A Risk Prediction Model for Screening Bacteremic Patients: A Cross Sectional Study

Abstract

BackgroundBacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia.MethodsIn this retrospective hospital-wide cohort study evaluating 15,985 patients with suspected bacteraemia, 51 variables were assessed for their diagnostic potency. A derivation cohort (n = 14.699) was used for feature and model selection as well as for cut-off specification. Models were established using the A2DE classifier, a supervised Bayesian classifier. Two internally validated models were further evaluated by a validation cohort (n = 1,286).ResultsThe proportion of neutrophile leukocytes in differential blood count was the best individual variable to predict bacteraemia (ROC-AUC: 0.694). Applying the A2DE classifier, two models, model 1 (20 variables) and model 2 (10 variables) were established with an area under the receiver operating characteristic curve (ROC-AUC) of 0.767 and 0.759, respectively. In the validation cohort, ROC-AUCs of 0.800 and 0.786 were achieved. Using predefined cut-off points, 16% and 12% of patients were allocated to the low risk group with a negative predictive value of more than 98.8%.ConclusionApplying the proposed models, more than ten percent of patients with suspected blood stream infection were identified having minimal risk for bacteraemia. Based on these data the application of this model as an automated decision support tool for physicians is conceivable leading to a potential increase in the cost-effectiveness of blood culture sampling. External prospective validation of the model's generalizability is needed for further appreciation of the usefulness of this tool.

Sepsis biomarkers in neutropaenic systemic inflammatory response syndrome patients on standard care wards

“…Therefore, we restricted our statistical analyses mostly to single variable methods. Predictive models analysis might increase the discriminatory power of biomarkers [37][38][39]. In a logistic regression model, including LBP, PCT and body temperature, we found 0Á899 ROC-AUC (CI: 0Á813-0Á985) for identifying bacteraemic SIRS patients with neutropaenia.…”

“…In contrast, in a prospective cohort study including 47 children, no significant difference was found in LBP levels when it came to predicting clinical sepsis or bacteraemia [31]. Predictive models analysis might increase the discriminatory power of biomarkers [37][38][39]. The function of LBP is the binding of bacterial surface patterns including lipopolysaccharides from Gram-negative bacteria and lipoteichoic acid from Gram-positive bacteria [35,36].…”

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

“…Only a few studies have prospectively analyzed the relationship between chills and bacteremia [3,4,[6][7][8][9]28], and none of them have included all patients at the ED where a blood culture had been obtained. Several studies have tried to develop clinical prediction rules to calculate the risk of bacteremia in patients seeking health care with suspected infection [3,4,[11][12][13][14][15][16][18][19][20][21][22][23]. The proposed clinical prediction rules contain between 6 and 20 variables, including clinical parameters, laboratory results, and anamnestic data.…”

“…Conversely, it is important at the population level to identify patients who do not have a bacterial infection to avoid unnecessary use of antibiotics. Other investigators have developed clinical prediction rules for the risk of bacteremia in patients with suspected infection [3,4,[11][12][13][14][15][16][17][18][19][20][21][22][23]. However, these studies have yielded conflicting results.…”

Synergistic interactions between NOD receptors and TLRs: Mechanisms and clinical implications

“…86 In this case, BCG-induced training can be interpreted as a particular case of positive NOD-TLR interaction, whereby the first stimulus Fathi et al 91 ), and some studies propose multiparametric prognostic classifiers to identify high-risk patients. 92,93 It is then a matter of clinical trials to determine whether stimulation of innate immunity in these patients would reduce sepsis risk. Given the link between BCG vaccination and reduced sepsis-related mortality in infants, 87 a good starting point could be randomized trials of BCG vaccine administered to highrisk patients on admission to hospital.…”