The level of serum albumin is associated with renal prognosis and renal function decline in patients with chronic kidney disease

OBJECTIVE

The study's purpose is to explore the link of serum albumin on renal progression in patients with chronic kidney disease (CKD).

METHODS

This study was a secondary analysis of a prospective cohort study in which a total of 954 participants were non-selectively and consecutively collected from the research of CKD-ROUTE in Japan between November 2010 and December 2011. We evaluated the association between baseline ALB and renal prognosis (initiation of dialysis or 50% decline in eGFR from baseline) and renal function decline (annual eGFR decline) using the Cox proportional-hazards and linear regression models, respectively. We performed a number of sensitivity analyses to ensure the validity of the results. In addition, we performed subgroup analyses.

RESULTS

The included patients had a mean age of (66.86 ± 13.41) years, and 522 (69.23%) were male. The mean baseline ALB and eGFR were (3.89 ± 0.59) g/dL and (33.43 ± 17.97) ml/min/1.73 m². The annual decline in eGFR was 2.65 mL/min/1.73 m²/year. 218 (28.9%) individuals experienced renal prognosis during a median follow-up period of 36.0 months. The baseline ALB was inversely linked with renal prognosis (HR = 0.61, 95%CI: 0.45, 0.81) and renal function decline (β = -1.41, 95%CI: -2.11, -0.72) after controlling for covariates. The renal prognosis and ALB had a non-linear connection, with ALB's inflection point occurring at 4.3 g/dL. Effect sizes (HR) were 0.42 (0.32, 0.56) and 6.11 (0.98, 38.22) on the left and right sides of the inflection point, respectively. There was also a non-linear relationship between ALB and renal function decline, and the inflection point of ALB was 4.1 g/dL. The effect sizes(β) on the left and right sides of the inflection point were -2.79(-3.62, -1.96) and 0.02 (-1.97, 1.84), respectively.

CONCLUSION

This study shows a negative and non-linear association between ALB and renal function decline as well as renal prognosis in Japanese CKD patients. When ALB is lower than 4.1 g/dL, ALB decline was closely related to poor renal prognosis and renal function decline. From a therapeutic point of view, reducing the decline in ALB makes sense for delaying CKD progression.

An updated lymphohematopoietic and bladder cancers risk evaluation for occupational and environmental exposures to 1,3-butadiene

EPA designated 1,3-butadiene (BD) as a high priority chemical in December 2019 and is presently performing an evaluation under the Toxic Substances Control Act (TSCA). EPA's cancer dose-response assessment for BD was published in 2002 and was primarily based on a study on workers exposed to BD in the North American synthetic Styrene-Butadiene Rubber (SBR) Industry developed by the University of Alabama at Birmingham (UAB). EPA relied upon a Poisson regression of leukemia mortality data from this cohort (hereinafter referred to as the SBR study) to estimate the cancer potency of BD. At the time, the SBR cohort included more than 15,000 male workers that were followed up through 1991. The SBR cohort has undergone multiple updates over the past two decades. Most recently, Sathiakumar et al. (2021a, b) published an update, with 18 more years of follow up in addition to approximately 5,000 female workers and updated exposure concentration estimates. Recent EPA assessments (e.g., for ethylene oxide, USEPA 2016) based on epidemiological studies use Cox proportional hazards models because they offer better control of the effect of age in cancer development and are less restrictive than Poisson regression models. Here, we develop exposure-response models using standard Cox proportional hazards regression. We explore the relationship between six endpoints (all leukemia, lymphoid leukemia, myeloid leukemia, multiple myeloma, non-Hodgkin's lymphoma, and bladder cancer) and exposures to BD using the most recent exposure metrics and the most recent update of the SBR study. After adjusting for statistically significant covariates, an upper 95% confidence level on the cancer potency based on leukemia derived herein is 0.000086 per ppm, which is approximately 1,000-fold less than EPA's (2002) estimate of 0.08 per ppm and about 10-fold less than TCEQ's (2008) estimate of 0.0011 per ppm.

[Development and validation of nomograms for predicting stroke recurrence after firstepisode ischemic stroke]

OBJECTIVE

To explore the risk factors for recurrence in first-episode ischemic stroke survivors and establish a model for predicting stroke recurrence using a nomogram.

METHODS

We collected the data from a total of 821 first-episode ischemic stroke survivors admitted in the Department of Neurology, West China Hospital, Sichuan University from January, 2010 to December, 2018. R software was used for random sampling of the patients, and 70% of the patients were included in the training set to establish the prediction model and 30% were included in the validation set. Cox proportional risk regression model was used to analyze the factors affecting stroke recurrence, and R software rms package was used to construct the histogram and establish the visual prediction model. C-index and calibration curve were used to evaluate the performance of the model for predicting stroke occurrence.

RESULTS

Among the 821 survivors, the recurrence rate was 16.81% at 3 years and 19.98% at 5 years. Multivariate analysis of the training set by Cox regression model showed that an age over 65 years (HR= 2.596, P=0.024), an age of 45-64 years (HR=2.510, P=0.006), a mRS score beyond 3 (HR=2.284, P=0.004) and a history of coronary heart disease (HR=1.353, P=0.034) were all risk factors for stroke recurrence. The C-indexes of the nomogram for the 3-and 5-year relapse prediction model were 0.640 and 0.671, respectively.

CONCLUSION

Age, mRS score and peripheral vascular disease are the factors affecting stroke recurrence in first-episode ischemic stroke survivors, and the nomogram has a high discrimination and predictive power for predicting ischemic stroke recurrence.

Corrigendum: A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer

[This corrects the article DOI: 10.3389/fonc.2021.554779.].

A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer

Background

Gastric cancer (GC) is a major public health problem worldwide. In recent decades, the treatment of gastric cancer has improved greatly, but basic research and clinical application of gastric cancer remain challenges due to the high heterogeneity. Here, we provide new insights for identifying prognostic models of GC.

Methods

We obtained the gene expression profiles of GSE62254 containing 300 samples for training. GSE15459 and TCGA-STAD for validation, which contain 200 and 375 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules. We performed Lasso regression and Cox regression analyses to identify the most significant five genes to develop a novel prognostic model. And we selected two representative genes within the model for immunohistochemistry staining with 105 GC specimens from our hospital to verify the prediction efficiency. Moreover, we estimated the correlation coefficient between our model and immune infiltration using the CIBERSORT algorithm. The data from GSE15459 and TCGA cohort validated the robustness and predictive accuracy of this prognostic model.

Results

Of the 12 gene modules identified, 1,198 green-yellow module genes were selected for further analysis. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using the Cox proportional hazards regression model. Finally, we constructed a five gene prognostic model: Risk Score = [(-0.7547) * Expression (ARHGAP32)] + [(-0.8272) * Expression (KLF5)] + [1.09 * Expression (MAMLD1)] + [0.5174 * Expression (MATN3)] + [1.66 * Expression (NES)]. The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (p = 6.503e-11). The risk model was also regarded as an independent predictor of prognosis (HR, 1.678, p < 0.001). The observed correlation with immune cells suggested that this risk model could potentially predict immune infiltration.

Conclusion

This study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.

[Expression of HOX transcript antisense intergenic RNA in salivary adenoid cystic carcinoma and its influence on prognosis]

OBJECTIVE

This study aimed to investigate the expression of HOX transcript antisense intergenic RNA (HOTAIR) in salivary adenoid cystic carcinoma (SACC) and explore its influence on prognosis.

METHODS

A total of 86 cases of patients with SACC who underwent surgical resection treatment from March 2007 to March 2014 were selected. In the same period, 45 cases of normal salivary gland tissues were obtained. The expression of HOTAIR was detected via real-time fluorescent quantitative polymerase chain reaction. The patients were followed up after the surgery, and the follow-up deadline was March 31, 2019. The deaths and survival times of patients were recorded. Based on the quartile value of the relative expression level of HOTAIR in SACC patients, the patients were divided into low expression group and high expression group. Kaplan-Meier method and Log-Rank test were used to compare the survival time of the two groups. Taking the age, sex, tumor location, pathological type, tumor diameter, TNM stage, nerve invasion and lymph node metastasis as independent variables, Cox proportional hazards regression model was used to analyze the multiple factors affecting survival time.

RESULTS

The relative expression of HOTAIR in SACC tissue was 2.48±0.22, which was higher than that in normal salivary gland tissue at 1.03±0.13, and the difference was statistically significant (t=39.812, P<0.001). No nerve invasion and lymph node metastasis were observed in these patients compared with those patients with TNM stages Ⅰ or Ⅱ, while the relative expression of HOTAIR in the tissues of patients with TNM stages Ⅲ or Ⅳ, nerve invasion, and lymph node metastasis increased (P<0.05). Kaplan-Meier survival analysis showed that the average survival time and cumulative survival rate in the low expression group were higher than those in the high expression group [(113.32±10.77) months vs. (59.75±6.50) months and 72.73% vs. 39.06%, respectively, P=0.004]. Cox proportional hazard regression analysis showed that nerve invasion, lymph node metastasis, and the high expression of HOTAIR were the inde-pendent risk factors for the prognosis of patients with SACC (HR=3.274, 2.971, and 2.911, respectively, P<0.05).

CONCLUSIONS

HOTAIR was highly expressed in patients with SACC tissues and associated with poor prognosis. It is a risk factor for prog-nosis, and it is expected to be a potential marker for the prognostic assessment of patients with SACC.

A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank

With increasing biobanking efforts connecting electronic health records and national registries to germline genetics, the time-to-event data analysis has attracted increasing attention in the genetics studies of human diseases. In time-to-event data analysis, the Cox proportional hazards (PH) regression model is one of the most used approaches. However, existing methods and tools are not scalable when analyzing a large biobank with hundreds of thousands of samples and endpoints, and they are not accurate when testing low-frequency and rare variants. Here, we propose a scalable and accurate method, SPACox (a saddlepoint approximation implementation based on the Cox PH regression model), that is applicable for genome-wide scale time-to-event data analysis. SPACox requires fitting a Cox PH regression model only once across the genome-wide analysis and then uses a saddlepoint approximation (SPA) to calibrate the test statistics. Simulation studies show that SPACox is 76-252 times faster than other existing alternatives, such as gwasurvivr, 185-511 times faster than the standard Wald test, and more than 6,000 times faster than the Firth correction and can control type I error rates at the genome-wide significance level regardless of minor allele frequencies. Through the analysis of UK Biobank inpatient data of 282,871 white British European ancestry samples, we show that SPACox can efficiently analyze large sample sizes and accurately control type I error rates. We identified 611 loci associated with time-to-event phenotypes of 12 common diseases, of which 38 loci would be missed within a logistic regression framework with a binary phenotype defined as event occurrence status during the follow-up period.

Personalized analysis of breast cancer using sample-specific networks

Breast cancer is a disease with high heterogeneity. Cancer is not usually caused by a single gene, but by multiple genes and their interactions with others and surroundings. Estimating breast cancer-specific gene–gene interaction networks is critical to elucidate the mechanisms of breast cancer from a biological network perspective. In this study, sample-specific gene–gene interaction networks of breast cancer samples were established by using a sample-specific network analysis method based on gene expression profiles. Then, gene–gene interaction networks and pathways related to breast cancer and its subtypes and stages were further identified. The similarity and difference among these subtype-related (and stage-related) networks and pathways were studied, which showed highly specific for subtype Basal-like and Stages IV and V. Finally, gene pairwise interactions associated with breast cancer prognosis were identified by a Cox proportional hazards regression model, and a risk prediction model based on the gene pairs was established, which also performed very well on an independent validation data set. This work will help us to better understand the mechanism underlying the occurrence of breast cancer from the sample-specific network perspective.

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Prevalence, Risk Factors, and Prognosis of Postoperative Complications after Surgery for Hirschsprung Disease

BACKGROUND

Although most of patients do well after surgery for Hirschsprung disease (HSCR), there are complications in some instances that impact social aspects and quality of life. The aim of this study was to explore the prevalence, risk factors, and prognosis of these complications, providing guidance for surgeons and healthcare personnel.

METHODS

A cohort of patients (N = 229) was retrospectively reviewed in the aftermath of surgery for HSCR. All medical data and operative notes were assessed. Early and late postoperative complications were solicited by questionnaire, using logistic regression and the Cox proportional hazards regression model for analysis.

RESULTS

A total of 181 patients qualified for the study. Enterocolitis and soiling/incontinence constituted the most frequent complications, whether early or late in the postoperative period. Risk factors for developing enterocolitis included low weight, low-level IgA, preoperative enterocolitis, and lengthy aganglionic segment in the early term; whereas preoperative enterocolitis and diet control impacted complications emerging later. Risk factors in early soiling/incontinence were low weight, operative age of < 2 months, low IgA level, and lengthy aganglionic segment. Lengthy aganglionic segment, operative age of < 2 months, and toilet training were factors long-term. Prognostic factors included diet control and toilet training.

CONCLUSION

Enterocolitis and soiling/incontinence remain the most frequent complications after surgery for HSCR. Risk factors in early and late postoperative periods differed, with diet control and toilet training contributing favorably to enterocolitis and soiling/incontinence, respectively.

Relative rates not relative risks: addressing a widespread misinterpretation of hazard ratios

The use of the Cox proportional hazards model is ubiquitous in modern medical research. Despite the widespread implementation of this model, the terminology and interpretation that is used to describe the estimate hazard ratio (HR) has become loose and, unfortunately, often incorrect. Although some journals offer guidelines that advise against reporting HRs as relative risks, these guidelines are frequently overlooked. Perhaps due to a lack of understanding, authors continue to interpret the resultant HR as a relative risk-such an interpretation is inappropriate and can be misleading. The HR should be described as a relative rate, not as a relative risk. This article demonstrates that although the direction of the HR can be used to explain the direction of the relative risk, the magnitude of the HR alone cannot be used to explain the magnitude of the relative risk. This article clarifies the relationship between HRs and relative risks in a way that may be better suited for the applied clinical researcher. We also provide a convenient table illustrating the magnitude of relative risk under various values of the HR; the table demonstrates that for a given constant HR, the magnitude of the relative risk can vary substantially. As a take-home message, authors should refrain from using the magnitude of the HR to describe the magnitude of the relative risk. Authors should be strongly encouraged to ascribe accurate interpretations to the statistics derived from fitted Cox proportional hazards regression models.

Effect of depression before breast cancer diagnosis on mortality among postmenopausal women

BACKGROUND

Few previous studies investigating depression before the diagnosis of breast cancer and breast cancer-specific mortality have examined depression measured at more than 1 time point. This study investigated the effect of depression (combining depressive symptoms alone with antidepressant use) measured at 2 time points before the diagnosis of breast cancer on all-cause mortality and breast cancer-specific mortality among older postmenopausal women.

METHODS

A large prospective cohort, the Women's Health Initiative, was used. The study included 3095 women with incident breast cancer who had measures of depressive symptoms and antidepressant use before their diagnosis at the baseline and at year 3. Multivariate Cox proportional hazards regression was used to estimate adjusted hazard ratios (HRs) between depression at the baseline, depression at year 3, and combinations of depression at these time points and all-cause mortality and breast cancer-specific mortality.

RESULTS

Depression at year 3 before a breast cancer diagnosis was associated with higher all-cause mortality after adjustments for multiple covariates (HR, 1.35; 95% confidence interval [CI], 1.02-1.78). There was no statistically significant association of baseline depression and all-cause mortality or breast cancer-specific mortality whether or not depression was also present at year 3. In women with late-stage (regional- or distant-stage) breast cancer, newly developed depression at year 3 was significantly associated with both all-cause mortality (HR, 2.00; 95% CI, 1.13-3.56) and breast cancer-specific mortality (HR, 2.42; 95% CI, 1.24-4.70).

CONCLUSIONS

Women with newly developed depression before the diagnosis of breast cancer had a modestly but significantly increased risk for death from any cause and for death from breast cancer at a late stage. Cancer 2017;123:3107-15. © 2017 American Cancer Society.

How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling

BACKGROUND

Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data.

AIM

The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data.

MATERIALS AND METHODS

This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years.

RESULTS

The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation.

CONCLUSION

Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Nomogram for 5-year relapse-free survival of a patient with advanced gastric cancer after surgery

BACKGROUND

Prognoses vary substantially among patients with advanced gastric cancer following curative surgery. The aim of the current study was to develop and verify the validity of a novel nomogram that predicts the probability of 5-year relapse-free survival (RFS) in patients who underwent curative resection for stage II/III gastric cancer.

MATERIALS AND METHODS

A nomogram to predict 5-year RFS following surgical resection of gastric cancer was constructed based on the data of patients who underwent surgery for primary gastric carcinoma at three institutions in Japan in January 2001-December 2006. Multivariate analysis using a Cox proportional hazards regression model was performed, and the nomogram's predictive accuracy (discrimination) and the agreement between observed outcomes and predictions (calibration) were evaluated by internal validation.

RESULTS

Multivariate analyses revealed that age at operation, depth of tumor, tumor location, lymph node classification, and presence of combined resection were significant prognostic factors for RFS. In the internal validation, discrimination of the developed nomogram for 5-year RFS was superior to that of the American Joint Committee on Cancer TNM classification (concordance indices of 0.80 versus 0.67; P < 0.001). Moreover, calibration appeared to be accurate. Based on these results, we have created free software to more easily predict 5-year RFS.

CONCLUSION

We developed and validated a nomogram to predict 5-year RFS after curative surgery for stage II/III gastric cancer. This tool will be useful for the assessing a patient's individual recurrence risk when considering additional therapy in clinical practice.

Using fractional polynomials to model the effect of cumulative duration of exposure on outcomes: applications to cohort and nested case-control designs

Purpose

Determining the nature of the relationship between cumulative duration of exposure to an agent and the hazard of an adverse outcome is an important issue in environmental and occupational epidemiology, public health and clinical medicine. The Cox proportional hazards regression model can incorporate time-dependent covariates. An important class of continuous time-dependent covariates is that denoting cumulative duration of exposure.

Methods

We used fractional polynomial methods to describe the association between cumulative duration of exposure and adverse outcomes. We applied these methods in a cohort study to examine the relationship between cumulative duration of use of the antiarrhythmic drug amiodarone and the risk of thyroid dysfunction. We also used these methods with a conditional logistic regression model in a nested case-control study to examine the relationship between cumulative duration of use of bisphosphonate medication and the risk of atypical femur fracture.

Results

Using a cohort design and a Cox proportional hazards model, we found a non-linear relationship between cumulative duration of use of the antiarrhythmic drug amiodarone and the risk of thyroid dysfunction. The risk initially increased rapidly with increasing cumulative use. However, as cumulative duration of use increased, the rate of increase in risk attenuated and eventually levelled off. Using a nested case-control design and a conditional logistic regression model, we found evidence of a linear relationship between duration of use of bisphosphonate medication and risk of atypical femur fractures.

Conclusions

Fractional polynomials allow one to model the relationship between cumulative duration of medication use and adverse outcomes.

Prognosis of patients with esophageal carcinoma after radiotherapy: an analysis of 85 cases

AIM: To investigate the efficacy of radiotherapy for esophageal carcinoma and to analyze prognostic factors for esophageal carcinoma after radiotherapy.

METHODS: Eighty-five patients with esophageal carcinoma who underwent comprehensive non-surgical treatment from April 2004 to April 2009 were enrolled in this study. Clinical factors influencing prognosis were evaluated. Survival was analyzed by Kaplan-Meier method. Univariate analysis was completed by using log-rank test (Log-rank test method), and multivariate analysis was performed using Cox proportional hazards regression model.

RESULTS: The follow-up rate was 100%. The 1- and 3-year survival rates were 65.9% and 29.4%, respectively. Univariate analysis showed that age, tumor site, lesion length, clinical stage, treatment mode, radiation techniques, and short-term effect were prognostic factors for esophageal carcinoma. Multivariate Cox regression analysis revealed that treatment mode, clinical stage and short-term effect were independent prognostic factors.

CONCLUSION: The long-term survival of patients with esophageal carcinoma after radiotherapy is poor. Treatment mode, clinical stage and short-term effect are main factors affecting the prognosis of esophageal carcinoma.