\n use and patient outcomes in contemporary practice. INR stability refers to achieving
\n and maintaining target INR range (typically 2\u20133, but not always). Therefore, if target
\n INR range is not achieved or maintained this would be considered INR in<\/em>stability.\n <\/p>\n
We will determine: 1) to what extent INR instability can be anticipated, 2) whether
\n INR instability is predictable, and 3) the consequences of INR instability.\n <\/p>\n
Metrics of INR<\/h4>\n
Despite 60\u00a0years of clinical experience, the maintenance of stable INR in patients
\n using VKA remains a challenging task. While numerous metrics have been used in clinical
\n studies of VKAs to assess the quality of anticoagulation control 9<\/a>], 10<\/a>], time in therapeutic range (TTR) (most commonly calculated using Rosendaal\u2019s method
\n of linear interpolation 11<\/a>]) is the most frequently reported. Experts have suggested that the minimum target
\n TTR should be no less than 65\u00a0% 12<\/a>]\u201315<\/a>] but this goal is often not met 16<\/a>]\u201322<\/a>] even in modern day RCTs 23<\/a>]\u201332<\/a>] (Table\u00a01<\/a>). A large observational assessment of 40,404 patients in the VA population demonstrated
\n that 42\u00a0% of patients had INR stability (defined as TTR??70\u00a0%) while 34\u00a0% had moderate
\n instability (TTR 50 to 70\u00a0%), and 23\u00a0% had high instability (TTR 50\u00a0%) 33<\/a>]. A recently published retrospective analysis from the CoagClinic\u2122 database assessed
\n 9433 patients who met the inclusion criteria and had been using warfarin for over
\n 6\u00a0months 34<\/a>]. In these chronic warfarin patients, more than 90\u00a0% had at least one value below
\n 2 and 82\u00a0% had at least one value above 3 (Fig.\u00a01<\/a>).<\/p>\n
Table 1.<\/strong><\/a> Mean time in the therapeutic range observed in recent atrial fibrillation and venous Using data from the multicenter ORBIT-AF (Outcomes Registry for Better Informed Treatment Multiple meta-analyses of randomized and real-world studies have been performed in Table 2.<\/strong><\/a> Results of meta-analyses evaluating the international normalized ratio stability in The literature from clinical trials and observational studies substantiate that INR The consequences of INR instability are multifaceted. INR instability was associated An observational study by Nelson et al, 38<\/a>] using the Veterans Health Administration (VHA) dataset, explored the relationship Further evidence showed the link between INR instability and clinical events. In meta-analyses Inability to achieve high TTR in clinical practice is associated with non-persistence INR instability was associated with higher healthcare utilization and costs. In an The literature suggests that INR instability has important clinical and financial Based on data from adjusted meta-regression or multivariate analyses of large datasets, Table 3.<\/strong><\/a> Summative assessment of factors shown to positively or negatively impact INR stability\n <\/p>\n Two of the most extensive studies were conducted by Apostolakis et al. (SAMe-TT2R2) Table 4.<\/strong><\/a> SAMe-TT2R2 scoring system and implications\n <\/p>\n The Veterans AffaiRs study to Improve Anticoagulation (VARIA) 48<\/a>] used data from over 124,000 veterans receiving warfarin for any indication (55\u00a0% Two reasons why the clinical prediction tools are inadequate may be related to genetics Medication adherence was highlighted as an important variable in VKA INR stability Existing research provided good understanding of the drivers behind poor INR control. There are several modalities to improve the clinical management of patients on VKAs Point of care testing by the patient or clinician allow rapid determination of the In an 8-month open label crossover trial conducted in Canadian primary care offices, Vitamin K antagonists (VKAs) such as warfarin inhibit the enzyme vitamin K epoxide reductase and consequently the recycling of inactive vitamin K epoxide back to its active, reduced form 1]. Vitamin K in its active form is required for the synthesis of various clotting factors (II, VII, IX and X) involved in the coagulation cascade Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-83239","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/posts\/83239","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/comments?post=83239"}],"version-history":[{"count":0,"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/posts\/83239\/revisions"}],"wp:attachment":[{"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/media?parent=83239"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/categories?post=83239"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/healthmedicinet.com\/i\/wp-json\/wp\/v2\/tags?post=83239"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n thromboembolism randomized controlled trials of novel target oral anticoagulants\n <\/p>\n![\"thumbnail\"](\"\/content\/figures\/s12959-016-0088-y-1.gif\")
Fig. 1.<\/strong><\/a> Percent of patients with ?1 INRs outside the normal therapeutic range. This figure
\n displays the % of people in an analysis 34<\/a>] of the CoagCheckTM database with at least one INR value outside of the normal therapeutic
\n range with blue boxes showing the percent of patients who were either below 2.0 (90\u00a0%)
\n or above 3.0 (82\u00a0%). The red, green, purple, and orange boxes display the percent
\n of people who ever achieved a level of 3.0\u20134.0, 4.0\u20135.0, 5.0\u20136.0, and 6.0, respectively.
\n The same individual could be represented in multiple categories given their INRs achieved
\n over time including being below 2.0 and above 3.0\n <\/p>\n
\n of Atrial Fibrillation) registry, the INR stability of 3749 patients on chronic warfarin
\n therapy for 6\u00a0months was assessed 35<\/a>]. Only 26\u00a0% (95%CI: 24 to 27\u00a0%) of patients had 80\u00a0% or more of their INRs between
\n 2 and 3. Among this subgroup with INR stability, 92\u00a0% (95%CI: 90 to 94\u00a0%) had at least
\n one value outside of the normal INR range while 36\u00a0% (95%CI: 33 to 39\u00a0%) had an INR
\n below 1.5 or above 4 over the subsequent year. Thus, even the \u201ccream of the crop\u201d
\n \u2013 those patients able to achieve most of their values within target range within a
\n 6-month period \u2013 had at least occasional out-of-range values over longer-term follow-up.\n <\/p>\n
\n order to estimate the quality of INR control in AF and VTE populations receiving VKAs
\n 16<\/a>]\u201320<\/a>], 22<\/a>]. These meta-analyses demonstrate poor INR control to be \u2018the rule rather than the
\n exception\u2019 with TTRs and proportion of INR measurement in range typically falling
\n near or below 60\u00a0% and nearly twice the amount of time being spent below versus above
\n the therapeutic INR range (Table\u00a02<\/a>) 16<\/a>]\u201320<\/a>], 22<\/a>].<\/p>\n
\n atrial fibrillation or venous thromboembolism patients\n <\/p>\n
\n stability is not readily attainable and when it occurs, is rarely sustainable over
\n time.\n <\/p>\nConsequences of INR Instability<\/h4>\n
Outcomes<\/h4>\n
\n with clinical events, higher level of medication non-persistence and discontinuation,
\n utilization of more healthcare resources, and therefore, higher costs. According to
\n meta-analyses of AF or mixed populations assessing INR control and associated events
\n 18<\/a>], 36<\/a>]\u201338<\/a>], greater than half of all thromboembolic events occurred when patients have an INR??2.0,
\n while over 40\u00a0% of all hemorrhagic events occurred at an INR 3.0. In VTE patients,
\n subtherapeutic INRs were found to be present during 58\u00a0% of recurrent VTEs 17<\/a>].\n <\/p>\n
\n between out-of-range INRs and clinical outcomes in 34,346 patients with non-valvular
\n AF (NVAF) who were newly initiated on warfarin therapy. When INR values were below
\n range (2), patients were much more likely to experience adverse thrombotic or embolic
\n events (Fig.\u00a02<\/a>). Patients were at an increased risk of major bleeding with both subtherapeutic INR
\n values (RR?=?2.58 95%CI: 2.19\u20133.03) as well as supratherapeutic INR values, (RR?=?1.55,
\n 95%CI: 1.21\u20131.97). All event rates were qualitatively the highest when patients had
\n an INR??2. While most of these events were stroke associated with sub-therapeutic
\n values, increased bleeding events were also observed. While only speculative (and
\n we could not identify supportive literature) it is possible the increased bleeding
\n associated with sub-therapeutic INRs is due a lag in time between the actual event
\n and the true INR value. This emphasizes the need for close INR monitoring to prevent
\n subtherapeutic warfarin dosing.<\/p>\n![\"thumbnail\"](\"\/content\/figures\/s12959-016-0088-y-2.gif\")
Fig. 2.<\/strong><\/a> Risks of adverse outcomes for people with INRs 2.0 or 3.0. Adapted from data from
\n an observational study using the Veterans Health Administration dataset 38<\/a>] showing the relative risk (RR) of adverse thrombotic or embolic events in patients
\n with subtherapeutic INRs versus normal INRs and then major bleeding vents with supertherapeutic
\n INRs versus normal INRs. The diamond represents the actual RR with the line representing
\n the 95\u00a0% confidence interval and the blue dashed line representing a RR of 1.0, where
\n the risk of outcomes would have been the same as those with normal INRs\n <\/p>\n
\n that examine the relationship between TTR and the prediction of adverse events, a
\n significant negative relationship has been observed 19<\/a>]. In patients with AF, 1 thrombotic or major hemorrhagic event per 100 patient-years
\n could be avoided by improving TTR by 7\u00a0% or 12\u00a0%, respectively 19<\/a>]. Likewise in patients with VTE, for every 1\u00a0% increase in TTR, recurrent thromboembolic
\n events may be reduced by 0.46\u00a0% per year and major hemorrhagic events reduced by 0.30\u00a0%
\n 17<\/a>]. Furthermore, in a nested case control analysis of the Atrial fibrillation Clopidogrel
\n Trial with Irbesartan for prevention of Vascular Events (ACTIVE W) study, patients
\n who experienced an ischemic stroke had a TTR 9.5\u00a0% lower than those without any ischemic
\n event 39<\/a>]. The TTR of patients with a major hemorrhage was 7.2\u00a0% lower when compared to those
\n without an event, again, suggesting that TTR is a useful predictor for both hemorrhagic
\n and thromboembolic events. Of note, ACTIVE W also found that patients spent a greater
\n amount of time out of range in the 1\u20132 months preceding a major bleeding event or
\n stroke which suggests even a temporary period out of range can lead to a bleeding
\n event or stroke.\n <\/p>\n
\n and medication discontinuation 40<\/a>]. In an analysis of longitudinal anticoagulation management records from 15,276 US
\n patients with NVAF, discontinuation of therapy occurred in less than 4\u00a0months among
\n patients with unstable INR. Patients who achieved INR stabilization were 10 times
\n more likely to remain on warfarin therapy beyond 1\u00a0year. In another observational
\n study using the Symphony Health Solutions\u2019 Patient Transactional Database, patients
\n who were prescribed rivaroxaban had a lower risk of treatment nonpersistence [HR 0.66
\n (95%CI: 0.60\u20130.72)] compared to patients who were prescribed warfarin 41<\/a>]. A similar analysis of the Truven Health Market Scan Research Databases showed comparable
\n findings, that NVAF patients who received rivaroxaban were 46\u00a0% less likely to discontinue
\n therapy compared to those receiving warfarin 42<\/a>]. Continued protection by anticoagulation is particularly important for patients with
\n NVAF, since the risk of stroke is expected to increase with age and additional comorbidities
\n 43<\/a>].\n <\/p>\nCosts<\/h4>\n
\n observational study using the Premier Perspective Comparative Hospital Database, hospital
\n length of stay was 5.27\u00a0days vs. 4.46\u00a0days, leading to significant differences in
\n hospitalization costs ($13,255 vs. $11,993, P??0.001) 44<\/a>], 45<\/a>]. In another comprehensive cost analysis of 23,588 patients with NVAF who were on
\n warfarin for at least 30\u00a0days from the US Veteran\u2019s Administration, investigators
\n randomly selected an INR value from a patient and classified it as being below 2,
\n 2\u20133, or above 3 and then evaluated total direct costs (i.e. inpatient, outpatient
\n medical, and outpatient pharmacy costs) over the next 30\u00a0days. Mean direct costs over
\n 30-days after exposure to an INR 2.0, between 2 and 3, and 3.0 were $5126, $2355
\n and $3419 (Fig.\u00a03<\/a>) 46<\/a>]. These findings remained robust in a sensitivity analysis with a more stringent definition
\n of the cohort. The substantial cost difference between in-range and out-of-range time
\n is significant across a broad warfarin population with atrial fibrillation.<\/p>\n![\"thumbnail\"](\"\/content\/figures\/s12959-016-0088-y-3.gif\")
Fig. 3.<\/strong><\/a> Costs Associated with In Range and Out of Range INRs. Adapted from data from a US
\n Veterans Administration dataset 46<\/a>] where the total costs are displayed in blue and the constituent costs of inpatient,
\n outpatient, and outpatient pharmacy costs are in red, green, and purple, respectively.
\n The total costs in the therapeutic INR group is significantly lower than those with
\n abnormally low or high INR groups. Note that the highest costs were associated with
\n suboptimal INR values (i.e., INR 2.0)\n <\/p>\n
\n consequences which underscore the need for greater vigilance on achieving INR stability
\n or the use of a novel oral anticoagulant which provides more consistent pharmacologic
\n effects.\n <\/p>\nPredicting INR Instability<\/h4>\n
Predictors<\/h4>\n
\n INR stability is known to vary greatly based upon various study- and patient-level
\n factors (Table\u00a03<\/a>) 15<\/a>]\u201317<\/a>], 21<\/a>], 47<\/a>], 48<\/a>]. The use of anticoagulation clinics can positively impact higher TTR attainment but
\n only ~1\/3 of VKA patients have access to these advanced services 49<\/a>]. Therefore, a broad understanding of factors predicting INR instability is beneficial
\n for clinical practice.<\/p>\n
\n 14<\/a>] and Rose et al. (VARIA) 48<\/a>] and provided insight into factors affecting anticoagulation control. Apostolakis
\n and colleagues 14<\/a>] used data from the 1061 patients in the Atrial Fibrillation Follow-up Investigation
\n of Rhythm Management (AFFIRM) trial to identify clinical factors associated with TTR.
\n Based upon these results, the SAMe-TT2R2 score was derived (and eventually validated)
\n whereby 1 or 2 points are assigned for important patient factors (Table\u00a04<\/a>). Scores ?2 were found to be associated with decreased odds of achieving a TTR ?65\u00a0%
\n (previously described as the minimum target TTR) 14<\/a>].<\/p>\n
\n AF, 35\u00a0% VTE, 10\u00a0% other) between 2006 and 2008; and evaluated the effect of various
\n patient characteristics on TTR in those starting warfarin (first 6\u00a0months of therapy)
\n and who were experienced (on therapy for 6\u00a0months). Like the SAMe-TT2R2 derivation\/validation
\n study, female gender, younger age, minority status and co-morbid physical conditions
\n were also found to be associated with lower TTR in VARIA (in both the inception or
\n experienced cohorts) but there were a large number of additional factors which were
\n identified, such as alcohol abuse, number of hospitalizations, and various comorbidities,
\n such as heart failure, diabetes, chronic kidney disease, and others. The VARIA investigators
\n also created a clinical prediction tool but eliminated race because they did not wish
\n to perpetuate disparities in care, eliminated poverty and distance to drive to receive
\n care because they felt it was hard to assess, and eliminated other factors to simplify
\n the model. Their model is available in a downloadable excel spreadsheet from Supplemental
\n Appendix 3S at http:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/j.1538-7836.2010.03996.x\/full<\/a>. In addition to the factors in the SAMeTT2R2 score, this tool also assesses the indication
\n for use, total number of chronic medications, substance abuse, mental illnesses, number
\n of hospitalizations, and the general quality of TTR attainment in other patients within
\n that healthcare setting. Even with all of these additional factors, the R-squared
\n value only ranged from 3.2 to 6.8\u00a0% suggesting that the much of the variability in
\n TTR is not explained by this model. Furthermore, while the study assessed TTR at therapy
\n initiation and after chronic therapy, the authors stated that the prediction tool
\n is not to be used as a means to assess long term control, and that clinical experience
\n from past VKA therapy is the preferred method 48<\/a>].\n <\/p>\nFactors that effect INR instability<\/h4>\n
\n and adherence to therapy. Patient genotype plays an important role in INR stability
\n 12<\/a>], 50<\/a>]\u201352<\/a>]. At least 30 genes contribute to the anticoagulant effects of VKAs, with one third
\n of the variance in warfarin dosing related to mutations in genes leading to the synthesis
\n of CYP2C9 and vitamin K epoxide reductase (VKORC1) 12<\/a>], 50<\/a>]\u201352<\/a>]. Patients with CYP2C9*2 and CYP2C9*3 polymorphisms have decreased enzymatic activity,
\n metabolize warfarin (and to a lesser extent acenocoumarol) more slowly, have a 1.4-
\n to 3.6-fold increased risk of supratherapeutic INR, and often take longer to achieve
\n stable dosing 53<\/a>]\u201355<\/a>]. VKORC1 polymorphisms can result in either a heightened (group A haplotype) or reduced
\n effect (group B haplotype) of warfarin which alters the risk of thromboembolism and
\n bleeding accordingly 12<\/a>]. Based on this data, the Food and Drug Administration altered the package insert
\n recommending clinicians consider genetic testing before initiating warfarin therapy
\n 56<\/a>]. However, the cost-effectiveness of this approach is questionable; with economic
\n models suggesting genotyping of patients would cost more than $170,000 per AF patient
\n quality-adjusted life-year (QALY) gained (far above the commonly accepted willingness-to-pay
\n threshold of $50,000 per QALY) 57<\/a>].\n <\/p>\n
\n in the American College of Chest Physicians (ACCP) guidelines 12<\/a>]. Identified predictors of VKA nonadherence include not being married, not having
\n a vehicle for transportation, education levels beyond high school, currently employed,
\n lower levels of mental health functioning, poor cognitive functioning, and greater
\n drug regimen complexity 58<\/a>], 59<\/a>]. In addition, studies have identified patient dissatisfaction with care as a cause
\n of medication non-adherence in patients with cardiovascular disease states 60<\/a>]. This is noteworthy since patient satisfaction with warfarin therapy has been shown
\n to be poor in recent studies of AF 61<\/a>] and VTE patients 62<\/a>]. In the above-mentioned studies, poor patient satisfaction in either AF or VTE patients
\n (measure by the Anti-Clot Treatment Scale) was related to the burden and frustration
\n of taking VKAs resulting from fear of bleeding\/bruising, diet and alcohol interactions
\n and the perceived hassle of INR monitoring. One of the frequently cited studies evaluating
\n the association between VKA non-adherence and INR control is the International Normalized
\n Ratio Adherence and Genetics (IN-RANGE) Study 63<\/a>]. IN-RANGE was a prospective cohort study conducted at 3 US anticoagulation clinics
\n and assessed warfarin adherence using a medication electronic monitoring system (MEMS).
\n The study followed 136 patients taking warfarin for a variety of reasons (but predominantly
\n AF and VTE) for a mean of 32-weeks and found that missed warfarin doses (missed MEMS
\n bottle openings) were common, with 92\u00a0% of patients missed at least 1 dose, and one-third
\n missed more than 20\u00a0% of their doses. A total of 1490 INRs values were collected,
\n with 40\u00a0% out of range and 26\u00a0% being below range. Upon multivariable regression analysis,
\n researchers found that for every 10\u00a0% increase in missed warfarin doses (days without
\n a dose), there was a 14\u00a0% increase in the adjusted odds of under-anticoagulation (having
\n an INR??2.0); and patients who missed 20\u00a0% of their doses (missed 20\u00a0days of warfarin
\n therapy) had a 2.10-fold (95%CI: 1.48\u20132.96) increase in their odds of having an INR??2.0
\n 63<\/a>].\n <\/p>\n
\n The research findings indicate that many of the patient factors are not modifiable
\n and are also not sufficiently reliable to predict whether VKA will perform well for
\n a particular patient.\n <\/p>\nModalities to optimize clinical management of VKAs<\/h4>\n
\n including computer assisted dosing and patient self-testing or management. In a randomized
\n study of 13,219 patients conducted in 32 centers around the world, the impact of software
\n program guided VKA therapy was compared with experienced clinician dosing 64<\/a>]. Unadjusted INR time in range was 67 and 66\u00a0% with computer-assisted versus experienced
\n clinician dosing. However, in order to elicit these comparable results, the experienced
\n medical staff randomized to use the computer assisted program provided 11\u00a0% of the
\n dosages because the computer failed to provide it and the dose was changed 11\u00a0% of
\n the time because the results were felt inaccurate. In addition, the computer-advised
\n appointment intervals were changed by experienced clinicians 34\u00a0% of the time. More
\n data is needed to truly determine the impact of computer assisted dosing in less experienced
\n clinicians versus very experienced clinicians\/anticoagulation specialists.\n <\/p>\n
\n INR without the need for a centralized laboratory to acquire and analyze the samples.
\n In a meta-analysis of 22 studies, including 4 studied deemed of high methodological
\n quality, the precision and accuracy of the CoaguChek XS, INRatio, ProTime\/ProTime3,
\n and Smartcheck INR coagulameter systems were assessed 65<\/a>]. The CoaguChek system was the most commonly assed and yielded a coefficient of variation
\n that ranged from 1.4 to 5.9 and the concordance ranged between 93 to 100\u00a0% showing
\n congruence. The other systems have coefficients of variation ranging from 3.7 to 8.4
\n and concordance values ranging from 81 to 97\u00a0% (with the exception of one trial of
\n the ProTime system where the concordance was only 39\u00a0%. In general, point of care
\n testing is accurate and can facilitate patient self-testing (where the patient self-tests
\n the INR but clinician doses) or patient self-management (where the patient self-tests
\n and self-adjusts VKA therapy based on the INR.\n <\/p>\n
\n patients (n<\/em>?=?11, 99 patient months, 122 INR determinations) underwent patient self-management
\n or physician management for 4\u00a0months 66<\/a>]. Patients were trained and given an algorithm to follow that specified the new dose
\n and the timeframe for which to reassess the INR. The mean proportion of INR values
\n in therapeutic range among subjects in the PSM and physician-management groups was
\n 82 and 80\u00a0%, respectively (p<\/em>?=?0.82). Ten of the 11 patients preferred PSM to physician management and elected
\n to continue with this strategy after study completion (P<\/em>?=?.001). No calls or visits were made to the physician regarding dose adjustment
\n during the patient self-management period. There were no episodes of major bleeding
\n or thromboembolic events. Studies like this are promising but preliminary and it is
\n unclear whether this is an effective therapy for highly motivated and intelligent
\n patients or patients with health disparities or care barriers.\n <\/p>\n","protected":false},"excerpt":{"rendered":"