MOESM1 of Alterations of blood coagulation in controlled human malaria infection

Additional file 1: Table S1. Peak thrombin generation at 3 different time points for all 22 individual study subjects who developed parasitemia, including information about PfSZ Challenge route of administration, parasite dose), development of symptoms during infection and parasitemia

Multivariable logistic regression analysis for delivery of an infant with low birth weight.

<p>LBW = low birth weight, OR = Odds Ratio, IPTp = intermittent preventive treatment of malaria during pregnancy.</p

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

Birth weight of term-newborns born to adolescent and adult mothers for each week of gestation.

<p>Bars show mean birth weight and standard deviations for adolescent (≤16 years) and adult (>16 years) mothers.</p

Univariable analysis of adolescent compared to adult women.

<p>LBW = low birth weight, IPTp = intermittent preventive treatment of malaria during pregnancy.</p

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⁻ 0.12,</p>3<p>Cut-off at LR⁺ 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

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

Selection process of the study population.

<p>¹unavailability of laboratory variables, ²Contaminations or fungal growth, ³blood culture results with less than 0.001% frequency, ⁴study patients treated between Jan 1, 2006 and Jul 31, 2010, ⁵study patients treated between Aug 1, 2010 and Dec 31, 2010.</p