Development of a Machine Learning-Based Model for Predicting the Incidence of Peripheral Intravenous Catheter-Associated Phlebitis

1. Yasuda H, Yamamoto R, Hayashi Y, et al. Occurrence and incidence rate of peripheral intravascular catheter-related phlebitis and complications in critically ill patients: a prospective cohort study (AMOR-VENUS study). J Intensive Care. 2021;9:3. YasudaH YamamotoR HayashiY Occurrence and incidence rate of peripheral intravascular catheter-related phlebitis and complications in critically ill patients: a prospective cohort study (AMOR-VENUS study) J Intensive Care 2021 9 3 Search in Google Scholar

2. Marsh N, Webster J, Ullman AJ, et al. Peripheral intravenous catheter non-infectious complications in adults: A systematic review and meta-analysis. J Adv Nurs 2020;76:3346–62. MarshN WebsterJ UllmanAJ Peripheral intravenous catheter non-infectious complications in adults: A systematic review and meta-analysis J Adv Nurs 2020 76 3346 62 Search in Google Scholar

3. Kleidon TM, Keogh S, Flynn J, Schults J, Mihala G, Rickard CM. Flushing of peripheral intravenous catheters: A pilot, factorial, randomised controlled trial of high versus low frequency and volume in paediatrics. J Paediatr Child Health. 2020;56:22–9. KleidonTM KeoghS FlynnJ SchultsJ MihalaG RickardCM Flushing of peripheral intravenous catheters: A pilot, factorial, randomised controlled trial of high versus low frequency and volume in paediatrics J Paediatr Child Health 2020 56 22 9 Search in Google Scholar

4. Rickard CM, Marsh N, Webster J, et al. Dressings and securements for the prevention of peripheral intravenous catheter failure in adults (SAVE): a pragmatic, randomised controlled, superiority trial. Lancet. 2018;392:419–30. RickardCM MarshN WebsterJ Dressings and securements for the prevention of peripheral intravenous catheter failure in adults (SAVE): a pragmatic, randomised controlled, superiority trial Lancet 2018 392 419 30 Search in Google Scholar

5. Rickard CM, Webster J, Wallis MC, et al. Routine versus clinically indicated replacement of peripheral intravenous catheters: a randomised controlled equivalence trial. Lancet. 2012;380:1066–74. RickardCM WebsterJ WallisMC Routine versus clinically indicated replacement of peripheral intravenous catheters: a randomised controlled equivalence trial Lancet 2012 380 1066 74 Search in Google Scholar

6. Günther SC, Schwebel C, Hamidfar-Roy R, et al. Complications of intravascular catheters in ICU: definitions, incidence and severity. A randomized controlled trial comparing usual transparent dressings versus new-generation dressings (the ADVANCED study). Intensive Care Med. 2016;42:1753–65. GüntherSC SchwebelC Hamidfar-RoyR Complications of intravascular catheters in ICU: definitions, incidence and severity. A randomized controlled trial comparing usual transparent dressings versus new-generation dressings (the ADVANCED study) Intensive Care Med 2016 42 1753 65 Search in Google Scholar

7. Larsen EN, Marsh N, O’Brien C, Monteagle E, Friese C, Rickard CM. Inherent and modifiable risk factors for peripheral venous catheter failure during cancer treatment: a prospective cohort study. Support Care Cancer. 2021;29:1487–96. LarsenEN MarshN O’BrienC MonteagleE FrieseC RickardCM Inherent and modifiable risk factors for peripheral venous catheter failure during cancer treatment: a prospective cohort study Support Care Cancer 2021 29 1487 96 Search in Google Scholar

8. Marsh N, Webster J, Larson E, Cooke M, Mihala G, Rickard CM. Observational study of peripheral intravenous catheter outcomes in adult hospitalized patients: A multivariable analysis of peripheral intravenous catheter failure. J Hosp Med. 2018;13:83–9. MarshN WebsterJ LarsonE CookeM MihalaG RickardCM Observational study of peripheral intravenous catheter outcomes in adult hospitalized patients: A multivariable analysis of peripheral intravenous catheter failure J Hosp Med 2018 13 83 9 Search in Google Scholar

9. Wallis MC, McGrail M, Webster J, et al. Risk factors for peripheral intravenous catheter failure: a multivariate analysis of data from a randomized controlled trial. Infect Control Hosp Epidemiol. 2014;35:63–8. WallisMC McGrailM WebsterJ Risk factors for peripheral intravenous catheter failure: a multivariate analysis of data from a randomized controlled trial Infect Control Hosp Epidemiol 2014 35 63 8 Search in Google Scholar

10. Abolfotouh MA, Salam M, Bani-Mustafa A, White D, Balkhy HH. Prospective study of incidence and predictors of peripheral intravenous catheter-induced complications. Ther Clin Risk Manag. 2014;10:993–1001. AbolfotouhMA SalamM Bani-MustafaA WhiteD BalkhyHH Prospective study of incidence and predictors of peripheral intravenous catheter-induced complications Ther Clin Risk Manag 2014 10 993 1001 Search in Google Scholar

11. Helm RE, Klausner JD, Klemperer JD, Flint LM, Huang E. Accepted but unacceptable: peripheral IV catheter failure. J Infus Nurs. 2019;42:151–64. HelmRE KlausnerJD KlempererJD FlintLM HuangE Accepted but unacceptable: peripheral IV catheter failure J Infus Nurs 2019 42 151 64 Search in Google Scholar

12. Carr PJ, Rippey JCR, Cooke ML, et al. Derivation of a clinical decision-making aid to improve the insertion of clinically indicated peripheral intravenous catheters and promote vessel health preservation. An observational study. PLOS ONE. 2019;14:e0213923. CarrPJ RippeyJCR CookeML Derivation of a clinical decision-making aid to improve the insertion of clinically indicated peripheral intravenous catheters and promote vessel health preservation. An observational study PLOS ONE 2019 14 e0213923 Search in Google Scholar

13. Ricard JD, Salomon L, Boyer A, et al. Central or peripheral catheters for initial venous access of ICU patients: a randomized controlled trial. Crit Care Med. 2013;41:2108–15. RicardJD SalomonL BoyerA Central or peripheral catheters for initial venous access of ICU patients: a randomized controlled trial Crit Care Med 2013 41 2108 15 Search in Google Scholar

14. Yasuda H, Rickard CM, Marsh N, et al. Risk factors for peripheral intravascular catheter-related phlebitis in critically ill patients: analysis of 3429 catheters from 23 Japanese intensive care units. Ann Intensive Care. 2022;12:33. YasudaH RickardCM MarshN Risk factors for peripheral intravascular catheter-related phlebitis in critically ill patients: analysis of 3429 catheters from 23 Japanese intensive care units Ann Intensive Care 2022 12 33 Search in Google Scholar

15. Goto T, Camargo CA Jr, Faridi MK, Freishtat RJ, Hasegawa K. Machine learning–based prediction of clinical outcomes for children during emergency department triage. JAMA Netw Open. 2019;2:e186937. GotoT CamargoCAJr FaridiMK FreishtatRJ HasegawaK Machine learning–based prediction of clinical outcomes for children during emergency department triage JAMA Netw Open 2019 2 e186937 Search in Google Scholar

16. Desai RJ, Wang SV, Vaduganathan M, Evers T, Schneeweiss S. Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes. JAMA Netw Open. 2020;3:e1918962. DesaiRJ WangSV VaduganathanM EversT SchneeweissS Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes JAMA Netw Open 2020 3 e1918962 Search in Google Scholar

17. Liang W, Liang H, Ou L, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med. 2020;180:1081–9. LiangW LiangH OuL Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19 JAMA Intern Med 2020 180 1081 9 Search in Google Scholar

18. Delahanty RJ, Alvarez J, Flynn LM, Sherwin RL, Jones SS. Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis. Ann Emerg Med. 2019;73:334–44. DelahantyRJ AlvarezJ FlynnLM SherwinRL JonesSS Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis Ann Emerg Med 2019 73 334 44 Search in Google Scholar

19. Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA Jr, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23:64. RaitaY GotoT FaridiMK BrownDFM CamargoCAJr HasegawaK Emergency department triage prediction of clinical outcomes using machine learning models Crit Care 2019 23 64 Search in Google Scholar

20. Rau CS, Wu SC, Chuang JF, et al. Machine learning models of survival prediction in trauma patients. J Clin Med. 2019;8:799. RauCS WuSC ChuangJF Machine learning models of survival prediction in trauma patients J Clin Med 2019 8 799 Search in Google Scholar

21. Okada Y, Matsuyama T, Morita S, et al. Machine learning-based prediction models for accidental hypothermia patients. J Intensive Care. 2021;9:6. OkadaY MatsuyamaT MoritaS Machine learning-based prediction models for accidental hypothermia patients J Intensive Care 2021 9 6 Search in Google Scholar

22. Nishioka N, Kobayashi D, Kiguchi T, et al. Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiacarrest. Resuscitation. 2021;168:142–50. NishiokaN KobayashiD KiguchiT Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiacarrest Resuscitation 2021 168 142 50 Search in Google Scholar

23. Gorski LA, Hadaway L, Hagle ME, et al. Infusion Therapy Standards of Practice, 8th Edition. J Infus Nurs. 2021;44:S1–224. GorskiLA HadawayL HagleME Infusion Therapy Standards of Practice, 8th Edition J Infus Nurs 2021 44 S1 224 Search in Google Scholar

24. Loveday HP, Wilson JA, Pratt RJ, et al. epic3: national evidence-based guidelines for preventing healthcare-associated infections in NHS hospitals in England. J Hosp Infect. 2014;86:S1–70. LovedayHP WilsonJA PrattRJ epic3: national evidence-based guidelines for preventing healthcare-associated infections in NHS hospitals in England J Hosp Infect 2014 86 S1 70 Search in Google Scholar

25. Lee S, Kim K, Kim JS. A Model of Phlebitis Associated with Peripheral Intravenous Catheters in Orthopedic Inpatients. Int J Environ Res Public Health. 2019;16:3412. LeeS KimK KimJS A Model of Phlebitis Associated with Peripheral Intravenous Catheters in Orthopedic Inpatients Int J Environ Res Public Health 2019 16 3412 Search in Google Scholar

26. 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. CollinsGS ReitsmaJB AltmanDG MoonsKG Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement BMJ 2015 350 g7594 Search in Google Scholar

27. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–29. KnausWA DraperEA WagnerDP ZimmermanJE APACHE II: a severity of disease classification system Crit Care Med 1985 13 818 29 Search in Google Scholar

28. 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;40:373–83. CharlsonME PompeiP AlesKL MacKenzieCR A new method of classifying prognostic comorbidity in longitudinal studies: development and validation J Chronic Dis 1987 40 373 83 Search in Google Scholar

29. Seymour CW, Liu VX, Iwashyna TJ, et al. Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315:762–74. SeymourCW LiuVX IwashynaTJ Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) JAMA 2016 315 762 74 Search in Google Scholar

30. Infusion Nurses Society. Infusion Nursing Standards of Practice. J Infus Nurs. 2006;29:S1–92. Infusion Nurses Society Infusion Nursing Standards of Practice J Infus Nurs 2006 29 S1 92 Search in Google Scholar

31. van Buuren S. Package ‘mice’. https://cran.r-project.org/web/packages/mice/mice.pdf. Accessed 12 June 2023. van BuurenS Package ‘mice’ https://cran.r-project.org/web/packages/mice/mice.pdf. Accessed 12 June 2023. Search in Google Scholar

32. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. RileyRD EnsorJ SnellKIE Calculating the sample size required for developing a clinical prediction model BMJ 2020 368 m441 Search in Google Scholar

33. Goto T, Camargo CA Jr, Faridi MK, Freishtat RJ, Hasegawa K. Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage. JAMA Network Open. 2019;2:e186937. GotoT CamargoCAJr FaridiMK FreishtatRJ HasegawaK Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage JAMA Network Open 2019 2 e186937 Search in Google Scholar

34. Tang H, Jin Z, Deng J, et al. Development and validation of a deep learning model to predict the survival of patients in ICU. J Am Med Inform Assoc. 2022;29:1567–76. TangH JinZ DengJ Development and validation of a deep learning model to predict the survival of patients in ICU J Am Med Inform Assoc 2022 29 1567 76 Search in Google Scholar

35. Ishwaran H. Package ‘randomforestSRC’. https://cran.r-project.org/web/packages/randomForestSRC/randomForestSRC.pdf. Accessed 12 June 2023. IshwaranH Package ‘randomforestSRC’ https://cran.r-project.org/web/packages/randomForestSRC/randomForestSRC.pdf. Accessed 12 June 2023. Search in Google Scholar

36. Kuhn M. Package ‘caret’. https://cran.r-project.org/web/packages/caret/caret.pdf. Accessed 12 June 2023. KuhnM Package ‘caret’ https://cran.r-project.org/web/packages/caret/caret.pdf. Accessed 12 June 2023. Search in Google Scholar

37. Chen T. Package ‘xgboost’. https://cran.r-project.org/web/packages/xgboost/xgboost.pdf. Accessed 12 June 2023. ChenT Package ‘xgboost’ https://cran.r-project.org/web/packages/xgboost/xgboost.pdf. Accessed 12 June 2023. Search in Google Scholar

38. Wright MN. Package ‘ranger’. https://cran.r-project.org/web/packages/ranger/ranger.pdf. Accessed 12 June 2023. WrightMN Package ‘ranger’ https://cran.r-project.org/web/packages/ranger/ranger.pdf. Accessed 12 June 2023. Search in Google Scholar

39. Robin X. Package ‘pROC’. https://cran.r-project.org/web/packages/pROC/pROC.pdf. Accessed 12 June 2023. RobinX Package ‘pROC’ https://cran.r-project.org/web/packages/pROC/pROC.pdf. Accessed 12 June 2023. Search in Google Scholar

40. Steyerberg EW. Clinical prediction models : a practical approach to development, validation, and updating. 2009th Edition. New York: Springer; 2009. SteyerbergEW Clinical prediction models : a practical approach to development, validation, and updating 2009th Edition New York Springer 2009 Search in Google Scholar

41. R Core Team. R: A language and environment for statistical computing. The R Project for Statistical Computing. https://www.R-project.org/. Accessed 12 June 2023. R Core Team R: A language and environment for statistical computing The R Project for Statistical Computing https://www.R-project.org/. Accessed 12 June 2023. Search in Google Scholar

42. Chopra V, Kaatz S, Conlon A, et al. The Michigan Risk Score to predict peripherally inserted central catheter-associated thrombosis. J Thromb Haemost. 2017;15:1951–62. ChopraV KaatzS ConlonA The Michigan Risk Score to predict peripherally inserted central catheter-associated thrombosis J Thromb Haemost 2017 15 1951 62 Search in Google Scholar

43. Govindan S, O’Malley ME, Flanders SA, Chopra V. The MI-PICC Score: A Risk-Prediction Model for PICC-associated Complications in the ICU. Am J Respir Crit Care Med. 2022;206:1286–89. GovindanS O’MalleyME FlandersSA ChopraV The MI-PICC Score: A Risk-Prediction Model for PICC-associated Complications in the ICU Am J Respir Crit Care Med 2022 206 1286 89 Search in Google Scholar

44. Song X, Lu H, Chen F, et al. A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients. Sci Rep. 2020;10:10090. SongX LuH ChenF A longitudinal observational retrospective study on risk factors and predictive model of PICC associated thrombosis in cancer patients Sci Rep 2020 10 10090 Search in Google Scholar

45. Liu B, Xie J, Sun X, et al. Development and Validation of a New Clinical Prediction Model of Catheter-Related Thrombosis Based on Vascular Ultrasound Diagnosis in Cancer Patients. Front Cardiovasc Med. 2020;7:571227. LiuB XieJ SunX Development and Validation of a New Clinical Prediction Model of Catheter-Related Thrombosis Based on Vascular Ultrasound Diagnosis in Cancer Patients Front Cardiovasc Med 2020 7 571227 Search in Google Scholar

46. Herc E, Patel P, Washer LL, Conlon A, Flanders SA, Chopra V. A Model to Predict Central-Line-Associated Bloodstream Infection Among Patients With Peripherally Inserted Central Catheters: The MPC Score. Infect Control Hosp Epidemiol. 2017;38:1155–66. HercE PatelP WasherLL ConlonA FlandersSA ChopraV A Model to Predict Central-Line-Associated Bloodstream Infection Among Patients With Peripherally Inserted Central Catheters: The MPC Score Infect Control Hosp Epidemiol 2017 38 1155 66 Search in Google Scholar

47. Perek S, Khatib A, Izhaki N, Khalaila AS, Brenner B, Horowitz NA. A prediction model for central venous catheter-related thrombosis in patients with newly-diagnosed acute myeloid leukemia: A derivation cohort analysis. Eur J Intern Med. 2022;101:68–75. PerekS KhatibA IzhakiN KhalailaAS BrennerB HorowitzNA A prediction model for central venous catheter-related thrombosis in patients with newly-diagnosed acute myeloid leukemia: A derivation cohort analysis Eur J Intern Med 2022 101 68 75 Search in Google Scholar

48. Lin S, Zhu N, Yihan Zhang, Du L, Zhang S. Development and validation of a prediction model of catheter-related thrombosis in patients with cancer undergoing chemotherapy based on ultrasonography results and clinical information. J Thromb Thrombolysis. 2022;54:480–91. LinS ZhuN ZhangYihan DuL ZhangS Development and validation of a prediction model of catheter-related thrombosis in patients with cancer undergoing chemotherapy based on ultrasonography results and clinical information J Thromb Thrombolysis 2022 54 480 91 Search in Google Scholar

49. Rahmani K, Garikipati A, Barnes G, et al. Early prediction of central line associated bloodstream infection using machine learning. Am J Infect Control. 2022;50:440–5. RahmaniK GarikipatiA BarnesG Early prediction of central line associated bloodstream infection using machine learning Am J Infect Control 2022 50 440 5 Search in Google Scholar

50. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240:1285–93. SwetsJA Measuring the accuracy of diagnostic systems Science 1988 240 1285 93 Search in Google Scholar