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.
Deprivation
Frailty
Primary care
Race
Sepsis
Journal
Infection
ISSN: 1439-0973
Titre abrégé: Infection
Pays: Germany
ID NLM: 0365307
Informations de publication
Date de publication:
16 Apr 2024
16 Apr 2024
Historique:
received:
03
01
2024
accepted:
12
03
2024
medline:
17
4
2024
pubmed:
17
4
2024
entrez:
16
4
2024
Statut:
aheadofprint
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 38627354
doi: 10.1007/s15010-024-02235-8
pii: 10.1007/s15010-024-02235-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
Singer M, Deutschman CS, Seymour C, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315:801–10. Available at: https://pubmed.ncbi.nlm.nih.gov/26903338/ . Accessed 27 Feb 2023.
Talisa VB, Yende S, Seymour CW, Angus DC. Arguing for adaptive clinical trials in sepsis. Front Immunol. 2018;9:1502.
doi: 10.3389/fimmu.2018.01502
pubmed: 30002660
pmcid: 6031704
Jones SL, Ashton CM, Kiehne LB, et al. Outcomes and resource use of sepsis-associated stays by presence on admission, severity, and hospital type. Med Care. 2016;54:303–10.
doi: 10.1097/MLR.0000000000000481
pubmed: 26759980
pmcid: 4751740
Page DB, Donnelly JP, Wang HE. Community-, healthcare-, and hospital-acquired severe sepsis hospitalizations in the university healthsystem consortium. Crit Care Med. 2015;43:1945–51.
doi: 10.1097/CCM.0000000000001164
pubmed: 26110490
pmcid: 4537676
Adrie C, Alberti C, Chaix-Couturier C, et al. Epidemiology and economic evaluation of severe sepsis in France: age, severity, infection site, and place of acquisition (community, hospital, or intensive care unit) as determinants of workload and cost. J Crit Care. 2005;20:46–58.
doi: 10.1016/j.jcrc.2004.10.005
pubmed: 16015516
Sousa C, Brandão M, Ribeiro O, Cardoso T. Community-acquired severe sepsis: a prospective cohort study. Open J Intern Med. 2015;05:37–49.
doi: 10.4236/ojim.2015.53008
Shah AD, MacCallum NS, Harris S, et al. Descriptors of sepsis using the sepsis-3 criteria: a cohort study in critical care units within the UK. National institute for health research critical care health informatics collaborative*. Crit Care Med. 2021;49:1883–94.
Tonai M, Shiraishi A, Karumai T, et al. Hospital-onset sepsis and community-onset sepsis in critical care units in Japan: a retrospective cohort study based on a Japanese administrative claims database. Crit Care. 2022;26:1–9.
doi: 10.1186/s13054-022-04013-0
NHS England » Core20PLUS5 an approach to reducing health inequalities: supporting information. Available at: https://www.england.nhs.uk/publication/core20plus5-an-approach-to-reducing-health-inequalities-supporting-information/ . Accessed 27 Feb 2023.
Bladon S, Ashiru-Oredope D, Cunningham N, et al. Rapid systematic review on risks and outcomes of sepsis: the influence of risk factors known to be associated with health inequalities on risks and outcomes (morbidity and mortality). Int J Equity Health. 2024;Available at: https://research.manchester.ac.uk/en/publications/rapid-systematic-review-on-risks-and-outcomes-of-sepsis-the-influ . Accessed 7 Feb 2024.
Herrett E, Gallagher AM, Bhaskaran K, et al. Data resource profile: clinical practice research datalink (CPRD). Int J Epidemiol. 2015;44:827–36. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4521131&tool=pmcentrez&rendertype=abstract . Accessed 7 June 2015.
Wolf A, Dedman D, Campbell J, et al. Data resource profile: clinical practice research datalink (CPRD) Aurum. Int J Epidemiol. 2019;48:1740–G. Available at: https://pubmed.ncbi.nlm.nih.gov/30859197/ . Accessed 28 Aug 2021.
Kontopantelis E, Stevens RJ, Helms PJ, Edwards D, Doran T, Ashcroft DM. Spatial distribution of clinical computer systems in primary care in England in 2016 and implications for primary care electronic medical record databases: a cross-sectional population study. BMJ Open. 2018;8:e020738. Available at: http://www.ncbi.nlm.nih.gov/pubmed/29490968 . Accessed 10 Aug 2019.
GOV.UK. English indices of deprivation. 2019.
Zhong X, Ashiru-Oredope D, Pate A, et al. Clinical and health inequality risk factors for non-COVID-related sepsis during the global COVID-19 pandemic: a national case-control and cohort study. eClinicalMedicine. 2023;66:102321. Available at: http://www.thelancet.com/article/S2589537023004984/fulltext . Accessed 3 Jan 2024.
van Staa TP, Pirmohamed M, Sharma A, Ashcroft DM, Buchan I. Adverse drug reactions and hospital admissions: large case-control study of patients aged 65–100 years using linked English primary care and hospital data. Pharmacoepidemiol Drug Saf. 2024;33. Available at: https://pubmed.ncbi.nlm.nih.gov/37609702/ . Accessed 7 Feb 2024.
Hippisley-Cox J, Coupland C. Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: cohort study. BMJ. 2017;358: j4208.
doi: 10.1136/bmj.j4208
pubmed: 28931509
pmcid: 5606253
Hippisley-Cox J, Coupland C. Predicting risk of emergency admission to hospital using primary care data: derivation and validation of QAdmissions score. BMJ Open. 2013;3:e003482. Available at: http://www.ncbi.nlm.nih.gov/pubmed/23959760 . Accessed 15 Sept 2016.
Zhu Y, Edwards D, Mant J, Payne RA, Kiddle S. Characteristics, service use and mortality of clusters of multimorbid patients in England: a population-based study. BMC Med. 2020;18.
Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat 2011;10:150–61. Available at: http://www.ncbi.nlm.nih.gov/pubmed/20925139 . Accessed 2 July 2019.
Breiman L. Random forests. Mach Learn. 2001;45:5–32.
doi: 10.1023/A:1010933404324
Austin PC, Tu J V., Ho JE, Levy D, Lee DS. Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. J Clin Epidemiol. 2013;66:398–407. Available at: https://pubmed.ncbi.nlm.nih.gov/23384592/ . Accessed 17 Apr 2022.
Fahmi A, Wong D, Walker L, et al. Combinations of medicines in patients with polypharmacy aged 65–100 in primary care: Large variability in risks of adverse drug related and emergency hospital admissions. PLoS One. 2023;18:e0281466. Available at: https://pubmed.ncbi.nlm.nih.gov/36753492/ . Accessed 12 Feb 2023.
Schwarzkopf D, Rose N, Fleischmann-Struzek C, et al. Understanding the biases to sepsis surveillance and quality assurance caused by inaccurate coding in administrative health data. Infection. 2023;Available at: https://pubmed.ncbi.nlm.nih.gov/37684496/ . Accessed 3 Jan 2024.
Goodwin AJ, Nadig NR, McElligott JT, Simpson KN, Ford DW. Where you live matters: the impact of place of residence on severe sepsis incidence and mortality. Chest. 2016; 150:829–836. Available at: https://pubmed.ncbi.nlm.nih.gov/27445093/ . Accessed 1 Mar 2023.
Hennessy DA, Soo A, Niven DJ, et al. Socio-demographic characteristics associated with hospitalization for sepsis among adults in Canada: a Census-linked cohort study. Can J Anaesth. 2020;67:408–20. Available at: https://pubmed.ncbi.nlm.nih.gov/31792835/ . Accessed 1 Mar 2023.
Stensrud VH, Gustad LT, Damås JK, Solligård E, Krokstad S, Nilsen TIL. Direct and indirect effects of socioeconomic status on sepsis risk and mortality: a mediation analysis of the HUNT Study. J Epidemiol Community Health. 2023;77:jech-2022-219825. Available at: https://pubmed.ncbi.nlm.nih.gov/36707239/ . Accessed 1 Mar 2023.
Donnelly JP, Lakkur S, Judd SE, et al. Association of neighborhood socioeconomic status with risk of infection and sepsis. Clin Infect Dis. 2018;66:1940–1947. Available at: https://pubmed.ncbi.nlm.nih.gov/29444225/ . Accessed 1 Mar 2023.
Wang HE, Donnelly JP, Griffin R, et al. Derivation of novel risk prediction scores for community-acquired sepsis and severe sepsis. Crit Care Med. 2016;44:1285–94. Available at: https://pubmed.ncbi.nlm.nih.gov/27031381/ . Accessed 15 Feb 2023.
Kempker JA, Wang HE, Martin GS. Sepsis is a preventable public health problem. Crit Care. 2018;22:1–3. Available at: https://ccforum.biomedcentral.com/articles/ https://doi.org/10.1186/s13054-018-2048-3 . Accessed 1 Mar 2023.
Overview | Sepsis: recognition, diagnosis and early management | Guidance | NICE. NICE, 2016. Available at: https://www.nice.org.uk/guidance/ng51 . Accessed 1 Mar 2023.
Kumar A, Abbenbroek B, Delaney A, Hammond N, Grattan S, Finfer S. Sepsis triggers and tools to support early identification in healthcare settings: an integrative review. Aust Crit Care. 2023;0. Available at: http://www.australiancriticalcare.com/article/S103673142300005X/fulltext . Accessed 1 Mar 2023.
Lane DJ, Wunsch H, Saskin R, et al. Screening strategies to identify sepsis in the prehospital setting: a validation study. CMAJ. 2020;192:E230–9. Available at: https://pubmed.ncbi.nlm.nih.gov/32152051/ . Accessed 1 Mar 2023.
Roney JK, Whitley BE, Maples JC, Futrell LS, Stunkard KA, Long JD. Modified early warning scoring (MEWS): evaluating the evidence for tool inclusion of sepsis screening criteria and impact on mortality and failure to rescue. J Clin Nurs. 2015;24:3343–54. Available at: https://onlinelibrary.wiley.com/doi/full/ https://doi.org/10.1111/jocn.12952 . Accessed 1 Mar 2023.
Wallgren UM, Castrén M, Svensson AEV, Kurland L. Identification of adult septic patients in the prehospital setting: a comparison of two screening tools and clinical judgment. Eur J Emerg Med. 2014;21:260–5. Available at: https://journals.lww.com/euro-emergencymed/Fulltext/2014/08000/Identification_of_adult_septic_patients_in_the.4.aspx . Accessed 1 Mar 2023.
Liu B, Li D, Cheng Y, et al. Development and internal validation of a simple prognostic score for early sepsis risk stratification in the emergency department. BMJ Open. 2021;11. Available at: https://pubmed.ncbi.nlm.nih.gov/34233976/ . Accessed 28 Feb 2023.
Kijpaisalratana N, Sanglertsinlapachai D, Techaratsami S, Musikatavorn K, Saoraya J. Machine learning algorithms for early sepsis detection in the emergency department: A retrospective study. Int J Med Inform. 2022;160. Available at: https://pubmed.ncbi.nlm.nih.gov/35078027/ . Accessed 1 Mar 2023.
National Early Warning Score (NEWS) 2 | RCP London. Available at: https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2 . Accessed 8 Mar 2023.
Burns A. NEWS 2 sepsis score is not validated in primary care. BMJ. 2018;361. Available at: https://pubmed.ncbi.nlm.nih.gov/29685958/ . Accessed 8 Mar 2023.
Gale BM, Hall KK. The use of patient monitoring systems to improve sepsis recognition and outcomes: a systematic review. J Patient Saf. 2020;16:S8–S11. Available at: https://pubmed.ncbi.nlm.nih.gov/32809995/ . Accessed 8 Mar 2023.
Alboksmaty A, Beaney T, Elkin S, et al. Effectiveness and safety of pulse oximetry in remote patient monitoring of patients with COVID-19: a systematic review. Lancet Digit Heal. 2022;4:e279–e289. Available at: https://pubmed.ncbi.nlm.nih.gov/35337644/ . Accessed 8 Mar 2023.