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Keehner J, Abeles SR, Longhurst CA, Horton LE, Myers FE, Riggs-Rodriguez L, Ahmad M, Baxter S, Boussina A, Cantrell K, Cardenas P, De Hoff P, El-Kareh R, Holland J, Ikeda D, Kurashige K, Laurent LC, Lucas A, Pride D, Sathe S, Tran AR, Vasylyeva TI, Yeo G, Knight R, Wertheim JO, Torriani FJ. Integrated Genomic and Social Network Analyses of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in the Healthcare Setting. Clin Infect Dis 2024:ciad738. [PMID: 38227643 DOI: 10.1093/cid/ciad738] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2. METHODS We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs. RESULTS Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001). CONCLUSIONS IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.
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Affiliation(s)
- Jocelyn Keehner
- Division of Infectious Diseases, Department of Medicine, University of California-SanFrancisco, San Francisco, California, USA
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
| | - Shira R Abeles
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
- Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
| | - Christopher A Longhurst
- Division of Biomedical Informatics, Department of Medicine, UC San Diego Health, La Jolla, California, USA
- Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
| | - Lucy E Horton
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
- Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
- Vaccine Research and Development Unit, Pfizer Inc, San Diego, California, USA
| | - Frank E Myers
- Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
| | - Lindsay Riggs-Rodriguez
- Population Health Services Organization-Programs and Strategy, UC San Diego Health, San Diego, California, USA
| | - Mohammed Ahmad
- Information Services EMR, UC San Diego Health, San Diego, California, USA
| | - Sally Baxter
- Division of Biomedical Informatics at the University of California-San Diego, San Diego, California, USA
| | - Aaron Boussina
- Division of Biomedical Informatics, University of California-San Diego, La Jolla, California, USA
| | - Kalen Cantrell
- Department of Computer Science & Engineering, Jacobs School of Engineering, University of California, San Diego, California, USA
| | - Priscilla Cardenas
- UC San Diego Health's Contact Tracing Team, Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
| | - Peter De Hoff
- Sanford Consortium of Regenerative Medicine, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
- Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, UC San Diego Health, San Diego, California, USA
| | - Robert El-Kareh
- Division of Biomedical Informatics, Department of Medicine, UC San Diego Health, La Jolla, California, USA
- Division of Hospital Medicine, Department of Medicine, UC San Diego Health, La Jolla, California, USA
| | - Jennifer Holland
- Analytics and Population Health Department, UC San Diego Health, San Diego, California, USA
| | - Daryn Ikeda
- UC San Diego Health's Contact Tracing Team, Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
| | - Kirk Kurashige
- Analytics and Population Health Department, UC San Diego Health, San Diego, California, USA
| | - Louise C Laurent
- Sanford Consortium of Regenerative Medicine, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
- Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, UC San Diego Health, San Diego, California, USA
| | - Andrew Lucas
- Information Services EMR, UC San Diego Health, San Diego, California, USA
| | - David Pride
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
- Department of Pathology, UC San Diego Health, La Jolla, California, USA
| | - Shashank Sathe
- Sanford Consortium of Regenerative Medicine, University of California-San Diego, La Jolla, California, USA
- Department of Cellular and Molecular Medicine, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
| | - Allen R Tran
- Information Services EMR, UC San Diego Health, San Diego, California, USA
| | - Tetyana I Vasylyeva
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
| | - Gene Yeo
- Sanford Consortium of Regenerative Medicine, University of California-San Diego, La Jolla, California, USA
- Department of Cellular and Molecular Medicine, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
| | - Rob Knight
- Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
- Department of Bioengineering, University of California-San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California-San Diego, La Jolla, California, USA
- Expedited COVID Identification Environment Laboratory, Department of Pediatrics, University of California-San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, University of California-San Diego, La Jolla, California, USA
| | - Joel O Wertheim
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
| | - Francesca J Torriani
- Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego Health, San Diego, California, USA
- Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health, San Diego, California, USA
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Kulasa K, Serences B, Nies M, El-Kareh R, Kurashige K, Box K. Insulin Infusion Computer Calculator Programmed Directly Into Electronic Health Record Medication Administration Record. J Diabetes Sci Technol 2021; 15:214-221. [PMID: 33118415 PMCID: PMC8256066 DOI: 10.1177/1932296820966616] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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] [Indexed: 01/04/2023]
Abstract
BACKGROUND Computerized insulin infusion protocols have demonstrated higher staff satisfaction, better compliance with protocols, and increased time with glucose in range compared to paper protocols. At University of California San Diego Health (UCSDH), we implemented an insulin infusion computer calculator (IICC) and transitioned it from a web-based platform directly into the electronic medication administration record (eMAR) of our primary electronic health record (EHR). METHODS This is a retrospective analysis of 6306 adult patients at UCSDH receiving intravenous (IV) insulin infusion from March 7, 2013 to May 30, 2019. We created three periods of the study-(1) the pre-eMAR integration period; (2) the eMAR integration period; and (3) the post-eMAR integration period-and looked at the percentage of readings within goal range (90-150 mg/dL for intensive care unit [ICU], 90-180 mg/dL for non-ICU) in patients with and without hyperglycemic emergencies. As our safety endpoints, we elected to look at incidence of blood glucose (BG) readings <70 mg/dL, <54 mg/dL, and <40 mg/dL. RESULTS Pre-eMAR 69.8% of readings were in the 90-150 mg/dL range compared to 70.2% post-eMAR (P = .03) and 82.7% of readings were in the 90-180 mg/dL range pre-eMAR versus 82.9% (P = .09) post-eMAR in patients without hyperglycemic emergencies. Rates of hypoglycemia with BG <70 mg/dL were 0.43%, <54 mg/dL were 0.07%, and <40 mg/dL were 0.01% of readings pre- and post-eMAR. CONCLUSIONS At UCSDH, our IICC has shown to be safe and effective in a wide variety of clinical situations and we were able to successfully transition it from a web-based platform directly into the eMAR of our primary EHR.
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Affiliation(s)
- Kristen Kulasa
- Division of Endocrinology, Diabetes and
Metabolism, University of California, San Diego, San Diego, CA, USA
- Kristen Kulasa, MD, University of California, San
Diego, 200 W Arbor Drive MC 8409, San Diego, CA 92103, USA.
| | - Brittany Serences
- Department of Nursing Education, Development
and Research, University of California, San Diego, San Diego, CA, USA
| | - Michael Nies
- Department of Information Services, University
of California San Diego Health, San Diego, CA, USA
| | - Robert El-Kareh
- Health Department of Biomedical Informatics,
University of California, San Diego, San Diego, CA, USA
| | - Kirk Kurashige
- Department of Information Services-Analytics,
University of California San Diego Health, San Diego, CA, USA
| | - Kevin Box
- Department of Pharmacy, University of
California, San Diego, San Diego, CA, USA
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