Changes in Life-Sustaining Treatment Limitations in Swedish ICUs During the COVID-19 Pandemic

Changes in Life-Sustaining Treatment Limitations in Swedish ICUs During the COVID-19 Pandemic | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Changes in Life-Sustaining Treatment Limitations in Swedish ICUs During the COVID-19 Pandemic Lisa Maria Wiltz, Tobias Siöland, Joar Björk, Kasper Glerup Lauridsen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8044900/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background The use of limitations of life-sustaining treatment (LLST) in Intensive Care Units (ICUs) varies internationally. The COVID-19 pandemic led to significant changes worldwide, but its impact on ICU LLST remains unclear. This study aimed to assess the prevalence of LLST in Swedish ICUs from 2018 to 2022 and whether the pandemic and COVID-19 influenced their utilization. Methods All ICU admissions registered in the Swedish Intensive Care Registry from 2018–2022 were screened. Cases with post-anesthesia care of less than 24 hours and subsequent ICU admissions were excluded. Logistic regression was used to analyze associations with LLST, defined as any limitation of therapy, including withholding or withdrawing therapy. March 16, 2020, marked the start of the pandemic. Results In total, 77 735 ICU admissions were analyzed: 39 396 pre-pandemic and 38 338 during the pandemic. Patients admitted during the pandemic had slightly higher odds (OR 1.06, 95% CI 1.03–1.10, p < 0.001) of receiving LLST compared to before the pandemic. COVID-19 patients had an increased odds of receiving a LLST compared to non COVID-19 patients (OR 1.17, 95% CI 1.09–1.25, p < 0.001). ICU capacity utilization was not associated with LLST use in our study. Conclusions The findings suggest a shift in LLST use, with increased odds during the pandemic and among COVID-19 patients specifically. This may reflect greater attention to active decision-making regarding life-sustaining therapy during this period. Figures Figure 1 Figure 2 Figure 3 1 INTRODUCTION 1.1 Background The COVID-19 pandemic spread abruptly in 2020, causing a global humanitarian crisis [ 1 ]. This posed challenges in healthcare prioritization and emphasized the need for ethical crisis standards to guide clinicians in deciding when standard prioritization approaches can no longer be applied [ 2 ]. The diagnostic uncertainty of a new illness, together with resource constraints and workforce shortages, led to debates and recommendations in prioritization within Sweden’s healthcare system. Among these were national guidelines on LLST (limitations of life-sustaining therapies) and ethical frameworks for resource allocation [ 3 ]. This prompted extensive debates on increasing the use of LLST such as withholding ventilator treatment, withholding ICU (Intensive Care Unit) care and Do-Not-Attempt Cardiopulmonary Resuscitation (DNACPR) orders [ 4 ]. The COVID-19 pandemic challenged the priority of care especially for patients with chronic, life-limiting disease and many countries issued recommendations of advance care planning and decisions about LLST [ 5 ]. The first reports during the pandemic indicated that outcomes for COVID-19 treated patients following in-hospital cardiac arrest were poor, suggesting considerations on DNACPR orders for COVID-19 patients [ 6 ]. However, to what extent the COVID-19 pandemic affected DNACPR rates and utilization of other LSTT in the ICU remain uncertain. ICU care targets major organ dysfunction and should only be provided if it benefits the patient and is not futile or against their interests. Determining whether ICU interventions will benefit the patient or are futile is often complex and not always easy to ascertain. LLST—such as DNACPR orders, withholding mechanical ventilation or renal replacement therapy—may be considered when interventions are unlikely to provide meaningful benefit. There is a variability in the use of LLST in the ICU within countries, cultures, hospitals and single sites [ 7 , 8 , 9 ]. The reasons for the variability are partly due to differences between countries, health care systems, hospitals, cultures and individual health care providers [ 7 ]. One Swedish study before the pandemic [ 8 ] looked at LLST in all Swedish ICUs in the Swedish Intensive Care Registry from 2014–2016 and found a LLST prevalence of 15,4%. Another study looked at LLST in hospitals (not only ICUs) from one Swedish region during the pandemic from 2020–2021 and found a LLST prevalence of 30% [ 9 ]. The decisions to LLST are generally perceived difficult for care givers and may raise controversies within care teams, with patients or with next-of-kin [ 10 ]. Two previous Swedish studies have shown that factors associated with LLST in the ICU pre-pandemic in general include higher age, female sex, severe comorbidity, longer length of stay (LOS) and higher SAPS3-score [ 8 , 11 ]. 1.2 Objectives We aimed to investigate differences in LLST before and during the COVID-19 pandemic and whether covariates affecting LLST utilization differed. 2 METHODS 2.1 Study design This is a registry-based cohort study from the Swedish Intensive Care Registry [ 12 ]. The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines were followed [ 13 ]. The Swedish Intensive Care Registry was launched 2001 and in 2019, 83 of 84 Swedish ICUs contributed to the registry. The registry contains patient level data, registered daily by physicians and nurses in the Swedish ICUs during the patient stay. Physicians are encouraged to document daily decisions to continue “Full Care” or to withdraw/withhold treatment, aided by reminders in the Electronic Health Record; however, they may choose not to, resulting in a “No Documented Decision.” The information from the registry is confidential and is only to be handed over after ethical review with de-identification of the data [ 14 ]. 2.2 Participants All ICU-admissions for adult patients (≥ 18 years of age) in Sweden from January 1st, 2018, to April 14th, 2022, were screened for inclusion. Exclusion criteria were post anesthesia care < 24 hours and subsequent ICU admissions (including index events only), see Fig. 1 . 2.3 Data collection and variables The registry documentation regarding LLST includes data variables that are registered both as withdraw, withhold and what type of LLST. Data completeness regarding LLST varies, as not all hospitals register all variables. To enable correct comparisons, only cases with a verified registration relating to LLST were included (“Full Care or LLST). In Fig. 4, we include patients without documented decisions to illustrate how the frequency of decision making changed over time. Since only a small proportion of hospitals reported what kind of therapy limitation was present (withhold, withdraw care, DNACPR, no renal replacement therapy, no invasive ventilation), we chose not to report that data. For a detailed description of missing data, see Supplement 1. From the registry, data was extracted for hospital type (community, county, district), patient characteristics, acute and chronic conditions (including COVID-19 status), and SAPS3 (see definitions, 15]. We defined pre–COVID-19 as before March 16, 2020, and the COVID-19 pandemic as March 16, 2020 onward. Within the COVID-19 pandemic we stratified admissions by COVID-19 or non COVID-19 patients. Patients with an active SARS-CoV-2 infection presenting the primary clinical manifestations that required ICU admission were classified as COVID-19 cases. 2.4 Exposure and outcomes Exposures were COVID-19 pandemic period and COVID-19 vs non COVID-19 patients. A COVID-19 patient was defined as such if COVID-19 was the main reason for ICU admission. The primary outcome was the presence of LLST. Covariates associated with a LLST decision were secondary outcomes. 2.5 Statistical analysis Baseline characteristics are presented using means, medians and proportions, with appropriate measurements of dispersion. Differences between our groups were analyzed using chi-square test to compare the baseline categorical variables, and students t-test and ANOVA for continuous continuous variables. To visualize potentially complex interactions between covariates, and identify confounders, mediators, and potential collider bias, we used Directed Acyclic Graphs (DAGs) [ 16 ]. We utilized the DAG (Supplement 2) to identify covariates for adjustment for the effect of the pandemic on LLST. Multivariable logistic regression analysis was performed to study independent associations with the presence of LLST. Covariates included were; age, sex, SAPS3-core, hospital type, LOS, COVID-19 status, pandemic period or not and ICU occupancy. The most simple model possible including exposure, outcome and confounders was built. To understand the impact of possible mediators, we built additional logistic regression models including COVID-19 status, LOS and ICU occupancy. P-values < 0.05 were considered significant. Standardized mean difference (SMD) was used to quantify differences between groups (SMD = difference between the two groups means divided by a standard deviation). SMD values < 0.1 (10%) were considered negligible. All analysis was performed using R-studio version 4.3.1. 3 RESULTS 77 735 ICU admissions were included in the final cohort, 39 396 pre- and 38 338 during the COVID-19 pandemic (Supplement 3). 3.1 Baseline characteristics There was no difference in prevalence of LLST between the pre-pandemic to the pandemic period. ICU-survival was lower in the pandemic period whereas 30-day survival was not significantly different (Table 1 ). Table 1 Baseline characteristics during the pre-pandemic and pandemic Variables Pre pandemic Pandemic p test SMD ICU admissions (n) 39396 38339 Age (mean (SD)) 62.39 (18.97) 62.11 (17.98) 00.33 0.015 Sex = F (n, %) 16848 (42.8) 15256 (39.8) < 0.001 0.06 Hospital type (n, %) < 0.001 0.033 Community hospital 10033 (25.5) 9327 (24.3) County hospital 17257 (43.8) 17367 (45.3) District (University) hospital 12106 (30.7) 11645 (30.4) ICU LOS*, days (median [IQR]) 1.13 [0.58, 2.75] 1.42 [0.64, 3.91] < 0.001 nonnorm 0.226 Surgery (n, %) < 0.001 0.073 Yes - Emergency 5550 (14.1) 4920 (12.8) Yes - elective 2981 (7.6) 2328 (6.1) No 30865 (78.3) 31091 (81.1) SAPS3 Score (mean (SD)) 57.07 (16.46) 57.02 (15.56) 0,614 0.004 Body Temp Max (median [IQR]) 37.00 [36.20, 37.50] 37.00 [36.30, 37.50] < 0.001 nonnorm 0.037 Heart Rate Max (mean (SD)) 98.93 (27.16) 97.46 (26.76) < 0.001 0.054 SBP* Min (mean (SD)) 106.43 (34.40) 109.32 (34.04) < 0.001 0.085 Bilirubin Max (median [IQR]) 10.00 [6.00, 17.00] 10.00 [6.00, 17.00] 0,368 nonnorm 0.003 Creatinine Max (median [IQR]) 86.00 [65.00, 131.00] 83.00 [62.00, 124.00] < 0.001 nonnorm 0.017 Leukocytes Max (median [IQR]) 11.70 [8.20, 16.10] 11.20 [7.90, 15.70] < 0.001 nonnorm 0.012 Platelets Min (median [IQR]) 221.00 [163.00, 286.00] 225.00 [167.00, 292.00] < 0.001 nonnorm 0.049 pH-level Min (median [IQR]) 7.36 [7.28, 7.42] 7.38 [7.30, 7.44] < 0.001 nonnorm 0.093 FiO2 (mean (SD)) 49.81 (22.19) 55.67 (24.30) < 0.001 0.252 PaO2 (median [IQR]) 11.50 [9.40, 15.00] 11.00 [9.00, 14.20] < 0.001 nonnorm 0.063 Ventilator treatment = No (n, %) 18382 (55.5) 17120 (51.6) < 0.001 0.079 FiO2 (mean (SD)) 40.68 (21.18) 43.45 (22.95) < 0.001 0.125 PaO2 (mean (SD)) 13.30 (9.43) 12.97 (7.25) < 0.05 0.04 Thrombocytes (mean (SD)) 230.00 (114.74) 237.91 (120.09) < 0.001 0.067 Bilirubin (mean (SD)) 18.00 (35.90) 18.43 (36.66) 0,408 0.012 MAP (mean (SD)) 68.09 (19.28) 69.44 (18.52) < 0.001 0.071 Length, cm (mean (SD)) 1.72 (0.11) 1.72 (0.11) 0,85 0.003 Weight, kg (mean (SD)) 79.93 (20.40) 81.75 (20.86) < 0.001 0.088 Non COVID-19 (%) 39384 (100.0) 31168 (81.3) < 0.001 0.677 ICU Occupancy nr. (mean (SD)) 338.43 (29.09) 428.65 (123.85) 80% (n, %) 941 (2.4) 17373 (45.3) < 0.001 1.166 ICU survival (n, %) 34976 (88.8) 33529 (87.5) < 0.001 0.041 30-day survival (n, %) 29645 (77.2) 28951 (76.9) 0,423 0.006 90-day survival (n, %) 10146 (73.4) 10080 (73.1) 0,374 0.007 LLST (n, %) 9608 (24.4) 9398 (24.5) 0,887 0.001 Note: Baseline table for all patients included in the primary analysis stratified in two groups: Pre Covid19-pandemic: before March 16, 2020. Covid19-pandemic: from March 16, 2020. SMD: the standardized mean difference is the difference between the means for the two groups divided by their standard There were 31 168 non COVID-19 patients, vs 7 171 COVID-19 patients during the pandemic period. In the COVID-19 group there were more men, the LOS was days longer and the ICU- and 30-day survival rate were lower. There was a minimal difference in LLST use. COVID-19 vs non COVID-19 differed substantially regarding the proportion of patients treated with invasive ventilation (Table 2 ). Table 2 Baseline characteristics grouped by COVID-19 status Variables Non COVID-19 COVID-19 p test SMD ICU admissions (n) 31168 7171 Age (mean (SD)) 62.34 (18.78) 61.10 (13.95) < 0.001 0.075 Sex = F (n, %) 13090 (42.0) 2166 (30.2) < 0.001 0.247 Hospital type (n, %) < 0.001 0.234 Community hospital 8138 (26.1) 1189 (16.6) County hospital 13799 (44.3) 3568 (49.8) District (University) hospital 9231 (29.6) 2414 (33.7) ICU LOS, days (median [IQR]) 1.07 [0.55, 2.56] 6.57 [2.49, 14.04] < 0.001 nonnorm 0.916 Surgery (n, %) < 0.001 0.636 Yes - Emergency 4758 (15.3) 162 (2.3) Yes - elective 2303 (7.4) 25 (0.3) No 24107 (77.3) 6984 (97.4) SAPS3 Score (mean (SD)) 57.12 (16.38) 56.54 (11.25) 0,004 0.041 Body Temp Max (median [IQR]) 36.80 [36.20, 37.30] 37.50 [36.90, 38.20] < 0.001 nonnorm 0.63 Heart Rate Max (mean (SD)) 98.33 (27.38) 93.71 (23.57) < 0.001 0.181 SBP Min (mean (SD)) 106.94 (34.61) 119.62 (29.32) < 0.001 0.395 Bilirubin Max (median [IQR]) 11.00 [6.50, 18.00] 9.00 [6.00, 12.00] < 0.001 nonnorm 0.273 Creatinine Max (median [IQR]) 86.00 [64.00, 133.00] 71.00 [57.00, 95.00] < 0.001 nonnorm 0.271 Leukocytes Max (median [IQR]) 11.80 [8.30, 16.40] 9.20 [6.70, 12.50] < 0.001 nonnorm 0.194 Platelets Min (median [IQR]) 221.00 [163.00, 287.00] 240.00 [182.00, 313.00] < 0.001 nonnorm 0.204 pH-level Min (median [IQR]) 7.36 [7.28, 7.42] 7.44 [7.38, 7.48] < 0.001 nonnorm 0.681 FiO2 (mean (SD)) 49.74 (23.20) 71.39 (19.74) < 0.001 1.002 PaO2 (median [IQR]) 11.80 [9.60, 15.10] 9.00 [7.80, 10.80] < 0.001 nonnorm 0.49 Ventilator treatment = No (n, %) 15042 (57.0) 2078 (30.6) < 0.001 0.552 FiO2 (mean (SD)) 39.27 (21.01) 64.91 (20.39) < 0.001 1.239 PaO2 (mean (SD)) 13.60 (7.61) 9.78 (3.68) < 0.001 0.641 Thrombocytes (mean (SD)) 232.68 (121.25) 259.71 (112.58) < 0.001 0.231 Bilirubin (mean (SD)) 19.99 (37.96) 11.92 (29.81) < 0.001 0.236 MAP (mean (SD)) 68.41 (18.77) 73.63 (16.81) < 0.001 0.293 ICU Occupancy nr. (mean (SD)) 408.96 (116.07) 514.21 (120.22) 80% (n, %) 11839 (38.0) 5534 (77.2) < 0.001 0.864 30-day survival (n, %) 23644 (77.3) 5307 (75.3) < 0.001 0.047 90-day survival (n, %) 22386 (73.6) 4938 (70.1) < 0.001 0.062 LLST (n, %) 7693 (24.7) 1675 (23.4) 0,019 0.031 Note: Baseline table for all patients during the COVID-19 pandemic stratified in two groups: Non COVID-19: Tested negative for SARS-CoV-2 virus COVID-19: Tested positive for SARS-CoV-2 virus Definition of COVID-19 pandemic: March 16, 2020, to April 14, 2022. SMD: the standardized mean difference is the difference between the means for the two groups divided by their standard deviation (SD). Values below 0.1 (10%) are considered inconsequential (i.e., no difference between the groups). LLST: limitations of life-sustaining therapies ICU, intensive care unit; SAPS3, Simplified Acute Physiology Score 3 3.2 Main results In the multivariable model (Fig. 1 ), patients during the pandemic period had slightly higher odds ratio (OR 1.06, 95% CI 1.03–1.10, p < 0.001) of receiving a LLST compared to before the pandemic. Higher age and sex were associated with an increased risk of a LLST both before and during the pandemic (Figs. 1 and 2 ). In contrast, University-/district, compared to community hospitals in Sweden, were associated with lower odds ratios of a LLST both before and during the pandemic (OR 0.76, 95% CI 0.72–0.80, p < 0,01 vs OR 0.79, 95% CI 0.73–0.85, p < 0.01) (Figs. 1 and 2 ). To investigate potential mediation of the effect of the pandemic on LLST, we performed regression analyses with/ without COVID-19 status, ICU occupancy, SAPS-III score, and length of stay (Supplement 4–9). The analyses did not suggest significant mediation of either factor (Supplement 4–9). COVID-19 patients received LLST a higher degree compared to non COVID-19 patients (OR 1.17, 95% CI 1.09–1.25, p < 0.001) (Fig. 2 ). 4 DISCUSSION 4.1 Key results We observed that the pandemic period was independently associated with an increased likelihood of receiving LLST, and that this association was more pronounced among patients with COVID-19. The effects of key covariates on LLST also varied depending on COVID-19 status and the timing of the pandemic. (Supplement 4–9). 4.2 Increased likelihood of LLST in the COVID-19 period The observed increase in LLST during the COVID-19 pandemic, although modest, suggests a shift in clinical practice and decision-making. The change over time, however, was surprisingly small. The reasons for this merit further discussion. Before the pandemic, a larger proportion of patients had no documented LLST decision (“No Documented Decision”; Fig. 3 ). Because these cases were excluded from the analysis, LLST use before the pandemic may have been overestimated. During the pandemic, the number of patients with “No Documented Decisions” decreased, meaning that fewer such cases were excluded. Consequently, the observed increase in LLST use during the pandemic may appear smaller than it truly was, since part of the pre-pandemic LLST rate may reflect missing documentation rather than actual treatment limitation decisions. This interpretation aligns with findings by Jönson et al. (2022) [ 17 ], who reported similar survival among patients with “Full Care” and those with “No Documented Decision”, suggesting that the latter group may have been more comparable to “Full Care” than to LLST cases. Another reason why the increase in LLST was only marginal in the ICU during the pandemic, may be a higher threshold for ICU admission due to actual or perceived bed shortages. As shown in Table 1 , mean ICU occupancy was substantially higher during the COVID-19 pandemic, and higher LLST rates outside the ICU [ 18 , 19 ] indicate that more patients were deemed “Not for ICU” during this period. Our study did not capture LLSTs for patients on general wards and could not adjust for this. In our multivariable analysis, ICU occupancy was not independently associated with LLST presence (Supplement 4 and 9). Sotoodeh et al. (2025) studied 20,261 hospitalized Swedish patients and found that higher occupancy increased the likelihood of patients being ‘Not for ICU’ [ 20 ]. Since our analysis only included admitted patients, ICU occupancy likely influenced ICU admission decisions more than LLST use among those admitted. Limited ICU resources may have necessitated prioritizing patients with more severe acute conditions, raising ethical considerations. However, the mean SAPS3 score was similar before and during the pandemic (p = 0.64), suggesting that illness severity among admitted patients remained largely unchanged despite these pressures 4.3 COVID-19 status and the pandemic During the COVID-19 pandemic period, we found a significantly increased risk of LLST among COVID-19 compared to non-COVID-19 patients (OR 1.17, 95%, CI 1.07–1.25). This somewhat contradicts findings from an Australian study [ 21 ], where frail patients with, compared to without, COVID-19 had the same frequency of LLST. As shown in our study, SAPS3 scores—used to predict hospital mortality at ICU admission—did not differ significantly before or during the pandemic, and only minimally between COVID-19 and non-COVID-19 patients (Tables 1 and 2 ). Multiple studies demonstrated that the pandemic, and more specifically, COVID-19 status, had a significant but divergent impact on time to surgery and surgical outcomes [22, 23] and delayed or avoided acute care [24, 25, 26]. The mechanisms underlying the increased likelihood of LLST among COVID-19 patients remain unclear. Syrous et al. [ 27 ] examined end-of-life decision-making in critically ill elderly patients during the pandemic and found that LLST decisions in COVID-19 patients were influenced by different factors compared with non-COVID-19 patients. Similarly, the European multicenter COVID-ICU study [ 28 ] identified age, frailty, and early severity of respiratory failure as key determinants of LLST, with notable variation across centers. LLST was, as expected, strongly associated with higher mortality, highlighting a fundamental challenge for intensivists: whether LLST contributes to mortality, reflects the severity of illness, or represents a combination of both. Although the overall rise in LLST was modest during the COVID-19 pandemic, the qualitative change in documentation practices—from 18.5% of patients lacking a recorded decision in 2018 to 11.4% in 2022, a 39% relative decrease (see Fig. 3 )—may reflect a heightened ethical awareness, which would make it a sign of meaningful evolution. Future research could explore whether these changes have persisted beyond the acute phase of the pandemic and how they have impacted patient outcomes, staff experiences, and ethical climate in healthcare settings. 4.2 Strengths and Limitations This is a nationwide registry study with strengths including high completeness and limited selection bias. Limitations include accuracy variations due to human error at registration and measurement bias due to variability in data completeness collections across different sites. There are limitations to the observational design with possible unmeasured confounders and residual confounding. Although we used DAGs and attempted to analyse mediation, such analyses rely on multiple assumptions that are difficult to meet, why the mediation analyses should be interpreted with caution. 5 CONCLUSION The results of this study suggest a shift in use of LLST during the COVID-19 pandemic as evidenced by the increased use of LLST during the pandemic and among COVID-19 patients. It is possible that the increased emphasis on active decision making during the pandemic was the driver of this change. Declarations 6.1 Ethics approval and consent to participate This retrospective registry-based study used pseudonymized data from the Swedish Intensive Care Registry (Svenska Intensivvårdsregistret, SIR; Dnr 202224). Ethical approval was obtained from the Swedish Ethical Review Authority (Etikprövningsmyndigheten), approval numbers Dnr 2022-02760-01 and Dnr 2023-02923-02. The requirement for informed consent was waived because the study was based on existing registry data that were pseudonymized prior to access. All data handling complied with the General Data Protection Regulation (GDPR) and institutional data protection policies. Clinical trial registration: Not applicable. 6.2 Consent for publication Not applicable. 6.5 Funding This study received funding from the Department of Anaesthesiology and Intensive Care at Sahlgrenska University Hospital, Mölndal, Sweden. Author Contribution Lisa Wiltz drafted the manuscript with substantial input from Fredrik Hessulf, Kasper Glerup Lauridsen, Adam Piasecki, and Joar Björk. Lisa Wiltz and Fredrik Hessulf performed the main statistical analyses. Kasper Glerup Lauridsen and Tobias Siöland provided statistical support and guidance. All authors contributed to interpretation, critically revised the text, and approved the final version. Acknowledgement We thank the Swedish Intensive Care Registry (SIR) for providing data and the clinical teams for their contributions. Data Availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request, subject to ethical and legal restrictions. References World Health Organization [WHO] (2025, May 7.) “Corona virus disease (COVID-19)”. https://www.who.int/emergencies/diseases/novel-coronavirus-2019 Kramer, Daniel B., Bernard Lo, and Neal W. Dickert. 2020. “CPR in the Covid-19 Era, An Ethical Framework.” New England Journal of Medicine 383 (2): e6. https://doi.org/10.1056/nejmp2010758. Socialstyrelsen. (2020, april). 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J Am Geriatr Soc. 2022;70(5):1314-1324. doi:10.1111/jgs.17722. ﷟HYPERLINK "https://www.researchgate.net/publication/358853265_Risk_from_delayed_or_missed_care_and_non-COVID-19_outcomes_for_older_patients_with_chronic_conditions_during_the_pandemic?utm_source=chatgpt.com" Tripathy S, Vijayaraghavan BKT, Panigrahi MK, Shetty AP, Haniffa R, Mishra RC, Beane A. Collateral Impact of the COVID-19 Pandemic on Acute Care of Non-COVID Patients: An Internet-based Survey of Critical Care and Emergency Personnel. Indian J Crit Care Med. 2021;25(4):374-381. doi:10.5005/jp-journals-10071-23782. ﷟HYPERLINK "https://pubmed.ncbi.nlm.nih.gov/34045802/?utm_source=chatgpt.com" Frey A, Tilstra AM, Verhagen MD. Inequalities in healthcare use during the COVID-19 pandemic. Nat Commun. 2024;15(1):1894. doi:10.1038/s41467-024-45720-2. Syrous AN, Gudnadottir G, Oras J, Ferguson T, Lilja D, Odenstedt Herges H, Larsson E, Block L. End-of-life decision-making in critically ill old patients with and without coronavirus disease 2019. Acta Anaesthesiol Scand. 2024 Jan;68(1):63-70. doi: 10.1111/aas.14326. Epub 2023 Sep 5. PMID: 37670491. Giabicani, M., Le Terrier, C., Poncet, A. et al. Limitation of life-sustaining therapies in critically ill patients with COVID-19: a descriptive epidemiological investigation from the COVID-ICU study. Crit Care 27, 103 (2023). https://doi.org/10.1186/s13054-023-04349-1. Additional Declarations No competing interests reported. Supplementary Files WiltzSupplementChangesinLifeSustainingTreatmentLimitationsinSwedishICUsDuringtheCOVID19Pandemic251030.docx.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 12 Dec, 2025 Editor invited by journal 14 Nov, 2025 Editor assigned by journal 12 Nov, 2025 Submission checks completed at journal 12 Nov, 2025 First submitted to journal 06 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":86529,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable logistic regression analysis of predictors of LLST among ICU treated patients in Sweden 2018-2022.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8044900/v1/0691f52872632af1c8a17329.jpeg"},{"id":98752063,"identity":"911589ac-8026-4cdb-9246-55528c758c67","added_by":"auto","created_at":"2025-12-22 09:13:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":434171,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable logistic regression analysis of predictors of LST limitations among ICU treated patients during the COVID-19 pandemic with COVID-19 status\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8044900/v1/fcafb9473921e83724c7f4e0.jpeg"},{"id":98778216,"identity":"faf7d84d-540e-4114-920b-dce38090003a","added_by":"auto","created_at":"2025-12-22 12:29:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197895,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in the use of LLST in Swedish ICUs 2018-2022.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8044900/v1/c1aa80c83705d8bc8fd5173a.png"},{"id":98785495,"identity":"1eef1d2d-ed2f-40e5-8220-1620d86a7d42","added_by":"auto","created_at":"2025-12-22 12:43:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2093706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8044900/v1/557c5883-aa0a-4a40-a5c8-87bce1372030.pdf"},{"id":98752068,"identity":"074229af-87d6-4147-85af-4e9771baddfc","added_by":"auto","created_at":"2025-12-22 09:13:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":628890,"visible":true,"origin":"","legend":"","description":"","filename":"WiltzSupplementChangesinLifeSustainingTreatmentLimitationsinSwedishICUsDuringtheCOVID19Pandemic251030.docx.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8044900/v1/79023681e49e36bf8a8d413a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changes in Life-Sustaining Treatment Limitations in Swedish ICUs During the COVID-19 Pandemic","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Background\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic spread abruptly in 2020, causing a global humanitarian crisis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This posed challenges in healthcare prioritization and emphasized the need for ethical crisis standards to guide clinicians in deciding when standard prioritization approaches can no longer be applied [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diagnostic uncertainty of a new illness, together with resource constraints and workforce shortages, led to debates and recommendations in prioritization within Sweden\u0026rsquo;s healthcare system. Among these were national guidelines on LLST (limitations of life-sustaining therapies) and ethical frameworks for resource allocation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This prompted extensive debates on increasing the use of LLST such as withholding ventilator treatment, withholding ICU (Intensive Care Unit) care and Do-Not-Attempt Cardiopulmonary Resuscitation (DNACPR) orders [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic challenged the priority of care especially for patients with chronic, life-limiting disease and many countries issued recommendations of advance care planning and decisions about LLST [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The first reports during the pandemic indicated that outcomes for COVID-19 treated patients following in-hospital cardiac arrest were poor, suggesting considerations on DNACPR orders for COVID-19 patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, to what extent the COVID-19 pandemic affected DNACPR rates and utilization of other LSTT in the ICU remain uncertain.\u003c/p\u003e \u003cp\u003eICU care targets major organ dysfunction and should only be provided if it benefits the patient and is not futile or against their interests. Determining whether ICU interventions will benefit the patient or are futile is often complex and not always easy to ascertain. LLST\u0026mdash;such as DNACPR orders, withholding mechanical ventilation or renal replacement therapy\u0026mdash;may be considered when interventions are unlikely to provide meaningful benefit. There is a variability in the use of LLST in the ICU within countries, cultures, hospitals and single sites [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The reasons for the variability are partly due to differences between countries, health care systems, hospitals, cultures and individual health care providers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. One Swedish study before the pandemic [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] looked at LLST in all Swedish ICUs in the Swedish Intensive Care Registry from 2014\u0026ndash;2016 and found a LLST prevalence of 15,4%. Another study looked at LLST in hospitals (not only ICUs) from one Swedish region during the pandemic from 2020\u0026ndash;2021 and found a LLST prevalence of 30% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The decisions to LLST are generally perceived difficult for care givers and may raise controversies within care teams, with patients or with next-of-kin [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Two previous Swedish studies have shown that factors associated with LLST in the ICU pre-pandemic in general include higher age, female sex, severe comorbidity, longer length of stay (LOS) and higher SAPS3-score [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Objectives\u003c/h2\u003e \u003cp\u003eWe aimed to investigate differences in LLST before and during the COVID-19 pandemic and whether covariates affecting LLST utilization differed.\u003c/p\u003e \u003c/div\u003e"},{"header":"2 METHODS","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis is a registry-based cohort study from the Swedish Intensive Care Registry [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. \u003cem\u003eThe Strengthening the Reporting of Observational studies in Epidemiology (STROBE)\u003c/em\u003e guidelines were followed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The Swedish Intensive Care Registry was launched 2001 and in 2019, 83 of 84 Swedish ICUs contributed to the registry. The registry contains patient level data, registered daily by physicians and nurses in the Swedish ICUs during the patient stay. Physicians are encouraged to document daily decisions to continue \u0026ldquo;Full Care\u0026rdquo; or to withdraw/withhold treatment, aided by reminders in the Electronic Health Record; however, they may choose not to, resulting in a \u0026ldquo;No Documented Decision.\u0026rdquo; The information from the registry is confidential and is only to be handed over after ethical review with de-identification of the data [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003eAll ICU-admissions for adult patients (\u0026ge;\u0026thinsp;18 years of age) in Sweden from January 1st, 2018, to April 14th, 2022, were screened for inclusion. Exclusion criteria were post anesthesia care\u0026thinsp;\u0026lt;\u0026thinsp;24 hours and subsequent ICU admissions (including index events only), see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection and variables\u003c/h2\u003e \u003cp\u003eThe registry documentation regarding LLST includes data variables that are registered both as withdraw, withhold and what type of LLST. Data completeness regarding LLST varies, as not all hospitals register all variables. To enable correct comparisons, only cases with a verified registration relating to LLST were included (\u0026ldquo;Full Care or LLST). In Fig.\u0026nbsp;4, we include patients without documented decisions to illustrate how the frequency of decision making changed over time. Since only a small proportion of hospitals reported what kind of therapy limitation was present (withhold, withdraw care, DNACPR, no renal replacement therapy, no invasive ventilation), we chose not to report that data. For a detailed description of missing data, see Supplement 1.\u003c/p\u003e \u003cp\u003eFrom the registry, data was extracted for hospital type (community, county, district), patient characteristics, acute and chronic conditions (including COVID-19 status), and SAPS3 (see definitions, 15]. We defined pre\u0026ndash;COVID-19 as before March 16, 2020, and the COVID-19 pandemic as March 16, 2020 onward. Within the COVID-19 pandemic we stratified admissions by COVID-19 or non COVID-19 patients. Patients with an active SARS-CoV-2 infection presenting the primary clinical manifestations that required ICU admission were classified as COVID-19 cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Exposure and outcomes\u003c/h2\u003e \u003cp\u003eExposures were COVID-19 pandemic period and COVID-19 vs non COVID-19 patients. A COVID-19 patient was defined as such if COVID-19 was the main reason for ICU admission. The primary outcome was the presence of LLST. Covariates associated with a LLST decision were secondary outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics are presented using means, medians and proportions, with appropriate measurements of dispersion. Differences between our groups were analyzed using chi-square test to compare the baseline categorical variables, and students t-test and ANOVA for continuous continuous variables. To visualize potentially complex interactions between covariates, and identify confounders, mediators, and potential collider bias, we used Directed Acyclic Graphs (DAGs) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. We utilized the DAG (Supplement 2) to identify covariates for adjustment for the effect of the pandemic on LLST. Multivariable logistic regression analysis was performed to study independent associations with the presence of LLST. Covariates included were; age, sex, SAPS3-core, hospital type, LOS, COVID-19 status, pandemic period or not and ICU occupancy. The most simple model possible including exposure, outcome and confounders was built. To understand the impact of possible mediators, we built additional logistic regression models including COVID-19 status, LOS and ICU occupancy. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. Standardized mean difference (SMD) was used to quantify differences between groups (SMD\u0026thinsp;=\u0026thinsp;difference between the two groups means divided by a standard deviation). SMD values\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (10%) were considered negligible. All analysis was performed using R-studio version 4.3.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cp\u003e77 735 ICU admissions were included in the final cohort, 39 396 pre- and 38 338 during the COVID-19 pandemic (Supplement 3).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eThere was no difference in prevalence of LLST between the pre-pandemic to the pandemic period. ICU-survival was lower in the pandemic period whereas 30-day survival was not significantly different (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics during the pre-pandemic and pandemic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre pandemic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePandemic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU admissions (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.39 (18.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.11 (17.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e00.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u0026thinsp;=\u0026thinsp;F (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16848 (42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15256 (39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital type (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10033 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9327 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17257 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17367 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict (University) hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12106 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11645 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU LOS*, days (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 [0.58, 2.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 [0.64, 3.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes - Emergency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5550 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4920 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes - elective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2981 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2328 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30865 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31091 (81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSAPS3 Score (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.07 (16.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.02 (15.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Temp Max (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.00 [36.20, 37.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.00 [36.30, 37.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart Rate Max (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.93 (27.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.46 (26.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP* Min (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.43 (34.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.32 (34.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin Max (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00 [6.00, 17.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.00 [6.00, 17.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine Max (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.00 [65.00, 131.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.00 [62.00, 124.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeukocytes Max (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.70 [8.20, 16.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.20 [7.90, 15.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelets Min (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221.00 [163.00, 286.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225.00 [167.00, 292.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epH-level Min (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.36 [7.28, 7.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.38 [7.30, 7.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFiO2 (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.81 (22.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.67 (24.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO2 (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.50 [9.40, 15.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00 [9.00, 14.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVentilator treatment\u0026thinsp;=\u0026thinsp;No (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18382 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17120 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFiO2 (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.68 (21.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.45 (22.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO2 (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.30 (9.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.97 (7.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThrombocytes (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230.00 (114.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237.91 (120.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.00 (35.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.43 (36.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAP (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.09 (19.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.44 (18.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength, cm (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight, kg (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.93 (20.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.75 (20.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon COVID-19 (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39384 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31168 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU Occupancy nr. (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338.43 (29.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428.65 (123.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU Occupancy\u0026thinsp;\u0026gt;\u0026thinsp;80% (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e941 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17373 (45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU survival (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34976 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33529 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e30-day survival (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29645 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28951 (76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e90-day survival (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10146 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10080 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLLST (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9608 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9398 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote: Baseline table for all patients included in the primary analysis stratified in two groups:\u003c/p\u003e \u003cp\u003ePre Covid19-pandemic: before March 16, 2020.\u003c/p\u003e \u003cp\u003eCovid19-pandemic: from March 16, 2020.\u003c/p\u003e \u003cp\u003eSMD: the standardized mean difference is the difference between the means for the two groups divided by their standard\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere were 31 168 non COVID-19 patients, vs 7 171 COVID-19 patients during the pandemic period. In the COVID-19 group there were more men, the LOS was days longer and the ICU- and 30-day survival rate were lower. There was a minimal difference in LLST use. COVID-19 vs non COVID-19 differed substantially regarding the proportion of patients treated with invasive ventilation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics grouped by COVID-19 status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon COVID-19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU admissions (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.34 (18.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.10 (13.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u0026thinsp;=\u0026thinsp;F (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13090 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2166 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital type (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8138 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1189 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13799 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3568 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict (University) hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9231 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2414 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU LOS, days (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 [0.55, 2.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.57 [2.49, 14.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes - Emergency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4758 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes - elective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2303 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24107 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6984 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSAPS3 Score (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.12 (16.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.54 (11.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody Temp Max (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.80 [36.20, 37.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.50 [36.90, 38.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeart Rate Max (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.33 (27.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.71 (23.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP Min (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.94 (34.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.62 (29.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin Max (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.00 [6.50, 18.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.00 [6.00, 12.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine Max (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.00 [64.00, 133.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.00 [57.00, 95.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeukocytes Max (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.80 [8.30, 16.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.20 [6.70, 12.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelets Min (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221.00 [163.00, 287.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240.00 [182.00, 313.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epH-level Min (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.36 [7.28, 7.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.44 [7.38, 7.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFiO2 (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.74 (23.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.39 (19.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO2 (median [IQR])\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.80 [9.60, 15.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.00 [7.80, 10.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enonnorm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVentilator treatment\u0026thinsp;=\u0026thinsp;No (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15042 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2078 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFiO2 (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.27 (21.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.91 (20.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO2 (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.60 (7.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.78 (3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThrombocytes (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232.68 (121.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259.71 (112.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBilirubin (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.99 (37.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.92 (29.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMAP (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.41 (18.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.63 (16.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU Occupancy nr. (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408.96 (116.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e514.21 (120.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU Occupancy\u0026thinsp;\u0026gt;\u0026thinsp;80% (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11839 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5534 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e30-day survival (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23644 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5307 (75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e90-day survival (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22386 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4938 (70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLLST (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7693 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1675 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote: Baseline table for all patients during the COVID-19 pandemic stratified in two groups:\u003c/p\u003e \u003cp\u003eNon COVID-19: Tested negative for SARS-CoV-2 virus\u003c/p\u003e \u003cp\u003eCOVID-19: Tested positive for SARS-CoV-2 virus\u003c/p\u003e \u003cp\u003eDefinition of COVID-19 pandemic: March 16, 2020, to April 14, 2022.\u003c/p\u003e \u003cp\u003eSMD: the standardized mean difference is the difference between the means for the two groups divided by their standard deviation (SD). Values below 0.1 (10%) are considered inconsequential (i.e., no difference between the groups).\u003c/p\u003e \u003cp\u003eLLST: limitations of life-sustaining therapies ICU, intensive care unit; SAPS3, Simplified Acute Physiology Score 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Main results\u003c/h2\u003e \u003cp\u003eIn the multivariable model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), patients during the pandemic period had slightly higher odds ratio (OR 1.06, 95% CI 1.03\u0026ndash;1.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of receiving a LLST compared to before the pandemic. Higher age and sex were associated with an increased risk of a LLST both before and during the pandemic (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, University-/district, compared to community hospitals in Sweden, were associated with lower odds ratios of a LLST both before and during the pandemic (OR 0.76, 95% CI 0.72\u0026ndash;0.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0,01 vs OR 0.79, 95% CI 0.73\u0026ndash;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To investigate potential mediation of the effect of the pandemic on LLST, we performed regression analyses with/ without COVID-19 status, ICU occupancy, SAPS-III score, and length of stay (Supplement 4\u0026ndash;9). The analyses did not suggest significant mediation of either factor (Supplement 4\u0026ndash;9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCOVID-19 patients received LLST a higher degree compared to non COVID-19 patients (OR 1.17, 95% CI 1.09\u0026ndash;1.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Key results\u003c/h2\u003e \u003cp\u003eWe observed that the pandemic period was independently associated with an increased likelihood of receiving LLST, and that this association was more pronounced among patients with COVID-19. The effects of key covariates on LLST also varied depending on COVID-19 status and the timing of the pandemic. (Supplement 4\u0026ndash;9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Increased likelihood of LLST in the COVID-19 period\u003c/h2\u003e \u003cp\u003eThe observed increase in LLST during the COVID-19 pandemic, although modest, suggests a shift in clinical practice and decision-making. The change over time, however, was surprisingly small. The reasons for this merit further discussion.\u003c/p\u003e \u003cp\u003eBefore the pandemic, a larger proportion of patients had no documented LLST decision (\u0026ldquo;No Documented Decision\u0026rdquo;; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Because these cases were excluded from the analysis, LLST use before the pandemic may have been overestimated. During the pandemic, the number of patients with \u0026ldquo;No Documented Decisions\u0026rdquo; decreased, meaning that fewer such cases were excluded. Consequently, the observed increase in LLST use during the pandemic may appear smaller than it truly was, since part of the pre-pandemic LLST rate may reflect missing documentation rather than actual treatment limitation decisions. This interpretation aligns with findings by J\u0026ouml;nson et al. (2022) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who reported similar survival among patients with \u0026ldquo;Full Care\u0026rdquo; and those with \u0026ldquo;No Documented Decision\u0026rdquo;, suggesting that the latter group may have been more comparable to \u0026ldquo;Full Care\u0026rdquo; than to LLST cases.\u003c/p\u003e \u003cp\u003eAnother reason why the increase in LLST was only marginal in the ICU during the pandemic, may be a higher threshold for ICU admission due to actual or perceived bed shortages. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, mean ICU occupancy was substantially higher during the COVID-19 pandemic, and higher LLST rates outside the ICU [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] indicate that more patients were deemed \u0026ldquo;Not for ICU\u0026rdquo; during this period. Our study did not capture LLSTs for patients on general wards and could not adjust for this. In our multivariable analysis, ICU occupancy was not independently associated with LLST presence (Supplement 4 and 9).\u003c/p\u003e \u003cp\u003eSotoodeh et al. (2025) studied 20,261 hospitalized Swedish patients and found that higher occupancy increased the likelihood of patients being \u0026lsquo;Not for ICU\u0026rsquo; [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Since our analysis only included admitted patients, ICU occupancy likely influenced ICU admission decisions more than LLST use among those admitted. Limited ICU resources may have necessitated prioritizing patients with more severe acute conditions, raising ethical considerations. However, the mean SAPS3 score was similar before and during the pandemic (p\u0026thinsp;=\u0026thinsp;0.64), suggesting that illness severity among admitted patients remained largely unchanged despite these pressures\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 COVID-19 status and the pandemic\u003c/h2\u003e \u003cp\u003eDuring the COVID-19 pandemic period, we found a significantly increased risk of LLST among COVID-19 compared to non-COVID-19 patients (OR 1.17, 95%, CI 1.07\u0026ndash;1.25). This somewhat contradicts findings from an Australian study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], where frail patients with, compared to without, COVID-19 had the same frequency of LLST. As shown in our study, SAPS3 scores\u0026mdash;used to predict hospital mortality at ICU admission\u0026mdash;did not differ significantly before or during the pandemic, and only minimally between COVID-19 and non-COVID-19 patients (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultiple studies demonstrated that the pandemic, and more specifically, COVID-19 status, had a significant but divergent impact on time to surgery and surgical outcomes [22, 23] and delayed or avoided acute care [24, 25, 26].\u003c/p\u003e \u003cp\u003eThe mechanisms underlying the increased likelihood of LLST among COVID-19 patients remain unclear. Syrous \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e27\u003c/span\u003e] examined end-of-life decision-making in critically ill elderly patients during the pandemic and found that LLST decisions in COVID-19 patients were influenced by different factors compared with non-COVID-19 patients. Similarly, the European multicenter COVID-ICU study [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e28\u003c/span\u003e] identified age, frailty, and early severity of respiratory failure as key determinants of LLST, with notable variation across centers. LLST was, as expected, strongly associated with higher mortality, highlighting a fundamental challenge for intensivists: whether LLST contributes to mortality, reflects the severity of illness, or represents a combination of both.\u003c/p\u003e \u003cp\u003eAlthough the overall rise in LLST was modest during the COVID-19 pandemic, the qualitative change in documentation practices\u0026mdash;from 18.5% of patients lacking a recorded decision in 2018 to 11.4% in 2022, a 39% relative decrease (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u0026mdash;may reflect a heightened ethical awareness, which would make it a sign of meaningful evolution. Future research could explore whether these changes have persisted beyond the acute phase of the pandemic and how they have impacted patient outcomes, staff experiences, and ethical climate in healthcare settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThis is a nationwide registry study with strengths including high completeness and limited selection bias. Limitations include accuracy variations due to human error at registration and measurement bias due to variability in data completeness collections across different sites. There are limitations to the observational design with possible unmeasured confounders and residual confounding. Although we used DAGs and attempted to analyse mediation, such analyses rely on multiple assumptions that are difficult to meet, why the mediation analyses should be interpreted with caution.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eThe results of this study suggest a shift in use of LLST during the COVID-19 pandemic as evidenced by the increased use of LLST during the pandemic and among COVID-19 patients. It is possible that the increased emphasis on active decision making during the pandemic was the driver of this change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003e6.1 Ethics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis retrospective registry-based study used pseudonymized data from the Swedish Intensive Care Registry (Svenska Intensivv\u0026aring;rdsregistret, SIR; Dnr 202224). Ethical approval was obtained from the Swedish Ethical Review Authority (Etikpr\u0026ouml;vningsmyndigheten), approval numbers Dnr 2022-02760-01 and Dnr 2023-02923-02. The requirement for informed consent was waived because the study was based on existing registry data that were pseudonymized prior to access. All data handling complied with the General Data Protection Regulation (GDPR) and institutional data protection policies. Clinical trial registration: Not applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e6.2 Consent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e6.5 Funding\u003c/h2\u003e \u003cp\u003e This study received funding from the Department of Anaesthesiology and Intensive Care at Sahlgrenska University Hospital, M\u0026ouml;lndal, Sweden.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLisa Wiltz drafted the manuscript with substantial input from Fredrik Hessulf, Kasper Glerup Lauridsen, Adam Piasecki, and Joar Bj\u0026ouml;rk. Lisa Wiltz and Fredrik Hessulf performed the main statistical analyses. Kasper Glerup Lauridsen and Tobias Si\u0026ouml;land provided statistical support and guidance. All authors contributed to interpretation, critically revised the text, and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the Swedish Intensive Care Registry (SIR) for providing data and the clinical teams for their contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request, subject to ethical and legal restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWorld Health Organization [WHO] (2025, May 7.) \u0026ldquo;Corona virus disease (COVID-19)\u0026rdquo;.\u0026nbsp;https://www.who.int/emergencies/diseases/novel-coronavirus-2019\u003c/li\u003e\n \u003cli\u003eKramer, Daniel B., Bernard Lo, and Neal W. Dickert. 2020. \u0026ldquo;CPR in the Covid-19 Era, An Ethical Framework.\u0026rdquo; New England Journal of Medicine 383 (2): e6. https://doi.org/10.1056/nejmp2010758.\u003c/li\u003e\n \u003cli\u003eSocialstyrelsen. (2020, april). \u003cem\u003eNationella principer f\u0026ouml;r prioritering av rutinsjukv\u0026aring;rd under covid-19-pandemin: Kunskapsst\u0026ouml;d f\u0026ouml;r att utveckla regionala och lokala riktlinjer\u003c/em\u003e (Dnr: 13865/2020). https://www.socialstyrelsen.se/globalassets/sharepoint-dokument/dokument-webb/ovrigt/nationella-principer-for-prioritering-av-rutinsjukvard-covid19.pdf\u003c/li\u003e\n \u003cli\u003eLudvigsson, Jonas F. 2022. \u0026ldquo;How Sweden Approached the COVID-19 Pandemic: Summary and Commentary on the National Commission Inquiry.\u0026rdquo; Acta Paediatrica 112 (1): 19\u0026ndash;33. https://doi.org/10.1111/apa.16535.\u003c/li\u003e\n \u003cli\u003eCurtis, J. Randall, Erin K. Kross, and Renee D. Stapleton. 2020. \u0026ldquo;The Importance of Addressing Advance Care Planning and Decisions About Do-Not-Resuscitate Orders During Novel Coronavirus 2019 (COVID-19).\u0026rdquo; JAMA, March. https://doi.org/10.1001/jama.2020.4894.\u003c/li\u003e\n \u003cli\u003eMark Taubert, John Idris Baker, Anna Hudson, Elin Harding, \u0026lsquo;Do Not Attempt CPR\u0026rsquo;: how the pandemic changed perceptions and practice, Medicine, Volume 52, Issue 7, 2024, Pages 426-428, ISSN 1357-3039, https://doi.org/10.1016/j.mpmed.2024.04.008.\u003c/li\u003e\n \u003cli\u003eMark, N. M., S. G. Rayner, N. J. Lee, and J. R. Curtis. 2015. \u0026ldquo;Global Variability in Withholding and Withdrawal of Life-Sustaining Treatment in the Intensive Care Unit: A Systematic Review.\u0026rdquo; Intensive Care Medicine 41 (9): 1572\u0026ndash;85. https://doi.org/10.1007/s00134-015-3810-5.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBlock, Linda, Max Petzold, Alma Nordenskj\u0026ouml;ld Syrous, Birgitta Lindqvist, Helena Odenstedt Herg\u0026egrave;s, and Silvana Naredi. 2019. \u0026ldquo;Age, SAPS 3 and Female Sex Are Associated with Decisions to Withdraw or Withhold Intensive Care.\u0026rdquo; Acta Anaesthesiologica Scandinavica 63 (9): 1210\u0026ndash;15.\u0026nbsp;https://doi.org/10.1111/aas.13411.\u003c/li\u003e\n \u003cli\u003e\u0026Ouml;hrling G, Taxbro K. Inga skillnader i andel beslut att avst\u0026aring; livsuppeh\u0026aring;llande covidv\u0026aring;rd [Limitations of life-sustaining treatment in hospitalized patients with COVID-19: A retrospective study in a Swedish healthcare region]. Lakartidningen. 2022 Nov 30;119:22110. Swedish. PMID: 36448934.\u003c/li\u003e\n \u003cli\u003eK. Beck, A. Vincent, H. Cam, C. Becker, S. Gross, N. Loretz, et al. 2022. Medical futility regarding cardiopulmonary resuscitation in in-hospital cardiac arrests of adult patients: A systematic review and Meta-analysis. Resuscitation 2022 Vol. 172 Pages 181-193. DOI: 10.1016/j.resuscitation.2021.11.041\u003c/li\u003e\n \u003cli\u003eJ\u0026ouml;nsson, Nino, Niklas Pettersson, Peter Asplund, Anders Bremer, Stefan Lehtipalo, and Fredrik Hessulf. 2023. \u0026ldquo;Factors Associated with Treatment Limitations in Two Swedish Intensive Care Units: Prevalence and Patient Involvement.\u0026rdquo; Acta Anaesthesiologica Scandinavica 67 (3): 339\u0026ndash;46. https://doi.org/10.1111/aas.14185.\u003c/li\u003e\n \u003cli\u003eSvenska Intensivv\u0026aring;rdsregistret. (n.d.). \u003cem\u003eThe Swedish Intensive Care Registry (SIR)\u003c/em\u003e. Retrieved September 25, 2025, from https://www.icuregswe.org/en/\u003c/li\u003e\n \u003cli\u003eElm, Erik von, Douglas G. Altman, Matthias Egger, Stuart J. Pocock, Peter C. G\u0026oslash;tzsche, and Jan P. Vandenbroucke. 2014. \u0026ldquo;The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies.\u0026rdquo; International Journal of Surgery 12 (12): 1495\u0026ndash;99. https://doi.org/10.1016/j.ijsu.2014.07.013.\u003c/li\u003e\n \u003cli\u003eSvenska Intensivv\u0026aring;rdsregistret. (n.d.). \u003cem\u003eR \u0026amp; D applications\u003c/em\u003e. Retrieved September 25, 2025, from https://www.icuregswe.org/en/research/r--d-applications/\u003c/li\u003e\n \u003cli\u003eMoreno, Rui P., Philipp G. H. Metnitz, Eduardo Almeida, Barbara Jordan, Peter Bauer, Ricardo Abizanda Campos, Gaetano Iapichino, David Edbrooke, Maurizia Capuzzo, and Jean-Roger Le Gall. 2005. \u0026ldquo;SAPS 3From Evaluation of the Patient to Evaluation of the Intensive Care Unit. Part 2: Development of a Prognostic Model for Hospital Mortality at ICU Admission.\u0026rdquo; Intensive Care Medicine 31 (10): 1345\u0026ndash;55. https://doi.org/10.1007/s00134-005-2763-5.\u003c/li\u003e\n \u003cli\u003eKatikireddi SV, et al. Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs. \u003cem\u003eInt J Epidemiol.\u003c/em\u003e 2019;49(1):322\u0026ndash;329. doi:10.1093/ije/dyz150.\u003c/li\u003e\n \u003cli\u003eJ\u0026ouml;nsson N, Pettersson N, Asplund P, Bremer A, Lehtipalo S, Hessulf F. Actors associated with treatment limitations in two Swedish intensive care units: prevalence and patient involvement. \u003cem\u003eActa Anaesthesiol Scand.\u003c/em\u003e 2022 Dec 19;67(2):224\u0026ndash;232. doi:10.1111/aas.14185\u003c/li\u003e\n \u003cli\u003ePiscitello GM, Parker WF. Do-Not-Resuscitate Orders by COVID-19 Status Throughout the First Year of the COVID-19 Pandemic. Chest. 2024 Mar;165(3):601-609. doi: 10.1016/j.chest.2023.09.024. Epub 2023 Sep 29. PMID: 37778695; PMCID: PMC10925541.\u003c/li\u003e\n \u003cli\u003eConnellan D, Diffley K, McCabe J, Cotter A, McGinty T, Sheehan G, Ryan K, Cullen W, Lambert JS, Callaly EL, Kyne L. Documentation of Do-Not-Attempt-Cardiopulmonary-Resuscitation orders amid the COVID-19 pandemic. Age Ageing. 2021 Jun 28;50(4):1048-1051. doi: 10.1093/ageing/afab075. PMID: 33909020; PMCID: PMC8135469.\u003c/li\u003e\n \u003cli\u003eSotoodeh A, Hedberg P, M\u0026aring;rtensson J, Naucl\u0026eacute;r P. Association of hospital and intensive care unit occupancy and non-admission to the intensive care unit decisions: a retrospective cohort study. Intensive Care Med. 2025 Mar;51(3):621-623. doi: 10.1007/s00134-025-07790-8. Epub 2025 Jan 20. Erratum in: Intensive Care Med. 2025 Mar;51(3):660. doi: 10.1007/s00134-025-07817-0. PMID: 39831999; PMCID: PMC12018513.\u003c/li\u003e\n \u003cli\u003eSubramaniam A, Tiruvoipati R, Pilcher D, Bailey M. Treatment limitations and clinical outcomes in critically ill frail patients with and without COVID-19 pneumonitis. J Am Geriatr Soc. 2023 Jan;71(1):145-156. doi: 10.1111/jgs.18044. Epub 2022 Sep 24. PMID: 36151970; PMCID: PMC9539196.\u003c/li\u003e\n \u003cli\u003eCodner JA, Archer RH, Lynde GC, Sharma J. Timing is everything: surgical outcomes for SARS-CoV-2 positive patients.\u0026nbsp;\u003cem\u003eWorld J Surg.\u003c/em\u003e 2023;47(2):437-444. doi: 10.1007/s00268-022-06814-4.\u003cbr\u003e\u0026nbsp;﷟HYPERLINK \u0026quot;https://pubmed.ncbi.nlm.nih.gov/36316514/?utm_source=chatgpt.com\u0026quot;\u003c/li\u003e\n \u003cli\u003eKim HJ, Ahn E, Oh EJ, Bang SR. Perioperative Coronavirus Disease 2019 Infection and Its Impact on Postoperative Outcomes: Pulmonary Complications and Mortality Based on Korean National Health Insurance Data. \u003cem\u003eJ Pers Med.\u003c/em\u003e 2025;15(4):157. doi: 10.3390/jpm15040157.\u003c/li\u003e\n \u003cli\u003eSmith M, Vaughan Sarrazin M, Wang X, Nordby P, Yu M, DeLonay AJ, Jaffery J. Risk from delayed or missed care and non-COVID-19 outcomes for older patients with chronic conditions during the pandemic.\u0026nbsp;\u003cem\u003eJ Am Geriatr Soc.\u003c/em\u003e 2022;70(5):1314-1324. doi:10.1111/jgs.17722.\u003cbr\u003e\u0026nbsp;﷟HYPERLINK \u0026quot;https://www.researchgate.net/publication/358853265_Risk_from_delayed_or_missed_care_and_non-COVID-19_outcomes_for_older_patients_with_chronic_conditions_during_the_pandemic?utm_source=chatgpt.com\u0026quot;\u003c/li\u003e\n \u003cli\u003eTripathy S, Vijayaraghavan BKT, Panigrahi MK, Shetty AP, Haniffa R, Mishra RC, Beane A. Collateral Impact of the COVID-19 Pandemic on Acute Care of Non-COVID Patients: An Internet-based Survey of Critical Care and Emergency Personnel.\u0026nbsp;\u003cem\u003eIndian J Crit Care Med.\u003c/em\u003e 2021;25(4):374-381. doi:10.5005/jp-journals-10071-23782.\u003cbr\u003e\u0026nbsp;﷟HYPERLINK \u0026quot;https://pubmed.ncbi.nlm.nih.gov/34045802/?utm_source=chatgpt.com\u0026quot;\u003c/li\u003e\n \u003cli\u003eFrey A, Tilstra AM, Verhagen MD. Inequalities in healthcare use during the COVID-19 pandemic. \u003cem\u003eNat Commun.\u003c/em\u003e 2024;15(1):1894. doi:10.1038/s41467-024-45720-2.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSyrous AN, Gudnadottir G, Oras J, Ferguson T, Lilja D, Odenstedt Herges H, Larsson E, Block L. End-of-life decision-making in critically ill old patients with and without coronavirus disease 2019. Acta Anaesthesiol Scand. 2024 Jan;68(1):63-70. doi: 10.1111/aas.14326. Epub 2023 Sep 5. PMID: 37670491.\u003c/li\u003e\n \u003cli\u003eGiabicani, M., Le Terrier, C., Poncet, A. \u003cem\u003eet al.\u003c/em\u003e Limitation of life-sustaining therapies in critically ill patients with COVID-19: a descriptive epidemiological investigation from the COVID-ICU study. \u003cem\u003eCrit Care\u003c/em\u003e 27, 103 (2023). https://doi.org/10.1186/s13054-023-04349-1.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8044900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8044900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe use of limitations of life-sustaining treatment (LLST) in Intensive Care Units (ICUs) varies internationally. The COVID-19 pandemic led to significant changes worldwide, but its impact on ICU LLST remains unclear. This study aimed to assess the prevalence of LLST in Swedish ICUs from 2018 to 2022 and whether the pandemic and COVID-19 influenced their utilization.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAll ICU admissions registered in the Swedish Intensive Care Registry from 2018\u0026ndash;2022 were screened. Cases with post-anesthesia care of less than 24 hours and subsequent ICU admissions were excluded. Logistic regression was used to analyze associations with LLST, defined as any limitation of therapy, including withholding or withdrawing therapy. March 16, 2020, marked the start of the pandemic.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn total, 77 735 ICU admissions were analyzed: 39 396 pre-pandemic and 38 338 during the pandemic. Patients admitted during the pandemic had slightly higher odds (OR 1.06, 95% CI 1.03\u0026ndash;1.10, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of receiving LLST compared to before the pandemic. COVID-19 patients had an increased odds of receiving a LLST compared to non COVID-19 patients (OR 1.17, 95% CI 1.09\u0026ndash;1.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ICU capacity utilization was not associated with LLST use in our study.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings suggest a shift in LLST use, with increased odds during the pandemic and among COVID-19 patients specifically. This may reflect greater attention to active decision-making regarding life-sustaining therapy during this period.\u003c/p\u003e","manuscriptTitle":"Changes in Life-Sustaining Treatment Limitations in Swedish ICUs During the COVID-19 Pandemic","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:13:53","doi":"10.21203/rs.3.rs-8044900/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"164827983489738128079050787614245558346","date":"2025-12-29T09:50:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T12:28:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-14T09:45:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-13T01:59:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T01:58:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2025-11-06T07:13:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fad70903-bb55-494e-9ba1-966f5a11fb96","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T09:13:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 09:13:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8044900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8044900","identity":"rs-8044900","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}