» Articles » PMID: 28982676

Prediction Score for Anticoagulation Control Quality Among Older Adults

Overview
Date 2017 Oct 7
PMID 28982676
Citations 18
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Time in the therapeutic range (TTR) is associated with the effectiveness and safety of vitamin K antagonist (VKA) therapy. To optimize prescribing of VKA, we aimed to develop and validate a prediction model for TTR in older adults taking VKA for nonvalvular atrial fibrillation and venous thromboembolism.

Methods And Results: The study cohort comprised patients aged ≥65 years who were taking VKA for atrial fibrillation or venous thromboembolism and who were identified in the 2 US electronic health record databases linked with Medicare claims data from 2007 through 2014. With the predictors identified from a systematic review and clinical knowledge, we built a prediction model for TTR, using one electronic health record system as the training set and the other as the validation set. We compared the performance of the new models to that of a published prediction score for TTR, SAMe-TTR. Based on 1663 patients in the training set and 1181 in the validation set, our optimized score included 42 variables and the simplified model included 7 variables, abbreviated as PROSPER (Pneumonia, Renal dysfunction, Oozing blood [prior bleeding], Staying in hospital ≥7 days, Pain medication use, no Enhanced [structured] anticoagulation services, Rx for antibiotics). The PROSPER score outperformed SAMe-TTR when predicting both TTR ≥70% (area under the receiver operating characteristic curve 0.67 versus 0.55) and the thromboembolic and bleeding outcomes (area under the receiver operating characteristic curve 0.62 versus 0.52).

Conclusions: Our geriatric TTR score can be used as a clinical decision aid to select appropriate candidates to receive VKA therapy and as a research tool to address confounding and treatment effect heterogeneity by anticoagulation quality.

Citing Articles

Comparative Bleeding Risk in Older Patients With HIV and Atrial Fibrillation Receiving Oral Anticoagulants.

Quinlan C, Avorn J, Kesselheim A, Singer D, Zhang Y, Cervone A JAMA Intern Med. 2025; .

PMID: 39992678 PMC: 11851300. DOI: 10.1001/jamainternmed.2024.8335.


Identifying Functional Status Impairment in People Living With Dementia Through Natural Language Processing of Clinical Documents: Cross-Sectional Study.

Laurentiev J, Kim D, Mahesri M, Wang K, Bessette L, York C J Med Internet Res. 2024; 26:e47739.

PMID: 38349732 PMC: 10900085. DOI: 10.2196/47739.


Development and Validation of a Claims-Based Model to Predict Categories of Obesity.

Suissa K, Wyss R, Lu Z, Bessette L, York C, Tsacogianis T Am J Epidemiol. 2023; 193(1):203-213.

PMID: 37650647 PMC: 11484604. DOI: 10.1093/aje/kwad178.


Development and Validation of a Novel Tool to Predict Model for End-Stage Liver Disease (MELD) Scores in Cirrhosis, Using Administrative Datasets.

Simon T, Schneeweiss S, Wyss R, Lu Z, Bessette L, York C Clin Epidemiol. 2023; 15:349-362.

PMID: 36941978 PMC: 10024467. DOI: 10.2147/CLEP.S387253.


A Novel Chronic Kidney Disease Phenotyping Algorithm Using Combined Electronic Health Record and Claims Data.

Mansour O, Paik J, Wyss R, Mastrorilli J, Bessette L, Lu Z Clin Epidemiol. 2023; 15:299-307.

PMID: 36919110 PMC: 10008306. DOI: 10.2147/CLEP.S397020.


References
1.
Johnson E, Bartman B, Briesacher B, Fleming N, Gerhard T, Kornegay C . The incident user design in comparative effectiveness research. Pharmacoepidemiol Drug Saf. 2012; 22(1):1-6. DOI: 10.1002/pds.3334. View

2.
Rudd K, Dier J . Comparison of two different models of anticoagulation management services with usual medical care. Pharmacotherapy. 2010; 30(4):330-8. DOI: 10.1592/phco.30.4.330. View

3.
White R, Riggs K, Ege E, Petroski G, Koerber S, Flaker G . The effect of the amiodarone-warfarin interaction on anticoagulation quality in a single, high-quality anticoagulation center. Blood Coagul Fibrinolysis. 2015; 27(2):147-50. DOI: 10.1097/MBC.0000000000000397. View

4.
Austin P . Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009; 28(25):3083-107. PMC: 3472075. DOI: 10.1002/sim.3697. View

5.
Hart R, Pearce L, Aguilar M . Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med. 2007; 146(12):857-67. DOI: 10.7326/0003-4819-146-12-200706190-00007. View