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Record W2926771130 · doi:10.1161/hcq.12.suppl_1.15

Abstract 15: Telephone Intervention to Improve Quality and Safety After Percutaneous Coronary Intervention

2019· article· en· W2926771130 on OpenAlexaff
Jeffrey Chidester, Daniel Bennett, Kim Berger, Laurie Beall, Tiffany Denkins, Chris Mathew, Kristin Alvarez, Michael Luna, Tayo Addo, Rebecca Vigen, Sandeep R. Das

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2019
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsBerger (Canada)
Fundersnot available
KeywordsMedicineConventional PCIPercutaneous coronary interventionPsychological interventionContext (archaeology)PharmacyEmergency medicinePatient safetyMedical emergencyIntervention (counseling)Internal medicineIntensive care medicineHealth careMyocardial infarctionFamily medicineNursing

Abstract

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Context: Poor adherence to dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI) is associated with poor outcomes including stent thrombosis, rehospitalization, and mortality. Safety-net patients are at high risk for low adherence due to poor health literacy and financial constraints. Understanding and overcoming barriers to patient adherence to DAPT after PCI may reduce the risk of adverse outcomes in safety-net patients. Objective: A multidisciplinary team of Nurses, Pharmacists, Internal Medicine Residents, and Cardiologists designed and implemented a telephone-based intervention to improve adherence to DAPT after PCI at Parkland Health & Hospital System (PHHS), a safety-net hospital system in Dallas, Texas. Methods: Beginning 9/1/17, nurses from the Parkland Cardiac Catheterization Laboratory called all PCI patients 7, 30, and 90 days post-PCI. Patients were reminded of the importance of DAPT and asked if they had concerns about their medications. Specific issues that arose were handled by the multidisciplinary team via interventions such as: provision of vouchers for reduced-cost medications, change in pharmacy or medication to lower cost, appointment with Pharmacy or Cardiology clinicians to discuss medication, confirmation of active refills. The total number of patients with self-reported nonadherence was quantified, as was proportion of days covered (PDC) on DAPT at 6 months, defined as the total number of days filled in the first 6 months post-PCI divided by 180 days. Results: From 9/1/17 - 2/28/18, 189 patients underwent PCI at Parkland. Of these patients, 67% (127 of 189) were able to be contacted, with 10% (13 of 127) of contacted patients reporting problems with medications resulting in complete nonadherence and requiring intervention. Of the total treated patients, 65% (123 of 189) had prescriptions filled at PHHS pharmacies which allowed for manual calculation of adherence data and 72% (89 of 123) of these patients were successfully contacted. Median PDC was 94% at 6 months, with PDC >90% in 59% (72 of 123) of all patients and 62% (55 of 89) of successfully contacted patients. A historical control sample (N=154) who underwent PCI at Parkland from 1/1/16 - 8/31/17 and filled prescriptions at PHHS pharmacies had a median PDC of 92% at 6 months, with PDC >90% in 55% (84 of 154). There was no significant difference in median PDC (p=0.4) or proportion of patients with PDC >90% in either all-study (p=0.37) or successful contact (p=0.19) patient populations compared to this control. Conclusion: A telephone-based intervention allowed for identification and resolution of potentially catastrophic barriers to DAPT adherence in 1 in 10 contacted patients within 6 months post-PCI. However, it was unable to significantly improve medication adherence as measured by PDC, indicating that this adherence metric may not be an adequate measure of impact in this context.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.336
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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