Artificial Intelligence Solutions for Risk Prediction of Healthcare Associated Infections during and after COVID-19 Pandemic: A Systematic Literature Review

Document Type : Review Article/ Systematic Review Article/ Meta Analysis

Authors

1 Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran

2 Health Information Technology unit of Economic Health Department, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: Healthcare-associated infections (HAIs) pose a significant challenge to patient safety and healthcare systems worldwide. These infections, acquired during medical care, can lead to prolonged long hospital stays, increased morbidity and mortality, and substantial healthcare costs. Identifying and managing risk factors associated with HAIs is crucial for effective prevention and control strategies.
 
Objectives: This study aims to systematically review the application of artificial intelligence (AI) techniques in Healthcare Associated Infections (HAIs).
 
Methods: A systematic review was performed that follows the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. PubMed was used to search for HAI publications with an emphasis on AI that were published during and post-COVID-19 pandemic. The terms “artificial intelligence” and “HAIs” were used to search for the publications.
 
Results: A total of 29 articles were included in the systematic review. The most commonly studied healthcare-associated infections (HAIs) were ventilator-associated pneumonia (VAP) and hospital-acquired pneumonia (HAP). However, other HAIs such as hospital-acquired bloodstream infections (BSI), urinary tract infections (UTIs), surgical site infections (SSIs), Klebsiella pneumonia bloodstream infections (Kp-BSI), incubator infections, skin infections, central nervous system infections, meningitis, central line-associated bloodstream infections (CLABSIs), and tracheobronchitis were also examined, although to a lesser extent.
 
Conclusion: By providing a comprehensive overview of the current landscape of AI solutions in HAI research, this review seeks to facilitate knowledge exchange, promote further research collaborations, and ultimately contribute to the development of effective strategies for preventing and managing HAIs.
 

Keywords

Copyright © 2024, Razavi International Journal of Medicine. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References

1. Zingg, W., A. Holmes, M. Dettenkofer, T. Goetting, F. Secci, L. Clack, et al., Hospital organisation, management, and structure for prevention of health-care-associated infection: a systematic review and expert consensus. Lancet Infect Dis, 2015. 15(2): p. 212-224. doi: 10.1016/S1473-3099(14)70854-0. [PubMed:25467650).
https://doi.org/10.1016/S1473-3099(14)70854-0
PMid:25467650
 
2. Haque, M., M. Sartelli, J. McKimm and M.A. Bakar, Health care-associated infections-an overview. Infect Drug Resist, 2018: p. 2321-2333. doi: 10.2147/IDR.S177247. [PubMed: 6245375).
https://doi.org/10.2147/IDR.S177247
PMid:30532565 PMCid:PMC6245375
 
3. Despotović, A., N. Milić, A. Cirković, B. Milošević, S. Jovanović, V. Mioljević, et al., Incremental costs of hospital-acquired infections in COVID-19 patients in an adult intensive care unit of a tertiary hospital from a low-resource setting. Antimicrob. Resist. Infect. Control, 2023. 12(1): p. 39. doi: 10.1186/s13756-023-01240-0. [PubMed: 10120483).
https://doi.org/10.1186/s13756-023-01240-0
PMid:37085906 PMCid:PMC10120483
 
4. Sundermann, A.J., J. Chen, J.K. Miller, M.I. Saul, K.A. Shutt, M.P. Griffith, et al., Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance. Clin. Infect. Dis, 2021. 73(3): p. e638-e642. doi: 10.1093/cid/ciaa1887 . [PubMed: 33367518).
https://doi.org/10.1093/cid/ciaa1887
PMid:33367518 PMCid:PMC8326550
 
5. Mancini, A., L. Vito, E. Marcelli, M. Piangerelli, R. De Leone, S. Pucciarelli, et al., Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS". BMC bioinformatics, 2020. 21(Suppl 10): p. 347. doi: 10.1186/s12859-020-03566-7. [PubMed: 7446147).
https://doi.org/10.1186/s12859-020-03566-7
PMid:32838752 PMCid:PMC7446147
 
6. Ghaffar Nia, N., E. Kaplanoglu and A. Nasab, Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence, 2023. 3(1): p. 5. doi: 10.1007/s44163-023-00049-5. [PubMed: 9885935).
https://doi.org/10.1007/s44163-023-00049-5
PMCid:PMC9885935
 
7. Møller, J.K., M. Sørensen and C. Hardahl, Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study. PloS one, 2021. 16(3): p. e0248636. doi: 10.1371/journal.pone.0248636. [PubMed: 8011767).
https://doi.org/10.1371/journal.pone.0248636
PMid:33788888 PMCid:PMC8011767
 
8. Han, X., W. Wu, H. Zhao and S. Wang, Developing and validating a prediction model for in-hospital mortality in patients with ventilator-associated pneumonia in the ICU. Ann Palliat Med, 2022. 11(5): p. 1799-1810. doi: 10.21037/apm-22-502. [PubMed: 35672896).
https://doi.org/10.21037/apm-22-502
PMid:35672896
 
9. Atkinson, A., B. Ellenberger, V. Piezzi, T. Kaspar, L. Salazar-Vizcaya, O. Endrich, et al., Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism. Infect Control Hosp Epidemiol, 2023. 44(2): p. 246-252. doi: 10.1017/ice.2022.66. [PubMed: 36111457).
https://doi.org/10.1017/ice.2022.66
PMid:36111457 PMCid:PMC9929710
 
10. Zachariah, P., E. Sanabria, J. Liu, B. Cohen, D. Yao and E. Larson, Novel Strategies for Predicting Healthcare-Associated Infections at Admission: Implications for Nursing Care. Nurs Res, 2020. 69(5): p. 399-403.10.1097. doi: 10.1097/NNR.0000000000000449. [PubMed: 32604154).
https://doi.org/10.1097/NNR.0000000000000449
PMid:32604154
 
11. Zhang, J., F. Xue, S.D. Liu, D. Liu, Y.H. Wu, D. Zhao, et al., Risk factors and prediction model for inpatient surgical site infection after elective abdominal surgery. World J Gastrointest Surg, 2023. 15(3): p. 387-397. doi: 10.4240/wjgs.v15.i3.387. [PubMed: 10080607).
https://doi.org/10.4240/wjgs.v15.i3.387
PMid:37032800 PMCid:PMC10080607
 
12. Xu, Y., C. Lai, G. Xu, W. Meng, J. Zhang, H. Hou, et al., Risk factors of ventilator-associated pneumonia in elderly patients receiving mechanical ventilation. Clin Interv Aging, 2019. 14: p. 1027-1038. doi: 10.2147/cia.s197146. [PubMed: 6566835).
https://doi.org/10.2147/CIA.S197146
PMid:31289438 PMCid:PMC6566835
 
13. Wang, Y., Q. Lin, Z. Chen, H. Hou, N. Shen, Z. Wang, et al., Construction of a Risk Prediction Model for Subsequent Bloodstream Infection in Intestinal Carriers of Carbapenem-Resistant Enterobacteriaceae: A Retrospective Study in Hematology Department and Intensive Care Unit. Infect Drug Resist, 2021. 14: p. 815-824. doi: 10.2147/idr.s286401 . [PubMed: 7936666).
https://doi.org/10.2147/IDR.S286401
PMid:33688216 PMCid:PMC7936666
 
14. Wang, H., P. Zhao, D. Sun, X. Wu, Q.F. Yuan and K.W. Wang, [Epidemiological characteristics and the establishment and evaluation of a risk prediction model for nosocomial infection in burn patients). Zhonghua Shao Shang Za Zhi, 2022. 38(12): p. 1170-1178. doi: 10.3760/cma.j.cn501225-20220214-00025. [PubMed: 36594148).
 
15. Wang, C., C. Peng, L. Ning, X. Qiu, K. Wu, N. Yang, et al., Development and Validation of a Nomogram to Assist Monitoring Nosocomial SARS-CoV-2 Infection of Hospitalized Patients. J Inflamm Res, 2022. 15: p. 1471-1481. doi: 10.2147/jir.s351509.Pmc8896769. [PubMed: 35250295).
https://doi.org/10.2147/JIR.S351509
PMid:35250295 PMCid:PMC8896769
 
16. Pei, Y., Y. Huang, X. Pan, Z. Yao, C. Chen, A. Zhong, et al., Nomogram for predicting 90-day mortality in patients with Acinetobacter baumannii-caused hospital-acquired and ventilator-associated pneumonia in the respiratory intensive care unit. J Int Med Res, 2023. 51(3): p. 3000605231161481. doi: 10.1016/s0045-6535(98)00362-2 . [PubMed: 10028662).
https://doi.org/10.1016/S0045-6535(98)00362-2
PMid:10028662
 
17. Jiang, L., J. Ma, F. Li and N. Qin, Association between incubator standards and newborn nosocomial infection with machine-learning prediction. Transl Pediatr, 2023. 12(4): p. 655-662. doi: 10.21037/tp-23-171. [PubMed: 10167382).
https://doi.org/10.21037/tp-23-171
PMid:37181021 PMCid:PMC10167382
 
18. Li, S., L. Shang, L. Yuan, W. Li, H. Kang, W. Zhao, et al., Construction and Validation of a Predictive Model for the Risk of Ventilator-Associated Pneumonia in Elderly ICU Patients. Can Respir J, 2023. 2023: p. 7665184. doi: 10.1210/jcem.83.12.5329 . [PubMed: 9851783).
https://doi.org/10.1210/jcem.83.12.5329
PMid:9851783
 
19. Chen, Z., Z. Xu, H. Wu, S. Gao, H. Wang, J. Jiang, et al., Derivation and validation of a nomogram for predicting nonventilator hospital-acquired pneumonia among older hospitalized patients. BMC Pulm Med, 2022. 22(1): p. 144.doi: 10.1186/s12890-022-01941-z . [PubMed: 35428276).
https://doi.org/10.1186/s12890-022-01941-z
PMid:35428276 PMCid:PMC9011946
 
20. Ai, J., Y. Hu, F.F. Zhou, Y.X. Liao and T. Yang, Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery. World J Clin Oncol, 2022. 13(12): p. 967-979. doi: 10.5306/wjco.v13.i12.967 . [PubMed: 36618079).
https://doi.org/10.5306/wjco.v13.i12.967
PMid:36618079 PMCid:PMC9813835
 
21. Li, S., J. Liu, F. Chen, K. Cai, J. Tan, W. Xie, et al., A risk score based on pediatric sequential organ failure assessment predicts 90-day mortality in children with Klebsiella pneumoniae bloodstream infection. BMC Infect Dis, 2020. 20(1): p. 916. doi: 10.1186/s12879-020-05644-w . [PubMed: 7709332).
https://doi.org/10.1186/s12879-020-05644-w
PMid:33267829 PMCid:PMC7709332
 
22. Goodwin, T.R. and D. Demner-Fushman, Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients. MIA ... Annual Symposium proceedings / AMIA Symposium, 2019. 2019: p. 467-476. [PubMed: 32308840).
 
23. Wu, Z., Y. Liu, J. Xu, J. Xie, S. Zhang, L. Huang, et al., A Ventilator-associated Pneumonia Prediction Model in Patients With Acute Respiratory Distress Syndrome. Clin Infect Dis, 2020. 71(Suppl 4): p. S400-s408.10.1093/ cid/ciaa1518. doi: 10.1093/cid/ciaa1518 . [PubMed: 33367575).
https://doi.org/10.1093/cid/ciaa1518
PMid:33367575
 
24. Giang, C., J. Calvert, K. Rahmani, G. Barnes, A. Siefkas, A. Green-Saxena, et al., Predicting ventilator-associated pneumonia with machine learning. Medicine, 2021. 100(23): p. e26246. doi: 10.1097/MD.0000000000026246. [PubMed: 34115013).
https://doi.org/10.1097/MD.0000000000026246
PMid:34115013 PMCid:PMC8202554
 
25. Nistal-Nuño, B., A neural network for prediction of risk of nosocomial infection at intensive care units: a didactic preliminary model. Einstein (Sao Paulo, Brazil), 2020. 18: p. eAO5480. doi: 10.31744/einstein_journal/2020AO5480 . [PubMed: 33237246).
https://doi.org/10.31744/einstein_journal/2020AO5480
PMid:33237246 PMCid:PMC7664827
 
26. Liao, Y.H., Z.C. Wang, F.G. Zhang, M.F. Abbod, C.H. Shih and J.S. Shieh, Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit. Sensors (Basel), 2019. 19(8). doi: 10.3390/s19081866. [PubMed: 31003541).
https://doi.org/10.3390/s19081866
PMid:31003541 PMCid:PMC6514817
 
27. Kuo, K.M., P.C. Talley, C.H. Huang and L.C. Cheng, Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach. BMC Med Inform Decis Mak, 2019. 19(1): p. 42. doi: 10.1186/s12911-019-0792-1. [PubMed: 30866913).
https://doi.org/10.1186/s12911-019-0792-1
PMid:30866913 PMCid:PMC6417112
 
28. Tilton, C.S. and S.W. Johnson, Development of a risk prediction model for hospital-onset Clostridium difficile infection in patients receiving systemic antibiotics. Am J Infect Control, 2019. 47(3): p. 280-284.10.1016/j.ajic. 2018.08.021. doi: 10.1016/j.ajic.2018.08.021 . [PubMed: 30318399).
https://doi.org/10.1016/j.ajic.2018.08.021
PMid:30318399
 
29. Rabhi, S., J. Jakubowicz and M.H. Metzger, Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives. Am J Infect Control, 2019. 58(1): p. 31-41.10.1055/s-0039-1677692. doi: 10.1055/s-0039-1677692 . [PubMed: 30877683).
https://doi.org/10.1055/s-0039-1677692
PMid:30877683
 
30. Roimi, M., A. Neuberger, A. Shrot, M. Paul, Y. Geffen and Y. Bar-Lavie, Early diagnosis of bloodstream infections in the intensive care unit using machine-learning algorithms. Intensive Care Med, 2020. 46(3): p. 454-462. doi: 10.1007/s00134-019-05876-8 . [PubMed: 31912208).
https://doi.org/10.1007/s00134-019-05876-8
PMid:31912208
 
31. Montella, E., A. Ferraro, G. Sperlì, M. Triassi, S. Santini and G. Improta, Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study. Int J Environ Res Public Health, 2022. 19(5). doi: 10.3390/ijerph19052498. [PubMed: 35270190).
https://doi.org/10.3390/ijerph19052498
PMid:35270190 PMCid:PMC8909182
 
32. Karajizadeh, M., M. Nasiri, M. Yadollahi, A.H. Zolfaghari and A. Pakdam, Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset. Healthc Inform Res, 2020. 26(4): p. 284-294. doi:10.4258/hir.2020.26.4.284. [PubMed: 33190462).
https://doi.org/10.4258/hir.2020.26.4.284
PMid:33190462 PMCid:PMC7674815
 
33. Barchitta, M., A. Maugeri, G. Favara, P.M. Riela, G. Gallo, I. Mura, et al., A machine learning approach to predict healthcare-associated infections at intensive care unit admission: findings from the SPIN-UTI project. J Hosp Infect, 2021. 112: p. 77-86. doi: 10.1016/j.jhin.2021.02.025 . [PubMed: 33676936).
https://doi.org/10.1016/j.jhin.2021.02.025
PMid:33676936
 
34. Walker, L.W., A.J. Nowalk and S. Visweswaran, Predicting outcomes in central venous catheter salvage in pediatric central line-associated bloodstream infection. J Am Med Inform Assoc, 2021. 28(4): p. 862-867.10.1093/jamia/ocaa328.Pmc7973452. doi: 10.1093/jamia/ocaa328. [PubMed: 33463685).
https://doi.org/10.1093/jamia/ocaa328
PMid:33463685 PMCid:PMC7973452
 
35. Dos Santos, R.P., D. Silva, A. Menezes, S. Lukasewicz, C.H. Dalmora, O. Carvalho, et al., Automated healthcare-associated infection surveillance using an artificial intelligence algorithm. Infect Prev Pract, 2021. 3(3): p. 100167. doi: 10.1016/j.infpip.2021.100167. [PubMed: 34471868).
https://doi.org/10.1016/j.infpip.2021.100167
PMid:34471868 PMCid:PMC8387762
 
36. Myall, A., J.R. Price, R.L. Peach, M. Abbas, S. Mookerjee, N. Zhu, et al., Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study. The Lancet. Digital health, 2022. 4(8): p. e573-e583. doi: 10.1016/S2589-7500(22)00093-0 . [PubMed: 35868812).
https://doi.org/10.1016/S2589-7500(22)00093-0
PMid:35868812
 
37. Liang, Y., C. Zhu, C. Tian, Q. Lin, Z. Li, Z. Li, et al., Early prediction of ventilator-associated pneumonia in critical care patients: a machine learning model. BMC Pulm Med, 2022. 22(1): p. 250. doi: 10.1186/s12890-022-02031-w . [PubMed: 35752818).
https://doi.org/10.1186/s12890-022-02031-w
PMid:35752818 PMCid:PMC9233772
 
38. Thurzo, A., W. Urbanová, B. Novák, L. Czako, T. Siebert, P. Stano, et al., Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel, Switzerland), 2022. 10(7). doi: 10.3390/healthcare10071269 . [PubMed: 35885796).
https://doi.org/10.3390/healthcare10071269
PMid:35885796 PMCid:PMC9320442
 
39. Jin, P., X. Ji, W. Kang, Y. Li, H. Liu, F. Ma, et al., Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol , 2020. 146(9): p. 2339-2350.10.1007/s00432-020-03304-9. doi: 10.1007/s00432-020-03304-9. [PubMed: 32613386).
https://doi.org/10.1007/s00432-020-03304-9
PMid:32613386
 
40. Kirk, D., C. Catal and B. Tekinerdogan, Precision nutrition: A systematic literature review. Comput Biol Med, 2021. 133:
https://doi.org/10.1016/j.compbiomed.2021.104365
PMid:33866251
 
p. 104365. doi: 10.1016/j.compbiomed.2021.104365 . [PubMed: 33866251).
https://doi.org/10.1016/j.compbiomed.2021.104365
PMid:33866251
 
41. Frondelius, T., I. Atkova, J. Miettunen, J. Rello and M.M. Jansson, Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies. J Crit Care, 2022. 67: p. 44-56. doi: 10.1016/j.jcrc.2021.10.001 . [PubMed: 34673331).
https://doi.org/10.1016/j.jcrc.2021.10.001
PMid:34673331
 
42. Raoofi, S., F. Pashazadeh Kan, S. Rafiei, Z. Hosseinipalangi, Z. Noorani Mejareh, S. Khani, et al., Global prevalence of nosocomial infection: A systematic review and meta-analysis. PloS one, 2023. 18(1): p. e0274248. doi: 10.1371/journal.pone.0274248 . [PubMed: 36706112).
https://doi.org/10.1371/journal.pone.0274248
PMid:36706112 PMCid:PMC9882897
 
43. Akazawa, M. and K. Hashimoto, Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review. Artif Intell Med, 2021. 120: p. 102164.
https://doi.org/10.1016/j.artmed.2021.102164
PMid:34629152
 
doi: 10.1016/j.artmed.2021.102164 [PubMed: 34629152).
https://doi.org/10.1016/j.artmed.2021.102164
PMid:34629152
Razavi International Journal of Medicine
Volume 11, Issue 4
November 2023
Pages 1-9

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