ResearchPad - survival-analysis https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[<i>In silico</i> analyses identify lncRNAs: WDFY3-AS2, BDNF-AS and AFAP1-AS1 as potential prognostic factors for patients with triple-negative breast tumors]]> https://www.researchpad.co/article/elastic_article_13870 Long non-coding RNAs (lncRNAs) are characterized as having 200 nucleotides or more and not coding any protein, and several been identified as differentially expressed in several human malignancies, including breast cancer.MethodsHere, we evaluated lncRNAs differentially expressed in triple-negative breast cancer (TNBC) from a cDNA microarray data set obtained in a previous study from our group. Using in silico analyses in combination with a review of the current literature, we identify three lncRNAs as potential prognostic factors for TNBC patients.ResultsWe found that the expression of WDFY3-AS2, BDNF-AS, and AFAP1-AS1 was associated with poor survival in patients with TNBCs. WDFY3-AS2 and BDNF-AS are lncRNAs known to play an important role in tumor suppression of different types of cancer, while AFAP1-AS1 exerts oncogenic activity.ConclusionOur findings provided evidence that WDFY3-AS2, BDNF-AS, and AFAP1-AS1 may be potential prognostic factors in TNBC development. ]]> <![CDATA[Optimizing predictive performance of criminal recidivism models using registration data with binary and survival outcomes]]> https://www.researchpad.co/article/5c8c193ed5eed0c484b4d25f

In a recidivism prediction context, there is no consensus on which modeling strategy should be followed for obtaining an optimal prediction model. In previous papers, a range of statistical and machine learning techniques were benchmarked on recidivism data with a binary outcome. However, two important tree ensemble methods, namely gradient boosting and random forests were not extensively evaluated. In this paper, we further explore the modeling potential of these techniques in the binary outcome criminal prediction context. Additionally, we explore the predictive potential of classical statistical and machine learning methods for censored time-to-event data. A range of statistical manually specified statistical and (semi-)automatic machine learning models is fitted on Dutch recidivism data, both for the binary outcome case and censored outcome case. To enhance generalizability of results, the same models are applied to two historical American data sets, the North Carolina prison data. For all datasets, (semi-) automatic modeling in the binary case seems to provide no improvement over an appropriately manually specified traditional statistical model. There is however evidence of slightly improved performance of gradient boosting in survival data. Results on the reconviction data from two sources suggest that both statistical and machine learning should be tried out for obtaining an optimal model. Even if a flexible black-box model does not improve upon the predictions of a manually specified model, it can serve as a test whether important interactions are missing or other misspecification of the model are present and can thus provide more security in the modeling process.

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<![CDATA[Toxoplasmic retinochoroiditis: The influence of age, number of retinochoroidal lesions and genetic polymorphism for IFN-γ +874 T/A as risk factors for recurrence in a survival analysis]]> https://www.researchpad.co/article/5c6c756dd5eed0c4843cfd80

Purpose

To analyze risk factors for recurrent toxoplasmic retinochoroiditis.

Design

Single center prospective case series.

Population and Methods

A total of 230 patients with toxoplasmic retinochoroiditis were prospectively followed to assess recurrences. All patients were treated with a specific drug regime for toxoplasmosis in each episode of active retinochoroiditis. Individuals with chronic diseases and pregnant women were excluded. Survival analysis by extended Cox regression model (Prentice-Williams-Peterson counting process model) was performed to evaluate the time between recurrences according to some potential risk factors: age, number of retinochoroidal lesions at initial evaluation, sex and interferon gamma +874 T/A gene polymorphism. Hazard Ratios (HR) and 95% confidence intervals (CI) were provided to interpret the risk effects.

Results

One hundred sixty-two recurrence episodes were observed in 104 (45.2%) patients during follow-up that lasted from 269 to 1976 days. Mean age at presentation was 32.8 years (Standard deviation = 11.38). The risk of recurrence during follow up was influenced by age (HR = 1.02, 95% CI = 1.01–1.04) and number of retinochoroidal lesions at the beginning of the study (HR = 1.60, 95% CI = 1.07–2.40). Heterozygosis for IFN-γ gene polymorphism at position +874 T/A was also associated with recurrence (HR = 1.49, 95% CI = 1.04–2.14).

Conclusion

The risk of ocular toxoplasmosis recurrence after an active episode increased with age and was significantly higher in individuals with primary lesions, which suggests that individuals with this characteristic and the elderly could benefit from recurrence prophylactic strategies with antimicrobials. Results suggest an association between IFN-γ gene polymorphism at position +874T/A and recurrence.

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<![CDATA[EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma]]> https://www.researchpad.co/article/5c5ca30cd5eed0c48441f069

Various feature selection algorithms have been proposed to identify cancer prognostic biomarkers. In recent years, however, their reproducibility is criticized. The performance of feature selection algorithms is shown to be affected by the datasets, underlying networks and evaluation metrics. One of the causes is the curse of dimensionality, which makes it hard to select the features that generalize well on independent data. Even the integration of biological networks does not mitigate this issue because the networks are large and many of their components are not relevant for the phenotype of interest. With the availability of multi-omics data, integrative approaches are being developed to build more robust predictive models. In this scenario, the higher data dimensions create greater challenges. We proposed a phenotype relevant network-based feature selection (PRNFS) framework and demonstrated its advantages in lung cancer prognosis prediction. We constructed cancer prognosis relevant networks based on epithelial mesenchymal transition (EMT) and integrated them with different types of omics data for feature selection. With less than 2.5% of the total dimensionality, we obtained EMT prognostic signatures that achieved remarkable prediction performance (average AUC values >0.8), very significant sample stratifications, and meaningful biological interpretations. In addition to finding EMT signatures from different omics data levels, we combined these single-omics signatures into multi-omics signatures, which improved sample stratifications significantly. Both single- and multi-omics EMT signatures were tested on independent multi-omics lung cancer datasets and significant sample stratifications were obtained.

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<![CDATA[Predictive value of a nomogram for hepatocellular carcinoma with brain metastasis at initial diagnosis: A population-based study]]> https://www.researchpad.co/article/5c3667bbd5eed0c4841a6228

Background

Population-based estimates of the incidence and prognosis of brain metastases at diagnosis of hepatocellular carcinoma (HCC) are lacking. The aim of this study was to characterize the incidence proportion and survival of newly diagnosed hepatocellular carcinoma with brain metastases (HCCBM).

Materials and methods

Data from Surveillance, Epidemiology, and End Results (SEER) program between 2010 and 2014 was evaluated. Patients with HCCBM were included. Multivariable logistic and Cox regression were performed to identify predictors of the presence of brain metastases at diagnosis and prognostic factors of overall survival (OS). We also built a nomogram based on Cox model to predict prognosis for HCCBM patients.

Results

We identified 97 patients with brain metastases at the time of diagnosis of HCC, representing 0.33% of the entire cohort. Logistic regression showed patients with bone or lung metastases had greater odds of having brain metastases at diagnosis. Median OS for HCCBM was 2.40 months. Cox regression revealed unmarried and bone metastases patients suffered significantly shorter survival time. A nomogram was developed with internal validation concordance index of 0.639.

Conclusions

This study provided population-based estimates of the incidence and prognosis for HCCBM patients. The nomogram could be a convenient individualized predictive tool for prognosis.

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<![CDATA[High serum MMP-14 predicts worse survival in gastric cancer]]> https://www.researchpad.co/article/5c141efad5eed0c484d29187

Matrix metalloproteinases (MMPs), endopeptidases with diverse biochemical functions, can promote cancer cell invasion and metastasis by degrading the extracellular matrix. A high matrix metalloproteinase-14 (MMP-14) expression in gastric cancer tissue has been associated with metastasis and poor prognosis. To further understand this association, we investigated serum MMP-14 as a biomarker in gastric cancer patients. The patient cohort consisted of 240 gastric adenocarcinoma patients who underwent surgery at Helsinki University Hospital, Finland, between 2000 and 2009. We determined the soluble MMP-14 serum levels using an enzyme-linked immunosorbent assay. We then calculated the associations between serum levels and clinicopathologic variables using the Mann-Whitney U-test or the Kruskal-Wallis test. We constructed survival curves using the Kaplan-Meier method and calculating the hazard ratios using the Cox proportional hazard model. We revealed a positive association between a high serum MMP-14 level and stages III–IV (p = 0.029), and between a high serum MMP-14 and distant metastasis (p = 0.022). Patients with a low serum MMP-14 had a 5-year disease-specific survival of 49.2% (95% confidence interval [CI] 45.5–52.9), whereas patients with a high serum MMP-14 had a 5-year survival of 22.1% (95% CI 15.2–29.0; p = 0.001). High serum MMP-14 was a statistically significant prognostic factor among patients with an intestinal type of cancer (hazard ratio [HR] 3.54; 95% CI 1.51–8.33; p = 0.004), but not among patients with a diffuse type. The serum MMP-14 level remained an independent prognostic factor in our multivariate survival analysis (HR 1.55; 95% CI 1.02–2.35; p = 0.040). This study indicates for the first time that high serum soluble MMP-14 levels in gastric cancer serves as a marker for a poor prognosis, possibly indicating the presence of distant metastases.

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<![CDATA[Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma]]> https://www.researchpad.co/article/5c141e9bd5eed0c484d27646

We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application to survival prediction in High-Risk (HR) Neuroblastoma from transcriptomics data, a task that studies from the MAQC consortium have shown to remain the hardest among multiple diagnostic and prognostic endpoints predictable from the same dataset. To obtain a more accurate risk stratification needed for appropriate treatment strategies, CDRP combines a first component (CDRP-A) synthesizing a diagnostic task and a second component (CDRP-N) dedicated to one or more prognostic tasks. The approach leverages the advent of semi-supervised deep learning structures that can flexibly integrate multimodal data or internally create multiple processing paths. CDRP-A is an autoencoder trained on gene expression on the HR/non-HR risk stratification by the Children’s Oncology Group, obtaining a 64-node representation in the bottleneck layer. CDRP-N is a multi-task classifier for two prognostic endpoints, i.e., Event-Free Survival (EFS) and Overall Survival (OS). CDRP-A provides the HR embedding input to the CDRP-N shared layer, from which two branches depart to model EFS and OS, respectively. To control for selection bias, CDRP is trained and evaluated using a Data Analysis Protocol (DAP) developed within the MAQC initiative. CDRP was applied on Illumina RNA-Seq of 498 Neuroblastoma patients (HR: 176) from the SEQC study (12,464 Entrez genes) and on Affymetrix Human Exon Array expression profiles (17,450 genes) of 247 primary diagnostic Neuroblastoma of the TARGET NBL cohort. On the SEQC HR patients, CDRP achieves Matthews Correlation Coefficient (MCC) 0.38 for EFS and MCC = 0.19 for OS in external validation, improving over published SEQC models. We show that a CDRP-N embedding is indeed parametrically associated to increasing severity and the embedding can be used to better stratify patients’ survival.

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<![CDATA[GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome]]> https://www.researchpad.co/article/5c117b67d5eed0c4846990f6

Accurate assessment of the association between continuous variables such as gene expression and survival is a critical aspect of precision medicine. In this report, we provide a review of some of the available survival analysis and validation tools by referencing published studies that have utilized these tools. We have identified pitfalls associated with the assumptions inherent in those applications that have the potential to impact scientific research through their potential bias. In order to overcome these pitfalls, we have developed a novel method that enables the logrank test method to handle continuous variables that comprehensively evaluates survival association with derived aggregate statistics. This is accomplished by exhaustively considering all the cutpoints across the full expression gradient. Direct side-by-side comparisons, global ROC analysis, and evaluation of the ability to capture relevant biological themes based on current understanding of RAS biology all demonstrated that the new method shows better consistency between multiple datasets of the same disease, better reproducibility and robustness, and better detection power to uncover biological relevance within the selected datasets over the available survival analysis methods on univariate gene expression and penalized linear model-based methods.

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<![CDATA[Impact of combining the progesterone receptor and preoperative endocrine prognostic index (PEPI) as a prognostic factor after neoadjuvant endocrine therapy using aromatase inhibitors in postmenopausal ER positive and HER2 negative breast cancer]]> https://www.researchpad.co/article/5b8685d440307c7363ff63ae

The preoperative endocrine prognostic index (PEPI) predicts survival after neoadjuvant endocrine therapy (NAE) using aromatase inhibitors (AIs) for women with postmenopausal estrogen receptor (ER)-positive breast cancer irrespective of the human epidermal growth factor receptor 2 (HER2) status. Although the progesterone receptor (PgR) is also a prognostic factor for ER-positive breast cancer, the PgR status was not considered a prognostic factor in the original PEPI scoring system. In this study, we investigated the utility of a modified PEPI including the PgR status (PEPI-P) as a prognostic factor after NAE for postmenopausal patients with ER-positive and HER2-negative breast cancer. We enrolled 107 patients with invasive ER-positive and HER2-negative breast cancer treated with exemestane for ≥4 months as NAE. We initially assessed PEPI and compared survival between the groups. Additionally, we obtained an effective cutoff for PgR through survival analysis. Then, we assessed the survival significance of PEPI-P. A PgR staining rate of 50% was the most significant cutoff for predicting recurrence-free survival (RFS) and cancer-specific survival (CSS). PEPI was a significant prognostic factor; moreover, PEPI-P was the most significant prognostic indicator for RFS and CSS. PEPI-P is a potent prognostic indicator of survival after NAE using AIs for postmenopausal patients with ER-positive and HER2-negative breast cancer. This modified PEPI may be useful for therapeutic decision-making regarding postmenopausal ER-positive and HER2-negative breast cancer after NAE.

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<![CDATA[Prognostic impact of diabetes mellitus on hepatocellular carcinoma: Special emphasis from the BCLC perspective]]> https://www.researchpad.co/article/5989db50ab0ee8fa60bdc160

Background

Diabetes mellitus (DM) is associated with higher incidence and poorer prognosis of hepatocellular carcinoma (HCC). The influence of DM on patient survival in different HCC stages is not known.

Methods

A prospective dataset of 3,182 HCC patients was collected between 2002 and 2014. Patients were divided into three groups according to BCLC stages (BCLC stage 0 and stage A, BCLC stage B, BCLC stage C and stage D). We compared the cumulative survival rate of diabetic and non-diabetic patients in different BCLC groups. The correlation between DM and overall survival was also analyzed by multivariate Cox regression model within each group.

Results

DM is present in 25.2% of all patients. Diabetic patients had lower cumulative survival in BCLC stage 0 plus BCLC stage A group (log rank p<0.001), and BCLC stage B group (log rank p = 0.012), but not in BCLC stage C plus BCLC stage D group (log rank p = 0.132). Statistically significant differences in overall survival are found between diabetic and non-diabetic patients in BCLC stage 0 plus stage A group (adjusted hazard ratio [HR] = 1.45, 95% confidence interval [CI] 1.08–1.93, p = 0.013), and BCLC stage B (adjusted HR = 1.77, 95% CI 1.24–2.51, p = 0.002). In contrast, the survival difference is not seen in BCLC stage C plus stage D group (adjusted HR = 1.09, 95% CI 0.90–1.30, p = 0.387).

Conclusions

DM is prevalent in HCC, and is associated with lower survival rate in HCC patients with BCLC stage 0 plus stage A and B, but not in those with BCLC stage C plus stage D.

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<![CDATA[DGKI Methylation Status Modulates the Prognostic Value of MGMT in Glioblastoma Patients Treated with Combined Radio-Chemotherapy with Temozolomide]]> https://www.researchpad.co/article/5989db0aab0ee8fa60bc9e0a

Background

Consistently reported prognostic factors for glioblastoma (GBM) are age, extent of surgery, performance status, IDH1 mutational status, and MGMT promoter methylation status. We aimed to integrate biological and clinical prognostic factors into a nomogram intended to predict the survival time of an individual GBM patient treated with a standard regimen. In a previous study we showed that the methylation status of the DGKI promoter identified patients with MGMT-methylated tumors that responded poorly to the standard regimen. We further evaluated the potential prognostic value of DGKI methylation status.

Methods

399 patients with newly diagnosed GBM and treated with a standard regimen were retrospectively included in this study. Survival modelling was performed on two patient populations: intention-to-treat population of all included patients (population 1) and MGMT-methylated patients (population 2). Cox proportional hazard models were fitted to identify the main prognostic factors. A nomogram was developed for population 1. The prognostic value of DGKI promoter methylation status was evaluated on population 1 and population 2.

Results

The nomogram-based stratification of the cohort identified two risk groups (high/low) with significantly different median survival. We validated the prognostic value of DGKI methylation status for MGMT-methylated patients. We also demonstrated that the DGKI methylation status identified 22% of poorly responding patients in the low-risk group defined by the nomogram.

Conclusions

Our results improve the conventional MGMT stratification of GBM patients receiving standard treatment. These results could help the interpretation of published or ongoing clinical trial outcomes and refine patient recruitment in the future.

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<![CDATA[Comparative Persistence of the TNF Antagonists in Rheumatoid Arthritis – A Population-Based Cohort Study]]> https://www.researchpad.co/article/5989db3eab0ee8fa60bd5adc

Objective

To compare persistence with tumor necrosis factor alpha (TNF) antagonists among rheumatoid arthritis patients in British Columbia. Treatment persistence has been suggested as a proxy for real-world therapeutic benefit and harm of treatments for chronic non-curable diseases, including rheumatoid arthritis. We hypothesized that the different pharmacological characteristics of infliximab, adalimumab and etanercept cause statistically and clinically significant differences in persistence.

Methods

We conducted a population-based cohort study using administrative health data from the Canadian province of British Columbia. The study cohort included rheumatoid arthritis patients who initiated the first course of a TNF antagonist between 2001 and 2008. Persistence was measured as the time between first dispensing to discontinuation. Drug discontinuation was defined as a drug-free interval of 180 days or switching to another TNF antagonist, anakinra, rituximab or abatacept. Persistence was estimated and compared using survival analysis.

Results

The study cohort included 2,923 patients, 63% treated with etanercept. Median persistence in years (95% confidence interval) with infliximab was 3.7 (2.9–4.9), with adalimumab 3.3 (2.6–4.1) and with etanercept 3.8 (3.3–4.3). Similar risk of discontinuation was observed for the three drugs: the hazard ratio (95% confidence interval) was 0.98 (0.85–1.13) comparing infliximab with etanercept, 0.95 (0.78–1.15) comparing infliximab with adalimumab and 1.04 (0.88–1.22) comparing adalimumab with etanercept.

Conclusions

Similar persistence was observed with infliximab, adalimumab and etanercept in rheumatoid arthritis patients during the first 9 years of use. If treatment persistence is a good proxy for the therapeutic benefit and harm of these drugs, then this finding suggests that the three drugs share an overall similar benefit-harm profile in rheumatoid arthritis patients.

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<![CDATA[Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science]]> https://www.researchpad.co/article/5989db4dab0ee8fa60bdaf75

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

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<![CDATA[The Magnitude and Duration of Clostridium difficile Infection Risk Associated with Antibiotic Therapy: A Hospital Cohort Study]]> https://www.researchpad.co/article/5989da7cab0ee8fa60b98bff

Antibiotic therapy is the principal risk factor for Clostridium difficile infection (CDI), but little is known about how risks cumulate over the course of therapy and abate after cessation. We prospectively identified CDI cases among adults hospitalized at a tertiary hospital between June 2010 and May 2012. Poisson regression models included covariates for time since admission, age, hospitalization history, disease pressure, and intensive care unit stay. Impacts of antibiotic use through time were modeled using 4 measures: current antibiotic receipt, time since most recent receipt, time since first receipt during a hospitalization, and duration of receipt. Over the 24-month study period, we identified 127 patients with new onset nosocomial CDI (incidence rate per 10,000 patient days [IR] = 5.86). Of the 4 measures, time since most recent receipt was the strongest independent predictor of CDI incidence. Relative to patients with no prior receipt of antibiotics in the last 30 days (IR = 2.95), the incidence rate of CDI was 2.41 times higher (95% confidence interval [CI] 1.41, 4.13) during antibiotic receipt and 2.16 times higher when patients had receipt in the prior 1–5 days (CI 1.17, 4.00). The incidence rates of CDI following 1–3, 4–6 and 7–11 days of antibiotic exposure were 1.60 (CI 0.85, 3.03), 2.27 (CI 1.24, 4.16) and 2.10 (CI 1.12, 3.94) times higher compared to no prior receipt. These findings are consistent with studies showing higher risk associated with longer antibiotic use in hospitalized patients, but suggest that the duration of increased risk is shorter than previously thought.

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<![CDATA[Pregnancy-Associated Breast Cancer in Taiwanese Women: Potential Treatment Delay and Impact on Survival]]> https://www.researchpad.co/article/5989d9feab0ee8fa60b73308

This study investigated the clinicopathologic characteristics and survival of women diagnosed with pregnancy-associated breast cancer (PABC) in Taiwan. PABC is defined as breast cancer diagnosed during pregnancy or within 1 year after obstetric delivery. Our sample of PABC patients (N = 26) included all patients diagnosed at a major medical center in northern Taiwan from 1984 through 2009. Among these patients, 15 were diagnosed during pregnancy and 11 were diagnosed within 1 year after delivery. The comparison group included 104 patients within the same age range as the PABC patients and diagnosed with breast cancer not associated with pregnancy from 2004 through 2009 at the same hospital. Patients' initiating treatment delayed, 5-year and 10-year overall survival were delineated by stratified Kaplan-Meier estimates. Patients' characteristics were associated with initiating treatment delayed was evaluated with multivariate proportional hazards modeling. Antepartum PABC patients were younger and had longer time between diagnosis and treatment initiation than postpartum PABC patients. The predictor of treatment delayed was including birth parity, cancer stage, and pregnancy. The PABC group had larger tumors, more advanced cancer stage, and tumors with less progesterone receptor than the comparison group. The antepartum PABC patients had higher mortality than postpartum PABC and comparison groups within 5 years after diagnosis. Based on these results, we confirmed that pregnant women with breast cancer were more likely to delay treatment. Therefore, we recommend that breast cancer screening should be integrated into the prenatal and postnatal routine visits for early detection of the women's breast problems.

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<![CDATA[Cardiac Health Risk Stratification System (CHRiSS): A Bayesian-Based Decision Support System for Left Ventricular Assist Device (LVAD) Therapy]]> https://www.researchpad.co/article/5989da25ab0ee8fa60b806bb

This study investigated the use of Bayesian Networks (BNs) for left ventricular assist device (LVAD) therapy; a treatment for end-stage heart failure that has been steadily growing in popularity over the past decade. Despite this growth, the number of LVAD implants performed annually remains a small fraction of the estimated population of patients who might benefit from this treatment. We believe that this demonstrates a need for an accurate stratification tool that can help identify LVAD candidates at the most appropriate point in the course of their disease. We derived BNs to predict mortality at five endpoints utilizing the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) database: containing over 12,000 total enrolled patients from 153 hospital sites, collected since 2006 to the present day, and consisting of approximately 230 pre-implant clinical variables. Synthetic minority oversampling technique (SMOTE) was employed to address the uneven proportion of patients with negative outcomes and to improve the performance of the models. The resulting accuracy and area under the ROC curve (%) for predicted mortality were 30 day: 94.9 and 92.5; 90 day: 84.2 and 73.9; 6 month: 78.2 and 70.6; 1 year: 73.1 and 70.6; and 2 years: 71.4 and 70.8. To foster the translation of these models to clinical practice, they have been incorporated into a web-based application, the Cardiac Health Risk Stratification System (CHRiSS). As clinical experience with LVAD therapy continues to grow, and additional data is collected, we aim to continually update these BN models to improve their accuracy and maintain their relevance. Ongoing work also aims to extend the BN models to predict the risk of adverse events post-LVAD implant as additional factors for consideration in decision making.

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<![CDATA[Expression and prognostic impact of matrix metalloproteinase-2 (MMP-2) in astrocytomas]]> https://www.researchpad.co/article/5989db50ab0ee8fa60bdbd41

Astrocytomas are the most frequent primary brain tumors in adults, and despite aggressive treatment patients often experience recurrence. Survival decreases with increasing tumor grade, and especially patients with grade IV glioblastoma have poor prognosis due to the aggressive character of this tumor. Matrix metalloproteinase-2 (MMP-2) is an extracellular matrix degrading enzyme which has been shown to play important roles in different cancers. The aim of this study was to investigate the expression and prognostic potential of MMP-2 in astrocytomas. Tissue samples from 89 patients diagnosed with diffuse astrocytoma, anaplastic astrocytoma and glioblastoma were stained immunohistochemically using a monoclonal MMP-2 antibody. The MMP-2 intensity in cytoplasm/membrane was quantified by a trained software-based classifier using systematic random sampling in 10% of the tumor area. We found MMP-2 expression in tumor cells and blood vessels. Measurements of MMP-2 intensity increased with tumor grade, and MMP-2 expression was found to be significantly higher in glioblastomas compared to normal brain tissue (p<0.001), diffuse astrocytomas (p<0.001) and anaplastic astrocytomas (p<0.05). MMP-2 expression was associated with shorter overall survival in patients with grade II-IV astrocytic tumors (HR 1.60; 95% CI 1.03–2.48; p = 0.036). In glioblastoma, high MMP-2 was associated with poorer prognosis in patients who survived longer than 8.5 months independent of age and gender (HR 2.27; 95% CI 1.07–4.81; p = 0.033). We found a positive correlation between MMP-2 and tissue inhibitor of metalloproteinases-1 (TIMP-1), and combined MMP-2 and TIMP-1 had stronger prognostic value than MMP-2 alone also when adjusting for age and gender (HR 2.78; 95% CI 1.30–5.92; p = 0.008). These findings were validated in bioinformatics databases. In conclusion, this study indicates that MMP-2 is associated with aggressiveness in astrocytomas and may hold an unfavorable prognostic value in patients with glioblastoma.

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<![CDATA[Comparison of Regression Methods for Modeling Intensive Care Length of Stay]]> https://www.researchpad.co/article/5989db31ab0ee8fa60bd20de

Intensive care units (ICUs) are increasingly interested in assessing and improving their performance. ICU Length of Stay (LoS) could be seen as an indicator for efficiency of care. However, little consensus exists on which prognostic method should be used to adjust ICU LoS for case-mix factors. This study compared the performance of different regression models when predicting ICU LoS. We included data from 32,667 unplanned ICU admissions to ICUs participating in the Dutch National Intensive Care Evaluation (NICE) in the year 2011. We predicted ICU LoS using eight regression models: ordinary least squares regression on untransformed ICU LoS,LoS truncated at 30 days and log-transformed LoS; a generalized linear model with a Gaussian distribution and a logarithmic link function; Poisson regression; negative binomial regression; Gamma regression with a logarithmic link function; and the original and recalibrated APACHE IV model, for all patients together and for survivors and non-survivors separately. We assessed the predictive performance of the models using bootstrapping and the squared Pearson correlation coefficient (R2), root mean squared prediction error (RMSPE), mean absolute prediction error (MAPE) and bias. The distribution of ICU LoS was skewed to the right with a median of 1.7 days (interquartile range 0.8 to 4.0) and a mean of 4.2 days (standard deviation 7.9). The predictive performance of the models was between 0.09 and 0.20 for R2, between 7.28 and 8.74 days for RMSPE, between 3.00 and 4.42 days for MAPE and between −2.99 and 1.64 days for bias. The predictive performance was slightly better for survivors than for non-survivors. We were disappointed in the predictive performance of the regression models and conclude that it is difficult to predict LoS of unplanned ICU admissions using patient characteristics at admission time only.

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<![CDATA[Difference in Restricted Mean Survival Time for Cost-Effectiveness Analysis Using Individual Patient Data Meta-Analysis: Evidence from a Case Study]]> https://www.researchpad.co/article/5989da27ab0ee8fa60b812cb

Objective

In economic evaluation, a commonly used outcome measure for the treatment effect is the between-arm difference in restricted mean survival time (rmstD). This study illustrates how different survival analysis methods can be used to estimate the rmstD for economic evaluation using individual patient data (IPD) meta-analysis. Our aim was to study if/how the choice of a method impacts on cost-effectiveness results.

Methods

We used IPD from the Meta-Analysis of Radiotherapy in Lung Cancer concerning 2,000 patients with locally advanced non-small cell lung cancer, included in ten trials. We considered methods either used in the field of meta-analysis or in economic evaluation but never applied to assess the rmstD for economic evaluation using IPD meta-analysis. Methods were classified into two approaches. With the first approach, the rmstD is estimated directly as the area between the two pooled survival curves. With the second approach, the rmstD is based on the aggregation of the rmstDs estimated in each trial.

Results

The average incremental cost-effectiveness ratio (ICER) and acceptability curves were sensitive to the method used to estimate the rmstD. The estimated rmstDs ranged from 1.7 month to 2.5 months, and mean ICERs ranged from € 24,299 to € 34,934 per life-year gained depending on the chosen method. At a ceiling ratio of € 25,000 per life year-gained, the probability of the experimental treatment being cost-effective ranged from 31% to 68%.

Conclusions

This case study suggests that the method chosen to estimate the rmstD from IPD meta-analysis is likely to influence the results of cost-effectiveness analyses.

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<![CDATA[Tumor location as a novel high risk parameter for stage II colorectal cancers]]> https://www.researchpad.co/article/5989db5fab0ee8fa60be1207

Current studies do not accurately evaluate the influence of tumor location on survival of colorectal cancer patients. This study aimed to explore whether tumor location could be identified as another high-risk factor in stage II colorectal cancer by using data identified from the Surveillance, Epidemiology, and End Results database. All colorectal cancer patients between 2004 and 2008 were grouped into three according to tumor location. Of 33,789 patients diagnosed with stage II colorectal cancer, 46.8% were right colon cancer, 37.5% were left colon cancer and 15.7% were rectal cancer. The 5-year cancer specific survivals were examined. Right colon cancer was associated with the female sex, older age (> 50), and having over 12 lymph nodes resected. Conversely, rectal cancer was associated with the male sex, patients younger than 50 years of age and insufficient lymph node resection. The characteristics of left colon cancer were between them and associated with Asian or Pacific Islander populations, T4 stage, and Grade II patients. The prognostic differences between three groups were significant and retained after stratification by T stage, histological grade, number of regional nodes dissected, age at diagnose, race and sex. Furthermore, the significant difference of location was retained as an independent high-risk parameter. Thus, stage II colorectal cancers of different locations have different clinic-pathological features and cancer-specific survivals, and tumor location should be recognized as another high-risk parameter in stage II colorectal cancer.

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