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.
Article in Infection, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
14 authors at 3 institutions in 1 country.
Tjeerd Pieter van StaaCentre 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. tjeerd.vanstaa@manchester.ac.uk.ORCID http://orcid.org/0000-0001-9363-742X
Alexander PateCentre 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.ORCID http://orcid.org/0000-0002-0849-3458
Glen P MartinCentre 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.ORCID http://orcid.org/0000-0002-3410-9472
Anita SharmaChadderton South Health Centre, Eaves Lane, Chadderton, Oldham, OL9 8RG, UK.
Paul DarkDivision of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.ORCID http://orcid.org/0000-0003-3309-0164
Tim FeltonDivision of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.ORCID http://orcid.org/0000-0001-6868-6633
Xiaomin ZhongCentre 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 BladonCentre 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 CunninghamHealthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK.ORCID http://orcid.org/0000-0002-8578-7628
Ellie L GilhamHealthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK.ORCID http://orcid.org/0000-0003-4266-4844
Colin S BrownHealthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK.ORCID http://orcid.org/0000-0003-4776-3403
Mariyam MirfendereskyHealthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK.ORCID http://orcid.org/0000-0002-6795-6491
Victoria PalinCentre 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.ORCID http://orcid.org/0000-0002-0851-8619
Diane Ashiru-OredopeHealthcare-Associated Infection (HCAI), Fungal, Antimicrobial Resistance (AMR), Antimicrobial Use (AMU) & Sepsis Division, United Kingdom Health Security Agency (UKHSA), London, SW1P 3JR, UK.ORCID http://orcid.org/0000-0001-9579-2028
Manchester Academic Health Science Centre · GBImperial College London · GBUniversity of Nottingham · GB
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
purposeSepsis 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.
methodsTwo 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.
results108,317 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.
conclusionGiven 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.
Indexed as
Primary Health CareSepsisAgedAged, 80 and overCase-Control StudiesCommunity-Acquired InfectionsEnglandEthnicityFemaleHospitalizationHospitalsHumansMaleRisk FactorsDeprivationFrailtyPrimary careRaceSepsis
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
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. · full record | OpenQuestion