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Acute kidney injury (AKI) is a common and serious complication of cardiac surgery. It is associated with increased morbidity and mortality. On a population level, the financial impact and overall incremental annual index hospitalization costs associated with AKI exceed $1 billion in the United States alone. The pathogenesis is multifaceted and complex. Although novel biomarkers may be useful in detecting AKI at an earlier stage, treatment of AKI remains elusive. Prevention of AKI is still key to the management of AKI, and strategies include maintenance of renal perfusion and avoidance of nephrotoxins and blood transfusion-related insults. Dialysis in severe AKI is established, but the optimal modality and dose remains an area of ongoing research. This narrative review assesses the pathophysiology of AKI, role of biomarkers, risk assessment, and management in cardiac surgical patients. [ABSTRACT FROM AUTHOR]

Highlights • Machine learning techniques were accurate in predicting wind damage to trees. • Random forests proved the most accurate and discriminating methodology. • Models were sensitive to removal of site and stand but not tree characteristics. • All models were able to accurately replicate a mechanistic wind risk model. • Machine learning techniques could help the management of wind damage to forests. Abstract This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory plots damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle-Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested. All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm. However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results. Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage. [ABSTRACT FROM AUTHOR]

Copyright of Der Radiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: With the ever-increasing cure rates in breast cancer, radiotherapy-induced cancers have become an important issue. This study aimed to estimate secondary cancer risks for different treatment techniques, taking into account organs throughout the body.Material and Methods: Organ doses were evaluated for a tangential three-dimensional conformal (3D-CRT) and a multi-field intensity-modulated radiotherapy (IMRT) plan using a validated, Monte Carlo-based treatment planning system. Effects of wedges and of forward versus inverse planning were systematically investigated on the basis of phantom measurements. Organ-specific cancer risks were estimated using risk coefficients derived from radiotherapy patients or from the atomic bomb survivors.Results: In the 3D-CRT plan, mean organ doses could be kept below 1 Gy for more remote organs than the lung, heart, and contralateral breast, and decreased to a few cGy for organs in the lower torso. Multi-field IMRT led to considerably higher mean doses in organs at risk, the difference being higher than 50% for many organs. Likewise, the peripheral radiation burden was increased by external wedges. No difference was observed for forward versus inverse planning. Despite the lower doses, the total estimated secondary cancer risk in more remote organs was comparable to that in the lung or the contralateral breast. For multi-field IMRT it was 75% higher than for 3D-CRT without external wedges.Conclusion: Remote organs are important for assessment of radiation-induced cancer risk. Remote doses can be reduced effectively by application of a tangential field configuration and a linear accelerator set-up with low head scatter radiation. [ABSTRACT FROM AUTHOR]

Due to the undesirable implications of maritime mishaps such as ship collisions and the consequent damages to maritime property; the safety and security of waterways, ports and other maritime assets are of the utmost importance to authorities and researches. Terrorist attacks, piracy, accidents and environmental damages are some of the concerns. This paper provides a detailed literature review of over 180 papers about different threats, their consequences pertinent to the maritime industry, and a discussion on various risk assessment models and computational algorithms. The methods are then categorized into three main groups: statistical, simulation and optimization models. Corresponding statistics of papers based on year of publication, region of case studies and methodology are also presented. [ABSTRACT FROM AUTHOR]

This work reports the first survey of Phytophthora diversity in the forests soils of Andean Patagonia. It also discusses the role of anthropogenic impact on Phytophthora distribution inferred from the findings on Phytophthora diversity and on the distribution of Phytophthora austrocedri-diseased forests. Invasive pathogen species threatening ecosystems and human activities contribute to their entry and spread. Information on pathogens already established, and early detection of potential invasive ones, are crucial to disease management and prevention. Phytophthora austrocedri causes the most damaging forest disease in Patagonia, affecting the endemic species Austrocedrus chilensis (D. Don) Pic. Sern. and Bizzarri. However, the relationship between anthropogenic impacts and the disease distribution has not been analyzed enough. The aims of this work were: to evaluate Phytophthora diversity in soils of Andean Patagonia using a metabarcoding method, and analyze this information in relation to soil type and land use; to assess the distribution of Austrocedrus disease over time in relation to anthropogenic and abiotic gradients in an area of interest; and to discuss the role of human activities in Phytophthora spread. High throughput Illumina sequencing was used to detect Phytophthora DNA in soil samples. The distribution of Austrocedrus disease over time was assessed by satellite imagery interpretation. Twenty-three Phytophthora species, 12 of which were new records for Argentina, were detected. The most abundant species was P. austrocedri, followed by P. × cambivora, P. ramorum and P. kernoviae. The most frequent was P. × cambivora, followed by P. austrocedri and P. ramorum. Phytophthora richness and abundance, and Austrocedrus disease distribution, were influenced by land use, anthropogenic impact and soil drainage. Results showed several Phytophthoras, including well-known pathogenic species. The threat they could present to Patagonian ecosystems and their relations to human activities are discussed. This study evidenced the need of management measures to control the spread of P. austrocedri and other invasive Phytophthora species in Patagonia.

The IMPECAF is a research project that started at the end of 2018 and aims to deepen the knowledge on weather extremes, particularly droughts and heat waves, which affect agricultural and forest ecosystems of the Iberian Peninsula. Despite these events presenting different temporal scales, their simultaneous occurrence can intensify the observed impacts. In addition, these impacts may extend over large areas affecting different ecosystems. This project aims at transferring knowledge on fundamental research in meteorology for the agricultural and forestry sectors, and it is expected that the results may be an input in the decision-making process of farmers. To achieve this aim, appropriate measures will be developed to mitigate the impact of these extreme weather events in the forestry and agricultural sectors. This will be followed by an approach that ensures the involvement of stakeholders since the beginning of the project and even after its conclusion.

This paper examines capital adequacy regulation in Germany. The first part reviews capital adequacy regulation from the 1930s up to the financial crisis and identifies two main trends: a gradual softening of the eligibility criteria for equity and increasing reliance on internal risk models. While the first trend has been reversed following the financial crisis, internal risk models still play a central role. Therefore, the second part discusses the problems with the use of internal risk models and discusses the potentials of Basel 2.5 and Basel III to alleviate the identified problems. It is concluded that the relevant problems are not resolved. Therefore, in the final part some suggestions of how the problems could be addressed properly are given.

Background There is a need to develop electronic health record-based predictive models for worsening heart failure (WHF) events across clinical settings and across the spectrum of left ventricular ejection fraction (LVEF). Methods and Results We studied adults with heart failure (HF) from 2011 to 2019 within an integrated health care delivery system. WHF encounters were ascertained using natural language processing and structured data. We conducted boosted decision tree ensemble models to predict 1-year hospitalizations, emergency department visits/observation stays, and outpatient encounters for WHF and all-cause death within each LVEF category: HF with reduced ejection fraction (EF) (LVEF <40%), HF with mildly reduced EF (LVEF 40%-49%), and HF with preserved EF (LVEF ≥50%). Model discrimination was evaluated using area under the curve and calibration using mean squared error. We identified 338 426 adults with HF: 61 045 (18.0%) had HF with reduced EF, 49 618 (14.7%) had HF with mildly reduced EF, and 227 763 (67.3%) had HF with preserved EF. The 1-year risks of any WHF event and death were, respectively, 22.3% and 13.0% for HF with reduced EF, 17.0% and 10.1% for HF with mildly reduced EF, and 16.3% and 10.3% for HF with preserved EF. The WHF model displayed an area under the curve of 0.76 and mean squared error of 0.13, whereas the model for death displayed an area under the curve of 0.83 and mean squared error of 0.076. Performance and predictors were similar across WHF encounter types and LVEF categories. Conclusions We developed risk prediction models for 1-year WHF events and death across the LVEF spectrum using structured and unstructured electronic health record data and observed no substantial differences in model performance or predictors except for death, despite differences in underlying HF cause.

Multiple tools exist to assess a patient's breast cancer risk. The choice of risk model depends on the patient's risk factors and how the calculation will impact care. High-risk patients-those with a lifetime breast cancer risk of ≥20%-are, for instance, eligible for supplemental screening with breast magnetic resonance imaging. Those with an elevated short-term breast cancer risk (frequently defined as a 5-year risk ≥1.66%) should be offered endocrine prophylaxis. High-risk patients should also receive guidance on modification of lifestyle factors that affect breast cancer risk., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: A third of familial epithelial ovarian cancer (EOC) is explained by BRCA1/2 pathogenic variants. Polygenic risk scores (PRSs) for BRCA1/2 heterozygotes associated with EOC have been created, but impact of combination with clinical and hormonal risk factors is unclear., Methods: We genotyped 300 cases and 355 controls and constructed modified PRSs based on those validated by Barnes et al. Model discrimination and EOC risk was assessed by area under the curve (AUC) values and difference between lowest and highest quintile odds ratios (ORs). We investigated model optimization using logistic regression to combine models with clinical and hormonal data., Results: Unadjusted AUC values ranged from 0.526 to 0.551 with 2.2- to 2.3-fold increase in OR between lowest and highest quintiles (BRCA1 heterozygotes) and 0.574 to 0.585 AUC values with a 6.3- to 7.7-fold increase (BRCA2 heterozygotes). The optimized model (parity, age at menarche, menopause, and first full-term pregnancy) estimated AUC values of 0.872 to 0.876 and 21- to 23-fold increase in OR (BRCA1 heterozygotes) and AUC values of 0.857 to 0.867 and 40- to 41-fold increase (BRCA2 heterozygotes)., Conclusion: The combination of PRS with age, family history, and hormonal factors significantly improved the EOC risk discrimination ability. However, the contribution of the PRS was small. Larger prospective studies are needed to assess if combined-PRS models could provide information to inform risk-reducing decisions., Competing Interests: Conflict of Interest The authors declare no conflicts of interest., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: With observational epidemiologic studies, there is often concern that an unmeasured variable might confound an observed association. Investigators can assess the impact from such unmeasured variables on an observed relative risk (RR) by utilizing externally sourced information and applying an indirect adjustment procedure, for example, the "Axelson adjustment." Although simple and easy to use, this approach applies to exposure and confounder variables that are binary. Other approaches eschew specific values and provide only bounds on the potential bias.Methods: For both multiplicative and additive RR models, we present formulae for indirect adjustment of observed RRs for unmeasured potential confounding variables when there are multiple categories. In addition, we suggest an alternative strategy to identify the characteristics that the confounder must have to explain fully the observed association.Results and Conclusions: We provide examples involving studies of pediatric computer tomography scanning and leukemia and nuclear radiation workers and smoking to demonstrate that with externally sourced information, an investigator can assess whether confounding from unmeasured factors is likely to occur. [ABSTRACT FROM AUTHOR]

Background: Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. This study evaluated the accuracy of an integrated proteomic classifier in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%.Methods: A prospective, multicenter observational trial of 685 patients with 8- to 30-mm lung nodules was conducted. Multiple reaction monitoring mass spectrometry was used to measure the relative abundance of two plasma proteins, LG3BP and C163A. Results were integrated with a clinical risk prediction model to identify likely benign nodules. Sensitivity, specificity, and negative predictive value were calculated. Estimates of potential changes in invasive testing had the integrated classifier results been available and acted on were made.Results: A subgroup of 178 patients with a clinician-assessed pCA ≤ 50% had a 16% prevalence of lung cancer. The integrated classifier demonstrated a sensitivity of 97% (CI, 82-100), a specificity of 44% (CI, 36-52), and a negative predictive value of 98% (CI, 92-100) in distinguishing benign from malignant nodules. The classifier performed better than PET, validated lung nodule risk models, and physician cancer probability estimates (P < .001). If the integrated classifier results were used to direct care, 40% fewer procedures would be performed on benign nodules, and 3% of malignant nodules would be misclassified.Conclusions: When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules with good performance characteristics. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance.Trial Registry: ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov). [ABSTRACT FROM AUTHOR]

Background: More than 1.5 million women per year have a benign breast biopsy resulting in concern about their future breast cancer (BC) risk. This study examined the performance of 2 BC risk models that integrate clinical and histologic findings in this population.Methods: The BC risk at 5 and 10 years was estimated with the Breast Cancer Surveillance Consortium (BCSC) and Benign Breast Disease to Breast Cancer (BBD-BC) models for women diagnosed with benign breast disease (BBD) at the Mayo Clinic from 1997 to 2001. Women with BBD were eligible for the BBD-BC model, but the BCSC model also required a screening mammogram. Calibration and discrimination were assessed.Results: Fifty-six cases of BC were diagnosed among the 2142 women with BBD (median age, 50 years) within 5 years (118 were diagnosed within 10 years). The BBD-BC model had slightly better calibration at 5 years (0.89; 95% confidence interval [CI], 0.71-1.21) versus 10 years (0.81; 95% CI, 0.70-1.00) but similar discrimination in the 2 time periods: 0.68 (95% CI, 0.60-0.75) and 0.66 (95% CI, 0.60-0.71), respectively. In contrast, among the 1089 women with screening mammograms (98 cases of BC within 10 years), the BCSC model had better calibration (0.94; 95% CI, 0.85-1.43) and discrimination (0.63; 95% CI, 0.56-0.71) at 10 years versus 5 years (calibration, 1.31; 95% CI, 0.94-2.25; discrimination, 0.59; 95% CI, 0.46-0.71) where discrimination was not different from chance.Conclusions: The BCSC and BBD-BC models were validated in the Mayo BBD cohort, although their performance differed by 5-year risk versus 10-year risk. Further enhancement of these models is needed to provide accurate BC risk estimates for women with BBD. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ibero-Latinoamericana de Seguros is the property of Pontificia Universidad Javeriana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Warfarin decreases stroke risk in atrial fibrillation patients, with efficacy and safety impacted by the quality of warfarin control, as measured by time in therapeutic range (TTR). Stroke and bleed risk scores are calculated prior to commencing warfarin, so it would be beneficial if these scores also identified likely warfarin control. Some studies have investigated CHADS2, CHA2DS2VASc, and HASBLED individually for this purpose, but application of all scores to diverse ethnic populations and at sites with differing overall control has not been investigated. The aim of this study was to determine if these commonly used risk scores could identify poor warfarin control. Retrospective data was collected for non-valvular AF patients receiving warfarin between January and June 2014 in Australia and Singapore. Patient data was used to calculate TTR and risk scores. Mean TTR was used for analysis and comparison to categorised scores. There were 3199 patients in Australia and 1171 in Singapore. At both sites, mean TTR decreased according to HASBLED category, and there was a statistically higher percentage of patients achieving a TTR>65% in the low HASBLED category. The association between HASBLED scores and TTR was independent of lower dosing in higher risk patients, particularly in Australia. No significant differences were found in mean TTR according to CHADS2 at either site. Time in therapeutic range significantly decreased according to high CHA2DS2VASc category in Singapore, but no differences were found in Australia. Of the bleed and stroke risk models, HASBLED is most suitable to identify a patient's potential TTR and ability to achieve TTR>65%. A high HASBLED score may assist prescribers in determining potential suitability to warfarin, and assist prescribers in deciding on the most suitable anticoagulant for patients. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the precise local large deviation probabilities for random sums of independent real-valued random variables with a common heavy-tailed distribution F, where is an -regularly varying function for some fixed constant (finite or infinite). We also obtain some results on precise local large deviation probabilities for the claim surplus process of generalized risk models in which the premium income until time t is simply assumed to be a nondecreasing and nonnegative stochastic process. In particular, the results we obtained are also valid for the global case, i.e. case . [ABSTRACT FROM AUTHOR]

Objectives: Evaluate the accuracy of different ICU risk models repurposed as continuous markers of severity of illness.Design: Nonintervention cohort study.Setting: eICU Research Institute ICUs using tele-ICU software calculating continuous ICU Discharge Readiness Scores between January 2013 and March 2016.Patients: Five hundred sixty-one thousand four hundred seventy-eight adult ICU patients with an ICU length of stay between 4 hours and 30 days.Interventions: Not available.Measurements and Main Results: Hourly Acute Physiology and Chronic Health Evaluation IV, Sequential Organ Failure Assessment, and Discharge Readiness Scores were calculated beginning hour 4 of the ICU stay. Primary outcome was the area under the receiver operating characteristic curve for the mean score with ICU mortality. Secondary outcomes included area under the receiver operating characteristic curves for ICU mortality with admission, median, maximum and last scores, and for death within 24 hours. The trajectories of each score were visualized by plotting the hourly averages against time in the ICU, stratified by mortality and length of stay. The area under the receiver operating characteristic curves for mean Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores were 0.90 (0.89-0.90), 0.86 (0.86-0.86), and 0.94 (0.94-0.94), respectively. The area under the receiver operating characteristic curves for hourly Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores predicting 24-hour mortality were 0.81 (0.81-0.81), 0.76 (0.76-0.76), and 0.86 (0.86-0.86). Discharge Readiness Scores had a higher area under the receiver operating characteristic curve than both Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment for each metric. Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment scores increased throughout the first 24 hours in both survivors and nonsurvivors; Discharge Readiness Scores continuously decreased in survivors and temporarily decreased before increasing by hour 36 in nonsurvivors with longer length of stays.Conclusions: Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores all have relatively high discrimination for ICU mortality when used continuously; Discharge Readiness Scores tended to have slightly higher area under the receiver operating characteristic curves for each endpoint. These findings validate the use of these models on a population level for continuous risk adjustment in the ICU, although Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment appear slower to respond to improvements in patient status than Discharge Readiness Scores, and Discharge Readiness Scores may reflect physiologic improvement from interventions, potentially underestimating risk. [ABSTRACT FROM AUTHOR]

Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects. [ABSTRACT FROM AUTHOR]

Introduction: Targeting resources for a designated higher-risk subgroup is a strategy for chronic care management. However, risk-designation has several limitations: it is inaccurate, seldom helpful for care guidance, and potentially misallocates care away from many patients.Methods: To address limitations of risk designation, we tested a "what matters index" (WMI) in 19,593 adult patients with chronic conditions. The WMI contains five binary measures: insufficient confidence to manage health problems, level of pain, emotional problems, polypharmacy, and adverse medication effects. We examined its sum for association with patient-reported quality of life and prior emergency or hospital use. We compared its accuracy to a prototypic risk-designation model.Results: The WMI was a good indicator for quality of life and in three diverse test populations it was strongly associated with the use of hospital and emergency services. For example, a sum of WMI ≥2 was associated with twice as many average uses as none; for ≥3, uses were three times higher. However, since relatively few patients use costly care, both the WMI and a prototypic risk-designation model had comparably low-positive predictive values. The WMI uses the patient voice to identify needs strongly associated with quality of life. Akin to risk designation models, the WMI can be used to place patients into groups associated with levels of costly services, but neither is likely to forecast costly service use for individuals. However, unlike risk-designation models, the WMI is based on measures that will immediately guide care for every patient. [ABSTRACT FROM AUTHOR]

• Renal cell carcinoma with associated tumor thrombus poses significant surgical risk. • Current survival models are based on patients with metastatic disease, and it is not known if these are models are associated with immediate perioperative outcomes. • We found that individual clinical variables are inconsistently associated with perioperative outcomes. • Comparatively, out of the established survival models, the Moffitt Cancer Center risk stratification model is associated with more perioperative outcomes. Despite modern advances in surgical and perioperative technologies, management of renal cell carcinoma (RCC) with tumor thrombus (TT) is a morbid procedure that necessitates careful patient selection. It is not known whether established prognostic models for metastatic RCC are suitable prognostic tools for more immediate perioperative outcomes in patients with RCC with TT. We evaluated if established risk models for cytoreductive nephrectomy, as a potential extension of their purpose-built use, are associated with immediate perioperative outcomes in patients undergoing nephrectomy and tumor thrombectomy. Perioperative outcomes of patients who underwent radical nephrectomy and tumor thrombectomy for RCC were compared to presences of established predictors of long-term outcomes from prior risk models individually and as stratified by risk grouping (International Metastatic Renal-Cell Carcinoma Database Consortium [IMDC], Memorial Sloan Kettering Cancer Center [MSKCC], M.D. Anderson Cancer Center [MDACC], and Moffitt Cancer Center [MCC]). Wilcoxon rank-sum test or the Kruskal-Wallis test compared continuous variables and the chi-square test or Fisher's exact test compared categorical variables. Fifty-five patients were analyzed with 17 (30.9%) being cytoreductive. Eighteen (32.7%) patients had a level III or higher TT. Individually, preoperative variables were inconsistently associated with perioperative outcomes. Poorer risk patients per the IMDC model had more major postoperative complications (Clavien-Dindo grade≥3, P = 0.008). For the MSKCC model, poorer risk patients had increased intraoperative estimated blood loss (EBL), longer length of stay (LOS), more major postoperative complications, and more likely to discharge to a rehabilitation facility (P < 0.05). Less favorable risk patients per MDACC model had increased LOS (P = 0.038). Poorer risk patients per the MCC model had increased EBL, LOS, major postoperative complications, and 30-day hospital readmissions (P < 0.05). Overall, cytoreductive risks models were heterogeneously associated with perioperative outcomes in patients undergoing nephrectomy and tumor thrombectomy. Of available models, the MCC model is associated with more perioperative outcomes including EBL, LOS, major postoperative complications, and readmissions within 30 days when compared to the IMDC, MSKCC, and MDACC models. [ABSTRACT FROM AUTHOR]

Numerous models have been developed to quantify the combined effect of various risk factors to predict either risk of developing breast cancer, risk of carrying a high-risk germline genetic mutation, specifically in the BRCA1 and BRCA2 genes, or the risk of both. These breast cancer risk models can be separated into those that utilize mainly hormonal and environmental factors and those that focus more on hereditary risk. Given the wide range of models from which to choose, understanding what each model predicts, the populations for which each is best suited to provide risk estimations, the current validation and comparative studies that have been performed for each model, and how to apply them practically is important for clinicians and researchers seeking to utilize risk models in their practice. This review provides a comprehensive guide for those seeking to understand and apply breast cancer risk models by summarizing the majority of existing breast cancer risk prediction models including the risk factors they incorporate, the basic methodology in their development, the information each provides, their strengths and limitations, relevant validation studies, and how to access each for clinical or investigative purposes. [ABSTRACT FROM AUTHOR]

Background: Recent clinical trials highlight the need for better models to identify patients at higher risk of sudden death.Objectives: The authors hypothesized that the Seattle Heart Failure Model (SHFM) for overall survival and the Seattle Proportional Risk Model (SPRM) for proportional risk of sudden death, including death from ventricular arrhythmias, would predict the survival benefit with an implantable cardioverter-defibrillator (ICD).Methods: Patients with primary prevention ICDs from the National Cardiovascular Data Registry (NCDR) were compared with control patients with heart failure (HF) without ICDs with respect to 5-year survival using multivariable Cox proportional hazards regression.Results: Among 98,846 patients with HF (87,914 with ICDs and 10,932 without ICDs), the SHFM was strongly associated with all-cause mortality (p < 0.0001). The ICD-SPRM interaction was significant (p < 0.0001), such that SPRM quintile 5 patients had approximately twice the reduction in mortality with the ICD versus SPRM quintile 1 patients (adjusted hazard ratios [HR]: 0.602; 95% confidence interval [CI]: 0.537 to 0.675 vs. 0.793; 95% CI: 0.736 to 0.855, respectively). Among patients with SHFM-predicted annual mortality ≤5.7%, those with a SPRM-predicted risk of sudden death below the median had no reduction in mortality with the ICD (adjusted ICD HR: 0.921; 95% CI: 0.787 to 1.08; p = 0.31), whereas those with SPRM above the median derived the greatest benefit (adjusted HR: 0.599; 95% CI: 0.530 to 0.677; p < 0.0001).Conclusions: The SHFM predicted all-cause mortality in a large cohort with and without ICDs, and the SPRM discriminated and calibrated the potential ICD benefit. Together, the models identified patients less likely to derive a survival benefit from primary prevention ICDs. [ABSTRACT FROM AUTHOR]

Objective To evaluate the predictive efficacies including sensitivity and positive predictive value of the genetic risk prediction model BRCAPRO and the Myriad BRCA risk calculator in Korean ovarian cancer patients. Methods Individuals undergoing genetic testing for BRCA mutations from November 2010–August 2016 were recruited from the Department of Obstetrics and Gynecology at a single institute in Korea. The observed BRCA1 and BRCA2 mutation statuses were compared with the predicted carrier probabilities using BRCAPRO and the Myriad BRCA risk calculator. Results Two hundred thirty-two patients were recruited, of whom 99.1% (230/232) were of Korean ethnicity. Of the 232 individuals, 206 and 26 had ovarian and double primary breast/ovarian cancer, respectively. Thirty-six individuals had a family history of breast/ovarian cancer in first-degree relatives. Fifty-seven patients (24.6%) tested positive for BRCA mutation (41 BRCA1 , 16 BRCA2 ). The mean BRCAPRO and Myriad scores for all patients were 6.4% and 7.7%, respectively. The scores were significantly higher for patients with positive BRCA mutation status (29.0% vs. 6.1%, P < 0.001, 12.1% vs. 7.7%, P < 0.001, respectively). For all patients, the respective areas under the receiver operating characteristics curves were 0.720 and 0.747 for the BRCAPRO and Myriad models to predict the risk of carrying a BRCA mutation. Both models overestimated the mutation probability in patients with a family history of breast/ovarian cancer (1.55-fold and 1.50-fold, respectively) and underestimated the probability in patients without a family history (both, 0.54-fold). Conclusion BRCAPRO and Myriad seem to be acceptable risk assessment tools for determining the risk of carrying BRCA mutations in Korean ovarian cancer patients. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is one of the most common fatal cancers in women. Identifying new biomarkers is thus of great significance for the diagnosis and prognosis of BC., Methods: In this study, 1,030 BC cases from The Cancer Genome Atlas (TCGA) were obtained for differential expression analysis and Short Time-series Expression Miner (STEM) analysis to identify characteristic BC development genes, which were further divided into upregulated and downregulated genes. Two predictive prognosis models were both defined by Least Absolute Shrinkage and Selection Operator (LASSO). Survival analysis and receiver operating characteristic (ROC) curve analysis were used to determine the diagnostic and prognostic capabilities of the two gene set model scores, respectively., Results: Our findings from this study suggested that both the unfavorable (BC1) and favorable (BC2) gene sets are reliable biomarkers for the diagnosis and prognosis of BC, although the BC1 model presents better diagnostic and prognostic value. Associations between the models and M2 macrophages and sensitivity to Bortezomib were also found, indicating that unfavorable BC genes are significantly involved in the tumor immune microenvironment., Conclusions: We successfully established one predictive prognosis model (BC1) based on characteristic gene sets of BC to diagnose and predict the survival time of BC patients using a cluster of 12 differentially expressed genes (DEGs)., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-2444/coif). The authors have no conflicts of interest to declare., (2023 Translational Cancer Research. All rights reserved.)

Background: Reducing the burden of dementia in First Nations populations may be addressed through developing population specific methods to quantify future risk of dementia., Objective: To adapt existing dementia risk models to cross-sectional dementia prevalence data from a First Nations population in the Torres Strait region of Australia in preparation for follow-up of participants. To explore the diagnostic utility of these dementia risk models at detecting dementia., Methods: A literature review to identify existing externally validated dementia risk models. Adapting these models to cross-sectional data and assessing their diagnostic utility through area under the receiver operating characteristic curve (AUROC) analyses and calibration using Hosmer-Lemeshow Chi 2 ., Results: Seven risk models could be adapted to the study data. The Aging, Cognition and Dementia (AgeCoDe) study, the Framingham Heart Study (FHS), and the Brief Dementia Screening Indicator (BDSI) had moderate diagnostic utility in identifying dementia (i.e., AUROC >0.70) before and after points for older age were removed., Conclusion: Seven existing dementia risk models could be adapted to this First Nations population, and three had some cross-sectional diagnostic utility. These models were designed to predict dementia incidence, so their applicability to identify prevalent cases would be limited. The risk scores derived in this study may have prognostic utility as participants are followed up over time. In the interim, this study highlights considerations when transporting and developing dementia risk models for First Nations populations., Competing Interests: The authors have no conflict of interest to declare., (© 2023 – The authors. Published by IOS Press.)

Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.

Objective: Develop a stakeholder-informed ethical framework to provide practical guidance to health systems considering implementation of suicide risk prediction models., Methods: In this multi-method study, patients and family members participating in formative focus groups ( n  = 4 focus groups, 23 participants), patient advisors, and a bioethics consultant collectively informed the development of a web-based survey; survey results ( n  = 1,357 respondents) and themes from interviews with stakeholders (patients, health system administrators, clinicians, suicide risk model developers, and a bioethicist) were used to draft the ethical framework., Results: Clinical, ethical, operational, and technical issues reiterated by multiple stakeholder groups and corresponding questions for risk prediction model adopters to consider prior to and during suicide risk model implementation are organized within six ethical principles in the resulting stakeholder-informed framework. Key themes include: patients' rights to informed consent and choice to conceal or reveal risk (autonomy); appropriate application of risk models, data and model limitations and consequences including ambiguous risk predictors in opaque models (explainability); selecting actionable risk thresholds (beneficence, distributive justice); access to risk information and stigma (privacy); unanticipated harms (non-maleficence); and planning for expertise and resources to continuously audit models, monitor harms, and redress grievances (stewardship)., Conclusions: Enthusiasm for risk prediction in the context of suicide is understandable given the escalating suicide rate in the U.S. Attention to ethical and practical concerns in advance of automated suicide risk prediction model implementation may help avoid unnecessary harms that could thwart the promise of this innovation in suicide prevention. HIGHLIGHTSPatients' desire to consent/opt out of suicide risk prediction models.Recursive ethical questioning should occur throughout risk model implementation.Risk modeling resources are needed to continuously audit models and monitor harms.

In the assessment of health risks after nuclear accidents, some health consequences require special attention. For example, in their 2013 report on health risk assessment after the Fukushima nuclear accident, the World Health Organisation (WHO) panel of experts considered risks of breast cancer, thyroid cancer and leukaemia. For these specific cancer types, use was made of already published excess relative risk (ERR) and excess absolute risk (EAR) models for radiation-related cancer incidence fitted to the epidemiological data from the Japanese A-bomb Life Span Study (LSS). However, it was also considered important to assess all other types of solid cancer together and the WHO, in their above-mentioned report, stated 'No model to calculate the risk for all other solid cancer excluding breast and thyroid cancer risks is available from the LSS data'. Applying the LSS models for all solid cancers along with the models for the specific sites means that some cancers have an overlap in the risk evaluations. Thus, calculating the total solid cancer risk plus the breast cancer risk plus the thyroid cancer risk can overestimate the total risk by several per cent. Therefore, the purpose of this paper was to publish the required models for all other solid cancers, i.e. all solid cancers other than those types of cancer requiring special attention after a nuclear accident. The new models presented here have been fitted to the same LSS data set from which the risks provided by the WHO were derived. Although it is known already that the EAR and ERR effect modifications by sex are statistically significant for the outcome 'all solid cancer', it is shown here that sex modification is not statistically significant for the outcome 'all solid cancer other than thyroid and breast cancer'. It is also shown here that the sex-averaged solid cancer risks with and without the sex modification are very similar once breast and thyroid cancers are factored out. Some other notable model differences between those already published for all solid cancers and those presented here for all other solid cancers are also given here. The models presented here can be used to improve on the methodology adopted by WHO after Fukushima and could contribute to emergency preparedness for future nuclear accidents. [ABSTRACT FROM AUTHOR]