Analysis of chronic kidney disease epidemiology in Kazakhstan using nationwide data for 2014-2020 and forecasting future trends of prevalence and mortality for 2030

According to the Global Burden of Disease (GBD) study, chronic kidney disease (CKD) was prevalent in 697.5 million individuals worldwide in 2017. By 2040, it is anticipated that CKD will rank as the fifth most common cause of death. This study aims to examine the epidemiology of CKD in Kazakhstan and to project future trends in CKD prevalence and mortality by 2030. The retrospective analysis was performed on a database acquired from the Unified National Electronic Health System for 703,122 patients with CKD between 2014 and 2020. During the observation period, 444,404 women and 258,718 men were registered with CKD, 459,900 (66%) were Kazakhs and 47% were older than 50. The incidence rate notably decreased: 6365 people per million population (PMP) in 2014 and 4040 people PMP in 2020. The prevalence changed from 10,346 to 38,287 people PMP, and the mortality rate increased dramatically from 279 PMP to 916 PMP. Kazakhstan's central regions, Turkestan and Kyzylorda were identified as the most burdensome ones. The ARIMA model projected 1,504,694 expected prevalent cases in 2030. The predicted mortality climbed from 17,068 cases in 2020 to 37,305 deaths in 2030. By 2030, the prevalence and mortality of CKD will significantly increase, according to the predicted model. A thorough action plan with effective risk factor management, enhanced screening among risk populations, and prompt treatment are required to lessen the burden of disease in Kazakhstan.

RMST-based multiple contrast tests in general factorial designs

Several methods in survival analysis are based on the proportional hazards assumption. However, this assumption is very restrictive and often not justifiable in practice. Therefore, effect estimands that do not rely on the proportional hazards assumption are highly desirable in practical applications. One popular example for this is the restricted mean survival time (RMST). It is defined as the area under the survival curve up to a prespecified time point and, thus, summarizes the survival curve into a meaningful estimand. For two-sample comparisons based on the RMST, previous research found the inflation of the type I error of the asymptotic test for small samples and, therefore, a two-sample permutation test has already been developed. The first goal of the present paper is to further extend the permutation test for general factorial designs and general contrast hypotheses by considering a Wald-type test statistic and its asymptotic behavior. Additionally, a groupwise bootstrap approach is considered. Moreover, when a global test detects a significant difference by comparing the RMSTs of more than two groups, it is of interest which specific RMST differences cause the result. However, global tests do not provide this information. Therefore, multiple tests for the RMST are developed in a second step to infer several null hypotheses simultaneously. Hereby, the asymptotically exact dependence structure between the local test statistics is incorporated to gain more power. Finally, the small sample performance of the proposed global and multiple testing procedures is analyzed in simulations and illustrated in a real data example.

Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy

Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy Registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that prostate-specific antigen is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal prostate-specific antigen history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package JMbayes2.

Impact of presentation timing in metastatic hormone-sensitive prostate cancer: Characterization of patients and identification of prognostic factors

BACKGROUND

The treatment and surveillance of metastatic hormone-sensitive prostate cancer (mHSPC) has evolved since the introduction of several treatment intensification options associated with hormonal blockade and classifications based on the timing of metastatic disease presentation and disease volume. Using a hospital-based registry, we aimed to assess whether these new classifications are applicable to our population, as few studies have demonstrated their prognostic value for overall survival (OS) and time to development of castration-resistant prostate cancer (CRPC), and to establish prognostic factors in our population.

METHODS

A retrospective cohort of mHSPC patients who were attended at an oncology referral hospital in Bogota between 2017 and 2021 were included in this study. The primary and secondary endpoints were OS and time to CRPC. The distribution of outcome measures was estimated using the Kaplan-Meier method. Proportional hazard models were constructed using the Cox regression approach and stratified according to risk factors.

RESULTS

The study cohort included 373 patients. The median castration resistance-free survival was 48 months (CI: 32-73 months), and OS was 43 months (CI: 37-48 months). In multivariate analysis, nodal staging, ECOG status, and surgical castration were independent prognostic factors.

CONCLUSION

In our hospital-based registry, the independent impact of the time of presentation on castration-resistant-free survival or OS could not be demonstrated, nor could the grouping of prognostic categories based on metastatic presentation temporality and volume. Other independent prognostic factors have been proposed.

Robust best linear weighted estimator with missing covariates in survival analysis

Missing data in covariates can result in biased estimates and loss of power to detect associations. We consider Cox regression in which some covariates are subject to missing. The inverse probability weighted approach is often applied to regression analysis with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood-based estimators, but in general are more robust against model misspecification. In this article, we propose a robust best linear weighted estimator for Cox regression with missing covariates. Our proposed estimator is the projection of the simple inverse probability weighted estimator onto the orthogonal complement of the score space based on a working regression model of the observed data. The efficiency gain is from the use of the association between the survival outcome variable and the available covariates, which is the working regression model. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via extensive simulation studies. The methods are applied to a colorectal cancer study to assess the association of the microsatellite instability status with colorectal cancer-specific mortality.

Shape restricted additive hazards models: Monotone, unimodal, and U-shape hazard functions

We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards model is limited in such setting due to the complicated structure of the partial likelihood. Our model defines a quadratic loss function, and its simple structure allows a global Hessian matrix that does not involve parameters. Thus, once the global Hessian matrix is computed, a standard quadratic programming method can be applicable by profiling all possible locations of the mode. However, the quadratic programming method may be inefficient to handle a large global Hessian matrix in the profiling algorithm due to a large dimensionality, where the dimension of the global Hessian matrix and number of hypothetical modes are the same order as the sample size. We propose the quadratic pool adjacent violators algorithm to reduce computational costs. The proposed algorithm is extended to the model with a time-dependent covariate with monotone or U-shape hazard function. In simulation studies, our proposed method improves computational speed compared to the quadratic programming method, with bias and mean square error reductions. We analyze data from a recent cardiovascular study.

Random forests for survival data: which methods work best and under what conditions?

Few systematic comparisons of methods for constructing survival trees and forests exist in the literature. Importantly, when the goal is to predict a survival time or estimate a survival function, the optimal choice of method is unclear. We use an extensive simulation study to systematically investigate various factors that influence survival forest performance - forest construction method, censoring, sample size, distribution of the response, structure of the linear predictor, and presence of correlated or noisy covariates. In particular, we study 11 methods that have recently been proposed in the literature and identify 6 top performers. We find that all the factors that we investigate have significant impact on the methods' relative accuracy of point predictions of survival times and survival function estimates. We use our results to make recommendations for which methods to use in a given context and offer explanations for the observed differences in relative performance.

The diameter of cutaneous melanoma serves as a prognostic indicator for survival among acral-melanoma predominant East Asian patients

BACKGROUND

Tumor staging plays a pivotal role in melanoma management, where the depth of tumor invasion has been traditionally used as the cornerstone of staging. Paradoxically, the tumor diameter has not been integrated into the staging system. The aim of this study is to elucidate the clinical implications and prognostic value of tumor diameter in cutaneous melanoma, with a particular emphasis on the acral-melanoma predominant East Asian population, thus potentially enriching the clinical evaluation and treatment strategies for cutaneous melanoma.

METHODS

From January 1st, 2006 to December 31st, 2022, a total of 352 patients were diagnosed with melanoma in our center. Among them, there were 135 patients diagnosed as cutaneous melanoma who received complete surgical wide excision and regional lymph nodes assessment. The diameter of the tumor, the depth of tumor invasion, lymph node status and patient survival were all collected and analyzed.

RESULTS

The diameter of cutaneous melanoma had a weak positive correlation with tumor thickness (r = 0.26), however, it still had a significant predictive value for patients' overall survival (p = 0.005) and disease free survival (p = 0.023). As for lymph node metastasis prediction, the Breslow thickness had a better predictive value than tumor diameter (p = 0.002 vs. p = 0.565).

CONCLUSIONS

In this study, though with only weak positive correlation to tumor thickness, the tumor diameter of melanoma showed a statistically significant correlation with the patients' overall survival and disease free survival. However, the larger tumor diameter cannot be used as an indicator of high risk of lymph node metastasis.

Risk of chronic kidney disease in 260 patients with lupus nephritis-analysis of a nationwide multicentre cohort with up to 35 years of follow-up

OBJECTIVES

To compare proliferative (PLN) and membranous (MLN) lupus nephritis (LN) regarding clinical and laboratory presentation and long-term outcomes; To investigate predictors of progression to chronic kidney disease (CKD).

METHODS

Multicentre observational study, with retrospective analysis of a prospective cohort, using data from the Rheumatic Diseases Portuguese Registry-Reuma.pt. Patients with biopsy-proven PLN, MLN and mixed LN were included. Cox regression survival analysis was used to investigate predictors of CKD.

RESULTS

260 patients were included. Median follow-up was 8 years (IQR 11; minimum 1, maximum 35 years). MLN patients presented with significantly lower serum creatinine (0.70 (IQR 0.20; minimum 0.50, maximum 1.30) mg/dl vs 0.80 (IQR 0.31; minimum 0.26, maximum 2.60) in PLN, p= 0.003). Proteinuria levels did not differ between groups (p= 0.641). Levels of complement were reduced in PLN but nearly normal in MLN patients, and there were fewer patients with positive anti-dsDNA antibodies in the MLN group (p< 0.001). One year after the beginning of treatment, 62% of the patients achieved EULAR/ERA-EDTA complete response, with further 5% achieving partial response. Patients with lower proteinuria at diagnosis were more likely to achieve a complete renal response at one year, however, proteinuria at diagnosis or at one year did not predict long term CKD. Estimated glomerular filtration rate (eGFR) ≤75 mL/min/1.73 m2 at one year was the strongest predictor of progression to CKD (HR 23 [95% CI 8-62], p< 0.001). Other possible predictors included the use of azathioprine for induction of remission, older age at diagnosis and male sex.

CONCLUSION

Proteinuria levels did not predict LN histologic class in our cohort. eGFR cutoff of 75 mL/min/1.73 m2 after one year of treatment was strongly predictive of progression to CKD.

Derivative survival analyses: Analysis methods to derive survival outcomes for the remainder patient cohort without individual patient data

It is not uncommon for industry-sponsored randomized controlled trials to publish survival curves/data for the overall patient cohort("A+B") and for a favorable subgroup ("A") pre-specified or post hoc, but not the survival curves/data for the remainder cohort("B"). Consequently, following regulatory approval of the intervention treatment for the overall patient population if the primary endpoint is met, it is common for cancer patients representing the remainder cohort (B) to be treated as per the results of the overall cohort (A+B). To overcome this important issue in clinical decision-making, this study aimed to identify methods to accurately derive the survival curves and/or hazard ratio (95% confidence interval) for the remainder cohort (B), utilizing published curves and hazard ratios (95% confidence intervals) of the overall (A+B) and favorable subgroup (A) cohorts. The analysis methods (method I and method II) presented here, termed "derivative survival analyses," enable accurate assessment of survival outcomes in the remainder cohort without individual patient data.

Rolling the DICE (Design, Interpret, Compute, Estimate): Interactive Learning of Biostatistics With Simulations

Despite the increasing relevance of statistics in health sciences, teaching styles in higher education are remarkably similar across disciplines: lectures covering the theory and methods, followed by application and computer exercises in given data sets. This often leads to challenges for students in comprehending fundamental statistical concepts essential for medical research. To address these challenges, we propose an engaging learning approach-DICE (design, interpret, compute, estimate)-aimed at enhancing the learning experience of statistics in public health and epidemiology. In introducing DICE, we guide readers through a practical example. Students will work in small groups to plan, generate, analyze, interpret, and communicate their own scientific investigation with simulations. With a focus on fundamental statistical concepts such as sampling variability, error probabilities, and the construction of statistical models, DICE offers a promising approach to learning how to combine substantive medical knowledge and statistical concepts. The materials in this paper, including the computer code, can be readily used as a hands-on tool for both teachers and students.

Risk of Malaria Following Untreated Subpatent Plasmodium falciparum Infections: Results Over 4 Years From a Cohort in a High-Transmission Area in Western Kenya

BACKGROUND

People with suspected malaria may harbor Plasmodium falciparum undetected by rapid diagnostic test (RDT). The impact of these subpatent infections on the risk of developing clinical malaria is not fully understood.

METHODS

We analyzed subpatent P. falciparum infections using a longitudinal cohort in a high-transmission site in Kenya. Weighted Kaplan-Meier models estimated the risk difference (RD) for clinical malaria during the 60 days following a symptomatic subpatent infection. Stratum-specific estimates by age and transmission season assessed modification.

RESULTS

Over 54 months, we observed 1128 symptomatic RDT-negative suspected malaria episodes, of which 400 (35.5%) harbored subpatent P. falciparum. Overall, the 60-day risk of developing clinical malaria was low following all episodes (8.6% [95% confidence interval, 6.7%-10.4%]). In the low-transmission season, the risk of clinical malaria was slightly higher in those with subpatent infection, whereas the opposite was true in the high-transmission season (low-transmission season RD, 2.3% [95% confidence interval, .4%-4.2%]; high-transmission season RD, -4.8% [-9.5% to -.05%]).

CONCLUSIONS

The risk of developing clinical malaria among people with undetected subpatent infections is low. A slightly elevated risk in the low-transmission season may merit alternate management, but RDTs identify clinically relevant infections in the high-transmission season.

Self-Rated Health in Middle Age and Risk of Hospitalizations and Death: Recurrent Event Analysis of the ARIC Study

BACKGROUND

Self-rated health is a simple measure that may identify individuals who are at a higher risk for hospitalization or death.

OBJECTIVE

To quantify the association between a single measure of self-rated health and future risk of recurrent hospitalizations or death.

PARTICIPANTS

Atherosclerosis Risk in Communities (ARIC) study, a community-based prospective cohort study of middle-aged men and women with follow-up beginning from 1987 to 1989.

MAIN MEASURES

We quantified the associations between initial self-rated health with risk of recurrent hospitalizations and of death using a recurrent events survival model that allowed for dependency between the rates of hospitalization and hazards of death, adjusted for demographic and clinical factors.

KEY RESULTS

Of the 14,937 ARIC cohort individuals with available self-rated health and covariate information, 34% of individuals reported "excellent" health, 47% "good," 16% "fair," and 3% "poor" at study baseline. After a median follow-up of 27.7 years, 1955 (39%), 3569 (51%), 1626 (67%), and 402 (83%) individuals with "excellent," "good," "fair," and "poor" health, respectively, had died. After adjusting for demographic factors and medical history, a less favorable self-rated health status was associated with increased rates of hospitalization and death. As compared to those reporting "excellent" health, adults with "good," "fair," and "poor" health had 1.22 (1.07 to 1.40), 2.01 (1.63 to 2.47), and 3.13 (2.39 to 4.09) times the rate of hospitalizations, respectively. The hazards of death also increased with worsening categories of self-rated health, with "good," "fair," and "poor" health individuals experiencing 1.30 (1.12 to 1.51), 2.15 (1.71 to 2.69), and 3.40 (2.54 to 4.56) times the hazard of death compared to "excellent," respectively.

CONCLUSIONS

Even after adjusting for demographic and clinical factors, having a less favorable response on a single measure of self-rated health taken in middle age is a potent marker of future hospitalizations and death.

Methods for non-proportional hazards in clinical trials: A systematic review

For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic literature search to identify statistical methods and software appropriate under non-proportional hazard. Our literature search identified 907 abstracts, out of which we included 211 articles, mostly methodological ones. Review articles and applications were less frequently identified. The articles discuss effect measures, effect estimation and regression approaches, hypothesis tests, and sample size calculation approaches, which are often tailored to specific non-proportional hazard situations. Using a unified notation, we provide an overview of methods available. Furthermore, we derive some guidance from the identified articles.

Prognostic factors and treatment outcomes in patients with pleomorphic rhabdomyosarcoma: a population-based cohort study

BACKGROUND

Pleomorphic rhabdomyosarcoma is a rare sarcoma in adults. The clinical characteristics, outcomes and prognostic factors associated with pleomorphic rhabdomyosarcoma remain unclear.

METHODS

We retrospectively analyzed data from the Bone and Soft Tissue Tumor Registry of Japan, and enrolled patients with pleomorphic rhabdomyosarcoma. Disease-specific overall survival, local recurrence-free survival and distant metastasis-free survival were estimated using the Kaplan-Meier method; Cox regression model was used to identify prognostic factors.

RESULTS

In total, 182 patients with pleomorphic rhabdomyosarcoma were included. Median age was 63 (range 20-95) years. The lower extremity (48%) was the most frequent tumor origin site, while head and neck were rare (4%). A total of 43 patients (24%) had distant or regional nodal metastases at first presentation. In all cases, the 2-year and 5-year survival rates were 66.3% and 54.1%, respectively. Distant metastasis was a significant poor prognostic factor (Hazard ratio 6.65; 95% confidence intervals, 3.00-14.75, P < 0.0001), with median survival of such patients being 9.4 (95% confidence intervals: 5.3-12.2) months. In 134 localized cases, the 2-year and 5-year survival rates were 91.5% and 68.3%, respectively. Large tumor size and older age were associated with poorer prognosis. Through data from localized and locally curative cases extracted and adjusted by propensity score matching, we found that perioperative chemotherapy did not improve disease-specific overall survival, distant metastasis-free survival or local recurrence-free survival.

CONCLUSIONS

Clinical characteristics and outcomes of pleomorphic rhabdomyosarcoma are similar to those of other high-grade soft tissue sarcomas. Pleomorphic rhabdomyosarcoma may be less chemosensitive, and a strategy other than the standard cytotoxic chemotherapy is required to improve its prognosis.

Localized Myxofibrosarcoma: A Retrospective Analysis of Primary Therapy and Prognostic Factors in 134 Patients in a Single Institution

BACKGROUND

Primary therapy of localized myxofibrosarcoma (MFS) remains controversial. Primary resection is complicated by a high rate of local recurrence, and the refractoriness to non-surgical treatment results in a higher risk of metastasis. The aim of the present study was to contribute the findings of a single sarcoma-specialized center and encourage investigating new treatment options.

PATIENTS AND METHODS

We analyzed 134 patients treated with localized MFS in our center regarding prognostic factors defining overall survival, local recurrence, and metastasis. We focused on multimodal treatment of localized MFS: surgery, radiation, chemotherapy, hyperthermia, and isolated limb perfusion.

RESULTS

The 5-year OS was 74.9%. From a total of 134 patients: 74 (55.2%) stayed disease free, 48 (35.8%) had a local recurrence (LR), and 23 (17.2%) developed a distant metastasis (DM). The 5-year LR-free survival (LRFS) and DM-free survival (DMFS) were 66.1% and 80.8%, respectively. Older age, tumor size (cT) cT ≥ 2, non-extremity localization, and distant metastasis were adverse predictive factors for OS. Performing an incision biopsy, surgery in a sarcoma-center, wide local excision or compartment-oriented excision, negative margins, and radiotherapy were positive predictive factors for LR. Tumor size cT ≥ 3 was a negative predictive factor for DM. Grading was a negative predictive factor for LR (G ≥ 2) and for DM (G3) in the multivariable analysis.

CONCLUSION

Adjuvant radiation had a positive impact on LRFS in all localized tumor stages, even in cT1 tumors. Chemotherapy did not have a significant impact on DMFS, regardless of tumor stage. Our findings indicate that myxofibrosarcoma may be a chemotherapy-resistant entity and a much closer monitoring is required, in case of neoadjuvant treatment.

Radioactive iodine therapy improves overall survival outcome in oncocytic carcinoma of the thyroid by reducing death risks from noncancer causes: A competing risk analysis of 4641 patients

BACKGROUND

Oncocytic carcinoma of the thyroid (OCA) is an independent type of thyroid cancer. Radioactive iodine (RAI) therapy was frequently administered to OCA patients, but its contribution to improving survival is indefinite.

METHODS

4641 OCA patients from 2000 to 2018 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Cox proportional hazard regression and competing risk analysis were applied.

RESULTS

Tumor size, SEER stage, primary surgery, and neck dissection were prognostic factors for cancer-specific survival. The results of competing risk analysis demonstrated that age over 55 years dramatically increased non-OCA death risks. Treatments that improve non-OCA survival (including total thyroidectomy, RAI therapy, and systemic therapy) should be recommended in OCA patients older than 55 years of age. Neck lymphadenectomy should not be recommended for OCA, since the metastatic lymph node ratio was low (about 3%).

CONCLUSIONS

RAI therapy can improve survival in OCA by reducing noncancer death risks.

Bioinformatics-driven identification of prognostic biomarkers in kidney renal clear cell carcinoma

Renal cell carcinoma (RCC), particularly the clear cell subtype (ccRCC), poses a significant global health concern due to its increasing prevalence and resistance to conventional therapies. Early detection of ccRCC remains challenging, resulting in poor patient survival rates. In this study, we employed a bioinformatic approach to identify potential prognostic biomarkers for kidney renal clear cell carcinoma (KIRC). By analyzing RNA sequencing data from the TCGA-KIRC project, differentially expressed genes (DEGs) associated with ccRCC were identified. Pathway analysis utilizing the Qiagen Ingenuity Pathway Analysis (IPA) tool elucidated key pathways and genes involved in ccRCC dysregulation. Prognostic value assessment was conducted through survival analysis, including Cox univariate proportional hazards (PH) modeling and Kaplan-Meier plotting. This analysis unveiled several promising biomarkers, such as MMP9, PIK3R6, IFNG, and PGF, exhibiting significant associations with overall survival and relapse-free survival in ccRCC patients. Cox multivariate PH analysis, considering gene expression and age at diagnosis, further confirmed the prognostic potential of MMP9, IFNG, and PGF genes. These findings enhance our understanding of ccRCC and provide valuable insights into potential prognostic biomarkers that can aid healthcare professionals in risk stratification and treatment decision-making. The study also establishes a foundation for future research, validation, and clinical translation of the identified prognostic biomarkers, paving the way for personalized approaches in the management of KIRC.

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

BACKGROUND AND HYPOTHESIS

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 real-world clinical outcomes in individuals at clinical high risk for psychosis (CHR-P) is unknown.

STUDY DESIGN

This observational cohort study used electronic health record data from CHR-P individuals to investigate whether BDZ exposure (including hypnotics, eg, 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.

STUDY RESULTS

567 CHR-P individuals (306 male, mean[±SD] age = 22.3[±4.9] years) were included after data cleaning. The BDZ-exposed (n = 105) and BDZ-unexposed (n = 462) 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 = .037), psychiatric hospital admission (HR = 1.93; 95% CI: 1.13-3.29; P = .017), home visit (HR = 1.64; 95% CI: 1.18-2.28; P = .004), and Accident and Emergency department attendance (HR = 1.88; 95% CI: 1.31-2.72; P < .001). However, after controlling for confounding-by-indication through PSM, BDZ exposure did not modulate the risk of any outcomes (all P > .05). In an analysis restricted to antipsychotic-naïve individuals, BDZ exposure reduced the risk of transition to psychosis numerically, although this was not statistically significant (HR = 0.59; 95% CI: 0.32-1.08; P = .089).

CONCLUSIONS

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

Transformer-based time-to-event prediction for chronic kidney disease deterioration

OBJECTIVE

Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture for electronic health records.

MATERIALS AND METHODS

The input for STRAFE is a sequence of visits with SNOMED-CT codes in OMOP-CDM format. A Transformer-based architecture was developed to calculate probabilities of the occurrence of the event in each of 48 months. Performance was evaluated using a real-world claims dataset of over 130 000 individuals with stage 3 chronic kidney disease (CKD).

RESULTS

STRAFE showed improved mean absolute error (MAE) compared to other time-to-event algorithms in predicting the time to deterioration to stage 5 CKD. Additionally, STRAFE showed an improved area under the receiver operating curve compared to binary outcome algorithms. We show that STRAFE predictions can improve the positive predictive value of high-risk patients by 3-fold. Finally, we suggest a novel visualization approach to predictions on a per-patient basis.

DISCUSSION

Time-to-event predictions are the most appropriate approach for clinical predictions. Our deep-learning algorithm outperformed not only other time-to-event prediction algorithms but also fixed-time algorithms, possibly due to its ability to train on censored data. We demonstrated possible clinical usage by identifying the highest-risk patients.

CONCLUSIONS

The ability to accurately identify patients at high risk and prioritize their needs can result in improved health outcomes, reduced costs, and more efficient use of resources.

The Construction of a Multi-Gene Risk Model for Colon Cancer Prognosis and Drug Treatments Prediction

In clinical practice, colon cancer is a prevalent malignant tumor of the digestive system, characterized by a complex and progressive process involving multiple genes and molecular pathways. Historically, research efforts have primarily focused on investigating individual genes; however, our current study aims to explore the collective impact of multiple genes on colon cancer and to identify potential therapeutic targets associated with these genes. For this research, we acquired the gene expression profiles and RNA sequencing data of colon cancer from TCGA. Subsequently, we conducted differential gene expression analysis using R, followed by GO and KEGG pathway enrichment analyses. To construct a protein-protein interaction (PPI) network, we selected survival-related genes using the log-rank test and single-factor Cox regression analysis. Additionally, we performed LASSO regression analysis, immune infiltration analysis, mutation analysis, and cMAP analysis, as well as an investigation into ferroptosis. Our differential expression and survival analyses identified 47 hub genes, and subsequent LASSO regression analysis refined the focus to 23 key genes. These genes are closely linked to cancer metastasis, proliferation, apoptosis, cell cycle regulation, signal transduction, cancer microenvironment, immunotherapy, and neurodevelopment. Overall, the hub genes discovered in our study are pivotal in colon cancer and are anticipated to serve as important biological markers for the diagnosis and treatment of the disease.

A 22-year single institution review of 119 cases of salivary duct carcinoma

Objective

Salivary duct carcinoma (SDC) is a rare and aggressive salivary gland malignancy. Herein, we present the largest single-institution review of SDC to date.

Methods

This is a retrospective cohort study of all histologically confirmed cases of SDC seen at our institution from January 1, 2002, to August 1, 2022. Patient demographics, treatment, histological characteristics, tumor staging, and outcomes were extracted from the electronic medical record. Kaplan-Meier and Cox regression survival analyses were performed.

Results

This study included 119 patients with a mean age of 66.2 years. Most primary tumors arose from the parotid gland (72.3%), and 23.5% were noted to be carcinoma ex-pleomorphic adenoma. 57.1% of patients presented with regional lymph node metastasis, whereas 23.5% presented with distant disease. Kaplan-Meier analysis demonstrated a 62.4% 5-year overall survival (OS) and a 69.0% 5-year disease-specific survival (DSS). Univariate analyses indicated that presence of regional lymph node disease (p<.001), distant metastasis (p<.001), perineural invasion (p = .027), and lymphovascular invasion (p = .018) were predictive of decreased OS and DSS. Trastuzumab administration was not associated with survival in HER-2-positive patients receiving chemotherapy. Multivariate analyses demonstrated that presence of nodal disease (HR 30.337, 95% CI 2.782-330.851, p = .005) and carcinoma ex pleomorphic adenoma (HR 5.54, 95% CI 1.024-29.933, p = .047) were associated with decreased OS.

Conclusion

Our patients had more favorable survival rates compared to prior studies, which may be due to lower incidence of nodal disease. Factors associated with worse survival included nodal and distant metastases, perineural invasion, lymphovascular invasion, and tumor size.

Level of Evidence

Level 3.

To study the survival analysis and recurrence pattern in women treated for breast cancer: Retrospective study, a tertiary cancer center experience from Sub-Himalayan Region of India

BACKGROUND

Breast cancer is a leading health concern in India, comprising 25% of female cancers with significant mortality. This study was conducted at the Cancer Research Institute in the Northern Sub-Himalayan region of India from 2016 to 2021, evaluated 674 breast cancer cases to analyze factors that influence recurrence.

METHODOLOGY

Retrospective clinical audit assessing patients' survival outcomes using Kaplan-Meier curves and Cox proportional hazard regression. Factors including age, molecular subtype, TNM staging, and treatment modalities were evaluated.

RESULTS

Notable findings include a high occurrence of breast cancer in young patients (24.48% ≤ 40 years) and varying recurrence rates among molecular subtypes with human epidermal growth factor receptor 2 neu-enriched (25.24%) and triple‑negative breast cancer (22.58%) being the most common. Advanced T and N stages, neoadjuvant chemotherapy, and the number of nodes dissected showed significant associations with higher recurrence rates.

CONCLUSION

This study sheds light on survival and recurrence patterns in Northern Sub-Himalayan breast cancer patients, emphasizing the need for tailored treatment strategies, comprehensive follow-up care, with improved understanding of regional outcomes. These findings contribute valuable insights for optimizing patient care and improving survival rates in this region.

Phase 1/2 multicenter trial of acalabrutinib in Chinese patients with relapsed/refractory mantle cell lymphoma

Acalabrutinib studies have limited Asian participation. This phase 1/2 study (NCT03932331) assessed acalabrutinib in Chinese patients with relapsed/refractory (R/R) mantle cell lymphoma (MCL). Primary endpoint was blinded independent central review (BICR)-assessed overall response rate (ORR). Overall, 34 patients were enrolled. Most patients were men (88%); median age was 63 years and 59% had ≥3 prior treatments. Median treatment duration was 14 months (range, 1-24). Any-grade adverse events (AEs) and grade ≥3 AEs occurred in 85.3% and 44.1% of patients, respectively. AEs causing treatment discontinuation were aplastic anemia, thrombocytopenia, and gastrointestinal infection (n = 1 each). Fatal AEs occurred in 2 patients (aplastic anemia and multiple organ dysfunction syndrome [n = 1 each]). BICR-assessed ORR was 82.4% (95% confidence interval [CI]: 65.5, 93.2); 12 (35.3%) patients achieved complete response. Estimated 12-month OS was 84.5% (95% CI: 66.6, 93.3). Acalabrutinib yielded tolerable safety and high response rates in Chinese patients with R/R MCL.

Clinical outcomes for previously treated patients with advanced biliary tract cancer: a meta-analysis

Aim: A systematic review and meta-analysis were performed to evaluate the efficacy of treatments for previously treated advanced biliary tract cancer (BTC) patients. Materials & methods: Databases were searched for studies evaluating treatments for advanced (unresectable and/or metastatic) BTC patients who progressed on prior therapy. Pooled estimates of objective response rate (ORR), median overall survival (OS) and median progression-free survival (PFS) were calculated using random effects meta-analysis. Results: Across 31 studies evaluating chemotherapy or targeted treatment regimens in an unselected advanced BTC patient population, pooled ORR was 6.9%, median OS was 6.6 months and median PFS was 3.2 months. Conclusion: The efficacy of conventional treatments for previously treated advanced BTC patients is poor and could be improved by novel therapies.

Survival and safety analysis of COVID-19 vaccine in Chinese patients with non-small cell lung cancer

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 disease (COVID-19) has caused a worldwide challenging and threatening pandemic. We aimed to assess the safety and efficacy of the COVID-19 vaccines in Non-Small Cell Lung Cancer (NSCLC) patients.

METHODS

Patient self-reported adverse events related to vaccines were recorded by follow-up through a uniform questionnaire. Survival analysis was performed by Kaplan-Meier method. A multivariate analysis was performed by the Cox proportional hazard regression model to determine the effect of each variable on the survival of lung cancer patients.

RESULTS

A total of 860 patients with NSCLC on treatment were enrolled. Mean age was 57 years in patients with early stage group and 62 years in advanced stage group. The vaccination rate was 71.11% for early-stage patients and 19.48% for advanced-stage patients; most of them (86.5%) received the COVID-19 inactivated virus (Vero cell) vaccine (Coronavac; Sinovac). The most common systemic adverse reaction was weakness. The main reason for vaccine refusal in those unvaccinated patients was concern about the safety of vaccination in the presence of a tumor and undergoing treatment (56.9% and 53.4%). The 1-year disease-free survival (DFS) rate was 100% for vaccinated and 97.4% for unvaccinated early-stage patients. Then we compared the progression-free survival (PFS) of vaccinated (median PFS 9.0 months) and unvaccinated (median PFS 7.0 months) advanced stage patients (p = 0.815). Advanced NSCLC patients continued to be divided into groups receiving radio-chemotherapy, immunotherapy, and targeted therapy, with no statistical difference in PFS between the groups (p > 0.05). The median overall survival (OS) of vaccinated patients was 20.5 months, and that of unvaccinated patients was 19.0 months (p = 0.478) in advanced NSCLC patients.

CONCLUSIONS

COVID-19 vaccination is safe for Chinese NSCLC patients actively receiving different antitumor treatments without increasing the incidence of adverse reactions, and vaccination does not affect cancer patient survival.

Estimating Vitamin K Antagonist Anticoagulation Benefit in People With Atrial Fibrillation Accounting for Competing Risks: Evidence From 12 Randomized Trials

BACKGROUND

Patients with atrial fibrillation have a high mortality rate that is only partially attributable to vascular outcomes. The competing risk of death may affect the expected anticoagulant benefit. We determined if competing risks materially affect the guideline-endorsed estimate of anticoagulant benefit.

METHODS

We conducted a secondary analysis of 12 randomized controlled trials that randomized patients with atrial fibrillation to vitamin K antagonists (VKAs) or either placebo or antiplatelets. For each participant, we estimated the absolute risk reduction (ARR) of VKAs to prevent stroke or systemic embolism using 2 methods-first using a guideline-endorsed model (CHA2DS2-VASc) and then again using a competing risk model that uses the same inputs as CHA2DS2-VASc but accounts for the competing risk of death and allows for nonlinear growth in benefit. We compared the absolute and relative differences in estimated benefit and whether the differences varied by life expectancy.

RESULTS

A total of 7933 participants (median age, 73 years, 36% women) had a median life expectancy of 8 years (interquartile range, 6-12), determined by comorbidity-adjusted life tables and 43% were randomized to VKAs. The CHA2DS2-VASc model estimated a larger ARR than the competing risk model (median ARR at 3 years, 6.9% [interquartile range, 4.7%-10.0%] versus 5.2% [interquartile range, 3.5%-7.4%]; P<0.001). ARR differences varied by life expectancies: for those with life expectancies in the highest decile, 3-year ARR difference (CHA2DS2-VASc model - competing risk model 3-year risk) was -1.3% (95% CI, -1.3% to -1.2%); for those with life expectancies in the lowest decile, 3-year ARR difference was 4.7% (95% CI, 4.5%-5.0%).

CONCLUSIONS

VKA anticoagulants were exceptionally effective at reducing stroke risk. However, VKA benefits were misestimated with CHA2DS2-VASc, which does not account for the competing risk of death nor decelerating treatment benefit over time. Overestimation was most pronounced when life expectancy was low and when the benefit was estimated over a multiyear horizon.

Prognostic biomarker CPEB3 and its associations with immune infiltration in clear cell renal cell carcinoma

The role and underlying mechanism of cytoplasmic polyadenylation element binding protein 3 (CPEB3) in clear cell renal cell carcinoma [ccRCC progression remain poorly characterized. The present study was designed to evaluate the role of CPEB3 in ccRCC and its clinical associations. The overall response rate of first-line therapies (ICIs combined with VEGFR-TKIs or ICI combination) for ccRCC] is 42.0-59.3%, so a number of patients with ccRCC do not benefit from these therapies. To avoid immunosurveillance and immune killing, tumor cells decrease immunogenicity and recruit immunosuppressive cells such as regulatory T cells (Tregs). Tregs inhibit the development of anti-tumor immunity, thereby hindering immune surveillance of cancer and preventing effective anti-tumor immune response in tumor-bearing hosts. The present study analyzed clinical specimens from patients ccRCC and then examined the role of CPEB3 in ccRCC via bioinformatics analysis. CPEB3 expression was significantly reduced in ccRCC compared with normal tissue and low CPEB3 expression was associated with poor overall survival. Moreover, CPEB3 expression was an independent predictor of survival. CPEB3 expression was positively associated with immune biomarkers [CD274, programmed cell death 1 ligand 2, Hepatitis a virus cellular receptor 2, Chemokine (C-X-C motif) ligand (CXCL)9, CXCL10, Inducible T cell costimulatory, CD40, CD80 and CD38] that improve the outcome of anti-tumor immune responses. CPEB3 expression in ccRCC also affected the status of 24 types of infiltrating immune cell, of which Tregs were the most significantly negatively correlated cell type. CPEB3 may serve as a prognostic biomarker in ccRCC and its mechanism may be related to the regulation of Tregs.

Weight calibration in the joint modelling of medical cost and mortality

Joint modelling of longitudinal and time-to-event data is a method that recognizes the dependency between the two data types, and combines the two outcomes into a single model, which leads to more precise estimates. These models are applicable when individuals are followed over a period of time, generally to monitor the progression of a disease or a medical condition, and also when longitudinal covariates are available. Medical cost datasets are often also available in longitudinal scenarios, but these datasets usually arise from a complex sampling design rather than simple random sampling and such complex sampling design needs to be accounted for in the statistical analysis. Ignoring the sampling mechanism can lead to misleading conclusions. This article proposes a novel approach to the joint modelling of complex data by combining survey calibration with standard joint modelling. This is achieved by incorporating a new set of equations to calibrate the sampling weights for the survival model in a joint model setting. The proposed method is applied to data on anti-dementia medication costs and mortality in people with diagnosed dementia in New Zealand.

Incidence and survival for childhood cancer by endorsed non-stage prognostic indicators in Australia

BACKGROUND

An international expert panel recently recommended 15 'non-stage prognostic indicators' (NSPIs) across eight childhood cancers, classified as essential or additional, for collection in population-based cancer registries. We aimed to describe the incidence distribution and survival of each of these NSPIs.

PROCEDURES

Cases were extracted from the Australian Childhood Cancer Registry. The study cohort (n = 4187) comprised all children aged under 15 years diagnosed with an eligible cancer between 2010 and 2018, with follow-up until 31 December 2020. NSPI data were collected directly from each patient's medical records. Differences in 5-year relative survival were assessed using multivariable flexible parametric models, adjusted for sex and age group at diagnosis.

RESULTS

The availability of data varied, exceeding 85% for all essential NSPIs apart from histologic subtype for Wilms tumours (69%) and lineage for acute lymphoblastic leukaemia (78%). Information on additional NSPIs tended to be recorded less often, particularly cytogenetic subtype for non-alveolar rhabdomyosarcoma (28%) and astrocytoma (4%). Eight NSPIs exhibited a significant difference in survival, with the largest disparity occurring among children with astrocytoma according to tumour grade (5-year relative survival of 18% for grade IV disease compared with 99% for grade I disease; p < .001).

CONCLUSIONS

Our findings demonstrate that most of the recommended NSPIs can be retrieved from medical records in Australia in recent years, allowing the capability of assessing survival within patient subgroups of clinical interest. Reporting of NSPI data has the capability to inform local and global understanding of population-level disparities in childhood cancer survival.

Haploidentical and matched unrelated donor allogeneic hematopoietic stem cell transplantation offer similar survival outcomes for acute leukemia

BACKGROUND

Haploidentical hematopoietic stem cell transplantation (haplo-HSCT) has emerged as an effective approach for acute leukemia, primarily due to the inherent difficulty in finding human leukocyte antigen-matched unrelated donors (MUD). Nevertheless, it remains uncertain whether haplo-HSCT and MUD-HSCT can provide comparable outcomes in patients with acute leukemia.

AIMS

This study aimed to assess the overall survival (OS) and leukemia-free survival (LFS) outcomes between the MUD-HSCT and haplo-HSCT groups.

METHODS AND RESULTS

This retrospective analysis encompassed adult patients with acute leukemia undergoing the initial allo-HSCT. Among these 85 patients, we stratified 33 patients into the MUD-HSCT group and 52 to the haplo-HSCT group. The primary outcomes were OS and LFS. The median OS was not reached in the haplo-HSCT group, while it reached 29.8 months in patients undergoing MUD-HSCT (p = .211). Likewise, the median LFS periods were 52.6 months in the haplo-HSCT group and 12.7 months in the MUD-HSCT group (p = .212). Importantly, neither the OS nor LFS showed substantial differences between the MUD-HSCT and haplo-HSCT groups. Furthermore, univariate analyses revealed that haplo-HSCT did not demonstrate a significantly higher risk of worse LFS (hazard ratio [HR], 0.69; 95% confidence interval [CI], 0.38-1.25; p = .216) or OS (HR, 0.67; 95% CI, 0.36-1.26; p = .214) than MUD-HSCT. Notably, a high European Group for Blood and Marrow Transplantation risk score (HR, 1.44; 95% CI, 1.10-1.87; p = .007) and non-complete remission (HR, 2.48; 95% CI, 1.17-5.23; p = .017) were significantly correlated with worse OS.

CONCLUSION

Haplo-HSCT may serve as an alternative to MUD-HSCT for the treatment of acute leukemia, offering similar survival outcomes.

Detecting potential outliers in longitudinal data with time-dependent covariates

BACKGROUND

Outliers can influence regression model parameters and change the direction of the estimated effect, over-estimating or under-estimating the strength of the association between a response variable and an exposure of interest. Identifying visit-level outliers from longitudinal data with continuous time-dependent covariates is important when the distribution of such variable is highly skewed.

OBJECTIVES

The primary objective was to identify potential outliers at follow-up visits using interquartile range (IQR) statistic and assess their influence on estimated Cox regression parameters.

METHODS

Study was motivated by a large TEDDY dietary longitudinal and time-to-event data with a continuous time-varying vitamin B12 intake as the exposure of interest and development of Islet Autoimmunity (IA) as the response variable. An IQR algorithm was applied to the TEDDY dataset to detect potential outliers at each visit. To assess the impact of detected outliers, data were analyzed using the extended time-dependent Cox model with robust sandwich estimator. Partial residual diagnostic plots were examined for highly influential outliers.

RESULTS

Extreme vitamin B12 observations that were cases of IA had a stronger influence on the Cox regression model than non-cases. Identified outliers changed the direction of hazard ratios, standard errors, or the strength of association with the risk of developing IA.

CONCLUSION

At the exploratory data analysis stage, the IQR algorithm can be used as a data quality control tool to identify potential outliers at the visit level, which can be further investigated.

Estimating the Effect of Engagement in Community-Based Going-Out Program on Incidence Disability in Older Adults

OBJECTIVES

Although going out has been reported to be associated with the incidence of disability, few studies have investigated the effect of community-based programs to promote going out on the incidence of disability. This study aimed to estimate the effects of a program fostering going-out on the incidence of disability in community-dwelling older adults.

DESIGN

Longitudinal, observational study.

SETTING AND PARTICIPANTS

Nonengaged (n = 1086) and engaged older adults (n = 1086) enrolled in the National Center for Geriatrics and Gerontology Study of Geriatric Syndrome by using a one-to-one nearest neighbor propensity score-matching scheme.

METHODS

After the baseline assessments, participants in the community-based going-out program received a specialized physical activity tracker, monitored their daily physical activity, and received personalized feedback on going out to community facilities with a system for reading the device for 12 months. Disability onset was defined as a new case of long-term care under the public insurance certification in Japan within 48 months of program completion. The absolute risk reduction and the number needed to treat for the incidence of disability were calculated for the nonengaged and engaged groups. Cox proportional hazard regression analysis, using inverse probability weighting was used to obtain the hazard ratio.

RESULTS

Disabilities occurred in 112 individuals in the matched nonengaged group and 51 individuals in the engaged group. The absolute risk reduction was 5.67% (95% CI 3.46%-7.88%). The number needed to treat was 18 (95% CI 13-29). The hazard ratio, with the nonengaged group as the reference, was 0.49 (95% CI 0.36-0.67).

CONCLUSIONS AND IMPLICATIONS

This longitudinal observational study suggested that a community-based program could prevent 1 disability in every 18 participants. This program does not require a professional instructor, only the distribution of devices and system installation, and it could be beneficial as a population-based approach to preventing disabilities.

Optimizing dynamic predictions from joint models using super learning

Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.

Actuarial Analysis of Survival after Breast Cancer Diagnosis among Lithuanian Females

Breast cancer is the most common cause of mortality due to cancer for women both in Lithuania and worldwide. The chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis and other factors. One way to estimate survival is to construct a Kaplan-Meier estimate for each factor value separately. However, in cases when it is impossible to observe a large number of patients (for example, in the case of countries with lower numbers of inhabitants), dividing the data into subsets, say, by stage at diagnosis, may lead to results where some subsets contain too few data, thus causing the results of a Kaplan-Meier (or any other) method to become statistically incredible. The problem may become even more acute if researchers want to use more risk factors, such as stage at diagnosis, sex, place of living, treatment method, etc. Alternatively, Cox models can be used to analyse survival data with covariates, and they do not require the data to be divided into subsets according to chosen risks factors (hazards). We estimate the chances of survival for up to 5 years after a breast cancer diagnosis for Lithuanian females during the period of 1995-2016. Firstly, we construct Kaplan-Meier estimates for each stage separately; then, we apply a (stratified) Cox model using stage, circumstance of diagnosis, and year of diagnosis as (potential) hazards. Some directions of further research are provided in the last section of the paper.

Tumour Size and Overall Survival in a Cohort of Patients with Unifocal Glioblastoma: A Uni- and Multivariable Prognostic Modelling and Resampling Study

Published models inconsistently associate glioblastoma size with overall survival (OS). This study aimed to investigate the prognostic effect of tumour size in a large cohort of patients diagnosed with GBM and interrogate how sample size and non-linear transformations may impact on the likelihood of finding a prognostic effect. In total, 279 patients with a IDH-wildtype unifocal WHO grade 4 GBM between 2014 and 2020 from a retrospective cohort were included. Uni-/multivariable association between core volume, whole volume (CV and WV), and diameter with OS was assessed with (1) Cox proportional hazard models +/- log transformation and (2) resampling with 1,000,000 repetitions and varying sample size to identify the percentage of models, which showed a significant effect of tumour size. Models adjusted for operation type and a diameter model adjusted for all clinical variables remained significant (p = 0.03). Multivariable resampling increased the significant effects (p < 0.05) of all size variables as sample size increased. Log transformation also had a large effect on the chances of a prognostic effect of WV. For models adjusted for operation type, 19.5% of WV vs. 26.3% log-WV (n = 50) and 69.9% WV and 89.9% log-WV (n = 279) were significant. In this large well-curated cohort, multivariable modelling and resampling suggest tumour volume is prognostic at larger sample sizes and with log transformation for WV.

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

BACKGROUND AND AIMS

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

DESIGN

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

SETTING

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

PARTICIPANTS

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

MEASUREMENTS

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

FINDINGS

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

CONCLUSIONS

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

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

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

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

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

A comparison of radiographically determined periapical healing and tooth survival outcomes of root canal (re)treatment performed in two care pathways within the United Kingdom Armed Forces

AIMS

To compare radiographic periapical healing and tooth survival outcomes of root canal (re)treatment performed within two care pathways (Routine Dental Care and Referred Treatment Pathway), in the United Kingdom Armed Forces (UKAF), and determine the effects of endodontic complexity on outcomes.

METHODOLOGY

This retrospective cohort study included 1466 teeth in 1252 personnel who received root canal (re)treatment between 2015 and 2020. General Dental Practitioners treated 661 teeth (573 patients) (Routine cohort), whilst Dentists with a Special Interest treated 805 teeth (678 patients) (Referred cohort). The latter group were graduates of an MSc programme in Endodontics with 4-8 years of postgraduation experience. Case complexity was retrospectively determined for each tooth using the endodontic component of Restorative Index of Treatment Need (RIOTN) guidelines. Periapical healing was determined using loose radiographic criteria. The data were analysed using chi-square tests, univariate logistic regression and Cox proportional hazards models.

RESULTS

A significantly (p < 0.0001) larger proportion of cases of low complexity had undergone root canal treatment within the Routine versus Referred cohort. The odds of periapical healing was significantly higher within the Referred versus Routine cohort, regardless of analyses using pooled (OR = 1.17; 95% CI: 1.11, 1.22) or moderate complexity (OR = 4.71; 95% CI: 2.73, 8.11) data. Within the Routine cohort, anterior teeth had higher odds of periapical healing than posterior teeth (OR = 1.13; 95% CI: 1.04, 1.22). The 60-month cumulative tooth survival was lower (p = 0.03) in the Routine (90.5%) than the Referred (96.0%) cohort. Within the Routine cohort, the hazard of tooth loss was higher amongst posterior teeth (HR = 4.03; 95% CI: 1.92, 8.45) but lower if posterior teeth had cast restorations (HR = 0.36; 95% CI: 0.19, 0.70). For the Referred cohort, posterior teeth restored with cast restoration (vs not) had significantly lower risk of tooth loss (HR = 0.21; 95% CI: 0.08, 0.55).

CONCLUSIONS

For UKAF patients, root canal (re)treatment provided within the Referred pathway was significantly more likely to achieve periapical healing and better tooth survival than those provided within the Routine pathway. Posterior teeth restored with an indirect restoration had a higher proportion of tooth survival. This study supported the utility of the endodontic component of RIOTN for assessing case complexity.

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

Introduction

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

Methods

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

Results

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

Conclusion

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

Wetness severity increases abrupt shifts in ecosystem functioning in arid savannas

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

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

BACKGROUND AND AIMS

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

DESIGN, SETTINGS, PARTICIPANTS AND MEASUREMENT

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

FINDINGS

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

CONCLUSIONS

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

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

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