Comparisons of Cocaine-Only, Opioid-Only, and Users of Both Substances in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)

<p><i>Background</i>: Cocaine and opioid co-use is a notable public health concern, but little is known about correlates of this behavior. Most prior findings come from treatment samples and concern cocaine and heroin. Findings from a nationally representative sample involving primarily prescription opioid misuse would expand knowledge. <i>Methods</i>: Past-12-month cocaine and/or opioid users in Wave 1 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) formed the sample (<i>N</i> = 839). Cocaine-only, opioid-only, and cocaine/opioid co-users were compared regarding sociodemographics, other substance involvement, psychiatric, and medical conditions/events. <i>Results</i>: Opioid-only users were the largest group (<i>n</i> = 622), followed by cocaine-only (<i>n</i> = 144) and co-users (<i>n</i> = 73). The vast majority of opioid misuse was of prescription opioids (1.4% with past-12-month use of heroin). Notably, co-users did not differ from single drug users in frequency of use of either drug. Co-users did not have significantly greater incidence of any psychiatric conditions, medial conditions, or events. In preliminary analyses, co-users were more likely than either single use group to report several classes of other drug use. However, for most comparisons, opioid use did not add substantial risk beyond cocaine use. Differences on multiple sociodemographic variables suggested opioid-only users were at lowest risk of negative outcomes. These results may relate to a finding that opioid-only users were less likely to have sought treatment. <i>Conclusions/importance</i>: This sample of past-12-month cocaine and/or opioid users had greater involvement with other substances, more psychiatric and medical conditions compared to the general population. Co-users had greater involvement with other substances than opioid-only users in particular.</p

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

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

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

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

Comparison of prognostic models in the validation cohort.

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