An algorithm for identifying visits due to pediatric lower acute respiratory infections in electronic clinical records
DOI:
https://doi.org/10.31053/1853.0605.v78.n3.30162Keywords:
electronic health records, child, Respiratory Tract InfectionsAbstract
Background: Due to ambiguities in terminology, acute lower respiratory infections (ALRI) in childhood are frequently not properly recorded, especially during outpatient visits. A tool that accurately identifies them, would assess the impact on respiratory health of massive harms, and design policies to prevent or mitigate their effects. We aimed to design an algorithm that allows identifying children with ALRI based on data from the electronic clinical record (ECR) of the Government of the City of Buenos Aires (GCBA).
Methodos: From the ECR-GCBA database, we randomly selected 1000 outpatient visits of patients aged under 2 years. Terms showing that the visit was due to LARI were searched using an algorithm based on hard rules. Another dataset including 800 visits was used to adjust the algorithm and, finally, its performance was tested in a third dataset of 800 queries corresponding to the entire year 2018.
Results: In the validation set, our tool identified LARI with sensitivity 88.24%, specificity 97.5%, PPV 86.07% and NPV 97.93%.
Conclusion: Our search algorithm allows us to identify with acceptable precision the outpatient visits related to LARI in children under 2 years of age from electronic clinical records.
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Nair H, Simões EA, Rudan I, Gessner BD, Azziz-Baumgartner E, Zhang JSF, Feikin DR, Mackenzie GA, Moiïsi JC, Roca A, Baggett HC, Zaman SM, Singleton RJ, Lucero MG, Chandran A, Gentile A, Cohen C, Krishnan A, Bhutta ZA, Arguedas A, Clara AW, Andrade AL, Ope M, Ruvinsky RO, Hortal M, McCracken JP, Madhi SA, Bruce N, Qazi SA, Morris SS, El Arifeen S, Weber MW, Scott JAG, Brooks WA, Breiman RF, Campbell H; Severe Acute Lower Respiratory Infections Working Group. Global and regional burden of hospital admissions for severe acute lower respiratory infections in young children in 2010: a systematic analysis. Lancet. 2013 Apr 20;381(9875):1380-1390. doi: 10.1016/S0140-6736(12)61901-1.
Liu L, Johnson HL, Cousens S, Perin J, Scott S, Lawn JE, Rudan I, Campbell H, Cibulskis R, Li M, Mathers C, Black RE; Child Health Epidemiology Reference Group of WHO and UNICEF. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet. 2012 Jun 9;379(9832):2151-61. doi:10.1016/S0140-6736(12)60560-1. Epub 2012 May 11. Erratum in: Lancet. 2012 Oct 13;380(9850):1308.
Ministerio de Salud, República Argentina. Boletín Integrado de Vigilancia. N° 479. Available: https://www.argentina.gob.ar/sites/default/files/biv_479.pdf.
Lanata CF, Rudan I, Boschi-Pinto C, Tomaskovic L, Cherian T, Weber M, Campbell H. Methodological and quality issues in epidemiological studies of acute lower respiratory infections in children in developing countries. Int J Epidemiol. 2004 Dec;33(6):1362-72. doi: 10.1093/ije/dyh229.
Fauroux B, Hascoët JM, Jarreau PH, Magny JF, Rozé JC, Saliba E, et al. Risk factors for bronchiolitis hospitalization in infants: A French nationwide retrospective cohort study over four consecutive seasons (2009-2013). PLoS One. 2020; 15(3):e0229766. https://doi.org/10.1371/journal.pone.0229766
Kukafka R, Ancker JS, Chan C, Chelico J, Khan S, Mortoti S, Natarajan K, Presley K, Stephens K. Redesigning electronic health record systems to support public health. J Biomed Inform. 2007 Aug;40(4):398-409. doi: 10.1016/j.jbi.2007.07.001.
Legislatura de la Ciudad Autónoma de Buenos Aires. Ley de historia clínica electrónica. Ley 5669. Buenos Aires, 27 de octubre de 2016.
Tenny S, Hoffman MR. Prevalence. [Updated 2021 May 30]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2021 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK430867/
Esteban S, Rodríguez Tablado M, Ricci RI, Terrasa S, Kopitowski K. A rule-based electronic phenotyping algorithm for detecting clinically relevant cardiovascular disease cases. BMC Res Notes. 2017 Jul 14;10(1):281. doi: 10.1186/s13104-017-2600-2.
Kottke TE, Baechler CJ, Parker ED. Accuracy of heart disease prevalence estimated from claims data compared with an electronic health record. Prev Chronic Dis. 2012;9:E141. doi: 10.5888/pcd9.120009.
Borja-Aburto VH. Estudios ecológicos [Ecological studies]. Salud Publica Mex. 2000 Nov-Dec;42(6):533-8.
Cimino JJ. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med. 1998 Nov;37(4-5):394-403.
Rees G. Staff use of acronyms in electronic care records. Mental Health Pract 2013; 16(10):28-31.
Walsh KE, Gurwitz JH. Medical abbreviations: writing little and communicating less. Arch Dis Child. 2008 Oct;93(10):816-7. doi: 10.1136/adc.2008.141473.
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