Mingyuan Zhang

Changes in Oral Anticoagulant Treatment Rates in Atrial Fibrillation before and after the Introduction of Direct Oral Anticoagulants

Background: Direct oral anticoagulants (DOACs) have the potential to improve stroke prevention among atrial fibrillation (AF) patients. We sought to determine if oral anticoagulation (OAC) treatment rates have increased since the approval of DOACs. Methods: We identified 6,688 patients with AF at an academic medical center from January 2008 to June 2015. We examined OAC prescription rates over time and according to CHA2DS2VASc score using multivariable Poisson regression models, with an interaction term between risk score and year of AF diagnosis. Results: Among 6,688 AF patients, 78% had CHA2DS2VASc scores ≥2, 51.6% of whom received an OAC prescription within 90 days of diagnosis. The OAC prescription rate was 47.8% in the pre-DOAC era and peaked at 56.4% in 2014. Relative to the pre-DOAC era, prescription rates increased in 2012 and leveled off thereafter. The prescription rate for the highest risk group was 58.5%, compared with 45.0% in patients with a CHA2DS2VASc score of 2 (p < 0.01). In the adjusted analysis, prescription rates were higher for the higher risk group (adjusted relative risk 1.24 for CHA2DS2VASc score 7-9 vs. 2, 95% CI 1.09-1.40). Conclusions: OAC treatment rates have increased since DOAC introduction, but substantial treatment gaps remain, specifically among the higher risk patients.

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study

BackgroundThe aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time.MethodsOur analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak.ResultsThe analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%.ConclusionThe proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

The perils of meta-regression to identify clinical decision support system success factors

Clinical decision support interventions are typically heterogeneous in nature, making it difficult to identify why some interventions succeed while others do not. One approach to identify factors important to the success of health information systems is the use of meta-regression techniques, in which potential explanatory factors are correlated with the outcome of interest. This approach, however, can result in misleading conclusions due to several issues. In this manuscript, we present a cautionary case study in the context of clinical decision support systems to illustrate the limitations of this type of analysis. We then discuss implications and recommendations for future work aimed at identifying success factors of medical informatics interventions. In particular, we identify the need for head-to-head trials in which the importance of system features is directly evaluated in a prospective manner.

Extracting Intrauterine Device Usage from Clinical Texts Using Natural Language Processing

Intrauterine devices (IUDs) are highly-effective contraceptive methods for preventing unintended pregnancy and related adverse outcomes. Clinical Decision Support (CDS) systems could aid care providers in identifying patients at risk for pregnancy due to lack of contraceptive use. However, research suggests that this information is not reliably documented in structured data fields for query, but rather in the clinical notes. As a first step towards developing a robust CDS tool to identify high-risk patients for contraceptive counseling, we developed a clinical information extraction tool, EasyCIE, that readily identifies mentions of IUD usage and classifies whether a note contains evidence that an IUD is present or not for review by domain experts. In this preliminary study, EasyCIE produced high recall and excellent precision distinguishing notes of patients with current IUD usage from notes of patients with historical or no usage.