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Predictive Validity of a QTc Interval Prolongation Risk Score in the Intensive Care Unit
Ke Su1, Rumi McGloin2,3, Rochelle M Gellatly2,3,4
1Pharmacy Department, Royal Columbian Hospital, New Westminster, British Columbia, Canada.
The Tisdale et al. Risk Score (TRS) effectively identifies critically ill patients at high risk for QT interval prolongation, a precursor to Torsade de pointes. While highly sensitive, its low specificity means it may not reliably identify all low-risk patients.
Area of Science:
- Cardiology
- Critical Care Medicine
- Clinical Prediction Tools
Background:
- Torsade de pointes is a dangerous heart rhythm linked to QT interval prolongation.
- QT interval prolongation affects 24-61% of critically ill patients.
- A predictive tool can aid monitoring and management in intensive care units (ICUs).
Purpose of the Study:
- To evaluate the predictive validity of the Tisdale et al. Risk Score (TRS).
- To assess the sensitivity, specificity, and likelihood ratios of the TRS in a medical ICU setting.
Main Methods:
- A longitudinal, retrospective cohort study of 264 patients from October 2017 to June 2018.
- Data collected from electronic health records included baseline characteristics and risk factors.
- Primary endpoint was the first occurrence of QT interval prolongation (QTc > 500 ms or increase ≥ 60 ms).
Main Results:
- The TRS demonstrated a sensitivity of 97% for identifying patients at risk.
- Specificity for low-risk patients was 16%.
- Positive and negative likelihood ratios were 1.15 and 0.20, respectively.
Conclusions:
- The TRS is highly sensitive for identifying patients at risk of QT interval prolongation.
- Patients classified as low risk by the TRS can be considered to have minimal risk.
- The tool's low specificity limits its ability to reliably exclude all low-risk patients.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.