An external validation study of a clinical prediction rule for medical patients with suspected bacteraemia

Hodgson, Luke; Dragolea, Nicholas; Venn, Richard; Dimitrov, Borislav D.; Forni, Lui G.

doi:10.1136/emermed-2015-204926

Cited by 23 publications

(22 citation statements)

References 46 publications

(32 reference statements)

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“…Some studies include chills as a variable and show that 14–95% of patients with bacteremia and 9–75% of patients without bacteremia have chills [3, 4, 6, 11, 13, 15, 22, 30, 31]. The reason for this very large variation may be that the data usually derives from retrospectively reviewing clinical admission notes.…”

Section: Discussionmentioning

confidence: 99%

Risk of bacteremia in patients presenting with shaking chills and vomiting - a prospective cohort study

et al. 2020

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Chills and vomiting have traditionally been associated with severe bacterial infections and bacteremia. However, few modern studies have in a prospective way evaluated the association of these signs with bacteremia, which is the aim of this prospective, multicenter study. Patients presenting to the emergency department with at least one affected vital sign (increased respiratory rate, increased heart rate, altered mental status, decreased blood pressure or decreased oxygen saturation) were included. A total of 479 patients were prospectively enrolled. Blood cultures were obtained from 197 patients. Of the 32 patients with a positive blood culture 11 patients (34%) had experienced shaking chills compared with 23 (14%) of the 165 patients with a negative blood culture, P = 0.009. A logistic regression was fitted to show the estimated odds ratio (OR) for a positive blood culture according to shaking chills. In a univariate model shaking chills had an OR of 3.23 (95% CI 1.35–7.52) and in a multivariate model the OR was 5.9 (95% CI 2.05–17.17) for those without prior antibiotics adjusted for age, sex, and prior antibiotics. The presence of vomiting was also addressed, but neither a univariate nor a multivariate logistic regression showed any association between vomiting and bacteremia. In conclusion, among patients at the emergency department with at least one affected vital sign, shaking chills but not vomiting were associated with bacteremia.

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Section: Discussionmentioning

confidence: 99%

Risk of bacteremia in patients presenting with shaking chills and vomiting - a prospective cohort study

et al. 2020

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“…Other well-studied clinical predictive models are the SIRS criteria (systemic inflammatory response syndrome) and the Shapiro criteria (11, 12) (see Figure 2). Both these models have demonstrated high sensitivity but low specificity (14–16) and rely on laboratory parameters that are not readily available in most LMICs. Their usefulness in daily practice is therefore rather limited.…”

Section: Introductionmentioning

confidence: 99%

Best Practices of Blood Cultures in Low- and Middle-Income Countries

et al. 2019

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Bloodstream infections (BSI) have a substantial impact on morbidity and mortality worldwide. Despite scarcity of data from many low- and middle-income countries (LMICs), there is increasing awareness of the importance of BSI in these countries. For example, it is estimated that the global mortality of non-typhoidal Salmonella bloodstream infection in children under 5 already exceeds that of malaria. Reliable and accurate diagnosis of these infections is therefore of utmost importance. Blood cultures are the reference method for diagnosis of BSI. LMICs face many challenges when implementing blood cultures, due to financial, logistical, and infrastructure-related constraints. This review aims to provide an overview of the state-of-the-art of sampling and processing of blood cultures, with emphasis on its use in LMICs. Laboratory processing of blood cultures is relatively straightforward and can be done without the need for expensive and complicated equipment. Automates for incubation and growth monitoring have become the standard in high-income countries (HICs), but they are still too expensive and not sufficiently robust for imminent implementation in most LMICs. Therefore, this review focuses on “manual” methods of blood culture, not involving automated equipment. In manual blood cultures, a bottle consisting of a broth medium supporting bacterial growth is incubated in a normal incubator and inspected daily for signs of growth. The collection of blood for blood culture is a crucial step in the process, as the sensitivity of blood cultures depends on the volume sampled; furthermore, contamination of the blood culture (accidental inoculation of environmental and skin bacteria) can be avoided by appropriate antisepsis. In this review, we give recommendations regarding appropriate blood culture sampling and processing in LMICs. We present feasible methods to detect and speed up growth and discuss some challenges in implementing blood cultures in LMICs, such as the biosafety aspects, supply chain and waste management.

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“…Thus, they concluded that for low-risk patients BC analysis might be omitted. In independent external validation studies, this rule resulted in similar ROC-AUCs 34 , 35 . Several similar scores and modifications of the Shapiro score have been established, resulting in a similar outcome 36 – 39 .…”

Section: Discussionmentioning

confidence: 78%

Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study

Ratzinger

Haslacher

Perkmann

et al. 2018

Sci Rep

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Bacteraemia is a life-threating condition requiring immediate diagnostic and therapeutic actions. Blood culture (BC) analyses often result in a low true positive result rate, indicating its improper usage. A predictive model might assist clinicians in deciding for whom to conduct or to avoid BC analysis in patients having a relevant bacteraemia risk. Predictive models were established by using linear and non-linear machine learning methods. To obtain proper data, a unique data set was collected prior to model estimation in a prospective cohort study, screening 3,370 standard care patients with suspected bacteraemia. Data from 466 patients fulfilling two or more systemic inflammatory response syndrome criteria (bacteraemia rate: 28.8%) were finally used. A 29 parameter panel of clinical data, cytokine expression levels and standard laboratory markers was used for model training. Model tuning was performed in a ten-fold cross validation and tuned models were validated in a test set (80:20 random split). The random forest strategy presented the best result in the test set validation (ROC-AUC: 0.729, 95%CI: 0.679–0.779). However, procalcitonin (PCT), as the best individual variable, yielded a similar ROC-AUC (0.729, 95%CI: 0.679–0.779). Thus, machine learning methods failed to improve the moderate diagnostic accuracy of PCT.

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An external validation study of a clinical prediction rule for medical patients with suspected bacteraemia

Cited by 23 publications

References 46 publications

Risk of bacteremia in patients presenting with shaking chills and vomiting - a prospective cohort study

Risk of bacteremia in patients presenting with shaking chills and vomiting - a prospective cohort study

Best Practices of Blood Cultures in Low- and Middle-Income Countries

Machine learning for fast identification of bacteraemia in SIRS patients treated on standard care wards: a cohort study

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