
==== Front
Ann Noninvasive Electrocardiol
Ann Noninvasive Electrocardiol
10.1111/(ISSN)1542-474X
ANEC
Annals of Noninvasive Electrocardiology
1082-720X
1542-474X
John Wiley and Sons Inc. Hoboken

10.1111/anec.70001
ANEC70001
ANEC-23-4816.R1
Original Article
Original Article
Monitoring of myocardial injury by serial measurements of QRS area and T area: The MaastrICCht cohort
Ghossein et al.
Ghossein M. A. MD https://orcid.org/0000-0003-2359-8167
1
de Kok J. W. T. M. MS 1 2
Eerenberg F. MD https://orcid.org/0009-0008-3500-549X
1
van Rosmalen F. PhD 1 2
Boereboom R. MS 2
Duisberg F. MS 2
Verharen K. MS 2
Sels J. E. M. MD, PhD 1 2 3
Delnoij T. MD https://orcid.org/0000-0003-2427-7613
2 3
Geyik Z. MD 2 3
Mingels A. M. A. MD, PhD 1 4
Meex S. J. R. MD, PhD 1 4
van Kuijk S. M. J. PhD 5
van Stipdonk A. M. W. MD, PhD 1 3
Ghossein C. MD, PhD 1 3 6
Prinzen F. W. MD, PhD 1
van der Horst I. C. C. MD, PhD https://orcid.org/0000-0003-3891-8522
1 2
Vernooy K. MD, PhD https://orcid.org/0000-0002-8818-5964
1 3
van Bussel B. C. T. MD, PhD https://orcid.org/0000-0003-1621-7848
1 2 7
Driessen R. G. H. MD, PhD https://orcid.org/0000-0002-8287-6166
1 2 3 rob.driessen@mumc.nl

1 Cardiovascular Research Institute Maastricht (CARIM) Maastricht University Maastricht The Netherlands
2 Department of Intensive Care Medicine Maastricht University Medical Center+ Maastricht The Netherlands
3 Department of Cardiology Maastricht University Medical Center+ Maastricht The Netherlands
4 Department of Clinical Chemistry, Central Diagnostic Laboratory Maastricht University Medical Center+ Maastricht The Netherlands
5 Clinical Epidemiology & Medical Technology Assessment (KEMTA) Maastricht University Medical Center+ Maastricht The Netherlands
6 School for Oncology and Developmental Biology (GROW) Maastricht University Maastricht The Netherlands
7 Care and Public Health Research Institute (CAPHRI) Maastricht University Maastricht The Netherlands
* Correspondence
R. G. H. Driessen, Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Email: rob.driessen@mumc.nl

04 9 2024
9 2024
29 5 10.1111/anec.v29.5 e7000103 5 2024
03 7 2023
14 7 2024
© 2024 The Author(s). Annals of Noninvasive Electrocardiology published by Wiley Periodicals LLC.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

Background

Manually derived electrocardiographic (ECG) parameters were not associated with mortality in mechanically ventilated COVID‐19 patients in earlier studies, while increased high‐sensitivity cardiac troponin‐T (hs‐cTnT) and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) were. To provide evidence for vectorcardiography (VCG) measures as potential cardiac monitoring tool, we investigated VCG trajectories during critical illness.

Methods

All mechanically ventilated COVID‐19 patients were included in the Maastricht Intensive Care Covid Cohort between March 2020 and October 2021. Serum hs‐cTnT and NT‐proBNP concentrations were measured daily. Conversion of daily 12‐lead ECGs to VCGs by a MATLAB‐based script provided QRS area, T area, maximal QRS amplitude, and QRS duration. Linear mixed‐effect models investigated trajectories in serum and VCG markers over time between non‐survivors and survivors, adjusted for confounders.

Results

In 322 patients, 5461 hs‐cTnT, 5435 NT‐proBNP concentrations and 3280 ECGs and VCGs were analyzed. Non‐survivors had higher hs‐cTnT concentrations at intubation and both hs‐cTnT and NT‐proBNP significantly increased compared with survivors. In non‐survivors, the following VCG parameters decreased more when compared to survivors: QRS area (−0.27 (95% CI) (−0.37 to −0.16, p < .01) μVs per day), T area (−0.39 (−0.62 to −0.16, p < .01) μVs per day), and maximal QRS amplitude (−0.01 (−0.01 to −0.01, p < .01) mV per day). QRS duration did not differ.

Conclusion

VCG‐derived QRS area and T area decreased in non‐survivors compared with survivors, suggesting that an increase in myocardial damage and tissue loss play a role in the course of critical illness and may drive mortality. These VCG markers may be used to monitor critically ill patients.

Graphical abstract of myocardial injury monitoring by serial measurements of VCG parameters.

electrocardiogram
intensive care monitoring
myocardial injury
serial
vectorcardiogram
source-schema-version-number2.0
cover-dateSeptember 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:04.09.2024
Ghossein, M. A. , de Kok, J. W. T. M. , Eerenberg, F. , van Rosmalen, F. , Boereboom, R. , Duisberg, F. , Verharen, K. , Sels, J. E. M. , Delnoij, T. , Geyik, Z. , Mingels, A. M. A. , Meex, S. J. R. , van Kuijk, S. M. J. , van Stipdonk, A. M. W. , Ghossein, C. , Prinzen, F. W. , van der Horst, I. C. C. , Vernooy, K. , van Bussel, B. C. T. , & Driessen, R. G. H. (2024). Monitoring of myocardial injury by serial measurements of QRS area and T area: The MaastrICCht cohort. Annals of Noninvasive Electrocardiology, 29 , e70001. 10.1111/anec.70001

B. C. T. van Bussel and R. G. H. Driessen contributed equally.
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pmc1 INTRODUCTION

In mechanically ventilated COVID‐19 patients, increasing concentrations of serum markers high‐sensitivity cardiac troponin‐T (hs‐cTnT) and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) over time have been associated with worsening disease and poor prognosis (An et al., 2021; Dawson et al., 2020; Ghossein et al., 2022; Guo et al., 2020; Habets et al., 2021; Shi et al., 2020; Stefanini et al., 2020). These markers predicted in‐hospital mortality independent of cardiovascular risk factors and pre‐existent cardiovascular disease (CVD) (An et al., 2021; Dawson et al., 2020; Ghossein et al., 2022; Guo et al., 2020; Habets et al., 2021; Shi et al., 2020; Stefanini et al., 2020). Daily ECG assessment in a relatively small cohort of mechanically ventilated COVID‐19 patients showed widespread ECG abnormalities. Indeed, important ECG abnormalities observed include RV‐strain characteristics, P‐wave splitting, QRS fragmentations, and changes reflective of myocardial ischemia/inflammation, including ST‐segment deviations and flat T waves (Ghossein et al., 2022). No meaningful differences during critical illness between non‐survivors and survivors were identified (Ghossein et al., 2022).

For the present study, we aimed at a more detailed cardiac phenotyping using the vectorcardiogram (VCG) in a larger cohort. The VCG combines data from the unidirectional 12‐lead ECG to provide 3D‐information about the electrical events within the heart. In cardiac resynchronization therapy (CRT), a higher value of VCG‐derived QRS area has been shown to provide CRT‐patient selection that is superior to guideline ECG‐derived QRS duration and QRS morphology (Emerek et al., 2019; Ghossein et al., 2021; Kabutoya et al., 2018; Marinko et al., 2022; Okafor et al., 2019; van Stipdonk et al., 2018). A previous study also showed that a lower QRS area was inversely related to a larger myocardial scar size (Nguyen et al., 2018).

In the present study, therefore, trajectories during critical illness in VCG‐derived markers QRS area, T area, and QRS T area were investigated for their ability to detect myocardial injury and to differentiate non‐survivors from survivors. For this purpose, myocardial serum biomarkers were additionally investigated. We hypothesized that the progression of COVID‐19‐related damage in the heart will result in a decreasing area of the QRS complex and possibly also of the T wave. The association between the development of QRS area—or any VCG‐derived marker—and mortality in mechanically ventilated patients with cardiac involvement has not been studied. Eight additional VCG‐derived markers were investigated for exploratory purposes and those additional results only serve to be hypothesis generating to direct future investigations into intensive care monitoring.

2 METHODS

The prospective observational Maastricht Intensive Care COVID (MaastrICCht) cohort is comprehensive and particularly designed to investigate serial data (Bels et al., 2021; Ghossein et al., 2022; Heines et al., 2022; Hulshof et al., 2021; Martens et al., 2022; Mulder et al., 2021; van de Berg et al., 2022) and includes all patients on mechanical ventilation for COVID‐19 admitted to the intensive care unit (ICU) of Maastricht University Medical Centre+ (MUMC+), the Netherlands (Trial Register number [NL8613]). The study protocol has been described previously in more detail (Tas et al., 2020) and has been approved by the institutional review board of Maastricht University Medical Center+ (MUMC+) (METc, 2020‐1565/300523). This manuscript was written following the “STrengthening the Reporting of OBservational studies in Epidemiology” (STROBE) guideline (von Elm et al., 2008).

2.1 Study population

The MUMC+ is a university teaching hospital in the Netherlands that provides tertiary care. All patients admitted to the ICU with respiratory insufficiency requiring mechanical ventilation, including at least one positive PCR for SARS‐CoV‐2 and/or a chest CT scan indicating a SARS‐CoV‐2 infection based on a CORADS‐score of 4–5 scored by a radiologist (Wang et al., 2020), were included. ECGs were converted into VCGs, and data on myocardial serum biomarkers were collected daily from intubation until death or ICU discharge. Patients were included from March 15th 2020, until October 1st 2021. Those transferred to other hospitals after ICU discharge were followed and classified as non‐survivor in case of in‐hospital death. Population characteristics and potential confounding variables have been described extensively elsewhere (Bels et al., 2021; Hulshof et al., 2021; Tas et al., 2020).

2.2 Myocardial serum biomarkers

The presence of myocardial injury and wall stress was investigated by daily assessment of serum samples of hs‐cTnT (ng/L) and NT‐proBNP (pmol/L), respectively, using the Cobas 8000 analyzer (Roche Diagnostics, Mannheim, Germany). In addition, serum creatinine (μmol/L) as the measure for kidney function was also measured daily on the Cobas 8000 analyzer.

2.3 VCG‐derived markers

Standard 12‐lead ECGs were performed daily at about the same time in the morning throughout patients' ICU stay until death or discharge and were recorded at 25 mm/s speed and 10 mm/mV amplitude. All ECGs were stored digitally as PDF files (MUSE Cardiology, GE Medical System) and were converted into three orthogonal leads (X‐, Y‐, and Z‐) using the Kors matrix (Kors et al., 1990), with a MATLAB‐based script (MathWorks Inc.) (Figure S1). When converting into VCG, the start and end of the QRS complex and the T wave in the superimposed X, Y, and Z leads were determined manually. Subsequently, a 3D electrical activation sequence of the ventricles is automatically constructed, and VCG‐derived markers, including markers derivable from the ECG, are automatically calculated. Electrophysiological markers derived from the construction of the VCG included in the main analysis are QRS T area (ventricular gradient), QRS area, T area (Figures S1 and S2), and maximal QRS amplitude, in addition to ECG markers QRS duration and QTc interval (Figure S2).

QRS T area was calculated as the sum of QRS area and T area, which were calculated as the sum of the integrals under the QRS complex and T wave in the orthogonal X, Y, and Z leads (Figure S1). QRS area was mainly determined by QRS duration and QRS amplitude, which were also semi‐automatically determined by the same software (Figures S1 and S2). QTc interval was investigated as this is the time component of the QRS‐T area (Figure S2).

2.4 Statistical analysis

All serially assessed VCG‐derived markers and myocardial serum biomarkers were collected and used for the current analysis. Pre‐existent CVD was defined based on the historical presence of cardiomyopathy, coronary artery disease, myocardial infarction, valvular disease, and/or any arrhythmia. VCG‐derived markers and serum biomarker data were analyzed based on days since intubation. When more than one value per day was available, the mean value was used. Hs‐cTnT and NT‐proBNP were log‐transformed to meet normality assumptions. For each model, all samples with missing values for any co‐variable used in that model were excluded. As only a few data were missing, the crude models had a slightly higher sample size than the adjusted models. Because the effect of a few missing patients on the results is minimal (Twisk, 2023), the choice was made not to impute the data.

Patients were categorized into non‐survivors and survivors. Relevant clinical characteristics were described and differences between non‐survivors and survivors were tested using the Wilcoxon signed‐rank test for continuous variables and Pearson's chi‐squared test for categorical variables. First, we aimed to replicate previous results with hs‐cTnT and NT‐proBNP as a means of cohort face validity (Ghossein et al., 2022). Linear mixed‐effects models were used to investigate the development of trajectories of myocardial serum biomarkers over time by modeling myocardial serum biomarkers, time and the interaction between myocardial serum biomarkers and time. We reported the difference at intubation and in trajectory over time for both hs‐cTnT and NT‐proBNP for the non‐survivors compared to survivors, with the latter as the reference category. Next, the crude models were adjusted for sex, age, body mass index (BMI), APACHE II score, diabetes mellitus, pre‐existent CVD, smoking, chronic lung disease and chronic kidney disease, as these confounders have been associated with both CVD and COVID‐19 disease severity (Grasselli et al., 2020; He et al., 2020; Hernández‐Garduño, 2020). Additionally, for hs‐cTnT and NT‐proBNP only, the model was further adjusted for dialysis and daily serum creatinine concentrations, as kidney function affects biomarker concentrations. An additional analysis was performed with exclusion of ECGs that were performed when patients were in prone position. For the main analyses, crude and adjusted linear mixed‐effect models, similar to those of myocardial serum biomarkers described above, except the model adjusted for daily creatinine concentrations, were used to compare the development of trajectories of the VCG‐derived markers QRS T area, QRS area, T area, maximal QRS amplitude, QRS duration, and QTc interval between non‐survivors and survivors. For illustration, we show the trajectories of myocardial serum biomarkers and VCG‐derived markers over time with their 95% confidence intervals for non‐survivors and survivors.

For reasons of exploration, eight additional VCG‐derived markers were investigated using similar linear mixed‐effect models (Table S1). Data were analyzed in R version 4.2.1 (R Core Team, 2020). We report regression coefficients β and 95% confidence intervals (95% CI). We considered two‐sided p‐values and interaction terms <.05 as statistically significant.

3 RESULTS

Of the 324 patients in the MaastrICCht cohort, 322 had at least one VCG‐derived marker or myocardial serum biomarker available and were included in the analyses (Figure 1). 126 patients (39%) were ICU non‐survivors. Compared with survivors, non‐survivors were older on average (67 ± 9 vs. 60 ± 12 years; p < .001), included fewer females (21% vs. 31%, p = .048), had a lower mean BMI (28 ± 5 vs. 30 ± 6 kg/m2; p = .020), and a higher mean APACHE‐II score (17 vs. 14; p < .001) (Table 1). In total, 320 patients had serial measurements of myocardial serum biomarkers available (of whom 125 (39%) died), and 286 patients had serial ECGs available that could be converted into VCGs (of which 115 (40%) died) (Figure 1). A total of 5461 hs‐cTnT and 5435 NT‐pro‐BNP serum samples and 3280 ECGs/VCGs were included.

FIGURE 1 Study population flowchart. Hs‐cTnT, high‐sensitive Troponine‐T; ICU, intensive care unit; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide; VCG, vectorcardiogram.

TABLE 1 Patient characteristics, stratified for non‐survivors and survivors.

	Patients (N = 322)	Non‐survivors (N = 126)	Survivors (N = 196)	p‐Value for difference	
Age (years)	63 (±11)	67 (±9)	60 (±12)	<.001	
Female sex (%)	86 (27%)	26 (21%)	60 (31%)	.048	
Body mass index (kg/m2)	29 (±5)	28 (±5)	30 (±6)	.020	
Diabetes Mellitus (%)	69 (21%)	27 (21%)	42 (21%)	1.000	
Smoking (%)	18 (6%)	8 (6%)	10 (5%)	.640	
Pre‐existent CVD a (%)	84 (26%)	38 (30%)	46 (24%)	.200	
Chronic lung disease (%)	52 (16%)	19 (15%)	33 (17%)	.680	
Chronic kidney disease (%)	10 (3%)	5 (4%)	5 (3%)	.470	
APACHE‐II score b	15 (±5)	17 (±5)	14 (±5)	<.001	
Hs‐cTnT (ng/L)	16 (9–43)	22 (11–53)	13 (8–25)	<.001	
NT‐proBNP (pmol/L)	73 (27–194)	95 (34–284)	58 (23–138)	<.001	
Data are presented as mean ± standard deviation, numbers and percentages, or as median with interquartile range. p‐Values tested using Wilcoxon signed‐rank test, Pearson's chi‐squared test, or Mann–Whitney U test.

a Pre‐existent CVD = cardiovascular disease, defined as a history of cardiomyopathy, coronary artery disease, myocardial infarction, valvular disease, and/or any arrhythmia.

b APACHE‐II, Acute Physiology And Chronic Health Evaluation, score based on age, body temperature, blood pressure, pH, heart rate, respiratory rate, sodium‐, potassium‐, and creatinine plasma levels, presence of acute kidney failure, hematocrit, white blood cell count, Glasgow Coma Scale, and need for oxygen supply.

3.1 Myocardial serum biomarkers

At intubation, median hs‐cTNT was 22 ng/L (interquartile range (IQR): 11–53 ng/L) in non‐survivors and 13 ng/L (IQR: 8–25 ng/L) in survivors. NT‐pro‐BNP was 95 pmol/L (IQR: 34–284 pmol/L) in non‐survivors and 58 pmol/L (23–138 pmol/L) in survivors (Table 1). After log‐transformation, hs‐cTnT was higher in non‐survivors at intubation and increased more over time in crude (Table 2) and adjusted models (Figure 2a, Table 2) than in survivors. In adjusted models, non‐survivors had a higher log hs‐cTnT of 0.27 ng/L (95% CI (0.01 to 0.52); p = .04) at intubation, that increased with 0.01 ng/L (0.00 to 0.02; p = .02) per day compared with survivors (Figure 2a; Table 2). After adjustment, log NT‐proBNP increased with 0.07 pmol/L (0.05 to 0.09, p < .01) per day in non‐survivors compared with a decrease in survivors (Figure 2b; Table 2). Adjustment for daily creatinine did not change the results (data not shown).

TABLE 2 Linear mixed‐effects models: Difference in myocardial serum biomarkers at intubation and their trajectories over time between non‐survivors and survivors.

Non‐survivors compared to survivors	Crude (N = 320)	Adjusted a (N = 312)	
β; [95% CI]; p‐value	β; [95% CI]; p‐value	
Log hs‐cTnT (ng/L)	
Average difference at intubation	0.52; [0.25 to 0.79]; <.01	0.27; [0.01 to 0.52]; .04	
Changes over time (per day)	0.01; [0.01 to 0.02]; <.01	0.01; [0.00 to 0.02]; <.01	
Interaction between group and time	0.01; [0.00 to 0.02]; .06	0.01; [0.00 to 0.02]; .02	
Non‐survivors compared to survivors	Crude (N = 314)	Adjusted a (N = 307)	
β [95% CI]; p‐value	β [95% CI]; p‐value	
Log NT‐proBNP (pmol/L)	
Average difference at intubation	0.33; [−0.02 to 0.68]; .06	−0.04; [−0.36 to 0.27]; .79	
Changes over time (per day)	−0.02; [−0.02 to −0.01]; <.01	−0.02; [−0.03 to −0.01]; <.01	
Interaction between group and time	0.07; [0.05 to 0.08]; <.01	0.07; [0.05 to 0.09]; <.01	
Note: Data are presented as regression coefficient β with 95% confidence interval (95% CI) representing differences in average biomarkers at time = 0 (intubation), the trajectories of biomarkers over time and the difference in change over time between non‐survivors and survivors (i.e., interaction between group and time), with survivors as reference category.

Abbreviations: Hs‐cTnT, high‐sensitive troponin‐T; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide.

a Adjusted for age, sex, body mass index, APACHE II score, diabetes mellitus, pre‐existent CVD, smoking, chronic lung disease, chronic kidney disease, dialysis, and daily serum creatinine concentrations.

FIGURE 2 Predicted values for Log hs‐cTnT (panel a) and log NT‐proBNP (panel b) over time for non‐survivors and survivors based on the adjusted mixed‐effect models. The adjusted* lines represent the average slope‐steepness (based on the regression coefficients = β of models in Table 2). The bands represent 95% confidence intervals (95% CI). Panel (a): Log hs‐cTnT shows a difference at intubation (timepoint 0) and in its trajectory with a significantly steeper increase over time in non‐survivors. Panel (b): NT‐proBNP shows no difference at intubation (time‐point 0), but an increase over time in non‐survivors and a decrease over time in survivors. *Adjusted for age, sex, body mass index, APACHE II score, diabetes mellitus, pre‐existent CVD, smoking, chronic lung disease, and chronic kidney disease.

3.2 VCG‐derived markers

None of the main VCG‐derived markers (QRS T area, QRS area, T area, QRS duration, maximal QRS amplitude, and QTc interval) differed significantly at intubation between non‐survivors and survivors in the adjusted models. Over time, however, the trajectories of all main markers, except QRS duration (Figure 3d, Table 3), differed between non‐survivors and survivors as follows. After adjustment and compared with survivors, in non‐survivors QRS T area decreased more by −0.54 μVs (−0.83 to −0.26, p < .01) per day, QRS area by −0.27 μVs (−0.37 to −0.16, p < .01), and T area by −0.39 μVs (−0.62 to −0.16, p < .01) per day (Figure 3a–c; Table 3). After adjustment, in non‐survivors maximal QRS amplitude decreased with −0.01 mV (−0.01 to −0.01, p < .01) per day (Figure 3e, Table 3). QTc interval decreased less in non‐survivors with 0.85 ms (0.14 to 1.55, p = .02) per day when compared to survivors (Figure 3f; Table 3). After exclusion of 519 ECGs that were taken on days of prone positioning in 162 patients, results remained similar (data not shown).

FIGURE 3 Predicted values for QRS T area (panel a), QRS area (panel b), T area (panel c), QRS duration (panel d), maximal QRS amplitude (panel e), and QTc interval (panel f) over time for non‐survivors and survivors based on the adjusted mixed‐effect models. The adjusted* lines represent the average slope‐steepness (based on the regression coefficients = β of models in Table 3). The bands represent 95% confidence intervals. Panel (a–c): Trajectories for QRS‐(T)‐area with a steeper decrease over time in non‐survivors compared with survivors; no difference at intubation (timepoint 0). Panel (d): No difference between the non‐survivors and survivors in QRS duration at intubation (timepoint 0) or in its trajectory over time. Panel (e): Trajectories for QRS amplitude with a decrease in non‐survivors and an increase in survivors; no difference at intubation (timepoint 0). Panel (f): Trajectories for QTc interval with a decrease in non‐survivors compared to survivors; no difference at intubation (timepoint 0). *Adjusted for age, sex, body mass index, APACHE II score, diabetes mellitus, cardiovascular risk factors, smoking, chronic lung disease, and chronic kidney disease.

TABLE 3 Linear mixed‐effects models: Difference in vector‐/electrocardiographic markers at intubation and their trajectories over time between non‐survivors and survivors.

Non‐survivors compared to survivors	Crude (N = 286) β; [95% CI]; p‐value	Adjusted a (N = 278) β; [95% CI]; p‐value	
QRS T area (μVs)	
Average difference at intubation	−5.92; [−12.58 to 0.74]; .08	−1.35; [−7.94 to 5.25]; .69	
Changes over time (per day)	−0.16; [−0.32 to 0.00]; .05	−0.11; [−0.27 to 0.05]; .19	
Interaction between group and time	−0.46; [−0.74 to −0.18]; <.01	−0.54; [−0.83 to −0.26]; <.01	
QRS area (μVs)	
Average difference at intubation	0.03; [−3.5 to 3.57]; .99	−0.14; [−3.93 to 3.66]; .94	
Changes over time (per day)	0.01; [−0.05 to 0.08]; .67	0.00; [−0.07 to 0.06]; .93	
Interaction between group and time	−0.28; [−0.39 to −0.17]; <.01	−0.27; [−0.37 to −0.16]; <.01	
T area (μVs)	
Average difference at intubation	0.84; [−3.41 to 5.09]: .70	1.49; [−2.84 to 5.83]; .50	
Changes over time (per day)	−0.13; [−0.26 to 0.01]; .06	−0.12; [−0.25 to 0.02]; .09	
Interaction between group and time	−0.37; [−0.59 to −0.14]; <.01	−0.39; [−0.62 to −0.16]; <.01	
QRS duration (ms)	
Average difference at intubation	5.66; [0.33 to 10.98]; .04	2.95; [−2.26 to 8.16]; .27	
Changes over time (per day)	−0.36; [−0.47 to −0.25]; <.01	−0.35; [−0.45 to −0.25]; <.01	
Interaction between group and time	0.10; [−0.09 to 0.29]; .29	0.11; [−0.07 to 0.28]; .23	
QRS amplitude (mV)	
Average difference at intubation	−0.02; [−0.11 to 0.06]; .59	−0.01; [−0.10 to 0.08]; .81	
Changes over time (per day)	0.01; [0.00 to 0.01]; <.01	0.00; [0.00 to 0.01]; <.01	
Interaction between group and time	−0.01; [−0.01 to −0.01]; <.01	−0.01; [−0.01 to −0.01]; <.01	
QTc interval (ms)	
Average difference at intubation	5.98; [−4.58 to 16.53]; .27	2.60; [−7.96v13.17]; .63	
Changes over time (per day)	−1.55; [−1.97 to −1.14]; <.01	−1.65; [−2.08 to −1.22]; <.01	
Interaction between group and time	0.75; [0.06 to 1.44]; .03	0.85; [0.14 to 1.55]; .02	
Note: Data are presented as regression coefficient (β) with 95% confidence interval (95% CI) representing differences in average biomarkers at time = 0 (intubation), the trajectories of VCG‐derived markers over time and the difference in change over time between non‐survivors and survivors (i.e., interaction between group and time), with survivors as reference category.

Abbreviations: Hs‐cTnT, high‐sensitive troponin‐T; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide.

a Adjusted for age, sex, body mass index, APACHE II score, diabetes mellitus, pre‐existent CVD, smoking, chronic lung disease, and chronic kidney disease.

3.3 Additional exploratory analyses

Exploration of eight additional VCG‐derived markers showed that in non‐survivors QT‐interval decreased 1.12 ms (0.32 to 1.93, p = .01) less over time compared with survivors (Table S1).

4 DISCUSSION

The present study on mechanically ventilated COVID‐19 patients provides evidence for a potential role of VCG‐derived trajectories for cardiac monitoring in critical illness and has two main findings. First, serial VCG analyses showed that trajectories of decreasing QRS area, T area, and QRS T area during critical illness are associated with mortality. The decrease in QRS area in non‐survivors compared with survivors appears to be mainly driven by a decrease in QRS amplitude over time, and not QRS duration. These results may suggest loss of myocardial tissue during critical illness that is associated with mortality. Second, we confirmed previously reported trajectories of myocardial serum biomarkers' association with mortality and showed that hs‐cTnT and NT‐proBNP increased more during critical illness in non‐survivors than survivors. Thus, serum biomarkers of myocardial injury and wall stress behave in a way that contributes to face validity when examining VCG‐derived markers and their associations with mortality. Taken together, this study suggests that loss of myocardial tissue (reflected by QRS‐(T)‐area decrease), increasing myocardial injury (hs‐cTnT increase), and increasing wall stress (NT‐proBNP increase) contribute to critical illness‐associated mortality. This suggests that more advanced cardiac monitoring during critical illness is required.

Previous studies that found an association between manually determined ECG alterations in COVID‐19 patients and in‐hospital mortality mainly included patients not admitted to the ICU (Angeli et al., 2020; Bergamaschi et al., 2021). In a smaller study, ECG alterations at admission and throughout ICU stay were present throughout the whole population, but no differences between non‐survivors and survivors were observed (Ghossein et al., 2022). This could mean that the ECG only detects myocardial injury associated with mortality early in the disease. When the patient reaches the stage of critical illness and mechanical ventilation, the results indicate that manual ECG assessment cannot differentiate between non‐survivors and survivors.

Compared to the ECG, the VCG goes one step further by calculating QRS amplitude in a 3D direction, thereby determining the true maximal QRS amplitude more precisely. In a previous study conducted on chronic heart failure patients, lower QRS area was associated with loss of viable myocardial tissue by myocardial scarring associated with myocardial infarction (Nguyen et al., 2018). QRS area's inverse association with myocardial scar size post‐infarction fits with less recent data that QRS amplitude on the ECG—as part of QRS area—decreases after myocardial infarction, as part of an overall loss of electromotive force or a slowing of conduction associated with loss of viable myocardial tissue (Goldberger et al., 1980). The amount of viable myocardial tissue is to be depolarized and repolarized, which explains why T area and, consequently, QRS T area show the same distinctive pattern between non‐survivors and survivors, similarly to that of QRS area. Thus, it is likely that the stronger increase in hs‐cTnT and concomitant stronger decrease in QRS area, T area, QRS T area, and maximal QRS amplitude in non‐survivors all reflect increased loss of viable myocardial tissue. Consequently this affects myocardial wall stress, which is reflected by an increase in NT‐proBNP that drives clinical deterioration in critical illness and likely increases the chance of death, at least in COVID‐19 patients.

With regard to future perspectives towards clinical implications, a VCG is easily constructed from a standard 12‐lead ECG, which only needs additional computer software (e.g., MATLAB, Mathworks Inc.), and could therefore be cost‐effective to implement in clinical practice. Furthermore, the VCG gives objective measurements without the confounding effect of the physician's subjective interpretation (van Stipdonk et al., 2019). Continuous VCG monitoring could be used to predict outcome in critically ill COVID‐19 patients and possibly in other groups of critically ill patients with cardiac involvement. Future studies need to validate these findings to establish a firm basis that myocardial damage, mirrored by the development of VCG‐derived markers and myocardial serum biomarker concentrations, is indeed an important contributor to mortality. Moreover, we need a deeper understanding of the cardiovascular mechanism by which either COVID‐19 or critical illness itself, or both, increases the risk of death so that advanced cardiac monitoring could be explored in a way to target critical illness favorably. For this reason, we have also reported on exploration of eight additional VCG‐derived markers for hypothesis‐generating purposes, of which only QT‐interval showed a significant difference during critical illness between non‐survivors and survivors.

This study has strengths and limitations. The main strength of this study is the prospective nature and the daily assessment of VCG‐derived markers, which included the automatic calculation of “classic” ECG markers, combined with the myocardial serum biomarkers over time. Also, we have extensively dealt with confounding factors by using adjusted models in all analyses. However, we cannot rule out residual confounding, for example that the decrease in QRS area associated with mortality, is driven by subcutaneous edema with fluid loading, that could have created a larger space between the ECG electrode and the heart reducing QRS amplitude. Nevertheless, the trajectory of QRS area could still be useful as an advanced monitoring variable on the ICU.

We chose to use a cohort of mechanically ventilated patients with COVID‐19. Therefore, our results cannot be generalized to other populations. Results for QRS area were similar when accounting for prone positioning, although such clinical interventions could be have increased measurement error. Furthermore, it was not always possible to perform an ECG on all patients. Nevertheless, we show that advanced cardiac phenotyping using serial assessments reveals potential monitoring variables associated with mortality in mechanical ventilation, which could have implications for more advanced cardiac monitoring in the ICU.

5 CONCLUSIONS

In mechanically ventilated COVID‐19 patients, decreasing QRS area, T area, and QRS T area during critical illness are associated with mortality, which is driven by decreasing QRS amplitude but not QRS duration. This is in line with the results that hs‐cTnT and consequently NT‐proBNP increase more during critical illness in non‐survivors than survivors. The results suggest that loss of myocardial tissue (QRS‐(T)‐area decrease) probably after myocardial injury (hs‐cTnT increase) may drive greater wall stress on the remaining tissue (NT‐proBNP increase), thereby each contributing to critical illness‐associated mortality. In this regard, the easily obtainable VCG should be investigated as an advanced cardiac monitoring tool for critically ill patients.

AUTHOR CONTRIBUTIONS

M. A. Ghossein: writing original draft, data collection, conceptualization. J. W. T. M. de Kok: formal analysis. F. Eerenberg: formal analysis, writing original draft, data collection. F. van Rosmalen: formal analysis. R. Boereboom: data curation. F. Duisberg: data curation. K. Verharen: data curation. J. E. M. Sels: supervision, review and editing. T. Delnoij: supervision, review and editing. Z. Geyik: supervision, review and editing. A. M. A. Mingels: formal analysis, review and editing. S. J. R. Meex: formal analysis, review and editing. S. M. J. van Kuijk: formal analysis, review and editing. A. M. W. van Stipdonk: formal analysis, review and editing. C. Ghossein: formal analysis, review and editing. F. W. Prinzen: supervision, conceptualization, review and editing. I. C. C. van der Horst: supervision, conceptualization, review and editing. K. Vernooy: supervision, conceptualization, review and editing. B. C. T. van Bussel: supervision, conceptualization, review and editing. R. G. H. Driessen: supervision, conceptualization, review and editing.

CONFLICT OF INTEREST STATEMENT

None to be declared.

ETHICS STATEMENT

The study has been approved by the Medical Ethical Committee of the MUMC+ (METc, 2020‐1565/300523) and conforms to the Declaration of Helsinki.

Supporting information

Appendix S1

ACKNOWLEDGMENTS

None.

DATA AVAILABILITY STATEMENT

The data underlying this article will be shared upon reasonable request to the corresponding author.
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