Survival Models Research Articles

Impact of European Stroke Organisation secondary prevention guideline for ischaemic stroke / transient ischaemic attack

ObjectivesWe aimed to assess secondary prevention strategies after ischaemic stroke or transient ischaemic attack (TIA). Materials and MethodsWe investigated the impact of European Stroke Organisation (ESO) Guideline recommendations for secondary prevention on recurrent events among people with non-cardioembolic ischaemic stroke or TIA. We assessed the following interventions by survival analysis or modelling impacts from clinical trial data: two blood pressure (BP) drugs compared to one drug; LDL-cholesterol target <1.8 mmol/L; and pioglitazone therapy. Outcomes were mortality, major adverse cardiovascular events (MACE) and recurrent stroke or myocardial infarction (MI). ResultsWe included 4,037 people admitted between 01/12/2015 to 31/12/2018: mean (SD) age 68.6 (12.9) years; 1984 (49.1 %) female and median (IQR) follow-up 2.2 (1.5-3.1) years.Prescription of two BP drugs was associated with reduced mortality in our sample of 2238 people with hypertension (HR 0.64, 95 %CI 0.51-0.81; P<0.001).We estimate an LDL-cholesterol target <1.8 mmol/L could reduce MACE incidence from 128 to 114 events (95 %CI 103-127) in our sample of 1024 people with LDL-cholesterol 1.8 mmol/L who were not already prescribed intensive lipid-lowering therapy over median (IQR) 2.2 (1.5-2.9) years follow-up (ARR 1.38 %, NNT 73).We estimate pioglitazone therapy could reduce incidence of recurrent stroke or MI from 192 to 169 events (95 %CI 156-185) in our sample of 1587 people with diabetes or insulin resistance over median (IQR) 2.4 (1.7-3.2) years follow-up (ARR 1.45 %, NNT 69). ConclusionsWe estimate that implementing ESO guidelines in a Scottish population after ischaemic stroke or TIA would reduce mortality and recurrent cardiovascular events.

A Cost-Effectiveness Analysis of Axicabtagene Ciloleucel versus Tisagenlecleucel in the Treatment of Diffuse Large B-cell Lymphoma Based on a Real-World French Registry.

Axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) are chimeric antigen receptor T-cell therapies that were evaluated in third and later line (3L+) relapsed or refractory (r/r) diffuse large B-cell lymphoma (DLBCL) in the ZUMA-1 and JULIET trials, respectively. As of October 2021, the DESCAR-T registry included 729 French patients with 3L+r/r DLBCL who received axi-cel or tisa-cel. Using these data, propensity score matching was used to conduct an adjusted comparison between axi-cel and tisa-cel. Axi-cel was associated with statistically significant improvements in overall survival (OS) and progression-free survival (PFS), and significantly more frequent Grade≥3 immune effector cell-associated neurotoxicity syndrome (ICANS), compared with tisa-cel. There was no significant difference in Grade≥3 cytokine release syndrome (CRS). The current analysis assessed the cost-effectiveness of axi-cel versus tisa-cel in the treatment of 3L+r/r DLBCL using propensity score-matched data from the DESCAR-T registry. A partitioned survival model was used to extrapolate costs and quality-adjusted life years (QALYs) over a lifetime. Survival curves for PFS and OS were based on independent mixture cure models fitted to digitized Kaplan-Meier data for the propensity score-matched DESCAR-T populations. Average duration of intensive care unit stays for each of axi-cel and tisa-cel in DESCAR-T were used to inform adverse event costs. Selected parametric survival distributions were based on clinical expert validation. Utility values were derived from ZUMA-1, and costs were obtained from French registries and published sources. List prices were used for both axi-cel and tisa-cel. Costs and outcomes were discounted at an annual rate of 2.5%. Axi-cel is associated with an incremental cost-effectiveness ratio of €15,520 per QALY compared with tisa-cel. Based on explicit willingness-to-pay thresholds applied in Europe, axi-cel is expected to be a cost-effective use of healthcare resources in real-world clinical settings compared with tisa-cel in 3L+r/r DLBCL.

Bayesian estimation of spatially varying mortality risk using tagged animal data

Abstract The survival of animals is impacted by landscapes of spatially varying mortality factors including habitat type, predation risk or harvest risk, among others. Characterization of these spatial mortality processes is important for managing animal populations and their habitats, yet this information has proved challenging to capture. Advances in telemetry now make it possible to monitor tagged individuals' locations through time for a wide range of taxa, providing opportunity to assess movement and mortality simultaneously through spatial time‐to‐event data. Existing time‐to‐event modelling frameworks have largely ignored spatially varying mortality processes or have treated an animal's location as fixed at a regional level. Here we present a general spatial survival model for analysing time‐to‐event data arising from animal telemetry. Our model has a flexible Cox regression form and can estimate the effects of conventional non‐spatial risk factors (size, seasonality, etc.), spatial covariates (e.g. habitat type) and spatial variation in risk not explained by covariates on mortality. We show how to fit our model using Bayesian methods and demonstrate applications of our model with several simulated examples and two animal telemetry case studies. Our model produced consistent and unbiased parameter estimates throughout simulations with a variety of spatial and non‐spatial hazards. In the first case study, data from 147 tagged caribou in British Columbia, Canada, revealed a spatially heterogeneous mortality landscape with caribou survival varying by elevation, likely in response to space use by predators. Our second case study involved a dataset of 390 acoustically tagged Atlantic cod in a southern Norwegian fjord where a marine protected area (MPA) was established during the study. We found that the MPA led to a shift from mostly fishing mortality to mostly natural mortality within the fjord and that these risks had markedly different spatial hazard patterns. Our spatial time‐to‐event model can make use of data from a variety of widely used telemetry technologies to characterize landscapes of mortality risks for different taxa. Our work provides new opportunities to inform the spatial ecology and management of fish and wildlife populations.

Robust variable selection methods with Cox model-a selective practical benchmark study.

With the advancement of biological and medical techniques, we can now obtain large amounts of high-dimensional omics data with censored survival information. This presents challenges in method development across various domains, particularly in variable selection. Given the inherently skewed distribution of the survival time outcome variable, robust variable selection methods offer potential solutions. Recently, there has been a focus on extending robust variable selection methods from linear regression models to survival models. However, despite these developments, robust methods are currently rarely used in practical applications, possibly due to a limited appreciation of their overall good performance. To address this gap, we conduct a selective review comparing the variable selection performance of twelve robust and non-robust penalised Cox models. Our study reveals the intricate relationship among covariates, survival outcomes, and modeling approaches, demonstrating how subtle variations can significantly impact the performance of methods considered. Based on our empirical research, we recommend the use of robust Cox models for variable selection in practice based on their superior performance in presence of outliers while maintaining good efficiency and accuracy when there are no outliers. This study provides valuable insights for method development and application, contributing to a better understanding of the relationship between correlated covariates and censored outcomes.

Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques

BackgroundBladder cancer (BCa), one of the most common cancers worldwide, is characterized by high rates of recurrence, progression, and mortality. Machine learning algorithms offer promising advancements in enhancing predictive models. This study aims to develop robust machine learning models for predicting BCa survival using clinical and gene expression data. MethodsClinical data from BCa patients were obtained from the Surveillance, Epidemiology, and End Results database. Cox proportional hazards regression models assessed the association between clinical variables and overall survival. Machine learning algorithms, including logistic regression, random forest, XGBoost, decision tree, and LightGBM, were employed to predict survival at 1, 3, and 5 years. The TAGO database, combined with the data from The Cancer Genome Atlas and four databases from the Gene Expression Omnibus, which have available genomic data and clinical data, were selected. Gene expression data were transformed into gene sets data, and the performance of models based on clinical data and gene sets data and their combination were compared. Furthermore, the impact of model-derived scores on overall survival was evaluated. ResultsAmong 138,741 BCa patients with available clinical data, key independent predictors of survival included age, race, marital status, surgery, chemotherapy, radiation, and TNM stages. Clinical data machine learning (CML) models used these clinical predictors to achieve AUC values of 0.860, 0.821, and 0.804 in the testing sets for predicting survival at 1, 3, and 5 years, respectively. In the TAGO database, which has 863 patients with clinical and genomic data, the integrated clinical and gene expression machine learning model (IML) outperformed the CML and gene expression machine learning (GML) models in survival prediction. Patients with higher IML and GML model scores exhibited poorer survival outcomes. ConclusionsThis study successfully identifies key clinical and genomic predictors, a significant step forward in BCa research. The development of predictive models for BCa survival underscores the potential of integrated data approaches in improving BCa management and treatment strategies.

Assessment of NSCLC disease burden: A survival model-based meta-analysis study

We present a meta-analytics approach to quantify NSCLC disease burden by integrative survival models. Aggregated survival data from public sources were used to parameterize the models for early as well as advanced NSCLC stages incorporating chemotherapies, targeted therapies, and immunotherapies. Overall survival (OS) was predicted in a heterogeneous patient cohort based on various stratifications and initial conditions. Pharmacoeconomic metrics (life years gained (LYG) and quality-adjusted life years (QALY) gained), were evaluated to quantify the benefits of specialized treatments and improved early detection of NSCLC. Simulations showed that the introduction of novel therapies for the advanced NSCLC sub-group increased median survival by 8.1 months (95 % CI: 5.9, 10.0), with corresponding gains of 2.9 months (95 % CI: 2.2, 3.6) in LYG and 1.65 months (95 % CI: 1.2, 2.0) in QALY. Scenarios representing improved detection of early cancer in the whole patient cohort, revealed up to 17.6 (95 % CI: 16.5, 19.0) and 15.7 months (95 % CI: 14.8, 16.6) increase in median survival, with respective gains of 6.2 months (95 % CI: 5.9, 6.4) and 5.2 months (95 % CI: 4.9, 5.4) in LYG and 6.6 months (95 % CI: 6.4, 6.7) and 6.0 months (95 % CI: 5.9, 6.2) in QALY for conventional and optimal treatment. This integrative modeling platform, aimed at characterizing cancer burden, allows to precisely quantify the cumulative benefits of introducing specialized therapies into the treatment schemes and survival prolongation upon early detection of the disease.

Comparative Cost-Effectiveness of Atezolizumab Versus Durvalumab as First-Line Combination Treatment with Chemotherapy for Patients with Extensive-Disease Small-Cell Lung Cancer in Japan.

IMpower 133 and CASPIAN data were used to create a partitioned survival model. Medical expenses and quality-adjusted life years (QALYs) were considered. The analysis period, discount rate, and threshold were set at 20 years, 2%, and 15 million Japanese yen (JPY) [114,068 US dollars (USD)] per QALY, respectively. The incremental cost-effectiveness ratio (ICER) was calculated by gathering reasonable parameters from published reports and combining the costs and effects using parametric models. Monte Carlo simulations, scenario analysis, and one-way sensitivity analyses were employed to quantify uncertainty. After comparing atezolizumab plus CE (ACE) and durvalumab plus CE (DCE) with CE, it was found that the ICERs exceeded the threshold at 35,048,299 JPY (266,527 USD) and 36,665,583 JPY (278,826 USD) per QALY, respectively. For one-way sensitivity and scenario assessments, the ICERs exceeded the threshold, even with considerably adjusted parameters. For the probabilistic sensitivity analyses, there was no probability that the ICER of the ICI combination treatment with chemotherapy would fall below the threshold. ACE and DCE were not cost-effective compared with CE as first-line therapy for ED-SCLC in Japan. Both these therapies exhibited high ICERs.

Statistical Power and Performance of Strategies to Analyze Composites of Survival and Duration of Ventilation in Clinical Trials.

Patients with acute hypoxemic respiratory failure are at high risk of death and prolonged time on the ventilator. Interventions often aim to reduce both mortality and time on the ventilator. Many methods have been proposed for analyzing these endpoints as a single composite outcome (days alive and free of ventilation), but it is unclear which analytical method provides the best performance. Thus, we aimed to determine the analysis method with the highest statistical power for use in clinical trials. Using statistical simulation, we compared multiple methods for analyzing days alive and free of ventilation: the t, Wilcoxon rank-sum, and Kryger Jensen and Lange tests, as well as the proportional odds, hurdle-Poisson, and competing risk models. We compared 14 scenarios relating to: 1) varying baseline distributions of mortality and duration of ventilation, which were based on data from a registry of patients with acute hypoxemic respiratory failure and 2) the varying effects of treatment on mortality and duration of ventilation. All methods have good control of type 1 error rates (i.e., avoid false positive findings). When data are simulated using a proportional odds model, the t test and ordinal models have the highest relative power (92% and 90%, respectively), followed by competing risk models. When the data are simulated using survival models, the competing risk models have the highest power (100% and 92%), followed by the t test and a ten-category ordinal model. All models struggled to detect the effect of the intervention when the treatment only affected one of mortality and duration of ventilation. Overall, the best performing analytical strategy depends on the respective effects of treatment on survival and duration of ventilation and the underlying distribution of the outcomes. The evaluated models each provide a different interpretation for the treatment effect, which must be considered alongside the statistical power when selecting analysis models.

Bayesian analyses of multiple random change points in survival models with applications to clinical trials

ABSTRACT Single and multiple random change points (RCPs) in survival analysis have arisen naturally in oncology trials, where the time to hazard rate change differs from one subject to another. Recently, Xu formulated and discovered important properties of these survival models using a frequentist approach, allowing us to estimate the hazard rates, rate parameters of the exponential distributions for the RCPs, expected survival and hazard functions. However, these methods did not provide an estimation of the uncertainty or the confidence intervals for the parameters and their differences or ratios. Therefore, statistical inferences were not able to be drawn on the parameters and their comparisons. To solve this issue, this article implements a Gibbs sampler method to estimate the above parameters and the differences or ratios alongside the 100(1 − α )% highest posterior density (HPD) intervals calculated from Chen–Shao’s algorithm. The estimated rate parameters from the methods in Xu serve as empirical values in the Gibbs sampler method. Thus, formal statistical inferences can now be readily drawn. Simulation studies demonstrate that the proposed methods yield robust estimates, with the samples from the marginal posterior distributions converging rapidly and exhibiting favorable behavior. The 95% HPD intervals also demonstrate excellent coverage probabilities. This proposed method has a multitude of applications in clinical trials such as efficient clinical trial design and sample size adjustment based on the estimated parameter values at interim analyses.

Mortality Risk Assessment Using Deep Learning-Based Frequency Analysis of EEG and EOG in Sleep

Abstract Study Objectives To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality. Methods Power spectra from PSGs of 8,716 participants, included from the MrOS Sleep Study and the Sleep Heart Health Study (SHHS), were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models. Results Survival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found an all-cause mortality hazard ratio (HR) of 0.90 (CI95% 0.85–0.96) for 12–15 Hz in N2, 0.86 (CI95% 0.82–0.91) for 0.75–1.5 Hz in N3, and 0.87 (CI95% 0.83–0.92) for 14.75–33.5 Hz in REM sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI95% 1.11–1.28) for 0.25 Hz in N3, 1.11 (CI95% 1.03–1.21) for 0.75 Hz in N1, and 1.11 (CI95% 1.03–1.20) for 1.25–1.75 Hz in Wake. The gain in the concordance index (C-index) for all-cause mortality is minimal, with only a 0.24% increase: The best single mortality predictor was EEG N3 (0-0.5 Hz) with C-index of 77.78% compared to 77.54% for confounders alone. Conclusion Spectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality.

Bridging the gap: improve neural survival models with interpolation techniques

Bridging the gap: improve neural survival models with interpolation techniques

Galectin-3 Predicts Long-Term Risk of Cerebral Disability and Mortality in Out-of-Hospital Cardiac Arrest Survivors.

Out-of-hospital cardiac arrest (OHCA) is associated with high mortality and cerebral disability in survivors. Current models of risk prediction and survival are mainly based on resuscitation duration. We examined the prognostic value of circulating biomarkers in predicting mortality and severe cerebral disability for OHCA survivors, alongside traditional clinical risk indicators. Biomarkers including BNP, troponin I, and galectin-3 were measured at hospital admission in resuscitated OHCA patients. Prognostic significance for mortality and cerebral disability involving circulating biomarkers, resuscitation duration, demographics, and laboratory and clinical characteristics was examined via univariate and multivariate Cox proportional hazards regression models. The incremental prognostic value of the index covariates was examined through model diagnostics, focusing on the Akaike information criterion (AIC) and Harrell's concordance statistic (c-statistic). In a combinatorial analysis of 144 OHCA survivors (median follow-up 5.7 years (IQR 2.9-6.6)), BNP, galectin-3, arterial pH, and resuscitation time were significant predictors of all-cause death and severe cerebral disability, whereas troponin I levels were not. Multivariate regression, adjusting for BNP, arterial pH, and resuscitation time, identified galectin-3 as an independent predictor of long-term mortality. Multiple linear regression models also confirmed galectin-3 as the strongest predictor of cerebral disability. The incorporation of galectin-3 into models for predicting mortality and cerebral disability enhanced fit and discrimination, demonstrating the incremental value of galectin-3 beyond traditional risk predictors. Galectin-3 is a significant, independent long-term risk predictor of cerebral disability and mortality in OHCA survivors. Incorporating galectin-3 into current risk stratification models may enhance early prognostication and guide targeted clinical interventions.

SolBeePopecotox: A Population Model for Pesticide Risk Assessments of Solitary Bees.

In agricultural landscapes, solitary bees occur in a large diversity of species and are important for crop and wildflower pollination. They are distinguished from honey bees and bumble bees by their solitary lifestyle as well as different nesting strategies, phenologies, and floral preferences. Their ecological traits and presence in agricultural landscapes imply potential exposure to pesticides and suggest a need to conduct ecological risk assessments for solitary bees. However, assessing risks to the large diversity of managed and wild bees across landscapes and regions poses a formidable challenge. Population models provide tools to estimate potential population-level effects of pesticide exposures, can support field study design and interpretation, and can be applied to expand study data to untested conditions. We present a population model for solitary bees, SolBeePopecotox, developed for use in the context of ecological risk assessments. The trait-based model extends a previous version with the explicit representation of exposures to pesticides from relevant routes. Effects are implemented in the model using a simplified toxicokinetic-toxicodynamic model, BeeGUTS (GUTS = generalized unified threshold model for survival), adapted specifically for bees. We evaluated the model with data from semifield studies conducted with the red mason bee, Osmia bicornis, in which bees were foraging in tunnels over control and insecticide-treated oilseed rape fields. We extended the simulations to capture hypothetical semifield studies with two soil-nesting species, Nomia melanderi and Eucera pruinosa, which are difficult to test in empirical studies. The model provides a versatile tool for higher-tier risk assessments, for instance, to estimate effects of potential exposures, expanding available study data to untested species, environmental conditions, or exposure scenarios. Environ Toxicol Chem 2024;00:1-17. © 2024 SETAC.

Outcomes of Medicaid Rebalancing May Differ Across Enrollee Populations.

Access to home- and community-based services (HCBS) may prevent or delay nursing home transitions among older adults. Medicaid's Balancing Incentive Program (BIP) (2011-2015) provided assistance for U.S. states to increase access to HCBS through infrastructure changes and spending benchmarks. We combined longitudinal data from the 2008-2019 Medicare Current Beneficiary Survey and Minimum Data Set and used survival modeling to examine the association between BIP exposure (living in a BIP-participant state vs. not) and time to long-term institutionalization (LTI, defined as a nursing home episode of 90+ days) among dual enrollees ages 65 and older. In the main effects model, BIP exposure was not associated with hazard of LTI. Interaction models showed that BIP exposure was associated with a lower hazard of LTI among Hispanic/Latinx enrollees, while the opposite was true among non-Hispanic White enrollees. Our findings suggest the outcomes of Medicaid rebalancing efforts may differ across enrollee subgroups.

Economic evaluation of NALIRIFOX vs. nab-paclitaxel and gemcitabine regimens for first-line treatment of metastatic pancreatic ductal adenocarcinoma from U.S. perspective

BackgroundThe cost-effectiveness of NALIRIFOX as a potential new standard of care for patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) has yet to be established. Our objective was to evaluate the cost-effectiveness of NALIRIFOX vs. nab-paclitaxel and gemcitabine in this indication from the perspective of U.S. public payers.MethodsA partitioned survival model was constructed from the perspective of U.S. public payers, drawing on baseline patient characteristics and vital clinical data from the NAPOLI-3 trial. Costs and utilities were sourced from publicly accessible databases and literature. A lifetime horizon was applied, with an annual discount rate of 3%. We calculated and compared cumulative costs, life years, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICER). To evaluate the model’s robustness, sensitivity analyses, scenario analyses, and subgroup analyses were carried out. Additionally, a price simulation for the costly liposomal irinotecan was conducted to inform the pricing strategy at the given willingness to pay (WTP) threshold.ResultsIn the base-case analysis, NALIRIFOX provided an additional 0.29 QALYs with an ICER of $206,340.69 /QALY compared to nab-paclitaxel and gemcitabine, indicating it is not cost-effective at a $150,000/QALY threshold. Sensitivity analysis showed the model was most sensitive to the costs of liposomal irinotecan, capecitabine, and post-progression care. Probabilistic sensitivity analysis indicated a 17.66% probability of NALIRIFOX being cost-effective at $150,000/QALY, rising to 47.48% at $200,000/QALY. Pricing simulations suggested NALIRIFOX could become cost-effective at $150,000/QALY if the price of irinotecan liposome drops to $53.24/mg (a 14.8% reduction).ConclusionsNALIRIFOX may not be cost-effective at its current price as a first-line treatment for patients with mPDAC in the long term. The cost of liposomal irinotecan has the greatest impact. It may become cost-effective only if its cost is reduced by 14.8%, with a WTP threshold of $150,000 /QALY.

The Economic Impact of Treatment Sequencing in Chronic Lymphocytic Leukemia in Canada Using Venetoclax plus Obinutuzumab.

Bruton tyrosine kinase inhibitors (BTKis) represent an advancement in chronic lymphocytic leukemia; however, these agents are administered continuously until disease progression or unacceptable toxicity, raising concerns about their affordability. Venetoclax in combination with obinutuzumab (VO) is a fixed-duration (12-month) treatment, approved in Canada in 2020. This study estimated the total cumulative cost of different treatment sequences and evaluated the economic impact of introducing treatment sequences with/without VO, from a Canadian health care system perspective. A 10-year partitioned survival model was developed, considering key clinical parameters and direct medical costs. Results were stratified by TP53 aberration. Treatment sequences starting with first-line (1L) VO resulted in lower 10-year cumulative costs compared to sequences starting with BTKis administered until disease progression, across both TP53 aberration subgroups. With a maximum of three lines of treatment over a 10-year period, cumulative costs were largely determined by the first two lines of treatment. When comparing sequences with the same 1L treatment, sequences with BTKis in second-line incurred greater costs compared to fixed-duration regimens. Overall, the economic impact of treating all patients with VO led to 10-year cumulative savings of CAD 169,341 and CAD 293,731 per patient, without and with TP53 aberration, respectively. These savings are mainly due to reductions in treatment costs associated with fixed treatment duration.

Developing a Novel Prognostic Model Based on Muscle-Invasive Bladder Cancer Types: A Multicenter RetrospectiveCohort Study of Patients Who Received Radical Cystectomy and Chemotherapy.

To develop a prognostic model to manage patients with muscle-invasive bladder cancer (MIBC) undergoing radical cystectomy (RC) and chemotherapy. Clinicopathologic characteristics and survival data were collated from a North American database to develop a model. Genomic and clinicopathologic data were also obtained from European and Asian databases to externally validate the model. Patients were classified as either "primary" or "progressive" MIBC according to non-muscle invasive stage history. Optimized cancer-specific survival (CSS) models, based on MIBC types, were constructed using Cox's proportional hazard regression. Differences of biological function and tumor immunity, between two risk-based groups stratified according to the prognostic model, were estimated. There were 2631 participants in the American cohort, 291 in the European cohort and 142 in the Asian cohort. Under Cox's regression analysis, tumor stage, lymph node stage, age, ethnicity, and MIBC types were independent CSS predictors (all p < 0.05). The constructed nomogram, whichintegratedthese variables, improved the predictivepower. This model had good discrimination and calibration. Patients were categorized into high- or low-risk groups according to the total points calculated. Kaplan-Meier curves revealed that patients in the high-risk group had poorer survival (p < 0.001). This was confirmed with two external validation cohorts (both with p < 0.001). Higher stromal scores and increased M0 and M2 macrophage numbers were observed in samples from the high-risk group, whereas regulatory T cells had lower infiltration in these populations (all with p < 0.05). This MIBC type-based nomogram provides accurate CSS predictions, which could help improve patient management and clinical decision-making.

Survival Analysis of Penile Cancer Patients Using Cox-Proportional Hazard and GPT-4

Today, in 2024, the evolution of the evolution of artificial intelligence (AI) is very impressive. An AI product that continues to develop is ChatGPT. The threat of ChatGPT to some job professions is real. Beside that, ChatGPT also helps many professions make their work easier. In this research, we analyze the survival analysis of penile cancer patients. The design of this research is mixed-method (quantitative-qualitative). The quantitative and qualitative methods that were used are Cox-proportional hazards and GPT-4, respectively. Based on quantitative analysis, it can be concluded that the Survival and Hazard model for study penile cancer are and , respectively. The predicted chances of survival and death for the patient in stage 2, 14 days after surgery, are 40% and 60%, respectively. Based on qualitative analysis, GPT-4 cannot quantitatively obtain the cox proportional hazard number. However, even so, GPT4 can read the cox proportional hazard output image, providing Python and R survival analysis coding quite perfectly. Thus, the qualitative method's conclusion is that GPT-4 can greatly assist data analysts in their analysis of survival data.

Health Insurance Continuity and Mortality in Children and Adolescents/Young Adults with Blood Cancer.

Many uninsured patients do not receive Medicaid coverage until a cancer diagnosis, potentially delaying access to care for early cancer detection and treatment. We examine the association of Medicaid enrollment timing and patterns with survival among children and adolescents/young adults (AYAs) diagnosed with blood cancers, where disease onset can be acute and early detection is critical. We identified 28,750 children and AYAs (0-39 years) newly diagnosed with blood cancers from the 2006-2013 SEER-Medicaid data. Enrollment patterns included continuous Medicaid (preceding through diagnosis), newly gained Medicaid (at/shortly after diagnosis), other noncontinuous Medicaid enrollment, and private/other insurance. We assessed cumulative incidence of death from diagnosis, censoring at last follow-up, five years post-diagnosis, or December 2018, whichever occurred first. Multivariable survival models estimated the association of insurance enrollment patterns with risk of death. One-fourth (26.1%) of the cohort were insured by Medicaid; of these, 41.1% had continuous Medicaid, 34.9% had newly gained Medicaid, and 24.0% had other noncontinuous enrollment. The cumulative incidence of all-cause death five-year post-diagnosis was highest in patients with newly gained Medicaid (30.2%, 95%CI = 28.4-31.9%), followed by other noncontinuous enrollment (23.2%, 95%CI = 21.3-25.2%), continuous Medicaid (20.5%, 95%CI = 19.1-21.9%), and private/other insurance (11.2%; 95%CI = 10.7-11.7%). In multivariable models, newly gained Medicaid was associated with a higher risk of all-cause (hazard ratio = 1.39, 95%CI = 1.27-1.53) and cancer-specific death (hazard ratio = 1.50, 95%CI = 1.35-1.68), compared to continuous Medicaid. Continuous Medicaid coverage is associated with survival benefits among pediatric and AYA patients diagnosed with blood cancers; however, less than half of Medicaid-insured patients have continuous coverage before diagnosis.

Prognostic model development for risk of curve progression in adolescent idiopathic scoliosis: a prospective cohort study of 127 patients.

The study's purpose was to develop and internally validate a prognostic survival model exploring baseline variables for adolescent idiopathic scoliosis curve progression. A longitudinal prognostic cohort analysis was performed on trial data (n = 135) including girls and boys, Cobb angle 25-40°, aged 9-17 years, remaining growth > 1 year, and previously untreated. Prognostic outcome was defined as curve progression of Cobb angle of > 6° prior to skeletal maturity. 34 candidate prognostic variables were tested. Time-to-event was measured with 6-month intervals. Cox proportional hazards regression survival model (CoxPH) was used for model development and validation in comparison with machine learning models (66.6/33.3 train/test data set). The models were adjusted for treatment exposure. The final primary prognostic model included 127 patients, predicting progress with acceptable discriminative ability (concordance = 0.79, 95% confidence interval [CI] 0.72-0.86). Significant prognostic risk factors were Risser stage of 0 (HR 4.6, CI 2.1-10.1, P < 0.001), larger major curve Cobb angle (HRstandardized1.5, CI 1.1-2.0, P = 0.005), and higher score on patient-reported pictorial Spinal Appearance Questionnaire (pSAQ) (HRstandardized 1.4, CI 1.0-1.9, P = 0.04). Treatment exposure, entered as a covariate adjustment, contributed significantly to the final model (HR 3.1, CI 1.5-6.0, P = 0.001). Sensitivity analysis displayed that CoxPH maintained acceptable discriminative ability (AUC 0.79, CI 0.65-0.93) in comparison with machine learning algorithms. The prognostic model (Risser stage, Cobb angle, pSAQ, and menarche) predicted curve progression of > 6° Cobb angle with acceptable discriminative ability. Adding patient report of the pSAQ may be of clinical importance for the prognosis of curve progression.