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Development of a Risk Score for QT Prolongation in the Intensive Care Unit Using Time-Series Electrocardiogram Data and Electronic Medical Records

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Objectives: Drug-induced QT prolongation can lead to life-threatening arrhythmia. In the intensive care unit (ICU), various drugs are administered concurrently, which can increase the risk of QT prolongation. However, no well-validated method to evaluate the risk of QT prolongation in real-world clinical practice has been established. We developed a risk scoring model to continuously evaluate the quantitative risk of QT prolongation in real-world clinical practice in the ICU. Methods: Continuous electrocardiogram (ECG) signals measured by patient monitoring devices and Electronic Medical Records data were collected for ICU patients. QT and RR intervals were measured from raw ECG data, and a corrected QT interval (QTc) was calculated by Bazett’s formula. A case-crossover study design was adopted. A case was defined as an occurrence of QT prolongation ≥12 hours after any previous QT prolongation. The patients served as their own controls. Conditional logistic regression was conducted to analyze prescription, surgical history, and laboratory test data. Based on the regression analysis, a QTc prolongation risk scoring model was established. Results: In total, 811 ICU patients who experienced QT prolongation were included in this study. Prescription information for 13 drugs was included in the risk scoring model. In the validation dataset, the high-risk group showed a higher rate of QT prolongation than the low-and low moderate-risk groups. Conclusions: Our proposed model may facilitate risk stratification for QT prolongation during ICU care as well as the selection of appropriate drugs to prevent QT prolongation.

Abstract

Objectives: Drug-induced QT prolongation can lead to life-threatening arrhythmia. In the intensive care unit (ICU), various drugs are administered concurrently, which can increase the risk of QT prolongation. However, no well-validated method to evaluate the risk of QT prolongation in real-world clinical practice has been established. We developed a risk scoring model to continuously evaluate the quantitative risk of QT prolongation in real-world clinical practice in the ICU. Methods: Continuous electrocardiogram (ECG) signals measured by patient monitoring devices and Electronic Medical Records data were collected for ICU patients. QT and RR intervals were measured from raw ECG data, and a corrected QT interval (QTc) was calculated by Bazett’s formula. A case-crossover study design was adopted. A case was defined as an occurrence of QT prolongation ≥12 hours after any previous QT prolongation. The patients served as their own controls. Conditional logistic regression was conducted to analyze prescription, surgical history, and laboratory test data. Based on the regression analysis, a QTc prolongation risk scoring model was established. Results: In total, 811 ICU patients who experienced QT prolongation were included in this study. Prescription information for 13 drugs was included in the risk scoring model. In the validation dataset, the high-risk group showed a higher rate of QT prolongation than the low-and low moderate-risk groups. Conclusions: Our proposed model may facilitate risk stratification for QT prolongation during ICU care as well as the selection of appropriate drugs to prevent QT prolongation.

참고문헌 (23)

  1. Pickham D, "High prevalence of corrected QT interval prolongation in acutely ill patients is associated with mortality : results of the QT in Practice(QTIP)Study", Crit Care Med, 40, pp.394~399 (2012)
  2. Etchegoyen CV, "Drug-induced QT interval prolongation in the intensive care unit", Curr Clin Pharmacol, 12, pp.210~222 (2017)
  3. Beitland S, "Drug-induced long QT syndrome and fatal arrhythmias in the intensive care unit", Acta Anaesthesiol Scand, 58, pp.266~272 (2014)
  4. George TK, "Association between a prolonged corrected QT interval and outcomes in patients in a medical Intensive Care Unit", Indian J Crit Care Med, 19, pp.326~332 (2015)
  5. Fernandes FM, "QTc interval prolongation in critically ill patients : prevalence, risk factors and associated medications", PLoS One, 13, pp.e0199028~ (2018)
  6. Tisdale JE, "Prevalence of QT interval prolongation in patients admitted to cardiac care units and frequency of subsequent administration of QT intervalprolonging drugs : a prospective, observational study in a large urban academic medical center in the US", Drug Saf, 35, pp.459~470 (2012)
  7. Kozik TM, "Acquired long QT syndrome : frequency, onset, and risk factors in intensive care patients", Crit Care Nurse, 32, pp.32~41 (2012)
  8. Hoogstraaten E, "Corrected QT-interval prolongation and variability in intensive care patients", J Crit Care, 29, pp.835~839 (2014)
  9. Nagele P, "Postoperative QT interval prolongation in patients undergoing noncardiac surgery under general anesthesia", Anesthesiology, 117, pp.321~328 (2012)
  10. Pham JC, "The prevalence of long QT interval in post-operative intensive care unit patients", J Clin Monit Comput, 30, pp.437~443 (2016)
  11. Vandael E, "Development of a risk score for QTc-prolongation : the RISQ-PATH study", Int J Clin Pharm, 39, pp.424~432 (2017)
  12. Bindraban AN, "Development of a risk model for predicting QTc interval prolongation in patients using QTc-prolonging drugs", Int J Clin Pharm, 40, pp.1372~1379 (2018)
  13. Berger FA, "Development and validation of a tool to assess the risk of QT drug-drug interactions in clinical practice", BMC Med Inform Decis Mak, 20, pp.171~ (2020)
  14. Su K, "Predictive validity of a QTc interval prolongation risk score in the intensive care unit", Pharmacotherapy, 40, pp.492~499 (2020)
  15. 정다희, "Construction of an Electrocardiogram Database Including 12 Lead Waveforms", Healthcare Informatics Research, 24(3), pp.242~246 (2018), 대한의료정보학회[DOI]
  16. Chesnokov YC, "Individually adaptable automatic QT detector", Proceedings of 2006 Computers in Cardiology, pp.337~340 (2016)
  17. Mehta HB, "Regression coefficient-based scoring system should be used to assign weights to the risk index", J Clin Epidemiol, 79, pp.22~28 (2016)
  18. 윤덕용, "System for Collecting Biosignal Data from Multiple Patient Monitoring Systems", Healthcare Informatics Research, 23(4), pp.333~337 (2017), 대한의료정보학회[DOI]
  19. Sharma S, "Providers’ response to clinical decision support for QT prolonging drugs", J Med Syst, 41, pp.161~ (2017)
  20. Tisdale JE, "Effectiveness of a clinical decision support system for reducing the risk of QT interval prolongation in hospitalized patients", Circ Cardiovasc Qual Outcomes, 7, pp.381~390 (2014)
  21. "QTdrugs list" (2013)[URL]
  22. Kim NY, "Effect of dexmedetomidine on heart rate-corrected QT and Tpeak-Tend intervals during robot-assisted laparoscopic prostatectomy with steep Trendelenburg position: a prospective, randomized, double-blinded, controlled study", Medicine (Baltimore), 95, pp.e3645~ (2016)
  23. Agdanli D, "Effects of highdose rocuronium on the QTc interval during anaesthesia induction in patients undergoing coronary artery bypass graft surgery", Turk J Anaesthesiol Reanim, 42, pp.245~250 (2014)

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