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van Staa TP, Pate A, Martin GP, Sharma A, Dark P, Felton T, Zhong X, Bladon S, Cunningham N, Gilham EL, Brown CS, Mirfenderesky M, Palin V, Ashiru-Oredope D. Sepsis and case fatality rates and associations with deprivation, ethnicity, and clinical characteristics: population-based case-control study with linked primary care and hospital data in England. Infection 2024; 52:1469-1479. [PMID: 38627354 PMCID: PMC11288984 DOI: 10.1007/s15010-024-02235-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 03/12/2024] [Indexed: 08/02/2024]
Abstract
PURPOSE Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. The purpose of the study was to measure the associations of specific exposures (deprivation, ethnicity, and clinical characteristics) with incident sepsis and case fatality. METHODS Two research databases in England were used including anonymized patient-level records from primary care linked to hospital admission, death certificate, and small-area deprivation. Sepsis cases aged 65-100 years were matched to up to six controls. Predictors for sepsis (including 60 clinical conditions) were evaluated using logistic and random forest models; case fatality rates were analyzed using logistic models. RESULTS 108,317 community-acquired sepsis cases were analyzed. Severe frailty was strongly associated with the risk of developing sepsis (crude odds ratio [OR] 14.93; 95% confidence interval [CI] 14.37-15.52). The quintile with most deprived patients showed an increased sepsis risk (crude OR 1.48; 95% CI 1.45-1.51) compared to least deprived quintile. Strong predictors for sepsis included antibiotic exposure in prior 2 months, being house bound, having cancer, learning disability, and diabetes mellitus. Severely frail patients had a case fatality rate of 42.0% compared to 24.0% in non-frail patients (adjusted OR 1.53; 95% CI 1.41-1.65). Sepsis cases with recent prior antibiotic exposure died less frequently compared to non-users (adjusted OR 0.7; 95% CI 0.72-0.76). Case fatality strongly decreased over calendar time. CONCLUSION Given the variety of predictors and their level of associations for developing sepsis, there is a need for prediction models for risk of developing sepsis that can help to target preventative antibiotic therapy.
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Affiliation(s)
- Tjeerd Pieter van Staa
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK.
| | - Alexander Pate
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
| | - Glen P Martin
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
| | - Anita Sharma
- Chadderton South Health Centre, Eaves Lane, Chadderton, Oldham, OL9 8RG, UK
| | - Paul Dark
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Tim Felton
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Intensive Care Unit, Manchester University NHS Foundation Trust, Wythenshawe Hospital, Manchester, UK
| | - Xiaomin Zhong
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
| | - Sian Bladon
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
| | - Neil Cunningham
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK
| | - Ellie L Gilham
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK
| | - Colin S Brown
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK
- NIHR Health Protection Unit in Healthcare-Associated Infection & Antimicrobial Resistance, Imperial College London, London, UK
| | - Mariyam Mirfenderesky
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK
| | - Victoria Palin
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Vaughan House, The University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, UK
- Maternal and Fetal Health Research Centre, Division of Developmental Biology and Medicine, The University of Manchester, Manchester, M13 9WL, UK
| | - Diane Ashiru-Oredope
- Healthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK
- School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, UK
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Ferreira LD, McCants D, Velamuri S. Using machine learning for process improvement in sepsis management. J Healthc Qual Res 2023; 38:304-311. [PMID: 36319584 DOI: 10.1016/j.jhqr.2022.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/18/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
INTRODUCTION In the U.S., sepsis afflicts 1.7 million adults, causing 270,000 deaths each year. Early detection of sepsis could decrease the number of deaths by 92,000 annually and decrease hospital expenditures by 1.5 billion USD. Few prior studies and reviews have presented a holistic understanding of the relationship between machine learning and existing process improvement measures. This study, in addition to discussing machine learning and existing process improvements measures, elaborates on the disadvantages and the barriers to integrating machine learning into the clinic. This article synthesizes previous studies to educate healthcare professionals on effectively managing sepsis by leveraging the benefits of machine learning. METHODS This study used the PubMed database. Search terms include sepsis antibiotics, sepsis process improvement, sepsis machine learning. Our search criteria included previous studies published between January 1, 2017, and February 1, 2022. RESULTS/DISCUSSION Although machine learning algorithms have better predictive capabilities, their effectiveness in the clinical setting is limited as studies show mixed results because the medical staff often fails to intervene. To overcome poor interventional response, clinicians need to work with the facility's IT department to ensure integration into clinical workflow and minimize alert-fatigue. Algorithms should enhance the productivity of clinical teams, not attempt to replace them entirely. CONCLUSION Hospitals can employ process improvement measures that effectively utilize machine learning algorithms to ensure integration into clinical workflows. Healthcare professionals can utilize workflow tools in addition to the predictive capabilities of machine learning to enhance clinical decisions in sepsis.
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Affiliation(s)
- L D Ferreira
- Department of Student Affairs, Baylor College of Medicine, United States.
| | - D McCants
- Department of Internal Medicine, Baylor College of Medicine, United States
| | - S Velamuri
- Department of Internal Medicine, Baylor College of Medicine, United States; Luminare, Inc. United States
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Shermock SB, Shermock KM, Schepel LL. Closed-Loop Medication Management with an Electronic Health Record System in U.S. and Finnish Hospitals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6680. [PMID: 37681820 PMCID: PMC10488169 DOI: 10.3390/ijerph20176680] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/13/2023] [Accepted: 08/25/2023] [Indexed: 09/09/2023]
Abstract
Many medication errors in the hospital setting are due to manual, error-prone processes in the medication management system. Closed-loop Electronic Medication Management Systems (EMMSs) use technology to prevent medication errors by replacing manual steps with automated, electronic ones. As Finnish Helsinki University Hospital (HUS) establishes its first closed-loop EMMS with the new Epic-based Electronic Health Record system (APOTTI), it is helpful to consider the history of a more mature system: that of the United States. The U.S. approach evolved over time under unique policy, economic, and legal circumstances. Closed-loop EMMSs have arrived in many U.S. hospital locations, with myriad market-by-market manifestations typical of the U.S. healthcare system. This review describes and compares U.S. and Finnish hospitals' EMMS approaches and their impact on medication workflows and safety. Specifically, commonalities and nuanced differences in closed-loop EMMSs are explored from the perspectives of the care/nursing unit and hospital pharmacy operations perspectives. As the technologies are now fully implemented and destined for evolution in both countries, perhaps closed-loop EMMSs can be a topic of continued collaboration between the two countries. This review can also be used for benchmarking in other countries developing closed-loop EMMSs.
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Affiliation(s)
- Susan B. Shermock
- Howard County Medical Center, The Johns Hopkins Health System, Department of Pharmacy Services, 5755 Cedar Lane, Columbia, MD 21044, USA;
| | - Kenneth M. Shermock
- Center for Medication Quality and Outcomes, The Johns Hopkins Health System, 600 North Wolfe Street Carnegie 180, Baltimore, MD 21287, USA;
- Division of Pharmacology and Pharmacotherapy, Faculty of Pharmacy, University of Helsinki, 00029 Helsinki, Finland
| | - Lotta L. Schepel
- Quality and Patient Safety Unit and HUS Pharmacy, HUS Joint Resources, Helsinki University Hospital and University of Helsinki, 00029 Helsinki, Finland
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