Risk prediction models to guide antibiotic prescribing: a study on adult patients with uncomplicated upper respiratory tract infections in an emergency department

Joshua Guoxian Wong, Aung-Hein Aung, Weixiang Lian, David Chien Lye, Chee-Kheong Ooi, Angela Chow, Joshua Guoxian Wong, Aung-Hein Aung, Weixiang Lian, David Chien Lye, Chee-Kheong Ooi, Angela Chow

Abstract

Background: Appropriate antibiotic prescribing is key to combating antimicrobial resistance. Upper respiratory tract infections (URTIs) are common reasons for emergency department (ED) visits and antibiotic use. Differentiating between bacterial and viral infections is not straightforward. We aim to provide an evidence-based clinical decision support tool for antibiotic prescribing using prediction models developed from local data.

Methods: Seven hundred-fifteen patients with uncomplicated URTI were recruited and analysed from Singapore's busiest ED, Tan Tock Seng Hospital, from June 2016 to November 2018. Confirmatory tests were performed using the multiplex polymerase chain reaction (PCR) test for respiratory viruses and point-of-care test for C-reactive protein. Demographic, clinical and laboratory data were extracted from the hospital electronic medical records. Seventy percent of the data was used for training and the remaining 30% was used for validation. Decision trees, LASSO and logistic regression models were built to predict when antibiotics were not needed.

Results: The median age of the cohort was 36 years old, with 61.2% being male. Temperature and pulse rate were significant factors in all 3 models. The area under the receiver operating curve (AUC) on the validation set for the models were similar. (LASSO: 0.70 [95% CI: 0.62-0.77], logistic regression: 0.72 [95% CI: 0.65-0.79], decision tree: 0.67 [95% CI: 0.59-0.74]). Combining the results from all models, 58.3% of study participants would not need antibiotics.

Conclusion: The models can be easily deployed as a decision support tool to guide antibiotic prescribing in busy EDs.

Keywords: Adult; Antibiotic prescribing; ED; Machine learning; Prediction model; URTI.

Conflict of interest statement

All authors have no conflict of interest related to the submitted work.

Figures

Fig. 1
Fig. 1
Summary of virus positivity by C-reactive protein levels
Fig. 2
Fig. 2
Algorithm for decision tree
Fig. 3
Fig. 3
ROC curves for the 3 models
Fig. 4
Fig. 4
User interface for the “Abx SteWARdS” app

References

    1. Kenealy T, Arroll B. Antibiotics for the common cold and acute purulent rhinitis. Cochrane Database of Systematic Reviews. 2013. 10.1002/14651858.CD000247.pub3/abstract cited 2019 13 Dec.
    1. Gonzales R, Malone DC, Maselli JH, Sande MA. Excessive antibiotic use for acute respiratory infections in the United States. Clin Infect Dis. 2001;33:757–762. doi: 10.1086/322627.
    1. Gonzales R, Bartlett JG, Besser RE, Cooper RJ, Hickner JM, Hoffman JR, et al. Principles of appropriate antibiotic use for treatment of acute respiratory tract infections in adults: background, specific aims, and methods. Ann Emerg Med. 2001;37:690–697. doi: 10.1067/S0196-0644(01)70087-X.
    1. Smith SM, Fahey T, Smucny J, Becker LA. Antibiotics for acute bronchitis. Cochrane Database Syst Rev. 2017. 10.1002/14651858.CD000245.pub4/full cited 2019 13 Dec.
    1. Aabenhus R, Jensen J-US, Jørgensen KJ, Hróbjartsson A, Bjerrum L. Biomarkers as point-of-care tests to guide prescription of antibiotics in patients with acute respiratory infections in primary care. Cochrane Database of Syst Rev. 2014. 10.1002/14651858.CD010130.pub2/full cited 2019 13 Dec.
    1. Xu KT, Roberts D, Sulapas I, Martinez O, Berk J, Baldwin J. Over-prescribing of antibiotics and imaging in the management of uncomplicated URIs in emergency departments. BMC Emerg Med. 2013;13:7. doi: 10.1186/1471-227X-13-7.
    1. Thorpe JM, Smith SR, Trygstad TK. Trends in emergency department antibiotic prescribing for acute respiratory tract infections. Ann Pharmacother. 2004;38:928–935. doi: 10.1345/aph.1D380.
    1. Mainous AG, Saxena S, Hueston WJ, Everett CJ, Majeed A. Ambulatory antibiotic prescribing for acute bronchitis and cough and hospital admissions for respiratory infections: time trends analysis. J R Soc Med. 2006;99:358–362. doi: 10.1177/014107680609900719.
    1. Ebell MH, Radke T. Antibiotic use for viral acute respiratory tract infections remains common. Am J Manag Care. 2015;21:e567–e575.
    1. Donnelly JP, Baddley JW, Wang HE. Antibiotic utilization for acute respiratory tract infections in U.S. emergency departments. Antimicrob Agents Chemother. 2014;58:1451–1457. doi: 10.1128/AAC.02039-13.
    1. Paul P, Heng BH, Seow E, Molina J, Tay SY. Predictors of frequent attenders of emergency department at an acute general hospital in Singapore. Emerg Med J. 2010;27:843–848. doi: 10.1136/emj.2009.079160.
    1. Chan JS-E, Tin AS, Chow WL, Tiah L, Tiru M, Lee CE. Frequent attenders at the emergency department: an analysis of characteristics and utilisation trends. Proceed Singapore Healthcare. 2018;27:12–19. doi: 10.1177/2010105817715271.
    1. Lee W. Antibiotic prescribing for patients with upper respiratory tract infections by emergency physicians in a Singapore tertiary hospital. Hong Kong J Emerg Med. 2005;12:70–76. doi: 10.1177/102490790501200207.
    1. Costelloe C, Metcalfe C, Lovering A, Mant D, Hay AD. Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis. BMJ. 2010;340:c2096. doi: 10.1136/bmj.c2096.
    1. Bell BG, Schellevis F, Stobberingh E, Goossens H, Pringle M. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect Dis. 2014;14:13. doi: 10.1186/1471-2334-14-13.
    1. Olesen SW, Barnett ML, MacFadden DR, Brownstein JS, Hernández-Díaz S, Lipsitch M, et al. The distribution of antibiotic use and its association with antibiotic resistance. eLife:7 Available from: [cited 2019 18 Oct].
    1. Temkin E, Fallach N, Almagor J, Gladstone BP, Tacconelli E, Carmeli Y. Estimating the number of infections caused by antibiotic-resistant Escherichia coli and Klebsiella pneumoniae in 2014: a modelling study. Lancet Glob Health. 2018;6:e969–e979. doi: 10.1016/S2214-109X(18)30278-X.
    1. Cassini A, Högberg LD, Plachouras D, Quattrocchi A, Hoxha A, Simonsen GS, et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European economic area in 2015: a population-level modelling analysis. Lancet Infect Dis. 2019;19:56–66. doi: 10.1016/S1473-3099(18)30605-4.
    1. Founou RC, Founou LL, Essack SY. Clinical and economic impact of antibiotic resistance in developing countries: A systematic review and meta-analysis. PLoS One. 2017;12 Available from: [cited 2019 18 Jul].
    1. Teng CB, Lee W, Yeo CL, Lee SY, Ng TM, Yeoh SF, et al. Guidelines for antimicrobial stewardship training and practice. Ann Acad Med Singap. 2012;41:29–34.
    1. Chow AL, Ang A, Chow CZ, Ng TM, Teng C, Ling LM, et al. Implementation hurdles of an interactive, integrated, point-of-care computerised decision support system for hospital antibiotic prescription. Int J Antimicrob Agents. 2016;47:132–139. doi: 10.1016/j.ijantimicag.2015.12.006.
    1. Chan YY, MAB I, Wong CM, Ooi CK, Chow A. Determinants of antibiotic prescribing for upper respiratory tract infections in an emergency department with good primary care access: a qualitative analysis. Epidemiol Infect. 2019;147 Available from: [cited 2019 15 Jul].
    1. May L, Gudger G, Armstrong P, Brooks G, Hinds P, Bhat R, et al. Multisite exploration of clinical decision-making for antibiotic use by emergency medicine providers using quantitative and qualitative methods. Infect Control Hosp Epidemiol. 2014;35:1114–1125. doi: 10.1086/677637.
    1. Lee T-H, Wong JG, Lye DC, Chen MI, Loh VW, Leo Y-S, et al. Medical and psychosocial factors associated with antibiotic prescribing in primary care: survey questionnaire and factor analysis. Br J Gen Pract. 2017;67:e168–e177. doi: 10.3399/bjgp17X688885.
    1. Rebnord IK, Sandvik H, Mjelle AB, Hunskaar S. Factors predicting antibiotic prescription and referral to hospital for children with respiratory symptoms: secondary analysis of a randomised controlled study at out-of-hours services in primary care. BMJ Open. 2017;7:e012992. doi: 10.1136/bmjopen-2016-012992.
    1. Oonsivilai M, Mo Y, Luangasanatip N, Lubell Y, Miliya T, Tan P, et al. Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children’s hospital in Cambodia. Wellcome Open Res. 2018;3 Available from: [cited 2019 15 Jul].
    1. van de Maat J, Nieboer D, Thompson M, Lakhanpaul M, Moll H, Oostenbrink R. Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric emergency care. PLoS One. 2019;14 Available from: [cited 2019 18 Jul].
    1. Irwin AD, Grant A, Williams R, Kolamunnage-Dona R, Drew RJ, Paulus S, et al. Predicting risk of serious bacterial infections in febrile children in the emergency department. Pediatrics. 2017;28(2):140. 10.1542/peds.2016-2853.
    1. Flanders SA, Stein J, Shochat G, Sellers K, Holland M, Maselli J, et al. Performance of a bedside c-reactive protein test in the diagnosis of community-acquired pneumonia in adults with acute cough. Am J Med. 2004;116:529–535. doi: 10.1016/j.amjmed.2003.11.023.
    1. Hopstaken RM, Muris JW, Knottnerus JA, Kester AD, Rinkens PE, Dinant GJ. Contributions of symptoms, signs, erythrocyte sedimentation rate, and C-reactive protein to a diagnosis of pneumonia in acute lower respiratory tract infection. Br J Gen Pract. 2003;53:358–364.
    1. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–383. doi: 10.1016/0021-9681(87)90171-8.
    1. Therneau TM, Atkinson EJ, Foundation M. An introduction to recursive partitioning using the RPART routines. R Journal. 2019.
    1. Williams GJ. Rattle: a data mining GUI for R. R Journal. 2009;1:45. doi: 10.32614/RJ-2009-016.
    1. Hastie T, Qian J. An Introduction to glmnet. The R Journal. 2016; Available from: .
    1. Spurling GK, Mar CBD, Dooley L, Foxlee R, Farley R. Delayed antibiotic prescriptions for respiratory infections. Cochrane Database Syst Rev. 2017. 10.1002/14651858.CD004417.pub5/full cited 2019 Jul 16.
    1. Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22. doi: 10.1016/j.jclinepi.2019.02.004.
    1. Nijman RG, Vergouwe Y, Thompson M, van Veen M, AHJ v M, van der Lei J, et al. Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study. BMJ. 2013;346:f1706. doi: 10.1136/bmj.f1706.
    1. den Bruel AV, Hall H, Aertgeerts B, Bruyninckx R, Aerts M, Buntinx F. Signs and symptoms for diagnosis of serious infections in children: a prospective study in primary careCommentary. Br J Gen Pract. 2007;57:538–546.
    1. Monto AS, Gravenstein S, Elliott M, Colopy M, Schweinle J. Clinical signs and symptoms predicting influenza infection. Arch Intern Med. 2000;160:3243–3247. doi: 10.1001/archinte.160.21.3243.
    1. Ho ZJM, Zhao X, Cook AR, Loh JP, Ng SH, Tan BH, et al. Clinical differences between respiratory viral and bacterial mono- and dual pathogen detected among Singapore military servicemen with febrile respiratory illness. Influenza Other Respir Viruses. 2015;9:200–208. doi: 10.1111/irv.12312.
    1. Lam P-P, Coleman BL, Green K, Powis J, Richardson D, Katz K, et al. Predictors of influenza among older adults in the emergency department. BMC Infect Dis. 2016;16:615. doi: 10.1186/s12879-016-1966-4.
    1. Michiels B, Thomas I, Van Royen P, Coenen S. Clinical prediction rules combining signs, symptoms and epidemiological context to distinguish influenza from influenza-like illnesses in primary care: a cross sectional study. BMC Fam Pract. 2011;12:4. doi: 10.1186/1471-2296-12-4.
    1. Haran JP, Beaudoin FL, Suner S, Lu S. C-reactive protein as predictor of bacterial infection among patients with an influenza-like illness. Am J Emerg Med. 2013;31:137–144. doi: 10.1016/j.ajem.2012.06.026.
    1. Estabragh ZR, Mamas MA. The cardiovascular manifestations of influenza: a systematic review. Int J Cardiol. 2013;167:2397–2403. doi: 10.1016/j.ijcard.2013.01.274.
    1. Li X, Dunn J, Salins D, Zhou G, Zhou W, Rose SMS-F, et al. Digital health: tracking Physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 2017;15:e2001402. doi: 10.1371/journal.pbio.2001402.
    1. Ang LW, Cutter J, James L, Goh KT. Factors associated with influenza vaccine uptake in older adults living in the community in Singapore. Epidemiol Infect. 2017;145:775–786. doi: 10.1017/S0950268816002491.
    1. Melbye H, Hvidsten D, Holm A, Nordbø SA, Brox J. The course of C-reactive protein response in untreated upper respiratory tract infection. Br J Gen Pract. 2004;54:653–658.
    1. Hu L, Shi Q, Shi M, Liu R, Wang C. Diagnostic Value of PCT and CRP for Detecting Serious Bacterial Infections in Patients With Fever of Unknown Origin: A Systematic Review and Meta-analysis. Appl Immunohistochem Mol Morphol. 2017:25 Available from: [cited 2020 10 Jan].

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