ECG pathology and its association with death in critically ill COVID-19 patients, a cohort study.

BACKGROUND: We investigated the prevalence of ECG abnormalities and their association with mortality, organ dysfunction and cardiac biomarkers in a cohort of COVID-19 patients admitted to the intensive care unit (ICU).
METHODS: This cohort study included patients with COVID-19 admitted to the ICU of a tertiary hospital in Sweden. ECG, clinical data and laboratory findings during ICU stay were extracted from medical records and ECGs obtained near ICU admission were reviewed by two independent physicians.
RESULTS: Eighty patients had an acceptable ECG near ICU-admission. In the entire cohort 30-day mortality was 28%. Compared to patients with normal ECG, among whom 30-day mortality was 16%, patients with ECG fulfilling criteria for prior myocardial infarction had higher mortality, 63%, odds ratio (OR) 9.61 (95% confidence interval (CI) 2.02-55.6) adjusted for Simplified Acute Physiology Score 3 and patients with ST-T abnormalities had 50% mortality and OR 6.05 (95% CI 1.82-21.3) in univariable analysis. Both prior myocardial infarction pattern and ST-T pathology were associated with need for vasoactive treatment and higher peak plasma levels of troponin-I, NT-pro-BNP (N-terminal pro-Brain Natriuretic Peptide), and lactate during ICU stay compared to patients with normal ECG.
CONCLUSION: ECG with prior myocardial infarction pattern or acute ST-T pathology at ICU admission is associated with death, need for vasoactive treatment and higher levels of biomarkers of cardiac damage and strain in severely ill COVID-19 patients, and should alert clinicians to a poor prognosis.

Introduction

Although Corona virus disease 2019 (COVID-19) primarily affects the respiratory system and may cause severe pneumonia and hypoxemic respiratory failure [1], other organ systems are also frequently affected, such as the renal [2] and the cardiovascular system [3]. Typical cardiovascular risk factors, such as hypertension, diabetes and cerebrovascular disease are associated with a higher risk of severe disease [4, 5]. Elevated cardiac biomarkers during hospitalization due to COVID-19 are associated with higher risk of severe disease and mortality [6-11]. Proposed mechanisms for cardiac injury include demand ischemia in patients with pre-existing coronary artery stenosis, diffuse myocardial injury due to hypoxemia or arrythmia, Takotsubo syndrome, myocarditis and pulmonary hypertension secondary to adult respiratory distress syndrome (ARDS) or pulmonary embolism [3, 12–14].

The electrocardiogram (ECG) is a readily available, non-invasive, radiation free diagnostic mainstay of cardiac pathology [15] used at some point in nearly all severely ill patients [16]. ECG abnormalities in patients with COVID-19 were described in small case series early during the pandemic [17-20] and analyses of larger cohorts found that ECG pathology at hospital admission is associated with more severe disease and higher mortality [9, 21–24]. Unfortunately, previous studies have not specifically investigated patients admitted to the intensive care unit (ICU), who have the highest mortality [5]. ECG at ICU admission may differ from ECG at hospital admission and further knowledge of ECG abnormalities at ICU admission may add important diagnostic and prognostic information.

The primary aim of this study was to describe the prevalence of ECG pathology among COVID-19 patients at ICU admission and its association with mortality. We further aimed to compare laboratory findings including cardiac biomarkers in patients with and without ECG pathology.

Methods

Ethics statement

The protocol was approved by the National Ethical Review Agency, Uppsala, Sweden (EPM; 2020–01623), and registered at ClinicalTrials (NCT04316884). Written informed consent for access to electronic medical records was obtained from all participants, or next of kin if the patient was unable to give consent in accordance with the decision from the ethics committee. Data was acquired from electronic medical records and pseudonymised after collection. The study was performed in compliance with the Declaration of Helsinki and STROBE guidelines were followed for reporting.

Study design, setting and population

This study was performed as a subgroup analysis of the PronMed cohort study [25] in patients consecutively admitted to a mixed medical and surgical ICU between March 23 and July 14, 2020 at Uppsala University Hospital, a tertiary care teaching hospital in Sweden.

Patients >18 years old admitted to the ICU with COVID-19 diagnosis confirmed by positive SARS-CoV-2 reverse transcription polymerase chain reaction tests on naso- or oropharyngeal swabs were eligible for inclusion in this study if they had an ECG recorded within 48 hours prior to, or within 72 hours after ICU admission. Patients were admitted to ICU based on the clinical judgement of the attending ICU physician. The main criterium was need for organ-support, most commonly respiratory support beyond high-flow nasal oxygen with 60% oxygen at 60L min-1, or high risk of progression based on clinical judgement.

Data collection and ECG interpretation

Demographic data, medical history, laboratory findings, treatment measures and ECG were extracted from the medical records during ICU stay. If a patient had several ECGs recorded within the predefined time limit, the ECG recorded closest to ICU-admission was chosen for analysis. Two physicians with profound experience of ECG interpretation (JR, MN) analysed the ECGs independently according to pre-specified criteria [26-29] and recorded findings in a standardized case report form. The physicians were blinded to patient outcomes during ECG interpretation. Discrepancies were resolved by consensus.

ECG data [30] included rhythm, premature atrial and ventricular contractions (PAC and PVC), frontal plane axis, PQ-time, QRS-duration, Bazett-corrected [31] QT-interval (QTc), poor R-wave progression, P-wave pathology, left and right ventricular hypertrophy (LVH and RVH), S1Q3T3 pattern, conduction blocks, ST-segment abnormality (depression or elevation), T-wave inversion and right ventricular strain pattern.

We defined and focused the analysis on two composite ECG patterns to represent chronic and acute cardiac conditions: ECG with prior myocardial infarction (MI) pattern and ECG with ST-T pathology. ECG with prior myocardial infarction (MI) pattern was defined as presence of pathological Q-waves [32] and/or poor precordial R-wave progression [33, 34]. We did not classify poor R-wave progression as myocardial infarction if it was clearly due to LVH, RVH or conduction blocks [33]. ECG with ST-T pathology was defined as presence of pathological ST-elevation, ST-depression or T-wave inversion [27]. Laboratory data was compared in patients with either composite ECG patterns to those with normal ECG.

Statistical analysis

All data was analysed using Microsoft Excel (Redmond, WA, USA) and the R package”rcmdr” (Rcmdr: R Commander. R package version 3.6.3). Data were presented as mean and standard deviations (SD) or median and interquartile range (IQR) for normally and non-normally distributed data respectively. Categorical variables were presented as numbers (percentages). Univariable logistic regression was performed to investigate the relationship between ECG pathology and mortality. Multivariable analyses were performed for composite ECG pathology. Due to the limited sample size and events, the multivariable analyses were adjusted for Simplified Acute Physiology Score (SAPS 3) [35] only. SAPS 3 was chosen to adjust for baseline differences, as it is a validated scoring system used for prediction of hospital mortality. Uni- and multivariable logistic regression were further performed for composite ECG pathologies for patients who had an ECG recorded within 24 hours from ICU-admission. This sensitivity analysis is presented in the Supporting information. Normally and non-normally distributed continuous data was compared using independent t-test and Mann-Whitney U-test respectively and Chi-square or Fischer’s exact tests were used to compare categorical data as appropriate. Two-sided p-values <0.05 were considered statistically significant. Due to the exploratory nature of this study, we did not adjust for multiple statistical testing.

Results

Patient characteristics

Between March 23 and July 14, 2020, 168 patients were admitted to the ICU with confirmed COVID-19, of which 123 patients (76%) were included in the PronMed cohort. 83 patients had an ECG recorded within 48 hours prior to up to 72 hours following ICU admission. Three patients were excluded due to poor ECG quality, leaving 80 patients (65% of included patients) for final analysis (Fig 1). The mean age was 60.6 (SD 13.6), mean body mass index 30.0 kg m-2 (SD 5.9) and 20 patients (25%) were female (Table 1). The most common comorbidities were hypertension (54%) and diabetes (26%). Ten patients (13%) had pre-existing ischemic heart disease, 14 patients (18%) had atherosclerotic vascular disease and three patients (4%) had heart failure. Patients had a mean SAPS 3 of 53 (SD 10) and a median PaO2/FiO2 ratio of 17.8 (IQR 15.5–23.5) kPa at ICU admission. Patients with an ECG pattern of prior MI and ST-T pathology were older and had higher prevalence of hypertension, diabetes, cardiovascular and vascular disease than patients with normal ECG.

Fig 1

Flow chart of included patients.

Table 1

Patient characteristics at ICU admission.

	All patients (n = 80)	Normal ECG (n = 51)	Prior MI pattern (n = 11)	ST-T pathology (n = 17)
Anthropometry
Age (years)	61 (14)	56 (12)	66 (11)	71.5 (13.3)
Sex (female)	20 (25%)	14 (27%)	2 (18%)	6 (35%)
Weight (kg)	89 (20)	90 (20)	90 (16)	85 (20)
Length (cm)	175 (9)	175 (10)	175 (10)	170 (7)
BMI (kg m-2)	30 (6)	29 (6)	32 (6)	31 (6)
Pre-existing comorbidities
Hypertension	43 (54%)	25 (49%)	8(73%)	13 (76%)
Diabetes Mellitus	21 (26%)	10 (20%)	6 (55%)	7 (41%)
Ischemic heart disease	10 (13%)	3 (6%)	4 (36%)	3 (18%)
Heart failure	3 (4%)	1 (2%)	2 (18%)	2 (12%)
Pulmonary embolism	1 (1%)	0 (0%)	1 (9%)	1 (6%)
Pulmonary hypertension	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Malignant disease	7 (9%)	2 (4%)	0 (0%)	5 (29%)
Liver failure	1 (1%)	1 (2%)	0 (0%)	0 (0%)
Pulmonary disease	22 (28%)	16 (31%)	1 (9%)	5 (29%)
Vessel disease	14 (18%)	4 (8%)	6 (55%)	4 (24%)
ICU characteristics
Days with symptoms at ICU-admission	8.5 (5–16)	8.5 (5–15)	16 (9–18)	7 (6–12)
SAPS 3	53 (10)	51 (8)	54 (7)	63 (8)
Average PaO2/FiO2 ratio ICU-day 1 (kPa)	17.7 (15.5–23.5)	19.0 (15.7–24.3)	16.1 (15.1–18.4)	16.0 (15.1–18.4)
Pulmonary embolism diagnosed during ICU stay	9 (11%)	7 (13%)	2 (18%)	0 (0%)

Data are presented as mean (standard deviation), median (interquartile range) and numbers (percentages). ICU: Intensive care unit. MI: myocardial infarction. SAPS 3: Simplified Acute Physiology Score 3 [35].

ECG characteristics

ECG were recorded at median day 0 (IQR -1 to 0) of ICU stay. The majority of patients were in sinus rhythm at ICU admission (Table 2) and 51 (64%) had a normal ECG. The mean heart rate was 89 (SD 17) and 24 patients were tachycardic (defined as a heart rate ≥100 min-1). Eleven patients (14%) had an ECG pattern consistent with prior MI although only four of these had a prior diagnosis of ischemic heart disease. 17 patients (21%) had an ECG with ST-T-pathology. Twenty-seven (34%) patients had a conduction block but LBBB and RBBB were rare. T-wave inversion (n = 13, 16%) was the most common repolarization abnormalities, whereas ST-depression or ST-elevation were found in six patients (8%). Although RBBB or S1Q3T3 morphology were noticed in isolation in three patients, there were no patients with an ECG compatible with acute right ventricular strain and none of the 9 patients (11%) who were diagnosed with pulmonary embolism prior to or following ICU admission (Table 1) had ECG characteristics indicative of the diagnosis.

Table 2

ECG characteristics within 48 hours prior to, or up to 72 hours after, admission to the intensive care unit.

ECG characteristics	All patients (n = 80)
Composite EGG patterns
Normal ECG	51 (64%)
Prior myocardial infarction	11 (14%)
ST-T-pathology	17 (21%)
Rhythm
Sinus rhythm	76 (95%)
Heart rate	89 (17)
Atrial fibrillation	3 (4%)
Heart rate (range)	(100–178)
SVT	1 (1%)
Tachycardia (HR>100)	24 (30%)
Bradycardia (HR<50)	1 (1.3%)
Premature contraction
Atrial	4 (5%)
Ventricular	5 (6%)
ECG measurements
QRS-axis (°)	14 (39)
Left axis deviation (<-30°)	7 (9%)
Right axis deviation (>+90°)	2 (3%)
PR interval (ms)	153 (24)
QRS duration (ms)	91 (16)
QRS > 120 ms n(%)	2 (3%)
QTc (ms)	440 (27)
QTc>500 ms	2 (3%)
Conduction blocks
AV-block I, II or III	0 (0%)
Intraventricular block	14 (18%)
Left hemiblock	5 (6%)
Partial RBBB	4 (5%)
Partial LBBB	2 (3%)
RBBB	1 (1%)
LBBB	1 (1%)
Morphology
Early repolarisation	2 (3%)
Pathological Q-wave	7 (9%)
Poor R-wave progression	5 (6%)
S1Q3T3-morphology	2 (3%)
Left ventricular hypertrophy	6 (8%)
ST-elevation	1 (1%)
ST-depression	5 (6%)
T-wave inversion	13 (16%)
U-wave	1 (1%)

Data are presented as mean (standard deviation) or n (%) if not stated otherwise. Prior myocardial infarction includes ECG with Q-wave and/or poor R-wave progression. ST-T-pathology includes ECG with ST-elevation, ST-depression or T-wave inversion. SVT: supraventricular tachycardia, QTc: Corrected QT interval according to Bazett. RBBB: Right bundle branch block. LBBB: Left bundle branch block.

Mortality

At 30 days follow-up, 22 patients (28%) had died. Among patients with normal ECG, 30-day mortality was 16%. Among patients with an ECG consistent with prior MI pattern mortality was 63% and among patients with ST-T pathology, mortality was 50%. In univariable logistic regression analysis, prior MI pattern, ST-T pathology, PAC and/or PVC, pathological Q-wave, poor R-wave progression, ST-depression and T-wave inversion were associated with higher odds of death (Table 3). In multivariable analysis for the composite ECG-patterns, adjusting for SAPS 3, prior MI pattern was associated with higher odds of death. Kaplan-Meier survival analyses in patients with normal ECG versus patients with prior MI pattern or ST-T pathology are presented in Figs 2 and 3, respectively. Patients with ECG-abnormalities had their ECG recorded at similar time in relation to ICU admission compared with patients who had a normal ECG. Analysis of ECGs recorded within 24 hours from ICU admission (n = 60) did not differ from the main analysis (S1 Appendix).

Table 3

Analysis of odds ratio for death within 30 days of intensive care unit admission.

				Univariable analysis		Multivariable analysis
ECG-abnormalities	Day of ECG recordinga median (IQR)	Survived (n = 58)	Died (n = 22)	OR (95% CI)	P-value	OR (95% CI)	P-value
Composite abnormalities
Normal ECG	0 (-1, 1)	43 (74%)	8 (36%)	Ref.	n.a.	Ref.	n.a.
Prior MI pattern	0 (-1, 1)	4 (7%)	7 (31%)	9.63 (2.38–44.8)	0.002	9.61 (2.02–55.6)	0.006
ST-T pathology	0 (0, 0)	8 (14%)	9 (41%)	6.05 (1.82–21.3)	0.004	1.95 (0.42–8.52)	0.38
Single abnormalities
Heart rate (min-1)	n.a.	91 (20)	92 (21)	1.00 (0.98–1.03)	0.86
QTc (ms)	n.a.	441 (27)	437 (27)	0.99 (0.97–1.01)	0.57
Tachycardia (>100 min-1)	0 (-1, 0)	16 (28%)	8 (36%)	3.30 (0.95–12.5)	0.065
PAC and/or PVC	0 (0, 1)	4 (7%)	5 (23%)	7.32 (1.57–36.9)	0.011
Conduction block	0 (-1, 1)	21 (36%)	6 (27%)	1.18 (0.33–4.07)	0.79
LVH	0 (0, 0)	3 (5%)	3 (14%)	5.50 (0.86–34.8)	0.059
Q-wave	-1 (-1, 1)	3 (5%)	4 (18%)	7.33 (1.38–43.8)	0.020
Poor R-wave progression	0 (0, 0)	1 (2%)	4 (18%)	22.0 (2.82–462)	0.009
ST-depression	0 (0, 1)	2 (3%)	3 (14%)	8.25 (1.19–70.6)	0.033
T-wave inversion	0 (0–1)	6 (10%)	7 (31%)	6.42 (1.73–25.4)	0.006

Data are presented as absolute numbers (percentages), mean (standard deviation) or median (IQR). Logistic regression analysis was performed with normal ECG (n = 51) as reference. Multivariable analysis performed for composite ECG patterns and adjusted for SAPS 3. CI: Confidence interval. SAPS 3: Simplified Acute Physiology Score [35]. Prior MI (myocardial infarction) pattern includes ECG with Q-wave and/or poor R-wave progression. ST-T-pathology includes ECG with ST-elevation, ST-depression or T-wave inversion. PAC: premature atrial contraction, PVC: premature ventricular contraction. QTc = Corrected QT time according to Bazett.

aDay zero defined as the day of intensive care unit admission.

Fig 2

Kaplan-Meier survival analysis.

Comparison of patients with normal ECG and patients with prior myocardial infarction pattern (pathological Q-waves and/or poor R-wave progression), log-rank test p<0.001.

Fig 3

Kaplan-Meier survival analysis.

Comparison of patients with normal ECG and patients with ST-T pathology (ST-elevation, ST-depression or T-wave inversion), log-rank test p<0.001.

Biomarkers and organ dysfunction

Patients with prior MI pattern or ST-T-pathology at ICU admission had higher peak plasma values during ICU stay of troponin-I, NT-pro-BNP (N-terminal pro-Brain Natriuretic Peptide) and lactate compared to patients who had normal ECGs, but similar markers of inflammation (Table 4). More patients with prior MI pattern required treatment with vasoactive drugs compared to patients with normal ECG. Peak plasma levels of creatinine were higher in patients with prior MI pattern and higher but not statistically significant among patients with ST-T pathology compared to patients with normal ECG.

Table 4

Comparison of peak plasma laboratory values and organ support during intensive care unit stay between patients with normal ECG (reference) and ECG with prior myocardial infarction pattern or ST-T pathology respectively at intensive care unit admission.

	Normal ECG (n = 51)	Prior MI pattern (n = 11)	P-value	ST-T pathology (n = 17)	P-value
CRP	281 (166–378)	294 (251–375)	0.34	261 (222–352)	0.99
IL-6	118 (32–342)	301 (181–369)	0.11	260 (121–458)	0.52
Ferritin	2587 (831–3913)	1786 (1160–3954)	0.84	2253 (821–5145)	0.89
Troponin-I	15 (7–26)	104 (32–189)	0.003	116 (75–293)	<0.001
NT-pro-BNP	555 (274–1210)	3390 (529–5480)	0.023	4810 (3160–6090)	<0.001
D-dimer	3.4 (1.7–7.8)	8.1 (3.1–21)	0.043	2.8 (2.5–6.6)	0.50
Lactate	2.2 (1.7–2.6)	2.6 (2.4–3.5)	0.045	2.6 (2.3–3.4)	0.043
Creatinine	95 (79–142)	128 (100–218)	0.047	121 (97–215)	0.057
Lowest PaO2/FiO2 ratio	10.4 (9.6–13.2)	9.4 (8.6–10.6)	0.037	9.4 (7.6–12.3)	0.11
Mechanical ventilation	29 (57%)	7 (64%)	0.68	10 (59%)	0.89
CRRT	6 (12%)	2 (18%)	0.57	3 (18%)	0.54
Vasoactive treatment	28 (55%)	10 (91%)	0.029	13 (77%)	0.12

Data are presented as median (interquartile range) or number (percentages). Mann-Whitney U-test was used to compare continuous variables and chi2-test was used to compare categorical variables between patients with normal ECG and prior MI pattern and ST-T pathology. MI: myocardial infarction, CRP: C-reactive protein, IL-6: Interleukin 6, NT-pro-BNP: N-Terminal pro brain natriuretic peptide, CRRT: Continuous renal replacement therapy. Laboratory reference ranges: CRP <5 mg/L; IL-6 <7,0 ng/L; Ferritin male patients 25–310 μg/L, female patients 10–155 μg/L (non-age-adjusted); Troponin I male patients <35 ng/L, female patients <16 ng/L; NT-pro-BNP male patients <230 ng/L, female patients <330 ng/L (non-age-adjusted); D-dimer <0.50 mg/L (non-age-adjusted); Lactate 0.5–1.6 mmol/L; Creatinine male patients 60–105 μmol/L, female patients 45–90 μmol/L.

Discussion

In this cohort study of 80 critically ill COVID-19 patients, several ECG pathologies were associated with death in the univariable analysis, including both prior MI pattern and ST-T pathology. An ECG consistent with prior MI pattern was associated with death in multivariable analysis adjusting for SAPS 3. Patients with prior MI pattern probably represents a population with significant cardiovascular comorbidity that are at high risk for death independently of the disease severity of COVID-19 at ICU admission. On the contrary, ST-T pathology in this population may have several aetiologies, some of which may be a direct cause of COVID-19, such as myocarditis, right ventricular strain and demand ischemia due to hypoxemia [36-38]. ST-T pathology may thus be more likely to depend on the disease severity and consequently less likely to display a mortality association independent of SAPS 3 compared with prior MI pattern. Both chronic and acute ECG pathology at ICU admission was associated with higher peak values of biomarkers of cardiac strain and damage, lactate and more frequent requirement for vasoactive treatment. This indicates that ECG at ICU admission may be an important prognostic tool in COVID-19.

Similar to our study, a cohort study of 756 patients with COVID-19 found that prior MI pattern and T-wave inversion at hospital admission were associated with death while sinus tachycardia was not [21]. Further, patients with ST-T pathology at hospital admission have a higher risk of developing more severe disease [22]. One study of 850 patients with ECG recorded at presentation to the emergency department, and another study of 269 patients that analysed ECG at hospital admission and the seventh day of hospitalization found that ST-T-pathology is predictive of death and invasive ventilation [23, 39]. RBBB [21], AF [23], and LVH [39] at hospital admission also have been reported to indicate higher risk of death in patients with COVID-19. These abnormalities were uncommon in our study and therefore lacked sufficient statistical power for analysis. Contrary to our findings, a study describing ECG findings at hospital admission in 431 patients who later died or underwent invasive ventilation reported abnormal ECG in 93% of patients, a high prevalence of AF (22%) and signs of right ventricular strain (30%) [24]. In their cohort, patients were older (74 vs 61 years) and had higher over-all mortality (46% vs 28%) compared to our study, which may account for some of the differences between our study and theirs. Previous studies reported longer QT interval in patients with COVID-19 compared to patients without COVID-19 [40] and QT prolongation after falling ill with COVID-19 compared to before [41]. Previously reported QT intervals ranges from a mean of 443–450 ms [40-43], which is somewhat longer than the mean of 440 ms reported in this study. This difference may at least in part be explained by that the present study includes younger patients and fewer patients with previous heart disease, both of which are risk factors for QT prolongation [40, 41, 44].

Both ECG pathology and elevated troponin have previously been associated with death [9]. There are several plausible explanations why severely ill COVID-19 patients develop ECG abnormalities and myocardial damage. Patients with pre-existing cardiovascular disease are more prone to develop secondary myocardial ischemia due to non-cardiac conditions [45] such as hypoxia [36]. Consistent with this, we found that abnormalities associated with prior MI was associated with death and developed higher peak troponin-I and NT-pro-BNP values compared to patients with normal ECG. Patients with ST-T pathology in our study also had higher odds of death and developed higher peak values of cardiac biomarkers compared to patients with normal ECG, which may be caused by several different pathophysiological mechanisms, such as ischemia due to pre-existing coronary stenosis with oxygen supply-demand mismatch [45], acute coronary syndrome due to plaque rupture secondary [19], myocardial microthrombi due to complement activation [37] or myocarditis [38]. T-wave inversion, present in 16% of our cohort, may be present in up to 57% of patients with myocarditis [46]. In a study of unselected patients recently recovered from COVID-19, 60% of patients had findings consistent with myocardial inflammation on magnetic resonance imaging [13]. Myocarditis may be non-viral [38], as a part of the hyperinflammatory response reported in COVID-19, but also due to direct viral infiltration in myocardial cells [12, 47, 48].

Although patients with COVID-19 have high risk of pulmonary embolism [49] and 11% of the patients in our cohort were diagnosed with pulmonary embolism, none of them had an ECG consistent with right ventricular strain. In a case series of 15 hospitalized patients with confirmed COVID-19 and pulmonary embolism, 33% had right ventricular strain pattern on ECG while two-thirds had non-specific ECG findings, such as sinus tachycardia [50]. The absence of right ventricular strain pattern in our study could simply be due to the absence of pulmonary embolism at the time of the ECG-recording. However, critically ill patients with COVID-19 related pulmonary embolism may also have less clot burden [51] compared to a general ICU population with pulmonary embolism, and right ventricular strain pattern may therefore not manifest on the ECG. Further, the incidence of pulmonary embolism was lower in our cohort than in other studies [52], possibly due to a higher dose low molecular weight heparin thromboprophylaxis at our ICU.

In a previous study, patients with an abnormal ECG developed higher peak plasma creatinine and had a higher incidence of continuous renal replacement therapy compared to patients with normal ECG [53]. In our study, peak plasma creatinine was similarly higher in patients with prior MI pattern compared to those with normal ECG. Patients with ST-T pathology also developed higher peak plasma creatinine values compared to patients with normal ECG, but this finding was only borderline significant, likely due to low statistical power. Kidney injury in COVID-19 is likely multifactorial and may be caused by several mechanisms, in part common to those responsible for cardiac injury, including both direct viral pathophysiological effects, systemic inflammation, hypovolemia and cardiopulmonary instability related to the degree of illness severity [2, 45, 54]. Pre-existing cardiovascular risk factors are frequent in COVID-19 patients [5] and may further contribute to the development of simultaneous cardiac and renal dysfunction.

Contrary to previous studies, where immune dysregulation has been proposed as a major mechanism for cardiac injury in COVID-19 [3] and cardiac biomarkers have been positively associated with inflammatory biomarkers [55, 56], we found no statistically significant difference in CRP, ferritin and IL-6, between patients with prior MI pattern or ST-T pathology compared to patients with normal ECG in our study. Patients admitted to the ICU may represent a cohort of patients with severe inflammatory response regardless of myocardial injury which could explain the lack of difference in our study. Pathological ECG changes may thus not primarily be caused by more severe inflammation, but rather similar levels of inflammation causing ECG abnormalities and cardiac injury primarily in patients with pre-existing cardiac disease.

Strengths of this study included that ECG interpretation was conducted according to pre-specified criteria by two independent physicians blinded to patient outcomes. The study was conducted at a large tertiary referral centre with a large catchment area and all inhabitants in Sweden are covered by the public health insurance increasing generalizability of our study. Further, no patients were lost to follow up and there was minimal missing data.

There are also limitations of this study. The single centre design reduces generalizability and the small sample size hampers statistical power, especially in the multivariable analysis where only additional adjustment for SAPS 3 was feasible. A larger study would have allowed for adjustment for more covariates, thus reducing the risk of confounding [57]. Several patients did not have an ECG recorded at ICU admission, which may have led to selection bias if patients with pre-existing comorbidities or more severe disease were more likely to have an ECG recorded. However, the baseline characteristics of this sub-cohort were similar to the entire PronMed cohort of COVID-19 patients admitted to the ICU at Uppsala University Hospital [58]. The inclusion of patients who had an ECG recorded within 48 hours prior to and up to 72 hours after ICU admission may have led to comparison of ECGs recorded late, at a time of clinical decompensation to ECGs recorded early at a time of relatively less severe disease. However, patients who had ECG pathology had their ECGs recorded at similar time in relation to ICU admission, compared with patients who had normal ECGs. Furthermore, the results of the main analysis were robust in the sensitivity analysis which was restricted to patients with ECGs recorded within one day from ICU admission.

This study contains important new information for bedside clinicians and future studies. If confirmed, ECG findings presented herein may be used in prognostic tools for severe COVID-19 and the apparent lack of association between pathological ECG and inflammatory markers may further the understanding how COVID-19 affects the cardiovascular system.

Conclusion

ECG indicative of both chronic and acute cardiac conditions at ICU admission due to severe COVID-19 were associated with higher mortality and higher levels of cardiac biomarkers. ECG is an invaluable low-risk investigation in a range of clinical scenarios. Our study suggests that an ECG provides important prognostic information in severe COVID-19 and should be considered in all critically ill COVID-19 patients.

Sensitivity analysis.

(DOCX)

Click here for additional data file.

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30 Oct 2021

PONE-D-21-28860ECG pathology and its association with death in critically ill COVID-19 patients, a cohort studyPLOS ONE

Dear Dr. Jacob Rosen,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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ACADEMIC EDITOR: Thank you very much for having submitted this paper to the journal for consideration. Although the topic is not particularly novel and some criticisms about the statistical method I would like to give you  the opportunity to try and improve the quality of our work. Besides all the reviewers' comments I'd like you to  discuss more about the QT interval in COVID 19 patients and in particular why the rate of prolongation was so low in your series and why it was not a associated to death.

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Additional Editor Comments (if provided):

Thank you very much for having submitted this paper to the journal for consideration. Although the topic is not particularly novel and some criticisms about the statistical method I would like to give you the opportunity to try and improve the quality of our work. Besides all the reviewers' comments I'd like you to discuss more about the QT interval in COVID 19 patients and in particular why the rate of prolongation was so low in your series and why it was not a associated to death.

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2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records/samples used in your retrospective study. Specifically, please ensure that you have discussed whether all data/samples were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data/samples from their medical records used in research, please include this information

3. Thank you for stating the following in the Funding Section of your manuscript:

“The study was funded by the SciLifeLab/KAW national COVID-19 research program project grant to MH (KAW 2020.0182), the Swedish Research Council grant to RF (2014-02569 and 2014-07606).”

We note that you have provided additional information within the Funding Section. Please note that funding information should not appear in other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

“The study was funded by

1. The SciLifeLab/Knut and Alice Wallenbergs foundations national COVID-19 research program project grant (https://kaw.wallenberg.org) to MH (grant number 2020.0182). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

2. The Swedish Research Council grant (https://www.vr.se) to RF (grant numbers 2014-02569 and 2014-07606). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

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Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.  Any potentially identifying patient information must be fully anonymized.

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We will update your Data Availability statement on your behalf to reflect the information you provide.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: PONE-D-21-28860: statistical review

SUMMARY. This is a cohort study that focuses on the association between ECG abnormalities and total mortality. The statistical analysis relies on a battery of univariate and multivariate logistic regressions, followed by the computation of survival curves that compare subjects with and without ECG abnormalities. My major concern with this paper is that not all the available information has been exploited in both the logistics regression analysis (see major issue 1) and in the survival analysis (major issue 2). I futhermore list below some specific points that should be addressed.

MAJOR ISSUES

1) Table 3 displays the outputs of univariate logistic regressions where single and composite ECG abnormalities are separately included and the output of (bivariate) logistics regressions where the effect of composite abnormalities is adjusted for SAPS 3. Why are all the available covariates (described in Table 1) not included in the analysis? Ignoring the observed heterogeneity of the sample could bias the final results. Either the authors should motivate this choice, or they should include such information. In the latter case, given the limited sample size, a careful model selection procedure should be implemented, in order to include only the relevant covariates.

2) Survival analysis reduces to comparing the survival curves of subjects with and without ECG abnormalities. Again, why are the additional covariates not included in the analysis? Mortality risks should be adjusted for the variables of Table 1. The natural approach would rely on the estimation of a Cox regression model, which would be much more informative than the plots displayed by Figures 1 and 2.

SPECIFIC ISSUES

1) Page 4: subjects were included "in this study if they had an ECG recorded within 48 hours prior to, or within 72 hours after ICU admission". Could the authors clarify that they are not introducing selection bias with this choice? Is the final sample still a random subset of the target population?

2) I understand that the study aims at investigating the effects of ECG abnormalities within COVID subjects. However, shouldn't the study compare subjects with covid and without covid? Under this design, we could test whether ECG abnormalities interact with Covid and see whether ECG and covid influence additively or multiplicatively mortality risk.

3) Figures 1 and 2 should include the confidence bands of the survival curves. In addition, please specify the test that has been run to compare the curves (log-ranks test?).

Reviewer #2: Thank you for the opportunity to review your manuscript entitled "ECG pathology and its association with death in critically ill COVID-19 patients, a cohort study". First of all, I would like to suggest you specify the total word count at the beginning of the manuscript and insert line numbers to make it easier to review and to correct any mistake. Then, you can find my specific comments here below.

METHODS

-Which is the rationale for including patients with an ECG recorded in the time interval between 48 hours before and 72 hours after ICU admission, instead of patients with an ECG recorded at ICU admission or patients admitted to ICU with an ECG recorded at hospital admission? In 5 hours, it is not likely that a patient will undergo pharmacological treatments or procedures which can modify ECG pattern, is it? Furthermore, in case of more than one ECG recorded, which one did you consider for the analysis? Please, specify these details.

-Which pre-specified criteria were used to analyse ECG? Please, explain.

-Why did you exclude the criteria for LVH, RVH and conduction blocks among the ECGs with prior myocardial infarction pattern? These conditions could co-exist and the firsts do not exclude the latters. Consider changing this definition, which moreover contrasts with the one written in the caption of Table 2 (see comment below in Results section).

RESULTS

-Consider adding a figure with a flow chart to clarify the inclusion and exclusion criteria of the study population.

-In Table 1 “Patient characteristics at baseline and ICU admission” you miss the percentage symbol in the cell about the females among all patients.

-As mentioned in the previous comment in Methods section, in the Table 2 caption you rightly defined prior myocardial infarction as ECG with Q-wave and/or poor R-wave progression. Please, correct the definition used in the Methods section, which is different.

-In Table 3 remove the percentage symbol in the cell about the heart rate among dead patients (the number in the brackets should indicate the standard deviation).

-Why did you use NT-proBNP and not BNP? The results could be influenced by renal dysfunction, which should be taken into consideration when interpreting these data. Circulating levels of both BNP and NT-proBNP increase indeed with kidney failure, but the impact of kidney function on NT-proBNP is much more pronounced than that on BNP (Takase H, Dohi Y. Kidney function crucially affects B-type natriuretic peptide (BNP), N-terminal proBNP and their relationship. Eur J Clin Invest. 2014;44(3):303-8. doi: 10.1111/eci.12234. Epub 2014 Jan 20. PMID: 24372567).

DISCUSSION

-Citing the paper of Bertini M et al. (Bertini M, Ferrari R, Guardigli G, et al. Electrocardiographic features of 431 consecutive, critically ill COVID-19 patients: an insight into the mechanisms of cardiac involvement. Europace. Epub ahead of print 18 September 2020. DOI: 10.1093/europace/euaa258), you wrote that you reported the proportion of patients who were not eligible for ICU admission, unlike them. However, I have not found this information throughout your manuscript. You should add your criteria of ICU admission in the Methods section, before indicating the inclusion criteria of the study population.

**********

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Reviewer #1: No

Reviewer #2: No

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12 Nov 2021

Authors’ response to reviewers (also provided as a word file)

Additional Editor Comments (if provided):

Thank you very much for having submitted this paper to the journal for consideration. Although the topic is not particularly novel and some criticisms about the statistical method I would like to give you the opportunity to try and improve the quality of our work. Besides all the reviewers' comments I'd like you to discuss more about the QT interval in COVID 19 patients and in particular why the rate of prolongation was so low in your series and why it was not a associated to death.

Thank you for the opportunity to revise our manuscript. We have made several alterations according to the reviewers’ comments, which we believe have greatly improved our manuscript. References are found at the end of this response letter. References to page and line numbers are

QT prolongation has been widely reported among patients with COVID. In our study, the mean QTc, corrected according to Bazett, was 440 ms and two patients (3%) had QTc >500 ms. The mean QTc in our study is comparable, but often marginally shorter, than figures reported by Rubin[1] (450 ms), Akthar[2] (461 and 449 ms), Changal[3] (446 ms), Garcia-Rodriguez[4] (443 ms). This incongruity may be due to differences in patient characteristics, as our cohort is younger and have fewer cardiovascular comorbidities than patients in previous studies, both of which are risk factors for prolonged QTc.

The difference in QTc among patients who survived (441 ms) and patients who died (437 ms) was very small, and a lack of association with death may be due to lack of statistical power or due to our younger cohort of patients. We have added a paragraph to the manuscript discussing this (p 15, lines 278-284).

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

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https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

The manuscript has been altered to adhere to PLOS ONE style requirements.

2. In ethics statement in the manuscript and in the online submission form, please provide additional information about the patient records/samples used in your retrospective study. Specifically, please ensure that you have discussed whether all data/samples were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data/samples from their medical records used in research, please include this information

Patients provided written informed consent. If consent was not possible to obtain from the patient, written informed consent was provided by the patients’ next of kin, as per decision from the ethics committee. Data was collected from electronic patient records, and as such anonymization before collection was not possible. Collected data was entered into a pseudoanynymized dataset and all analyses were performed on pseudoanonymized data. This has been clarified in the methods section (p 4, lines 80-86).

3. Thank you for stating the following in the Funding Section of your manuscript:

“The study was funded by the SciLifeLab/KAW national COVID-19 research program project grant to MH (KAW 2020.0182), the Swedish Research Council grant to RF (2014-02569 and 2014-07606).”

We note that you have provided additional information within the Funding Section. Please note that funding information should not appear in other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

“The study was funded by

1. The SciLifeLab/Knut and Alice Wallenbergs foundations national COVID-19 research program project grant (https://kaw.wallenberg.org) to MH (grant number 2020.0182). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

2. The Swedish Research Council grant (https://www.vr.se) to RF (grant numbers 2014-02569 and 2014-07606). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

Please include your amended statements within your cover letter; we will change the online submission form on your behalf.

The manuscript has been changed accordingly. An amended Funding Statement is included in the cover letter.

4. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Unfortunately, it is not possible to upload the study’s minimal underlying dataset. Data privacy regulations prohibit deposition of individual level data to public repositories and the ethical approval does not cover public sharing of data for unknown purposes. Upon contact with the authors or SciLifeLab (https://doi.org/10.17044/scilifelab.14229410.v1) an institutional data transfer agreement may be established, and data shared if the aims of data use are covered by ethical approval and patient consent.

5. One of the noted authors is a group or consortium Uppsala Intensive Care COVID-19 Research Group. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address

The Acknowledgements section has been edited accordingly.

6. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Unfortunately, it is not possible to upload the study’s minimal underlying dataset. Data privacy regulations prohibit deposition of individual level data to public repositories and the ethical approval does not cover public sharing of data for unknown purposes. Upon contact with the authors or SciLifeLab (https://doi.org/10.17044/scilifelab.14229410.v1) an institutional data transfer agreement may be established, and data shared if the aims of data use are covered by ethical approval and patient consent.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: PONE-D-21-28860: statistical review

SUMMARY. This is a cohort study that focuses on the association between ECG abnormalities and total mortality. The statistical analysis relies on a battery of univariate and multivariate logistic regressions, followed by the computation of survival curves that compare subjects with and without ECG abnormalities. My major concern with this paper is that not all the available information has been exploited in both the logistics regression analysis (see major issue 1) and in the survival analysis (major issue 2). I futhermore list below some specific points that should be addressed.

Thank you for your time and expertise reviewing our manuscript. We hope our responses, alterations to the manuscript and new analyses are satisfactory. References are found at the end of this response letter.

MAJOR ISSUES

1) Table 3 displays the outputs of univariate logistic regressions where single and composite ECG abnormalities are separately included and the output of (bivariate) logistics regressions where the effect of composite abnormalities is adjusted for SAPS 3. Why are all the available covariates (described in Table 1) not included in the analysis? Ignoring the observed heterogeneity of the sample could bias the final results. Either the authors should motivate this choice, or they should include such information. In the latter case, given the limited sample size, a careful model selection procedure should be implemented, in order to include only the relevant covariates.

Not all available covariates are included in the analysis, as our study unfortunately was not powered to do so. Including more than one variable per ten events in a multivariable model may introduce bias.[5] Our study includes 22 deaths, and hence inclusion of more than two variables may be inappropriate. Considering this limitation, we believe SAPS 3 to be the best variable to adjust for baseline differences, as it is a well-validated scoring system used for prediction of hospital mortality based on several variables of physiological derangements, current and previous conditions. The rationale for using SAPS 3 as adjusting covariate is added to the methods section and the discussion (p 6, lines 136-138) has been edited to emphasize this limitation (p 17, 345-346).

2) Survival analysis reduces to comparing the survival curves of subjects with and without ECG abnormalities. Again, why are the additional covariates not included in the analysis? Mortality risks should be adjusted for the variables of Table 1. The natural approach would rely on the estimation of a Cox regression model, which would be much more informative than the plots displayed by Figures 1 and 2.

The Kaplan-Meier curves illustrate mortality in patients with normal ECG compared to patients with prior MI pattern and ST-T pathology respectively. The results section was not well worded and has been edited for clarity.

This analysis is also limited by the study size and the number of events (deaths). The 22 deaths in the study only reliably permits the inclusion of two variables in the adjusted model. As the time perspective is rather short, reporting 30-day mortality, we chose to perform a logistic regression for odds of death instead of a Cox regression. The Kaplan-Meier plots are an unadjusted illustration detailing the timing of the deaths, whereas the adjusted logistic regression attempts to control for potential confounding to the extent permitted by the study size.

SPECIFIC ISSUES

1) Page 4: subjects were included "in this study if they had an ECG recorded within 48 hours prior to, or within 72 hours after ICU admission". Could the authors clarify that they are not introducing selection bias with this choice? Is the final sample still a random subset of the target population?

Good point. Knowledge of prior heart or vascular disease, or risk factors thereof, may have alerted the clinician to the risk of current cardiac involvement and prompted ECG recording. Thus, this study may include more patients with heart disease than other cohorts of COVID patients. However, this subcohort did not differ substantially in patient characteristics compared to the entire PronMed cohort[6,7] of COVID patients admitted to the ICU at Uppsala University Hospital (not limited to only patients with an ECG recorded). Further, this would not affect the internal validity of the results.

We chose a time interval restriction because the aim of the study was to compare the prevalence of ECG abnormalities in COVID patients at ICU admission and their association with mortality, as we thought this association would be interesting to bedside clinicians. However, we agree that inclusion of patients who had an ECG recorded within 48 hours prior to and up to 72 hours after ICU admission may have led to comparison of ECGs recorded late, at a time of clinical decompensation to ECGs recorded early at a time of relatively less severe disease. However, patients who had ECG pathology had their ECGs recorded at similar time in relation to ICU admission (information added to Table 3), compared with patients who had normal ECGs. Furthermore, the results of the main analysis were robust in a sensitivity analysis which was restricted to patients with ECGs recorded within one day from ICU admission. The sensitivity analysis was added as separate file (S1 Appendix) and the limitations paragraph has been edited to include this weakness (p 17-18, lines 348-357).

2) I understand that the study aims at investigating the effects of ECG abnormalities within COVID subjects. However, shouldn't the study compare subjects with covid and without covid? Under this design, we could test whether ECG abnormalities interact with Covid and see whether ECG and covid influence additively or multiplicatively mortality risk.

This would indeed be a very interesting analysis! However, the aim of the study was to investigate ECG abnormalities and their association with death in COVID patients. It is beyond the scope of the present study to also investigate how ECG abnormalities interact with different diseases.

3) Figures 1 and 2 should include the confidence bands of the survival curves. In addition, please specify the test that has been run to compare the curves (log-ranks test?).

Thank you, this would improve the figures. Confidence bands have been added and the use of log rank test specified.

Reviewer #2: Thank you for the opportunity to review your manuscript entitled "ECG pathology and its association with death in critically ill COVID-19 patients, a cohort study". First of all, I would like to suggest you specify the total word count at the beginning of the manuscript and insert line numbers to make it easier to review and to correct any mistake. Then, you can find my specific comments here below.

Thank you for reviewing our manuscript. It has been changed accordingly. References are found at the end of this response letter.

METHODS

-Which is the rationale for including patients with an ECG recorded in the time interval between 48 hours before and 72 hours after ICU admission, instead of patients with an ECG recorded at ICU admission or patients admitted to ICU with an ECG recorded at hospital admission? In 5 hours, it is not likely that a patient will undergo pharmacological treatments or procedures which can modify ECG pattern, is it? Furthermore, in case of more than one ECG recorded, which one did you consider for the analysis? Please, specify these details.

ECG at hospital admission has previously been studied, and abnormalities are associated with increased mortality. We wanted to specifically study patients admitted to the ICU, as this is a group with higher mortality. Furthermore, patients are admitted to the ICU at a time of clinical deterioration, which may reveal other ECG patterns than ECG recorded at hospital admission. Many patients were admitted to the hospital several days prior to ICU admission. In order to collect and compare ECGs at ICU admission, we had to arbitrarily define time constraints.

However, we agree that inclusion of patients who had an ECG recorded within 48 hours prior to and up to 72 hours after ICU admission may have led to comparison of ECGs recorded late, at a time of clinical decompensation to ECGs recorded early at a time of relatively less severe disease. However, patients who had ECG pathology had their ECGs recorded at similar time in relation to ICU admission (information added to Table 3), compared with patients who had normal ECGs. Furthermore, the results of the main analysis were robust in a sensitivity analysis which was restricted to patients with ECGs recorded within one day from ICU admission. The sensitivity analysis was added as separate file (S1 Appendix) and the limitations paragraph has been edited to include this weakness (p 17-18, lines 348-357).

-Which pre-specified criteria were used to analyse ECG? Please, explain.

We used criteria that adhered to the published guidelines[8-11]. The interpretation data were entered in a pre-defined case report form.

-Why did you exclude the criteria for LVH, RVH and conduction blocks among the ECGs with prior myocardial infarction pattern? These conditions could co-exist and the firsts do not exclude the latters. Consider changing this definition, which moreover contrasts with the one written in the caption of Table 2 (see comment below in Results section).

This section was poorly phrased. We only excluded these patterns if it was obvious that poor R-wave progression was due to one of these conditions. The diagnosis of myocardial infarction based on poor R-wave progression on surface-ECG in the prescence of LVH, RVH and conduction block may be difficult or impossible. We therefore did not include ECG with poor R-wave progression that was obviously due to other conditions than myocardial infarction in this group to increase specificity, as demonstrated by Zema et al.[12] We have rephrased this to clarify (p 5, lines 123-126).

RESULTS

-Consider adding a figure with a flow chart to clarify the inclusion and exclusion criteria of the study population.

A flow chart has been added as Fig 1.

-In Table 1 “Patient characteristics at baseline and ICU admission” you miss the percentage symbol in the cell about the females among all patients.

Thank you, Table 1 has been corrected.

-As mentioned in the previous comment in Methods section, in the Table 2 caption you rightly defined prior myocardial infarction as ECG with Q-wave and/or poor R-wave progression. Please, correct the definition used in the Methods section, which is different.

See comment concerning ECG patterns above. The text has been altered for consistency.

-In Table 3 remove the percentage symbol in the cell about the heart rate among dead patients (the number in the brackets should indicate the standard deviation).

It has been removed.

-Why did you use NT-proBNP and not BNP? The results could be influenced by renal dysfunction, which should be taken into consideration when interpreting these data. Circulating levels of both BNP and NT-proBNP increase indeed with kidney failure, but the impact of kidney function on NT-proBNP is much more pronounced than that on BNP (Takase H, Dohi Y. Kidney function crucially affects B-type natriuretic peptide (BNP), N-terminal proBNP and their relationship. Eur J Clin Invest. 2014;44(3):303-8. doi: 10.1111/eci.12234. Epub 2014 Jan 20. PMID: 24372567).

We display NT-proBNP as it is the standard analysis at our institution. Although listing BNP was not an alternative, we agree that it may have reduced the risk of confounding by kidney function.

DISCUSSION

-Citing the paper of Bertini M et al. (Bertini M, Ferrari R, Guardigli G, et al. Electrocardiographic features of 431 consecutive, critically ill COVID-19 patients: an insight into the mechanisms of cardiac involvement. Europace. Epub ahead of print 18 September 2020. DOI: 10.1093/europace/euaa258), you wrote that you reported the proportion of patients who were not eligible for ICU admission, unlike them. However, I have not found this information throughout your manuscript. You should add your criteria of ICU admission in the Methods section, before indicating the inclusion criteria of the study population.

This section was poorly worded and has been edited for clarity. We do indeed not report which patients were ineligible for ICU admission. Thank you for your careful reading. Patients were admitted to ICU based on the clinical judgement of the attending ICU physician. The main criterium was need for organ-support, most commonly need for respiratory support beyond high-flow nasal oxygen with 60% oxygen at 60L min-1, or high risk of progression based on clinical judgement. The methods section has been edited (p 4-5, lines 100-103).

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References

1. Rubin GA, Desai AD, Chai Z, Wang A, Chen Q, Wang AS, et al. Cardiac Corrected QT Interval Changes Among Patients Treated for COVID-19 Infection During the Early Phase of the Pandemic. JAMA Network Open. 2021;4: e216842. doi:10.1001/jamanetworkopen.2021.6842

2. Akhtar Z, Gallagher MM, Yap YG, Leung LWM, Elbatran AI, Madden B, et al. Prolonged QT predicts prognosis in COVID-19. Pacing Clin Electrophysiol. 2021;44: 875–882. doi:10.1111/pace.14232

3. Changal K, Paternite D, Mack S, Veria S, Bashir R, Patel M, et al. Coronavirus disease 2019 (COVID-19) and QTc prolongation. BMC Cardiovascular Disorders. 2021;21: 158. doi:10.1186/s12872-021-01963-1

4. García-Rodríguez D, Remior P, García-Izquierdo E, Toquero J, Castro V, Fernández Lozano I. Drug-induced QT prolongation in COVID-19 pneumonia: influence on in-hospital survival. Rev Esp Cardiol. 2021;74: 111–112. doi:10.1016/j.rec.2020.09.027

5. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology. 1996;49: 1373–1379. doi:10.1016/S0895-4356(96)00236-3

6. PRONMED Uppsala COVID-19 ICU Biobank. SciLifeLab; 2021. doi:10.17044/scilifelab.14229410.v1

7. Sancho Ferrando E, Hanslin K, Hultström M, Larsson A, Frithiof R, Lipcsey M, et al. Soluble TNF receptors predict acute kidney injury and mortality in critically ill COVID-19 patients: A prospective observational study. Cytokine. 2021;149: 155727. doi:10.1016/j.cyto.2021.155727

8. Surawicz B, Childers R, Deal BJ, Gettes LS, Bailey JJ, Gorgels A, et al. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part III: intraventricular conduction disturbances: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society. Endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol. 2009;53: 976–981. doi:10.1016/j.jacc.2008.12.013

9. Rautaharju PM, Surawicz B, Gettes LS. AHA/ACCF/HRS Recommendations for the Standardization and Interpretation of the Electrocardiogram: Part IV: The ST Segment, T and U Waves, and the QT Interval A Scientific Statement From the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society Endorsed by the International Society for Computerized Electrocardiology. Journal of the American College of Cardiology. 2009;53: 982–991. doi:10.1016/j.jacc.2008.12.014

10. Hancock EW, Deal BJ, Mirvis DM, Okin P, Kligfield P, Gettes LS, et al. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part V: electrocardiogram changes associated with cardiac chamber hypertrophy: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society. Endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol. 2009;53: 992–1002. doi:10.1016/j.jacc.2008.12.015

11. Wagner GS, Macfarlane P, Wellens H, Josephson M, Gorgels A, Mirvis DM, et al. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part VI: acute ischemia/infarction: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society. Endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol. 2009;53: 1003–1011. doi:10.1016/j.jacc.2008.12.016

12. Zema MJ, Collins M, Alonso DR, Kligfield P. Electrocardiographic Poor R-Wave Progression: Correlation with Postmortem Findings. CHEST. 1981;79: 195–200. doi:10.1378/chest.79.2.195

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ECG pathology and its association with death in critically ill COVID-19 patients, a cohort study

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PONE-D-21-28860R1

ECG pathology and its association with death in critically ill COVID-19 patients, a cohort study.

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