Improving Patient Access to Hospital Pharmacists Using eConsults: Retrospective Descriptive Study.

BACKGROUND: eConsults are increasingly used worldwide to reduce specialist referrals and increase access to medical care. An additional benefit of using an eConsult tool is a reduction of health care costs while improving the quality of health care and patient participation. Currently, shared decision making is increasingly implemented and preferred by patients. eConsults are also a promising tool to improve access to the hospital pharmacist. Patients often have questions about their medication. When medication is started during a hospital admission or outpatient visit, community pharmacists are not always sufficiently informed to answer patient questions. Direct contact with hospital pharmacists may be more appropriate and efficient. This contact is facilitated through the eConsult feature in the hospital's patient portal. OBJECTIVE: This study aims to evaluate the prevalence and contents of the eConsults sent by patients to hospital pharmacists. METHODS: A first retrospective descriptive study was conducted at the Leiden University Medical Center in the Netherlands. Patients who sent at least one eConsult to a hospital pharmacist between March 2017 and December 2021 were included. Patient characteristics and the number of medications taken were extracted from electronic health records. The content of eConsults was analyzed and grouped into different subjects. Time of sending of the eConsults was analyzed. A comparison was made between the number of eConsults sent to the hospital pharmacy and the number sent to the medical center. Finally, the appropriateness for evaluation by the hospital pharmacist was assessed in all eConsults. RESULTS: During the study period, 983 eConsults (from 808 patients) were sent to the hospital pharmacist. The average patient age was 56 (SD 15.9) years, and 51.4% (415/808) were male; 47.8% (386/808) of the patients used 0 to 4 medications, 33.0% (267/808) used 5 to 9 medications, and 19.2% (155/808) used ≥10 medications. Of the eConsults, 10.9% (107/983) were excluded due to not being medication-related or not intended for the hospital pharmacist. Patients being treated in 31 medical specialties sent eConsults to the hospital pharmacist. The most common medical specialty was cardiology with 22.5% (197/876) of the eConsults. Most eConsults were sent during office hours (614/876, 70.2%). eConsult subjects were medication verification (372/876, 42.5%), logistics (243/876, 27.7%), therapeutic effect and adverse events (100/876, 11.4%), use of medication (87/876, 9.9%), and other subjects (74/876, 8.4%). CONCLUSIONS: Introducing eConsults allows patients to ask medication-related questions directly to hospital pharmacists. Our study shows that patients send medication reconciliation-related eConsults most often. Use of the eConsult tool leads to fast, direct, and documented communication between patient and hospital pharmacist. This can reduce medication-related errors, improve patient empowerment, and increase access to the hospital pharmacist.

Application of Electronic Health Record Text Mining: Real-World Tolerability, Safety, and Efficacy of Adjuvant Melanoma Treatments.

Introduction: Nivolumab (N), pembrolizumab (P), and dabrafenib plus trametinib (D + T) have been registered as adjuvant treatments for resected stage III and IV melanoma since 2018. Electronic health records (EHRs) are a real-world data source that can be used to review treatments in clinical practice. In this study, we applied EHR text-mining software to evaluate the real-world tolerability, safety, and efficacy of adjuvant melanoma treatments. Methods: Adult melanoma patients receiving adjuvant treatment between January 2019 and October 2021 at the Leiden University Medical Center, the Netherlands, were included. CTcue text-mining software (v3.1.0, CTcue B.V., Amsterdam, The Netherlands) was used to construct rule-based queries and perform context analysis for patient inclusion and data collection from structured and unstructured EHR data. Results: In total, 122 patients were included: 54 patients treated with nivolumab, 48 with pembrolizumab, and 20 with D + T. Significantly more patients discontinued treatment due to toxicity on D + T (N: 16%, P: 6%, D + T: 40%), and X2 (6, n = 122) = 14.6 and p = 0.024. Immune checkpoint inhibitors (ICIs) mainly showed immune-related treatment-limiting adverse events (AEs), and chronic thyroid-related AE occurred frequently (hyperthyroidism: N: 15%, P: 13%, hypothyroidism: N: 20%, P: 19%). Treatment-limiting toxicity from D + T was primarily a combination of reversible AEs, including pyrexia and fatigue. The 1-year recurrence-free survival was 70.3% after nivolumab, 72.4% after pembrolizumab, and 83.0% after D + T. Conclusions: Text-mining EHR is a valuable method to collect real-world data to evaluate adjuvant melanoma treatments. ICIs were better tolerated than D + T, in line with RCT results. For BRAF+ patients, physicians must weigh the higher risk of reversible treatment-limiting AEs of D + T against the risk of long-term immune-related AEs.

Real-world evaluation of supportive care using an electronic health record text-mining tool: G-CSF use in breast cancer patients.

PURPOSE: Chemotherapy-induced febrile neutropenia (FN) is a life-threatening and chemotherapy dose-limiting adverse event. FN can be prevented with granulocyte-colony stimulating factors (G-CSFs). Guidelines recommend primary G-CSF use for patients receiving either high (> 20%) FN risk (HR) chemotherapy, or intermediate (10-20%) FN risk (IR) chemotherapy if the overall risk with additional patient-related risk factors exceeds 20%. In this study, we applied an EHR text-mining tool for real-world G-CSF treatment evaluation in breast cancer patients. METHODS: Breast cancer patients receiving IR or HR chemotherapy treatments between January 2015 and February 2021 at LUMC, the Netherlands, were included. We retrospectively collected data from EHR with a text-mining tool and assessed G-CSF use, risk factors, and the FN and neutropenia (grades 3-4) and incidence. RESULTS: A total of 190 female patients were included, who received 77 HR and 113 IR treatments. In 88.3% of the HR regimens, G-CSF was administered; 7.3% of these patients developed FN vs. 33.3% without G-CSF. Although most IR regimen patients had ≥ 2 risk factors, only 4% received G-CSF, of which none developed neutropenia. However, without G-CSF, 11.9% developed FN and 31.2% severe neutropenia. CONCLUSIONS: Our text-mining study shows high G-CSF use among HR regimen patients, and low use among IR regimen patients, although most had ≥ 2 risk factors. Therefore, current practice is not completely in accordance with the guidelines. This shows the need for increased awareness and clarity regarding risk factors. Also, text-mining can effectively be implemented for the evaluation of patient care.

The Effect of Medication Reconciliation via a Patient Portal on Medication Discrepancies: A Randomized Noninferiority Study.

BACKGROUND: Medication reconciliation has become standard care to prevent medication transfer errors. However, this process is time-consuming but could be more efficient when patients are engaged in medication reconciliation via a patient portal. OBJECTIVES: To explore whether medication reconciliation by the patient via a patient portal is noninferior to medication reconciliation by a pharmacy technician. DESIGN (INCLUDING INTERVENTION): Open randomized controlled noninferiority trial. Patients were randomized between medication reconciliation via a patient portal (intervention) or medication reconciliation by a pharmacy technician at the preoperative screening (usual care). SETTING AND PARTICIPANTS: Patients scheduled for elective surgery using at least 1 chronic medication were included. MEASURES: The primary endpoint was the number of medication discrepancies compared to the electronic nationwide medication record system (NMRS). For the secondary endpoint, time investment of the pharmacy technician for the medication reconciliation interview and patient satisfaction were studied. Noninferiority was analyzed with an independent t test, and the margin was set at 20%. RESULTS: A total of 499 patients were included. The patient portal group contained 241 patients; the usual care group contained 258 patients. The number of medication discrepancies was 2.6 ± 2.5 in the patient portal group and 2.8 ± 2.7 in the usual care group. This was not statistically different and within the predefined noninferiority margin. Patients were satisfied with the use of the patient portal tool. Also, the use of the portal can save on average 6.8 minutes per patient compared with usual care. CONCLUSIONS AND IMPLICATIONS: Medication reconciliation using a patient portal is noninferior to medication reconciliation by a pharmacy technician with respect to medication discrepancies, and saves time in the medication reconciliation process. Future studies should focus on identifying patient characteristics for successful implementation of patient portal medication reconciliation.

Liver and kidney function in patients with Covid-19 treated with remdesivir.

For the treatment of Covid-19 patients with remdesivir, poor renal and liver function were both exclusion criteria in randomized clinical trials and contraindication for treatment. Also, nephrotoxicity and hepatotoxicity are reported as adverse events. We retrospectively reviewed renal and liver functions of Covid-19 103 patients who received remdesivir in the 15 days after treatment initiation. Approximately 20% of the patient population met randomized clinical trial exclusion criteria. In total, 11% of the patients had a decrease in estimated glomerular filtration rate >10 mL/min/1.73m2 . Also, 25 and 35% had increased alanine transaminase and aspartate transaminase levels, respectively. However, serious adverse events were limited. Therefore, based on these preliminary results, contraindications based on kidney and liver function should not be absolute for remdesivir treatment in patients with Covid-19 if these functions are monitored regularly. A larger patient cohort is warranted to confirm our results.

An Electronic Health Record Text Mining Tool to Collect Real-World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma.

Real-world evidence can close the inferential gap between marketing authorization studies and clinical practice. However, the current standard for real-world data extraction from electronic health records (EHRs) for treatment evaluation is manual review (MR), which is time-consuming and laborious. Clinical Data Collector (CDC) is a novel natural language processing and text mining software tool for both structured and unstructured EHR data and only shows relevant EHR sections improving efficiency. We investigated CDC as a real-world data (RWD) collection method, through application of CDC queries for patient inclusion and information extraction on a cohort of patients with metastatic renal cell carcinoma (RCC) receiving systemic drug treatment. Baseline patient characteristics, disease characteristics, and treatment outcomes were extracted and these were compared with MR for validation. One hundred patients receiving 175 treatments were included using CDC, which corresponded to 99% with MR. Calculated median overall survival was 21.7 months (95% confidence interval (CI) 18.7-24.8) vs. 21.7 months (95% CI 18.6-24.8) and progression-free survival 8.9 months (95% CI 5.4-12.4) vs. 7.6 months (95% CI 5.7-9.4) for CDC vs. MR, respectively. Highest F1-score was found for cancer-related variables (88.1-100), followed by comorbidities (71.5-90.4) and adverse drug events (53.3-74.5), with most diverse scores on international metastatic RCC database criteria (51.4-100). Mean data collection time was 12 minutes (CDC) vs. 86 minutes (MR). In conclusion, CDC is a promising tool for retrieving RWD from EHRs because the correct patient population can be identified as well as relevant outcome data, such as overall survival and progression-free survival.

Medication non-adherence after kidney transplantation: A critical appraisal and systematic review.

Medication non-adherence is one of the most important causes for shortened graft survival subsequently leading to a reduction in kidney graft survival results. Our aim was to provide an overview of its prevalence, risk factors, diagnostic methods and interventions to improve adherence in kidney transplant recipients. Therefore, we systematically searched the databases PubMed, COCHRANE Library, Web of Science and EMBASE for studies addressing "medication adherence", "compliance", "adherence", "kidney transplantation" and "life style factors". We identified 96 studies that satisfied our inclusion criteria. A problematic lack of a uniformly accepted definition for non-adherence was found, consequently leading to a wide range in non-adherence prevalence (36-55%). Using one uniformly accepted non-adherence definition should therefore be encouraged. A wide range in diagnostic methods makes it difficult to accurately detect non-adherence. Heterogeneous results of intervention studies make it difficult to select the best adherence enhancing method, challenging the battle against medication non-adherence. Literature suggests a combination of personalized interventions, based on patient-specific non-adherent behavior, to be most successful in improvement of adherence. High quality diagnostic methods and multidisciplinary, personalized interventions with focus on relevant clinical outcome are essential in overcoming medication non-adherence in kidney transplant recipients.

Prospective Validation of a Risk Prediction Model to Identify High-Risk Patients for Medication Errors at Hospital Admission.

BACKGROUND: Pharmacy-led medication reconciliation in elective surgery patients is often performed at the preoperative screening (POS). Because of the time lag between POS and admission, changes in medication may lead to medication errors at admission (MEAs). In a previous study, a risk prediction model for MEA was developed. OBJECTIVE: To validate this risk prediction model to identify patients at risk for MEAs in a university hospital setting. METHODS: The risk prediction model was derived from a cohort of a Dutch general hospital and validated within a comparable cohort from a Dutch University Medical Centre. MEAs were assessed by comparing the POS medication list with the reconciled medication list at hospital admission. This was considered the gold standard. For every patient, a risk score using the risk prediction model was calculated and compared with the gold standard. The risk prediction model was assessed with receiver operating characteristic (ROC) analysis. RESULTS: Of 368 included patients, 167 (45.4%) had at least 1 MEA. ROC analysis revealed significant differences in the area under the curve of 0.535 ( P = 0.26; validation cohort) versus 0.752 ( P < 0.0001; derivation cohort). The sensitivity in this validating cohort was 66%, with a specificity of 40%. Conclusion and Relevance: The risk prediction model developed in a general hospital population is not suitable to identify patients at risk for MEA in a university hospital population. However, number of medications is a common risk factor in both patient populations and should, thus, form the basis of an adapted risk prediction model.

Risk factors for medication errors at admission in preoperatively screened patients.

BACKGROUND: Preoperative screening (POS) may help to reduce medication errors at admission (MEA). However, due to the time window between POS and hospital admission, unintentional medication discrepancies may still occur and thus a second medication reconciliation at hospital admission can be necessary. Insight into potential risk factors associated with these discrepancies would be helpful to focus the second medication reconciliation on high-risk patients. OBJECTIVE: To determine the proportion of POS patients with MEA and to identify risk factors for MEA. METHODS: This single-centre observational cross-sectional study included elective surgical patients between October 26 and December 18, 2015. Main exclusion criteria were age younger than 18 years and daycare admissions. Medication reconciliation took place at the POS and was repeated within 30 hours of admission. Unintended discrepancies between the first and second medication reconciliation were defined as MEA. The primary outcome was the proportion of patients with one or more MEA. The association of this outcome with potential risk factors was analysed using multivariate logistic regression analysis. RESULTS: Of the 183 included patients, 60 (32.8%) patients had at least one MEA. In a multivariate model, the number of medications at POS (adjusted odds ratio 1.16 [95% confidence interval, 1.04-1.30]) and respiratory disease (4.25 [1.52-11.83]) were significantly associated with MEA. CONCLUSION: In one-third of preoperatively screened patients, an MEA was found. The number of medications and respiratory comorbidities are risk factors for MEA in preoperatively screened patients.

Evaluation of the concurrent use of potassium-influencing drugs as risk factors for the development of hyperkalemia.

BACKGROUND: Hyperkalemia is a potentially dangerous electrolyte abnormality with a reported incidence of 1-10 % in hospitals. Patients are especially at risk of developing this complication if they use a combination of potassium supplements and potassium sparing diuretics or renin-angiotensin-aldosterone-system (RAAS) inhibitors. Previous studies on the occurrence of hyperkalemia in patients who use multiple potassium influencing drugs simultaneously were either small in sample size or did not investigate the full range of drugs involved. OBJECTIVE: To assess the prevalence of hyperkalemia and to identify risk factors for its development in hospitalised patients using potassium supplements, potassium-sparing diuretics and/or RAAS-inhibitors concurrently. SETTING: The study was conducted at the Onze Lieve Vrouwe Hospital in Amsterdam, The Netherlands from January 2009 to May 2010. METHOD: A retrospective, nested case-control study included hospitalised patients who used a combination of potassium-influencing drugs. Cases were patients with serum potassium ≥ 5.5 mmol/l, controls were patients with normal serum potassium levels. Cases and controls were included in a ratio of 1:2. The following known risk factors associated with hyperkalemia were recorded and statistically analyzed: diabetes mellitus, congestive heart failure, decreased renal function, advanced age, gender and use of heparin, digoxin, non-steroidal anti-inflammatory drugs, beta-blockers, calcineurin antagonists and trimethoprim. MAIN OUTCOME MEASURE: Identify risk factors for the development of hyperkalemia as a result of the concurrent use of potassium supplements, RAAS inhibitors and/or potassium-sparing diuretics. RESULTS: Of 774 patients included in this study, 52 patients developed hyperkalemia; a prevalence of 6.7 %. The only risk factor found to be significantly associated with hyperkalemia was lowered renal function, expressed as estimated glomerular filtration rate (eGFR) <50 ml/min (adjusted OR 5.08; 95 % CI 2.46-10.48). None of the other tested risk factors was identified as significant. CONCLUSION: This study showed that decreased renal function (eGFR <50 ml/min) was associated with a fivefold increased risk for hyperkalemia in patients using potassium-influencing drugs. While previous studies showed that hyperkalemia substantially increases below a threshold of eGFR <30 or 40 ml/min, we observed a lower threshold of eGFR <50 ml/min. It is therefore recommended that physicians should be particularly alert while monitoring the use of potassium-influencing drugs in patients with decreased renal function.

Cost-effectiveness of ward-based pharmacy care in surgical patients: protocol of the SUREPILL (Surgery & Pharmacy In Liaison) study.

BACKGROUND: Preventable adverse drug events (pADEs) are widely known to be a health care issue for hospitalized patients. Surgical patients are especially at risk, but prevention of pADEs in this population is not demonstrated before. Ward-based pharmacy interventions seem effective in reducing pADEs in medical patients. The cost-effectiveness of these preventive efforts still needs to be assessed in a comparative study of high methodological standard and also in the surgical population. For these aims the SUREPILL (Surgery & Pharmacy in Liaison) study is initiated. METHODS/DESIGN: A multi-centre controlled trial, with randomisation at ward-level and preceding baseline assessments is designed. Patients admitted to the surgical study wards for elective surgery with an expected length of stay of more than 48 hours will be included. Patients admitted to the intervention ward, will receive ward-based pharmacy care from the clinical pharmacy team, i.e. pharmacy practitioners and hospital pharmacists. This ward-based pharmacy intervention includes medication reconciliation in consultation with the patient at admission, daily medication review with face-to-face contact with the ward doctor, and patient counselling at discharge. Patients admitted in the control ward, will receive standard pharmaceutical care.The primary clinical outcome measure is the number of pADEs per 100 elective admissions. These pADEs will be measured by systematic patient record evaluation using a trigger tool. Patient records positive for a trigger will be evaluated on causality, severity and preventability by an independent expert panel. In addition, an economic evaluation will be performed from a societal perspective with the costs per preventable ADE as the primary economic outcome. Other outcomes of this study are: severity of pADEs, number of patients with pADEs per total number of admissions, direct (non-)medical costs and indirect non-medical costs, extra costs per prevented ADE, number and type of pharmacy interventions, length of hospital stay, complications registered in a national complication registration system for surgery, number of readmissions within three months after initial admission (follow-up), quality of life and number of non-institutionalized days during follow-up. DISCUSSION: This study will assess the cost-effectiveness of ward-based pharmacy care on preventable adverse drug events in surgical patients from a societal perspective, using a comparative study design. TRIAL REGISTRATION: Netherlands Trial Register (NTR): NTR2258.

Influence of chemical structure on hypersensitivity reactions induced by antiepileptic drugs: the role of the aromatic ring.

OBJECTIVE: Antiepileptic drugs (AEDs) can cause various 'idiosyncratic' hypersensitivity reactions, i.e. the mechanism by which AEDs induce hypersensitivity is unknown. The aim of this study was to assess whether the presence of an aromatic ring as a commonality in chemical structures of AEDs can explain symptoms of hypersensitivity. METHODS: Between January 1985 and January 2007, all adverse drug reactions (ADRs) reported to the Netherlands Pharmacovigilance Centre Lareb related to AEDs as suspected drugs were included in this study. ADRs were analysed using a case/non-case design. Cases were defined as those patients with ADRs involving symptoms of hypersensitivity. Non-cases were patients with all other ADR reports. Symptoms of hypersensitivity were classified according to the Gell and Coombs classification (type I-IV) and the organ involved (e.g. cutaneous, hepatic). AEDs were classified as aromatic anticonvulsant if their chemical structure contained at least one aromatic ring. All other AEDs were classified as non-aromatic. We assessed the strength of the association between aromatic AEDs versus non-aromatic AEDs and reported hypersensitivity reactions with logistic regression analysis and expressed these as reporting odds ratios (RORs). RESULTS: In total, 303 cases of hypersensitivity associated with the use of AEDs were reported. Aromatic AEDs were suspected in 64.4% of these reports versus 41.3% (574/1389) of the non-hypersensitivity reports. A significant ROR of 2.15 (95% CI 1.63, 2.82) was found for aromatic AEDs and all hypersensitivity reactions. Aromatic AEDs were significantly associated with immunoglobin E-mediated type I hypersensitivity reactions (ROR 2.15; 95% CI 1.23, 3.78) and T-cell-mediated type IV reactions (ROR 6.06; 95% CI 3.41, 10.75). Type II and III reactions did not show an association. Cutaneous symptoms represented 39.9% of the hypersensitivity-related ADRs. Aromatic AEDs were significantly associated with cutaneous hypersensitivity reactions (ROR 5.81; 95% CI 3.38, 9.99). CONCLUSION: This study confirms that the presence of an aromatic ring as a common feature in chemical structures of AEDs partly explains apparent 'idiosyncratic' hypersensitivity reactions. Symptoms of hypersensitivity were reported twice as frequently with aromatic AEDs than with non-aromatic AEDs. Strong associations for aromatic AEDs versus non-aromatic AEDs were found for T-cell-mediated (type IV) reactions, as well as for cutaneous reactions.

Drug treatment-related factors of inadequate seizure control.

To optimize seizure control it is important to identify modifiable factors. We conducted a case-control study to explore to what extent drug treatment-related factors are associated with seizures. Eighty-six patients with epilepsy were evaluated: 45 cases (recently experienced a seizure) and 41 controls (seizure-free for at least 2 months). There was a significant association between low AED serum concentration and seizures (odds ratio (OR)=8.9, 95% confidence interval (CI)=1.7-47.8), compliance was not associated with seizures (OR=0.9, 95% CI=0.2-4.0), and changes in medication (mainly non-AEDs) were more frequently observed in the case group than in the control group (OR=4.1, 95% CI=0.9-18.3). These findings indicate that patients with low AED serum levels have a nine times higher risk of seizures compared with patients with therapeutic AED levels and that changes in medication regimens in patients with epilepsy should be made with care.

Changes in medication associated with epilepsy-related hospitalisation: a case-crossover study.

AIM: To assess the association between changes in medication and epilepsy-related hospitalisation. METHODS: Data were obtained from the PHARMO Record Linkage System (Jan 1998 to Dec 2002). We conducted a case-crossover study among patients with a first epilepsy-related hospital admission who had continuously used at least one antiepileptic drug (AED) during a 28-week period before admission. For each patient, changes in medication in a 28-day window before hospitalisation were compared with changes in four earlier 28-day windows. Evaluated changes were: changes in AEDs (pattern and dosage), changes in interacting co-medication and changes in non-interacting co-medication (i.e. introduction of non-interacting drugs). The strength of the association between changes in medication and epilepsy-related hospitalisation was estimated using conditional logistic regression analysis and expressed as odds ratios (ORs) with 95% confidence intervals (CI). RESULTS: Out of 1185 patients with a first epilepsy-related hospitalisation, 217 patients met the inclusion criteria. Of the changes in antiepileptic therapy, discontinuation showed a trend towards an increased risk of hospitalisation (OR: 2.57; 95%CI: 0.81-8.17). Drug interactions influencing antiepileptic therapy rarely occurred. Introduction of three or more non-interacting drugs was significantly associated with epilepsy-related hospitalisation (OR: 4.80; 95%CI: 2.12-10.87). Of individual drugs, addition of antimicrobial agents was significantly associated with epilepsy-related hospitalisation (OR: 1.99; 95%CI: 1.06-3.75). CONCLUSIONS: Changes in AED therapy were not significantly associated with epilepsy-related hospitalisation and few drug interactions influencing antiepileptic therapy occurred. However, patients starting three or more new non-AEDs had a nearly five times increased risk of epilepsy-related hospital admission.

Risk of aplastic anemia in patients using antiepileptic drugs.

PURPOSE: To assess the association between exposure to antiepileptic drugs (AEDs) and the occurrence of aplastic anemia. METHODS: A retrospective case-control study was conducted using data from the U.K. General Practitioners Research Database (GPRD). Cases were defined as patients diagnosed with aplastic anemia. For each case, up to three control patients were matched on age, sex, and medical practice. Cases and controls were compared with respect to AED use. The effects of duration of AED use were assessed. Characteristics of individual cases with AED use were reviewed. RESULTS: The study population comprised 173 cases and 497 controls. AED use was more prevalent among cases (9.2%) than among controls (0.8%). After adjustment for confounders, the use of AEDs was significantly associated with aplastic anemia (adjusted odds ratio (OR), 9.5; 95% confidence interval (CI), 3.0-39.7). The most frequently used AEDs were carbamazepine (CBZ), valproic acid (VPA), and phenytoin. The 16 exposed cases were heterogeneous with respect to patient and exposure characteristics: the age of these patients varied from 1 to 92 years, and the duration of AED use varied from 17 days to 6.8 years. CONCLUSIONS: This study indicates that use of AEDs, in particular CBZ and VPA, is associated with a ninefold increased risk of aplastic anemia. Physicians should be alert to the possibility of AED-associated aplastic anemia.