scholarly journals Survival prediction models for patients with anal carcinoma receiving definitive chemoradiation: A population‑based study

2019 ◽  
Author(s):  
Yinhang Wu ◽  
Xiaoyang Han ◽  
Yan Li ◽  
Kunli Zhu ◽  
Jinming Yu
Keyword(s):  
Prediction Models ◽  
Anal Carcinoma ◽  
Population Based ◽  
Survival Prediction ◽  
Population Based Study ◽  
Definitive Chemoradiation

scholarly journals Risk Factors for Pneumonia and Death in Adult Patients With Seasonal Influenza and Establishment of Prediction Scores: A Population-Based Study

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Koichi Miyashita ◽  
Eiji Nakatani ◽  
Hironao Hozumi ◽  
Yoko Sato ◽  
Yoshiki Miyachi ◽  
...  
Keyword(s):  
High Risk ◽  
Seasonal Influenza ◽  
Prediction Models ◽  
Validation Cohort ◽  
Risk Groups ◽  
Population Based ◽  
Limited Data ◽  
Population Based Study ◽  
Relative Risks ◽  
Primary Care Settings

Abstract Background Seasonal influenza remains a global health problem; however, there are limited data on the specific relative risks for pneumonia and death among outpatients considered to be at high risk for influenza complications. This population-based study aimed to develop prediction models for determining the risk of influenza-related pneumonia and death. Methods We included patients diagnosed with laboratory-confirmed influenza between 2016 and 2017 (main cohort, n = 25 659), those diagnosed between 2015 and 2016 (validation cohort 1, n = 16 727), and those diagnosed between 2017 and 2018 (validation cohort 2, n = 34 219). Prediction scores were developed based on the incidence and independent predictors of pneumonia and death identified using multivariate analyses, and patients were categorized into low-, medium-, and high-risk groups based on total scores. Results In the main cohort, age, gender, and certain comorbidities (dementia, congestive heart failure, diabetes, and others) were independent predictors of pneumonia and death. The 28-day pneumonia incidence was 0.5%, 4.1%, and 10.8% in the low-, medium-, and high-risk groups, respectively (c-index, 0.75); the 28-day mortality was 0.05%, 0.7%, and 3.3% in the low-, medium-, and high-risk groups, respectively (c-index, 0.85). In validation cohort 1, c-indices for the models for pneumonia and death were 0.75 and 0.87, respectively. In validation cohort 2, c-indices for the models were 0.74 and 0.87, respectively. Conclusions We successfully developed and validated simple-to-use risk prediction models, which would promptly provide useful information for treatment decisions in primary care settings.

Chemoradiotherapy for anal cancer: A population-based study of treatment interruption, treatment completion, and associated outcomes.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3573-3573
Author(s):  
Michael J. Raphael ◽  
Sunil Patel ◽  
Christopher M. Booth ◽  
Timothy P Hanna ◽  
Maria Kalyvas ◽  
...  
Keyword(s):  
Anal Cancer ◽  
Anal Carcinoma ◽  
Treatment Completion ◽  
Treatment Interruption ◽  
Population Based ◽  
Individual Treatment ◽  
Routine Practice ◽  
Curative Intent ◽  
Population Based Study ◽  
Incident Cases

3573 Background: Chemoradiotherapy (CRT) is the standard treatment for squamous cell anal carcinoma (SCCA). Here, we describe CRT delivery in routine practice and explore the association between treatment interruption, non-completion and outcomes. Methods: The Ontario Cancer Registry was used to identify all incident cases of SCCA treated with curative intent RT in Ontario, Canada (2007-2015). Treatment interruption was defined a priori as > 7 days between consecutive fractions. Completed CRT was defined as receiving ≥50 Gy and 30+ fractions of RT along with 2 concurrent doses of chemotherapy. Log-binomial regression models were used to estimate risk ratios (RR) between patient characteristics and 1) failure to complete CRT and 2) salvage abdominoperineal resection (APR). Cox proportional hazard models were used to estimate hazards ratios (HR) between treatment interruption or non-completion and 1) cancer specific survival (CSS) and 2) overall survival (OS). Results: We identified 1593 patients with SCCA; 73% (n = 1161) initiated curative intent RT. Median RT dose and duration was 54 Gy (IQR, 50.4-67.8) and 46 days (IQR, 42-53), respectively. Treatment interruption > 7 days occurred in 23%. CRT was completed by 59%. Factors associated with CRT non-completion were age > 70 vs. < 50 (RR 0.70, 95% CI: 0.59-0.93) and greater comorbidity (1+ vs. 0, RR 0.57, 95% CI: 0.39-0.85). Treatment interruption > 7 days appears to be associated with salvage APR (RR 1.39, 95% CI: 0.91-2.12). In an exploratory analysis, the association between treatment interruption > 10 days and salvage APR reached statistical significance (RR 1.63, 95% CI, 1.06-2.53). Treatment interruption was not associated with inferior CSS (HR 0.87, 95% CI: 0.60-1.25) nor OS (HR 0.96, 95% CI: 0.76-1.23). Failure to complete CRT was not associated with higher rates of salvage APR nor inferior CSS, but was associated with inferior OS (HR 1.40, 95% CI: 1.13-1.72). Conclusions: In routine clinical practice, treatment interruption and non-completion among patients with SCCA are common. Quality improvement initiatives to optimize treatment continuity and completion are needed. The observed association between failure to complete CRT and OS is likely a reflection of confounding by indication, which is highlighted by the lack of association between CRT completion and CSS. Publisher's Note The abstract by Raphael et al entitled, “Chemoradiotherapy for anal cancer: A population-based study of treatment interruption, treatment completion, and associated outcomes,” published in the Journal of Clinical Oncology 37, no. 15 suppl (May 20 2019) 3573–3573, contained errors. In updated analyses, the authors discovered that the main exposure (radiation therapy) may have been incorrectly coded into the population-level databases from several individual treatment centers. Some of the coded radiation doses and fractionation numbers are considerably beyond what would be clinically plausible. Until this issue has been resolved, the authors believe the results of their study cannot be considered reliable. This article was retracted on 17-07-2019.

A population-based validation of a clinical age-based prognostic tool to predict survival in melanoma patients (Preprint)

2021 ◽  
Author(s):  
Yu-Jen Wang ◽  
Mingchih Chen ◽  
Yen Chun Huang ◽  
Tian-Shyug Lee
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Population Based ◽  
Survival Prediction ◽  
Prediction Tool ◽  
Old Patients ◽  
Melanoma Patients

BACKGROUND Melanoma is the most serious form of skin cancer, and the treatment can be challenging if the disease progresses to the metastatic stage. Depth of invasion is a good prognostic factor for predicting outcome. However, no good outcome prediction system that combines the staging system with other chronic systemic diseases is available to date. We investigated melanoma-related data from a population-based database and developed an outcome prediction tool for melanoma patients via machine learning. OBJECTIVE Build up a prediction tool for melanoma patients METHODS The clinical data of patients with melanoma were extracted from Taiwan’s National Health Insurance Research Database between 2008 and 2015 and were analysed in this study. Clinical data including demographic, pathologic, staging, and treatment data from melanoma patients over 18 years old were abstracted and collected. Prognostic factors were analyzed. Logistic regression (LR), random forest (RF) modelling, and multivariate adaptive regression splines (MARS) were applied to calculate predicted overall survival (OS). A 5-fold cross-validation method was applied. Two age groups (≥64 years old as the older age group and <64 years old as the general population group) with different prognostic factors were identified, and prognostic models for survival outcomes were built. RESULTS A total of 3481 patients were enrolled in our study. The 1-, 3-, and 5-year overall survival rates were 92.2%, 80.1%, and 70.3%, respectively. The Cox proportional hazard model showed that older age, male sex, higher grade, higher clinical stage, larger tumour size, positive surgical margins, no surgical intervention, and a higher Charlson comorbidity index (CCI) were associated with higher hazard ratios. LR, RF, and MARS techniques were used to validate the overall survival without tracking time, the accuracy of the MARS model for the <64-year-old patients and ≥64-year-old patients was 90.4% and 80.7%, respectively, with 3-, and 5-year the accuracy of prediction models are 94% and 89.6%. CONCLUSIONS Machine learning techniques offer excellent survival prediction in melanoma patients. Age-based survival prediction models may be applied for better clinical decision making. CLINICALTRIAL N/A

Prognostic factors and nomogram for survival prediction in patients with primary pulmonary lymphoma: a SEER population-based study

2019 ◽  
Vol 60 (14) ◽  
pp. 3406-3416 ◽  
Author(s):  
Soon Khai Low ◽  
Ahmad Helmy Zayan ◽  
Obaida Istanbuly ◽  
Minh Duc Nguyen Tran ◽  
Amr Ebied ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Population Based ◽  
Survival Prediction ◽  
Population Based Study ◽  
Primary Pulmonary Lymphoma ◽  
Pulmonary Lymphoma

scholarly journals Incidence trends and survival prediction of hepatoblastoma in children: a population-based study

2019 ◽  
Vol 39 (1) ◽  
pp. 62 ◽  
Author(s):  
Jincheng Feng ◽  
Georgios Polychronidis ◽  
Ulrike Heger ◽  
Giovanni Frongia ◽  
Arianeb Mehrabi ◽  
...  
Keyword(s):  
Population Based ◽  
Survival Prediction ◽  
Population Based Study ◽  
Incidence Trends

The informative error: A framework for the construction of individualized phenotypes

2018 ◽  
Vol 28 (5) ◽  
pp. 1427-1438 ◽  
Author(s):  
Johannes Hertel ◽  
Stefan Frenzel ◽  
Johanna König ◽  
Katharina Wittfeld ◽  
Georg Fuellen ◽  
...  
Keyword(s):  
Prediction Error ◽  
Prediction Models ◽  
Individualized Medicine ◽  
Population Based ◽  
Model Fit ◽  
Biological Traits ◽  
Population Based Study ◽  
Statistical Framework ◽  
Definition Of ◽  
The Individual

For the goal of individualized medicine, it is critical to have clinical phenotypes at hand which represent the individual pathophysiology. However, for most of the utilized phenotypes, two individuals with the same phenotype assignment may differ strongly in their underlying biological traits. In this paper, we propose a definition for individualization and a corresponding statistical operationalization, delivering thereby a statistical framework in which the usefulness of a variable in the meaningful differentiation of individuals with the same phenotype can be assessed. Based on this framework, we develop a statistical workflow to derive individualized phenotypes, demonstrating that under specific statistical constraints the prediction error of prediction scores contains information about hidden biological traits not represented in the modeled phenotype of interest, allowing thereby internal differentiation of individuals with the same assigned phenotypic manifestation. We applied our procedure to data of the population-based Study of Health in Pomerania to construct a refined definition of obesity, demonstrating the utility of the definition in prospective survival analyses. Summarizing, we propose a framework for the individualization of phenotypes aiding personalized medicine by shifting the focus in the assessment of prediction models from the model fit to the informational content of the prediction error.

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