Objective:To examine the relationship between unit-wide Clostridium difficile infection (CDI) susceptibility and inpatient mobility and to create contagion centrality as a new predictive measure of CDI.Design:Retrospective cohort study.Methods:A mobility network was constructed using 2 years of patient electronic health record data for a 739-bed hospital (n = 72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms of patient transfers between units (ie, edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and were compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility.Results:Closeness centrality was a statistically significant measure associated with unit susceptibility (P < .05), highlighting the importance of incoming patient mobility in CDI prevention at the unit level. Contagion centrality (CC) was calculated using inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. The contagion centrality measure was statistically significant (P < .05) with our outcome of hospital-onset CDI cases, and it captured the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create easily interpretable clinical tools showing this relationship as well as the risk of hospital-onset CDI in real time, and these tools can be implemented in hospital EHR systems.Conclusions:Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak and, thus, provide clinicians and infection prevention staff with advanced warning and specific location data to inform prevention efforts.

Context.— Intraductal carcinoma of the prostate (IDC-P) is considered a distinct form of aggressive prostate cancer where comedonecrosis, a grade 5 pattern, is occasionally present. Meanwhile, assigning a Gleason grade to IDC-P remains controversial. Objective.— To assess the clinical significance of necrosis associated with IDC-P. Design.— We compared radical prostatectomy (RP) findings and oncologic outcomes in men with prostate cancer exhibiting IDC-P with (IDC-P+/N+) versus without (IDC-P+/N−) comedonecrosis. Results.— Of the 558 RPs examined, IDC-P was present in 213 cases (38.2%), including 167 (78.4%) with IDC-P+/N− and 46 (21.6%) with IDC-P+/N+. When comparing IDC-P+/N− versus IDC-P+/N+ cases, the presence of necrosis was significantly associated with higher tumor grade, higher incidence of pT3/pT3b or pN1 disease, and larger estimated tumor volume. Outcome analysis revealed a significantly higher risk of disease progression in IDC-P+/N+ patients than in IDC-P+/N− patients (P &lt; .001). Significant differences in progression-free survival between IDC-P+/N− and IDC-P+/N+ patients were also seen in subgroups, such as those without (P = .01) or with (P = .03) adjuvant therapy immediately after RP, those with pN0 disease (P &lt; .001), and, more interestingly, those exhibiting conventional Gleason pattern 5 component (P = .02). Multivariate analysis showed significance for IDC-P+/N+ when IDC-P (grade 4) and IDC-P+/N+ (grade 5) were (hazard ratio, 1.768; P = .049) or were not (hazard ratio, 2.000; P = .008) incorporated into the Gleason score. Conclusions.— IDC-P+/N+ was found to be associated with worse histopathologic features on RP and poorer prognosis as an independent predictor. Pathologists may thus need to report the presence or absence of not only IDC-P but also comedonecrosis within IDC-P.

Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the first hours of hospitalization. Here, the focus is instead on rehospitalized patients, a cohort in which rich longitudinal features from prior hospitalizations can be analyzed. Our objective is to provide a risk score directly at hospital re-entry. Gradient boosting, penalized logistic regression (with and without stability selection), and a recurrent neural network are trained on two years of adult inpatient EHR data (3,387 attributes for 34,505 patients who generated 90,013 training samples with 5,618 cases and 84,395 controls). Predictions are internally evaluated with 50 iterations of 5-fold grouped cross-validation with special emphasis on calibration, an analysis of which is performed at the patient as well as hospitalization level. Error is assessed with respect to diagnosis, race, age, gender, AKI identification method, and hospital utilization. In an additional experiment, the regularization penalty is severely increased to induce parsimony and interpretability. Predictors identified for rehospitalized patients are also reported with a special analysis of medications that might be modifiable risk factors. Insights from this study might be used to construct a predictive tool for AKI in rehospitalized patients. An accurate estimate of AKI risk at hospital entry might serve as a prior for an admitting provider or another predictive algorithm.

BackgroundTo characterize the regional and national variation in prescribing patterns in the Medicare Part D program using dimensional reduction visualization methods.MethodsUsing publicly available Medicare Part D claims data, we identified and visualized regional and national provider prescribing profile variation with unsupervised clustering and t-distributed stochastic neighbor embedding (t-SNE) dimensional reduction techniques. Additionally, we examined differences between regionally representative prescribing patterns for major metropolitan areas.ResultsDistributions of prescribing volume and medication diversity were highly skewed among over 800,000 Medicare Part D providers. Medical specialties had characteristic prescribing patterns. Although the number of Medicare providers in each state was highly correlated with the number of Medicare Part D enrollees, some states were enriched for providers with > 10,000 prescription claims annually. Dimension-reduction, hierarchical clustering and t-SNE visualization of drug- or drug-class prescribing patterns revealed that providers cluster strongly based on specialty and sub-specialty, with large regional variations in prescribing patterns. Major metropolitan areas had distinct prescribing patterns that tended to group by major geographical divisions.ConclusionsThis work demonstrates that unsupervised clustering, dimension-reduction and t-SNE visualization can be used to analyze and visualize variation in provider prescribing patterns on a national level across thousands of medications, revealing substantial prescribing variation both between and within specialties, regionally, and between major metropolitan areas. These methods offer an alternative system-wide and pattern-centric view of such data for hypothesis generation, visualization, and pattern identification.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0670-2) contains supplementary material, which is available to authorized users.

With hospital-onset Clostridium difficile Infection (CDI) still a common occurrence in the U.S., this paper examines the relationship between unit-wide CDI susceptibility and inpatient mobility and creates a predictive measure of CDI called "Contagion Centrality". A mobility network was constructed using two years of patient electronic health record (EHR) data within a 739-bed hospital (Jan. 2013 -Dec. 2014; n=72,636 admissions). Network centrality measures were calculated for each hospital unit (node) providing clinical context for each in terms and patient transfers between units (edges). Daily unit-wide CDI susceptibility scores were calculated using logistic regression and compared to network centrality measures to determine the relationship between unit CDI susceptibility and patient mobility. Closeness centrality was a statistically significant measure associated with unit susceptibility (p-value < 0.05), highlighting the importance of incoming patient mobility in CDI prevention at the unit-level. Contagion Centrality (CC) was calculated using incoming inpatient transfer rates, unit-wide susceptibility of CDI, and current hospital CDI infections. This measure is statistically significant (p-value <0.05) with our outcome of hospital-onset CDI cases, and captures the additional opportunities for transmission associated with inpatient transfers. We have used this analysis to create an easily interpretable and informative clinical tool showing this relationship and risk of hospital-onset CDI in real-time. Quantifying and visualizing the combination of inpatient transfers, unit-wide risk, and current infections help identify hospital units at risk of developing a CDI outbreak, and thus provide clinicians and infection prevention staff with advanced warning and specific location data to concentrate prevention efforts.