Background: Efforts to reduce unnecessary antibiotic prescribing have coincided with increasing awareness of sepsis. We aimed to estimate the probability of sepsis following infection consultations in primary care when antibiotics were or were not prescribed.
Methods And Findings: We conducted a cohort study including all registered patients at 706 general practices in the United Kingdom Clinical Practice Research Datalink, with 66.2 million person-years of follow-up from 2002 to 2017. There were 35,244 first episodes of sepsis (17,886, 51%, female; median age 71 years, interquartile range 57-82 years). Consultations for respiratory tract infection (RTI), skin or urinary tract infection (UTI), and antibiotic prescriptions were exposures. A Bayesian decision tree was used to estimate the probability (95% uncertainty intervals [UIs]) of sepsis following an infection consultation. Age, gender, and frailty were evaluated as association modifiers. The probability of sepsis was lower if an antibiotic was prescribed, but the number of antibiotic prescriptions required to prevent one episode of sepsis (number needed to treat [NNT]) decreased with age. At 0-4 years old, the NNT was 29,773 (95% UI 18,458-71,091) in boys and 27,014 (16,739-65,709) in girls; over 85 years old, NNT was 262 (236-293) in men and 385 (352-421) in women. Frailty was associated with greater risk of sepsis and lower NNT. For severely frail patients aged 55-64 years, the NNT was 247 (156-459) in men and 343 (234-556) in women. At all ages, the probability of sepsis was greatest for UTI, followed by skin infection, followed by RTI. At 65-74 years, the NNT following RTI was 1,257 (1,112-1,434) in men and 2,278 (1,966-2,686) in women; the NNT following skin infection was 503 (398-646) in men and 784 (602-1,051) in women; following UTI, the NNT was 121 (102-145) in men and 284 (241-342) in women. NNT values were generally smaller for the period from 2014 to 2017, when sepsis was diagnosed more frequently. Lack of random allocation to antibiotic therapy might have biased estimates; patients may sometimes experience sepsis or receive antibiotic prescriptions without these being recorded in primary care; recording of sepsis has increased over the study period.
Conclusions: These stratified estimates of risk help to identify groups in which antibiotic prescribing may be more safely reduced. Risks of sepsis and benefits of antibiotics are more substantial among older adults, persons with more advanced frailty, or following UTIs.
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http://dx.doi.org/10.1371/journal.pmed.1003202 | DOI Listing |
Front Cell Infect Microbiol
January 2025
Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
Background: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with sepsis-related deaths contributing substantially to the overall burden on healthcare systems worldwide. The primary objective was to construct and evaluate a machine learning (ML) model for forecasting 28-day all-cause mortality among ICU sepsis patients.
Methods: Data for the study was sourced from the eICU Collaborative Research Database (eICU-CRD) (version 2.
Trials
January 2025
Women's Health, Te Whatu Ora Te Toka Tumai Auckland, 2 Park Road, Grafton, Auckland, 1023, New Zealand.
Background: The approach to induction of labour differs internationally, with timing of amniotomy being controversial. Some institutions favour performing artificial rupture of membranes prior to commencement of oxytocin infusion, with the belief that the labour will progress more efficiently. In other institutions, the approach recommended is for oxytocin infusion with intact amniotic membranes until the person has reached the active phase of labour, citing risk of infection with early amniotomy.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.
Background: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma.
View Article and Find Full Text PDFEur J Orthop Surg Traumatol
January 2025
Stony Brook University Hospital, Stony Brook, USA.
Purpose: Diabetes mellitus (DM) is a well-established risk factor for postoperative complications. Distal radius fractures (DRFs) are a common orthopedic injury and often require open reduction and internal fixation (ORIF). The rise of ORIF utilization warrants investigation into factors that may expose patients to postoperative complications following DRF ORIF.
View Article and Find Full Text PDFBr J Clin Pharmacol
January 2025
Department of Medical Microbiology, Haaglanden Medisch Centrum, The Hague, The Netherlands.
Aims: The beta-lactam antibiotic temocillin is increasingly used to treat extended-spectrum beta-lactamase (ESBL-producing) strains; however, its protein binding is complex. This study aims to predict unbound temocillin concentrations in various participant groups to determine its impact on the probability of target attainment (PTA) and to improve dosing recommendations.
Methods: The plasma pharmacokinetics were analysed using non-linear mixed-effects modelling.
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