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Record W2956470099 · doi:10.17533/udea.iee.v37n2e04

Metabolic fatigue in resuscitators using personal protection equipment against biological hazard

2019· article· en· W2956470099 on OpenAlexaboutno aff
Francisco Martín‐Rodriguez

Bibliographic record

VenueInvestigación y Educación en Enfermería · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsPersonal protective equipmentMedicineAnaerobic exerciseResuscitationHazard ratioPhysical therapyEmergency medicineInternal medicineConfidence intervalCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

AbstractObjective. To describe the effects of wearing individual protection equipment against biological hazard when performing a simulated resuscitation.Methods. Uncontrolled quasi-experimental study involving 47 volunteers chosen by random sampling stratified by sex and professional category. We determined vital signs, anthropometric parameters and baseline lactate levels; subsequently, the volunteers put on level D individual protection equipment against biological hazard and performed a simulated resuscitation for 20 minutes. After undressing and 10 minutes of rest, blood was extracted again to determine lactate levels. Metabolic fatigue was defined as a level of lactic acid above 4 mmol/L at the end of the intervention.Results. 25.5% of the participants finished the simulation with an unfavorable metabolic tolerance pattern. The variables that predict metabolic fatigue were the level of physical activity and bone mass -in a protective formand muscle mass. People with a low level of physical activity had ten times the probability of metabolic fatigue compared to those with higher levels of activity (44% versus 4.5%, respectively).Conclusion. Professionals who present a medium or high level of physical activity tolerate resuscitation tasks better with a level D individual biological protection suit in a simulated resuscitation.Descriptors: cardiopulmonary resuscitation; personal protective equipment; anaerobic threshold; containment of biohazards; stress, physiological.How to cite this article: Martín-Rodríguez F. Metabolic fatigue in resuscitators using personal protection equipment against biological hazard. Invest. Educ. Enferm. 2019; 37(2):e04ReferencesBarsuk JH, Cohen ER, Wayne DB, Siddall VJ, McGaghie WC. Developing a Simulation-Based Mastery Learning Curriculum: Lessons from 11 Years of Advanced Cardiac Life Support. Simul. Healthc. 2016; 11(1):52–9. Kwon JH, Burnham CAD, Reske KA, Liang SY, Hink T, Wallace MA, et al. Assessment of Healthcare Worker Protocol Deviations and Self-Contamination During Personal Protective Equipment Donning and Doffing. Infect. Control Hosp. Epidemiol. 2017; 38(9):1077-83. Schoch-Spana M, Cicero A, Adalja A, Gronvall G, Kirk Sell T, Meyer D, et al. Global Catastrophic Biological Risks: Toward a Working Definition. Health Secur. 2017; 15(4):323-8. Millett P, Snyder-Beattie A. Existential Risk and Cost-Effective Biosecurity. Health Secur. 2017; 15(4):373-83. Fogel I, David O, Balik CH, Eisenkraft A, Poles L, Shental O, et al. The association between self-perceived proficiency of personal protective equipment and objective performance: An observational study during a bioterrorism simulation drill. Am. J. Infect. Control. 2017; 45(11): 1238-42. Calfee MW, Tufts J, Meyer K, McConkey K, Mickelsen L, Rose L, et al. Evaluation of standardized sample collection, packaging, and decontamination procedures to assess cross-contamination potential during Bacillus anthracis incident response operations. J. Occup. Environ. Hyg. 2016; 13(12): p. 980-92. Narayanan N, Lacy CR, Cruz JE, Nahass M, Karp J, Barone JA, et al. Disaster Preparedness: Biological Threats and Treatment Options. Pharmacotherapy. 2018; 38(2):217-34. Hunt L, Gupta-Wright A, Simms V, al. e. Clinical presentation, biochemical, and haematological parameters and their association with outcome in patients with Ebola virus disease: an observational cohort study. Lancet Infect. Dis. 2015; 15(11):1292–9. Nicaise V. The Sensitivity And Specificity Of The IPAQ For Detecting Intervention Related Changes In Physical Activity. Med. Sci. Sports Exerc. 2011; 43(Sup. 1):607. van Poppel MNM, Chinapaw MJM, Mokkink LB, van Mechelen W, Terwee CB. Physical activity questionnaires for adults: A systematic review of measurement properties. Sports Med. 2010; 40(7):565-600. Baur DA, Bach CW, Hyder WJ, Ormsbee MJ. Fluid retention, muscle damage, and altered body composition at the Ultraman triathlon. Eur. J. Appl. Physiol. 2016; 116(3):447-58. Spartano LN, Lyass GA, Larson DM, Lewis SG, Vasan SR. Abstract 19256: Predicting Exercise Systolic Blood Pressure and Heart Rate at 20 Years of Follow-up: Correlates in the Framingham Heart Study. Circulation. 2015; 132(3):A19256-A19256. Jayasinghe S, Lambert G, Torres S, Fraser S, Eikelis N, Turner A. Hypothalamo-pituitary adrenal axis and sympatho-adrenal medullary system responses to psychological stress were not attenuated in women with elevated physical fitness levels. Endocrine. 2016; 51(2):369-79. Pattani R, Marquez C, Dinyarian C, Sharma M, Bain J, Moore JE, et al. The perceived organizational impact of the gender gap across a Canadian department of medicine and proposed strategies to combat it: a qualitative study. BMC Medicine. 2018; 16(1): p. 48. ¿Morales‐Alamo D, Losa‐Reyna J, Torres‐Peralta R, Martin‐Rincon M, Perez‐Valera M, Curtelin D, ¿et al. What limits performance during whole‐body incremental exercise to exhaustion in humans? J. Physiol. 2015; 593(20):4631–48. Hall MM, Rajasekaran S, Thomsen TW, Peterson AR. Lactate: Friend or Foe. PM R. 2016; 8(3):S8-S15. Ekkekakis P, Hall EE, Petruzzello SJ. Practical markers of the transition from aerobic to anaerobic metabolism during exercise: rationale and a case for affect-based exercise prescription. Prev. Med. 2014; 38(2):149-59. Vikmoen O, Raastad T, Seynnes O, Bergstrøm K, Ellefsen S, Rønnestad BR. Effects of Heavy Strength Training on Running Performance and Determinants of Running Performance in Female Endurance Athletes. PLoS One. 2016; 11(3):e0150. Devlin J, Paton B, Poole L, Sun W, Ferguson C, Wilson J, et al. d lactate clearance after maximal exercise depends on active recovery intensity. J. Sports Med. Phys. Fitness. 2014; 54(3):271-8. Szarpak L, Madziała M, Smereka J. Comparison of endotracheal intubation performed with 3 devices by paramedics wearing chemical, biological, radiological, and nuclear personal protective equipment. Am. J. Emerg Med. 2016; 34(9):1902-3. Szarpak L, Ramirez JG, Buljan D, Drozd A, Madziała M, Czyzewski L. Comparison of Bone Injection Gun and Jamshidi intraosseous access devices by paramedics with and without chemical-biological-radiological-nuclear personal protective equipment: a randomized, crossover, manikin trial. Am. J. Emerg. Med. 2016; 34(7):1307-8. Szarpak L, Truszewski Z, Smereka J, Madziała M, Czyzewski L. Comparison of two intravascular access techniques when using CBRN-PPE: A randomized crossover manikin trial. Am. J. Emerg. Med. 2016; 34(6):1170-2. Szarpak L, Truszewski Z, Gałązkowski R, Czyzewski L. Comparison of two chest compression techniques when using CBRN-PPE: a randomized crossover manikin trial. Am. J. Emerg. Med. 2016; 34(5): 913-5. Stein C, Makkink A, Vincent-Lambert C. The effect of physical exertion in chemical and biological personal protective equipment on physiological function and reaction time. Prehosp Emerg Care. 2010; 14(1):36-44. Ji T, Qian X, Yuan M, Jiang J. Experimental study of thermal comfort on stab resistant body armor. Springerplus. 2016; 5(1):1168. Carter H, Amlôt R. Mass Casualty Decontamination Guidance and Psychosocial Aspects of CBRN Incident Management: A Review and Synthesis. PLoS Curr. 2016; September 27; 8. Verbeek JH. Personal protective equipment for preventing highly infectious diseases due to exposure to contaminated body fluids in healthcare staff. Cochrane Database Syst. Rev. 2016; 4:CD011621. Coca A. Physiological Evaluation of Personal Protective Ensembles Recommended for Use in West Africa. Disaster Med. Public Health Prep. 2017; 11(5): 580-6.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.320
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations5
Published2019
Admission routes1
Has abstractyes

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