scholarly journals Parsimonious Eurolung risk models to predict cardiopulmonary morbidity and mortality following anatomic lung resections: an updated analysis from the European Society of Thoracic Surgeons database

2019 ◽  
Author(s):  
Alessandro Brunelli ◽  
Silvia Cicconi ◽  
Herbert Decaluwe ◽  
Zalan Szanto ◽  
Pierre Emmanuel Falcoz
Keyword(s):  
Risk Model ◽  
Lung Resection ◽  
Predictive Ability ◽  
Risk Models ◽  
Multiple Imputations ◽  
Risk Of Mortality ◽  
Parsimonious Models ◽  
Parsimonious Model ◽  
User Friendly ◽  
Lung Resections

Abstract OBJECTIVES To develop a simplified version of the Eurolung risk model to predict cardiopulmonary morbidity and 30-day mortality after lung resection from the ESTS database. METHODS A total of 82 383 lung resections (63 681 lobectomies, 3617 bilobectomies, 7667 pneumonectomies and 7418 segmentectomies) recorded in the ESTS database (January 2007–December 2018) were analysed. Multiple imputations with chained equations were performed on the predictors included in the original Eurolung models. Stepwise selection was then applied for determining the best logistic model. To develop the parsimonious models, different models were tested eliminating variables one by one starting from the less significant. The models’ prediction power was evaluated estimating area under curve (AUC) with the 10-fold cross-validation technique. RESULTS Cardiopulmonary morbidity model (Eurolung1): the best parsimonious Eurolung1 model contains 5 variables. The logit of the parsimonious Eurolung1 model was as follows: −2.852 + 0.021 × age + 0.472 × male −0.015 × ppoFEV1 + 0.662×thoracotomy + 0.324 × extended resection. Pooled AUC is 0.710 [95% confidence interval (CI) 0.677–0.743]. Mortality model (Eurolung2): the best parsimonious model contains 6 variables. The logit of the parsimonious Eurolung2 model was as follows: −6.350 + 0.047 × age + 0.889 × male −0.055 × BMI −0.010 × ppoFEV1 + 0.892 × thoracotomy + 0.983 × pneumonectomy. Pooled AUC is 0.737 (95% CI 0.702–0.770). An aggregate parsimonious Eurolung2 was also generated by repeating the logistic regression after categorization of the numeric variables. Patients were grouped into 7 risk classes showing incremental risk of mortality (P < 0.0001). CONCLUSIONS We were able to develop simplified and updated versions of the Eurolung risk models retaining the predictive ability of the full original models. They represent a more user-friendly tool designed to inform the multidisciplinary discussion and shared decision-making process of lung resection candidates.

scholarly journals Bio-artificial pleura using autologous dermal fibroblast sheets to mitigate air leaks during thoracoscopic lung resection

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Masato Kanzaki ◽  
Ryo Takagi ◽  
Kaoru Washio ◽  
Mami Kokubo ◽  
Shota Mitsuboshi ◽  
...  
Keyword(s):  
Dermal Fibroblast ◽  
Lung Resection ◽  
Fibroblast Cell ◽  
Patient Specific ◽  
Cell Content ◽  
Cell Monolayers ◽  
Air Leaks ◽  
Skin Biopsies ◽  
Dermal Cells ◽  
Lung Resections

AbstractLung air leaks (LALs) due to visceral pleura injury during surgery are a difficult-to-avoid complication in thoracic surgery (TS). Reliable LAL closure is an important patient management issue after TS. We demonstrated both safeties of transplantation of a cultured human autologous dermal fibroblast sheet (DFS) to LALs. From May 2016 to March 2018, five patients who underwent thoracoscopic lung resection met all the inclusion criteria. Skin biopsies were acquired from each patient to source autologous dermal cells for DFS fabrication. During the primary culture, fibroblasts migrated from the dermal tissue pieces and proliferated to form cell monolayers. These fibroblasts were subcultured to confluence. Transplantable DFSs were fabricated from these subcultured fibroblasts that were trypsinized and seeded onto temperature-responsive culture dishes. After 10 days of fabrication culture, intact patient-specific DFS were harvested. DFSs were analyzed for fibroblast cell content and tissue contaminants prior to application. For closing intraoperative LAL, mean number of transplanted autologous DFS per patient was 6 ± 2 sheets. Mean chest drainage duration was 5.0 ± 4.8 days. The two patients with major LAL had a drainage duration of more than 7 days. All patients currently have no LAL recurrence after discharge. DFSs effectively maintain LAL closure via remodeling of the deposited extracellular matrix. The use of autologous DFSs to permanently close air leaks using a patient-derived source is expected to reduce surgical complications during high-risk lung resections.

scholarly journals Ruin problems in Markov-modulated risk models

2017 ◽  
Vol 12 (1) ◽  
pp. 23-48 ◽  
Author(s):  
David C.M. Dickson ◽  
Marjan Qazvini
Keyword(s):  
Continuous Time ◽  
Risk Model ◽  
Numerical Algorithms ◽  
Binomial Model ◽  
Risk Models ◽  
Time Of Ruin ◽  
Compound Binomial ◽  
Compound Binomial Model ◽  
Markov Modulated ◽  
The Time Of Ruin

AbstractChen et al. (2014), studied a discrete semi-Markov risk model that covers existing risk models such as the compound binomial model and the compound Markov binomial model. We consider their model and build numerical algorithms that provide approximations to the probability of ultimate ruin and the probability and severity of ruin in a continuous time two-state Markov-modulated risk model. We then study the finite time ruin probability for a discrete m-state model and show how we can approximate the density of the time of ruin in a continuous time Markov-modulated model with more than two states.

Determining the Number of Factors When Population Models Can Be Closely Approximated by Parsimonious Models

2021 ◽  
pp. 001316442199283
Author(s):  
Yan Xia
Keyword(s):  
Factor Model ◽  
Population Level ◽  
Parallel Analysis ◽  
Model Data ◽  
Behavioral Studies ◽  
Number Of Factors ◽  
Parsimonious Models ◽  
Parsimonious Model ◽  
Fit Index ◽  
Tucker Lewis Index

Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser’s rule, minimum average partial, sequential χ2, and sequential root mean square error of approximation, comparative fit index, and Tucker–Lewis index under a realistic scenario in behavioral studies, where researchers employ a closing–fitting parsimonious model with K factors to approximate a population model, leading to a trivial model-data misfit. Results show that while traditional and RPA both stand out when zero population-level misfits exist, the accuracy of RPA substantially deteriorates when a K-factor model can closely approximate the population. TPA is the least sensitive to trivial misfits and results in the highest accuracy across most simulation conditions. This study suggests the use of TPA for the investigated models. Results also imply that RPA requires further revision to accommodate a degree of model–data misfit that can be tolerated.

scholarly journals On a Class of Semi-Markov Risk Models Obtained as Classical Risk Models in a Markovian Environment

1984 ◽  
Vol 14 (1) ◽  
pp. 23-43 ◽  
Author(s):  
Jean-Marie Reinhard
Keyword(s):  
Risk Model ◽  
Risk Process ◽  
Ruin Probabilities ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
Risk Models ◽  
Markovian Environment ◽  
Necessary And Sufficient ◽  
Natural Way ◽  
Quantities Of Interest

AbstractWe consider a risk model in which the claim inter-arrivals and amounts depend on a markovian environment process. Semi-Markov risk models are so introduced in a quite natural way. We derive some quantities of interest for the risk process and obtain a necessary and sufficient condition for the fairness of the risk (positive asymptotic non-ruin probabilities). These probabilities are explicitly calculated in a particular case (two possible states for the environment, exponential claim amounts distributions).

scholarly journals Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

2020 ◽  
Author(s):  
Dai Zhang ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
Jia Yao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gynecological Cancer ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.

scholarly journals Dietary Information Improves Model Performance and Predictive Ability of a Noninvasive Type 2 Diabetes Risk Model

PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0166206 ◽  
Author(s):  
Tianshu Han ◽  
Shuang Tian ◽  
Li Wang ◽  
Xi Liang ◽  
Hongli Cui ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Model ◽  
Model Performance ◽  
Predictive Ability ◽  
Diabetes Risk

An Evaluation of a Test-Driven Security Risk Analysis Approach Based on Two Industrial Case Studies

2019 ◽  
pp. 69-103 ◽  
Author(s):  
Gencer Erdogan ◽  
Phu H. Nguyen ◽  
Fredrik Seehusen ◽  
Ketil Stølen ◽  
Jon Hofstad ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Case Studies ◽  
Risk Model ◽  
Case Study Research ◽  
Security Risk ◽  
Risk Models ◽  
First Case ◽  
Assessment Approach ◽  
Security Risk Assessment

Risk-driven testing and test-driven risk assessment are two strongly related approaches, though the latter is less explored. This chapter presents an evaluation of a test-driven security risk assessment approach to assess how useful testing is for validating and correcting security risk models. Based on the guidelines for case study research, two industrial case studies were analyzed: a multilingual financial web application and a mobile financial application. In both case studies, the testing yielded new information, which was not found in the risk assessment phase. In the first case study, new vulnerabilities were found that resulted in an update of the likelihood values of threat scenarios and risks in the risk model. New vulnerabilities were also identified and added to the risk model in the second case study. These updates led to more accurate risk models, which indicate that the testing was indeed useful for validating and correcting the risk models.

Assessing the Usefulness of Testing for Validating and Correcting Security Risk Models Based on Two Industrial Case Studies

2016 ◽  
pp. 1016-1037
Author(s):  
Gencer Erdogan ◽  
Fredrik Seehusen ◽  
Ketil Stølen ◽  
Jon Hofstad ◽  
Jan Øyvind Aagedal
Keyword(s):  
Risk Assessment ◽  
Case Studies ◽  
Web Application ◽  
Risk Model ◽  
Security Risk ◽  
Risk Models ◽  
First Case ◽  
New Information ◽  
Financial Application

The authors present the results of an evaluation in which the objective was to assess how useful testing is for validating and correcting security risk models. The evaluation is based on two industrial case studies. In the first case study the authors analyzed a multilingual financial Web application, while in the second case study they analyzed a mobile financial application. In both case studies, the testing yielded new information which was not found in the risk assessment phase. In particular, in the first case study, new vulnerabilities were found which resulted in an update of the likelihood values of threat scenarios and risks in the risk model. New vulnerabilities were also identified and added to the risk model in the second case study. These updates led to more accurate risk models, which indicate that the testing was indeed useful for validating and correcting the risk models.

scholarly journals Partial anomalous pulmonary venous connection detected during right pneumonectomy

2019 ◽  
Vol 30 (3) ◽  
pp. 497-498
Author(s):  
Bülent Mustafa Yenigün ◽  
Gökhan Kocaman ◽  
Ayşegül Gürsoy Çoruh ◽  
Rıfat Murat Akal
Keyword(s):  
Lung Cancer ◽  
Congenital Anomaly ◽  
Case Report ◽  
Surgical Treatment ◽  
Septal Defect ◽  
Lung Resection ◽  
Rare Congenital Anomaly ◽  
The Right ◽  
Anomalous Pulmonary Venous Connection ◽  
Lung Resections

Abstract Partial anomalous pulmonary venous connection (PAPVC) is a rare congenital anomaly. Generally, it is seen on the right side and is associated with an atrial septal defect. Herein, we present a case of a 50-year-old male patient with a supracardiac type PAPVC detected during pneumonectomy for a right hilar mass. This is the second case report in the literature presenting surgical treatment of both lung cancer and PAPVC using pneumonectomy. Thoracic surgeons should be aware of this anomaly when they are planning to perform a major lung resection. If PAPVC and lung cancer are in the same lobe, anatomical lung resections including pneumonectomy can be safely performed.

scholarly journals Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

2016 ◽  
Vol 34 (21) ◽  
pp. 2534-2540 ◽  
Author(s):  
Kathleen F. Kerr ◽  
Marshall D. Brown ◽  
Kehao Zhu ◽  
Holly Janes
Keyword(s):  
Prostate Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Model ◽  
Characteristic Curve ◽  
Clinical Impact ◽  
Adjuvant Radiation ◽  
Risk Models ◽  
Personal Benefit ◽  
The Impact

The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man’s risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics.

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