Type 2 Diabetes Mellitus is a progressive disease with increased risk of developing serious complications. Identifying subpopulations and their relevant risk factors can contribute to the prevention ...and effective management of diabetes. We use a novel divisive hierarchical clustering technique to identify clinically interesting subpopulations in a large cohort of Olmsted County, MN residents. Our results show that our clustering algorithm successfully identified clinically interesting clusters consisting of patients with higher or lower risk of diabetes than the general population. The proposed algorithm offers fine control over the granularity of the clustering, has the ability to seamlessly discover and incorporate interactions among the risk factors, and can handle non-proportional hazards, as well. It has the potential to significantly impact clinical practice by recognizing patients with specific risk factors who may benefit from an alternative management approach potentially leading to the prevention of diabetes and its complications.
Prediabetes is the most important risk factor for developing type-2 diabetes mellitus, an important and growing epidemic. Prediabetes is often associated with comorbidities including ...hypercholesterolemia. While statin drugs are indicated to treat hypercholesterolemia, recent reports suggest a possible increased risk of developing overt diabetes associated with the use of statins. Association rule mining is a data mining technique capable of identifying interesting relationships between risks and treatments. However, it is limited in its ability to accurately calculate the effect of a treatment, as it does not appropriately account for bias and confounding. We propose a novel combination of propensity score matching and association rule mining to account for this bias, and find meaningful associations between a treatment and outcome for various subpopulations. We demonstrate this technique on a real diabetes data set examining the relationship between statin use and diabetes, and identify risk and protective factors previously not clearly defined.
Early detection of patients with elevated risk of developing diabetes mellitus is critical to the improved prevention and overall clinical management of these patients. We aim to apply association ...rule mining to electronic medical records (EMR) to discover sets of risk factors and their corresponding subpopulations that represent patients at particularly high risk of developing diabetes. Given the high dimensionality of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, strengths and weaknesses. We proposed extensions to incorporate risk of diabetes into the process of finding an optimal summary. We evaluated these modified techniques on a real-world prediabetic patient cohort. We found that all four methods produced summaries that described subpopulations at high risk of diabetes with each method having its clear strength. For our purpose, our extension to the Buttom-Up Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.
To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can ...deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR).
We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR.
The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance.
This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.
The level of CYP2D6 metabolic activity can be predicted by pharmacogenomic testing, and concomitant use of clinical decision support has the potential to prevent adverse effects from those drugs ...metabolized by this enzyme. Our initial findings after implementation of clinical decision support alerts integrated in the electronic health records suggest high feasibility, but also identify important challenges.
Computer-based clinical decision-support systems are effective interventions to improve compliance with guidelines and quality measures. However, understanding of their long-term impact, including ...unintended consequences, is limited. The authors assessed the clinical impact of the sequential implementation of 2 such systems to improve the use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs) in inpatients with heart failure. Compliance with the core measure improved from 91.0% at baseline to 93.6% with the Pharmacy Care (P-Care) Rule and to 96.4% with the Centricity-Blaze (CE-Blaze) Rule. At the same time, prescriptions for ACEIs/ARBs documented in the hospital discharge summary decreased from 83.2% at baseline to 75.8% with the P-Care rule and to 64.1% with the CE-Blaze Rule. The inpatient mortality rate and the 30-day readmission rate did not change significantly. Better documentation of contraindications in the electronic medical record seems to account for the core measure improvement, even as ACEI/ARB therapy has unexpectedly declined.
Pharmacogenomics (PGx) is often promoted as the domain of precision medicine with the greatest potential to readily impact everyday healthcare. Rapid advances in PGx knowledge derived from extensive ...basic and clinical research along with decreasing costs of laboratory testing have led to an increased interest in PGx and expectations of imminent clinical translation with substantial clinical impact. However, the implementation of PGx into clinical workflows is neither simple nor straightforward, and comprehensive processes and multidisciplinary collaboration are required. Several national and international institutions have pioneered models for implementing clinical PGx, and these initial models have led to a better understanding of unresolved challenges. In this review, we have categorized and explored the most relevant of these challenges to highlight potential gaps and present possible solutions. We describe the ongoing need for basic and clinical research to drive further developments in evidence-based medicine. Integration into daily clinical workflows introduces new challenges requiring innovative solutions; specifically those related to the electronic health record and embedded clinical decision support. We describe advances in PGx testing and result reporting and describe the critical need for increased standardization in these areas across laboratories. We also explore the complexity of the PGx knowledge required for clinical practice and the need for educational strategies to ensure adequate understanding among members of current and future healthcare teams. Finally, we evaluate knowledge obtained from previous implementation efforts and discuss how to best apply these learnings to future projects. Despite these challenges, the future of precision medicine appears promising due to the rapidity of recent advances in the field and current multidisciplinary efforts to effectively translate PGx to everyday clinical practice. Keywords: precision medicine, pharmacogenomics, clinical implementation, clinical decision support, delivery of health care, medication therapy management
•This work presents an empirical analysis that relates modularity anomalies with Technical Debt.•The work is based on the application of a framework to identify modularity anomalies.•The study has ...been applied to three different software product lines.•The study provides information to anticipate refactoring decisions (reducing interest).
It is widely claimed that Technical Debt is related to quality problems being often produced by poor processes, lack of verification or basic incompetence. Several techniques have been proposed to detect Technical Debt in source code, as identification of modularity violations, code smells or grime buildups. These approaches have been used to empirically demonstrate the relation among Technical Debt indicators and quality harms. However, these works are mainly focused on programming level, when the system has already been implemented. There may also be sources of Technical Debt in non-code artifacts, e.g. requirements, and its identification may provide important information to move refactoring efforts to previous stages and reduce future Technical Debt interest. This paper presents an empirical study to evaluate whether modularity anomalies at requirements level are directly related to maintainability attributes affecting systems quality and increasing, thus, system's interest. The study relies on a framework that allows the identification of modularity anomalies and its quantification by using modularity metrics. Maintainability metrics are also used to assess dynamic maintainability properties. The results obtained by both sets of metrics are pairwise compared to check whether the more modularity anomalies the system presents, the less stable and more difficult to maintain it is.
Causal inference aims to estimate the causal relationships and effect sizes among treatments and outcomes. Electronic health record (EHR) data is a valuable healthcare data source that can support ...causal inference. However, a large percentage of the data is missing in EHRs and they are missing not at random (MNAR). Ignoring MNAR can lead to severe biases, to the extent where the causal structure underlying the data gets distorted. We proposed a new causal inference methodology that addresses the MNAR problem and thus helps preserve the causal structure. We compared the performance of our proposed method with the traditional causal inference method, structural equation modeling (SEM). We evaluated these methods for their accuracy in estimating the causal effect sizes and their ability to converge at all. We employed both simulation studies and real-world EHR data sets. We demonstrated that imputation under the improper missingness mechanism distorted the causal structure to a degree where SEM found it incompatible with the data and failed to. converge. Even when 20 to 30 % of the values were missing, SEM failed to converge in as many as 50% of the runs. The proposed causal inference method achieved a higher convergence rate and more accurate estimation of latent treatment effects both on the synthetic data and on a real EHR data set. We proposed a new methodology that incorporates the knowledge of missing data mechanisms. It significantly mitigated the biases associated with MNAR in the EHR dataset and substantially outperformed SEM that uses the improper missing data mechanism.
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