Strengths and limitations
We identified several strengths in our study. First, we performed a thorough review of data and risk factors associated with QTc prolongation, which allowed us to build and evaluate the risk score for all components and their association with QTc prolongation in our patient population. We used the corrected QTcF formula and identified patients in sinus rhythm to better assess QTc prolongation on a follow up ECG reading. Furthermore, the preliminary risk score that was built in the EHR was capable of accurately extracting the data from the patients’ charts. The ECG timeframe we set was appropriate to detect medication-related QTc prolongation, eliminating other possible causes. In addition, we compared our custom alert and DDI warning for the same patients and within the same timeframe. This added to the accuracy of our analysis which was based on rigorous data. Performing a multiple logistic regression also helped us identify the risk factors that were mostly associated with QTc prolongation, thus allowing us to re-arrange the score points and increase our sensitivity and specificity results through an optimized risk score. Moreover, using the missing-indicator method in our regression analysis to deal with missing values contributes to a more realistic view and broadens applicability.
Limitations in our study include the inability of the score to automatically extract a QTc interval from the EHR to be included in our risk score or to be used as a reference for clinical decision support. In the future, implementing electronic waveform readings in the EHR would allow for a more accurate assessment of alerts by incorporating this component in the risk score. Additionally, the patients we included already had a baseline ECG reading, which may suggest that these patients were already at high risk for QTc prolongation and were being followed closely by cardiology. Smoking is one of the risk factors for QTc prolongation, however, our score could not reliably extract smoking habits from the chart. As mentioned earlier, the DDI warning is based on the FDB database, whereas the custom alert is based on CredibleMeds list. Most of the DDI warnings were filtered at that time, and the triggering medications were those with the greatest risk for QTc prolongation. This explains the high specificity of the DDI warning compared with the custom alert which included all drugs listed in CredibleMeds lists 1, 2 and 3, regardless of severity. Furthermore, our study was conducted in an oncology setting where patient characteristics and risk factors differ and are affected by various therapies and comorbidities, which makes extrapolating this data to other patient populations challenging.