LENGTH OF HOSPITAL STAY AND SURVIVAL OF HOSPITALIZED 
COVID-19 PATIENTS DURING THE SECOND WAVE OF THE PANDEMIC:  
A SINGLE CENTRE RETROSPECTIVE STUDY FROM SLOVENIA
Aleš ROZMAN1,3*, Boštjan RITUPER1,2, Mark KAČAR1,3, Peter KOPAČ1,3, Mihaela ZIDARN1,3, Maja POHAR PERME4
1University Clinic of Respiratory and Allergic Diseases Golnik, Golnik 36, 4204 Golnik, Slovenia
2University of Ljubljana, Medical Faculty, Institute of Pathophysiology, Zaloška cesta 4, 1000 Ljubljana, Slovenia
3University of Ljubljana, Medical Faculty, Vrazov trg 2, 1000 Ljubljana, Slovenia
4University of Ljubljana, Medical Faculty, Institute for Biostatistics and 
Medical Informatics, Vrazov trg 2, 1000 Ljubljana, Slovenia
Received: Jan 30, 2022
Accepted: Jul 28, 2022
Original scientific article
*Corresponding author: Tel. + 386 41 313 811; E-mail: ales.rozman@klinika-golnik.si
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TRAJANJE HOSPITALIZACIJE IN UMRLJIVOST HOSPITALIZIRANIH 
BOLNIKOV S COVID-19 MED DRUGIM VALOM PANDEMIJE: 
RETROSPEKTIVNA RAZISKAVA SLOVENSKE KLINIKE
© National Institute of Public Health, Slovenia. 
Rozman A, Rituper B, Kačar M, Kopač P Zidarn M, Pohar Perme M. Length of hospital stay and survival of hospitalized COVID-19 patients during the second wave of 
the pandemic: a single centre retrospective study from Slovenia. Zdr Varst. 2022;61(4):201-208. doi: 10.2478/sjph-2022-0027.
ABSTRACT
Keywords: 
COVID-19, mortality, 
length of stay, 
intensive care units, 
hospital wards
IZVLEČEK
Ključne besede: 
COVID-19, umrljivost, 
trajanje hospitalizacije, 
intenzivni oddelki, 
bolnišnični oddelki
Background: As of writing, there are no publications pertaining to the prediction of COVID-19-related outcomes 
and length of stay in patients from Slovene hospitals.
Objectives: To evaluate the length of regular ward and ICU stays and assess the survival of COVID-19 patients 
to develop better prediction models to forecast hospital capacity and staffing demands in possible further 
pandemic peaks.
Methods: In this retrospective, single-site study we analysed the length of stay and survival of all patients, 
hospitalized due to the novel coronavirus (COVID-19) at the peak of the second wave, between November 18th 
2020 and January 27th 2021 at the University Clinic Golnik, Slovenia. 
Results: Out of 407 included patients, 59% were male. The median length of stay on regular wards was 7.5 
(IQR 5–13) days, and the median ICU length of stay was 6 (IQR 4–11) days. Age, male sex, and ICU stay were 
significantly associated with a higher risk of death. The probability of dying in 21 days at the regular ward was 
14.4% (95% CI [10.9–18%]) and at the ICU it was 43.6% (95% CI [19.3-51.8%]).
Conclusion: The survival of COVID-19 is strongly affected by age, sex, and the fact that a patient had to be 
admitted to ICU, while the length of hospital bed occupancy is very similar across different demographic 
groups. Knowing the length of stay and admission rate to ICU is important for proper planning of resources 
during an epidemic.
Ozadje: Podatki o času bolnišničnega bivanja in preživetju bolnikov, hospitaliziranih zaradi COVID-19 v slovenskih 
bolnišnicah, v strokovni literaturi še niso objavljeni.
Cilji: Opredeliti čas bolnišničnega bivanja na navadnem in intenzivnem oddelku ter hospitalno umrljivost bolnikov 
zaradi s koronavirusne bolezni 2019 (COVID-19) povezanih vzrokov, kar bi lahko olajšalo načrtovanje bolnišničnih 
kapacitet v prihodnjih valovih pandemije.
Metode: Opravili smo retrospektivno analizo podatkov o vseh bolnikih, ki so bili zaradi COVID-19 hospitalizirani 
na Univerzitetni kliniki Golnik med vrhom drugega vala epidemije, in sicer med 18. novembrom 2020 in 27. 
januarjem 2021.
Rezultati: Od vključenih 407 bolnikov jih je bilo 59 % moškega spola. Srednja vrednost dolžine bolnišničnega 
bivanja je na navadnem oddelku znašala 7,5 (IKR 5–13) dni, v enoti intenzivne terapije (EIT) pa 6 (IKR 4–11) 
dni. Starost, spol in premestitev v EIT so bili značilno povezani z večjo umrljivostjo. 21-dnevna umrljivost na 
navadnem oddelku je znašala 14,4 % (95 % (IZ [10,9–18 %]), v EIT pa 43,6 % (IZ [19,3–51,8 %]).
Zaključek: Na preživetje bolnikov s COVID-19 močno vplivajo starost, spol in dejstvo, da je bil bolnik sprejet na 
oddelek intenzivne terapije, nasprotno pa je dolžina ležalne dobe v bolnišnici podobna v različnih demografskih 
skupinah. Poznavanje dolžine bivanja in deleža sprejemov na intenzivni oddelek je pomembno za načrtovanje 
virov med epidemijo.
1 INTRODUCTION
Since its first appearance in Wuhan in late 2019, COVID-19 
quickly became an important cause of global morbidity 
and mortality, that placed unprecedented strain on health 
care systems in developing and developed countries alike 
(1, 2). COVID-19 presents with varying levels of severity. 
Hospital beds and intensive care unit (ICU) beds equipped 
with ventilators and staffed accordingly are vital for the 
treatment of patients with a severe form of the illness. 
In addition to the estimated number of patients requiring 
hospitalization, data about the number of hospital bed-
days and ICU-days from COVID-19 are necessary for 
mathematical models to calculate future demand on 
health care service due to COVID-19, and to allow for better 
planning of non-COVID-19 health services (3). Estimating 
the length of hospital stay (LoS) requires observation of 
individual patient pathways. Age and comorbidities of 
patients influence disease severity and LoS. Increasing 
knowledge of the disease and its management also affects 
the duration and level of care needed.
At the beginning of the pandemic, the prediction models 
for estimated bed utilization rates used data from studies 
published in the pre-COVID era (4, 5). The majority of 
studies on COVID-19 LoS originate from China, and show a 
widely variable median LoS. LoS in China was also longer 
than elsewhere (6). 
Second only to the search for treatments, the development 
of a reliable prognostic toolset for predicting the 
disease’s course has been a leading objective among 
scientists and clinicians, with more than 100 articles 
already written about the topic, assessing the effects of 
numerous variables (demographic characteristics, clinical 
manifestations, laboratory results or radiological findings) 
on common endpoints, including duration of stay, the need 
for intensive care, intubation, mechanical ventilation, and 
death. A recent review article assessing these studies 
found high bias in most publications, also noting the 
intensely country-specific nature of most such studies (7). 
It is important to note that the epidemiological models 
estimating the reproduction number and studying and 
forecasting the number of positive cases, hospitalizations 
and ICU bed occupancy are typically based on aggregated 
data that do not allow for the estimation of length of stay, 
and therefore strong but unverifiable assumptions need 
to be made in the process, thus lowering the value of 
models, where the Slovene epidemiological models are no 
exception (8, 9). Due to the lack of data from individual-
based studies, common references remain Chinese data 
(10, 11). An additional reason for non-transferability of 
the estimated length of stay from different countries 
is the possible dependency on age and sex. Therefore, 
a different demographic distribution of the patients 
may affect the average length of stay to a considerable 
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extent. Obtaining even a rudimentary predictive tool, be 
it universal or tailored to a region, would allow clinicians 
and policymakers to better distribute resources, allocate 
hospital beds and bolster staff numbers, reducing the 
risk of a system becoming overwhelmed. At the time 
of writing, there were no publications pertaining to the 
prediction of COVID-19-related outcomes in patients from 
Slovene hospitals, and this report aims to at least partly 
remedy that.
The goal of this work is to estimate the average length of 
stay for Slovene patients on regular wards and ICUs, and 
understand how it depends on basic demographic variables 
that may be available for improving the prediction models, 
i.e. age and sex.
2 METHODS
2.1 Study design, time frame
Data on a cohort of patients treated for COVID-19 at the 
peak of the second wave in late 2020 and early 2021 were 
gathered retrospectively to analyse the probabilities of 
events in terms of time. 
2.2 Target population and sampling, data collection 
procedure
The target population are the Slovene patients treated 
for COVID-19. Our sample is formed by the cohort of 
patients who were treated between November 18th 2020 
and January 27th 2021 at the University Clinic Golnik, 
Slovenia, a tertiary clinic with 190 patient beds before 
the pandemic. All admitted patients in this time frame 
form our sample, the sole exception being those who 
transferred to our ICU from other health care settings, 
who were excluded from the analysis. 
The data were extracted from the hospital records. Two 
separate datasets, one considering the patients on a regular 
ward and the other those in ICU, were merged. The data 
has been thoroughly checked for possible inconsistencies.
During the peak of the second wave, our hospital provided 
4.5% of Slovene COVID-19 hospital beds and 5.7% of Slovene 
ICU beds. The Slovene Ministry of Health organized central 
coordination at the national level to provide hospital or 
ICU beds for all patients; consequently, patients were 
not always admitted to the nearest hospital, but were 
transferred to one with available capacity. As the data 
are collected at the peak of the second wave, the sample 
of the patients can be thus seen as representative for our 
target – the Slovene population in this observation period.
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3 RESULTS
3.1 Description of the sample
In the observed period, 439 patients were treated at our 
clinic. We excluded 32 patients who were transferred from 
other health care settings and were treated in our ICU 
for advanced care and/or weaning purposes. Our sample 
thus consists of 407 patients. In total, 33 patients were 
admitted to ICU at some point of their stay in the hospital, 
and of these 11 were admitted to ICU on the day of their 
hospital admission. These 11 patients were excluded in the 
analysis of the total length of stay and in the Cox model 
for transition intensities. All 33 ICU patients were included 
for the estimation of the length of stay in the ICU. The 
analysis of the length of stay in the regular ward after ICU 
is limited to the 17 patients who meet this criterion. 
Fifty-five of the 407 patients were admitted before 
November 18th, they are included in the analyses 
from November 18th onwards. On the final date of our 
observation period 46 patients were still in hospital, 
while 64 and 297 patients had died or were discharged, 
respectively, by that time. 
Out of 407 patients, 240 (59%) were males. The average 
age of the admitted patients was 73 years, and the age 
and sex distribution of patients is shown in Figure 1.
2.3 Observed outcomes and explanatory factors
The key outcomes of interest were the time to ICU 
admittance and the time to discharge or death. Patients 
still in hospital at the end of the study were considered 
censored. The time was measured either from the 
admission to hospital or from the intermediate events (ICU 
admittance or ICU discharge) onwards. 
Patients in our cohort who were admitted to the hospital 
before November 18th were followed only from November 
18th onwards, and this is considered as late entry in the 
survival analysis (i.e., patients are considered at risk only 
from November 18th onwards). This ensures the maximum 
possible sample size and the longest possible follow-up 
time while avoiding the bias that could have arisen if they 
were considered from their time of hospital admission.
The two key explanatory variables of interest are age at 
hospital admission and sex. For the analysis of the hazard 
of dying and discharge, the intermediate stay in ICU is 
regarded as an additional explanatory covariate. The time-
varying variable ICU equals 0 and switches to 1 when a 
patient is transferred to ICU, it remains equal to 1 after the 
ICU discharge. Sex can take the value male (M) or female (F), 
and females are taken as the reference group in the models. 
Age is calculated exactly as the difference in days between 
the date of birth and the date of hospital admittance, and 
when reporting we report the hazard ratio for individuals 
that differ by 10 years to ease the interpretation. 
2.4 Methods of analysis
The age and sex distribution and the outcome frequencies 
are presented with descriptive statistics and graphs. The 
probability of length of stay is estimated using the Kaplan-
Meier curve, in each of the cases conditional on the 
time of entry into that state. The median length of stay 
and the interquartile range are reported. Patients who 
were admitted directly to ICU were excluded from the 
estimate of total length of stay in the hospital, but were 
included for the estimate of the length of stay in ICU. The 
association between the hazard of different events and 
the variables (sex, age, and the time-dependent covariate 
of having been in ICU) is modelled using a Cox model. The 
proportional hazards assumption is tested with Schoenfeld 
residuals, the linearity of the covariate effect is checked 
with splines. The transition intensities of moving between 
states are joined in a multistate model. The state 
occupation probabilities are estimated using the Aalen-
Johansen estimator and multistate model predictions 
(based on Cox models for state transition intensities) are 
reported for chosen ages and with respect to sex. P-values 
below 5 % are considered statistically significant.
The analysis is performed using the packages survival (12) 
and mstate (13) of the statistical software R (14), version 
3.6.2.
Figure 1. Age distribution of the hospitalized COVID-19 
patients with respect to sex. Left: all patients 
(n=407); right: patients in intensive care at any time 
point (n=33).
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3.2. Hospital stay
The probability of hospital stay in terms of time is 
presented in the left-hand panel of Figure 2. The median 
length of hospital stay is 7.5 days (IQR [5–13] days).
Table 1 presents the results of the competing risks analysis. 
Age is strongly associated with both the hazard of dying 
and the hazard of being discharged. An age increase of 10 
years results in a 3.39-fold increase in the hazard of dying, 
and a lower probability of being discharged at any given 
time (0.74 of the hazard of a 10-year younger patient). In 
our sample, males have a significantly increased hazard 
of dying (2.29-fold), there seems to be no important 
association between sex and the hazard of discharge. If 
a patient has been (or still is) in ICU, the hazard of dying 
increases substantially (2.88 times higher than the hazard 
of a patient who has only been treated in a regular ward), 
at the same time, the hazard of discharge is 4 times lower.
The proportional hazard assumption does not seem to be 
violated to any considerable degree, and all the hazard 
ratios are constant over time. On the other hand, the 
association between age and the logarithm of the hazard 
of discharge does not seem linear – the decrease of hazard 
with age only lasts until approximately the age of 70, 
after that, the hazard of discharge remains constant with 
increasing age. 
3.3 ICU 
The probability of being transferred to ICU from a regular 
ward equals 4.6% (95% CI [2.6–6.6]), in our sample all 
patients admitted to the ICU were transferred within the 
first two weeks of being admitted to the hospital. 
The median length of stay in ICU was 6 days (IQR [4–11]), 
the patients in our sample stayed in ICU for up to 22 days. 
Seventeen patients were transferred back to a regular 
ward after ICU, and the estimated median remaining 
length of hospital stay based on these patients was 10 
days (IQR [7-15]) (Figure 2).
3.4 State occupation probability
Figure 3 presents the patients’ probability of being 
in a certain state over time. The probability of being 
discharged in the first 7 days after hospital admission 
equals 36.5% (95% CI [31.4–41.6]), the probability of dying 
in the same period equals 7.9% (95% CI [5.2–10.7]). By day 
21, the probability of discharge increases to 72.9% (95% 
CI [68.2–77.6]) and the probability of death to 14.4% (95% 
CI [10.9–18%]). At any given time, fewer than 3.5% of the 
patients can be expected to be in the ICU (the upper limit 
of 95% CI is below 5%). 
The right-hand graph of Figure 3 presents the state 
occupation probabilities following admission to the ICU. 
The probability of death in the first 7 days equals 16% (95% 
CI [2.1–29.9]) and increases to 35.6% (95% CI [19.3–51.8]) in 
21 days. The probability of discharge for these patients is 
substantially lower and reaches 31.3% (95% CI [18.6–44]) 
by day 21, whereas the probability of remaining in ICU for 
more than 21 days equals 19.7% (95% CI [4.7–34.8]).
Figure 4 presents the predictions of a multistate model, 
combining the Cox models modelling the hazards with 
respect to sex and age. Our main observation is that the 
probability of remaining in the hospital does not vary much 
with age and is only slightly lower in younger compared to 
older patients. On the other hand, the proportion of those 
discharged compared to those dead depends strongly 
on age and sex (Figure 4). In males, we can observe a 
substantial difference in the probability of dying already 
in the younger ages, in women, however, this probability 
stays lower and only increases at later ages.
Figure 2. The probability of length of stay for the hospitalized patients. Left: total time in hospital (based on n=396), middle: in ICU 
(n=33), right: in a regular ward after ICU discharge (n=17). 
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Age (10yrs)
Sex (M vs F)
ICU 
HRHR 95% CI95% CI
Outcome = Death Outcome = Discharge
P valueP value
0.74
0.92
0.25
3.39
2.29
2.88
[0.67-0.82]
[0.73-1.17]
[0.14-0.45]
[2.35-4.83]
[1.33-3.96]
[1.54-5.41]
<0.001
  0.492
<0.001
<0.001
  0.003
<0.001
Table 1. The results of the multivariate Cox models modelling the hazard of dying and the hazard of being discharged.
Figure 3.
Figure 4.
The probability of being in a hospital ward, 
ICU, discharged or dead in terms of time for the 
hospitalized COVID-19 patients. Left: probability 
of being in a certain state with respect to days 
since being admitted to the hospital (n=396); right: 
probability of being in a certain state with respect 
to days since being transferred to ICU (n=33) (from 
darkest to lightest grey: death, regular ward, ICU 
and discharge). 
Multistate model predictions: probability of state 
occupation in terms of time since hospital admission 
with respect to sex and age (from darkest to lightest 
grey: death, regular ward, ICU and discharge).
4 DISCUSSION
In this study we report the length of hospital stay and 
survival analysis of 407 COVID-19 patients hospitalized 
during the peak of the second wave of the pandemic in a 
single Slovenian centre. We find that age, male gender, and 
admittance to ICU strongly affect the survival of COVID-19 
patients. In contrast, the length of hospital bed occupancy 
is very similar across different demographic groups.
Understanding the duration of hospital stay and patient 
outcomes is important for planning bed occupancy and 
associated staff and equipment needs. Since the published 
results vary substantially, national, and local data are 
important. 
In this study, we report a median regular ward LoS that is 
comparable to the LoS reported from other European and 
US studies, but shorter than that in Chinese hospitals, as 
reported previously (6, 10, 11). The ICU LoS in our hospital 
appears to be somewhat shorter compared to the reported 
data (15-17), as well as predictions from statistical models 
(18). This may be due to the high case rate fatality and 
short LoS of ICU patients who die (19), while surviving ICU 
patients usually have longer LoS (20). The same is also true 
for our cohort (see Figures 3 and 4). Moreover, patients 
in our respiratory disease-oriented facility were usually 
transferred from ICU to a regular ward very early in the 
process of disease resolution, which could have affected 
the ICU LoS statistics.
The ICU admission rates in our study appear to be 
substantially lower compared to published data (22–30.5%) 
(21-24), which can likely be attributed to differences in 
the ICU admission criteria and demographic properties 
of studied cohorts. Nevertheless, it should be noted that 
our ICU figures do not include the proportion of patients 
who received non-invasive mechanical ventilation and 
high flow oxygen therapy outside the ICU wards. These 
patients are included in the regular ward figures.
Global death rate due to COVID-19, according to a global 
counter (25), appears to be roughly 2%, but is likely 
underestimated (26). The case rate fatality is much higher 
in a hospital setting, and this is especially true for patients 
admitted to ICU (see Table 1 and Figure 3). Moreover, 
mortality from COVID-19 in ICUs appears to be much higher 
than usually seen in ICU admissions with other types of 
viral pneumonia (25). The case rate fatality reported for 
our cohort appears to be well in line with the reported 
figures (e.g. 15, 16, 25, 27-36). Moreover, a subgroup of 
patients undergoing mechanical ventilation have even 
higher mortality rates (16, 37-40). Unfortunately, we do 
not have sufficient data to compare our cohort to the 
published figures.
We also found that LoS and well as the survival rates 
are sensitive to age and sex, as corroborated by several 
studies, e.g. (15, 17, 41-44). In their meta-analysis, 
Peckham et al. show that while there is no difference 
in the proportion of males and females with confirmed 
COVID-19, male patients have almost three times the odds 
of requiring ICU admission and higher odds of death (45). 
Similarly, the data from a very large US study (46) show 
that – compared to females – males had a higher rate of in-
hospital mortality, a higher rate of intubation and a longer 
LoS. In our cohort, the effect of sex on the hazard of ICU 
could not be studied due to the small sample. 
This study has several limitations. First, the patients 
were followed up to January 27th 2021, and there was 
no follow-up after this date, so the survival could be 
lower than reported. Second, the vaccination status of 
patients was not considered in the analysis, since it had 
not been widely implemented at that time. Third, there 
was no official treatment protocol consensus in Slovenia, 
and therefore our data might not be representative of 
all Slovene hospitals. Fourth, we do not include data 
pertaining to the modes of patient respiratory support 
either in the ICU or regular ward. Moreover, it has been 
reported many times that comorbidities such as arterial 
hypertension and diabetes mellitus (47, 48), are associated 
with higher a COVID-related risk of dying, and this issue 
was not addressed in this study.
One the other hand, this study has several strengths. Except 
for transfers from other hospitals, we included all patients 
hospitalized in our centre during the specified period. All 
patients had PCR confirmed SARS-CoV-2 infection, and all 
were treated according to hospital guidelines.
As the pandemic has progressed the case fatality rate 
is reported to be declining, an effect not related to 
demographic data or COVID-19 severity (49). The factors 
that influenced this phenomenon are not entirely known 
and are most likely multifactorial, including preventive 
behaviour in general and vitamin D supplementation in 
vitamin D deficient individuals. (50, 51). It is not clear 
whether the same is true for the Slovenian cohort, and 
thus this question warrants further research.
The median length of stay in regular wards and ICU, 
including gender distribution and risk of death, are often 
country or even institution specific. Real-life data are very 
valuable, especially in situations with limited resources, 
as in ongoing epidemics. They offer a reliable basis for 
effective resource allocation planning, can then be used 
when dealing with further COVID-19 epidemics.
5 CONCLUSION
This analysis of 407 adults hospitalized in a single Slovenian 
health care centre showed a similar length of hospital 
stay, mortality, and risk factors for death to those seen 
in data published elsewhere in the EU and US, but not 
in China. Additionally, we noticed slightly shorter ICU LoS 
and significantly lower ICU admission rates than published. 
These data mirror country and perhaps even institution 
specific situations and workflows, and are essential for 
proper planning of resources during an epidemic.
ACKNOWLEDGMENTS
We kindly thank and acknowledge the contribution of 
many doctors, nurses, and support staff, without whom 
this contribution would not be possible. 
The authors thank the students of the Applied Statistics 
program Anja Žavbi Kunaver, Eva Lavrenčič, and Daša 
Gorjan for the initial data cleaning and analysis.
CONFLICTS OF INTEREST
The authors declare that no conflicts of interest exist.
FUNDING
The research of MPP is supported by Slovenian Research 
Agency (grants P3-0154 and J3-1761).
ETHICAL APPROVAL
The study was approved by the National Research and 
Ethics Board, approval number 0120-201/2020/7, and 
informed consent was obtained from the patients or their 
representatives.
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