Risk Classification Scheme Research Articles

A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study.

Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient's OUD risk level, which was then compared to the ML application's risk assignments. A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients' different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.

Adverse childhood experiences, cognitive functioning, depression, and anxiety in adulthood.

Evidence suggests that adverse childhood experiences (ACEs) predict cognitive dysfunction, possibly through direct (e.g., brain structure/function changes) and indirect (e.g., increased psychopathology risk) pathways. However, extant studies have focused on young and older adults, with limited understanding of how ACEs affect cognitive health in midadulthood. This study compared psychiatric and cognitive differences between adults at high- and low-risk of adverse health outcomes based on the ACE risk classification scheme. Adult patients (N = 211; 46.9% female; Mage = 44.1, SD = 17.1; Meducation = 13.8, SD = 3.0) consecutively referred for outpatient neuropsychological evaluation within a large, Midwestern academic medical center. Patients were divided into high and low ACE groups based on the number of ACEs endorsed. Subsequently, a series of one-way analyses of variances were conducted to compare high versus low ACE groups on the Test of Premorbid Functioning, Wechsler Adult Intelligence Scale-Fourth Edition Digit Span Test, Trail Making Test-Parts A and B, Rey Auditory Verbal Learning Test, Beck Depression Inventory-II, and Beck Anxiety Inventory scores. Significant group differences were detected for anxiety and depression with the high ACE group endorsing significantly greater depression and anxiety symptoms relative to the low ACE group. High and low ACE groups did not significantly differ on any cognitive measures. Results indicate that an individual's psychological health, but not cognitive functioning, is impacted by the level of ACE exposure. Study findings highlight the importance of including ACE measures in neuropsychological evaluations, as it will aid in case conceptualization and tailoring treatment recommendations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Genomic classifiers and prognosis of localized prostate cancer: a systematic review.

Refinement of the risk classification for localized prostate cancer is warranted to aid in clinical decision making. A systematic analysis was undertaken to evaluate the prognostic ability of three genomic classifiers, Decipher, GPS, and Prolaris, for biochemical recurrence, development of metastases and prostate cancer-specific mortality in patients with localized prostate cancer. Data sources: MEDLINE, Embase, and Web of Science were queried for reports published from January 2010 to April 2022. prospective or retrospective studies reporting prognosis for patients with localized prostate cancer. relevant data were extracted into a customized database by one researcher with a second overreading. Risk of bias was assessed using a validated tool for prognostic studies, Quality in Prognosis Studies (QUIPS). Disagreements were resolved by consensus or by input from a third reviewer. We assessed the certainty of evidence by GRADE incorporating adaptation for prognostic studies. Data synthesis: a total of 39 studies (37 retrospective) involving over 10,000 patients were identified. Twenty-two assessed Decipher, 5 GPS, and 14 Prolaris. Thirty-four studies included patients who underwent prostatectomy. Based on very low to low certainty of evidence, each of the three genomic classifiers modestly improved upon the prognostic ability for biochemical recurrence, development of metastases, and prostate cancer-specific mortality compared to standard clinical risk-classification schemes. downgrading of confidence in the evidence stemmed largely from bias due to the retrospective nature of the studies, heterogeneity in treatment received, and era in which patients were treated (i.e., prior to the 2000s). Genomic classifiers provide a small but consistent improvement upon the prognostic ability of clinical classification schemes, which may be helpful when treatment decisions are uncertain. However, evidence from current management-era data and of the predictive ability of these tests is needed.

Are Guideline-recommended Risk Classification Schemes in Pulmonary Hypertension Adequately Robust to Guide the Real-world Setting?

Pulmonary hypertension is a complex syndrome that encompasses a diverse group of pathophysiologies predisposed by different environmental and genetic factors. It is not clear to which extent the universal risk classification schemes can be applied to cohorts in individual pulmonary hypertension centers with differing environmental backgrounds, genetic pools, referral networks. To explore whether the recommended risk classification schemes could reliably be used for mortality prediction in an unselected pulmonary hypertension population of a tertiary pulmonary hypertension center. A retrospective cross-sectional study. We retrospectively screened our hospital database for the patients with pulmonary hypertension between 2015 and 2022. The grouping of pulmonary hypertension was made as follows in accordance with current guidelines: Group 1: patients with pulmonary arterial hypertension, Group 2: patients with pulmonary hypertension associated with left heart disease, Group 3: patients with pulmonary hypertension associated with lung disease and/or hypoxia, and Group 4: patients with pulmonary hypertension associated with pulmonary artery obstructions. Then, we compared the predicted and observed mortality rates of four different risk classification schemes (REVEAL, REVEAL-Lite, ESC/ERS and COMPERA). We identified 723 cases in our pulmonary hypertension database, the final study population consisted of 549 patients. The REVEAL, REVEAL-Lite and European Society of Cardiology/European Respiratory Society risk scores significantly underestimated the mortality risk in the low-risk stratum (5.3% vs. 1.9%, P < 0.001; 5.3% vs. 2.9%, P = 0.015 and 6.3% vs. 1%, P < 0.001, respectively) and overestimated the mortality risk in the high-risk stratum (11.8% vs. 25.8%, P < 0.001; 10.4% vs. 25.1%, P < 0.001 and 13.2% vs. 30%, P < 0.001, respectively). Although the COMPERA 4-strata model significantly underestimated the risk in low- and intermediate-low risk strata (4.9% vs. 1.5%, P < 0.001 and 6.8% vs. 2.8%, P = 0.001, respectively), it was accurate in intermediate-high and high-risk groups (10.1% vs. 8.7%, P = 0.592 and 15.6% vs. 22%, P = 0.384, respectively). The analyses limited only to group 1 pulmonary hypertension patients gave similar results. The established risk classification schemes may not perform as good as expected in unselected pulmonary hypertension populations and this may have important implications on management decisions. Tertiary centers should not uncritically accept the published risk prediction models and consider modifying current risk scores according to their own patient characteristics.

Risk management at the stages of the life cycle of NPP projects

The article discusses the issues of risk management at all stages of the life cycle of nuclear power plants. The definition and classification by types of risks that arise during the stages of the life cycle and operation of difficult objects such as nuclear power plants. Risk is anything that has the potential to affect each stage of a facility’s life cycle, performance, longevity, safety, or project budget. Thus, risk management is the process of identifying, classifying, prioritizing and planning risks. This article not only discusses the identification and classification by types of risks that arise during the construction and operation of such complex facilities as nuclear power plants, but also describes the methods for assessing and managing risks, taking into account the specifics of the construction of nuclear power facilities. There are two methods of risk assessment: quantitative and qualitative. The article describes such Quantitative methods as “Scenario Technology”, “Method - Sensitivity Analysis”, “Diagramming Technique - Tree Analysis”, as well as such Qualitative risk assessment methods as “Assessment of the probability of risk and its impact”, “Evaluation Matrix probability/risk of impact”, “Probability and impact”, “Risk classification and assessment of risk urgency. As a result of the analysis of existing risk management methods at all stages of the life cycle, the following conclusions were drawn: Risk identification can occur at any stage of the life cycle of a nuclear power plant, even after completion of construction work and delivery of the project. The maximum number of risks for all stages of the NPP life cycle should be identified at the stage of Sitting and design. The article proposes the format of the risk register table, provides a risk classification scheme, and develops a risk management flow chart at the stages of the NPP life cycle.

Cyber risk definition and classification for financial risk management

Cyber risk is undeniably one of the most critical emerging risks to the financial industry. However, even though cyber risk is recognized as a significant threat to financial institutions and, more generally, to financial stability, the lack of proper data on cyber risk losses impedes efforts to effectively measure and manage this risk. This paper aims to address this gap by providing a cyber risk definition and classification scheme for risk management purposes, to be used as a data collection template for financial institutions. As such, the proposed scheme would ensure that the adopting institutions utilize common language and would allow consistent data collection and sharing.We provide a deeper dive into the reasoning behind the variables we propose to collect and demonstrate how some of the existing cyber security events map into our proposed scheme.

The highest percentage of Gleason Pattern 4 is a predictor in intermediate-risk prostate cancer.

This study aims to clarify the clinicopathological significance of several novel pathological markers, including the percentage of Gleason pattern 4 and small/non-small cribriform pattern, in intermediate-risk Gleason score 3+4=7 prostate cancer. Two-hundred and twenty-eight patients with Gleason score 3+4=7 intermediate-risk prostate cancer who underwent radical prostatectomy between 2009 and 2019 at our institute were selected. Preoperative clinicopathological characteristics, including serum prostate-specific antigen level, clinical T stage, percentage of cancer-positive cores at biopsy, small/non-small cribriform pattern, the highest percentage of Gleason pattern 4, the total length of Gleason pattern 4 and percentage of Gleason score 7 cores were examined in univariate/multivariate logistic regression analysis to determine their predictive value for postoperative adverse pathological findings, defined as an upgrade to Gleason score 4+3=7 or higher, pN1 or pT3b disease. Fifty-four cases (23.7%) showed adverse pathological findings. Although a non-small cribriform pattern, highest Gleason pattern 4 percentage and total length of Gleason pattern 4 were predictive of adverse pathological findings in univariate analysis, only the highest Gleason pattern 4 percentage was an independent predictive factor in multivariate analysis (odds ratio: 1.610; 95% confidence interval: 1.260-2.070; P= 0.0002). The highest Gleason pattern 4 percentage was a potent predictive parameter for Gleason score 3+4=7 intermediate-risk prostate cancer and should be considered in the risk classification scheme for prostate cancer.

Why make it hard? - Usage of aggregated statistical data for risk assessment of damage scenarios in the context of ISO/SAE 21434

ISO/SAE 21434 "Road vehicles - Cybersecurity engineering" is a standard for cyber security, which has been published since August 2021. The standard covers the concept phase. The concept phase is characterized by the cooperation of various mostly high-level stakeholders from different disciplines, departments and possibly companies. The cooperation takes place mainly in workshops. ISO/SAE 21434 requires a Threat and Risk Assessment (TARA) to be carried out for the concept phase. Within TARA, damage scenarios are to be identified for the vehicle/components to be developed. Furthermore, the effects of these damage scenarios are to be assessed. In this context, ISO/SAE 21434 refers to the risk classification scheme ASIL of ISO 26262, which is established in the automotive industry. For the effective application of these schemes, expert knowledge is necessary on the one hand, and verified data on accident types and accident causes are required on the other hand. Access to this data is often difficult and the data itself is not suitable for direct use in workshops.In this paper we present tools for the assessment of damage scenarios. For this purpose, we use data from the Federal Statistical Office (StBA) with over 2 million registered traffic accidents in Germany per year. We have analyzed the StBA data and evaluated them according to their usefulness for workshops. The result of this work are several concrete ASIL tables which address different types of accidents and causes of accidents. In two workshop with 17 experts from the automotive sector and with experts from product development, we applied the elaborated tables to evaluate different damage scenarios.

Evaluation of Early Treatment Response Utilizing the MAS-ALL18 Adapted Management Guideline in Four “Mexico in Alliance with St. Jude” (MAS) Member Hospitals

Introduction: Acute lymphoblastic leukemia (ALL) represents approximately 50% of all childhood cancers in Latin America. Mexico is not the exception. The impact of ALL survival on overall childhood cancer survival is significant. According to government data, five-year survival is about 52%. Mexico in Alliance with St. Jude (MAS) is a multi-site, intersectoral collaboration. The collaborative network has explored and reported on factors associated with this suboptimal outcome and have identified challenges with ALL risk group classification as a leading cause. For example, as many as 82% of children diagnosed with ALL in Mexico receive high-risk treatment and the tendency for higher-risk group assignment often occurs in response to limited access to cytogenetic testing and minimal residual disease (MRD) testing. This leads to higher intensity treatment and may explain the high rate of treatment-related death (TRD) (12%) documented during the induction but also subsequently. In our previous case series, only 75% of patients were alive at the end of the first year of treatment. These findings, led MAS to develop a consensus-derived standardized diagnosis and treatment schema (MAS-ALL18), which takes into account clinical, cytogenetic, and MRD results. Diagnostic testing is performed in a centralized laboratory. Although centred on delivery of standard of care, this experience represents is the first prospective multi-site cooperative group effort in Mexico. We report on early treatment (first 90 days) clinical and implementation outcomes utilizing the MAS-ALL18 adapted management guideline (AMG) in four member hospitals of the MAS collaboration network.Results: From June 2019 to June 2020, 137 patients received treatment utilizing the MAS-ALL18 AMG in four publicly funded hospitals in Mexico. B-cell ALL represented 91.9% of the cases, 20.4% of patients were older than 9 years of age, 25.5% had a white blood cell count greater than 50,000 at diagnosis and 58.3% were male. Complete remission at the end of the induction was achieved in 90.6% of patients. TRD during the induction phase was 8%. MRD at Day 15 in 123 patients with B-cell ALL, 84.5% of them had MRD <1% and 7.3% had MRD ≥5%. MRD at Day 29 was assessed for the 10 patients diagnosed with T-cell ALL, 4 patients had MRD <0.01%, 2 had 0.02%, and 4 died during the induction phase. MRD was also assessed during consolidation (at Day 84) in 99 patients, 94.9% had MRD <0.01% and 5 patients MRD ≥0.01%, from which 3 had MRD at day 15 >1% and none registered an MRD result <0.5%. Utilizing the MAS-ALL18 risk group stratification, 50% of patients were assigned to a favorable risk group at the end of the consolidation. In 34 patients, the risk group was reclassified following the standardized algorithm; 30 reclassification events happened at the end of the induction and four at the end of the consolidation. Only two events were reassigned to a lower risk group, while the rest of the reclassifications were conducted to assign patients to a higher risk group due to unfavorable cytogenetics or an inadequate early response to treatment. High-risk treatment was ultimately assigned to 30% of patients using the MAS-ALL18 risk classification schema.Conclusion: It is feasible to implement a standardized multi-site adapted management guideline for ALL risk group stratification and treatment allocation in Mexico in the context of a collaborative network. TRD remains high during the induction phase, nevertheless, this number shows an improvement (8%) compared to the 2015 data report (12%). The ALL risk group classification is transitioning from a rigid scheme that placed 80% of patients in a high-risk group to a dynamic classification system that considers cytogenetic testing and MRD conducted in a centralized and externally validated laboratory that serves as reference for all the participating hospitals. The standardized MAS-ALL18 approach allowed us to classify 50% of patients in a favorable risk group during and at the end of the induction phase, which implies receiving a lower intensity treatment with a high probability of cure and a lower risk of TRD. The implementation of MAS-ALL18 risk classification scheme reduced the number of children requiring high-risk treatment in the participating hospitals. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

REVIEW AND CLASSIFICATION OF ART-PROJECTS’ FEATURES AS RISK FACTORS

An overview of the features of art projects as possible threats to the planning and implementation of projects in the creative sphere in the following areas is offered: psychological aspects of personality and the specificity of art projects, which are characteristic exclusively for projects in this sphere. It has been determined that the combination of factors of the internal and external environment is the basis for the formation of the list and ranking of risks of art projects. A detailed analysis of scientific works devoted to research in the implementation of the design approach in the creative field, the study of psychological traits that significantly distinguish a creative person from the psychology of other people and research in the field of risk management, has proved that the attention of the scientific community on the study of risks and their nature origin in art projects today is very insignificant. The introduction of the methodology of the project approach to the organization and implementation of creative events in Ukraine is still rather weak, and anti-risk measures begin to be developed only after the onset of risk. Therefore, the issue of studying the risks in the implementation of art projects remains relevant and in demand. Analysis of the psychological traits of creative personalities and the specificity of art projects as sources of origin of risks made it possible to develop types of risks for their subsequent classification. It is concluded that the presented classification scheme is of practical value for art project managers. It will allow in the future to envisage options for the development of events, to determine in advance the factors of influence of the external and internal environment of the art project and to develop in advance measures, methods and techniques to prevent a risk event or, in case of its occurrence, to minimize negative consequences for achieving the goals of the art project (time, budget, content, quality). Now the list of types of risks of art projects includes only two sources of risks’ origin. But work on drawing up a risk classification scheme for art projects will continue to include the maximum possible sources of risk origin and provide the project manager with an effective tool for identifying and analyzing possible threats (risks) or opportunities.

A risk stratification scheme for synchronous oligometastatic non-small cell lung cancer developed by a multicentre analysis

BackgroundsOligometastatic Non-Small Cell Lung Cancer (NSCLC) patients represent a category without a standard therapeutic approach. However, in selected oligometastatic NSCLC, radical surgery seems to offer a good prognosis. This retrospective study aimed to analyse the long-term outcomes of synchronous oligometastatic patients treated with curative intent and identify the factors associated with better results and the proposal of a risk stratification system for classifying the synchronous oligometastatic NSCLC. MethodsThe medical records of patients from 18 centres with pathologically diagnosed synchronous oligometastatic NSCLC were retrospectively reviewed. The inclusion criteria were synchronous oligometastatic NSCLC, radical surgical treatment of the primary tumour with or without neoadjuvant/adjuvant therapy and radical treatment of all metastatic sites. The Kaplan – Meier method estimated survivals. A stratified backward stepwise Cox regression model was assessed for multivariable survival analyses. Results281 patients were included. The most common site of metastasis was the brain, in 50.89 % patients. Median overall survival was 40 months (95 % CI: 29–53). Age ≤65 years (HR = 1.02, 95 % CI: 1.00–1.05; p = 0.019), single metastasis (HR = 0.71, 95 % CI: 0.45–1.13; p = 0.15) and presence of contralateral lung metastases (HR = 0.30, 95 % CI: 0.15 – 0.62; p = 0.001) were associated with a good prognosis. The presence of pathological N2 metastases negatively affected survival (HR = 2.00, 95 % CI: 1.21–3.32; p = 0.0065). These prognostic factors were used to build a simple risk classification scheme. ConclusionsTreatment of selected synchronous oligometastatic NSCLC with curative purpose could be conducted safely and at acceptable 5-year survival levels, especially in younger patients with pN0 disease.

Copy Number Alterations are Associated with the Risk of Very Early Relapse in Pediatric B-lineage Acute Lymphoblastic Leukemia: A Nested Case-control MIGICCL Study

Refining risk stratification to avoid very early relapses (VER) in Mexican patients with B-lineage acute lymphoblastic leukemia (B-ALL) could lead to better survival rates in our population. The purpose of this study was to investigate the association between the United Kingdom ALL (UKALL)-CNA classifier and VER risk in Mexican patients with childhood B-ALL. A nested case-control study of 25 cases with VER and 38 frequency-matched controls without relapse was conducted within the MIGICCL study cohort. They were grouped into the categories of the UKALL-CNA risk classifier (good [reference], intermediate and poor), according to the results obtained by multiplex ligation dependent probe amplification. Overall and disease-free survival (DFS) were estimated using the Kaplan-Meier method. Univariate and multivariate Cox proportional hazards analyses were conducted. The CDKN2A/B genes were most frequently deleted in the group with relapse. According to UKALL-CNA classifier, 33 (52.4%) patients were classified as good, 21 (33.3%) intermediate and 9 (14.3%) poor-risk B-ALL. The intermediate and poor risk groups were associated with an increased risk of VER (HR=4.94, 95% CI=1.87-13.07 and HR=7.42, 95% CI=2.37-23.26, respectively) in comparison to the good-risk patients. After adjusting by NCI risk classification and chemotherapy scheme in a multivariate model, the risks remained significant. Our data support the clinical utility of profiling CNAs to potentially refine current risk stratification strategies of patients with B-ALL.

Approach to the selection of strategies for emerging risk management considering uncertainty as the main decision variable in occupational contexts

Emerging risk models are still scarce and far from agreed upon. They are currently the focus of increasing interest in the occupational context. Consequently, frameworks that deal with emerging risk management in industrial contexts are very recent or, even still, in the development and maturation stage. Uncertainty should be considered as the main characteristic of emerging risk in this context.It is as such that the main objective of this paper is to develop a qualitative approach inspired by meta-learning lessons to the selection of strategies for emerging risk management, considering uncertainty as the main decision variable in occupational contexts. To this end, uncertainty has been integrated, as a combination of knowledge and understanding, in a theoretical framework on emerging risk. An emerging risk classification scheme has been developed with the results obtained. This scheme makes it possible to estimate the level of emerging risk and management strategies based on the combination of uncertainty and the potential consequences of emerging risk. Such approach has been applied to three case studies with different evolutionary degrees of emerging risk: exoskeletons; nanomaterials; and industrial automation. The proposed approach could be considered primarily as a qualitative tool applicable to the process of pre-assessment and communication of emerging risk.

Environmental risk classification of emerging contaminants in an alpine stream influenced by seasonal tourism

Over the last few decades, there has been increasing concern about the risks posed by pharmaceuticals and personal care products (PPCPs) in water resources. Despite the awareness of the widespread presence of PPCPs in many aquatic systems, very few studies have investigated the potential risk of these chemicals to wildlife communities of high-altitude freshwaters such as in the Italian Alps. In this context, the two-fold aim of this study was to increase the knowledge about the potential risks of PPCPs to alpine streams subjected to tourism pressure and to investigate the capability of a predictive approach as a useful tool for risk management. The Vermigliana stream (Tonale Pass, Trento Province, Italy) was selected as a case study. Although regional in nature, the selected site is representative of Italian alpine tourism areas. Risk analysis was performed on 54 PPCPs potentially released by the local sewage treatment plant during tourism periods in winter and summer. PPCPs were ranked by risk based on either measured environmental concentrations (MECs) or predicted environmental concentrations (PECs). The results highlighted the potential risks to aquatic organisms, particularly in winter. For instance, the antibiotics amoxicillin and clarithromycin as well as the nonsteroidal anti-inflammatory drugs ibrupofen and diclofenac were above the risk threshold showing MECs or PECs higher than their relative predicted no-effect concentrations. Finally, the risk classifications of PPCPs obtained using PECs and MECs were generally comparable, thereby indicating the potential usefulness of the predictive approach to plan adequate risk management actions. However, over the counter drugs were the only exceptions. The predictive approach misclassified these substances and indicated a lower risk. This highlights the difficulties in identifying the concern posed by these substances in alpine areas characterised by fluctuating population owing to seasonal tourism. Overall, these findings indicate that the proposed risk classification scheme may well be appropriate for prioritization of PPCPs in the alpine environments characterized by high tourism pressure, when robust and local data are available and reflective of most source inputs.

A novel MRI-based classification of spinal cord shape and CSF presence at the curve apex to assess risk of intraoperative neuromonitoring data loss with thoracic spinal deformity correction.

Retrospective cohort. We present a simple classification system that is able to identify patients with increased odds of losing intraoperative neuromonitoring data during thoracic deformity correction. Type 3 spinal cords, with the cord deformed against the concave pedicle in the axial plane, have ×28 greater odds of losing monitoring data during surgery. Assess preoperative morphology of the spinal cord across the thoracic concavity to predict intraoperative loss of neuromonitoring data. 128 consecutive patients undergoing surgical correction of a thoracic deformity with pedicle screw/rod constructs were included. Spinal cords were classified into 3 types based on the appearance of the cord on the axial-T2 MRI at the apex of the curve. Type 1 is defined as a circular/symmetric cord with visible CSF between the cord and the apical concave pedicle/vertebral body. Type 2 is a circular/oval/symmetric cord with no visible CSF between the concave pedicle and the cord. Type 3 is a spinal cord that is flattened/deformed by the apical concave pedicle or vertebral body, with no intervening CSF (Fig.1). 128 patients were reviewed: 81 (63%) Type 1; 32 (25%) Type 2; and 12 (11.7%) Type 3 spinal cords. Lower extremity trans-cranial motor-evoked Potentials (MEPs) and/or somatosensory evoked potentials (SSEPs) were lost intraoperatively in 21 (16%) cases, with full recovery of data in 20 of those cases. On regression analysis, a Type 1 cord was protective against intraoperative data loss (OR = 0.17, p = 0.0003). Type 2 cords had no association with data loss (OR = 0.66, p = 0.49). Type 3 cords had significantly higher odds of intraoperative data loss (OR = 28.3, p < 0.0001). We present a new spinal cord risk classification scheme to identify patients with increased odds of losing spinal cord monitoring data with thoracic deformity correction. The odds of losing intraoperative MEPs/SSEPs are greater in type 3 spinal cords. III.

IMPACT OF A NOVEL MYOCARDIAL PERFUSION IMAGING RISK CLASSIFICATION SCHEME ON REFERRAL FOR CORONARY ANGIOGRAPHY, REVASCULARIZATION, AND MORTALITY

Background: To risk stratify patients undergoing SPECT-MPI in accordance with AUC for referral of angiography and revascularization, we developed a novel risk classification scheme incorporating AU...

Additive Manufacturing Cyber-Physical System: Supply Chain Cybersecurity and Risks

Additive Manufacturing (AM) methods have become increasingly efficient and industrially viable in the past ten years. These methods offer the freedom of complexity to the designers and choices of localized and pull-based production system to the managers. These propositions of AM have been enabling custom manufacturing and are catalysts for rapid growth of additive manufacturing (AM). This paper analyzes the general characteristics of AM supply chain and proposes three AM supply chain models based on the specific nature of the industry. Our description of the models emphasizes on adopting an holistic view of the AM supply chain and therefore includes raw material, printer hardware and the virtual supply chain. Throughout the product life cycle of additively manufactured products, the interlacing of the virtual supply chain ( digital thread ) with the physical supply chain and their operations fundamentally make the AM process a cyber-physical system (CPS). Therefore, the technology brings along with it benefits of a CPS as well as a new class of attack vectors. We discuss the possible attacks (printer, raw material and design level), risks (reverse engineering, counterfeiting and theft) and provide an enhanced risk classification scheme. We contend that the traditional cybersecurity methods need to evolve to address the new class of attack vectors that threaten the AM supply chain and also discuss the nature of existing solutions that help in addressing the risks and attack threats. In providing an holistic view of the AM supply chain the interdependencies of the processes in the AM supply chain are presented and we elucidate the effects of local attack vectors on the entire supply chain. Further, we discuss the existing security measures to mitigate the risk and identify the existing gap in AM security that needs to be bridged.

The Genomic and Molecular Pathology of Prostate Cancer: Clinical Implications for Diagnosis, Prognosis, and Therapy.

Prostate cancer (PCa) is the most common noncutaneous malignancy affecting American men and the second most common cause of cancer death. The traditional risk classification schemes for PCa are limited due to the vast clinical and molecular heterogeneity of the disease. Fortunately, recent advancements in sequencing technologies have provided us with valuable insight into the genomics of PCa. To date, a wide array of recurrent genomic alterations in PCa have been identified. Incorporating these distinct molecular subtypes of PCa into prediction models provides opportunities for improved risk stratification and ultimately better patient outcomes. In this review, we summarize the key molecular subtypes of PCa and focus on those genomic alterations that have clinical implications for diagnosis, prognosis, and therapeutic response.

A dynamic neural network model for predicting risk of Zika in real time

BackgroundIn 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak’s expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner.MethodsIn this work, we present a dynamic neural network model to predict the geographic spread of outbreaks in real time. The modeling framework is flexible in three main dimensions (i) selection of the chosen risk indicator, i.e., case counts or incidence rate; (ii) risk classification scheme, which defines the high-risk group based on a relative or absolute threshold; and (iii) prediction forecast window (1 up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future.ResultsThe model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, and vector habitat suitability, socioeconomic, and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks.ConclusionsSensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at any time during an epidemic. The predictive capability was superior for shorter forecast windows and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.

Predicting 90-Day Mortality in Locoregionally Advanced Head and Neck Squamous Cell Carcinoma after Curative Surgery

Purpose: To propose a risk classification scheme for locoregionally advanced (Stages III and IV) head and neck squamous cell carcinoma (LA-HNSCC) by using the Wu comorbidity score (WCS) to quantify the risk of curative surgeries, including tumor resection and radical neck dissection. Methods: This study included 55,080 patients with LA-HNSCC receiving curative surgery between 2006 and 2015 who were identified from the Taiwan Cancer Registry database; the patients were classified into two groups, mortality (n = 1287, mortality rate = 2.34%) and survival (n = 53,793, survival rate = 97.66%), according to the event of mortality within 90 days of surgery. Significant risk factors for mortality were identified using a stepwise multivariate Cox proportional hazards model. The WCS was calculated using the relative risk of each risk factor. The accuracy of the WCS was assessed using mortality rates in different risk strata. Results: Fifteen comorbidities significantly increased mortality risk after curative surgery. The patients were divided into low-risk (WCS, 0–6; 90-day mortality rate, 0–1.57%), intermediate-risk (7–11; 2.71–9.99%), high-risk (12–16; 17.30–20.00%), and very-high-risk (17–18 and >18; 46.15–50.00%) strata. The 90-day survival rates were 98.97, 95.85, 81.20, and 53.13% in the low-, intermediate-, high-, and very-high-risk patients, respectively (log-rank p < 0.0001). The five-year overall survival rates after surgery were 70.86, 48.62, 22.99, and 18.75% in the low-, intermediate-, high-, and very-high-risk patients, respectively (log-rank p < 0.0001). Conclusion: The WCS is an accurate tool for assessing curative-surgery-related 90-day mortality risk and overall survival in patients with LA-HNSCC.