Journal of Clinical Medicine Article Prolonged QT Interval in SARS-CoV-2 Infection: Prevalence and Prognosis Núria Farré 1,2,3, * , Diana Mojón 1 , Marc Llagostera 1 , Laia C. Belarte-Tornero 1,2,3 , Alicia Calvo-Fernández 1,2 , Ermengol Vallés 1,2,3 , Alejandro Negrete 1 , Marcos García-Guimaraes 1 , Yolanda Bartolomé 1 , Camino Fernández 1 , Ana B. García-Duran 1 , Jaume Marrugat 4,5 and Beatriz Vaquerizo 1,2,3 1 Department of Cardiology, Hospital del Mar, 08003 Barcelona, Spain; 62834@parcdesalutmar.cat (D.M.); 62205@parcdesalutmar.cat (M.L.); 62547@parcdesalutmar.cat (L.C.B.-T.); 61725@parcdesalutmar.cat (A.C.-F.); 99056@parcdesalutmar.cat (E.V.); negrete_alejandro@hotmail.com (A.N.); marcos.garcia.guimaraes@gmail.com (M.G.-G.); 97096@parcdesalutmar.cat (Y.B.); 60851@parcdesalutmar.cat (C.F.); abgarciaduran@psmar.cat (A.B.G.-D.); 97912@parcdesalutmar.cat (B.V.) 2 Department of Medicine, School of Medicine, Univ Autonoma de Barcelona, 08003 Barcelona, Spain 3 Heart Diseases Biomedical Research Group (GREC), IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain 4 REGICOR (Registre Gironí del Cor) Study Group, IMIM (Hospital del Mar Medical Research Institute), 08003 Barcelona, Spain; jaume@imim.es 5 CIBER (Centro de Investigación Biomédica en Red) of Cardiovascular Diseases (CIBERCV), Instituto de Salud Carlos III (ISCIII), 08003 Barcelona, Spain * Correspondence: NFarreLopez@parcdesalutmar.cat  Received: 15 July 2020; Accepted: 20 August 2020; Published: 21 August 2020  Abstract: Background: The prognostic value of a prolonged QT interval in SARS-Cov2 infection is not well known. Objective: To determine whether the presence of a prolonged QT on admission is an independent factor for mortality in SARS-Cov2 hospitalized patients. Methods: Single-center cohort of 623 consecutive patients with positive polymerase-chain-reaction test (PCR) to SARS Cov2, recruited from 27 February to 7 April 2020. An electrocardiogram was taken on these patients within the first 48 h after diagnosis and before the administration of any medication with a known effect on QT interval. A prolonged QT interval was defined as a corrected QT (QTc) interval >480 milliseconds. Patients were followed up with until 10 May 2020. Results: Sixty-one patients (9.8%) had prolonged QTc and only 3.2% had a baseline QTc > 500 milliseconds. Patients with prolonged QTc were older, had more comorbidities, and higher levels of immune-inflammatory markers. There were no episodes of ventricular tachycardia or ventricular fibrillation during hospitalization. All-cause death was higher in patients with prolonged QTc (41.0% vs. 8.7%, p < 0.001, multivariable HR 2.68 (1.58–4.55), p < 0.001). Conclusions: Almost 10% of patients with COVID-19 infection have a prolonged QTc interval on admission. A prolonged QTc was independently associated with a higher mortality even after adjustment for age, comorbidities, and treatment with hydroxychloroquine and azithromycin. An electrocardiogram should be included on admission to identify high-risk SARS-CoV-2 patients. Keywords: QT interval; COVID-19; hydroxychloroquine; azithromycin; prognosis; death 1. Introduction Previous reports have highlighted the potential risk of cardiac complications and arrhythmias in patients with severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection [1]. The presence of a prolonged QT interval can further worsen prognosis. However, most of the information about the prognostic role of QT interval in SARS-Cov-2 infection has been derived from studies analyzing the J. Clin. Med. 2020, 9, 2712; doi:10.3390/jcm9092712 www.mdpi.com/journal/jcm J. Clin. Med. 2020, 9, 2712 2 of 15 effects of the treatment with hydroxychloroquine and azithromycin [2–6], a treatment associated with QT interval prolongation. The benefits of these treatments on prognosis are currently controversial. Baseline QT interval abnormalities in the setting of SARS-Cov-2 infection can be secondary to the viral infection per se, the inflammatory state associated with SARS-Cov-2 infection, and ischemia or hypoxia [1]. Indeed, several viral infections like human immunodeficiency virus (HIV) and dengue have been independently associated with a prolonged QT interval [7–9]. Interestingly, acute coronavirus infection has been associated with a prolonged QT interval in rabbits [10], which suggests that the virus might have a direct effect on the heart. On the other hand, in the absence of infection, systemic inflammation and elevated C-reactive protein (CRP) have also been associated with QT prolongation [11–16]. These associations seem to be mediated, at least in part, by elevated interleukin-6 (IL-6) levels. Treatment with tocilizumab, an anti-IL-6 receptor antibody, has been associated with QT interval shortening [12,13]. Intriguingly, in men with HIV infections, those with elevated IL-6 had more prolonged QT [17], suggesting a potential additive effect of infection and inflammation on the QT interval. Thus, the presence of a prolonged QT interval on admission might be a marker of worse prognosis irrespective of the treatment the patients receive. Therefore, the aim of this study was to test the hypothesis that the presence of prolonged QT on admission is an independent factor for mortality in patients with SARS-Cov-2 infection. 2. Experimental Section A single-center cohort study conducted at Hospital del Mar, Barcelona, Spain, from 27 February to 7 April 2020. Patients were followed up until 10 May 2020. All consecutive patients with laboratory-confirmed COVID-19 by means of polymerase-chain-reaction (PCR) test were included in the study. We collected demographic data, laboratory findings, comorbidities, and treatment received. Baseline electrocardiogram (ECG) was defined as the ECG taken within the first 48 h after laboratory-confirmed COVID-19 diagnosis and always before the administration of any medication with a known effect on the QT interval. QT was automatically calculated as the time from the start of the Q wave to the end of the T wave and corrected for heart rate by the Bazett formula (QTc). All ECGs were done with the Philips PageWriter TC30 Cardiograph (Koninklijke Philips, Eindhoven, The Netherlands). Prolonged QTc was defined as a QTc > 480 milliseconds (ms) [18]. Although ECG was recommended in all patients, and especially in those who would receive medication that potentially modifies the QT interval, the decision to order the ECG was left to clinicians and adapted to the logistic capabilities of the center during the pandemic. Therefore, in the current analysis, we focused on patients who had a baseline ECG (Figure 1). However, patients who had a baseline ECG were also compared to those who did not have a baseline ECG. When patients had more than one ECG during hospitalization, maximum QTc interval was also collected. QTc prolongation was defined as an increase of at least one millisecond in QTc compared to baseline QTc. According to the protocol at our center at the time of the study, treatment with hydroxychloroquine and azithromycin was recommended to all patients. Azithromycin was given once a day (500 mg) for three days and hydroxychloroquine was given five days at a dose of 400 mg twice a day the first day and 200 mg twice a day the following four days. This treatment was contraindicated when QTc was longer than 550 ms. If QTc was longer than 500 ms, a daily ECG was mandatory. The use of tocilizumab was decided based on the presence of pulmonary infiltrates on chest X-ray or worsening of previous infiltrates, PaO2/FiO2 <300, and at least one of these parameters: IL6 ≥ 40 ng/L (or PCR ≥ 100 mg/L), D dimer ≥ 1000 ng/mL, or ferritin ≥ 700 ng/mL. The primary endpoint was all-cause death at 30 days after COVID-19 diagnosis. This study was performed in accordance with the provisions of the Declaration of Helsinki, ISO 14155 and clinical practice guidelines. The study protocol was approved by the Institutional Ethics Committee and the hospital’s research commission (number CEIm 2020/9178). Oral informed consent was obtained, but the need for written informed consent was waived in light of the infectious disease hazard. J. Clin. Med. 2020, 9, 2712 3 of 15 J. Clin. Med. 2020, 9, x FOR PEER REVIEW 3 of 15 Figure 1. Flowchart of patient selection. Figure 1. Flowchart of patient selection. Statistical Analysis According to the protocol at our center at the time of the study, treatment with Categorical hydroxychloroquine variables were summarized and azithromycin as number andtopercentages, was recommended all patients. and continuouswas Azithromycin variables given were summarized as the mean and standard deviation (SD), or the median and interquartile once a day (500 mg) for three days and hydroxychloroquine was given five days at a dose of 400 range (IQR), mg depending twice a dayonthe thefirst variable day distribution. and 200 mg Patients’ characteristics twice a day werefour the following compared between days. This prolonged treatment was QTc contraindicated > 480QTc (cut-off pointwhen ms) was and outcome status longer than 550categories ms. If QTc (death) by Student’s was longer than 500t-test ms,ora Mann–Whitney daily ECG was U test for continuous variables, and by Pearson’s chi-squared test for mandatory. The use of tocilizumab was decided based on the presence of pulmonary categorical variables. infiltrates on chest X-ray or worsening of previous infiltrates, PaO2/FiO2 <300, and at least one incidence Kaplan–Meier survival estimates were used to calculate the 30-day observed cumulative of these of death, andIL6 parameters: statistical ≥ 40 ng/L significance (or PCR ≥was100 tested mg/L),by D the log-rank dimer ≥ 1000test. The or ng/mL, adjusted ferritinhazard ratio (HR) of ≥ 700 ng/mL. deathThe for primary QTc status was analyzed endpoint using Cox was all-cause proportional death at 30 days hazard models. The after COVID-19 models were adjusted diagnosis. for potential confounders This study was performedselected by stepwisewith in accordance forward inclusion, of the provisions among patient characteristics the Declaration of Helsinki, that ISO were significantly associated with a prolonged QTc status as well as with 14155 and clinical practice guidelines. The study protocol was approved by the Institutionalthe composite endpoint Ethics (death). Committee Because and the the number of end-points hospital’s was low, it was research commission not possible (number CEIm to include allOral 2020/9178). variables with informed < 0.05. was pconsent We chose the variables with p-value < 0.001 and prevalence >5%, therefore obtained, but the need for written informed consent was waived in light of the moderate to severe valve heartdisease infectious diseasehazard. was not included in the model (overall prevalence 3.7%). The variables included in the model were age, baseline QTc > 480 ms, chronic kidney disease, treatment with azithromycin and hydroxychloroquine, Statistical Analysis ischemic chronic disease, atrial fibrillation or flutter, heart failure, and the presence of any cardiovascular risk factor. We acknowledge that there might be a survival bias associated with Categorical variables were summarized as number and percentages, and continuous variables treatment (or an immortal time bias) wherein you must survive long enough to be treated. However, were summarized as the mean and standard deviation (SD), or the median and interquartile range since the treatment with HCQ and AZM are known to prolong QT and might predispose to ventricular (IQR), depending on the variable distribution. Patients’ characteristics were compared between arrhythmias, we thought that the inclusion of treatment in the model was warranted. However, in order prolonged QTc (cut-off point > 480 ms) and outcome status categories (death) by Student’s t-test or to minimize the bias, we created a model with the same variables except did not include the treatment Mann–Whitney U test for continuous variables, and by Pearson’s chi-squared test for categorical received. Second, we also did a sensitivity analysis excluding patients who died during the first 48 h of variables. admission. Finally, standardized differences were calculated, and a difference >0.10 was considered Kaplan–Meier survival estimates were used to calculate the 30-day observed cumulative clinically significant. In addition, p-values < 0.05 were considered statistically significant. All tests incidence of death, and statistical significance was tested by the log-rank test. The adjusted hazard were performed with SPSS version 25 (IBM SPSS versión 25, Armonk, NY, USA). ratio (HR) of death for QTc status was analyzed using Cox proportional hazard models. The models were adjusted for potential confounders selected by stepwise forward inclusion, among patient characteristics that were significantly associated with a prolonged QTc status as well as with the composite endpoint (death). Because the number of end-points was low, it was not possible to include all variables with p < 0.05. We chose the variables with p-value < 0.001 and prevalence >5%, therefore moderate to severe valve heart disease was not included in the model (overall prevalence J. Clin. Med. 2020, 9, 2712 4 of 15 3. Results Sixty-one patients (9.8%) had prolonged QTc on admission. Only 20 patients (3.2%) had a baseline QTc > 500 ms. Baseline characteristics are described in Table 1. Briefly, patients with prolonged QTc were older and had more comorbidities. Moreover, they had higher levels of C-reactive protein, leucocytes, lactate, and procalcitonin. Similar results were seen in patients who died (Table 2). Table 1. Comparison of baseline characteristics and clinical presentation between patients with and without prolonged QTc on admission. QTc ≤ 480 QTc > 480 Standardized p-Value (n = 562) (n = 61) Differences Women 237 (42.2) 30 (49.2) 0.29 0.14107 Age, years 62.9 ± 16.9 76.6 ± 12.6 <0.001 −0.92172 Diabetes 88 (15.7) 30 (49.2) <0.001 0.76703 Hypertension 244 (43.4) 46 (75.4) <0.001 0.68911 Dyslipidemia 179 (31.9) 33 (54.1) <0.001 0.46121 CV Risk factors 360 (64.1) 55 (90.2) <0.001 0.65377 Obesity 97 (21.7) 15 (28.8) 0.239 0.16617 Ischemic chronic disease 36 (6.4) 15 (24.6) <0.001 0.51915 Atrial fibrillation or flutter 37 (6.6) 15 (24.6) <0.001 0.51245 Heart failure 20 (3.6) 13 (21.3) <0.001 0.55858 Moderate to severe valve heart disease 14 (2.5) 9 (14.8) <0.001 0.44769 COPD 42 (7.5) 10 (16.4) 0.017 0.27780 Cancer 64 (11.4) 14 (23.0) 0.010 0.31028 CKD 39 (6.9) 18 (29.5) <0.001 0.61132 Peripheral vascular disease 20 (3.6) 7 (1.5) 0.004 0.30370 Stroke 27 (4.8) 8 (13.1) 0.007 0.29411 Systolic blood pressure, mmHg 128.3 ± 19.0 130.4 ± 24.1 0.43 −0.09693 Diastolic blood pressure, mmHg 77.1 ± 13.3 74.5 ± 16.4 0.23 0.17954 Heart rate, bpm 91.2 ± 18.0 88.3 ± 21.5 0.27 0.13995 Respiratory rate, rpm 24.5 ± 7.1 25.6 ± 7.1 0.37 −0.15164 Oxygen saturation, % 95.1 ± 4.4 93.8 ± 6.1 0.13 0.23793 Baseline FiO2, % 25.9 ± 16.0 33.3 ± 25.4 0.033 −0.34956 PaO2/FiO2 < 300, n (%) 180 (42.8) 30 (60.0) 0.020 0.35028 Hemoglobin, g/dL 13.6 ± 1.6 12.5 ± 2.0 <0.001 0.59990 White blood cell count, per µL 7.04 ± 3.54 8.99 ± 4.76 0.003 −0.46572 Lymphocytes, per µL 1.40 ± 2.2 1.74 ± 2.64 0.35 −0.13720 Platelet count 209.0 ± 78.3 223.1 ± 109.0 0.33 −0.14919 Creatinine, mg/dL 1.02 ± 0.56 1.66 ± 1.44 0.001 −0.58761 eGFR, mL/min/1.73 m2 83.0 ± 29.2 59.1 ± 32.0 <0.001 0.77871 Creatine phosphokinase, U/L 93 (57–170) 95 (47–290) 0.97 −0.18600 Serum lactate, mmoL/L 1.44 ± 0.67 1.76 ± 0.92 0.028 −0.39636 CRP, mg/dL 9.4 ± 8.4 13.6 ± 11.0 0.005 −0.43681 Procalcitonin, ng/mL 0.11 (0.08–0.21) 0.22 (0.11–0.70) <0.001 0.06087 J. Clin. Med. 2020, 9, 2712 5 of 15 Table 1. Cont. QTc ≤ 480 QTc > 480 Standardized p-Value (n = 562) (n = 61) Differences Lactate dehydrogenase, U/L 309.9 ± 131.3 368.1 ± 159.1 0.003 −0.39636 D-dimer, ng/mL 670 (430–1120) 980 (650–1950) <0.001 −0.30192 High sensitivity troponin T > 14 ng/L 142 (30.7) 42 (77.8) <0.001 1.07117 NT-proBNP, pg/mL 157 (39–417) 2065 (416–6780) <0.001 −0.68728 Baseline QTc duration, milliseconds 430.2 ± 23.5 505.4 ± 31.9 <0.001 −2.68874 Abnormal chest radiography 495 (88.7) 54 (91.5) 0.51 0.09446 Results are expressed as mean ± standard deviation, median and (interquartile range) or number and (percentage). COPD: chronic obstruction pulmonary disease, CKD: chronic kidney disease, CRP: C-reactive protein. CV: Cardiovascular. eGFR: estimated glomerular filtration rate. Table 2. Comparison of baseline characteristics and clinical presentation between patients alive and dead during hospitalization. Alive Dead Standardized p-Value (n = 549) (n = 74) Differences Women 230 (41.9) 37 (50.0) 0.486 0.16319 Age, years 62.0 ± 16.6 81.2 ± 8.6 <0.001 −1.45609 Diabetes 90 (16.4) 28 (37.8) <0.001 0.49705 Hypertension 234 (42.6) 56 (75.7) <0.001 0.71397 Dyslipidemia 175 (31.9) 37 (50.0) 0.002 0.37500 CV Risk factors 345 (62.8) 70 (94.6) <0.001 0.95029 Obesity 96 (21.8) 16 (26.7) 0.40 0.11332 Ischemic chronic disease 31 (5.6) 20 (27.0) <0.001 0.60412 Atrial fibrillation or flutter 35 (6.4) 17 (23.0) <0.001 0.48252 Heart failure 18 (3.3) 15 (20.3) <0.001 0.54652 Moderate to severe valve heart disease 14 (2.6) 9 (12.2) <0.001 0.37460 COPD 38 (6.9) 14 (18.9) <0.001 0.36353 Cancer 61 (11.1) 17 (23.0) 0.004 0.31947 CKD 33 (6.0) 24 (32.4) <0.001 0.71171 Peripheral vascular disease 17 (3.1) 10 (13.5) <0.001 0.38439 Stroke 26 (4.7) 9 (12.2) 0.009 0.26943 Systolic blood pressure, mmHg 128.2 ± 18.6 130.8 ± 25.4 0.400 −0.11696 Diastolic blood pressure, mmHg 77.4 ± 13.2 72.8 ± 16.1 0.022 0.31195 Heart rate, bpm 91.2 ± 17.9 88.5 ± 21.3 0.24 0.13723 Respiratory rate, rpm 24.1 ± 6.9 28.7 ± 7.1 <0.001 −0.66193 Oxygen saturation, % 95.2 ± 4.3 93.2 ± 5.9 0.006 0.38792 Baseline FiO2, % 25.1 ± 15.0 37.6 ± 26.7 <0.001 −0.57585 PaO2/FiO2 < 300, n (%) 165 (40.4) 45 (71.4) <0.001 0.65697 Hemoglobin, g/dL 13.6 ± 1.6 12.6 ± 2.1 0.006 0.57111 White blood cell count, per µL 6.86 ± 3.05 9.99 ± 6.26 <0.001 −0.63711 Lymphocytes, per µL 1.46 ± 2.33 1.24 ± 1.55 0.43 0.11148 J. Clin. Med. 2020, 9, 2712 6 of 15 Table 2. Cont. Alive Dead Standardized p-Value (n = 549) (n = 74) Differences Platelet count 210.8 ± 77.9 207.5 ± 107.3 0.80 0.03488 Creatinine, mg/dL 0.99 ± 0.49 1.72 ± 1.46 <0.001 −0.66936 eGFR, mL/min/1.73 m2 84.0 ± 28.5 55.5 ± 31.9 <0.001 0.94242 Creatine phosphokinase, U/L 91 (57–167) 125 (49–290) 0.28 −0.33445 Serum lactate, mmoL/L 1.42 ± 0.66 1.79 ± 0.95 0.007 −0.45164 CRP, mg/dL 8.89 ± 7.78 16.41 ± 12.35 <0.001 −0.72780 Procalcitonin, ng/mL 0.11 (0.08–0.20) 0.26 (0.13–0.81) <0.001 0.03618 Lactate dehydrogenase, U/L 1.42 ± 0.66 1.79 ± 0.95 <0.001 −0.66081 D-dimer, ng/mL 640 (420–1060) 1190 (780–3570) <0.001 −0.47772 High sensitivity troponin T > 14 ng/L 135 (29.7) 49 (79.0) <0.001 1.13899 NT-proBNP, pg/mL 144 (39–367) 1530 (550–4730) <0.001 −0.67258 Baseline QTc duration, milliseconds 434.0 ± 27.8 464.3 ± 51.9 <0.001 −0.72743 QTc ≥ 480 ms 36 (6.6) 25 (33.8) <0.001 0.72127 Abnormal chest radiography 481 (88.3) 68 (94.4) 0.115 0.22147 Results are expressed as mean ± standard deviation, median and (interquartile range) or number and (percentage). COPD: chronic obstruction pulmonary disease, CKD: chronic kidney disease, CRP: C-reactive protein. CV: Cardiovascular. eGFR: estimated glomerular filtration rate. Only 245 patients (39% of the cohort) had a follow-up ECG during hospitalization. Of those, 77 patients (31.4%) had the longest QTc interval on admission, whilst 68.6% had QTc prolongation during hospitalization. Baseline characteristics, treatment, and prognosis of patients who had QTc prolongation on follow-up ECG during hospitalization are described in Table 3. Interestingly, both baseline QTc duration (441.75 ± 38.5 ms vs. 435.38 ± 31.6 ms, p = 0.17) and the percentage of patients with baseline QTc > 480 ms (10.4% vs. 11.9%, p = 0.73) were similar in those who prolonged QTc during hospitalization compared with those who did not. As expected, patients with QTc interval prolongation during hospitalization had higher prescription of hydroxychloroquine and azithromycin. Table 3. Comparison of baseline characteristics, clinical presentation, treatment, and outcome between patients with and without QTc prolongation during hospitalization. No QTc Prolongation QTc Prolongation p-Value (n = 77) (n = 168) Women 32 (41.6) 61 (36.3) 0.43 Age, years 62.1 ± 17.4 65.4 ± 15.0 0.14 Diabetes 10 (13) 40 (23.8) 0.051 Hypertension 32 (41.6) 96 (57.1) 0.023 Dyslipidemia 21 (27.3) 65 (38.7) 0.082 Obesity 13 (22.4) 42 (28.8) 0.36 Ischemic chronic disease 8 (10.4) 14 (8.3) 0.60 Atrial fibrillation or flutter 7 (9.1) 23 (13.7) 0.31 Heart failure 4 (5.2) 10 (6.0) 0.54 Moderate to severe valve heart disease 2 (2.6) 6 (3.6) 0.54 COPD 4 (5.2) 21 (12.5) 0.058 Cancer 13 (16.9) 29 (17.3) 0.94 J. Clin. Med. 2020, 9, 2712 7 of 15 Table 3. Cont. No QTc Prolongation QTc Prolongation p-Value (n = 77) (n = 168) CKD 5 (5.2) 14 (8.3) 0.28 Peripheral vascular disease 7 (9.1) 5 (3.0) 0.04 Stroke 2 (2.6) 13 (7.7) 0.097 Systolic blood pressure, mmHg 123.0 ± 18.8 128.4 ± 19.6 0.074 Diastolic blood pressure, mmHg 76.2 ± 14.7 75.2 ± 14.0 0.60 Heart rate, bpm 89.0 ± 15.0 91.2 ± 20.6 0.35 Respiratory rate, rpm 24.4 ± 6.4 26.2 ± 7.8 0.13 Oxygen saturation, % 95.9 ± 2.9 93.2 ± 6.3 <0.001 Baseline FiO2, % 24.9 ± 14.3 30.4 ± 22.3 0.022 PaO2/FiO2 < 300, n (%) 12 (21.4) 82 (58.2) <0.001 Hemoglobin, g/dL 13.4 ± 1.5 13.6 ± 1.7 0.36 White blood cell count, per µL 6.3 ± 2.2 7.8 ± 3.6 <0.001 Lymphocytes, per µL 1.56 ± 2.60 1.37 ± 1.81 0.51 Platelet count 205.1 ± 72.6 202.6 ± 75.0 0.81 Creatinine, mg/dL 1.04 ± 0.51 1.15 ± 0.73 0.26 eGFR, mL/min/1.73 m2 81.1 ± 28.9 77.3 ± 29.7 0.34 Creatine phosphokinase, U/L 82 (50–156) 108 (63–181) 0.051 Serum lactate, mmoL/L 1.35 ± 0.59 1.55 ± 0.70 0.04 CRP, mg/dL 8.5 ± 7.7 11.6 ± 9.7 0.009 Procalcitonin, ng/mL 0.12 (0.07–0.17) 0.13 (0.08–0.25) 0.31 Lactate dehydrogenase, U/L 293.0 ± 92.5 348.8 ± 151.2 0.01 D-dimer, ng/mL 630 (410–940) 760 (470–1240) 0.027 High sensitivity troponin T > 14 ng/L 20 (33.9) 60 (41.4) 0.34 NT-proBNP, pg/mL 145 (42–303) 246 (76–756) 0.014 Baseline QTc duration, milliseconds 441.8 ± 38.5 435.38 ± 31.6 0.17 Baseline QTc ≥ 480 ms 8 (10.4) 20 (11.9) 0.73 Abnormal chest radiography 69 (90.8) 156 (92.9) 0.58 Tocilizumab 14 (18.2) 54 (32.1) 0.023 No hydroxychloroquine or azithromycin 6 (7.8) 1 (0.6) Hydroxychloroquine alone 2 (2.6) 1 (1.8) 0.015 Azithromycin alone 1 (1.3) 1 (0.6) Hydroxychloroquine + Azithromycin 68 (88.3) 163 (97.0) Oxygen support 46 (59.7) 91 (54.2) High Flow Nasal Cannula 2 (2.6) 5 (3.0) <0.001 Non-invasive ventilation 1 (1.3) 16 (9.5) Intubation and invasive ventilation 1 (1.3) 37 (22.0) Longest QTc duration, milliseconds 440.4 ± 37.9 476.3 ± 48.9 <0.001 Length of hospitalization, days 10 (6–18) 17 (9–29.5) <0.001 Dead 5 (6.5) 25 (14.9) 0.045 Results are expressed as mean ± standard deviation, median and (interquartile range) or number and (percentage). COPD: chronic obstruction pulmonary disease, CKD: chronic kidney disease, CRP: C-reactive protein. eGFR: estimated glomerular filtration rate. J. Clin. Med. 2020, 9, 2712 8 of 15 In-hospital treatment and prognosis are shown in Tables 4 and 5. There were no episodes of ventricular tachycardia or ventricular fibrillation during hospitalization. When analyzed by sex, the presence of QTc ≥ 480 ms was associated with a higher mortality in both sexes. In women, mortality was 56.7% (17/30) in those with QTc ≥ 480 ms compared with 8.4% (20/237) in the non-prolonged QTc interval, p < 0.001. Similar results were seen in men: mortality was 25.8% (8/31) in the prolonged QTc interval group vs. 8.9% (29/325) in the non-prolonged QTc interval group, p = 0.003. This cut-off was independently associated with death in women (univariable HR 8.53 (95% CI: 4.45–16.36), p < 0.001, multivariable HR 4.04 (1.98–8.27), p < 0.001), whereas there was a strong tendency in the same direction in men (univariate HR 2.27 (95% CI: 0.99–5.23), p = 0.053). Table 4. In-hospital treatment and outcome in patients with and without prolonged QTc on admission. QTc ≤ 480 QTc > 480 p-Value (n = 562) (n = 61) Tocilizumab 94 (16.7) 11 (18.0) 0.80 No hydroxychloroquine or azithromycin 9 (1.6) 7 (11.5) Hydroxychloroquine 15 (2.7) 1 (1.6) <0.001 Azithromycin 7 (1.2) 3 (4.9) Hydroxychloroquine + Azithromycin 531 (94.5) 50 (82.0) Oxygen support 337 (60.0) 40 (65.6) High Flow Nasal Cannula 9 (1.6) 1 (1.6) 0.12 Non-invasive ventilation 27 (4.8) 3 (4.9) Intubation and invasive ventilation 36 (6.4) 8 (13.1) QTc prolongation during hospitalization 148 (68.2) 20 (71.4) 0.73 Longest QTc duration, milliseconds 455.8 ± 40.5 536.5 ± 47.8 <0.001 Length of hospitalization, days 10 (5–19) 10 (3–23) 0.68 Death 49 (8.7) 25 (41.0) <0.001 Results are expressed as mean ± standard deviation, median and (interquartile range) or number and (percentage). Table 5. Comparison of in-hospital treatment and outcomes between patients alive and dead during hospitalization. Alive Dead p-Value (n = 549) (n = 74) Tocilizumab 96 (17.5) 9 (12.2) 0.25 No hydroxychloroquine or azithromycin 9 (1.6) 7 (9.5) Hydroxychloroquine 12 (2.2) 4 (5.4) <0.001 Azithromycin 6 (1.1) 4 (5.4) Hydroxychloroquine + Azithromycin 522 (95.1) 59 (79.7) Oxygen support 324 (59.0) 53 (71.6) High Flow Nasal Cannula 9 (1.6) 1 (1.4) <0.001 Non-invasive ventilation 20 (3.6) 8 (10.8) Intubation and invasive ventilation 33 (6.0) 11 (14.9) QTc prolongation during hospitalization 143 (66.5) 25 (83.3) 0.045 Longest QTc duration, milliseconds 460.3 ± 45.3 499.0 ± 58.2 <0.001 Length of hospitalization, days 10 (5–20) 6 (3–16) 0.007 Results are expressed as mean ± standard deviation, median and (interquartile range) or number and (percentage). J. Clin. Med. 2020, 9, 2712 9 of 15 Mortality rate was much higher in patients with prolonged QTc at admission (41.0% vs. 8.7%, p < 0.001), as shown in Table 6 and the Kaplan–Meier survival curves in Figure 2. A baseline QTc > 480 ms was independently associated with higher mortality (HR 2.68 (1.58–4.55), p < 0.001). This result was similar when treatment was not included in the model (HR 2.78 (95% CI 1.66–4.66), p < 0.001). In a sensitivity analysis excluding patients who died during the first 48 h of admission (18 patients, 24.3% of all patients who died), the results were also similar (HR: 2.073 (95% CI: 1.073–4.005), p = 0.03). Table 6. Hazard ratios (HR) of 30-day death for baseline QTc > 480 ms adjusted for potential confounders. Univariate HR (95%CI) Adjusted HR (95%CI) * Age (per every year) 1.01 (1.08–1.12), p < 0.001 1.08 (1.06–1.11), p < 0.001 Baseline QTc > 480 ms 4.87 (2.98–7.96), p < 0.001 2.68 (1.58–4.55), p < 0.001 Chronic kidney disease 6.07 (3.70–10.05), p < 0.001 2.62 (1.55–4.46), p < 0.001 Treatment with azithromycin and hydroxychloroquine 0.12 (0.05–0.26), p < 0.001 0.31 (0.13–0.72), p = 0.007 Ischemic chronic disease 3.60 (2.13–6.09), p < 0.001 - Atrial fibrillation or flutter 3.04 (1.74–5.30), p < 0.001 - Heart failure 5.43 (3.05–9.66), p < 0.001 - Any cardiovascular risk factor 7.09 (2.58–19.45), p < 0.001 - * Model adjusted for age, baseline QTc > 480 ms, chronic kidney disease, treatment with azithromycin and hydroxychloroquine, ischemic chronic disease, atrial fibrillation or flutter, heart failure, and the presence of any cardiovascular risk factor. J. Clin. Med. 2020, 9, x FOR PEER REVIEW 9 of 15 Figure 2. Kaplan–Meier 30-day survival curves for mortality by QTc during the time from medical Figure 2. Kaplan–Meier 30-day survival curves for mortality by QTc during the time from medical contact. contact. The baseline characteristics, prognosis, and presentation of patients without a baseline ECG are summarized in Table 7. This group of patients was younger and had less cardiovascular risk factors and comorbidities. The clinical presentation was less severe and 24% were not treated with J. Clin. Med. 2020, 9, 2712 10 of 15 The baseline characteristics, prognosis, and presentation of patients without a baseline ECG are summarized in Table 7. This group of patients was younger and had less cardiovascular risk factors and comorbidities. The clinical presentation was less severe and 24% were not treated with hydroxychloroquine or azithromycin. Death rate was similar to those with a baseline ECG (10.8 vs. 11.9, p = 0.66). Table 7. Comparison of baseline characteristics, clinical presentation, treatment, and outcome between patients with and without ECG on presentation. Baseline ECG Without Baseline ECG p-Value (n = 623) (n = 249) Women 267 (42.9) 119 (47.8) 0.19 Age, years 64.2 ± 17.0 57.3 ± 19.6 <0.001 Diabetes 118 (18.9) 55 (22.19) 0.29 Hypertension 290 (46.5) 93 (37.3) 0.013 Dyslipidemia 69 (27.7) 212 (34.0) 0.07 Any CV risk factor 415 (66.6) 150 (60.2) 0.08 Obesity 112 (22.4) 46 (21.8) 0.86 Ischemic chronic disease 51 (8.2) 9 (3.6) 0.016 Atrial fibrillation or flutter 52 (8.3) 18 (7.2) 0.58 Heart failure 33 (5.3) 10 (4.0) 0.43 Moderate to severe valve heart disease 23 (3.7) 6 (2.4) 0.34 COPD 52 (8.3) 14 (5.6) 0.17 Cancer 78 (12.5) 33 (13.3) 0.77 CKD 57 (9.1) 18 (7.2) 0.36 Peripheral vascular disease 27 (4.3) 7 (2.8) 0.29 Stroke 35 (5.6) 16 (6.4) 0.65 Systolic blood pressure, mmHg 128.5 ± 19.5 129.1 ± 19.4 0.68 Diastolic blood pressure, mmHg 76.9 ± 13.7 76.5 ± 13.5 0.72 Heart rate, bpm 90.9 ± 18.4 89.4 ± 16.4 0.27 Respiratory rate, rpm 24.6 ± 7.1 22.4 ± 8.2 0.011 Oxygen saturation, % 95.1 ± 4.4 93.8 ± 6.1 0.13 Baseline FiO2, % 26.6 ± 17.2 24.9 ± 8.2 0.42 PaO2/FiO2 < 300, n (%) 210 (44.6) 41 (41.4) 0.56 Hemoglobin, g/dL 13.5 ± 1.7 13.4 ± 1.8 0.34 White blood cell count, per µL 7.23 ± 3.7 6.31 ± 2.8 0.001 Lymphocytes, per µL 1.44 ± 2.26 1.48 ± 1.18 0.71 Platelet count 210.4 ± 81.8 220.5 ± 86.5 0.13 Creatinine, mg/dL 1.08 ± 0.72 0.95 ± 0.46 0.002 eGFR, mL/min/1.73 m2 80.7 ± 30.3 88.3 ± 32.4 0.003 Creatine phosphokinase, U/L 93 (57–175) 78 (50–151) 0.079 Serum lactate, mmoL/L 1.3 (1.03–1.67) 1.25 (0.93–1.6) 0.16 CRP, mg/dL 7.3 (3.1–14.1) 4.0 (1.5–9.3) <0.001 Procalcitonin, ng/mL 0.12 (0.08–0.24) 0.09 (0.05–0.20) <0.001 J. Clin. Med. 2020, 9, 2712 11 of 15 Table 7. Cont. Baseline ECG Without Baseline ECG p-Value (n = 623) (n = 249) Lactate dehydrogenase, U/L 315.9 ± 135.1 295.6 ± 160.6 0.15 D-dimer, ng/mL 690 (440–1190) 610 (370–910) 0.004 High sensitivity troponin T > 14 ng/L 184 (35.7) 41 (30.4) 0.25 NT-proBNP, pg/mL 180 (48–540) 97 (25–346) 0.013 Abnormal chest radiography 549 (89.0) 161 (72.2) <0.001 Tocilizumab 105 (16.9) 14 (5.6) <0.001 No hydroxychloroquine or azithromycin 16 (2.6) 60 (24.1) Hydroxychloroquine 16 (2.6) 11 (4.4) <0.001 Azithromycin 10 (1.6) 11 (4.4) Hydroxychloroquine + Azithromycin 581 (93.3) 167 (67.1) Oxygen support 377 (60.5) 78 (31.3) High Flow Nasal Cannula 10 (1.6) 3 (1.2) <0.001 Non-invasive ventilation 28 (4.5) 4 (1.6) Intubation and invasive ventilation 44 (7.1) 11 (4.4) Length of hospitalization, days 10 (5–19) 8 (1–17) <0.001 Death 74 (11.9) 27 (10.8) 0.66 Results are expressed as mean ± standard deviation, median and (interquartile range) or number and (percentage). CV: Cardiovascular. COPD: chronic obstruction pulmonary disease, CKD: chronic kidney disease, CRP: C-reactive protein. eGFR: estimated glomerular filtration rate. 4. Discussion In this study, we found that a prolonged QTc interval at admission is present in almost 10% of patients with SARS-CoV-2 infection. Even though these patients had more comorbidities and worse clinical profile at presentation, the presence of a prolonged QTc was independently associated with increased mortality. The mean baseline QTc interval in our study was 437.0 ± 34.5 ms. Several studies in SARS-CoV-2 infection have reported similar baseline QTc intervals, with mean values ranging from 415 to 455 ms [2–6]. There are different definitions of prolonged QTc and the use of any of them might have affected the results of our study. We chose the cut-off value of 480 ms following the ESC Guidelines [18]. Although the prevalence of QTc > 480 ms was 9.8%, the prevalence of very prolonged QTc (QTc > 500 ms) was very low, only affecting 3.2% of patients and similar to other studies [6]. Therefore, using this restrictive cut-off as a screening tool would have had limited clinical value. The majority of studies have focused on QTc interval and risk of arrhythmias, especially in the setting of hydroxychloroquine treatment. This treatment (with or without azithromycin) is associated with a prolongation of the QT interval in 2.8 to 18.9% of patients [2,4,6,19,20]. However, these results depend on the definition of QT prolongation used and the dose of hydroxychloroquine. Interestingly, the risk of ventricular arrhythmias was very low and, consistent with our results, several studies did not show any episode of torsade de pointes or arrhythmic death [2,4,6,20,21]. In rheumatologic disease studies, the use of hydroxychloroquine has also been associated with QTc interval prolongation but not to increased mortality [22]. Thus, if randomized controlled trials were to show increased survival in SARS-CoV-2 infection with this treatment, data available show that the fear of malignant arrhythmias should not be a deterrent to its use with proper QT interval monitoring. However, the interest in the prognostic value of QTc interval goes beyond its potential interaction with treatment. The electrocardiogram (ECG) is a cheap non-invasive tool that can be found in all J. Clin. Med. 2020, 9, 2712 12 of 15 healthcare settings, from local clinics to tertiary hospitals. However, ECG is an underused tool in risk stratification. In our cohort, all-cause death was higher in patients with prolonged QTc (41.0% vs. 8.7%, p < 0.001, multivariable HR 2.68 (1.58–4.55), p < 0.001). As expected from previous research, age and comorbidities were associated with prolonged QTc interval and worse prognosis [23–26]. Some studies have shown that almost 20% of patients with chronic kidney disease (CKD) have a prolonged QTc interval than patients without CKD, and the presence of a prolonged QTc interval in this group is associated with increased cardiovascular and all-cause mortality [24]. Moreover, age per se is associated with a prolonged QT interval [23,27]. In patients with acute heart failure, the QTc interval has been associated with 5-year all-cause mortality in J-shape with nadir of 440 to 450 ms in male and 470 to 480 ms in female, although its significance decreased in females [28]. Similar results are seen in chronic heart failure, where the presence of prolonged QTc is also associated with higher mortality (41% vs. 14%, p = 0.001) [29]. In patients with prior cardiovascular disease, both cardiovascular mortality and sudden death were higher in patients with prolonged QTc, with relative risks that ranged from 1.1 to 3.8 for total mortality, from 1.2 to 8.0 for cardiovascular mortality, and from 1.0 to 2.1 for sudden death [30]. There are well documented sex-dependent differences in normal QT interval and age- and sex- specific cut-offs for prolonged QTc (>450 ms for men and >470 ms for women) have been proposed [27]. Therefore, by using a cut-off of 480 ms, it is possible that high-risk men were not identified. When analyzed separately by sex, we saw that patients with prolonged QTc had higher mortality (56.7% vs. 8.4% in women, p < 0.001, and 25.8% vs. 8.9% in men, p = 0.003). The 480 ms cut-off was independently associated with death in women, whereas there was a strong tendency in the same direction in men (univariate HR 2.27 (95% CI: 0.99–5.23), p = 0.053). It is worth mentioning that the number of events was very low in men (only eight patients in the QTc ≥ 480 ms died) and that might explain the lack of statistical significance in men. Although the use of a different cut-off according to sex could be useful, using several cut-off points depending on sex might not be feasible in clinical practice when different types of healthcare professionals at several levels of complexity are involved. The fact that patients with prolonged QTc had higher immune-inflammatory parameters and cardiac biomarkers (i.e., C-reactive protein, white blood cell count, serum lactate, procalcitonin, lactate dehydrogenase, D-dimer, troponin T, and NTproBNP) is intriguing. Although these differences could be due to a more severe presentation in a group of elderly comorbid patients, SARS-CoV-2 infection could be the cause of this prolonged QTc interval, either as a direct effect of the virus or through systemic inflammation. Studies done in rabbits showed that coronavirus infection was associated with QT interval prolongation [10], and coronavirus infection caused right and left ventricular dilation, myocardial fibrosis, and myocarditis [31,32]. Similarly to what had been observed in the animal model, echocardiograms done in patients with SARS-CoV-2 infection have shown a predominant right ventricular dilation, which was associated with increased troponin levels and worse prognosis [33]. On the other hand, several studies have described abnormal immune-inflammatory response to SARS-CoV-2 infection. A recent study has shown that levels of interleukin (IL)-1β, IL-6, IL-8, IL-10 and soluble TNF receptor 1 (sTNFR1) were all increased in patients with SARS-CoV-2 infection compared to healthy volunteers and cytokine ratios may predict outcomes in this population [34]. A recent meta-analysis has shown that other immune-inflammatory parameters, such as C-reactive protein, white blood cell count, and procalcitonin, were higher in severe SARS-CoV-2 infection compared to milder presentations [35]. Given that inflammation can also lead to QT interval prolongation [14,15], it is possible that SARS-CoV-2 infection prolongs a QTc interval through an inflammatory response. Hence, a prolonged QTc interval in SARS-CoV-2 infection could be the result of direct virus activity or be mediated by inflammation, which would help explain why a prolonged QTc is independently associated with 30-day mortality. Limitations There are some limitations to our study. First, asymptomatic patients were not included in this registry, which confers a selection bias. Second, although this is the largest study assessing QTc J. Clin. Med. 2020, 9, 2712 13 of 15 prognostic value in SARS-CoV-2 infection, this is a single-center study with a limited number of patients. Third, data on prolongation of QTc during hospitalization should be viewed with caution because only 39% of patients had a repeated ECG during hospitalization, hence the risk of bias is potentially high. Fourth, the measurement of the QT interval can be difficult [27,36]. Previous studies have shown that only 60% of physicians were able to accurately measure a sample QT interval, even though the majority stated that their area of specialization was cardiology [37]. Several studies have shown that automated QTc measurements are accurate in comparison with manual QTc measurements [6,22,38]. Therefore, the use of automated ECG measurement is likely to offer greater accuracy and allow a wider use of this tool in all healthcare levels than the manual assessment. Finally, we cannot exclude that some of the deaths might be due to ventricular tachycardia or ventricular fibrillation that went unnoticed and were ultimately attributed to other causes. 5. Conclusions Up to 10% of patients with SARS-CoV-2 infection had a prolonged QTc interval (i.e., >480 milliseconds) on admission. A prolonged QTc was independently associated with a higher risk of mortality even after adjustment for age, comorbidities, and treatment with hydroxychloroquine and azithromycin. Thus, the QTc interval should be measured in all patients with SARS-CoV-2 infection as a non-invasive and low cost tool for identifying high-risk patients. Author Contributions: Conceptualization, N.F. and B.V.; methodology, N.F., J.M., and B.V.; formal analysis, N.F. and J.M.; investigation, N.F., D.M., M.L., E.V., A.N., M.G.-G., Y.B., C.F., and A.B.G.-D.; data curation, N.F. and L.C.B.-T.; writing—original draft preparation, N.F.; writing—review and editing, N.F., D.M., M.L., L.C.B.-T., A.C.-F., E.V., A.N., M.G.-G., Y.B., C.F., and A.B.G.-D.; supervision, N.F. and B.V.; project administration, N.F., B.V., and A.C.-F. 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