.7% (n = 20) of the RCTs and 23.8% (n = 30) of the observational studies. Mean was the most frequently applied measure of labor duration in the RCTs (89.1%, n = 82), and median in the observational studies (54.8%, n = 69). Most RCTs (83%, n = 76) and observational studies (70.6%, n = 89) analyzed labor duration using a bivariate method, with the t-test being the most frequently applied (45.7% and 27%, respectively). Only 10.8% (n = 10) of the RCTs and 52.4% (n = 66) of the observational studies conducted a multivariable regression: 3 (30%; out of 10) RCTs and 37 (56%; out of 66) observational studies used a time-to-event adapted model.

CONCLUSION

This survey reports a lack of agreement with respect to how the onset of labor and phases of labor duration are presented. Concerning the statistical approaches, few studies used survival analysis, which is the appropriate statistical framework to analyze time-to-event data.

Reference-free inferring of transcriptomic events in cancer cells on single-cell data

BACKGROUND

Cancerous cells' identity is determined via a mixture of multiple factors such as genomic variations, epigenetics, and the regulatory variations that are involved in transcription. The differences in transcriptome expression as well as abnormal structures in peptides determine phenotypical differences. Thus, bulk RNA-seq and more recent single-cell RNA-seq data (scRNA-seq) are important to identify pathogenic differences. In this case, we rely on k-mer decomposition of sequences to identify pathogenic variations in detail which does not need a reference, so it outperforms more traditional Next-Generation Sequencing (NGS) analysis techniques depending on the alignment of the sequences to a reference.

RESULTS

Via our alignment-free analysis, over esophageal and glioblastoma cancer patients, high-frequency variations over multiple different locations (repeats, intergenic regions, exons, introns) as well as multiple different forms (fusion, polyadenylation, splicing, etc.) could be discovered. Additionally, we have analyzed the importance of less-focused events systematically in a classic transcriptome analysis pipeline where these events are considered as indicators for tumor prognosis, tumor prediction, tumor neoantigen inference, as well as their connection with respect to the immune microenvironment.

CONCLUSIONS

Our results suggest that esophageal cancer (ESCA) and glioblastoma processes can be explained via pathogenic microbial RNA, repeated sequences, novel splicing variants, and long intergenic non-coding RNAs (lincRNAs). We expect our application of reference-free process and analysis to be helpful in tumor and normal samples differential scRNA-seq analysis, which in turn offers a more comprehensive scheme for major cancer-associated events.

Confronting Inequalities and Bridging the Divide: A Retrospective Study Assessment of Country-Level COVID-19 Vaccine Equality with a Cox Regression Model

COVID-19 vaccination is vital in reducing illness, hospitalization, and mortality in the face of this global pandemic. However, COVID-19 vaccination rates worldwide remain below WHO public health targets, and persistent structural inequities reduce vaccine uptake likelihood among populations of low socioeconomic status. We conducted a cross-sectional study based on publicly available data from the Our World in Data project. We included all 124 countries with available open epidemic data and a population of more than 5 million. We used a Cox Regression Model, with population, population density, median age, human development index, GDP per capita, gender inequality index, healthcare access and quality index, hospital beds per thousand people, completion rate of primary education, infection cases of COVID-19 by the end of 2022, and death rate due to COVID-19 by the end of 2022 as predictors for model hazard rates of completion of 50% population vaccination. According to our study, countries with higher populations, higher population density, higher human development index, lower gender inequality index, and lower hospital beds per 1000 people had a higher hazard rate, which means they were more likely to achieve 50% population vaccination faster. By utilizing the time to achieve vaccination rate goals as our primary endpoint, we evaluated inequity from a dual perspective, considering both the differences in vaccination rates and the duration required to attain them. Consequently, this study employed survival analysis approaches to gain a comprehensive understanding of vaccine drivers and population-level trends nationally and inform all communities from a statistical perspective to prepare for health emergencies. Development-level standing modified the effects of equal access to COVID-19 vaccination on cumulative cases and mortality, for which countries of low or medium human development tended to fare worse in outcomes than high human development countries. As COVID-19 vaccination efforts evolve, healthcare professionals, scholars, and policymakers need to identify the structural impediments to equitable vaccination awareness, access, and uptake so that future vaccination campaigns are not impeded by these barriers to immunization. Recognizing the complex nature of this significant barrier, it is evident that no single statistical analysis method can comprehensively address all intricacies.

Time to first birth and its predictors among reproductive-age women in Ethiopia: multilevel analysis using shared frailty model

OBJECTIVE

To determine the time to first birth and its predictors among reproductive-age women in Ethiopia.

DESIGN, SETTING AND PARTICIPANTS

Nationwide secondary data analysis using mini Ethiopian Demographic and Health Survey, 2019. A stratified, two-stage cluster sampling technique was used to select the sample. Among 9012 women of age 15-49 years, 8885 completed the interview yielding a response rate of 99%.

MAIN OUTCOMES MEASURED

Time to first birth was determined. Kaplan-Meier method was used to estimate the time to first birth. The lognormal inverse Gaussian shared frailty model was used to model the data at a 95% CI. CI and adjusted time ratio (ATR) were reported as effect size. Statistical significance was declared at p-value<0.05.

RESULTS

The overall median time to give first birth was 18 years (IQR: 15, 21). Age 20-29 (ATR=1.08; 95% CI, 1.05 to 1.12), Age>29 years (ATR=1.08; 95% CI, 1.05 to 1.11), northern regions (ATR=1.06; 95% CI, 1.03 to 1.08), rural residence (ATR=0.95; 95% CI, 0.93 to 0.98), never using contraceptive methods (ATR=0.98; 95% CI, 0.96 to 0.99), sex of household head (ATR=1.01; 95% CI, 1 to 1.03), poorest wealth index (ATR=1.04; 95% CI, 1.02 to 1.06) and richest wealth index (ATR=1.07; 95% CI, 1.04 to 1.1) were the significant predictors of time to first birth among reproductive-age women.

CONCLUSION

In Ethiopia, women often gave birth to their first child at a younger age than what is recommended. Explicitly, women aged 20-29 and over 29, living in the northern region, leading a household as females, and belonging to the poorest or wealthiest wealth index tended to have a slightly delayed first childbirth. On the other hand, women in rural areas and those who had never used contraception were more likely to have an early first birth. The findings indicated the necessity of implementing targeted measures for rural Ethiopian women, especially those lacking knowledge about contraception.

Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients

RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In order to study the connection between RNA editing and CESC patients' prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group. An RNA editing-based risk model for CESC patients was established by Cox regression analysis and least absolute shrinkage and selection operator (LASSO). According to the median score generated by this RNA editing-based risk model, patients were categorized into subgroups with high and low risks. We further constructed the nomogram by risk scores and clinical characteristics and analyzed the impact of RNA editing levels on host gene expression levels and adenosine deaminase acting on RNA. Finally, we also compared the biological functions and pathways of differentially expressed genes (DEGs) between different subgroups by enrichment analysis. In this risk model, we screened out 6 RNA editing sites with significant prognostic value. The constructed nomogram performed well in forecasting patients' prognoses. Furthermore, the level of RNA editing at the prognostic site exhibited a strong correlation with host gene expression. In the high-risk subgroup, we observed multiple biological functions and pathways associated with immune response, cell proliferation, and tumor progression. This study establishes an RNA editing-based risk model that helps forecast patients' prognoses and offers a new understanding of the underlying mechanism of RNA editing in CESC.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

BACKGROUND

Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis.

METHODS

Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.

RESULTS

Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.

CONCLUSIONS

The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

Temperature influences desiccation resistance of bumble bees

Ongoing climate change has increased temperatures and the frequency of droughts in many parts of the world, potentially intensifying the desiccation risk for insects. Because resisting desiccation becomes more difficult at higher temperatures and lower humidity, avoiding water loss is a key challenge facing terrestrial insects. However, few studies have examined the interactive effects of temperature and environmental humidity on desiccation resistance in insects. Such studies on bees (Hymenoptera: Apoidea: Anthophila) are especially rare, despite their ecological and economic importance. Here, we crossed temperature (20, 25, and 30 °C) with humidity (<5, 50, >95 % RH) manipulations and measured time to mortality, water loss rates, and the water content at mortality of bumble bees (Bombus impatiens). We found that both higher temperature and lower humidity increased water loss rates, while warmer temperatures reduced survival time and lower humidity decreased water content at mortality. Additionally, we observed large intraspecific variation in water balance traits between colonies, and larger individuals survived longer and could tolerate more water loss before mortality. This study raises important questions about the mechanisms underpinning water loss in bumble bees and suggests that frequent access to nectar may be especially important for bumble bees' water balance and survival in a warming and drying climate.

Identification of risk factors on rabies vaccine efficacy from censored data: Pre-travel tests for dogs and cats from Yaoundé (2005-2015)

Little research is available on acquired immunity to rabies in dogs and cats from Central Africa, particularly regarding the legal movements of pets. Movement of domestic animals from rabies-endemic countries like Cameroon to rabies free areas poses one of the main risks for rabies introduction into rabies-free areas. Thus, the aim of this study was to assess the effect of various risk factors on rabies vaccine efficacy in Cameroonian. Since the dependent variable, rabies neutralizing titres, were censored from above (right-censoring), Generalized Additive Model for Location, Scale and Shape (GAMLSS) was used in the analysis. Overall, 85.7% of dogs and 100% of cats had titres greater than or equal to 0.5 IU/mL, which is considered protective. Additionally, compared to cats, the value of the rabies-neutralizing serum titres in dogs was on average smaller by 2.3 IU/mL. For each additional year of age, the value of the rabies-neutralizing serum titre, on average, increased by approximately 0.14 IU/mL. Finally, for each 30 additional days between the date of the last rabies vaccination and the date of the sampling, the value the rabies neutralizing titre, on average, decreased by approximately 0.10 IU/mL, given the species and age at sampling were equivalent. These results are useful for assessing risk and improving surveillance to prevent the introduction of rabies into a country via the international movement of animals.

Pattern electroretinogram, blue-yellow visual evoked potentials and the risk of developing visual field defects in glaucoma suspects: a longitudinal "survival" analysis with a very long follow-up

PURPOSE

Estimating glaucoma suspects' risk for visual field defects helps to avoid under- and over-treatment. In this retrospective, longitudinal cohort study with a very long follow-up, we studied whether pattern electroretinograms (PERG) amplitudes and blue-on-yellow visual evoked potential (BY-VEP) latencies can predict visual field defects.

METHODS

Participants of the Erlangen Glaucoma Study were examined with PERG and BY-VEP between 9/1991 and 8/2001. Stimuli were created using an optical bench with Maxwellian view and consisted of vertical gratings (0,88 cpd) in a 32° field for both PERG and BY-VEP. Patients were treated according to clinical standards and performed standard automated perimetry (SAP) annually. Retrospectively, patients with normal SAP at baseline were selected. Primary endpoint was conversion to perimetric glaucoma. Predictive value was modeled using Kaplan-Meier analyses and a multivariate cox proportional hazards model with the continuous variables PERG amplitude, BY-VEP peak time and SAP square-root of loss variance (sLV) after stratification for Jonas classification of the optic discs.

RESULTS

Of 412 patients (288: Jonas 0, 103: I, and 21: II; baseline age: 20-60 years), 65 converted to perimetric glaucoma during follow-up (0.5-23.3 years; median 5.5 years). Optic disc classification was a strong risk factor for conversion (log rank p < 0.0001), and patients with more advanced changes progressed earlier. In the multivariate analysis (log rank p = 0.005), only PERG amplitude remained an independent risk factor after stratification for optic disc morphology (p = 0.021), with a ~ 30% higher risk per μV amplitude decrease.

CONCLUSIONS

PERG helps to estimate glaucoma suspects' risk for visual field defects.

Genetic dependencies associated with transcription factor activities in human cancer cell lines

Transcription factors (TFs) are important mediators of aberrant transcriptional programs in cancer cells. In this study, we focus on TF activity (TFa) as a biomarker for cell-line-selective anti-proliferative effects, in that high TFa predicts sensitivity to loss of function of a given gene (i.e., genetic dependencies [GDs]). Our linear-regression-based framework identifies 3,047 pan-cancer and 3,952 cancer-type-specific candidate TFa-GD associations from cell line data, which are then cross-examined for impact on survival in patient cohorts. One of the most prominent biomarkers is TEAD1 activity, whose associations with its predicted GDs are validated through experimental evidence as proof of concept. Overall, these TFa-GD associations represent an attractive resource for identifying innovative, biomarker-driven hypotheses for drug discovery programs in oncology.

Impact of lymph node dissection on cancer-specific survival in non-small cell lung cancer patients: a SEER database analysis

Background

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide, and lymph node dissection (LND) is a significant surgical procedure employed in its management. Although some studies suggest benefits of LND, the extent of its impact on survival, the optimal range of lymph nodes to be examined, and the specific patient groups that benefit most remain areas of active debate and investigation.

Methods

A population-based analysis was conducted using the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed with NSCLC between 2004 and 2017, undergoing primary tumor resection, were included. Descriptive, univariate, and multivariate analyses assessed the effect of LND on survival, and a restricted cubic spline method determined the optimal range for lymph node examination.

Results

This study of 37,323 NSCLC patients delved into the impact of LND on lung cancer-specific survival. Key findings revealed a median survival of 19.58 months, with 85% mortality. Baseline characteristics included a majority of White patients (81%), distant stage diagnoses (63%), and 64% with Grade IV tumors. LND emerged as a crucial predictor, influencing survival across age, gender, race, and tumor characteristics. Univariate analysis highlighted its significance, with higher T, N, and M categories, advanced stage, and poorer grade associating with elevated hazard ratios. Multivariate Cox proportional hazards (PH) analysis reinforced LND's impact, showcasing lower hazard ratios post-removal. Hazard ratios for biopsy/aspiration and removal of regional lymph nodes were 0.85 [95% confidence interval (CI): 0.81-0.89; P<0.001] and 0.43 (95% CI: 0.39-0.46; P<0.001), underscoring the protective effect. Visualizations and a U-shaped curve analysis identified an optimal range (24-32 nodes) for examination, emphasizing the nuanced benefits across NSCLC stages.

Conclusions

The study findings suggest that LND plays a critical role in improving cancer-specific survival in NSCLC patients, particularly when tailored to the early stages of the disease. The optimal range of lymph nodes examined, between 24 and 32, offers crucial insights for personalized NSCLC treatment strategies and may enhance overall survival. These results underscore the need for refined surgical guidelines that incorporate the extent of LND, supporting the utility of a more personalized approach in NSCLC management.

Associations between antipsychotics exposure and dementia risk: A prospective cohort study of 415,100 participants

BACKGROUND

Antipsychotics (APs) are among the most widely prescribed medications, and have been shown to cause cognitive decline. But previous studies on their effects on dementia risk are controversial and scarce. We aimed to examine the relationships of APs exposure with the risk of dementia.

METHODS

Data were obtained from a prospective cohort of 415,100 UK Biobank (UKB) participants. We investigated the effects of APs exposure and their various classes on dementia risk by using multivariable Cox proportional hazard models and further the dose-response effects of oral APs.

RESULTS

After a mean follow-up of 8.64 years, 5235 (1.3 %) participants developed all-cause dementia (ACD), among whom 2313 (0.6 %) developed Alzheimer's disease (AD), and 1213 (0.3 %) developed vascular dementia (VaD). Exposure to any APs conferred increased risks of ACD (HR: 1.33, 95 % CI = 1.17-1.51, P < 0.001) and VaD (HR: 1.90, 95 % CI = 1.51-2.40, P < 0.001), but not AD (HR: 1.22, 95 % CI = 1.00-1.48, P = 0.051). Cumulative dose-response relationships of oral APs with the risks of ACD and VaD were observed (P for trend, P < 0.05).

LIMITATIONS

Our study is observational and does not show evidence of causality. Since there are relatively few cases of dementia in the UKB, APs exposure may be higher than estimated in our study.

CONCLUSIONS

APs exposure increased the risk of developing dementia. Dose-response relationships were found between oral APs and dementia risk. Efforts to raise awareness of doctors and patients about this potential drug-related risk are critical to reducing APs use.

A parametric additive hazard model for time-to-event analysis

BACKGROUND

In recent years, the use of non- and semi-parametric models which estimate hazard ratios for analysing time-to-event outcomes is continuously criticized in terms of interpretation, technical implementation, and flexibility. Hazard ratios in particular are critically discussed for their misleading interpretation as relative risks and their non-collapsibility. Additive hazard models do not have these drawbacks but are rarely used because they assume a non- or semi-parametric additive hazard which renders computation and interpretation complicated.

METHODS

As a remedy, we propose a new parametric additive hazard model that allows results to be reported on the original time rather than on the hazard scale. Being an essentially parametric model, survival, hazard and probability density functions are directly available. Parameter estimation is straightforward by maximizing the log-likelihood function.

RESULTS

Applying the model to different parametric distributions in a simulation study and in an exemplary application using data from a study investigating medical care to lung cancer patients, we show that the approach works well in practice.

CONCLUSIONS

Our proposed parametric additive hazard model can serve as a powerful tool to analyze time-to-event outcomes due to its simple interpretation, flexibility and facilitated parameter estimation.

Insulin resistance is a cardiovascular risk factor in hypertensive adults without type 2 diabetes mellitus

BACKGROUND

Metabolic syndrome refers to the association among several cardiovascular risk factors: obesity, dyslipidemia, hyperglycemia, and hypertension. It is associated with increased cardiovascular risk and the development of type 2 diabetes mellitus. Insulin resistance is the underlying mechanism of metabolic syndrome, although its role in increased cardiovascular risk has not been directly identified.

OBJECTIVE

We investigated the association between insulin resistance and increased cardiovascular risk in hypertensive adults without diabetes mellitus.

DESIGN AND PARTICIPANTS

We enrolled participants without diabetes from an outpatient setting in a retrospective, longitudinal study. Several demographic, clinical, and laboratory parameters were recorded during the observation period. Plasma insulin and homeostatic model assessment for insulin resistance (HOMA-IR) were used to determine insulin resistance and four cardiovascular events (acute coronary disease, acute cerebrovascular disease, incident heart failure, and cardiovascular mortality) were combined into a single outcome. Logistic regression and Cox proportional hazards models were fitted to evaluate the association between covariates and outcomes.

RESULTS

We included 1899 hypertensive adults without diabetes with an average age of 53 years (51.3% women, 23% had prediabetes, and 64.2% had metabolic syndrome). In a logistic regression analysis, male sex (odds ratio, OR = 1.66) having high levels of low-density lipoprotein (LDL, OR = 1.01), kidney function (OR = 0.97), and HOMA-IR (OR = 1.06) were associated with the incidence of cardiovascular events; however, in a survival multivariate analysis, only HOMA-IR (hazard ratio, HR 1.4, 95% confidence interval, CI: 1.05-1.87, p = 0.02) and body mass index (HR 1.05, 95% CI: 1.02-1.08, p = 0.002) were considered independent prognostic variables for the development of incident cardiovascular events.

CONCLUSION

Insulin resistance and obesity are useful for assessing cardiovascular risk in hypertensive people without diabetes but with preserved kidney function. This work demonstrates the predictive value of the measurement of insulin, and therefore of insulin resistance, in an outpatient setting and attending to high-risk patients.

Assessing the hazard of death, cardiac tamponade, and pericardial constriction among HIV and tuberculosis pericarditis patients using the extended Cox-hazard model: Intervention study

Background and Aims

Tuberculous (TB) pericarditis (TBP), a TB of the heart, is linked to significant morbidity and mortality rates. Administering glucocorticoid therapy to individuals with TBP might enhance overall results and lower the likelihood of fatality. However, the actual clinical effectiveness of supplementary glucocorticoids remains uncertain. This study specifically evaluated the effects of prednisolone, prednisolone-antiretroviral therapy (ART) interaction, and other potential risk factors in reducing the hazard of the composite outcome, death, cardiac tamponade, and constriction, among TBP and human immunodeficiency virus (HIV) patients.

Methods

The data used in this study were obtained from the investigation of the Management of Pericarditis trial, a multicentre international randomized double-blind placebo-controlled 2 × 2 factorial study that investigated the effects of two TB treatments, prednisolone and Mycobacterium indicus pranii  immunotherapy in patients with TBP in Africa. This study used a sample size of 587 TBP and HIV-positive patients randomized into prednisolone and its corresponding placebo arm. We used the extended Cox-proportional hazard model to evaluate the effects of the covariates on the hazard of the survival outcomes. Models fitting and parameter estimation were carried out using R version 4.3.1.

Results

Prednisolone reduces the hazard of composite outcome (hazrad ratio [HR] = 0.32, 95% confidence interval [CI] = 0.19 , 0.54 , p < 0.001), cardiac tamponade (HR = 0.14, 95% CI = 0.05, 0.42, p < 0.001) and constriction (HR = 0.81, 95% CI = 0.41, 1.61, p = 0.55). However, prednisolone increases the hazard of death (HR = 1.58, 95% CI = 1.11, 2.24, p = 0.01). Consistent usage of ART reduces the hazard of composite outcome, death, and constriction but insignificantly increased the hazard of cardiac tamponade.

Conclusion

The study offers valuable insights into how prednisolone impact the hazard of different outcomes in patients with TBP and HIV. The findings hold potential clinical significance, particularly in guiding treatment decisions and devising strategies to enhance outcomes in this specific patient group. However, there are concerns about prednisolone potentially increasing the risk of death due to HIV-related death.

Transcriptome Sequencing Unveils a Molecular-Stratification-Predicting Prognosis of Sarcoma Associated with Lipid Metabolism

Sarcomas are heterogeneous connective tissue malignancies that have been historically categorized into soft tissue and bone cancers. Although multimodal therapies are implemented, many sarcoma subtypes are still difficult to treat. Lipids play vital roles in cellular activities; however, ectopic levels of lipid metabolites have an impact on tumor recurrence, metastasis, and drug resistance. Thus, precision therapies targeting lipid metabolism in sarcoma need to be explored. In this study, we performed a comprehensive analysis of molecular stratification based on lipid metabolism-associated genes (LMAGs) using both public datasets and the data of patients in our cohort and constructed a novel prognostic model consisting of squalene epoxidase (SQLE) and tumor necrosis factor (TNF). We first integrated information on gene expression profile and survival outcomes to divide TCGA sarcoma patients into high- and low-risk subgroups and further revealed the prognosis value of the metabolic signature and immune infiltration of patients in both groups, thus proposing various therapeutic recommendations for sarcoma. We observed that the low-risk sarcoma patients in the TCGA-SARC cohort were characterized by high proportions of immune cells and increased expression of immune checkpoint genes. Subsequently, this lipid metabolic signature was validated in four external independent sarcoma datasets including the CHCAMS cohort. Notably, SQLE, a rate-limiting enzyme in cholesterol biosynthesis, was identified as a potential therapeutic target for sarcoma. Knockdown of SQLE substantially inhibited cell proliferation and colony formation while promoting the apoptosis of sarcoma cells. Terbinafine, an inhibitor of SQLE, displayed similar tumor suppression capacity in vitro. The prognostic predictive model and the potential drug target SQLE might serve as valuable hints for further in-depth biological, diagnostic, and therapeutic exploration of sarcoma.

Establishment and Validation of a Four-stress Granule-related Gene Signature in Hepatocellular Carcinoma

Background and Aims

Stress granules (SGs) as membrane-less cytoplasmic foci formed in response to unfavorable external stimuli could promote cancer cells to adapt to hostile environments. Hepatocellular carcinoma (HCC) is prone to be highly aggressive once diagnosed, which markedly reduces patient survival time. Therefore, it is crucial to develop valid diagnostic markers to prognosticate HCC patient prognosis, which promotes individualized precision therapeutics in HCC. Considering the pro-tumorigenic activity of SGs, it is of great potential value to construct a prognostic tool for HCC based on the expression profiles of SG-related genes (SGGs).

Methods

Bioinformatic analysis was employed to establish an SGG-based prognostic signature. Western blotting and real-time polymerase chain reaction assays were used to assess the expression patterns of the related SGGs. Loss-of-function experiments were performed to analyze the effect of the SGGs on SG formation and cell survival.

Results

A four-SGG signature (KPNA2, MEX3A, WDR62, and SFN) targeting HCC was established and validated to exhibit a robust performance in predicting HCC prognosis. Consistently, all four genes were further found to be highly expressed in human HCC tissues. More important, we demonstrated that individually knocking down the four SGGs significantly reduced HCC cell proliferation and metastasis by compromising the SG formation process.

Conclusions

We developed an SGG-based predictive signature that can be used as an independent prognostic tool for HCC. The strong predictive power of this signature was further elucidated by the carcinogenic activity of KPNA2, MEX3A, WDR62, and SFN in HCC cells by regulating SG formation.

Associations of traditional Chinese medicine body constitution and all-cause mortality in patients with type 2 diabetes mellitus: a prospective cohort study of a Taiwanese medical center

Introduction

The objective of this study was to investigate associations between baseline body constitutions (BCs) in traditional Chinese Medicine (TCM) and all-cause mortality in Chinese individuals with type 2 diabetes.

Methods

A total of 887 individuals with type 2 diabetes who were enrolled in managed care in 2010 were included. These individuals were followed up until 2015, and their mortality status was determined through the use of Taiwan National Death Datasets. At baseline, BC status of participants, including Yin deficiency, Yang deficiency, and phlegm stasis, was assessed using a well-developed Body Constitutions Questionnaire. Hazard ratios (HR) were calculated using a multivariate Cox proportional hazards model.

Results

During 6807.2 person-years of follow-up of 887 participants, with an average follow-up period of 7.7 years, a total of 190 individuals died, resulting in an incidence density of 0.0279 person-years. Yin deficiency was associated with all-cause mortality (HR, 95% CI: 1.39, 1.02-1.90). This study indicates that individuals diagnosed with Yin deficiency in TCM, characterized by symptoms such as thirst, reduced urine volume, hard stool, and hot flushes, had a 39% higher risk of all-cause mortality.

Discussion

The findings may provide information for TCM practitioners on tailoring treatment plans for persons with type 2 diabetes. No conclusive statements can be made on the basis of the preliminary data presented here. Controlled prospective studies are warranted.

The prognostic role of PD-L1 expression and the presence of polyomavirus in Merkel cell carcinoma cases

BACKGROUND

Merkel cell carcinoma (MCC) comprises a rare malignant primary skin tumor presenting neuroendocrine differentiation. Recently, agents blocking the programmed cell death protein 1 and programmed cell death protein ligand 1 pathway (PD-1/PD-L1) have demonstrated objective and durable tumor regressions in patients presenting advanced MCC. This study aimed to describe the sociodemographic, clinical, and histopathological characteristics of MCC patients, also assessing the prevalence of PD-L1 expression and Merkel cell Polyomavirus (MCPyV), as well as their prognostic roles.

METHODS

Data from patients diagnosed with MCC between 1996 and 2019 at a reference cancer center in Rio de Janeiro, southeastern Brazil, were evaluated in a retrospective study. Tumor samples were tested for MCPyV and PD-L1 employing immunohistochemistry. Survival analyses were carried out employing the Kaplan-Meier method and curves were compared using the log-rank test. A multiple semiparametric Cox model was used. Values p < 0.05 were considered significant.

RESULTS

A total of 65 patients were included in the study, with a mean age at diagnosis of 72 (standard deviation 13.9). A total of 56.9% (37/65) of the patients were male, 86.2% (56/65) were white, and 56.9% (37/64) were illiterate or with incomplete elementary school. MCPyV immunohistochemistry was positive in 29 cases (44.6%) and PD-L1 positivity was ≥ 1% in 42 cases (64.6%). Significant associations between MCPyV and PD-L1 expression ≥ 1% (p = 0.003) and PD-L1 expression ≥ 5% (p = 0.005) were noted. Concerning the multivariate analysis, only education level and advanced MCC stage indicated statistically significant worse progression-free survival. Regarding overall survival (OS), being male, education level and advanced stage comprised risk factors. The estimated OS at 60 months for stages I to III was of 48.9% and for stage IV, 8.9%.

CONCLUSIONS

This is the first large Brazilian cohort to assess the prevalence of MCPyV in MCC tumors, as well as PD-L1 expression and their associations. No correlations were noted between MCPyV infection or PD-L1 expression and survival rates.

A Novel Mathematical Approach for Analysis of Integrated Cell-Patient Data Uncovers a 6-Gene Signature Linked to Endocrine Therapy Resistance

A significant number of breast cancers develop resistance to hormone therapy. This progression, while posing a major clinical challenge, is difficult to predict. Despite important contributions made by cell models and clinical studies to tackle this problem, both present limitations when taken individually. Experiments with cell models are highly reproducible but do not reflect the indubitable heterogenous landscape of breast cancer. On the other hand, clinical studies account for this complexity but introduce uncontrolled noise due to external factors. Here, we propose a new approach for biomarker discovery that is based on a combined analysis of sequencing data from controlled MCF7 cell experiments and heterogenous clinical samples that include clinical and sequencing information from The Cancer Genome Atlas. Using data from differential gene expression analysis and a Bayesian logistic regression model coupled with an original simulated annealing-type algorithm, we discovered a novel 6-gene signature for stratifying patient response to hormone therapy. The experimental observations and computational analysis built on independent cohorts indicated the superior predictive performance of this gene set over previously known signatures of similar scope. Together, these findings revealed a new gene signature to identify patients with breast cancer with an increased risk of developing resistance to endocrine therapy.

De-stabilizing innate immunity in COVID-19: effects of its own positive feedback and erratic viraemia on the alternative pathway of complement

Complement provides powerful, fast responses in the human circulation to SARS-CoV-2 (COVID-19 virus) infection of the lower respiratory tract. COVID-19 effects were investigated in a revised human in silico Mass Action model of complement's alternative pathway (AP) responses. Bursts of newly circulating virions increased the fission of Complement protein C3 into C3a and C3b via stimulation of the lectin pathway or inhibited complement factor H. Viral reproduction sub-models incorporated smoothly exponential or step-wise exponential growth. Starting complement protein concentrations were drawn randomly from published normal male or female ranges and each infection model run for 10 days. C3 and factor B (FB) syntheses driven by Lectin Pathway stimulation led to declining plasma C3 and increasing FB concentrations. The C3-convertase concentration, a driver of viral elimination, could match viral growth over three orders of magnitude but near-complete exhaustion of circulating C3 was more prevalent with step-wise than with 'smooth' increases in viral stimulation. C3 exhaustion could be prolonged. Type 2 Diabetes and hypertension led to greatly increased peak C3-convertase concentrations, as did short-term variability of COVID-19 viraemia, pulmonary capillary clotting and secondary acidosis. Positive feedback in the AP greatly extends its response range at the expense of stability.

Optimizing Skin Cancer Survival Prediction with Ensemble Techniques

The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan-Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy.

Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan-Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.

How plastic are upper thermal limits? A comparative study in tsetse (family: Glossinidae) and wider Diptera

Critical thermal maximum (CTmax) describes the upper thermal tolerance of an animal where biological functions start to fail. A period of acclimation can enhance CTmax through plasticity, potentially buffering animals from extreme temperatures caused by climate change. Basal and acclimated CTmax vary within and between species and may be explained by traits related to thermal physiology, such as body size and sex. Differences in CTmax have not been established among species of tsetse fly (Glossina spp.), vectors of animal and human African trypanosomiasis. Here, we investigated basal CTmax and its plasticity for five tsetse species following adult acclimation at constant 25 or 30 °C for five days. We then set our findings in context using a meta-analysis on 33 species of Diptera. We find that, of the five tsetse species considered, only Glossina palpalis gambiensis and Glossina brevipalpis exhibited plasticity of CTmax, with an increase of 0.12 °C and 0.10 °C per 1 °C acclimation respectively. Within some species, higher basal CTmax values were associated with larger body size and being female, while variation in plasticity (i.e., response to the acclimation temperature) could not be explained by sex or size. Our broader meta-analysis across Diptera revealed overall CTmax plasticity of 0.06 °C per 1 °C acclimation, versus a similar 0.05 °C mean increase in tsetse. In contrast, there was greater CTmax plasticity in males compared to females in Diptera. Our study highlights that CTmax and its plasticity varies even among closely related species. Broader patterns across groups are not always reflected at a finer resolution; we thus emphasise the need for detailed experimental studies across a wide range of insect species to capture their capacity to cope with rapidly warming temperatures.

Estimation of the onset time of diabetic complications in type 2 diabetes patients in Thailand: a survival analysis

BACKGROUND

This study aimed to identify factors associated with the onset time of diabetic complications in patients with type 2 diabetes mellitus (T2DM) and determine the best-fitted survival model.

METHODS

A retrospective cohort study was conducted among T2DM patients enrolled from October 1, 2016 to July 15, 2020 at the National Health Security Office (NHSO). In total, 388 T2DM patients were included. Cox proportional-hazard and parametric models were used to identify factors related to the onset time of diabetic complications. The Akaike information criterion, Bayesian information criterion, and Cox-Snell residual were compared to determine the best-fitted survival model.

RESULTS

Thirty diabetic complication events were detected among the 388 patients (7.7%). A 90% survival rate for the onset time of diabetic complications was found at 33 months after the first T2DM diagnosis. According to multivariate analysis, a duration of T2DM ≥42 months (time ratio [TR], 0.56; 95% confidence interval [CI], 0.33-0.96; p=0.034), comorbid hypertension (TR, 0.30; 95% CI, 0.15-0.60; p=0.001), mildly to moderately reduced levels of the estimated glomerular filtration rate (eGFR) (TR, 0.43; 95% CI, 0.24-0.75; p=0.003) and an eGFR that was severely reduced or indicative of kidney failure (TR, 0.38; 95% CI, 0.16-0.88; p=0.025) were significantly associated with the onset time of diabetic complications (p<0.05).

CONCLUSION

Patients with T2DM durations of more than 42 months, comorbid hypertension, and decreased eGFR were at risk of developing diabetic complications. The NHSO should be aware of these factors to establish a policy to prevent diabetic complications after the diagnosis of T2DM.

HoTDAM! An easy-to-use automated assay expands the inducible thermotolerance phenotype in Drosophila melanogaster: Heat hardening reduces motility

To quantify heat tolerance in insects, two manual observation measures are typically implemented: the time to physiological collapse at a static noxious temperature (time to knockdown; TKD) or the temperature at which collapse occurs as temperature increases (critical thermal maximum; CTmax). Both assay modalities focus on physiological collapse, neglecting the prior behavioral processes. In this study, the locomotion response of Drosophila melanogaster to relatively high temperature (39 and 40.5 °C) was quantified using the TriKinetics Drosophila Activity Monitor (DAM2 system). The absence of locomotion was defined as the state of physiological collapse resulting from extended exposure to high temperature. An easy-to-use executable application that allows the user to automatically extract individual TKD from the activity data was developed. For validation, manual TKD assays were performed in parallel to automated assays across multiple factors, including sex, hardening, recovery time after hardening, and assay temperature, which gave similar results. In terms of behavioral aspects, heat hardening consistently led to reduced activity during a subsequent heat stress, irrespective of assay temperature, sex, or recovery time after hardening. Our automated heat tolerance assay utilizing the DAM2 system is one way to expand the scope of the heat tolerance phenotype to include a behavioral component in conjunction with the traditional TKD measure.

Non-Mixture Cure Model Estimation in Bladder Cancer Patients: A Novel Approach with Exponentiated Weibull Exponential Distribution

OBJECTIVE

Cure models are frequently used in survival analysis to account for a cured fraction in the data. When there is a cure rate present, researchers often prefer cure models over parametric models to analyse the survival data. These models enable the ability to define the probability distribution of survival durations for patients who are at risk. Various distributions can be considered for the survival times, such as Exponentiated Weibull Exponential (EWE), Exponential Exponential (EE), Weibull and lognormal distribution. The objective of this research is to choose the most appropriate distribution that accurately represents the survival times of patients who have not been cured. This will be accomplished by comparing various non-mixture cure models that are based on the EWE distribution with its sub-distributions, and distributions distinct from those belonging to the EWE distribution family.

MATERIAL AND METHODS

A sample of 85 patients diagnosed with superficial bladder tumours was selected to be used in fitting the non-mixture cure model. In order to estimate the parameters of the suggested model, which takes into account the presence of a cure rate, censored data, and covariates, we utilized the maximum likelihood estimation technique using R software version 3.5.7.

RESULT

Upon conducting a comparison of various parametric models fitted to the data, both with and without considering the cure fraction and without incorporating any predictors, the EE distribution yields the lowest AIC, BIC, and HQIC values among all the distributions considered in this study, (1191.921/1198.502, 1201.692/1203.387, 1195.851/1200.467). Furthermore, when considering a non-mixture cure model utilizing the EE distribution along with covariates, an estimated ratio was obtained between the probabilities of being cured for placebo and thiotepa groups (and its 95% confidence intervals) were 0.76130 (0.13914, 6.81863).

CONCLUSION

The findings of this study indicate that EE distribution is the optimal selection for determining the duration of survival in individuals diagnosed with bladder cancer.

Impact of a simplified treatment protocol for moderate acute malnutrition with a decentralized treatment approach in emergency settings of Niger

Introduction

Of the 45.4 million children under five affected by acute malnutrition in the world, the majority (31.8 million) are affected by moderate acute malnutrition (MAM). Its treatment is particularly complex in emergency settings such as the Diffa region in Niger. This study aims to evaluate the effectiveness and coverage of a simplified treatment protocol with Community Health Workers (CHWs) as treatment providers.

Methods

This study is a non-randomized controlled trial. The control group (n = 181) received the standard protocol currently used in country, delivered by nursing staff only in health centres and health posts, while the intervention group (n = 483) received the simplified protocol which included nursing at health centres and CHWs at health post as treatment providers.

Results

The recovery rate was higher in the simplified protocol group (99.6% vs. 79.56%, p < 0.001) recording lower time to recover and higher anthropometric gain. Treatment coverage in the intervention group increased from 28.8% to 84.9% and reduced in the control group (25.3% to 13.6%). No differences were found in the recovery rate of children treated by CHWs and nursing staff.

Conclusion

The outcomes using the simplified protocol exceeded humanitarian requirements and demonstrated improvements compared to the standard protocol showing that the simplified protocol could be safely provided by CHWs in an emergency context. Further research in other contexts is needed to scale up this intervention.

Application of machine learning in predicting survival outcomes involving real-world data: a scoping review

BACKGROUND

Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.

METHODS

PUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).

RESULTS

Of 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6-0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).

CONCLUSIONS

The ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

Histologic margin status is a predictor of relapse in lentigo maligna melanoma

BACKGROUND

Most surgical margins for lentigo maligna melanomas reported in the literature are clinical and not histologic.

OBJECTIVES

We sought to determine whether histologic margin status is an independent predictor of progression.

METHODS

Clinicopathologic information of 268 invasive lentigo maligna melanomas diagnosed from 1990-2019 were analyzed. Statistical analyses were performed using Cox proportional hazards model and Boruta method.

RESULTS

A total of 75% of the lesions were located on the head and neck. The range of follow-up for all patients was 0 to 31.8 years (median, 10.2 years). Time to local recurrence ranges from 0 to 20 years (median, 3 years). Progression developed in 54 (20.1%) of 268 patients. Local recurrence was seen only in 36 (13.4%), both local recurrence and subsequent metastasis in 7 (2.6%), and only metastasis in 11 (4.1%) of 268 patients. Histologic margin status (positive and close/<3 mm) and tumor site (head and neck location) significantly correlated with worse progression-free survival.

LIMITATIONS

Single institution and retrospective study.

CONCLUSIONS

Histologic margin status is the strongest predictor of progression for lentigo maligna melanoma. Patients with positive or close/<3 mm histologic margins should consider a re-excision due to the increased risk of relapse.

No evidence for direct thermal carryover effects on starvation tolerance in the obligate blood-feeder, Glossina morsitans morsitans

Thermal stress during development can prime animals to cope better with similar conditions in later life. Alternatively, negative effects of thermal stress can persist across life stages and result in poorer quality adults (negative carryover effects). As mean temperatures increase due to climate change, evidence for such effects across diverse taxa is required. Using Glossina morsitans morsitans, a species of tsetse fly and vector of trypanosomiasis, we asked whether (i) adaptive developmental plasticity allows flies to survive for longer under food deprivation when pupal and adult temperatures are matched; or (ii) temperature stress during development persists into adulthood, resulting in a greater risk of death. We did not find any advantage of matched pupal and adult temperature in terms of improved starvation tolerance, and no direct negative carryover effects were observed. There was some evidence for indirect carryover effects-high pupal temperature produced flies of lower body mass, which, in turn, resulted in greater starvation risk. However, adult temperature had the largest impact on starvation tolerance by far: flies died 60% faster at 31°C than those experiencing 25°C, consequently reducing survival time from a median of 8 (interquartile range (IQR) 7-9) to 5 (IQR 5-5.25) days. This highlights differences in temperature sensitivity between life stages, as there was no direct effect of pupal temperature on starvation tolerance. Therefore, for some regions of sub-Saharan Africa, climate change may result in a higher mortality rate in emerging tsetse while they search for their first blood meal. This study reinforces existing evidence that responses to temperature are life stage specific and that plasticity may have limited capacity to buffer the effects of climate change.

Identification of atrial fibrillation phenotypes at low risk of stroke in patients with CHA2DS2-VASc ≥2: Insight from the China-AF study

OBJECTIVE

Patients with atrial fibrillation (AF) are highly heterogeneous, and current risk stratification scores are only modestly good at predicting an individual's stroke risk. We aim to identify distinct AF clinical phenotypes with cluster analysis to optimize stroke prevention practices.

METHODS

From the prospective Chinese Atrial Fibrillation Registry cohort study, we included 4337 AF patients with CHA2 DS2 -VASc≥2 for males and 3 for females who were not treated with oral anticoagulation. We randomly split the patients into derivation and validation sets by a ratio of 7:3. In the derivation set, we used outcome-driven patient clustering with metric learning to group patients into clusters with different risk levels of ischemic stroke and systemic embolism, and identify clusters of patients with low risks. Then we tested the results in the validation set, using the clustering rules generated from the derivation set. Finally, the survival decision tree was applied as a sensitivity analysis to confirm the results.

RESULTS

Up to the follow-up of 1 year, 140 thromboembolic events (ischemic stroke or systemic embolism) occurred. After supervised metric learning from six variables involved in CHA2 DS2 -VASc scheme, we identified a cluster of patients (255/3035, 8.4%) at an annual thromboembolism risk of 0.8% in the derivation set. None of the patients in the low-risk cluster had prior thromboembolism, heart failure, diabetes, or age older than 70 years. After applying the regularities from metric learning on the validation set, we also identified a cluster of patients (137/1302, 10.5%) with an incident thromboembolism rate of 0.7%. Sensitivity analysis based on the survival decision tree approach selected a subgroup of patients with the same phenotypes as the metric-learning algorithm.

CONCLUSIONS

Cluster analysis identified a distinct clinical phenotype at low risk of stroke among high-risk [CHA2 DS2 -VASc≥2 (3 for females)] patients with AF. The use of the novel analytic approach has the potential to prevent a subset of AF patients from unnecessary anticoagulation and avoid the associated risk of major bleeding.

Breast Cancer in Pregnant Young Women: Clinicopathological Profile, Survival, and Pregnancy Outcomes

Background Breast cancer is one of the most common cancer types diagnosed during pregnancy; the presence of any neoplasm in pregnant women faces clinical dilemmas and challenges in cancer and pregnancy management. Pregnancy-associated breast cancer (PABC) is defined as breast cancer diagnosed during pregnancy or within one year after delivery. The aim of this study was to describe tumor clinicopathological characteristics and pregnancy outcomes in PABC patients. Materials and methods This is a retrospective cohort assessing PABC patients. Qualitative variables were compared using Fisher's exact test. Kaplan-Meier method was used to calculate survival. Cox regression and logistic regression methods were used to estimate the hazard ratio (HR) and odds ratio (OR), respectively. Results We assessed 16 PABC patients. Women ≤ 35 years of age were mainly diagnosed at advanced stage (88.8%) with ER-negative disease (77.8%). Patients with >4 pathological lymph nodes (25%; p = 0.001) and ER-negative disease (50%; p = 0.646) showed poor five-year overall survival (OS). In the multivariate analysis, nodal involvement was the main predictor associated with poorer OS (HR = 1.4, 90% confidence interval [CI]: 1.14 to 1.8). The following risk factors might influence the risk of preterm delivery: maternal older age, gestational age at diagnosis, and intrauterine exposure to chemotherapy, but an adjusted OR of 0.61 (90% CI: 0.34 to 1), 0.80 (90% CI: 0.66 to 0.9), and 0.013 (90% CI: 0.00 to 0.9), respectively, did not statistically support such an effect. Conclusions Younger women with PABC had a more aggressive pathological profile that might partly explain the poor OS. Obstetrical adverse events related to preterm delivery should be avoided with better planning of specialized strategies.

CESCProg: a compact prognostic model and nomogram for cervical cancer based on miRNA biomarkers

Cervical squamous cell carcinoma, more commonly cervical cancer, is the fourth common cancer among women worldwide with substantial burden of disease, and less-invasive, reliable and effective methods for its prognosis are necessary today. Micro-RNAs are increasingly recognized as viable alternative biomarkers for direct diagnosis and prognosis of disease conditions, including various cancers. In this work, we addressed the problem of systematically developing an miRNA-based nomogram for the reliable prognosis of cervical cancer. Towards this, we preprocessed public-domain miRNA -omics data from cervical cancer patients, and applied a cascade of filters in the following sequence: (i) differential expression criteria with respect to controls; (ii) significance with univariate survival analysis; (iii) passage through dimensionality reduction algorithms; and (iv) stepwise backward selection with multivariate Cox modeling. This workflow yielded a compact prognostic DEmiR signature of three miRNAs, namely hsa-miR-625-5p, hs-miR-95-3p, and hsa-miR-330-3p, which were used to construct a risk-score model for the classification of cervical cancer patients into high-risk and low-risk groups. The risk-score model was subjected to evaluation on an unseen test dataset, yielding a one-year AUROC of 0.84 and five-year AUROC of 0.71. The model was validated on an out-of-domain, external dataset yielding significantly worse prognosis for high-risk patients. The risk-score was combined with significant features of the clinical profile to establish a predictive prognostic nomogram. Both the miRNA-based risk score model and the integrated nomogram are freely available for academic and not-for-profit use at CESCProg, a web-app (https://apalania.shinyapps.io/cescprog).

Can sarcopenia predict survival in locally advanced rectal cancer patients?

BACKGROUND

There is mounting evidence that suggests sarcopenia can be used to predict survival outcomes in patients with colon cancer. However, the effect on locally advanced rectal cancer (LARC) is less clear. We sought to determine the association between sarcopenia on Overall Survival and Recurrence-free Survival (OS and RFS) in patients with LARC undergoing multimodal treatment.

METHODS

A retrospective study was undertaken of all pre-treatment stage 2-3 rectal cancer patients who underwent neo-adjuvant treatment and surgery with curative intent between January 2010 and September 2016 at Western Health. Sarcopenia was measured on pre-treatment staging scans at the third lumbar vertebrae and defined using cohort-derived, sex-specific thresholds. Primary outcomes were OS and RFS.

RESULTS

A total of 132 patients with LARC were analysed. Sarcopenia: Hazard ratio (HR) 3.71; 95% CI, 1.28-10.75, P = 0.016 was independently associated with worse Overall Survival following multivariate analysis. There was no significant relationship between sarcopenia and RFS: Time ratio (TR) 1.67; 95% CI 0.52-5.34, P = 0.386.

CONCLUSION

Sarcopenia was found to be an independent risk factor for worse overall survival, but not recurrence free survival, in patients with locally advanced rectal cancer undergoing neo-adjuvant chemo-radiotherapy and surgery with curative intent.

No Effect of Return to Sport Test Batteries with and without Psychological PROs on the Risk of a Second ACL Injury: A Critical Assessment of Four Different Test Batteries

Background

Patients report psychological barriers as important when returning to sport, however, psychological outcome measures are seldom included in return to sport (RTS) assessment. There is a need for clinical trials to integrate psychological patient-reported outcomes (PROs) in return to sport batteries assessing patients treated with ACL reconstruction.

Objective

The aim of this study was to determine the association between passing clinical tests of muscle function and psychological PROs and sustaining a second ACL injury in patients who RTS after primary ACL reconstruction.

Design

Retrospective Cohort study.

Methods

Patients' sex, age, height and weight, and the results of strength and hop tests, as well as answers to PRO's (including Tegner activity scale, the ACL Return to Sport after Injury scale (ACL-RSI) as well as the Quality of Life (QoL) subscale of the Knee injury and Osteoarthritis Outcome Score [KOOS]), were extracted from a rehabilitation-specific registry. Four different test batteries comprising muscle function tests and PROs were created to assess whether patients were ready to RTS. Passing each of the test batteries (yes/no) was used as an independent variable. A multivariable Cox proportional hazard model analysis was performed, with sustaining a second ACL injury (either ipsi- or contralateral; yes/no) within two years of RTS as the dependent variable.

Results

A total of 419 patients (male, n=214; 51%) were included, of which 51 (12.2%) suffered a second ACL injury within the first two years after RTS. There were no differences in passing rates in the different RTS test batteries comprising muscle function tests and PROs for patients who suffered a second ACL injury compared to patients who did not.

Conclusion

No association between passing the RTS clinical tests batteries comprising muscle function and psychological PROs used, and the risk of a second ACL injury could be found.

Level of Evidence

3©The Author(s).

FGCNSurv: dually fused graph convolutional network for multi-omics survival prediction

MOTIVATION

Survival analysis is an important tool for modeling time-to-event data, e.g. to predict the survival time of patient after a cancer diagnosis or a certain treatment. While deep neural networks work well in standard prediction tasks, it is still unclear how to best utilize these deep models in survival analysis due to the difficulty of modeling right censored data, especially for multi-omics data. Although existing methods have shown the advantage of multi-omics integration in survival prediction, it remains challenging to extract complementary information from different omics and improve the prediction accuracy.

RESULTS

In this work, we propose a novel multi-omics deep survival prediction approach by dually fused graph convolutional network (GCN) named FGCNSurv. Our FGCNSurv is a complete generative model from multi-omics data to survival outcome of patients, including feature fusion by a factorized bilinear model, graph fusion of multiple graphs, higher-level feature extraction by GCN and survival prediction by a Cox proportional hazard model. The factorized bilinear model enables to capture cross-omics features and quantify complex relations from multi-omics data. By fusing single-omics features and the cross-omics features, and simultaneously fusing multiple graphs from different omics, GCN with the generated dually fused graph could capture higher-level features for computing the survival loss in the Cox-PH model. Comprehensive experimental results on real-world datasets with gene expression and microRNA expression data show that the proposed FGCNSurv method outperforms existing survival prediction methods, and imply its ability to extract complementary information for survival prediction from multi-omics data.

AVAILABILITY AND IMPLEMENTATION

The codes are freely available at https://github.com/LiminLi-xjtu/FGCNSurv.

Rate advancement measurement for lung cancer and pleural mesothelioma in asbestos-exposed workers

INTRODUCTION

Exposure to asbestos increases the risk of lung cancer and mesothelioma. Few studies quantified the premature occurrence of these diseases in asbestos-exposed workers. Focus on premature disease onset (rate advancement or acceleration) can be useful in risk communication and for the evaluation of exposure impact. We estimated rate advancement for total mortality, lung cancer and pleural mesothelioma deaths, by classes of cumulative asbestos exposure in a pooled cohort of asbestos cement (AC) workers in Italy.

METHOD

The cohort study included 12 578 workers from 21 cohorts, with 6626 deaths in total, 858 deaths from lung cancer and 394 from pleural malignant neoplasm (MN). Rate advancement was estimated by fitting a competitive mortality Weibull model to the hazard of death over time since first exposure (TSFE).

RESULT

Acceleration time (AT) was estimated at different TSFE values. The highest level of cumulative exposure compared with the lowest, for pleural MN AT was 16.9 (95% CI 14.9 to 19.2) and 33.8 (95% CI 29.8 to 38.4) years at TSFE of 20 and 40 years, respectively. For lung cancer, it was 13.3 (95% CI 12.0 to 14.7) and 26.6 (95% CI 23.9 to 29.4) years, respectively. As for total mortality, AT was 3.35 (95% CI 2.98 to 3.71) years at 20 years TSFE, and 6.70 (95% CI 5.95 to 7.41) at 40 years TSFE.

CONCLUSION

The current study observed marked rate advancement after asbestos exposure for lung cancer and pleural mesothelioma, as well as for total mortality.

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"?

The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.

NK/T-cell Lymphoma With Orbital Involvement: A Case Report and Systematic Review of the Literature

PURPOSE

To present the clinical course of a patient with recurrent NK/T-cell lymphoma (NKTL) involving the orbit and to review the literature on patients with NKTL involving the orbit.

METHODS

The PubMed database was searched for all cases of NKTL involving orbital, intraocular, or adnexal ocular structures.

RESULTS

Ninety-six patients were included in the final analysis. The mean age of diagnosis was 48.1 ± 16.8 years. The patients were 53/96 (55.2%) male and 43/96 (44.8%) female. Tumor location varied and included the orbit in 80/96 (83.3%), nasosinus in 56/96 (58.3%), uvea in 11/96 (11.5%), lacrimal gland in 9/96 (9.4%), lacrimal drainage system in 11/96 (11.5%), and conjunctiva in 7/96 (7.3%) cases. Management included surgical debulking in 29/96 (30.2%) cases, radiotherapy in 52/96 (54.2%) cases, and chemotherapy in 82/96 (85.4%) cases. Median survival was 6 months (95% CI: 5-9). Chemotherapy (hazard ratio = 0.80, 95% CI: 0.67-0.95, p = 0.013), radiotherapy (hazard ratio = 0.75, 95% CI: 0.64-0.87, p < 0.001), and orbital involvement being a recurrence of disease (hazard ratio = 0.79, 95% CI: 0.67-0.95, p = 0.009) were associated with improved survival. Advanced Ann Arbor stage (III-IV) at diagnosis (hazard ratio = 1.22, 95% CI: 1.08-1.38, p = 0.001), vision loss (hazard ratio = 1.18, 95% CI: 1.04-1.34, p = 0.009), proptosis (hazard ratio = 1.15, 95% CI: 1.01-1.30, p = 0.035) and periorbital swelling (hazard ratio = 1.15, 95% CI: 1.00-1.33, p = 0.048) were associated with poor survival.

CONCLUSIONS

NK/T-cell lymphoma involving the orbit, globe, or ocular adnexa heralds a poor prognosis where early diagnosis and therapy are critical. The use of radiotherapy and chemotherapy is associated with improved survival.

Usage and Daily Attrition of a Smartphone-Based Health Behavior Intervention: Randomized Controlled Trial

BACKGROUND

Although most adolescents have access to smartphones, few of them use mobile health (mHealth) apps for health improvement, highlighting the apparent lack of interest in mHealth apps among adolescents. Adolescent mHealth interventions have been burdened with high attrition rates. Research on these interventions among adolescents has frequently lacked detailed time-related attrition data alongside analysis of attrition reasons through usage.

OBJECTIVE

The objective was to obtain daily attrition rates among adolescents in an mHealth intervention to gain a deeper understanding of attrition patterns, including the role of motivational support, such as altruistic rewards, through analysis of app usage data.

METHODS

A randomized controlled trial was conducted with 304 adolescent participants (152 boys and 152 girls) aged 13-15 years. Based on 3 participating schools, participants were randomly assigned to control, treatment as usual (TAU), and intervention groups. Measures were obtained at baseline, continuously throughout the 42-day trial period (research groups), and at the trial end. The mHealth app is called SidekickHealth and is a social health game with the following 3 main categories: nutrition, mental health, and physical health. Primary measures were attrition based on time from launch, and the type, frequency, and time of health behavior exercise usage. Outcome differences were obtained through comparison tests, while regression models and survival analyses were used for attrition measures.

RESULTS

Attrition differed significantly between the intervention and TAU groups (44.4% vs 94.3%; χ²₁=61.220; P<.001). The mean usage duration was 6.286 days in the TAU group and 24.975 days in the intervention group. In the intervention group, male participants were active significantly longer than female participants (29.155 vs 20.433 days; χ²₁=6.574; P<.001). Participants in the intervention group completed a larger number of health exercises in all trial weeks, and a significant decrease in usage was observed from the first to second week in the TAU group (t₁₀₅=9.208; P<.001) but not in the intervention group. There was a significant increase in health exercises in the intervention group from the fifth to sixth week (t₁₀₅=3.446; P<.001). Such a significant increase in usage was not evident in the TAU group. The research group was significantly related to attrition time (hazard ratio 0.308, 95% CI 0.222-0.420), as well as the numbers of mental health exercises (P<.001) and nutrition exercises (P<.001).

CONCLUSIONS

Differences in attrition rates and usage between groups of adolescents were identified. Motivational support is a significant factor for lowering attrition in adolescent mHealth interventions. The results point to sensitivity periods in the completion of diverse health tasks, and emphasis on time-specific attrition, along with the type, frequency, and time of health behavior exercise usage, is likely a fruitful avenue for further research on mHealth interventions for adolescent populations, in which attrition rates remain excessive.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05912439; https://clinicaltrials.gov/study/NCT05912439.

BatMan: Mitigating Batch Effects Via Stratification for Survival Outcome Prediction

Reproducible translation of transcriptomics data has been hampered by the ubiquitous presence of batch effects. Statistical methods for managing batch effects were initially developed in the setting of sample group comparison and later borrowed for other settings such as survival outcome prediction. The most notable such method is ComBat, which adjusts for batches by including it as a covariate alongside sample groups in a linear regression. In survival prediction, however, ComBat is used without definable groups for survival outcome and is done sequentially with survival regression for a potentially batch-confounded outcome. To address these issues, we propose a new method called BATch MitigAtion via stratificatioN (BatMan). It adjusts batches as strata in survival regression and uses variable selection methods such as the regularized regression to handle high dimensionality. We assess the performance of BatMan in comparison with ComBat, each used either alone or in conjunction with data normalization, in a resampling-based simulation study under various levels of predictive signal strength and patterns of batch-outcome association. Our simulations show that (1) BatMan outperforms ComBat in nearly all scenarios when there are batch effects in the data and (2) their performance can be worsened by the addition of data normalization. We further evaluate them using microRNA data for ovarian cancer from the Cancer Genome Atlas and find that BatMan outforms ComBat while the addition of data normalization worsens the prediction. Our study thus shows the advantage of BatMan and raises caution about the use of data normalization in the context of developing survival prediction models. The BatMan method and the simulation tool for performance assessment are implemented in R and publicly available at LXQin/PRECISION.survival-GitHub.

Incidence, trends, and survival of oropharyngeal squamous cell cancer in Aotearoa New Zealand, 2006-2020

BACKGROUND

An increasing trend of oropharyngeal cancer (OPC) has been reported in several countries with different demographic characteristics, and often attributed to increases in human papillomavirus (HPV) infection. The survival of patients with OPC has steadily improved, especially for those with positive HPV status. This study assessed the incidence, trends, and survival of OPC in Aotearoa New Zealand (NZ) by age at diagnosis, sex and ethnicity.

METHODS

The study included all 2109 patients resident in NZ with a primary diagnosis of oropharyngeal squamous cell carcinoma from 2006 to 2020, identified from the National Cancer Registry. We assessed age-standardised incidence rate (ASR), annual percent change (APC) and overall and relative survival rates.

RESULTS

The average annual incidence of OPC was 2.2 per 100,000 population. There was a steady increase of 4.9% per year over 15 years. Although the incidence rates were higher in males over the study period, the overall rate of increase was similar in males (4.9%) and in females (4.3%). The incidence was highest in the 50-69-year group (8.8/100,000 population). This age group had an incidence that increased by 7.5% per year to 2018, and then declined. The main increase in rates was seen between the birth cohort of 1946-50 and that of 1956-60. The increase in incidence was seen in Māori and Pākehā/European populations, but no increase was seen in Pacific or Asian populations. The 5-year overall relative survival rate improved from 69% in 2006-13 to 78% in 2014-20. Survival rates were lower in older patients, females, and Māori patients.

CONCLUSION

This study confirmed a substantial increase in OPC incidence in NZ, with some evidence to suggest a recent slowing in this increase. Māori and Pākehā/European had the highest incidence, while Pacific and Asian populations showed the lowest rates and no increase over the study period. Survival rates have improved over time, but remained lower in some demographic groups.

Accumulation of Chronic Disease Among Survivors of Childhood Cancer Predicts Early Mortality

PURPOSE

Cancer survivors develop cancer and treatment-related morbidities at younger than normal ages and are at risk for early mortality, suggestive of an aging phenotype. The Cumulative Illness Rating Scale for Geriatrics (CIRS-G) is specifically designed to describe the accumulation of comorbidities over time with estimates of severity such as total score (TS) which is a sum of possible conditions weighted by severity. These severity scores can then be used to predict future mortality.

METHODS

CIRS-G scores were calculated in cancer survivors and their siblings from Childhood Cancer Survivor Study cohort members from two time points 19 years apart and members of the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2004. CIRS-G metrics were analyzed using Cox proportional hazards regression to determine subsequent mortality risk.

RESULTS

In total, 14,355 survivors with a median age of 24 (IQR, 18-30) years and 4,022 siblings with a median age of 26 (IQR, 19-33) years provided baseline data; 6,138 survivors and 1,801 siblings provided follow-up data. Cancer survivors had higher median baseline TS than siblings at baseline (5.75 v 3.44) and follow-up (7.76 v 4.79), all P < .01. The mean increase in TS from baseline to follow-up was significantly steeper in cancer survivors (2.89 males and 3.18 females) vs. siblings (1.79 males and 1.69 females) and NHANES population (2.0 males and 1.94 females), all P < .01. Every point increase in baseline TS increased hazard for death by 9% (95% CI, 8 to 10) among survivors.

CONCLUSION

Application of a geriatric rating scale to characterize disease supports the hypothesis that morbidity accumulation is accelerated in young adult survivors of childhood cancer when compared with siblings and the general population.

Systemic Analyses of Cuproptosis-Related lncRNAs in Pancreatic Adenocarcinoma, with a Focus on the Molecular Mechanism of LINC00853

Pancreatic cancer (PC) is a deadly malignant digestive tumor with poor prognoses and a lack of effective treatment options. Cuproptosis, a recently identified copper-dependent programmed cell death type, has been implicated in multiple cancers. Long non-coding RNAs (lncRNAs) are also linked to the progression of PC. However, the role and prognostic values of cuproptosis-related lncRNAs in pancreatic adenocarcinoma (PAAD) remain unclear. In this study, we systemically analyzed the differential expressions and prognostic values of 672 cuproptosis-related lncRNAs in PAAD. Based on this, a prognostic signature including four lncRNAs (LINC00853, AC099850.3, AC010719.1, and AC006504.7) was constructed and was able to divide PAAD patients into high- and low-risk groups with significantly different prognoses. Next, we focused on lncRNA LINC00853. The differential expressions of LINC00853 between normal tissue and PAAD samples were validated by qRT-PCR. LINC00853 was knocked down by siRNA in PC cell lines BxPC-3 and PANC-1 and the oncogenic role of LINC00853 was validated by CCK8, colony formation, and EdU assays. Subsequently, LINC00853 knockdown cells were subjected to tumor xenograft tests and exhibited decreased tumor growth in nude mice. Mechanistically, knockdown of LINC00853 significantly reduced cellular glycolysis and enhanced cellular mitochondrial respiration levels in PC cells. Moreover, knockdown of LINC00853 decreased the protein level of a glycolytic kinase PFKFB3. Finally, glycolysis tests and functional tests using LINC00853 and HA-PFKFB3 indicated that the effects of LINC00853 on glycolysis and cell proliferation were mediated by PFKFB3. In conclusion, our systemic analyses have highlighted the important roles of cuproptosis-related lncRNAs in PAAD while the prognostic signature based on them showed excellent performance in PAAD patients and is expected to provide clinical guidance for individualized treatment. In addition, our findings provide a novel mechanism by which the LINC00853-PFKFB3 axis critically regulates aerobic glycolysis and cell proliferation in PC cells.

Survival analysis 101: an easy start guide to analysing time-to-event data

Survival analysis, also called time-to-event analysis, is a common approach to handling event data in cardiovascular nursing and health-related research. Survival analysis is used to describe, explain, and/or predict the occurrence and timing of events. There is a specific language used and methods designed to handle the unique nature of event data. In this methods paper, we provide an 'easy start guide' to using survival analysis by (i) providing a step-by-step guide and (ii) applying the steps with example data. Specifically, we analyse cardiovascular event data over 6 months in a sample of patients with heart failure.

Partially linear Bayesian modeling of longitudinal rank and time-to-event data using accelerated failure time model with application to brain tumor data

Joint modeling of longitudinal rank and time-to-event data with random effects model using a Bayesian approach is presented. Accelerated failure time (AFT) models can be used for the analysis of time-to-event data to estimate the effects of covariates on acceleration/deceleration of the survival time. The parametric AFT models require determining the event time distribution. So, we suppose that the time variable is modeled with Weibull AFT distribution. In many real-life applications, it is difficult to determine the appropriate distribution. To avoid this restriction, several semiparametric AFT models were proposed, containing spline-based model. So, we propose a flexible extension of the accelerated failure time model. Furthermore, the usual joint linear model, a joint partially linear model, is also considered containing the nonlinear effect of time on the longitudinal rank responses and nonlinear and time-dependent effects of covariates on the hazard. Also, a Bayesian approach that yields Bayesian estimates of the model's parameters is used. Some simulation studies are conducted to estimate parameters of the considered models. The model is applied to a real brain tumor patient's data set that underwent surgery. The results of analyzing data are presented to represent the method.

Multimodal deep learning to predict prognosis in adult and pediatric brain tumors

BACKGROUND

The introduction of deep learning in both imaging and genomics has significantly advanced the analysis of biomedical data. For complex diseases such as cancer, different data modalities may reveal different disease characteristics, and the integration of imaging with genomic data has the potential to unravel additional information than when using these data sources in isolation. Here, we propose a DL framework that combines these two modalities with the aim to predict brain tumor prognosis.

METHODS

Using two separate glioma cohorts of 783 adults and 305 pediatric patients we developed a DL framework that can fuse histopathology images with gene expression profiles. Three strategies for data fusion were implemented and compared: early, late, and joint fusion. Additional validation of the adult glioma models was done on an independent cohort of 97 adult patients.

RESULTS

Here we show that the developed multimodal data models achieve better prediction results compared to the single data models, but also lead to the identification of more relevant biological pathways. When testing our adult models on a third brain tumor dataset, we show our multimodal framework is able to generalize and performs better on new data from different cohorts. Leveraging the concept of transfer learning, we demonstrate how our pediatric multimodal models can be used to predict prognosis for two more rare (less available samples) pediatric brain tumors.

CONCLUSIONS

Our study illustrates that a multimodal data fusion approach can be successfully implemented and customized to model clinical outcome of adult and pediatric brain tumors.

Utilizing shared frailty with the Cox proportional hazards regression: Post discharge survival analysis of CHF patients

Understanding patients' survival probability as well as the factors affecting it constitute a significant concern for researchers and practitioners, in particular for patients with severe chronic illnesses such as congestive heart failure (CHF). CHF is a clinical syndrome characterized by comorbidities and adverse medical events. Risk stratification to identify patients most likely to die shortly after hospital discharge can improve the quality of care by better allocating organizational resources and personalized interventions. Probability assessment improves clinical decision-making, contributes to personalized care, and saves costs. Although one of the most informative indices is the time to an adverse event for each patient, commonly analyzed using survival analysis methods, these are often challenging to implement due to the complexity of the medical data. Numerous studies have used the Cox proportional hazards (PH) regression method to generate the survival distribution pattern and factors affecting survival. This model, although advantageous for survival analysis, assumes the homogeneity of the hazard ratio across patients and independence of the observations in terms of survival time. These assumptions are often violated in real-world data, especially when the dataset is composed of readmission data for chronically ill patients, since these recurring observations are inherently dependent. This study ran the Cox PH regression on a feature set selected by machine learning algorithms from a rich hospital dataset. The event modeled here was patient mortality within 90 days post-hospital discharge. The sample was composed of medical records of patients hospitalized in the Israeli Sheba Medical Center more than once, with CHF as the primary diagnosis. We modeled the survival of CHF patients using the Cox PH regression with and without the shared frailty correction that addresses the shortcomings of the Cox Model. The results of the two models of the Cox PH regression - with and without the shared frailty correction were compared. The results demonstrate that the shared frailty correction, which was statistically significant in our analysis, improved the performance of the basic Cox PH model. While this is the main contribution, we also show that this model outperforms two commonly used measures (ADHERE and EFFECT) for predicting early mortality of CHF patients. Thus, the results illustrate how applying advanced analytics can outperform traditional methods. An additional contribution is the feature set selected using machine-learning methods that is different from those used in the extant literature.