Risk Estimation Model Research Articles

Predictive performance of the Garvan Fracture Risk Calculator: a registry-based cohort study.

The optimal model for fracture risk estimation to guide treatment decision-making remains controversial. Our objective was toevaluate the predictive performance of the Garvan Fracture Risk Calculator (FRC) in a large clinical registry from Manitoba, Canada. Using the population-based Manitoba Bone Mineral Density (BMD) registry, we identified women and men aged 50-95years undergoing baseline BMD assessment from September 1, 2012, onwards. Five-year Garvan FRC predictions were generated from clinical risk factors (CRFs) with and without femoral neck BMD. We identified incident non-traumatic osteoporotic fractures (OFs) and hip fractures (HFs) from population-based healthcare data sources to March 31, 2018. Fracture risk was assessed from area under the receiver operating characteristic curve (AUROC). Cox regression analysis and calibration ratios (5-year observed/predicted) were assessed for risk quintiles. All analyses were sex stratified. We included 16,682 women (mean age 66.6 + / - SD 8.7years) and 2839 men (mean age 68.7 + / - SD 10.2years). During a mean observation time of 2.6years, incident OFs were identified in 681 women and 140 men and HFs in 199 women and 22 men. AUROC showed significant fracture risk stratification with the Garvan FRC. Tool predictions without BMD were better than from age or decreasing weight, and the tool with BMD performed better than BMD alone. Garvan FRC with BMD performed better than without BMD, especially for HF prediction (AUROC 0.86 in women, 0.82 in men). There was a strong gradient of increasing risk across Garvan FRC quintiles (highest versus lowest, hazard ratios women 5.75 and men 3.43 for any OF; women 101.6 for HF). Calibration differences were noted, with both over- and underestimation in risk. Garvan FRC outperformed CRFs and BMD alone for fracture risk stratification, particularly for HF, but may require recalibration for accurate predictions in this population.

Mathematical methods for studying risks (resumptive article)

We define risk as an unwanted opportunity and divide risk theory into three stages — risk analysis, risk estimation, risk management. Safety and risk are directly related to each other, being like a «mirror image» of each other which necessitates developing both the general theory of risk and particular theories of risk in specific areas. General risk theory allows for a uniform approach to the analysis, estimation and management of risks in specific situations. Currently, three main approaches to accounting for the uncertainty and describing risks are used — probabilistic and statistical approach, fuzzy sets, and the approach based on interval mathematics. The methods of risk estimation primarily based on probabilistic and statistical models are considered. The mathematical apparatus for estimating and managing risks is based on nonparametric formulations, limit relations, and multi-criteria optimization. Asymptotic nonparametric point estimates and confidence limits for the probability of a risk event are constructed on the base of binomial distribution and the Poisson distribution. Rules for testing statistical hypotheses regarding the equality (or difference) of two probabilities of risk events are proposed. An additive-multiplicative risk estimation model based on a hierarchical risk system based on a three-level risk system has become widespread: private risks — group risks — final risk. For this model, the role of expert estimation is revealed. The prospects of using (in the future) the theory of fuzzy sets are shown. The article deals with the main components of the mathematical apparatus of the theory of risks, in particular, the mathematical support of private theories of risks related to the quality management, innovations and investments. The simplest risk assessment in a probabilistic-statistical model is the product of the probability of a risk event and the mathematical expectation of the accidental damage. Mathematical and instrumental methods for studying global economic and environmental risks are discussed.

Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care.

We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.

Association of Clinicopathologic and Molecular Tumor Features With Recurrence in Resected Early-Stage Epidermal Growth Factor Receptor–Positive Non–Small Cell Lung Cancer

The recently published ADAURA study has posed a significant dilemma for clinicians in selecting patients for adjuvant osimertinib. Risk factors for recurrence in early-stage epidermal growth factor receptor (EGFR)-positive non-small cell lung cancer (NSCLC) also remain undefined. To determine clinicopathologic characteristics and recurrence patterns of resected early-stage EGFR-positive NSCLC, using wildtype EGFR as a comparator cohort, and identify features associated with recurrence. This is a cohort study including patients diagnosed with AJCC7 Stage IA to IIIA NSCLC between January 1, 2010, and June 30, 2018, who underwent curative surgical procedures at a specialist cancer center in Singapore. The cutoff for data analysis was October 15, 2020. Patient demographic characteristics, treatment history, and survival data were collated. In exploratory analysis, whole-exome sequencing was performed in a subset of 86 patients. Data were analyzed from September 3, 2020, to June 6, 2021. Adjuvant treatment was administered per investigator's discretion. The main outcome was 2-year disease-free survival (DFS). A total of 723 patients were included (389 patients with EGFR-positive NSCLC; 334 patients with wildtype EGFR NSCLC). There were 366 women (50.6%) and 357 men (49.4%), and the median (range) age was 64 (22-88) years. A total of 299 patients (41.4%) had stage IA NSCLC, 155 patients (21.4%) had stage IB NSCLC, 141 patients (19.5%) had stage II NSCLC, and 125 patients (17.3%) had stage IIIA NSCLC. Compared with patients with wildtype EGFR NSCLC, patients with EGFR-positive NSCLC were more likely to be women (106 women [31.7%] vs 251 women [64.5%]) and never smokers (121 never smokers [36.2%] vs 317 never smokers [81.5%]). At median (range) follow up of 46 (0-123) months, 299 patients (41.4%) had cancer recurrence. There was no statistically significant difference in 2-year DFS for EGFR-positive and wildtype EGFR NSCLC (70.2% [95% CI, 65.3%-74.5%] vs 67.6% [95% CI, 62.2%-72.4%]; P = .70), although patients with EGFR-positive NSCLC had significantly better 5-year overall survival (77.7% [95% CI, 72.4%-82.1%] vs 66.6% [95% CI, 60.5%-72.0%]; P = .004). Among patients with EGFR-positive NSCLC, 2-year DFS was 81.0% (95% CI, 74.0%-86.3%) for stage IA, 78.4% (95% CI, 68.2%-85.6%) for stage IB, 57.1% (95% CI, 43.7%-68.4%) for stage II, and 46.6% (95% CI, 34.7%-57.7%) for stage IIIA. Overall, 5-year DFS among patients with stage IB through IIIA was 37.2% (95% CI, 30.1%-44.3%). Sites of disease at recurrence were similar between EGFR-positive and wildtype EGFR NSCLC, with locoregional (64 patients [16.5%] vs 56 patients [16.8%]), lung (41 patients [10.5%] vs 40 patients [12.0%]), and intracranial (37 patients [9.5%] vs 22 patients [6.6%]) metastases being the most common. A risk estimation model incorporating genomic data and an individual patient nomogram using clinicopathologic features for stage I EGFR-positive NSCLC was developed to improve risk stratification. This cohort study found that recurrence rates were high in early-stage EGFR-positive NSCLC including stage IA, yet 37.2% of patients with stage IB through IIIA were cured without adjuvant osimertinib. Further studies are needed to elucidate individualized surveillance and adjuvant treatment strategies for early-stage EGFR-positive NSCLC.

Bariatric surgery increases the rate of major fracture: self-controlled case series study in UK Clinical Practice Research Datalink.

ABSTRACTConflicting results exist about the relationship between bariatric surgery and fracture risk. Also, prediction of who is at increased risk of fracture after bariatric surgery is not currently available. Hence, we used a combination of a self‐controlled case series (SCCS) study to establish the association between bariatric surgery and fracture, and develop a prediction model for postoperative fracture risk estimation using a cohort study. Patients from UK Primary care records from the Clinical Practice Research Datalink GOLD linked to Hospital Episode Statistics undergoing bariatric surgery with body mass index (BMI) ≥30 kg/m2 between 1997 and 2018 were included in the cohort. Those sustaining one or more fractures in the 5 years before or after surgery were included in the SCCS. Fractures were considered in three categories: (i) any except skull and digits (primary outcome); (ii) major (hip, vertebrae, wrist/forearm, and humerus); and (iii) peripheral (forearm and lower leg). Of 5487 participants, 252 (4.6%) experienced 272 fractures (of which 80 were major and 135 peripheral) and were included in the SCCS analyses. Major fracture risk increased after surgery, incidence rate ratios (IRRs) and 95% confidence intervals (CIs): 2.77 (95% CI, 1.34–5.75) and 3.78 (95% CI, 1.42–10.08) at ≤3 years and 3.1 to 5 years postsurgery when compared to 5 years prior to surgery, respectively. Any fracture risk was higher only in the 2.1 to 5 years following surgery (IRR 1.73; 95% CI, 1.08–2.77) when compared to 5 years prior to surgery. No excess risk of peripheral fracture after surgery was identified. A prediction tool for major fracture was developed using 5487 participants included in the cohort study. It was also internally validated (area under the receiver‐operating characteristic curve [AUC ROC] 0.70) with use of anxiolytics/sedatives/hypnotics and female as major predictors. Hence, major fractures are nearly threefold more likely after bariatric surgery. A simple prediction tool with five variables identifies high risk patients for major fracture. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID-19.

Aerosol transmission has played a significant role in the transmission of COVID‐19 disease worldwide. We developed a COVID‐19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aerosol particles that convey the SARS‐CoV‐2 virus. The model calculates the concentration of size‐resolved, virus‐laden aerosol particles in well‐mixed indoor air challenged by emissions from an index case(s). The model uses a mechanistic approach, accounting for particle emission dynamics, particle deposition to indoor surfaces, ventilation rate, and single‐zone filtration. The novelty of this model relates to the concept of “inhaled & deposited dose” in the respiratory system of receptors linked to a dose–response curve for human coronavirus HCoV‐229E. We estimated the volume of inhaled & deposited dose of particles in the 0.5–4 μm range expressed in picoliters (pL) in a well‐documented COVID‐19 outbreak in restaurant X in Guangzhou China. We anchored the attack rate with the dose–response curve of HCoV‐229E which provides a preliminary estimate of the average SARS‐CoV‐2 dose per person, expressed in plaque forming units (PFUs). For a reasonable emission scenario, we estimate approximately three PFU per pL deposited, yielding roughly 10 PFUs deposited in the respiratory system of those infected in restaurant X. To explore the model's utility, we tested it with four COVID‐19 outbreaks. The risk estimates from the model fit reasonably well with the reported number of confirmed cases given available metadata from the outbreaks and uncertainties associated with model assumptions.

New prevention scenarios: polygenic risk.

Although coronary heart disease is a highly preventable disease, it is still the leading cause of morbidity and mortality in developed countries. This is also due to the fact that the risk models used in clinical practice have proved ineffective in identifying people at risk: up to 30% of cases of myocardial infarction do not have traditional risk factors used in risk estimation models. Although the genetic component of myocardial infarction has been known for many years, with an inheritance rate of between 40% and 60%, it is not yet used as a risk factor in primary prevention models such as the Heart Card or the European SCORE. Recent advances in genomics and the use of clinical big data have allowed the development of genetic risk scores called Polygenic Risk Score (PRS), capable of identifying populations with average LDL-C levels, but with the same risk of heart attack of carriers of hypercholesterolaemia. The clinical usefulness of the PRS lies precisely in identifying high-risk individuals who are invisible to traditional models. The clinical applications of PRS for coronary artery disease are discussed in this report.

Coupling of redundancy analysis with geochemistry and mineralogy to assess the behavior of dust arsenic as a base of risk estimation in Dhaka, Bangladesh

Exposure to dust particles enriched with arsenic (As) is a significant health threat for populations living in Southeast Asian megacities. The mineralogical composition of dust particles is the key factor that controls the retention and release of As. This study investigated the degree of metal(oid)s pollution (As, Ca, Fe, K, Ga, Rb, Sr, Ti, V, Y, and Zr) in road dust of Dhaka city, Bangladesh. Enrichment factor and geoaccumulation index suggested that the road dust was heavily enriched with As, which triggers a comprehensive investigation of its controlling mechanisms and potential health risks by combining physicochemical and mineralogical information with multivariate analysis and a simulated probabilistic risk estimation model. Alkaline road dust (pH1:5 ranges from 8.02 to 10.34) in Dhaka city was found to have significant enrichment of As. Dust alkalinity was possibly controlled by the presence of carbonate minerals, such as calcite. Quartz was identified as the dominant mineral phase followed by magnesium carbon arsenide (MgCAs2). Carbonate mineral driven alkaline pH conditions in road dust would potentially trigger the release and mobilization of As to the environment. However, organic complexation can stabilize As on particle surfaces. Monte Carlo simulation-based health risk forecast suggested that the probability of As associated cancer risk has greatly exceeded the threshold value of 1E-4 for adults and children, and children are more vulnerable than adults. According to sensitivity analysis, the concentration of As and exposure duration (ED) posed the most significant impact (>58%) on risk estimation.

Abstract 2531: Function-related indicator and outcomes of screening mammography in older women from the BCSC-Medicare Cohort

Abstract Background: Although there is evidence pointing to the role of comorbidity and age in screening mammography outcomes among older women, the impact of functional decline on mortality in this population is unknown. Methods: We used data from 238,849 women in the Breast Cancer Surveillance Consortium-Medicare (BCSC) linked database from 1999-2015 who had at least one screening mammogram at ages 66-94 years. We estimated the 10-year cumulative incidence of breast cancer, breast cancer death, and non-breast cancer death by function-related indicator (FRI) which incorporated 16 claims-based items reflecting functional or health status in older people. In the analysis, FRI score was categorized as an ordinal variable (0, 1, and 2+) and a higher score indicated a higher burden of functional limitations. Fine and Gray proportional sub-distribution hazards models were applied with incident breast cancer and death treated as competing events. Risk estimates by FRI scores (0, 1, and 2+) were adjusted by age and NCI comorbidity index separately and stratified by these factors. Results: During a median follow-up of 101 months, 9,252 women were diagnosed with breast cancer. During follow-up, 406 women died of breast cancer and 41,640 died from non-breast cancer causes. The 10-year age-adjusted cumulative incidence of invasive breast cancer slightly decreased with increasing FRI score (FRI=0: 4.0%, 95% CI=3.8%-4.1%; FRI=1: 3.9%, 95% CI=3.7%-4.2%; FRI=2+: 3.5%, 95% CI=3.1%-3.9%). Risk of death from non-breast cancer causes increased with FRI score (FRI=0: 18.8%, 95% CI=18.5%-19.1%; FRI=1: 24.4%, 95% CI=23.9%-25.0%; FRI=2+: 39.8%, 95% CI=38.8%-40.9%). Risk of breast cancer death was low with minimal risk differences across FRI scores (FRI=0: 0.24%, 95%CI=0.20%-0.28%; FRI=1: 0.20%, 95%CI=0.14%-0.26%; FRI=2+: 0.30%, 95% CI=0.18%-0.42%). Risk estimates in NCI comorbidity index-adjusted models were largely similar to outcomes in age-adjusted models. Stratified analyses suggested similar patterns of risk estimates by FRI. Conclusion: Cumulative risk of death from non-breast cancer causes substantially increases with FRI score, suggesting that the potential of screening mammography to benefit older women is low among those with a high burden of functional limitation. Citation Format: Dongyu Zhang, Linn Abraham, Joshua Demb, Diana L. Miglioretti, Shailesh Advani, Brian L. Sprague, Louise M. Henderson, Tracy Onega, Karen Wernli, Louise C. Walter, Karla Kerlikowske, John T. Schousboe, Ellen S. O'Meara, Dejana Braithwaite. Function-related indicator and outcomes of screening mammography in older women from the BCSC-Medicare Cohort [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 2531.

Performance of early risk assessment tools to predict the later development of gestational diabetes.

BackgroundSeveral prognostic models for gestational diabetes mellitus (GDM) are provided in the literature; however, their clinical significance has not been thoroughly evaluated, especially with regard to application at early gestation and in accordance with the most recent diagnostic criteria. This external validation study aimed to assess the predictive accuracy of published risk estimation models for the later development of GDM at early pregnancy.MethodsIn this cohort study, we prospectively included 1132 pregnant women. Risk evaluation was performed before 16 + 0 weeks of gestation including a routine laboratory examination. Study participants were followed‐up until delivery to assess GDM status according to the IADPSG 2010 diagnostic criteria. Fifteen clinical prediction models were calculated according to the published literature.ResultsGestational diabetes mellitus was diagnosed in 239 women, that is 21.1% of the study participants. Discrimination was assessed by the area under the ROC curve and ranged between 60.7% and 76.9%, corresponding to an acceptable accuracy. With some exceptions, calibration performance was poor as most models were developed based on older diagnostic criteria with lower prevalence and therefore tended to underestimate the risk of GDM. The highest variable importance scores were observed for history of GDM and routine laboratory parameters.ConclusionsMost prediction models showed acceptable accuracy in terms of discrimination but lacked in calibration, which was strongly dependent on study settings. Simple biochemical variables such as fasting glucose, HbA1c and triglycerides can improve risk prediction. One model consisting of clinical and laboratory parameters showed satisfactory accuracy and could be used for further investigations.

Survival benefit of induction chemotherapy for locally advanced nasopharyngeal carcinoma: prognosis based on a new risk estimation model

BackgroundAlthough the National Comprehensive Cancer Network (NCCN) Guidelines recommend CCRT+AC and IC + CCRT as level 2A evidence for treatment of the locoregionally advanced NPC (II-IVa), IC + CCRT+AC could also be an alternative but it is seldom used because of the low completion rates. This article aimed to compare the effectiveness of the three radiotherapy regimens using a large-scale retrospective study.MethodsThis retrospective single center analysis enrolled 1812 diagnosed NPC patients at Nanfang Hospital from January 2005 to December 2015 and only 729 patients met the inclusion criteria and were analyzed. Patients without distant metastasis, age of 18–70 years, Karnofsky scores of at least 70,stage III-IVb, and adequate adequate bone marrow, liver and renal function. Were enrolled. Adverse events and other categorical variables were compared by Pearson chi-square test or Fishier exact test. Time-to-event data were described with the Kaplan-Meier curves, time-to-event intervals compared with the log-rank test. We did multivariable analyses with the Cox proportional hazards model to test the independent signifi cance of diff erent factors. Cox proportional hazards model was used to estimate the β regression coeffi cient, p value, and hazard ratio and its 95% CI for each of the selected risk predictors.ResultsThe median follow-up time was 47 months. Kaplan-Meier analyses revealed no significant differences among three groups in 3-year failure-free survival (FFS, P = 0.225), 3-year overall survival (OS, P = 0.992), 3-year locoregional failure-free survival (LFFS, P = 0.549), and 3-year distant failure-free survival (DFFS, P = 0.174). Stratified survival analysis based on the risk scoring model revealed no differences in FFS, OS, LFFS, and DFFS between IC + CCRT and CCRT+AC groups for low-risk patients, however, the 3-year OS (88.3% vs. 77.6%, P = 0.049) and 3-year DFFS (84.0% vs.66.8%, P = 0.032) were respectively significantly better in IC + CCRT group compared with CCRT+AC group for high-risk patients.ConclusionsCompared with CCRT+AC, IC + CCRT lowers distant metastasis rate and improves OS among patients with locally advanced NPC in high risk group.

A technical and clinical evaluation of the new ThermoFisher BRAHMS unconjugated estriol and inhibin-A assays and their use in second trimester Down syndrome screening

To evaluate second-trimester Down syndrome screening performance of the new ThermoFisher BRAHMS GOLD unconjugated estriol (uE3) and inhibin-A assays. Serum samples were analyzed for levels of uE3 and inhibin-A using the ThermoFisher BRAHMS GOLD immunoanalyzer and compared to other platforms. Levels were transformed to multiples of the median (MoM) in unaffected pregnancies. Log10 MoM distributions in unaffected and Down syndrome pregnancies were assessed for central tendency (mean) and dispersion (SD). Empirical and estimated screening performances were determined. Correlation between BRAHMS and AutoDELFIA® uE3 and inhibin-A were 0.63 and 0.97, respectively, the respective mean difference was 31.3% [95%CI 50.2% to −112.8%] and −23.3% [95%CI −41.9% to −4.7%]. Passing–Bablok indicated significant systematic (−2.78 [95%CI −3.57 to −2.04]) and proportional bias (1.30 [95%CI 1.15 to −1.47]) between uE3 assays and significant proportional bias (0.71[95%CI 0.65–0.78]) between inhibin-A assays. The uE3 and inhibin-A log10 MoM distribution mean [SD] in unaffected and Down syndrome pregnancies were 0.0024 [SD = 0.2341] and −0.0001 [SD = 0.2078], and −0.2028 [SD = 0.2495] and 0.3645 [SD = 0.2576], respectively. The new BRAHMS uE3 and inhibin-A assays had an 81–83% detection rate for Trisomy21 for a 5% false-positive rate. The new BRAHMS assays achieved the expected screening performance provided the risk estimation model is adjusted to account for the higher BRAHMS uE3 MoM measurement distribution variance.

Gene methylation and cytological atypia in random fine needle aspirates for assessment of breast cancer risk: 10-year follow up in average risk women.

10520 Background: Alterations in DNA methylation occur early in tumorigenesis, and are a potential breast cancer risk biomarker. We previously reported a study where healthy volunteers underwent random fine needle aspiration (rFNA) of the breasts; cumulative methylation index of eight preselected tumor suppressor genes (CMI) was associated with the presence of cytological atypia in rFNA samples. We now report 10-year follow-up of this population, to evaluate whether increased CMI is associated with subsequent breast cancer development. Methods: 380 women, unselected by breast cancer risk, were enrolled. Demographics, breast cancer risk factors, lifetime Gail model risk estimates (Gail-LR), and %breast density were obtained at baseline. rFNA samples were assessed for cytopathology (Masood Score, MS) and CMI. Patients were contacted annually for 10 years to ascertain development of invasive or non-invasive breast cancer. In univariate analysis, log-rank test was used to compare breast cancer incidence rates between individuals with high and low baseline measures (separated by median). Area under the ROC curve was used to evaluate the cancer prediction accuracy. In multivariate analysis, the effect of CMI (after log-transformation to reduce skewness) was further studied using Cox regression model adjusting for confounding baseline variables. Results: 362 women participated in follow up. At a median follow up time of 9.5 years after rFNA sampling, 16 women developed invasive or non-invasive breast cancer. There were no significant differences between women who developed cancer and those that did not in regard to demographic factors, %breast density, MS, or Gail-LR. On univariate analysis, Gail-LR was higher in women who developed cancer (13.0 vs. 16.5, p=0.08). The largest hazard ratios were observed from high breast density (2.30, 95% CI 0.8, 6.6) and high CMI (2.26, 95% CI 0.8, 6.6, p=0.07). In breast cancer prediction, the AUC for CMI was 0.64 (95% CI 0.51, 0.77). In separate bivariable models that adjusted for age, Gail-LR, MS, and %breast density, the HR for log CMI was consistently above unity, with a p value consistently below 0.1, except for the model that included MS (see Table). Conclusions: Elevated CMI has potential as a robust predictor of future breast cancer occurrence in average risk women, even when adjusted for breast density or cytologic atypia. Our prior analysis established that CMI is not susceptible to variation with menstrual cycle phase and menopausal status. These features support its further evaluation in larger trials. Clinical trial information: NCT00896636 .[Table: see text]

The going-public decision and firm risk

We investigate the relationship between the going-public decision and firm risk. We employ a comprehensive sample of firms that went public on European stock exchanges from 2000 to 2015 and examine how the risks of these newly listed firms are different from those of private firms and long-listing firms. We find that compared with private firms, newly listed firms have significantly increased risks of financial distress. This difference is largely attributable to the increase in leverage and the decline in liquidity, profitability and retained earnings. The results are consistent after controlling for selection bias, the effect of stock issuance, and the impact of the financial crisis and are robust to different risk indicators and estimation models (namely, the treatment effect model and DID). Finally, we find that the risks of newly listed firms are much higher than those of long-listing firms, and the risk effect of newly listed firms gradually weakens after listing. We argue that the increase in risk of IPO firms is temporary and is likely to be caused by the transition to public listing.

Development of an Early Warning and Incident Response System for the Protection of Visitors from Natural Hazards in Important Outdoor Sites in Greece

Safety in touristic destinations is of utmost importance since tourists’ preferences change frequently in response to emerging threats. Natural hazards are a significant risk and, as such, they need to be considered in the effort for safe tourism. Services and systems monitoring and predicting extreme natural phenomena and disasters in sites of special tourist and cultural interest can lead to more effective risk management and incident response. This paper presents Xenios, a system under development in Greece that provides early warning and risk communication services via web-based and mobile phone applications. We present the user requirements analysis contacted, which led to the design of a modular system architecture through a formal Business Process Model procedure. Currently, early warning systems for wildfire, floods, and extreme weather events are offered, based on a fusion of information from satellite imagery, meteorological forecasts, and risk estimation models. Moreover, visitors’ dispersion monitoring via unmanned aerial vehicles (UAVs) and Wi-Fi connection signals is also offered, along with emergency response planning and ticketing system’s interfacing. The system is built around a modular architecture that permits the easy integration of new subsystems or other danger forecasting modules, depending on the site’s actual needs and limitations. Xenios also provides a mobile app for site visitors, which establishes a communication link for sending alarms, but also serves them with useful tourist information, so that they are encouraged to download and use the app. Finally, the opportunities for supporting a viable business model are also discussed. The results of this study could prove useful in designing other natural risk management systems for sites of cultural and natural interest.

WITHDRAWN: AI-based framework for risk estimation in workplace

Abstract A significant problem for industrial management has always been the frequency of workplace fatalities and injuries. From production plants to offices to construction sites and beyond, every workplace has safety threats and hazards. To recognize certain objects, circumstances, practices, etc., that can cause harm, especially to individuals, a detailed look at the workplace becomes crucial. It is important to evaluate and determine how severe and likely the danger is after identification is made. The range of cumulative challenges, partially linked to technology developments with rising expectations, has to be broadly considered. Currently, it needs systematic risk estimation, enhancement of past lessons in learning, and the concept of appropriate data processing techniques to be combined with adequate capacity to cope with unforeseen events and provide the right support to enable risk management. Therefore, this paper suggests a risk management methodology focused on artificial intelligence (AI). A Deep Neural Network for Workplace Risk Estimation (DNN-WRE) model is explicitly generated and trained to predict the working environment's risks. The DNN-WRE is evaluated by a common risk prediction seen in the workplace, i.e., Musculoskeletal Disorders (MDs). Furthermore, this paper re-tuned the transfer learning network ResNet 18 to predict MDs. The training and validation of the proposed DNN-WRE have been observed with the highest prediction accuracy of 93.86% compared with the pre-trained ResNet 18 model.

Pulmonary infection after cardiopulmonary bypass surgery in children: a risk estimation model in China

ObjectivesThe occurrence of pulmonary infection after congenital heart disease (CHD) surgery can lead to significant increases in intensive care in cardiac intensive care unit (CICU) retention time, medical expenses, and risk of death risk. We hypothesized that patients with a high risk of pulmonary infection could be screened out as early after surgery. Hence, we developed and validated the first risk prediction model to verify our hypothesis.MethodsPatients who underwent CHD surgery from October 2012 to December 2017 in the Children’s Hospital of Chongqing Medical University were included in the development group, while patients who underwent CHD surgery from December 2017 to October 2018 were included in the validation group. The independent risk factors associated with pulmonary infection following CHD surgery were screened using univariable and multivariable logistic regression analyses. The corresponding nomogram prediction model was constructed according to the regression coefficients. Model discrimination was evaluated by the area under the receiver operating characteristic curve (ROC) (AUC), and model calibration was conducted with the Hosmer-Lemeshow test.ResultsThe univariate and multivariate logistic regression analyses identified the following six independent risk factors of pulmonary infection after cardiac surgery: age, weight, preoperative hospital stay, risk-adjusted classification for congenital heart surgery (RACHS)-1 score, cardiopulmonary bypass time and intraoperative blood transfusion. We established an individualized prediction model of pulmonary infection following cardiopulmonary bypass surgery for CHD in children. The model displayed accuracy and reliability and was evaluated by discrimination and calibration analyses. The AUCs for the development and validation groups were 0.900 and 0.908, respectively, and the P-values of the calibration tests were 0.999 and 0.452 respectively. Therefore, the predicted probability of the model was consistent with the actual probability.ConclusionsIdentified the independent risk factors of pulmonary infection after cardiopulmonary bypass surgery. An individualized prediction model was developed to evaluate the pulmonary infection of patients after surgery. For high-risk patients, after surgery, targeted interventions can reduce the risk of pulmonary infection.

Analysis of the Influence of DTM Source Data on the LS Factors of the Soil Water Erosion Model Values with the Use of GIS Technology

Counteracting soil degradation is one of the strategic priorities for sustainable development. One of the most important current challenges is effective management of available resources. Multiple studies in various aspects of soil water erosion are conducted in many research institutions in the world. They concern, among others, the development of risk estimation models and the use of new data for modelling. The aim of the presented research was a discussion on the impact of the accuracy and detail of elevation data sources on the results of soil water erosion topographic factors modelling. Elevation data for this research were chosen to reflect various technologies of data acquisition, differences in the accuracy and detail of field forms mapping and, consequently, the spatial resolution of the digital terrain models (DTMs). The methodology of the universal soil loss equation USLE/RUSLE was used for the L and S factors modelling and calculation. The research was carried out in three study areas located in different types of geographical regions in Poland: uplands, highlands and lake districts. The proposed methodology consisted of conducting detailed comparative elevation and slope value assessments, calculating the values of topographical factors of the universal soil loss equation: slope length (L) and slope (S) and a detailed analysis of the total LS factors values. An approach to assess LS factors values within homogeneous areas such as agricultural plots has also been proposed. The studies draw the conclusion that the values of topographical factors obtained from various DTM sources were significantly different. It was shown that the choice of the right modelling data has a significant impact on the L and S factors values and, thus, also, on the decision-making process. The conducted research has definitely shown that data of the highest accuracy and detail should be used to study local phenomena (inter alia erosion), even analysing a large area.

Gamma frailty model for survival risk estimation: an application to cancer data

Abstract Objectives: In time to event analysis, the risk for an event is usually estimated using Cox proportional hazards (CPH) model. But CPH model has the limitation of biased estimate due to unobserved hidden heterogeneity among the covariates, which can be tackled using frailty models. The best models were usually being identified using Akaike information criteria (AIC). Apart from AIC, the present study aimed to assess predictability of risk models using survival concordance measure. Methods: CPH model and frailty models were used to estimate the risk for breast cancer patient survival, and the frailty variable was assumed to follow gamma distribution. Schoenfeld global test was used to check the proportionality assumption. Survival concordance, AIC and simulation studies were used to identify the significance of frailty. Results: From the univariate analysis it was observed that for the covariate age, the frailty has a significant role (θ = 2.758, p-value: 0.0004) and the corresponding hazard rate was 1.93 compared to that of 1.38 for CPH model (age > 50 vs. ≤ 40). Also the covariates radiotherapy and chemotherapy were found to be significant (θ = 5.944, p-value: <0.001 and θ = 16, p-value: <0.001 respectively). Even though there were only minor differences in hazard rates, the concordance was higher for frailty than CPH model for all the covariates. Further the simulation study showed that the bias and root mean square error (RMSE) obtained for both the methods was almost the same and the concordance measures were higher for frailty model by 12–15%. Conclusions: We conclude that the frailty model is better compared to CPH model as it can account for unobserved random heterogeneity, and if the frailty coefficient doesn’t have an effect it gives exactly the same risk as that of CPH model and this has been established using survival concordance.

Deviation-Based Model Risk Measures

In practice, risk forecasts are obtained by risk measures based on a given probability measure on a measurable space. In our study, we consider the probability measures as alternative scenarios, which refer to, for instance, different distribution assumptions, models, or economic situations. Using an improper probability measure can affect risk forecasting and lead to wrong financial decisions. In this context, we propose a Deviation-based approach for quantifying model risk associated with choosing an inappropriate probability measure for risk forecasting. This measuring approach provides us with information about how far our risk measurement process could be affected by model risk. We provide examples of Deviation-based model risk measures defined in the literature. Moreover, we are proposing new alternatives to quantify model risk, for example, Gini and Extended Gini-type model risk measures. We provide a practical example using Value-at-risk (VaR) and Expected Shortfall forecasting to illustrate our approach. Our results indicate that using an inadequate probability measure (distribution assumptions) can largely affect risk forecasting. We verify that model risk estimates present skewness and heavy tail, have significant auto-correlation and do increase in periods that coincide with the highest variability of returns.