Approaches to Predicting Outcomes in Patients with Acute Kidney Injury

<div><p>Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into training and validation cohorts. Outcomes of interest were death within 7 days, dialysis within 7 days, and length of stay. Data elements eligible for model-building included lab values, medications and dosages, procedures, and demographics. We assessed model discrimination using the area under the receiver operator characteristic curve and r-squared values. 2241 individuals were available for analysis. Both modeling techniques created viable models with very good discrimination ability, with AUCs exceeding 0.85 for dialysis and 0.8 for death prediction. Model performance was similar across model building strategies, though the strategy employing more advanced variable selection was more parsimonious. Very good to excellent prediction of outcome events is feasible in patients with AKI. More advanced techniques may lead to more parsimonious models, which may facilitate adoption in other settings.</p></div

Baseline Characteristics at the Onset of AKI¹.

<p>Baseline Characteristics at the Onset of AKI<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169305#t001fn002" target="_blank">¹</a>.</p

Principal Components Analysis.

<p>Colored points reflect individual level data, where individuals are mapped to a coordinate plane based upon 2 principal components derived from laboratory (panel A) and medication (panel B) data. Next to the colored plots, the covariate map appears. Covariates are mapped along the same two principal component vectors, helping to illustrate the correlations among several of the covariates. <b>A)</b> Laboratory covariates as mapped on two principal components. Based on laboratory values, a patient (represented as a dot) can be put anywhere on the coordinate plane. For the outcome of death within 7 days, red dots indicate an individual who died in that time frame, black an individual who did not. For LOS analyses, blue dots indicate shorter lengths of stay, with red dots indicating longer lengths of stay. Clustering of colors along one dimension of the plot suggests a significant relationship between that principal component and the outcome. Next to the patient plots is a plot showing each lab on the same two principal coordinate axes. Labs that are closer together a more correlated (for example, creatinine and BUN). Size of the text indicates strength of association between a given lab and that principal component. <b>B)</b> Medication covariates as mapped on two principal components. Based on medications received, a patient (represented as a dot) can be put anywhere on the coordinate plane. For the outcome of death within 7 days, red dots indicate an individual who died in that time frame, black an individual who did not. For LOS analyses, blue dots indicate shorter lengths of stay, with red dots indicating longer lengths of stay. Clustering of colors along one dimension of the plot suggests a significant relationship between that principal component and the outcome. Next to the patient plots is a plot showing each medication on the same two principal coordinate axes. Medications that are closer together a more correlated (for example, vancomycin and fentanyl). Size of the text indicates strength of association between a given lab and that principal component. Covariates ending in "category" are binary (ie D50 category is a 1 if the patient has received 50% dextrose infusion), whereas those ending in "dose" reflect the actual dose received. Higher resolution figures are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169305#pone.0169305.s002" target="_blank">S2 File</a>.</p

Comparison of prognostic models in the validation cohort.

<p>Comparison of prognostic models in the validation cohort.</p

Receiver-Operator Characteristic curves for death.

<p>Comparing the performance of conventional vs. alternative models in the prediction of death in the validation cohort. Area under the curve for conventional model: 0.80 (0.75–0.84), alternative model 0.80 (0.76–0.85).</p

Lignes et lignages dans la littérature arthurienne

Le lignage et les « amis charnels » qui le constituent repose au Moyen Âge sur une communauté de sang, dont le rôle est certainement antérieur à la mise en place des liens féodaux. En littérature, le lignage est un cadre essentiel : il permet aux personnages de s’intégrer et d’organiser des réseaux relationnels qui conditionnent souvent l’action. C’est aussi un moteur puissant de création littéraire, puisque les textes inventent volontiers, à partir d’un héros souche, l’histoire des pères, des fils, des neveux. C’est enfin un point névralgique, tant la paternité des grands rois fondateurs, Charlemagne, Alexandre ou Arthur, est douteuse. La matière arthurienne, centrée sur Arthur et Merlin, deux figures dont la naissance pose problème, s’épanouissant dans des reprises, des continuations et des récritures qui inventent des fils, des neveux, des ancêtres, est un champ particulièrement intéressant pour qui s’intéresse au lignage. La diversité des approches, mythologiques, folkloriques, poétiques, sémantiques, historiques, iconographiques, permet de saisir la richesse de cette problématique et d’en dégager les lignes de force. Ce volume contient les actes du troisième colloque arthurien de Rennes, « Lignes et lignages », qui eut lieu à l’université de Rennes 2 les 13 et 14 octobre 2005.À Emmanuèle Baumgartne