Association of a Composite Respiratory Support Index with Extubation Failure in ICU Patients: Insights from the MIMIC-IV Database

Association of a Composite Respiratory Support Index with Extubation Failure in ICU Patients: Insights from the MIMIC-IV Database | 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 Association of a Composite Respiratory Support Index with Extubation Failure in ICU Patients: Insights from the MIMIC-IV Database Xin Yi, Weijing Sun, Chongshuang Yang, Raja Abdul Wafy Raja Muhammad Rooshdi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8554436/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective To investigate the association between a newly derived Composite Respiratory Support Index (CRSI) and extubation failure (EF) among critically ill patients receiving invasive mechanical ventilation (IMV), and to evaluate its predictive performance compared with the conventional Rapid Shallow Breathing Index (RSBI). Methods This retrospective cohort study was conducted using the MIMIC-IV version 3.1 database, including adult patients who underwent IMV for at least 24 hours followed by their first planned extubation. Patients who died within 48 hours post-extubation without reintubation were excluded. Key demographic, clinical, laboratory, and ventilatory parameters were extracted within 24 hours before extubation. The CRSI was derived by principal component analysis based on positive end-expiratory pressure, fraction of inspired oxygen, observed tidal volume, invasive mechanical ventilation duration, and observed respiratory rate. Logistic regression models were used to examine associations between CRSI and EF, with adjustments for demographics, comorbidities, and SOFA score. Subgroup analyses explored consistency across age, gender, BMI, and comorbidity strata. Restricted cubic spline (RCS) analysis assessed nonlinear risk patterns. The predictive performance of CRSI was compared with RSBI. Results A total of 3,589 patients were analyzed, including 2,960 in the Extubation Success group and 629 in the Extubation Failure group. Patients in the Extubation Failure group demonstrated higher ventilatory demands, impaired gas exchange, and elevated CRSI and RSBI values compared with those successfully extubated. Across all models, higher CRSI was independently associated with increased risk of EF (fully adjusted OR = 1.44, 95% CI 1.34–1.54, P < 0.001). Subgroup analysis confirmed consistent associations across most strata, except among patients with COPD and cancer. Notably, the association was stronger in patients with prior stroke (OR = 2.03, 95% CI 1.32–3.41, P = 0.003). RCS analysis revealed a nonlinear escalation of risk beyond a CRSI threshold of -0.42. When jointly modeled with RSBI, only CRSI remained an independent predictor of EF. Conclusion The CRSI provides a comprehensive assessment of ventilatory dependence by integrating multiple respiratory support parameters. It demonstrated superior predictive accuracy and stability compared with RSBI, with potential to improve individualized extubation decision-making. Incorporation of CRSI into bedside tools may facilitate timely interventions, reduce extubation-related complications, and enhance clinical outcomes in the ICU setting. extubation failure mechanical ventilation weaning assessment respiratory support index mimic-iv database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Invasive mechanical ventilation (IMV) is a mainstay of intensive care medicine, delivering life-sustaining respiratory support for patients with severe organ dysfunction ( 1 ). In routine ICU practice, approximately 30–40% of admissions receive IMV, underscoring its central role in contemporary critical care worldwide ( 2 , 3 ). Liberation from the ventilator—particularly successful extubation—marks a key step along the recovery trajectory. Across cohorts, patients extubated without subsequent re-intubation have been reported to experience markedly better outcomes, including reductions in ICU mortality of up to 70% and a roughly 20% shorter ICU length of stay ( 4 , 5 ). However, extubation is not without risk. Extubation failure (EF), commonly defined as the need for re-intubation within a short interval, is linked to longer ICU stays, greater rates of ventilator-associated complications, and higher mortality ( 6 , 7 ). Prolonged weaning is likewise hazardous: unnecessary continuation of IMV is associated with ventilator-induced lung injury, nosocomial infection, and poorer long-term prognosis ( 8 ). Identifying the right moment for extubation is therefore integral to patient safety and overall outcomes. The Rapid Shallow Breathing Index (RSBI) is widely used as a practical bedside gauge of extubation readiness ( 9 ). Its performance, however, has shown variability across studies. An RSBI threshold of ≤ 72 breaths/min/L during spontaneous breathing trials has been associated with higher likelihood of extubation success ( 10 ), yet other investigations indicate that RSBI alone provides limited discrimination between success and failure ( 11 ). Taken together, these observations suggest that single-parameter thresholds are insufficient for the complex physiology underpinning weaning and extubation. To address this gap, we derived a Composite Respiratory Support Index (CRSI) using principal component analysis (PCA) of multiple ventilatory parameters from the MIMIC-IV database. By integrating several dimensions of respiratory support into a single quantitative measure, CRSI aims to provide a more comprehensive appraisal of extubation readiness. We also implemented a web-based interface for bedside use to generate real-time risk estimates and support multidisciplinary discussion. Our expectation is that CRSI will improve predictive accuracy, enable timely extubation, and ultimately reduce complications and ICU length of stay while improving survival in critically ill patients. 2 Methods 2.1 Study Design and Data Source This study was conducted as a retrospective analysis based on the MIMIC-IV, version 3.1 database. MIMIC-IV contains detailed information from patients admitted to the intensive care units of the Beth Israel Deaconess Medical Center (Boston, MA, USA) between 2008 and 2019. The database provides a rich spectrum of clinical variables, including demographic profiles, comorbidities, vital signs, laboratory results, therapeutic interventions, and hospitalization outcomes. All data within MIMIC-IV are de-identified in compliance with the Health Insurance Portability and Accountability Act (HIPAA). The database has received approval for research use from the Institutional Review Boards of both the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center, with the requirement for individual informed consent waived. Access for this study was granted to author Xin Yi following completion of the prescribed training program (certification number: 70538661). 2.2 Study Participants and Selection We restricted the analysis to the first ICU admission for each individual and retained only the earliest episode of IMV followed by the first planned extubation. Eligibility required an ICU stay of at least 24 hours and IMV lasting at least 24 hours. Patients who died within 48 hours after extubation without undergoing reintubation were removed to avoid misclassification. Records with physiologically implausible ventilatory parameters or with missing critical variables were excluded during data quality checks. After applying all criteria, a total of 3,589 patients were included in the final cohort, with 2,960 in the Extubation Success group and 629 in the Extubation Failure group. The detailed process of data screening and cleaning is shown in Fig. 1. 2.3 Data Extraction and Processing All study variables were obtained from the 24-hour window prior to the first planned extubation. When repeated measurements occurred within this interval, the median value was retained to limit the influence of outlying observations. Demographic data comprised age, gender, and body mass index (BMI). Physiological observations included heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), observed respiratory rate (RR_observed), and peripheral oxygen saturation (SpO₂). Laboratory profiles were also reviewed, covering white blood cell count (WBC), red blood cell count (RBC), platelet count (PLT), hemoglobin (HGB), serum sodium (Sodium), serum potassium (Potassium), serum calcium (Calcium), anion gap (AG), blood pH (PH), partial pressure of arterial carbon dioxide (PaCO₂), partial pressure of arterial oxygen (PaO₂), blood urea nitrogen (BUN), serum creatinine (Cr), and blood glucose (Glucose). Ventilatory and clinical characteristics were assessed in parallel. These included observed tidal volume (TV_observed), positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO₂), urine output, Glasgow Coma Scale (GCS), and the duration of IMV (MV duration). Rigorous data quality control was applied. Values outside the physiological range were excluded. Variables with more than 25 percent missingness were removed, while missing values in the remaining variables were imputed using a random forest–based multiple imputation method. Comorbidities, including congestive heart failure (CHF), chronic kidney disease (CKD), diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD), cancer, and stroke, were identified through structured diagnostic codes 2.4 Derivation of the CRSI We derived a CRSI using PCA based on key respiratory parameters. Five variables reflecting ventilatory support and respiratory mechanics were included: PEEP, FiO₂, TV_observed, MV duration, and RR_observed. For CRSI derivation, all variables were standardized to account for differences in measurement scale. PCA was performed to reduce dimensionality and capture the main variance in ventilatory support. The first principal component (PC1), which explained 31.8% of the total variance, was extracted and assigned as the CRSI score for each patient. To validate the derived CRSI, we inspected both numerical and visual PCA outputs. The PCA loadings indicated that PC1 had positive contributions from PEEP (0.543), FiO₂ (0.431), MV duration (0.318), and RR_observed (0.589), and a negative contribution from TV_observed (− 0.266), consistent with the expected directionality of respiratory support intensity as shown in Table 1. The proportion of variance explained by each component, as shown in Table 2, confirmed that PC1 captured the largest share of variance. Furthermore, the scree plot and variable contribution plot, as shown in Fig. 2A and Fig. 2B, respectively, demonstrated that the derived CRSI is a reasonable and interpretable composite measure of ventilatory support. 2.5 Sequential Organ Failure (SOFA) score Assessment and RSBI Calculation The SOFA score was calculated to assess the severity of organ dysfunction, incorporating five organ systems: respiratory, coagulation, cardiovascular, neurological, and renal. The respiratory component was based on the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO₂/FiO₂) and scored as 0 for ≥ 400, 1 for 300–399, 2 for 200–299, 3 for 100–199, and 4 for < 100. Coagulation was assessed using platelet count, scored 0 for ≥ 150 ×10⁹/L, 1 for 100–149 ×10⁹/L, 2 for 50–99 ×10⁹/L, 3 for 20–49 ×10⁹/L, and 4 for < 20 ×10⁹/L. Cardiovascular function was evaluated using the minimum mean arterial pressure (MAP), scored 0 for MAP ≥ 70 mmHg and 1 for MAP < 70 mmHg. Neurological function was assessed using the lowest Glasgow Coma Scale (GCS) score, scored 0 for GCS = 15, 1 for 13–14, 2 for 10–12, 3 for 6–9, and 4 for < 6. Renal function was assessed based on total 24-hour urine output, scored 0 for ≥ 500 mL, 3 for 200–499 mL, and 4 for < 200 mL. The total SOFA score was computed as the sum of these five components. The RSBI was calculated as a conventional measure of weaning readiness using the formula: $$\:\text{R}\text{S}\text{B}\text{I}=\frac{\text{R}\text{R}\_\text{o}\text{b}\text{s}\text{e}\text{r}\text{v}\text{e}\text{d}}{\text{T}\text{V}\_\text{o}\text{b}\text{s}\text{e}\text{r}\text{v}\text{e}\text{d}\:}$$ where RR_observed is the number of breaths per minute and TV_observed is expressed in liters. 2.6 Statistical Analysis All analysis were conducted using R software (version 4.5.0). Given the total sample size of 3,589, continuous variables were first assessed for normality using the Kolmogorov–Smirnov test. Variables with non-significant deviations from normality or skewness ≤ 1 were presented as mean ± SD, whereas variables with significant deviations and skewness > 1 were expressed as median (IQR). Logistic regression was used to examine the association between CRSI and EF across three models: unadjusted, adjusted for age, gender, and BMI, and further adjusted for comorbidities and SOFA score. Subgroup Analysis explored consistency across age, BMI, gender, and relevant comorbidities. Restricted cubic spline (RCS) analysis assessed potential nonlinear associations between CRSI and EF. Comparative analysis were performed between CRSI and RSBI to examine their associations with EF.Two-sided P < 0.05 was considered statistically significant. 3 Results 3.1 Baseline Characteristics of the Participates Baseline characteristics of the participates are presented in Table 3 . Patients who experienced EF had higher HR and RR_observed, increased WBC, and lower HBG levels compared with those who succeeded. Arterial blood gases was more impaired in the failure group, as reflected by higher PaCO₂ and lower PaO₂. They also required greater ventilatory support, with higher PEEP, higher FiO₂, and longer ventilation duration. Notably, both CRSI and RSBI were significantly elevated in the failure group, whereas SOFA scores showed no meaningful difference between groups. 3.2 Logistic Regression Analysis of CRSI in Different Models In all three models, CRSI consistently demonstrated a strong association with EF. In the unadjusted model (Model 1), higher CRSI was significantly associated with an increased risk of EF (OR = 1.39, 95%CI = 1.30–1.49, P < 0.001). After adjustment for age, gender, and BMI (Model 2), this association remained robust (OR = 1.44, 95%CI = 1.35–1.55, P < 0.001). Further adjustment for comorbidities and SOFA score (Model 3) yielded comparable results (OR = 1.44, 95%CI = 1.34–1.54, P < 0.001), underscoring the independent predictive value of CRSI. Among the covariates, BMI was inversely associated with EF (OR = 0.98, 95%CI = 0.97–0.99, P = 0.003), whereas age, gender, comorbidities, and SOFA score showed no significant associations. As shown in Table 4 and Figure 3 . 3.3 Subgroup Analysis Subgroup analysis demonstrated that CRSI was consistently associated with EF across most clinical strata. The association was significant in patients aged ≥65 years (OR=1.51, 95%CI=1.36–1.68, P<0.001) and <65 years (OR=1.33, 95%CI=1.23–1.45, P<0.001). Similar patterns were observed in patients with BMI ≥28 (OR=1.37, 95%CI=1.26–1.50, P<0.001) and BMI <28 (OR=1.49, 95%CI=1.34–1.65, P<0.001). The association remained robust among patients with CHF (OR=1.45, 95%CI=1.19–1.77, P<0.001), CKD (OR=1.61, 95%CI=1.22–2.15, P<0.001), and diabetes (OR=1.64, 95%CI=1.35–2.01, P<0.001), as well as their counterparts without these comorbidities. Consistency was also observed in both men (OR=1.42, 95%CI=1.30–1.55, P<0.001) and women (OR=1.36, 95%CI=1.23–1.50, P<0.001). Patients with SOFA ≤5 (OR=1.47, 95%CI=1.36–1.59, P5 (OR=1.25, 95%CI=1.11–1.40, P<0.001) both demonstrated significant associations. In contrast, no significant association was found among patients with COPD or cancer. Notably, patients with prior stroke showed a stronger effect (OR=2.03, 95%CI=1.32–3.41, P=0.003). As shown in Table 5 and Figure 4 . 3.4 RCS Analysis of CRSI RCS analysis based on Model 3 revealed a robust association between CRSI and the risk of EF (overall P < 0.001), with significant evidence of nonlinearity (nonlinear P = 0.013). The risk of EF increased only modestly when CRSI values were below the inflection point (–0.42), whereas beyond this threshold the odds rose sharply, indicating a threshold-dependent acceleration of risk. These findings suggest that higher CRSI values may capture a nonlinear escalation in vulnerability, with clinically meaningful risk emerging once CRSI surpasses the turning point. As shown in Figure 5 . 3.5 Comparison of CRSI and RSBI In the unadjusted model, both CRSI and RSBI were significantly associated with EF (CRSI: OR = 1.39, 95% CI 1.30–1.49, P < 0.001; RSBI: OR = 1.02, 95% CI 1.01–1.02, P < 0.001). After adjusting for age, gender, and BMI, the associations remained robust for both indices (CRSI: OR = 1.44, 95% CI 1.35–1.55, P < 0.001; RSBI: OR = 1.02, 95% CI 1.01–1.02, P < 0.001). Further adjustment for comorbidities and SOFA score (Model 3) did not materially alter the results (CRSI: OR = 1.44, 95% CI 1.34–1.54, P < 0.001; RSBI: OR = 1.02, 95% CI 1.01–1.02, P < 0.001). When both indices were simultaneously included in the fully adjusted model, CRSI retained a strong independent association with EF (OR = 1.48, 95% CI 1.36–1.62, P < 0.001), whereas RSBI lost statistical significance (OR = 0.996, 95% CI 0.988–1.00, P = 0.282). These findings indicate that CRSI demonstrated greater predictive strength and stability across all models compared with RSBI, and only CRSI remained an independent predictor in the joint model. As shown in Table 6 . 3.6 Development of a Clinical Web Application for CRSI To facilitate bedside application of CRSI, a web-based tool was developed based on the fully adjusted logistic regression model (Model 3). This application allows clinicians to input readily available ventilatory parameters, including PEEP, FiO₂, TV_observed, MV_duration, and RR_observed, and automatically computes the corresponding CRSI. In addition, the tool provides an estimated probability of EF derived from the underlying Model 3, enabling rapid individualized risk assessment. As shown in Figure 6. The CRSI extubation risk calculator is publicly accessible online at https://eyu666.shinyapps.io/CRSI_extubation_risk/. 4 Discussion Using a large-scale ICU cohort from the MIMIC-IV database, this study confirmed that the CRSI was consistently and independently associated with EF across multiple statistical modeling frameworks. When other covariates were held constant, each one-unit increase in CRSI was linked to an approximately 44% higher risk of EF, and this magnitude remained nearly unchanged from unadjusted to fully adjusted models. This suggests that CRSI reflects more than a transient change in a single respiratory mechanics variable; it captures an integrated measure of a patient’s dependence on ventilatory assistance, functioning as a comprehensive indicator of the overall extubation risk burden. The strong association of CRSI with EF has a clear physiological and clinical basis. In this study, CRSI was derived through PCA integrating five core variables: PEEP, FiO₂, MV duration, RR_observed, and TV_observed. The PCA loading directions were consistent with physiological expectations: higher PEEP generally indicates more severe oxygenation impairment and reduced lung compliance, requiring greater airway pressure ( 12 ); higher FiO₂ denotes stronger dependency on oxygen supplementation, as seen in severe hypoxemia ( 13 ); longer MV duration signals both greater illness severity and heightened risk of respiratory muscle weakness due to diaphragmatic inactivity ( 14 ); elevated RR_observed often reflects increased respiratory workload or metabolic demand, suggesting reduced ventilatory reserve ( 15 ). Conversely, TV_observed carried a negative loading, implying that a larger tidal volume is indicative of better spontaneous breathing capacity and lower reliance on mechanical support, aligning with a reduced probability of EF ( 16 ). Compared with individual weaning parameters, such combined evaluation—integrating mechanical ventilation settings with intrinsic respiratory capacity—offers a broader and more nuanced reflection of readiness for extubation. Although the RSBI was also related to EF in all models, its effect size was smaller than that of CRSI. When both indices were entered into the fully adjusted model, the association for RSBI lost statistical significance, while the link between CRSI and EF remained stable, suggesting that CRSI incorporates most of the information RSBI represents, along with additional elements from ventilatory settings and disease progression, thereby achieving an “information integration” advantage ( 17 , 18 , 19 ). BMI displayed an inverse association with EF risk, with each 1 kg/m² increment linked to an approximately 2% lower risk. This aligns with the “obesity paradox” described in critical care, where higher BMI has been related to more favorable short-term outcomes in certain critically ill populations ( 20 , 21 ). Possible mechanisms include greater nutritional and muscular reserves providing metabolic and immune buffering during illness and weaning, and greater respiratory muscle mass delaying respiratory decompensation ( 23 ). However, this link may vary with baseline metabolic profile, muscle composition, and fat distribution ( 24 , 25 ), and some studies have found no corresponding association ( 6 , 26 ), underscoring the need for cautious interpretation and further multicenter, prospective evaluation. CRSI also exhibited a nonlinear, threshold-dependent relationship with EF: when CRSI exceeded − 0.42, the risk rose sharply. This pattern may indicate a critical physiological tipping point beyond which respiratory reserve is markedly compromised, consistent with the “critical range” phenomenon reported for key ventilatory parameters ( 27 , 28 , 29 ). Clinically, this finding may aid in identifying patients approaching a high-risk state, prompting earlier intervention. Subgroup analyses indicated that CRSI maintained consistent associations with EF across most strata defined by age, sex, BMI, and common critical illness comorbidities. Notably, no significant association was observed in patients with COPD, perhaps due to adaptation of central respiratory drive to long-standing hypercapnia ( 30 , 31 ). The association was weaker in patients with malignancy, potentially due to multifactorial effects of the disease and its treatment on respiratory and immune systems ( 32 ). In contrast, patients with prior stroke exhibited stronger links between higher CRSI and EF, likely reflecting central neural control impairment, reduced swallowing function, and diminished airway protective reflexes ( 33 , 34 ). CRSI offers practical utility for clinical risk assessment: its quantified score allows for more individualized extubation planning, and its accompanying online tool can provide near real-time EF probability estimates at the bedside, supporting dynamic, evidence-based decision-making. For patients with markedly elevated CRSI, particularly near or beyond the threshold point, strategies such as gradual weaning, prophylactic post-extubation noninvasive ventilation, or prolonged high-level monitoring may be considered. Limitations include the retrospective nature of the study, which precludes causal inference and necessitates testing in forward-looking designs. CRSI was constructed solely from ventilator parameters and course data, excluding key clinical factors such as neurological status, cough strength, or secretion load. Moreover, only single time-point measurements were analyzed, thus not capturing temporal trends. Finally, the findings are derived from a single database, and external validity in broader, multicenter populations remains to be established. Future work may incorporate CRSI into dynamic assessment models alongside diaphragm and respiratory muscle function metrics, or apply machine-learning frameworks to integrate multidimensional clinical and physiological datasets, with the aim of refining its clinical relevance and broadening applicability across diverse patient settings. In conclusion, the CRSI provides a robust framework for evaluating extubation readiness in critically ill patients. By integrating ventilatory settings with intrinsic respiratory performance, it captures the overall burden of ventilatory dependence more comprehensively than conventional single-parameter indices. This multidimensional approach enables refined risk stratification and may facilitate more individualized strategies for weaning and extubation management in the ICU. Declarations 10 Competing Interests The authors declare that they have no competing interests. 11 Consent to Participate Informed consent was not required for this study, as the data used were fully de-identified and publicly available. The Institutional Review Boards waived the need for individual consent due to the retrospective design and anonymized nature of the datasets. 12 Ethics Approval This study utilized the MIMIC-IV (version 3.1), approved for research use by the Institutional Review Boards of the Beth Israel Deaconess Medical Center (BIDMC) and the Massachusetts Institute of Technology (MIT). 13 Consent for Publication Not applicable. 6 Funding Not applicable. Author Contribution Xin Yi and Weijing Sun contributed equally to this work and share first authorship. Xin Yi conceived and designed the study, performed data extraction and analysis, and drafted the initial manuscript. Weijing Sun contributed to study design, supervised statistical analyses, and provided critical revisions of the manuscript. Chongshuang Yang and Raja Abdul Wafy Raja Muhammad Rooshdi assisted with data interpretation and contributed to manuscript editing. Jie Yan and Canzhang Liu provided expertise in clinical methodology and supported data validation. Ummi Nadira Daut and Razif Abas supervised the overall project, contributed to critical revision of the manuscript, and approved the final version for submission. 5 Acknowledgements Not applicable. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Saavedra SN, Barisich PVS, Maldonado JBP, Lumini RB, Gómez-González A, Gallardo A. Asynchronies during invasive mechanical ventilation: narrative review and update. Acute Crit Care. 2022;37(4):491–501. 10.4266/acc.2022.01158 . Epub 2022 Nov 30. PMID: 36480901; PMCID: PMC9732206. Ohbe H, Shime N, Yamana H, Goto T, Sasabuchi Y, Kudo D, Matsui H, Yasunaga H, Kushimoto S. Hospital and regional variations in intensive care unit admission for patients with invasive mechanical ventilation. J Intensive Care. 2024;12(1):21. 10.1186/s40560-024-00736-0 . PMID: 38840225; PMCID: PMC11155017. McLarty J, Litton E, Beane A, Aryal D, Bailey M, Bendel S, Burghi G, Christensen S, Christiansen CF, Dongelmans DA, Fernandez AL, Ghose A, Hall R, Haniffa R, Hashmi M, Hashimoto S, Ichihara N, Kumar Tirupakuzhi Vijayaraghavan B, Lone NI, Arias López MDP, Mat Nor MB, Okamoto H, Priyadarshani D, Reinikainen M, Soares M, Pilcher D, Salluh J. Linking of Global Intensive Care (LOGIC) Collaboration. Non-COVID-19 intensive care admissions during the pandemic: a multinational registry-based study. Thorax. 2024;79(2):120–127. 10.1136/thorax-2022-219592 . PMID: 37225417. Cinotti R, Mijangos JC, Pelosi P, Haenggi M, Gurjar M, Schultz MJ, Kaye C, Godoy DA, Alvarez P, Ioakeimidou A, Ueno Y, Badenes R, Suei Elbuzidi AA, Piagnerelli M, Elhadi M, Reza ST, Azab MA, McCredie V, Stevens RD, Digitale JC, Fong N, Asehnoune K, ENIO Study Group. the PROtective VENTilation network, the European Society of Intensive Care Medicine, the Colegio Mexicano de Medicina Critica, the Atlanréa group and the Société Française d’Anesthésie-Réanimation–SFAR research network. Extubation in neurocritical care patients: the ENIO international prospective study. Intensive Care Med. 2022;48(11):1539–50. 10.1007/s00134-022-06825-8 . Epub 2022 Aug 29. PMID: 36038713. Yang H, Ni Y, Huang D, Liang Z. Ventilatory ratio as a predictor for extubation failure in critical ill patients based on MIMIC-IV database (from 2008 to 2019). Front Physiol. 2023;14:1137115. 10.3389/fphys.2023.1137115 . PMID: 37324397; PMCID: PMC10267390. Chapin KC, Dragnich AG, Gannon WD, Martel AK, Bacchetta M, Erasmus DB, Shaver CM, Trindade AJ. Risk factors and clinical consequences of early extubation failure in lung transplant recipients. JHLT open. 2023;4:100046. https://doi.org/10.1016/j.jhlto.2023.100046 . Burns KEA, Rizvi L, Cook DJ, Lebovic G, Dodek P, Villar J, Slutsky AS, Jones A, Kapadia FN, Gattas DJ, Epstein SK, Pelosi P, Kefala K, Meade MO, Canadian Critical Care Trials Group. Ventilator Weaning and Discontinuation Practices for Critically Ill Patients. JAMA. 2021;325(12):1173–84. https://doi.org/10.1001/jama.2021.2384 . Chai X, Ma M, Hu W, Bai L, Duan J. Two-year survival after scheduled extubation in patients with pneumonia or ARDS: a prospective observational study. BMC Anesthesiol. 2024;24(1):232. https://doi.org/10.1186/s12871-024-02603-9 . Baptistella AR, Sarmento FJ, da Silva KR, Baptistella SF, Taglietti M, Zuquello RÁ, Nunes Filho JR. Predictive factors of weaning from mechanical ventilation and extubation outcome: A systematic review. J Crit Care. 2018;48:56–62. Epub 2018 Aug 20. PMID: 30172034. Turhan S, Tutan D, Şahiner Y, Kısa A, Özdemir SÖ, Tutan MB, Kayır S, Doğan G. Predictive Value of Serial Rapid Shallow Breathing Index Measurements for Extubation Success in Intensive Care Unit Patients. Med (Kaunas). 2024;60(8):1329. 10.3390/medicina60081329 . PMID: 39202610; PMCID: PMC11356565. Albashir SM, Robert RC, Jairath NN, Raub CB, Alzumai OA, Salem SS. The rapid shallow breathing index (RSBI) as a predictor for extubation success in medical and surgical ICU patients: A retrospective cohort study. Heart Lung 2025 Mar-Apr;70:321–8. doi: 10.1016/j.hrtlng.2025.01.007. Epub 2025 Jan 22. PMID: 39848164. Yan Y, Luo J, Wang Y, Chen X, Du Z, Xie Y, Li X. Development and validation of a mechanical power-oriented prediction model of weaning failure in mechanically ventilated patients: a retrospective cohort study. BMJ open. 2022;12(12):e066894. https://doi.org/10.1136/bmjopen-2022-066894 . Arrive F, Le Pape S, Bruhn A, et al. Physiological comparison of noninvasive ventilation and high-flow nasal oxygen on inspiratory efforts and tidal volumes after extubation: a randomized crossover trial. Crit Care. 2025;29:185. https://doi.org/10.1186/s13054-025-05366-y . Bureau C, Van Hollebeke M, Dres M. Managing respiratory muscle weakness during weaning from invasive ventilation. Eur respiratory review: official J Eur Respiratory Soc. 2023;32(168):220205. https://doi.org/10.1183/16000617.0205-2022 . Muttini S, Jona Falco J, Cuevas Cairo I, Umbrello M. A High Respiratory Drive Is Associated with Weaning Failure in Patients with COVID-19-Associated Acute Respiratory Distress Syndrome: The Role of the Electrical Activity of the Diaphragm. J Clin Med. 2024;13(4):1120. https://doi.org/10.3390/jcm13041120 . Trivedi V, Chaudhuri D, Jinah R, Piticaru J, Agarwal A, Liu K, McArthur E, Sklar MC, Friedrich JO, Rochwerg B, Burns KEA. The Usefulness of the Rapid Shallow Breathing Index in Predicting Successful Extubation: A Systematic Review and Meta-analysis. Chest. 2022;161(1):97–111. Epub 2021 Jun 26. PMID: 34181953. Sterr F, Reintke M, Bauernfeind L, Senyol V, Rester C, Metzing S, Palm R. Predictors of weaning failure in ventilated intensive care patients: a systematic evidence map. Crit Care (London England). 2024;28(1):366. https://doi.org/10.1186/s13054-024-05135-3 . Varón-Vega F, Tuta-Quintero E, Maldonado-Franco A, Robayo-Amórtegui H, Giraldo-Cadavid LF, Botero-Rosas D. (2025). Machine learning to predict extubation success using the spontaneous breathing trial, objective cough measurement, and diaphragmatic contraction velocity: Secondary analysis of the COBRE-US trial. Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures), 11(1), 70–7. https://doi.org/10.2478/jccm-2025-0009 Abplanalp LA, Ionescu F, Calvo-Ayala E, et al. Static Respiratory System Compliance as a Predictor of Extubation Failure in Patients with Acute Respiratory Failure. Lung. 2023;201:309–14. https://doi.org/10.1007/s00408-023-00625-7 . Zhou D, Wang C, Lin Q, Li T. The obesity paradox for survivors of critically ill patients. Crit Care (London England). 2022;26(1):198. https://doi.org/10.1186/s13054-022-04074-1 . Roberts KJ. (2023). 2022 Year in Review: Ventilator Liberation. Respiratory care, 68(12), 1728–1735. https://doi.org/10.4187/respcare.11114 Jaitovich A, Hall JB. The flux of energy in critical illness and the obesity paradox. Physiol Rev. 2025;105(3):1487–552. 10.1152/physrev.00029.2024 . Epub 2025 Feb 21. PMID: 39982115; PMCID: PMC12107714. Wei S, Nguyen TT, Zhang Y, Ryu D, Gariani K. Sarcopenic obesity: epidemiology, pathophysiology, cardiovascular disease, mortality, and management. Front Endocrinol. 2023;14:1185221. https://doi.org/10.3389/fendo.2023.1185221 . Graziano E, Peghin M, De Martino M, De Carlo C, Da Porto A, Bulfone L, Casarsa V, Sozio E, Fabris M, Cifù A, Grassi B, Curcio F, Isola M, Sechi LA, Tascini C, GIRA-COVID study group. The impact of body composition on mortality of COVID-19 hospitalized patients: A prospective study on abdominal fat, obesity paradox and sarcopenia. Clin Nutr ESPEN. 2022;51:437–44. https://doi.org/10.1016/j.clnesp.2022.07.003 . De Jong A, Capdevila M, Aarab Y, Cros M, Pensier J, Lakbar I, Monet C, Quintard H, Cinotti R, Asehnoune K, Arnal JM, Guitton C, Paugam-Burtz C, Abback P, Mekontso-Dessap A, Lakhal K, Lasocki S, Plantefeve G, Claud B, Pottecher J, Corne P, Ichai C, Molinari N, Chanques G, Papazian L, Azoulay E, Jaber S, FREE-REA Study Group. Incidence, Risk Factors, and Long-Term Outcomes for Extubation Failure in ICU in Patients With Obesity: A Retrospective Analysis of a Multicenter Prospective Observational Study. Chest. 2025;167(1):139–51. 10.1016/j.chest.2024.07.171 . Epub 2024 Sep 7. PMID: 39182573. Yan Y, Du Z, Chen H, Liu S, Chen X, Li X, Xie Y. The relationship between mechanical power normalized to dynamic lung compliance and weaning outcomes in mechanically ventilated patients. PLoS ONE. 2024;19(8):e0306116. https://doi.org/10.1371/journal.pone.0306116 . Zhang Z, Guo L, Wang H, Zhang Z, Shen L, Zhao H. Diagnostic accuracy of lung ultrasound to predict weaning outcome: a systematic review and meta-analysis. Front Med (Lausanne). 2024;11:1486636. 10.3389/fmed.2024.1486636 . PMID: 39554497; PMCID: PMC11563988. Wahlster S, Sharma M, Taran S, Town JA, Stevens RD, Cinotti R, Asehnoune K, Robba C. Associations between Driving Pressure and Clinical Outcomes in Acute Brain Injury: A Subanalysis of ENIO. Am J Respir Crit Care Med. 2024;209(11):1400–4. 10.1164/rccm.202402-0402LE . PMID: 38502247; PMCID: PMC11146561. Gupta A, Singh O, Juneja D. Clinical prediction scores predicting weaning failure from invasive mechanical ventilation: Role and limitations. World J Crit care Med. 2024;13(4):96482. https://doi.org/10.5492/wjccm.v13.i4.96482 . Nakornnoi B, Tscheikuna J, Rittayamai N. The effects of real-time waveform analysis software on patient ventilator synchronization during pressure support ventilation: a randomized crossover physiological study. BMC Pulm Med. 2024;24(1):212. https://doi.org/10.1186/s12890-024-03039-0 . Murphy BT, Mackrill JJ, O'Halloran KD. Impact of cancer cachexia on respiratory muscle function and the therapeutic potential of exercise. J Physiol. 2022;600(23):4979–5004. https://doi.org/10.1113/JP283569 . Muhle P, Claus I, Labeit B, Roderigo M, Warnecke T, Dziewas R, Suntrup-Krueger S. Pharyngeal Electrical Stimulation prior to extubation - Reduction of extubation failure rate in acute stroke patients? J Crit Care. 2024;82:154808. 10.1016/j.jcrc.2024.154808 . Epub 2024 Apr 5. PMID: 38581884. da Silva AR, Novais MCM, Neto MG, Correia HF. Predictors of extubation failure in neurocritical patients: A systematic review. Aust Crit Care. 2023;36(2):285–91. Epub 2022 Feb 21. PMID: 35197209. Tables Table 1. PCA Loadings of Ventilatory Parameters for the PC1 of CRSI Principal Component Standard Deviation Proportion of Variance Cumulative Proportion PC1 1.262 0.318 (31.8%) 0.318 (31.8%) PC2 1.132 0.256 (25.6%) 0.575 (57.5%) PC3 0.981 0.192 (19.2%) 0.767 (76.7%) PC4 0.798 0.127 (12.7%) 0.894 (89.4%) PC5 0.726 0.106 (10.6%) 1.000 (100%) Note : PC1–PC5 represent the first to fifth principal components derived from the ventilatory parameters. Table 2. Proportion of Variance Explained by Each Principal Component Variable PC1 PC2 PC3 PC4 PC5 PEEP 0.543 -0.337 -0.167 0.746 0.082 FiO₂ 0.431 -0.526 -0.223 -0.634 0.293 TV_observed -0.266 -0.692 0.288 0.012 -0.606 MV_duration 0.318 0.046 0.915 -0.032 0.242 RR_observed 0.589 0.359 -0.046 -0.201 -0.694 Note : PC1–PC5 represent the first to fifth principal components derived from the ventilatory parameters; RR_observed : Observed Respiratory Rate; PEEP: Positive End-Expiratory Pressure; FIO2: Fraction of Inspired Oxygen; TV_observed: Observed Tidal Volume. Table 3. Baseline Characteristics of Participants Variable Overall (n=3589) Extubation Success (n=2960) Extubation Failure (n=629) P Continuous variables , mean ± SD or median, IQR Age (years) 61.36 ± 17.32 61.37 ± 17.54 61.32 ± 16.22 0.948 HR (bpm/min) 83.19 ± 15.63 82.81 ± 15.48 84.94 ± 16.21 0.003 SBP (mmHg) 116.21 ± 18.28 116.34 ± 18.17 115.60 ± 18.80 0.365 DBP (median) 62.35 ± 11.54 62.35 ± 11.45 62.31 ± 12.00 0.924 RR_observed (/min) 18.86 ± 4.22 18.64 ± 4.11 19.89 ± 4.58 <0.001 SpO₂ (%) 97.91 ± 1.91 97.96 ± 1.91 97.69 ± 1.93 0.001 WBC (×10⁹/L) 10.90 (8.20, 14.40) 10.85 (8.20, 14.26) 11.30 (8.30, 14.80) 0.035 RBC (×10¹²/L) 3.35 ± 0.63 3.37 ± 0.64 3.26 ± 0.59 <0.001 PLT (×10⁹/L) 178.50 (121.50, 254.00) 176.75 (121.50, 249.62) 190.00 (124.00, 284.00) 0.013 Sodium (mmol/L) 140.13 ± 4.97 140.04 ± 4.92 140.51 ± 5.16 0.037 Potassium (mmol/L) 4.05 ± 0.50 4.04 ± 0.50 4.09 ± 0.53 0.036 Calcium (mmol/L) 8.24 ± 0.64 8.24 ± 0.63 8.24 ± 0.67 0.915 AG 12.93 ± 3.42 12.89 ± 3.42 13.15 ± 3.41 0.079 pH 7.40 ± 0.06 7.40 ± 0.06 7.40 ± 0.06 0.218 PaCO₂ (mmHg) 41.00 (36.00, 46.50) 41.00 (36.00, 46.00) 42.00 (37.00, 48.00) 0.001 PaO₂ (mmHg) 99.00 (72.00, 127.00) 100.00 (73.00, 129.00) 95.00 (68.00, 121.00) 0.003 BUN (mg/dL) 21.50 (13.50, 35.00) 21.00 (13.00, 34.00) 24.50 (14.50, 41.00) <0.001 Creatinine (mg/dL) 0.95 (0.70, 1.50) 0.95 (0.70, 1.50) 1.00 (0.60, 1.65) 0.815 Glucose (mg/dL) 125.00 (106.00, 151.00) 124.50 (106.00, 150.00) 129.00 (109.00, 156.00) 0.017 HGB (g/dL) 10.00 ± 1.84 10.07 ± 1.85 9.70 ± 1.74 <0.001 BMI (kg/m²) 27.99 (24.20, 33.05) 28.01 (24.24, 32.95) 27.92 (23.98, 33.15) 0.703 PEEP (cmH₂O) 5.00 (5.00, 8.00) 5.00 (5.00, 7.50) 5.00 (5.00, 8.00) <0.001 FiO₂ (%) 40.00 (40.00, 50.00) 40.00 (40.00, 50.00) 40.00 (40.00, 50.00) <0.001 Tidal Volume (mL) 467.84 ± 104.27 470.34 ± 104.19 456.03 ± 103.94 0.002 Urine Output (mL/24h) 1865.00 (1070.00, 2987.00) 1871.00 (1086.00, 3015.00) 1800.00 (995.00, 2820.00) 0.027 GCS 15.00 (15.00, 15.00) 15.00 (15.00, 15.00) 15.00 (15.00, 15.00) 0.766 MAP (mmHg) 75.92 ± 11.74 75.90 ± 11.68 76.02 ± 11.99 0.821 MV Duration (hours) 68.52 (38.00, 130.88) 68.52 (38.00, 130.88) 68.52 (38.00, 130.88) <0.001 SOFA Score 5.00 (4.00, 6.00) 5.00 (4.00, 6.00) 5.00 (4.00, 6.00) 0.398 CRSI 0.00 ± 1.26 -0.10 ± 1.22 0.47 ± 1.35 <0.001 RSBI 40.38 (32.06, 51.05) 39.82 (31.62, 50.13) 44.64 (35.35, 56.50) <0.001 Categorical variables, n (%) Gender Male 1517 (42.3%) 1246 (42.1%) 271 (43.1%) 0.680 Female 2072 (57.7%) 1714 (57.9%) 358 (56.9%) CHF No 3080 (85.8%) 2534 (85.6%) 546 (86.8%) 0.473 Yes 509 (14.2%) 426 (14.4%) 83 (13.2%) CKD No 3275 (91.3%) 2689 (90.8%) 586 (93.2%) 0.073 Yes 314 (8.7%) 271 (9.2%) 43 (6.8%) Table 3. (Continued) Variable Overall (n = 3592) EF Success (n = 2962) EF Failure (n = 630) P DM No 3067 (85.5%) 2516 (85.0%) 551 (87.6%) 0.106 Yes 522 (14.5%) 444 (15.0%) 78 (12.4%) COPD No 3402 (94.8%) 2804 (94.7%) 598 (95.1%) 0.801 Yes 187 (5.2%) 156 (5.3%) 31 (4.9%) Cancer No 3497 (97.4%) 2890 (97.6%) 607 (96.5%) 0.135 Yes 92 (2.6%) 70 (2.4%) 22 (3.5%) Stroke No 3488 (97.2%) 2883 (97.4%) 605 (96.2%) 0.124 Yes 101 (2.8%) 77 (2.6%) 24 (3.8%) Note: HR: Heart Rate; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; RR_observed : Observed Respiratory Rate; SPO 2 : Peripheral Capillary Oxygen Saturation; WBC: White Blood Cell Count; RBC: Red Blood Cell Count; PLT: Platelet Count; AG: Anion Gap; PaCO 2 : Partial Pressure of Carbon Dioxide; PaO 2 : Partial Pressure of Oxygen; INR: International Normalized Ratio; BUN: Blood Urea Nitrogen; CR: Creatinine; GLU: Glucose; HGB: Hemoglobin; BMI: Body Mass Index; PEEP: Positive End-Expiratory Pressure; FIO 2 : Fraction of Inspired Oxygen; TV_observed: Observed Tidal Volume; GCS: Glasgow Coma Scale; MAP: Mean Arterial Pressure; MV duration: Mechanical Ventilation Duration; SOFA: Sequential Organ Failure Assessment; CKD: Chronic Kidney Disease; DM: Diabetes Mellitus; COPD: Chronic Obstructive Pulmonary Disease; CHF: Congestive Heart Failure; RSBI: Rapid Shallow Breathing Index; CRSI: Composite Respiratory Support Index. Table 4. Three Logistic Regression Models Variable OR 95%CI (lower, upper) Stander Error P Model 1 (unadjusted) CRSI 1.39 1.30 - 1.49 0.033 <0.001 Model 2 (adjusted for Age, Gender, and BMI) CRSI 1.44 1.35 – 1.55 0.035 <0.001 age 1.00 0.996 – 1.01 0.003 0.605 gender 1.01 0.849 – 1.21 0.091 0.878 BMI 0.982 0.970 – 0.993 0.006 0.001 Model 3 (fully adjusted) Including Age, Gender, BMI, CHF, CKD, Diabetes, COPD, Cancer, and Stroke CRSI 1.44 1.34 – 1.54 0.035 <0.001 age 1.00 0.997 – 1.01 0.003 0.483 gender 1.02 0.851 – 1.22 0.091 0.855 BMI 0.984 0.972 – 0.995 0.006 0.005 CHF 0.991 0.741 – 1.31 0.146 0.951 CKD 0.778 0.533 – 1.11 0.188 0.182 DM 0.873 0.655 – 1.15 0.144 0.346 COPD 0.960 0.625 – 1.43 0.211 0.845 Cancer 1.37 0.815 – 2.23 0.256 0.214 Stroke 1.56 0.948 – 2.47 0.243 0.069 SOFA score 1.03 0.970 – 1.09 0.029 0.356 Note: SOFA: Sequential Organ Failure Assessment; CKD: Chronic Kidney Disease; DM: Diabetes Mellitus; COPD: Chronic Obstructive Pulmonary Disease; CHF: Congestive Heart Failure; CRSI: Composite Respiratory Support Index. Table 5. Subgroups Analysis Variable n OR 95%CI (lower, upper) P Model 3 (fully adjusted) Including Age, Gender, BMI, CHF, CKD, Diabetes, COPD, Cancer, and Stroke Age ≥65 1661 1.511 1.358 - 1.683 <0.001 <65 1928 1.331 1.226 - 1.446 <0.001 BMI ≥28 1798 1.372 1.258 - 1.498 <0.001 <28 1791 1.487 1.340 - 1.653 <0.001 CHF Yes 509 1.448 1.190 - 1.768 <0.001 No 3080 1.385 1.293 - 1.484 <0.001 CKD Yes 314 1.612 1.219 - 2.148 0.001 No 3275 1.377 1.289 - 1.473 <0.001 COPD Yes 187 1.208 0.862 - 1.672 0.258 No 3402 1.400 1.310 - 1.497 <0.001 Cancer Yes 92 1.178 0.799 - 1.731 0.397 No 3497 1.398 1.309 - 1.494 <0.001 Diabetes Yes 522 1.645 1.352 - 2.014 <0.001 No 3067 1.363 1.273 - 1.461 <0.001 Gender Male 2072 1.419 1.303 - 1.547 <0.001 Female 1517 1.356 1.227 - 1.499 <0.001 SOFA score ≤5 2505 1.471 1.359 - 1.595 5 1084 1.247 1.114 - 1.395 <0.001 Stroke Yes 101 2.029 1.315 - 3.408 0.003 No 3488 1.380 1.292 - 1.474 <0.001 SOFA: Sequential Organ Failure Assessment; CKD: Chronic Kidney Disease; DM: Diabetes Mellitus; COPD: Chronic Obstructive Pulmonary Disease; CHF: Congestive Heart Failure; Table 6. Comparison of CRSI and RSBI in Different Logistic Models Models CRSI RSBI OR (95%CI) P OR (95%CI) P Model 1 1.39 (1.30–1.49) <0.001 1.02 (1.01–1.02) <0.001 Model 2 1.44 (1.35–1.55) <0.001 1.02 (1.01–1.02) <0.001 Model 3 1.44 (1.34–1.54) <0.001 1.02 (1.01–1.02) <0.001 Joint Model 1.48 (1.36–1.62) <0.001 0.996 (0.988–1.00) 0.282 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor invited by journal 12 Jan, 2026 Editor assigned by journal 10 Jan, 2026 Submission checks completed at journal 10 Jan, 2026 First submitted to journal 08 Jan, 2026 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. 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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-8554436","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587213264,"identity":"c35fedca-baa7-4928-98e8-6a1e7c489f11","order_by":0,"name":"Xin Yi","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Yi","suffix":""},{"id":587213265,"identity":"cad9982a-fdf0-4cc3-a7bc-cd9900389635","order_by":1,"name":"Weijing Sun","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Weijing","middleName":"","lastName":"Sun","suffix":""},{"id":587213266,"identity":"1fb0b3d2-7e70-4759-9bde-741f9ae84adc","order_by":2,"name":"Chongshuang Yang","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Chongshuang","middleName":"","lastName":"Yang","suffix":""},{"id":587213267,"identity":"fbb598bf-d340-4741-903a-47622ccdcf31","order_by":3,"name":"Raja Abdul Wafy Raja Muhammad Rooshdi","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Raja","middleName":"Abdul Wafy Raja Muhammad","lastName":"Rooshdi","suffix":""},{"id":587213268,"identity":"e71781d7-6985-4183-97c5-7f7589983cba","order_by":4,"name":"Jie Yan","email":"","orcid":"","institution":"North China University of Science and Technology Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yan","suffix":""},{"id":587213269,"identity":"31f2ec52-b178-4673-b78b-f62fa4e1896b","order_by":5,"name":"Canzhang Liu","email":"","orcid":"","institution":"North China University of Science and Technology Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Canzhang","middleName":"","lastName":"Liu","suffix":""},{"id":587213270,"identity":"d7304581-a7d0-4ac9-80f5-97a3c2466d5c","order_by":6,"name":"Razif Abas","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Razif","middleName":"","lastName":"Abas","suffix":""},{"id":587213271,"identity":"216c42d7-7903-4a28-a258-4c08cfffb6fc","order_by":7,"name":"Ummi Nadira Daut","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYJCCAwwMzDxAfADOJVYLWwLxWoCAGYh5DIjTYnDtdOLhghprGfP2M9+kC3MY5PhuJLBu5sGn5XbuhsMzjqXzyJzJ3SY9cxuDseSNBLbbBLXwsB3mkWAAauHdxpC4gTgt/4Ba+N88A2mpJ04LbxtQi0QOG0hLggEhLZJgLX3pQC3PjK1nbpMwnHnmYdvNOXi08N3O3fyZ55u1vQR/8sPbhdts5PmOJx+78QaPFhQAjB0JIMXYwITPYehaIIDxB7FaRsEoGAWjYCQAAK/NTzQzQSitAAAAAElFTkSuQmCC","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Ummi","middleName":"Nadira","lastName":"Daut","suffix":""}],"badges":[],"createdAt":"2026-01-08 18:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8554436/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8554436/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102296950,"identity":"84fcc0b1-4080-4efc-9d86-2a355cb160ba","added_by":"auto","created_at":"2026-02-10 10:23:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184132,"visible":true,"origin":"","legend":"\u003cp\u003eThe Detailed Process of Data Screening and Cleaning\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8554436/v1/f481119cbf44283acd5b451f.jpg"},{"id":102217849,"identity":"cdb77b99-4bd7-4724-9ecd-d65d7569faa6","added_by":"auto","created_at":"2026-02-09 13:13:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133864,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the PCA for CRSI\u003c/p\u003e\n\u003cp\u003e(A: Scree plot of principal components. B: Variable contribution plot for PC1)\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8554436/v1/a7a24107320d5deac6b300f4.jpg"},{"id":102297131,"identity":"08c7cdc8-75ad-4945-b3ff-60e4f90c3fbf","added_by":"auto","created_at":"2026-02-10 10:26:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99444,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of CRSI in Three Logistic Regression Models\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8554436/v1/e1deb8313defa9af4e664e9f.jpg"},{"id":102217853,"identity":"74e1de9b-c94f-4849-b137-d8f038f54ab6","added_by":"auto","created_at":"2026-02-09 13:13:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":194434,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of CRSI Subgroup Analysis\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8554436/v1/1a5a35ad25591542b1f642f6.jpg"},{"id":102217851,"identity":"f34a280d-2745-4912-8e03-31e8cf5cb72e","added_by":"auto","created_at":"2026-02-09 13:13:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":128069,"visible":true,"origin":"","legend":"\u003cp\u003eRCS Analysis of CRSI\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8554436/v1/8bc009000af0bf43ffecf4b2.jpg"},{"id":102297110,"identity":"4f58525a-45ec-43a0-9e71-bedd693af713","added_by":"auto","created_at":"2026-02-10 10:25:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":39790,"visible":true,"origin":"","legend":"\u003cp\u003eWeb-Based Application for Automatic Calculation of CRSI and Estimated Risk of EF\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8554436/v1/64bd7ef762676ae1d93bb270.jpg"},{"id":102300436,"identity":"83aa825d-0a13-490f-a269-1b85ba5e6f33","added_by":"auto","created_at":"2026-02-10 11:14:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2149940,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8554436/v1/a766dde9-9052-436b-b454-375774aaec8d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of a Composite Respiratory Support Index with Extubation Failure in ICU Patients: Insights from the MIMIC-IV Database","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eInvasive mechanical ventilation (IMV) is a mainstay of intensive care medicine, delivering life-sustaining respiratory support for patients with severe organ dysfunction (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In routine ICU practice, approximately 30\u0026ndash;40% of admissions receive IMV, underscoring its central role in contemporary critical care worldwide (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Liberation from the ventilator\u0026mdash;particularly successful extubation\u0026mdash;marks a key step along the recovery trajectory. Across cohorts, patients extubated without subsequent re-intubation have been reported to experience markedly better outcomes, including reductions in ICU mortality of up to 70% and a roughly 20% shorter ICU length of stay (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, extubation is not without risk. Extubation failure (EF), commonly defined as the need for re-intubation within a short interval, is linked to longer ICU stays, greater rates of ventilator-associated complications, and higher mortality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Prolonged weaning is likewise hazardous: unnecessary continuation of IMV is associated with ventilator-induced lung injury, nosocomial infection, and poorer long-term prognosis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Identifying the right moment for extubation is therefore integral to patient safety and overall outcomes.\u003c/p\u003e \u003cp\u003eThe Rapid Shallow Breathing Index (RSBI) is widely used as a practical bedside gauge of extubation readiness (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Its performance, however, has shown variability across studies. An RSBI threshold of \u0026le;\u0026thinsp;72 breaths/min/L during spontaneous breathing trials has been associated with higher likelihood of extubation success (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), yet other investigations indicate that RSBI alone provides limited discrimination between success and failure (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Taken together, these observations suggest that single-parameter thresholds are insufficient for the complex physiology underpinning weaning and extubation.\u003c/p\u003e \u003cp\u003eTo address this gap, we derived a Composite Respiratory Support Index (CRSI) using principal component analysis (PCA) of multiple ventilatory parameters from the MIMIC-IV database. By integrating several dimensions of respiratory support into a single quantitative measure, CRSI aims to provide a more comprehensive appraisal of extubation readiness. We also implemented a web-based interface for bedside use to generate real-time risk estimates and support multidisciplinary discussion. Our expectation is that CRSI will improve predictive accuracy, enable timely extubation, and ultimately reduce complications and ICU length of stay while improving survival in critically ill patients.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Study Design and Data Source\u003c/h2\u003e\n \u003cp\u003eThis study was conducted as a retrospective analysis based on the MIMIC-IV, version 3.1 database. MIMIC-IV contains detailed information from patients admitted to the intensive care units of the Beth Israel Deaconess Medical Center (Boston, MA, USA) between 2008 and 2019. The database provides a rich spectrum of clinical variables, including demographic profiles, comorbidities, vital signs, laboratory results, therapeutic interventions, and hospitalization outcomes. All data within MIMIC-IV are de-identified in compliance with the Health Insurance Portability and Accountability Act (HIPAA). The database has received approval for research use from the Institutional Review Boards of both the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center, with the requirement for individual informed consent waived. Access for this study was granted to author Xin Yi following completion of the prescribed training program (certification number: 70538661).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Study Participants and Selection\u003c/h2\u003e\n \u003cp\u003eWe restricted the analysis to the first ICU admission for each individual and retained only the earliest episode of IMV followed by the first planned extubation. Eligibility required an ICU stay of at least 24 hours and IMV lasting at least 24 hours. Patients who died within 48 hours after extubation without undergoing reintubation were removed to avoid misclassification. Records with physiologically implausible ventilatory parameters or with missing critical variables were excluded during data quality checks. After applying all criteria, a total of 3,589 patients were included in the final cohort, with 2,960 in the Extubation Success group and 629 in the Extubation Failure group. The detailed process of data screening and cleaning is shown in Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Data Extraction and Processing\u003c/h2\u003e\n \u003cp\u003eAll study variables were obtained from the 24-hour window prior to the first planned extubation. When repeated measurements occurred within this interval, the median value was retained to limit the influence of outlying observations. Demographic data comprised age, gender, and body mass index (BMI). Physiological observations included heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), observed respiratory rate (RR_observed), and peripheral oxygen saturation (SpO₂). Laboratory profiles were also reviewed, covering white blood cell count (WBC), red blood cell count (RBC), platelet count (PLT), hemoglobin (HGB), serum sodium (Sodium), serum potassium (Potassium), serum calcium (Calcium), anion gap (AG), blood pH (PH), partial pressure of arterial carbon dioxide (PaCO₂), partial pressure of arterial oxygen (PaO₂), blood urea nitrogen (BUN), serum creatinine (Cr), and blood glucose (Glucose). Ventilatory and clinical characteristics were assessed in parallel. These included observed tidal volume (TV_observed), positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO₂), urine output, Glasgow Coma Scale (GCS), and the duration of IMV (MV duration).\u003c/p\u003e\n \u003cp\u003eRigorous data quality control was applied. Values outside the physiological range were excluded. Variables with more than 25 percent missingness were removed, while missing values in the remaining variables were imputed using a random forest\u0026ndash;based multiple imputation method. Comorbidities, including congestive heart failure (CHF), chronic kidney disease (CKD), diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD), cancer, and stroke, were identified through structured diagnostic codes\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Derivation of the CRSI\u003c/h2\u003e\n \u003cp\u003eWe derived a CRSI using PCA based on key respiratory parameters. Five variables reflecting ventilatory support and respiratory mechanics were included: PEEP, FiO₂, TV_observed, MV duration, and RR_observed. For CRSI derivation, all variables were standardized to account for differences in measurement scale. PCA was performed to reduce dimensionality and capture the main variance in ventilatory support. The first principal component (PC1), which explained 31.8% of the total variance, was extracted and assigned as the CRSI score for each patient.\u003c/p\u003e\n \u003cp\u003eTo validate the derived CRSI, we inspected both numerical and visual PCA outputs. The PCA loadings indicated that PC1 had positive contributions from PEEP (0.543), FiO₂ (0.431), MV duration (0.318), and RR_observed (0.589), and a negative contribution from TV_observed (\u0026minus;\u0026thinsp;0.266), consistent with the expected directionality of respiratory support intensity as shown in Table\u0026nbsp;1. The proportion of variance explained by each component, as shown in Table\u0026nbsp;2, confirmed that PC1 captured the largest share of variance. Furthermore, the scree plot and variable contribution plot, as shown in Fig.\u0026nbsp;2A and Fig.\u0026nbsp;2B, respectively, demonstrated that the derived CRSI is a reasonable and interpretable composite measure of ventilatory support.\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n \u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 Sequential Organ Failure (SOFA) score Assessment and RSBI Calculation\u003c/h2\u003e\n \u003cp\u003eThe SOFA score was calculated to assess the severity of organ dysfunction, incorporating five organ systems: respiratory, coagulation, cardiovascular, neurological, and renal. The respiratory component was based on the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO₂/FiO₂) and scored as 0 for \u0026ge;\u0026thinsp;400, 1 for 300\u0026ndash;399, 2 for 200\u0026ndash;299, 3 for 100\u0026ndash;199, and 4 for \u0026lt;\u0026thinsp;100. Coagulation was assessed using platelet count, scored 0 for \u0026ge;\u0026thinsp;150 \u0026times;10⁹/L, 1 for 100\u0026ndash;149 \u0026times;10⁹/L, 2 for 50\u0026ndash;99 \u0026times;10⁹/L, 3 for 20\u0026ndash;49 \u0026times;10⁹/L, and 4 for \u0026lt;\u0026thinsp;20 \u0026times;10⁹/L. Cardiovascular function was evaluated using the minimum mean arterial pressure (MAP), scored 0 for MAP\u0026thinsp;\u0026ge;\u0026thinsp;70 mmHg and 1 for MAP\u0026thinsp;\u0026lt;\u0026thinsp;70 mmHg. Neurological function was assessed using the lowest Glasgow Coma Scale (GCS) score, scored 0 for GCS\u0026thinsp;=\u0026thinsp;15, 1 for 13\u0026ndash;14, 2 for 10\u0026ndash;12, 3 for 6\u0026ndash;9, and 4 for \u0026lt;\u0026thinsp;6. Renal function was assessed based on total 24-hour urine output, scored 0 for \u0026ge;\u0026thinsp;500 mL, 3 for 200\u0026ndash;499 mL, and 4 for \u0026lt;\u0026thinsp;200 mL. The total SOFA score was computed as the sum of these five components.\u003c/p\u003e\n \u003cp\u003eThe RSBI was calculated as a conventional measure of weaning readiness using the formula:\u003c/p\u003e\n \u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{R}\\text{S}\\text{B}\\text{I}=\\frac{\\text{R}\\text{R}\\_\\text{o}\\text{b}\\text{s}\\text{e}\\text{r}\\text{v}\\text{e}\\text{d}}{\\text{T}\\text{V}\\_\\text{o}\\text{b}\\text{s}\\text{e}\\text{r}\\text{v}\\text{e}\\text{d}\\:}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere RR_observed is the number of breaths per minute and TV_observed is expressed in liters.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll analysis were conducted using R software (version 4.5.0). Given the total sample size of 3,589, continuous variables were first assessed for normality using the Kolmogorov\u0026ndash;Smirnov test. Variables with non-significant deviations from normality or skewness\u0026thinsp;\u0026le;\u0026thinsp;1 were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, whereas variables with significant deviations and skewness\u0026thinsp;\u0026gt;\u0026thinsp;1 were expressed as median (IQR). Logistic regression was used to examine the association between CRSI and EF across three models: unadjusted, adjusted for age, gender, and BMI, and further adjusted for comorbidities and SOFA score. Subgroup Analysis explored consistency across age, BMI, gender, and relevant comorbidities. Restricted cubic spline (RCS) analysis assessed potential nonlinear associations between CRSI and EF. Comparative analysis were performed between CRSI and RSBI to examine their associations with EF.Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e3.1 \u0026nbsp;Baseline Characteristics of the Participates\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of the participates are presented in \u003cstrong\u003eTable 3\u003c/strong\u003e. Patients who experienced EF had higher HR and RR_observed, increased WBC, and lower HBG levels compared with those who succeeded. Arterial blood gases was more impaired in the failure group, as reflected by higher PaCO₂ and lower PaO₂. They also required greater ventilatory support, with higher PEEP, higher FiO₂, and longer ventilation duration. Notably, both CRSI and RSBI were significantly elevated in the failure group, whereas SOFA scores showed no meaningful difference between groups.\u003c/p\u003e\n\u003cp\u003e3.2 \u0026nbsp;Logistic Regression Analysis of CRSI in Different Models\u003c/p\u003e\n\u003cp\u003eIn all three models, CRSI consistently demonstrated a strong association with EF. In the unadjusted model (Model 1), higher CRSI was significantly associated with an increased risk of EF (OR = 1.39, 95%CI = 1.30\u0026ndash;1.49, P \u0026lt; 0.001). After adjustment for age, gender, and BMI (Model 2), this association remained robust (OR = 1.44, 95%CI = 1.35\u0026ndash;1.55, P \u0026lt; 0.001). Further adjustment for comorbidities and SOFA score (Model 3) yielded comparable results (OR = 1.44, 95%CI = 1.34\u0026ndash;1.54, P \u0026lt; 0.001), underscoring the independent predictive value of CRSI. Among the covariates, BMI was inversely associated with EF (OR = 0.98, 95%CI = 0.97\u0026ndash;0.99, P = 0.003), whereas age, gender, comorbidities, and SOFA score showed no significant associations. As shown in \u003cstrong\u003eTable 4\u003c/strong\u003e and \u003cstrong\u003eFigure 3\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e3.3 Subgroup Analysis\u003c/p\u003e\n\u003cp\u003eSubgroup analysis demonstrated that CRSI was consistently associated with EF across most clinical strata. The association was significant in patients aged \u0026ge;65 years (OR=1.51, 95%CI=1.36\u0026ndash;1.68, P\u0026lt;0.001) and \u0026lt;65 years (OR=1.33, 95%CI=1.23\u0026ndash;1.45, P\u0026lt;0.001). Similar patterns were observed in patients with BMI \u0026ge;28 (OR=1.37, 95%CI=1.26\u0026ndash;1.50, P\u0026lt;0.001) and BMI \u0026lt;28 (OR=1.49, 95%CI=1.34\u0026ndash;1.65, P\u0026lt;0.001). The association remained robust among patients with CHF (OR=1.45, 95%CI=1.19\u0026ndash;1.77, P\u0026lt;0.001), CKD (OR=1.61, 95%CI=1.22\u0026ndash;2.15, P\u0026lt;0.001), and diabetes (OR=1.64, 95%CI=1.35\u0026ndash;2.01, P\u0026lt;0.001), as well as their counterparts without these comorbidities. Consistency was also observed in both men (OR=1.42, 95%CI=1.30\u0026ndash;1.55, P\u0026lt;0.001) and women (OR=1.36, 95%CI=1.23\u0026ndash;1.50, P\u0026lt;0.001). Patients with SOFA \u0026le;5 (OR=1.47, 95%CI=1.36\u0026ndash;1.59, P\u0026lt;0.001) and SOFA \u0026gt;5 (OR=1.25, 95%CI=1.11\u0026ndash;1.40, P\u0026lt;0.001) both demonstrated significant associations. In contrast, no significant association was found among patients with COPD or cancer. Notably, patients with prior stroke showed a stronger effect (OR=2.03, 95%CI=1.32\u0026ndash;3.41, P=0.003). As shown in \u003cstrong\u003eTable 5\u003c/strong\u003e and \u003cstrong\u003eFigure 4\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e3.4 RCS Analysis of CRSI\u003c/p\u003e\n\u003cp\u003eRCS analysis based on Model 3 revealed a robust association between CRSI and the risk of EF (overall P \u0026lt; 0.001), with significant evidence of nonlinearity (nonlinear P = 0.013). The risk of EF increased only modestly when CRSI values were below the inflection point (\u0026ndash;0.42), whereas beyond this threshold the odds rose sharply, indicating a threshold-dependent acceleration of risk. These findings suggest that higher CRSI values may capture a nonlinear escalation in vulnerability, with clinically meaningful risk emerging once CRSI surpasses the turning point. As shown in \u003cstrong\u003eFigure 5\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e3.5 Comparison of CRSI and RSBI\u003c/p\u003e\n\u003cp\u003eIn the unadjusted model, both CRSI and RSBI were significantly associated with EF (CRSI: OR = 1.39, 95% CI 1.30\u0026ndash;1.49, P \u0026lt; 0.001; RSBI: OR = 1.02, 95% CI 1.01\u0026ndash;1.02, P \u0026lt; 0.001). After adjusting for age, gender, and BMI, the associations remained robust for both indices (CRSI: OR = 1.44, 95% CI 1.35\u0026ndash;1.55, P \u0026lt; 0.001; RSBI: OR = 1.02, 95% CI 1.01\u0026ndash;1.02, P \u0026lt; 0.001). Further adjustment for comorbidities and SOFA score (Model 3) did not materially alter the results (CRSI: OR = 1.44, 95% CI 1.34\u0026ndash;1.54, P \u0026lt; 0.001; RSBI: OR = 1.02, 95% CI 1.01\u0026ndash;1.02, P \u0026lt; 0.001). When both indices were simultaneously included in the fully adjusted model, CRSI retained a strong independent association with EF (OR = 1.48, 95% CI 1.36\u0026ndash;1.62, P \u0026lt; 0.001), whereas RSBI lost statistical significance (OR = 0.996, 95% CI 0.988\u0026ndash;1.00, P = 0.282). These findings indicate that CRSI demonstrated greater predictive strength and stability across all models compared with RSBI, and only CRSI remained an independent predictor in the joint model. As shown in \u003cstrong\u003eTable 6\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e3.6 Development of a Clinical Web Application for CRSI\u003c/p\u003e\n\u003cp\u003eTo facilitate bedside application of CRSI, a web-based tool was developed based on the fully adjusted logistic regression model (Model 3). This application allows clinicians to input readily available ventilatory parameters, including PEEP, FiO₂, TV_observed, MV_duration, and RR_observed, and automatically computes the corresponding CRSI. In addition, the tool provides an estimated probability of EF derived from the underlying Model 3, enabling rapid individualized risk assessment. As shown in \u003cstrong\u003eFigure 6.\u0026nbsp;\u003c/strong\u003eThe CRSI extubation risk calculator is publicly accessible online at https://eyu666.shinyapps.io/CRSI_extubation_risk/.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eUsing a large-scale ICU cohort from the MIMIC-IV database, this study confirmed that the CRSI was consistently and independently associated with EF across multiple statistical modeling frameworks. When other covariates were held constant, each one-unit increase in CRSI was linked to an approximately 44% higher risk of EF, and this magnitude remained nearly unchanged from unadjusted to fully adjusted models. This suggests that CRSI reflects more than a transient change in a single respiratory mechanics variable; it captures an integrated measure of a patient\u0026rsquo;s dependence on ventilatory assistance, functioning as a comprehensive indicator of the overall extubation risk burden.\u003c/p\u003e \u003cp\u003eThe strong association of CRSI with EF has a clear physiological and clinical basis. In this study, CRSI was derived through PCA integrating five core variables: PEEP, FiO₂, MV duration, RR_observed, and TV_observed. The PCA loading directions were consistent with physiological expectations: higher PEEP generally indicates more severe oxygenation impairment and reduced lung compliance, requiring greater airway pressure (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e); higher FiO₂ denotes stronger dependency on oxygen supplementation, as seen in severe hypoxemia (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e); longer MV duration signals both greater illness severity and heightened risk of respiratory muscle weakness due to diaphragmatic inactivity (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e); elevated RR_observed often reflects increased respiratory workload or metabolic demand, suggesting reduced ventilatory reserve (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Conversely, TV_observed carried a negative loading, implying that a larger tidal volume is indicative of better spontaneous breathing capacity and lower reliance on mechanical support, aligning with a reduced probability of EF (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompared with individual weaning parameters, such combined evaluation\u0026mdash;integrating mechanical ventilation settings with intrinsic respiratory capacity\u0026mdash;offers a broader and more nuanced reflection of readiness for extubation. Although the RSBI was also related to EF in all models, its effect size was smaller than that of CRSI. When both indices were entered into the fully adjusted model, the association for RSBI lost statistical significance, while the link between CRSI and EF remained stable, suggesting that CRSI incorporates most of the information RSBI represents, along with additional elements from ventilatory settings and disease progression, thereby achieving an \u0026ldquo;information integration\u0026rdquo; advantage (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBMI displayed an inverse association with EF risk, with each 1 kg/m\u0026sup2; increment linked to an approximately 2% lower risk. This aligns with the \u0026ldquo;obesity paradox\u0026rdquo; described in critical care, where higher BMI has been related to more favorable short-term outcomes in certain critically ill populations (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Possible mechanisms include greater nutritional and muscular reserves providing metabolic and immune buffering during illness and weaning, and greater respiratory muscle mass delaying respiratory decompensation (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, this link may vary with baseline metabolic profile, muscle composition, and fat distribution (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and some studies have found no corresponding association (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e), underscoring the need for cautious interpretation and further multicenter, prospective evaluation.\u003c/p\u003e \u003cp\u003eCRSI also exhibited a nonlinear, threshold-dependent relationship with EF: when CRSI exceeded \u0026minus;\u0026thinsp;0.42, the risk rose sharply. This pattern may indicate a critical physiological tipping point beyond which respiratory reserve is markedly compromised, consistent with the \u0026ldquo;critical range\u0026rdquo; phenomenon reported for key ventilatory parameters (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Clinically, this finding may aid in identifying patients approaching a high-risk state, prompting earlier intervention.\u003c/p\u003e \u003cp\u003eSubgroup analyses indicated that CRSI maintained consistent associations with EF across most strata defined by age, sex, BMI, and common critical illness comorbidities. Notably, no significant association was observed in patients with COPD, perhaps due to adaptation of central respiratory drive to long-standing hypercapnia (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The association was weaker in patients with malignancy, potentially due to multifactorial effects of the disease and its treatment on respiratory and immune systems (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In contrast, patients with prior stroke exhibited stronger links between higher CRSI and EF, likely reflecting central neural control impairment, reduced swallowing function, and diminished airway protective reflexes (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCRSI offers practical utility for clinical risk assessment: its quantified score allows for more individualized extubation planning, and its accompanying online tool can provide near real-time EF probability estimates at the bedside, supporting dynamic, evidence-based decision-making. For patients with markedly elevated CRSI, particularly near or beyond the threshold point, strategies such as gradual weaning, prophylactic post-extubation noninvasive ventilation, or prolonged high-level monitoring may be considered. Limitations include the retrospective nature of the study, which precludes causal inference and necessitates testing in forward-looking designs. CRSI was constructed solely from ventilator parameters and course data, excluding key clinical factors such as neurological status, cough strength, or secretion load. Moreover, only single time-point measurements were analyzed, thus not capturing temporal trends. Finally, the findings are derived from a single database, and external validity in broader, multicenter populations remains to be established. Future work may incorporate CRSI into dynamic assessment models alongside diaphragm and respiratory muscle function metrics, or apply machine-learning frameworks to integrate multidimensional clinical and physiological datasets, with the aim of refining its clinical relevance and broadening applicability across diverse patient settings.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn conclusion, the CRSI provides a robust framework for evaluating extubation readiness in critically ill patients. By integrating ventilatory settings with intrinsic respiratory performance, it captures the overall burden of ventilatory dependence more comprehensively than conventional single-parameter indices. This multidimensional approach enables refined risk stratification and may facilitate more individualized strategies for weaning and extubation management in the ICU.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e10 Competing Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e11 Consent to Participate\u003c/strong\u003e \u003cp\u003eInformed consent was not required for this study, as the data used were fully de-identified and publicly available. The Institutional Review Boards waived the need for individual consent due to the retrospective design and anonymized nature of the datasets.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e12 Ethics Approval\u003c/strong\u003e \u003cp\u003e This study utilized the MIMIC-IV (version 3.1), approved for research use by the Institutional Review Boards of the Beth Israel Deaconess Medical Center (BIDMC) and the Massachusetts Institute of Technology (MIT).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e13 Consent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e6 Funding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXin Yi and Weijing Sun contributed equally to this work and share first authorship. Xin Yi conceived and designed the study, performed data extraction and analysis, and drafted the initial manuscript. Weijing Sun contributed to study design, supervised statistical analyses, and provided critical revisions of the manuscript. Chongshuang Yang and Raja Abdul Wafy Raja Muhammad Rooshdi assisted with data interpretation and contributed to manuscript editing. Jie Yan and Canzhang Liu provided expertise in clinical methodology and supported data validation. Ummi Nadira Daut and Razif Abas supervised the overall project, contributed to critical revision of the manuscript, and approved the final version for submission.\u003c/p\u003e\u003ch2\u003e5 Acknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaavedra SN, Barisich PVS, Maldonado JBP, Lumini RB, G\u0026oacute;mez-Gonz\u0026aacute;lez A, Gallardo A. Asynchronies during invasive mechanical ventilation: narrative review and update. Acute Crit Care. 2022;37(4):491\u0026ndash;501. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4266/acc.2022.01158\u003c/span\u003e\u003cspan address=\"10.4266/acc.2022.01158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Nov 30. PMID: 36480901; PMCID: PMC9732206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhbe H, Shime N, Yamana H, Goto T, Sasabuchi Y, Kudo D, Matsui H, Yasunaga H, Kushimoto S. Hospital and regional variations in intensive care unit admission for patients with invasive mechanical ventilation. J Intensive Care. 2024;12(1):21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40560-024-00736-0\u003c/span\u003e\u003cspan address=\"10.1186/s40560-024-00736-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 38840225; PMCID: PMC11155017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLarty J, Litton E, Beane A, Aryal D, Bailey M, Bendel S, Burghi G, Christensen S, Christiansen CF, Dongelmans DA, Fernandez AL, Ghose A, Hall R, Haniffa R, Hashmi M, Hashimoto S, Ichihara N, Kumar Tirupakuzhi Vijayaraghavan B, Lone NI, Arias L\u0026oacute;pez MDP, Mat Nor MB, Okamoto H, Priyadarshani D, Reinikainen M, Soares M, Pilcher D, Salluh J. Linking of Global Intensive Care (LOGIC) Collaboration. Non-COVID-19 intensive care admissions during the pandemic: a multinational registry-based study. Thorax. 2024;79(2):120\u0026ndash;127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/thorax-2022-219592\u003c/span\u003e\u003cspan address=\"10.1136/thorax-2022-219592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37225417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCinotti R, Mijangos JC, Pelosi P, Haenggi M, Gurjar M, Schultz MJ, Kaye C, Godoy DA, Alvarez P, Ioakeimidou A, Ueno Y, Badenes R, Suei Elbuzidi AA, Piagnerelli M, Elhadi M, Reza ST, Azab MA, McCredie V, Stevens RD, Digitale JC, Fong N, Asehnoune K, ENIO Study Group. the PROtective VENTilation network, the European Society of Intensive Care Medicine, the Colegio Mexicano de Medicina Critica, the Atlanr\u0026eacute;a group and the Soci\u0026eacute;t\u0026eacute; Fran\u0026ccedil;aise d\u0026rsquo;Anesth\u0026eacute;sie-R\u0026eacute;animation\u0026ndash;SFAR research network. Extubation in neurocritical care patients: the ENIO international prospective study. Intensive Care Med. 2022;48(11):1539\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-022-06825-8\u003c/span\u003e\u003cspan address=\"10.1007/s00134-022-06825-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Aug 29. PMID: 36038713.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Ni Y, Huang D, Liang Z. Ventilatory ratio as a predictor for extubation failure in critical ill patients based on MIMIC-IV database (from 2008 to 2019). Front Physiol. 2023;14:1137115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphys.2023.1137115\u003c/span\u003e\u003cspan address=\"10.3389/fphys.2023.1137115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37324397; PMCID: PMC10267390.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapin KC, Dragnich AG, Gannon WD, Martel AK, Bacchetta M, Erasmus DB, Shaver CM, Trindade AJ. Risk factors and clinical consequences of early extubation failure in lung transplant recipients. JHLT open. 2023;4:100046. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhlto.2023.100046\u003c/span\u003e\u003cspan address=\"10.1016/j.jhlto.2023.100046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurns KEA, Rizvi L, Cook DJ, Lebovic G, Dodek P, Villar J, Slutsky AS, Jones A, Kapadia FN, Gattas DJ, Epstein SK, Pelosi P, Kefala K, Meade MO, Canadian Critical Care Trials Group. Ventilator Weaning and Discontinuation Practices for Critically Ill Patients. JAMA. 2021;325(12):1173\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2021.2384\u003c/span\u003e\u003cspan address=\"10.1001/jama.2021.2384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChai X, Ma M, Hu W, Bai L, Duan J. Two-year survival after scheduled extubation in patients with pneumonia or ARDS: a prospective observational study. BMC Anesthesiol. 2024;24(1):232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12871-024-02603-9\u003c/span\u003e\u003cspan address=\"10.1186/s12871-024-02603-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaptistella AR, Sarmento FJ, da Silva KR, Baptistella SF, Taglietti M, Zuquello R\u0026Aacute;, Nunes Filho JR. Predictive factors of weaning from mechanical ventilation and extubation outcome: A systematic review. J Crit Care. 2018;48:56\u0026ndash;62. Epub 2018 Aug 20. PMID: 30172034.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurhan S, Tutan D, Şahiner Y, Kısa A, \u0026Ouml;zdemir S\u0026Ouml;, Tutan MB, Kayır S, Doğan G. Predictive Value of Serial Rapid Shallow Breathing Index Measurements for Extubation Success in Intensive Care Unit Patients. Med (Kaunas). 2024;60(8):1329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/medicina60081329\u003c/span\u003e\u003cspan address=\"10.3390/medicina60081329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 39202610; PMCID: PMC11356565.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbashir SM, Robert RC, Jairath NN, Raub CB, Alzumai OA, Salem SS. The rapid shallow breathing index (RSBI) as a predictor for extubation success in medical and surgical ICU patients: A retrospective cohort study. Heart Lung 2025 Mar-Apr;70:321\u0026ndash;8. doi: 10.1016/j.hrtlng.2025.01.007. Epub 2025 Jan 22. PMID: 39848164.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Luo J, Wang Y, Chen X, Du Z, Xie Y, Li X. Development and validation of a mechanical power-oriented prediction model of weaning failure in mechanically ventilated patients: a retrospective cohort study. BMJ open. 2022;12(12):e066894. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2022-066894\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2022-066894\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArrive F, Le Pape S, Bruhn A, et al. Physiological comparison of noninvasive ventilation and high-flow nasal oxygen on inspiratory efforts and tidal volumes after extubation: a randomized crossover trial. Crit Care. 2025;29:185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13054-025-05366-y\u003c/span\u003e\u003cspan address=\"10.1186/s13054-025-05366-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBureau C, Van Hollebeke M, Dres M. Managing respiratory muscle weakness during weaning from invasive ventilation. Eur respiratory review: official J Eur Respiratory Soc. 2023;32(168):220205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1183/16000617.0205-2022\u003c/span\u003e\u003cspan address=\"10.1183/16000617.0205-2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuttini S, Jona Falco J, Cuevas Cairo I, Umbrello M. A High Respiratory Drive Is Associated with Weaning Failure in Patients with COVID-19-Associated Acute Respiratory Distress Syndrome: The Role of the Electrical Activity of the Diaphragm. J Clin Med. 2024;13(4):1120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm13041120\u003c/span\u003e\u003cspan address=\"10.3390/jcm13041120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrivedi V, Chaudhuri D, Jinah R, Piticaru J, Agarwal A, Liu K, McArthur E, Sklar MC, Friedrich JO, Rochwerg B, Burns KEA. The Usefulness of the Rapid Shallow Breathing Index in Predicting Successful Extubation: A Systematic Review and Meta-analysis. Chest. 2022;161(1):97\u0026ndash;111. Epub 2021 Jun 26. PMID: 34181953.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSterr F, Reintke M, Bauernfeind L, Senyol V, Rester C, Metzing S, Palm R. Predictors of weaning failure in ventilated intensive care patients: a systematic evidence map. Crit Care (London England). 2024;28(1):366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13054-024-05135-3\u003c/span\u003e\u003cspan address=\"10.1186/s13054-024-05135-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVar\u0026oacute;n-Vega F, Tuta-Quintero E, Maldonado-Franco A, Robayo-Am\u0026oacute;rtegui H, Giraldo-Cadavid LF, Botero-Rosas D. (2025). Machine learning to predict extubation success using the spontaneous breathing trial, objective cough measurement, and diaphragmatic contraction velocity: Secondary analysis of the COBRE-US trial. Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures), 11(1), 70\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2478/jccm-2025-0009\u003c/span\u003e\u003cspan address=\"10.2478/jccm-2025-0009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbplanalp LA, Ionescu F, Calvo-Ayala E, et al. Static Respiratory System Compliance as a Predictor of Extubation Failure in Patients with Acute Respiratory Failure. Lung. 2023;201:309\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00408-023-00625-7\u003c/span\u003e\u003cspan address=\"10.1007/s00408-023-00625-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou D, Wang C, Lin Q, Li T. The obesity paradox for survivors of critically ill patients. Crit Care (London England). 2022;26(1):198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13054-022-04074-1\u003c/span\u003e\u003cspan address=\"10.1186/s13054-022-04074-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts KJ. (2023). 2022 Year in Review: Ventilator Liberation. Respiratory care, 68(12), 1728\u0026ndash;1735. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4187/respcare.11114\u003c/span\u003e\u003cspan address=\"10.4187/respcare.11114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaitovich A, Hall JB. The flux of energy in critical illness and the obesity paradox. Physiol Rev. 2025;105(3):1487\u0026ndash;552. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/physrev.00029.2024\u003c/span\u003e\u003cspan address=\"10.1152/physrev.00029.2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2025 Feb 21. PMID: 39982115; PMCID: PMC12107714.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei S, Nguyen TT, Zhang Y, Ryu D, Gariani K. Sarcopenic obesity: epidemiology, pathophysiology, cardiovascular disease, mortality, and management. Front Endocrinol. 2023;14:1185221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fendo.2023.1185221\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2023.1185221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraziano E, Peghin M, De Martino M, De Carlo C, Da Porto A, Bulfone L, Casarsa V, Sozio E, Fabris M, Cif\u0026ugrave; A, Grassi B, Curcio F, Isola M, Sechi LA, Tascini C, GIRA-COVID study group. The impact of body composition on mortality of COVID-19 hospitalized patients: A prospective study on abdominal fat, obesity paradox and sarcopenia. Clin Nutr ESPEN. 2022;51:437\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clnesp.2022.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.clnesp.2022.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Jong A, Capdevila M, Aarab Y, Cros M, Pensier J, Lakbar I, Monet C, Quintard H, Cinotti R, Asehnoune K, Arnal JM, Guitton C, Paugam-Burtz C, Abback P, Mekontso-Dessap A, Lakhal K, Lasocki S, Plantefeve G, Claud B, Pottecher J, Corne P, Ichai C, Molinari N, Chanques G, Papazian L, Azoulay E, Jaber S, FREE-REA Study Group. Incidence, Risk Factors, and Long-Term Outcomes for Extubation Failure in ICU in Patients With Obesity: A Retrospective Analysis of a Multicenter Prospective Observational Study. Chest. 2025;167(1):139\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chest.2024.07.171\u003c/span\u003e\u003cspan address=\"10.1016/j.chest.2024.07.171\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Sep 7. PMID: 39182573.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Du Z, Chen H, Liu S, Chen X, Li X, Xie Y. The relationship between mechanical power normalized to dynamic lung compliance and weaning outcomes in mechanically ventilated patients. PLoS ONE. 2024;19(8):e0306116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0306116\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0306116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Guo L, Wang H, Zhang Z, Shen L, Zhao H. Diagnostic accuracy of lung ultrasound to predict weaning outcome: a systematic review and meta-analysis. Front Med (Lausanne). 2024;11:1486636. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2024.1486636\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2024.1486636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 39554497; PMCID: PMC11563988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahlster S, Sharma M, Taran S, Town JA, Stevens RD, Cinotti R, Asehnoune K, Robba C. Associations between Driving Pressure and Clinical Outcomes in Acute Brain Injury: A Subanalysis of ENIO. Am J Respir Crit Care Med. 2024;209(11):1400\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.202402-0402LE\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202402-0402LE\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 38502247; PMCID: PMC11146561.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta A, Singh O, Juneja D. Clinical prediction scores predicting weaning failure from invasive mechanical ventilation: Role and limitations. World J Crit care Med. 2024;13(4):96482. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5492/wjccm.v13.i4.96482\u003c/span\u003e\u003cspan address=\"10.5492/wjccm.v13.i4.96482\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakornnoi B, Tscheikuna J, Rittayamai N. The effects of real-time waveform analysis software on patient ventilator synchronization during pressure support ventilation: a randomized crossover physiological study. BMC Pulm Med. 2024;24(1):212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12890-024-03039-0\u003c/span\u003e\u003cspan address=\"10.1186/s12890-024-03039-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy BT, Mackrill JJ, O'Halloran KD. Impact of cancer cachexia on respiratory muscle function and the therapeutic potential of exercise. J Physiol. 2022;600(23):4979\u0026ndash;5004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1113/JP283569\u003c/span\u003e\u003cspan address=\"10.1113/JP283569\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhle P, Claus I, Labeit B, Roderigo M, Warnecke T, Dziewas R, Suntrup-Krueger S. Pharyngeal Electrical Stimulation prior to extubation - Reduction of extubation failure rate in acute stroke patients? J Crit Care. 2024;82:154808. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcrc.2024.154808\u003c/span\u003e\u003cspan address=\"10.1016/j.jcrc.2024.154808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2024 Apr 5. PMID: 38581884.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Silva AR, Novais MCM, Neto MG, Correia HF. Predictors of extubation failure in neurocritical patients: A systematic review. Aust Crit Care. 2023;36(2):285\u0026ndash;91. Epub 2022 Feb 21. PMID: 35197209.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u0026nbsp; PCA Loadings of Ventilatory Parameters for the PC1 of CRSI\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrincipal Component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion of Variance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative Proportion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.318 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 239px;\"\u003e\n \u003cp\u003e0.318 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e1.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.256 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 239px;\"\u003e\n \u003cp\u003e0.575 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.192 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 239px;\"\u003e\n \u003cp\u003e0.767 (76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.127 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 239px;\"\u003e\n \u003cp\u003e0.894 (89.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 153px;\"\u003e\n \u003cp\u003e0.106 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 239px;\"\u003e\n \u003cp\u003e1.000 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote : PC1\u0026ndash;PC5 represent the first to fifth principal components derived from the ventilatory parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Proportion of Variance Explained by Each Principal Component\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePEEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eFiO₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eTV_observed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eMV_duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eRR_observed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e-0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e-0.694\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\u003eNote : PC1\u0026ndash;PC5 represent the first to fifth principal components derived from the ventilatory parameters; RR_observed : Observed Respiratory Rate; PEEP: Positive End-Expiratory Pressure; FIO2: Fraction of Inspired Oxygen; TV_observed: Observed Tidal Volume.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Baseline Characteristics of Participants\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"106%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (n=3589)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtubation Success (n=2960)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtubation Failure (n=629)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 686px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous variables\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;or median, IQR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e61.36 \u0026plusmn; 17.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e61.37 \u0026plusmn; 17.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e61.32 \u0026plusmn; 16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eHR (bpm/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e83.19 \u0026plusmn; 15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e82.81 \u0026plusmn; 15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e84.94 \u0026plusmn; 16.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e116.21 \u0026plusmn; 18.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e116.34 \u0026plusmn; 18.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e115.60 \u0026plusmn; 18.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eDBP (median)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e62.35 \u0026plusmn; 11.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e62.35 \u0026plusmn; 11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e62.31 \u0026plusmn; 12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eRR_observed\u0026nbsp;(/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e18.86 \u0026plusmn; 4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e18.64 \u0026plusmn; 4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e19.89 \u0026plusmn; 4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSpO₂ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e97.91 \u0026plusmn; 1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e97.96 \u0026plusmn; 1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e97.69 \u0026plusmn; 1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eWBC (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e10.90 (8.20, 14.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e10.85 (8.20, 14.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e11.30 (8.30, 14.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eRBC (\u0026times;10\u0026sup1;\u0026sup2;/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e3.35 \u0026plusmn; 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e3.37 \u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e3.26 \u0026plusmn; 0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003ePLT (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e178.50 (121.50, 254.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e176.75 (121.50, 249.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e190.00 (124.00, 284.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSodium (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e140.13 \u0026plusmn; 4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e140.04 \u0026plusmn; 4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e140.51 \u0026plusmn; 5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003ePotassium (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e4.05 \u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e4.04 \u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e4.09 \u0026plusmn; 0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eCalcium (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e8.24 \u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e8.24 \u0026plusmn; 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e8.24 \u0026plusmn; 0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e12.93 \u0026plusmn; 3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e12.89 \u0026plusmn; 3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e13.15 \u0026plusmn; 3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e7.40 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e7.40 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e7.40 \u0026plusmn; 0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003ePaCO₂ (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e41.00 (36.00, 46.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e41.00 (36.00, 46.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e42.00 (37.00, 48.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003ePaO₂ (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e99.00 (72.00, 127.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e100.00 (73.00, 129.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e95.00 (68.00, 121.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eBUN (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e21.50 (13.50, 35.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e21.00 (13.00, 34.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e24.50 (14.50, 41.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e0.95 (0.70, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e0.95 (0.70, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e1.00 (0.60, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.815\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e125.00 (106.00, 151.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e124.50 (106.00, 150.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e129.00 (109.00, 156.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eHGB (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e10.00 \u0026plusmn; 1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e10.07 \u0026plusmn; 1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e9.70 \u0026plusmn; 1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e27.99 (24.20, 33.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e28.01 (24.24, 32.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e27.92 (23.98, 33.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003ePEEP (cmH₂O)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e5.00 (5.00, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e5.00 (5.00, 7.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e5.00 (5.00, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eFiO₂ (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e40.00 (40.00, 50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e40.00 (40.00, 50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e40.00 (40.00, 50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eTidal Volume (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e467.84 \u0026plusmn; 104.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e470.34 \u0026plusmn; 104.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e456.03 \u0026plusmn; 103.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eUrine Output (mL/24h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e1865.00 (1070.00, 2987.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e1871.00 (1086.00, 3015.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e1800.00 (995.00, 2820.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e15.00 (15.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e15.00 (15.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e15.00 (15.00, 15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eMAP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e75.92 \u0026plusmn; 11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e75.90 \u0026plusmn; 11.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e76.02 \u0026plusmn; 11.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eMV Duration (hours)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e68.52 (38.00, 130.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e68.52 (38.00, 130.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e68.52 (38.00, 130.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSOFA Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e5.00 (4.00, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e5.00 (4.00, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e5.00 (4.00, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eCRSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e0.00 \u0026plusmn; 1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e-0.10 \u0026plusmn; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e0.47 \u0026plusmn; 1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eRSBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e40.38 (32.06, 51.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e39.82 (31.62, 50.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e44.64 (35.35, 56.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 741px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategorical variables, n (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 741px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e1517 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e1246 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e271 (43.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e2072 (57.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e1714 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e358 (56.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 741px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e3080 (85.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e2534 (85.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e546 (86.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e509 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e426 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e83 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 741px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e3275 (91.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e2689 (90.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e586 (93.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003e314 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e271 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179px;\"\u003e\n \u003cp\u003e43 (6.8%)\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\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e\u0026nbsp; \u0026nbsp;(Continued)\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (n = 3592)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEF Success (n = 2962)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEF Failure (n = 630)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 721px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e3067 (85.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e2516 (85.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003e551 (87.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e522 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003e444 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e78 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 721px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e3402 (94.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e2804 (94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003e598 (95.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e187 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e156 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003e31 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 721px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e3497 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e2890 (97.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003e607 (96.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e92 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e70 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003e22 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 721px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e3488 (97.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e2883 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003e605 (96.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\n \u003cp\u003e101 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e77 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003e24 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: HR: Heart Rate; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; RR_observed : Observed Respiratory Rate; SPO\u003csub\u003e2\u003c/sub\u003e: Peripheral Capillary Oxygen Saturation; WBC: White Blood Cell Count; RBC: Red Blood Cell Count; PLT: Platelet Count; AG: Anion Gap; PaCO\u003csub\u003e2\u003c/sub\u003e: Partial Pressure of Carbon Dioxide; PaO\u003csub\u003e2\u003c/sub\u003e: Partial Pressure of Oxygen; INR: International Normalized Ratio; BUN: Blood Urea Nitrogen; CR: Creatinine; GLU: Glucose; HGB: Hemoglobin; BMI: Body Mass Index; PEEP: Positive End-Expiratory Pressure; FIO\u003csub\u003e2\u003c/sub\u003e: Fraction of Inspired Oxygen; TV_observed: Observed Tidal Volume; GCS: Glasgow Coma Scale; MAP: Mean Arterial Pressure; MV duration: Mechanical Ventilation Duration; SOFA: Sequential Organ Failure Assessment; CKD: Chronic Kidney Disease; DM: Diabetes Mellitus; COPD: Chronic Obstructive Pulmonary Disease; CHF: Congestive Heart Failure; RSBI: Rapid Shallow Breathing Index; CRSI: Composite Respiratory Support Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e\u0026nbsp; Three Logistic Regression Models\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI (lower, upper)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStander Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1 (unadjusted)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCRSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e1.30 - 1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 698px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 698px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2 (adjusted for Age, Gender, and BMI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCRSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e1.35 \u0026ndash; 1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.996 \u0026ndash; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.849 \u0026ndash; 1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.970 \u0026ndash; 0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 698px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 698px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3 (fully adjusted)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIncluding Age, Gender, BMI, CHF, CKD, Diabetes, COPD, Cancer, and Stroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCRSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e1.34 \u0026ndash; 1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.997 \u0026ndash; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.851 \u0026ndash; 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.972 \u0026ndash; 0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.741 \u0026ndash; 1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.533 \u0026ndash; 1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.655 \u0026ndash; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.625 \u0026ndash; 1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.815 \u0026ndash; 2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.948 \u0026ndash; 2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eSOFA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0.970 \u0026ndash; 1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.356\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\u003eNote: SOFA: Sequential Organ Failure Assessment; CKD: Chronic Kidney Disease; DM: Diabetes Mellitus; COPD: Chronic Obstructive Pulmonary Disease; CHF: Congestive Heart Failure; CRSI: Composite Respiratory Support Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e\u0026nbsp; Subgroups Analysis\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 190px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI (lower, upper)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 698px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3 (fully adjusted)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIncluding Age, Gender, BMI, CHF, CKD, Diabetes, COPD, Cancer, and Stroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.358 - 1.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.226 - 1.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026ge;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.258 - 1.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026lt;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.340 - 1.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.190 - 1.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.293 - 1.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.219 - 2.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.289 - 1.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e0.862 - 1.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.310 - 1.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e0.799 - 1.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.309 - 1.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.352 - 2.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.273 - 1.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.303 - 1.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.227 - 1.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSOFA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026le;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.359 - 1.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026gt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.114 - 1.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e2.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.315 - 3.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 190px;\"\u003e\n \u003cp\u003e1.292 - 1.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\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\u003eSOFA: Sequential Organ Failure Assessment; CKD: Chronic Kidney Disease; DM: Diabetes Mellitus; COPD: Chronic Obstructive Pulmonary Disease; CHF: Congestive Heart Failure;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Comparison of CRSI and RSBI in Different Logistic Models\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 273px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRSBI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.39 (1.30\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.44 (1.35\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.44 (1.34\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1.02 (1.01\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eJoint Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1.48 (1.36\u0026ndash;1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.996 (0.988\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"extubation failure, mechanical ventilation, weaning assessment, respiratory support index, mimic-iv database","lastPublishedDoi":"10.21203/rs.3.rs-8554436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8554436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the association between a newly derived Composite Respiratory Support Index (CRSI) and extubation failure (EF) among critically ill patients receiving invasive mechanical ventilation (IMV), and to evaluate its predictive performance compared with the conventional Rapid Shallow Breathing Index (RSBI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study was conducted using the MIMIC-IV version 3.1 database, including adult patients who underwent IMV for at least 24 hours followed by their first planned extubation. Patients who died within 48 hours post-extubation without reintubation were excluded. Key demographic, clinical, laboratory, and ventilatory parameters were extracted within 24 hours before extubation. The CRSI was derived by principal component analysis based on positive end-expiratory pressure, fraction of inspired oxygen, observed tidal volume, invasive mechanical ventilation duration, and observed respiratory rate. Logistic regression models were used to examine associations between CRSI and EF, with adjustments for demographics, comorbidities, and SOFA score. Subgroup analyses explored consistency across age, gender, BMI, and comorbidity strata. Restricted cubic spline (RCS) analysis assessed nonlinear risk patterns. The predictive performance of CRSI was compared with RSBI.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 3,589 patients were analyzed, including 2,960 in the Extubation Success group and 629 in the Extubation Failure group. Patients in the Extubation Failure group demonstrated higher ventilatory demands, impaired gas exchange, and elevated CRSI and RSBI values compared with those successfully extubated. Across all models, higher CRSI was independently associated with increased risk of EF (fully adjusted OR\u0026thinsp;=\u0026thinsp;1.44, 95% CI 1.34\u0026ndash;1.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analysis confirmed consistent associations across most strata, except among patients with COPD and cancer. Notably, the association was stronger in patients with prior stroke (OR\u0026thinsp;=\u0026thinsp;2.03, 95% CI 1.32\u0026ndash;3.41, P\u0026thinsp;=\u0026thinsp;0.003). RCS analysis revealed a nonlinear escalation of risk beyond a CRSI threshold of -0.42. When jointly modeled with RSBI, only CRSI remained an independent predictor of EF.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe CRSI provides a comprehensive assessment of ventilatory dependence by integrating multiple respiratory support parameters. It demonstrated superior predictive accuracy and stability compared with RSBI, with potential to improve individualized extubation decision-making. Incorporation of CRSI into bedside tools may facilitate timely interventions, reduce extubation-related complications, and enhance clinical outcomes in the ICU setting.\u003c/p\u003e","manuscriptTitle":"Association of a Composite Respiratory Support Index with Extubation Failure in ICU Patients: Insights from the MIMIC-IV Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 13:13:32","doi":"10.21203/rs.3.rs-8554436/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-10T15:38:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14271769028064094309137027122319888907","date":"2026-02-04T17:19:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T16:51:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-12T17:51:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-10T14:15:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-10T14:14:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-01-08T18:31:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abdd8703-f8eb-446f-ac78-0da72a4fad03","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T13:13:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 13:13:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8554436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8554436","identity":"rs-8554436","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}