Survival Analysis of Patients with Heart Failure: Implications of Time-Varying Regression Effects in Modeling Mortality

Background: Several models have been designed to predict survival of patients with heart failure. These, while available and widely used for both stratifying and deciding upon different treatment options on the individual level, have several limitations. Specifically, some clinical variables that may influence prognosis may have an influence that change over time. Statistical models that include such characteristic may help in evaluating prognosis. The aim of the present study was to analyze and quantify the impact of modeling heart failure survival allowing for covariates with time-varying effects known to be independent predictors of overall mortality in this clinical setting. Methodology: Survival data from an inception cohort of five hundred patients diagnosed with heart failure functional class III and IV between 2002 and 2004 and followed-up to 2006 were analyzed by using the proportional hazards Cox model and variations of the Cox's model and also of the Aalen's additive model. Principal Findings: One-hundred and eighty eight (188) patients died during follow-up. For patients under study, age, serum sodium, hemoglobin, serum creatinine, and left ventricular ejection fraction were significantly associated with mortality. Evidence of time-varying effect was suggested for the last three. Both high hemoglobin and high LV ejection fraction were associated with a reduced risk of dying with a stronger initial effect. High creatinine, associated with an increased risk of dying, also presented an initial stronger effect. The impact of age and sodium were constant over time. Conclusions: The current study points to the importance of evaluating covariates with time-varying effects in heart failure models. The analysis performed suggests that variations of Cox and Aalen models constitute a valuable tool for identifying these variables. The implementation of covariates with time-varying effects into heart failure prognostication models may reduce bias and increase the specificity of such models.

Survival analysis and iniquities in older Brazilians: a six year follow up survey in São Paulo, Brazil

Maria Lucia Lebrão is the Coordinator of the SABE study. Jair LF Santos and Yeda AO Duarte receive support from National Council of Research (CNPq). The SABE study is supported by The São Paulo Research Foundation (FAPESP).

Survival, Causes of Death, and Prognostic Factors in Systemic Sclerosis: Analysis of 947 Brazilian Patients

Objective. To analyze survival, prognostic factors, and causes of death in a large cohort of patients with systemic sclerosis (SSc). Methods. From 1991 to 2010, 947 patients with SSc were treated at 2 referral university centers in Brazil. Causes of death were considered SSc-related and non-SSc-related. Multiple logistic regression analysis was used to identify prognostic factors. Survival at 5 and 10 years was estimated using the Kaplan-Meier method. Results. One hundred sixty-eight patients died during the followup. Among the 110 deaths considered related to SSc, there was predominance of lung (48.1%) and heart (24.5%) involvement. Most of the 58 deaths not related to SSc were caused by infection, cardiovascular or cerebrovascular disease, and cancer. Male sex, modified Rodnan skin score (mRSS) > 20, osteoarticular involvement, lung involvement, and renal crisis were the main prognostic factors associated to death. Overall survival rate was 90% for 5 years and 84% for 10 years. Patients presented worse prognosis if they had diffuse SSc (85% vs 92% at 5 yrs, respectively, and 77% vs 87% at 10 yrs, compared to limited SSc), male sex (77% vs 90% at 5 yrs and 64% vs 86% at 10 yrs, compared to female sex), and mRSS > 20 (83% vs 90% at 5 yrs and 66% vs 86% at 10 yrs, compared to mRSS <20). Conclusion. Survival was worse in male patients with diffuse SSc, and lung and heart involvement represented the main causes of death in this South American series of patients with SSc. (First Release Aug 15 2012; J Rheumatol 2012;39:1971-8; doi:10.3899/jrheum.111582)

A Bayesian analysis of the Conway-Maxwell-Poisson cure rate model

The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals may be present in the population from which the data is taken. In our approach the number of competing causes of the event of interest follows the Conway-Maxwell-Poisson distribution which generalizes the Poisson distribution. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model selection and an illustration with a real data set are considered.

Smad2 and Smad6 as predictors of overall survival in oral squamous cell carcinoma patients

Abstract Background To test if the expression of Smad1-8 mRNAs were predictive of survival in patients with oral squamous cell carcinoma (SCC). Patients and Methods We analyzed, prospectively, the expression of Smad1-8, by means of Ribonuclease Protection Assay in 48 primary, operable, oral SCC. In addition, 21 larynx, 10 oropharynx and 4 hypopharynx SCC and 65 matched adjacent mucosa, available for study, were also included. For survival analysis, patients were categorized as positive or negative for each Smad, according to median mRNA expression. We also performed real-time quantitative PCR (QRTPCR) to asses the pattern of TGFβ1, TGFβ2, TGFβ3 in oral SCC. Results Our results showed that Smad2 and Smad6 mRNA expression were both associated with survival in Oral SCC patients. Cox Multivariate analysis revealed that Smad6 positivity and Smad2 negativity were both predictive of good prognosis for oral SCC patients, independent of lymph nodal status (P = 0.003 and P = 0.029, respectively). In addition, simultaneously Smad2- and Smad6+ oral SCC group of patients did not reach median overall survival (mOS) whereas the mOS of Smad2+/Smad6- subgroup was 11.6 months (P = 0.004, univariate analysis). Regarding to TGFβ isoforms, we found that Smad2 mRNA and TGFβ1 mRNA were inversely correlated (p = 0.05, R = -0.33), and that seven of the eight TGFβ1+ patients were Smad2-. In larynx SCC, Smad7- patients did not reach mOS whereas mOS of Smad7+ patients were only 7.0 months (P = 0.04). No other correlations were found among Smad expression, clinico-pathological characteristics and survival in oral, larynx, hypopharynx, oropharynx or the entire head and neck SCC population. Conclusion Smad6 together with Smad2 may be prognostic factors, independent of nodal status in oral SCC after curative resection. The underlying mechanism which involves aberrant TGFβ signaling should be better clarified in the future.

Beta 1 integrin predicts survival in breast cancer: a clinicopathological and immunohistochemical study

Abstract Background The main focus of several studies concerned with cancer progression and metastasis is to analyze the mechanisms that allow cancer cells to interact and quickly adapt with their environment. Integrins, a family of transmembrane glycoproteins, play a major role in invasive and metastatic processes. Integrins are involved in cell adhesion in both cell-extracellular matrix and cell-cell interactions, and particularly, β1 integrin is involved in proliferation and differentiation of cells in the development of epithelial tissues. This work aimed to investigate the putative role of β1 integrin expression on survival and metastasis in patients with breast invasive ductal carcinoma (IDC). In addition, we compared the expression of β1 integrin in patients with ductal carcinoma in situ (DCIS). Methods Through tissue microarray (TMA) slides containing 225 samples of IDC and 67 samples of DCIS, β1 integrin expression was related with several immunohistochemical markers and clinicopathologic features of prognostic significance. Results β1 integrin was overexpressed in 32.8% of IDC. In IDC, β1 integrin was related with HER-2 (p = 0.019) and VEGF (p = 0.011) expression and it had a significant relationship with metastasis and death (p = 0.001 and p = 0.05, respectively). Kaplan-Meier survival analysis showed that the overexpression of this protein is very significant (p = 0.002) in specific survival (number of months between diagnosis and death caused by the disease). There were no correlation between IDC and DCIS (p = 0.559) regarding β1 integrin expression. Conclusions Considering that the expression of β1 integrin in breast cancer remains controversial, specially its relation with survival of patients, our findings provide further evidence that β1 integrin can be a marker of poor prognosis in breast cancer. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/6652215267393871

The relationship between lymphatic vascular density and vascular endothelial growth factor A (VEGF-A) expression with clinical-pathological features and survival in pancreatic adenocarcinomas

Abstract: Background Pancreatic cancer is a rare tumor with an extremely low survival rate. Its known risk factors include the chronic use of tobacco and excessive alcohol consumption and the presence of chronic inflammatory diseases, such as pancreatitis and type 2 diabetes. Angiogenesis and lymphangiogenesis, which have been the focus of recent research, are considered prognostic factors for cancer development. Knowing the angiogenic and lymphangiogenic profiles of a tumor may provide new insights for designing treatments according to the different properties of the tumor. The aim of this study was to evaluate the density of blood and lymphatic vessels, and the expression of VEGF-A, in pancreatic adenocarcinomas, as well as the relationship between blood and lymphatic vascular density and the prognostically important clinical-pathological features of pancreatic tumors. Methods Paraffin blocks containing tumor samples from 100 patients who were diagnosed with pancreatic cancer between 1990 and 2010 were used to construct a tissue microarray. VEGF expression was assessed in these samples by immunohistochemistry. To assess the lymphatic and vascular properties of the tumors, 63 cases that contained sufficient material were sectioned routinely. The sections were then stained with the D2-40 antibody to identify the lymphatic vessels and with a CD34 antibody to identify the blood vessels. The vessels were counted individually with the Leica Application Suite v4 program. All statistical analyses were performed using SPSS 18.0 (Chicago, IL, USA) software, and p values ≤ 0.05 were considered significant. Results In the Cox regression analysis, advanced age (p=0.03) and a history of type 2 diabetes (p=0.014) or chronic pancreatitis (p=0.02) were shown to be prognostic factors for pancreatic cancer. Blood vessel density (BVD) had no relationship with clinical-pathological features or death. Lymphatic vessel density (LVD) was inversely correlated with death (p=0.002), and by Kaplan-Meyer survival analysis, we found a significant association between low LVD (p=0.021), VEGF expression (p=0.023) and low patient survival. Conclusions Pancreatic carcinogenesis is related to a history of chronic inflammatory processes, such as type 2 diabetes and chronic pancreatitis. In pancreatic cancer development, lymphangiogenesis can be considered an early event that enables the dissemination of metastases. VEGF expression and low LVD can be considered as poor prognostic factors as tumors with this profile are fast growing and highly aggressive. Virtual slides. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/5113892881028514

A general threshold stress hybrid hazard model for lifetime data

In this paper we propose a hybrid hazard regression model with threshold stress which includes the proportional hazards and the accelerated failure time models as particular cases. To express the behavior of lifetimes the generalized-gamma distribution is assumed and an inverse power law model with a threshold stress is considered. For parameter estimation we develop a sampling-based posterior inference procedure based on Markov Chain Monte Carlo techniques. We assume proper but vague priors for the parameters of interest. A simulation study investigates the frequentist properties of the proposed estimators obtained under the assumption of vague priors. Further, some discussions on model selection criteria are given. The methodology is illustrated on simulated and real lifetime data set.

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.

The polysurvival model with long-term survivors

Long-term survival models have historically been considered for analyzing time-to-event data with long-term survivors fraction. However, situations in which a fraction (1 - p) of systems is subject to failure from independent competing causes of failure, while the remaining proportion p is cured or has not presented the event of interest during the time period of the study, have not been fully considered in the literature. In order to accommodate such situations, we present in this paper a new long-term survival model. Maximum likelihood estimation procedure is discussed as well as interval estimation and hypothesis tests. A real dataset illustrates the methodology.

The log-exponentiated generalized gamma regression model for censored data

For the first time, we introduce a generalized form of the exponentiated generalized gamma distribution [Cordeiro et al. The exponentiated generalized gamma distribution with application to lifetime data, J. Statist. Comput. Simul. 81 (2011), pp. 827-842.] that is the baseline for the log-exponentiated generalized gamma regression model. The new distribution can accommodate increasing, decreasing, bathtub- and unimodal-shaped hazard functions. A second advantage is that it includes classical distributions reported in the lifetime literature as special cases. We obtain explicit expressions for the moments of the baseline distribution of the new regression model. The proposed model can be applied to censored data since it includes as sub-models several widely known regression models. It therefore can be used more effectively in the analysis of survival data. We obtain maximum likelihood estimates for the model parameters by considering censored data. We show that our extended regression model is very useful by means of two applications to real data.

A log-linear regression model for the beta-Birnbaum-Saunders distribution with censored data

The beta-Birnbaum-Saunders (Cordeiro and Lemonte, 2011) and Birnbaum-Saunders (Birnbaum and Saunders, 1969a) distributions have been used quite effectively to model failure times for materials subject to fatigue and lifetime data. We define the log-beta-Birnbaum-Saunders distribution by the logarithm of the beta-Birnbaum-Saunders distribution. Explicit expressions for its generating function and moments are derived. We propose a new log-beta-Birnbaum-Saunders regression model that can be applied to censored data and be used more effectively in survival analysis. We obtain the maximum likelihood estimates of the model parameters for censored data and investigate influence diagnostics. The new location-scale regression model is modified for the possibility that long-term survivors may be presented in the data. Its usefulness is illustrated by means of two real data sets. (C) 2011 Elsevier B.V. All rights reserved.

The Real Importance of Pre-Existing Comorbidities on Long-Term Mortality after Acute Kidney Injury

Background: The causes of death on long-term mortality after acute kidney injury (AKI) have not been well studied. The purpose of the study was to evaluate the role of comorbidities and the causes of death on the long-term mortality after AKI. Methodology/Principal Findings: We retrospectively studied 507 patients who experienced AKI in 2005-2006 and were discharged free from dialysis. In June 2008 (median: 21 months after AKI), we found that 193 (38%) patients had died. This mortality is much higher than the mortality of the population of Sao Paulo City, even after adjustment for age. A multiple survival analysis was performed using Cox proportional hazards regression model and showed that death was associated with Khan's index indicating high risk [adjusted hazard ratio 2.54 (1.38-4.66)], chronic liver disease [1.93 (1.15-3.22)], admission to non-surgical ward [1.85 (1.30-2.61)] and a second AKI episode during the same hospitalization [1.74 (1.12-2.71)]. The AKI severity evaluated either by the worst stage reached during AKI (P=0.20) or by the need for dialysis (P=0.12) was not associated with death. The causes of death were identified by a death certificate in 85% of the non-survivors. Among those who died from circulatory system diseases (the main cause of death), 59% had already suffered from hypertension, 34% from diabetes, 47% from heart failure, 38% from coronary disease, and 66% had a glomerular filtration rate <60 previous to the AKI episode. Among those who died from neoplasms, 79% already had the disease previously. Conclusions: Among AKI survivors who were discharged free from dialysis the increased long-term mortality was associated with their pre-existing chronic conditions and not with the severity of the AKI episode. These findings suggest that these survivors should have a medical follow-up after hospital discharge and that all efforts should be made to control their comorbidities.

A 15-year study on the treatment of undernourished children at a nutrition rehabilitation centre (CREN), Brazil

Objective: To build a life table and determine the factors related to the time of treatment of undernourished children at a nutrition rehabilitation centre (CREN), Sao Paulo, Brazil. Design: Nutritional status was assessed from weight-for-age, height-for-age and BMI-for-age Z-scores, while neuropsychomotor development was classified according to the milestones of childhood development. Life tables, Kaplan-Meier survival curves and Cox multiple regression models were employed in data analysis. Setting: CREN (Centre of Nutritional Recovery and Education), Sao Paulo, Brazil. Subjects: Undernourished children (n 228) from the southern slums of Sao Paulo who had received treatment at CREN under a day-hospital regime between the years 1994 and 2009. Results: The Kaplan-Meier curves of survival analysis showed statistically significant differences in the periods of treatment at CREN between children presenting different degrees of neuropsychomotor development (log-rank = 6.621; P = 0.037). Estimates based on the multivariate Cox model revealed that children aged >= 24 months at the time of admission exhibited a lower probability of nutritional rehabilitation (hazard ratio (HR) = 0.49; P = 0.046) at the end of the period compared with infants aged up 12 months. Children presenting slow development were better rehabilitated in comparison with those exhibiting adequate evolution (HR = 4.48; P = 0.023). No significant effects of sex, degree of undernutrition or birth weight on the probability of nutritional rehabilitation were found. Conclusions: Age and neuropsychomotor developmental status at the time of admission to CREN are critical factors in determining the duration of treatment.

The impact of psychiatric diagnosis on treatment adherence and duration among victimized children and adolescents in Sao Paulo, Brazil

OBJECTIVE: Despite the high prevalence of substance abuse and mood disorders among victimized children and adolescents, few studies have investigated the association of these disorders with treatment adherence, represented by numbers of visits per month and treatment duration. We aimed to investigate the effects of substance abuse and mood disorders on treatment adherence and duration in a special programfor victimized children in Sao Paulo, Brazil. METHODS: A total of 351 participants were evaluated for psychiatric disorders and classified into one of five groups: mood disorders alone; substance abuse disorders alone; mood and substance abuse disorders; other psychiatric disorders; no psychiatric disorders. The associations between diagnostic classification and adherence to treatment and the duration of program participation were tested with logistic regression and survival analysis, respectively. RESULTS: Children with mood disorders alone had the highest rate of adherence (79.5%); those with substance abuse disorders alone had the lowest (40%); and those with both disorders had an intermediate rate of adherence (50%). Those with other psychiatric disorders and no psychiatric disorders also had high rates of adherence (75.6% and 72.9%, respectively). Living with family significantly increased adherence for children with substance abuse disorders but decreased adherence for those with no psychiatric disorders. The diagnostic correlates of duration of participation were similar to those for adherence. CONCLUSIONS: Mood and substance abuse disorders were strong predictive factors for treatment adherence and duration, albeit in opposite directions. Living with family seems to have a positive effect on treatment adherence for patients with substance abuse disorders. More effective treatment is needed for victimized substance-abusing youth.