Evidence map›Paper›PMID 39308772›Full record

ArticleInfection prevention in practice2024

Performance of an Autonomous Sanitary Sterilisation Ultraviolet Machine (ASSUM) on terminal disinfection of surgical theaters and rooms of an intensive-intermediate care unit.

Sabina Herrera, Ignasi Roca, Ana Del Río, Javier Fernández, Cristina Pitart, Isabel Fortes, Blanca Torralbo, Gemina Santana, Romina Parejo-González, Andreu Veà-Baró and 6 more

Abstract read
In one paragraph

Article in Infection prevention in practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Sabina HerreraInfectious Disease Service, Hospital Clínic, University of Barcelona, IDIBAPS, Barcelona, Spain.
Ignasi RocaDepartment of Microbiology, Biomedical Diagnostic Center (CDB) and ISGlobal, Hospital Clínic, University of Barcelona, Barcelona, Spain.
Ana Del RíoInfectious Disease Service, Hospital Clínic, University of Barcelona, IDIBAPS, Barcelona, Spain.
Javier FernándezLiver ICU, Liver Unit, Hospital Clinic, University of Barcelona, IDIBAPS and CIBERehd, Spain.
Cristina PitartDepartment of Microbiology, Biomedical Diagnostic Center (CDB) and ISGlobal, Hospital Clínic, University of Barcelona, Barcelona, Spain.
Isabel FortesPreventive Medicine Service, Hospital Clinic, University of Barcelona, IDIBAPS, Barcelona, Spain.
Blanca TorralboPreventive Medicine Service, Hospital Clinic, University of Barcelona, IDIBAPS, Barcelona, Spain.
Gemina SantanaPreventive Medicine Service, Hospital Clinic, University of Barcelona, IDIBAPS, Barcelona, Spain.
Romina Parejo-GonzálezPreventive Medicine Service, Hospital Clinic, University of Barcelona, IDIBAPS, Barcelona, Spain.
Andreu Veà-BaróAndreu Veà, Ph.D. advisor to the CEO (on Digital-Transformation & Optimization) Hospital Clinic Barcelona CovidWarrior, Barcelona, Spain.
Josep Maria CampistolHospital Clínic, University of Barcelona, August Pi i Sunyer Biomedical Research Institute Barcelona, Spain.
Mireia AguilarDepartment of Microbiology, Biomedical Diagnostic Center (CDB) and ISGlobal, Hospital Clínic, University of Barcelona, Barcelona, Spain.
Sergi DegeaDepartment of Microbiology, Biomedical Diagnostic Center (CDB) and ISGlobal, Hospital Clínic, University of Barcelona, Barcelona, Spain.
Climent Casals-PascualDepartment of Microbiology, Biomedical Diagnostic Center (CDB) and ISGlobal, Hospital Clínic, University of Barcelona, Barcelona, Spain.
Alex SorianoInfectious Disease Service, Hospital Clínic, University of Barcelona, IDIBAPS, Barcelona, Spain.
José A MartínezInfectious Disease Service, Hospital Clínic, University of Barcelona, IDIBAPS, Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ultraviolet- C (UV-C) light is effective for reducing environmental bioburden in hospitals, and the use of robots to deliver it may be advantageous. Aim: To evaluate the feasibility and clinical efficacy of an autonomous programmable UV-C robot in surgical and intensive care unit (ICU) rooms of a tertiary hospital. Method: During ten consecutive months, the device was used in six theatres where cardiac, colorectal and orthopaedic surgeries were performed, and in the rooms previously occupied by patients subjected to contact precautions of a 14-bed ICU. Surgical site infection (SSI) rates of procedures performed in the UV-cleaned theatres were compared with those of the previous year. Incidence in clinical samples of ICU-acquired multiple-drug resistant (MDR) microorganisms was compared with that of the same period of the previous year. An UV-C exposure study done by semi-quantitative dosimeters and a survey of the bioburden on surfaces were carried out. Findings: SSI rates in the pre- and post-intervention periods were 8.67% (80/922) and 7.5% (61/813), respectively (p=0.37). Incidence of target microorganisms in clinical samples remained unchanged (38.4 vs. 39.4 per 10,000 patient-days, p=0.94). All the dosimeters exposed to ≤1 meter received ≥500 mJ/cm Conclusion: Deployment of an UV-C robot in surgical and ICU rooms is feasible, ensures adequate delivery of germicidal UV-C light and reduces the environmental bacterial burden. Rates of surgical site infections or acquisition of MDR in clinical samples of critically-ill patients remained unchanged.

Indexed as

(MDR) microorganismsUltraviolet- C (UV–C)UVC robot

Identifiers

PMID39308772
PMCPMC11415570

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.