Prognostic Value of Electrical Cardiometry–Derived Thoracic Fluid Content in Respiratory Intensive Care Unit Patients: A Prospective Observational Study

Prognostic Value of Electrical Cardiometry–Derived Thoracic Fluid Content in Respiratory Intensive Care Unit Patients: A Prospective Observational 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 Prognostic Value of Electrical Cardiometry–Derived Thoracic Fluid Content in Respiratory Intensive Care Unit Patients: A Prospective Observational Study Mohamed AbdElmoniem Mohamed, Mohsen Mohammad Elshafey, Amany Ragab Elsaid, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7895080/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background : Hemodynamic instability is common among respiratory intensive care unit (RICU) patients and is associated with multi-organ dysfunction and high mortality. Electrical cardiometry (EC) is a noninvasive method that continuously measures thoracic fluid content (TFC), providing real-time assessment of fluid status. This study evaluated the prognostic value of EC-derived TFC in predicting outcomes among critically ill respiratory patients. Methods : A prospective observational study was conducted on 130 adult patients admitted to the RICU of Mansoura University Hospitals. Daily hemodynamic measurements were obtained using the ICON™ noninvasive cardiometer. Primary outcomes included duration of invasive mechanical ventilation (MV) and RICU stay, while in-hospital mortality was the secondary endpoint. Correlations between TFC and clinical, hemodynamic, and biochemical parameters were analyzed. Results : Pneumonia was the most frequent diagnosis (65.4%), followed by COPD (15.4%). Non-survivors showed significantly higher mean TFC values on all follow-up days compared with survivors (p < 0.001). TFC correlated positively with RICU stay and serum lactate levels and negatively with mean arterial pressure. At a cutoff value of 43 kΩ⁻¹, TFC predicted mortality with an AUC of 0.656, sensitivity of 85.7%, and specificity of 67.3%. Elevated TFC values were also significantly associated with mechanical ventilation and vasopressor use (p < 0.001). Conclusion : Electrical cardiometry provides a reliable, noninvasive technique for continuous hemodynamic monitoring in respiratory critical care. Elevated TFC values were independently associated with higher mortality, prolonged mechanical ventilation, and extended RICU stay. Routine TFC monitoring may assist in early detection of fluid overload and guide individualized fluid management. Trial registration : ClinicalTrials.gov identifier: NCT07100821 Electrical cardiometry thoracic fluid content mortality respiratory intensive care unit hemodynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Hemodynamic instability represents one of the most common and serious challenges in critically ill patients, especially those admitted to respiratory intensive care units (RICUs). It frequently leads to tissue hypoperfusion, multiple organ dysfunction, and higher mortality rates. Accurate evaluation of the circulatory status and fluid balance is therefore crucial for proper management. Although traditional invasive techniques such as pulmonary artery catheterization provide precise measurements, they carry notable drawbacks, including procedural risks, technical difficulty, and patient discomfort. These concerns have driven the search for dependable, noninvasive methods that allow continuous, real-time hemodynamic monitoring at the bedside [ 1 ]. Electrical cardiometry (EC) is a modern, noninvasive technique based on thoracic bioimpedance analysis. It provides continuous assessment of cardiac output and thoracic fluid content (TFC), which reflects total thoracic fluid volume, including intravascular, interstitial, and alveolar components. TFC is inversely related to thoracic impedance, thus increasing in conditions of pulmonary congestion or fluid overload. EC allows the detection of rapid changes in hemodynamics and has the advantages of simplicity, safety, reproducibility, and minimal operator dependency, making it suitable for continuous bedside use in the RICU [ 2 ]. Thoracic fluid content (TFC) represents an integrated marker of the patient’s pulmonary and circulatory fluid status. It provides a quantitative assessment of the total water content within the thoracic cavity, encompassing both intravascular and extravascular compartments. In pathological states such as pneumonia, acute respiratory distress syndrome (ARDS), and congestive heart failure, the accumulation of extravascular lung water increases TFC values. Monitoring this parameter may therefore help in early detection of pulmonary edema, fluid overload, or inadequate fluid resuscitation before overt clinical manifestations occur [ 3 ]. In the intensive care setting, optimal fluid management is crucial. Both excessive and inadequate fluid administration are harmful. Over-resuscitation can result in pulmonary edema, impaired gas exchange, and prolonged mechanical ventilation, while under-resuscitation may cause tissue hypoxia and organ dysfunction. Traditional static indices, such as central venous pressure (CVP), pulmonary artery occlusion pressure, and mean arterial pressure (MAP), often fail to accurately reflect intravascular volume or predict fluid responsiveness. Dynamic indices and impedance-based methods, including EC-derived TFC, have gained growing attention for their ability to provide continuous and more physiologically relevant measurements of fluid status [ 4 ]. Several studies have demonstrated the usefulness of EC-derived parameters in evaluating cardiac output, stroke volume, and systemic vascular resistance. Albert et al. 2004 reported that impedance cardiography provides a reliable and cost-effective method for continuous monitoring of central hemodynamics [ 5 ]. Similarly, Peyton and Chong emphasized the noninvasive and beat-to-beat capability of EC, highlighting its ease of use and ability to track rapid hemodynamic changes during different clinical conditions. The technology is particularly beneficial in critically ill respiratory patients where invasive catheterization may not be feasible or safe [ 6 ]. Recent advances have strengthened the clinical application of TFC monitoring. Gho et al. 2021 found that elevated TFC values were associated with higher mortality and longer hospital stays among patients with community-acquired pneumonia [ 7 ]. Hammad et al. 2019 confirmed the correlation between EC-measured TFC and lung ultrasound findings in preeclamptic women with pulmonary edema, showing excellent diagnostic accuracy [ 8 ]. Narula et al. 2017 further demonstrated that changes in TFC accurately reflected intrathoracic fluid shifts during blood withdrawal, validating its role as a dynamic indicator of thoracic hydration [ 9 ]. In Egypt, studies using EC technology remain limited, especially in the field of respiratory critical care. However, a recent study comparing EC with lung ultrasound among mechanically ventilated patients revealed a strong correlation between TFC and lung water content, underscoring EC’s potential role in predicting weaning failure. This correlation between TFC and extravascular lung water supports EC as a feasible alternative to ultrasound for continuous, noninvasive assessment of pulmonary fluid status [ 10 ]. Moreover, international studies between 2022 and 2025 have expanded the understanding of EC’s prognostic significance. EC has been validated against transthoracic echocardiography for evaluating cardiac output and fluid responsiveness in patients with circulatory failure [ 11 ]. In patients with ARDS, cardiometry-guided fluid management was associated with reduced mechanical ventilation duration and shorter ICU stay compared with conventional fluid therapy [ 12 ]. Additionally, studies in pediatric cardiac surgery and post-cardiopulmonary bypass patients demonstrated that increased TFC predicted the development of secondary capillary leak syndrome, highlighting its sensitivity to early hemodynamic derangements [ 13 ]. Despite this growing body of evidence, the prognostic value of EC-derived TFC among adult patients admitted to the RICU remains underexplored. Most previous investigations have focused on cardiac surgery, sepsis, or perioperative care, while data in respiratory critical illness are scarce. Determining whether elevated TFC values can predict adverse outcomes—such as prolonged mechanical ventilation, extended RICU stay, and increased mortality—would add important insight into fluid management and risk stratification in this patient population [ 14 ]. The current study, therefore, aimed to evaluate the predictive role of electrical cardiometry–derived thoracic fluid content in patients admitted to the RICU of Mansoura University Hospitals. Specifically, the study examined the relationship between TFC and clinical outcomes, including mortality, duration of invasive mechanical ventilation, and RICU length of stay. We hypothesized that higher TFC values would correlate with prolonged hospitalization, hemodynamic instability, and poorer prognosis among critically ill respiratory patients [ 15 ]. Methods Study Design and Setting A prospective observational analytical study was conducted in the Respiratory Intensive Care Unit (RICU) of the Chest Medicine Department, Mansoura University Hospitals, Egypt. The study enrolled 130 adult patients between December 2022 and December 2023. Ethical approval was obtained from the Mansoura Faculty of Medicine Institutional Research Board (MFM-IRB: MS.19.12.354). Written informed consent was obtained from all participants or their first-degree relatives. Sample size calculation : The minimum required sample size was estimated using the single-proportion formula: n = Z21 − α/2 p(1 − p)/d2 Based on an expected mortality rate of 43%, a 95% confidence level, and a margin of error of 7.5%, the target sample size was 130 patients. A total of 120 patients were eventually included due to practical and logistical limitations during the study period, which still provided acceptable precision for the primary outcomes. Inclusion criteria Patients aged > 18 years admitted to the RICU for any indication were included. Exclusion criteria presence of malignancy (primary or metastatic lung cancer), advanced pulmonary fibrosis, significant pleural or pericardial effusion, or refusal to participate. Clinical and Laboratory Assessment: Each patient underwent a full clinical evaluation, including APACHE II scoring, within 24 hours of admission. Radiological investigations (chest X-ray and/or CT chest) and laboratory tests (CBC, ABGs, liver and kidney function, electrolytes, CRP, and serum lactate) were performed according to standard protocols. Electrical Cardiometry Monitoring: Thoracic fluid content (TFC) and related hemodynamic parameters were measured daily at 10 a.m. using the ICON™ noninvasive cardiometer (Model C3, OSYPKA Medical, Germany). Four surface electrodes were placed on the left neck and thorax according to the manufacturer’s protocol (Fig. 1 ). Parameters including cardiac output (CO), stroke volume variation (SVV), flow time corrected (FTc), and TFC were recorded three times at 5-minute intervals and averaged (Fig. 2 ). Study Endpoints Primary endpoints: Duration of invasive mechanical ventilation and length of RICU stay. Secondary endpoint: Mortality during hospitalization. Ethical considerations: After obtaining approval from the Institutional Research Board (IRB) of the Faculty of Medicine, Mansoura University (MS.19.12.354), written informed consent was secured from all participants or their first-degree relatives before initiation of the study. Statistical analysis Data were analyzed using SPSS version 18. Quantitative variables were expressed as mean ± SD or median (IQR) according to distribution, and qualitative data as frequencies and percentages. Between-group comparisons were made using Student’s t-test, chi-square, or Fisher’s exact-test as appropriate. Correlations between TFC and other variables (MAP, serum lactate, RICU stay) were assessed using Spearman’s correlation. ROC analysis was used to determine predictive accuracy of TFC and APACHE II for mortality. A p-value < 0.05 was considered statistically significant. Results A total of 130 patients were included, with a mean age of 52.8 ± 13.1 years; 80% were females, and 80.8% were non-smokers. Pneumonia was the most common cause of admission (65.4%), followed by COPD (15.4%). The overall survival rate was 57% (Tables 1 , 2 ). Table 1 Sociodemographic characteristics of the studied patients . Variable Mean ± SD (Range) Age (years) 52.83 ± 13.12 (18–70) BMI (kg/m²) 35.47 ± 7.66 Variable n (%) Sex Male Female 26 (20%) 104 (80%) Smoking status Nonsmokers passive smokers Active smokers 105 (80%) 10 (7.7%) 15 (11.5%) BMI = Body Mass Index; SD = Standard Deviation. Table 2 Main causes of admission and outcome of the studied patients. Causes of admission n(%) Pneumonia COPD Near drowning OHS PE 85 (65.4%) 20 (15.4%) 5 (3.8%) 5 (3.8%) 15 (11.5%) Outcome n(%) Survival Died 74 (56.9%) 56 (43.1%) COPD = Chronic Obstructive Pulmonary Disease; OHS = Obesity Hypoventilation Syndrome. Table 3 shows a statistically significant difference in mortality between non-smokers and active smokers (p = 0.01). However, no significant differences were observed between survivors and non-survivors with respect to age, sex, or body mass index (BMI) (p > 0.05 for all). Table 3 Relationship between outcome and sociodemographic characteristics. Survived (n = 74) Died (n = 56) Test of significance P-value Age (years) mean ± SD 51.81 ± 14.66 54.18 ± 10.73 t = 1.02 p = 0.310 n(%) n(%) Test of significance P-value Sex Male Female 14 (18.9) 60 (81.1) 12 (21.4) 44 (78.6) χ²=0.125 p = 0.723 Smoking status Nonsmokers Passive smokers Active smokers 55 (74.3) 5 (6.8) 14 (18.9) 50 (89.3) 5 (8.9) 1 (1.8) MC test P = 0.01* BMI (Kg/m2 ) mean ± SD 35.59 ± 7.91 35.30 ± 7.38 t = 0.216 p = 0.829 t: Student t-test, MC: Monte Carlo test χ2 = Chi-Square test *p < 0.05 was considered statistically significant. Table (4) demonstrates that non-survivors had significantly higher mean thoracic fluid content (TFC) values on all follow-up days compared with survivors. The differences in TFC between the two groups were statistically significant throughout the entire monitoring period (p < 0.001). Table 4 Comparison of thoracic fluid content (TFC) between survivors and non-survivors over follow-up days. TFC survived (n = 74) mean ± SD Died (n = 56) mean ± SD Test of significance P-value Follow-up day 1 48.89 ± 8.94 k ohm⁻¹ 54.61 ± 7.39 k ohm⁻¹ t = 3.88 p < 0.001* Follow-up day 2 47.08 ± 6.51 k ohm⁻¹ 56.88 ± 8.82 k ohm⁻¹ t = 7.11 p < 0.001* Follow-up day 3 44.36 ± 5.84 k ohm⁻¹ 63.12 ± 7.01 k ohm⁻¹ t = 13.69 p < 0.001* Follow-up day 4 45.31 ± 6.20 k ohm⁻¹ 63.64 ± 9.51 k ohm⁻¹ t = 8.32 p < 0.001* Follow-up day 5 46.0 ± 6.45 k ohm⁻¹ 63.88 ± 8.85 k ohm⁻¹ t = 6.53 p < 0.001* Follow-up day 6 48.0 ± 3.13 k ohm⁻¹ 59.80 ± 6.71 k ohm⁻¹ t = 5.44 p < 0.001* t-test used; TFC = Thoracic Fluid Content (kΩ⁻¹) *p < 0.05 was considered statistically significant. Figure (3): Comparison of thoracic fluid content (TFC) values between survivors and non-survivors across follow-up days. Table (5) shows that higher thoracic fluid content (TFC) correlated positively with the duration of mechanical ventilation, RICU stay, and serum lactate levels (p = 0.001), and negatively with mean arterial pressure (MAP) from day 2 to day 6. These findings indicate that elevated TFC reflects hemodynamic instability and prolonged illness severity. Table 5 Correlation between TFC and clinical parameters. TFC Invasive MV. duration hospital stay (days) MAP s.lactate TFC day 1 r .249 .316 -0.02 .306 TFC day 1 P .038* .001* 0.823 .001* TFC day 2 r .165 .416 -0.410 .560 TFC day 2 P .192 .001* < 0.001* .001* TFC day 3 r − .200 .399 -0.683 .656 TFC day 3 P .155 .001* < 0.001* .001* TFC day 4 r .362 .027 -0.637 .422 TFC day 4 P .022* .848 < 0.001* .001* TFC day 5 r − .357 − .166 -0.887 .576 TFC day 5 P .103 .398 < 0.001* .001* TFC day 6 r .894 − .163 -0.963 0.777 TFC day 6 P .001* .373 < 0.001* 0.001* r: Spearman correlation coefficient *p < 0.05 was considered statistically significant Table (6) shows that APACHE II had excellent predictive accuracy for mortality (AUC = 0.991, sensitivity 92.9%, specificity 91.9%), while thoracic fluid content (TFC) demonstrated moderate predictive value (AUC = 0.656, sensitivity 85.7%, specificity 67.3%) at a cutoff of 43 kΩ⁻¹. Table 6 Predictive performance of APACHE II and TFC for mortality Test Result Variable(s) Area p Asymptotic 95% Confidence Interval cut off Sensitivity% Specificity % PPV% NPV% Accuracy % Lower bound Upper bound APACHII .991 .001* .981 1.001 45.5 92.9 91.9 89.7 94.4 92.3 TFC .656 .002* .562 .750 43 k ohm⁻¹ 85.7 67.3 46.7 94.3 72.3 AUC = Area under ROC curve; PPV = Positive Predictive Value; NPV = Negative Predictive Value. Table (7) revealed that mean thoracic fluid content (TFC) values were significantly higher among non-survivors with pneumonia and COPD compared to survivors (p = 0.049 and p = 0.023, respectively). No deaths occurred among patients with near-drowning or obesity hypoventilation syndrome, whose mean TFC values were 54.60 ± 12.07 and 41.40 ± 3.28, respectively. Figure (4): Receiver Operating Characteristic (ROC) curves for APACHE II score and thoracic fluid content (TFC) in predicting mortality among RICU patients. Table 7 Mean Thoracic fluid content (TFC) within survived, died cases in each cause of hospital admission Main causes of hospital admission Mean TFC of Survived (n = 74) Mean TFC of Died (n = 56) test of significance P-value Pneumonia 51.87 ± 7.94 55.17 ± 7.39 t = 1.98 p = 0.051 COPD 45.29 ± 6.41 54.67 ± 0.58 t = 2.48 p = 0.023* Near drowning 54.60 ± 12.07 0 - - OHS 41.40 ± 3.28 0 - - PE 43.13 ± 11.21 50.86 ± 8.53 t = 1.48 p = 0.162 COPD = Chronic Obstructive Pulmonary Disease; OHS = Obesity Hypoventilation Syndrome. PE = pulmonary embolism t-test used; TFC = Thoracic Fluid Content (kΩ⁻¹) *p < 0.05 was considered statistically significant. Patients with type I respiratory failure had significantly higher mean thoracic fluid content (TFC) values than those with type II respiratory failure during follow-up days 1 to 4 (p < 0.001, p = 0.001, p 0.05). Thus, higher TFC values can be used to predict type I respiratory failure Table (8). Table 8 Comparison of Thoracic fluid content (TFC) between cases with Respiratory failure Type 1 & Type 2 TFC (mean ± SD) RF1 RF2 Test of significance P-value Follow-up day 1 52.71 ± 8.68 45.64 ± 6.58 t = 3.82 p < 0.001* Follow-up day 2 52.39 ± 8.96 45.64 ± 6.44 t = 3.54 p = 0.001* Follow-up day 3 54.58 ± 10.58 42.83 ± 7.31 t = 5.01 p < 0.001* Follow-up day 4 57.27 ± 12.39 47.08 ± 7.99 t = 2.78 p = 0.008* Follow-up day 5 55.07 ± 12.24 53.0 ± 0.0 t = 0.235 p = 0.816 Follow-up day 6 53.60 ± 8.15 51.0 ± 0.0 t = 0.664 p = 0.170 RF1 = Respiratory failure Type 1, RF2 = Respiratory failure Type 2, t:Student t-test, *p < 0.05 statistical significant Regarding patient outcomes, mortality was significantly associated with the use of invasive mechanical ventilation and the presence of hypotension requiring vasopressors (p < 0.001 for both). Notably, 90% of patients who required both mechanical ventilation and vasopressor support were non-survivors. However, the duration of mechanical ventilation showed no significant association with mortality (p = 0.798) (Table 9 ). Table 9 Relation between outcome and hospitalization characteristics of the studied cases. Survived (n = 74) n(%) Died (n = 56) n(%) Test of significance P-value Invasive MV No Yes 56 (75.7) 18 (24.3) 4 (7.1) 52 (92.9) χ2 = 60.24 P < 0.001* MV duration/days mean ± SD 3.33 ± 0.97 3.23 ± 1.59 t = 0.257 p = 0.798 Non-hypotensive Hypotensive treated with Fluids & vasopressors 70 (94.6) 4 (5.4) 4 (7.1) 52 (92.9) MC test P < 0.001* MV = Mechanical ventilation, t: Student t-test, MC: Monte Carlo test χ2 = Chi-Square test *p < 0.05 statistically significant The mean thoracic fluid content (TFC) values were significantly higher in patients who required both fluids and vasopressors compared with those who received fluids only or required neither (p < 0.001). These findings indicate that TFC changes closely reflect hemodynamic status and correlate well with vasopressor requirement Table (10). Table 10 The correlation between Thoracic fluid content (TFC) and requirement of fluids and vasopressors vasopressor requirement N mean ± SD Test of significance P-value TFC day 1 Non 70 49.17 ± 9.11 F = 6.70 P = 0.002* Fluids only 8 49 ± 5.34 F = 6.70 P = 0.002* fluids, vasopressors 52 54.65 ± 7.67 F = 6.70 P = 0.002* TFC day 2 Non 70 47.42 ± 6.52 F = 17.75 P < 0.001* Fluids only 8 52 ± 11.75 F = 17.75 P < 0.001* fluids, vasopressors 46 56.34 ± 9.00 F = 17.75 P < 0.001* TFC day 3 Non 52 44.84 ± 5.77 F = 99.77 P < 0.001* Fluids only 4 38 ± .00 F = 99.77 P < 0.001* fluids, vasopressors 34 63.11 ± 7.01 F = 99.77 P < 0.001* TFC day 4 Non 22 46.63 ± 5.81 F = 38.71 P < 0.001* Fluids only 4 38 ± .00 F = 38.71 P < 0.001* fluids, vasopressors 28 63.64 ± 9.51 F = 38.71 P < 0.001* TFC day 5 Non 16 46 ± 6.44 F = 42.66 P < 0.001* Fluids only 0 . F = 42.66 P < 0.001* fluids, vasopressors 16 63.87 ± 8.84 F = 42.66 P < 0.001* TFC day 6 Non 12 48 ± 3.13 F = 29.58 P < 0.001* Fluids only 0 . F = 29.58 P < 0.001* fluids, vasopressors 10 59.80 ± 6.71 F = 29.58 P < 0.001* TFC = Thoracic Fluid Content (kΩ⁻¹), F: One Way ANOVA test *p < 0.05 statistically significant Discussion Functional hemodynamic monitoring has gradually evolved from static measures to dynamic and continuous assessment of cardiovascular performance. This shift reflects growing evidence that functional parameters are more reliable in predicting fluid responsiveness than traditional static indicators such as central venous pressure or pulmonary capillary wedge pressure [ 1 , 4 , 16 ]. This approach allows for individualized fluid management, helping to prevent both hypoperfusion and volume overload, which are associated with pulmonary edema, poor oxygenation, and longer mechanical ventilation [ 17 ]. Continuous hemodynamic assessment has therefore become an essential part of critical care management, supporting improved perfusion and better patient outcomes [ 16 , 18 ]. Accurate evaluation of volume status and prediction of fluid responsiveness remain key challenges in intensive care. Both hypovolemia and overhydration impair oxygen delivery and tissue recovery [ 16 ]. Studies have reported that only about half of unstable patients respond positively to fluid resuscitation [ 17 ], emphasizing the need for practical, real-time, noninvasive monitoring tools. Electrical cardiometry (EC) fulfills this role by continuously measuring cardiac output, stroke volume, and related parameters based on thoracic bioimpedance [ 2 , 5 ]. One of its main indices, thoracic fluid content (TFC), reflects the total thoracic fluid volume, including both intravascular and extravascular components [ 3 , 9 ]. EC is easy to use, reproducible, and operator-independent, making it especially useful in respiratory intensive care units (RICUs) where close and frequent monitoring is needed [ 5 , 6 ]. In the present study, higher TFC values were significantly associated with increased mortality, longer mechanical ventilation, and extended RICU stay. These findings suggest that increased thoracic fluid accumulation reflects more severe disease and poor hemodynamic stability. Our results are consistent with those of Gho et al. (2021), who found that elevated TFC predicted higher mortality and prolonged hospitalization in patients with community-acquired pneumonia [ 7 ]. Similarly, Hammad et al. (2019) demonstrated that EC-derived TFC correlated strongly with lung ultrasound findings in preeclamptic women with pulmonary edema [ 8 ]. These consistent results confirm that TFC is a reliable, noninvasive marker of pulmonary congestion and overall disease severity. The present findings are also in agreement with Narula et al. (2017), who reported that TFC changes accurately reflected thoracic volume shifts during blood withdrawal [ 9 ]. In this study, patients who required vasopressors or aggressive fluid therapy showed higher TFC values, indicating that EC effectively tracks real-time circulatory variations. The positive correlation between TFC, duration of mechanical ventilation, and RICU stay aligns with the findings of Fathy et al. (2020) and Choudhury et al. (2023), who reported delayed weaning and longer ventilation periods with elevated TFC [ 12 , 16 ]. An inverse correlation between TFC and mean arterial pressure (MAP) was also observed, suggesting that increased thoracic fluid is associated with poor circulatory performance. Similar findings were described by Kossari et al. (2009) and Mahmoud et al. (2016), who reported that reductions in TFC following fluid removal were linked to improvements in MAP [ 18 , 19 ]. Moreover, the strong positive correlation between TFC and serum lactate (p < 0.001) highlights the relationship between pulmonary congestion and tissue hypoxia. These observations are supported by previous studies showing that impedance-derived indices can indicate metabolic stress and reduced oxygen delivery [3, 21]. Continuous TFC monitoring may therefore serve as an early indicator of hemodynamic deterioration before clinical signs appear. When compared with the APACHE II score, TFC showed a moderate predictive ability (AUC = 0.656), while APACHE II demonstrated superior discriminative power (AUC = 0.991). Nevertheless, TFC provides continuous, dynamic information that complements static scoring systems. Combining both measures may enhance risk stratification and guide clinical decisions in RICU patients. Van De Water et al. (2005) also verified the accuracy of impedance cardiography for evaluating thoracic fluid status and differentiating pulmonary from cardiac causes of dyspnea [ 15 ]. Recent research has further supported EC as a reliable and noninvasive alternative to imaging methods such as echocardiography and lung ultrasound. El-Sherif et al. (2025) found a strong correlation between EC-derived TFC and lung ultrasound estimates of extravascular lung water in mechanically ventilated patients [ 10 ]. EC enables early detection of hemodynamic changes and allows clinicians to adjust therapy before decompensation occurs. Similar findings were reported by Garutti et al. (2015) and Choudhury et al. (2023), who observed that increased thoracic fluid indices were associated with prolonged ventilation and postoperative complications [ 12 , 17 ]. In this study, TFC was not influenced by age, sex, or body mass index but was significantly higher among smokers and non-survivors. This is consistent with evidence suggesting that smoking impairs endothelial function and alveolar fluid clearance. The positive association between TFC and serum lactate underscores the relationship between pulmonary congestion and tissue hypoxia, similar to findings by Sanidas et al. (2009) [ 20 ]. Collectively, these results confirm that EC-derived TFC is a practical adjunct to conventional hemodynamic measures. The combination of higher TFC, lower MAP, and elevated lactate strongly indicates poor outcomes, a pattern observed in several studies [ 7 – 9 , 12 , 18 , 22 ]. Overall, this study highlights the value of electrical cardiometry as a continuous, noninvasive, and dependable tool for monitoring critically ill respiratory patients. Elevated TFC reflects pulmonary and systemic fluid overload and may serve as an early warning sign of hemodynamic compromise. Routine use of TFC monitoring could support more precise fluid management, improve oxygenation, and ultimately enhance survival. Conclusion Electrical cardiometry offers a noninvasive, continuous method for assessing hemodynamic status in RICU patients. In this study, elevated thoracic fluid content (TFC) was significantly associated with higher mortality, longer ventilation, and extended ICU stay. These results indicate that TFC can serve as a useful dynamic marker of fluid overload and circulatory dysfunction. When combined with the APACHE II score, EC-derived TFC may facilitate early identification of high-risk patients and guide individualized management. Incorporating EC monitoring into clinical practice may help optimize fluid therapy, reduce complications, and improve outcomes in respiratory critical care. Recommendations Routine monitoring of thoracic fluid content (TFC) using electrical cardiometry is recommended for RICU patients to guide fluid resuscitation and prevent volume overload. Future multicenter studies with larger sample sizes are needed to validate the prognostic value of TFC in different critical care settings and to establish standard cutoff points for mortality prediction. More frequent or continuous EC recordings are encouraged, as TFC values can fluctuate rapidly with clinical changes. Combining TFC monitoring with cardiac biomarkers such as brain natriuretic peptide (BNP) or troponin could provide a more comprehensive evaluation of cardiopulmonary fluid dynamics and improve outcome prediction. Clinician education and training are essential to ensure proper interpretation of EC-derived parameters and to integrate them effectively into individualized patient management.implementation and for tailoring management to each patient’s hemodynamic profile. Limitations: The study was conducted in a single tertiary center with a relatively modest sample size (n = 130), which may limit generalizability. The heterogeneity of underlying diagnoses, such as pneumonia, pulmonary embolism, and near-drowning, might have influenced TFC variability. However, although EC is noninvasive and reproducible, it is sensitive to electrode placement and patient movement, which could introduce measurement bias. Also, the study did not compare EC data directly with gold-standard methods such as pulmonary artery catheterization or transpulmonary thermodilution, limiting the ability to quantify absolute measurement accuracy. Finally, long-term outcomes after ICU discharge were not evaluated, and future multicenter studies with larger cohorts and longitudinal follow-up are warranted to validate these findings. Abbreviations ABG Arterial Blood Gases APACHE II Acute Physiology and Chronic Health Evaluation II ARDS Acute Respiratory Distress Syndrome AUC Area Under the Curve BMI Body Mass Index BNP Brain Natriuretic Peptide CO Cardiac Output COPD Chronic Obstructive Pulmonary Disease CRP C-Reactive Protein EC Electrical Cardiometry FTc Flow Time Corrected ICG Impedance Cardiography MAP Mean Arterial Pressure MV Mechanical Ventilation NPV Negative Predictive Value OHS Obesity Hypoventilation Syndrome PE Pulmonary Embolism PPV Positive Predictive Value RICU Respiratory Intensive Care Unit ROC Receiver Operating Characteristic ScvO₂ Central Venous Oxygen Saturation SD Standard Deviation SVRI Systemic Vascular Resistance Index SVV Stroke Volume Variation TEB Thoracic Electrical Bioimpedance TFC Thoracic Fluid Content Declarations The authors have no relevant financial or non-financial interests to disclose. Ethics approval and consent to participate This study was approved by the research ethics committee of the chest medicine department of the Faculty of Medicine at Mansoura University. Reference number of approval: MS.19.12.354. All patients included in this study gave written informed consent to participate in the research. Consent for publication : All patients included in this study gave written informed consent to publish the data contained in this study. Availability of data and materials: Available on request with the corresponding author. Competing interests : The authors declare that they have no competing interests. Funding: Not applicable (no funding was received for this study). Authors’ Contributions: MME and DAA conceived and designed the study. AGA performed patient enrollment and data collection. MAM analyzed the data and drafted the manuscript. MAM, ARE, DAA, and MME critically reviewed and revised the manuscript for important intellectual content. 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Minimally invasive measurement of cardiac output during surgery and critical care: a meta-analysis of accuracy and precision. Anesthesiology. 2010;113(5):1220–1235. Gho KY, Lee DH, Cho JY, et al. Prognostic significance of thoracic fluid content measured by electrical cardiometry in community-acquired pneumonia. Sci Rep. 2021;11:19865. Hammad Y, Hassan H, Youssef M, et al. Electrical cardiometry for early detection of pulmonary edema in preeclamptic parturients. J Obstet Gynaecol Res. 2019;45(12):2378–2386. Narula J, Kapoor PM, Chauhan S, et al. Changes in thoracic fluid content during autologous blood harvest: correlation with intrathoracic volume shifts. J Cardiothorac Vasc Anesth. 2017;31(2):587–593. El-Sherif S, El-Sayed T, Khalifa M, et al. Comparison between electrical cardiometry and lung ultrasound in the assessment of lung water in mechanically ventilated patients. Egypt J Bronchol. 2025;19(1):Article 45. Mahajan R, Agrawal A, Das S, et al. Evaluation of electrical cardiometry to assess fluid responsiveness in patients with acute circulatory failure compared with echocardiography. J Clin Monit Comput. 2024;38(6):1153–1162. Choudhury M, Narula J, Saini V, Kapoor PM, Kiran U. Fluid management using cardiometry versus simplified FACTT protocol in ARDS patients. Anaesth Pain Intensive Care. 2023;27(4):456–462. Zhang L, Chen W, Lu J, et al. Thoracic fluid content as a rapid diagnostic indicator of secondary capillary leak syndrome in pediatric patients after cardiopulmonary bypass. Front Pediatr. 2025;13:1494533. Standl T, Annecke T, Cascorbi I, Heller AR, Sabashnikov A, Teske W. The new definition of shock states: current classification and terminology. Curr Med Res Opin. 2018;34(2):161–168. Van De Water JM, Miller TW, Vogel R, Mount BE, Dalton ML. Impedance cardiography: the next vital sign technology? Chest. 2005;128(4):287–297. Fathy S, Elshazly M, Ghaleb A, Khalil A. Thoracic fluid content as a predictor of weaning outcome in surgical critically ill patients. Egypt J Bronchol. 2020;14(1):22–29. Garutti I, Cruz P, Olmedilla L, et al. Extravascular lung water and postoperative pulmonary complications after orthotopic liver transplantation: a prospective observational study. Transplant Proc. 2015; 47(9): 2015;47(9):2630–2634. Kossari N, Hufnagel C, Squara P. Changes in thoracic fluid content in patients undergoing hemodialysis: comparison with classical fluid removal indices. Intensive Care Med. 2009; 35(2): 2009;35(2):343–349. Mahmoud K, Mokhtar A, Soliman M, Khaled H. Relationship between thoracic fluid content and amount of fluid removal during hemodialysis session. Egypt J Crit Care Med. 2016;4(3):135–142. Sanidas EA, Papadopoulos DP, Velliou M, et al. Hemodynamic effects of diuretics assessed with impedance cardiography in hypertensive patients. Clin Exp Hypertens. 2009;31(1):43–54. Eucker W, Brunetti I, Krüger S, Müssigbrodt U, Pahernik SA. A novel noninvasive impedance-based technique for central venous pressure measurement. Crit Care Med. 2009;37(1):83–89. Abraham WT, Compton S, Haas G, et al. Intrathoracic impedance monitoring for early detection of worsening heart failure: results of the Fluid Accumulation Status Trial (FAST). J Card Fail. 2004;10(5):365–371. Folan J, Funk M. Thoracic fluid content measurement to detect changes in fluid status in patients with heart failure. Heart Lung. 2008;37(4):295–302. Cecconi M, De Backer D, Antonelli M, et al. Consensus on circulatory shock and hemodynamic monitoring: task force of the European Society of Intensive Care Medicine. Intensive Care Med. 2014;40(12):1795–1815. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Jan, 2026 Reviews received at journal 07 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviews received at journal 05 Jan, 2026 Reviewers agreed at journal 03 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 29 Oct, 2025 Editor assigned by journal 24 Oct, 2025 Submission checks completed at journal 21 Oct, 2025 First submitted to journal 18 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7895080","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538419891,"identity":"cbcc5592-01f0-4f62-a544-4085b1346dc1","order_by":0,"name":"Mohamed AbdElmoniem Mohamed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDCCA0CcUGFjxw/lMzYQpeXBmbRkyQZStDA+bDvMuOEAsVr4bh9g3ZDYdpjZ+NrhZw9/MNjIbjjAfPgFPi2S5xLYbiScS+czu51mbszDkGa84QBbmgU+LQZnGIBayqyZzW4nmEkzMBxO3HCAx8yAsBY2ZsbNs9O/Sf5g+A/Uwv+NCC1tzowbpHPMJHgYDoBsYX6A1y9nGNtuJAADWeJ2Tpk0j0Gy8czDbGb4dDDwnWE+dvMHKCpnp2+T/FFhJ9t3vPnxB7x6UCMC5AlmBjYJ/FqwAGYCtoyCUTAKRsEIAwDHtlAivAc7mQAAAABJRU5ErkJggg==","orcid":"","institution":"Mansoura University","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"AbdElmoniem","lastName":"Mohamed","suffix":""},{"id":538419892,"identity":"3634865f-d132-4c31-82b7-6e2ceb0d6554","order_by":1,"name":"Mohsen Mohammad Elshafey","email":"","orcid":"","institution":"Mansoura 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16:37:12","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117036,"visible":true,"origin":"","legend":"","description":"","filename":"d5fc5a3bc58147d793d7187ea98b90fb1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7895080/v1/003d0e12399aa0fa16ede620.xml"},{"id":95662180,"identity":"18490c3e-3913-42b5-a117-15133e040e48","added_by":"auto","created_at":"2025-11-11 16:37:15","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129248,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7895080/v1/3d7f07df5401772d9976d853.html"},{"id":95661866,"identity":"dc34fc77-d408-4508-bf93-f634e8227ea0","added_by":"auto","created_at":"2025-11-11 16:36:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44364,"visible":true,"origin":"","legend":"\u003cp\u003eStandard electrode placement for electrical cardiometry (ICON™ device). Four surface electrodes are typically positioned on the left side of the neck and thorax, following the manufacturer’s protocol. Adapted from Albert et al. [5] and OSYPKA Medical user manual.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7895080/v1/5d8cc59f2a3be4bbdffa3a49.png"},{"id":95662287,"identity":"1b8e06cd-0009-4dce-84a1-2d056e4e1d1c","added_by":"auto","created_at":"2025-11-11 16:37:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12987,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative screen display from the ICON™ monitor showing real-time hemodynamic parameters, including cardiac output (CO), stroke volume variation (SVV), and thoracic fluid content (TFC). This view illustrates how the device continuously tracks key circulatory indices. Adapted from Albert et al. [5].\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7895080/v1/471970b47dec14650ac6fd89.png"},{"id":95662008,"identity":"0074bf88-e5a2-482c-8b96-c7d996cba191","added_by":"auto","created_at":"2025-11-11 16:37:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24191,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of thoracic fluid content (TFC) values between survivors and non-survivors across follow-up days. Higher TFC levels were consistently observed among non-survivors, reflecting increased thoracic water accumulation and hemodynamic compromise. Created by the authors.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7895080/v1/1058edf79699145f883ad410.png"},{"id":95662016,"identity":"0ebbe30b-97f5-43bb-bc92-acdab9b56025","added_by":"auto","created_at":"2025-11-11 16:37:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14431,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for APACHE II score and thoracic fluid content (TFC) in predicting mortality among RICU patients. The APACHE II score demonstrated excellent discriminative ability (AUC = 0.991), while TFC showed moderate predictive performance (AUC = 0.656). Created by the authors.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7895080/v1/7c83c6531fa7854ea9d9e2a8.png"},{"id":95663522,"identity":"3dddc1e1-a1b1-4113-b012-d4ce799ea2d8","added_by":"auto","created_at":"2025-11-11 16:39:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1528189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7895080/v1/6f0883ec-649b-4fa1-ad99-6d99e387be2d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of Electrical Cardiometry–Derived Thoracic Fluid Content in Respiratory Intensive Care Unit Patients: A Prospective Observational Study","fulltext":[{"header":"Background","content":"\u003cp\u003eHemodynamic instability represents one of the most common and serious challenges in critically ill patients, especially those admitted to respiratory intensive care units (RICUs). It frequently leads to tissue hypoperfusion, multiple organ dysfunction, and higher mortality rates. Accurate evaluation of the circulatory status and fluid balance is therefore crucial for proper management. Although traditional invasive techniques such as pulmonary artery catheterization provide precise measurements, they carry notable drawbacks, including procedural risks, technical difficulty, and patient discomfort. These concerns have driven the search for dependable, noninvasive methods that allow continuous, real-time hemodynamic monitoring at the bedside [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eElectrical cardiometry (EC) is a modern, noninvasive technique based on thoracic bioimpedance analysis. It provides continuous assessment of cardiac output and thoracic fluid content (TFC), which reflects total thoracic fluid volume, including intravascular, interstitial, and alveolar components. TFC is inversely related to thoracic impedance, thus increasing in conditions of pulmonary congestion or fluid overload. EC allows the detection of rapid changes in hemodynamics and has the advantages of simplicity, safety, reproducibility, and minimal operator dependency, making it suitable for continuous bedside use in the RICU [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThoracic fluid content (TFC) represents an integrated marker of the patient\u0026rsquo;s pulmonary and circulatory fluid status. It provides a quantitative assessment of the total water content within the thoracic cavity, encompassing both intravascular and extravascular compartments. In pathological states such as pneumonia, acute respiratory distress syndrome (ARDS), and congestive heart failure, the accumulation of extravascular lung water increases TFC values. Monitoring this parameter may therefore help in early detection of pulmonary edema, fluid overload, or inadequate fluid resuscitation before overt clinical manifestations occur [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the intensive care setting, optimal fluid management is crucial. Both excessive and inadequate fluid administration are harmful. Over-resuscitation can result in pulmonary edema, impaired gas exchange, and prolonged mechanical ventilation, while under-resuscitation may cause tissue hypoxia and organ dysfunction. Traditional static indices, such as central venous pressure (CVP), pulmonary artery occlusion pressure, and mean arterial pressure (MAP), often fail to accurately reflect intravascular volume or predict fluid responsiveness. Dynamic indices and impedance-based methods, including EC-derived TFC, have gained growing attention for their ability to provide continuous and more physiologically relevant measurements of fluid status [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral studies have demonstrated the usefulness of EC-derived parameters in evaluating cardiac output, stroke volume, and systemic vascular resistance. Albert et al. 2004 reported that impedance cardiography provides a reliable and cost-effective method for continuous monitoring of central hemodynamics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similarly, Peyton and Chong emphasized the noninvasive and beat-to-beat capability of EC, highlighting its ease of use and ability to track rapid hemodynamic changes during different clinical conditions. The technology is particularly beneficial in critically ill respiratory patients where invasive catheterization may not be feasible or safe [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent advances have strengthened the clinical application of TFC monitoring. Gho et al. 2021 found that elevated TFC values were associated with higher mortality and longer hospital stays among patients with community-acquired pneumonia [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hammad et al. 2019 confirmed the correlation between EC-measured TFC and lung ultrasound findings in preeclamptic women with pulmonary edema, showing excellent diagnostic accuracy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Narula et al. 2017 further demonstrated that changes in TFC accurately reflected intrathoracic fluid shifts during blood withdrawal, validating its role as a dynamic indicator of thoracic hydration [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Egypt, studies using EC technology remain limited, especially in the field of respiratory critical care. However, a recent study comparing EC with lung ultrasound among mechanically ventilated patients revealed a strong correlation between TFC and lung water content, underscoring EC\u0026rsquo;s potential role in predicting weaning failure. This correlation between TFC and extravascular lung water supports EC as a feasible alternative to ultrasound for continuous, noninvasive assessment of pulmonary fluid status [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, international studies between 2022 and 2025 have expanded the understanding of EC\u0026rsquo;s prognostic significance. EC has been validated against transthoracic echocardiography for evaluating cardiac output and fluid responsiveness in patients with circulatory failure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In patients with ARDS, cardiometry-guided fluid management was associated with reduced mechanical ventilation duration and shorter ICU stay compared with conventional fluid therapy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, studies in pediatric cardiac surgery and post-cardiopulmonary bypass patients demonstrated that increased TFC predicted the development of secondary capillary leak syndrome, highlighting its sensitivity to early hemodynamic derangements [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite this growing body of evidence, the prognostic value of EC-derived TFC among adult patients admitted to the RICU remains underexplored. Most previous investigations have focused on cardiac surgery, sepsis, or perioperative care, while data in respiratory critical illness are scarce. Determining whether elevated TFC values can predict adverse outcomes\u0026mdash;such as prolonged mechanical ventilation, extended RICU stay, and increased mortality\u0026mdash;would add important insight into fluid management and risk stratification in this patient population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe current study, therefore, aimed to evaluate the predictive role of electrical cardiometry\u0026ndash;derived thoracic fluid content in patients admitted to the RICU of Mansoura University Hospitals. Specifically, the study examined the relationship between TFC and clinical outcomes, including mortality, duration of invasive mechanical ventilation, and RICU length of stay. We hypothesized that higher TFC values would correlate with prolonged hospitalization, hemodynamic instability, and poorer prognosis among critically ill respiratory patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eA prospective observational analytical study was conducted in the Respiratory Intensive Care Unit (RICU) of the Chest Medicine Department, Mansoura University Hospitals, Egypt. The study enrolled 130 adult patients between December 2022 and December 2023. Ethical approval was obtained from the Mansoura Faculty of Medicine Institutional Research Board (MFM-IRB: MS.19.12.354). Written informed consent was obtained from all participants or their first-degree relatives.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample size calculation\u003c/b\u003e: The minimum required sample size was estimated using the single-proportion formula: n\u0026thinsp;=\u0026thinsp;Z21\u0026thinsp;\u0026minus;\u0026thinsp;α/2 p(1\u0026thinsp;\u0026minus;\u0026thinsp;p)/d2\u003c/p\u003e\u003cp\u003eBased on an expected mortality rate of 43%, a 95% confidence level, and a margin of error of 7.5%, the target sample size was 130 patients.\u003c/p\u003e\u003cp\u003eA total of 120 patients were eventually included due to practical and logistical limitations during the study period, which still provided acceptable precision for the primary outcomes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003cp\u003ePatients aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years admitted to the RICU for any indication were included.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003cp\u003epresence of malignancy (primary or metastatic lung cancer), advanced pulmonary fibrosis, significant pleural or pericardial effusion, or refusal to participate.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical and Laboratory Assessment:\u003c/h3\u003e\n\u003cp\u003eEach patient underwent a full clinical evaluation, including APACHE II scoring, within 24 hours of admission. Radiological investigations (chest X-ray and/or CT chest) and laboratory tests (CBC, ABGs, liver and kidney function, electrolytes, CRP, and serum lactate) were performed according to standard protocols.\u003c/p\u003e\n\u003ch3\u003eElectrical Cardiometry Monitoring:\u003c/h3\u003e\n\u003cp\u003eThoracic fluid content (TFC) and related hemodynamic parameters were measured daily at 10 a.m. using the ICON\u0026trade; noninvasive cardiometer (Model C3, OSYPKA Medical, Germany). Four surface electrodes were placed on the left neck and thorax according to the manufacturer\u0026rsquo;s protocol (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Parameters including cardiac output (CO), stroke volume variation (SVV), flow time corrected (FTc), and TFC were recorded three times at 5-minute intervals and averaged (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eStudy Endpoints\u003c/h3\u003e\n\u003cp\u003ePrimary endpoints: Duration of invasive mechanical ventilation and length of RICU stay.\u003c/p\u003e\u003cp\u003eSecondary endpoint: Mortality during hospitalization.\u003c/p\u003e\n\u003ch3\u003eEthical considerations:\u003c/h3\u003e\n\u003cp\u003e After obtaining approval from the Institutional Research Board (IRB) of the Faculty of Medicine, Mansoura University (MS.19.12.354), written informed consent was secured from all participants or their first-degree relatives before initiation of the study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003cp\u003eData were analyzed using SPSS version 18. Quantitative variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) according to distribution, and qualitative data as frequencies and percentages. Between-group comparisons were made using Student\u0026rsquo;s t-test, chi-square, or Fisher\u0026rsquo;s exact-test as appropriate. Correlations between TFC and other variables (MAP, serum lactate, RICU stay) were assessed using Spearman\u0026rsquo;s correlation. ROC analysis was used to determine predictive accuracy of TFC and APACHE II for mortality. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 130 patients were included, with a mean age of 52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1 years; 80% were females, and 80.8% were non-smokers. Pneumonia was the most common cause of admission (65.4%), followed by COPD (15.4%). The overall survival rate was 57% (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic characteristics of the studied patients\u003c/strong\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Range)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12 (18\u0026ndash;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e26 (20%)\u003c/p\u003e\n \u003cp\u003e104 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNonsmokers\u003c/p\u003e\n \u003cp\u003epassive smokers\u003c/p\u003e\n \u003cp\u003eActive smokers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e105 (80%)\u003c/p\u003e\n \u003cp\u003e10 (7.7%)\u003c/p\u003e\n \u003cp\u003e15 (11.5%)\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\u003eBMI\u0026thinsp;=\u0026thinsp;Body Mass Index; SD\u0026thinsp;=\u0026thinsp;Standard Deviation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMain causes of admission and outcome of the studied patients.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCauses of admission\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia\u003c/p\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003cp\u003eNear drowning\u003c/p\u003e\u003cp\u003eOHS\u003c/p\u003e\u003cp\u003ePE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (65.4%)\u003c/p\u003e\u003cp\u003e20 (15.4%)\u003c/p\u003e\u003cp\u003e5 (3.8%)\u003c/p\u003e\u003cp\u003e5 (3.8%)\u003c/p\u003e\u003cp\u003e15 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003en(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvival\u003c/p\u003e\u003cp\u003eDied\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (56.9%)\u003c/p\u003e\u003cp\u003e56 (43.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCOPD\u0026thinsp;=\u0026thinsp;Chronic Obstructive Pulmonary Disease; OHS\u0026thinsp;=\u0026thinsp;Obesity Hypoventilation Syndrome.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a statistically significant difference in mortality between non-smokers and active smokers (p\u0026thinsp;=\u0026thinsp;0.01). However, no significant differences were observed between survivors and non-survivors with respect to age, sex, or body mass index (BMI) (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between outcome and sociodemographic characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvived (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDied (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest of significance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.81\u0026thinsp;\u0026plusmn;\u0026thinsp;14.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003en(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003en(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eTest of significance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (18.9)\u003c/p\u003e\u003cp\u003e60 (81.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (21.4)\u003c/p\u003e\u003cp\u003e44 (78.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ\u0026sup2;=0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNonsmokers\u003c/p\u003e\u003cp\u003ePassive smokers\u003c/p\u003e\u003cp\u003eActive smokers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (74.3)\u003c/p\u003e\u003cp\u003e5 (6.8)\u003c/p\u003e\u003cp\u003e14 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (89.3)\u003c/p\u003e\u003cp\u003e5 (8.9)\u003c/p\u003e\u003cp\u003e1 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMC test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.01*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (Kg/m2 )\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.30\u0026thinsp;\u0026plusmn;\u0026thinsp;7.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003et: Student t-test, MC: Monte Carlo test χ2\u0026thinsp;=\u0026thinsp;Chi-Square test *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;(4) demonstrates that non-survivors had significantly higher mean thoracic fluid content (TFC) values on all follow-up days compared with survivors. The differences in TFC between the two groups were statistically significant throughout the entire monitoring period (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eComparison of thoracic fluid content (TFC) between survivors and non-survivors over follow-up days.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTFC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003esurvived (n\u0026thinsp;=\u0026thinsp;74) mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDied (n\u0026thinsp;=\u0026thinsp;56) mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest of significance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.61\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.08\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.82 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;7.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.36\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.12\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;13.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.31\u0026thinsp;\u0026plusmn;\u0026thinsp;6.20 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.64\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;8.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;6.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.71 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;5.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003et-test used; TFC\u0026thinsp;=\u0026thinsp;Thoracic Fluid Content (kΩ⁻\u0026sup1;) *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure (3): Comparison of thoracic fluid content (TFC) values between survivors and non-survivors across follow-up days.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;(5) shows that higher thoracic fluid content (TFC) correlated positively with the duration of mechanical ventilation, RICU stay, and serum lactate levels (p\u0026thinsp;=\u0026thinsp;0.001), and negatively with mean arterial pressure (MAP) from day 2 to day 6. These findings indicate that elevated TFC reflects hemodynamic instability and prolonged illness severity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eCorrelation between TFC and clinical parameters.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTFC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInvasive MV. duration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehospital stay (days)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMAP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003es.lactate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.038*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.422\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.022*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003er\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC day 6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003er: Spearman correlation coefficient *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;(6) shows that APACHE II had excellent predictive accuracy for mortality (AUC\u0026thinsp;=\u0026thinsp;0.991, sensitivity 92.9%, specificity 91.9%), while thoracic fluid content (TFC) demonstrated moderate predictive value (AUC\u0026thinsp;=\u0026thinsp;0.656, sensitivity 85.7%, specificity 67.3%) at a cutoff of 43 kΩ⁻\u0026sup1;.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredictive performance of APACHE II and TFC for mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTest Result Variable(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eArea\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAsymptotic 95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ecut off\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSensitivity%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSpecificity %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePPV%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNPV%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAccuracy %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower bound\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003cp\u003ebound\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAPACHII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e92.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e91.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e89.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e94.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e92.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTFC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43 k ohm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e85.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e67.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e46.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e94.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e72.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAUC\u0026thinsp;=\u0026thinsp;Area under ROC curve; PPV\u0026thinsp;=\u0026thinsp;Positive Predictive Value; NPV\u0026thinsp;=\u0026thinsp;Negative Predictive Value.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;(7) revealed that mean thoracic fluid content (TFC) values were significantly higher among non-survivors with pneumonia and COPD compared to survivors (p\u0026thinsp;=\u0026thinsp;0.049 and p\u0026thinsp;=\u0026thinsp;0.023, respectively). No deaths occurred among patients with near-drowning or obesity hypoventilation syndrome, whose mean TFC values were 54.60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07 and 41.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure (4): Receiver Operating Characteristic (ROC) curves for APACHE II score and thoracic fluid content (TFC) in predicting mortality among RICU patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean Thoracic fluid content (TFC) within survived, died cases in each cause of hospital admission\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMain causes of hospital admission\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean TFC of Survived (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean TFC of Died (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003etest of significance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePneumonia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e51.87\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e45.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.023*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNear drowning\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e54.60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOHS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e41.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e43.13\u0026thinsp;\u0026plusmn;\u0026thinsp;11.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCOPD\u0026thinsp;=\u0026thinsp;Chronic Obstructive Pulmonary Disease; OHS\u0026thinsp;=\u0026thinsp;Obesity Hypoventilation Syndrome. PE\u0026thinsp;=\u0026thinsp;pulmonary embolism t-test used; TFC\u0026thinsp;=\u0026thinsp;Thoracic Fluid Content (kΩ⁻\u0026sup1;) *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003ePatients with type I respiratory failure had significantly higher mean thoracic fluid content (TFC) values than those with type II respiratory failure during follow-up days 1 to 4 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and p\u0026thinsp;=\u0026thinsp;0.008, respectively). No significant difference was observed on days 5 and 6 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Thus, higher TFC values can be used to predict type I respiratory failure Table\u0026nbsp;(8).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Thoracic fluid content (TFC) between cases with Respiratory failure Type 1 \u0026amp; Type 2\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTFC (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRF1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRF2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest of significance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e52.71\u0026thinsp;\u0026plusmn;\u0026thinsp;8.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e45.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e52.39\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e45.64\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e54.58\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e42.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;5.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e57.27\u0026thinsp;\u0026plusmn;\u0026thinsp;12.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e47.08\u0026thinsp;\u0026plusmn;\u0026thinsp;7.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.008*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e55.07\u0026thinsp;\u0026plusmn;\u0026thinsp;12.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e53.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up day 6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e53.60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e51.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eRF1\u0026thinsp;=\u0026thinsp;Respiratory failure Type 1, RF2\u0026thinsp;=\u0026thinsp;Respiratory failure Type 2, t:Student t-test, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistical significant\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding patient outcomes, mortality was significantly associated with the use of invasive mechanical ventilation and the presence of hypotension requiring vasopressors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). Notably, 90% of patients who required both mechanical ventilation and vasopressor support were non-survivors. However, the duration of mechanical ventilation showed no significant association with mortality (p\u0026thinsp;=\u0026thinsp;0.798) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelation between outcome and hospitalization characteristics of the studied cases.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvived (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDied (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest of significance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvasive MV\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (75.7)\u003c/p\u003e\u003cp\u003e18 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (7.1)\u003c/p\u003e\u003cp\u003e52 (92.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;60.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMV duration/days\u003c/p\u003e\u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.798\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-hypotensive\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHypotensive treated with Fluids \u0026amp; vasopressors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (94.6)\u003c/p\u003e\u003cp\u003e4 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (7.1)\u003c/p\u003e\u003cp\u003e52 (92.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMC test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eMV\u0026thinsp;=\u0026thinsp;Mechanical ventilation, t: Student t-test, MC: Monte Carlo test χ2\u0026thinsp;=\u0026thinsp;Chi-Square test *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mean thoracic fluid content (TFC) values were significantly higher in patients who required both fluids and vasopressors compared with those who received fluids only or required neither (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate that TFC changes closely reflect hemodynamic status and correlate well with vasopressor requirement Table\u0026nbsp;(10).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe correlation between Thoracic fluid content (TFC) and requirement of fluids and vasopressors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003evasopressor requirement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTest of significance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTFC day 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.17\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;9.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluids only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;5.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efluids, vasopressors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.65\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;7.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;6.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTFC day 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.42\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;6.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;17.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluids only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;11.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;17.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efluids, vasopressors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.34\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;9.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;17.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTFC day 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.84\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;5.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;99.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluids only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;99.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efluids, vasopressors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.11\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;7.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;99.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTFC day 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.63\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;5.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;38.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluids only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;38.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efluids, vasopressors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.64\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;9.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;38.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTFC day 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;42.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluids only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;42.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efluids, vasopressors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.87\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;8.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;42.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTFC day 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;29.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFluids only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;29.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efluids, vasopressors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.80\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;6.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;29.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eTFC\u0026thinsp;=\u0026thinsp;Thoracic Fluid Content (kΩ⁻\u0026sup1;), F: One Way ANOVA test *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFunctional hemodynamic monitoring has gradually evolved from static measures to dynamic and continuous assessment of cardiovascular performance. This shift reflects growing evidence that functional parameters are more reliable in predicting fluid responsiveness than traditional static indicators such as central venous pressure or pulmonary capillary wedge pressure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This approach allows for individualized fluid management, helping to prevent both hypoperfusion and volume overload, which are associated with pulmonary edema, poor oxygenation, and longer mechanical ventilation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Continuous hemodynamic assessment has therefore become an essential part of critical care management, supporting improved perfusion and better patient outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAccurate evaluation of volume status and prediction of fluid responsiveness remain key challenges in intensive care. Both hypovolemia and overhydration impair oxygen delivery and tissue recovery [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies have reported that only about half of unstable patients respond positively to fluid resuscitation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], emphasizing the need for practical, real-time, noninvasive monitoring tools. Electrical cardiometry (EC) fulfills this role by continuously measuring cardiac output, stroke volume, and related parameters based on thoracic bioimpedance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. One of its main indices, thoracic fluid content (TFC), reflects the total thoracic fluid volume, including both intravascular and extravascular components [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. EC is easy to use, reproducible, and operator-independent, making it especially useful in respiratory intensive care units (RICUs) where close and frequent monitoring is needed [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the present study, higher TFC values were significantly associated with increased mortality, longer mechanical ventilation, and extended RICU stay. These findings suggest that increased thoracic fluid accumulation reflects more severe disease and poor hemodynamic stability. Our results are consistent with those of Gho et al. (2021), who found that elevated TFC predicted higher mortality and prolonged hospitalization in patients with community-acquired pneumonia [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, Hammad et al. (2019) demonstrated that EC-derived TFC correlated strongly with lung ultrasound findings in preeclamptic women with pulmonary edema [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These consistent results confirm that TFC is a reliable, noninvasive marker of pulmonary congestion and overall disease severity.\u003c/p\u003e\u003cp\u003eThe present findings are also in agreement with Narula et al. (2017), who reported that TFC changes accurately reflected thoracic volume shifts during blood withdrawal [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In this study, patients who required vasopressors or aggressive fluid therapy showed higher TFC values, indicating that EC effectively tracks real-time circulatory variations. The positive correlation between TFC, duration of mechanical ventilation, and RICU stay aligns with the findings of Fathy et al. (2020) and Choudhury et al. (2023), who reported delayed weaning and longer ventilation periods with elevated TFC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAn inverse correlation between TFC and mean arterial pressure (MAP) was also observed, suggesting that increased thoracic fluid is associated with poor circulatory performance. Similar findings were described by Kossari et al. (2009) and Mahmoud et al. (2016), who reported that reductions in TFC following fluid removal were linked to improvements in MAP [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, the strong positive correlation between TFC and serum lactate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) highlights the relationship between pulmonary congestion and tissue hypoxia. These observations are supported by previous studies showing that impedance-derived indices can indicate metabolic stress and reduced oxygen delivery [3, 21]. Continuous TFC monitoring may therefore serve as an early indicator of hemodynamic deterioration before clinical signs appear.\u003c/p\u003e\u003cp\u003eWhen compared with the APACHE II score, TFC showed a moderate predictive ability (AUC\u0026thinsp;=\u0026thinsp;0.656), while APACHE II demonstrated superior discriminative power (AUC\u0026thinsp;=\u0026thinsp;0.991). Nevertheless, TFC provides continuous, dynamic information that complements static scoring systems. Combining both measures may enhance risk stratification and guide clinical decisions in RICU patients. Van De Water et al. (2005) also verified the accuracy of impedance cardiography for evaluating thoracic fluid status and differentiating pulmonary from cardiac causes of dyspnea [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent research has further supported EC as a reliable and noninvasive alternative to imaging methods such as echocardiography and lung ultrasound. El-Sherif et al. (2025) found a strong correlation between EC-derived TFC and lung ultrasound estimates of extravascular lung water in mechanically ventilated patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. EC enables early detection of hemodynamic changes and allows clinicians to adjust therapy before decompensation occurs. Similar findings were reported by Garutti et al. (2015) and Choudhury et al. (2023), who observed that increased thoracic fluid indices were associated with prolonged ventilation and postoperative complications [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, TFC was not influenced by age, sex, or body mass index but was significantly higher among smokers and non-survivors. This is consistent with evidence suggesting that smoking impairs endothelial function and alveolar fluid clearance. The positive association between TFC and serum lactate underscores the relationship between pulmonary congestion and tissue hypoxia, similar to findings by Sanidas et al. (2009) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Collectively, these results confirm that EC-derived TFC is a practical adjunct to conventional hemodynamic measures. The combination of higher TFC, lower MAP, and elevated lactate strongly indicates poor outcomes, a pattern observed in several studies [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOverall, this study highlights the value of electrical cardiometry as a continuous, noninvasive, and dependable tool for monitoring critically ill respiratory patients. Elevated TFC reflects pulmonary and systemic fluid overload and may serve as an early warning sign of hemodynamic compromise. Routine use of TFC monitoring could support more precise fluid management, improve oxygenation, and ultimately enhance survival.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eElectrical cardiometry offers a noninvasive, continuous method for assessing hemodynamic status in RICU patients. In this study, elevated thoracic fluid content (TFC) was significantly associated with higher mortality, longer ventilation, and extended ICU stay. These results indicate that TFC can serve as a useful dynamic marker of fluid overload and circulatory dysfunction. When combined with the APACHE II score, EC-derived TFC may facilitate early identification of high-risk patients and guide individualized management. Incorporating EC monitoring into clinical practice may help optimize fluid therapy, reduce complications, and improve outcomes in respiratory critical care.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRecommendations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eRoutine monitoring of thoracic fluid content (TFC) using electrical cardiometry is recommended for RICU patients to guide fluid resuscitation and prevent volume overload.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFuture multicenter studies with larger sample sizes are needed to validate the prognostic value of TFC in different critical care settings and to establish standard cutoff points for mortality prediction.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMore frequent or continuous EC recordings are encouraged, as TFC values can fluctuate rapidly with clinical changes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCombining TFC monitoring with cardiac biomarkers such as brain natriuretic peptide (BNP) or troponin could provide a more comprehensive evaluation of cardiopulmonary fluid dynamics and improve outcome prediction.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eClinician education and training are essential to ensure proper interpretation of EC-derived parameters and to integrate them effectively into individualized patient management.implementation and for tailoring management to each patient\u0026rsquo;s hemodynamic profile.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations:\u003c/h2\u003e\u003cp\u003eThe study was conducted in a single tertiary center with a relatively modest sample size (n\u0026thinsp;=\u0026thinsp;130), which may limit generalizability. The heterogeneity of underlying diagnoses, such as pneumonia, pulmonary embolism, and near-drowning, might have influenced TFC variability. However, although EC is noninvasive and reproducible, it is sensitive to electrode placement and patient movement, which could introduce measurement bias. Also, the study did not compare EC data directly with gold-standard methods such as pulmonary artery catheterization or transpulmonary thermodilution, limiting the ability to quantify absolute measurement accuracy. Finally, long-term outcomes after ICU discharge were not evaluated, and future multicenter studies with larger cohorts and longitudinal follow-up are warranted to validate these findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eABG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArterial Blood Gases\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\"\u003eARDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute Respiratory Distress Syndrome\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\"\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\"\u003eBNP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBrain Natriuretic Peptide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiac Output\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic Obstructive Pulmonary Disease\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\"\u003eEC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eElectrical Cardiometry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFTc\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFlow Time Corrected\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImpedance Cardiography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMean Arterial Pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMechanical Ventilation\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\"\u003eOHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eObesity Hypoventilation Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePulmonary Embolism\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\"\u003eRICU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRespiratory Intensive Care Unit\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\"\u003eScvO₂\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCentral Venous Oxygen Saturation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSVRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic Vascular Resistance Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSVV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStroke Volume Variation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTEB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThoracic Electrical Bioimpedance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTFC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThoracic Fluid Content\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the research ethics committee of the chest medicine department of the Faculty of Medicine at Mansoura University. Reference number of approval: MS.19.12.354.\u003c/p\u003e\n\u003cp\u003eAll patients included in this study gave written informed consent to participate in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: All patients included in this study gave written informed consent to publish the data contained in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAvailable on request with the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNot applicable (no funding was received for this study).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e MME and DAA conceived and designed the study. AGA performed patient enrollment and data collection. MAM analyzed the data and drafted the manuscript. MAM, ARE, DAA, and MME critically reviewed and revised the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTeboul JL, Saugel B, Cecconi M, Scheeren TWL, Teboul JL. Update on hemodynamic monitoring in critically ill patients. Intensive Care Med. 2016;42(9):1350–1362.\u003c/li\u003e\n\u003cli\u003eBera TK. Bioelectrical impedance methods for noninvasive health monitoring: a review. J Med Eng Technol. 2014;38(5):253–269.\u003c/li\u003e\n\u003cli\u003eDovancescu S, Pellicori P, Mabote T, Clark AL. Bioimpedance-derived indices and lung water content in acute decompensated heart failure. Eur J Heart Fail. 2017;19(11):1548–1559.\u003c/li\u003e\n\u003cli\u003eMagder S. Central venous pressure: a useful but not so simple measurement. Crit Care Med. 2006;34(8):2224–2227.\u003c/li\u003e\n\u003cli\u003eAlbert NM, Hail MD, Li J, Young JB. Impedance cardiography for noninvasive measurement of cardiac output: clinical and research applications. Heart Lung. 2004;33(6):437–446.\u003c/li\u003e\n\u003cli\u003ePeyton PJ, Chong SW. Minimally invasive measurement of cardiac output during surgery and critical care: a meta-analysis of accuracy and precision. Anesthesiology. 2010;113(5):1220–1235.\u003c/li\u003e\n\u003cli\u003eGho KY, Lee DH, Cho JY, et al. Prognostic significance of thoracic fluid content measured by electrical cardiometry in community-acquired pneumonia. Sci Rep. 2021;11:19865.\u003c/li\u003e\n\u003cli\u003eHammad Y, Hassan H, Youssef M, et al. Electrical cardiometry for early detection of pulmonary edema in preeclamptic parturients. J Obstet Gynaecol Res. 2019;45(12):2378–2386.\u003c/li\u003e\n\u003cli\u003eNarula J, Kapoor PM, Chauhan S, et al. Changes in thoracic fluid content during autologous blood harvest: correlation with intrathoracic volume shifts. J Cardiothorac Vasc Anesth. 2017;31(2):587–593.\u003c/li\u003e\n\u003cli\u003eEl-Sherif S, El-Sayed T, Khalifa M, et al. Comparison between electrical cardiometry and lung ultrasound in the assessment of lung water in mechanically ventilated patients. Egypt J Bronchol. 2025;19(1):Article 45.\u003c/li\u003e\n\u003cli\u003eMahajan R, Agrawal A, Das S, et al. Evaluation of electrical cardiometry to assess fluid responsiveness in patients with acute circulatory failure compared with echocardiography. J Clin Monit Comput. 2024;38(6):1153–1162.\u003c/li\u003e\n\u003cli\u003eChoudhury M, Narula J, Saini V, Kapoor PM, Kiran U. Fluid management using cardiometry versus simplified FACTT protocol in ARDS patients. Anaesth Pain Intensive Care. 2023;27(4):456–462.\u003c/li\u003e\n\u003cli\u003eZhang L, Chen W, Lu J, et al. Thoracic fluid content as a rapid diagnostic indicator of secondary capillary leak syndrome in pediatric patients after cardiopulmonary bypass. Front Pediatr. 2025;13:1494533.\u003c/li\u003e\n\u003cli\u003eStandl T, Annecke T, Cascorbi I, Heller AR, Sabashnikov A, Teske W. The new definition of shock states: current classification and terminology. Curr Med Res Opin. 2018;34(2):161–168.\u003c/li\u003e\n\u003cli\u003eVan De Water JM, Miller TW, Vogel R, Mount BE, Dalton ML. Impedance cardiography: the next vital sign technology? Chest. 2005;128(4):287–297.\u003c/li\u003e\n\u003cli\u003eFathy S, Elshazly M, Ghaleb A, Khalil A. Thoracic fluid content as a predictor of weaning outcome in surgical critically ill patients. Egypt J Bronchol. 2020;14(1):22–29.\u003c/li\u003e\n\u003cli\u003eGarutti I, Cruz P, Olmedilla L, et al. Extravascular lung water and postoperative pulmonary complications after orthotopic liver transplantation: a prospective observational study. Transplant Proc. 2015; 47(9): 2015;47(9):2630–2634.\u003c/li\u003e\n\u003cli\u003eKossari N, Hufnagel C, Squara P. Changes in thoracic fluid content in patients undergoing hemodialysis: comparison with classical fluid removal indices. Intensive Care Med. 2009; 35(2): 2009;35(2):343–349.\u003c/li\u003e\n\u003cli\u003eMahmoud K, Mokhtar A, Soliman M, Khaled H. Relationship between thoracic fluid content and amount of fluid removal during hemodialysis session. Egypt J Crit Care Med. 2016;4(3):135–142.\u003c/li\u003e\n\u003cli\u003eSanidas EA, Papadopoulos DP, Velliou M, et al. Hemodynamic effects of diuretics assessed with impedance cardiography in hypertensive patients. Clin Exp Hypertens. 2009;31(1):43–54.\u003c/li\u003e\n\u003cli\u003eEucker W, Brunetti I, Krüger S, Müssigbrodt U, Pahernik SA. A novel noninvasive impedance-based technique for central venous pressure measurement. Crit Care Med. 2009;37(1):83–89.\u003c/li\u003e\n\u003cli\u003eAbraham WT, Compton S, Haas G, et al. Intrathoracic impedance monitoring for early detection of worsening heart failure: results of the Fluid Accumulation Status Trial (FAST). J Card Fail. 2004;10(5):365–371.\u003c/li\u003e\n\u003cli\u003eFolan J, Funk M. Thoracic fluid content measurement to detect changes in fluid status in patients with heart failure. Heart Lung. 2008;37(4):295–302.\u003c/li\u003e\n\u003cli\u003eCecconi M, De Backer D, Antonelli M, et al. Consensus on circulatory shock and hemodynamic monitoring: task force of the European Society of Intensive Care Medicine. Intensive Care Med. 2014;40(12):1795–1815.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-bronchology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Bronchology](https://ejb.springeropen.com/)","snPcode":"43168","submissionUrl":"https://submission.nature.com/new-submission/43168/3","title":"The Egyptian Journal of Bronchology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electrical cardiometry, thoracic fluid content, mortality, respiratory intensive care unit, hemodynamics","lastPublishedDoi":"10.21203/rs.3.rs-7895080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7895080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Hemodynamic instability is common among respiratory intensive care unit (RICU) patients and is associated with multi-organ dysfunction and high mortality. Electrical cardiometry (EC) is a noninvasive method that continuously measures thoracic fluid content (TFC), providing real-time assessment of fluid status. This study evaluated the prognostic value of EC-derived TFC in predicting outcomes among critically ill respiratory patients.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA prospective observational study was conducted on 130 adult patients admitted to the RICU of Mansoura University Hospitals. Daily hemodynamic measurements were obtained using the ICON™ noninvasive cardiometer. Primary outcomes included duration of invasive mechanical ventilation (MV) and RICU stay, while in-hospital mortality was the secondary endpoint. Correlations between TFC and clinical, hemodynamic, and biochemical parameters were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePneumonia was the most frequent diagnosis (65.4%), followed by COPD (15.4%). Non-survivors showed significantly higher mean TFC values on all follow-up days compared with survivors (p \u0026lt; 0.001). TFC correlated positively with RICU stay and serum lactate levels and negatively with mean arterial pressure. At a cutoff value of 43 kΩ⁻¹, TFC predicted mortality with an AUC of 0.656, sensitivity of 85.7%, and specificity of 67.3%. Elevated TFC values were also significantly associated with mechanical ventilation and vasopressor use (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eElectrical cardiometry provides a reliable, noninvasive technique for continuous hemodynamic monitoring in respiratory critical care. Elevated TFC values were independently associated with higher mortality, prolonged mechanical ventilation, and extended RICU stay. Routine TFC monitoring may assist in early detection of fluid overload and guide individualized fluid management.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e: \u003c/em\u003eClinicalTrials.gov identifier: NCT07100821\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Electrical Cardiometry–Derived Thoracic Fluid Content in Respiratory Intensive Care Unit Patients: A Prospective Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:20:18","doi":"10.21203/rs.3.rs-7895080/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-07T09:07:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T07:56:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T11:30:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T20:48:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266034668888264471888212457173562727178","date":"2026-01-03T10:00:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224122377642389979928432396456761874102","date":"2026-01-02T21:45:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154213396311162647703205201755615719952","date":"2025-12-30T03:31:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-29T17:17:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T06:46:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-21T23:35:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Bronchology","date":"2025-10-18T18:14:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-bronchology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Bronchology](https://ejb.springeropen.com/)","snPcode":"43168","submissionUrl":"https://submission.nature.com/new-submission/43168/3","title":"The Egyptian Journal of Bronchology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60e575cb-df2a-44ef-948a-1b341e5c89d6","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-15T05:23:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 16:20:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7895080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7895080","identity":"rs-7895080","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}