BackgroundDrug safety precautions recommend monitoring of the corrected QT interval. To determine which QT correction formula to use in an automated QT‐monitoring algorithm in our electronic medical record, we studied rate correction performance of different QT correction formulae and their impact on risk assessment for mortality.Methods and ResultsAll electrocardiograms (ECGs) in patients >18 years with sinus rhythm, normal QRS duration and rate <90 beats per minute (bpm) in the University Hospitals of Leuven (Leuven, Belgium) during a 2‐month period were included. QT correction was performed with Bazett, Fridericia, Framingham, Hodges, and Rautaharju formulae. In total, 6609 patients were included (age, 59.8±16.2 years; 53.6% male and heart rate 68.8±10.6 bpm). Optimal rate correction was observed using Fridericia and Framingham; Bazett performed worst. A healthy subset showed 99% upper limits of normal for Bazett above current clinical standards: men 472 ms (95% CI, 464–478 ms) and women 482 ms (95% CI 474–490 ms). Multivariate Cox regression, including age, heart rate, and prolonged QTc, identified Framingham (hazard ratio [HR], 7.31; 95% CI, 4.10–13.05) and Fridericia (HR, 5.95; 95% CI, 3.34–10.60) as significantly better predictors of 30‐day all‐cause mortality than Bazett (HR, 4.49; 95% CI, 2.31–8.74). In a point‐prevalence study with haloperidol, the number of patients classified to be at risk for possibly harmful QT prolongation could be reduced by 50% using optimal QT rate correction.ConclusionsFridericia and Framingham correction formulae showed the best rate correction and significantly improved prediction of 30‐day and 1‐year mortality. With current clinical standards, Bazett overestimated the number of patients with potential dangerous QTc prolongation, which could lead to unnecessary safety measurements as withholding the patient of first‐choice medication.

Background QTc-interval prolongation has been associated with serious adverse events, such as Torsade de Pointes and sudden cardiac death. In the prevention of QTc-prolongation, special attention should go to high-risk patients. Aim of the review The aim of this review is to summarize and assess the evidence for different risk factors for QTc-prolongation (demographic factors, comorbidities, electrolytes, QTc-prolonging medication). Methods Potential studies were retrieved based on a systematic search of articles published until June 2015 in the databases Medline and Embase. Both terms about QTc-prolongation/Torsade de Pointes and risk factors were added in the search strategy. The following inclusion criteria were applied: randomized controlled trials and observational studies; inclusion of ≥500 patients from a general population (not limited to specific disease states); assessment of association between QTc-interval and risk factors. For the articles that met the inclusion criteria, the following data were extracted: study design, setting and study population, number of patients and cases of QTc-prolongation, method of electrocardiogram-monitoring, QTc-correction formula, definition of QTc-prolongation, statistical methods and results. Quality assessment was performed using the GRADE approach (for randomized controlled trials) and the STROBE-recommendations (for observational studies). Based on the number of significant results and the level of significance, a quotation of the evidence was allocated. Results Ten observational studies could be included, with a total of 89,532 patients [prospective cohort design: N = 6; multiple regression analyses: N = 5; median STROBE score = 17/22 (range 15-18)]. Very strong evidence was found for hypokalemia, use of diuretics, antiarrhythmic drugs and QTc-prolonging drugs of list 1 of CredibleMeds. Little or no evidence was found for hyperlipidemia, the use of digoxin or statins, neurological disorders, diabetes, renal failure, depression, alcohol abuse, heart rate, pulmonary disorders, hormone replacement therapy, hypomagnesemia, history of a prolonged QTc-interval/Torsade de Pointes, familial history of cardiovascular disease, and the use of only QTc-prolonging drugs of list 2 or 3 of CredibleMeds. Conclusion This systematic review gives a clear overview of the available evidence for a broad range of risk factors for QTc-prolongation.

Residents in long-term care facilities (LTCF) are a vulnerable population group. Coronavirus disease (COVID-19)-related deaths in LTCF residents represent 30–60% of all COVID-19 deaths in many European countries. This situation demands that countries implement local and national testing, infection prevention and control, and monitoring programmes for COVID-19 in LTCF in order to identify clusters early, decrease the spread within and between facilities and reduce the size and severity of outbreaks.

Background More than 170 drugs are linked with QTc-prolongation, which in extreme cases can lead to Torsade de Pointes. Monitoring of this potential side effect is an important challenge in clinical practice. Objective To investigate the risk of QTc-prolongation in hospital patients who started a QTc-prolonging drug, and to develop a risk score to identify patients at high/low risk for QTc-prolongation. Setting University Hospitals Leuven, Belgium. Method All patients starting with haloperidol or a QTc-prolonging antibiotic/antimycotic were eligible for this observational study. Twelve-lead electrocardiograms were recorded at baseline and follow-up (steady state). Demographic, medical and drug data were collected. The obtained data were used to calculate the performance characteristics of a preliminary risk score (RISQ-PATH score), based on a systematic review of risk factors. ROC analysis determined a score of <10 points as a low risk for QTc-prolongation. Main outcome measure QTc-interval in a baseline and follow-up electrocardiogram. Results 178 patients (46.6% female; mean age 69 ± 14 years) were included (levofloxacin: N = 80; haloperidol: N = 41; fluconazole: N = 41). Overall, no significant difference between the mean QTc-values at baseline (425.7 ± 31.7 ms) and follow-up (428.0 ± 30.7 ms) was found (p = 0.328). However, 26 patients (14.6%) did develop a prolonged QTc-interval (≥450(♂)/470(♀) ms) of whom 25 with a RISQ-PATH score ≥10. This score had a sensitivity of 96.2% (95% CI 78.4-99.8%) and a negative predictive value of 98.0% (95% CI 88.2-99.9%). Conclusion This RISQ-PATH score is able to rule out low-risk patients with a negative predictive value of 98.0% and is promising to exclude patients from further follow-up when starting QTc-prolonging drugs. Clinicaltrials.gov Registration Number: NCT02068170.

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