A novel, integrated approach for understanding and investigating Healthcare Associated Infections: A risk factors constellation analysis

Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) are major public health threats in upper- and lower-middle-income countries. Electronic health records (EHRs) are an invaluable source of data for achieving different goals, including the early detection of HAIs and AMR clust...

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Bibliographic Details
Published in:PloS one 2023-03, Vol.18 (3), p.e0282019-e0282019
Main Authors: Carestia, Mariachiara, Andreoni, Massimo, Buonomo, Ersilia, Ciccacci, Fausto, De Angelis, Luigi, De Carolis, Gerardo, De Filippis, Patrizia, Di Giovanni, Daniele, Emberti Gialloreti, Leonardo, Fontana, Carla, Guarente, Luca, Magrini, Andrea, Mattei, Marco, Moramarco, Stefania, Morciano, Laura, Mosconi, Claudia, Orlando, Stefano, Quintavalle, Giuseppe, Riccardi, Fabio, Santoro, Viviana, Palombi, Leonardo
Format: Article
Language:English
Subjects:
Anti-Infective Agents
Antibiotics
Antimicrobial resistance
Artificial Intelligence
Availability
Biology and Life Sciences
Cardiovascular diseases
Codification
Coronaviruses
COVID-19
Cross infection
Cross Infection - epidemiology
Cross Infection - microbiology
Data analysis
Data base management systems
Data recording
Diagnosis
Drug resistance in microorganisms
E coli
Electronic health records
Electronic medical records
Epidemiology
Health aspects
Health care
Health care industry
Health risks
Health surveillance
Heart diseases
Hospitals
Hospitals, University
Humans
Infection
Infections
Information systems
Intensive care
Intubation
Ischemia
Klebsiella
Laboratories
Medical records
Medical research
Medicine and Health Sciences
Medicine, Experimental
Microbiology
Microorganisms
Monitoring
Mortality
Nosocomial infections
Observational studies
Patients
Personal information
Physical Sciences
Public health
Research and Analysis Methods
Risk analysis
Risk Factors
Safety and security measures
Severe acute respiratory syndrome coronavirus 2
Standardization
Staphylococcus infections
Streptococcus infections
Surveillance
Telemedicine
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Summary:Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) are major public health threats in upper- and lower-middle-income countries. Electronic health records (EHRs) are an invaluable source of data for achieving different goals, including the early detection of HAIs and AMR clusters within healthcare settings; evaluation of attributable incidence, mortality, and disability-adjusted life years (DALYs); and implementation of governance policies. In Italy, the burden of HAIs is estimated to be 702.53 DALYs per 100,000 population, which has the same magnitude as the burden of ischemic heart disease. However, data in EHRs are usually not homogeneous, not properly linked and engineered, or not easily compared with other data. Moreover, without a proper epidemiological approach, the relevant information may not be detected. In this retrospective observational study, we established and engineered a new management system on the basis of the integration of microbiology laboratory data from the university hospital "Policlinico Tor Vergata" (PTV) in Italy with hospital discharge forms (HDFs) and clinical record data. All data are currently available in separate EHRs. We propose an original approach for monitoring alert microorganisms and for consequently estimating HAIs for the entire period of 2018. Data extraction was performed by analyzing HDFs in the databases of the Hospital Information System. Data were compiled using the AREAS-ADT information system and ICD-9-CM codes. Quantitative and qualitative variables and diagnostic-related groups were produced by processing the resulting integrated databases. The results of research requests for HAI microorganisms and AMR profiles sent by the departments of PTV from 01/01/2018 to 31/12/2018 and the date of collection were extracted from the database of the Complex Operational Unit of Microbiology and then integrated. We were able to provide a complete and richly detailed profile of the estimated HAIs and to correlate them with the information contained in the HDFs and those available from the microbiology laboratory. We also identified the infection profile of the investigated hospital and estimated the distribution of coinfections by two or more microorganisms of concern. Our data were consistent with those in the literature, particularly the increase in mortality, length of stay, and risk of death associated with infections with Staphylococcus spp, Pseudomonas aeruginosa, Klebsiella pneumoniae, Clos
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0282019