Application Of Risk Assessment Research Articles

Usefulness of risk assessment tools in predicting hemodynamic outcome after balloon pulmonary angioplasty: a comparative analysis

ABSTRACT Objectives Several parameters of widely used risk assessment tools for pulmonary arterial hypertension (PAH) have been linked to hemodynamic outcomes of balloon pulmonary angioplasty (BPA). Therefore, we aimed to determine whether these risk assessment tools could be used to predict hemodynamic outcomes following BPA. Methods In this retrospective study, we included 139 patients with chronic thromboembolic pulmonary hypertension who had undergone BPA at Center for Pulmonary Vascular Diseases, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College (Beijing, China). We compared the accuracies of seven well-validated risk assessment tools for predicting hemodynamic outcomes following BPA. A favorable hemodynamic outcome was defined as a mean pulmonary arterial pressure < 30 mmHg at follow-up. Results The baseline risk profiles varied significantly among the risk assessment tools. The US Registry to Evaluate Early and Long-Term PAH Disease Management risk scales and the French risk assessment tools rated most patients as high-risk, while the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) series and laboratory examination-based risk scales categorized most patients as having intermediate-risk profile. COMPERA 2.0 (4-strata) exhibited the highest predictive power among all risk stratifications. Noninvasive risk stratification (COMPERA 2.0 [3-strata]) showed a comparable predictive ability to that of invasive risk stratification (COMPERA 1.0) (area under the curve 0.649 vs. 0.648). Moreover, incorporating diffusing capacity of the lungs for carbon monoxide and tricuspid regurgitation velocity into COMPERA 2.0 (4-strata) further enhanced its predictive power (net reclassification index 0.153, 95% confidence interval 0.009–0.298, p = 0.038). Additionally, this refined COMPERA version had a high calibration accuracy (slope 0.96). Conclusion Although the risk strata distribution varied among different risk assessment tools, the proportion of patients achieving favorable hemodynamics decreased with the escalation of risk stratification in most models. The well-validated risk assessment tools for PAH could also predict hemodynamic outcomes following BPA, and the refined COMPERA 2.0 model exhibited the highest predictive ability among these. Applying risk assessment tools before BPA can facilitate early identification of patients in need of closer monitoring and more intensive interventions, contributing to a better prognosis after BPA.

Application of the hazard identification risk assessment and risk control method in the welding frame body process (a case study in indonesian company)

Application of the hazard identification risk assessment and risk control method in the welding frame body process (a case study in indonesian company)

Credit default swap spreads modeling and forecasting with a stochastic square-root three-factor model

In this study, we consider the CIR3 model, a three-factor stochastic model with correlated trends and volatilities for modeling and forecasting credit default swap (CDS) spreads. After recalling existence and uniqueness results, together with a generalized Feller condition to ensure positivity, we use a Lamperti-type transform to rewrite our SDEs system in a form in which the stochastic part of the leading process is uncorrelated with those of its mean and volatility processes. Finally, we calibrate the model through the estimating function approach for ergodic diffusions and simulate the CDS prices by discretizing our (transformed) system. These findings contribute to a deeper understanding of stochastic models with correlated trends and volatilities, with applications in pricing, trading and risk assessment.

Double-quantitative multi-granularity kernel fuzzy rough sets model and its application in rheumatoid arthritis risk assessment

Double-quantitative multi-granularity kernel fuzzy rough sets model and its application in rheumatoid arthritis risk assessment

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia.

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review.

Respiratory tract infections are major contributors to the global disease burden. Quantitative microbial risk assessment (QMRA) holds potential as a rapidly deployable framework to understand respiratory pathogen transmission and inform policy on infection control. The goal of this paper was to evaluate, motivate, and inform further development of the use of QMRA as a rapid tool to understand the transmission of respiratory pathogens and improve the evidence base for infection control policies. We conducted a literature review to identify peer-reviewed studies of complete QMRA frameworks on aerosol inhalation or contact transmission of respiratory pathogens. From each of the identified studies, we extracted and summarized information on the applied exposure model approaches, dose-response models, and parameter values, including risk characterization. Finally, we reviewed linkages between model outcomes and policy. We identified 93 studies conducted in 16 different countries with complete QMRA frameworks for diverse respiratory pathogens, including SARS-CoV-2, Legionella spp., Staphylococcus aureus, influenza, and Bacillus anthracis. Six distinct exposure models were identified across diverse and complex transmission pathways. In 57 studies, exposure model frameworks were informed by their ability to model the efficacy of potential interventions. Among interventions, masking, ventilation, social distancing, and other environmental source controls were commonly assessed. Pathogen concentration, aerosol concentration, and partitioning coefficient were influential exposure parameters as identified by sensitivity analysis. Most (84%, ) studies presented policy-relevant content including a) determining disease burden to call for policy intervention, b) determining risk-based threshold values for regulations, c) informing intervention and control strategies, and d) making recommendations and suggestions for QMRA application in policy. We identified needs to further the development of QMRA frameworks for respiratory pathogens that prioritize appropriate aerosol exposure modeling approaches, consider trade-offs between model validity and complexity, and incorporate research that strengthens confidence in QMRA results. https://doi.org/10.1289/EHP12695.

Оценка риска совершения преступлений лицами, освобожденными из мест лишения свободы

Introduction. While studying recidivism prediction, criminal risk assessment is considered in many countries a mandatory procedure. The paper presents the most widely known and frequently applied risk assessment tools. Special attention is paid to predicted risk assessment tools for individuals released in the Russian Federation and the Republic of Kazakhstan. Taking into account the emerging tradition of using these risk assessment tools for practical purposes and the opportunities for conducting relevant research, much attention is paid to reviewing the tools used abroad. The purpose of the study is to provide the theoretical grounds for recidivism risk assessment tools, as well as the analysis of foreign experience in application and verification of this tool. Research methods. The research uses general scientific methods: (analysis, synthesis, systematization, generalization, analogy), special methods: comparative (when studying tools for assessing the risk of recidivism), formal legal (for the study of normative legal acts). Results. The research demonstrates a wide range of approaches and patterns in the area of criminal behavior risk assessment. Criminal behavior risk assessment tools were classified, and they can be summarised in a historical perspective into four generations according to chronology. The first and least reliable approach is to assess the risk of recidivism based on the clinical opinion of professionals. At this stage the measurement was characterised by its subjectivity. The second generation was based on actuarial valuation using reliable statistical predictors and significance levels for recidivism. The third generation tended to combine risk factors based on the theory of static risk assessment. The fourth generation of tools is based on the understanding that risk should be assessed as a continuous and dynamic process related to both the risk itself and the needs and resources of the individual. This approach considers that offenders’ supervision in post-penitentiary probation is an effective means of successful risk assessment based on their individual psychological characteristics and resources.

A baseline approach for probabilistic blast risk analysis of building cladding under external explosions

The state of the practice in blast-resistant applications against explosions is to design the structural components for a prescribed combination of explosive mass and location, namely the design basis threat. In this context, the blast source is represented by specific scenarios, mostly associated with expert judgement, rating systems or code provisions. While offering a useful basis for practical applications, the level of detail can be significantly enhanced within a probabilistic framework for risk assessment. In research practice, plenty simplified probabilistic approaches have been proposed on external explosions in order to perform risk assessment. A rigorous methodology for such an assessment is presented herein, using tools and techniques derived from seismic risk-assessment applications. Specifically, the mean annual frequency of different explosive mass hazards is represented via a recurrence law, while the potential locations are modelled as a 2D spatial distribution, accounting for the various layers of defense that exist around the structure, i.e., perimeter protection, landscape, public or private spaces, etc. The methodology is finally substantiated with the case study of a typical building subjected to potential blast loadings from external aggressors.

Evaluation of in silico model predictions for mammalian acute oral toxicity and regulatory application in pesticide hazard and risk assessment

The United States Environmental Protection Agency (USEPA) uses the lethal dose 50% (LD50) value from in vivo rat acute oral toxicity studies for pesticide product label precautionary statements and environmental risk assessment (RA). The Collaborative Acute Toxicity Modeling Suite (CATMoS) is a quantitative structure-activity relationship (QSAR)-based in silico approach to predict rat acute oral toxicity that has the potential to reduce animal use when registering a new pesticide technical grade active ingredient (TGAI). This analysis compared LD50 values predicted by CATMoS to empirical values from in vivo studies for the TGAIs of 177 conventional pesticides. The accuracy and reliability of the model predictions were assessed relative to the empirical data in terms of USEPA acute oral toxicity categories and discrete LD50 values for each chemical. CATMoS was most reliable at placing pesticide TGAIs in acute toxicity categories III (>500–5000 mg/kg) and IV (>5000 mg/kg), with 88% categorical concordance for 165 chemicals with empirical in vivo LD50 values ≥ 500 mg/kg. When considering an LD50 for RA, CATMoS predictions of 2000 mg/kg and higher were found to agree with empirical values from limit tests (i.e., single, high-dose tests) or definitive results over 2000 mg/kg with few exceptions.

Fractionation of metals in soil for strawberry cultivation: Effect on metal migration in food chain and application in risk assessment

Although trace metals in strawberry production system have attracted growing attention, little is known about metal fractionation in soil for strawberry cultivation. We hypothesized that the metal fractions in soil influenced by strawberry production had significant effect on food chain transport of metals and their risk in soil. Here, samples of strawberries and soil were gathered in the Yangtze River Delta, China to verify the hypothesis. Results showed that the acid-soluble Cr, Cd, and Ni in soil for strawberry cultivation were 21.5%–88.3% higher than those in open field soil, which enhanced uptake and bioaccessible levels of these metals in strawberries. Overall, the ecological, mobility, and health risks of Pb, Zn, Ni, and Cu in soil were at a low level. However, the ecological risk of bioavailable Cd, mobility risk of Cd, and cancer risk of bioavailable Cr in over 70% of the soil samples were at moderate, high, and acceptable levels, respectively. Since the increased acid-soluble Cr and Ni in soil were related to soil acidification induced by strawberry production, nitrogen fertilizer application should be optimized to prevent soil acidification and reduce transfer of Cr and Ni. Additionally, as Cd and organic matter accumulated in soil, the acid-soluble Cd and the ecological and mobility risks of Cd in soil were enhanced. To decrease transfer and risk of Cd in soil, organic fertilizer application should be optimized to mitigate Cd accumulation, alter organic matter composition, and subsequently promote the transformation of bioavailable Cd into residual Cd in soil.

Theoretical frameworks in AI for credit risk assessment: Towards banking efficiency and accuracy

This paper delves into theoretical frameworks in AI for credit risk assessment, exploring how these frameworks enhance banking efficiency and accuracy. It discusses various AI techniques such as machine learning algorithms, neural networks, and natural language processing, and their application in credit risk assessment. Furthermore, it examines the challenges and opportunities presented by these frameworks, highlighting their potential to revolutionize the banking sector. Revolutionizing Credit Risk Assessment in Banking, The Role of Artificial Intelligence In the dynamic realm of finance, the assessment of credit risk stands as a fundamental pillar for banking institutions. Traditionally, this process has heavily relied on statistical models and historical data. However, the emergence of Artificial Intelligence (AI) has catalyzed a transformative shift in this domain. This paper elucidates the theoretical underpinnings of AI frameworks employed in credit risk assessment and investigates their profound implications for enhancing the efficiency and accuracy of banking operations. The exploration begins by delineating various theoretical frameworks in AI pertinent to credit risk assessment. Leveraging machine learning algorithms, neural networks, and natural language processing techniques, these frameworks offer innovative approaches to evaluate creditworthiness. Unlike conventional methods, AI-driven models possess the capacity to ingest vast datasets, identify intricate patterns, and adapt dynamically to evolving market dynamics. Such capabilities empower banks to make more informed and timely decisions regarding lending activities. Moreover, this paper delves into the practical application of AI techniques in credit risk assessment. Through case studies and empirical evidence, it elucidates how these advanced methodologies enable banks to mitigate risks while maximizing profitability. By harnessing AI, financial institutions can optimize credit scoring processes, identify potential defaulters with greater accuracy, and customize lending terms based on individual risk profiles. Additionally, AI facilitates real-time monitoring of credit portfolios, allowing proactive risk management and timely interventions to prevent adverse outcomes.

Bifenthrin induces changes in clinical poisoning symptoms, oxidative stress, DNA damage, histological characteristics, and transcriptome in Chinese giant salamander (Andrias davidianus) larvae

Bifenthrin (BF) is a broad-spectrum insecticide that has gained widespread use due to its high effectiveness. However, there is limited research on the potential toxic effects of bifenthrin pollution on amphibians. This study aimed to investigate the 50 % lethal concentration (LC50) and safety concentration of Chinese giant salamanders (CGS) exposed to BF (at 0, 6.25,12.5,25 and 50 μg/L BF) for 96 h. Subsequently, CGS were exposed to BF (at 0, 0.04, and 4 μg/L BF) for one week to investigate its toxic effects. Clinical poisoning symptoms, liver pathology, oxidative stress factors, DNA damage, and transcriptome differences were observed and analyzed. The results indicate that exposure to BF at 4 μg/L significantly decreased the adenosine-triphosphate (ATP), superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT) contents in the brain, liver, and kidney of CGS. Additionally, the study found that the malondialdehyde (MDA), reactive oxygen species (ROS), and 8-hydroxydeoxyguanosine (8-OHdG) contents were increased. The liver tissue exhibited significant inflammatory reactions and structural malformations. RNA-seq analysis of the liver showed that BF caused abnormal antioxidant indices of CGS. This affected molecular function genes such as catalytic activity, ATP-dependent activity, metabolic processes, signaling and immune system processes, behavior, and detoxification, which were significantly upregulated, resulting in the differential genes significantly enriched in the calcium signaling pathway, PPARα signaling pathway and NF-kB signaling pathway. The results suggest that BF induces the abnormal production of free radicals, which overwhelms the body's self-defense system, leading to varying degrees of oxidative stress. This can result in oxidative damage, DNA damage, abnormal lipid metabolism, autoimmune diseases, clinical poisoning symptoms, and tissue inflammation. This work provides a theoretical basis for the rational application of bifenthrin and environmental risk assessment, as well as scientific guidance for the conservation of amphibian populations.

ATT&CK-based Advanced Persistent Threat attacks risk propagation assessment model for zero trust networks

In recent years, the growing frequency and intensity of Advanced Persistent Threats (APTs) have significantly undermined the legitimacy and financial stability of government agencies, enterprises, and other entities. Moreover, these attacks have shown the inherent vulnerabilities in conventional border defense strategies. The emergence of the zero trust network architecture can be attributed to the increasing complexity of the cyber threat landscape. With the application of risk assessment, this paper effectively tackles the challenges posed by conventional network defense limitations and enhances the efficiency of the access control decision-making process. Nevertheless, the existing risk assessment approaches primarily focus on conventional security assessment objectives, which exhibits a deficiency in the ability to dynamically assess APT attacks. The Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) Framework introduced in this paper is a novel approach to mitigating APT attacks. This paper aims to mine and analyze the frequent item set and correlation of cyber threat penetration attack techniques. The paper also intends to construct an attack technique relationship diagram and develop a tactical prediction model for cyber threat penetration attacks using the Markov chain model. Finally, our study aims to establish a risk propagation model for APT threats based on the aforementioned model. The approach presented in this paper significantly enhances the capacity of zero trust networks in addressing sophisticated cyber threats.

Biomonitoring for workplace exposure to copper and its compounds is currently not interpretable

This paper sets out to explore the requirements needed to recommend a useable and reliable biomonitoring system for occupational exposure to copper and its inorganic compounds. Whilst workplace environmental monitoring of copper is used to measure ambient air concentrations for comparison against occupational exposure limits, biological monitoring could provide complementary information about the internal dose of workers, taking into account intra-individual variability and exposure from all routes. For biomonitoring to be of reliable use for copper, a biomarker and the analytical ability to measure it with sufficient sensitivity must be identified and this is discussed in a range of matrices. In addition, there needs to be a clear understanding of the dose-response relationship of the biomarker with any health-effect (clinical or sub-clinical) or, between the level of external exposure (by any route) and the level of the copper biomarker in the biological matrix being sampled, together with a knowledge of the half-life in the body to determine accurate sampling times. For many biologically non-essential metals the requirements for reliable biomarkers can be met, however, for ‘essential’ metals such as copper that are under homeostatic control, the relationship between exposure (short- or long-term) and the level of any copper biomarker in the blood or urine is complex, which may limit the use and interpretation of measured levels. There are a number of types of biomarker guidance values currently in use which are discussed in this paper, but no values have yet been determined for copper (or its inorganic compounds) due to the complexity of its essential nature; the US The American Conference of Governmental Industrial Hygienists (ACGIH) has however indicated that it is considering the development of a biological exposure index for copper and its compounds. In light of this, we present a review of the reliability of current copper biomarkers and their potential use in the occupational context to evaluate whether there is value in carrying out human biomonitoring for copper exposure. Based on the available evidence we have concluded that the reliable use of biomonitoring of occupational exposure to copper and its application in risk assessment is not possible at the present time.

Storm surge risk assessment and sensitivity analysis based on multiple criteria decision-making methods: a case study of Huizhou City

Coastal areas face escalating storm surge disasters due to rising sea levels and urban growth, posing greater risks to lives and property. Comprehensive storm surge risk assessment and sensitivity analysis in coastal areas are crucial for effective disaster prevention and mitigation. This research focuses on Huizhou, China, conducting a comparative study of storm surge risk assessment and sensitivity analysis based on an integrated approach, which includes the storm surge inundation numerical model (FVCOM-SWAN), Geographic Information System (GIS) and Remote Sensing (RS) techniques, and Multiple Criteria Decision-Making (MCDM) methods. Ten flood-related risk indicators are selected from the hazard, exposure and vulnerability, the weights of which are evaluated through a comprehensive comparison among Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight (EW), AHP-EW, and FAHP-EW methods. High-precision risk level maps are generated subsequently utilizing GIS and RS techniques. Sensitivities of the indicators are analyzed using One-At-A-Time (OAT) and Fourier Amplitude Sensitivity Test (FAST) methods. The proposed storm surge risk assessment framework, the MCDM comparative study and the sensitivity analysis can offer insights for better understanding and management of storm surge risks, and contribute to the standardization and application of storm surge risk assessment.

The advantage of artificial intelligence application in financial risk assessment and management

The increasing perfection of artificial intelligence technology has brought subversive changes to the field of financial risk management. The application of artificial models such as neural networks, support vector machines, and mixed intelligence in financial risk management can improve the speed of data processing, provide deep insight into data analysis, reduce human labour costs, and hence improve the efficiency of financial risk control. Meanwhile, the increasing amount of data and the application of AI also bring new challenges to financial risk management, such as the risk of program error and information security. This paper introduces in detail the application status of three models, including Support Vector Machine, Support Vector Machine, and Large Language Model in risk management Based on this, this paper analyses the advantages of AI applications in promoting and reforming the financial industry. The goal is to provide an in-depth examination of present implementations and their respective benefits, as well as to investigate potential future advances in this sector.

Empirical analysis of lead neurotoxicity mode of action and its application in health risk assessment

The mode of action (MOA) framework is proposed to inform a biological link between chemical exposures and adverse health effects. Despite a significant increase in knowledge and awareness, the application of MOA in human health risk assessment (RA) remains limited. This study aims to discuss the adoption of MOA for health RA within a regulatory context, taking our previously proposed but not yet validated MOA for lead neurotoxicity as an example. We first conducted a quantitative weight of evidence (qWOE) assessment, which revealed that the MOA has a moderate confidence. Then, targeted bioassays were performed within an in vitro blood-brain barrier (BBB) model to quantitatively validate the scientific validity of key events (KEs) in terms of essentiality and concordance of empirical support (dose/temporal concordance), which increases confidence in utilizing the MOA for RA. Building upon the quantitative validation data, we further conducted benchmark dose (BMD) analysis to map dose-response relationships for the critical toxicity pathways, and the lower limit of BMD at a 5% response (BMDL5) was identified as the point of departure (POD) value for adverse health effects. Notably, perturbation of the Aryl Hydrocarbon Receptor (AHR) signaling pathway exhibited the lowest POD value, measured at 0.0062 μM. Considering bioavailability, we further calculated a provisional health-based guidance value (HBGV) for children's lead intake, determining it to be 2.56 μg/day. Finally, the health risk associated with the HBGV was assessed using the hazard quotient (HQ) approach, which indicated that the HBGV established in this study is a relative safe reference value for lead intake. In summary, our study described the procedure for utilizing MOA in health RA and set an example for MOA-based human health risk regulation.

Development and validation of a targeted mass spectrometry method for the quantification of milk protein allergens in multiple complex food matrices using matrix-independent calibration

Quantitative detection of food allergens is crucial in assessing the risk of allergic responses in susceptible individuals and ensuring regulatory compliance. Targeted mass spectrometry methods provide an opportunity to analyze allergens from complex, processed food matrices. However, probable interferences of diverse food matrices mandates preparation of matrix-matched calibration curves, challenging adaptation of methods into multiple matrices. In this study, we have developed a novel matrix-independent calibration strategy employing exogenous carrier proteins as background and demonstrated its adaptability in quantification of milk from multiple food matrices at concentrations relevant for risk assessment applications. Peptides representing casein or whey fractions of milk were detectable from concentrations as low as 1 ppm nonfat dry milk (NFDM) in baked cookie matrix at 46–99% accuracy, 0.5 ppm total milk protein (TMP) in pasteurized beverages at 94–138% accuracy, and 1 ppm casein in chocolate at 35–51% accuracy. Quantitative estimations of milk from matrix-independent calibration curves in the background of egg white powder as exogenous carrier were comparable to those from a matrix-matched calibration curve, and the quantitative precision was within 30% CV across the inter-day replicates. The sensitivity, accuracy, and precision of this matrix-independent approach ensure robustness of the method for high throughput food allergen analysis as required in routine testing labs.

Comprehensive risk assessment of a renewables‐based stand‐alone electric vehicle charging station with multiple energy storage technologies

AbstractThis study presents the application of a comprehensive risk assessment and risk management framework on a grid‐independent and renewable energy‐based electric vehicle charging station with multiple energy storage techniques including hydrogen and ammonia. The design comprises complex subsystems such as concentrated photovoltaic thermal, wind turbine, biomass standby generator, hydrogen storage, fuel cell, Ammonia storage and fuel cell, Lithium‐ion battery storage, Salt‐hydrate thermal storage, lithium‐bromide absorption cooling, and fast charging units. Systematic risk‐based approaches compromising multiple phases and qualitative and quantitative risk assessment techniques are applied to identify the causes and consequences of potentially critical events associated with the incorporated subsystems and components. The results show that out of 15 identified technical, economic, environmental, and health and safety risks, three health and safety risks were found unacceptable with risk values above 14. In contrast, three technical, three environmental, two health and safety, and one economic risks were found undesirable with risk values between 8 and 12. Appropriate preventive and protective measures are applied to lessen the high‐risk levels to acceptable safe limits below 6. Proper safety barriers and distances are considered while recommending the associated subsystems and components' optimal physical site distribution plan.

Reproducibility of heart rate recovery measures across differing modalities and intensities of submaximal exercise in healthy volunteers: A healthy volunteer study

PurposeSubmaximal exercise tests (SET) are an easier to conduct and less physically demanding alternative to cardiopulmonary exercise testing for assessment of pre-operative fitness. Assessment of heart rate recovery (HRR) following SETs offers an easily obtained marker of vagal reactivation and sympathetic withdrawal and therefore provides an indication of an individual’s fitness. This study sought to assess the reproducibility of HRR across different workloads and modalities of SET.MethodsThirty-four healthy volunteers were recruited to undergo two SETs at 40% and 60% of their predicted maximal workload. A second group of thirty-one healthy volunteers were recruited to undergo three SETs of differing modalities; Cycle ergometry, Step and Shuttle Walk tests. HRR was quantified using the conventional indices HRR1 and HRR2 (calculated as heart rate (HR) on exercise cessation minus HR at 1 and 2 min of rest) and a novel area under the HRR vs. time curve (HRRAUC) method. Reproducibility of results was assessed using intra-class correlation coefficient (ICC) and limits of agreements.ResultsThe results showed that HRR1 and HRR2 were poorly reproducible across differing workloads and exercise modalities (ICC < 0.45 for all comparisons) whereas HRRAUC proved to be at least moderately reproducible (ICC > 0.52 for all comparisons).ConclusionsThese results suggest that HRRAUC may be a superior way of quantifying HRR following SETs, adding objectivity to SET results. Quantifying HRRAUC could prove to have useful clinical applications for pre-operative risk assessment, assessing fitness to undergo treatment and monitoring disease progression.