Sepsis-associated mortality in England: an analysis of multiple cause of death data from 2001 to 2010

McPherson, Duncan; Griffiths, Clare; Williams, Matthew; Baker, Allan; Klodawski, E; Jacobson, Bobbie; Donaldson, Liam

doi:10.1136/bmjopen-2013-002586

Cited by 109 publications

(74 citation statements)

References 19 publications

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“…5 Only 7.0% of deaths definitely associated with sepsis did not occur in hospital. 5 Incidence of sepsis is particularly high in patients who are admitted to ICUs. A large retrospective analysis of 56,673 admissions of adult patients to ICUs in England Wales and Northern Ireland, between 1995 and 2000, found that 27.1% met the criteria for severe sepsis with the first 24 hours of admission.…”

Section: Populationmentioning

confidence: 99%

“…4 In addition, a recently published analysis of the 2001-10 Office for National Statistics (ONS) mortality data found that, during this period, 4.7% of all deaths recorded in England were 'definitely directly associated with sepsis'. 5 Ninety-nine per cent of deaths definitely associated with sepsis had at least one of the ICD-10 codes -A40 (sepsis due to pneumonia), A41 (other sepsis) or P36 (sepsis of newborn due to streptococcus group B) -on the death certificate; however, only 8.6% of deaths definitely associated with sepsis in 2010 had a sepsis-related condition as the underlying cause of death. 5 Only 7.0% of deaths definitely associated with sepsis did not occur in hospital.…”

Section: Populationmentioning

confidence: 99%

“…5 Ninety-nine per cent of deaths definitely associated with sepsis had at least one of the ICD-10 codes -A40 (sepsis due to pneumonia), A41 (other sepsis) or P36 (sepsis of newborn due to streptococcus group B) -on the death certificate; however, only 8.6% of deaths definitely associated with sepsis in 2010 had a sepsis-related condition as the underlying cause of death. 5 Only 7.0% of deaths definitely associated with sepsis did not occur in hospital. 5 Incidence of sepsis is particularly high in patients who are admitted to ICUs.…”

Section: Populationmentioning

confidence: 99%

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Procalcitonin testing to guide antibiotic therapy for the treatment of sepsis in intensive care settings and for suspected bacterial infection in emergency department settings: a systematic review and cost-effectiveness analysis

Westwood

Ramaekers

Whiting

et al. 2015

Health Technol Assess

122

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BackgroundDetermination of the presence or absence of bacterial infection is important to guide appropriate therapy and reduce antibiotic exposure. Procalcitonin (PCT) is an inflammatory marker that has been suggested as a marker for bacterial infection.ObjectivesTo assess the clinical effectiveness and cost-effectiveness of adding PCT testing to the information used to guide antibiotic therapy in adults and children (1) with confirmed or highly suspected sepsis in intensive care and (2) presenting to the emergency department (ED) with suspected bacterial infection.MethodsTwelve databases were searched to June 2014. Randomised controlled trials were assessed for quality using the Cochrane Risk of Bias tool. Summary relative risks (RRs) and weighted mean differences (WMDs) were estimated using random-effects models. Heterogeneity was assessed visually using forest plots and statistically using theI2andQstatistics and investigated through subgroup analysis. The cost-effectiveness of PCT testing in addition to current clinical practice was compared with current clinical practice using a decision tree with a 6 months’ time horizon.ResultsEighteen studies (36 reports) were included in the systematic review. PCT algorithms were associated with reduced antibiotic duration [WMD –3.19 days, 95% confidence interval (CI) –5.44 to –0.95 days,I2 = 95.2%; four studies], hospital stay (WMD –3.85 days, 95% CI –6.78 to –0.92 days,I2 = 75.2%; four studies) and a trend towards reduced intensive care unit (ICU) stay (WMD –2.03 days, 95% CI –4.19 to 0.13 days,I2 = 81.0%; four studies). There were no differences for adverse clinical outcomes. PCT algorithms were associated with a reduction in the proportion of adults (RR 0.77, 95% CI 0.68 to 0.87; seven studies) and children (RR 0.86, 95% CI 0.80 to 0.93) receiving antibiotics, reduced antibiotic duration (two studies). There were no differences for adverse clinical outcomes. All but one of the studies in the ED were conducted in people presenting with respiratory symptoms.Cost-effectiveness: the base-case analyses indicated that PCT testing was cost-saving for (1) adults with confirmed or highly suspected sepsis in an ICU setting; (2) adults with suspected bacterial infection presenting to the ED; and (3) children with suspected bacterial infection presenting to the ED. Cost-savings ranged from £368 to £3268. Moreover, PCT-guided treatment resulted in a small quality-adjusted life-year (QALY) gain (ranging between < 0.001 and 0.005). Cost-effectiveness acceptability curves showed that PCT-guided treatment has a probability of ≥ 84% of being cost-effective for all settings and populations considered (at willingness-to-pay thresholds of £20,000 and £30,000 per QALY).ConclusionsThe limited available data suggest that PCT testing may be effective and cost-effective when used to guide discontinuation of antibiotics in adults being treated for suspected or confirmed sepsis in ICU settings and initiation of antibiotics in adults presenting to the ED with respiratory symptoms and suspected bacterial infection. However, it is not clear that observed costs and effects are directly attributable to PCT testing, are generalisable outside people presenting with respiratory symptoms (for the ED setting) and would be reproducible in the UK NHS. Further studies are needed to assess the effectiveness of adding PCT algorithms to the information used to guide antibiotic treatment in children with suspected or confirmed sepsis in ICU settings. Additional research is needed to examine whether the outcomes presented in this report are fully generalisable to the UK.Study registrationThis study is registered as PROSPERO CRD42014010822.FundingThe National Institute for Health Research Health Technology Assessment programme.

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Section: Populationmentioning

confidence: 99%

Section: Populationmentioning

confidence: 99%

Section: Populationmentioning

confidence: 99%

See 1 more Smart Citation

Procalcitonin testing to guide antibiotic therapy for the treatment of sepsis in intensive care settings and for suspected bacterial infection in emergency department settings: a systematic review and cost-effectiveness analysis

Westwood

Ramaekers

Whiting

et al. 2015

Health Technol Assess

122

View full text Add to dashboard Cite

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“…However, the kidney function of inpatients is monitored more closely, partly due to the increase in AKI risk factors once admitted to hospital, such as the greater rate of sepsis [7]. Given this closer monitoring and the greater prevalence of AKI amongst inpatients [8], algorithms have been developed that can accurately identify AKI within this population in real-time [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of acute kidney injury episodes from primary care data

Tirunagari

Bull

Vehtari

et al. 2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Abstract-Acute kidney injury (AKI) is characterised by a rapid deterioration in kidney function, and can be identified by examining the rate of change in a patient's estimated glomerular filtration rate (eGFR) signal. Due to the potentially irreversible nature of the damage AKI episodes cause to renal function, their detection plays a significant role in predicting a kidney's effectiveness. Although algorithms for this are available for patients under constant monitoring, e.g. inpatients, their applicability to primary care settings is less clear as the eGFR signal often contains large lapses in time between measurements. However, waiting for hospital admittance is undesirable, as detecting AKI early can help to mitigate the degradation of kidney function and the associated increase in morbidity and mortality. Traditionally, a clinician in a primary care setting would manually identify AKI episodes within an eGFR signal. While this approach may work for individual patients, the time consuming nature of it precludes quick large-scale monitoring. We therefore present two alternative automated approaches for detecting AKI: as the outlier points when using Gaussian process regression and using a novel technique called Surrey AKI detection algorithm (SAKIDA). Using SAKIDA, we can identify the exact number of AKI episodes a patient experiences with an accuracy of 70%, when evaluated against the performance of human experts.

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“…According to the present criteria many of them are regarded as sepsis.The results of the analysis of mortality in England for the period of 2001 2010 showed that 5.1% of deaths in the country were associated with sepsis [1], which is one of the most serious health problems according to the authors. In the United States sepsis is on the 11th position [2] in the list of the most frequent causes of death.…”

mentioning

confidence: 99%