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\begin{document}

\title{Cardiac arrhythmias among hospitalized Coronavirus 2019 (COVID-19)
patients: prevalence, characterization, and clinical algorithm to
classify arrhythmic risk}



\author[1]{Moshe Rav Acha}%
\author[1]{Amir Orlev}%
\author[2]{Itay  Itzhaki}%
\author[1]{Frederic S Zimmerman}%
\author[1]{Bashar Fteiha}%
\author[1]{Davina  Bohm}%
\author[1]{Ramzi Kurd}%
\author[1]{Tal Y Samuel}%
\author[1]{Elad  Asher}%
\author[1]{Yigal Helviz}%
\author[1]{Michael Glikson}%
\author[1]{Yoav  Michowitz }%
\affil[1]{Shaare Zedek Medical Center}%
\affil[2]{Hebrew University Hadassah Medical School}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Objectives: A significant proportion of COVID-19 patients may have
cardiac involvement including arrhythmias. Although arrhythmia
characterization and possible predictors were previously reported, there
are conflicting data regarding the exact prevalence of arrhythmias.
Clinically applicable algorithms to classify COVID patients' arrhythmic
risk are still lacking, and are the aim of our study. Methods: We
describe a single center cohort of hospitalized patients with a positive
nasopharyngeal swab for COVID-19 during the initial Israeli outbreak
between 1/2/2020 --30/5/2020. The study's outcome was any documented
arrhythmia during hospitalization, based on daily physical examination,
routine ECG's, periodic 24-hour Holter, and continuous monitoring.
Multivariate analysis was used to find predictors for new arrhythmias
and create classification trees for discriminating patients with high
and low arrhythmic risk. Results: Out of 390 COVID-19 patients included,
28 (7.2\%) had documented arrhythmias during hospitalization, including:
23 atrial tachyarrhythmias, combined atrial fibrillation (AF) and
ventricular fibrillation, ventricular tachycardia storm, and 3
bradyarrhythmias. Only 7/28 patients had previous arrhythmias. Our study
showed significant correlation between disease severity and arrhythmia
prevalence (p\textless{}0.001) with a low arrhythmic prevalence among
mild disease patients (2\%). Multivariate analysis revealed background
heart failure (CHF) and disease severity are independently associated
with overall arrhythmia while age, CHF, disease severity, and arrhythmic
symptoms are associated with tachyarrhythmias. A novel decision tree
using age, disease severity, CHF, and troponin levels was created to
stratify patients into high and low risk for developing arrhythmia.
Conclusions: Dominant arrhythmia among COVID-19 patients is AF.
Arrhythmia prevalence is dependent on age, disease severity, CHF, and
troponin levels. A novel simple Classification tree, based on these
parameters, can discriminate between high and low arrhythmic risk
patients.%
\end{abstract}%



\sloppy




\textbf{WHAT'S KNOWN?}

\begin{itemize}
\tightlist
\item
  A significant proportion of COVID-19 patients may have cardiac
  involvement including arrhythmias.
\item
  There is a correlation between disease severity in general and cardiac
  involvement specifically to occurrence of cardiac arrhythmias.
\item
  Arrhythmia characterization and possible predictors.
\end{itemize}

\textbf{WHAT'S NEW?}

\begin{itemize}
\tightlist
\item
  Using a 24-hour Holter monitoring among hospitalized COVID-19
  patients, for better arrythmias detection.
\item
  Among of all hospitalized COVID-19 patients, 7.2\% had new arrhythmias
  during hospitalization.
\item
  Classification tree which discriminate between high and low arrhythmic
  risk patients
\end{itemize}



\textbf{Introduction}

During the last year the world has faced a new Corona virus pandemic,
named COVID-19. Although COVID-19 clinical manifestations are mainly
respiratory, cardiac involvement exists in a substantial number of
patients, and cardiac arrhythmias have been reported in variable percent
of patients, ranging from 6\% to 19\% of overall hospitalized COVID
patients and from 9\% to 44\% of COVID patients hospitalized in
intensive care units (ICU).\textsuperscript{1,2,3} In some patients,
arrhythmias represent the first clinical manifestation of COVID
infection,\textsuperscript{4,5} while in others arrhythmia may occur
only during the recovery out-of-hospital
period.\textsuperscript{6}Overall, there seems to be a correlation
between disease severity in general and cardiac involvement specifically
to occurrence of cardiac arrhythmias,\textsuperscript{2,4,6} although
sudden cardiac death was described among mild COVID-19 patients as
well.\textsuperscript{4}Moreover, a significant minority of severe COVID
ICU patients with arrhythmias had normal troponin levels, suggesting
other mechanisms rather than cardiac injury, triggering arrhythmias in
these patients.\textsuperscript{3,7} Various potential causes for
cardiac involvement and arrhythmias were suggested, including: a) direct
viral myocardial injury- myocarditis,\textsuperscript{2,4,6} b)
cytokine-induced myocardial injury as well as cytokine-mediated
conduction disturbances\textsuperscript{8,9} and QT
prolongation;\textsuperscript{3} c) hypoxemia, d) hypotension, e)
hyper-adrenergic state, f) drug effect, such as hydroxychloroquine or
azithromycin , which can lead to prolonged QT-related Torsades de
Pointes as well as to nodal and infra-nodal conduction
disturbances.\textsuperscript{4,10,11} Although much data has
accumulated regarding various cardiac complications in COVID-19, the
characterization and exact prevalence of cardiac arrhythmias among
COVID-19 patients and more so among the mild-moderate ones is still
unclear.\textsuperscript{4,6} Furthermore, although various possible
predictors for cardiac arrhythmias in COVID patients were
speculated,\textsuperscript{2,4,6,7,9} a simple clinical algorithm to
classify arrhythmic risk has not been developed so far. Such algorithm
is critical to guide cardiac monitoring level during the hospitalization
period and maybe during the recovery period as well. The primary aim of
our study was to characterize the various arrhythmias encountered among
hospitalized COVID-19 patients of various severity levels, describe
their prevalence and develop a clinical algorithm to classify arrhythmic
risk.

\textbf{Methods}

\textbf{Study configuration and patients}

Retrospective analysis of a single center cohort of hospitalized
COVID-19 patients. A computer-based search of all patients who were
hospitalized in Shaare Zedek medical center in Jerusalem during the
period of 1/2/2020 -- 30/5/2020 (including those admitted to regular
corona departments as well as those admitted or transferred to corona
intensive care unit) with nasopharyngeal swab PCR positive for severe
acute respiratory syndrome-coronavirus 2 (SARS-Cov -2), who were at the
age of [?]18 years old at the day of admission. Notably, the research
period represents the period of the first COVID-19 outbreak in Israel.

\textbf{Study outcomes}

The study primary outcome was any arrhythmia documented during the
hospitalization. The search for arrhythmias was based on review of all
patients' medical records, 12-lead ECG's, 24-hour Holters or continuous
ECG monitoring (telemetry).

Our secondary aim was to evaluate possible predictors for arrhythmia
occurrence, with regard to:

\begin{enumerate}
\tightlist
\item
  Patients' medical history; including age, gender, hypertension (HTN),
  diabetes mellitus (DM), ischemic heart disease (IHD), background
  congestive heart failure (CHF), and previous documented arrhythmia
\item
  Presenting ''arrhythmic'' symptom as palpitations or syncope
\item
  Disease severity. Disease severity was categorized primarily according
  to the patients' respiratory status, and categorized as mild for those
  who did not need any O\textsubscript{2} support during their
  hospitalization; moderate for those who required some
  O\textsubscript{2} support via nasal cannula or mask; severe for those
  who required non-invasive ventilation (high-flow, CPAP, etc); and
  critical for those who needed mechanical ventilation or alternatively
  those with multi-organ failure who were supported by inotropes.
  Notably, disease severity was determined according to the patients'
  worst respiratory or hemodynamic status during their hospitalization
  period.
\item
  Presence of cardiac injury as assessed by high-sensitive cardiac
  troponin-I (hsTnI) and/or Trans Thoracic Echocardiography (TTE).
\item
  Inflammatory status based on D-DIMER and CRP lab measurements
\item
  QTc duration, as evidenced by review of all patients ECG's performed
  during the hospitalization period, determining the ECG with longest
  QTc duration. Notably, to look for other potential contributors for
  prolonged QTc we documented the patient's electrolytes and medications
  around the day of longest QTc ECG. The QTc was calculated using the
  Bazett formula. In patients with heart rate of less than 60 QT and not
  QTc was measured.
\item
  COVID-19 treatments, especially regarding use of Hydroxychloroquine
  and Azithromycin and other QT-prolonging medications
\end{enumerate}

\textbf{ECG, Holter monitoring, and laboratory data}

- Physical examination including checking for heart regularity and rate
was done on daily basis. - Routine ECG was performed on admission and
whenever new cardiac symptoms appeared or an arrhythmia was suspected on
physical examination or telemetry. - Laboratory examination was not done
routinely but on clinical basis for patients with cardiac symptoms,
general deterioration and before treatment with Hydroxychloroquine
and/or Azithromycin. . - 24-hour Holter monitoring was performed in a
minority of patients, primarily those with evidence of cardiac injury,
abnormal ECG on admission, or those with significant illness.

\textbf{Statistical analysis}

Categorical variables summarized as frequency and percentage. Continuous
variables were evaluated for normal distribution using histograms and
Q-Q plots and reported as median and interquartile range. Independent
samples T-test and Mann-Whitney test were applied to compare between
patients with and without new arrhythmia. Chi-square test and Fisher's
exact test were used to compare categorical variables. Multi variate
Logistic regression using forward stepwise (likelihood ratio) selection
method (p\textless{}0.05 was used as criteria for inclusion) was used to
explore variables associated with new arrhythmia. Variables available
for inclusion were: age, gender, BMI, history of HTN, DM, IHD, CHF,
valvular heart disease, lung disease, presentation with syncope or
palpitations, past arrhythmia, and COVID-19 illness severity.
Classification trees were applied to identify subgroup of patients that
are in increased risk for arrhythmia. Classification and Regression Tree
(CART) and Chi-square Automatic Interaction Detection (CHAID) growing
algorithms were used. The following variables were available for the
classification trees: age, gender, previous history of HTN, DM, IHD,
CHF, valvular heart disease, lung disease, presentation with syncope or
palpitations, past arrhythmia, and COVID-19 illness severity. A second
model included also hsTnI and CRP levels. All statistical tests were 2
sided and p\textless{} 0.05 was considered statistically significant.
SPSS software was used for all statistical analyses (IBM SPSS statistics
for windows, version 24, IBM cooperation, Armonk, NY, USA, 2016).

The study was approved by the hospital ethical committee.

\textbf{Results}

\textbf{Study population}

After exclusion of 6 patients whose age was less than 18 years old, the
study included 390 patients with confirmed COVID-19 infection. Median
patients age was 57.5 years (IQR 43-74.3 years), the prevalence of HTN,
DM, ischemic heart disease (IHD) and CHF was 30\%, 20.3\%, 8.7\% and
6.4\%, respectively. Twenty-six patients had a past history of
arrhythmias: 2 with bradyarrhythmias due to a previous atrioventricular
(AV) block with an implanted pacemaker for both; and 24 with
tachyarrhythmias including atrial fibrillation (AF) in 21 (4 persistent
and 17 paroxysmal), ventricular fibrillation in 1 patient {[}(patient
with dilated cardiomyopathy implanted with an implantable
cardioverter-defibrillator (ICD){]}, supraventricular tachycardia ( SVT)
in 1 patient and atypical atrial flutter in 1 patient. On presentation,
most patients had respiratory symptoms (67.7\%) while a minority
presented also with either palpitations or syncope (4.4\%). Thirty-eight
(9.7\%) patients were hospitalized in intensive care unit (including 8
with new arrhythmias). Relevant blood test, ECG findings and
distribution of disease severity are further presented in Table 1.
Twenty-nine (7.4\%) patients died during the index hospitalization.

\textbf{New arrhythmias}

Of the study cohort, 28/390 patients (7.2\%) presented or developed new
arrhythmias during hospitalization (Table 2). Seven of these 28 patients
(25\%) had previous documented arrhythmias. There were 3 cases of
bradyarrhythmias and 25 tachyarrhythmias. The first bradyarrhythmic
patient was a 92-year-old woman who presented with complete AV block
associated with a QT of 550 ms and normal electrolytes; the second case
was a 51-year-old man who presented with 2:1 AV block with left bundle
branch block (LBBB) and developed complete AVB within hours from his
admission. Notably, the patient had narrow complex QRS on a clinic visit
two years earlier. Both patients did not receive any negative
dromotropic drugs; laboratory examinations revealed normal electrolytes,
mildly elevated hsTnI (100 and 83 ng/L, respectively; normal values for
men women \textless{} 35ng/L and 20ng/L, respectively) and elevated CRP
(15 and 4.5 mg/dl, respectively; normal value \textless{} 0.5 mg/dL).
Both had pacemakers implanted during their index hospitalization. A
third patient was a 33 year-old man who presented in sinus rhythm
without any conduction abnormality and who developed on day 2 transient
sinus bradycardia with slow ventricular escape rhythm that resolved
within a few hours (Figures 1A and 1B). Notably, the patient's
O\textsubscript{2} saturation at that time was 84\% and later rose to
90\%, his electrolytes were normal and no drugs with negative
chronotropic effects were given. Laboratory examinations revealed
elevated CRP of 16.58 mg/dl and normal hsTnI (10 ng/L).

There were 25 patients with new tachyarrhythmias: 24 of them had new
atrial tachyarrhythmias including 20 with paroxysmal AF. Of the 24
patients with atrial tachyarrhythmias, 7 had a prior history of similar
arrhythmias. Notably, during his index hospitalization, one of these
patients with new-onset AF also had a documented ventricular
fibrillation which was successfully cardioverted (Table 2). This patient
did not have prolonged QT and his electrolytes were normal; however, he
did have elevated hsTnI of 7049 ng/L and mildly elevated CRP (6.8
mg/dl). Three patients had atrial flutter and 1 patient had an SVT
episode (narrow complex regular tachycardia of 195 bpm seen on monitor
and terminated with a Valsalva maneuver). Lastly, there was a patient
with ventricular tachycardia (VT) storm (Table 1). This patient had an
ischemic cardiomyopathy for which he was previously implanted with an
ICD. He presented with normal sinus rhythm, however, a week later had
respiratory deterioration necessitating mechanical ventilation. He was
transferred to ICU where he developed ventricular bigeminy (Figures 1 C
and 1D) which deteriorated within an hour to recurrent monomorphic VT
episodes. The patient received multiple ICD therapies and his VT's
eventually resolved after intravenous lidocaine and amiodarone. Notably,
the patient did not receive ionotropic medications, his QTc was stable
around 480 ms and electrolytes were normal, hsTnI increased to 17,578
ng/L and his maximal CRP was 16.58 mg/dL. Importantly, none of the new
arrhythmias were discovered by Holter.

\textbf{Comparison of patients with and without new arrhythmia (overall
arrhythmias)}

Comparison of the 28 patients with new arrhythmias with those who did
not experience any arrhythmia is presented in Table 1. Patients with new
arrhythmia were older and had more comorbidities as DM, HTN, IHD and
valvular heart disease. Also, their disease was more significant, with
28.6\% and 17.9\% of patients categorized as critical or severe disease
in the new arrhythmia group compared with 7.2\% and 8.8\% in the non
arrhythmia group, respectively (p \textless{} 0.001). Among the new
arrhythmia group there were more patients presenting with respiratory
symptoms and lower proportion of patients who had mild illness (14.3\%
versus 55\%, p \textless{} 0.001). The new arrhythmia group patients
were more likely to receive a QT prolonging drug, mostly an
antiarrhythmic agent, while there was no difference regarding percent of
patients on ''anti-COVID'' medications as Hydroxychloroquine and/or
Azithromycin. On ECG, the QRS width and QTc were longer in patients with
new arrhythmias and the prevalence of bundle branch block was higher.
Laboratory tests of patients with arrhythmia also reflected a more
severe illness, with higher creatinine, troponin, CRP and D-Dimer
levels. Noteworthy, there was a significantly increased mortality among
the arrhythmia group (32.1\% versus 5.5\%, p \textless{} 0.001).

\textbf{Comparison of patients with and without new tachyarrhythmias}

Comparison of patients with new tachyarrhythmias with the no arrhythmias
group (Supplementry Table 1) shows that patients with new
tachyarrhythmias (n=25, 6.4\%) were older, had more comorbidities, and
experienced a more severe disease. Rate of respiratory symptoms was
higher among patients with new tachyarrhythmias, however it was not
statistically significant (p=0.07). As expected, more patients with new
tachyarrhythmias received a QT prolonging drug which included mainly
anti-arrhythmic medications. Differences in other ECG and laboratory
results are further described in Supplementary table 1.

\textbf{Multivariate Logistic regression model for new arrhythmia and
new tachyarrhythmias.}

Multivariate logistic regression models were conducted to explore
variables that were associated with new arrhythmias and new
tachyarrhythmias. As presented in Figure 2, background CHF (OR 4.66, 95
CI 1.45-14.97, p=0.01) and disease severity (p=0.003) were associated
with overall new arrhythmias. A stepwise increase in odds ratio (OR) for
overall new onset arrhythmia was seen with increased disease severity.
As compared with mild disease, patients with moderate, severe and
critical disease had OR of 4.81, 8.86 and 15.79 to develop new
arrhythmia, respectively.

Factors associated with new tachyarrhythmias are presented in Figure 3.
Age, background CHF, presentation with syncope or palpitations and
disease severity were significantly associated with new tachyarrhythmia
in the multivariate analysis.

\textbf{Classification trees.}

Classification trees were applied in order to identify subgroup of
patients who are at low or high risk for arrhythmia. Figure 4
demonstrates a classification tree incorporating baseline patients'
characteristics including prior arrhythmias, as well as clinical
symptoms and disease severity. As seen, the first division for
discriminating patients with and without new arrhythmia is based on age.
Patients aged [?] 70 compared to \textless{} 70 years had 18.1\% vs. 2.6\%
prevalence of arrhythmias, respectively. The next divisions of the model
include illness severity and background CHF. The highest prevalence of
arrhythmias was 33.3\% seen in patients aged [?] 70 years and critical
disease severity. The lowest prevalence of 1.6\% was seen among patients
aged \textless{} 70 years with mild to moderate disease severity. A
second classification tree incorporated the same clinical parameters of
the first tree with addition of hsTnI levels and CRP (Figure 5). Of
note, hsTnI levels were measured in 219/390 patients (56\%). As seen in
Figure 5, hsTnI was the best discriminator for the new arrhythmia
occurrence and obviated the contribution of other clinical parameters.
Patients with negative or not measured hsTnI had an arrhythmia
prevalence of 2.1\% compared to patients with troponin [?] 48 ng/L who had
an arrhythmia prevalence of 34.1\%.

\textbf{Discussion}

Our study including a single center cohort of 390 COVID-19 patients
revealed 28/390 (7.2\%) cases with documented cardiac arrhythmias,
including 20 cases of AF, 3 atrial flutter, 1 VF (in a patient who also
had paroxysmal AF), 1 VT case and 3 bradyarrhythmic cases. The study
showed significant correlation between disease severity and arrhythmia
prevalence, revealing significant increase in arrhythmia prevalence with
increasing disease severity {[}11/116 (9.5\%); 5/37 (13.5\%); 8/34
(23.5\%) for moderate, severe, and critical severity, respectively,
p\textless{} 0.001{]} with a very low prevalence of arrhythmias among
patients with mild COVID-19 disease (4/203; 2\%). Multivariate forward
regression analysis showed that background CHF and disease severity are
independently associated with overall arrhythmias while age, background
CHF, disease severity, and arrhythmic symptoms (syncope or palpitations)
are associated with tachy-arrhythmias. Importantly, a classification
tree using specific age (\textgreater{}70 years old) and troponin ([?] 48
ng/L) cutoffs, as well as disease severity, and history of CHF could
further stratify patients into high and low risk for developing
arrhythmia. To our knowledge, these relevant cutoffs were not previously
described and our presented classification tree is novel in this regard.

The overall arrhythmia prevalence in our study was 7.2\%, which is lower
than reported in some studies\textsuperscript{1,12} and similar to
others.\textsuperscript{13,14} Notably, the initial study from China
reported an overall prevalence of 16.7\% among 138 hospitalized COVID-19
patients in Wuhan,\textsuperscript{1} but there was a significant
difference of arrhythmia incidence between non-ICU and ICU patients
(6.3\%, vs. 44.4\%; p 0.001) and their relatively high overall
prevalence was clearly influenced by the high number of ICU patients
(36/138, 26\%) in whom there was very high prevalence of arrhythmias.
Similarly, among 115 American COVID-19 patients,\textsuperscript{12} a
dramatic change in arrhythmia prevalence was noticed between non-ICU and
ICU patients (0\% versus 27.5\%, p 0.0002), with an overall arrhythmia
prevalence of 16.5\% given the fact that 69/115 study patients (60\%)
were ICU patients.\textsuperscript{12} In contrast, an Italian study
focusing on 132 stable non-ICU COVID-19 patients, reported 12/132 (9\%)
new arrhythmias documented during the admission
period.\textsuperscript{13} Our overall arrhythmic prevalence is similar
to Wuhan's non-ICU group and to the Italian stable non-ICU patients,
probably reflecting the low percentage of ICU patients (9.7\%) in our
cohort. Notably, the arrhythmic prevalence among our ICU patients was
significantly greater than our non-ICU patients (21\% versus 5.7\%; p
0.003), in accordance with the previous studies. Similar findings were
reported in a single center study including 700 patients (11\% ICU
patients), revealing an overall arrhythmia prevalence of 7.5\% with
marked difference of arrhythmia prevalence between ICU and non-ICU
patients.\textsuperscript{14} Lastly, the low arrhythmic prevalence
among the non-ICU group compared with the ICU one, suggests that need
for mechanical ventilation or presence of multi-organ dysfunction
necessitating ionotropic support, which characterized our ''critical''
severity and most of our ICU patients, have major impact on arrhythmic
prevalence as was suggested by previous
studies.\textsuperscript{1,6,9,12.14}

AF or atrial flutter was the dominant arrhythmia in our study, occurring
for 23/28 (82\%) of cases with documented arrhythmia, while only a
minority of these cases had previous arrhythmias (5/23, 22\%). This
result is in line with multiple previous studies, revealing AF to be the
most prevalent arrhythmia in COVID-19
disease.\textsuperscript{1,6,12,13,14,15} The absence of previous atrial
arrhythmias in most of these patients and the fact that some of these
new arrhythmia was timely correlated with respiratory deterioration, may
suggest an association or perhaps even a causative relation between
COVID illness and arrhythmia development. Indeed, some have suggested
that the increased cytokine levels among severe COVID patients may
trigger atrial arrhythmias.\textsuperscript{7,8} Nevertheless, one
should not interpret the above results as a definite cause and effect
relation between COVID disease and AF, as most of these patients are
elderly with multiple comorbidities as HTN, diabetes and IHD and most if
not all have significant respiratory symptoms, all of which are well
known risk factors for AF occurrence. Thus, we like
others,\textsuperscript{13,14} find it hard to confirm a necessary
pathophysiologic link between AF occurrence and COVID-19 infection.
Regardless of whether AF is a direct result of COVID or circumstantial
to the multiple comorbidities of these patients, multiple publications
suggest AF to predict bad prognosis among COVID-19
patients.\textsuperscript{8,12,14,16}

There were 3 cases with new onset bradyarrhythmias in our study,
including two cases with advanced AVB and one with a transient slow
ventricular escape rhythm. Notably, 2 of these cases were relatively
young male patients (50 and 33 year old) without prior conduction
abnormalities who presented with advanced AVB or developed a slow
ventricular escape rhythm, respectively. Both did not receive any
negative dromotropic or chronotropic drug. Both had normal electrolytes
and markedly elevated inflammatory markers with mildly increased or
normal hsTnI. Accordingly, we raise the possibility, which we cannot
prove, that these bradyarrhythmias had been related to the cytokine
storm and exacerbated inflammatory state associated with COVID-19
illness. Interestingly, inflammatory-mediated conduction abnormalities
have been suggested in previous reports of COVID-19 patients who
developed otherwise unexplained AVB during the acute COVID illness, with
markedly elevated inflammatory markers.\textsuperscript{8,9}

Disease severity, categorized by the level of respiratory support
needed, was the most robust clinical predictor for new-onset arrhythmia
among COVID-19 patients in our study. This correlation was shown in most
previous studies, with special attention to the remarkably high
arrhythmia prevalence among COVID-19 ICU patients, many of whom were
mechanically ventilated and/or with multi-organ failure necessitating
inotropic support.\textsuperscript{1,2,3,12,14} The other clinical
predictor for new-onset overall arrhythmias was background CHF, which is
well-established risk factor for atrial and ventricular
arrhythmias.\textsuperscript{17,18}

Regarding laboratory predictors, previous studied suggested both
myocardial injury as well as inflammatory process to have impact on
arrhythmic prevalence or even underlie arrhythmia
development,\textsuperscript{7,12} with the notion of ''inflammatory and
cardiac injury mediated atrial tachycardia and conduction
disturbances''.\textsuperscript{7,8,9} Indeed, in our study both hsTnI
and CRP were significantly associated with new arrhythmias in the
univariate analysis. However, in the classification tree model, hsTnI
per se was found to best differentiate between low and high arrhythmic
activities (Fig 5). This is in line with many previous studies
suggesting myocardial injury, assessed via troponin levels, to correlate
with need for mechanical ventilation, ICU admission, mortality and
arrhythmias.\textsuperscript{1,2,3,4,9,19}

Importantly, our classification trees (Fig 4\&5) are noval and
clinically relevant as they can discriminate between patients with high
and low risk for new onset arrhythmia. Although, some of these variables
as age and troponin are clinically intuitive and were previously
reported,\textsuperscript{2,4,9,14} no cutoffs were previously given to
guide clinical decisions. Accordingly, we suggest that patients whose
age is below 70 with mild to moderate disease (1.6\% arrhythmic
prevalence) or with negative hsTnI (2.1\% arrhythmic prevalence) may be
hospitalized to a ''non-monitored bed'' and even discharged early; while
patients aged [?]70 (18.1\% arrhythmic prevalence) or patients with hsTnI
[?] 48 ng/L (34.1\% arrhythmic prevalence) should be carefully monitored
for the occurrence of arrhythmias. These algorithms might be of crucial
importance to direct optimal utilization of medical staff and monitoring
equipment in COVID-19 outbreaks, especially in areas with limited
medical resources.

\textbf{Conclusion}

Arrhythmias, mostly AF episodes, may be seen in 7.2\% of hospitalized
patients with COVID-19 disease. Baseline clinical factors including age
[?] 70, disease severity and hsTnI levels may separate between patients
with high and low probability for arrhythmias and thus dictate further
level of patient monitoring during hospitalization, or on the other hand
in the low risk patients, the ability for early patient discharge.

\textbf{Limitations}

The main limitation of our study is its retrospective nature.
Nevertheless, given the unexpected nature of this pandemia, prospective
study would not be realistic. Other limitations includes the fact that
not all hospitalized COVID-19 patients underwent laboratory measurements
of troponin and/or CRP, thus, having a built-in bias for patients with
cardiac symptoms or higher disease severity levels. Moreover, since only
part of our patients underwent a 24-hour Holter and continuous ECG
monitoring, some transient arrhythmias might have been missed on
examination and 12-lead ECG's. Lastly, we do not have post-discharge
follow-up data; thus, our study has probable some underestimation of
true COVID-related arrhythmia prevalence, as some arrhythmia could
probably develop post discharge.

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\textbf{Figure legends}

\textbf{Figure 1:} \textbf{ECG example of 2 patients with new
arrhythmias} .

(A) and (B) ECG of a 33-year-old patient. On 8:22 the ECG shows normal
sinus rhythm with heart rate of 90 beats per minute (bpm) (A). Ninty
minutes later while awake and with low O\textsubscript{2} saturation he
developed extreme sinus bradycardia with ventricular escape (B). ECG
done a few hours later (not shown) demonstrated return of sinus at 90
bpm. (C) ECG during sinus rhythm of 78 bpm showing RBBB of a patient who
later exhibited VT storm. (D) ECG a day later showed sinus rhythm and
ventricular bigeminy. Few hours later the patient developed VT storm.
(see text for further discussion on both patients).

\textbf{Figure 2: Multi variate Logistic regression analysis for the
occurrence of new arrhythmia.} History of CHF and disease severity in a
step wise fashion were independently associated with the occurrence of
new arrhythmias. OR= odds ratio.

\textbf{Figure 3: Multi variate Logistic regression analysis for the
occurrence of new tachyarrhythmia.} Age, history of CHF, symptoms of
palpitations or syncope and disease severity were independently
associated with the occurrence of new tachyarrhythmias. OR= odds ratio.

\textbf{Figure 4: Classification tree for new arrhythmia based on
clinical parameters.} Age and disease severity level were found to
discriminate best new arrhythmia prevalence. Age \selectlanguage{english}[?] 70 was associated
with 18.1\% prevalence of new arrhythmia, while age \textless{} 70 in
patients with mild-moderate disease was associated with low (1.6\%)
prevalence. NL= normal, no arrhythmia; NA= new arrhythmia; CHF=
congestive heart failure.

\textbf{Figure 5: Classification tree for new arrhythmia based on
clinical and laboratory parameters.} This classification tree
incorporated the same clinical parameters as in first tree with the
addition of hsTnl and CRP as potential variables. In this model HsTnl
(Normal \textless{}35 ng/L) was found as the best discriminator for
arrhythmia prevalence; as HsTnl \selectlanguage{english}[?] 48 ng/L was associated with 34.1\%
prevalence of new arrhythmia, while in patients with negative or
not-measured hsTnl the prevalence was 2.1\%. HsTnl= high sensitivity
troponin-I; NA= new arrhythmia.

\textbf{Hosted file}

\verb`Tables.docx`  available at \url{https://authorea.com/users/360522/articles/482187-cardiac-arrhythmias-among-hospitalized-coronavirus-2019-covid-19-patients-prevalence-characterization-and-clinical-algorithm-to-classify-arrhythmic-risk}

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\verb`Figure 1.pptx`  available at \url{https://authorea.com/users/360522/articles/482187-cardiac-arrhythmias-among-hospitalized-coronavirus-2019-covid-19-patients-prevalence-characterization-and-clinical-algorithm-to-classify-arrhythmic-risk}\selectlanguage{english}
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\textbf{Hosted file}

\verb`figure 4.pptx`  available at \url{https://authorea.com/users/360522/articles/482187-cardiac-arrhythmias-among-hospitalized-coronavirus-2019-covid-19-patients-prevalence-characterization-and-clinical-algorithm-to-classify-arrhythmic-risk}\selectlanguage{english}
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