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Botelho SF, Neiva Pantuzza LL, Moreira Reis AM. Development, content validation and standardization of an adult patient prioritization tool for hospital clinical pharmacy services. Res Social Adm Pharm 2024:S1551-7411(24)00174-8. [PMID: 38760312 DOI: 10.1016/j.sapharm.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/06/2024] [Accepted: 05/11/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Patient prioritization is a effective strategy to identify high risk patients for targeted Clinical Pharmacy Service (CPS) in hospital pharmacy. However, there is a paucity of patient prioritization tool to use in clinical practice. OBJECTIVES Describe the development, content validation and standardization of an adult patient prioritization tool for hospital CPS named, PrioFarClinH. METHODS The tool was developed using a stepwise design multi: Scoping Review to identify prioritization criteria/sub-criteria; Delphi technique to obtain consensus under the identified criteria/sub-criteria; Survey with pharmacists evaluating applicability of the criteria/sub-criteria obtained from Delphi; Definition of criteria/sub-criteria to be included in PrioFarClinH attribution of scores. Content validation was performed by a panel of experts evaluating relevance, feasibility, clarity and adequacy of the score. Content Validity Index (CVI) was calculated. Standardization occurred through a retrospective observational study carried out at 24 and 72 h and median of the patient's hospital stay. An intragroup norm was performed, determining percentile ranks of the instrument's total scores. Patients with a P90 score were classified with a high level of prioritization for CPS. RESULTS PrioFarClinH is divided into three sections, with prioritization criteria for health issues; therapeutic classes; laboratory parameters. It comprises 51 criteria with specific scores with simple total calculation. None of the criteria presented CVI <0.78, maintaining the items from the initial version of PrioFarClinH. The scores were adjusted per suggestions from the panel of judges. Data were collected from 393 patients. The P90 percentile in the three hospitalization stages (24 h, 72 h, and median) was found, respectively, in the following scores: 18.0, 20.0, and 22.6. CONCLUSIONS PrioFarClinH is a comprehensive tool to target and to prioritize adults patients most likely to benefit from CPS. Evidence for adequate content validity was provided. However, further validation of this tool is necessary to establish tool performance.
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Affiliation(s)
- Stephanie Ferreira Botelho
- Programa de Pós-graduação em Medicamentos e Assistência Farmacêutica, Faculdade de Farmácia, Universidade Federal de Minas Gerais, 6627 Presidente Antônio Carlos Ave., Pampulha, Belo Horizonte, Minas Gerais, Brazil
| | - Laís Lessa Neiva Pantuzza
- Programa de Pós-graduação em Medicamentos e Assistência Farmacêutica, Faculdade de Farmácia, Universidade Federal de Minas Gerais, 6627 Presidente Antônio Carlos Ave., Pampulha, Belo Horizonte, Minas Gerais, Brazil
| | - Adriano Max Moreira Reis
- Programa de Pós-graduação em Medicamentos e Assistência Farmacêutica, Faculdade de Farmácia, Universidade Federal de Minas Gerais, 6627 Presidente Antônio Carlos Ave., Pampulha, Belo Horizonte, Minas Gerais, Brazil.
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Mahomedradja RF, Tichelaar J, Mokkink LB, Sigaloff KCE, van Agtmael MA. Quality indicators for appropriate in-hospital pharmacotherapeutic stewardship: An international modified Delphi study. Br J Clin Pharmacol 2024; 90:1280-1300. [PMID: 38369619 DOI: 10.1111/bcp.16015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/30/2023] [Accepted: 01/08/2024] [Indexed: 02/20/2024] Open
Abstract
AIMS In-hospital prescribing errors may result in patient harm, such as prolonged hospitalisation and hospital (re)admission, and may be an emotional burden for the prescribers and healthcare professionals involved. Despite efforts, in-hospital prescribing errors and related harm still occur, necessitating an innovative approach. We therefore propose a novel approach, in-hospital pharmacotherapeutic stewardship (IPS). The aim of this study was to reach consensus on a set of quality indicators (QIs) as a basis for IPS. METHODS A three-round modified Delphi procedure was performed. Potential QIs were retrieved from two systematic searches of the literature, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. In two written questionnaires and a focus meeting (held between the written questionnaire rounds), potential QIs were appraised by an international, multidisciplinary expert panel composed of members of the European Association for Clinical Pharmacology and Therapeutics (EACPT). RESULTS The expert panel rated 59 QIs and four general statements, of which 35 QIs were accepted with consensus rates ranging between 79% and 97%. These QIs describe the activities of an IPS programme, the team delivering IPS, the patients eligible for the programme and the outcome measures that should be used to evaluate the care delivered. CONCLUSIONS A framework of 35 QIs for an IPS programme was systematically developed. These QIs can guide hospitals in setting up a pharmacotherapeutic stewardship programme to reduce in-hospital prescribing errors and improve in-hospital medication safety.
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Affiliation(s)
- Rashudy F Mahomedradja
- Department of Internal Medicine, Unit Pharmacotherapy, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Research and Expertise Center in Pharmacotherapy Education (RECIPE), De Boelelaan 1117, Amsterdam, The Netherlands
| | - Jelle Tichelaar
- Department of Internal Medicine, Unit Pharmacotherapy, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Research and Expertise Center in Pharmacotherapy Education (RECIPE), De Boelelaan 1117, Amsterdam, The Netherlands
| | - Lidwine B Mokkink
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Kim C E Sigaloff
- Department of Internal Medicine, Unit Pharmacotherapy, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Research and Expertise Center in Pharmacotherapy Education (RECIPE), De Boelelaan 1117, Amsterdam, The Netherlands
| | - Michiel A van Agtmael
- Department of Internal Medicine, Unit Pharmacotherapy, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
- Research and Expertise Center in Pharmacotherapy Education (RECIPE), De Boelelaan 1117, Amsterdam, The Netherlands
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Rodriguez R, Joseph H, Macrito R, Lee TA, Sweiss K. Risk prediction models for antineoplastic-associated cardiotoxicity in treatment of breast cancer: A systematic review. Am J Health Syst Pharm 2023; 80:1315-1325. [PMID: 37368407 DOI: 10.1093/ajhp/zxad147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Indexed: 06/28/2023] Open
Abstract
PURPOSE The objective of this systematic review is to assess methodology of published models to predict the risk of antineoplastic-associated cardiotoxicity in patients with breast cancer. METHODS We searched PubMed and Embase for studies that developed or validated a multivariable risk prediction model. Data extraction and quality assessments were performed according to the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS We identified 2,816 unique publications and included 8 eligible studies (7 new risk models and 1 validation of a risk stratification tool) that modeled risk with trastuzumab (n = 5), anthracyclines (n = 2), and anthracyclines with or without trastuzumab (n = 1). The most common final predictors were previous or concomitant chemotherapy (n = 5) and age (n = 4). Three studies incorporated measures of myocardial mechanics that may not be frequently available. Model discrimination was reported in 7 studies (range of area under the receiver operating characteristic curve, 0.56-0.88), while calibration was reported in 1 study. Internal and external validation were performed in 4 studies and 1 study, respectively. Using the PROBAST methodology, we rated the overall risk of bias as high for 7 of 8 studies and unclear for 1 study. Concerns for applicability were low for all studies. CONCLUSION Among 8 models to predict the risk of cardiotoxicity of antineoplastic agents for breast cancer, 7 were rated as having a high risk of bias and all had low concerns for clinical applicability. Most evaluated studies reported positive measures of model performance but did not perform external validation. Efforts to improve development and reporting of these models to facilitate their use in practice are warranted.
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Affiliation(s)
- Ryan Rodriguez
- Department of Pharmacy Practice, University of Illinois Chicago College of Pharmacy, Chicago, IL, USA
| | - Honey Joseph
- Department of Pharmacy Practice, University of Illinois Chicago College of Pharmacy, Chicago, IL, USA
| | - Rosa Macrito
- Department of Pharmacy Practice, University of Illinois Chicago College of Pharmacy, Chicago, IL, USA
| | - Todd A Lee
- Department of Pharmacy Systems, Outcomes, and Policy, University of Illinois Chicago College of Pharmacy, Chicago, IL, USA
| | - Karen Sweiss
- Department of Pharmacy Practice, University of Illinois Chicago College of Pharmacy, Chicago, IL, USA
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Keller MS, Qureshi N, Albertson E, Pevnick J, Brandt N, Bui A, Sarkisian CA. Comparing risk prediction models aimed at predicting hospitalizations for adverse drug events in community dwelling older adults: a protocol paper. RESEARCH SQUARE 2023:rs.3.rs-2429369. [PMID: 36711695 PMCID: PMC9882666 DOI: 10.21203/rs.3.rs-2429369/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background The objective of this paper is to describe the creation, validation, and comparison of two risk prediction modeling approaches for community-dwelling older adults to identify individuals at highest risk for adverse drug event-related hospitalizations. One approach will use traditional statistical methods, the second will use a machine learning approach. Methods We will construct medication, clinical, health care utilization, and other variables known to be associated with adverse drug event-related hospitalizations. To create the cohort, we will include older adults (≥ 65 years of age) empaneled to a primary care physician within the Cedars-Sinai Health System primary care clinics with polypharmacy (≥ 5 medications) or at least 1 medication commonly implicated in ADEs (certain oral hypoglycemics, anti-coagulants, anti-platelets, and insulins). We will use a Fine-Gray Cox proportional hazards model for one risk modeling approach and DataRobot, a data science and analytics platform, to run and compare several widely used supervised machine learning algorithms, including Random Forest, Support Vector Machine, Extreme Gradient Boosting (XGBoost), Decision Tree, Naïve Bayes, and K-Nearest Neighbors. We will use a variety of metrics to compare model performance and to assess the risk of algorithmic bias. Discussion In conclusion, we hope to develop a pragmatic model that can be implemented in the primary care setting to risk stratify older adults to further optimize medication management.
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Affiliation(s)
| | | | | | | | | | - Alex Bui
- David Geffen School of Medicine: University of California Los Angeles David Geffen School of Medicine
| | - Catherine A Sarkisian
- David Geffen School of Medicine: University of California Los Angeles David Geffen School of Medicine
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Jung-Poppe L, Nicolaus HF, Roggenhofer A, Altenbuchner A, Dormann H, Pfistermeister B, Maas R. Systematic Review of Risk Factors Assessed in Predictive Scoring Tools for Drug-Related Problems in Inpatients. J Clin Med 2022; 11:jcm11175185. [PMID: 36079114 PMCID: PMC9457151 DOI: 10.3390/jcm11175185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/15/2022] [Accepted: 08/18/2022] [Indexed: 11/16/2022] Open
Abstract
Drug-related problems (DRP, defined as adverse drug events/reactions and medication errors) are a common threat for patient safety. With the aim to aid improved allocation of specialist resources and to improve detection and prevention of DRP, numerous predictive scoring tools have been proposed. The external validation and evidence for the transferability of these tools still faces limitations. However, the proposed scoring tools include partly overlapping sets of similar factors, which may allow a new approach to estimate the external usability and validity of individual risk factors. Therefore, we conducted this systematic review and analysis. We identified 14 key studies that assessed 844 candidate risk factors for inclusion into predictive scoring tools. After consolidation to account for overlapping terminology and variable definitions, we assessed each risk factor in the number of studies it was assessed, and, if it was found to be a significant predictor of DRP, whether it was included in a final scoring tool. The latter included intake of ≥ 8 drugs, drugs of the Anatomical Therapeutic Chemical (ATC) class N, ≥1 comorbidity, an estimated glomerular filtration rate (eGFR) <30 mL/min and age ≥60 years. The methodological approach and the individual risk factors presented in this review may provide a new starting point for improved risk assessment.
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Affiliation(s)
- Lea Jung-Poppe
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
- Correspondence: (L.J.-P.); (R.M.)
| | - Hagen Fabian Nicolaus
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
- University Hospital Erlangen, 91054 Erlangen, Germany
| | - Anna Roggenhofer
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Anna Altenbuchner
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Harald Dormann
- Central Emergency Department, Fürth Hospital, 90766 Fürth, Germany
| | | | - Renke Maas
- Institute of Experimental and Clinical Pharmacology and Toxicology, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
- Correspondence: (L.J.-P.); (R.M.)
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