Discrepancies requiring clarification in cancer patients: a risk predictive model
- Triana González-Carrascosa Vega1,
- Jesús Francisco Sierra-Sánchez2,
- María José Martínez-Bautista1,
- María Victoria Manzano Martín1,
- Jose Manuel Baena Cañada3,
- Irene Romero-Hernández1
- 1Department of Pharmacy, Hospital Universitario Puerta del Mar, Cádiz, Spain
- 2Department of Pharmacy, Hospital Universitario de Fuenlabrada, Fuenlabrada, Spain
- 3Department of Oncology, Hospital Universitario Puerta del Mar, Cádiz, Spain
- Correspondence to Dr Triana González-Carrascosa Vega, Department of Pharmacy, Hospital Universitario Puerta del Mar, C/Ana de Viya, 21, Cádiz CP 11105, Spain;
- Received 23 August 2012
- Revised 18 November 2012
- Accepted 23 November 2012
- Published Online First 20 December 2012
Purpose Medication reconciliation has been proved to be a safe and effective strategy for preventing medication errors. However, the use of this strategy and the need for its implementation in cancer patients has not been adequately studied. This study was performed to develop a predictive model of the risk of discrepancies that need clarification (DNCs) in cancer patients.
Material and methods A retrospective observational study was designed in order to develop a predictive model of the risk of DNCs in cancer patients. Patients diagnosed with breast or colon cancer during 2010 who received chemotherapy and were treated with any chronic medication were included. As endpoints, the incidence of DNCs, drugs involved and risk factors for DNCs were measured. After detecting DNCs, a statistical analysis was performed using multivariate logistic regression with dependent variable discrepancies. The predictive model building was performed using the Hosmer–Lemeshow test.
Results A total of 168 patients were included, 83% of whom were taking home medication. At least one DNC was detected in 50.7% of patients, with a total of 131 DNCs. Multivariate analysis identified two independent variables as predictors of the occurrence of DNCs: the number of cytostatic agents (OR 1.24, 95% 1.03 to 1.49, p=0.026) and the number of target drugs (OR 1.77, 95% CI 1.37 to 2.29, p<0.0001). The proposed predictive model includes two variables to determine the probability (Pr) of suffering a DNC using the following mathematical expression: Pr (%)= 1/(1+e−(−2.21+0.21×Number of cytostatic drugs+0.57×Number of target drugs))×100. The area under the receiver operating characteristic curve was 0.78 (95% CI 0.7 to 0.85, p<0.001), so the model is able to discriminate between patients with and without DNCs with moderate accuracy.
Conclusions Half of all patients who are receiving chemotherapy and are taking home medication have some DNCs. The resulting model can predict the probability of DNCs in cancer patients from the information about their medication.