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Taddei E, Pafundi PC, Masciocchi C, Fiori B, Segala FV, Antenucci L, Guerriero S, Pastorino R, Scarsi N, Damiani A, Sanguinetti M, De Pascale G, Fantoni M, Murri R, De Angelis G. Epidemiology, time course, and risk factors for hospital-acquired bloodstream infections in a cohort of 14,884 patients before and during the COVID-19 pandemic. Infect Dis (Lond) 2023; 55:776-785. [PMID: 37750316 DOI: 10.1080/23744235.2023.2243327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 07/24/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE COVID-19 pandemic has changed in-hospital care and was linked to superimposed infections. Here, we described epidemiology and risk factors for hospital-acquired bloodstream infections (HA-BSIs), before and during COVID-19 pandemic. METHODS This retrospective, observational, single-center real-life study included 14,884 patients admitted to hospital wards and intensive care units (ICUs) with at least one blood culture, drawn 48 h after admission, either before (pre-COVID, N = 7382) or during pandemic (N = 7502, 1203 COVID-19+ and 6299 COVID-19-). RESULTS Two thousand two hundred and forty-five HA-BSI were microbiologically confirmed in 14,884 patients (15.1%), significantly higher among COVID-19+ (22.9%; ptrend < .001). COVID-19+ disclosed a significantly higher mortality rate (33.8%; p < .001) and more ICU admissions (29.7%; p < .001). Independent HAI-BSI predictors were: COVID-19 (OR: 1.43, 95%CI: 1.21-1.69; p < .001), hospitalization length (OR: 1.04, 95%CI: 1.03-1.04; p < .001), ICU admission (OR: 1.38, 95%CI: 1.19-1.60; p < .001), neoplasms (OR:1.48, 95%CI: 1.34-1.65; p < .001) and kidney failure (OR: 1.81, 95%CI: 1.61-2.04; p < .001). Of note, HA-BSI IRs for Acinetobacter spp. (0.16 × 100 patient-days) and Staphylococcus aureus (0.24 × 100 patient-days) peaked during the interval between first and second pandemic waves in our National context. CONCLUSIONS Patients with HA-BSI admitted before and during pandemic substantially differed. COVID-19 represented a risk factor for HA-BSI, though not confirmed in the sole pandemic period. Some etiologies emerged between pandemic waves, suggesting potential COVID-19 long-term effect on HA-BSIs.
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Affiliation(s)
- Eleonora Taddei
- Department of Laboratory and Infectious Diseases Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Pia Clara Pafundi
- Epidemiology & Biostatistics Research Core Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Carlotta Masciocchi
- Real World Data Research Core Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Barbara Fiori
- Department of Laboratory and Infectious Diseases Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesco Vladimiro Segala
- Department of Laboratory and Infectious Diseases Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Laura Antenucci
- Real World Data Research Core Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento di Diagnostica per Immagini, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Guerriero
- Department of Laboratory and Infectious Diseases Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Roberta Pastorino
- Epidemiology & Biostatistics Research Core Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Life Sciences and Public Health, Hygiene Section, Catholic University of the Sacred Heart, Rome, Italy
| | - Nicolò Scarsi
- Department of Life Sciences and Public Health, Hygiene Section, Catholic University of the Sacred Heart, Rome, Italy
| | - Andrea Damiani
- Real World Data Research Core Facility, Gemelli Generator, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maurizio Sanguinetti
- Department of Laboratory and Infectious Diseases Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Gennaro De Pascale
- Department of Emergency, Anesthesiological and Resuscitation Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Biotechnological, Intensivologic and Perioperative Clinics, Catholic University of the Sacred Heart, Rome, Italy
| | - Massimo Fantoni
- Department of Laboratory and Infectious Diseases Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Rita Murri
- Department of Laboratory and Infectious Diseases Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Safety and Bioethics, Catholic University of the Sacred Heart, Rome, Italy
| | - Giulia De Angelis
- Department of Laboratory and Infectious Diseases Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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Abu-Rish Blakeney E, Wolpin S, Lavallee DC, Dardas T, Cheng R, Zierler B. Developing and implementing a heart failure data mart for research and quality improvement. Inform Health Soc Care 2018; 44:164-175. [PMID: 29672242 DOI: 10.1080/17538157.2018.1455202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
OBJECTIVE The purpose of this project was to build and formatively evaluate a near-real time heart failure (HF) data mart. Heart Failure (HF) is a leading cause of hospital readmissions. Increased efforts to use data meaningfully may enable healthcare organizations to better evaluate effectiveness of care pathways and quality improvements, and to prospectively identify risk among HF patients. METHODS AND PROCEDURES We followed a modified version of the Systems Development Life Cycle: 1) Conceptualization, 2) Requirements Analysis, 3) Iterative Development, and 4) Application Release. This foundational work reflects the first of a two-phase project. Phase two (in process) involves the implementation and evaluation of predictive analytics for clinical decision support. RESULTS We engaged stakeholders to build working definitions and established automated processes for creating an HF data mart containing actionable information for diverse audiences. As of December 2017, the data mart contains information from over 175,000 distinct patients and >100 variables from each of their nearly 300,000 visits. CONCLUSION The HF data mart will be used to enhance care, assist in clinical decision-making, and improve overall quality of care. This model holds the potential to be scaled and generalized beyond the initial focus and setting.
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Affiliation(s)
- Erin Abu-Rish Blakeney
- a Department of Biobehavioral Nursing and Health Informatics , University of Washington School of Nursing
| | - Seth Wolpin
- a Department of Biobehavioral Nursing and Health Informatics , University of Washington School of Nursing
| | | | - Todd Dardas
- c Department of Medicine , University of Washington School of Medicine
| | - Richard Cheng
- c Department of Medicine , University of Washington School of Medicine
| | - Brenda Zierler
- a Department of Biobehavioral Nursing and Health Informatics , University of Washington School of Nursing
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