Assessment of Patient Medication Adherence, Medical Record Accuracy, and Medication Blood Concentrations for Prescription and Over-The-Counter Medications
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Supplementary Online Content Sutherland JJ, Morrison RD, McNaughton CD, et al. Assessment of patient medication adherence, medical record accuracy, and medication blood concentrations for prescription and over-the-counter medications. JAMA Netw Open. 2018;1(7):e184196. doi:10.1001/jamanetworkopen.2018.4196 eMethods eTable 1. Multiplex Assay Panel Medications eTable 2. Multiplex Assay Panel Medications, Medication Classes, Limits of Detection, Reference Ranges and Biological Half Life eTable 3. Proportion of Samples With Detected Medication, by Medication Category eTable 4. Proportion of Medication Detections Without Evidence of Prescription, by Medication Category eFigure 1. Medication Detection Rate in Two Cohorts eFigure 2. Comparison of Differential Prescription and Detection Rates in ED vs Residuals Cohorts eFigure 3. Adherence to Prescription Drugs eFigure 4. Comparison of Naïve Estimates of Adherence by Medication Class vs. Coefficients From Logistic Regression Modelling eFigure 5. Comparison of Detection Rates for Medications not Listed in Subjects’ Health Record in Two Cohorts This supplementary material has been provided by the authors to give readers additional information about their work. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/26/2021 eMethods Sample collection Samples were collected in serum phlebotomy tubes and processed within four hours of collection. Resulting serum was frozen at -70°C until shipment on dry ice to Precera Biosciences for analysis. The key linking study-specific identifiers to EHR information was maintained by study personnel at the site and not shared with laboratory or analysis personnel. Laboratory personnel were blinded to study participants’ records, including the EHR medication list, during the measurement phase of the study. LC/MS/MS analysis Sample analysis was executed under the guidelines set forth by the CAP and standard operating procedures commensurate with CLIA-registered operations. Samples were thawed, mixed, and transferred to 96-well plates for processing. Internal standard working solution was added and protein precipitation was performed using Phenomenex Impact Protein Precipitation Plates. Eluate was transferred to a new plate and dried under Nitrogen prior to reconstitution for LC/MS/MS analysis. Reconstituted samples were processed using a Shimadzu Nexera X2 liquid chromatography system (Columbia, MD) fitted with a Phenomenex 2.1 x 50 mm, 1.7um C18 column (Torrence, CA). Sample analysis was performed on a Sciex 5500 Q-Trap Mass Spectrometer (Framingham, MA) with TurboV ion source. Data collection was performed with Sciex Analyst software, version 1.6.2, and data analysis was performed using Indigo BioAutomation Ascent software (Indianapolis, IN). Optimal grade methanol and acetonitrile were obtained from Fisher Scientific (Waltham, MA). Formic acid, ammonium acetate, ammonium formate, and water were LC/MS grade and obtained from Sigma-Aldrich (St. Louis, MO). Dimethylsulfoxide was obtained from Sigma-Aldrich. Ammonium hydroxide was obtained from Thermo Fisher Scientific. Drug naïve human serum used in validation studies was obtained from Bioreclamation IVT (Westbury, NY). All analytical standards were obtained at the highest purity available. Stock solutions were prepared individually in DMSO, water, methanol, or acetonitrile, then combined. Standard Curve and Quality Control samples were prepared in drug naïve human serum. Assay linearity, precision, accuracy, and detection were assessed by adding various amounts of each test drug to human serum. Each of the analytes assayed passed strict analytical validation criteria. The final test panel detected the presence of 277 unique analytes, corresponding to 263 parent drugs (eTable 2). Quantitative Medication Reporting After measurement, medication lists were compared to LC/MS/MS data. The primary information source was the AGNP Consensus Guidelines for Therapeutic Drug Monitoring in Psychiatry1, which provides evidence-based reference ranges for 128 marketed psychiatric medications. If a medication was not listed in this primary source, secondary sources derived from primary literature were utilized as cited. Finally, if no literature values could be obtained, drug label information was utilized. Annotation of drugs by class Medications were mapped to drug classes according to the National Health and Nutrition Examination Survey (NHANES) resource (https://wwwn.cdc.gov/nchs/nhanes/1999-2000/RXQ_DRUG.htm accessed 11/20/2017). Medications were grouped using the variables RXDDCN1A and RXDDCN1B. Groupings were adjusted manually to achieve ≥20 prescriptions and/or detections per category. The categories of medications as used throughout this work are provided in eTable 2 (column: medication class). Coverage of US medication prescribing The most recent NHANES prescription drug survey for 2013-2014 (https://wwwn.cdc.gov/Nchs/Nhanes/2013- 2014/RXQ_RX_H.htm, accessed 03/26/2018) was used to evaluate coverage of the assay. Only 1870 subjects aged 20 years or older and reported to be using 3 or more medications were retained (i.e. a common definition of polypharmacy). Medications were classified as small molecule oral drugs using resources such as Wikipedia © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/26/2021 (https://www.wikipedia.org; accessed 03/26/2018) and DrugBank (https://www.drugbank.ca; accessed 03/26/2018). This definition excluded therapeutic antibodies, hormones, and vitamins/supplements (e.g. potassium chloride). Hormones are small molecules that can be detected via LC/MS/MS as employed herein, but their natural presence in the body reduces the utility of blood-based detection for medication management. Combination drugs (e.g. ACETAMINOPHEN; HYDROCODONE) were mapped to individual agents and treated as individual prescriptions. Each prescription was weighted according to the subject variable WINT2YR. The weighted sum for all prescriptions in polypharmacy adults was used to normalize each of 554 unique drugs, ranging from 4.9% (lisinopril, i.e. the most commonly reported medication and accounting for 4.9% of all oral drug prescriptions) to 0.002% (sofosbuvir, the least commonly reported medication). Detection rate for prescribed drugs and drug classes The proportion of prescribed drugs detected in patient serum was calculated by excluding prescriptions with PRN status (i.e. prescribed for use “as-needed”). Unless indicated otherwise, when calculating the proportion of medications detected for a given drug, ≥ 20 prescriptions were required in order to avoid estimating detection proportions from very few prescriptions. Calculation of detection proportions in Figure 1 were obtained by pooling prescriptions from the three clinic sample cohorts and our previously described cohorts7,12. For those parent drugs with metabolites on the panel, we considered the prescribed drug ‘detected’ if parent and/or metabolites were detected. Patient medication adherence (adherence drug subset) For evaluating a patient’s overall adherence to prescribed medications, we excluded medications that may be dosed infrequently or administered non-orally: methotrexate, budesonide, buprenorphine, clonidine, dexamethasone, diclofenac, fentanyl, ketorolac, and prednisolone. Further, we excluded 54 drugs with biological half-life ≤ 4 hours having low probability of detection (Figure 1) or prescribed fewer than 20 times across our pooled datasets and hence insufficiently prescribed to estimate detection rates. We also exclude drugs prescribed PRN (“as-needed”), as non-detection does not indicate non-adherence. The complete set of excluded drugs are denoted in eTable 2. In total, 189 drugs were used to calculate each patient’s overall adherence to prescribed medications, ranging from 0% to 100% adherence. In our analyses of factors affecting overall medication adherence, we focused on polypharmacy patients, defined as 3 or more prescribed non-PRN medications from the 189 drug subset. Drug-drug interactions Elsevier Gold Standard database (https://www.elsevier.com/solutions/drug-database; accessed 03/22/2018) was used as the source of DDI information. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/26/2021 eTable 1. Multiplex Assay Panel Medications Medication category Drugs anti-infectives cephalexin, efavirenz, emtricitabine, hydroxychloroquine, itraconazole, ketoconazole, lamivudine, nitrofurantoin, oseltamivir, penicillin, sofosbuvir antineoplastics abiraterone, raloxifene angiotensin converting benazepril, enalapril, lisinopril, quinapril, ramipril enzyme inhibitors angiotensin II inhibitors candesartan, irbesartan, losartan, olmesartan, telmisartan, valsartan antiadrenergic agents, alfuzosin, doxazosin, prazosin, reserpine, tamsulosin, terazosin peripherally acting antiarrhythmic agents amiodarone, diltiazem, dofetilide, dronedarone, flecainide, fosphenytoin, phenytoin, procainamide, quinidine, verapamil beta-adrenergic acebutolol, atenolol, bisoprolol, carvedilol, labetalol, metoprolol, nadolol, blocking agents nebivolol, pindolol, propranolol calcium channel amlodipine, felodipine, nifedipine blocking agents diuretics acetazolamide, bumetanide, chlorothiazide, chlorthalidone, furosemide, hydrochlorothiazide, indapamide, metolazone, torsemide, triamterene other cardiovascular cilostazol, clonidine, guanfacine, ranolazine agents anticonvulsants carbamazepine, divalproex,