Risk Calculation Model Research Articles

METODE SIMULASI HISTORIS UNTUK PERHITUNGAN NILAI VALUE AT RISK PADA PORTOFOLIO DENGAN MODEL MARKOWITZ

A portfolio concerns the formation of the composition of multiple assets to obtain optimum results. At the same time, Value at Risk is a technique in risk management to measure and assess parametrically (variant and co-variant), Monte-Carlo, and historical simulation. This research employed historic simulation because normal distribution is not required from returns and is a Value at Risk calculation model that is determined by the past value on produced return asset, in which this research aimed to determine the Markowitz model positive shares and Value at Risk in the portfolio by using historical simulation. The Markowitz model found eight shares with positive expected returns, which are as follows: BBCA, BBRI, BRPT, EXCL, ICBP, INDF, MNCN, and TPIA. The BBCA has the most significant exposure of all the shares with the amount of Rp 2.287.200.440.000, while the TPIA has the smallest exposure of all the shares with the amount of Rp 58.899.375.000. Further, the EXCL has the largest VaR with the amount of Rp 236.189.538.497, while the TPIA and ICBP had no VaR losses because the VaR of TPIA and ICBP is Rp 0 and Rp -1.407.719.893, respectively, along with the INDF as the share with the smallest VaR of Rp 18.513.213.620. The most significant exposure average is Rp 719.246.318.375, while the largest VaR average is Rp 76.827.608.341,3. As long as the VaR did not exceed the exposure value, the investors will be safe and have no loss.

An improved density-based approach to risk assessment on railway investment

PurposeDensity-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.Design/methodology/approachFirst, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.FindingsThe IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.Originality/valueThis study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

Abstract 883: Parsimonious model to predict length of stay following cystectomy

Abstract BACKGROUND: The gold standard treatment for urinary bladder cancer is cystectomy, which can result in postoperative complications, patient morbidities, and lengthy hospital visits post-surgery. At present, there are limited models that can predict length of stay for patients post cystectomy. Identifying risk factors associated with increased length of stay could have translational implications in mitigating such risk factors. As such, the goal of this study was to generate parsimonious models to predict length of hospital stay (LOHS) following cystectomy in bladder cancer patients. METHODS: Data from the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) was used to analyze patients who underwent cystectomy between 2014 and 2016. Modeling building approaches were used to identify the most informative preoperative and postoperative covariates to predict LOHS. Separate models were identified for preoperative covariates and for postoperative covariates and the most informative pre- and postoperative covariates were combined into a single model. RESULTS: This analysis includes 6,074 patients (mean LOHS = 9.4 days). The preoperative (5 covariates; R squared= 0.245 and Adj R-squared = 0.240) and postoperative models (13 covariates; R squared= 0.237 and Adj R-squared = 0.236) were significantly better predictors compared to the ACS risk calculator model (19 covariates; R-square= 0.026 and Adj R-squared = 0.025). Likewise, the combined model (15 covariates) was a significantly better predictor (R squared= 0.241 & Adj R-squared = 0.239) when compared to the ACS risk calculator model. The combined model was subjected into CART analysis to stratify patients into five risk-groups based on ventilator over 48 hours, organ/space surgical site infection, wound disruption, and pneumonia. Patients in the high-risk group had significantly longer mean LOHS of 26.29 days compared to low-risk group of 8.4 days. CONCLUSIONS: This study identified and validated a clinically important combined model that is to the ACS NSQIP surgical risk calculator to predict LOHS in patients who underwent cystectomy. Patients at high-risk may benefit from allocation of additional follow-up to prevent longer LOHS. Citation Format: Rashmi Pathak, Jaileene Perez-Morales, Sephalie Y. Patel, Michael A. Poch, Matthew B. Schabath. Parsimonious model to predict length of stay following cystectomy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 883.

EnviRisk – Automated Environmental Risk Assessment for Ship Traffic in Norwegian Waters

ABSTRACT A near real-time environmental calculation of oil spill risk along the entire coast of Norway is developed as the EnviRisk model. Previous risk assessments utilize older decision models and repeated manual calculations that are costly as well as not accounting for the complexity of and changes in, ship traffic. Furthermore, cloud-providers have enabled enough data ingest and processing power to utilize high resolution shore and satellite based AIS data (Automated Identification System), to develop more dynamic and accurate risk calculation models than before. EnviRisk builds upon AISyRisk, an automated risk calculation model for marine traffic developed by the Norwegian Coastal Administration (NCA) and DNV GL. AISyRisk, includes a long-term data collection on probability of ship accidents and consequences for fatalities and oil spills for Norwegian waters (Norwegian Coastal Administration 2020). From AISyRisk, the probabilities for a certain oil spill (location, oil type and volume) is developed further to assess the environmental consequence in the EnviRisk model. As part of EnviRisk, extensive oil spill modelling is being performed in the cloud with the open source OpenDrift model (https://github.com/opendrift/opendrift/wiki) released by the Norwegian Meteorological Institute. This, combined with environmental sensitivity for both seabirds, marine mammals, fish and shoreline habitats, makes it feasible to quantify the environmental consequence and risk. Environmental risk is presented on a 10x10 km grid for the previous month of ship traffic and also accumulates statistics for risk over time. This paper presents the automated oil spill modelling and environmental risk calculation in EnviRisk. The method builds upon previous risk assessments for NCA for the Norwegian Coast (Braathen and Brude, 2011), for Svalbard and Jan Mayen (Braathen et. al., 2014) and for Greenland for Defence Command Denmark also in 2014 (Eikeland et. al., 2014). The approach is significantly improved particularly with respect to the oil spill modelling. Updates of AISyRisk and EnviRisk data and calculations are done monthly and the results published on a web portal administered by the Norwegian Coastal Administration where aggregated results are publicly available.

Risk variation of reservoir regulation during flood season based on bivariate statistical approach under climate change: a case study in the Chengbihe reservoir, China

The risk analysis of reservoir regulation in the flood season is crucial and provides the valuable information for reservoir flood control, safety operation, and decision making, especially under climate change. The purpose of this study is to propose a framework for reasonably estimating the variation of reservoir regulation risk including the dam extreme risk and the overtopping risk during the flood season under climate change. The framework consists of an integrated diagnostic system for detecting the climate abrupt change time, a copula function-based bivariate statistical approach for modeling the dependence between the flood peak and flood volume, a Monte Carlo simulation for generating numerous random flood peak–volume pairs, and a risk calculation model for routing the design flood hydrographs to obtain the frequency curve of the maximum water level reached in front of dam and evaluating the reservoir regulation risk. The methodology was implemented in the Chengbihe reservoir in south China by using the 55-year (1963–2017) hydrometeorological data, including temperature, evaporation, precipitation, and streamflow, in the flood season. Results show that the hydrometeorological series during the flood season changed abruptly in 1992 and the entire data can be divided into two periods (1963–1992 and 1993–2017). The dam extreme risk and overtopping risk during the two periods are assessed, respectively, and a comparison analysis is made based on different flood limit water-level schemes (185.00–188.50 m). It demonstrates that both the dam extreme risk and the dam overtopping risk increase under the influence of climate change. The dam extreme risk increases by 22.91–95.03%, while the climate change-induced increase in the dam overtopping risk is between 38.62 and 123.59%, which indicates that the dam overtopping risk is more sensitive to climate change than the dam extreme risk. The risk evaluations in the study are of great significance in the safety operation and risk management of reservoirs under future climate change.

Evolution and Outcomes of Premature Coronary Artery Disease.

The purpose of this review is to explore the evolution and outcomes of premature coronary artery disease (PCAD) while reviewing strategies for effective screening of those at high risk for developing this disease. Premature coronary artery disease (PCAD) affects a population of patients not typically identified as high risk by current risk stratification guidelines or traditional risk calculation tools. Not only does PCAD represent a large proportion of overall cardiovascular disease, it also afflicts a population in which the rate of mortality from cardiovascular disease has plateaued despite an overall declining population-wide cardiovascular mortality rate. There is ample opportunity for behavioral change strategies, screening tools, adapted imaging modalities, and precision pharmacotherapies to be more precisely targeted toward those at highest risk for premature coronary artery disease. Premature coronary artery disease (PCAD) is pervasive and not frequently represented within contemporary risk calculation models. Providers should pursue proactive screening and aggressive risk factor modification and deploy appropriate preventative therapies in caring for younger populations.

Second trimester maternal serum D-dimer combined with alpha-fetoprotein and free \u03b2-subunit of human chorionic gonadotropin predict hypertensive disorders of pregnancy: a systematic review and retrospective case\u2013control study

BackgroundThis study investigated whether maternal serum D-dimer (DD) alone or DD combined with alpha-fetoprotein (AFP) and free β-subunit of human chorionic gonadotropin (free β-hCG) in the second trimester could be used to predict hypertensive disorders of pregnancy (HDP).Materials and methodsIn this retrospective case–control study, the data of gravidas patients who delivered at hospital were divided into the following groups: control (n = 136), gestational hypertension (GH, n = 126), preeclampsia (PE, n = 53), and severe preeclampsia (SPE, n = 41). Receiver operator characteristic (ROC) curves were used to evaluate the diagnostic value of maternal serum DD, AFP, and free β-hCG levels for HDP.ResultsDD levels of the GH, PE, and SPE groups were significantly higher than that of the control group (P < 0.001). The order of effectiveness for models predicting HDP was as follows: DD + AFP + free β-hCG > DD > DD + AFP > DD + free β-hCG > AFP + free β-hCG > AFP > free β-hCG. For predicting different types of HDP, DD alone had the best diagnostic value for SPE, followed by PE and GH. DD alone had a sensitivity of 100% with a 0% false negative rate and had the highest positive likelihood ratio (+ LR) for SPE. DD alone in combination with AFP alone, free β-hCG alone and AFP + free β-hCG could reduce false positive rate and improve + LR.ConclusionDD is possible the best individual predictive marker for predicting HDP. Levels of DD alone in the second trimester were positively correlated with the progression of elevated blood pressure in the third trimester, demonstrating the predicting the occurrence of HDP. The risk calculation model constructed with DD + free β-hCG + AFP had the greatest diagnostic value for SPE.

Dynamic risk calculation model applied to gas compressors

Dynamic risk calculation model applied to gas compressors

First-trimester presentation of ultrasound findings in trisomy 13 and validation of multiparameter ultrasound-based risk calculation models to detect trisomy 13 in the late first trimester.

To identify the most common ultrasound patterns of markers and anomalies associated with Patau syndrome (PS), to explore the efficacy of multiparameter sonographic protocols in detecting trisomy 13 (T13) and to analyze the influence of maternal age (MA) on screening performance. The project was a prospective study based on singleton pregnancies referred for a first-trimester screening examination. The scan protocol included nuchal translucency (NT), fetal heart rate (FHR), secondary ultrasound markers [nasal bone (NB), tricuspid regurgitation (TR), ductus venosus reversed a-wave (revDV)] and major anomaly findings. The study population comprised 6133 pregnancies: 6077 cases of euploidy and 56 cases of T13. Statistically significant differences were found in MA, FHR, NT, absence of NB, presence of revDV, TR and single umbilical artery. Fourteen cases of T13 (25%) demonstrated no markers of aneuploidy. The best general detection rate (DR) (DR of 78.6% with an false positive rate (FPR) of 1.2%) was obtained for a cutoff of 1/300 utilizing the "NT+T13" algorithm. The logistic regression model revealed that the central nervous system (CNS) anomalies had the greatest odds ratio (of 205.4) for T13. The effectiveness of the multiparameter sonographic protocol used for T13 screening showed promising results in patients older than 36 years and suboptimal results in patients between 26 and 36 years old. When screening for T13 left heart defects, CNS anomalies, abdominal anomalies, FHR above the 95th percentile, increased NT, revDV and lack of NB should receive specific attention.

Incorporating cortisol into the NAPLS2 individualized risk calculator for prediction of psychosis

BackgroundRisk calculators are useful tools that can help clinicians and researchers better understand an individual's risk of conversion to psychosis. The North American Prodrome Longitudinal Study (NAPLS2) Individualized Risk Calculator has good predictive accuracy but could be potentially improved by the inclusion of a biomarker. Baseline cortisol, a measure of hypothalamic-pituitary-adrenal (HPA) axis functioning that is impacted by biological vulnerability to stress and exposure to environmental stressors, has been shown to be higher among individuals at clinical high-risk for psychosis (CHRP) who eventually convert to psychosis than those who do not. We sought to determine whether the addition of baseline cortisol to the NAPLS2 risk calculator improved the performance of the risk calculator. MethodsParticipants were drawn from the NAPLS2 study. A subset of NAPLS2 participants provided salivary cortisol samples. A multivariate Cox proportional hazards regression evaluated the likelihood of an individual's eventual conversion to psychosis based on demographic and clinical variables in addition to baseline cortisol levels. ResultsA total of 417 NAPLS2 participants provided salivary cortisol and were included in the analysis. Higher levels of cortisol were predictive of conversion to psychosis in a univariate model (C-index = 0.59, HR = 21.5, p-value = 0.004). The inclusion of cortisol in the risk calculator model resulted in a statistically significant improvement in performance from the original risk calculator model (C-index = 0.78, SE = 0.028). ConclusionsSalivary cortisol is an inexpensive and non-invasive biomarker that could improve individual predictions about conversion to psychosis and treatment decisions for CHR-P individuals.

Dynamic risk calculation model applied to gas compressor

Abstract The present article proposes to apply a Risk-Based Maintenance methodology in a gas compression system. This risk calculation is based on a semi-quantitative mathematical model that uses the interdependence of process variables and predictive maintenance data applied in a Condition-Based Maintenance methodology, in order to obtain the calculation of the dynamic risk. This promotes a review of maintenance and reliability strategies proactively in real time. The dynamic risk applied to the system involves the interface of predictive maintenance and process variables with the reliability and risk analyses of the system. This value, which is associated with the financial aspects, safety, people and environment, can be compared to the tolerable risk of production plant operation, providing operation or maintenance decisions. An example demonstrates the integration of process and maintenance data using this model methodology for a gas compression system. This framework can be applied to evaluate the integrity of systems, providing critical risk-based information for the maintenance and operation teams, assisting then in the mitigation of failures in the production systems.

Non-coding RNA model improves prognostic prediction in patients with nephroblastoma

Background. Nephroblastoma (Wilms tumor) is a common abdominal malignancy in children, ranking second among abdominal malignancies in children, but the pathogenesis is still unclear, and further research on their molecular mechanisms is needed. Method. We obtained lncRNA expression and clinical data from the TARGET database of the GDC data portal of the American Cancer Institute. Effective patient samples were determined based on gene differential expression analysis and clinical data screening. The risk calculation model was established by univariate and multivariate Cox regression analysis, after which the samples were divided into training group and test group to predict the prognosis of patients with nephroblastoma, and then the independent validation on gender was carried out for all samples. Finally, the corresponding target genes of lncRNA were predicted for functional enrichment analysis to explore the enrichment of genes and the interaction between them. Result. 125 valid samples were identified after screening 136 samples. After experimental analysis, five significant lncRNAs (AC004233.3, AC055764.1, SCAT8, LINC02623, AL118505.1) associated with the prognosis of nephroblastoma were found and validated. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve on the test set was 0.732, which indicated that the model was accurate and the independence verification had good discrimination. In the enrichment analysis, we can intuitively see the significant situation and promotion or inhibition of genes. It can be inferred that the experimental five lncRNAs may regulate the expression of related protein-coding genes and the production process of nephroblastoma, thus affecting the relevant pathways of cancer development. Conclusions. This study systematically analyzed the lncRNA expression and clinical data of patients with nephroblastoma. The data obtained by establishing Cox proportional hazard regression model can further understand the molecular mechanism related to nephroblastoma and play an auxiliary role in the clinical diagnosis of doctors, thereby improving the diagnosis, treatment and prognosis of patients with nephroblastoma.

Risk assessment of landslide-induced surge disaster of river type reservoir in mountainous area

The landslide-induced surge of river type reservoir in the mountainous area has become one of the most important disasters affecting the waterborne transportation and the safety of human life and property in the reservoir area. Based on the field observation of landslide and landslide-induced surge in the Three Gorges Reservoir (TGR) area and relevant research results, this paper develops the basic principles for selecting the risk factors of landslide-induced surge, analyses the risk factors of landslide-induced surge, and gives the main and secondary risk factors and their characteristics. According to the characteristics of landslide-induced surge disaster, the hierarchical structure, judgment matrix, and risk calculation model suitable for landslide-induced surge disaster are established by using analytic hierarchy process method, and the classification standard of the action index of each main evaluation factor is determined. This well-developed evaluation method is then applied to evaluate the risk of Hongyanzi landslide-induced surge disaster in the TGR area. The evaluation result is high risk, which is consistent with the actual disaster situation.

Dynamic Semi-Quantitative Risk Research in Chemical Plants

When a major accident occurs in a chemical industry park, it directly affects the personal safety of operators and neighboring residents and causes major losses; therefore, we should take measures to strengthen the management of chemical industry parks. This article proposes and analyzes a new dynamic semi-quantitative risk calculation model for chemical plants that can be applied digitally. This model provides a sustainable, standardized, and comprehensive management strategy for the safety management of chemical plants and chemical industry park managers. The model and its determined parameters were applied to the safety management of chemical companies within the chemical industry park of Quzhou, Zhejiang Province. From the point of view of the existing semi-quantitative model, the existing problems of the current model are analyzed, the current model is optimized, and a new dynamic semi-quantitative calculation model scheme is proposed. The new model uses an analytical hierarchy process targeting the factors affecting the risks in chemical plants, and chemical plant semi-quantitative dynamic calculation system consisting of the operator, process/equipment, risk, building environment, safety management, and domino effect, and the comprehensive risk of the chemical plant was calculated. The model is ultimately a real-time quantitative value, but its calculation process can compare and analyze the causes of high risk in a chemical plant as they relate to these six factors. Its implementation requires only software, which will greatly help chemical plant safety management.

Risk Prediction Tools Available for Germline BRCA1/2 Mutations Underperform in Prostate Cancer Patients

Early identification of germline BRCA1/2 mutations may be relevant for the management of patients with prostate cancer (PC) and to prevent future breast and ovarian cancers in their relatives. Several prediction tools have been developed to estimate the likelihood of a germline BRCA1/2 mutation and are widely used to optimize screening in breast and ovarian cancer patients. We aimed to elucidate the proportion of PC patients with known BRCA1/2 mutations who would have qualified for testing using two risk calculation models (BRCAPRO and the Manchester scoring system [MSS]). We analyzed 106 families with known BRCA1/BRCA2 mutations, including 23 with PC cases. Only 30% and 48% of PC patients who were known BRCA1/BRCA2 mutations carriers would have qualified for testing using BRCAPRO and MSS, respectively. A median of two breast and/or ovarian cancer cases per family had occurred between the first PC identified in a carrier and the cancer case leading to germline testing. Patient summaryWe tested two models developed to predict the probability of inherited BRCA1/BRCA2 mutations and found that these tools underperform in men with prostate cancer and should not be used to optimize testing in this population.

Model for calculating the risk of venous thrombosis

Aim. To develop a model for calculating the risk of venous thrombosis, taking into account the presence of known risk factors, comorbidity and congenital thrombophilia. Material and methods . During the study (2015 to 2017), 79 patients with venous thrombosis were examined (36 men and 43 women, mean age — 56,76±15,570). The control group consisted of 83 patients and healthy volunteers without thrombosis at the moment and in history (35 men and 48 women, average age — 43,95±18,136). All individuals included in the study were analyzed for the presence of G1691A mutations in the factor V gene, G20210A in the prothrombin gene, C677T polymorphism in the 5,10-methylenetetrahydrofolate reductase gene, and polymorphism in the SERPINE1 gene of plasminogen activator inhibitor. Real-time polymerase chain reaction was used to identify mutations. To create a risk calculation model, a linear regression analysis was performed. Results. We have developed a model for calculating the risk of venous thrombosis. The resulting formula showed high prognostic accuracy (the area under the ROC curve is 95,9%). For patients who do not have data on the presence of these mutations, a short version of the risk calculation model was developed (the area under the ROC curve is 94,6%). Conclusion. We have developed a risk calculation model taking into account the presence of known risk factors, congenital thrombophilia and comorbidities. Thromboprophylaxis is necessary in &gt;0,45 individual risk, which corresponds to a high risk of developing venous thrombosis. Patients who have not previously been diagnosed with thrombophilia and are in the middle risk group for venous thrombosis, according to a short version of the model, must be screened for congenital thrombophilia to clarify the risk.

Comparative study of Alternative Fistula Risk Score (A-FRS) and Fistula Risk Score (FRS) For Predicting the Risk of Postoperative Pancreatic Fistula After Pancreaticoduodenectomy

Introduction: Postoperative pancreatic fistula (POPF) remains the most serious complication after pancreatico-duodenectomy (PD) and the risk of developing POPF is more or less equal with different techniques of reconstruction. Various risk calculation models have been developed for predicting / calculating risk of pancreatic fistula. Fistula risk score (FRS), described in 2013 is the most commonly used scoring system for this purpose. Recently alternative fistula score (a-FRS), was developed and validated in western population. Both FRS & a-FRS are developed and validated in western population with predominantly pancreatic head tumors; whereas, in India we mostly encounter periampullary tumors. Aim: to compare the a-FRS and FRS models for predicting POPF. Method: We performed a retrospective analysis from 1stJanuary 2011 to 31stMarch 2018. All the patients undergoing PD with pancreatico-enteric reconstruction by PJ method were included and patients undergoing distal /median/ total pancreatectomy were excluded. A-FRS and FRS scores were calculated and compared with the actual POPF rates (defined by 2016 ISGPS definition). Result: study cohort contained 825 patients with male preponderance. Median age of the study cohort was 55 years. 61.8% tumors were located in periampullary region and 77.4% were adenocarcinoma. Overall CR-POPF rate in our study population was 16.8% and was represented with more accuracy by a-FRS model (p value-0.003). The performance of each model was assessed byarea under the receiver operating curve (AUC) and calibration plots. Conclusion: a-FRS model performed better in as compared to FRS model (AUC- 0.70 Vs 0.65, p value-0.003) for prediction of POPF but, AUC’s of both the models were still not the best for the perfect prediction model.

Validation of prediction models for risk stratification of incidentally detected pulmonary subsolid nodules: a retrospective cohort study in a Korean tertiary medical centre

ObjectivesTo validate the performances of two prediction models (Brock and Lee models) for the differentiation of minimally invasive adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions among subsolid...

Determining Risk of Colorectal Cancer and Starting Age of Screening Based on Lifestyle, Environmental, and Genetic Factors

Guidelines for initiating colorectal cancer (CRC) screening are based on family history but do not consider lifestyle, environmental, or genetic risk factors. We developed models to determine risk of CRC, based on lifestyle and environmental factors and genetic variants, and to identify an optimal age to begin screening. We collected data from 9748 CRC cases and 10,590 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colorectal Transdisciplinary study, from 1992 through 2005. Half of the participants were used to develop the risk determination model and the other half were used to evaluate the discriminatory accuracy (validation set). Models of CRC risk were created based on family history, 19 lifestyle and environmental factors (E-score), and 63 CRC-associated single-nucleotide polymorphisms identified in genome-wide association studies (G-score). We evaluated the discriminatory accuracy of the models by calculating area under the receiver operating characteristic curve values, adjusting for study, age, and endoscopy history for the validation set. We used the models to project the 10-year absolute risk of CRC for a given risk profile and recommend ages to begin screening in comparison to CRC risk for an average individual at 50 years of age, using external population incidence rates for non-Hispanic whites from the Surveillance, Epidemiology, and End Results program registry. In our models, E-score and G-score each determined risk of CRC with greater accuracy than family history. A model that combined both scores and family history estimated CRC risk with an area under the receiver operating characteristic curve value of 0.63 (95% confidence interval, 0.62-0.64) for men and 0.62 (95% confidence interval, 0.61-0.63) for women; area under the receiver operating characteristic curve values based on only family history ranged from 0.53 to 0.54 and those based only E-score or G-score ranged from 0.59 to 0.60. Although screening is recommended to begin at age 50 years for individuals with no family history of CRC, starting ages calculated based on combined E-score and G-score differed by 12 years for men and 14 for women, forindividuals with thehighest vs the lowest 10% of risk. We used data from 2 large international consortia to develop CRC risk calculation models that included genetic and environmental factors along with family history. These determine risk of CRC and starting ages for screening with greater accuracy than the family history only model, whichis based on the current screening guideline. These scoring systems might serve as a first step toward developing individualized CRC prevention strategies.

Can we predict postoperative complications in elderly Chinese patients with hip fractures using the surgical risk calculator?

PurposeHip fractures are associated with poor prognosis in elderly patients partly due to the high rate of postoperative complications. This study was aimed to investigate whether the surgical risk calculator is suitable for predicting postoperative complications in elderly Chinese patients with hip fractures.MethodsThe incidence of postoperative complications among 410 elderly patients with hip fractures was predicted by the surgical risk calculator and then compared with the actual value. The risk calculator model was evaluated using the following three metrics: Hosmer–Lemeshow test for the goodness-of-fit of the model, receiver operating characteristic curve (ROC) (also referred as C-statistic) for the predictive specificity and sensitivity, and the Brier’s score test for predictive accuracy.ResultsPreoperative risk factors including gender, age, preoperative functional status, American Society of Anesthesiologists grade, hypertension, dyspnea, dialysis, previous cardio-vascular history, and cerebrovascular disease were positively correlated with the incidence of postoperative complications in elderly patients with hip fractures. The predicted complication incidence rate was well matched with the actual complication rate by Hosmer–Lemeshow test. The model had high sensitivity and specificity for predicting the mortality rate of these patients with a C-statistic index of 0.931 (95% CI [0.883, 0.980]). The surgical calculator model had an accuracy of 90% for predicting the reoperation rate (Brier’s score <0.01).ConclusionsThe surgical risk calculator could be useful for predicting mortality and reoperation in elderly patients with hip fracture. Patients and surgeons may use this simple calculator to better manage the preoperative risks.