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Riester MR, Zhang Y, Hayes KN, Beaudoin FL, Zullo AR. Use of electronic health record data to examine administrations of pro re nata analgesics during hip fracture post-acute care. Pharmacoepidemiol Drug Saf 2024; 33:e5846. [PMID: 38825963 PMCID: PMC11149906 DOI: 10.1002/pds.5846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 05/03/2024] [Accepted: 05/15/2024] [Indexed: 06/04/2024]
Abstract
PURPOSE Medications prescribed to older adults in US skilled nursing facilities (SNF) and administrations of pro re nata (PRN) "as needed" medications are unobservable in Medicare insurance claims. There is an ongoing deficit in our understanding of medication use during post-acute care. Using SNF electronic health record (EHR) datasets, including medication orders and barcode medication administration records, we described patterns of PRN analgesic prescribing and administrations among SNF residents with hip fracture. METHODS Eligible participants resided in SNFs owned by 11 chains, had a diagnosis of hip fracture between January 1, 2018 to August 2, 2021, and received at least one administration of an analgesic medication in the 100 days after the hip fracture. We described the scheduling of analgesics, the proportion of available PRN doses administered, and the proportion of days with at least one PRN analgesic administration. RESULTS Among 24 038 residents, 57.3% had orders for PRN acetaminophen, 67.4% PRN opioids, 4.2% PRN non-steroidal anti-inflammatory drugs, and 18.6% PRN combination products. The median proportion of available PRN doses administered per drug was 3%-50% and the median proportion of days where one or more doses of an ordered PRN analgesic was administered was 25%-75%. Results differed by analgesic class and the number of administrations ordered per day. CONCLUSIONS EHRs can be leveraged to ascertain precise analgesic exposures during SNF stays. Future pharmacoepidemiology studies should consider linking SNF EHRs to insurance claims to construct a longitudinal history of medication use and healthcare utilization prior to and during episodes of SNF care.
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Affiliation(s)
- Melissa R Riester
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Yuan Zhang
- Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Kaleen N Hayes
- Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
- Graduate Department of Pharmaceutical Sciences, University of Toronto Leslie Dan Faculty of Pharmacy, Toronto, Ontario, Canada
| | - Francesca L Beaudoin
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Andrew R Zullo
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
- Center of Innovation in Long-Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, Rhode Island, USA
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2
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Battini V, Barbieri MA, Carnovale C, Spina E, Clementi E, Sessa M. Comparing major and mild cognitive impairment risks in older type-2 diabetic patients: a Danish register-based study on dipeptidyl peptidase-4 inhibitors vs. glucagon-like peptide-1 analogues. J Neurol 2024; 271:3417-3425. [PMID: 38517522 PMCID: PMC11136777 DOI: 10.1007/s00415-024-12300-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/01/2024] [Accepted: 03/02/2024] [Indexed: 03/24/2024]
Abstract
INTRODUCTION The prevalence of major and mild cognitive impairment (CI) in type-2 diabetes older patients is 15-25% and 30-60%, respectively, thus affecting quality of life and health outcomes. There is, therefore, the need of head-to-head studies aiming at identifying the optimal treatment for individuals with type-2 diabetes at increased risk of mild and major CI. This study focuses on the risk of developing mild and major CI in Danish patients treated with dipeptidyl peptidase-4 inhibitors (DPP-4i) and glucagon-like peptide-1 analogues (GLP-1a) using administrative and healthcare registers. METHODS An active comparator design with a 3-year follow-up period was used. The main outcome was the hospital admission with a diagnosis of mild CI or major CI. Multivariate Cox Regression analysis was performed using the high-dimensional propensity score to obtain adjusted Hazard Ratio (HR) estimates. Inverse probability of treatment weighting (IPTW) and marginal structured model were used to calculate risk differences while accounting for the variations of confounders throughout the follow-up period. RESULTS Our results show a significant higher risk of major CI between DPP-4i and GLP-1a in unadjusted [HR (95% CI) = 3.13 (2.45-4.00), p < 0.001] and adjusted analyses [HR (95% CI) = 1.58 (1.22-2.06), p = 0.001]. No statistically significant differences were observed for mild CI. IPTW resulted stable throughout the follow-up period. Marginal structure modeling (β (95% CI) = 0.022 (0.020-0.024), p < 0.001) resulted in a higher risk of major CI for DPP-4i when compared to GLP-1a. DISCUSSION DPP-4i was associated with an increased risk of developing major CI when compared to GLP-1a among older individuals with type-2 diabetes.
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Affiliation(s)
- Vera Battini
- Department of Drug Design and Pharmacology, University of Copenhagen, Jagtvej 160, 2100, Copenhagen, Denmark
- Pharmacovigilance and Clinical Research, International Centre for Pesticides and Health Risk Prevention, Department of Biomedical and Clinical Sciences (DIBIC), ASST Fatebenefratelli-Sacco University Hospital, Università Degli Studi Di Milano, Milan, Italy
| | - Maria Antonietta Barbieri
- Department of Drug Design and Pharmacology, University of Copenhagen, Jagtvej 160, 2100, Copenhagen, Denmark
- Department of Clinical and Experimental Medicine, University of Messina, 98125, Messina, Italy
| | - Carla Carnovale
- Pharmacovigilance and Clinical Research, International Centre for Pesticides and Health Risk Prevention, Department of Biomedical and Clinical Sciences (DIBIC), ASST Fatebenefratelli-Sacco University Hospital, Università Degli Studi Di Milano, Milan, Italy
| | - Edoardo Spina
- Department of Clinical and Experimental Medicine, University of Messina, 98125, Messina, Italy
| | - Emilio Clementi
- Pharmacovigilance and Clinical Research, International Centre for Pesticides and Health Risk Prevention, Department of Biomedical and Clinical Sciences (DIBIC), ASST Fatebenefratelli-Sacco University Hospital, Università Degli Studi Di Milano, Milan, Italy
- Scientific Institute, IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Jagtvej 160, 2100, Copenhagen, Denmark.
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Wang W, Battini V, Carnovale C, Noordam R, van Dijk KW, Kragholm KH, van Heemst D, Soeorg H, Sessa M. A novel approach for pharmacological substantiation of safety signals using plasma concentrations of medication and administrative/healthcare databases: a case study using Danish registries for an FDA warning on lamotrigine. Pharmacol Res 2023:106811. [PMID: 37268178 DOI: 10.1016/j.phrs.2023.106811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 05/29/2023] [Accepted: 05/29/2023] [Indexed: 06/04/2023]
Abstract
PHARMACOM-EPI is a novel framework to predict plasma concentrations of drugs at the time of occurrence of clinical outcomes. In early 2021, the U.S. Food and Drug Administration (FDA) issued a warning on the antiseizure drug lamotrigine claiming that it has the potential to increase the risk of arrhythmias and related sudden cardiac death due to a pharmacological sodium channel-blocking effect. We hypothesized that the risk of arrhythmias and related death is due to toxicity. We used the PHARMACOM-EPI framework to investigate the relationship between lamotrigine's plasma concentrations and the risk of death in older patients using real-world data. Danish nationwide administrative and healthcare registers were used as data sources and individuals aged 65 years or older during the period 1996 - 2018 were included in the study. According to the PHARMACOM-EPI framework, plasma concentrations of lamotrigine were predicted at the time of death and patients were categorized into non-toxic and toxic groups based on the therapeutic range of lamotrigine (3-15mg/L). Over 1 year of treatment, the incidence rate ratio (IRR) of all-cause mortality was calculated between the propensities score matched toxic and non-toxic groups. In total, 7286 individuals were diagnosed with epilepsy and were exposed to lamotrigine, 432 of which had at least one plasma concentration measurement The pharmacometric model by Chavez et al. was used to predict lamotrigine's plasma concentrations considering the lowest absolute percentage error among identified models (14.25%, 95% CI: 11.68-16.23). The majority of lamotrigine associated deaths were cardiovascular-related and occurred among individuals with plasma concentrations in the toxic range. The IRR of mortality between the toxic group and non-toxic group was 3.37 [95% CI: 1.44-8.32] and the cumulative incidence of all-cause mortality exponentially increased in the toxic range. Application of our novel framework PHARMACOM-EPI provided strong evidence to support our hypothesis that the increased risk of all-cause and cardiovascular death was associated with a toxic plasma concentration level of lamotrigine among older lamotrigine users.
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Affiliation(s)
- Wenyi Wang
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Vera Battini
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Italy; Department of Drug Design and Pharmacology, University of Copenhagen, Denmark
| | - Carla Carnovale
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Italy
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics; Leiden University Medical Center, Leiden, Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, Netherlands; Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, Netherlands
| | | | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics; Leiden University Medical Center, Leiden, Netherlands
| | - Hiie Soeorg
- Department of Microbiology, Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Estonia.
| | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Denmark.
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Mathieu C, Bezin J, Pariente A. Impact of COVID-19 epidemic on antihypertensive drug treatment disruptions: results from a nationwide interrupted time-series analysis. Front Pharmacol 2023; 14:1129244. [PMID: 37256233 PMCID: PMC10225585 DOI: 10.3389/fphar.2023.1129244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 04/27/2023] [Indexed: 06/01/2023] Open
Abstract
Background: The COVID-19 epidemic has disrupted care and access to care in many ways. It was accompanied by an excess of cardiovascular drug treatment discontinuations. We sought to investigate a deeper potential impact of the COVID-19 epidemic on antihypertensive drug treatment disruptions by assessing whether the epidemic induced some changes in the characteristics of disruptions in terms of duration, treatment outcome, and patient characteristics. Methods: From March 2018 to February 2021, a repeated cohort analysis was performed using French national health insurance databases. The impact of the epidemic on treatment discontinuations and resumption of antihypertensive medications was assessed using preformed interrupted time series analyses either on a quarterly basis. Results: Among all adult patients on antihypertensive medication, we identified 2,318,844 (18.7%) who discontinued their antihypertensive treatment during the first blocking period in France. No differences were observed between periods in the characteristics of patients who interrupted their treatment or in the duration of treatment disruptions. The COVID-19 epidemic was not accompanied by a change in the proportion of patients who fully resumed treatment after a disruption, neither in level nor in trend/slope [change in level: 2.66 (-0.11; 5.42); change in slope: -0.67 (-1.54; 0.20)]. Results were similar for the proportion of patients who permanently discontinued treatment within 1 year of disruption [level change: -0.21 (-2.08; 1.65); slope change: 0.24 (-0.40; 0.87)]. Conclusion: This study showed that, although it led to an increase in cardiovascular drug disruptions, the COVID-19 epidemic did not change the characteristics of these. First, disruptions were not prolonged, and post-disruption treatment outcomes remained unchanged. Second, patients who experienced antihypertensive drug disruptions during the COVID-19 outbreak were essentially similar to those who experienced disruptions before it.
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Affiliation(s)
- Clément Mathieu
- Inserm, Bordeaux Population Health Research Center, Team AHeaD, UMR 1219, University Bordeaux, Bordeaux, France
| | - Julien Bezin
- Inserm, Bordeaux Population Health Research Center, Team AHeaD, UMR 1219, University Bordeaux, Bordeaux, France
- CHU de Bordeaux, Service de Pharmacologie Médicale, Bordeaux, France
| | - Antoine Pariente
- Inserm, Bordeaux Population Health Research Center, Team AHeaD, UMR 1219, University Bordeaux, Bordeaux, France
- CHU de Bordeaux, Service de Pharmacologie Médicale, Bordeaux, France
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Bharat C, Degenhardt L, Pearson S, Buizen L, Wilson A, Dobbins T, Gisev N. A data-informed approach using individualised dispensing patterns to estimate medicine exposure periods and dose from pharmaceutical claims data. Pharmacoepidemiol Drug Saf 2023; 32:352-365. [PMID: 36345837 PMCID: PMC10947320 DOI: 10.1002/pds.5567] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 10/28/2022] [Accepted: 11/04/2022] [Indexed: 11/10/2022]
Abstract
Pharmaceutical claims data are often used as the primary information source to define medicine exposure periods in pharmacoepidemiological studies. However, often critical information on directions for use and the intended duration of medicine supply are not available. In the absence of this information, alternative approaches are needed to support the assignment of exposure periods. This study summarises the key methods commonly used to estimate medicine exposure periods and dose from pharmaceutical claims data; and describes a method using individualised dispensing patterns to define time-dependent estimates of medicine exposure and dose. This method extends on important features of existing methods and also accounts for recent changes in an individual's medicine use. Specifically, this method constructs medicine exposure periods and estimates the dose used by considering characteristics from an individual's prior dispensings, accounting for the time between prior dispensings and the amount supplied at prior dispensings. Guidance on the practical applications of this method is also provided. Although developed primarily for application to databases, which do not contain duration of supply or dose information, use of this method may also facilitate investigations when such information is available and there is a need to consider individualised and/or changing dosing regimens. By shifting the reliance on prescribed duration and dose to determine exposure and dose estimates, individualised dispensing information is used to estimate patterns of exposure and dose for an individual. Reflecting real-world individualised use of medicines with complex and variable dosing regimens, this method offers a pragmatic approach that can be applied to all medicine classes.
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Affiliation(s)
- Chrianna Bharat
- National Drug and Alcohol Research CentreUNSW SydneySydneyNew South WalesAustralia
| | - Louisa Degenhardt
- National Drug and Alcohol Research CentreUNSW SydneySydneyNew South WalesAustralia
| | - Sallie‐Anne Pearson
- Centre for Big Data Research in HealthFaculty of Medicine, UNSW SydneyKensingtonNew South WalesAustralia
| | - Luke Buizen
- National Drug and Alcohol Research CentreUNSW SydneySydneyNew South WalesAustralia
| | - Andrew Wilson
- Menzies Centre for Health Policy and EconomicsSydney School of Public Health, University of SydneySydneyNew South WalesAustralia
| | - Timothy Dobbins
- School of Population HealthUNSW SydneySydneyNew South WalesAustralia
| | - Natasa Gisev
- National Drug and Alcohol Research CentreUNSW SydneySydneyNew South WalesAustralia
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Breitenstein PS, Mahmoud I, Al-Azzawi F, Shakibfar S, Sessa M. A machine-learning guided method for predicting add-on and switch in secondary data sources: A case study on anti-seizure medications in Danish registries. Front Pharmacol 2022; 13:954393. [PMID: 36438810 PMCID: PMC9685793 DOI: 10.3389/fphar.2022.954393] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/19/2022] [Indexed: 12/14/2023] Open
Abstract
Purpose: There is a lack of available evidence regarding the treatment pattern of switches and add-ons for individuals aged 65 years or older with epilepsy during the first years from the time they received their first anti-seizure medication because of the lack of valid methods. Therefore, this study aimed to develop an algorithm for identifying switches and add-ons using secondary data sources for anti-seizure medication users. Methods: Danish nationwide databases were used as data sources. Residents in Denmark between 1996 and 2018 who were diagnosed with epilepsy and redeemed their first prescription for anti-seizure medication after epilepsy diagnosis were followed up for 730 days until the end of the follow-up period, death, or emigration to assess switches and add-ons occurred during the follow-up period. The study outcomes were the overall accuracy of the classification of switch or add-on of the newly developed algorithm. Results: In total, 15870 individuals were included in the study population with a median age of 72.9 years, of whom 52.0% were male and 48.0% were female. A total of 988 of the 15879 patients from the study population were present during the 730-day follow-up period, and 988 individuals (6.2%) underwent a total of 1485 medication events with co-exposure to two or more anti-seizure medications. The newly developed algorithmic method correctly identified 9 out of 10 add-ons (overall accuracy 92%) and 9 out of 10 switches (overall accuracy 88%). Conclusion: The majority of switches and add-ons occurred early during the first 2 years of disease and according to clinical recommendations. The newly developed algorithm correctly identified 9 out of 10 switches/add-ons.
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Affiliation(s)
| | | | | | | | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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