A Training for Parents Prevents Clinically Relevant Handling Errors in the Use of Long-Term Antiseizure Medication - Kaune, Almuth; Schumacher, Pia Madeleine; Neininger, Martina Patrizia; Syrbe, Steffen; Hiemisch, Andreas; Bernhard, Matthias Karl; Merkenschlager, Andreas; Kiess, Wieland; Bertsche, Astrid; Bertsche, Thilo (Klinische Pädiatrie, 2024)
Background Medication handling errors (ME) in long-term antiseizure medication (ASM) compromise patient safety. Training programs to prevent those errors by parents are scarce.Patients The intervention concept is designed for parents of children and adolescents aged 0–18 years with at least one long-term ASM.Method In a controlled prospective study, we assessed ME of ASM during home visits 3–6 weeks after a patient’s in- or outpatient hospital visit (outpatient neuropediatric clinic and Social-Pediatric Center (SPZ)). We investigated the effectiveness of a patient specific, risk-adapted training (intervention group, IG) compared to routine care (control group, CG). For 54 ME in ASM handling, an expert panel classified the clinical risk ranging from Score-0 (no risk) to Score-6 (maximum risk) with the lowest risk actually classified as Score-3.Results We analyzed data from 83 parents in the CG and 85 in the IG who administered 140 ASM per group. The intervention reduced ME per patient from 5 (median; Q25/Q75 3/9) to 4 (2/8; p=0.018). A total number of 589 ME occurred in the CG, 432 in the IG. ME in ASM handling rated Score-6 occurred once in the CG and not in the IG. A relative-risk-reduction (RRR) of ME (with p<0.001) was observed, with a RRR of 55.0% for Score-5, 27.6% for Score-4, and 23.1% for Score-3. RRR was 56.6% for ASM preparation (p<0.001) and 22.4% for oral administration (p=0.045).Conclusion Compared to controls, ME with high clinical risk significantly decreased in the IG after the training. Drug safety in chronically ill children with ASM was thereby improved.
Keywords: medication handling error; clinical risk; pediatric; antiseizure medication; training; dummy model