6 research outputs found
A Risk Prediction Model for Screening Bacteremic Patients: A Cross Sectional Study
<div><p>Background</p><p>Bacteraemia 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.</p><p>Methods</p><p>In 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).</p><p>Results</p><p>The 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%.</p><p>Conclusion</p><p>Applying 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.</p></div
Differences between derivation cohort and validation cohort.
<p>green: selected variables, red: deselected variables; for variable selection the derivation set was used. After application of the Bonferroni-Holm procedure, haemoglobin and magnesium was found to significantly differ between the sets. CRP =  C-reactive protein, ALP =  alkaline phosphatase, CK =  creatinine kinases, GGT =  gamma-glutamyl transpeptidase, MG =  magnesium, RBC =  red blood count;</p><p>Differences between derivation cohort and validation cohort.</p
Results of the models'diagnostic performances at predefined cut-off points.
<p>model 1: 20 variables; model 2: 10 variables;</p>1<p>Youden Index method,</p>2<p>Cut-off at LR<sup>−</sup> 0.12,</p>3<p>Cut-off at LR<sup>+</sup> 4.93.</p><p>Results of the models'diagnostic performances at predefined cut-off points.</p
Graphical result of the validation cohort.
<p>model 1: 16% low risk cohort with 2 false negative patients; model 2: 12% low risk cohort with 3 false negative patients.</p
Selection process of the study population.
<p><sup>1</sup>unavailability of laboratory variables, <sup>2</sup>Contaminations or fungal growth, <sup>3</sup>blood culture results with less than 0.001% frequency, <sup>4</sup>study patients treated between Jan 1, 2006 and Jul 31, 2010, <sup>5</sup>study patients treated between Aug 1, 2010 and Dec 31, 2010.</p
Patient characteristics and variables analysed.
<p>total study population (n = 15.985); green: selected variables, red: deselected variables; for variable selection the derivation set was used. M1 =  model 1, M2 =  model 2, CRP =  C-reactive protein, ALP =  alkaline phosphatase, CK =  creatinine kinases, GGT =  gamma-glutamyl transpeptidase, MG =  magnesium, RBC =  red blood count, ALAT =  alanine transaminase, ASAT =  aspartate transaminase, BUN =  blood urea nitrogen, CHE =  cholinesterase, LDH =  lactate dehydrogenase, MCH =  mean corpuscular hemoglobin, MCV =  mean corpuscular volume, PAMY =  pancreas amylase, RDW =  red blood cell distribution width, TP =  total protein, PDW =  platelet distribution width, aPTT =  activated partial thromboplastin time, MCHC =  Mean corpuscular hemoglobin concentration, MPV =  mean platelet volume, WBC =  white blood count;</p><p>Patient characteristics and variables analysed.</p