DESEMPEÑO DE INDICADORES PREANALÍTICOS EN LABORATORIOS CLÍNICOS
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Resumo
Introducción: Dentro del proceso del laboratorio clínico, la fase preanalítica es la fuente más frecuente de errores que pueden impactar en la fiabilidad de los resultados y su utilidad en la práctica clínica. Los indicadores de calidad son herramientas útiles para monitorear y mejorar todos los pasos del proceso de análisis. Objetivo: Evaluar el nivel de desempeño de indicadores preanalíticos en las áreas de química y hematología para identificar errores en el proceso de preanálisis. Métodos: Investigación de tipo descriptiva, de corte transversal, se desarrolló en 11 laboratorios clínicos utilizando seis indicadores preanalíticos del modelo de la International Federation of Clinical Chemistry and Laboratory medicine (IFCC). Resultados: La prevalencia de errores en solicitudes para el área de hematología fue 42,88% y en el área de química 36,37%, observándose que el indicador con mayor registro fue solicitudes con identificación incompleta del paciente 1920 (20,86%) en hematología y en el área de química 1600 (16,09%) (p<0.005). En las muestras, la prevalencia de errores para hematología fue de 41.02% y en el área de química 39,03%, notándose que el indicador con mayor registro fue “muestras hemolizadas” 1950 (22,41%) en el área de bioquímica y 1030 (10,28%) en hematología (p<0.005); considerando ambas áreas, entre 27,27-45.45% de los laboratorios tiene los indicadores analizados con un desempeño no aceptable, destacándose los relacionados con la identificación de paciente y muestras. Conclusión La situación es indicativa de un proceso de preanálisis que requiere de intervención y acciones correctivas para atender las causas que conllevan a generar los errores detectados.
Palabras clave: indicadores de calidad, errores preanalíticos, laboratorios clínicos, seguridad del paciente
ABSTRACT
Introduction: Within the clinical laboratory process, the preanalytical phase is the most frequent source of errors that can impact the reliability of the results and their usefulness in clinical practice. Quality indicators are useful tools to monitor and improve all steps of the analysis process. Objective: To evaluate the level of performance of preanalytical indicators in the areas of chemistry and hematology to identify errors in the preanalysis process. Methods: Descriptive, cross-sectional research. The research was developed in 11 clinical laboratories using six preanalytical indicators of the International Federation of Clinical Chemistry and Laboratory medicine (IFCC) model. Results: The prevalence of errors in requests for the hematology area was 42.88% and in the chemistry area 36.37%, observing that the indicator with the highest record was requests with incomplete identification of the patient 1920 (20.86%) in hematology and in the area of ??chemistry 1600 (16.09%) (p<0.005). In the samples, the prevalence of errors for hematology was 41.02% and in the area of ??chemistry 39.03%, noting that the indicator with the highest record was "hemolyzed samples" 1950 (22.41%) in the area of biochemistry and 1030 (10.28%) in hematology (p<0.005); Considering both areas, between 27.27-45.45% of the laboratories have the indicators analyzed with unacceptable performance, highlighting those related to patient identification and samples. Conclusion: The situation is indicative of a pre-analysis process that requires intervention and corrective actions to address the causes that lead to generating the errors detected.
Keywords: quality indicators; pre-analytical phase; preanalytical errors; laboratory; patient safety
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Referências
2. Plebani M. Quality in laboratory medicine: 50 years on. Clin Biochem.2017;50(3):101-104. doi: 10.1016/j.clinbiochem.2016.10.007.
3. Plebani M; EFLM Task Force on Performance Specifications for the extra-analytical phases. Performance specifications for the extra-analytical phases of laboratory testing: Why and how. Clin Biochem.2017;50(10-11):550-554. doi: 10.1016/j.clinbiochem.2017.02.002.
4. Plebani M. System-related and cognitive errors in laboratory medicine. Diagnosis (Berl). 2018 27;5(4):191-196. doi: 10.1515/dx-2018-0085.
5. Carraro P, Plebani M. Errors in a stat laboratory: types and frequency 10 years later. Clin Chem 2007;53: 1338-42. Available in: https://pubmed.ncbi.nlm.nih.gov/17525103/
6. Plebani M.2016 Harmonization in laboratory medicine: Request, samples, measurements and reports. Crit Rev Clin Lab Sci. 2016; 53(3):184-96 doi: 10.3109/10408363.2015.1116851
7. Sciacovelli L, Plebani M The IFCC Working Group on laboratory errors and patient safety. Clin Chim Acta. 2009;404(1):79-85. doi:10.016/j.cca.2009.03.025
8. Sciacovelli L, Lippi G, Sumarac Z, West J, Garcia Del Pino Castro I, Furtado Vieira K, Ivanov A, Plebani M; Working Group “Laboratory Errors and PatientSafety” of International Federation of Clinical Chemistryand Laboratory Medicine (IFCC). Quality Indicators in Laboratory Medicine:the status of progress of IFCC Working Group "Laboratory Errors and Patient Safety" Project Clin Chem Lab Med. 2017 1;55(3):348-357. doi: 10.1515/cclm-2016-0929
9. International Organization for Standardization. ISO 15189:2012: Medical Laboratories-Requirements for quality and competence. ISO: Geneva, Switzerland; 2012. Available in: https://www.iso.org/standard/56115.html
10. Van Dongen-Lases EC, Cornes MP, Grankvist K, Ibarz M, Kristensen GB, Lippi G, Nybo M, Simundic AM. Working Group for Preanalytical Phase (WG-PRE), European Federation of Clinical Chemistry and Laboratory Medicine (EFLM). Patient identification and tube labelling - a call for harmonisation. Clin Chem Lab Med. 2016 1;54(7):1141-5. doi: 10.1515/cclm-2015-1089.
11. Salinas M, López-Garrigós M, Flores E, Santo-Quiles A, Gutierrez M, Lugo J, Lillo R, Leiva-Salinas C. Ten years of preanalytical monitoring and control: Synthetic Balanced Score Card Indicator Biochemia Medica 2015;25(1):49–56 doi: 10.11613/BM.2015.005.
12. Lippi G, Caola I, Cervellin G, Ferrari A, MIlanesi B, Morandini M, Piva E, Ramponi C, Giavarina D. A multicentre observational study evaluating the effectiveness of a phlebotomy check-list in reducing preanalytical errors Biochimica clinica 2015, vol. 39, n. 6. Available in: https://bc.sibioc.it/bc/numero/bcnum/155
13. Plebani M, Carraro P. Mistakes in a stat laboratory: types and frequency. Clin Chem. 1997;43(8):1348–51. Available in: https://pubmed.ncbi.nlm.nih.gov/9267312/
14. Hawkins R. Managing the pre- and post-analytical phases of the total testing process. Ann Lab Med. 2012;32(1):5. doi: 10.3343/alm.2012.32.1.5
15. Patel S, Nanda R, Sahoo S, Mohapatra E. Congruity in Quality Indicators and Laboratory Performance. Indian J Clin Biochem. 2018;33(3):341-347. doi: 10.1007/s12291-017-0687-9.
16. Simundic AM, Nikolac N, Vukasovic I, Vrkic N. The prevalence of preanalytical errors in a Croatian ISO 15189 accredited laboratory. Clin Chem Lab Med. 2010;48(7):1009-14. doi: 10.1515/CCLM.2010.221
17. Cornes MP, Atherton J, Pourmahram G, Borthwick H, Kyle B, West J, Costelloe SJ. Monitoring and reporting of preanalytical errors in laboratory medicine: the UK situation Ann Clin Biochem. 2016 Mar;53(Pt 2):279-84. Doi: 10.1177/0004563215599561
18. Llopis MA, Bauca JM, Barba N, Alvarez V, Ventura M, Ibarz M, et al. Spanish Preanalytical Quality Monitoring Program (SEQC), an overview of 12 years’ experience. Clin Chem Lab Med. 2017;55:530-8. Doi: org/10.1515/cclm-2016-0382
19. Kang F, Li W, Xia X, Shan Z. Three years' experience of quality monitoring program on pre-analytical errors in china. J Clin Lab Anal. 2021 Mar;35(3):e23699. doi: 10.1002/jcla.23699
20. Chen A, Anderson J, Frater JL. Preanalytical errors in a satellite stat laboratory: A Six Sigma analysis of seven years' data. Clin Chim Acta. 2021 Dec;523:26-30. doi: 10.1016/j.cca.2021.08.031
21. Zaninotto M, Tasinato A, Vecchiato G, Legnaro A, Pinato A, Plebani M. Performance specifications in extra-analytical phase of laboratory testing: Sample handling and transportation. Clin Biochem. 2017 Jul;50(10-11):574-578. doi: 10.1016/j.clinbiochem.2017.04.008
22. Nybo M, Cadamuro J, Cornes MP, Gomez Rioja R, Grankvist K. Sample transportation - an overview. Diagnosis (Berl).2019;6:39-43. https://doi.org/10.1515/dx-2018-0051
23. Brun M, Füzéry AK, Henschke B, Rozak K, Venner AA. Identifying sources of error and selecting quality indicators for point of care testing. Pract Lab Med. 2021 Mar 21;25:e00216. doi: 10.1016/j.plabm.2021.e00216.
24. Sengupta, S.; Handoo, A. Pre-analytical errors in the clinical laboratory: A risk assessment analysis. Clinica Chimica Acta, 2019;493: S502. doi:10.016/j.cca.2019.03.1058
25. Mrazek C, Lippi G, Keppel MH, Felder TK, Oberkofler H, Haschke-Becher E, Cadamuro J. Errors within the total laboratory testing process, from test selection to medical decision-making - A review of causes, consequences, surveillance and solutions. Biochem Med (Zagreb). 2020;30(2):020502. doi: 10.11613/BM.2020.020502
26. Cadamuro J, Gaksch M, Mrazek C, Haschke-Becher E, Plebani M. How do we use the data from pre-analytical quality indicators and how should we? JLPM. 2018;3:1-9. doi.org/10.21037/jlpm.2018.04.04