Forecasting ESKAPE infections through a time-varying auto-adaptive algorithm using laboratory-based surveillance data

• Published in: BMC Infectious Diseases Vol. 14; no. 1; p. 634
• Main Authors: Ballarin, A, Posteraro, Brunella, Demartis, G, Gervasi, S, Panzarella, F, Torelli, Riccardo, Paroni Sterbini, Francesco, Morandotti, Grazia Angela, Posteraro, P, Ricciardi, Gualtiero, Gervasi Vidal, Ka, Sanguinetti, Maurizio
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 06.12.2014; BioMed Central; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Anti-Bacterial Agents; Anti-Bacterial Agents - therapeutic use; Antibiotic resistance; Bacterial and fungal diseases; Bacterial diseases; Bacterial Infections; Bacterial Infections - drug therapy; Bacterial Infections - epidemiology; Bacterial Infections - microbiology; Drug resistance; Drug resistance in microorganisms; Drug Resistance, Bacterial; Drug therapy; ESKAPE; Female; Forecasting; Forecasting - methods; Forecasts and trends; Gram-Negative Bacteria; Gram-Negative Bacteria - isolation & purification; Gram-Positive Bacteria; Gram-Positive Bacteria - isolation & purification; Health aspects; Health care; Humans; Infectious Diseases; Internal Medicine; Italy; Italy - epidemiology; Klebsiella pneumoniae; Laboratories; Male; Medical Microbiology; Medicine; Medicine & Public Health; Microbiology; Middle Aged; Models, Statistical; Parasitology; Population Surveillance; Pseudomonas aeruginosa; Research Article; Risk factors; Staphylococcus aureus; Time Factors; Time series; Tropical Medicine; Italy; Rome Italy; ESKAPE infections; Time series analysis; TVA algorithm; Clinical microbiology laboratory data; Forecasting
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-014-0634-9

Background Mathematical or statistical tools are capable to provide a valid help to improve surveillance systems for healthcare and non-healthcare-associated bacterial infections. The aim of this work is to evaluate the t ime- v arying a uto-adaptive (TVA) algorithm-based use of clinical microbiology laboratory database to forecast medically important drug-resistant bacterial infections. Methods Using TVA algorithm, six distinct time series were modelled, each one representing the number of episodes per single ‘ESKAPE’ ( E nterococcus faecium , S taphylococcus aureus , K lebsiella pneumoniae , A cinetobacter baumannii , P seudomonas aeruginosa and E nterobacter species) infecting pathogen, that had occurred monthly between 2002 and 2011 calendar years at the Università Cattolica del Sacro Cuore general hospital. Results Monthly moving averaged numbers of observed and forecasted ESKAPE infectious episodes were found to show a complete overlapping of their respective smoothed time series curves. Overall good forecast accuracy was observed, with percentages ranging from 82.14% for E. faecium infections to 90.36% for S. aureus infections. Conclusions Our approach may regularly provide physicians with forecasted bacterial infection rates to alert them about the spread of antibiotic-resistant bacterial species, especially when clinical microbiological results of patients’ specimens are delayed.