Predictive Analytics for Identification of Patients at Risk for <scp>QT</scp> Interval Prolongation: A Systematic Review

Muensterman, Elena Tomaselli; Tisdale, James E.

doi:10.1002/phar.2146

Cited by 40 publications

(27 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We aim here to fill this gap by using several data mining techniques first to predict survival of the patients, and then to rank the most important features included in the medical records. As major result, we show that the top predictive performances can be reached by machine learning methods with just two features, none of them coming unexpected: one is ejection fraction, and the other is serum creatinine, well known in the literature as a major driver of heart failure [57][58][59][60][61][62], and also a key biomarker in renal dysfunction [63][64][65].…”

Section: Introductionmentioning

confidence: 84%

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

380

171

View full text Add to dashboard Cite

Background: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body. Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. Methods: In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Results: Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. Conclusions: This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

show abstract

Section: Introductionmentioning

confidence: 84%

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

380

171

View full text Add to dashboard Cite

show abstract

“…Although RALP has fewer post‐operative complications and better outcomes compared to open radical prostatectomy, 23,24 both have a similar incidence of peri‐operative cardiovascular adverse events, including arrhythmia 24 . This may be attributable to the older age of patients undergoing RALP as old age is a known risk factor for QTc interval prolongation 25 . Moreover, under sevoflurane anaesthesia, elderly patients (≥70 years) develop greater QTc interval prolongation than younger patients (20–69 years), and age and QTc interval prolongation show a linear relationship 15 .…”

Section: Discussionmentioning

confidence: 99%

Effect of different general anaesthetics on ventricular repolarisation in robot‐assisted laparoscopic prostatectomy

Park

Lee

Choi

et al. 2020

Acta Anaesthesiol Scand

View full text Add to dashboard Cite

Background Ventricular repolarisation is affected differently by the types of anaesthetics used. This study aimed to compare the effect of different types of anaesthetics on ventricular repolarisation during robot‐assisted laparoscopic radical prostatectomy (RALP). Methods Sixty‐nine patients were randomly assigned in a 1:1:1 ratio to the Sevoflurane (sevoflurane/remifentanil), Desflurane (desflurane/remifentanil) or total intravenous anaesthesia (TIVA [propofol/remifentanil]) groups; however, only 67 patients completed the study. The primary outcome was heart rate‐corrected QT (QTc) interval collected at nine time points during RALP. Bazett’s (QTcB) and Fridericia’s (QTcF) formulae were used for QT interval correction. The secondary outcomes were Tpeak‐Tend (Tp‐e) interval and Tp‐e/QT ratio that were collected at the same time points. Results The QTcB and QTcF intervals were significantly prolonged during surgery in all groups; however, these values showed significant intergroup differences with time. After assuming the Trendelenburg position, the QTcB and QTcF intervals were significantly longer in the Desflurane group than in the other two groups, and this prolongation continued until the end of surgery. Intra‐operatively, the QTcB and QTcF intervals exceeded 450 ms in six and five patients, respectively, in the Desflurane group, but in none in the TIVA group. Moreover, the incidence of intra‐operative QTc interval prolongation >20 ms and >60 ms was significantly higher in the Desflurane group than in the TIVA group. There were no significant differences in Tp‐e intervals and Tp‐e/QT ratio among the three groups during surgery. Conclusions To minimise QTc interval prolongation during RALP, TIVA with propofol/remifentanil is recommended for general anaesthesia.

show abstract

“…A prolonged QTc interval is also associated with an increased risk of life-threatening arrhythmias, including polymorphic ventricular tachycardia (torsade de points), and a significant increase in the risk for such pathology increases markedly in patients with QTc intervals longer than 500 ms. However, the risk of cardiac arrhythmia also increases when QTc lengthens more than 60 ms compared with the baseline value [39].…”

Section: Discu/ssionmentioning

confidence: 99%

Elective lung resection increases spatial QRS-T angle and QTc interval

et al. 2020

View full text Add to dashboard Cite

Background: Lung resection changes intra-thoracic anatomy, which may affect electrocardiographic results. While postoperative cardiac arrhythmias have been recognized after lung resection, no study has documented changes in vectorcardiographic variables in patients undergoing this surgery. The purpose of this study was to analyse changes in spatial QRS-T angle (spQRS-T) and corrected QT interval (QTc) after lung resection. Methods: Adult patients undergoing elective lung resection under general anaesthesia were studied. The patients were allocated into four groups: those undergoing (1) left lobectomy (LL); (2) left pneumonectomy (LP); (3) right lobectomy (RL); and (4) right pneumonectomy (RP). The spQRS-T angle and QTc interval were measured one day before surgery (baseline) and 24, 48 and 72 h after surgery. Results: Seventy-one adult patients (47 men and 24 women) aged 47-80 (65 ± 7) years were studied. In the study group as a whole, lung resection was associated with significant increases in spQRS-T (p < 0.001) and QTc (p < 0.05 at 24 and 48 h and p < 0.01 at 72 h). The greatest changes were noted in patients undergoing LP. Postoperative atrial fibrillation (AF) was noted in 6.4% of patients studied, in whom the widest spQRS-T angle and the most prolonged QTc intervals were also noted. Conclusions: Lung resection widens the spQRS-T angle and prolongs the QTc interval, especially in patients undergoing LP. While postoperative AF was a relatively rare complication after lung resection in this study, it was associated with the widest spQRS-T angles and most prolonged QTc intervals.

show abstract

Predictive Analytics for Identification of Patients at Risk for QT Interval Prolongation: A Systematic Review

Cited by 40 publications

References 31 publications

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Effect of different general anaesthetics on ventricular repolarisation in robot‐assisted laparoscopic prostatectomy

Elective lung resection increases spatial QRS-T angle and QTc interval

Contact Info

Product

Resources

About