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Jerry G Myers

Publications and source records attributed to Jerry G Myers.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Numerical Characterization of Astronaut CaOx Renal Stone Incidence Rates to Quantify In-flight and Post-flight Relative Risk

Changes in urine chemistry potentially alter the risk of renal stone formation in astronauts. Quantifying spaceflight renal stone incidence risk compared to pre-flight levels remains a significant challenge for assessing the appropriate vehicle, mission, and countermeasure design. A computational biochemistry model representing CaOx crystal precipitation, growth, and agglomeration is combined with a probabilistic analysis to predict the in- and post-flight CaOx renal stone incidence risk ratio (IRR) relative to pre-flight values using 1517 astronaut 24-hour urine chemistries. Our simulations predict that in-flight fluid intake alone would need to increase from current prescriptions of 2.0 - 2.5 L/day to approximately 3.2 L/day to approach CaOx IRR of the pre-flight population. Bone protective interventions would reduce CaOx risk to pre-flight levels if Ca excretion alone is reduced to < 150 mg/day or if current levels are diminished to 190mg/day in combination with increasing fluid intake to 2.5 - 2.7 L/day. This analysis provides a quantitative risk assessment that can influence the critical balance between engineering and astronaut health requirements.

renal stones↗
NASA NTRS2021

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤ 15 cGy of individual Galactic Cosmic Radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in Attentional Set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the Simple Discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the Compound Discrimination (CD) stage. On a subject-based level, implementing Machine Learning (ML) classifiers such as the Gaussian Naïve Bayes, Support Vector Machine, and Artificial Neural Networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreenperformance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Feis the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4Hein SD and 28Siin CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for morerobust predictionsof cognitive decrements due to space radiation exposure.

space radiation↗
NASA NTRS2020

Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective

The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the devel-opment and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder com-munity survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing imple-mentations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.

Credibility↗
NASA NTRSDate not supplied

Prediction of Calcium Oxalate Stone Risk with Interventions

Introduction Mitigations intended to reduce bone mineral loss in-flight also influence the level of renal stone risk to astronauts. Extending a recent study completed by our group [1], we investigate the likely influence of combined fluid intake and Ca excretion mitigation strategies. Methods A computational biochemistry model representing CaOx crystal precipitation, growth, and agglomeration [2] is combined with a probabilistic analysis to predict the in-flight CaOx renal stone incidence risk ratio (IRR) relative to pre-flight values using 1517 astronaut 24-hour urine chemistries. The output of this simulation is used to assess the combined impact of controlling fluid intake and Ca excretion on maintaining IRR < 1.2 for > 95% of the population, a threshold determined from initial analysis of the model output compared to referent populations [1]. Results and Conclusions Figure 1 illustrates the simulated in-flight astronaut population (n=40000) with CaOx IRR > 1.2. The red lines indicate the known clinical risk boundary of each factor, and the white box is considered the area of high combined clinical risk. The simulation and identified risk threshold conform well to the expected clinical risk boundaries. The results, assuming insensible water losses equal or exceed 0.7 L/day, imply mitigations achieve risks similar to pre-flight levels when either Ca < 150 mg/day or fluid volume intake > 3.2 L/day individually, or combined Ca < 200 and fluid volume intake > 2.5 L/day. We continue to investigate similar compounded effects and look to apprise engineering trades by informing relative risk changes during mission design. References [1] D. A. Goodenow-Messman, S. A. Gokoglu, M. Kassemi, and J. Myers, Jerry G., “Characterization of Astronaut CaOx Renal Stone Incidence Rates to Quantify In-flight and Post-flight Relative Risk,” npj Microgravity, in review, 2021. [2] M. Kassemi and D. Thompson, “Prediction of renal crystalline size distributions in space using a PBE analytic model. 1. Effect of microgravity-induced biochemical alterations,” Am. J. Physiol. Physiol., vol. 311, no. 3, pp. F520–F530, Sep. 2016, doi: 10.1152/ajprenal.00401.2015.

Debra A Goodenow↗
NASA NTRSDate not supplied

Assessing Long-Term Health (LTH) Outcomes in Astronauts

NASA has linked potential Long-Term Health (LTH) risks of astronauts to their spaceflight experience, including the effects of space radiation, microgravity, and stressors such as isolation, sleep deprivation, and stress. Therefore, assessing the LTH outcomes for astronauts exposed to these hazards becomes a critical investigation in ensuring any deleterious effects are minimal. Prior research conducted into LTH outcomes of astronauts has not revealed increases in non-accidental mortality rates when compared to the U.S. general population, analog healthy cohorts, and professional athletes. One study did report an increased incidence of non-fatal cardiovascular disease events (Hazard Ratio=2.41, 95% Confidence Interval 1.26 to 4.63). Additional evidence indicates that astronauts may have increases in incidences of melanoma, prostate, and hematologic cancers, though the former is consistent with rates observed among aircraft pilots and the latter two are likely caused by detection bias due to increased screening on former astronauts.

long term health↗
NASA NTRSDate not supplied

Investigating Risks Due to Artemis EVA Tempo Via Probabilistic Risk Assessment

Spaceflight operations pose unique challenges to crew health, safety, and resource management. As space agencies and private companies continue to push the boundaries of human exploration, it is essential to understand the risks associated with Extravehicular Activities (EVAs) and develop strategies to mitigate them. The tempo at which EVAs are conducted – the total number and frequency of these activities – can have a profound impact on medical risks, resource consumption, and overall mission success. Probabilistic risk assessment (PRA) provides a powerful framework for evaluating complex systems and identifying potential hazards. Our work employs the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) [1] to simulate mission events, occurrence and treatment of medical conditions, and track the utilization of resources. Coupled with the Evidence Library [2], a medical evidence base for exploration-class missions developed by the Exploration Medical Capability within NASA’s Human Research Program, we can estimate these risks with increased fidelity and optimize medical kit contents to meet specific mission requirements. This presentation provides a detailed examination of how EVA tempo influences medical risk estimates for a lunar surface design reference mission. A comprehensive analysis is conducted to assess the additional mass and volume burden imposed on medical kits required to maintain adequate levels of risk mitigation. Furthermore, we estimate the distribution of the number of successful EVAs completed based on the level of task impairment imposed by medical events and flight rules related to specific medical events, such as decompression sickness.

Modeling↗
NASA NTRSDate not supplied

The Future of Integrated Performance Modeling in the Crew Health and Performance – Probabilistic Risk Assessment Project

The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Human Research Program’s Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) was a significant step forward in efforts to robustly quantify the risk to crew health for exploration missions. However, there remains a significant gap in the ability to comprehensively assess and characterize risk across the disparate functionalities and capabilities which comprise the Crew Health and Performance (CHP) system. The Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA) project seeks to characterize CHP risks by expanding beyond the foundation established by its PRA predecessors like IMM and MEDPRAT, that simulate medical risk metrics like loss of crew life and evacuations. One of the new risk measures in the CHP-PRA system is embodied in our Performance Risk Model (PRisM). PRisM provides a novel way of assessing crew performance on mission tasks, using a generalized framework which relates back to NASA-STD-3001. This approach allows PRisM to capture and integrate data from a variety of different domains into a single, unified, reproducible representation of astronaut performance. In this presentation, we discuss the motivation for the CHP-PRA work and give a high level overview of the goals of the project, outline the forward work for PRisM, and discuss collaboration opportunities for the community who might explore if their domain knowledge and data could be represented, integrated, and quantified with these tools, whose outcomes are metrics useful for supporting operational mission planning and decision making.

Lauren McIntyre↗