Sex, environment, and death rate in a dementia cohort: a seven-years Bayesian survival analysis using medications data from a contaminated area in Italy.

Introduction: Studies have analyzed the effects of industrial installations on the environment and human health in Taranto, Southern Italy. Literature documented associations between different variables and dementia mortality among both women and men. The present study aims to investigate the associations between sex, environment, age, disease duration, pandemic years, anti-dementia drugs, and death rate. Methods: Data from the regional medication registry were used. All women and men with an anti-dementia medication between 2015 and 2021 were included and followed-up to 2021. Bayesian mixed effects logistic and Cox regression models with time varying exposures were fitted using integrated nested Laplace approximations and adjusting for patients and therapy characteristics. Results: A total of 7,961 person-years were observed. Variables associated with lower prevalence of acetylcholinesterase inhibitors (AChEIs) medication were male sex (OR 0.63, 95% CrI 0.42-0.96), age 70-79 years (OR 0.17, 95% CrI 0.06-0.47) and ≥ 80 years (OR 0.08, 95% CrI 0.03-0.23), disease duration of 2-3 years (OR 0.43, 95% CrI 0.32-0.56) and 4-6 years (OR 0.21, 95% CrI 0.13-0.33), and pandemic years 2020 (OR 0.50, 95% CrI 0.37-0.67) and 2021 (OR 0.47, 95% CrI 0.33-0.65). Variables associated with higher mortality were male sex (HR 2.14, 95% CrI 1.75-2.62), residence in the contaminated site of national interest (SIN) (HR 1.25, 95% CrI 1.02-1.53), age ≥ 80 years (HR 6.06, 95% CrI 1.94-18.95), disease duration of 1 year (HR 1.50, 95% CrI 1.12-2.01), 2-3 years (HR 1.90, 95% CrI 1.45-2.48) and 4-6 years (HR 2.21, 95% CrI 1.60-3.07), and pandemic years 2020 (HR 1.38, 95% CrI 1.06-1.80) and 2021 (HR 1.56, 95% CrI 1.21-2.02). Variables associated with lower mortality were therapy with AChEIs alone (HR 0.69, 95% CrI 0.56-0.86) and in combination with memantine (HR 0.54, 95% CrI 0.37-0.81). Discussion: Male sex, age, disease duration, and pandemic years appeared to be associated with lower AChEIs medications. Male sex, residence in the SIN of Taranto, age, disease duration, and pandemic years seemed to be associated with an increased death rate, while AChEIs medication seemed to be associated with improved survival rate.

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC.

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

Tisotumab Vedotin as Second- or Third-Line Therapy for Recurrent Cervical Cancer.

BACKGROUND: Recurrent cervical cancer is a life-threatening disease, with limited treatment options available when disease progression occurs after first-line combination therapy. METHODS: We conducted a phase 3, multinational, open-label trial of tisotumab vedotin as second- or third-line therapy in patients with recurrent or metastatic cervical cancer. Patients were randomly assigned, in a 1:1 ratio, to receive tisotumab vedotin monotherapy (2.0 mg per kilogram of body weight every 3 weeks) or the investigator's choice of chemotherapy (topotecan, vinorelbine, gemcitabine, irinotecan, or pemetrexed). The primary end point was overall survival. RESULTS: A total of 502 patients underwent randomization (253 were assigned to the tisotumab vedotin group and 249 to the chemotherapy group); the groups were similar with respect to demographic and disease characteristics. The median overall survival was significantly longer in the tisotumab vedotin group than in the chemotherapy group (11.5 months [95% confidence interval {CI}, 9.8 to 14.9] vs. 9.5 months [95% CI, 7.9 to 10.7]), results that represented a 30% lower risk of death with tisotumab vedotin than with chemotherapy (hazard ratio, 0.70; 95% CI, 0.54 to 0.89; two-sided P = 0.004). The median progression-free survival was 4.2 months (95% CI, 4.0 to 4.4) with tisotumab vedotin and 2.9 months (95% CI, 2.6 to 3.1) with chemotherapy (hazard ratio, 0.67; 95% CI, 0.54 to 0.82; two-sided P<0.001). The confirmed objective response rate was 17.8% in the tisotumab vedotin group and 5.2% in the chemotherapy group (odds ratio, 4.0; 95% CI, 2.1 to 7.6; two-sided P<0.001). A total of 98.4% of patients in the tisotumab vedotin group and 99.2% in the chemotherapy group had at least one adverse event that occurred during the treatment period (defined as the period from day 1 of dose 1 until 30 days after the last dose); grade 3 or greater events occurred in 52.0% and 62.3%, respectively. A total of 14.8% of patients stopped tisotumab vedotin treatment because of toxic effects. CONCLUSIONS: In patients with recurrent cervical cancer, second- or third-line treatment with tisotumab vedotin resulted in significantly greater efficacy than chemotherapy. (Funded by Genmab and Seagen [acquired by Pfizer]; innovaTV 301 ClinicalTrials.gov number, NCT04697628.).

A prognostic model for anoikis-related genes in pancreatic cancer.

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

Racial disparities in colorectal cancer outcomes and access to care: a multi-cohort analysis.

Introduction: Non-Hispanic Black (NHB) Americans have a higher incidence of colorectal cancer (CRC) and worse survival than non-Hispanic white (NHW) Americans, but the relative contributions of biological versus access to care remain poorly characterized. This study used two nationwide cohorts in different healthcare contexts to study health system effects on this disparity. Methods: We used data from the Surveillance, Epidemiology, and End Results (SEER) registry as well as the United States Veterans Health Administration (VA) to identify adults diagnosed with colorectal cancer between 2010 and 2020 who identified as non-Hispanic Black (NHB) or non-Hispanic white (NHW). Stratified survival analyses were performed using a primary endpoint of overall survival, and sensitivity analyses were performed using cancer-specific survival. Results: We identified 263,893 CRC patients in the SEER registry (36,662 (14%) NHB; 226,271 (86%) NHW) and 24,375 VA patients (4,860 (20%) NHB; 19,515 (80%) NHW). In the SEER registry, NHB patients had worse OS than NHW patients: median OS of 57 months (95% confidence interval (CI) 55-58) versus 72 months (95% CI 71-73) (hazard ratio (HR) 1.14, 95% CI 1.12-1.15, p = 0.001). In contrast, VA NHB median OS was 65 months (95% CI 62-69) versus NHW 69 months (95% CI 97-71) (HR 1.02, 95% CI 0.98-1.07, p = 0.375). There was significant interaction in the SEER registry between race and Medicare age eligibility (p < 0.001); NHB race had more effect in patients <65 years old (HR 1.44, 95% CI 1.39-1.49, p < 0.001) than in those ≥65 (HR 1.13, 95% CI 1.11-1.15, p < 0.001). In the VA, age stratification was not significant (p = 0.21). Discussion: Racial disparities in CRC survival in the general US population are significantly attenuated in Medicare-aged patients. This pattern is not present in the VA, suggesting that access to care may be an important component of racial disparities in this disease.

[Clinicopathologic characteristics and survival analysis of primary large B-cell lymphoma of the central nervous system].

Objective: To retrospectively analyze the clinical and pathologic characteristics, response to treatment, survival, and prognosis of patients with primary large B-cell lymphoma of the central nervous system (PCNSLBCL) . Methods: Clinical and pathologic data of 70 patients with PCNSLBCL admitted to Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from December 2010 to November 2022 were collected for retrospective analysis. Survival analysis was performed using the Kaplan-Meier method and log-rank test, and prognosis analysis was conducted using the Cox proportional hazards model. Results: Among 70 patients with PCNSLBCL, complete remission (CRs) were achieved in 49 (70.0% ) and partial remission in 4 (5.7% ) after the first-line induction therapy; the overall remission rate was 75.7%. The 2-year progression-free survival (PFS) rate was 55.8% and the median progression-free survival (mPFS) time was 35.9 months, whereas the 2-year overall survival (OS) rate was 79.1% with a median OS time not reached. After CR induced by first-line therapy, cumulative incidence of relapse (CIR) was lower in patients who had received auto-HSCT than in those who had not received consolidation therapy (P=0.032), whose 2-year PFS rate was 54.4% and mPFS time was 35.9 months; comparatively, the 2-year PFS rate in patients having received oral maintenance of small molecule drugs reached 84.4% with a mPFS time of 79.5 months (P=0.038). Multivariant analysis demonstrated that Class 3 in the Memorial Sloan-Kettering Cancer Center (MSKCC) prognostic model is an independent adverse prognostic factor of OS in patients with PCNSLBCL (HR=3.127, 95% CI 1.057-9.253, P=0.039) . Conclusions: In patients with PCNSLBCL achieving CR after the first-line induction therapy, auto-HSCT as consolidation therapy would lead to a decreased CIR, and PFS time could be prolonged by oral maintenance of small molecule drugs. Class 3 MSKCC prognostic model is independently associated with poorer OS.

The effect of matrine and glycyrrhizic acid on porcine reproductive and respiratory syndrome virus in Vitro and in vivo.

Porcine reproductive and respiratory syndrome (PRRS) is endemic worldwide, seriously affecting the development of the pig industry, but vaccines have limited protective effects against PRRSV transmission. The aim of this study was to identify potential anti-PRRSV drugs. We examined the cytotoxicity of seven compounds formulated based on the mass ratio of glycyrrhizic acid to matrine and calculated their inhibition rates against PRRSV in vitro. The results showed that the seven compounds all had direct killing and therapeutic effects on PRRSV, and the compounds inhibited PRRSV replication in a time- and dose-dependent manner. The compound with the strongest anti-PRRSV effect was selected for subsequent in vivo experiments. Pigs were divided into a control group and a medication group for the in vivo evaluation. The results showed that pigs treated with the 4:1 compound had 100% morbidity after PRRSV challenge, and the mortality rate reached 75% on the 8th day of the virus challenge. These results suggest that this compound has no practical anti-PRRSV effect in vivo and can actually accelerate the death of infected pigs. Next, we further analyzed the pigs that exhibited semiprotective effects following vaccination with the compound to determine whether the compound can synergize with the vaccine in vivo. The results indicated that pigs treated with the compound had higher mortality rates and more severe clinical reactions after PRRSV infection (p < 0.05). The levels of proinflammatory cytokines (IL-6, IL-8, IL-1ß, IFN-γ, and TNF-α) were significantly greater in the compound-treated pigs than in the positive control-treated pigs (p < 0.05), and there was no synergistic enhancement with the live attenuated PRRSV vaccine (p < 0.05). The compound enhanced the inflammatory response, prompted the body to produce excessive levels of inflammatory cytokines and caused body damage, preventing a therapeutic effect. In conclusion, the present study revealed that the in vitro effectiveness of these agents does not indicate that they are effective in vivo or useful for developing anti-PRRSV drugs. Our findings also showed that, to identify effective anti-PRRSV drugs, comprehensive drug screening is needed, for compounds with solid anti-inflammatory effects both in vitro and in vivo. Our study may aid in the development of new anti-PRRSV drugs.

DR-70 (fibrinogen-fibrin degradation products) as a prognostic biomarker in dogs with neoplasms.

Fibrinogen-fibrin degradation products (DR-70) are derived from tumor cells or metastases. Our previous study reported the diagnostic values in dogs with tumors, but no research has yet to be conducted to establish DR-70 as a prognostic marker. Herein, we investigated changes in DR-70 concentrations and disease courses in dogs with tumors. Overall survival time (OST) analysis was performed in 195 dogs with tumors, stratified with a recommended cut-off (1.514 µg/mL). Continual DR-70 measurements were performed during the medical interventions of 27 dogs with neoplasms. Clinical conditions and medical records were retrospectively reviewed. According to a cut-off value, dogs with plasma DR-70 concentrations above 1.514 µg/mL had shorter survival rates than those with concentrations below this threshold. In cases with complete or partial remission in response to treatment, the DR-70 concentration was decreased compared with that at the first visit, whereas it was increased in patients with disease progression. Our study suggested that changes in DR-70 concentration can be used as a prognostic biomarker for canine neoplasms. Furthermore, increased plasma DR-70 levels might be associated with shorter survival, and DR-70 concentrations may reflect responses to medical intervention.

ctGAN: combined transformation of gene expression and survival data with generative adversarial network.

Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p-value (0.041) compared with using only the real COAD (p-value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field.

Tumor purity-related genes for predicting the prognosis and drug sensitivity of DLBCL patients.

Background: Diffuse large B-cell lymphoma (DLBCL) is the predominant type of malignant B-cell lymphoma. Although various treatments have been developed, the limited efficacy calls for more and further exploration of its characteristics. Methods: Datasets from the Gene Expression Omnibus (GEO) database were used for identifying the tumor purity of DLBCL. Survival analysis was employed for analyzing the prognosis of DLBCL patients. Immunohistochemistry was conducted to detect the important factors that influenced the prognosis. Drug-sensitive prediction was performed to evaluate the value of the model. Results: VCAN, CD3G, and C1QB were identified as three key genes that impacted the outcome of DLBCL patients both in GEO datasets and samples from our center. Among them, VCAN and CD3G+ T cells were correlated with favorable prognosis, and C1QB was correlated with worse prognosis. The ratio of CD68 + macrophages and CD8 + T cells was associated with better prognosis. In addition, CD3G+T cells ratio was significantly correlated with CD68 + macrophages, CD4 + T cells, and CD8 +T cells ratio, indicating it could play an important role in the anti-tumor immunity in DLBCL. The riskScore model constructed based on the RNASeq data of VCAN, C1QB, and CD3G work well in predicting the prognosis and drug sensitivity. Conclusions: VCAN, CD3G, and C1QB were three key genes that influenced the tumor purity of DLBCL, and could also exert certain impact on drug sensitivity and prognosis of DLBCL patients. Funding: This work is supported by the Shenzhen High-level Hospital Construction Fund and CAMS Innovation Fund for Medical Sciences (CIFMS) (2022-I2M-C&T-B-062).

[A simulation study of the reliability and accuracy of Cox-TEL method for estimating hazard ratio and difference in proportions for long-term survival data containing cured patients].

OBJECTIVE: To explore the applicable conditions of the Cox-TEL (Cox PH-Taylor expansion adjustment for long-term survival data) method for analysis of survival data that contain cured patients. METHODS: The simulated survival data method based on Weibull distribution was used to simulate and generate the survival data with different cure rates, censored rates, and cure rate differences. The Cox-TEL method was used for analysis of the generated simulation data, and its performance was evaluated by calculating its type Ⅰ error and power. RESULTS: Almost all the type Ⅰ error of the hazard ratios (HRs) obtained by the Cox-TEL method under different conditions were slightly greater than 0.05, and this method showed a good test power for estimating the HRs for data with a large sample size and a large difference in proportions (DPs). For the data of cured patients, the type Ⅰ error of the DPs obtained by the Cox-TEL method was well around 0.05, and its test power was robust in most of the scenarios. CONCLUSION: The Cox-TEL method is effective for analyzing data of uncured patients and obtaining reliable HRs for most of the survival data with a sample size, a low censored rates, and a large difference in cure rates. The method is capable of accurately estimating the DPs regardless of the sample size, censored rates, or the cure rates.

One-year survival of patients admitted for sepsis to intensive care units in Colombia.

BACKGROUND: Sepsis is a frequent cause of admission to intensive care units (ICUs). High mortality rates are estimated globally, and in our country, few studies have reported one-year survival. The objective of this study is to determine one-year survival in patients with sepsis admitted to the ICU in Colombia, compared with the survival of patients admitted for other conditions. METHODS: Retrospective cohort study using administrative databases from the Ministry of Health of Colombia. One-year survival and the adjusted hazard ratio for survival, adjusted for comorbidities included in the Charlson Index, were determined using a Cox proportional hazards model for patients admitted for other causes as well as for those admitted for sepsis. This was then compared with an inverse propensity score weighting model. RESULTS: A total of 116.407 patients were initially admitted to the ICUs, with 12.056 (10.36%) diagnosed with sepsis. Within the first year, 4.428 (36.73%) patients died due to sepsis. Age and male gender were associated with an increased risk of death from sepsis, and the covariates associated with one-year mortality were as follows: age over 80 years with HR 9.91 (95% CI: 9.22-10.65), renal disease with HR 3.16 (95% CI: 3.03-3.29), primary tumoral disease with HR 2.07 (95% CI: 1.92-2.23), liver disease with HR 2.27 (95% CI: 2.07-2.50), and metastatic solid tumor with HR 2.03 (95% CI: 1.92-2.15). CONCLUSION: This study revealed a high one-year sepsis mortality rate in the population, associated with variables such as age over 80 years, the presence of renal disease, liver disease, connective tissue diseases, and cancer. Men exhibited higher mortality compared to women.

The intratumoral microbiota biomarkers for predicting survival and efficacy of immunotherapy in patients with ovarian serous cystadenocarcinoma.

BACKGROUND: Ovarian serous cystadenocarcinoma, accounting for about 90% of ovarian cancers, is frequently diagnosed at advanced stages, leading to suboptimal treatment outcomes. Given the malignant nature of the disease, effective biomarkers for accurate prediction and personalized treatment remain an urgent clinical need. METHODS: In this study, we analyzed the microbial contents of 453 ovarian serous cystadenocarcinoma and 68 adjacent non-cancerous samples. A univariate Cox regression model was used to identify microorganisms significantly associated with survival and a prognostic risk score model constructed using LASSO Cox regression analysis. Patients were subsequently categorized into high-risk and low-risk groups based on their risk scores. RESULTS: Survival analysis revealed that patients in the low-risk group had a higher overall survival rate. A nomogram was constructed for easy visualization of the prognostic model. Analysis of immune cell infiltration and immune checkpoint gene expression in both groups showed that both parameters were positively correlated with the risk level, indicating an increased immune response in higher risk groups. CONCLUSION: Our findings suggest that microbial profiles in ovarian serous cystadenocarcinoma may serve as viable clinical prognostic indicators. This study provides novel insights into the potential impact of intratumoral microbial communities on disease prognosis and opens avenues for future therapeutic interventions targeting these microorganisms.

Effects of pre-existing type 1 diabetes mellitus on survival outcome following out-of-hospital cardiac arrest: a registry-based observational study in Sweden.

BACKGROUND: It has been estimated that 80% of cases of out-of-hospital cardiac arrest (OHCA) are due to cardiac causes. It is well-documented that diabetes is a risk factor for conditions associated with sudden cardiac arrest. Type 1 diabetes (T1D) displays a threefold to fivefold increased risk of cardiovascular disease and death compared with the general population. OBJECTIVE: This study aims to assess the characteristics and survival outcomes of individuals with and without T1D who experienced an OHCA. Design: A registry-based nationwide observational study with two cohorts, patients with T1D and patients without T1D. Setting: All emergency medical services and hospitals in Sweden were included in the study. PARTICIPANTS: Using the Swedish Cardiopulmonary Resuscitation Registry, we enrolled 54 568 cases of OHCA where cardiopulmonary resuscitation was attempted between 2010 and 2020. Among them, 448 patients with T1D were identified using International Classification of Diseases-code: E10. METHODS: Survival analysis was performed using Kaplan-Meier and logistic regression. Multiple regression was adjusted for age, sex, cause of arrest, prevalence of T1D and time to cardiopulmonary resuscitation. MAIN OUTCOME MEASURES: The outcomes were discharge status (alive vs dead), 30 days survival and neurological outcome at discharge. RESULTS: There were no significant differences in patients discharged alive with T1D 37.3% versus, 46% among cases without T1D. There was also no difference in neurological outcome. Kaplan-Meier curves yielded no significant difference in long-term survival. Multiple regression showed no significant association with survival after accounting for covariates, OR 0.99 (95% CI 0.96 to 1.02), p value=0.7. Baseline characteristics indicate that patients with T1D were 5 years younger at OHCA occurrence and had proportionally fewer cases of heart disease as the cause of arrest (57.6% vs 62.7%). CONCLUSION: We conclude, with the current sample size, that there is no statistically significant difference in long-term or short-term survival between patients with and without T1D following OHCA.

SurvdigitizeR: an algorithm for automated survival curve digitization.

BACKGROUND: Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves. METHODS: The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm's performance was also evaluated by Bland-Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves. RESULTS: The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement. CONCLUSIONS: The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/ .

Construction and validation of a prognostic model for gastric cancer patients with tumor deposits.

Background: Tumor deposits (TD) was a significant risk factor impacting the prognosis of patients diagnosed with gastric cancer (GC), yet it was not currently incorporated into TNM staging systems. The objective of this research was to develop a predictive model for assessing the prognosis of patients with TD-positive GC. Methods: Retrospective analysis was performed on the data of 4,972 patients treated for GC with D2 radical gastrectomy at Wannan Medical College's Yijishan Hospital between January 2012 and December 2021. The patients were categorized based on the number of TD (L1: 1, L2: 2-3, L3: ≥4) and the anatomical location of TD (Q1: single area, Q2: multiple areas). In a 3:1 ratio, patients were randomly assigned to one of two groups: training or validation. Results: The study included a total of 575 patients who were divided into the training group (n = 432) and validation group (n = 143). Survival analysis showed that the number and anatomical location of TD had a significant impact on the prognosis of patients with TD-positive GC. Univariate analysis of the training group data revealed that tumor size, T-stage, N-stage, histological grade, number and distribution of TD, neural invasion, and postoperative chemotherapy were associated with prognosis. Multivariate Cox regression analysis identified poor histological grade, T4 stage, N3 stage, number of TD, neural invasion, and postoperative chemotherapy as independent prognostic factors for GC patients with TD. A nomogram was developed using these variables, demonstrating well predictive ability for 1, 3, and 5-year overall survival (OS) in the validation set. The DCA curve shows that the constructed model shows a large positive net gain compared to the eighth edition Tumour, Node, Metastasis (TNM) staging system. Conclusion: The prognostic model developed for patients with TD-positive GC has a higher clinical utility compared to the eighth edition of TNM staging.

Application of information from external data to correct for collider bias in a Covid-19 hospitalised cohort.

BACKGROUND: Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to collider bias as they restrict the population of Covid-19 patients to only those with severe disease. Inverse probability weighting is typically used to correct for this bias but requires information from the unrestricted population. Using electronic health records from a South London NHS trust, this work demonstrates a method to correct for collider bias using externally sourced data while examining the relationship between minority ethnicities and poor Covid-19 outcomes. METHODS: The probability of inclusion within the observed hospitalised cohort was modelled based on estimates from published national data. The model described the relationship between patient ethnicity, hospitalisation, and death due to Covid-19 - a relationship suggested to be susceptible to collider bias. The obtained probabilities (as applied to the observed patient cohort) were used as inverse probability weights in survival analysis examining ethnicity (and covariates) as a risk factor for death due to Covid-19. RESULTS: Within the observed cohort, unweighted analysis of survival suggested a reduced risk of death in those of Black ethnicity - differing from the published literature. Applying inverse probability weights to this analysis amended this aberrant result to one more compatible with the literature. This effect was consistent when the analysis was applied to patients within only the first wave of Covid-19 and across two waves of Covid-19 and was robust against adjustments to the modelled relationship between hospitalisation, patient ethnicity, and death due to Covid-19 made as part of a sensitivity analysis. CONCLUSIONS: In conclusion, this analysis demonstrates the feasibility of using external publications to correct for collider bias (or other forms of selection bias) induced by the restriction of a population to a hospitalised cohort using an example from the recent Covid-19 pandemic.

Pediatric Axial Ewing Sarcoma: A Retrospective Population-Based Survival Analysis.

INTRODUCTION: Ewing sarcomas of the axial skeleton represent a notable challenge for clinicians because of their aggressive presentation and tendency to obstruct neurovascular structures; however, little data exist regarding axial tumors in children. This study is the first population-based analysis assessing treatment regimens for axial Ewing sarcomas and their effects on cancer-specific survival and overall survival (OS). METHODS: Data from 2004 to 2019 were collected for all patients aged 1 to 24 years from the Surveillance, Epidemiology, and End Results (SEER) database. Primary groups included pelvic tumors, thoracic tumors, and vertebral tumors. Chi-squared and Kaplan-Meier tests were used to assess associations between demographic variables, clinical and treatment characteristics, and patient survival. RESULTS: Pelvic tumors were most common, and 49.7% received chemotherapy/radiation. Vertebral tumors were least common, and 56.7% received chemotherapy/surgery/radiation. 53.5% of thoracic tumors received chemotherapy/surgery. Surgery was most common for thoracic tumors (80.2%) and rare for pelvic tumors (38.9%). Radiation therapy was most common for vertebral tumors (83.6%) and least common for thoracic tumors (36.0%). Pelvic tumors exhibited the lowest OS (1-year, 5-year, and 10-year OS: 96%, 70%, and 59%), followed by thoracic tumors (1-year, 5-year, and 10-year OS: 97%, 79%, and 66%) and vertebral tumors (1-year, 5-year, and 10-year OS: 92%, 77%, and 68%). CONCLUSION: This study underpins the importance of both early detection and chemotherapy-based multimodal therapy in the treatment of axial Ewing sarcoma in a pediatric population. A comparatively large decline in OS was observed between 5 and 10 years for patients with thoracic tumors, and this cohort's 10-year OS has not improved when compared with a similar SEER cohort from 1973 to 2011. Despite a growing body of research supporting definitive radiation therapy, a notable portion of patients with pelvic Ewing sarcoma did not receive radiation, representing an unmet need for this population.