Submitted:
03 July 2024
Posted:
03 July 2024
You are already at the latest version
Abstract
Keywords:
Introduction
Methodology
Outbreaks Genesis
Risk Factors for Outbreak

Strategies
- NURSE-TO-PATIENT RATIO
- 2.
- PHYSICIAN/PATIENT RATIO
- 3.
- EDUCATION


- 4.
- ISOLATION
Respiratory Isolation
- A negative-pressure room for patients who resulted to be colonized or infected by potential air-spreading pathogens;
- A positive-pressure for patients who are likely more susceptible of acquiring an infection, such as solid-organ transplanted (SOT) recipients, hematopoietic stem cell transplanted (HSCT) patients, presence of hematological disorders, chronic use of corticosteroids, calcineurin inhibitors, anti-metabolites and other immunosuppressants.
Contact Isolation
- 5.
- MDROs DECOLONIZATION
- 6.
- HAND HYGIENE
- 7.
- SHOE HYGIENE (SH)
- 8.
- SCREENING
- Risk-assessment scores
- b.
- CRAB screening
- c.
- Rectal screening for carbapenem resistant Gram negative bacteria (CR- GNB)
- d.
- Skin screening for MRSA
- e.
- Environmental samples surveillance
- f.
- Whole genome sequencing (WGS)

- 9.
- ENVIRONMENTAL CLEANING
Air Cleaning
Surfaces Cleaning
- 10.
- ANTIMICROBIAL STEWARDSHIP PROGRAM
- 11.
- OUTBREAK REPORTING
- 12.
- CRE PREVENTION AMONG SPECIAL POPULATIONS
- 13.
- COST-EFFECTIVENESS AND MDROs REPRODUCTIVE NUMBER (R0)
- 14.
- NEW EXPERIMENTED STRATEGIES

- 15.
- FUTURE PERSPECTIVES OF IPC

Limitations
Conclusions
Author Contributions
Acknowledgements
Conflicts of Interest
References
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| Routes of transmission in ICU |
|---|
| Direct or indirect contact Bacteria: MDR-GNB [9] (including CRE, ESBL carriers [10], MDR-Klebsiella spp, MDR-Acinetobacter baumanii [11], and MDR-Pseudomonas aeruginosa), MRSA [12], VRE [13], Clostridium difficile [13] Fungi: Candida auris [14], Scedosporium spp [15] Virus: Ebola virus [16] |
| Water contamination [17] Bacteria: Legionella spp., Pseudomonas spp, Acinetobacter spp, and Serratia Fungi: Aspergillus spp., Mucor spp., Trichosporon spp., Scedosporium spp [18] and Fusarium Virus: Norovirus |
| Air contamination Bacteria [19]: CRE, Acinetobacter baumanii, Pseudomonas aeruginosa, Corynebacterium striatum*, Legionella spp., MRSA [20] Fungi: Aspergillus spp [21], Fusarium spp [18], Scedosporium spp [15,18], Lomentospora spp. [18] Virus: human coronaviruses (including SARS-CoV-2 [22]), Ebola virus [16] |
| Droplet and airborne spread [23] Bacteria: Mycobacterium tuberculosis, Bordetella spp, Pertussis Virus: human coronaviruses (including SARS-CoV-2 [22]), Varicela-Zoster virus, Measles virus, influenza viruses (including H1N1, H2N3, H5N1), Parainfluenza viruses, Respiratory Syncytial Virus, Adenoviruses |
| Type of study | Study author and year of publishing | Country | Time period | Sample size | Suggested ICU PNR | Higher ratios are associated with higher mortality |
|---|---|---|---|---|---|---|
| Guidelines | Bray K et al. 2010 (the British Association of Critical Care Nurses, the Critical Care Networks National Nurse Leads) [35] | UK | - | - | 1: 1 | Yes |
| American Nurses Association (ANA) and California Legislation (Assembly Bill No. 394) | California, USA | - | - | 2 : 1 | Yes | |
| Narrative review | Suresh K. Sharma et Ritu Rani 2020 [36] | India | - | - | 1 : 1 | Yes |
| Retrospective observational study | Falk AC 2023 [37] | Sweden | 15 years | 2 ICUs (9,814 patients) | 1 : 1 | Yes |
| Cross-sectional, retrospective, risk adjusted observational study | West E et al. 2014 [38] | UK | 16 years | 65 ICUs (38,168 patients) | 0,5 : 1 | Yes* |
| Study author and year of publication | Country | Type of study | Time period | Sample size | Median PIR | Higher ratios resulted to be associated with higher mortality |
|---|---|---|---|---|---|---|
| Neuraz A et al. 2015 [40] | France | Multicenter observational study | 2013 |
5,718 patients (8 ICUs) |
5.6 | Yes |
| Gershengorn HB et al. 2017 [41] | UK | Retrospective cohort study | 2010-2013 |
49,686 patients (94 ICUs) |
8.5 | Yes |
| Dara SI et al. 2005 [44] | USA | Retrospective cohort study | 2001-2003 | 2,492 patients (1 ICU) |
8.4* |
No |
| Gershengorn HB et al. 2022 [42] | Australia and New Zealand | Retrospective cohort study | 2016-2019 | 27,380 patients (67 ICUs) in the “narrow cohort” and 91,206 patients (73 ICUs) in the “broad cohort” | 10.1 | No |
| Agarwal A et al. 2022 [45] | USA | Cross-sectional observational study | 2020-2021 | 1,322 patients (62 ICUs) |
12 | No |
| Kahn JM et al. 2023 [46] | USA | Retrospective cohort study | 2018-2020 | 51,656 patients (29 ICUs) |
11.8 | No |
| Estenssoro E et al. 2017 [47] | Latin America (51% from Brazil, 17% Chile, 13% Argentina, 6% Ecuador, 5% Uruguay, 3% Colombia, and 5% between Mexico, Peru, and Paraguay.) | Cross-sectional observational study | 2015-2016 | 257 ICUs | 1:1-1:3 (11%) 1:4 to 1:7 (46%) > 8 (41%) |
Not evaluated |
| Study author and year of publication | Country | Type of infection | Most relevant proposed solution |
|---|---|---|---|
| Menegueti MG et al. 2019 [49] | Brazil | CAUTI |
|
| McNett et al 2020 [54] | USA | VAP |
|
| Mogyodi et al. 2023 [56] | Hungary | VAP |
|
| Phan et al. 2018 [57] | Vietnam | All HCAI |
|
| Moghnieh et al. 2023 [53] | Eastern Mediterranean Region (Afghanistan. Barhain, Iraq, Kuwait, Jordan, Lebanon, Oman, Pakistan, Palestine, Qatar, Sudan, Syria, United Arab Emirates, Yemen) | All HCAI |
|
| Candida spp. [93] | Colonization | Candida Colonization Index [94] | Ratio of the number of (non-blood) sites colonized with Candida spp /total number of sites cultured Threshold = 0.5 |
PPV = 66% NPV = 100% |
| Infection | Candida score [95] | Candida Score = TPN (1 point), surgery (1 point), severe sepsis (2 points), Multifocal Candida colonization (1 point). Threshold = 2.5 |
Sensitivity = 81% Specificity = 74% PPV = 16% NPV = 98% |
|
| Ostrosky-Zeichner Clinical Prediction Rule [96] |
Mechanical ventilation ≥ 48hours AND Systemic antibiotic AND CVP (on any of day 1–3 of ICU admission) plus ≥1 of: any major surgery (days 7–0), pancreatitis (days 7–0), use of steroids/other immunosuppressive agents (days 7–0), use of TPN (days 1–3), or dialysis (days 1–3) | Sensitivity = 50% Specificity= 83% PPV = 10% NPV = 97% |
||
| ESBL-producing Enterobacteriacae | Colonization | Tumbarello et al. [97] | Recent (≤12 months before admission) hospitalization, transfer from another health care facility, Charlson comorbidity score ≥ 4, recent (≤3 months before admission) β-lactam and/or fluoroquinolone treatment, recent urinary catheterization, and age ≥ 70 years. | With cutoff score ≥3: Sensitivity = 94% Specificity = 41% PPV = 44% NPV = 93% |
| Infection (BSI) | ESBL Prediction Score (ESBL-PS) [98] | Outpatient procedures within 1 month, prior infections or colonization with ESBLE within 12 months, and number of prior courses of β-lactams and/or fluoroquinolones used within 3 months of BSI. | With cutoff score ≥1: Sensitivity = 88% Specificity = 77% PPV = 16% NPV = 99% With cutoff score ≥3: Sensitivity = 43% Specificity = 96% PPV = 33% NPV = 97% |
|
| CPE | Colonization | Papafotiou et al. [99] | Karnofsky score, previous hospitalization, stay in a Long-term care facilty, history of ≥2 different interventional procedures, previous CPE colonization or infection, renal replacement therapy, and diabetes with end-organ damage | With cutoff score ≥27: Sensitivity = 72% Specificity = 81% PPV = 15% NPV = 98% |
| CRAB | Infection | Cogliati Dezza et al. [100] | CRAB colonization, higher CCI, multisite colonization and the need for mechanical ventilation. | Unknown |
| XDR A. baumanii | Colonization | Moghnieh et al. [101] | Urinary catheter placement >6 days, ICU contact pressure for >4 days, presence of gastrostomy tube, and previous use of carbapenems or piperacillinetazobactam | Unknown |
| MRSA | Colonization | Torres et Sampathkumar [102] | Nursing home residence, diabetes, hospitalization in the past year, and chronic skin condition/infection | With cutoff score ≥8: Sensitivity = 54% Specificity = 80% |
| VRE | Colonization | The PREVENT score [103] | Age of ≥60 years, hemato-oncological disease, cumulative antibiotic treatment for >4 weeks, and a VRE infection | Sensitivity = 82% Specificity = 77% PPV = 57% NPV = 92% |
| MDROs | Colonization | AutoRAS- MDRO [104] | Electronic health records (EHRs) | Sensitivity = 81% Specificity= 79% PPV = 49% NPV = 94% |
| Type of pathogen | Estimated mean single-patient cost per hospital length of stay i | Estimated mean R₀ | Estimated mean outbreak cost | IPC implementation threshold (up to) |
|---|---|---|---|---|
| CRE | $ 639,48 | 11 | €1.1 millions | $572,000 |
| CRAB | $55,122-$ 60,000 | 1.5 | €1.0 millions | $75,000- $93,822/QALY |
| VRE | $17,949 | 1.32 | € 60.524 | $50.000/QALY |
| MRSA | $ 9.275 | 0.97-1.6 [160] | $30.225 | $9.275 |
| C. auris | € 35.818* | Unknown | € 1.2 millions | $3.730.480,26 |
| (HO-CDI) | $30,049 - $34,149 [165,167] | 0.55-7.0 [168] | €1.2 millions | $150 000/QALY [169] |
| Type of transmission | Type of pathogen | Estimated R₀ (mean) | Country |
|---|---|---|---|
| Airborne | SARS-CoV-2 | 1.4 to 6.7 [170] (4.1) | China, Italy, Korea, Peru |
| SARS virus | 1.7 to 1.9 [171] (1.8) | Hong Kong | |
| MERS virus | 2.0 to 6.7 [172] (4.4) | Saudi Arabia | |
| H1N1 | 1.9 [173] | China | |
| Mycobacterium tuberculosis (MTB) | 0.8 to 1.2 [174] 0.2 to 0.4 [175] (0.29) |
USA |
|
| Measles virus | 0.7 [176] to 25.3 [171] (13)* 12-18 (15) [177] |
USA, Italy, Japan Systemic review |
|
| Vectorborne | Zika virus | 2.3 [178] to 27.2 [179] (14.9) | Brazil, Chile |
| Dengue virus | 1.1 [180]-1.7 [178,181] (1.4) | Indonesia, Brazil | |
| Bloodborne/ Body fluids contact |
Ebola virus | 1.1 to 10 [182] (1.95) [182] | West Africa |
| HIV (viremic) | (36.8) [183] | Uganda |
| Study author and year of publication | Country | Study design | Pathogen | Experimental period | Name of the new strategies |
|---|---|---|---|---|---|
| De Freitas DalBen et al. 2016 [184] | Brazil | Prospective study | CRE | Baseline period: 10 monthsIntervention period: 24 weeks | Educational model based on:
|
| Stachel et al. 2017 [185] | USA | Prospective study | MDROs | 8 months | Automated surveillance system to detect hospital outbreak |
| Fitzpatrick et al. 2020 | Ireland | Narrative review | All pathogens | - | Artificial Intelligence in IPC: driven by “big data”, it could find correlations that may indicate medically relevant conditions or identify potential risk factors for outbreaks |
| Meschiari et al. 2021 [51] | Italy | Prospective study | CRAB | 6-years (2013-2019) | Cycling radical cleaning and disinfection |
| Piaggio et al. 2023 [186] | Italy | Systemic review | All pathogens | - |
|
| Zwerwer et al. 2024 [132] | Netherlands | Prospective study | All pathogens | 3-years (2014-2017) | Machine-learning model to predict the need for infection-related consultations in ICU |
| First Author and year of publication | Country | Target pathogen | Aim of the study | Suggested technique |
|---|---|---|---|---|
| Hatfull GF et al. 2022 [192] | USA | MDRB | Fighting antibiotic resistance | Phage therapy |
| Wang J et al. 2024 [193] | China | MDR-Corynebacterium striatum | Fighting antibiotic resistance | Phage therapy |
| Skurnik et al. 2016 [194] | USA | CPE | Vaccine against CPE (including NDM-producers E. coli, E. cloacae, K. pneumoniae, K. pneumoniae carbapenemase (KPC)-producing and PNAG-producing P. aeruginosa) | Vaccine targeting polysaccharide poly-(β-1,6)-N-acetyl glucosamine (PNAG) in CPE |
| Kalfopoulou et Huebner. 2020 [195] | Germany | VRE | Vaccine against Enterococci and VRE | Vaccine targeting capsular polysaccharides and surface-associated proteins in Enterococci |
| Miller et al. 2020 [196] | USA | MRSA | Vaccine against MRSA | Vaccine targeting superantigens and pore-forming toxins in MRSA |
| Meschiari et al. 2021 [51] | Italy | CRAB | IPC measures in CRAB’s outbreaks | Targeting inactivated adeN gene in CRAB |
| Ji Yun Bae et al. 2023 [197] | Korea | CRAB | Identifying virulents CRAB’s genes associated with higher mortality in VAP | Targeting hisF and uspA genes in CRAB |
| Choi et al. 2022 [82] | South Korea | VRE and CRE | New non-antibiotic decolonization strategy | 4-items bundle:
|
| Wong et al. 2023 [198] | USA | All pathogens | Use of artificial intelligence for new antinfective drugs discovery, pathogens’ pathophysiology and transmission understanding, and diagnostics | Artificial intelligence implementation |
| Zwerwer et al. 2024 [132] | Netherlands | All pathogens | Use a machine-learning model to predict the need for infection-related consultations in ICU | Machine-learning model |
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