Guillaume Hébert1, Florence Netzer2, Marie Ferrua3, Michel Ducreux4, François Lemare5, Etienne Minvielle3. 1. Gustave Roussy, Projet CAPRI, Villejuif F-94805, France; Equipe d'accueil "Management des organisations de santé", Ecoles de Hautes Etudes en Santé Publique, Rennes F-35000, France. Electronic address: guillaume.hebert@gustaveroussy.fr. 2. Gustave Roussy, Département de Pharmacie Clinique, Villejuif F-94805, France. 3. Gustave Roussy, Projet CAPRI, Villejuif F-94805, France; Equipe d'accueil "Management des organisations de santé", Ecoles de Hautes Etudes en Santé Publique, Rennes F-35000, France. 4. Gustave Roussy, Département d'Oncologie Médicale, Villejuif F-94805, France. 5. Gustave Roussy, Département de Pharmacie Clinique, Villejuif F-94805, France; Département de pharmacie clinique, PRES Sorbonne Paris-Cité, Paris F-75006, France.
Abstract
BACKGROUND: Drug-related iatrogenic effects are common in oncology because chemotherapy is toxic. The evaluation of the application of the guidelines may be a way to understand the occurrence of adverse drug-related event (ADE). There is no specific method for identifying ADEs and measuring harm to patients in oncology. OBJECTIVE: Our objective was to develop and test an Oncology Trigger Tool (OTT) for ADEs and to describe ADE characteristics and incidence. METHODS: A clinical advisory panel identified situations at high risk of ADE occurrence and built 22 triggers with, in each case, an analysis flowchart to confirm or refute occurrence. The OTT was used to review 288 random admissions (Oct. 2010-Sept. 2011) and measure ADE incidence and severity (CTCAE 4.03 - Common Terminology Criteria for Adverse Events). Tool feasibility (time required), inter-rater (IR) reproducibility and positive predictive value (PPV) were measured. RESULTS: Overall, 884 triggers were detected and 122 ADEs, with 42.4 ADEs/100 admissions or 46.0 ADEs/1000 patient-days, and a 31.1% rate of severe ADEs. The most common ADEs were hyperglycaemia (14.5%), unplanned drug-related admission within 30 days (13.7%) and opiate-induced constipation (12.1%). Unplanned drug-related admission was the most serious (82.4% incidence of severe harm). Mean time for OTT implementation was 21.8 min; IR reproducibility was high (κ=0.965 (trigger); κ=0.935 (ADE); κ=0.853 (harm)); PPV 22-trigger version was 20.7%. CONCLUSIONS: ADE analysis flowcharts coupled with standardised grading of harm considerably reduced IR variability, thus providing a robust oncology-focused trigger tool for use in ADE audits and hospital comparisons. The involvement of a clinical advisory panel in tool development should help drive changes for improving practice. Further research on the OTT is warranted.
BACKGROUND: Drug-related iatrogenic effects are common in oncology because chemotherapy is toxic. The evaluation of the application of the guidelines may be a way to understand the occurrence of adverse drug-related event (ADE). There is no specific method for identifying ADEs and measuring harm to patients in oncology. OBJECTIVE: Our objective was to develop and test an Oncology Trigger Tool (OTT) for ADEs and to describe ADE characteristics and incidence. METHODS: A clinical advisory panel identified situations at high risk of ADE occurrence and built 22 triggers with, in each case, an analysis flowchart to confirm or refute occurrence. The OTT was used to review 288 random admissions (Oct. 2010-Sept. 2011) and measure ADE incidence and severity (CTCAE 4.03 - Common Terminology Criteria for Adverse Events). Tool feasibility (time required), inter-rater (IR) reproducibility and positive predictive value (PPV) were measured. RESULTS: Overall, 884 triggers were detected and 122 ADEs, with 42.4 ADEs/100 admissions or 46.0 ADEs/1000 patient-days, and a 31.1% rate of severe ADEs. The most common ADEs were hyperglycaemia (14.5%), unplanned drug-related admission within 30 days (13.7%) and opiate-induced constipation (12.1%). Unplanned drug-related admission was the most serious (82.4% incidence of severe harm). Mean time for OTT implementation was 21.8 min; IR reproducibility was high (κ=0.965 (trigger); κ=0.935 (ADE); κ=0.853 (harm)); PPV 22-trigger version was 20.7%. CONCLUSIONS:ADE analysis flowcharts coupled with standardised grading of harm considerably reduced IR variability, thus providing a robust oncology-focused trigger tool for use in ADE audits and hospital comparisons. The involvement of a clinical advisory panel in tool development should help drive changes for improving practice. Further research on the OTT is warranted.
Authors: Saul N Weingart; Jason Nelson; Benjamin Koethe; Omar Yaghi; Stephan Dunning; Albert Feldman; David Kent; Allison Lipitz-Snyderman Journal: Cancer Med Date: 2020-04-13 Impact factor: 4.452
Authors: Saul N Weingart; Jason Nelson; Benjamin Koethe; Omar Yaghi; Stephan Dunning; Albert Feldman; David M Kent; Allison Lipitz-Snyderman Journal: Cancer Med Date: 2020-01-03 Impact factor: 4.452