To develop clinical decision support (CDS) for familial hypercholesterolemia (FH), based on physician input obtained by a mixed methods approach.
Awareness, detection, and control of FH—a relatively ...common genetic disorder—is low. Clinical decision support could address knowledge gaps and provide point-of-care guidance for the management of FH.
A 16-question survey that assessed familiarity with FH and sought input on potential content of the CDS tool was emailed to 1161 clinicians including 208 cardiologists. In addition, 4 physician focus groups were held to gather input on the structure and form of the CDS tool. This study took place between September 12, 2016, and January 16, 2017.
The response rate to the survey was 18.1%. Clinicians were overwhelmingly (97.6%) in favor of a CDS tool that assists in managing patients with FH at the point of care and this was confirmed in the focus group discussions. Key themes emerged during the focus groups including providers' knowledge and understanding of FH, facilitators and barriers to implementing a CDS tool, and suggestions for its design and content.
Clinicians were supportive of development of a CDS tool to assist with the evaluation and treatment of FH and provided feedback related to the design and implementation of such a tool.
QT prolongation is an independent risk factor for cardiovascular mortality in adults. However, there is little information available on pediatric patients with QT prolongation and their outcomes. ...Herein, we evaluated the prevalence of QT prolongation in pediatric patients identified by an institution-wide QT alert system, and the spectrum of their phenotype. Patients with documented QT prolongation on an ECG obtained between November 2010 and June 2011 were included. There were 1303 pediatric ECGs, and 68 children had electrographically isolated QT prolongation. Comprehensive review of medical records was performed with particular attention to QT-prolonging clinical, laboratory, and medication data, which were summarized into a pro-QTc score. Overall, 68 (5 %) pediatric patients had isolated QT prolongation. The mean age of this pediatric cohort was 9 ± 6 years, and the average QTc was 494 ± 42 ms. All children had 1 or more QT-prolonging risk factor(s), most commonly QT-prolonging medications. One patient was identified with congenital long QT syndrome (LQTS), which was not previously diagnosed. In one-year follow-up, only one pediatric death (non-cardiac) occurred (1.5 %). Potentially QT-offending/pro-arrhythmic medications were changed in 80 % of pediatric patients after the physician received the QT alert. Children with QT prolongation had very low mortality and minimal polypharmacy. Still, medications and other modifiable conditions were the most common causes of QT prolongation. Children with a prolonged QTc should be evaluated for modifiable QT-prolonging factors. However, if no risk factors are present or the QTc does not attenuate after risk factor modification/removal, the child should be evaluated for congenital LQTS.
Type-2 Diabetes Mellitus is a growing epidemic that often leads to severe complications. Effective preventive measures exist and identifying patients at high risk of diabetes is a major health-care ...need. The use of association rule mining (ARM) is advantageous, as it was specifically developed to identify associations between risk factors in an interpretable form. Unfortunately, traditional ARM is not directly applicable to survival outcomes and it lacks the ability to compensate for confounders and to incorporate dosage effects. In this work, we propose Survival Association Rule (SAR) Mining, which addresses these shortcomings. We demonstrate on a real diabetes data set that SARs are naturally more interpretable than the traditional association rules, and predictive models built on top of these rules are very competitive relative to state of the art survival models and substantially outperform the most widely used diabetes index, the Framingham score.
Because deterioration in overall metabolic health underlies multiple complications of Type 2 Diabetes Mellitus, a substantial overlap among risk factors for the complications exists, and this makes ...the outcomes difficult to distinguish. We hypothesized each risk factor had two roles: describing the extent of deteriorating overall metabolic health and signaling a particular complication the patient is progressing towards. We aimed to examine feasibility of our proposed methodology that separates these two roles, thereby, improving interpretation of predictions and helping prioritize which complication to target first. To separate these two roles, we built models for six complications utilizing Multi-Task Learning-a machine learning technique for modeling multiple related outcomes by exploiting their commonality-in 80% of EHR data (N=9,793) from a university hospital and validated them in remaining 20% of the data. Additionally, we externally validated the models in claims and EHR data from the OptumLabs™ Data Warehouse (N=72,720). Our methodology successfully separated the two roles, revealing distinguishing outcome-specific risk factors without compromising predictive performance. We believe that our methodology has a great potential to generate more understandable thus actionable clinical information to make a more accurate and timely prognosis for the patients.
Clinical use of pharmacogenomic (PGx) knowledge at the bedside is new and complex. Our program has implemented multiple PGx-CDS interventions in different clinical settings and in multiple commercial ...EHRs. Herein, we discuss lessons learned and propose general technical guidelines related to PGx implementation.
Significant barriers, such as lack of professional guidelines, specialized training for interpretation of pharmacogenomics (PGx) data, and insufficient evidence to support clinical utility, prevent ...preemptive PGx testing from being widely clinically implemented. The current study, as a pilot project for the Right Drug, Right Dose, Right Time–Using Genomic Data to Individualize Treatment Protocol, was designed to evaluate the impact of preemptive PGx and to optimize the workflow in the clinic setting. We used an 84-gene next-generation sequencing panel that included SLCO1B1 , CYP2C19, CYP2C9, and VKORC1 together with a custom-designed CYP2D6 testing cascade to genotype the 1013 subjects in laboratories approved by the Clinical Laboratory Improvement Act. Actionable PGx variants were placed in patient's electronic medical records where integrated clinical decision support rules alert providers when a relevant medication is ordered. The fraction of this cohort carrying actionable PGx variant(s) in individual genes ranged from 30% ( SLCO1B1 ) to 79% ( CYP2D6 ). When considering all five genes together, 99% of the subjects carried an actionable PGx variant(s) in at least one gene. Our study provides evidence in favor of preemptive PGx testing by identifying the risk of a variant being present in the population we studied.
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.
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