Subscribe to RSS
Identifying Pneumonia Subtypes from Electronic Health Records Using Rule-Based AlgorithmsFunding Research reported in this publication was supported by the National Institute of Dental & Craniofacial Research of the National Institutes of Health under Award Number 1R03DE027020–01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Background The International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations where pneumonia is standardly subtyped by settings, exposures, and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHRs), frequently in nonstructured formats including radiological interpretation or clinical notes that complicate electronic classification.
Objective The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR.
Methods Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for “rule of two” pneumonia-related codes or one ICD code and radiologically confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support subclassification based on features including symptomatic patient point of entry into the health care system timing of pneumonia emergence and identification of clinical, laboratory, or medication orders that informed definition of the pneumonia subclassification algorithm.
Results Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following Natural Language Processing classification of pneumonia status as “negative” or “unknown.” Subtyping of 83,387 episodes identified: community-acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), health care–acquired (5%), and ventilator-associated (0.4%) cases, and 9.4% cases were not classifiable by the algorithm.
Conclusion Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored.
Keywordspneumonia - classification - natural language processing - electronic health record - health informatics
Received: 28 August 2021
Accepted: 15 March 2022
Accepted Manuscript online:
17 March 2022
Article published online:
28 June 2022
© 2022. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
- 1 World Health Organization. Global health estimates: life expectancy and leading causes of death and disability. Accessed August 28, 2021 at: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates
- 2 Micek ST, Kollef KE, Reichley RM, Roubinian N, Kollef MH. Health care-associated pneumonia and community-acquired pneumonia: a single-center experience. Antimicrob Agents Chemother 2007; 51 (10) 3568-3573
- 3 Rothberg MB, Haessler S, Lagu T. et al. Outcomes of patients with healthcare-associated pneumonia: worse disease or sicker patients?. Infect Control Hosp Epidemiol 2014; 35 (03, Suppl 3): S107-S115
- 4 American Thoracic Society; Infectious Diseases Society of America. Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. Am J Respir Crit Care Med 2005; 171 (04) 388-416
- 5 Cascini S, Agabiti N, Incalzi RA. et al. Pneumonia burden in elderly patients: a classification algorithm using administrative data. BMC Infect Dis 2013; 13 (01) 559
- 6 Hegde H, Shimpi N, Pack G, Rostami R, Acharya A. Smoking status classification of clinical notes using natural language processing. Presented in: 2016 AADR/CADR Annual Meeting, March 16–19: 2016; Los Angeles); 2016
- 7 Franchini M, Pieroni S, Passino C, Emdin M, Molinaro S. The CARPEDIEM algorithm: a rule-based system for identifying heart failure phenotype with a precision public health approach. Front Public Health 2018; 6: 6
- 8 Bustos Á, Fuenzalida I, Santibáñez R, Pérez-Acle T, Martin AJM. Rule-based models and applications in biology. Methods in Molecular Biology 2018; 1819: 3-32
- 9 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; 10 (01) 281
- 10 Heckerling PS, Gerber BS, Tape TG, Wigton RS. Prediction of community-acquired pneumonia using artificial neural networks. Med Decis Making 2003; 23 (02) 112-121
- 11 Yu O, Nelson JC, Bounds L, Jackson LA. Classification algorithms to improve the accuracy of identifying patients hospitalized with community-acquired pneumonia using administrative data. Epidemiol Infect 2011; 139 (09) 1296-1306
- 12 McLaughlin JM, Khan FL, Thoburn EA, Isturiz RE, Swerdlow DL. Rates of hospitalization for community-acquired pneumonia among US adults: a systematic review. Vaccine 2020; 38 (04) 741-751
- 13 Glurich I, Shimpi N, Scannapieco F, Vedre J, Acharya A. Interdisciplinary care model: pneumonia and oral health. In: Acharya A, Powell V, Torres-Urquidy MH, Posteraro RH, Thyvalikakath TP. eds. Integration of Medical and Dental Care and Patient Data. Cham, Switzerland: Springer; 2019: 123-139
- 14 File TM. Epidemiology, pathogenesis, and microbiology of community-acquired pneumonia in adults. In: Ramirez JA, Bond S, eds. UpToDate. Waltham, MA: Wolters Kluwer; 2020
- 15 Regunath H, Oba Y. Community-acquired pneumonia. StatPearls Publishing. Accessed March 30, 2022 at: https://www.statpearls.com/ArticleLibrary/viewarticle/27357
- 16 Ferreira-Coimbra J, Sarda C, Rello J. Burden of community-acquired pneumonia and unmet clinical needs. Adv Ther 2020; 37 (04) 1302-1318
- 17 Shimpi N, Glurich I, Acharya A. Integrated care case study: Marshfield clinic health system. In: Acharya A, Powell V, Torres-Urquidy MH, Posteraro RH, Thyvalikakath TP. ed. Integration of Medical and Dental Care and Patient Data. Health Informatics. 2nd ed.. Cham, Switzerland: Springer; 2019: 315-326
- 18 Savova GK, Masanz JJ, Ogren PV. et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010; 17 (05) 507-513
- 19 U. S. Government Printing Office, Washington.. Promoting Disease Management in Medicare. Vol 4. Accessed March 30, 2022 at: https://www.govinfo.gov/content/pkg/CHRG-107hhrg82324/html/CHRG-107hhrg82324.htm
- 20 Mandell LA, Wunderink RG, Anzueto A. et al; Infectious Diseases Society of America, American Thoracic Society. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clin Infect Dis 2007; 44 (Suppl. 02) S27-S72
- 21 Jain V, Vashisht R, Yilmaz G, Bhardwaj A. Pneumonia pathology. In: Abai B, Abu-Ghosh A, Acharya AB. et al. StatPearls (Internet). Treasure Island, FL: StatPearls Publishing; 2021
- 22 Anand N, Kollef MH. The alphabet soup of pneumonia: CAP, HAP, HCAP, NHAP, and VAP. Semin Respir Crit Care Med 2009; 30 (01) 3-9
- 23 Ramya CM. Bacterial pneumonia. Res J Pharm Technol 2014; 7 (08) 942-945
- 24 Marik PE. Aspiration pneumonitis and aspiration pneumonia. N Engl J Med 2001; 344 (09) 665-671
- 25 Japanese Respiratory Society. Aspiration pneumonia. Respirology 2009; 14 (Suppl. 02) S59-S64
- 26 Arsigny C, Myers M, Hammond S. et al; Canadian Critical Care Trials Group. A randomized trial of diagnostic techniques for ventilator-associated pneumonia. N Engl J Med 2006; 355 (25) 2619-2630
- 27 American Thoracic Society, Infectious Diseases Society of America. Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. Am J Respir Crit Care Med 2005; 171 (04) 388-416
- 28 Metlay JP, Waterer GW, Long AC. et al. Diagnosis and treatment of adults with community-acquired pneumonia. Am J Respir Crit Care Med 2019; 200 (07) e45-e67
- 29 Roberts S. Acute community-acquired pneumonia (CAP and VAP/HAP). Accessed March 30, 2022 at: https://www.infectiousdiseaseadvisor.com/home/decision-support-in-medicine/infectious-diseases/acute-community-acquired-pneumonia-cap-and-vap-hap/
- 30 Janssen KJM, Donders ART, Harrell Jr. FE. et al. Missing covariate data in medical research: to impute is better than to ignore. J Clin Epidemiol 2010; 63 (07) 721-727
- 31 Schmitt P, Mandel J, Guedj M. A comparison of six methods for missing data imputation. J Biom Biostat 2015;06(01):
- 32 Chowdhury MH, Islam MK, Khan SI. Imputation of missing healthcare data. Presented in: 2017 20th International Conference of Computer and Information Technology (ICCIT). IEEE; Dhaka, Bangladesh: 22–24 December; 2017: 1-6
- 33 Li P, Stuart EA, Allison DB. Multiple imputation. JAMA 2015; 314 (18) 1966-1967
- 34 Hegde H, Shimpi N, Panny A, Glurich I, Christie P, Acharya A. MICE vs PPCA: Missing data imputation in healthcare. Informat Med Unlocked 2019; 17: 100275