Prevalence of frailty and its association with six-month mortality in critically ill COVID-19 patients: a prospective observational cohort study

Prevalence of frailty and its association with six-month mortality in critically ill COVID-19 patients: a prospective observational cohort study | 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 Prevalence of frailty and its association with six-month mortality in critically ill COVID-19 patients: a prospective observational cohort study Sinan Değirmencioğlu, Mehmet Sargın, Jale Bengi Çelik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8776291/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The aim of this study was to determine the prevalence of frailty using the Clinical Frailty Scale (CFS) in patients admitted to a tertiary intensive care unit (ICU) due to COVID-19 and to evaluate the association between this score and long-term mortality. Methods This single-center, prospective, observational cohort study was conducted at Selçuk University Faculty of Medicine Hospital between January 1 and June 4, 2022, and included 137 patients admitted to the intensive care unit (ICU) with COVID-19. CFS, SOFA and APACHE II scores were evaluated along with demographic, clinical, laboratory and mortality data of the patients. Risk factors associated with six-month mortality were analyzed using multivariate logistic regression. The prognostic performance of the CFS was determined via Receiver Operating Characteristic (ROC) curve analysis. Results The prevalence of clinical frailty was found to be 57.7%. The six-month mortality rate was 68.6%. CFS, SOFA, and APACHE II scores were significantly associated with six-month mortality ( p < 0.001). The AUC value of the CFS in predicting six-month mortality was 0.765, and the optimal cut-off value was identified as 4.5. In addition, albumin, lymphocyte, and platelet levels were higher in survivors, whereas ferritin, CRP/albumin ratio, and procalcitonin levels were found to be higher in non-survivors. Conclusions Clinical Frailty Score is an independent risk factor for predicting long-term mortality in patients with COVID-19.. CFS is a valuable prognostic tool that can be used in addition to classical scoring systems for patient management and resource planning in intensive care units. Trial registration: ClinicalTrials.gov identifier: NCT06330883 COVID-19 intensive care frailty clinical frailty score mortality prognosis Figures Figure 1 Background Many people have been affected by the pandemic after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection spread from the city of Wuhan to the world (1–3). In the first wave of the pandemic, geriatric patients over the age of 70 were the most affected patient group, and mortality due to SARS-CoV-2 was very high (4–6). In the next waves of the pandemic, the age group of patients occupying most of the beds in intensive care units shifted from geriatric patients to between the ages of 50 and 70. Mortality rates in intensive care units decreased in parallel with the shift toward younger patient populations, improvements in healthcare quality, and increased clinical experience with the infection (7, 8). Predicting the prognosis of patients admitted to intensive care units enables healthcare system planning (9). A variety of methods have been employed to assess the prognosis of patients in intensive care units. The most commonly used parameters include age, comorbidities, the Sepsis-related Organ Failure Assessment (SOFA) score, the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, laboratory values, and frailty assessments. There is a need for more comprehensive scoring systems and prognostic factors that encompass adult patient populations in intensive care units (ICUs) (10). Frailty was initially studied in geriatric patient populations and was first investigated in the intensive care setting among elderly individuals (11). Recently, frailty has been recognized as an important factor not only in geriatric patient groups but also in younger populations (11), however, its impact on clinical outcomes in younger patients remains unclear (11–13). It is well established that frailty increases the risk of short-term mortality (14), however, evidence regarding its long-term impact in patients with COVID-19 remains limited (15). In this study, we aimed to determine the prevalence of frailty in COVID-19 patients followed in the intensive care unit and to evaluate its association with six-month mortality. Methods This study was designed as a single-center, prospective, observational cohort study conducted between January 1, 2022, and June 4, 2022, at Selçuk University Faculty of Medicine Hospital. Ethical approval for the study was obtained from the Selçuk University Faculty of Medicine Institutional Ethics Committee (Approval No: 2021/507). Written informed consent forms were obtained directly from patients who were conscious and capable of providing consent; for sedated, mechanically ventilated, unconscious, or otherwise incapacitated patients, written consent forms were obtained from their legal representatives. The study included patients aged 18 years and older who were admitted to a tertiary intensive care unit (ICU). Patients who met the following criteria were included individuals with a confirmed diagnosis of COVID-19 via a positive reverse transcription–polymerase chain reaction (RT-PCR) test, as well as those with suspected false-negative RT-PCR results at admission who were clinically diagnosed based on clinical symptoms, physical examination, and radiological findings. The following patients were excluded from the study: those who were pregnant, those admitted to the intensive care unit (ICU) due to trauma, patients lost to follow-up during the six-month observation period, and cases involving readmission to the ICU for COVID-19. Demographic data including age, sex, and body mass index (BMI) of the patients included in the study were recorded. In relation to the diagnosis of COVID-19, RT-PCR test results (positive/negative) obtained either prior to admission or during the ICU course were evaluated. The length of stay in the intensive care unit (in days) and mortality data within the six-month period following ICU admission were systematically recorded. The frailty level of each patient was assessed by the attending intensive care physician at the time of ICU admission. Assessments were conducted directly with the patient if they were conscious and communicative. In cases of impaired consciousness, data were obtained from first-degree relatives or the healthcare professionals responsible for the patient's clinical follow-up. The level of frailty was determined using the Turkish-validated version of the Clinical Frailty Scale (CFS). The scale is a nine-point visual and descriptive system that categorizes individuals on a spectrum ranging from a score of 1, representing 'very fit,' to a score of 9, indicating 'terminally ill'. The CFS scores of all patients were recorded individually. Vaccination status, SOFA and APACHE II scores, and the smoking histories of the patients were documented. Patients were evaluated for the presence of comorbidities, including diabetes mellitus, hypertension, coronary artery disease, congestive heart failure, chronic kidney disease, chronic lung disease, chronic neurological disorders, and malignancy; all identified conditions were systematically documented. During daily clinical follow-up, the administration of renal replacement therapy (RRT) and the requirement for vasopressor support were categorized as received or not received and incorporated into the dataset. Biochemical and hematological laboratory parameters obtained at the time of ICU admission were systematically documented. The following parameters were analyzed and documented: white blood cell (WBC) count, neutrophil and lymphocyte counts, platelet count, hematocrit level, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin and CRP/albumin ratio, serum creatinine level, estimated glomerular filtration rate (eGFR), ferritin, D-dimer, C-reactive protein (CRP) levels, and procalcitonin (PCT) levels. Statistical Analysis The study data were entered and analyzed using SPSS for Mac OS, Version 26.0 Multilingual (ISO Version) (Statistical Package for the Social Sciences Inc., Chicago, IL, USA). Descriptive statistical methods were used to summarize the data. Numerical data were reported as median and interquartile range (IQR), whereas categorical variables were expressed as frequencies and percentages (%). The normality of data distribution was assessed using both visual methods (histograms and probability plots) and analytical tests (Kolmogorov–Smirnov and Shapiro–Wilk tests). Categorical variables were analyzed using the Chi-square test or Fisher’s exact test. Numerical variables were compared using the Mann–Whitney U test, as they were found to be non-parametrically distributed. In the multivariate analysis, logistic regression was performed to identify independent predictors of in-ICU and six-month mortality, incorporating variables that demonstrated statistical significance in the univariate analyses. Furthermore, Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the discriminative power of the models. For all statistical analyses, a p -value of < 0.05 was considered statistically significant. A formal a priori sample size calculation was not performed due to the observational nature of the study. Results A total of 148 patients admitted with a diagnosis of COVID-19 to the tertiary intensive care unit (ICU) of Selçuk University Faculty of Medicine Hospital between January 1 and June 4, 2022, were included in the study. Based on the eligibility criteria, 11 patients were excluded from the study: two due to pregnancy, three due to admission following trauma, three due to missing clinical data, and three due to loss to follow-up at six months. The final analysis was conducted with a total of 137 patients who fulfilled the eligibility criteria and had complete data available. The demographic and clinical characteristics of the patients are presented in Table 1 The median age of the patients was 72 years (IQR: 60–79), with 49.6% being female and 50.4% male. The median value of body mass index (BMI) was 25.95 kg/m² (IQR: 22.06–29.38). A total of 76.6% of the patients had a diagnosis of COVID-19 confirmed by RT-PCR. The median length of stay in the intensive care unit was 8 days (IQR: 4–14), and the median Clinical Frailty Score (CFS) was 6 (IQR: 4–7). The patients were vaccinated (76.6%). SOFA score was 7 (IQR: 4–10) and APACHE II score was 22 (IQR: 16–29). A total of 8.8% of the participants were smokers. The most prevalent comorbidities were hypertension (48.2%), diabetes mellitus (30.7%), malignancy (28.5%), chronic neurological disease (25.5%), and chronic lung disease (21.2%), in descending order of frequency. Renal replacement therapy (RRT) was administered to 8% of the patients, while 20.4% required vasopressor support ( Table 1). Table 1. Demographic and General Characteristics of the Study Population Variables Total Patients (n=137) Median (IQR) or n (%) Age, years 72 (60-79) Gender, n (%) Female Male 68 (49.6%) 69 (50.4%) BMI, kg/m² 25.95 (22.06-29.38) COVID-19 RT-PCR, n (%) Positive Negative 105 (76.60%) 32 (23.40%) ICU Length of Stay, days 8 (4-14) Clinical Frailty Score (CFS) 6 (4-7) Vaccination Status, n (%) Vaccinated Unvaccinated 105 (76.60%) 32 (23.40%) SOFA Score 7 (4-10) APACHE II Score 22 (16-29) Smoking Status, n (%) 12 (8.80%) Diabetes Mellitus, n (%) 42 (30.70%) Hypertension, n (%) 66 (48.20%) Coronary Artery Disease, n (%) 32 (23.40%) Congestive Heart Failure, n (%) 15 (10.90%) Chronic Kidney Disease, n (%) 21 (15.30%) Chronic Lung Disease, n (%) 29 (21.20%) Chronic Neurological Disorders, n (%) 35 (25.50%) Malignancy, n (%) 39 (28.50%) Renal Replacement Therapy, n(%) 11 (8.00%) Vasopressor Support, n(%) 28 (20.40%) IQR, Interquartile Range; BMI, Body Mass Index; RT-PCR, Reverse Transcriptase-Polymerase Chain Reaction; SOFA, Sepsis-related Organ Failure Assessment; APACHE II, Acute Physiology and Chronic Health Evaluation II. Laboratory parameters at ICU admission are summarized in Table 2 . Based on inflammatory and organ function parameters, the median values were as follows: white blood cell (WBC) count 10.56 × 10⁹/L, neutrophil count 8.94 × 10⁹/L, lymphocyte count 0.85 × 10⁹/L, platelet count 212 × 10⁹/L, and hematocrit level 34.90%. The neutrophil/lymphocyte ratio (NLR) was 10.00 (IQR: 4.52–17.87) and the platelet/lymphocyte ratio (PLR) was 224.44 (IQR: 135.40–413.04). The median serum albumin level was 3.16 g/dL (IQR: 2.78–3.50), with a CRP/albumin ratio of 24.17, serum creatinine of 1.03 mg/dL, an estimated glomerular filtration rate (eGFR) of 70.07 mL/min/1.73 m², and a ferritin level of 760 ng/mL. The D-dimer level was 1599 ng/mL, CRP was 72.60 mg/L, and procalcitonin (PCT) was 0.36 μg/L ( Table 2). Table 2. Laboratory Values at Intensive Care Unit Admission Variables Total Patients (n=137) Median (IQR) WBC, *10^9/L 10.56 (6.56–15.91) Neutrophil, *10^9/L 8.94 (4.99–14.03) Lymphocyte, *10^9/L 0.85 (0.52–1.51) Platelet count, *10^9/L 212.00 (139.50–309.00) Hematocrit, % 34.90 (29.45–40.25) Neutrophil-to-Lymphocyte Ratio (NLR) 10.00 (4.52–17.87) Platelet-to-Lymphocyte Ratio (PLR) 224.44 (135.40–413.04) CRP/Albumin ratio 24.17 (6.91–53.54) Albumin, g/dL 3.16 (2.78–3.50) Serum Creatinine, mg/dL 1.03 (0.70–2.01) eGFR, mL/min/1.73m 2 70.07 (30.60–95.43) Ferritin, ng/ml 760 (274–1132) D-Dimer, ng/mL 1599 (691–3132) CRP, mg/L 72.60 (23.30–158.50) PCT, mg/L 0.36 (0.12–1.53) IQR, Interquartile Range; WBC, White Blood Cell; CRP, C-Reactive Protein; eGFR, estimated Glomerular Filtration Rate; PCT, Procalcitonin. The findings regarding factors associated with six-month mortality are summarized in Table 3 At the end of the six-month follow-up, the overall mortality rate was 68.6% (n = 94), with 31.4% (n = 43) of patients surviving. The median age was significantly higher in non-survivors than in survivors (75 [IQR: 64–82] vs. 65 [IQR: 49–75] years; p < 0.0001). The median body mass index (BMI) was significantly higher in survivors compared to non-survivors (27.34 vs. 24.97 kg/m²; p = 0.034). The Clinical Frailty Score, SOFA, and APACHE II scores were significantly higher in non-survivors compared to survivors ( p < 0.0001 for all). A history of malignancy was associated with a significantly higher mortality rate compared to patients without malignancy (36.2% vs. 11.6%, p = 0.014). Lymphocyte ( p = 0.002), platelet ( p = 0.002), and albumin ( p = 0.001) levels were significantly higher in survivors, whereas NLR ( p = 0.035), CRP/albumin ratio ( p = 0.002), ferritin ( p < 0.0001), CRP ( p = 0.002), and PCT ( p < 0.0001) levels were higher in the non-survivor group. Furthermore, survivors had significantly higher eGFR levels compared to non-survivors ( p = 0.007) ( Table 3). [Table 3 here] Table 3. Univariate and Multivariate Analysis of 6-Month ICU Mortality Univariate Multivariate Variables Survivors (n=43) Median (IQR), n(%) Non-Survivors (n=94) Median (IQR), n(%) P P Age, years 65 (49–75) 75 (64–82) <0.0001 <0.0001 Gender, n (%) Female Male 17(39.5%) 26(60.5%) 51(54.3%) 43(45.7%) 0.11 BMI, kg/m² 27.34 (23.43–32.00) 24.97 (21.57–29.17) 0.029 0.034 COVID-19 RT-PCR, n (%) Positive Negative 37(86%) 6(14%) 68(72.3%) 26(27.7%) 0.078 ICU Length of Stay, days 10 (6–14) 7 (3–14) 0.13 Clinical Frailty Score (CFS) 4 (3–6) 6 (4–8) <0.0001 <0.0001 Vaccination Status, n (%) Vaccinated Unvaccinated 34(79.1%) 9(20.9%) 71(75.5%) 23(24.5%) 0.650 SOFA Score 5 (3–8) 8 (5–11) <0.0001 <0.0001 APACHE II Score 16(12–22) 24 (18–31) <0.0001 <0.0001 Smoking Status, n (%) Smokers Non-smokers 4(9.3%) 39(90.7%) 8(8.5%) 86(91.5%) 0.879 Diabetes Mellitus, n (%) Yes No 15(34.9%) 28(65.1%) 27(28.7%) 67(71.3%) 0.468 Hypertension, n (%) Yes No 15(34.9%) 28(65.1%) 51(54.3%) 43(45.7%) 0.035 0.067 Coronary Artery Disease, n (%) Yes No 9(20.9%) 34(79.1%) 23(24.5%) 71(75.5%) 0.650 Congestive Heart Failure, n (%) Yes No 5(11.6%) 38(88.4%) 10(10.6%) 84(89.4%) 0.863 Chronic Kidney Disease, n (%) Yes No 6(14%) 37(86%) 15(16%) 79(84%) 0.763 Chronic Lung Disease, n (%) Yes No 10(23.3%) 33(76.7%) 19(20.2%) 75(79.8%) 0.686 Chronic Neurological Disorders, n (%) Yes No 8(18.6%) 35(81.4%) 27(28.7%) 67(71.3%) 0.208 Malignancy, n (%) Yes No 5(11.6%) 38(88.4%) 34(36.2%) 60(63.8%) 0.003 0.014 Renal Replacement Therapy , n(%) Yes No 3(8.5%) 40(93%) 8(8.5%) 86(91.5%) 0.759 Vasopressor Support , n(%) Yes No 4(9.3%) 39(90.7%) 24(25.5%) 70(74.5%) 0.029 NS WBC, *10^9/L 10.52 (6.86–15.11) 10.76 (6.11–16.16) 0.867 Neutrophil, *10^9/L 7.49 (4.44–11.97) 9.40 (5.14–14.38) 0.481 Lymphocyte, *10^9/L 1.32 (0.74–2.17) 0.75 (0.41–1.19) <0.0001 0.002 Platelet count, *10^9/L 287.00 (182.00–358.00) 198.50 (111.75–267.50) <0.0001 0.002 Hematocrit, % 37.10 (32.30–40.10) 34.00 (29.15–40.40) 0.077 Neutrophil-to-Lymphocyte Ratio (NLR) 7.36 (2.85–13.01) 11.58 (5.95–23.34) 0.002 0.035 Platelet-to-Lymphocyte Ratio (PLR) 184.33 (134.93–332.00) 247.02 (138.00–455.22) 0.17 CRP/Albumin ratio 9.35 (2.37–33.42) 32.33 (11.73–59.87) <0.0001 0.002 Albumin, g/dL 3.30 (3.04–3.60) 3.08 (2.60–3.40) 0.001 0.001 Serum Creatinine, mg/dL 0.93 (0.63–1.67) 1.14 (0.74–2.20) 0.133 eGFR, mL/min/1.73m 2 83.30 (42.28–106.10) 56.55 (26.89–89.18) 0.016 0.007 Ferritin, ng/ml 371 (233–803) 801 (376–1611) 0.001 <0.0001 D-Dimer, ng/mL 979 (340–2511) 1836 (926–3498) 0.009 NS CRP, mg/L 28.00 (9.91–117.00) 90.15 (36.37–182.75) 0.001 0.002 PCT, mg/L 0.14 (0.06–0.28) 0.81 (0.18–2.13) <0.0001 NS IQR, Interquartile Range; BMI, Body Mass Index; RT-PCR, Reverse Transcriptase-Polymerase Chain Reaction; SOFA, Sepsis-related Organ Failure Assessment; APACHE II, Acute Physiology and Chronic Health Evaluation II; WBC, White Blood Cell; CRP, C-Reactive Protein; eGFR, estimated Glomerular Filtration Rate; PCT, Procalcitonin; NS: Non-significant. The predictive performance of the Clinical Frailty Score (CFS) for six-month mortality, evaluated via ROC analysis, is presented in Table 4 ROC analysis revealed an area under the curve (AUC) of 0.765 (95% CI: 0.682–0.848), demonstrating significant predictive ability for six-month mortality ( p < 0.0001). The optimal cut-off value was determined to be 4.5. Using this optimal cut-off, the CFS demonstrated a sensitivity of 72%, a specificity of 69%, a positive predictive value of 83%, and a negative predictive value of 53% for predicting mortality. Furthermore, the positive likelihood ratio was 2.39, and the negative likelihood ratio was 0.40. These results indicate that the CFS score has significant prognostic value for predicting long-term mortality among critically ill patients (Table 4). Table 4. Clinical Frailty Score ROC Analysis for Predicting 6-Month Mortality in the Intensive Care Unit The distribution of mean Clinical Frailty Score (CFS) scores according to mortality status is illustrated in Figure 1. As shown in the figure, there was a marked difference in Clinical Frailty Score (CFS) between survivors and non-survivors at the six-month follow-up. The mean CFS score was significantly higher in non-survivors compared to survivors. Mortality rates were significantly higher in patients with a CFS score above the optimal cut-off value of 4.5 compared to those below this threshold. These visual data reinforce the role of the CFS score as both a statistically significant and clinically meaningful discriminator for patient outcomes (Figure 1). Discussion This study aimed to evaluate the predictive value of the Clinical Frailty Score (CFS) on 6-month mortality in critically ill patients admitted to a tertiary intensive care unit with a diagnosis of COVID-19. The findings demonstrated that advanced age, elevated frailty scores, increased inflammatory markers, and parameters indicative of physiological vulnerability were significantly associated with long-term mortality. Notably, the prognostic power of the CFS, as evidenced by the ROC analysis, underscores the importance of considering not only chronological age but also physiological reserve in the management of acute infections such as COVID-19. This study investigated the impact of the Clinical Frailty Score (CFS), alongside SOFA and APACHE II scores, on six-month mortality among patients admitted to a tertiary intensive care unit with COVID-19. Our findings align with previous literature—specifically studies by Fumagalli et al. [16] and Aliberti et al. [10] which identified CFS as an independent predictor of mortality. Frailty has been established as a significant correlate of 30-day, 3-month, and 6-month mortality, particularly in elderly and middle-aged populations [4, 5, 7, 10, 17]. Consistent with these reports, our results demonstrate that CFS serves as a robust predictor of long-term outcomes. Importantly, our study was conducted during a later phase of the pandemic when healthcare services were more structured and triage protocols were systematically implemented. This timing minimized the risk of patient exclusion due to the acute resource shortages prevalent during the initial waves, thereby yielding a more representative and comprehensive sample of admitted patients. In the present study, the prevalence of frailty was 57.7%, which exceeds the 46% reported by Guidet et al. in ICU patients aged > 80 years [17] and the 33% reported by Aliberti et al. in those aged > 65 years [10]. The inclusion of all adults over 18 years without age restrictions, coupled with the high acuity of COVID-19, may account for this finding. Furthermore, given that mortality risk escalates with advancing years, our results confirm that age remains an independent risk factor for six-month mortality. Some studies have suggested that frailty scores may have limited predictive value, particularly in younger frail patients, due to their lower mortality rates [18]. Nevertheless, significant declines in functional capacity and quality of life have been reported in this population [19, 20]. As these parameters were not monitored in our study, the long-term quality of life of younger patients could not be assessed. Aliberti et al. [10], demonstrated that the Clinical Frailty Score is a valid assessment tool for both young and middle-aged SARS-CoV-2 patients. Furthermore, Dres et al. [5] reported that frailty offers superior long-term mortality prediction compared to traditional prognostic models such as SOFA. In contrast, our study found that CFS, SOFA, and APACHE II scores were all significant predictors of outcome. These findings suggest that frailty should be integrated as a complementary assessment tool rather than a substitute for acute illness severity scores in the clinical evaluation of COVID-19 patients. Advanced age has been consistently linked to poor prognosis in COVID-19 patients requiring ICU admission [7, 20, 22, 23]. Our results corroborate this association, showing higher mortality rates among elderly patients. Notably, the older age of patients who died within 6 months suggests that late-term mortality is more strongly associated with age. However, our ability to further dissect the dimensions of this effect was limited by the lack of predefined age subgroup analyses. Both RT-PCR-confirmed and clinically diagnosed COVID-19 patients were included in our cohort. The lack of a significant difference in mortality rates between these two subgroups reinforces the validity of clinical diagnosis and the robustness of clinician-led assessments during the pandemic. Some studies, such as those by Günster [20] and Grasselli [22], have reported that male gender is associated with poor prognosis. However, in our study, gender did not have a statistically significant effect on mortality. Similarly, findings regarding obesity remain conflicting. While Gupta [24] did not observe increased mortality in patients with a BMI > 40, Günster identified BMI as a risk factor [20]. In our study, BMI was associated only with 6-month mortality. Regarding comorbidities, malignancy and hypertension were found to be associated with 6-month mortality. Interestingly, chronic pulmonary diseases and smoking history did not demonstrate a significant correlation with mortality in our cohort. However, as comorbidity data were obtained from patients or their surrogates, certain associations may have been underestimated due to potential recall bias or the subjective nature of self-reported medical histories. Azoulay et al. [9] linked the high mortality observed in COVID-19 patients to multiorgan failure. Consistent with this, our analysis revealed that—beyond the total SOFA score—specific subcomponents such as platelet count and vasopressor requirements, along with key inflammatory markers (CRP/albumin ratio, ferritin, and albumin), were independently associated with mortality. These results underscore the pivotal role of systemic inflammation in driving both the progression of frailty and poor clinical outcomes. This study has several limitations. First, frailty was assessed using the Clinical Frailty Score (CFS). However, this assessment was performed by the attending physicians at the time of ICU admission, and the potential for inter-observer variability and subjective interpretation cannot be excluded. The CFS evaluations were based on the patient's condition during the two weeks prior to ICU admission, and this information was often obtained from relatives or caregivers, introducing a potential for recall bias. Although physicians received training on how to apply the scale, inter-rater reliability could not be fully ensured. Furthermore, our analysis was restricted to patients admitted to the ICU, excluding those who were triaged to other departments or deemed ineligible for intensive care admission. Consequently, the study population may be biased toward more clinically severe cases, potentially limiting the generalizability of our findings to the broader population of COVID-19 patients. Furthermore, our analysis did not account for specific treatment protocols, medication dosages, or therapeutic interventions administered during the ICU course. Although these variations represent significant potential confounders for mortality, they were not evaluated due to a lack of granular data. Likewise, systemic variables that may influence the quality of care—such as nurse-to-patient ratios—fell outside the scope of this study. The single-center, observational nature of this study may have introduced selection bias, potentially limiting the representativeness of our sample. Furthermore, the absence of randomization and the non-interventional design necessitate caution when drawing causal inferences from the observed associations; our results should be interpreted as correlational rather than definitive evidence of causality. Lastly, comorbidity data were predominantly derived from subjective reports by patients or their next of kin. This reliance on self-reported or proxy-reported history increases the susceptibility to information bias and may have led to instances of missing or under-reported data for certain clinical variables. Conclusions This study demonstrated that the Clinical Frailty Score (CFS) is an independent risk factor for predicting 6-month mortality in patients admitted to the intensive care unit due to COVID-19. CFS can be utilized alongside classical scoring systems such as SOFA and APACHE II to enhance prognostic accuracy in patient management. Incorporating frailty assessment into intensive care practice may support clinical decision-making and facilitate individualized treatment planning. CFS may serve as a practical bedside tool to support triage decisions, resource allocation, and individualized treatment strategies in critically ill patients with COVID-19. Abbreviations COVID-19 Coronavirus disease 2019 ICU Intensive care unit CFS Clinical Frailty Scale SOFA Sequential Organ Failure Assessment APACHE II Acute Physiology and Chronic Health Evaluation II RT-PCR Reverse transcription–polymerase chain reaction BMI Body mass index WBC White blood cell CRP C-reactive protein eGFR Estimated glomerular filtration rate PCT Procalcitonin ROC Receiver operating characteristic AUC Area under the curve IQR Interquartile range NLR Neutrophil-to-lymphocyte ratio PLR Platelet-to-lymphocyte ratio RRT Renal replacement therapy CI Confidence interval PPV Positive predictive value NPV Negative predictive value LR Likelihood ratio Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Selçuk University Faculty of Medicine Institutional Ethics Committee (Approval No: 2021/507). Written informed consent forms were obtained directly from patients who were conscious and capable of providing consent; for sedated, mechanically ventilated, unconscious, or otherwise incapacitated patients, written consent forms were obtained from their legal representatives. All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki and its later amendments. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work. Authors’ contributions JBC and MS contributed to conceptualization. SD and MS contributed to study design. SD was responsible for data acquisition. SD and JBC performed clinical supervision and patient follow-up. SD and MS conducted the statistical analysis. SD, MS, and JBC performed data interpretation. SD drafted the manuscript. MS and JBC critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank the intensive care unit staff of Selçuk University Faculty of Medicine for their support during data collection. Authors’ information Sinan Değirmencioğlu , MD, is a Specialist in Anesthesiology and Intensive Care Medicine. ORCID: 0000-0003-4731-6937 e-mail: [email protected] Jale Bengi Çelik , MD, PhD, is a Professor of Intensive Care Medicine. Researcher ID: 3307 ORCID: 0000-0003-2167-9967 e-mail: [email protected] Mehmet Sargın , MD, PhD, is a Professor of Anesthesiology and Intensive Care Medicine. Researcher ID: 140460 ORCID: 0000-0002-6574-273X e-mail: [email protected] References Guarneri V, Bassan F, Zagonel V, Milella M, Zaninelli M, Cattelan AM, et al. Epidemiology and clinical course of severe acute respiratory syndrome coronavirus 2 infection in cancer patients in the Veneto Oncology Network: The Rete Oncologica Veneta COVID19 study. Eur J Cancer. 2021;147:120-7. Polack FP, Thomas SJ, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and Efficacy of the BNT162b2 mRNA COVID-19 Vaccine. N Engl J Med. 2020;383(27):2603-15. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708–20. Jung C, Flaatten H, Fjølner J, Bruno RR, Wernly B, Artigas A, et al. The impact of frailty on survival in elderly intensive care patients with COVID-19: the COVIP study. Crit Care. 2021;25(1):149. Dres M, Hajage D, Lebbah S, Kimmoun A, Pham T, Béduneau G, et al. Characteristics, management, and prognosis of elderly patients with COVID-19 admitted in the ICU during the first wave: insights from the COVID-ICU study. Ann Intensive Care. 2021;11(1):77. Richards-Belle A, Orzechowska I, Gould DW, Thomas K, Doidge JC, Mouncey PR, et al. COVID-19 in critical care: epidemiology of the first epidemic wave across England, Wales and Northern Ireland. Intensive Care Med. 2020;46(11):2035-47. Kurtz P, Bastos LSL, Dantas LF, Zampieri FG, Soares M, Hamacher S, et al. Evolving changes in mortality of 13,301 critically ill adult patients with COVID-19 over 8 months. Intensive Care Med. 2021;47(5):538-48. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020;323(20):2052-9. Azoulay E, Fartoukh M, Darmon M, Géri G, Voiriot G, Dupont T, et al. Increased mortality in patients with severe SARS-CoV-2 infection admitted within seven days of disease onset. Intensive Care Med. 2020;46(9):1714-22. Aliberti MJR, Szlejf C, Avelino‐Silva VI, Suemoto CK, Apolinario D, Dias MB, et al. COVID‐19 is not over and age is not enough: Using frailty for prognostication in hospitalized patients. J Am Geriatr Soc. 2021;69(5):1116-27. De Geer L, Fredrikson M, Chew MS. Frailty is a stronger predictor of death in younger intensive care patients than in older patients: a prospective observational study. Ann Intensive Care. 2022;12(1):120 Flaatten H, De Lange DW, Morandi A, Andersen FH, Artigas A, Bertolini G, et al. The impact of frailty on ICU and 30-day mortality and the level of care in very elderly patients (≥ 80 years). Intensive Care Med. 2017;43(12):1820-8. Hewitt J, Carter B, Vilches-Moraga A, Quinn TJ, Braude P, Verduri A, et al. The effect of frailty on survival in patients with COVID-19 (COPE): a multicentre, European, observational cohort study. Lancet Public Health. 2020;5(8):e444-e51. Wolff G, Wernly B, Flaatten H, Fjølner J, Bruno RR, Artigas A, et al. Sex-specific treatment characteristics and 30-day mortality outcomes of critically ill COVID-19 patients over 70 years of age—results from the prospective COVIP study. Can J Anaesth. 2022;69(11):1390-8. Brummel NE, Bell SP, Girard TD, Pandharipande PP, Jackson JC, Morandi A, et al. Frailty and Subsequent Disability and Mortality among Patients with Critical Illness. Am J Respir Crit Care Med. 2017;196(1):64-72. Fumagalli C, Ungar A, Rozzini R, Vannini M, Coccia F, Cesaroni G, et al. Predicting Mortality Risk in Older Hospitalized Persons With COVID-19: A Comparison of the COVID-19 Mortality Risk Score with Frailty and Disability. J Am Med Dir Assoc. 2021;22(8):1588-92.e1. Guidet B, De Lange DW, Boumendil A, Leaver S, Watson X, Boulanger C, et al. The contribution of frailty, cognition, activity of daily life and comorbidities on outcome in acutely admitted patients over 80 years in European ICUs: the VIP2 study. Intensive Care Med. 2020;46(1):57-69. Marengoni A, Zucchelli A, Vetrano DL, Armellini A, Botteri E, Nicosia F, et al. Beyond Chronological Age: Frailty and Multimorbidity Predict In-Hospital Mortality in Patients With Coronavirus Disease 2019. J Gerontol A Biol Sci Med Sci. 2021;76(3):e38-e45. Hodgson CL, Higgins AM, Bailey MJ, Mather AM, Beach L, Bellomo R, et al. The impact of COVID-19 critical illness on new disability, functional outcomes and return to work at 6 months: a prospective cohort study. Crit Care. 2021;25(1):382. Günster C, Busse R, Spoden M, Rombey T, Schillinger G, Hoffmann W, et al. 6-month mortality and readmissions of hospitalized COVID-19 patients: A nationwide cohort study of 8,679 patients in Germany. PLoS One. 2021;16(8):e0255427. Le Maguet P, Roquilly A, Lasocki S, Asehnoune K, Carise E, Saint Martin M, et al. Prevalence and impact of frailty on mortality in elderly ICU patients: a prospective, multicenter, observational study. Intensive Care Med. 2014;40(5):674-82 Grasselli G, Greco M, Zanella A, Albano G, Antonelli M, Bellani G, et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern Med. 2020;180(10):1345-55. Polok K, Fronczek J, Artigas A, Flaatten H, Guidet B, De Lange DW, et al. Noninvasive ventilation in COVID-19 patients aged ≥ 70 years—a prospective multicentre cohort study. Crit Care. 2022;26(1):224 Gupta S, Hayek SS, Wang W, Chan L, Mathews KS, Melamed ML, et al. Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US. JAMA Intern Med. 2020;180(11):1436-47. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8776291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617624379,"identity":"8041b139-0594-49cb-9f85-140ab172de68","order_by":0,"name":"Sinan Değirmencioğlu","email":"data:image/png;base64,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","orcid":"","institution":"Konya City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Sinan","middleName":"","lastName":"Değirmencioğlu","suffix":""},{"id":617624380,"identity":"eb134c2f-d01a-463e-8f12-b9ab43dfaa91","order_by":1,"name":"Mehmet Sargın","email":"","orcid":"","institution":"Selçuk University Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mehmet","middleName":"","lastName":"Sargın","suffix":""},{"id":617624381,"identity":"ae61e878-bffc-4045-a493-c1d88da7b175","order_by":2,"name":"Jale Bengi Çelik","email":"","orcid":"","institution":"Selçuk University Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jale","middleName":"Bengi","lastName":"Çelik","suffix":""}],"badges":[],"createdAt":"2026-02-03 13:10:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8776291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8776291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106534011,"identity":"a177e666-7723-4690-b33a-c383596db6a0","added_by":"auto","created_at":"2026-04-09 15:01:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39951,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve of the Clinical Frailty Score for predicting six-month mortality.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8776291/v1/d962a76d0cd92503586d2bb6.png"},{"id":107884331,"identity":"ab79701e-61c3-4394-aae1-69974410c7cf","added_by":"auto","created_at":"2026-04-27 09:13:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8776291/v1/6166af8b-732a-42af-b9c4-3f6524840306.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence of frailty and its association with six-month mortality in critically ill COVID-19 patients: a prospective observational cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eMany people have been affected by the pandemic after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection spread from the city of Wuhan to the world (1\u0026ndash;3). In the first wave of the pandemic, geriatric patients over the age of 70 were the most affected patient group, and mortality due to SARS-CoV-2 was very high (4\u0026ndash;6). In the next waves of the pandemic, the age group of patients occupying most of the beds in intensive care units shifted from geriatric patients to between the ages of 50 and 70. Mortality rates in intensive care units decreased in parallel with the shift toward younger patient populations, improvements in healthcare quality, and increased clinical experience with the infection (7, 8).\u003c/p\u003e \u003cp\u003ePredicting the prognosis of patients admitted to intensive care units enables healthcare system planning (9). A variety of methods have been employed to assess the prognosis of patients in intensive care units. The most commonly used parameters include age, comorbidities, the Sepsis-related Organ Failure Assessment (SOFA) score, the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, laboratory values, and frailty assessments.\u003c/p\u003e \u003cp\u003eThere is a need for more comprehensive scoring systems and prognostic factors that encompass adult patient populations in intensive care units (ICUs) (10). Frailty was initially studied in geriatric patient populations and was first investigated in the intensive care setting among elderly individuals (11). Recently, frailty has been recognized as an important factor not only in geriatric patient groups but also in younger populations (11), however, its impact on clinical outcomes in younger patients remains unclear (11\u0026ndash;13). It is well established that frailty increases the risk of short-term mortality (14), however, evidence regarding its long-term impact in patients with COVID-19 remains limited (15).\u003c/p\u003e \u003cp\u003eIn this study, we aimed to determine the prevalence of frailty in COVID-19 patients followed in the intensive care unit and to evaluate its association with six-month mortality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was designed as a single-center, prospective, observational cohort study conducted between January 1, 2022, and June 4, 2022, at Sel\u0026ccedil;uk University Faculty of Medicine Hospital. Ethical approval for the study was obtained from the Sel\u0026ccedil;uk University Faculty of Medicine Institutional Ethics Committee (Approval No: 2021/507). Written informed consent forms were obtained directly from patients who were conscious and capable of providing consent; for sedated, mechanically ventilated, unconscious, or otherwise incapacitated patients, written consent forms were obtained from their legal representatives.\u003c/p\u003e \u003cp\u003eThe study included patients aged 18 years and older who were admitted to a tertiary intensive care unit (ICU). Patients who met the following criteria were included individuals with a confirmed diagnosis of COVID-19 via a positive reverse transcription\u0026ndash;polymerase chain reaction (RT-PCR) test, as well as those with suspected false-negative RT-PCR results at admission who were clinically diagnosed based on clinical symptoms, physical examination, and radiological findings. The following patients were excluded from the study: those who were pregnant, those admitted to the intensive care unit (ICU) due to trauma, patients lost to follow-up during the six-month observation period, and cases involving readmission to the ICU for COVID-19.\u003c/p\u003e \u003cp\u003eDemographic data including age, sex, and body mass index (BMI) of the patients included in the study were recorded. In relation to the diagnosis of COVID-19, RT-PCR test results (positive/negative) obtained either prior to admission or during the ICU course were evaluated. The length of stay in the intensive care unit (in days) and mortality data within the six-month period following ICU admission were systematically recorded.\u003c/p\u003e \u003cp\u003eThe frailty level of each patient was assessed by the attending intensive care physician at the time of ICU admission. Assessments were conducted directly with the patient if they were conscious and communicative. In cases of impaired consciousness, data were obtained from first-degree relatives or the healthcare professionals responsible for the patient's clinical follow-up. The level of frailty was determined using the Turkish-validated version of the Clinical Frailty Scale (CFS). The scale is a nine-point visual and descriptive system that categorizes individuals on a spectrum ranging from a score of 1, representing 'very fit,' to a score of 9, indicating 'terminally ill'. The CFS scores of all patients were recorded individually.\u003c/p\u003e \u003cp\u003eVaccination status, SOFA and APACHE II scores, and the smoking histories of the patients were documented. Patients were evaluated for the presence of comorbidities, including diabetes mellitus, hypertension, coronary artery disease, congestive heart failure, chronic kidney disease, chronic lung disease, chronic neurological disorders, and malignancy; all identified conditions were systematically documented.\u003c/p\u003e \u003cp\u003eDuring daily clinical follow-up, the administration of renal replacement therapy (RRT) and the requirement for vasopressor support were categorized as received or not received and incorporated into the dataset.\u003c/p\u003e \u003cp\u003eBiochemical and hematological laboratory parameters obtained at the time of ICU admission were systematically documented. The following parameters were analyzed and documented: white blood cell (WBC) count, neutrophil and lymphocyte counts, platelet count, hematocrit level, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin and CRP/albumin ratio, serum creatinine level, estimated glomerular filtration rate (eGFR), ferritin, D-dimer, C-reactive protein (CRP) levels, and procalcitonin (PCT) levels.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe study data were entered and analyzed using SPSS for Mac OS, Version 26.0 Multilingual (ISO Version) (Statistical Package for the Social Sciences Inc., Chicago, IL, USA). Descriptive statistical methods were used to summarize the data. Numerical data were reported as median and interquartile range (IQR), whereas categorical variables were expressed as frequencies and percentages (%). The normality of data distribution was assessed using both visual methods (histograms and probability plots) and analytical tests (Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests). Categorical variables were analyzed using the Chi-square test or Fisher\u0026rsquo;s exact test. Numerical variables were compared using the Mann\u0026ndash;Whitney U test, as they were found to be non-parametrically distributed.\u003c/p\u003e \u003cp\u003eIn the multivariate analysis, logistic regression was performed to identify independent predictors of in-ICU and six-month mortality, incorporating variables that demonstrated statistical significance in the univariate analyses. Furthermore, Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the discriminative power of the models. For all statistical analyses, a \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eA formal a priori sample size calculation was not performed due to the observational nature of the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 148 patients admitted with a diagnosis of COVID-19 to the tertiary intensive care unit (ICU) of Sel\u0026ccedil;uk University Faculty of Medicine Hospital between January 1 and June 4, 2022, were included in the study.\u0026nbsp;Based on the eligibility criteria, 11 patients were excluded from the study: two due to pregnancy, three due to admission following trauma, three due to missing clinical data, and three due to loss to follow-up at six months.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;The final analysis was conducted with a total of 137 patients who fulfilled the eligibility criteria and had complete data available.\u003c/p\u003e\n\u003cp\u003eThe demographic and clinical characteristics of the patients are presented in \u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1\u003c/strong\u003e The median age of the patients was 72 years (IQR: 60\u0026ndash;79), with 49.6% being female and 50.4% male. The median value of body mass index (BMI) was 25.95 kg/m\u0026sup2; (IQR: 22.06\u0026ndash;29.38). A total of 76.6% of the patients had a diagnosis of COVID-19 confirmed by RT-PCR. The median length of stay in the intensive care unit was 8 days (IQR: 4\u0026ndash;14), and the median Clinical Frailty Score (CFS) was 6 (IQR: 4\u0026ndash;7). The patients were vaccinated (76.6%). SOFA score was 7 (IQR: 4\u0026ndash;10) and APACHE II score was 22 (IQR: 16\u0026ndash;29). \u003cstrong\u003eA total of 8.8% of the participants were smokers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The most prevalent comorbidities were hypertension (48.2%), diabetes mellitus (30.7%), malignancy (28.5%), chronic neurological disease (25.5%), and chronic lung disease (21.2%), in descending order of frequency. Renal replacement therapy (RRT) was administered to 8% of the patients, while 20.4% required vasopressor support (\u003cstrong\u003eTable 1).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" summary=\"IQR, Interquartile Range; BMI, Body Mass Index; RT-PCR, Reverse Transcriptase-Polymerase Chain Reaction; SOFA, Sepsis-related Organ Failure Assessment; APACHE II, Acute Physiology and Chronic Health Evaluation II.\" \"=\"\" width=\" 548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1. Demographic and General Characteristics of the Study Population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Patients (n=137) Median (IQR) or n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e72 (60-79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68 (49.6%)\u003c/p\u003e\n \u003cp\u003e69 (50.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e25.95 (22.06-29.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 RT-PCR, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePositive\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e105 (76.60%)\u003c/p\u003e\n \u003cp\u003e32 (23.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU Length of Stay, days\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e8 (4-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Frailty Score (CFS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e6 (4-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVaccination Status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Vaccinated\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Unvaccinated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e105 (76.60%)\u003c/p\u003e\n \u003cp\u003e32 (23.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e7 (4-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPACHE II Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e22 (16-29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e12 (8.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes Mellitus, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e42 (30.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e66 (48.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronary Artery Disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e32 (23.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCongestive Heart Failure, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e15 (10.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Kidney Disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e21 (15.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Lung Disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e29 (21.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Neurological Disorders, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e35 (25.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignancy, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e39 (28.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRenal Replacement Therapy, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e11 (8.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 56.9343%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVasopressor Support, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.0657%;\"\u003e\n \u003cp\u003e28 (20.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eIQR, Interquartile Range; BMI, Body Mass Index; RT-PCR, Reverse Transcriptase-Polymerase Chain Reaction; SOFA, Sepsis-related Organ Failure Assessment; APACHE II, Acute Physiology and Chronic Health Evaluation II.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Laboratory parameters at ICU admission are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Based on inflammatory and organ function parameters, the median values were as follows: white blood cell (WBC) count 10.56 \u0026times; 10⁹/L, neutrophil count 8.94 \u0026times; 10⁹/L, lymphocyte count 0.85 \u0026times; 10⁹/L, platelet count 212 \u0026times; 10⁹/L, and hematocrit level 34.90%. The neutrophil/lymphocyte ratio (NLR) was 10.00 (IQR: 4.52\u0026ndash;17.87) and the platelet/lymphocyte ratio (PLR) was 224.44 (IQR: 135.40\u0026ndash;413.04). The median serum albumin level was 3.16 g/dL (IQR: 2.78\u0026ndash;3.50), with a CRP/albumin ratio of 24.17, serum creatinine of 1.03 mg/dL, an estimated glomerular filtration rate (eGFR) of 70.07 mL/min/1.73 m\u0026sup2;, and a ferritin level of 760 ng/mL. The D-dimer level was 1599 ng/mL, CRP was 72.60 mg/L, and procalcitonin (PCT) was 0.36 \u0026mu;g/L (\u003cstrong\u003eTable 2).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Laboratory Values at Intensive Care Unit Admission\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003eTotal Patients (n=137)\u003c/p\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eWBC, *10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e10.56 (6.56\u0026ndash;15.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eNeutrophil, *10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e8.94 (4.99\u0026ndash;14.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eLymphocyte, *10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e0.85 (0.52\u0026ndash;1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003ePlatelet count, *10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e212.00 (139.50\u0026ndash;309.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eHematocrit, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e34.90 (29.45\u0026ndash;40.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eNeutrophil-to-Lymphocyte Ratio (NLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e10.00 (4.52\u0026ndash;17.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003ePlatelet-to-Lymphocyte Ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e224.44 (135.40\u0026ndash;413.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eCRP/Albumin ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e24.17 (6.91\u0026ndash;53.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e3.16 (2.78\u0026ndash;3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eSerum Creatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e1.03 (0.70\u0026ndash;2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e70.07 (30.60\u0026ndash;95.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eFerritin, ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e760 (274\u0026ndash;1132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eD-Dimer, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e1599 (691\u0026ndash;3132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003eCRP, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e72.60 (23.30\u0026ndash;158.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2742%;\"\u003e\n \u003cp\u003ePCT,\u0026nbsp;mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7258%;\"\u003e\n \u003cp\u003e0.36 (0.12\u0026ndash;1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIQR, Interquartile Range; WBC, White Blood Cell; CRP, C-Reactive Protein; eGFR, estimated Glomerular Filtration Rate; PCT, Procalcitonin.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;The findings regarding factors associated with six-month mortality are summarized in \u003cstrong\u003eTable 3\u003c/strong\u003e At the end of the six-month follow-up, the overall mortality rate was 68.6% (n = 94), with 31.4% (n = 43) of patients surviving. The median age was significantly higher in non-survivors than in survivors (75 [IQR: 64\u0026ndash;82] vs. 65 [IQR: 49\u0026ndash;75] years; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). The median body mass index (BMI) was significantly higher in survivors compared to non-survivors (27.34 vs. 24.97 kg/m\u0026sup2;; \u003cem\u003ep\u003c/em\u003e = 0.034). The Clinical Frailty Score, SOFA, and APACHE II scores were significantly higher in non-survivors compared to survivors (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 for all).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;A history of malignancy was associated with a significantly higher mortality rate compared to patients without malignancy (36.2% vs. 11.6%, \u003cem\u003ep\u003c/em\u003e = 0.014). Lymphocyte (\u003cem\u003ep\u003c/em\u003e = 0.002), platelet (\u003cem\u003ep\u003c/em\u003e = 0.002), and albumin (\u003cem\u003ep\u003c/em\u003e = 0.001) levels were significantly higher in survivors, whereas NLR (\u003cem\u003ep\u003c/em\u003e = 0.035), CRP/albumin ratio (\u003cem\u003ep\u003c/em\u003e = 0.002), ferritin (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), CRP (\u003cem\u003ep\u003c/em\u003e = 0.002), and PCT (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) levels were higher in the non-survivor group. Furthermore, survivors had significantly higher eGFR levels compared to non-survivors (\u003cem\u003ep\u003c/em\u003e = 0.007) (\u003cstrong\u003eTable 3).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;[Table 3 here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Univariate and Multivariate Analysis of 6-Month ICU Mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"122%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eSurvivors (n=43)\u003c/p\u003e\n \u003cp\u003eMedian (IQR), n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eNon-Survivors (n=94)\u003c/p\u003e\n \u003cp\u003eMedian (IQR), n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e65 (49\u0026ndash;75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e75 (64\u0026ndash;82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17(39.5%)\u003c/p\u003e\n \u003cp\u003e26(60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51(54.3%)\u003c/p\u003e\n \u003cp\u003e43(45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e27.34 (23.43\u0026ndash;32.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e24.97 (21.57\u0026ndash;29.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 RT-PCR, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePositive\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37(86%)\u003c/p\u003e\n \u003cp\u003e6(14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68(72.3%)\u003c/p\u003e\n \u003cp\u003e26(27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU Length of Stay, days\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e10 (6\u0026ndash;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e7 (3\u0026ndash;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Frailty Score (CFS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4 (3\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e6 (4\u0026ndash;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVaccination Status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Vaccinated\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Unvaccinated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34(79.1%)\u003c/p\u003e\n \u003cp\u003e9(20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e71(75.5%)\u003c/p\u003e\n \u003cp\u003e23(24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e5 (3\u0026ndash;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e8 (5\u0026ndash;11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPACHE II Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e16(12\u0026ndash;22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e24 (18\u0026ndash;31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSmokers\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Non-smokers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4(9.3%)\u003c/p\u003e\n \u003cp\u003e39(90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8(8.5%)\u003c/p\u003e\n \u003cp\u003e86(91.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes Mellitus, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15(34.9%)\u003c/p\u003e\n \u003cp\u003e28(65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e27(28.7%)\u003c/p\u003e\n \u003cp\u003e67(71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15(34.9%)\u003c/p\u003e\n \u003cp\u003e28(65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51(54.3%)\u003c/p\u003e\n \u003cp\u003e43(45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.067\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronary Artery Disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9(20.9%)\u003c/p\u003e\n \u003cp\u003e34(79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23(24.5%)\u003c/p\u003e\n \u003cp\u003e71(75.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCongestive Heart Failure, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5(11.6%)\u003c/p\u003e\n \u003cp\u003e38(88.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10(10.6%)\u003c/p\u003e\n \u003cp\u003e84(89.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Kidney Disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6(14%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37(86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15(16%)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e79(84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Lung Disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10(23.3%)\u003c/p\u003e\n \u003cp\u003e33(76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19(20.2%)\u003c/p\u003e\n \u003cp\u003e75(79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Neurological Disorders, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8(18.6%)\u003c/p\u003e\n \u003cp\u003e35(81.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e27(28.7%)\u003c/p\u003e\n \u003cp\u003e67(71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignancy, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5(11.6%)\u003c/p\u003e\n \u003cp\u003e38(88.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34(36.2%)\u003c/p\u003e\n \u003cp\u003e60(63.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRenal Replacement Therapy\u003c/strong\u003e\u003cstrong\u003e, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3(8.5%)\u003c/p\u003e\n \u003cp\u003e40(93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8(8.5%)\u003c/p\u003e\n \u003cp\u003e86(91.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVasopressor Support\u003c/strong\u003e\u003cstrong\u003e, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4(9.3%)\u003c/p\u003e\n \u003cp\u003e39(90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24(25.5%)\u003c/p\u003e\n \u003cp\u003e70(74.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eWBC, *10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e10.52 (6.86\u0026ndash;15.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e10.76 (6.11\u0026ndash;16.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNeutrophil, *10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e7.49 (4.44\u0026ndash;11.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9.40 (5.14\u0026ndash;14.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eLymphocyte, *10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1.32 (0.74\u0026ndash;2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.75 (0.41\u0026ndash;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003ePlatelet count, *10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e287.00 (182.00\u0026ndash;358.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e198.50 (111.75\u0026ndash;267.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eHematocrit, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e37.10 (32.30\u0026ndash;40.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e34.00 (29.15\u0026ndash;40.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eNeutrophil-to-Lymphocyte Ratio (NLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e7.36 (2.85\u0026ndash;13.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e11.58 (5.95\u0026ndash;23.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003ePlatelet-to-Lymphocyte Ratio (PLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e184.33 (134.93\u0026ndash;332.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e247.02 (138.00\u0026ndash;455.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eCRP/Albumin ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e9.35 (2.37\u0026ndash;33.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e32.33 (11.73\u0026ndash;59.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e3.30 (3.04\u0026ndash;3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3.08 (2.60\u0026ndash;3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eSerum Creatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.93 (0.63\u0026ndash;1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.14 (0.74\u0026ndash;2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e83.30 (42.28\u0026ndash;106.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e56.55 (26.89\u0026ndash;89.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eFerritin, ng/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e371 (233\u0026ndash;803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e801 (376\u0026ndash;1611)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eD-Dimer, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e979 (340\u0026ndash;2511)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1836 (926\u0026ndash;3498)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eCRP, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e28.00 (9.91\u0026ndash;117.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e90.15 (36.37\u0026ndash;182.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003ePCT,\u0026nbsp;mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.14 (0.06\u0026ndash;0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.81 (0.18\u0026ndash;2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIQR, Interquartile Range; BMI, Body Mass Index; RT-PCR, Reverse Transcriptase-Polymerase Chain Reaction; SOFA, Sepsis-related Organ Failure Assessment; APACHE II, Acute Physiology and Chronic Health Evaluation II; WBC, White Blood Cell; CRP, C-Reactive Protein; eGFR, estimated Glomerular Filtration Rate; PCT, Procalcitonin; NS: Non-significant.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;The predictive performance of the Clinical Frailty Score (CFS) for six-month mortality, evaluated via ROC analysis, is presented in \u003cstrong\u003eTable 4\u003c/strong\u003e ROC analysis revealed an area under the curve (AUC) of 0.765 (95% CI: 0.682\u0026ndash;0.848), demonstrating significant predictive ability for six-month mortality (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). The optimal cut-off value was determined to be 4.5.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Using this optimal cut-off, the CFS demonstrated a sensitivity of 72%, a specificity of 69%, a positive predictive value of 83%, and a negative predictive value of 53% for predicting mortality. Furthermore, the positive likelihood ratio was 2.39, and the negative likelihood ratio was 0.40. These results indicate that the CFS score has significant prognostic value for predicting long-term mortality among critically ill patients \u003cstrong\u003e(Table 4).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Clinical Frailty Score ROC Analysis for Predicting 6-Month Mortality in the Intensive Care Unit\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;The distribution of mean Clinical Frailty Score (CFS) scores according to mortality status is illustrated in \u003cstrong\u003eFigure 1.\u003c/strong\u003e As shown in the figure, there was a marked difference in Clinical Frailty Score (CFS) between survivors and non-survivors at the six-month follow-up. The mean CFS score was significantly higher in non-survivors compared to survivors. Mortality rates were significantly higher in patients with a CFS score above the optimal cut-off value of 4.5 compared to those below this threshold. These visual data reinforce the role of the CFS score as both a statistically significant and clinically meaningful discriminator for patient outcomes \u003cstrong\u003e(Figure 1).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to evaluate the predictive value of the Clinical Frailty Score (CFS) on 6-month mortality in critically ill patients admitted to a tertiary intensive care unit with a diagnosis of COVID-19. The findings demonstrated that advanced age, elevated frailty scores, increased inflammatory markers, and parameters indicative of physiological vulnerability were significantly associated with long-term mortality. Notably, the prognostic power of the CFS, as evidenced by the ROC analysis, underscores the importance of considering not only chronological age but also physiological reserve in the management of acute infections such as COVID-19.\u003c/p\u003e \u003cp\u003eThis study investigated the impact of the Clinical Frailty Score (CFS), alongside SOFA and APACHE II scores, on six-month mortality among patients admitted to a tertiary intensive care unit with COVID-19. Our findings align with previous literature\u0026mdash;specifically studies by Fumagalli et al. [16] and Aliberti et al. [10] which identified CFS as an independent predictor of mortality. Frailty has been established as a significant correlate of 30-day, 3-month, and 6-month mortality, particularly in elderly and middle-aged populations [4, 5, 7, 10, 17]. Consistent with these reports, our results demonstrate that CFS serves as a robust predictor of long-term outcomes.\u003c/p\u003e \u003cp\u003eImportantly, our study was conducted during a later phase of the pandemic when healthcare services were more structured and triage protocols were systematically implemented. This timing minimized the risk of patient exclusion due to the acute resource shortages prevalent during the initial waves, thereby yielding a more representative and comprehensive sample of admitted patients.\u003c/p\u003e \u003cp\u003eIn the present study, the prevalence of frailty was 57.7%, which exceeds the 46% reported by Guidet et al. in ICU patients aged\u0026thinsp;\u0026gt;\u0026thinsp;80 years [17] and the 33% reported by Aliberti et al. in those aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years [10]. The inclusion of all adults over 18 years without age restrictions, coupled with the high acuity of COVID-19, may account for this finding. Furthermore, given that mortality risk escalates with advancing years, our results confirm that age remains an independent risk factor for six-month mortality.\u003c/p\u003e \u003cp\u003eSome studies have suggested that frailty scores may have limited predictive value, particularly in younger frail patients, due to their lower mortality rates [18]. Nevertheless, significant declines in functional capacity and quality of life have been reported in this population [19, 20]. As these parameters were not monitored in our study, the long-term quality of life of younger patients could not be assessed.\u003c/p\u003e \u003cp\u003eAliberti et al. [10], demonstrated that the Clinical Frailty Score is a valid assessment tool for both young and middle-aged SARS-CoV-2 patients. Furthermore, Dres et al. [5] reported that frailty offers superior long-term mortality prediction compared to traditional prognostic models such as SOFA. In contrast, our study found that CFS, SOFA, and APACHE II scores were all significant predictors of outcome. These findings suggest that frailty should be integrated as a complementary assessment tool rather than a substitute for acute illness severity scores in the clinical evaluation of COVID-19 patients.\u003c/p\u003e \u003cp\u003eAdvanced age has been consistently linked to poor prognosis in COVID-19 patients requiring ICU admission [7, 20, 22, 23]. Our results corroborate this association, showing higher mortality rates among elderly patients. Notably, the older age of patients who died within 6 months suggests that late-term mortality is more strongly associated with age. However, our ability to further dissect the dimensions of this effect was limited by the lack of predefined age subgroup analyses.\u003c/p\u003e \u003cp\u003eBoth RT-PCR-confirmed and clinically diagnosed COVID-19 patients were included in our cohort. The lack of a significant difference in mortality rates between these two subgroups reinforces the validity of clinical diagnosis and the robustness of clinician-led assessments during the pandemic.\u003c/p\u003e \u003cp\u003eSome studies, such as those by G\u0026uuml;nster [20] and Grasselli [22], have reported that male gender is associated with poor prognosis. However, in our study, gender did not have a statistically significant effect on mortality. Similarly, findings regarding obesity remain conflicting. While Gupta [24] did not observe increased mortality in patients with a BMI\u0026thinsp;\u0026gt;\u0026thinsp;40, G\u0026uuml;nster identified BMI as a risk factor [20]. In our study, BMI was associated only with 6-month mortality.\u003c/p\u003e \u003cp\u003eRegarding comorbidities, malignancy and hypertension were found to be associated with 6-month mortality. Interestingly, chronic pulmonary diseases and smoking history did not demonstrate a significant correlation with mortality in our cohort. However, as comorbidity data were obtained from patients or their surrogates, certain associations may have been underestimated due to potential recall bias or the subjective nature of self-reported medical histories.\u003c/p\u003e \u003cp\u003eAzoulay et al. [9] linked the high mortality observed in COVID-19 patients to multiorgan failure. Consistent with this, our analysis revealed that\u0026mdash;beyond the total SOFA score\u0026mdash;specific subcomponents such as platelet count and vasopressor requirements, along with key inflammatory markers (CRP/albumin ratio, ferritin, and albumin), were independently associated with mortality. These results underscore the pivotal role of systemic inflammation in driving both the progression of frailty and poor clinical outcomes.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, frailty was assessed using the Clinical Frailty Score (CFS). However, this assessment was performed by the attending physicians at the time of ICU admission, and the potential for inter-observer variability and subjective interpretation cannot be excluded. The CFS evaluations were based on the patient's condition during the two weeks prior to ICU admission, and this information was often obtained from relatives or caregivers, introducing a potential for recall bias. Although physicians received training on how to apply the scale, inter-rater reliability could not be fully ensured.\u003c/p\u003e \u003cp\u003eFurthermore, our analysis was restricted to patients admitted to the ICU, excluding those who were triaged to other departments or deemed ineligible for intensive care admission. Consequently, the study population may be biased toward more clinically severe cases, potentially limiting the generalizability of our findings to the broader population of COVID-19 patients.\u003c/p\u003e \u003cp\u003eFurthermore, our analysis did not account for specific treatment protocols, medication dosages, or therapeutic interventions administered during the ICU course. Although these variations represent significant potential confounders for mortality, they were not evaluated due to a lack of granular data. Likewise, systemic variables that may influence the quality of care\u0026mdash;such as nurse-to-patient ratios\u0026mdash;fell outside the scope of this study.\u003c/p\u003e \u003cp\u003eThe single-center, observational nature of this study may have introduced selection bias, potentially limiting the representativeness of our sample. Furthermore, the absence of randomization and the non-interventional design necessitate caution when drawing causal inferences from the observed associations; our results should be interpreted as correlational rather than definitive evidence of causality.\u003c/p\u003e \u003cp\u003eLastly, comorbidity data were predominantly derived from subjective reports by patients or their next of kin. This reliance on self-reported or proxy-reported history increases the susceptibility to information bias and may have led to instances of missing or under-reported data for certain clinical variables.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrated that the Clinical Frailty Score (CFS) is an independent risk factor for predicting 6-month mortality in patients admitted to the intensive care unit due to COVID-19. CFS can be utilized alongside classical scoring systems such as SOFA and APACHE II to enhance prognostic accuracy in patient management. Incorporating frailty assessment into intensive care practice may support clinical decision-making and facilitate individualized treatment planning.\u003c/p\u003e \u003cp\u003eCFS may serve as a practical bedside tool to support triage decisions, resource allocation, and individualized treatment strategies in critically ill patients with COVID-19.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronavirus disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Frailty Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPACHE II\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Physiology and Chronic Health Evaluation II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReverse transcription\u0026ndash;polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite blood cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRenal replacement therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNegative predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLikelihood ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Ethical approval for this study was obtained from the Selçuk University Faculty of Medicine Institutional Ethics Committee (Approval No: 2021/507). Written informed consent forms were obtained directly from patients who were conscious and capable of providing consent; for sedated, mechanically ventilated, unconscious, or otherwise incapacitated patients, written consent forms were obtained from their legal representatives.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJBC and MS contributed to conceptualization.\u003cbr\u003e\u0026nbsp;SD and MS contributed to study design.\u003cbr\u003e\u0026nbsp;SD was responsible for data acquisition.\u003cbr\u003e\u0026nbsp;SD and JBC performed clinical supervision and patient follow-up.\u003cbr\u003e\u0026nbsp;SD and MS conducted the statistical analysis.\u003cbr\u003e\u0026nbsp;SD, MS, and JBC performed data interpretation.\u003cbr\u003e\u0026nbsp;SD drafted the manuscript.\u003cbr\u003e\u0026nbsp;MS and JBC critically revised the manuscript.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The authors thank the intensive care unit staff of Selçuk University Faculty of Medicine for their support during data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSinan Değirmencioğlu\u003c/strong\u003e, MD, is a Specialist in Anesthesiology and Intensive Care Medicine.\u003cbr\u003e\u0026nbsp;ORCID: 0000-0003-4731-6937\u003c/p\u003e\n\u003cp\u003ee-mail:[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJale Bengi Çelik\u003c/strong\u003e, MD, PhD, is a Professor of Intensive Care Medicine.\u003cbr\u003e\u0026nbsp;Researcher ID: 3307\u003cbr\u003e\u0026nbsp;ORCID: 0000-0003-2167-9967\u003c/p\u003e\n\u003cp\u003ee-mail:[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMehmet Sargın\u003c/strong\u003e, MD, PhD, is a Professor of Anesthesiology and Intensive Care Medicine.\u003cbr\u003e\u0026nbsp;Researcher ID: 140460\u003cbr\u003e\u0026nbsp;ORCID: 0000-0002-6574-273X\u003c/p\u003e\n\u003cp\u003ee-mail: [email protected]\u003c/p\u003e"},{"header":" References","content":"\u003col\u003e\n \u003cli\u003eGuarneri V, Bassan F, Zagonel V, Milella M, Zaninelli M, Cattelan AM, et al. Epidemiology and clinical course of severe acute respiratory syndrome coronavirus 2 infection in cancer patients in the Veneto Oncology Network: The Rete Oncologica Veneta COVID19 study. Eur J Cancer. 2021;147:120-7.\u003c/li\u003e\n \u003cli\u003ePolack FP, Thomas SJ, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and Efficacy of the BNT162b2 mRNA COVID-19 Vaccine. N Engl J Med. 2020;383(27):2603-15.\u003c/li\u003e\n \u003cli\u003eGuan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of coronavirus disease 2019 in China. \u003cstrong\u003eN Engl J Med.\u003c/strong\u003e 2020;382(18):1708\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003eJung C, Flaatten H, Fj\u0026oslash;lner J, Bruno RR, Wernly B, Artigas A, et al. The impact of frailty on survival in elderly intensive care patients with COVID-19: the COVIP study. \u003cstrong\u003eCrit Care.\u003c/strong\u003e 2021;25(1):149.\u003c/li\u003e\n \u003cli\u003eDres M, Hajage D, Lebbah S, Kimmoun A, Pham T, B\u0026eacute;duneau G, et al. Characteristics, management, and prognosis of elderly patients with COVID-19 admitted in the ICU during the first wave: insights from the COVID-ICU study. Ann Intensive Care. 2021;11(1):77.\u003c/li\u003e\n \u003cli\u003eRichards-Belle A, Orzechowska I, Gould DW, Thomas K, Doidge JC, Mouncey PR, et al. COVID-19 in critical care: epidemiology of the first epidemic wave across England, Wales and Northern Ireland. Intensive Care Med. 2020;46(11):2035-47.\u003c/li\u003e\n \u003cli\u003eKurtz P, Bastos LSL, Dantas LF, Zampieri FG, Soares M, Hamacher S, et al. Evolving changes in mortality of 13,301 critically ill adult patients with COVID-19 over 8 months. Intensive Care Med. 2021;47(5):538-48.\u003c/li\u003e\n \u003cli\u003eRichardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA. 2020;323(20):2052-9.\u003c/li\u003e\n \u003cli\u003eAzoulay E, Fartoukh M, Darmon M, G\u0026eacute;ri G, Voiriot G, Dupont T, et al. Increased mortality in patients with severe SARS-CoV-2 infection admitted within seven days of disease onset. Intensive Care Med. 2020;46(9):1714-22.\u003c/li\u003e\n \u003cli\u003eAliberti MJR, Szlejf C, Avelino‐Silva VI, Suemoto CK, Apolinario D, Dias MB, et al. COVID‐19 is not over and age is not enough: Using frailty for prognostication in hospitalized patients. J Am Geriatr Soc. 2021;69(5):1116-27.\u003c/li\u003e\n \u003cli\u003eDe Geer L, Fredrikson M, Chew MS. 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The contribution of frailty, cognition, activity of daily life and comorbidities on outcome in acutely admitted patients over 80 years in European ICUs: the VIP2 study. Intensive Care Med. 2020;46(1):57-69.\u003c/li\u003e\n \u003cli\u003eMarengoni A, Zucchelli A, Vetrano DL, Armellini A, Botteri E, Nicosia F, et al. Beyond Chronological Age: Frailty and Multimorbidity Predict In-Hospital Mortality in Patients With Coronavirus Disease 2019. J Gerontol A Biol Sci Med Sci. 2021;76(3):e38-e45.\u003c/li\u003e\n \u003cli\u003eHodgson CL, Higgins AM, Bailey MJ, Mather AM, Beach L, Bellomo R, et al. The impact of COVID-19 critical illness on new disability, functional outcomes and return to work at 6 months: a prospective cohort study. Crit Care. 2021;25(1):382.\u003c/li\u003e\n \u003cli\u003eG\u0026uuml;nster C, Busse R, Spoden M, Rombey T, Schillinger G, Hoffmann W, et al. 6-month mortality and readmissions of hospitalized COVID-19 patients: A nationwide cohort study of 8,679 patients in Germany. PLoS One. 2021;16(8):e0255427.\u003c/li\u003e\n \u003cli\u003eLe Maguet P, Roquilly A, Lasocki S, Asehnoune K, Carise E, Saint Martin M, et al. Prevalence and impact of frailty on mortality in elderly ICU patients: a prospective, multicenter, observational study. Intensive Care Med. 2014;40(5):674-82\u003c/li\u003e\n \u003cli\u003eGrasselli G, Greco M, Zanella A, Albano G, Antonelli M, Bellani G, et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern Med. 2020;180(10):1345-55.\u003c/li\u003e\n \u003cli\u003ePolok K, Fronczek J, Artigas A, Flaatten H, Guidet B, De Lange DW, et al. Noninvasive ventilation in COVID-19 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years\u0026mdash;a prospective multicentre cohort study. Crit Care. 2022;26(1):224\u003c/li\u003e\n \u003cli\u003eGupta S, Hayek SS, Wang W, Chan L, Mathews KS, Melamed ML, et al. Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US. JAMA Intern Med. 2020;180(11):1436-47.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, intensive care, frailty, clinical frailty score, mortality, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8776291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8776291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe aim of this study was to determine the prevalence of frailty using the Clinical Frailty Scale (CFS) in patients admitted to a tertiary intensive care unit (ICU) due to COVID-19 and to evaluate the association between this score and long-term mortality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis single-center, prospective, observational cohort study was conducted at Sel\u0026ccedil;uk University Faculty of Medicine Hospital between January 1 and June 4, 2022, and included 137 patients admitted to the intensive care unit (ICU) with COVID-19. CFS, SOFA and APACHE II scores were evaluated along with demographic, clinical, laboratory and mortality data of the patients. Risk factors associated with six-month mortality were analyzed using multivariate logistic regression. The prognostic performance of the CFS was determined via Receiver Operating Characteristic (ROC) curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of clinical frailty was found to be 57.7%. The six-month mortality rate was 68.6%. CFS, SOFA, and APACHE II scores were significantly associated with six-month mortality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AUC value of the CFS in predicting six-month mortality was 0.765, and the optimal cut-off value was identified as 4.5. In addition, albumin, lymphocyte, and platelet levels were higher in survivors, whereas ferritin, CRP/albumin ratio, and procalcitonin levels were found to be higher in non-survivors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eClinical Frailty Score is an independent risk factor for predicting long-term mortality in patients with COVID-19.. CFS is a valuable prognostic tool that can be used in addition to classical scoring systems for patient management and resource planning in intensive care units.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eClinicalTrials.gov identifier: NCT06330883\u003c/p\u003e","manuscriptTitle":"Prevalence of frailty and its association with six-month mortality in critically ill COVID-19 patients: a prospective observational cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 15:01:23","doi":"10.21203/rs.3.rs-8776291/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9addc00-ac78-40a6-bb9b-0934fffeac8e","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T09:12:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 15:01:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8776291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8776291","identity":"rs-8776291","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}