Diagnostic performance of biomarkers in urine.

<p>Receiver operating characteristic curves (AUC-ROC) show discriminative abilities for preoperative risk stratification (pre-OP) and postoperative diagnosis of AKI (at 2h, 4h and 24h after CPB) for all patients (solid thick line) and separately for patients with (sold thin line) and without (dashed line) preexisting CKD. Indicated are AUC (p-value).</p

Ranking of candidate plasma biomarkers.

<p>Ranking of biomarkers in plasma according to their preoperative (pre-OP) and postoperative (4h and 24h after CPB) AUC-ROC performance for risk stratification and detection of AKI. AUC values <0.5 were expressed as 1-AUC indicated by AUC−. Parameters marked with * were measured with the assays indicated in the methods section, all others were measured using the Human CustomMAP. The confidence intervals for AUCs were calculated with the DeLong method. Abbreviations: BAFF, B-cell activating factor; BLC, B lymphocyte chemoattractant, chemokine C-X-C motif ligand (CXCL) 13; CKINE-6, chemokine with 6 cysteines, chemokine C-C motif ligand (CCL) 21; CysC, cystatin C; ICAM-1, intercellular adhesion molecule 1, cluster of differentiation (CD) 54; IP10, interferon-γ-induced protein 10, CXCL 10; ITAC, interferon-inducible T-cell alpha chemoattractant, CXCL11, IP9; L-FABP, liver-type fatty acid-binding protein; MCP-1, monocyte chemotactic protein-1, CCL2; MIG, monokine induced by interferon-γ, CXCL9; MIP3B, macrophage inflammatory protein-3ß, CCL19; NGAL, neutrophil gelatinase-associated lipocalin; SDF-1, stromal cell-derived factor-1, CXCL12; S-RAGE, soluble receptor for advanced glycosylation end products.</p

Ranking of candidate urine biomarkers.

<p>Ranking of biomarkers in urine according to their preoperative (pre-OP) and postoperative (4h and 24h after CPB) AUC-ROC performance for risk stratification and detection of AKI. AUC values <0.5 were expressed as 1-AUC indicated by AUC−. Parameters marked with * were not included in the Human KidneyMAP^® and were measured separately. The confidence intervals for AUCs were calculated with the DeLong method. Abbreviations: CTGF, connective tissue growth factor; CysC, Cystatin C; GSTα, glutathione S-transferase-α; IL18, interleukin 18; KIM1, kidney injury molecule 1; L-FABP, liver-type fatty acid-binding protein; NGAL neutrophil gelatinase-associated lipocalin; THP, Tamm-Horsfall protein; TIMP1, tissue inhibitor of metalloproteinases 1; TFF3, trefoil factor 3; VEGF, vascular endothelial growth factor.</p

Towards a Computable Data Corpus of Temporal Correlations between Drug Administration and Lab Value Changes

<div><p>Background</p><p>The analysis of electronic health records for an automated detection of adverse drug reactions is an approach to solve the problems that arise from traditional methods like spontaneous reporting or manual chart review. Algorithms addressing this task should be modeled on the criteria for a standardized case causality assessment defined by the World Health Organization. One of these criteria is the temporal relationship between drug intake and the occurrence of a reaction or a laboratory test abnormality. Appropriate data that would allow for developing or validating related algorithms is not publicly available, though.</p><p>Methods</p><p>In order to provide such data, retrospective routine data of drug administrations and temporally corresponding laboratory observations from a university clinic were extracted, transformed and evaluated by experts in terms of a reasonable time relationship between drug administration and lab value alteration.</p><p>Result</p><p>The result is a data corpus of 400 episodes of normalized laboratory parameter values in temporal context with drug administrations. Each episode has been manually classified whether it contains data that might indicate a temporal correlation between the drug administration and the change of the lab value course, whether such a change is not observable or whether a decision between those two options is not possible due to the data. In addition, each episode has been assigned a concordance value which indicates how difficult it is to assess. This is the first open data corpus of a computable ground truth of temporal correlations between drug administration and lab value alterations.</p><p>Discussion</p><p>The main purpose of this data corpus is the provision of data for further research and the provision of a ground truth which allows for comparing the outcome of other assessments of this data with the outcome of assessments made by human experts. It can serve as a contribution towards systematic, computerized ADR detection in retrospective data. With this lab value curve data as a basis, algorithms for detecting temporal relationships can be developed, and with the classification made by human experts, these algorithms can immediately be validated. Due to the normalization of the lab value data, it allows for a generic approach rather than for specific or solitary drug/lab value combinations.</p></div

Distribution and characteristics of lab parameters in the raw data and in the data corpus.

<p>¹ Percent values are relative to the total number of datasets in the raw data (902) / data corpus (400).</p><p>² Percent values are relative to the frequency in the data corpus.</p><p>³ No episodes with at least five observations before, during, and after the drug administration were found.</p><p>Distribution and characteristics of lab parameters in the raw data and in the data corpus.</p

Screenshot of the reutilization tool that allows for comparing other assessment results with the ground truth classification.

<p>Screenshot of the reutilization tool that allows for comparing other assessment results with the ground truth classification.</p

Breakdown of classifications for correlation and intricacy for the Ground Truth Data Corpus (absolute numbers, percent values referred to the total number of curves and in brackets percent values referred to the number of curves of the corresponding intricacy).

<p>Breakdown of classifications for correlation and intricacy for the Ground Truth Data Corpus (absolute numbers, percent values referred to the total number of curves and in brackets percent values referred to the number of curves of the corresponding intricacy).</p

XML structure needed for loading external data into the DOG reutilization tool.

<p>XML structure needed for loading external data into the DOG reutilization tool.</p

Example of extracted drug administration days (red bars) with corresponding normalized lab values (blue dots).

<p>A complete episode consist of a starting administration-free interval of seven days with at least five lab value observations, a period of continuous administration (max. one administration-free day) with at least five lab value observations and finally another administration-free interval of seven days with at least five lab value observations.</p

Example for a curve with “no assessment” and high intricacy (curve #129).

<p>Example for a curve with “no assessment” and high intricacy (curve #129).</p