Risk of Early Intubation and High-Flow Nasal Cannula Failure in Pneumonia: Pre-Treatment Predictors using Triage Data from a Retrospective COVID-19 Cohort

Risk of Early Intubation and High-Flow Nasal Cannula Failure in Pneumonia: Pre-Treatment Predictors using Triage Data from a Retrospective COVID-19 Cohort | 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 Risk of Early Intubation and High-Flow Nasal Cannula Failure in Pneumonia: Pre-Treatment Predictors using Triage Data from a Retrospective COVID-19 Cohort José Alberto Choreño Parra, Daniela Alessandra Lázaro-Robles, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8427896/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 Background Despite high-flow nasal cannula (HFNC) having improved outcomes in acute hypoxemic respiratory failure (AHRF), identifying patients unlikely to benefit before therapy initiation remains crucial to avoid delays in invasive mechanical ventilation (IMV). The objective of this study is to describe the clinical characteristics and outcomes of patients with AHRF due to COVID-19 pneumonia, and to identify predictors of early IMV and HFNC failure. Methods We conducted a retrospective study of patients with AHRF secondary to COVID-19 pneumonia evaluated between March 2020 and February 2021. Clinical data were collected during the pre-treatment phase in the emergency department. Study groups included early IMV, HFNC success, and HFNC failure, according to outcomes. The inclusion of patients needing early IMV aimed to capture clinical profiles in which immediate intubation was deemed necessary on admission, precluding the safe initiation of HFNC. Prognostic factors were explored using univariate logistic regression analysis. Results The study enrolled 139 patients (62% male, aged 56 years). Among them, 75 required IMV, 43 in the early IMV group and 32 classified as HFNC failure. Early IMV was associated with age, male sex, SpO₂, PaO₂/FiO₂ ratio, albumin levels, respiratory rate, troponin, and pneumonia severity indices. HFNC failure was associated with age, prior conventional oxygen therapy flow rates, urea, BUN, low lymphocyte count, and symptoms duration. Conclusions Early clinical variables evaluated at triage can help predict the need for immediate IMV and HFNC failure, supporting timely clinical decision-making in patients with AHRF. Trial registration: not applicable. COVID-19 pneumonia high-flow nasal cannula invasive mechanical ventilation acute hypoxemic respiratory failure predictive factors early intubation Figures Figure 1 Introduction Acute hypoxemic respiratory failure (AHRF) remains a critical challenge in pulmonary medicine. High-flow nasal cannula (HFNC) oxygen therapy has gained widespread adoption given its physiological advantages, including: improved oxygenation, reduced respiratory effort, and enhanced patient comfort [ 1 , 2 ]. Recently, this modality has become a cornerstone for COVID-19-associated AHRF over conventional oxygen therapy (COT) [ 3 – 5 ], showing similar efficacy compared to non-invasive ventilation (NIV) [ 6 , 7 ]. However, HFNC therapy is not universally successful, with significant failure rates that delay intubation [ 1 , 8 ]. Remarkably, in patients with severe hypoxemia or in settings of COVID-19 outbreaks, late intubation may worsen outcomes [ 9 , 10 ]. Hence, the timely identification of cases unlikely to benefit from HFNC is crucial to guide prompt escalation to invasive mechanical ventilation (IMV). Several clinical markers and predictive tools can assist clinicians in assessing HFNC efficacy and identifying seriously ill patients. However, most of them stratify patients risk based on continuous monitoring of respiratory parameter trends that become evident hours after HFNC therapy onset [ 11 – 15 ]. Conversely, only a few studies have investigated prognostic factors relying on clinical data available before initiating respiratory support [ 9 , 16 , 17 ] [ 14 , 18 ]. These findings underscore the importance of integrating pre-treatment evaluations into predictive frameworks to identify at-risk patients early and provide a pivotal advantage in clinical decision-making. Predictive factors in HFNC therapy have not been validated in our environment. Thus, the objective of this research was to describe the clinical characteristics and outcomes of patients with COVID-19-associated AHRF, and to identify early predictors of IMV and HFNC failure using data collected during the pre-treatment phase. Methods A retrospective cohort study was conducted in patients with AHRF secondary to COVID-19 pneumonia. Participants were evaluated at the emergency department (ED) of a national reference center for respiratory diseases and subsequently hospitalized between March 2020 and February 2021. This timeframe precedes the SARS-CoV-2 vaccination campaign in the region, avoiding biases associated with varying immunization schemes. During the COVID-19 outbreak, the ED adopted a structured yet agile protocol to evaluate patients presenting with respiratory symptoms, based on rapid categorization into priority levels of care. This protocol facilitated the timely identification and recruitment of eligible individuals for the study ( Supplementary Methods ). Adults aged ≥ 18 years with confirmed COVID-19 via viral polymerase chain reaction testing of nasopharyngeal swab samples who received HFNC therapy and consented to participate were enrolled. A comparative group of patients who underwent early intubation without an initial HFNC trial (hereinafter named as the early IMV group) was included. Patients were assigned to this group if they presented clinical features deemed incompatible with safe initiation of HFNC, based on physician judgment on admission. Patients were excluded if they had concurrent viral or bacterial infections on admission, primarily hypercapnic acute respiratory failure, solid organ transplants on immunosuppressive therapy, active hematologic malignancy, prior management at other facilities involving IMV or HFNC before initial triage assessment, or NIV during hospitalization before HFNC initiation. Individuals on HFNC or IMV who died within the first 24 hours of hospitalization were also excluded from the analysis. All complete medical records available in the center's electronic database during the study period were reviewed for eligible participants. All data were extracted using a standardized template by two independent investigators. Discrepancies were resolved by consensus with a third reviewer to ensure data accuracy, reduce extraction bias, guarantee uniformity in variable definitions, and minimize misclassification errors. Collected data included demographic characteristics, anthropometrics, comorbidities, symptoms, triage vital signs, chest CT imaging findings, admission severity scores, and details of prior oxygen therapy. Laboratory parameters obtained within 24 hours of admission and prior to HFNC initiation included white blood cell counts, markers of inflammation, liver and kidney function, blood gases, tissue injury markers, and other relevant tests. Patients were monitored throughout their hospitalization, with records of specific complications, antibiotic, corticosteroid, and antiviral use, intensive care interventions, duration of HFNC and IMV therapy. The primary outcome was the need for IMV before or after a HFNC trial. Criteria for intubation are described in Supplementary Methods . Secondary outcomes included the incidence of hospital-acquired bacterial or fungal pneumonia (HAP) and acute kidney injury (AKI). Descriptive statistics were used to characterize the study population. Categorical variables were reported as frequencies and proportions. Continuous variables were presented as medians with interquartile ranges (IQR) or 95% confidence intervals (CI). Group comparisons were performed using Fisher's exact test, unpaired Mann–Whitney U test, or Kruskal–Wallis test with post hoc Dunn’s analysis, as appropriate. No sample size calculation was performed because this study was a retrospective census including all consecutive patients who met eligibility criteria. To contextualize the interpretability of our findings, we conducted an exploratory post hoc power assessment. Logistic regression was employed to assess the association of pre-treatment prognostic factors with outcomes. Receiver Operating Characteristic (ROC) curve analyses evaluated the diagnostic accuracy of potential predictors, using the area under the curve (AUC). Prognostic factors were dichotomized based on thresholds determined by the Youden index for maximum diagnostic performance. Kaplan-Meier curves and time-to-event analyses were not performed due to the relatively short follow-up period. Analyses were conducted using GraphPad Prism (version 9.5, La Jolla, CA, USA) and R software (version 4.1.2, R Core Team, Vienna, Austria). A p-value < 0.05 was considered statistically significant. False discovery rate (FDR) correction was applied to univariate logistic regression models using the Benjamini–Hochberg method, and q-values were calculated. While results were primarily interpreted as suggestive of association based on unadjusted p-values < 0.05 to identify potential predictors of intubation for future validation, q-values (< 0.1) were reported to improve transparency and highlight potentially robust findings. This study was approved by the institutional research and ethics committee under the protocol number E10-25. Results A total of 139 patients with confirmed COVID-19 met inclusion criteria and were eligible for analysis. This sample size was sufficient to detect clinically meaningful associations of moderate or greater magnitude ( Supplementary Results ). The median age was 56 years, and 62% were male. Of these, 43 patients (31%) were intubated immediately after admission and included in the early IMV group, while 96 patients (69%) received HFNC. Among HFNC recipients, 32 patients (33%) experienced failure. Age and gender significantly differed between groups (Table 1 ). Frequent manifestations included dyspnea (72%), fever (64%), cough (48%), myalgias (42%), and headache (36%; Table S1 ). The duration of symptoms from onset to admission was 8 days (6–11 IQR), which differed significantly between groups: 9 (7-11.3) days in HFNC success, 7 (5-8.3) days in HFNC failure, and 8 (7-13.5) days in early IMV. The most prevalent comorbidities included obesity (45%), overweight (35%), hypertension (30%), diabetes (29%), smoking history (27%), interstitial lung disease (ILD; 8.6%), and chronic obstructive pulmonary disease (COPD; 5%). A history of asthma, obstructive sleep apnea (OSA), and alcohol use was also documented. Imaging studies revealed bilateral infiltrates in 96% of cases, ground-glass opacities in 92%, consolidation in 46%, crazy paving in 17%, and reticular changes or bronchiectasis in less than 10% ( Table S1 ). Details on the duration and specific parameters of HFNC therapy and IMV support are described in Table S2 . Table 1 Clinical and laboratory parameters of study participants Characteristics Overall, N = 139 Group p-value 1 HFNC success, N = 64 HFNC failure, N = 32 Early IMV, N = 43 Demographics Age, years 56.0 (46.0, 64.5) 53.5 (42.8, 61.3) 60.0 (53.8, 67.5) 55.0 (44.0, 63.0) 0.024 Male 86 (62%) 33 (52%) 18 (56%) 35 (81%) 0.006 Vital signs GCS 15.0 (15.0, 15.0) 15.0 (15.0, 15.0) 15.0 (15.0, 15.0) 15.0 (15.0, 15.0) 0.3 SpO 2 , % 77.0 (67.0, 86.0) 83.0 (74.3, 88.0) 76.5 (65.8, 86.5) 70.0 (60.0, 79.5) 0.001 FiO 2 , % 21.0 (21.0, 21.0) 21.0 (21.0, 21.0) 21.0 (21.0, 23.3) 21.0 (21.0, 21.0) 0.7 RR, rpm 29.0 (25.0, 35.0) 26.5 (23.8, 30.0) 28.0 (25.8, 34.3) 32.0 (30.0, 38.0) < 0.001 HR, bpm 100.0 (89.5, 108.5) 98.0 (88.8, 107.5) 92.0 (85.5, 102.3) 104.0 (97.5, 112.0) 0.024 Temperature, ºC 36.8 (36.5, 37.4) 36.8 (36.5, 37.4) 37.0 (36.5, 37.5) 37.0 (36.5, 37.2) 0.4 SBP, mmHg 119.0 (109.5, 128.0) 118.0 (110.0, 129.0) 118.5 (109.3, 122.0) 119.0 (106.5, 129.0) 0.8 DBP, mmHg 73.0 (69.0, 83.0) 75.0 (70.0, 84.5) 73.0 (67.8, 85.3) 72.0 (69.0, 79.5) 0.8 MAP, mmHg 88.0 (81.0, 98.2) 88.2 (80.6, 99.0) 87.2 (83.2, 97.2) 89.3 (81.9, 96.5) > 0.9 Laboratory parameters Leucocytes, 10 9 /L 8.9 (6.3, 13.1) 8.6 (5.8, 12.9) 8.1 (6.2, 12.9) 11.0 (7.9, 14.1) 0.039 Neutrophils, 10 9 /L 7.5 (5.3, 11.0) 7.2 (4.7, 10.5) 6.8 (5.2, 11.0) 9.7 (6.5, 12.4) 0.067 Lymphocytes, 10 9 /L 0.7 (0.5, 1.0) 0.8 (0.6, 1.0) 0.6 (0.4, 1.0) 0.8 (0.5, 1.1) 0.10 Hb, g/dL 14.8 (13.6, 16.2) 15.1 (13.7, 16.4) 14.4 (13.2, 16.3) 14.4 (13.2, 15.9) 0.2 Htc, % 43.7 (40.0, 47.0) 44.4 (40.9, 47.6) 41.5 (38.3, 47.2) 42.3 (37.7, 45.8) 0.12 Platelets, 10 3 /µL 225.0 (187.0, 283.5) 220.5 (185.8, 247.0) 203.0 (142.3, 248.8) 246.0 (203.0, 297.5) 0.049 Glucose, mg/dL 122.0 (101.0, 166.0) 119.5 (101.8, 148.3) 127.5 (96.5, 176.5) 121.0 (103.0, 192.5) 0.6 Urea, mg/dL 39.0 (26.0, 54.0) 28.0 (21.0, 47.5) 45.0 (31.0, 57.0) 45.0 (32.1, 64.0) < 0.001 BUN, mg/dL 18.5 (12.0, 25.0) 13.5 (10.0, 22.3) 21.0 (14.5, 26.0) 21.0 (15.0, 30.0) < 0.001 Cr, mg/dL 0.9 (0.7, 1.1) 0.8 (0.7, 1.0) 0.9 (0.7, 1.2) 0.9 (0.8, 1.4) 0.012 Uric acid, mg/dL 4.4 (3.4, 5.8) 4.2 (3.5, 5.4) 4.4 (3.4, 5.7) 4.9 (3.3, 6.0) 0.8 Albumin, g/dL 3.4 (3.0, 3.7) 3.6 (3.3, 3.9) 3.3 (3.0, 3.7) 3.0 (2.6, 3.3) < 0.001 Bilirrubin, mg/dL 0.6 (0.5, 0.8) 0.6 (0.5, 0.7) 0.6 (0.5, 0.9) 0.7 (0.5, 0.9) 0.4 AST, U/L 42.0 (33.0, 59.0) 36.0 (28.0, 50.5) 50.0 (35.0, 62.0) 46.0 (37.0, 64.5) 0.014 ALT, U/L 35.0 (23.0, 53.0) 29.0 (20.8, 48.3) 35.8 (24.0, 51.0) 41.0 (30.3, 65.8) 0.019 GGT, IU/L 74.5 (36.3, 123.5) 60.0 (30.5, 105.0) 46.0 (33.0, 150.0) 95.0 (68.3, 198.8) 0.005 LDH, U/L 455.5 (358.3, 562.5) 422.0 (320.8, 532.8) 483.0 (366.5, 551.0) 504.0 (396.0, 672.0) 0.11 ALP, IU/L 95.2 (71.0, 117.0) 94.0 (65.8, 121.8) 96.0 (72.0, 110.5) 98.0 (72.0, 113.0) 0.9 CPK, µg/L 96.7 (57.0, 195.5) 84.0 (48.3, 140.3) 133.0 (68.8, 237.5) 124.0 (77.0, 211.0) 0.022 PCT, ng/mL 0.2 (0.1, 0.6) 0.1 (0.0, 0.3) 0.2 (0.1, 0.6) 0.3 (0.2, 0.8) 0.010 CRP, mg/dL 14.3 (9.5, 24.3) 11.6 (7.7, 21.4) 14.1 (12.2, 22.0) 22.7 (12.6, 31.5) 0.009 Troponin, ng/L 6.5 (2.8, 34.9) 6.2 (2.0, 34.7) 6.0 (2.5, 10.2) 10.4 (4.4, 145.1) 0.055 Mioglobin, g/L 67.8 (33.8, 146.7) 34.4 (22.9, 97.3) 118.3 (76.7, 196.1) 51.2 (39.3, 186.8) 0.2 BNP, pg/mL 24.9 (11.1, 83.3) 27.7 (10.6, 107.6) 17.0 (10.4, 39.9) 37.5 (13.4, 107.4) 0.4 Ferritin, ng/mL 855.3 (405.7, 2,127.5) 560.0 (278.9, 1,822.9) 963.1 (547.5, 1,245.1) 1,520.3 (703.3, 2,693.7) 0.2 PT, seconds 14.6 (14.0, 15.9) 14.3 (13.5, 15.2) 14.3 (13.9, 15.3) 15.6 (14.7, 16.8) < 0.001 aPTT, seconds 41.3 (36.0, 46.6) 40.4 (35.5, 46.0) 41.9 (35.4, 46.7) 41.7 (37.5, 46.3) 0.7 INR 1.0 (1.0, 1.1) 1.0 (1.0, 1.1) 1.0 (1.0, 1.1) 1.1 (1.0, 1.2) < 0.001 DD, mcg/mL 0.7 (0.4, 1.7) 0.7 (0.4, 1.4) 0.5 (0.4, 0.9) 1.4 (0.7, 5.8) 0.003 Na, mEq/L 138.0 (135.0, 140.0) 137.0 (135.0, 140.0) 137.0 (134.0, 140.0) 139.0 (134.5, 140.5) 0.3 K, mEq/L 4.1 (3.8, 4.5) 4.1 (3.7, 4.3) 4.1 (3.7, 4.6) 4.1 (4.0, 4.6) 0.3 Cl, mEq/L 102.0 (100.0, 105.0) 102.0 (100.0, 105.0) 101.0 (99.5, 104.0) 103.0 (100.0, 105.5) 0.5 Ca, mEq/L 8.3 (7.8, 8.8) 8.4 (7.9, 8.8) 8.4 (7.9, 8.8) 8.2 (7.7, 8.6) 0.2 Mg, mEq/L 2.0 (1.9, 2.3) 2.0 (1.9, 2.2) 2.1 (1.9, 2.3) 2.1 (2.0, 2.4) 0.12 P, mEq/L 3.3 (2.7, 3.8) 3.2 (2.7, 3.7) 3.2 (2.7, 3.8) 3.7 (3.2, 4.0) 0.008 Arterial gases on admission pH 7.5 (7.4, 7.5) 7.5 (7.4, 7.5) 7.5 (7.4, 7.5) 7.5 (7.4, 7.5) 0.6 PCO 2 , mmHg 30.8 (26.7, 37.3) 29.7 (27.1, 36.9) 30.6 (26.5, 35.4) 32.8 (26.9, 38.8) 0.5 PO 2 , mmHg 54.5 (41.7, 73.2) 55.1 (45.0, 71.1) 63.8 (44.3, 75.3) 49.7 (36.8, 72.8) 0.12 SO 2 , % 86.9 (77.6, 93.0) 86.0 (78.7, 92.6) 90.3 (79.7, 94.0) 85.0 (74.5, 92.2) 0.2 HCO 3 , mEq/L 21.8 (19.4, 24.6) 21.5 (19.4, 23.8) 21.7 (19.7, 25.0) 22.2 (19.4, 24.1) > 0.9 PaO 2 /FiO 2 184.5 (130.8, 224.5) 195.0 (162.5, 242.0) 183.5 (147.5, 245.5) 148.0 (110.0, 197.0) 0.005 BE, mmol/L -0.6 (-3.2, 1.8) -0.5 (-3.1, 1.7) 0.1 (-2.0, 1.9) -1.3 (-3.5, 1.3) 0.7 Lactate, mmol/L 1.3 (1.0, 2.0) 1.3 (0.9, 1.9) 1.2 (1.0, 1.9) 1.5 (1.1, 2.2) 0.4 The data are displayed as median (IQR) or n (%); N is the total number of participants with available information. 1 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test. ALP, alkaline phosphatase; ALT, alanine aminotransferase; aPTT, activated partial thromboplastin time; AST, aspartate aminotransferase; BE, base excess; BNP, brain natriuretic peptide; BUN, blood urea nitrogen; CPK, creatin phosphokinase; Cr, creatinin; CRP, C-reactive protein; DBP, diastolic blood pressure; DD, D-dimer; FiO 2 , fraction of inspired oxygen; GCS, Glasgow coma scale; GGT, gamma glutamil transpeptidase; Hb, hemoglobin; HCO 3 , bicarbonate; HR, heart rate; Htc, hematocrit; INR, international normalized ratio; LDH, lactate dehydrogenase; MAP, mean arterial pressure; PCO 2 , partial pressure of CO 2 ; PCT, procalcitonin; PO 2 , partial pressure of O 2 ; PT, prothrombin time; RR, respiratory rate; SBP, systolic blood pressure; SO 2 , saturation of oxygen in arterial blood; SpO 2 , pulse oximetry saturation of oxygen. Patients in the early IMV group had distinct clinical features. They were significantly older, more frequently male, and had a lower prevalence of ILD and hypertension. At triage, they had higher respiratory rates (RR), lower SpO₂, and more profound hypoxemia measured by the PaO₂/FiO₂ ratio (Table 1 ). Laboratory findings revealed higher leukocyte counts, lower albumin levels, and higher inflammatory markers. Troponin and GGT were elevated in this group. Kidney function markers, including urea and BUN, were also higher. Severity scores reflected greater baseline risk in early IMV compared to the HFNC success group (Table 2 ). Nearly all early IMV patients met criteria for ARDS, with 19% classified as severe (PaO₂/FiO₂ <100). Table 2 Admission severity scores in study participants Characteristics Overall, N = 139 Group p-value 1 HFNC success, N = 64 HFNC failure, N = 32 Early IMV, N = 43 Severity scores SOFA 2.0 (2.0, 4.0) 2.0 (2.0, 3.0) 3.0 (2.0, 4.0) 3.0 (2.0, 4.0) 0.017 APACHEII 9.0 (7.0, 14.0) 8.0 (6.0, 12.0) 10.0 (7.8, 15.3) 11.0 (8.0, 15.0) 0.016 PSI 74.0 (55.0, 95.0) 61.0 (47.0, 82.3) 80.0 (56.8, 106.0) 86.0 (70.0, 110.0) < 0.001 SMART-COP 4.0 (3.0, 5.0) 3.0 (3.0, 4.0) 4.0 (2.0, 4.3) 5.0 (4.0, 5.0) < 0.001 MuLBSTA 9.0 (7.0, 11.0) 9.0 (7.0, 11.0) 10.5 (9.0, 13.0) 9.0 (7.0, 11.0) 0.2 ARDS 123 (90%) 55 (87%) 25 (83%) 43 (100%) 0.011 ARDS grade, PaO 2 /FiO 2 No ARDS 13 (9.6%) 8 (13%) 5 (17%) 0 (0%) 0.065 Mild (< 300) 37 (27%) 21 (33%) 6 (20%) 10 (23%) Moderate (< 200) 67 (49%) 27 (43%) 15 (50%) 25 (58%) Severe (< 100) 19 (14%) 7 (11%) 4 (13%) 8 (19%) The data are displayed as median (IQR) or n (%); N is the total number of participants with available information. 1 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test. APACHEII, Acute Physiology and Chronic Health disease Classification System II; ARDS, acute respiratory distress syndrome; MuLBSTA, MuLBSTA score for viral pneumonia; PSI, pneumonia severity index; SMART-COP, SMART-COP score for pneumonia severity; SOFA, Sequential Organ Failure Assessment score. On the other hand, patients in the HFNC failure group were significantly older and with a higher proportion of male compared with those who succeeded with HFNC. They also presented to the hospital earlier after symptom onset compared with the HFNC success group. In terms of comorbidities, ILD and COPD were more common among patients who failed HFNC. Similarly, the HFNC failure group exhibited lower SpO₂ and a higher RR at admission. Biochemical analysis revealed that HFNC failure patients had significantly higher markers of renal dysfunction. Albumin was lower, suggesting early catabolism. Inflammatory and tissue damage markers were also elevated in the HFNC failure group, including AST, ALT, PCT, and CRP. In addition, patients who failed HFNC had higher severity-of-illness scores at admission compared to the HFNC success group. The prevalence of ARDS in the HFNC failure group was high (83%), with 50% meeting moderate and 13% severe criteria based on PaO₂/FiO₂ ratio. In univariate logistic regression, key predictors of early IMV included low SpO₂, PaO₂/FiO₂, serum albumin, male sex, SOFA, and SMART-COP scores. Although marginally statistically significant in the regression model, elevated RR, GGT, troponin levels, and pneumonia severity index (PSI) scores were consistently higher in this group (Fig. 1 A and Table S3 ). Late HFNC failure was associated with more gradual clinical deterioration. Significant predictors included age, BUN, lymphocyte count, and PSI scores (Fig. 1 B and Table S4 ). Additional observed trends included a higher oxygen flow requirement on COT devices before HFNC initiation and a longer duration of symptoms prior to hospital admission ( Table S2 ). In ROC analysis, serum albumin 50 U/L, and SpO₂ < 80%. Although troponin had lower overall accuracy, it exhibited high specificity (88%) at a threshold of 42 ng/L, suggesting its value as a rule-in marker for early IMV. For HFNC failure, prior COT oxygen flow rate > 6 L/min, urea > 36 mg/dL, symptom duration > 6 days, and lymphocyte count < 0.56 ×10⁹/L demonstrated fair predictive performance (Table 3 ). Table 3 Diagnostic performance of early predictors of intubation and HFNC failure Predictor AUC 95% CI Cut-off value Sensitivity 95% CI Specificity 95% CI Early IMV SpO₂ 0.68 0.59–0.77 80% 74% 80%-85% 56% 46%-66% Respiratory rate 0.71 0.61–0.80 30 breaths/min 79% 65%-89% 65% 55%-73% PaO₂/FiO₂ 0.66 0.56–0.75 170 mmHg 63% 48%-76% 68% 59%-77% Albumin 0.78 0.69–0.86 3.4 g/dL 79% 64%-88% 65% 55%-73% GGT 0.69 0.59–0.79 50 U/L 87% 73%-94% 47% 37%-58% Troponin 0.66 0.53–0.78 42 ng/L 44% 28%-63% 88% 77%-94% SOFA 0.61 0.51–0.71 2 points 65% 50%-78% 57% 47%-67% SMART-COP 0.70 0.61–0.80 3 points 79% 65%-89% 50% 40%-60% PSI 0.67 0.57–0.76 64 points 84% 70%-92% 48% 38%-58% HFNC failure Age 0.67 0.55–0.78 50 years 91% 76%-97% 38% 27%-50% Prior oxygen flow rate 0.70 0.57–0.83 6 L/min 71% 51%-85% 63% 50%-75% Urea 0.69 0.58–0.80 36 mg/dL 71% 53%-84% 62% 50%-73% BUN 0.68 0.57–0.79 17 mg/dL 71% 53%-84% 61% 49%-72% Lymphocytes 0.63 0.51–0.75 0.56 ×10⁹/L 50% 34%-66% 78% 67%-86% Symptoms duration 0.68 0.57–0.80 6 days 47% 31%-64% 83% 72%-90% PSI 0.68 0.57–0.79 67 points 69% 51%-82% 61% 49%-72% Cut-off values were selected based on the optimal Youden index. Predictors are grouped according to the outcome of interest: early IMV (within the first 24 hours) and HFNC failure (defined as the need for delayed intubation after HFNC initiation). AUC, Area under the receiver operating characteristic curve; BUN, blood urea nitrogen; CI, Confidence interval; GGT, gamma-glutamyl transferase; HFNC, High-flow nasal cannula; IMV, Invasive mechanical ventilation PaO₂/FiO₂, arterial oxygen partial pressure to inspired oxygen fraction ratio; PSI, Pneumonia Severity Index; SMART-COP: Systolic blood pressure, Multilobar infiltrates, Albumin level, Respiratory rate, Tachycardia, Confusion, Oxygenation, and arterial pH score; SOFA: Sequential Organ Failure Assessment score; SpO₂, peripheral oxygen saturation. Secondary outcomes are summarized in Table 4 . HAP occurred in 35% of early IMV, 19% of HFNC failure, and only 1.6% of HFNC success patients. AKI was more frequent in early IMV (14%) than in HFNC success group (1.6%). Medical management strategies required during hospitalization also differed according to respiratory support requirements. In-hospital antibiotic use, corticosteroid administration and antifungal therapy were significantly more common in the early IMV and HFNC failure groups ( Table S2 ). Table 4 Secondary outcomes of study participants Characteristics Overall, N = 139 Group p-value 1 HFNC success, N = 64 HFNC failure, N = 32 Early IMV, N = 43 HAP 22 (16%) 1 (1.6%) 6 (19%) 15 (35%) < 0.001 Suspected fungal infection 23 (17%) 3 (4.7%) 9 (28%) 11 (26%) 0.002 Confirmed fungal co-infection 3 (2.2%) 0 (0%) 0 (0%) 3 (7.0%) 0.040 AKI 10 (7.2%) 1 (1.6%) 3 (9.4%) 6 (14%) 0.031 KDIGO 1 2 (1.4%) 0 (0%) 0 (0%) 2 (4.7%) KDIGO 2 3 (2.2%) 0 (0%) 1 (3.1%) 2 (4.7%) KDIGO 3 5 (3.6%) 1 (1.6%) 2 (6.3%) 2 (4.7%) Reintubation 10 (14%) 0 (NA%) 0 (0%) 10 (23%) 0.004 IMV free days 7.0 (2.0, 12.0) NA (NA, NA) 7.0 (4.0, 11.3) 6.0 (0.0, 12.5) 0.4 The data are displayed as median (IQR) or n (%); N is the total number of participants with available information. 1 Kruskal-Wallis rank sum test; Pearson’s Chi-squared test; Fisher’s exact test. AKI, acute kidney injury; HAP, hospital-acquired pneumonia; KDIGO, Kidney Disease: Improving Global Outcomes classification of AKI; IMV, invasive mechanical ventilation. Odds ratios (OR) with 95% confidence intervals are shown for variables associated with early IMV and HFNC failure, based on multivariate logistic regression models. For early IMV, significant predictors included male gender, interstitial lung disease (ILD), systemic arterial hypertension (SAH), saturation of oxygen (SpO 2 ), low PaO2/FiO2, low serum albumin, and increased levels of gamma-glutamyl transferase (GGT) and troponin. Also, higher scores in SOFA and SMART-COP were associated with early IMV. For HFNC failure, higher age, pre-HFNC oxygen flow on conventional oxygen therapy (COT) devices, elevated urea and BUN, reduced lymphocyte count, and longer symptoms duration before admission were associated with increased risk. The Pneumonia Severity Index (PSI) score was significant in both models. Specific OR values are shown in Table S1 of the supplementary material accompanying this manuscript. Discussion HFNC have become a cornerstone for oxygen therapy across diverse clinical contexts. The RENOVATE trial, a large multicenter randomized clinical trial, showed HFNC to be noninferior to NIV for preventing intubation or death in patients with AHRF, with the added benefit of improved comfort [ 19 ]. Systematic reviews have similarly found that HFNC reduces treatment failure compared to COT and offers outcomes comparable to NIV. In post-extubation patients, HFNC is also well-tolerated and effective, particularly in those with COPD [ 1 , 20 ]. During the COVID-19 pandemic, HFNC therapy was widely adopted. Meta-analyses showed comparable failure and mortality rates between HFNC, NIV, and COT [ 3 – 7 , 21 ]. However, retrospective studies have raised concern about the risks of delayed intubation following HFNC failure. In both general ICU and high-risk populations, prolonged use of HFNC before intubation has been associated with higher mortality, fewer ventilator-free days, and lower weaning success [ 9 , 10 , 22 ]. Despite extensive data on HFNC outcomes, few studies have addressed how to predict HFNC failure before starting therapy. Most predictive models rely on post-HFNC metrics, missing a critical opportunity to guide initial therapy [ 11 – 13 ]. Notably, in a study by Kang et al. (2024), the ROX and ROX-HR indices (adjusted for heart rate), calculated before initiating HFNC in AHRF patients were modest predictors of failure [ 16 ]. Also, Yu et al. conducted a retrospective analysis of 69 adults with COVID‑19 and found lower ventilation in COVID-19 estimation (VICE) scores and higher ROX indices at baseline among patients succeeded to HFNC [ 23 ]. In this context, we evaluated whether triage-based clinical parameters obtained prior to HFNC initiation could predict respiratory outcomes in COVID-19 patients. This research yielded interesting clinical differences between patients who started early on IMV, those who received HFNC, and those who failed this treatment. Our findings demonstrate that early risk stratification is feasible and clinically informative, supporting the notion that not all patients with AHRF are appropriate candidates for a HFNC trial, and that in certain high-risk presentations, immediate intubation may be a more appropriate initial strategy [ 22 , 24 ]. Patients in the early IMV group presented with a profile of severe respiratory compromise and elevated markers of cardiac or tissue injury. Interestingly, this group had a lower prevalence of ILD and SAH, despite both conditions being associated with reduced pulmonary reserve and a higher likelihood of respiratory decompensation. This paradox may be explained by selection bias, as clinicians often choose not to intubate patients with ILD due to the expectation of poor outcomes [ 25 ], leading instead to a preference for noninvasive or palliative approaches [ 26 ]. Moreover, the fact that this group did not undergo a trial of HFNC underscores the real-world decision-making process in which bedside clinicians recognized the severity of the disease and opted for early IMV. Our results align with prior evidence suggesting that delayed intubation in severely hypoxemic patients may increase mortality, particularly in settings of respiratory fatigue or hemodynamic instability [ 9 , 10 ]. Therefore, a data-driven approach to identifying patients unlikely to benefit from HFNC, based on simple triage parameters, may contribute to more timely and effective airway management. In contrast, patients who initially received HFNC but ultimately failed were less likely to present with severe hypoxemia at triage but showed signs of systemic deterioration, including elevated BUN, lymphopenia, and longer delays between symptom onset and hospital admission. These findings suggest that HFNC failure may be influenced not only by respiratory mechanics but also by underlying immunologic or metabolic dysfunction. For example, lymphopenia has been consistently associated with disease severity and adverse outcomes in COVID-19 [ 27 , 28 ]. While lymphopenia is frequently associated with adverse outcomes in univariate analyses, the predictive value of lymphopenia alone may be diminished when adjusted for other relevant factors such as age, comorbidities, and additional laboratory findings [ 29 ]. Similarly, renal dysfunction markers have been linked to both hypoperfusion and inflammation and may serve as indirect indicators of multiorgan vulnerability. Multiple studies have shown that both serum and urinary biomarkers reflecting kidney injury, inflammation, and reduced perfusion, such as NGAL, KIM-1, MCP-1, soluble TNF receptors (sTNFR1 and sTNFR2), cystatin C, and LAP, are closely linked to adverse kidney outcomes in hospitalized patients with COVID-19 [ 30 – 33 ]. Elevated levels of these biomarkers reflect both tubular injury and systemic inflammation, and their combination improves risk stratification. Interestingly, the link between delayed presentation and HFNC failure identified in our study underscores the critical importance of early access to care and prompt escalation when needed. Multiple studies have shown that delays, whether in seeking medical attention, reaching a diagnosis, or transferring patients to higher levels of care, are associated with higher mortality, longer hospital and ICU stays, and increased use of healthcare resources. While there is limited direct evidence specifically tying delayed presentation to the emergency department with HFNC failure, some research suggests that postponing intubation beyond 6 to 48 hours after starting HFNC is linked to higher ICU mortality and poorer clinical outcomes, particularly in patients with acute respiratory failure, including those with COVID-19 [ 8 – 10 , 34 ]. Finally, patients who succeeded with HFNC had more favorable respiratory profiles and fewer abnormal laboratory values. This supports the role of HFNC in well-selected patients and highlights the importance of refined triage criteria. While tools like the ROX index are useful for monitoring therapy once started, they are less helpful for initial risk stratification. Our study complements existing models by focusing on predictors available before HFNC is initiated, offering a practical approach for guiding early management decisions. Several study limitations must be acknowledged. First, the retrospective, single-center design may limit the generalizability of our findings. Second, clinical decision-making regarding intubation in our study participants was based on physician judgment with some support from standardized protocols, introducing potential selection bias. In this matter, some patients who underwent early IMV might have potentially benefited from a HFNC trial. However, immediate IMV was prioritized based on clinical severity where HFNC would likely have been ineffective or risky. Third, given the observational nature of the study and limited sample size, only univariate logistic regressions were performed. This strategy mitigated the risk of model overfitting given the high number of potential predictors relative to the event rate. Therefore, our findings should be interpreted as hypothesis-generating, providing a foundation for future prospective studies designed to build predictive models with stronger statistical power. Moreover, although extensive, our dataset may not have captured all variables influencing outcomes. Additionally, our analyses were primarily powered to detect moderate-to-large associations. Smaller associations may have remained undetected, increasing the risk of type II error. Finally, although we reported the incidence of HAP and AKI, no statistical analyses were conducted to identify their predictors. This omission limits the clinical applicability of these secondary outcomes. Conclusions This study identified early clinical predictors associated with early IMV and HFNC failure in COVID-19 patients with AHRF. Variables such as older age, elevated RR, lower SpO₂, reduced PaO₂/FiO₂ ratio, low albumin, and elevated troponin were strongly linked to early respiratory deterioration and IMV, emphasizing the importance of evaluating both oxygenation status and systemic disease burden at triage. Intubation after a HFNC trial was associated with age, prior COT flow rates, urea, BUN, low lymphocyte count, and symptom duration. Our study brings together a group of simple and accessible clinical predictors from a retrospective COVID-19 cohort which can be used in the real world as they represent common variables assessed during the daily routine of respiratory emergency services. Our findings reinforce the need for early, comprehensive triage assessment to optimize respiratory management in AHRF. Recognition of key pre-treatment predictors may enable timely escalation of care, potentially improving patient outcomes. Abbreviations AHRF acute hypoxemic respiratory failure AKI acute kidney injury ALP alkaline phosphatase ALT alanine aminotransferase APACHE II Acute Physiology and Chronic Health Evaluation II aPTT activated partial thromboplastin time ARDS acute respiratory distress syndrome AST aspartate aminotransferase AUC area under the curve BE base excess BNP brain natriuretic peptide BUN blood urea nitrogen CI confidence interval COPD chronic obstructive pulmonary disease COT conventional oxygen therapy COVID-19 coronavirus disease 2019 CPK creatine phosphokinase Cr creatinine CRP C-reactive protein CT computed tomography DBP diastolic blood pressure DD D-dimer ED emergency department FDR false discovery rate FiO₂ fraction of inspired oxygen GCS Glasgow Coma Scale GGT gamma-glutamyl transferase HAP hospital-acquired pneumonia Hb hemoglobin HCO₃⁻ bicarbonate HFNC high-flow nasal cannula HR heart rate Htc hematocrit IC intensive care IMV invasive mechanical ventilation INR international normalized ratio IQR interquartile range KDIGO Kidney Disease: Improving Global Outcomes KIM-1 kidney injury molecule-1 LAP leucine aminopeptidase LDH lactate dehydrogenase MAP mean arterial pressure MCP-1 monocyte chemoattractant protein-1 MuLBSTA MuLBSTA score for viral pneumonia NGAL neutrophil gelatinase-associated lipocalin NIV non-invasive ventilation OSA obstructive sleep apnea PaO₂/FiO₂ arterial oxygen partial pressure to inspired oxygen fraction ratio PCO₂ partial pressure of carbon dioxide PCT procalcitonin PO₂ partial pressure of oxygen PSI Pneumonia Severity Index PT prothrombin time ROC receiver operating characteristic ROX index ratio of SpO₂/FiO₂ to respiratory rate RR respiratory rate SBP systolic blood pressure SMART-COP systolic blood pressure,multilobar infiltrates,albumin,respiratory rate,tachycardia,confusion,oxygenation,and pH score SO₂ arterial oxygen saturation SOFA Sequential Organ Failure Assessment SpO₂ peripheral oxygen saturation sTNFR1 soluble tumor necrosis factor receptor 1 sTNFR2 soluble tumor necrosis factor receptor 2 VICE Ventilation in COVID-19 Estimation score. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board and Ethics Committee of the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas” (INER), Mexico City. All study participants provided written informed consent. Given the retrospective observational design and the use of anonymized data, trial registration was not required. Consent for publication Not applicable. This study did not include any identifiable individual patient data. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution JACP: study design, supervision, writing, editing, statistical analysis, final review, and approval. DALR: study design, case follow-up, and data collection. JLCA: manuscript review, editing, corrections, final review, and approval. MARA: case follow-up, data collection, final review, and approval. KBP: case follow-up, data collection, final review, and approval. MSV: case follow-up, data collection, final review, and approval. AAG: case follow-up, data collection, final review, and approval. AHGM: case follow-up, data collection, final review, and approval. GBV: case follow-up, data collection, final review, and approval. JFAV: case follow-up, data collection, final review, and approval. IBG: case follow-up, data collection, final review, and approval. GLV: case follow-up, data collection, final review, and approval. AYGT: case follow-up, data collection, final review, and approval. JDCA: supervision, case follow-up, data collection, final review, and approval. CMHC: supervision, case follow-up, data collection, final review, and approval. MCL: study design, supervision, editing, statistical analysis, final review, and approval. Acknowledgements None. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Baldomero AK, Melzer AC, Greer N, Majeski BN, MacDonald R, Linskens EJ, Wilt TJ. Effectiveness and Harms of High-Flow Nasal Oxygen for Acute Respiratory Failure: An Evidence Report for a Clinical Guideline From the American College of Physicians. Ann Intern Med. 2021;174(7):952–66. Roca O, Li J, Mauri T. High-flow nasal cannula: evolving practices and novel clinical and physiological insights. Intensive Care Med. 2024;50(5):758–61. Le Pape S, Savart S, Arrivé F, Frat JP, Ragot S, Coudroy R, Thille AW. High-flow nasal cannula oxygen versus conventional oxygen therapy for acute respiratory failure due to COVID-19: a systematic review and meta-analysis. Ann Intensive Care. 2023;13(1):114. Li Y, Li C, Chang W, Liu L. 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Hernández-Cárdenas","email":"","orcid":"","institution":"Instituto Nacional de Enfermedades Respiratorias","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"M.","lastName":"Hernández-Cárdenas","suffix":""},{"id":578803472,"identity":"646077be-498a-4ac4-adfc-a9d1a408f6b3","order_by":15,"name":"Manuel Castillejos-López","email":"","orcid":"","institution":"Instituto Nacional de Enfermedades Respiratorias","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Castillejos-López","suffix":""}],"badges":[],"createdAt":"2025-12-22 19:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8427896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8427896/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101205457,"identity":"6b8f2aaa-dd3e-4d61-b366-ad15af01bd2e","added_by":"auto","created_at":"2026-01-27 09:49:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":199932,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/716cb0088c684ca808b284cc.docx"},{"id":101067590,"identity":"6fcd742c-abb5-4c94-b7e9-80175d7c8107","added_by":"auto","created_at":"2026-01-25 07:13:46","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16120,"visible":true,"origin":"","legend":"","description":"","filename":"2a7bf97ef9b949758ed5d5bbe282e874.json","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/0f46fddd69b8419732b4d554.json"},{"id":101067595,"identity":"165ee197-5530-41f9-bfec-c48f61660e1d","added_by":"auto","created_at":"2026-01-25 07:13:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44350,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental.docx","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/bc8d451b3acd1da17dce8640.docx"},{"id":101067598,"identity":"fc29d6b9-563a-4932-8035-4da92d42aa2c","added_by":"auto","created_at":"2026-01-25 07:13:47","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174355,"visible":true,"origin":"","legend":"","description":"","filename":"2a7bf97ef9b949758ed5d5bbe282e8741enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/61ae3169dab688cf5f5f8ae8.xml"},{"id":101067593,"identity":"230e176c-5dfc-4beb-9f9c-34b9ed2faed8","added_by":"auto","created_at":"2026-01-25 07:13:46","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20863,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/a1b686d363f3351157bb6f30.png"},{"id":101205715,"identity":"1a777c59-9608-4cba-8160-16479e42b5b3","added_by":"auto","created_at":"2026-01-27 09:50:11","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170895,"visible":true,"origin":"","legend":"","description":"","filename":"2a7bf97ef9b949758ed5d5bbe282e8741structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/6e37febfcb3aedde39c0b168.xml"},{"id":101067596,"identity":"342c5e3e-414f-4b7c-86db-af7dce521495","added_by":"auto","created_at":"2026-01-25 07:13:46","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185649,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/077ede7449fb66d2a2ce739b.html"},{"id":101205476,"identity":"811cb52f-7f69-42a4-8ead-66baba93cf63","added_by":"auto","created_at":"2026-01-27 09:49:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of independent predictors of early IMV and HFNC failure.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOdds ratios (OR) with 95% confidence intervals are shown for variables associated with early IMV and HFNC failure, based on multivariate logistic regression models. For early IMV, significant predictors included male gender, interstitial lung disease (ILD), systemic arterial hypertension (SAH), saturation of oxygen (SpO\u003csub\u003e2\u003c/sub\u003e), low PaO2/FiO2, low serum albumin, and increased levels of gamma-glutamyl transferase (GGT) and troponin. Also, higher scores in SOFA and SMART-COP were associated with early IMV. For HFNC failure, higher age, pre-HFNC oxygen flow on conventional oxygen therapy (COT) devices, elevated urea and BUN, reduced lymphocyte count, and longer symptoms duration before admission were associated with increased risk. The Pneumonia Severity Index (PSI) score was significant in both models. Specific OR values are shown in Table S1 of the supplementary material accompanying this manuscript.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/b328e1773e8176e296041a97.jpeg"},{"id":101208034,"identity":"8ae20e00-20d3-4be6-81f8-0d243638ada0","added_by":"auto","created_at":"2026-01-27 10:08:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1545952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/be9a65d7-b814-46f7-a0e2-8037f1cbb7c4.pdf"},{"id":101205525,"identity":"63429d4e-9afe-4f22-a2e2-840d0bd45769","added_by":"auto","created_at":"2026-01-27 09:49:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":44350,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental.docx","url":"https://assets-eu.researchsquare.com/files/rs-8427896/v1/12492cd16e10a98bd2b17840.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk of Early Intubation and High-Flow Nasal Cannula Failure in Pneumonia: Pre-Treatment Predictors using Triage Data from a Retrospective COVID-19 Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute hypoxemic respiratory failure (AHRF) remains a critical challenge in pulmonary medicine. High-flow nasal cannula (HFNC) oxygen therapy has gained widespread adoption given its physiological advantages, including: improved oxygenation, reduced respiratory effort, and enhanced patient comfort [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recently, this modality has become a cornerstone for COVID-19-associated AHRF over conventional oxygen therapy (COT) [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], showing similar efficacy compared to non-invasive ventilation (NIV) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, HFNC therapy is not universally successful, with significant failure rates that delay intubation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Remarkably, in patients with severe hypoxemia or in settings of COVID-19 outbreaks, late intubation may worsen outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hence, the timely identification of cases unlikely to benefit from HFNC is crucial to guide prompt escalation to invasive mechanical ventilation (IMV). Several clinical markers and predictive tools can assist clinicians in assessing HFNC efficacy and identifying seriously ill patients. However, most of them stratify patients risk based on continuous monitoring of respiratory parameter trends that become evident hours after HFNC therapy onset [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Conversely, only a few studies have investigated prognostic factors relying on clinical data available before initiating respiratory support [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings underscore the importance of integrating pre-treatment evaluations into predictive frameworks to identify at-risk patients early and provide a pivotal advantage in clinical decision-making.\u003c/p\u003e \u003cp\u003ePredictive factors in HFNC therapy have not been validated in our environment. Thus, the objective of this research was to describe the clinical characteristics and outcomes of patients with COVID-19-associated AHRF, and to identify early predictors of IMV and HFNC failure using data collected during the pre-treatment phase.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA retrospective cohort study was conducted in patients with AHRF secondary to COVID-19 pneumonia. Participants were evaluated at the emergency department (ED) of a national reference center for respiratory diseases and subsequently hospitalized between March 2020 and February 2021. This timeframe precedes the SARS-CoV-2 vaccination campaign in the region, avoiding biases associated with varying immunization schemes. During the COVID-19 outbreak, the ED adopted a structured yet agile protocol to evaluate patients presenting with respiratory symptoms, based on rapid categorization into priority levels of care. This protocol facilitated the timely identification and recruitment of eligible individuals for the study (\u003cb\u003eSupplementary Methods\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAdults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with confirmed COVID-19 via viral polymerase chain reaction testing of nasopharyngeal swab samples who received HFNC therapy and consented to participate were enrolled. A comparative group of patients who underwent early intubation without an initial HFNC trial (hereinafter named as the early IMV group) was included. Patients were assigned to this group if they presented clinical features deemed incompatible with safe initiation of HFNC, based on physician judgment on admission.\u003c/p\u003e \u003cp\u003ePatients were excluded if they had concurrent viral or bacterial infections on admission, primarily hypercapnic acute respiratory failure, solid organ transplants on immunosuppressive therapy, active hematologic malignancy, prior management at other facilities involving IMV or HFNC before initial triage assessment, or NIV during hospitalization before HFNC initiation. Individuals on HFNC or IMV who died within the first 24 hours of hospitalization were also excluded from the analysis.\u003c/p\u003e \u003cp\u003eAll complete medical records available in the center's electronic database during the study period were reviewed for eligible participants. All data were extracted using a standardized template by two independent investigators. Discrepancies were resolved by consensus with a third reviewer to ensure data accuracy, reduce extraction bias, guarantee uniformity in variable definitions, and minimize misclassification errors. Collected data included demographic characteristics, anthropometrics, comorbidities, symptoms, triage vital signs, chest CT imaging findings, admission severity scores, and details of prior oxygen therapy. Laboratory parameters obtained within 24 hours of admission and prior to HFNC initiation included white blood cell counts, markers of inflammation, liver and kidney function, blood gases, tissue injury markers, and other relevant tests. Patients were monitored throughout their hospitalization, with records of specific complications, antibiotic, corticosteroid, and antiviral use, intensive care interventions, duration of HFNC and IMV therapy.\u003c/p\u003e \u003cp\u003eThe primary outcome was the need for IMV before or after a HFNC trial. Criteria for intubation are described in \u003cb\u003eSupplementary Methods\u003c/b\u003e. Secondary outcomes included the incidence of hospital-acquired bacterial or fungal pneumonia (HAP) and acute kidney injury (AKI).\u003c/p\u003e \u003cp\u003eDescriptive statistics were used to characterize the study population. Categorical variables were reported as frequencies and proportions. Continuous variables were presented as medians with interquartile ranges (IQR) or 95% confidence intervals (CI). Group comparisons were performed using Fisher's exact test, unpaired Mann\u0026ndash;Whitney U test, or Kruskal\u0026ndash;Wallis test with post hoc Dunn\u0026rsquo;s analysis, as appropriate. No sample size calculation was performed because this study was a retrospective census including all consecutive patients who met eligibility criteria. To contextualize the interpretability of our findings, we conducted an exploratory post hoc power assessment. Logistic regression was employed to assess the association of pre-treatment prognostic factors with outcomes. Receiver Operating Characteristic (ROC) curve analyses evaluated the diagnostic accuracy of potential predictors, using the area under the curve (AUC). Prognostic factors were dichotomized based on thresholds determined by the Youden index for maximum diagnostic performance. Kaplan-Meier curves and time-to-event analyses were not performed due to the relatively short follow-up period.\u003c/p\u003e \u003cp\u003eAnalyses were conducted using GraphPad Prism (version 9.5, La Jolla, CA, USA) and R software (version 4.1.2, R Core Team, Vienna, Austria). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. False discovery rate (FDR) correction was applied to univariate logistic regression models using the Benjamini\u0026ndash;Hochberg method, and q-values were calculated. While results were primarily interpreted as suggestive of association based on unadjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify potential predictors of intubation for future validation, q-values (\u0026lt;\u0026thinsp;0.1) were reported to improve transparency and highlight potentially robust findings.\u003c/p\u003e \u003cp\u003e This study was approved by the institutional research and ethics committee under the protocol number E10-25.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 139 patients with confirmed COVID-19 met inclusion criteria and were eligible for analysis. This sample size was sufficient to detect clinically meaningful associations of moderate or greater magnitude (\u003cb\u003eSupplementary Results\u003c/b\u003e). The median age was 56 years, and 62% were male. Of these, 43 patients (31%) were intubated immediately after admission and included in the early IMV group, while 96 patients (69%) received HFNC. Among HFNC recipients, 32 patients (33%) experienced failure. Age and gender significantly differed between groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Frequent manifestations included dyspnea (72%), fever (64%), cough (48%), myalgias (42%), and headache (36%; \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The duration of symptoms from onset to admission was 8 days (6\u0026ndash;11 IQR), which differed significantly between groups: 9 (7-11.3) days in HFNC success, 7 (5-8.3) days in HFNC failure, and 8 (7-13.5) days in early IMV. The most prevalent comorbidities included obesity (45%), overweight (35%), hypertension (30%), diabetes (29%), smoking history (27%), interstitial lung disease (ILD; 8.6%), and chronic obstructive pulmonary disease (COPD; 5%). A history of asthma, obstructive sleep apnea (OSA), and alcohol use was also documented. Imaging studies revealed bilateral infiltrates in 96% of cases, ground-glass opacities in 92%, consolidation in 46%, crazy paving in 17%, and reticular changes or bronchiectasis in less than 10% (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Details on the duration and specific parameters of HFNC therapy and IMV support are described in \u003cb\u003eTable S2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and laboratory parameters of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;139\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c11\" namest=\"c10\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHFNC success,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHFNC failure,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;32\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eEarly IMV,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;43\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e56.0 (46.0, 64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e53.5 (42.8, 61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e60.0 (53.8, 67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e55.0 (44.0, 63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e86 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e33 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e18 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e35 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVital signs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15.0 (15.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15.0 (15.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e15.0 (15.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e15.0 (15.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e77.0 (67.0, 86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e83.0 (74.3, 88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e76.5 (65.8, 86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e70.0 (60.0, 79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e21.0 (21.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21.0 (21.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21.0 (21.0, 23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e21.0 (21.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR, rpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e29.0 (25.0, 35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e26.5 (23.8, 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e28.0 (25.8, 34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e32.0 (30.0, 38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR, bpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e100.0 (89.5, 108.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e98.0 (88.8, 107.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e92.0 (85.5, 102.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e104.0 (97.5, 112.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature, \u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e36.8 (36.5, 37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e36.8 (36.5, 37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e37.0 (36.5, 37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e37.0 (36.5, 37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e119.0 (109.5, 128.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e118.0 (110.0, 129.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e118.5 (109.3, 122.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e119.0 (106.5, 129.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e73.0 (69.0, 83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e75.0 (70.0, 84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e73.0 (67.8, 85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e72.0 (69.0, 79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e88.0 (81.0, 98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e88.2 (80.6, 99.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e87.2 (83.2, 97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e89.3 (81.9, 96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLaboratory parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucocytes, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8.9 (6.3, 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8.6 (5.8, 12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e8.1 (6.2, 12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e11.0 (7.9, 14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7.5 (5.3, 11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7.2 (4.7, 10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.8 (5.2, 11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e9.7 (6.5, 12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.7 (0.5, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.8 (0.6, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.6 (0.4, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.8 (0.5, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e14.8 (13.6, 16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15.1 (13.7, 16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e14.4 (13.2, 16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e14.4 (13.2, 15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHtc, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e43.7 (40.0, 47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e44.4 (40.9, 47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e41.5 (38.3, 47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e42.3 (37.7, 45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e225.0 (187.0, 283.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e220.5 (185.8, 247.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e203.0 (142.3, 248.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e246.0 (203.0, 297.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e122.0 (101.0, 166.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e119.5 (101.8, 148.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e127.5 (96.5, 176.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e121.0 (103.0, 192.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e39.0 (26.0, 54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e28.0 (21.0, 47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e45.0 (31.0, 57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e45.0 (32.1, 64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e18.5 (12.0, 25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e13.5 (10.0, 22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21.0 (14.5, 26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e21.0 (15.0, 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.9 (0.7, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.8 (0.7, 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.9 (0.7, 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.9 (0.8, 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.4 (3.4, 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.2 (3.5, 5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.4 (3.4, 5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4.9 (3.3, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.4 (3.0, 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.6 (3.3, 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.3 (3.0, 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.0 (2.6, 3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirrubin, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.6 (0.5, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.6 (0.5, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.6 (0.5, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.7 (0.5, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e42.0 (33.0, 59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e36.0 (28.0, 50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e50.0 (35.0, 62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e46.0 (37.0, 64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e35.0 (23.0, 53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29.0 (20.8, 48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e35.8 (24.0, 51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e41.0 (30.3, 65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT, IU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e74.5 (36.3, 123.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e60.0 (30.5, 105.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e46.0 (33.0, 150.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95.0 (68.3, 198.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e455.5 (358.3, 562.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e422.0 (320.8, 532.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e483.0 (366.5, 551.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e504.0 (396.0, 672.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP, IU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e95.2 (71.0, 117.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e94.0 (65.8, 121.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e96.0 (72.0, 110.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e98.0 (72.0, 113.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPK, \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e96.7 (57.0, 195.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e84.0 (48.3, 140.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e133.0 (68.8, 237.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e124.0 (77.0, 211.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.2 (0.1, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1 (0.0, 0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.2 (0.1, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.3 (0.2, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e14.3 (9.5, 24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e11.6 (7.7, 21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e14.1 (12.2, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e22.7 (12.6, 31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin, ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6.5 (2.8, 34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.2 (2.0, 34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.0 (2.5, 10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e10.4 (4.4, 145.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMioglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e67.8 (33.8, 146.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e34.4 (22.9, 97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e118.3 (76.7, 196.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e51.2 (39.3, 186.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e24.9 (11.1, 83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e27.7 (10.6, 107.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e17.0 (10.4, 39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e37.5 (13.4, 107.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerritin, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e855.3 (405.7, 2,127.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e560.0 (278.9, 1,822.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e963.1 (547.5, 1,245.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1,520.3 (703.3, 2,693.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT, seconds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e14.6 (14.0, 15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e14.3 (13.5, 15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e14.3 (13.9, 15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e15.6 (14.7, 16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaPTT, seconds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e41.3 (36.0, 46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e40.4 (35.5, 46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e41.9 (35.4, 46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e41.7 (37.5, 46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.0 (1.0, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.0 (1.0, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.0 (1.0, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.1 (1.0, 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD, mcg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.7 (0.4, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.7 (0.4, 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.5 (0.4, 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.4 (0.7, 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e138.0 (135.0, 140.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e137.0 (135.0, 140.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e137.0 (134.0, 140.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e139.0 (134.5, 140.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.1 (3.8, 4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.1 (3.7, 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.1 (3.7, 4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4.1 (4.0, 4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e102.0 (100.0, 105.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e102.0 (100.0, 105.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e101.0 (99.5, 104.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e103.0 (100.0, 105.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8.3 (7.8, 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e8.4 (7.9, 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e8.4 (7.9, 8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e8.2 (7.7, 8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.0 (1.9, 2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.0 (1.9, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.1 (1.9, 2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.1 (2.0, 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3.3 (2.7, 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.2 (2.7, 3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.2 (2.7, 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.7 (3.2, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eArterial gases on admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7.5 (7.4, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7.5 (7.4, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.5 (7.4, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7.5 (7.4, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e30.8 (26.7, 37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29.7 (27.1, 36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e30.6 (26.5, 35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e32.8 (26.9, 38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e54.5 (41.7, 73.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e55.1 (45.0, 71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e63.8 (44.3, 75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e49.7 (36.8, 72.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e86.9 (77.6, 93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e86.0 (78.7, 92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e90.3 (79.7, 94.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e85.0 (74.5, 92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e21.8 (19.4, 24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e21.5 (19.4, 23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21.7 (19.7, 25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e22.2 (19.4, 24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e184.5 (130.8, 224.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e195.0 (162.5, 242.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e183.5 (147.5, 245.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e148.0 (110.0, 197.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBE, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.6 (-3.2, 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.5 (-3.1, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.1 (-2.0, 1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1.3 (-3.5, 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.3 (1.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.3 (0.9, 1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.2 (1.0, 1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.5 (1.1, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eThe data are displayed as median (IQR) or n (%); N is the total number of participants with available information. \u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Kruskal-Wallis rank sum test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test.\u003c/p\u003e \u003cp\u003eALP, alkaline phosphatase; ALT, alanine aminotransferase; aPTT, activated partial thromboplastin time; AST, aspartate aminotransferase; BE, base excess; BNP, brain natriuretic peptide; BUN, blood urea nitrogen; CPK, creatin phosphokinase; Cr, creatinin; CRP, C-reactive protein; DBP, diastolic blood pressure; DD, D-dimer; FiO\u003csub\u003e2\u003c/sub\u003e, fraction of inspired oxygen; GCS, Glasgow coma scale; GGT, gamma glutamil transpeptidase; Hb, hemoglobin; HCO\u003csub\u003e3\u003c/sub\u003e, bicarbonate; HR, heart rate; Htc, hematocrit; INR, international normalized ratio; LDH, lactate dehydrogenase; MAP, mean arterial pressure; PCO\u003csub\u003e2\u003c/sub\u003e, partial pressure of CO\u003csub\u003e2\u003c/sub\u003e; PCT, procalcitonin; PO\u003csub\u003e2\u003c/sub\u003e, partial pressure of O\u003csub\u003e2\u003c/sub\u003e; PT, prothrombin time; RR, respiratory rate; SBP, systolic blood pressure; SO\u003csub\u003e2\u003c/sub\u003e, saturation of oxygen in arterial blood; SpO\u003csub\u003e2\u003c/sub\u003e, pulse oximetry saturation of oxygen.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePatients in the early IMV group had distinct clinical features. They were significantly older, more frequently male, and had a lower prevalence of ILD and hypertension. At triage, they had higher respiratory rates (RR), lower SpO₂, and more profound hypoxemia measured by the PaO₂/FiO₂ ratio (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Laboratory findings revealed higher leukocyte counts, lower albumin levels, and higher inflammatory markers. Troponin and GGT were elevated in this group. Kidney function markers, including urea and BUN, were also higher. Severity scores reflected greater baseline risk in early IMV compared to the HFNC success group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nearly all early IMV patients met criteria for ARDS, with 19% classified as severe (PaO₂/FiO₂ \u0026lt;100).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdmission severity scores in study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;139\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHFNC success,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHFNC failure,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;32\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEarly IMV,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;43\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (2.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (2.0, 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (2.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0 (2.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHEII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 (7.0, 14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (6.0, 12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0 (7.8, 15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.0 (8.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.0 (55.0, 95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0 (47.0, 82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.0 (56.8, 106.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.0 (70.0, 110.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMART-COP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0 (3.0, 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (3.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (2.0, 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0 (4.0, 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuLBSTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0 (7.0, 11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0 (7.0, 11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.5 (9.0, 13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.0 (7.0, 11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARDS grade, PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo ARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (\u0026lt;\u0026thinsp;300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (\u0026lt;\u0026thinsp;200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere (\u0026lt;\u0026thinsp;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eThe data are displayed as median (IQR) or n (%); N is the total number of participants with available information. \u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Kruskal-Wallis rank sum test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test.\u003c/p\u003e \u003cp\u003eAPACHEII, Acute Physiology and Chronic Health disease Classification System II; ARDS, acute respiratory distress syndrome; MuLBSTA, MuLBSTA score for viral pneumonia; PSI, pneumonia severity index; SMART-COP, SMART-COP score for pneumonia severity; SOFA, Sequential Organ Failure Assessment score.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, patients in the HFNC failure group were significantly older and with a higher proportion of male compared with those who succeeded with HFNC. They also presented to the hospital earlier after symptom onset compared with the HFNC success group. In terms of comorbidities, ILD and COPD were more common among patients who failed HFNC. Similarly, the HFNC failure group exhibited lower SpO₂ and a higher RR at admission. Biochemical analysis revealed that HFNC failure patients had significantly higher markers of renal dysfunction. Albumin was lower, suggesting early catabolism. Inflammatory and tissue damage markers were also elevated in the HFNC failure group, including AST, ALT, PCT, and CRP. In addition, patients who failed HFNC had higher severity-of-illness scores at admission compared to the HFNC success group. The prevalence of ARDS in the HFNC failure group was high (83%), with 50% meeting moderate and 13% severe criteria based on PaO₂/FiO₂ ratio.\u003c/p\u003e \u003cp\u003eIn univariate logistic regression, key predictors of early IMV included low SpO₂, PaO₂/FiO₂, serum albumin, male sex, SOFA, and SMART-COP scores. Although marginally statistically significant in the regression model, elevated RR, GGT, troponin levels, and pneumonia severity index (PSI) scores were consistently higher in this group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cb\u003eTable S3\u003c/b\u003e). Late HFNC failure was associated with more gradual clinical deterioration. Significant predictors included age, BUN, lymphocyte count, and PSI scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cb\u003eTable S4\u003c/b\u003e). Additional observed trends included a higher oxygen flow requirement on COT devices before HFNC initiation and a longer duration of symptoms prior to hospital admission (\u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn ROC analysis, serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.4 g/dL had the highest AUC for discrimination of patients requiring early IMV, followed by RR\u0026thinsp;\u0026ge;\u0026thinsp;30 rpm, GGT\u0026thinsp;\u0026gt;\u0026thinsp;50 U/L, and SpO₂ \u0026lt; 80%. Although troponin had lower overall accuracy, it exhibited high specificity (88%) at a threshold of 42 ng/L, suggesting its value as a rule-in marker for early IMV. For HFNC failure, prior COT oxygen flow rate\u0026thinsp;\u0026gt;\u0026thinsp;6 L/min, urea\u0026thinsp;\u0026gt;\u0026thinsp;36 mg/dL, symptom duration\u0026thinsp;\u0026gt;\u0026thinsp;6 days, and lymphocyte count\u0026thinsp;\u0026lt;\u0026thinsp;0.56 \u0026times;10⁹/L demonstrated fair predictive performance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of early predictors of intubation and HFNC failure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly IMV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u0026ndash;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80%-85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46%-66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 breaths/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65%-89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55%-73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO₂/FiO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u0026ndash;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48%-76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59%-77%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026ndash;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4 g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64%-88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55%-73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73%-94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37%-58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28%-63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77%-94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026ndash;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50%-78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47%-67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMART-COP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65%-89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40%-60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026ndash;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70%-92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38%-58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHFNC failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76%-97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27%-50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior oxygen flow rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 L/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51%-85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50%-75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53%-84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50%-73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53%-84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49%-72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51\u0026ndash;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56 \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34%-66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67%-86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31%-64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72%-90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51%-82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49%-72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCut-off values were selected based on the optimal Youden index. Predictors are grouped according to the outcome of interest: early IMV (within the first 24 hours) and HFNC failure (defined as the need for delayed intubation after HFNC initiation). AUC, Area under the receiver operating characteristic curve; BUN, blood urea nitrogen; CI, Confidence interval; GGT, gamma-glutamyl transferase; HFNC, High-flow nasal cannula; IMV, Invasive mechanical ventilation PaO₂/FiO₂, arterial oxygen partial pressure to inspired oxygen fraction ratio; PSI, Pneumonia Severity Index; SMART-COP: Systolic blood pressure, Multilobar infiltrates, Albumin level, Respiratory rate, Tachycardia, Confusion, Oxygenation, and arterial pH score; SOFA: Sequential Organ Failure Assessment score; SpO₂, peripheral oxygen saturation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSecondary outcomes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. HAP occurred in 35% of early IMV, 19% of HFNC failure, and only 1.6% of HFNC success patients. AKI was more frequent in early IMV (14%) than in HFNC success group (1.6%). Medical management strategies required during hospitalization also differed according to respiratory support requirements. In-hospital antibiotic use, corticosteroid administration and antifungal therapy were significantly more common in the early IMV and HFNC failure groups (\u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecondary outcomes of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;139\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHFNC success,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHFNC failure,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;32\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEarly IMV,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;43\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected fungal infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfirmed fungal co-infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKDIGO 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKDIGO\u0026nbsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKDIGO 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReintubation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (NA%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMV free days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (2.0, 12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA (NA, NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.0 (4.0, 11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0 (0.0, 12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eThe data are displayed as median (IQR) or n (%); N is the total number of participants with available information.\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u0026nbsp;Kruskal-Wallis rank sum test; Pearson\u0026rsquo;s Chi-squared test; Fisher\u0026rsquo;s exact test.\u003c/p\u003e \u003cp\u003eAKI, acute kidney injury; HAP, hospital-acquired pneumonia; KDIGO, Kidney Disease: Improving Global Outcomes classification of AKI; IMV, invasive mechanical ventilation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eOdds ratios (OR) with 95% confidence intervals are shown for variables associated with early IMV and HFNC failure, based on multivariate logistic regression models. For early IMV, significant predictors included male gender, interstitial lung disease (ILD), systemic arterial hypertension (SAH), saturation of oxygen (SpO\u003csub\u003e2\u003c/sub\u003e), low PaO2/FiO2, low serum albumin, and increased levels of gamma-glutamyl transferase (GGT) and troponin. Also, higher scores in SOFA and SMART-COP were associated with early IMV. For HFNC failure, higher age, pre-HFNC oxygen flow on conventional oxygen therapy (COT) devices, elevated urea and BUN, reduced lymphocyte count, and longer symptoms duration before admission were associated with increased risk. The Pneumonia Severity Index (PSI) score was significant in both models. Specific OR values are shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e of the supplementary material accompanying this manuscript.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHFNC have become a cornerstone for oxygen therapy across diverse clinical contexts. The RENOVATE trial, a large multicenter randomized clinical trial, showed HFNC to be noninferior to NIV for preventing intubation or death in patients with AHRF, with the added benefit of improved comfort [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Systematic reviews have similarly found that HFNC reduces treatment failure compared to COT and offers outcomes comparable to NIV. In post-extubation patients, HFNC is also well-tolerated and effective, particularly in those with COPD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the COVID-19 pandemic, HFNC therapy was widely adopted. Meta-analyses showed comparable failure and mortality rates between HFNC, NIV, and COT [\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, retrospective studies have raised concern about the risks of delayed intubation following HFNC failure. In both general ICU and high-risk populations, prolonged use of HFNC before intubation has been associated with higher mortality, fewer ventilator-free days, and lower weaning success [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite extensive data on HFNC outcomes, few studies have addressed how to predict HFNC failure before starting therapy. Most predictive models rely on post-HFNC metrics, missing a critical opportunity to guide initial therapy [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Notably, in a study by Kang et al. (2024), the ROX and ROX-HR indices (adjusted for heart rate), calculated before initiating HFNC in AHRF patients were modest predictors of failure [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Also, Yu et al. conducted a retrospective analysis of 69 adults with COVID‑19 and found lower ventilation in COVID-19 estimation (VICE) scores and higher ROX indices at baseline among patients succeeded to HFNC [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, we evaluated whether triage-based clinical parameters obtained prior to HFNC initiation could predict respiratory outcomes in COVID-19 patients. This research yielded interesting clinical differences between patients who started early on IMV, those who received HFNC, and those who failed this treatment. Our findings demonstrate that early risk stratification is feasible and clinically informative, supporting the notion that not all patients with AHRF are appropriate candidates for a HFNC trial, and that in certain high-risk presentations, immediate intubation may be a more appropriate initial strategy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients in the early IMV group presented with a profile of severe respiratory compromise and elevated markers of cardiac or tissue injury. Interestingly, this group had a lower prevalence of ILD and SAH, despite both conditions being associated with reduced pulmonary reserve and a higher likelihood of respiratory decompensation. This paradox may be explained by selection bias, as clinicians often choose not to intubate patients with ILD due to the expectation of poor outcomes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], leading instead to a preference for noninvasive or palliative approaches [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, the fact that this group did not undergo a trial of HFNC underscores the real-world decision-making process in which bedside clinicians recognized the severity of the disease and opted for early IMV. Our results align with prior evidence suggesting that delayed intubation in severely hypoxemic patients may increase mortality, particularly in settings of respiratory fatigue or hemodynamic instability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, a data-driven approach to identifying patients unlikely to benefit from HFNC, based on simple triage parameters, may contribute to more timely and effective airway management.\u003c/p\u003e \u003cp\u003eIn contrast, patients who initially received HFNC but ultimately failed were less likely to present with severe hypoxemia at triage but showed signs of systemic deterioration, including elevated BUN, lymphopenia, and longer delays between symptom onset and hospital admission. These findings suggest that HFNC failure may be influenced not only by respiratory mechanics but also by underlying immunologic or metabolic dysfunction. For example, lymphopenia has been consistently associated with disease severity and adverse outcomes in COVID-19 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. While lymphopenia is frequently associated with adverse outcomes in univariate analyses, the predictive value of lymphopenia alone may be diminished when adjusted for other relevant factors such as age, comorbidities, and additional laboratory findings [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, renal dysfunction markers have been linked to both hypoperfusion and inflammation and may serve as indirect indicators of multiorgan vulnerability. Multiple studies have shown that both serum and urinary biomarkers reflecting kidney injury, inflammation, and reduced perfusion, such as NGAL, KIM-1, MCP-1, soluble TNF receptors (sTNFR1 and sTNFR2), cystatin C, and LAP, are closely linked to adverse kidney outcomes in hospitalized patients with COVID-19 [\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Elevated levels of these biomarkers reflect both tubular injury and systemic inflammation, and their combination improves risk stratification.\u003c/p\u003e \u003cp\u003eInterestingly, the link between delayed presentation and HFNC failure identified in our study underscores the critical importance of early access to care and prompt escalation when needed. Multiple studies have shown that delays, whether in seeking medical attention, reaching a diagnosis, or transferring patients to higher levels of care, are associated with higher mortality, longer hospital and ICU stays, and increased use of healthcare resources. While there is limited direct evidence specifically tying delayed presentation to the emergency department with HFNC failure, some research suggests that postponing intubation beyond 6 to 48 hours after starting HFNC is linked to higher ICU mortality and poorer clinical outcomes, particularly in patients with acute respiratory failure, including those with COVID-19 [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, patients who succeeded with HFNC had more favorable respiratory profiles and fewer abnormal laboratory values. This supports the role of HFNC in well-selected patients and highlights the importance of refined triage criteria. While tools like the ROX index are useful for monitoring therapy once started, they are less helpful for initial risk stratification. Our study complements existing models by focusing on predictors available before HFNC is initiated, offering a practical approach for guiding early management decisions.\u003c/p\u003e \u003cp\u003eSeveral study limitations must be acknowledged. First, the retrospective, single-center design may limit the generalizability of our findings. Second, clinical decision-making regarding intubation in our study participants was based on physician judgment with some support from standardized protocols, introducing potential selection bias. In this matter, some patients who underwent early IMV might have potentially benefited from a HFNC trial. However, immediate IMV was prioritized based on clinical severity where HFNC would likely have been ineffective or risky. Third, given the observational nature of the study and limited sample size, only univariate logistic regressions were performed. This strategy mitigated the risk of model overfitting given the high number of potential predictors relative to the event rate. Therefore, our findings should be interpreted as hypothesis-generating, providing a foundation for future prospective studies designed to build predictive models with stronger statistical power. Moreover, although extensive, our dataset may not have captured all variables influencing outcomes. Additionally, our analyses were primarily powered to detect moderate-to-large associations. Smaller associations may have remained undetected, increasing the risk of type II error. Finally, although we reported the incidence of HAP and AKI, no statistical analyses were conducted to identify their predictors. This omission limits the clinical applicability of these secondary outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study identified early clinical predictors associated with early IMV and HFNC failure in COVID-19 patients with AHRF. Variables such as older age, elevated RR, lower SpO₂, reduced PaO₂/FiO₂ ratio, low albumin, and elevated troponin were strongly linked to early respiratory deterioration and IMV, emphasizing the importance of evaluating both oxygenation status and systemic disease burden at triage. Intubation after a HFNC trial was associated with age, prior COT flow rates, urea, BUN, low lymphocyte count, and symptom duration.\u003c/p\u003e \u003cp\u003eOur study brings together a group of simple and accessible clinical predictors from a retrospective COVID-19 cohort which can be used in the real world as they represent common variables assessed during the daily routine of respiratory emergency services. Our findings reinforce the need for early, comprehensive triage assessment to optimize respiratory management in AHRF. Recognition of key pre-treatment predictors may enable timely escalation of care, potentially improving patient outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute hypoxemic respiratory failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute kidney injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealkaline phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPACHE II\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Physiology and Chronic Health Evaluation II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaPTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eactivated partial thromboplastin time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute respiratory distress syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebase excess\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebrain natriuretic peptide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eblood urea nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econventional oxygen therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoronavirus disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatine phosphokinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eD-dimer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eemergency department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFiO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efraction of inspired oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlasgow Coma Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGGT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egamma-glutamyl transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehospital-acquired pneumonia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCO₃⁻\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebicarbonate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHFNC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-flow nasal cannula\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eheart rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHtc\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehematocrit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintensive care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einvasive mechanical ventilation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einternational normalized ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKDIGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney Disease: Improving Global Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKIM-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ekidney injury molecule-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleucine aminopeptidase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elactate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean arterial pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCP-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emonocyte chemoattractant protein-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMuLBSTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMuLBSTA score for viral pneumonia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGAL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil gelatinase-associated lipocalin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-invasive ventilation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eobstructive sleep apnea\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePaO₂/FiO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earterial oxygen partial pressure to inspired oxygen fraction ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epartial pressure of carbon dioxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprocalcitonin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epartial pressure of oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePneumonia Severity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprothrombin time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROX index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eratio of SpO₂/FiO₂ to respiratory rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erespiratory rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMART-COP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure,multilobar infiltrates,albumin,respiratory rate,tachycardia,confusion,oxygenation,and pH score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earterial oxygen saturation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSpO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperipheral oxygen saturation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esTNFR1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esoluble tumor necrosis factor receptor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esTNFR2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esoluble tumor necrosis factor receptor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVICE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVentilation in COVID-19 Estimation score.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board and Ethics Committee of the Instituto Nacional de Enfermedades Respiratorias \u0026ldquo;Ismael Cos\u0026iacute;o Villegas\u0026rdquo; (INER), Mexico City. All study participants provided written informed consent. Given the retrospective observational design and the use of anonymized data, trial registration was not required.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable. This study did not include any identifiable individual patient data.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJACP: study design, supervision, writing, editing, statistical analysis, final review, and approval. DALR: study design, case follow-up, and data collection. JLCA: manuscript review, editing, corrections, final review, and approval. MARA: case follow-up, data collection, final review, and approval. KBP: case follow-up, data collection, final review, and approval. MSV: case follow-up, data collection, final review, and approval. AAG: case follow-up, data collection, final review, and approval. AHGM: case follow-up, data collection, final review, and approval. GBV: case follow-up, data collection, final review, and approval. JFAV: case follow-up, data collection, final review, and approval. IBG: case follow-up, data collection, final review, and approval. GLV: case follow-up, data collection, final review, and approval. AYGT: case follow-up, data collection, final review, and approval. JDCA: supervision, case follow-up, data collection, final review, and approval. CMHC: supervision, case follow-up, data collection, final review, and approval. MCL: study design, supervision, editing, statistical analysis, final review, and approval.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated 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\u003eBaldomero AK, Melzer AC, Greer N, Majeski BN, MacDonald R, Linskens EJ, Wilt TJ. Effectiveness and Harms of High-Flow Nasal Oxygen for Acute Respiratory Failure: An Evidence Report for a Clinical Guideline From the American College of Physicians. Ann Intern Med. 2021;174(7):952\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoca O, Li J, Mauri T. High-flow nasal cannula: evolving practices and novel clinical and physiological insights. Intensive Care Med. 2024;50(5):758\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Pape S, Savart S, Arriv\u0026eacute; F, Frat JP, Ragot S, Coudroy R, Thille AW. High-flow nasal cannula oxygen versus conventional oxygen therapy for acute respiratory failure due to COVID-19: a systematic review and meta-analysis. Ann Intensive Care. 2023;13(1):114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Li C, Chang W, Liu L. High-flow nasal cannula reduces intubation rate in patients with COVID-19 with acute respiratory failure: a meta-analysis and systematic review. BMJ Open. 2023;13(3):e067879.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang JC, Peng Y, Dai B, Hou HJ, Zhao HW, Wang W, Tan W. Comparison between high-flow nasal cannula and conventional oxygen therapy in COVID-19 patients: a systematic review and meta-analysis. Ther Adv Respir Dis. 2024;18:17534666231225323.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeran A, Srour O, Malhas SE, Mhanna M, Ayesh H, Sajdeya O, Musallam R, Khokher W, Kalifa M, Srour K, et al. High-Flow Nasal Cannula Versus Noninvasive Ventilation in Patients With COVID-19. Respir Care. 2022;67(9):1177\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng Y, Dai B, Zhao HW, Wang W, Kang J, Hou HJ, Tan W. Comparison between high-flow nasal cannula and noninvasive ventilation in COVID-19 patients: a systematic review and meta-analysis. Ther Adv Respir Dis. 2022;16:17534666221113663.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang BJ, Koh Y, Lim CM, Huh JW, Baek S, Han M, Seo HS, Suh HJ, Seo GJ, Kim EY, et al. Failure of high-flow nasal cannula therapy may delay intubation and increase mortality. Intensive Care Med. 2015;41(4):623\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZablockis R, Šlekytė G, Mereškevičienė R, Kėvelaitienė K, Zablockienė B, Danila E. Predictors of Noninvasive Respiratory Support Failure in COVID-19 Patients: A Prospective Observational Study. Medicina. 2022;58(6):769.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNurok M, Friedman O, Driver M, Sun N, Kumaresan A, Chen P, Cheng S, Talmor DS, Ebinger J. Mechanically Ventilated Patients With Coronavirus Disease 2019 Had a Higher Chance of In-Hospital Death If Treated With High-Flow Nasal Cannula Oxygen Before Intubation. Anesth Analg. 2023;136(4):692\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoca O, Messika J, Caralt B, Garc\u0026iacute;a-de-Acilu M, Sztrymf B, Ricard JD, Masclans JR. Predicting success of high-flow nasal cannula in pneumonia patients with hypoxemic respiratory failure: The utility of the ROX index. J Crit Care. 2016;35:200\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoca O, Caralt B, Messika J, Samper M, Sztrymf B, Hern\u0026aacute;ndez G, Garc\u0026iacute;a-de-Acilu M, Frat JP, Masclans JR, Ricard JD. An Index Combining Respiratory Rate and Oxygenation to Predict Outcome of Nasal High-Flow Therapy. Am J Respir Crit Care Med. 2019;199(11):1368\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandel A, Patolia S, Brown AW, Collins AC, Sahjwani D, Khangoora V, Cameron PC, Desai M, Kasarabada A, Kilcullen JK, et al. High-Flow Nasal Cannula Therapy in COVID-19: Using the ROX Index to Predict Success. Respir Care. 2021;66(6):909\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu M, Zhou Q, Zheng R, Li X, Ling J, Chen Y, Jia J, Xie C. Application of high-flow nasal cannula in hypoxemic patients with COVID-19: a retrospective cohort study. BMC Pulm Med. 2020;20(1):324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalligaro GL, Lalla U, Audley G, Gina P, Miller MG, Mendelson M, Dlamini S, Wasserman S, Meintjes G, Peter J, et al. The utility of high-flow nasal oxygen for severe COVID-19 pneumonia in a resource-constrained setting: A multi-centre prospective observational study. EClinicalMedicine. 2020;28:100570.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang Y, Jung HM, Chung SP, Chung HS, Cho Y. Failure Prediction of High-Flow Nasal Cannula at the Conventional Oxygen Therapy Phase in the Emergency Department. Respiration. 2024;103(8):488\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamjai P, Hemvimol S, Bordeerat NK, Srimanote P, Angkasekwinai P. Evaluation of emerging inflammatory markers for predicting oxygen support requirement in COVID-19 patients. PLoS ONE. 2022;17(11):e0278145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObradović D, Milovančev A, Plećaš Đurić A, Sovilj-Gmizić S, Đurović V, Šović J, Đurđević M, Tubić S, Bulajić J, Mišić M, et al. High-Flow Nasal Cannula oxygen therapy in COVID-19: retrospective analysis of clinical outcomes - single center experience. Front Med (Lausanne). 2023;10:1244650.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInvestigators R, Authors, tB. High-Flow Nasal Oxygen vs Noninvasive Ventilation in Patients With Acute Respiratory Failure: The RENOVATE Randomized Clinical Trial. JAMA. 2025;333(10):875\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis SR, Baker PE, Parker R, Smith AF. High-flow nasal cannulae for respiratory support in adult intensive care patients. Cochrane Database Syst Rev. 2021;3(3):Cd010172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePisciotta W, Passannante A, Arina P, Alotaibi K, Ambler G, Arulkumaran N. High-flow nasal oxygen versus conventional oxygen therapy and noninvasive ventilation in COVID-19 respiratory failure: a systematic review and network meta-analysis of randomised controlled trials. Br J Anaesth. 2024;132(5):936\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaillard C, Lambert J, Tramier M, Chow-Chine L, Bisbal M, Servan L, Gonzalez F, de Guibert JM, Faucher M, Sannini A, et al. High-flow nasal cannula failure in critically ill cancer patients with acute respiratory failure: Moving from avoiding intubation to avoiding delayed intubation. PLoS ONE. 2022;17(6):e0270138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu PT, Chen CH, Wang CJ, Kuo KC, Wu JC, Chung HP, Chen YT, Tang YH, Chang WK, Lin CY, et al. Predicting the successful application of high-flow nasal oxygen cannula in patients with COVID-19 respiratory failure: a retrospective analysis. Expert Rev Respir Med. 2023;17(4):319\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsquinas AM, Parke R, Gifford AH. Failure of high-flow nasal cannula and delayed intubation: a new harmful sequence? Intensive Care Med. 2015;41(6):1170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRush B, Wiskar K, Berger L, Griesdale D. The use of mechanical ventilation in patients with idiopathic pulmonary fibrosis in the United States: A nationwide retrospective cohort analysis. Respir Med. 2016;111:72\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNolan TJ, Dwyer I, Geoghegan P. The use of mechanical ventilation in interstitial lung disease. Breathe (Sheff). 2025;21(2):240172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Li H, Luo M, Liu J, Wu L, Lin X, Li R, Wang Z, Zhong H, Zheng W, et al. Lymphopenia predicted illness severity and recovery in patients with COVID-19: A single-center, retrospective study. PLoS ONE. 2020;15(11):e0241659.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang G, Kovalic AJ, Graber CJ. Prognostic Value of Leukocytosis and Lymphopenia for Coronavirus Disease Severity. Emerg Infect Dis. 2020;26(8):1839\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHastak P, Cromer D, Malycha J, Andersen CR, Raith E, Davenport MP, Plummer M, Sasson SC. Defining the correlates of lymphopenia and independent predictors of poor clinical outcome in adults hospitalized with COVID-19 in Australia. Sci Rep. 2024;14(1):11102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorell-Garcia D, Ramos-Chavarino D, Bau\u0026ccedil;a JM, Del Argente P, Ballesteros-Vizoso MA, Garc\u0026iacute;a de Guadiana-Romualdo L, G\u0026oacute;mez-Cobo C, Pou JA et al. Amezaga-Men\u0026eacute;ndez R, Alonso-Fern\u0026aacute;ndez A : Urine biomarkers for the prediction of mortality in COVID-19 hospitalized patients. \u003cem\u003eSci Rep\u003c/em\u003e 2021, 11(1):11134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenez S, Moledina DG, Thiessen-Philbrook H, Wilson FP, Obeid W, Simonov M, Yamamoto Y, Corona-Villalobos CP, Chang C, Garibaldi BT, et al. Prognostic Significance of Urinary Biomarkers in Patients Hospitalized With COVID-19. Am J Kidney Dis. 2022;79(2):257\u0026ndash;e267251.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLablad Y, Vanhomwegen C, De Prez E, Antoine MH, Hasan S, Baudoux T, Nortier J. Longitudinal Follow-Up of Serum and Urine Biomarkers Indicative of COVID-19-Associated Acute Kidney Injury: Diagnostic and Prognostic Impacts. Int J Mol Sci 2023, 24(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenez S, Coca SG, Moledina DG, Wen Y, Chan L, Thiessen-Philbrook H, Obeid W, Garibaldi BT, Azeloglu EU, Ugwuowo U, et al. Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19. Am J Kidney Dis. 2023;82(3):322\u0026ndash;e332321.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishikimi M, Nishida K, Shindo Y, Shoaib M, Kasugai D, Yasuda Y, Higashi M, Numaguchi A, Yamamoto T, Matsui S, et al. Failure of non-invasive respiratory support after 6 hours from initiation is associated with ICU mortality. PLoS ONE. 2021;16(4):e0251030.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-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":"COVID-19 pneumonia, high-flow nasal cannula, invasive mechanical ventilation, acute hypoxemic respiratory failure, predictive factors, early intubation","lastPublishedDoi":"10.21203/rs.3.rs-8427896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8427896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite high-flow nasal cannula (HFNC) having improved outcomes in acute hypoxemic respiratory failure (AHRF), identifying patients unlikely to benefit before therapy initiation remains crucial to avoid delays in invasive mechanical ventilation (IMV). The objective of this study is to describe the clinical characteristics and outcomes of patients with AHRF due to COVID-19 pneumonia, and to identify predictors of early IMV and HFNC failure.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective study of patients with AHRF secondary to COVID-19 pneumonia evaluated between March 2020 and February 2021. Clinical data were collected during the pre-treatment phase in the emergency department. Study groups included early IMV, HFNC success, and HFNC failure, according to outcomes. The inclusion of patients needing early IMV aimed to capture clinical profiles in which immediate intubation was deemed necessary on admission, precluding the safe initiation of HFNC. Prognostic factors were explored using univariate logistic regression analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study enrolled 139 patients (62% male, aged 56 years). Among them, 75 required IMV, 43 in the early IMV group and 32 classified as HFNC failure. Early IMV was associated with age, male sex, SpO₂, PaO₂/FiO₂ ratio, albumin levels, respiratory rate, troponin, and pneumonia severity indices. HFNC failure was associated with age, prior conventional oxygen therapy flow rates, urea, BUN, low lymphocyte count, and symptoms duration.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEarly clinical variables evaluated at triage can help predict the need for immediate IMV and HFNC failure, supporting timely clinical decision-making in patients with AHRF.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Risk of Early Intubation and High-Flow Nasal Cannula Failure in Pneumonia: Pre-Treatment Predictors using Triage Data from a Retrospective COVID-19 Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-25 07:13:36","doi":"10.21203/rs.3.rs-8427896/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-31T12:38:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322449787005643997064423660129330908819","date":"2026-01-21T01:14:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T11:01:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-29T09:57:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-27T13:19:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-27T13:18:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-12-22T18:57:38+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":"3a8583f8-309b-4590-bfed-fe066abaefd5","owner":[],"postedDate":"January 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-25T07:13:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-25 07:13:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8427896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8427896","identity":"rs-8427896","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}