Surveillance of medication use: early identification of poor adherence

Background: We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence. Methods Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication possession, to measure adherence among subjects and to build models for predicting poor long-term adherence. Results: All prescription fills for a specific drug were sequenced chronologically into drug eras. 61.3% to 66.5% of the prescription patterns contained medication gaps >30 days during the first year of drug use. These interrupted drug eras include long-term discontinuations, where the subject never again filled a prescription for any drug in that category in the dataset, which represent 23.7% to 29.1% of all drug eras. Among the prescription-filling patterns without large medication gaps, 0.8% to 1.3% exhibited long-term poor adherence. Our models identified these subjects as early as 60 days after the first prescription fill, with an area under the curve (AUC) of 0.81. Model performance improved as the predictions were made at later time-points, with AUC values increasing to 0.93 at the 120-day time-point. Conclusions: Dispensed medication histories (widely available in real time) are useful for alerting providers about poorly adherent patients and those who will be non-adherent several months later. Efforts to use these data in point of care and decision support facilitating patient are warranted

Early Detection of Poor Adherers to Statins: Applying Individualized Surveillance to Pay for Performance

Background: Medication nonadherence costs $300 billion annually in the US. Medicare Advantage plans have a financial incentive to increase medication adherence among members because the Centers for Medicare and Medicaid Services (CMS) now awards substantive bonus payments to such plans, based in part on population adherence to chronic medications. We sought to build an individualized surveillance model that detects early which beneficiaries will fall below the CMS adherence threshold. Methods: This was a retrospective study of over 210,000 beneficiaries initiating statins, in a database of private insurance claims, from 2008-2011. A logistic regression model was constructed to use statin adherence from initiation to day 90 to predict beneficiaries who would not meet the CMS measure of proportion of days covered 0.8 or above, from day 91 to 365. The model controlled for 15 additional characteristics. In a sensitivity analysis, we varied the number of days of adherence data used for prediction. Results: Lower adherence in the first 90 days was the strongest predictor of one-year nonadherence, with an odds ratio of 25.0 (95% confidence interval 23.7-26.5) for poor adherence at one year. The model had an area under the receiver operating characteristic curve of 0.80. Sensitivity analysis revealed that predictions of comparable accuracy could be made only 40 days after statin initiation. When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value. Conclusions: To preserve their Medicare Star ratings, plan managers should identify or develop effective programs to improve adherence. An individualized surveillance approach can be used to target members who would most benefit, recognizing the tradeoff between improved model performance over time and the advantage of earlier detection

Knowledge-based instantiation of full atomic detail into coarse-grain RNA 3D structural models

Motivation: The recent development of methods for modeling RNA 3D structures using coarse-grain approaches creates a need to bridge low- and high-resolution modeling methods. Although they contain topological information, coarse-grain models lack atomic detail, which limits their utility for some applications

Differentiation and Transplantation of Embryonic Stem Cell-Derived Cone Photoreceptors into a Mouse Model of End-Stage Retinal Degeneration

The loss of cone photoreceptors that mediate daylight vision represents a leading cause of blindness, for which cell replacement by transplantation offers a promising treatment strategy. Here, we characterize cone differentiation in retinas derived from mouse embryonic stem cells (mESCs). Similar to in vivo development, a temporal pattern of progenitor marker expression is followed by the differentiation of early thyroid hormone receptor β2-positive precursors and, subsequently, photoreceptors exhibiting cone-specific phototransduction-related proteins. We establish that stage-specific inhibition of the Notch pathway increases cone cell differentiation, while retinoic acid signaling regulates cone maturation, comparable with their actions in vivo. MESC-derived cones can be isolated in large numbers and transplanted into adult mouse eyes, showing capacity to survive and mature in the subretinal space of Aipl1−/− mice, a model of end-stage retinal degeneration. Together, this work identifies a robust, renewable cell source for cone replacement by purified cell suspension transplantation

Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters

Understanding the function of complex RNA molecules depends critically on understanding their structure. However, creating three-dimensional (3D) structural models of RNA remains a significant challenge. We present a protocol (the nucleic acid simulation tool [NAST]) for RNA modeling that uses an RNA-specific knowledge-based potential in a coarse-grained molecular dynamics engine to generate plausible 3D structures. We demonstrate NAST's capabilities by using only secondary structure and tertiary contact predictions to generate, cluster, and rank structures. Representative structures in the best ranking clusters averaged 8.0 ± 0.3 Å and 16.3 ± 1.0 Å RMSD for the yeast phenylalanine tRNA and the P4-P6 domain of the Tetrahymena thermophila group I intron, respectively. The coarse-grained resolution allows us to model large molecules such as the 158-residue P4-P6 or the 388-residue T. thermophila group I intron. One advantage of NAST is the ability to rank clusters of structurally similar decoys based on their compatibility with experimental data. We successfully used ideal small-angle X-ray scattering data and both ideal and experimental solvent accessibility data to select the best cluster of structures for both tRNA and P4-P6. Finally, we used NAST to build in missing loops in the crystal structures of the Azoarcus and Twort ribozymes, and to incorporate crystallographic data into the Michel–Westhof model of the T. thermophila group I intron, creating an integrated model of the entire molecule. Our software package is freely available at https://simtk.org/home/nast

Model performance versus time of prediction.

<p>Separate curves are presented for members whose first statin fill was for 30 days or less (circles), and for members whose first statin fill was for more than 30 days (triangles). For the ≤30 day subset, performance improves sharply between days 30 and 31, and it improves steadily thereafter. However, for the >30 day subset, (most of whom had 90-day fills,) performance improves sharply only after day 90. Each Y coordinate expresses the performance of a model that uses prescription fills from days 1 to X to predict adherence from days X to 365.</p

Timeline showing periods used to calculate adherence measures and baseline variables.

<p>Outcome variable is proportion of days covered (PDC) by statin for days 91-365, respectively. Three baseline variables are calculated from the first non-statin prescription to the index date. Presence of ACS (acute coronary syndrome) as a baseline variable was determined in the 30 days prior to statin initiation. Nine baseline variables including early PDC are calculated from statin prescriptions in days 1-90. Time from eligibility to initiation is required to be 180 days (in both models) in order to include those truly initiating statins, and not those merely continuing statins after switching insurance plans. </p

Study flow diagram showing exclusion criteria.

<p>The sum of members who met each individual exclusion criterion does not equal the total number excluded because one member can meet more than one exclusion criterion.</p