Predicting urinary tract infections in the emergency department with machine learning

PLoS ONE

Volumn 13, Issue 3, 2018, Pages

  Taylor, R Andrew ^a   Moore, Christopher L ^a   Cheung, Kei Hoi ^a   Brandt, Cynthia ^a  

^a YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIBIOTIC AGENT;

ADULT; AGED; ALGORITHM; ARTICLE; COHORT ANALYSIS; COMPARATIVE STUDY; DEMOGRAPHY; ELECTRONIC HEALTH RECORD; EMERGENCY WARD; FALSE POSITIVE RESULT; FEMALE; HOSPITAL ADMISSION; HOSPITAL DISCHARGE; HUMAN; LABORATORY TEST; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAL DOCUMENTATION; MEDICAL HISTORY; PHYSICAL EXAMINATION; PREDICTION; PRESCRIPTION; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; URINARY TRACT INFECTION; URINE CULTURE; AREA UNDER THE CURVE; BIOLOGICAL MODEL; EARLY DIAGNOSIS; EVALUATION STUDY; FALSE NEGATIVE RESULT; HOSPITAL EMERGENCY SERVICE; MICROBIOLOGY; MIDDLE AGED; PREDICTIVE VALUE; RECEIVER OPERATING CHARACTERISTIC; URINE;

AREA UNDER CURVE; EARLY DIAGNOSIS; EMERGENCY SERVICE, HOSPITAL; FALSE NEGATIVE REACTIONS; FALSE POSITIVE REACTIONS; FEMALE; HUMANS; MACHINE LEARNING; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; PREDICTIVE VALUE OF TESTS; RETROSPECTIVE STUDIES; ROC CURVE; SENSITIVITY AND SPECIFICITY; URINARY TRACT INFECTIONS; URINE;

EID: 85042925770     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0194085     Document Type: Article

References (37)

1. National Center for Health Statistics. National hospital ambulatory medical care survey (NHAMCS), 2010. Hyattsville (MD). Public-use data file and documentation. Available at:ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/. Accessed May 18, 2017.
2. Gordon LB, Waxman MJ, Ragsdale L, Mermel LA. Overtreatment of presumed urinary tract infection in older women presenting to the emergency department. Journal of the American Geriatrics Society. 2013; 61(5):788–92. https://doi.org/10.1111/jgs.12203 PMID: 23590846.
3. Foxman B. Urinary tract infection syndromes: occurrence, recurrence, bacteriology, risk factors, and disease burden. Infect Dis Clin North Am. 2014; 28(1):1–13. https://doi.org/10.1016/j.idc.2013.09.003PMID: 24484571.
4. Brown P, Ki M, Foxman B. Acute pyelonephritis among adults: cost of illness and considerations for the economic evaluation of therapy. Pharmacoeconomics. 2005; 23(11):1123–42. PMID: 16277548.
5. Schito GC, Naber KG, Botto H, Palou J, Mazzei T, Gualco L, et al. The ARESC study: an international survey on the antimicrobial resistance of pathogens involved in uncomplicated urinary tract infections. Int J Antimicrob Agents. 2009; 34(5):407–13. https://doi.org/10.1016/j.ijantimicag.2009.04.012 PMID: 19505803.
6. Tomas ME, Getman D, Donskey CJ, Hecker MT. Overdiagnosis of Urinary Tract Infection and Underdi-agnosis of Sexually Transmitted Infection in Adult Women Presenting to an Emergency Department. J Clin Microbiol. 2015; 53(8):2686–92. https://doi.org/10.1128/JCM.00670-15 PMID: 26063863; PubMed Central PMCID: PMCPMC4508438.
7. Schmiemann G, Kniehl E, Gebhardt K, Matejczyk MM, Hummers-Pradier E. The diagnosis of urinary tract infection: a systematic review. Dtsch Arztebl Int. 2010; 107(21):361–7. https://doi.org/10.3238/arztebl.2010.0361 PMID: 20539810; PubMed Central PMCID: PMCPMC2883276.
8. McIsaac WJ, Hunchak CL. Overestimation error and unnecessary antibiotic prescriptions for acute cystitis in adult women. Med Decis Making. 2011; 31(3):405–11.
9. Aubin C. Does this woman have an acute uncomplicated urinary tract infection? Ann Emerg Med. 2007; 49(1):106–8. doi: 10.1016/j.annemergmed.2006.09.022. WOS:000243448300023. PMID: 17203544
10. Meister L, Morley EJ, Scheer D, Sinert R. History and physical examination plus laboratory testing for the diagnosis of adult female urinary tract infection. Acad Emerg Med. 2013; 20(7):631–45. https://doi.org/10.1111/acem.12171 PMID: 23859578.
11. Lachs MS, Nachamkin I, Edelstein PH, Goldman J, Feinstein AR, Schwartz JS. Spectrum Bias in the Evaluation of Diagnostic-Tests—Lessons from the Rapid Dipstick Test for Urinary-Tract Infection. Annals of internal medicine. 1992; 117(2):135–40. WOS:A1992JD12300008. PMID: 1605428
12. Wigton RS, Hoellerich VL, Ornato JP, Leu V, Mazzotta LA, Cheng IH. Use of clinical findings in the diagnosis of urinary tract infection in women. Arch Intern Med. 1985; 145(12):2222–7. PMID: 2934038.
13. Little P, Turner S, Rumsby K, Warner G, Moore M, Lowes JA, et al. Developing clinical rules to predict urinary tract infection in primary care settings: sensitivity and specificity of near patient tests (dipsticks) and clinical scores. Br J Gen Pract. 2006; 56(529):606–12. PMID: 16882379; PubMed Central PMCID: PMCPMC1874525.
14. McIsaac WJ, Moineddin R, Ross S. Validation of a decision aid to assist physicians in reducing unnecessary antibiotic drug use for acute cystitis. Arch Intern Med. 2007; 167(20):2201–6. https://doi.org/10.1001/archinte.167.20.2201 PMID: 17998492.
15. Winkens R, Nelissen-Arets H, Stobberingh E. Validity of the urine dipslide under daily practice conditions. Fam Pract. 2003; 20(4):410–2. PMID: 12876111.
16. Heckerling PS, Canaris GJ, Flach SD, Tape TG, Wigton RS, Gerber BS. Predictors of urinary tract infection based on artificial neural networks and genetic algorithms. Int J Med Inform. 2007; 76(4):289–96. https://doi.org/10.1016/j.ijmedinf.2006.01.005 PMID: 16469531.
17. Papageorgiou EI. Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection. Comput Methods Programs Biomed. 2012; 105(3):233–45. https://doi.org/10.1016/j.cmpb.2011.09.006 PMID: 22001398.
18. Obermeyer Z, Emanuel EJ. Predicting the Future—Big Data, Machine Learning, and Clinical Medicine. New Engl J Med. 2016; 375(13):1216–9. doi: 10.1056/NEJMp1606181. WOS:000384265000003. PMID: 27682033
19. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015; 350:g7594. https://doi.org/10.1136/bmj.g7594 PMID: 25569120.
20. Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, et al. Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. Acad Emerg Med. 2016; 23(3):269–78. https://doi.org/10.1111/acem.12876 PMID: 26679719.
21. Nicolle LE, Bradley S, Colgan R, Rice JC, Schaeffer A, Hooton TM, et al. Infectious Diseases Society of America guidelines for the diagnosis and treatment of asymptomatic bacteriuria in adults. Clin Infect Dis. 2005; 40(5):643–54. https://doi.org/10.1086/427507 PMID: 15714408.
22. Maslove DM, Podchiyska T, Lowe HJ. Discretization of continuous features in clinical datasets. J Am Med Inform Assoc. 2013; 20(3):544–53. Epub 2012/10/13. https://doi.org/10.1136/amiajnl-2012-000929 PMID: 23059731; PubMed Central PMCID: PMCPMC3628044.
23. Chen L, Zeng WM, Cai YD, Feng KY, Chou KC. Predicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS One. 2012; 7 (4):e35254. https://doi.org/10.1371/journal.pone.0035254 PMID: 22514724; PubMed Central PMCID: PMCPMC3325992.
24. HCUP Clinical Classification Software (CCS) for ICD-9-CM. Healthcare Cost and Utilization Project (HCUP). 2006–2009. Agency for Healthcare Research and Quality, Rockville, MD. Available at:http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed July 11, 2016.
25. Hooton TM, Roberts PL, Cox ME, Stapleton AE. Voided midstream urine culture and acute cystitis in premenopausal women. N Engl J Med. 2013; 369(20):1883–91. https://doi.org/10.1056/NEJMoa1302186PMID: 24224622; PubMed Central PMCID: PMCPMC4041367.
26. Rea S, Pathak J, Savova G, Oniki TA, Westberg L, Beebe CE, et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project. J Biomed Inform. 2012; 45(4):763–71. doi: 10.1016/j.jbi.2012.01.009. WOS:000308258200019. PMID: 22326800
27. Mazurowski MA, Habas PA, Zurada JA, Lo JY, Baker JA, Tourassi GD. Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Networks. 2008; 21(2–3):427–36. doi: 10.1016/j.neunet.2007.12.031. WOS:000255238800034. PMID: 18272329
28. Delong ER, Delong DM, Clarkepearson DI. Comparing the Areas under 2 or More Correlated Receiver Operating Characteristic Curves—a Nonparametric Approach. Biometrics. 1988; 44(3):837–45. doi: 10.2307/2531595. WOS:A1988Q069100016. PMID: 3203132
29. D C. Modelling Binary Data. Chapman & Hall/CRC, Boca Raton Florida, pp. 241999.
30. Simel DL, Samsa GP, Matchar DB. Likelihood ratios with confidence: sample size estimation for diagnostic test studies. J Clin Epidemiol. 1991; 44(8):763–70. PMID: 1941027.
31. Wenzel D, Zapf A. Difference of two dependent sensitivities and specificities: Comparison of various approaches. Biometrical J. 2013; 55(5):705–18. doi: 10.1002/bimj.201200186. WOS:000327816900005. PMID: 23828661
32. Wigton RS, Hoellerich VL, Ornato JP, Leu V, Mazzotta LA, Cheng IHC. Use of Clinical Findings in the Diagnosis of Urinary-Tract Infection in Women. Arch Intern Med. 1985; 145(12):2222–7. doi: 10.1001/archinte.145.12.2222. WOS:A1985AVM5000014. PMID: 2934038
33. Schriger DL, Elder JW, Cooper RJ. Structured Clinical Decision Aids Are Seldom Compared With Subjective Physician Judgment, and Are Seldom Superior. Ann Emerg Med. 2017; 70(3):338–44 e3. https://doi.org/10.1016/j.annemergmed.2016.12.004 PMID: 28238497.
34. Janke AT, Overbeek DL, Kocher KE, Levy PD. Exploring the Potential of Predictive Analytics and Big Data in Emergency Care. Ann Emerg Med. 2016; 67(2):227–36. doi: 10.1016/j.annemergmed.2015.06.024. WOS:000369124400013. PMID: 26215667
35. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016; 316(22):2402–10. https://doi.org/10.1001/jama.2016.17216 PMID: 27898976.
36. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? Plos One. 2017; 12(4). ARTN e0174944 10.1371/journal. pone.0174944. WOS:000399352000025.
37. Zhang YY, Jiao YQ. Design and Implementation of Predictive Model Markup Language Interpretation Engine. 2015 International Conference on Network and Information Systems for Computers (ICNISC). 2015:527–31. 10.1109/Icnisc.2015.105. WOS:000380542600064.