Intravenous chemotherapy preparation : risk analysis

Drug toxicity is important and creates risks for patient, pharmaceutical and nursing staff in contact with chemotherapies. This implies to secure the injectable drugs circuit and particularly in the pharmaceutical Chemotherapies Preparation Unit (CPU). In order to identify and quantify these risks, we chose to draw up a risk profile by using an a priori Failure Mode and Effects Analysis (FMEA).

This method consists in identifying the potential failures of the system and defining their causes and consequences. Each listed dysfunction is rated on a scale of 1 to 5 according to three criteria : gravity (G), frequency of occurrence (F) and detectability (D). A criticality index (CI) is calculated : G x F x D. After prioritization of potential failures, actions can be initiated and monitored in order to reduce high risks.

Among the 8 main stages defined in the CPU, we identified 129 potential failures distributed mainly on the validation stages of drug prescription (18%), purchasing, procurement and storage (16%) and manufacture of chemotherapy (47%). Twenty-one of them (16%) exceed the criticality limit defined at 27 : these concern the pharmaceutical validation of drug prescriptions (29%), the manufacture of chemotherapies (62%) and the drug release by the pharmacist (9%). The sub-step production included in the manufacturing stage lists 77% of the 13 failures which CI ≥ 27. The maximum CI found is 48 and concerns the failure "control preparation error by the manipulator in charge of controls“. An Ishikawa diagram applied to this manufacturing stage finds causes of failures especially in the areas of "staff" and "material”.

The FMEA is a time consuming method, but allows to carry out a quantitative study of risk during a multidisciplinary work. The dynamic enhancement has to be continued with the inclusion of other steps (prescription and administration) and actors (doctors and nurses). A new CI has to be calculated after the implementation of preventive measures to determine residual risks.