Behrouz Beiranvand; Ebrahim Hajizadeh; Aliakbar Rasekhi; Abdollah Amirfarhangi; Javad Nasseryan
Volume 22, Issue 5 , 2020
Abstract
Background: Restenosis after coronary angioplasty can have serious complications such as coronary artery bypass graft, myocardial infarction, and death.
Objectives: The present study aimed at investigating the factors affecting the recurrence of coronary artery stenosis in patients undergoing ...
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Background: Restenosis after coronary angioplasty can have serious complications such as coronary artery bypass graft, myocardial infarction, and death.
Objectives: The present study aimed at investigating the factors affecting the recurrence of coronary artery stenosis in patients undergoing angioplasty using the recurrent event data analysis.
Methods: A cohort study was performed on patients undergoing coronary angioplasty from March 23, 2009, to January 21, 2011. All patients were followed up from angioplasty to January 21, 2015. First, each of the independent variables was entered into the univariate Cox model with a frailty component. Then, variables with p-values of less than 0.2 were entered into the multivariate analysis. The statistical analysis was done using R software, version 3.6, at the significance level of 0.05.
Results: The present study was conducted on 1,000 patients who underwent coronary angioplasty. We found that 441 patients experienced restenosis at least once in the study period. The mean survival time to the first event of restenosis was 44.08 ± 1.06 months. Patients with a history of diabetes, unstable angina, and myocardial infarction had a significantly higher hazard of restenosis compared to other patients (P < 0.05).
Conclusions: The results of the recurrent event survival analysis confirmed the significant role of risk factors such as a history of diabetes, unstable angina, and myocardial infarction. Therefore, training to enhance the patients awareness and attitude seems necessary to prevent them from exposing whit known risk factors. The periodic follow-up of patients with risk factors and more ongoing care are also necessary.
Fatemeh Mohammadzadeh; Ebrahim Hajizadeh; Aliakbar Rasekhi; Sadegh Azimzadeh Jamalkandi
Volume 21, Issue 5 , 2019, Pages 1-9
Abstract
Background: Tumor stage is one of the most reliable prognostic factors in the clinical characterization of colorectal cancer. The identification of genes associated with tumor staging may facilitate the personalized molecular diagnosis and treatment along with better risk stratification in colorectal ...
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Background: Tumor stage is one of the most reliable prognostic factors in the clinical characterization of colorectal cancer. The identification of genes associated with tumor staging may facilitate the personalized molecular diagnosis and treatment along with better risk stratification in colorectal cancer.Objectives: The study aimed to identify genetic signatures associated with tumor staging and patients’ survival in colorectal cancer and recognize the patients’ risk category for clinical outcomes based on transcriptomic data.Methods: In this retrospective cohort study, two available transcriptomic datasets, including 232 patients with colorectal cancer under accession number GSE17537 and GSE17536 were used as discovery and validation sets, respectively. A Bayesian sparse group selection method in the discovery set was applied to identify the associated genes with the tumor staging. Then further screen- ing was performed using survival analysis, and significant genes were used to develop a gene signature model. Finally, the robust performance of the signature model was assessed in the validation set.Results: A total of 56 genes were significantly associated with the tumor staging in colorectal cancer. Survival analysis resulted in a shortlist of 19 genes, including ADH1B (P = 0.012), AHI (P = 0.006), AKAP12 (P = 0.018), BNIP3 (P = 0.015), CLDN11 (P = 0.015), CST9L (P = 0.028), DPP10 (P = 0.029), FBXO33 (P = 0.036), HEBP (P = 0.025), INTS4 (P = 0.003), LIPJ (P = 0.001), MMP21 (P = 0.006), NGRN (P = 0.014), PAFAH1B2 (P = 0.035), PCOLCE2 (P = 0.009), PIM1 (P = 0.007), TBKBP1 (P = 0.003), TCEB3B (P = 0.001), and TIPARP (P = 0.018), developing the signature model and validation. In both discovery and validation sets, the discrimination ability of the signature model to categorize patients with colorectal cancer into low- and high-risk subgroups for mortality and recurrence at 3- and 5-years showed good discrimination performances, with the area under the receiver operating characteristic curve (ROC) ranging from 0.64 to 0.88. It also had good sensitivity (discovery set 63.1%, validation set 61.7%) and specificity (discovery set 75.0%, validation set 59.3%) to discriminate between early- and late-stage groups.Conclusions: We identified a 19-gene signature associated with tumor staging and survival of colorectal cancer, which may repre- sent potential diagnosis and prognosis markers, and help to classify patients with colorectal cancer into low- or high-risk subgroups.
Rezvaneh Alvandi; Aliakbar Rasekhi; Mehdi Ariana
Volume 21, Issue 4 , 2019, Pages 1-7
Abstract
Background: Cancer is the second leading cause of death globally, and it was responsible for almost 9.6 million deaths in 2018. Breast cancer (BC) is the most common cancer among women with almost two million new cases worldwide in 2018. Thus, it is necessary to study new methods to estimate the survival ...
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Background: Cancer is the second leading cause of death globally, and it was responsible for almost 9.6 million deaths in 2018. Breast cancer (BC) is the most common cancer among women with almost two million new cases worldwide in 2018. Thus, it is necessary to study new methods to estimate the survival predictive factors in BC patients.Objectives: This cohort study aimed to fit a Cox model to BC data using partial likelihood (PL) and new maximum penalized likeli- hood (MPL) methods in order to determine the predictive factors of survival time and compare the accuracy of these two methods.Methods: This prospective cohort study used the data of 356 women with BC registered at the Cancer Research Center of Shahid Beheshti University of Medical Sciences in Tehran, Iran. The patients were identified from 1999 to 2015. The Cox model by new MPL and PL methods was used with variables such as the stage of cancer, tumor grade, estrogen receptor, and several other variables for univariate and multiple analyses.Results: The mean age ± standard deviation (SD) of patients at diagnosis was about 48 ± 11.27 years ranging from 24 to 84 years. Using the new MPL method, in addition to lymphovascular invasion and recurrence variables, estrogen receptor (P = 0.045) also had a statistically significant relationship with survival. The standard errors of most variables were smaller when using the MLP method than the PL method. The overall one-year, two-year, five-year, and 10-year survival rates based on the baseline hazard estimate were 96%, 92%, 70%, and 51%, respectively.Conclusions: In the analysis of BC data, new MPL method can help identify the factors that affect the survival of patients more accurately than usual methods do. This method decreases the standard error of most variables and can be applied for identifying predictive factors more accurately than previous methods.
