#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

A validation of machine learning-based risk scores in the prehospital setting


Autoři: Douglas Spangler aff001;  Thomas Hermansson aff002;  David Smekal aff001;  Hans Blomberg aff001
Působiště autorů: Uppsala Center for Prehospital Research, Department of Surgical Sciences—Anesthesia and Intensive Care, Uppsala University, Uppsala, Sweden aff001;  Uppsala Ambulance Service, Uppsala University Hospital, Uppsala, Sweden aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226518

Souhrn

Background

The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a machine learning-based approach to generating risk scores based on hospital outcomes using routinely collected prehospital data.

Methods

Dispatch, ambulance, and hospital data were collected in one Swedish region from 2016–2017. Dispatch center and ambulance records were used to develop gradient boosting models predicting hospital admission, critical care (defined as admission to an intensive care unit or in-hospital mortality), and two-day mortality. Composite risk scores were generated based on the models and compared to National Early Warning Scores (NEWS) and actual dispatched priorities in a prospectively gathered dataset from 2018.

Results

A total of 38203 patients were included from 2016–2018. Concordance indexes (or areas under the receiver operating characteristics curve) for dispatched priorities ranged from 0.51–0.66, while those for NEWS ranged from 0.66–0.85. Concordance ranged from 0.70–0.79 for risk scores based only on dispatch data, and 0.79–0.89 for risk scores including ambulance data. Dispatch data-based risk scores consistently outperformed dispatched priorities in predicting hospital outcomes, while models including ambulance data also consistently outperformed NEWS. Model performance in the prospective test dataset was similar to that found using cross-validation, and calibration was comparable to that of NEWS.

Conclusions

Machine learning-based risk scores outperformed a widely-used rule-based triage algorithm and human prioritization decisions in predicting hospital outcomes. Performance was robust in a prospectively gathered dataset, and scores demonstrated adequate calibration. Future research should explore the robustness of these methods when applied to other settings, establish appropriate outcome measures for use in determining the need for prehospital care, and investigate the clinical impact of interventions based on these methods.

Klíčová slova:

Algorithms – Ambulances – Clinical decision support systems – Hospitals – Intensive care units – Machine learning – Machine learning algorithms


Zdroje

1. Platts-Mills TF, Leacock B, Cabañas JG, Shofer FS, McLean SA. Emergency Medical Services Use by the Elderly: Analysis of a Statewide Database. Prehosp Emerg Care. 2010;14: 329–333. doi: 10.3109/10903127.2010.481759 20507220

2. Lowthian JA, Jolley DJ, Curtis AJ, Currell A, Cameron PA, Stoelwinder JU, et al. The challenges of population ageing: accelerating demand for emergency ambulance services by older patients, 1995–2015. Med J Aust. 2011;194. Available: https://www.mja.com.au/journal/2011/194/11/challenges-population-ageing-accelerating-demand-emergency-ambulance-services?inline=true

3. Hwang U, Shah MN, Han JH, Carpenter CR, Siu AL, Adams JG. Transforming Emergency Care For Older Adults. Health Aff (Millwood). 2013;32: 2116–2121. doi: 10.1377/hlthaff.2013.0670 24301394

4. Pines JM, Mullins PM, Cooper JK, Feng LB, Roth KE. National Trends in Emergency Department Use, Care Patterns, and Quality of Care of Older Adults in the United States. J Am Geriatr Soc. 2013;61: 12–17. doi: 10.1111/jgs.12072 23311549

5. Dale J, Williams S, Foster T, Higgins J, Snooks H, Crouch R, et al. Safety of telephone consultation for “non-serious” emergency ambulance service patients. Qual Saf Health Care. 2004;13: 363–373. doi: 10.1136/qshc.2003.008003 15465940

6. Haines CJ, Lutes RE, Blaser M, Christopher NC. Paramedic Initiated Non-Transport of Pediatric Patients. Prehosp Emerg Care. 2006;10: 213–219. doi: 10.1080/10903120500541308 16531379

7. Gray JT, Wardrope J. Introduction of non‐transport guidelines into an ambulance service: a retrospective review. Emerg Med J EMJ. 2007;24: 727–729. doi: 10.1136/emj.2007.048850 17901280

8. Magnusson C, Källenius C, Knutsson S, Herlitz J, Axelsson C. Pre-hospital assessment by a single responder: The Swedish ambulance nurse in a new role: A pilot study. Int Emerg Nurs. 2015 [cited 7 Dec 2015]. doi: 10.1016/j.ienj.2015.09.001 26472522

9. Krumperman K, Weiss S, Fullerton L. Two Types of Prehospital Systems Interventions that Triage Low-Acuity Patients to Alternative Sites of Care. South Med J. 2015;108: 381–386. doi: 10.14423/SMJ.0000000000000303 26192931

10. Eastwood K, Morgans A, Smith K, Hodgkinson A, Becker G, Stoelwinder J. A novel approach for managing the growing demand for ambulance services by low-acuity patients. Aust Health Rev Publ Aust Hosp Assoc. 2015. doi: 10.1071/AH15134 26568037

11. Höglund E, Schröder A, Möller M, Andersson‐Hagiwara M, Ohlsson‐Nevo E. The ambulance nurse experiences of non-conveying patients. J Clin Nurs. 2019;28: 235–244. doi: 10.1111/jocn.14626 30016570

12. Kirkland SW, Soleimani A, Rowe BH, Newton AS. A systematic review examining the impact of redirecting low-acuity patients seeking emergency department care: is the juice worth the squeeze? Emerg Med J. 2019;36: 97–106. doi: 10.1136/emermed-2017-207045 30510034

13. Heward A, Damiani M, Hartley-Sharpe C. Does the use of the Advanced Medical Priority Dispatch System affect cardiac arrest detection? Emerg Med J. 2004;21: 115–118. doi: 10.1136/emj.2003.006940 14734398

14. Bolorunduro OB, Villegas C, Oyetunji TA, Haut ER, Stevens KA, Chang DC, et al. Validating the Injury Severity Score (ISS) in different populations: ISS predicts mortality better among Hispanics and females. J Surg Res. 2011;166: 40–44. doi: 10.1016/j.jss.2010.04.012 20828742

15. Maddali A, Razack FA, Cattamanchi S, Ramakrishnan TV. Validation of the Cincinnati Prehospital Stroke Scale. J Emerg Trauma Shock. 2018;11: 111–114. doi: 10.4103/JETS.JETS_8_17 29937640

16. Silcock DJ, Corfield AR, Gowens PA, Rooney KD. Validation of the National Early Warning Score in the prehospital setting. Resuscitation. 2015;89: 31–35. doi: 10.1016/j.resuscitation.2014.12.029 25583148

17. Seymour CW, Kahn JM, Cooke CR, Watkins TR, Heckbert SR, Rea TD. Prediction of Critical Illness During Out-of-Hospital Emergency Care. JAMA. 2010;304: 747–754. doi: 10.1001/jama.2010.1140 20716737

18. Lane DJ, Wunsch H, Saskin R, Cheskes S, Lin S, Morrison LJ, et al. Assessing Severity of Illness in Patients Transported to Hospital by Paramedics: External Validation of 3 Prognostic Scores. Prehosp Emerg Care. 2019;0: 1–9. doi: 10.1080/10903127.2019.1632998 31210571

19. Pirneskoski J, Kuisma M, Olkkola KT, Nurmi J. Prehospital National Early Warning Score predicts early mortality. Acta Anaesthesiol Scand. 2019;63: 676–683. doi: 10.1111/aas.13310 30623422

20. Patel R, Nugawela MD, Edwards HB, Richards A, Le Roux H, Pullyblank A, et al. Can early warning scores identify deteriorating patients in pre-hospital settings? A systematic review. Resuscitation. 2018;132: 101–111. doi: 10.1016/j.resuscitation.2018.08.028 30171976

21. Hettinger AZ, Cushman JT, Shah MN, Noyes K. Emergency Medical Dispatch Codes Association with Emergency Department Outcomes. Prehosp Emerg Care. 2013;17: 29–37. doi: 10.3109/10903127.2012.710716 23140195

22. Veen M van, Steyerberg EW, Ruige M, Meurs AHJ van, Roukema J, Lei J van der, et al. Manchester triage system in paediatric emergency care: prospective observational study. BMJ. 2008;337: a1501. doi: 10.1136/bmj.a1501 18809587

23. Khorram-Manesh A, Montán KL, Hedelin A, Kihlgren M, Örtenwall P. Prehospital triage, discrepancy in priority-setting between emergency medical dispatch centre and ambulance crews. Eur J Trauma Emerg Surg. 2010;37: 73–78. doi: 10.1007/s00068-010-0022-0 26814754

24. Dami F, Golay C, Pasquier M, Fuchs V, Carron P-N, Hugli O. Prehospital triage accuracy in a criteria based dispatch centre. BMC Emerg Med. 2015;15. doi: 10.1186/s12873-015-0041-6

25. Newgard CD, Yang Z, Nishijima D, McConnell KJ, Trent SA, Holmes JF, et al. Cost-Effectiveness of Field Trauma Triage among Injured Adults Served by Emergency Medical Services. J Am Coll Surg. 2016;222: 1125–1137. doi: 10.1016/j.jamcollsurg.2016.02.014 27178369

26. Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, et al. Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index. Ann Emerg Med. 2018;71: 565–574.e2. doi: 10.1016/j.annemergmed.2017.08.005 28888332

27. Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLOS ONE. 2018;13: e0201016. doi: 10.1371/journal.pone.0201016 30028888

28. Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23: 64. doi: 10.1186/s13054-019-2351-7 30795786

29. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. Npj Digit Med. 2018;1: 18. doi: 10.1038/s41746-018-0029-1 31304302

30. Blomberg SN, Folke F, Ersbøll AK, Christensen HC, Torp-Pedersen C, Sayre MR, et al. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation. 2019;138: 322–329. doi: 10.1016/j.resuscitation.2019.01.015 30664917

31. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Intern Med. 2018;178: 1544–1547. doi: 10.1001/jamainternmed.2018.3763 30128552

32. Dugas AF, Kirsch TD, Toerper M, Korley F, Yenokyan G, France D, et al. An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes. J Emerg Med. 2016;50: 910–918. doi: 10.1016/j.jemermed.2016.02.026 27133736

33. Guttmann A, Schull MJ, Vermeulen MJ, Stukel TA. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada. BMJ. 2011;342: d2983. doi: 10.1136/bmj.d2983 21632665

34. Di Somma S, Paladino L, Vaughan L, Lalle I, Magrini L, Magnanti M. Overcrowding in emergency department: an international issue. Intern Emerg Med. 2015;10: 171–175. doi: 10.1007/s11739-014-1154-8 25446540

35. Berg LM, Ehrenberg A, Florin J, Östergren J, Discacciati A, Göransson KE. Associations Between Crowding and Ten-Day Mortality Among Patients Allocated Lower Triage Acuity Levels Without Need of Acute Hospital Care on Departure From the Emergency Department. Ann Emerg Med. 2019;74: 345–356. doi: 10.1016/j.annemergmed.2019.04.012 31229391

36. Newgard CD. The Validity of Using Multiple Imputation for Missing Out-of-hospital Data in a State Trauma Registry. Acad Emerg Med. 2006;13: 314–324. doi: 10.1197/j.aem.2005.09.011 16495420

37. Laudermilch DJ, Schiff MA, Nathens AB, Rosengart MR. Lack of Emergency Medical Services Documentation Is Associated with Poor Patient Outcomes: A Validation of Audit Filters for Prehospital Trauma Care. J Am Coll Surg. 2010;210: 220–227. doi: 10.1016/j.jamcollsurg.2009.10.008 20113943

38. Buuren S van, Groothuis-Oudshoorn K. Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45. Available: https://www.jstatsoft.org/article/view/v045i03

39. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM; 2016. pp. 785–794. doi: 10.1145/2939672.2939785

40. Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. Springer series in statistics New York; 2001.

41. Davison AC, Hinkley DV. Bootstrap Methods and Their Applications. Cambridge: Cambridge University Press; 1997. Available: http://statwww.epfl.ch/davison/BMA/

42. Harrell FE. rms: Regression Modeling Strategies. 2017. Available: https://CRAN.R-project.org/package=rms

43. R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2017. Available: https://www.R-project.org/

44. American College of Surgeons. Resources for optimal care of the injured patient. 6th ed. chicago, IL; 2014.

45. Cox S, Smith K, Currell A, Harriss L, Barger B, Cameron P. Differentiation of confirmed major trauma patients and potential major trauma patients using pre-hospital trauma triage criteria. Injury. 2011;42: 889–895. doi: 10.1016/j.injury.2010.03.035 20430387

46. Fosbøl EL, Granger CB, Peterson ED, Lin L, Lytle BL, Shofer FS, et al. Prehospital system delay in ST-segment elevation myocardial infarction care: A novel linkage of emergency medicine services and inhospital registry data. Am Heart J. 2013;165: 363–370. doi: 10.1016/j.ahj.2012.11.003 23453105

47. Crilly JL, O’Dwyer JA, O’Dwyer MA, Lind JF, Peters JAL, Tippett VC, et al. Linking ambulance, emergency department and hospital admissions data: understanding the emergency journey. Med J Aust. 2011;194: S34–S37. doi: 10.5694/j.1326-5377.2011.tb02941.x 21401486

48. Birk HO, Henriksen LO. Prehospital Interventions: On-scene-Time and Ambulance-Technicians’ Experience. Prehospital Disaster Med. 2002;17: 167–169. doi: 10.1017/s1049023x00000406 12627921

49. Hale KE, Gavin C, O’Driscoll BR. Audit of oxygen use in emergency ambulances and in a hospital emergency department. Emerg Med J. 2008;25: 773–776. doi: 10.1136/emj.2008.059287 18955625

50. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162: 55–63. doi: 10.7326/M14-0697 25560714

51. Swaminathan S, Qirko K, Smith T, Corcoran E, Wysham NG, Bazaz G, et al. A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLOS ONE. 2017;12: e0188532. doi: 10.1371/journal.pone.0188532 29166411

52. Goto T, Camargo CA, Faridi MK, Yun BJ, Hasegawa K. Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. Am J Emerg Med. 2018;36: 1650–1654. doi: 10.1016/j.ajem.2018.06.062 29970272

53. Hong WS, Haimovich AD, Taylor RA. Predicting 72-hour and 9-day return to the emergency department using machine learning. JAMIA Open. 2019 [cited 28 Aug 2019]. doi: 10.1093/jamiaopen/ooz019

54. Marinovich A, Afilalo J, Afilalo M, Colacone A, Unger B, Giguère C, et al. Impact of Ambulance Transportation on Resource Use in the Emergency Department. Acad Emerg Med. 2004;11: 312–315. doi: 10.1111/j.1553-2712.2004.tb02218.x 15001417

55. Ruger JP, Richter CJ, Lewis LM. Clinical and Economic Factors Associated with Ambulance Use to the Emergency Department. Acad Emerg Med. 2006;13: 879–885. doi: 10.1197/j.aem.2006.04.006 16825670

56. Squire BT, Tamayo A, Tamayo-Sarver JH. At-Risk Populations and the Critically Ill Rely Disproportionately on Ambulance Transport to Emergency Departments. Ann Emerg Med. 2010;56: 341–347. doi: 10.1016/j.annemergmed.2010.04.014 20554351

57. Bohm K, Kurland L. The accuracy of medical dispatch—a systematic review. Scand J Trauma Resusc Emerg Med. 2018;26: 94. doi: 10.1186/s13049-018-0528-8 30413213

58. Brangan E, Banks J, Brant H, Pullyblank A, Roux HL, Redwood S. Using the National Early Warning Score (NEWS) outside acute hospital settings: a qualitative study of staff experiences in the West of England. BMJ Open. 2018;8: e022528. doi: 10.1136/bmjopen-2018-022528 30368449

59. Spangler D. openTriage prehospital risk score demo. Uppsala: Uppsala University; 2019. Available: https://ucpr.se/openTriage_demo


Článek vyšel v časopise

PLOS One


2019 Číslo 12
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

KOST
Koncepce osteologické péče pro gynekology a praktické lékaře
nový kurz
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Svět praktické medicíny 5/2023 (znalostní test z časopisu)

Imunopatologie? … a co my s tím???
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#