2002. analyses. NIHMS1652514-supplement-Supplementary_Figures.pdf (20M) GUID:?E7C4C0B8-AF2C-48EF-BA5F-4BF78382D9B7 Abstract Drug-drug interactions (DDIs) with oral anticoagulants may lead to under-anticoagulation and increased risk of thromboembolism. While warfarin is susceptible to numerous DDIs, few studies have examined DDIs resulting in thromboembolism or those involving direct-acting oral anticoagulants (DOACs). We aimed to identify medications that increase the rate of hospitalization for thromboembolic events when taken concomitantly with oral anticoagulants. We conducted a high-throughput pharmacoepidemiologic screening study using Optum Clinformatics Data Mart, 2000C2016. We performed self-controlled case series studies among adult users of oral anticoagulants (warfarin, dabigatran, rivaroxaban, apixaban, and edoxaban) with at least one hospitalization for a thromboembolic event. Among eligible patients, we identified all oral medications frequently co-prescribed with oral anticoagulants as potential interacting precipitants. Conditional Poisson regression was used to estimate rate ratios comparing precipitant exposed vs. unexposed time for each anticoagulant-precipitant pair. To minimize within-person confounding by indication for the precipitant, we used pravastatin as a negative control object drug. Multiple estimation was adjusted using semi-Bayes shrinkage. We screened 1,622 oral anticoagulant-precipitant drug pairs and identified 226 (14%) drug pairs associated with statistically significantly elevated risk of thromboembolism. Using pravastatin as the negative control object drug, this list was reduced to 69 potential DDI signals for thromboembolism, 33 (48%) of which were not documented in the DDI knowledge databases Lexicomp and/or Micromedex. There were more DDI signals associated with warfarin than DOACs. This study reproduced several previously documented oral anticoagulant DDIs and identified potential DDI signals that deserve to be examined in future etiologic studies. codes from 2000 to 2016, and (algorithms have been validated in prior studies with positive predictive values of 73C90% (Table S1).18,19 The algorithms were translated from using General Equivalence Mappings (GEMs) published by the Centers for Medicare and Medicaid Services and Centers for Disease Control and Prevention using Forward Backward Mapping method.20C22 We identified all eligible anticoagulant and pravastatin users Rabbit Polyclonal to MLTK with at least one thromboembolic event during the time of an active prescription for that object drug. Any days of hospitalization were excluded because medication use during hospitalization is not recorded in the data. Exposure C Potential Interacting Precipitant Drugs The potential interacting precipitants were identified as all oral medications (by active ingredient) that were co-dispensed with each oral anticoagulant and with pravastatin among eligible patients. For each observation day of each patient, exposure indicators were generated to indicate if the patient was exposed (i.e., on the oral anticoagulant and the precipitant drug) or unexposed β-Sitosterol (i.e., on the oral anticoagulant without the precipitant drug) to the potential precipitant drug. To simplify the high-throughput screening of hundreds of precipitant drugs and to maximize the strength of resulting signals, no grace period was added following the precipitant days supply. Covariates The SCCS design intrinsically controls for fixed multiplicative covariates such as sex and genetic factors. We controlled for two time-varying covariates: the use of nonsteroidal anti-inflammatory drug (NSAIDs: celecoxib, diclofenac, diflunisal, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, meclofenamate, mefenamic acid, meloxicam, nabumetone, naproxen, oxaprozin, piroxicam, salsalate, sulindac, and tolmetin) and the use of antiplatelet agents (abciximab, anagrelide, aspirin, cilostazol, clopidogrel, dipyridamole, eptifibatide, prasugrel, ticagrelor, ticlopidine, and tirofiban) in the past 30 days, each calculated as a binary indicator for each observation day. In the model analyzing a NSAID or an antiplatelet agent as the precipitant.Although the clinical significance of this interaction was not previously examined, single doses of concurrent administration of raloxifene and warfarin were shown to reduce the prothrombin time by 10% within two weeks.32 We also found a positive association between rifampin and the rate of thromboembolism (RR=2.35, 95% CI:1.82C3.01, ratio of RRs=2.25, 95% CI:1.10C4.61). in the secondary analysis. Figure S4. Heatmaps presenting rate ratios comparing precipitant exposed time vs. precipitant unexposed time in the secondary analysis. Figure S5. Heatmaps presenting the overall rate ratio comparing precipitant exposed time vs. precipitant unexposed time from the sensitivity analyses. Figure S6. Heatmaps presenting the overall ratio of rate ratio comparing precipitant exposed time vs. precipitant unexposed time from the sensitivity analyses. NIHMS1652514-supplement-Supplementary_Figures.pdf (20M) GUID:?E7C4C0B8-AF2C-48EF-BA5F-4BF78382D9B7 Abstract Drug-drug interactions (DDIs) with oral anticoagulants may lead to under-anticoagulation and increased risk of thromboembolism. While warfarin is susceptible to numerous DDIs, few studies have examined DDIs resulting in thromboembolism or those involving direct-acting oral anticoagulants (DOACs). We aimed to identify medications that increase the rate of hospitalization for thromboembolic events when taken concomitantly with oral anticoagulants. We conducted a high-throughput pharmacoepidemiologic screening study using Optum Clinformatics Data Mart, 2000C2016. We performed self-controlled case series studies among adult users of oral anticoagulants (warfarin, dabigatran, rivaroxaban, apixaban, and edoxaban) with at least one hospitalization for a thromboembolic event. Among eligible patients, we identified all oral medications frequently co-prescribed with oral anticoagulants as potential interacting precipitants. Conditional Poisson regression was used to estimate rate ratios comparing precipitant exposed vs. unexposed time for each anticoagulant-precipitant pair. To minimize within-person confounding by indication for the precipitant, we used pravastatin as a negative control object drug. Multiple estimation was adjusted using semi-Bayes shrinkage. We screened 1,622 oral anticoagulant-precipitant drug pairs and identified 226 (14%) drug pairs associated with statistically significantly elevated risk of thromboembolism. Using pravastatin as the bad control object drug, this list was reduced to 69 potential DDI signals for thromboembolism, 33 (48%) of which were not recorded in the DDI knowledge databases Lexicomp and/or Micromedex. There were more DDI signals associated with warfarin than DOACs. This study reproduced several previously documented oral anticoagulant DDIs and recognized potential DDI signals that deserve to be examined in future etiologic studies. codes from 2000 to 2016, and (algorithms have been validated in previous studies with positive predictive ideals of 73C90% (Table S1).18,19 The algorithms were translated from using General Equivalence Mappings (GEMs) published from the Centers for Medicare and Medicaid Solutions and Centers for Disease Control and Prevention using Forward Backward Mapping method.20C22 We identified all eligible anticoagulant and pravastatin users with at least one thromboembolic event during the time of an active prescription for the object drug. Any days of hospitalization were excluded because medication use during hospitalization is not recorded in the data. Exposure C Potential Interacting Precipitant Medicines The potential interacting precipitants were identified as all oral medications (by active ingredient) that were co-dispensed with each oral anticoagulant and with pravastatin among qualified patients. For each observation day of each patient, exposure signals were generated to indicate if the patient was revealed (we.e., within the oral anticoagulant and the precipitant drug) or unexposed (i.e., within the oral anticoagulant without the precipitant drug) to the potential precipitant drug. To simplify the high-throughput screening of hundreds of precipitant medicines and to maximize the strength of producing signals, no elegance period was added following a precipitant days supply. Covariates The SCCS design intrinsically settings for fixed multiplicative covariates such as sex and genetic factors. We controlled for two time-varying covariates: the use of nonsteroidal anti-inflammatory drug (NSAIDs: celecoxib, diclofenac, diflunisal, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, meclofenamate, mefenamic acid, meloxicam, nabumetone, naproxen, oxaprozin, piroxicam, salsalate, sulindac, and tolmetin) and the use of antiplatelet providers (abciximab, anagrelide, aspirin, cilostazol, clopidogrel, dipyridamole, eptifibatide, prasugrel, ticagrelor, ticlopidine, and tirofiban) β-Sitosterol in the past 30 days, each determined like a binary indication for each observation day time. In the model analyzing a NSAID or an antiplatelet agent as the precipitant drug, we only controlled for the use of antiplatelet agent in the past 30 days or the use of NSAIDs in the past 30 days, respectively. Statistical analysis We used conditional Poisson regression to estimate rate ratios (RRs) and 95% confidence intervals (CI) comparing precipitant exposed time vs. precipitant unexposed time for each object-precipitant drug pair. An overall RR was estimated for each object-precipitant pair for each outcome over the entire observation period. To ensure statistically stable estimations of the model, we excluded the drug pair if there were fewer.Hatanaka T Clinical pharmacokinetics of pravastatin: mechanisms of pharmacokinetic events. Number S4. Heatmaps showing rate ratios comparing precipitant exposed time vs. precipitant unexposed time in the secondary analysis. Number S5. Heatmaps showing the overall rate ratio comparing precipitant exposed time vs. precipitant unexposed time from the level of sensitivity analyses. Number S6. Heatmaps showing the overall percentage of rate ratio comparing precipitant exposed time vs. precipitant unexposed time from the level of sensitivity analyses. NIHMS1652514-supplement-Supplementary_Numbers.pdf (20M) GUID:?E7C4C0B8-AF2C-48EF-BA5F-4BF78382D9B7 Abstract Drug-drug interactions (DDIs) with oral anticoagulants may lead to under-anticoagulation and increased risk of thromboembolism. While warfarin is definitely susceptible to several DDIs, few studies have examined DDIs resulting in thromboembolism or those including direct-acting oral anticoagulants (DOACs). We targeted to identify medications that increase the rate of hospitalization for thromboembolic events when taken concomitantly with oral anticoagulants. We carried out a high-throughput pharmacoepidemiologic screening study using Optum Clinformatics Data Mart, 2000C2016. We performed self-controlled case series studies among adult users of oral anticoagulants (warfarin, dabigatran, rivaroxaban, apixaban, and edoxaban) with at least one hospitalization for any thromboembolic event. Among qualified patients, we recognized all oral medications regularly co-prescribed with oral anticoagulants as potential interacting precipitants. Conditional Poisson regression was used to estimate rate ratios comparing precipitant revealed vs. unexposed time for each anticoagulant-precipitant pair. To minimize within-person confounding by indicator for the precipitant, we used pravastatin as a negative control object drug. Multiple estimation was modified using semi-Bayes shrinkage. We screened 1,622 oral anticoagulant-precipitant drug pairs and recognized 226 (14%) drug pairs associated with statistically significantly elevated risk of thromboembolism. Using pravastatin as the bad control object drug, this list was reduced to 69 potential DDI signals for thromboembolism, 33 (48%) of which were not recorded in the DDI knowledge databases Lexicomp and/or Micromedex. There were more DDI signals associated with warfarin than DOACs. This study reproduced several previously documented oral anticoagulant DDIs and recognized potential DDI signals that deserve to be examined in future etiologic studies. codes from 2000 to 2016, and (algorithms have been validated in previous studies with positive predictive ideals of 73C90% (Table S1).18,19 The algorithms were translated from using General Equivalence Mappings (GEMs) published from the Centers for Medicare and Medicaid Solutions and Centers for Disease Control and Prevention using Forward Backward Mapping method.20C22 We identified all eligible anticoagulant and pravastatin users with at least one thromboembolic event during the time of an active prescription for the object drug. Any days of hospitalization were excluded because medication use during hospitalization is not recorded in the data. Exposure C Potential Interacting Precipitant Medicines The potential interacting precipitants were defined as all oral medicaments (by active component) which were co-dispensed with each dental anticoagulant and with pravastatin among entitled patients. For every observation day of every patient, exposure indications were generated to point if the individual was open (i actually.e., in the dental anticoagulant as well as the precipitant medication) or unexposed (we.e., in the dental anticoagulant with no precipitant medication) towards the potential precipitant medication. To simplify the high-throughput testing of a huge selection of precipitant medications and to increase the effectiveness of ensuing signals, no sophistication period was added following precipitant days source. Covariates The SCCS style intrinsically handles for set multiplicative covariates such as for example sex and hereditary factors. We managed for just two time-varying covariates: the usage of nonsteroidal anti-inflammatory medication (NSAIDs: celecoxib, diclofenac, diflunisal, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, meclofenamate, mefenamic acidity, meloxicam, nabumetone, naproxen, oxaprozin, piroxicam, salsalate, sulindac, and tolmetin) and the usage of antiplatelet agencies (abciximab, anagrelide, aspirin, cilostazol, clopidogrel, dipyridamole, eptifibatide, prasugrel, ticagrelor, ticlopidine, and tirofiban) before thirty days, each computed being a binary sign for every observation time. In the model examining a NSAID or an antiplatelet agent as the precipitant medication, we only managed for the usage of antiplatelet agent before thirty days or the usage of NSAIDs before thirty days, respectively. Statistical evaluation We utilized conditional Poisson regression to estimation price ratios (RRs) and 95% self-confidence intervals (CI) evaluating precipitant exposed period vs. precipitant unexposed period for every object-precipitant medication pair. A standard RR was approximated for every object-precipitant pair for every outcome over the complete observation period. β-Sitosterol To make sure statistically stable quotes from the model, we excluded the medication pair if there have been less than 5 situations who had been ever subjected to that precipitant medication or if the variance from the approximated beta for the parameter appealing was bigger than 10. To examine the duration-response interactions, we divided the publicity period into 5 mutually distinctive risk home windows: 0C15, 16C30, 31C60, and 61C120 and 120+ times because the initiation of concomitancy. Individual RRs were approximated for each.