Factors Associated with Time to Receiving Gender-Affirming Hormones and Puberty Blockers at a Pediatric Clinic Serving Transgender and Nonbinary Youth

Purpose

To describe barriers to care for a cohort of transgender and nonbinary (TNB) youth and examine factors associated with delays in receiving puberty blockers (PBs) or gender-affirming hormones (GAHs).

Methods

We used longitudinal data from a prospective cohort of TNB youth seeking care at a multidisciplinary pediatric gender clinic between August 2017 and June 2018. We calculated the time between (i) initial clinic contact, (ii) phone intake, (iii) first medical appointment, and (iv) initiating PBs/GAHs. We estimated Kaplan-Meier curves for each time-to-care interval and used Cox regression models to estimate hazard ratios (HRs) for factors hypothesized to be barriers and facilitators of care.

Results

Our cohort included 104 youth aged 13-20 years. The median time from contacting the clinic to initiating PBs/GAHs was 307 days (range, 54-807). Lower income level, Medicaid insurance, and lack of family support were associated with longer times from contacting the clinic to completing the first medical appointment. In addition, older youth experienced longer times to first medical appointment relative to youth aged 13-14 years. Youth younger than 18 years of age who did not complete a mental health assessment before their first medical appointment experienced delays from first medical appointment to initiating PBs/GAHs (HR=0.44, 95% confidence interval, 0.22-0.88).

Conclusion

Certain subsets of youth disproportionately experienced delays in receiving gender-affirming medications, and these factors varied by stage of care engagement. Given the association between gender-affirming care and improved mental health, identifying sociostructural and clinic-level barriers to care is critically important to facilitating more equitable access.

Diffuse Isocitrate Dehydrogenase-Mutant Gliomas With Histone H3 Alterations Are Distinguished by Unique Clinical Characteristics, Molecular Expression Profile, and Survival Prognosis

BACKGROUND AND OBJECTIVES

Histopathological features and molecular biomarkers have been studied as potential prognostic factors. This study aimed to investigate the clinical features, molecular phenotypes, and survival prognosis of isocitrate dehydrogenase (IDH)-mutant (IDHmt) gliomas with histone H3 alterations (H3-alterations).

METHODS

A total of 236 and 657 patients with whole-exome sequencing data were separately collected from the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases. Survival analysis of patients with glioma was performed using Kaplan-Meier survival curves stratified by histone H3 status. Univariate and multivariate analyses were used to identify the associations between histone H3 status and other clinicopathological factors with survival in patients with IDH-mutant gliomas.

RESULTS

Diffuse gliomas with H3 alterations are more likely to be high grade in 2 cohorts ( P = .025 and P = .021, respectively). IDHmt glioma patients with H3-alteration had significantly less life expectancy than histone H3 wild-type ( P = .041 and P = .008, respectively). In the Chinese Glioma Genome Atlas cohort, Karnofsky performance scores ≤ 80 (HR 2.394, 95% CI 1.257-4.559, P = .008), extent of resection (HR 0.971, 95% CI 0.957-0.986, P < .001), high WHO grade (HR 6.938, 95% CI 2.787-17.269, P < .001), H3-alteration (HR 2.482, 95% CI 1.183-4.981, P = .016), and 1p/19q codeletion (HR 0.169, 95% CI 0.073-0.390, P < .001) were independently associated with IDHmt gliomas. In the The Cancer Genome Atlas cohort, age (HR 1.034, 95% CI 1.008-1.061, P = .010), high WHO grade (HR 2.365, 95% CI 1.263-4.427, P = .007), and H3-alteration (HR 2.501, 95% CI 1.312-4.766, P = .005) were independently associated with IDHmt gliomas.

CONCLUSION

Identification and assessment of histone H3 status in clinical practice might help improve prognostic prediction and develop therapeutic strategies for these patient subgroups.

Evaluating cancer screening programs using survival analysis

The main purpose of cancer screening programs is to provide early treatment to patients that are diagnosed with cancer on a screening test, thus increasing their chances of survival. To test this hypothesis directly, one should compare the survival of screen-detected cases to the survival of their counterparts not included to the program. In this study, we develop a general notation and use it to formally define the comparison of interest. We explain why the naive comparison between screen-detected and interval cases is biased and show that the total bias that arises in this case can be decomposed as a sum of lead time bias, length time bias, and bias due to overdetection. With respect to the estimation, we show what can be estimated using existing methods. To fill in the missing gap, we develop a new nonparametric estimator that allows us to estimate the survival of the control group, that is, the survival of cancer cases that would be screen-detected among those not included to the program. By joining the proposed estimator with existing methods, we show that the contrast of interest can be estimated without neglecting any of the biases. Our approach is illustrated using simulations and empirical data.

Temporal changes in childhood cancer incidence and survival by stage at diagnosis in Australia, 2000-2017

BACKGROUND

The Toronto Paediatric Cancer Stage Guidelines are a compendium of staging systems developed to facilitate collection of consistent and comparable data on stage at diagnosis for childhood cancers by cancer registries.

MATERIAL AND METHODS

This retrospective, observational cohort study investigated changes in stage-specific incidence and survival for children diagnosed between 2000-2008 compared to 2009-2017 using the population-based Australian Childhood Cancer Registry. Information on mortality for each patient was available to 31st December 2020. Shifts in incidence by stage were evaluated using chi-square tests, and differences in stage-specific five-year observed survival for all causes of death over time were assessed using flexible parametric models.

RESULTS

Stage was assigned according to the Toronto Guidelines for 96% (n = 7944) of the total study cohort (n = 8292). Changes in the distribution of incidence by stage between the two diagnosis periods were observed for retinoblastoma, with stage 0 increasing from 26% to 37% of cases (p = 0.02), and hepatoblastoma, with metastatic disease increasing from 22% to 39% of cases (p = 0.04). There were large gains in stage-specific survival over time for stage IV rhabdomyosarcoma (five-year adjusted mortality hazard ratio for 2009-2017 compared to 2000-2008 of 0.38, 95% CI 0.19-0.77; p = 0.01), stage M3 for medulloblastoma (HR = 0.41, 95% CI 0.21-0.79; p = 0.01) and metastatic neuroblastoma excluding stage MS (HR = 0.61, 95% CI 0.44-0.84; p < 0.01).

CONCLUSION

These results indicate that improvements in childhood cancer survival in Australia are most likely due to refined management rather than changes in stage at diagnosis, particularly for metastatic solid tumours. Wide international uptake of the Toronto Guidelines will allow comprehensive evaluation of differences in survival between countries.

Deciphering the Genetic Alteration in the ZEB2 Gene Network and Their Possible Association With Head and Neck Squamous Cell Carcinoma (HNSCC)

Background Head and neck squamous cell carcinoma (HNSCC) is an abnormal growth of cells that leads to tumor formation in the head and neck region. Several genes and genetic networks are involved in the process of carcinogenesis. Aim The aim of the present study is to unravel the prognostic marker from a pool of interacting networks governed by the ZEB2gene. Materials and methods Computational analysis was employed to identify the protein network interactions, genetic alterations, gene expression, and the survival analysis of the ZEB2 dysregulated network in the head and neck cancer dataset (HNSCC) from the Cancer Genome Atlas (TCGA), Firehose Legacy. The gene expression profiling and survival analysis were performed for the gene with the highest frequency of genetic alteration. Result The interaction network returned nine genes that interact with ZEB2. The ARHGAP31 gene was found to harbor the highest frequency of alteration at the genomic as well as the transcriptomic levels. Survival was also found to be significant with respect to the differential gene expression pattern while comparing the genders and different ethnic groups. Females with higher expression of ARHGAP31 and the Asian population exhibiting low/medium expression of the same were found to present with poor survival probability. Conclusion The identification of putative drivers or a candidate gene of a network could provide clues about the association with the disease phenotype of HNSCC. The present study identifies ARHGAP31 as the key gene of the ZEB2 gene network, wherein the genetic alterations correlate with the transcriptomics data and the survival probability of patients segregated based on gender and race. Further experimental evaluation is warranted to confirm the association of this infamous gene ARHGAP31 with the development of oral carcinoma.

Real-world effectiveness of regorafenib in the treatment of patients with metastatic colorectal cancer: A retrospective, observational study

AIM

To evaluate the real-world usage pattern and factors associated with the effectiveness of regorafenib in patients with metastatic colorectal cancer (mCRC).

METHODS

This retrospective study analyzed data for 209 patients with mCRC treated with regorafenib as third or later line of therapy. TheKaplan-Meier method was used to draw the survival curves. Cox proportional hazard regression models were used to analyze the prognostic value for overall survival (OS).

RESULTS

Of 209 patients, 156 (75%) were treated with regorafenib, and 53 (25%) were given regorafenib combined with programmed cell death-1 (PD-1) inhibitors. About 182 (87%) patients had a dose record of regorafenib. The initial daily doses of regorafenib were 160, 120, 80, and 40 mg, accounting for 29%, 17%, 48%, and 6% of patients, respectively. The median follow-up time was 11.3 months, and the median OS was 12.0 months (95% CI: 9.7-14.3). Patients treated with PD-1 inhibitors plus regorafenib had a longer OS than the non-PD-1 group (13.5 vs. 10.1 months, hazard ratio [HR] = .534, 95% CI: .325-.879; p = .014). A total of 49 patients with microsatellite stable or mismatch repair-proficient genotype treated with PD-1 inhibitors plus regorafenib had a longer OS than the non-PD-1 group (13.5 vs. 9.7 months; HR = .563, p = .027). The median OS of patients continuing treatment with regorafenib after progression (n = 19, with five patients receiving additional immunotherapy) was marginally longer than patients discontinuing regorafenib after progression (12.7 vs. 11.9 months, p = .425) observed in a smaller cohort.

CONCLUSION

In real-world practice, patients with mCRC in whom standard regimens had failed have a good survival benefit with regorafenib. Combination with PD-1 inhibitor may further prolong survival of the patients.

Distance-Metric Learning for Personalized Survival Analysis

Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to handle such outcomes effectively, they do not provide explanations for their predictions, lacking interpretability. In this paper, an alternative method for survival prediction by weighted nearest neighbors is proposed. Fitting this model to data entails optimizing the weights by learning a metric. An individual prediction of this method can be explained by providing the user with the most influential data points for this prediction, i.e., the closest data points and their weights. The strengths and weaknesses in terms of predictive performance are highlighted on simulated data and an application of the method on two different real-world datasets of breast cancer patients shows its competitiveness with established methods.

Greater Covid-19 vaccine uptake among enrollees offered health and social needs case management: Results from a randomized trial

OBJECTIVE

To investigate Covid-19 vaccination as a potential secondary public health benefit of case management for Medicaid beneficiaries with health and social needs.

DATA SOURCES AND STUDY SETTING

The CommunityConnect case management program for Medicaid beneficiaries is run by Contra Costa Health, a county safety net health system in California. Program enrollment data were merged with comprehensive county vaccination records.

STUDY DESIGN

Individuals with elevated risk of hospital and emergency department use were randomized each month to a case management intervention or usual care. Interdisciplinary case managers offered coaching, community referrals, healthcare connections, and other support based on enrollee interest and need. Using survival analysis with intent-to-treat assignment, we assessed rates of first-dose Covid-19 vaccination from December 2020 to September 2021. In exploratory sub-analyses we also examined effect heterogeneity by gender, race/ethnicity, age, and primary language.

DATA COLLECTION AND EXTRACTION METHODS

Data were extracted from county and program records as of September 2021, totaling 12,866 interventions and 25,761 control enrollments.

PRINCIPAL FINDINGS

Approximately 58% of enrollees were female and 41% were under age 35. Enrollees were 23% White, 12% Asian/Pacific Islander, 20% Black/African American, and 36% Hispanic/Latino, and 10% other/unknown. Approximately 35% of the intervention group engaged with their case manager. Approximately 56% of all intervention and control enrollees were vaccinated after 9 months of analysis time. Intervention enrollees had a higher vaccination rate compared to control enrollees (adjusted hazard ratio [aHR]: 1.06; 95% confidence interval [CI]: 1.02-1.10). In sub-analyses, the intervention was associated with stronger likelihood of vaccination among males and individuals under age 35.

CONCLUSIONS

Case management infrastructure modestly improved Covid-19 vaccine uptake in a population of Medicaid beneficiaries that over-represents social groups with barriers to early Covid-19 vaccination. Amidst mixed evidence on vaccination-specific incentives, leveraging trusted case managers and existing case management programs may be a valuable prevention strategy.

Nadir oxygen delivery during cardiopulmonary bypass in acute type A aortic dissection repair

Background

Acute type A aortic dissection (ATAAD) is associated with high mortality. Previous studies found that maintaining a high level of oxygen delivery (DO2) could decrease the postoperative mortality, but the minimum threshold of DO2 remained unclear. The present study aimed to investigate the relationship between maintaining intraoperative DO2 ≥280 mL/(min·m2) and the 90-day postoperative mortality of ATAAD patients.

Methods

The clinical data of 178 ATAAD patients who underwent Sun's procedure in our center from January 2018 to July 2022 were retrospectively analyzed in the present cohort study. The included patients were divided into hypoxic group [DO2 <280 mL/(min·m2)] and normoxic group [DO2 ≥280 mL/(min·m2)]. The primary endpoint was the 90-day all-cause mortality, and the secondary endpoints were postoperative mechanical ventilation time, the application of continuous renal replacement therapy (CRRT), brain complications, and other postoperative complications.

Results

Among all the patients, a total of 23 patients died 90 days postoperatively. Compared with the hypoxic group, blood flow, hematocrit (HCT), DO2, and DO2/VO2 ratio during cardiopulmonary bypass (CPB) were significantly higher, while the need for CRRT and the 90-day mortality were significantly lower in the normoxic group. The median follow-up time was 4 months. Kaplan-Meier curve indicated that the survival rate of ATAAD patients in the normoxic group was significantly higher. Univariate cox regression analysis demonstrated that 90-day mortality was reduced by 72.1% in the normoxic group.

Conclusions

Maintaining DO2 ≥280 mL/(min·m2) during CPB by increasing CPB flow and HCT level is associated with decreased 90-day mortality of ATAAD patients.

A Study on Survival Analysis Methods Using Neural Network to Prevent Cancers

Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality.

Mean platelet volume may predict all-cause and cardiovascular mortality among incident peritoneal dialysis patients

BACKGROUND

The association between mean platelet volume (MPV) and mortality in patients with cardiovascular disease has been demonstrated. However, the association between MPV and mortality in peritoneal dialysis (PD) patients remains unclear.

METHODS

Patients catheterized at the First Affiliated Hospital, Nanchang University, between November 1, 2005, and August 31, 2019, were enrolled. The primary endpoints were all-cause and cardiovascular mortality. Patients were divided into two groups according to the cutoff value, which was determined using maximally selected rank statistics. The mortality hazard ratio was evaluated using Cox regression models.

RESULTS

Among the 1322 PD patients enrolled, the mean age was 49.3 ± 14.5 years, 57.6% were men, and 18.8% had diabetes. During a median follow-up of 50 months (IQR: 30-80), 360 patients died; among these, 167 deaths were attributed to cardiovascular diseases. Survival analysis revealed that all-cause and cardiovascular mortality rates were lower in the higher-MPV group than in the lower-MPV group (p < .001 and p < .001, respectively). After full adjustment, a higher MPV was significantly associated with a hazard ratio of 0.77 for all-cause mortality (95% CI: 0.60-0.98, p = .036) and 0.75 for cardiovascular mortality (95% CI: 0.51-0.97, p = .041). Subgroup analysis showed that a significant interaction existed between age and MPV (p < .001). Decreased MPV was associated with higher mortality risk only in patients < 60 years old.

CONCLUSIONS

Our results showed that lower MPV can be associated with a higher risk of all-cause and cardiovascular mortality in patients with PD.

Generalized Pairwise Comparisons to Assess Treatment Effects: JACC Review Topic of the Week

A time-to-first-event composite endpoint analysis has well-known shortcomings in evaluating a treatment effect in cardiovascular clinical trials. It does not fully describe the clinical benefit of therapy because the severity of the events, events repeated over time, and clinically relevant nonsurvival outcomes cannot be considered. The generalized pairwise comparisons (GPC) method adds flexibility in defining the primary endpoint by including any number and type of outcomes that best capture the clinical benefit of a therapy as compared with standard of care. Clinically important outcomes, including bleeding severity, number of interventions, and quality of life, can easily be integrated in a single analysis. The treatment effect in GPC can be expressed by the net treatment benefit, the success odds, or the win ratio. This review provides guidance on the use of GPC and the choice of treatment effect measures for the analysis and reporting of cardiovascular trials.

Gene regulatory Networks Reveal Sex Difference in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. We observe that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue, as well as in tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also uncovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration

Vertical federated learning has gained popularity as a means of enabling collaboration and information sharing between different entities while maintaining data privacy and security. This approach has potential applications in disease healthcare, cancer prognosis prediction, and other industries where data privacy is a major concern. Although using multi-omics data for cancer prognosis prediction provides more information for treatment selection, collecting different types of omics data can be challenging due to their production in various medical institutions. Data owners must comply with strict data protection regulations such as European Union (EU) General Data Protection Regulation. To share patient data across multiple institutions, privacy and security issues must be addressed. Therefore, we propose an adaptive optimized vertical federated-learning-based framework adaptive optimized vertical federated learning for heterogeneous multi-omics data integration (AFEI) to integrate multi-omics data collected from multiple institutions for cancer prognosis prediction. AFEI enables participating parties to build an accurate joint evaluation model for learning more information related to cancer patients from different perspectives, based on the distributed and encrypted multi-omics features shared by multiple institutions. The experimental results demonstrate that AFEI achieves higher prediction accuracy (6.5% on average) than using single omics data by utilizing the encrypted multi-omics data from different institutions, and it performs almost as well as prognosis prediction by directly integrating multi-omics data. Overall, AFEI can be seen as an efficient solution for breaking down barriers to multi-institutional collaboration and promoting the development of cancer prognosis prediction.

Development and Validation of an XGBoost-Algorithm-Powered Survival Model for Predicting In-Hospital Mortality Based on 545,388 Isolated Severe Traumatic Brain Injury Patients from the TQIP Database

BACKGROUND

Traumatic brain injury (TBI) represents a significant global health issue; the traditional tools such as the Glasgow Coma Scale (GCS) and Abbreviated Injury Scale (AIS) which have been used for injury severity grading, struggle to capture outcomes after TBI.

AIM AND METHODS

This paper aims to implement extreme gradient boosting (XGBoost), a powerful machine learning algorithm that combines the predictions of multiple weak models to create a strong predictive model with high accuracy and efficiency, in order to develop and validate a predictive model for in-hospital mortality in patients with isolated severe traumatic brain injury and to identify the most influential predictors. In total, 545,388 patients from the 2013-2021 American College of Surgeons Trauma Quality Improvement Program (TQIP) database were included in the current study, with 80% of the patients used for model training and 20% of the patients for the final model test. The primary outcome of the study was in-hospital mortality. Predictors were patients' demographics, admission status, as well as comorbidities, and clinical characteristics. Penalized Cox regression models were used to investigate the associations between the survival outcomes and the predictors and select the best predictors. An extreme gradient boosting (XGBoost)-powered Cox regression model was then used to predict the survival outcome. The performance of the models was evaluated using the Harrell's concordance index (C-index). The time-dependent area under the receiver operating characteristic curve (AUC) was used to evaluate the dynamic cumulative performance of the models. The importance of the predictors in the final prediction model was evaluated using the Shapley additive explanations (SHAP) value.

RESULTS

On average, the final XGBoost-powered Cox regression model performed at an acceptable level for patients with a length of stay up to 250 days (mean time-dependent AUC = 0.713) in the test dataset. However, for patients with a length of stay between 20 and 213 days, the performance of the model was relatively poor (time-dependent AUC < 0.7). When limited to patients with a length of stay ≤20 days, which accounts for 95.4% of all the patients, the model achieved an excellent performance (mean time-dependent AUC = 0.813). When further limited to patients with a length of stay ≤5 days, which accounts for two-thirds of all the patients, the model achieved an outstanding performance (mean time-dependent AUC = 0.917).

CONCLUSION

The XGBoost-powered Cox regression model can achieve an outstanding predictive ability for in-hospital mortality during the first 5 days, primarily based on the severity of the injury, the GCS on admission, and the patient's age. These variables continue to demonstrate an excellent predictive ability up to 20 days after admission, a period of care that accounts for over 95% of severe TBI patients. Past 20 days of care, other factors appear to be the primary drivers of in-hospital mortality, indicating a potential window of opportunity for improving outcomes.

Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients

Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to the start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional radiomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. The radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. An ensemble method with 5-fold cross-validation over 1000 iterations offers an AUC of 0.793 ± 0.082 for REP versus non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up with until death) identifies significant features (p-value < 0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients' cohort produces a precision of 0.881 ± 0.056. The prognostic index (PI) calculated using the fused features shows that 84.62% of REP cases fall under the bad prognostic group, suggesting the potential of fused features for predicting a higher percentage of REP cases. The experimental results further show that multi-resolution fractal texture features perform better than conventional radiomics features for prediction of REP and survival outcomes.

The influence of adjunctive traditional Chinese medicine therapy on survival in primary liver cancer: a real-world study based on electronic medical records

Background: Traditional Chinese medicine (TCM) effectively improves the survival rate and quality of life of primary liver cancer patients, but high-level evidence is lacking. Patients and methods: Patients were selected from 5 tertiary hospitals in Henan Province, China. Two thousand sixty-seven patients with primary liver cancer were included in the study. The electronic medical records (EMRs) of the patients were collected. Patients who received adjunctive TCM treatment and underwent treatment cumulative time for more than 1 month were classified as the TCM intervention cohort. Patients who did not receive adjunctive TCM treatment or underwent treatment cumulative time for less than 1 month were classified as the non-TCM intervention cohort. The main outcome indicators were the survival rate and overall survival time. The propensity score inverse probability weighting method was used to balance the differences between the groups. Results: The primary cohort comprised 2,067 patients, including 462 patients who received adjunctive TCM treatment and 1,605 patients who did not receive adjunctive TCM treatment. The results of the Kaplan‒Meier survival curve indicated that the survival rate and median survival time of the exposure group before and after propensity score weighting were greater than those of the control group (p < 0.0001). Univariate Cox regression analysis after propensity score weighting showed that adjunctive TCM treatment was an independent protective factor for survival [regression coefficient = -0.215, hazard ratio (HR) = 0.8066, 95% confidence interval (CI) (0.6609-0.9844)]. Conclusion: Adjuvant treatment with TCM has a protective effect on the prognosis of patients with primary liver cancer; it can reduce the mortality and prolong the survival time.

Patterns of recurrence in head and neck squamous cell carcinoma to inform personalized surveillance protocols

BACKGROUND

Development of evidence-based post-treatment surveillance guidelines in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) is limited by comprehensive documentation of patterns of recurrence and metastatic spread.

METHODS

A retrospective analysis of patients diagnosed with R/M HNSCC at a National Cancer Institute-designated cancer center between 1998- 2019 was performed (n = 447). Univariate and multivariate analysis identified patterns of recurrence and predictors of survival.

RESULTS

Median overall survival (mOS) improved over time (6.7 months in 1998-2007 to 11.8 months in 2008-2019, p = .006). Predictors of worse mOS included human papillomavirus (HPV) negativity (hazard ratio [HR], 1.8; 95% confidence interval [CI], 1.2-2.6), high neutrophil/lymphocyte ratio (HR, 2.1 [1.4-3.0], disease-free interval (DFI) ≤6 months (HR, 1.4 [1.02-2.0]), and poor performance status (Eastern Cooperative Oncology Group, ≥2; HR, 1.91.1-3.4). In this cohort, 50.6% of recurrences occurred within 6 months of treatment completion, 72.5% occurred within 1 year, and 88.6% occurred within 2 years. Metachronous distant metastases were more likely to occur in patients with HPV-positive disease (odds ratio [OR], 2.3 [1.4-4.0]), DFI >6 months (OR, 2.4 [1.5-4.0]), and body mass index ≥30 (OR, 2.3 [1.1-4.8]). Oligometastatic disease treated with local ablative therapy was associated with improved survival over polymetastatic disease (HR, 0.36; 95% CI, 0.24-0.55).

CONCLUSION

These data regarding patterns of distant metastasis in HNSCC support the clinical utility of early detection of recurrence. Patterns of recurrence in this population can be used to inform individualized surveillance programs as well as to risk-stratify eligible patients for clinical trials.

PLAIN LANGUAGE SUMMARY

After treatment for head and neck cancer (HNC), patients are at risk of recurrence at prior sites of disease or at distant sites in the body. This study includes a large group of patients with recurrent or metastatic HNC and examines factors associated with survival outcomes and recurrence patterns. Patients with human papillomavirus (HPV)-positive HNC have good survival outcomes, but if they recur, this may be in distant regions of the body and may occur later than HPV-negative patients. These data argue for personalized follow-up schedules for patients with HNC, perhaps incorporating imaging studies or novel blood tests.

Impact of preoperative red blood cell transfusion on long-term mortality of liver transplantation: A retrospective cohort study

Preoperative red blood cell (RBC) transfusion can induce immune modulation and alloimmunization; however, few studies have investigated the effect of preoperative transfusion and hemoglobin levels that need to be corrected before surgery, especially in critically ill patients such as those with end-stage liver disease who undergo liver transplantation (LT). This study aimed to investigate the effects of preoperative RBC transfusion on long-term mortality in LT recipients. A total of 249 patients who underwent LT at a single center between January 2012 and December 2021 were included in this study. The patients were divided into 2 groups: preoperative transfusion and preoperative non-transfusion. Since the baseline characteristics were significantly different between the 2 groups, we performed propensity score matching, including factors such as the Model for End-Stage Liver Disease score and intraoperative RBC transfusion, to exclude possible biases that could affect prognosis. We analyzed the 5-year mortality rate as the primary outcome. The preoperative transfusion group showed a 4.84-fold higher hazard ratio than that in the preoperative non-transfusion group. There were no differences in 30-day mortality, duration of intensive care unit stay, or graft rejection rate between the 2 groups. Preoperative transfusion could influence long-term mortality in LT, and clinicians should pay attention to RBC transfusion before LT unless the patient is hemodynamically unstable. A large-scale randomized controlled trial is needed to determine the possible mechanisms related to preoperative RBC transfusion, long-term mortality, and the level of anemia that should be corrected before surgery.

Predictors of cessation of exclusive breastfeeding according to the Cox regression model: survey of mothers of children aged 6-12 months, Thiès, Senegal

Introduction

even though exclusive breastfeeding (EBF) for up to six months is recognised as essential infant care, it is still insufficiently practiced. The objective of this study was to identify predictors of EBF cessation in Thiès.

Methods

this was a survival analysis of data collected using a cross-sectional procedure. Data collection took place from 2nd December 2019 to 21st January 2020. The study population consisted of mothers of children aged 6 to 12 months residing in Thiès and seen at the reference health centre of the Thiès Health District during infant vaccination sessions. The number of subjects was 400 mothers recruited using a systematic survey, with a sampling interval equal to two. Data were collected through a face-to-face interview. Predictive factors were identified using the Cox regression model. The adjusted hazard ratio (AHR) and its 95% confidence interval (95% CI) are calculated.

Results

the average age of the mothers was 27.08 ± 6.34 years. The proportion of mothers who breastfed their child within one hour of birth was 29.25%. The proportion of those who practiced EBF was 41.50%. The incidence density of EBF cessation was 14 person-months per 100 breastfeeding mothers. The median duration of EBF was 5 months. Lack of advice on EBF during antenatal care (AHR=1.42; 95% CI =1.08-1.85), sources of information other than health professionals (AHR =1.51; 95% CI =1.05-2.19), late initiation of EBF, i.e. breastfeeding beyond 24 hours after birth (AHR =1.53; 95% CI =1.02-2.28) and low level of knowledge about EBF (AHR =1.46; 95% CI =1.11-1.92) were significantly associated with early termination of EBF.

Conclusion

the promotion of EBF for up to six months will necessarily involve the promotion of prenatal consultations during which professionals should raise awareness among future mothers.

Testing for an ignorable sampling bias under random double truncation

In clinical and epidemiological research doubly truncated data often appear. This is the case, for instance, when the data registry is formed by interval sampling. Double truncation generally induces a sampling bias on the target variable, so proper corrections of ordinary estimation and inference procedures must be used. Unfortunately, the nonparametric maximum likelihood estimator of a doubly truncated distribution has several drawbacks, like potential nonexistence and nonuniqueness issues, or large estimation variance. Interestingly, no correction for double truncation is needed when the sampling bias is ignorable, which may occur with interval sampling and other sampling designs. In such a case the ordinary empirical distribution function is a consistent and fully efficient estimator that generally brings remarkable variance improvements compared to the nonparametric maximum likelihood estimator. Thus, identification of such situations is critical for the simple and efficient estimation of the target distribution. In this article, we introduce for the first time formal testing procedures for the null hypothesis of ignorable sampling bias with doubly truncated data. The asymptotic properties of the proposed test statistic are investigated. A bootstrap algorithm to approximate the null distribution of the test in practice is introduced. The finite sample performance of the method is studied in simulated scenarios. Finally, applications to data on onset for childhood cancer and Parkinson's disease are given. Variance improvements in estimation are discussed and illustrated.

A general framework for subgroup detection via one-step value difference estimation

Recent statistical methodology for precision medicine has focused on either identification of subgroups with enhanced treatment effects or estimating optimal treatment decision rules so that treatment is allocated in a way that maximizes, on average, predefined patient outcomes. Less attention has been given to subgroup testing, which involves evaluation of whether at least a subgroup of the population benefits from an investigative treatment, compared to some control or standard of care. In this work, we propose a general framework for testing for the existence of a subgroup with enhanced treatment effects based on the difference of the estimated value functions under an estimated optimal treatment regime and a fixed regime that assigns everyone to the same treatment. Our proposed test does not require specification of the parametric form of the subgroup and allows heterogeneous treatment effects within the subgroup. The test applies to cases when the outcome of interest is either a time-to-event or a (uncensored) scalar, and is valid at the exceptional law. To demonstrate the empirical performance of the proposed test, we study the type I error and power of the test statistics in simulations and also apply our test to data from a Phase III trial in patients with hematological malignancies.

Bayesian bi-level variable selection for genome-wide survival study

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

Survival of people with untreated TB: effects of time, geography and setting

BACKGROUND: An estimated 40% of people who developed TB in 2021 were not diagnosed or treated. Pre-chemotherapy era data are a rich resource on survival of people with untreated TB. We aimed to identify heterogeneities in these data to inform their more precise use.METHODS: We extracted survival data from pre-chemotherapy era papers reporting TB-specific mortality and/or natural recovery data. We used Bayesian parametric survival analysis to model the survival distribution, stratifying by geography (North America vs. Europe), time (pre-1930 vs. post-1930), and setting (sanitoria vs. non-sanitoria).RESULTS: We found 12 studies with TB-specific mortality data. Ten-year survival was 69% in North America (95% CI 54-81) and 36% in Europe (95% CI 10-71). Only 38% (95% CI 18-63) of non-sanitorium individuals survived to 10 years compared to 69% (95% CI 41-87) of sanitoria/hospitalized patients. There were no significant differences between people diagnosed pre-1930 and post-1930 (5-year survival pre-1930: 65%, 95% CI 44-88 vs. post-1930: 72%, 95% CI 41-94).CONCLUSIONS: Mortality and natural recovery risks vary substantially by location and setting. These heterogeneities need to be considered when using pre-chemotherapy data to make inferences about expected survival of people with undiagnosed TB.

Model uncertainty quantification in Cox regression

We consider covariate selection and the ensuing model uncertainty aspects in the context of Cox regression. The perspective we take is probabilistic, and we handle it within a Bayesian framework. One of the critical elements in variable/model selection is choosing a suitable prior for model parameters. Here, we derive the so-called conventional prior approach and propose a comprehensive implementation that results in an automatic procedure. Our simulation studies and real applications show improvements over existing literature. For the sake of reproducibility but also for its intrinsic interest for practitioners, a web application requiring minimum statistical knowledge implements the proposed approach.

A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies

The hazard ratio (HR) is often reported as the main causal effect when studying survival data. Despite its popularity, the HR suffers from an unclear causal interpretation. As already pointed out in the literature, there is a built-in selection bias in the HR, because similarly to the truncation by death problem, the HR conditions on post-treatment survival. A recently proposed alternative, inspired by the Survivor Average Causal Effect, is the causal HR, defined as the ratio between hazards across treatment groups among the study participants that would have survived regardless of their treatment assignment. We discuss the challenge in identifying the causal HR and present a sensitivity analysis identification approach in randomized controlled trials utilizing a working frailty model. We further extend our framework to adjust for potential confounders using inverse probability of treatment weighting. We present a Cox-based and a flexible non-parametric kernel-based estimation under right censoring. We study the finite-sample properties of the proposed estimation methods through simulations. We illustrate the utility of our framework using two real-data examples.

Predictive impact of human papillomavirus circulating tumor DNA in treatment response monitoring of HPV-associated cancers; a meta-analysis on recurrent event endpoints

BACKGROUND

HPV infection can cause cancer, and standard treatments often result in recurrence. The extent to which liquid biopsy using HPV circulating tumor DNA (HPV ctDNA) can be used as a promising marker for predicting recurrence in HPV-related cancers remains to be validated. Here we conducted a systematic review and meta-analysis to assess its effectiveness in predicting treatment response.

METHODS

We conducted a systematic literature search of online databases, including PubMed, Embase, Scopus, and the Cochrane Library, up to December 2022. The goal was to identify survival studies that evaluated the potential of plasma HPV ctDNA at baseline and end-of-treatment (EoT) in predicting recurrence of related cancers. Hazard ratios were estimated directly from models or extracted from Kaplan-Meier plots.

RESULTS

The pooled effect of HPV ctDNA presence on disease recurrence was estimated to be HR = 7.97 (95% CI: [3.74, 17.01]). Subgroup analysis showed that the risk of recurrence was HR = 2.17 (95% CI: [1.07, 4.41]) for baseline-positive cases and HR = 13.21 (95% CI: [6.62, 26.36]) for EoT-positive cases. Significant associations were also observed between recurrence of oropharyngeal squamous cell carcinoma (HR = 12.25 (95% CI: [2.62, 57.36])) and cervical cancer (HR = 4.60 (95% CI: [2.08, 10.17])) in plasma HPV ctDNA-positive patients.

CONCLUSIONS

The study found that HPV ctDNA detection can predict the rate of relapse or recurrence after treatment, with post-treatment measurement being more effective than baseline assessment. HPV ctDNA could be used as a surrogate or incorporated with other methods for detecting residual disease.

Multi-locus homozygosity promotes actuarial senescence in a wild mammal

Genome-wide homozygosity, caused for example by inbreeding, is expected to have deleterious effects on survival and/or reproduction. Evolutionary theory predicts that any fitness costs are likely to be detected in late life because natural selection will filter out negative impacts on younger individuals with greater reproductive value. Here we infer associations between multi-locus homozygosity (MLH), sex, disease and age-dependent mortality risks using Bayesian analysis of the life histories of wild European badgers Meles meles in a population naturally infected with Mycobacterium bovis (the causative agent of bovine tuberculosis [bTB]). We find important effects of MLH on all parameters of the Gompertz-Makeham mortality hazard function, but particularly in later life. Our findings confirm the predicted association between genomic homozygosity and actuarial senescence. Increased homozygosity is particularly associated with an earlier onset, and greater rates of actuarial senescence, regardless of sex. The association between homozygosity and actuarial senescence is further amplified among badgers putatively infected with bTB. These results recommend further investigation into the ecological and behavioural processes that result in genome-wide homozygosity, and focused work on whether homozygosity is harmful or beneficial during early life-stages.

Fighting to the death: Does hockey fighting in the NHL affect players' longevity?

Hockey is a sport known for its violent nature. Specifically, hockey fights have consistently been an integral part of the National Hockey League. Past research has demonstrated that players are likely to be involved in fighting as a mechanism to gain fan support, generate game momentum or enhance team camaraderie. However, fighting naturally has negative health ramifications. In the current study, we sought to investigate if players' involvement in hockey fights throughout their career was associated with shorter life spans. Previous mortality studies have not distinguished hockey fighting from other aggressive aspects of this sport (e.g., in game physical contact with other players). We conducted an archival study examining the frequency of hockey fighting during the 1957-1971 NHL seasons and player longevity. A Kaplan-Meier survival analysis log-rank method and a subsequent Cox regression accounting for correlates showed no relationship between an elevated number of fights and a reduced lifespan. The lack of effect may indeed demonstrate a negligible influence on long-term health consequences in the context of a generally very physical game. However, with the relatively moderate levels of fighting in the period studied we recommend that the association be explored also in a subsequent era when NHL fighting peaked.

Risk factors influencing chemotherapy compliance and survival of elderly patients with non-small cell lung cancer

Objective

Non-small cell lung cancer (NSCLC) has high degree of malignance and proneness to recurrence and metastasis. The aim of this study was to analyse the risk factors influencing the chemotherapy compliance and survival status of elderly NSCLC patients.

Methods

The clinical data of 110 patients admitted from January 2014 to March 2018 were retrospectively analysed. They were assigned to non-chemotherapy (n=25), partial chemotherapy (n=30) and complete chemotherapy (n=55) groups according to chemotherapy compliance, and followed up until March 2021. Their clinicopathological characteristics were investigated by univariate analysis and then multivariate Cox regression analysis. The survival rates were compared by Kaplan-Meier survival curve and log-rank test.

Results

Among the 110 NSCLC patients, 25 did not receive chemotherapy, 30 underwent partial chemotherapy and 55 received complete chemotherapy. Educational level, pathological tumor-node-metastasis (TNM) stage, pathological type, surgical approach, place of residence, payment mode and chemotherapy stage were independent risk factors influencing the chemotherapy compliance (P<0.05).

Conclusion

Particular attention should be paid to improving the chemotherapy compliance of patients with low educational level, late TNM stage, medical history of squamous cell carcinoma, history of thoracotomy, living in rural areas and no medical insurance, and those in the recurrence period or consolidation period of chemotherapy.

Concordance indices with left-truncated and right-censored data

In the context of time-to-event analysis, a primary objective is to model the risk of experiencing a particular event in relation to a set of observed predictors. The Concordance Index (C-Index) is a statistic frequently used in practice to assess how well such models discriminate between various risk levels in a population. However, the properties of conventional C-Index estimators when applied to left-truncated time-to-event data have not been well studied, despite the fact that left-truncation is commonly encountered in observational studies. We show that the limiting values of the conventional C-Index estimators depend on the underlying distribution of truncation times, which is similar to the situation with right-censoring as discussed in Uno et al. (2011) [On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine 30(10), 1105-1117]. We develop a new C-Index estimator based on inverse probability weighting (IPW) that corrects for this limitation, and we generalize this estimator to settings with left-truncated and right-censored data. The proposed IPW estimators are highly robust to the underlying truncation distribution and often outperform the conventional methods in terms of bias, mean squared error, and coverage probability. We apply these estimators to evaluate a predictive survival model for mortality among patients with end-stage renal disease.

Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models

Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.

Pilonidal sinus disease carcinoma: Survival and recurrence analysis

BACKGROUND

The study aims to determine the survival and recurrence rates of pilonidal sinus disease (PSD) carcinoma.

METHODS

The data were collected retrospectively by searching the worldwide literature for all reports of carcinoma developing on the background of PSD. The results were presented using Kaplan-Meier curves.

RESULTS

Between 1900 and 2022, 140 cases of PSD carcinoma were published in 103 papers, with follow-up data available in 111 cases. Squamous cell carcinoma constituted 94.6% of the cases (n = 105). The disease-specific survival rate was 61.7% for 3 years, 59.8% for 5 years, and 53.2% for 10 years. There was a significant survival difference between stages: 80.0% in stages I and II, 70.8% in stage III, and 47.8% in stage IV (p = 0.01). The 5-year survival in G1-tumors was better than G2 and G3-tumors at 70.5% and 32.0%, respectively (p = 0.002). Recurrence occurred in 46.6% of the patients. The time-to-recurrence in patients treated with curative intention averaged 15.1 months (1-132 months). Local, regional, and distant recurrence was observed in 75.6%, 33.3%, and 28.9% of the recurrent tumors, respectively.

CONCLUSIONS

Pilonidal sinus carcinoma has a worse prognosis than primary cutaneous squamous cell carcinoma. Poor prognostic factors include advanced-stage disease and poor differentiation.

Real-world effectiveness of nivolumab and subsequent therapy in Japanese patients with metastatic renal cell carcinoma (POST-NIVO study): 36-month follow-up results of a clinical chart review

OBJECTIVES

To examine the long-term effectiveness of nivolumab monotherapy and following subsequent therapies for metastatic renal cell carcinoma (mRCC) in Japanese real-world settings.

METHODS

This was a multicenter, retrospective, observational study, with a 36-month follow-up, and conducted in Japanese patients with mRCC who initiated nivolumab monotherapy between 1 Feb 2017 and 31 Oct 2017. Endpoints included overall survival (OS), progression-free survival (PFS), and objective response rate (ORR).

RESULTS

Of the 208 patients, 36.5% received nivolumab monotherapy as second-line, 30.8% as third-line, and 31.7% as fourth- or later-line therapy. By 36 months, 12.0% of patients continued nivolumab monotherapy; 88.0% discontinued, mainly because of disease progression (66.7%). The median (m) OS was not reached irrespective of treatment line, with a 36-month OS rate of 54.3% (second-line, 57.4%; third-line, 52.6%; fourth- or later-line, 52.9%). The ORR was 24.2% and five patients achieved complete response. The OS from first-line therapy was 8.9 years. In the 95 patients receiving therapy after nivolumab, 87.4% received vascular endothelial growth factor receptor-tyrosine kinase inhibitors, with mOS and mPFS of 27.4 and 8.1 months, respectively. Irrespective of treatment line, the mOS was not reached in patients with International Metastatic RCC Database Consortium (IMDC) favorable or intermediate risk at mRCC diagnosis.

CONCLUSIONS

This 36-month real-world follow-up analysis showed a survival benefit of nivolumab monotherapy for patients with mRCC. The long-term effectiveness of sequential therapy from first-line therapy to therapy after nivolumab was also demonstrated. Additionally, nivolumab monotherapy was beneficial for patients with favorable IMDC risk at the time of mRCC diagnosis.

Lameness recovery rates following treatment of dairy cattle with claw horn lameness in the Waikato region of New Zealand

AIMS

To describe the time in days for lame dairy cows to recover after diagnosis and treatment of claw horn lameness, and to investigate whether cure rates differed between farms.

METHODS

Five dairy farms in the Waikato region were conveniently enrolled into a descriptive epidemiological study. Three of these farms had dairy cattle enrolled over two consecutive seasons, while two farms enrolled for one year. Lame cattle diagnosed by the farmers were enrolled into the study if they had a lameness score (LS ≥ 2 on a 0-3 scale) and claw horn lesions. All enrolled animals were treated by a single veterinarian following a consistent methodology, and subsequently assessed for LS at a median frequency of 4 days from enrolment until they were sound (LS = 0). The times (days) taken for animals to become sound and non-lame (LS < 2) were reported for all animals, and Kaplan-Meier survival curves used to present the results. A Cox-proportional hazard model was used to assess if the hazard of soundness was associated with farm, age, breed, lesion, number of limbs involved, and LS at enrolment.

RESULTS

A total of 241 lame cattle with claw horn lesions were enrolled across the five farms. White line disease was the predominant pain-causing lesion in 225 (93%) animals, and blocks were applied to 205 (85%) of enrolled animals. The overall median days from enrolment to becoming sound was 18 (95% CI = 14-21) days, and 7 (95% CI = 7-8) days to become non-lame. A difference in the hazards of lameness cure between farms was identified (p = 0.007), with median days to lameness cure between farms ranging from 11 to 21 days. No associations were identified between age, breed, limb, or LS at enrolment on the lameness cure rates.

CONCLUSIONS

Treatment of claw horn lameness following industry-standard guidelines in dairy cattle on five New Zealand dairy farms resulted in rapid cure, although cure rates differed between farms.

CLINICAL RELEVANCE

Following industry best-practice lameness treatment guidelines, including frequent use of blocks, can result in rapid lameness cure rates in New Zealand dairy cows. This study also suggests that management of lame cattle on pasture can positively benefit their welfare and recovery times. The reported cure rates provide veterinarians with benchmarks on the length of time after which a lame animal should be re-examined, and in the investigation of poor treatment response rates at the herd level.

Turnover intention and retention of newly licensed nurses in their first job: A longitudinal study

AIM

To examine newly licensed nurses' retention in their first job and factors related to their turnover.

BACKGROUND

Newly licensed nurses have a high turnover rate within three years of entering their first job.

INTRODUCTION

The factors affecting newly licensed nurses' turnover should be identified and prevented.

METHODS

This longitudinal study tracked newly licensed nurses for three years. Data were collected online, and nurses could participate four times in the survey. The Kaplan-Meier method was used to estimate survival curves and to present nurses' retention. Cox proportional hazards regression model was used to analyze factors related to turnover.

RESULTS

The estimated survival probabilities in nurses' first job at one, two, and three years were 0.927, 0.778, and 0.686, respectively. Nurses who reported turnover intention within one year in the first survey had significantly lower survival probabilities than those who did not have turnover intention. Turnover was more likely among male nurses. Turnover intention was associated with a significant increased likelihood of nurses leaving their first job.

DISCUSSION

More than one third of newly licensed nurses left their first job within three years of employment. To reduce nurses' turnover, it is necessary to understand and manage turnover intention in advance. Specific steps should be taken to address male nurses' turnover.

CONCLUSIONS

Turnover intention was the most powerful predictor of turnover. Further research should investigate reasons for turnover intention at the beginning of employment, and strategies to prevent turnover should be developed before nurses enter the workforce.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

Newly licensed nurses with turnover intention at the beginning of their career should receive particular attention, and wellness programs should be prepared to promote retention.

Long-term patency rate of the translocated autologous saphenous vein versus prosthetic material in vascular access surgery for haemodialysis and parenteral nutrition

OBJECTIVE

To evaluate the long-term patency rate of the arteriovenous angioaccess (AVA) with interposition of either autologous or prosthetic material as a last option for vascular access in the upper extremity.

METHODS

This is a retrospective chart review study of all patients who received an AVA with autologous saphenous vein (SV Group, n = 38) or prosthetic material (PTFE Group, n = 25) as a conduit from the year 1996 to 2020 in the Radboud University Medical Center (Radboudumc). Data were retrospectively extracted from two prospectively updated local databases for vascular access, one for haemodialysis (HD) and one for parenteral nutrition (PN). When required, the medical records of each patient were used. Data were eventually collected anonymously and analysed in SPSS 25. Kaplan-Meier life-tables were used for the statistical analysis.

RESULTS

Primary patency at 12 and 48 months was 30% and 20% in the SV group and 45% and 14% in the PTFE group. No significant difference was shown in the median primary patency rate (p = 0.715). Secondary patency at 12 and 48 months was 63% and 39% in the SV group and 55% and 19% in the PTFE group. This was considered a significant difference in median secondary patency in favour of the SV with 41.16 ± 17.67 months against 13.77 ± 10.22 months for PTFE (p = 0.032). The incidence of infection was significantly lower in the SV group (p = 0.0002). A Kaplan-Meier curve could not detect a significant difference in secondary patency between the access for haemodialysis and the access for parenteral nutrition. The secondary patency of the SV in parenteral nutrition access, was significantly higher when compared with PTFE (p = 0.004).

CONCLUSION

The SV can be preferred over PTFE when conduit material is needed for long-term vascular access for HD or PN treatment due to its higher secondary patency and lower infection risk.

OS and DFS are affected by different diagnostic methods and hysterectomy procedures in endometrial cancer patients: A single-center retrospective study

PURPOSE

We aimed to evaluate whether hysteroscopy increases the risk of intraperitoneal dissemination or worsens the prognosis of endometrial carcinoma (EC) patients and whether radical hysterectomy (RH) improves overall survival (OS) or disease-free survival (DFS) in patients with stage II to III EC and to investigate the effects of different procedures for identifying EC and the effects of different surgical methods on the OS and DFS of endometrial cancer patients.

METHODS

Four hundred sixty-five women with EC were included in this retrospective study. Log-rank tests and Kaplan-Meier analysis were used for the outcome comparisons of the effects of the EC diagnostic method and different hysterectomy procedures. A Cox proportional hazards model was used for univariate regression analysis.

RESULTS

Among the three procedures for diagnosing EC (diagnostic curettage, hysteroscopy, and hysterectomy), the incidences of fallopian tube and ovarian invasion were not significantly different (p = 0.506 and 0.066, respectively). The diagnostic methods for EC had no significant effect on OS (p = 0.577) or DFS (p = 0.294). In addition, type II RH and type III RH did not improve the prognosis of patients with FIGO stage II and III disease (log-rank p = 0.914 and 0.810 for OS; log-rank p = 0.707 and 0.771 for DFS, respectively).

CONCLUSION

Based on the current study evidence, the use of diagnostic hysteroscopy procedures is safe and does not increase the risk of fallopian tube and ovarian invasion of intraperitoneal dissemination or worsen the prognosis of EC patients. Type II and type III RH did not demonstrate a benefit for stage II-III EC patients.

Concave likelihood-based regression with finite-support response variables

We propose a unified framework for likelihood-based regression modeling when the response variable has finite support. Our work is motivated by the fact that, in practice, observed data are discrete and bounded. The proposed methods assume a model which includes models previously considered for interval-censored variables with log-concave distributions as special cases. The resulting log-likelihood is concave, which we use to establish asymptotic normality of its maximizer as the number of observations n tends to infinity with the number of parameters d fixed, and rates of convergence of L1 -regularized estimators when the true parameter vector is sparse and d and n both tend to infinity withlog ( d ) / n → 0 $\log (d) / n \rightarrow 0$ . We consider an inexact proximal Newton algorithm for computing estimates and give theoretical guarantees for its convergence. The range of possible applications is wide, including but not limited to survival analysis in discrete time, the modeling of outcomes on scored surveys and questionnaires, and, more generally, interval-censored regression. The applicability and usefulness of the proposed methods are illustrated in simulations and data examples.

Use of machine learning models for identification of predictors of survival and tumour recurrence in liver transplant recipients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the leading indications for liver transplantation (LT) however, selection criteria remain controversial. We aimed to identify survival factors and predictors for tumour recurrence using machine learning (ML) methods. We also compared ML models to the Cox regression model.

Methods

Thirty pretransplant donor and recipient general and tumour specific parameters were analysed from 170 patients who underwent orthotopic liver transplantation for HCC between March 2013 and December 2019 at the University Hospital Merkur, Zagreb. Survival rates were calculated using the Kaplan-Meier method and multivariate analysis was performed using the Cox proportional hazards regression model. Data was also processed through Coxnet (a regularized Cox regression model), Random Survival Forest (RSF), Survival Support Vector Machine (SVM) and Survival Gradient Boosting models, which included pre-processing, variable selection, imputation of missing data, training and cross-validation of the models. The cross-validated concordance index (CI) was used as an evaluation metric and to determine the best performing model.

Results

Kaplan-Meier curves for 5-year survival time showed survival probability of 80% for recipient survival and 82% for graft survival. The 5-year HCC recurrence was observed in 19% of patients. The best predictive accuracy was observed in the RSF model with CI of 0.72, followed by the Survival SVM model (CI 0.70). Overall ML models outperform the Cox regression model with respect to their limitations. Random Forest analysis provided several relevant outcome predictors: alpha fetoprotein (AFP), donor C-reactive protein (CRP), recipient age and neutrophil to lymphocyte ratio (NLR). Cox multivariate analysis showed similarities with RSF models in identifying detrimental variables. Some variables such as donor age and number of transarterial chemoembolization treatments (TACE) were pointed out, but these were not influential in our RSF model.

Conclusions

Using ML methods in addition to classical statistical analysis, it is possible to develop sufficient prognostic models, which, compared to established risk scores, could help us quantify survival probability and make changes in organ utilization.

An Immune-Related Gene Signature for Predicting Survival and Immunotherapy Efficacy in Esophageal Adenocarcinoma

BACKGROUND Immune checkpoint inhibitor (ICI) therapy has attracted wide attention in the treatment of malignant tumors. This study was designed to build a prognostic model based on immune-related genes for esophageal adenocarcinoma (EAC). MATERIAL AND METHODS The expression of immune-related differentially-expressed genes (IRDEGs) between EAC and normal samples from The Cancer Genome Atlas database was analyzed. Univariate and multivariate Cox regressions were used to identify the prognostic IRDEGs and construct an immune-related gene signature (IRGS) to predict the overall survival (OS) of EAC patients. Then, the molecular mechanisms and immune characteristics were comprehensively analyzed. RESULTS A total of 111 IRDEGs were obtained from the weighted gene co-expression network analysis. Univariate Cox regression analysis showed that 12 IRDEGs (P<0.05 for all) were linked with OS in the EAC patients. Four genes were used to construct the IRGS based on the multivariate Cox regression analysis. Patients in the high-risk group showed worse OS than those in the low-risk group (P<0.001). A high-risk score was related to DNA replication relevant pathways, an increase in mutation rate, and an increase in activated mast cell infiltration. Patients with high-risk scores had lower tumor immune dysfunction and exclusion scores (P<0.001). CONCLUSIONS IRDEGs may be involved in the progression of EAC. The high-risk group is more suitable for immunotherapy, which may provide a reference value for the treatment of clinical EAC patients. Therefore, it is possible to identify the patients who are better suited for ICI therapy.

Proteomic Profile Distinguishes New Subpopulations of Breast Cancer Patients with Different Survival Outcomes

As a highly heterogeneous disease, breast cancer (BRCA) demonstrates a diverse molecular portrait. The well-established molecular classification (PAM50) relies on gene expression profiling. It insufficiently explains the observed clinical and histopathological diversity of BRCAs. This study aims to demographically and clinically characterize the six BRCA subpopulations (basal, HER2-enriched, and four luminal ones) revealed by their proteomic portraits. GMM-based high variate protein selection combined with PCA/UMAP was used for dimensionality reduction, while the k-means algorithm allowed patient clustering. The statistical analysis (log-rank and Gehan-Wilcoxon tests, hazard ratio HR as the effect size ES) showed significant differences across identified subpopulations in Disease-Specific Survival (p = 0.0160) and Progression-Free Interval (p = 0.0264). Luminal subpopulations vary in prognosis (Disease-Free Interval, p = 0.0277). The A2 subpopulation is of the poorest, comparable to the HER2-enriched subpopulation, prognoses (HR = 1.748, referenced to Luminal B, small ES), while A3 is of the best (HR = 0.250, large ES). Similar to PAM50 subtypes, no substantial dependency on demographic and clinical factors was detected across Luminal subpopulations, as measured by χ2 test and Cramér's V for ES, and ANOVA with appropriate post hocs combined with η2 or Cohen's d-type ES, respectively. Progesterone receptors can serve as the potential A2 biomarker within Luminal patients. Further investigation of molecular differences is required to examine the potential prognostic or clinical applications.

Persistent increase and improved survival of stage I lung cancer based on a large-scale real-world sample of 26,226 cases

BACKGROUND

Lung cancer prevails and induces high mortality around the world. This study provided real-world information on the evolution of clinicopathological profiles and survival outcomes of lung cancer, and provided survival information within stage I subtypes.

METHODS

Patients pathologically confirmed with lung cancer between January 2009 and December 2018 were identified with complete clinicopathological information, molecular testing results, and follow-up data. Shifts in clinical characteristics were evaluated using χ2 tests. Overall survival (OS) was calculated through the Kaplan-Meier method.

RESULTS

A total of 26,226 eligible lung cancer patients were included, among whom 62.55% were male and 52.89% were smokers. Non-smokers and elderly patients took increasingly larger proportions in the whole patient population. The proportion of adenocarcinoma increased from 51.63% to 71.80%, while that of squamous carcinoma decreased from 28.43% to 17.60%. Gene mutations including EGFR (52.14%), KRAS (12.14%), and ALK (8.12%) were observed. Female, younger, non-smoking, adenocarcinoma patients and those with mutated EGFR had better survival prognoses. Importantly, this study validated that early detection of early-stage lung cancer patients had contributed to pronounced survival benefits during the decade. Patients with stage I lung cancer, accounted for an increasingly considerable proportion, increasing from 15.28% to 40.25%, coinciding with the surgery rate increasing from 38.14% to 54.25%. Overall, period survival analyses found that 42.69% of patients survived 5 years, and stage I patients had a 5-year OS of 84.20%. Compared with that in 2009-2013, the prognosis of stage I patients in 2014-2018 was dramatically better, with 5-year OS increasing from 73.26% to 87.68%. Regarding the specific survival benefits among stage I patients, the 5-year survival rates were 95.28%, 93.25%, 82.08%, and 74.50% for stage IA1, IA2, IA3, and IB, respectively, far more promising than previous reports.

CONCLUSIONS

Crucial clinical and pathological changes have been observed in the past decade. Notably, the increased incidence of stage I lung cancer coincided with an improved prognosis, indicating actual benefits of early detection and management of lung cancer.

Tumor Suppressor Properties of Small C-Terminal Domain Phosphatases in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) accounts for 80-90% of kidney cancers worldwide. Small C-terminal domain phosphatases CTDSP1, CTDSP2, and CTDSPL (also known as SCP1, 2, 3) are involved in the regulation of several important pathways associated with carcinogenesis. In various cancer types, these phosphatases may demonstrate either antitumor or oncogenic activity. Tumor-suppressive activity of these phosphatases in kidney cancer has been shown previously, but in general case, the antitumor activity may be dependent on the choice of cell line. In the present work, transfection of the Caki-1 cell line (ccRCC morphologic phenotype) with expression constructs containing the coding regions of these genes resulted in inhibition of cell growth in vitro in the case of CTDSP1 (p < 0.001) and CTDSPL (p < 0.05) but not CTDSP2. The analysis of The Cancer Genome Atlas (TCGA) data showed differential expression of some of CTDSP genes and of their target, RB1. These results were confirmed by quantitative RT-PCR using an independent sample of primary ccRCC tumors (n = 52). We observed CTDSPL downregulation and found a positive correlation of expression for two gene pairs: CTDSP1 and CTDSP2 (rs = 0.76; p < 0.001) and CTDSPL and RB1 (rs = 0.38; p < 0.05). Survival analysis based on TCGA data demonstrated a strong association of lower expression of CTDSP1, CTDSP2, CTDSPL, and RB1 with poor survival of ccRCC patients (p < 0.001). In addition, according to TCGA, CTDSP1, CTDSP2, and RB1 were differently expressed in two subtypes of ccRCC-ccA and ccB, characterized by different survival rates. These results confirm that CTDSP1 and CTDSPL have tumor suppressor properties in ccRCC and reflect their association with the more aggressive ccRCC phenotype.

Real-world survival outcomes of wedge resection versus lobectomy for cT1a/b cN0 cM0 non-small cell lung cancer: a single center retrospective analysis

Background

JCOG0802/WJOG4607L showed benefits in overall survival (OS) of segmentectomy. CALGB 140503 confirmed that sublobar resection was not inferior to lobectomy concerning recurrence-free survival (RFS) but did not provide specific OS and RFS according to the techniques of sublobar resections. Hence, we retrospectively analyze the survival differences between wedge resection and lobectomies for stage IA lung cancer.

Methods

We reviewed the clinical records of patients with clinical stage IA NSCLC over 20 years. The inclusion criteria were: preoperative staging with CT scan and whole body CT/PET; tumor size <20 mm; wedge resections or lobectomies with or without lymph node dissection; NSCLC as the only primary tumor during the follow-up period. We excluded: multiple invasive lung cancer; positive resection margin; preoperative evidence of nodal disease; distant metastasis at presentation; follow-up time <5 years. The reverse Kaplan - Meier method estimated the median OS and PFS and compared them by the log-rank test. The stratified backward stepwise Cox regression model was employed for multivariable survival analyses.

Results

539 patients were identified: 476 (88.3%) lobectomies and 63 (11.7%) wedge resections. The median OS time for the whole cohort was 189.7 months (range: 173.7 - 213.9 months). The 5-year wedge resection and lobectomy OS were 82.2% and 87.0%. The 5-year RFS of wedge resection and lobectomy were 17.8% and 28.9%. The log-rank test showed no significant differences (p = 0.39) between wedge resections and lobectomies regarding OS and RFS (p = 0.23).

Conclusions

Lobectomy and wedge resection are equivalent oncologic treatments for individuals with cN0/cM0 stage IA NSCLC <20 mm. Validating the current findings requires a prospective, randomized comparison between wedge resection and standard lobectomy to establish the prognostic significance of wedge resection.

Effects of Benzodiazepine Exposure on Real-World Clinical Outcomes in Individuals at Clinical High-Risk for Psychosis

Background

Animal models indicate GABAergic dysfunction in the development of psychosis, and that benzodiazepine (BDZ) exposure can prevent the emergence of psychosis-relevant phenotypes. However, whether BDZ exposure influences the risk of psychosis in humans is unknown.

Methods

This observational-cohort study used electronic health record data from 818 individuals at clinical high-risk for psychosis (CHR-P) to investigate whether BDZ exposure (including hypnotics e.g., zopiclone) reduces the risk of developing psychosis and adverse clinical outcomes. Cox proportional-hazards models were employed in both the whole-unmatched sample, and a propensity score matched (PSM) subsample.

Results

567 CHR-P individuals were included after data cleaning (105 BDZ-exposed, 462 BDZ-unexposed). 306 (54%) individuals were male, and the mean age was 22.3 years (SD 4.9). The BDZ-exposed and BDZ-unexposed groups differed on several demographic and clinical characteristics, including psychotic symptom severity. In the whole-unmatched sample, BDZ exposure was associated with increased risk of transition to psychosis (HR=1.61; 95%CI:1.03-2.52; P=0.037), psychiatric hospital admission (HR=1.93; 95%CI:1.13-3.29; P=0.017), home visit (HR=1.64; 95%CI:1.18-2.28; P=0.004), and A&E attendance (HR=1.88; 95%CI:1.31-2.72; P<0.001). However, after controlling for confounding-by-indication through PSM, BDZ exposure did not modulate the risk of any outcomes (all P>0.05). In analysis restricted to antipsychotic-naïve individuals, BDZ exposure reduced the risk of transition to psychosis at trend-level (HR=0.59; 95%CI:0.32-1.08; P=0.089).

Conclusions

BDZ exposure in CHR-P individuals was not associated with a reduction in the risk of psychosis transition or other adverse clinical outcomes. Results in the whole-unmatched sample suggest BDZ prescription may be more likely in CHR-P individuals with higher symptom severity.

TNFR2 promotes pancreatic cancer proliferation, migration, and invasion via the NF-κB signaling pathway

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant disease with low overall survival; chemotherapy and immunotherapy have limited efficacy. Tumor necrosis factor receptor 2 (TNFR2), a type II transmembrane protein, contributes to the development and progression of several tumors. In this study, we elucidated the effect and molecular mechanisms of TNFR2.

METHOD

We used The Cancer Genome Atlas and the Genotype-Tissue Expression database to compare the expression of the TNFR2 gene between normal and malignant pancreatic tissue. Using immunohistochemical staining, we divided the patients into high and low-expression groups, then investigated clinicopathologic data and survival curves of pancreatic cancer patients. We measured TNFR2 protein expression in PANC-1 and ASPC-1 pancreatic cancer cells subjected to TNFR2 small interfering RNA or negative control treatment. We performed proliferation, invasion, and migration assays to study the biological effects of TNFR2 in PDAC. The molecular mechanisms were validated using western blotting.

RESULTS

TNFR2 was more highly expressed in PDAC cells and tissues than controls. Abundant expression of TNFR2 was associated with aggressive clinicopathologic characteristics and poor outcomes. Overexpression of TNFR2 promoted PDAC cell proliferation, migration, and invasion in vitro. Mechanistically, TNFR2 binds to TNF-α and activates the NF-κB signaling pathway.

CONCLUSION

TNFR2 is a prognostic marker that facilitates the proliferation, migration, and invasion of PDAC via the NF-κB signaling pathway. TNFR2 may become a therapeutic target.

SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers

Survival models exist to study relationships between biomarkers and treatment effects. Deep learning-powered survival models supersede the classical Cox proportional hazards (CoxPH) model, but substantial performance drops were observed on high-dimensional features because of irrelevant/redundant information. To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms with the deep survival model. Furthermore, four objective functions were designed to optimize prognostic prediction while regularizing selected feature numbers. When testing on multicenter sets (n = 1,058) of four different cancer types, SwarmDeepSurv was less prone to overfitting and achieved optimal patient risk stratification compared with popular survival modeling algorithms. Strikingly, SwarmDeepSurv selected different features compared with classical feature selection algorithms, including the least absolute shrinkage and selection operator (LASSO), with nearly no feature overlapping across these models. Taken together, SwarmDeepSurv offers an alternative approach to model relationships between radiomics features and survival endpoints, which can further extend to study other input data types including genomics.

Adequate Pelvic Lymph Node Dissection in Radical Cystectomy in the Era of Neoadjuvant Chemotherapy: A Meta-Analysis and Systematic Review

Radical cystectomy (RC) with pelvic lymphadenectomy (PLND) serves as the gold-standard treatment for muscle-invasive bladder cancer (MIBC). Numerous studies have shown that the number of lymph nodes (LN) removed during RC could affect patient prognosis. However, these studies confirmed the association between PLND and survival outcomes prior to the widespread adoption of neoadjuvant chemotherapy (NAC). Consequently, this study aimed to investigate the prognostic role of PLND in patients previously pretreated with NAC. A systematic review and meta-analysis were performed using PubMed, Web of Knowledge, and Scopus databases. The selected studies contained a total of 17,421 participants. The meta-analysis indicated a significant correlation between adequate PLND and overall survival in the non-NAC group. However, a survival benefit was not observed in patients undergoing RC with preoperative systemic therapy, regardless of the LN cut-off thresholds. The pooled HR for ≥10 and ≥15 LN were 0.87 (95% CI 0.75-1.01) and 0.87 (95% CI 0.76-1.00), respectively. The study results suggest that NAC mitigates the therapeutic significance of PLND, as patients pre-treated with NAC no longer gain oncological benefits from more extensive lymphadenectomy. This highlights the analogous roles of NAC and PLND in eradication of micrometastases and in prevention of distal recurrence post-RC.

AUTOSURV: INTERPRETABLE DEEP LEARNING FRAMEWORK FOR CANCER SURVIVAL ANALYSIS INCORPORATING CLINICAL AND MULTI-OMICS DATA

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can reveal the underlying disease mechanisms at the molecular level. In this study, we developed a novel deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian cancer patients. Our model achieved significantly better prognosis prediction than the conventional Cox Proportional Hazard model and other competitive deep learning approaches in various settings. Moreover, an interpretation approach was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that made important contributions to distinguishing predicted high- and low-risk patients. The identified associations were partially supported by previous studies.