Relative sparsity for medical decision problems.

Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (eg, whether to administer hypotension treatment based on covariates blood pressure and heart rate). There is great interest in using such data-driven policies in healthcare. However, it is often important to explain to the healthcare provider, and to the patient, how a new policy differs from the current standard of care. This end is facilitated if one can pinpoint the aspects of the policy (ie, the parameters for blood pressure and heart rate) that change when moving from the standard of care to the new, suggested policy. To this end, we adapt ideas from Trust Region Policy Optimization (TRPO). In our work, however, unlike in TRPO, the difference between the suggested policy and standard of care is required to be sparse, aiding with interpretability. This yields "relative sparsity," where, as a function of a tuning parameter, λ $$ \lambda $$ , we can approximately control the number of parameters in our suggested policy that differ from their counterparts in the standard of care (eg, heart rate only). We propose a criterion for selecting λ $$ \lambda $$ , perform simulations, and illustrate our method with a real, observational healthcare dataset, deriving a policy that is easy to explain in the context of the current standard of care. Our work promotes the adoption of data-driven decision aids, which have great potential to improve health outcomes.

Visualizing nationwide variation in medicare Part D prescribing patterns.

BACKGROUND: To characterize the regional and national variation in prescribing patterns in the Medicare Part D program using dimensional reduction visualization methods. METHODS: Using 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. RESULTS: Distributions 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. CONCLUSIONS: This 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.

Open-Source Radiation Exposure Extraction Engine (RE3) with Patient-Specific Outlier Detection.

We present an open-source, picture archiving and communication system (PACS)-integrated radiation exposure extraction engine (RE3) that provides study-, series-, and slice-specific data for automated monitoring of computed tomography (CT) radiation exposure. RE3 was built using open-source components and seamlessly integrates with the PACS. RE3 calculations of dose length product (DLP) from the Digital imaging and communications in medicine (DICOM) headers showed high agreement (R (2) = 0.99) with the vendor dose pages. For study-specific outlier detection, RE3 constructs robust, automatically updating multivariable regression models to predict DLP in the context of patient gender and age, scan length, water-equivalent diameter (D w), and scanned body volume (SBV). As proof of concept, the model was trained on 811 CT chest, abdomen + pelvis (CAP) exams and 29 outliers were detected. The continuous variables used in the outlier detection model were scan length (R (2) = 0.45), D w (R (2) = 0.70), SBV (R (2) = 0.80), and age (R (2) = 0.01). The categorical variables were gender (male average 1182.7 ± 26.3 and female 1047.1 ± 26.9 mGy cm) and pediatric status (pediatric average 710.7 ± 73.6 mGy cm and adult 1134.5 ± 19.3 mGy cm).

The Clinical Impact of Comedonecrosis Within Intraductal Carcinoma of the Prostate.

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 < .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 < .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.

Predicting hospital-onset Clostridium difficile using patient mobility data: A network approach.

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.

Predicting acute kidney injury at hospital re-entry using high-dimensional electronic health record data.

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.