Etiologies and Outcomes of Acute Respiratory Failure in Patients Treated with Immune Checkpoint Inhibitors

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Psoter, Aliyah Pabani, Cheng Ting Lin, Mohammed Ghanbar, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9096889/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Indications for immune checkpoint inhibitors (ICIs) are expanding across cancer types and stages. While this carries promise for improved survival, increasing use has raised concern for respiratory complications in patients with multi-morbidities. Pulmonary complications such as checkpoint inhibitor pneumonitis (CIP), a recognized immune-related ICI toxicity, can lead to severe acute respiratory failure (ARF). However, not all ARF following ICI exposure is attributable to CIP, and while different causes imply distinct management and prognostic implications, the types of complications leading to ARF after ICI remain poorly understood. Methods We conducted a retrospective cohort study of ICI-treated adults who developed ARF, defined as ≥ 24 hours of invasive or noninvasive ventilatory support within 2 years of first ICI receipt, treated at five hospitals in a single health system (2017–2024). ARF etiology was determined by manual chart review, and suspected CIP cases underwent multidisciplinary adjudication. We evaluated associations between CIP versus non-CIP ARF and 90-day all-cause mortality using multivariable Cox proportional hazards models. Results Among 197 ICI-treated patients with ARF, the median age was 66 years, and the most frequent cancer type was non-small cell lung cancer. Among these patients, 29 (15%) had CIP, and 168 (85%) had other etiologies of ARF (e.g., pneumonia, progressive cancer, aspiration, fluid overload). In the first 24 hours, CIP patients exhibited more severe hypoxemia but less multiorgan dysfunction than non-CIP patients. Ninety-day mortality was 66% for CIP and 71% for non-CIP ARF. In adjusted analyses, CIP vs. non-CIP ARF was not significantly associated with 90-day mortality (adjusted hazard ratio [aHR] = 0.66; 95% CI: 0.40–1.08) but was associated with decreased in-hospital mortality (aHR = 0.43; 95% CI: 0.21–0.88). Among ARF survivors, 13% restarted ICI therapy: 5% of adjudicated CIP vs 17% of adjudicated non-CIP (p = 0.28); only 7% of suspected but non-adjudicated CIP cases restarted ICI. Conclusions In this cohort of ICI-treated patients who developed acute respiratory failure, most ARF was not attributable to CIP. While CIP and non-CIP ARF had similar 90-day mortality, CIP-related respiratory failure was associated with lower in-hospital mortality and lower likelihood of ICI continuation, underscoring the importance of accurate etiologic attribution in this vulnerable group. immune checkpoint inhibitors respiratory failure checkpoint inhibitor pneumonitis Figures Figure 1 Figure 2 Figure 3 BACKGROUND Immune checkpoint inhibitors (ICIs) are a class of cancer therapeutics that can generate durable responses in difficult-to-treat malignancies. In 2017, approximately 44% of patients with cancer in the United States were eligible for ICI therapy, and indications have continued to expand since that time and across a spectrum of strategies from curative to palliative intent. 1 – 3 As ICIs are increasingly used across cancer types and in patients with multiple comorbidities, understanding the presentation and targeted management of treatment-related toxicities is an important clinical consideration. 4 – 8 While pulmonary complications of ICI-therapy have been described, there is little known about which complications yield severe acute respiratory failure (ARF), which is generally likely to be life-threatening, and may have significant implications for survivors in regards to ongoing cancer care. 7 In oncologic patients broadly, ARF has many causes, including infections, pulmonary embolism, exacerbation of underlying lung disease, and progressive malignancy. In ICI-treated patients, ICI-related specific pulmonary toxicities include inflammatory lung injury, termed checkpoint inhibitor pneumonitis (CIP), in addition to other inflammatory-related sequelae such as sarcoid-like granulomatous disease, and serositis-related pleural effusions. In addition, non-pulmonary immune-related adverse events, including myocarditis and neuromuscular toxicity may precipitate ARF indirectly and further complicate diagnostic and management decisions in ICI treated patients presenting with ARF. 9 – 19 While, these etiologies of ARF differ substantially in diagnostic approach, management strategies, and implications for continuation of ICI therapy, 20 the etiologic distribution, and outcomes of severe ARF following ICI exposure remain incompletely characterized. Limited data exist to inform whether outcomes differ when ARF is attributable to CIP versus alternative etiologies, particularly among patients with severe disease requiring ventilatory support. 14 , 20 – 22 To address this gap, we conducted a multihospital retrospective cohort study of ICI-treated patients with severe ARF to describe ARF etiologies and to evaluate the association between CIP versus non-CIP ARF and survival. We hypothesized that CIP-related ARF is associated with better survival compared to non-CIP-related ARF. METHODS Study Design and Cohort We conducted a retrospective cohort study across two academic and three community hospitals within the Johns Hopkins Health System (JHHS). Data were extracted from an electronic health-record (EHR) registry of patients ever admitted to intensive care unit or intermediate care units in the JHHS and was housed on the Johns Hopkins Precision Medicine Analytics Platform (PMAP). 23 We included adults (≥ 18 years old) treated with ICIs in an inpatient or outpatient JHHS setting between July 1, 2017, and December 31, 2024, and who developed ARF within 2 years after first ICI exposure. This time window for ARF was selected based on prior reports of the range for CIP-related respiratory failure. 24 We pragmatically defined ARF as receipt of advanced respiratory support, either invasive mechanical ventilation (IMV) or non-invasive ventilation (NIV), including non-invasive positive-pressure ventilation (NIPPV) or high-flow nasal oxygen (HFNO), for ≥ 24 continuous hours. This threshold excludes brief procedural use and selects for clinically significant ARF as signified by persistent respiratory support requirements. For NIV, breaks of ≤ 2 hours off NIV support were allowed to capture patients who are intermittently but persistently supported with NIV over a 24-hour period. All patients meeting initial inclusion criteria were manually chart reviewed by author R.S. (pulmonary and critical care physician), and patients supported with IMV for non-respiratory failure related reasons (e.g. post-procedural, altered mental status only) were excluded. The cause of ARF was adjudicated by manual chart review of clinical documentation, laboratory results, and imaging (author R.S). Although multiple etiologies could coexist, patients were classified into mutually exclusive groups according to the clinically-suspected primary clinical driver: aspiration, suspected CIP, neuromuscular weakness, pneumonia (defined as positive respiratory viral panel, and/or positive bacterial or fungal respiratory culture), progressive cancer, pulmonary embolism, sepsis-related respiratory failure (defined as positive non-respiratory culture with other signs of sepsis), or fluid overload. CIP was diagnosed in accordance with national and international guidelines, including using a best practice of multi-disciplinary adjudication. 17 , 25 – 27 Patients were initially classified as having suspected CIP if the treating clinical team documented concern for immune checkpoint inhibitor-associated pneumonitis and initiated management for CIP (e.g. corticosteroid therapy and/or discontinuation of immune checkpoint inhibitor therapy). For each suspected CIP patient, we retrospectively determined CIP diagnosis via multidisciplinary adjudication by a panel including two board-certified pulmonologists, one oncologist, and one thoracic radiologist. A CIP diagnosis required compatible clinical features (new or worsening dyspnea, cough, or hypoxemia), characteristic radiologic patterns (ground-glass opacities, patchy consolidations, or organizing-pneumonia pattern), and exclusion of alternative etiologies through targeted microbiologic testing, including bronchoalveolar lavage studies when available, and comprehensive assessment for alternative etiologies including tumor progression, or other treatment-related lung injury ( Appendix A ). 17, 25–27 We evaluated inter-rater reliability among multidisciplinary reviewers using Cohen’s κ statistic. Covariates We extracted covariates electronically from the EHR registry and via manual chart review. Extracted variables included demographic characteristics (age, sex, race, ethnicity, body mass index [BMI], smoking history, hospital type [academic/community]), and comorbidities (Elixhauser comorbidity count). Cancer-specific covariates included cancer type, stage, and presence of brain metastases. ICI-specific data included which ICI was administered, cumulative instances of any ICI administration, and timing of administrations. We also assessed for receipt of concurrent chemotherapy, thoracic radiation (palliative or definitive intent), or tyrosine kinase inhibitors (TKIs), as these have been linked to CIP in prior studies. 28 , 29 We included the following acute-illness variables: type of respiratory support (NIV or IMV), presence of viral infection, lowest oxygen saturation-to-fraction-of-inspired-oxygen ratio (SpO₂/F I O₂ [S/F]) within the first 24 hours of ARF, non-respiratory Sequential Organ Failure Assessment (SOFA) score, vasopressor use, and presence of other non-pulmonary immune related adverse event (irAE) during admission. We additionally captured data related to other therapies received during hospitalization including administration of intravenous antibiotics for ≥ 2 days after start of ARF, corticosteroid use and dosing (≥ 1 mg/kg methylprednisolone equivalent, which is the recommended dosing for treating CIP 30 ), and use of additional immunosuppressive therapies including intravenous immunoglobulin (IVIG), infliximab, tocilizumab, and mycophenolate mofetil. For SOFA sub-scores (central nervous system, cardiovascular, liver, coagulation, and renal), we imputed missing values as normal, otherwise no missing data were imputed, and we performed complete-case analyses throughout ( Table E1 ). Exposures and Outcomes The primary exposure was ARF from CIP versus non-CIP causes. The primary outcome was 90-day all-cause mortality following the onset of ARF. Vital status for patients discharged from the hospital alive before day 90 was determined by linkage to the national death records. 31 Secondary outcomes included in-hospital mortality, and time to hospital discharge accounting for competing risk of death. For time-to-event analyses, individuals entered risk sets at ARF onset and were followed until death or administrative censoring at 90 days for the primary outcome, until in-hospital death or discharge for in-hospital mortality, and until hospital discharge for length of stay, with death treated as a competing event using Fine-Gray sub-distribution hazard models. 32 Statistical Analysis We summarized and compared baseline characteristics and clinical outcomes between patients diagnosed with CIP vs non-CIP ARF using Wilcoxon rank sum, and χ² or Fisher exact tests for continuous and categorical variables, respectively. Time to death and discharge for the CIP and non-CIP ARF groups were plotted using Kaplan-Meier curves and compared using a log-rank test. We used unadjusted and multivariable Cox proportional-hazards regression to evaluate the association between CIP status and 90-day mortality as well as in-hospital mortality. Covariates for adjusted models were pre-specified based on prior literature and expert study team consensus and were guided by a directed acyclic graph ( Figure E1 ). These included age, sex, Elixhauser comorbidity count, smoking status, presence of other immune-related adverse events, cancer type (lung vs. other), and cancer stage. The proportional hazards assumption was verified for all models. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs), where a HR < 1.0 indicates a lower hazard of death (better survival). For the secondary outcome of time-to-hospital discharge alive, in-hospital death was treated as competing risk using a Fine-Gray sub-distribution model. 32 Competing risk model estimates are presented as sub-distribution hazard ratios (sHRs), in which a sHR > 1.0 indicates faster time to discharge alive. As exploratory analyses, we evaluated patterns of ICI rechallenge among patients who survived to hospital discharge after the initial ARF episode. We stratified these analyses by CIP versus non-CIP adjudicated diagnosis, and by treatment for suspected CIP versus not during hospitalization. We also conducted several sensitivity analyses. These included analyses adding additional covariates for prior cancer-directed therapies to assess for potential treatment-related confounding, and included: concurrent chemotherapy with the ICI, definitive thoracic radiation (defined as curative-intent radiation to the thorax) prior to ARF event, TKI exposure prior to ARF, and ICI exposure duration (number of days from first to last ICI dose prior to ARF). We also repeated the primary analysis in a subset of patients with non-small cell lung cancer (NSCLC), the most common underlying malignancy in this cohort, to evaluate whether findings were consistent within this more homogeneous cancer population. Lastly, we evaluated the primary and secondary outcomes in models stratified by oxygenation status using a cutoff of SpO₂/FiO₂ ≤ 148 versus > 148, values which were selected for their correlation as they are the threshold for severe ARDS oxygenation in newly published criteria. 33 A two-sided p value < 0.05 was considered statistically significant. Statistical analyses were conducted using Stata version 18.0 (StataCorp, College Station, TX). Reporting follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement ( Table E1 ). This study was approved by the Johns Hopkins Medicine Institutional Review Board (IRB00513735) with a waiver of informed consent for secondary analysis of de-identified data. RESULTS Among 3,011 patients in the registry who received an ICI in the JHHS, 197 (7%) met our definition of ARF requiring at least 24 hours of noninvasive or invasive ventilatory support for confirmed respiratory failure ( Figure 1 ). Fifty-three patients were treated for suspected CIP at the bedside and underwent multidisciplinary adjudication. Among patients treated for suspected CIP by their inpatient clinicians, 55% (29/53) were ultimately adjudicated as having CIP-related respiratory failure. Over the entire cohort, this yielded 15% (29/197) with CIP-related ARF, and 85% (168/197) with non-CIP ARF. Agreement between pulmonology consensus adjudication and oncology review was perfect (κ = 1.00), with similarly high agreement between pulmonology and radiology reviewers (κ = 0.91). Among those with non-CIP ARF, the etiologies were pneumonia (n=54), progressive cancer (n=37), fluid overload (n=29), aspiration (n=26), sepsis-related respiratory failure (n=9), pulmonary embolism (n=9), and neuromuscular causes (n=3) ( Figure 2 ). The distribution of primary cancer sites for the cohort is shown in Figure E2. Figure 1: Flow Diagram of Patient Cohort Selection Flow diagram showing selection of ICI-treated patients with acute respiratory failure requiring ≥24 hours of invasive or noninvasive ventilatory support. Of 3,011 ICU or intermediate-care admissions, 197 patients met ARF criteria and comprised the analytic cohort, including 29 cases of CIP-related ARF and 168 non-CIP cases. Figure 2: Distribution of Acute Respiratory Failure Etiologies Bar chart showing the number of patients by adjudicated cause of acute respiratory failure among immune checkpoint inhibitor–treated patients. The median patient age was 66 years (interquartile range [IQR]: 59-74). Aside from lung cancer being more frequent in patients with CIP versus non-CIP (62 vs. 35% of patients), other baseline variables were similar between groups ( Table 1 ). Patients in both groups had received a median of 3 ICI administrations prior to respiratory failure, and the distribution of initial ICI agents and classes, and total duration of ICI exposure were similar. The median time from first ICI exposure to ARF was 112 days in CIP (IQR: 56-209) and 138 days in non-CIP etiologies (IQR:54-332; p=0.44). CIP patients were more frequently managed with noninvasive ventilation alone (69% vs 45%), but more often experienced escalation from noninvasive to invasive ventilation compared with non-CIP patients (28% vs 11%; p<0.01). Although CIP patients had lower non-respiratory SOFA scores (median 2 vs 5; p<0.01), they presented with more severe hypoxemia, reflected by a lower nadir SpO₂/FiO₂ ratio in the first 24 hours (median of 120 vs 148; p=0.01). All CIP patients received systemic glucocorticoids compared with 68% of non-CIP patients (p<0.01), and among patients who received glucocorticoids, high-dose therapy (≥1 mg/kg methylprednisolone equivalent) was more common in CIP vs. non-CIP (90% vs 67%; p=0.02). Intravenous immunoglobulin was administered to 14% of CIP patients and 2% of non-CIP patients (p<0.01); use of other immunosuppressive agents was rare. Table 1: Clinical Characteristics of Acute Respiratory Failure Cases after Immune Checkpoint Inhibitor Therapy, Stratified by Etiology of Acute Respiratory Failure Total (n= 197) CIP-related ARF (n=29) Non-CIP ARF (n=168) P- value Baseline Characteristics Age at admission, years 66 (59-74) 70 (64-76) 65 (58-74) 0.11 Female 67 (34%) 8 (28%) 59 (35%) 0.43 Race 0.82 White 125 (64%) 17 (59%) 108 (64%) Black 55 (28%) 10 (35%) 45 (27%) Asian 12 (6%) 1 (3%) 11 (7%) Other 5 (2%) 1 (3%) 4 (2%) Hispanic Ethnicity 17 (9%) 1 (3%) 16 (10%) 0.28 Body mass index 25 (22-30) 25 (22-30) 25 (22-29) 0.90 Ever smoker 126 (64%) 21 (73%) 105 (63%) 0.30 Elixhauser comorbidity count 3 (1-5) 3 (2-5) 3 (1-5) 0.13 Lung Cancer 77 (39%) 18 (62%) 59 (35%) 0.01 Cancer Stage 0.91 I 4 (2%) 1 (3%) 3 (2%) II 11 (6%) 2 (7%) 9 (5%) III 33 (17%) 5 (17%) 28 (17%) IV 140 (71%) 19 (66%) 121 (72%) Incomplete/Unknown 9 (4%) 2 (7%) 7 (4%) Brain Mets 54 (27%) 7 (24%) 47 (28%) 0.67 Immune Checkpoint Inhibitor Therapy Characteristics Number of ICI Administrations Prior to Respiratory Failure 3 (2-8) 3 (2-5) 3 (2-9) 0.54 First ICI Administered 0.47 Atezolizumab 15 (8%) 3 (10%) 12 (7%) Cemiplimab 8 (4%) 0 8 (5%) Dostarlimab 1 (0.5%) 0 1 (0.5%) Durvalumab 15 (8%) 5 (17%) 10 (6%) Ipilimumab-Nivolumab 20 (10%) 4 (14%) 16 (10%) Nivolumab 34 (17%) 5 (17%) 29 (17%) Nivolumab-Relatlimab 2 (1%) 0 2 (1%) Pembrolizumab 101 (51%) 12 (42%) 89 (53%) Tremelimumab-Durvalumab 1 (0.5%) 0 1 (0.5%) Total duration of ICI exposure (time from first ICI receipt to last ICI receipt occurring before respiratory failure) (days) 52 (21-156) 43 (21-84) 54 (21-182) 0.27 Median time from first ICI to Respiratory Failure (days) 133 (54-316) 112 (56-209) 138 (54-332) 0.44 Other Cancer Therapies Chemotherapy concurrent with ICI 68 (35%) 11 (38%) 57 (34%) 0.68 Thoracic radiation therapy Palliative dose 28 (14%) 6 (21%) 22 (13%) 0.28 Definitive dose 24 (12%) 9 (31%) 15 (9%) <0.01 Prior TKI therapy 5 (3%) 1 (3%) 4 (2%) 0.74 Acute Respiratory Failure Event Characteristics Admitted to academic hospital 147 (75%) 21 (72%) 126 (75%) 0.77 Respiratory support Non-invasive Ventilation Only (NIV) 96 (49%) 20 (69%) 76 (45%) <0.01 Invasive Ventilation Only (IMV) 75 (38%) 1 (3%) 74 (44%) Both NIV and IMV 26 (13%) 8 (28%) 18 (11%) Respiratory virus positive 26 (13%) 3 (10%) 23 (14%) 0.62 Coronavirus 19 positive 13 (7%) 1 (3%) 12 (7%) 0.33 Lowest SpO2/FiO2 ratio in first 24 hours 136 (115-168) 120 (110-139) 148 (116-182) 0.01 Non-respiratory SOFA score 5 (2-7) 2 (1-5) 5 (2-8) <0.01 Vasopressor use 62 (32%) 4 (14%) 58 (35%) 0.03 Other iRAE (any organ) 13 (7%) 0 13 (8%) 0.12 Other Therapies Received During Hospitalization Intravenous antibiotics for ≥ 48 hours 129 (66%) 18 (62%) 111 (66%) 0.68 Any corticosteroid 141 (72%) 29 (100%) 114 (68%) <0.01 ≥ 1 mg/kg methylprednisolone equivalent 140 (71%) 26 (90%) 112 (67%) 0.02 Intravenous Immunoglobulin 7 (4%) 4 (14%) 3 (2%) <0.01 Infliximab 0 0 0 Tocilizumab 0 0 0 Mycophenolate mofetil 3 (2%) 0 3 (2%) 0.08 Data are shown as median (interquartile range) for continuous variables and n (%) for categorical variables. Abbreviations: ARF, acute respiratory failure; CIP, checkpoint inhibitor pneumonitis; ICI, immune checkpoint inhibitor; NIV, non-invasive ventilation; IMV, invasive mechanical ventilation; SOFA, Sequential Organ Failure Assessment; SpO₂/FiO₂, peripheral oxygen saturation to fraction of inspired oxygen ratio; iRAE, immune-related adverse event; TKI, tyrosine kinase inhibitor; COVID-19, coronavirus disease 2019. All-cause mortality by day 90 occurred in 65% of patients with CIP-related ARF and 71% of patients with non-CIP ARF ( Table 2, Figure 3 ). In multivariable Cox proportional hazards models, time to death within 90 days did not differ significantly between CIP and non-CIP ARF (adjusted HR [aHR] 0.66; 95% CI, 0.40-1.08). In-hospital mortality was 31% in patients with CIP and 49% in patients with non-CIP ARF and was associated with a lower hazard of in-hospital death compared with non-CIP ARF (aHR 0.43; 95% CI, 0.21-0.88). Accounting for the competing risk of in-hospital death, patients with CIP-related ARF had a shorter time to discharge alive compared to non-CIP (sHR:1.70; 95% CI, 1.02-2.84) (Figure 3). Table 2: Association Between CIP-Related Respiratory Failure and Outcomes Outcome CIP-related ARF Non-CIP-related ARF Unadjusted Analysis HR (95% CI) Multivariable Analysis* HR (95% CI) Primary Outcome 90-day all-cause mortality (n=188) 19 (65%) 119 (71%) 0.73 (0.45-1.18) 0.66 (0.40-1.08) Secondary Outcomes In-hospital mortality (n=188) 9 (31%) 82 (49%) 0.52 (0.26-1.04) 0.43 (0.21-0.88) Median Days [IQR] Median Days [IQR] Time to hospital discharge (n=188) 11 (9-14) 12 (7-27) -- SHR 1.70 (1.02-2.84) Sensitivity/Exploratory Analyses Analysis Including Pre-ARF Additional Cancer Therapeutics (n=188) -- -- -- 1.32 (0.75-2.31) NSCLC only (n=67) 15 (52%) 52 (31%) 0.59 (0.27-1.31) 0.74 (0.31-1.74) S/F ≤148 (n=96) 22 (23%) 74 (77%) 0.65 (0.35-1.19) 0.58 (0.31-1.09) S/F >148 (n=82) 7 (9%) 75 (91%) 0.98 (0.42-2.27) 1.29 (0.51-3.23) *Adjusted for age, sex, Elixhauser comorbidity count, smoking status, presence of other immune-related adverse events, lung, and cancer stage. Abbreviations: ARF, acute respiratory failure; CIP, checkpoint inhibitor pneumonitis; NSCLC, non-small cell lung cancer; SHR, sub-hazard ratio; S/F, SpO2/FiO2 Figure 3: Ninety-Day Outcomes After Acute Respiratory Failure by Checkpoint Inhibitor Pneumonitis (CIP) Status (A) Kaplan–Meier estimates of 90-day survival following acute respiratory failure among patients with adjudicated CIP versus non-CIP etiologies. Numbers at risk are displayed below the x-axis. (B) Cumulative incidence of discharge alive over 90 days, accounting for the competing risk of in-hospital death, stratified by CIP status. In patients who survived ARF hospitalization (n=106), a total of 14 patients (13.2%) restarted ICI therapy after recovery from ARF. Re-initiation of ICI occurred in 5.0% of adjudicated CIP cases who survived, and 16.9% of adjudicated non-CIP patients who were not treated for CIP (p=0.28). In patients in whom clinicians suspected and treated for CIP, but the multidisciplinary panel did not think was CIP, only 6.7% of survivors reinitiated ICI therapy. Among patients who restarted ICI therapy, the median time from recovery to rechallenge was 435 days. Sensitivity analyses adjusting for recent cancer-directed therapies, including concurrent chemotherapy, definitive thoracic radiation, recent tyrosine kinase inhibitor exposure, and total duration of ICI therapy prior to ARF, also resulted in a non-significant difference in 90-day survival for CIP versus non-CIP, but did suggest the possibility of treatment-related confounding with a point estimate favoring non-CIP 1.32 (95% CI, 0.75-2.31). In analyses restricted to patients with non-small cell lung cancer (n=67), the adjusted HR for 90-day mortality was 0.74 (95% CI, 0.31-1.74) ( Table 2, Figure E2) . When stratified by hypoxemia severity (SpO₂/FiO₂ ≤148 [n=96] vs >148 [n=82]), adjusted hazard ratios for 90-day mortality were 0.56 (95% CI, 0.30-1.03) and 1.34 (95% CI, 0.49-3.61) ( Table 2 , Figure E3) . CONCLUSION In this retrospective cohort of patients who developed ARF within two years of initiating ICI therapy, CIP accounted for a minority of ARF cases. Ninety-day survival did not differ significantly between CIP and non-CIP ARF; however, patients with CIP experienced lower in-hospital mortality and shorter hospital length of stay. ICI therapy was infrequently reinitiated among survivors. Overall, the high morbidity and mortality observed in this cohort underscore the severity of acute respiratory failure in patients receiving immunotherapy. Prior studies of CIP resulting in ARF, largely limited to NSCLC populations, have reported substantial in-hospital mortality and poor overall survival. In a recent cohort of patients with severe CIP, median hospital length of stay was 8 days, in-hospital mortality was 32%, and median overall survival was 4.4 months. 34 Another study similarly reported a 29% mortality rate among patients with grade 3-4 pneumonitis, with deaths occurring exclusively in severe cases. 35 In another real-world cohort, severe-grade CIP was associated with a median overall survival of 3.0 months and a CIP-related mortality rate of 22.7%. 36 The in-hospital mortality observed in our CIP-related ARF cohort (31%) is comparable to these prior reports. Importantly, however, our study included patients across cancer types and additionally evaluated 90-day mortality, demonstrating that nearly two-thirds of patients with CIP-related ARF died within 90 days, suggesting substantial intermediate-term mortality beyond the index hospitalization. Although CIP-related ARF was associated with lower in-hospital mortality, this short-term advantage did not translate into improved intermediate-term outcomes. This may reflect the potentially reversible nature of immune-mediated lung injury compared with other causes of ARF in this population, such as progressive malignancy or aspiration. However, outcomes following ICU-level ARF in oncology patients likely reflect not only the mechanism of lung injury but also the broader burden of malignancy and critical illness. 4 37 Notably, ICI therapy was infrequently reinitiated following ARF, even among patients ultimately adjudicated as not having CIP. The occurrence of ARF itself—independent of confirmed immune-mediated pneumonitis—appears to function as a clinical inflection point, frequently leading to permanent discontinuation of immunotherapy. Despite access to multidisciplinary immunotherapy toxicity expertise within our health system, diagnostic uncertainty was common. Among patients treated for suspected CIP, just over half (55%) were ultimately adjudicated as having CIP-related respiratory failure. This reflects the significant overlap in presenting features between CIP and alternative etiologies such as infection, tumor progression, and aspiration. 37, 38 Although multidisciplinary adjudication is increasingly recommended as a gold standard approach for CIP diagnosis, real-time access to such expertise remains limited in many settings. 17, 25-27 Structured diagnostic pathways incorporating radiology input, targeted microbiologic evaluation, and timely multidisciplinary review may help reduce misclassification, limit unnecessary immunosuppression, and preserve opportunities for ICI continuation when appropriate. This study has several limitations. The number of adjudicated CIP cases was modest, limiting statistical power and precision. As an observational study, residual confounding is possible despite multivariable adjustment, and unmeasured differences in underlying cancer burden, functional status, or frailty could have influenced both ARF etiology and mortality risk. Misclassification of CIP diagnosis remains possible despite multidisciplinary adjudication, reflecting the inherent diagnostic uncertainty of CIP in critically ill patients, and may have influenced observed associations between ARF etiology and outcomes. Finally, the single health-system design may limit generalizability to settings with different patient populations, ICU admission practices, or access to multidisciplinary immunotherapy toxicity expertise. In summary, ARF after ICI therapy arises from heterogeneous etiologies, with CIP accounting for a minority of cases. CIP-related ARF was not associated with differential 90-day survival but was associated with lower in-hospital mortality and lower subsequent ICI reinitiation. These findings underscore the clinical complexity of ARF in ICI-treated patients and highlight the importance of precise etiologic attribution to inform subsequent immunotherapy decision-making. Declarations Ethics approval and consent to participate This study was approved by the Johns Hopkins University School of Medicine Institutional Review Board. The requirement for informed consent was waived due to the retrospective design of the study and use of de-identified electronic health record data. Consent for publication Not applicable. Funding RS was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under institutional training grant T32HL007534-41 and individual fellowship award F32HL182250-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Author Contribution RS conceptualized the study, performed data analysis, and drafted the manuscript. KJP contributed to study design and statistical methodology. AP, CTL, and MG contributed to data interpretation and clinical adjudication. DNH and SD contributed to study design and critical revision of the manuscript. KS and CHH supervised the study, contributed to interpretation of results, and critically revised the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to institutional privacy regulations but are available from the corresponding author on reasonable request and with appropriate Institutional Review Board approval. References Haslam A, Gill J, Prasad V. Estimation of the Percentage of US Patients With Cancer Who Are Eligible for Immune Checkpoint Inhibitor Drugs. JAMA Netw Open. 2020;3(3):e200423. 10.1001/jamanetworkopen.2020.0423 . Twomey JD, Zhang B. Cancer Immunotherapy Update: FDA-Approved Checkpoint Inhibitors and Companion Diagnostics. AAPS J. 2021;23(2):39. 10.1208/s12248-021-00574-0 . Ma W, Xue R, Zhu Z, et al. Increasing cure rates of solid tumors by immune checkpoint inhibitors. Exp Hematol Oncol. 2023;12(1):10. 10.1186/s40164-023-00372-8 . Silverstein J, Wright F, Wang M, et al. Evaluating Survival After Hospitalization Due to Immune-Related Adverse Events From Checkpoint Inhibitors. Oncologist. 2023;28(10):e950–9. 10.1093/oncolo/oyad135 . Joseph A, Simonaggio A, Stoclin A, et al. Immune-related adverse events: a retrospective look into the future of oncology in the intensive care unit. Ann Intensive Care. 2020;10(1):143. 10.1186/s13613-020-00761-w . Lin L, Houwink API, van Dieren JM, et al. Treatment patterns and survival outcomes of patients admitted to the intensive care unit due to immune-related adverse events of immune checkpoint inhibitors. Cancer Med. 2024;13(12):e7302. 10.1002/cam4.7302 . Toffart AC, Meert AP, Wallet F, et al. ICU admission for solid cancer patients treated with immune checkpoint inhibitors. Ann Intensive Care. 2023;13(1):29. 10.1186/s13613-023-01122-z . Kerekes DM, Frey AE, Prsic EH, et al. Immunotherapy Initiation at the End of Life in Patients With Metastatic Cancer in the US. JAMA Oncol. 2024;10(3):342–51. 10.1001/jamaoncol.2023.6025 . Nair VS, Eaton K, McGarry Houghton A. A case series of morbid COPD exacerbations during immune checkpoint inhibitor therapy in cancer patients. Respir Med Case Rep. 2021;34:101541. 10.1016/j.rmcr.2021.101541 . Reuss JE, Kunk PR, Stowman AM, et al. Sarcoidosis in the setting of combination ipilimumab and nivolumab immunotherapy: a case report & review of the literature. J Immunother Cancer. 2016;4:94. 10.1186/s40425-016-0199-9 . Soto F, Torre-Sada LF, Mott FE, et al. Sarcoidosis and Airway Disease After Immune Checkpoint Inhibitor Therapy: Case Study and Review of the Literature. J Immunother Precis Oncol. 2023;6(2):111–6. 10.36401/JIPO-22-30 . Sumi T, Nagahisa Y, Matsuura K, et al. Successful management of severe bronchial asthma exacerbated by anti-PD-L1 treatment: A report of two cases. Respirol Case Rep. 2021;9(11):e0868. 10.1002/rcr2.868 . Liu H, Luo SX, Jie J, et al. Immune checkpoint inhibitors related respiratory disorders in patients with lung cancer: A meta-analysis of randomized controlled trials. Front Immunol. 2023;14:1115305. 10.3389/fimmu.2023.1115305 . Naidoo J, Wang X, Woo KM, et al. Pneumonitis in Patients Treated With Anti-Programmed Death-1/Programmed Death Ligand 1 Therapy. J Clin Oncol. 2017;35(7):709–17. 10.1200/JCO.2016.68.2005 . Taccone FS, Artigas AA, Sprung CL, et al. Characteristics and outcomes of cancer patients in European ICUs. Crit Care. 2009;13(1):R15. 10.1186/cc7713 . Sears CR, Peikert T, Possick JD, et al. Knowledge Gaps and Research Priorities in Immune Checkpoint Inhibitor-related Pneumonitis. An Official American Thoracic Society Research Statement. Am J Respir Crit Care Med. 2019;200(6):e31–43. 10.1164/rccm.201906-1202ST . Brahmer JR, Lacchetti C, Schneider BJ, et al. Management of Immune-Related Adverse Events in Patients Treated With Immune Checkpoint Inhibitor Therapy: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol. 2018;36(17):1714–68. 10.1200/JCO.2017.77.6385 . Ghanbar MI, Suresh K. Pulmonary toxicity of immune checkpoint immunotherapy. J Clin Invest. 2024;134(2). 10.1172/JCI170503 . Safa H, Johnson DH, Trinh VA, et al. Immune checkpoint inhibitor related myasthenia gravis: single center experience and systematic review of the literature. J Immunother Cancer. 2019;7(1):319. 10.1186/s40425-019-0774-y . Abdel-Rahman O, Fouad M. Risk of pneumonitis in cancer patients treated with immune checkpoint inhibitors: a meta-analysis. Ther Adv Respir Dis. 2016;10(3):183–93. 10.1177/1753465816636557 . Lin MX, Zang D, Liu CG, et al. Immune checkpoint inhibitor-related pneumonitis: research advances in prediction and management. Front Immunol. 2024;15:1266850. 10.3389/fimmu.2024.1266850 . Cadranel J, Canellas A, Matton L, et al. Pulmonary complications of immune checkpoint inhibitors in patients with nonsmall cell lung cancer. Eur Respir Rev. 2019;28(153). 10.1183/16000617.0058-2019 . University JH. Precision Medicine Portal. Available at: https://pm.jh.edu/ . Accessed. Suresh K, Voong KR, Shankar B, et al. Pneumonitis in Non-Small Cell Lung Cancer Patients Receiving Immune Checkpoint Immunotherapy: Incidence and Risk Factors. J Thorac Oncol. 2018;13(12):1930–9. 10.1016/j.jtho.2018.08.2035 . Brahmer JR, Abu-Sbeih H, Ascierto PA, et al. Society for Immunotherapy of Cancer (SITC) clinical practice guideline on immune checkpoint inhibitor-related adverse events. J Immunother Cancer. 2021;9(6):e002435. 10.1136/jitc-2021-002435 . Haanen J, Obeid M, Spain L, et al. Management of toxicities from immunotherapy: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up ☆.Annals of Oncology. 2022;33(12):1217–1238. doi: 10.1016/j.annonc.2022.10.001. Network NCC. Management of Immunotherapy Related Toxicities 2024(Version 2.2024). Oshima Y, Tanimoto T, Yuji K, et al. EGFR-TKI-Associated Interstitial Pneumonitis in Nivolumab-Treated Patients With Non-Small Cell Lung Cancer. JAMA Oncol. 2018;4(8):1112–5. 10.1001/jamaoncol.2017.4526 . Cheung JM, Waliany S, Yeap BY, et al. Adverse events associated with sequential immune checkpoint inhibitor and alectinib in patients with ALK-rearranged advanced non-small-cell lung cancer. ESMO Open. 2025;10(10):105842. 10.1016/j.esmoop.2025.105842 . Reuss JE, Suresh K, Naidoo J. Checkpoint Inhibitor Pneumonitis: Mechanisms, Characteristics, Management Strategies, and Beyond. Curr Oncol Rep. 2020;22(6):56. 10.1007/s11912-020-00920-z . LexisNexis. Death Records. Available at: https://supportcenter.lexisnexis.com/app/answers/answer_view/a_id/1084520/~/death-records . Accessed. Fine JP, Gray, Robert J. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94:496–509. 10.1080/01621459.1999.10474144 . Matthay MA, Arabi Y, Arroliga AC, et al. A New Global Definition of Acute Respiratory Distress Syndrome. Am J Respir Crit Care Med. 2024;209(1):37–47. 10.1164/rccm.202303-0558WS . Manzano JM, Sahar H, Aldrich J, et al. Treatment patterns and outcomes of high-grade immune checkpoint inhibitor-related pneumonitis in an oncology hospitalist service. Support Care Cancer. 2024;32(3):160. 10.1007/s00520-024-08361-1 . Pang L, Xie M, Ma X, et al. Clinical characteristics and therapeutic effects of checkpoint inhibitor-related pneumonitis in patients with non-small cell lung cancer. BMC Cancer. 2023;23(1):203. 10.1186/s12885-023-10649-0 . Tone M, Izumo T, Awano N, et al. High mortality and poor treatment efficacy of immune checkpoint inhibitors in patients with severe grade checkpoint inhibitor pneumonitis in non-small cell lung cancer. Thorac Cancer. 2019;10(10):2006–12. 10.1111/1759-7714.13187 . Hsiehchen D, Watters MK, Lu R, et al. Variation in the Assessment of Immune-Related Adverse Event Occurrence, Grade, and Timing in Patients Receiving Immune Checkpoint Inhibitors. JAMA Netw Open. 2019;2(9):e1911519. 10.1001/jamanetworkopen.2019.11519 . Hindocha S, Hunter B, Linton-Reid K, et al. Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis. Radiother Oncol. 2024;195:110266. 10.1016/j.radonc.2024.110266 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9096889","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627951783,"identity":"42ea85e0-9022-4030-9f7e-bd6e4d7e68e8","order_by":0,"name":"Rupali Sood","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoklEQVRIiWNgGAWjYLACngobZgMgLUGCljNppGrhbTnMQLwW8/7DBx+8bTjPbs7AfPA2DzFaZG6kJRvO3XGb2bKBLdmaKC0SEjxm0rxnbjMbHAAyiNPCfwaope0cUAv/NyK1MOSAtBwA2cJGpBYJoF/mnElmNjjMZmw5hziHAUPsTYVdssHx5oc33hCjBQaSGZhJUQ4CdqRqGAWjYBSMghEEAFewKwsdYX1BAAAAAElFTkSuQmCC","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":true,"prefix":"","firstName":"Rupali","middleName":"","lastName":"Sood","suffix":""},{"id":627951784,"identity":"d70d0113-c3b3-41bd-9645-e97e20258663","order_by":1,"name":"Kevin J. Psoter","email":"","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"J.","lastName":"Psoter","suffix":""},{"id":627951785,"identity":"7184222d-2b2c-460e-9d63-e5c97deb8086","order_by":2,"name":"Aliyah Pabani","email":"","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":false,"prefix":"","firstName":"Aliyah","middleName":"","lastName":"Pabani","suffix":""},{"id":627951786,"identity":"f4e1f6b9-9c5d-4b41-9815-222f975daaa9","order_by":3,"name":"Cheng Ting Lin","email":"","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"Ting","lastName":"Lin","suffix":""},{"id":627951787,"identity":"1cd371d0-8f66-4819-93a0-4512877d8dea","order_by":4,"name":"Mohammed Ghanbar","email":"","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Ghanbar","suffix":""},{"id":627951788,"identity":"c9b99a4c-6cd5-49b9-9eb5-2c5030dbcdcf","order_by":5,"name":"David N. Hager","email":"","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"N.","lastName":"Hager","suffix":""},{"id":627951789,"identity":"6d236dce-635e-4998-bd33-a97344117dcf","order_by":6,"name":"Sonye Danoff","email":"","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sonye","middleName":"","lastName":"Danoff","suffix":""},{"id":627951790,"identity":"49f424ea-c077-4954-bafd-1d394195525b","order_by":7,"name":"Karthik Suresh","email":"","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":false,"prefix":"","firstName":"Karthik","middleName":"","lastName":"Suresh","suffix":""},{"id":627951791,"identity":"25e621bf-a9c2-4933-9cf3-3e9adb269b34","order_by":8,"name":"Chad H. Hochberg","email":"","orcid":"","institution":"The Johns Hopkins Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chad","middleName":"H.","lastName":"Hochberg","suffix":""}],"badges":[],"createdAt":"2026-03-11 17:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9096889/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9096889/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107675448,"identity":"3d9dae90-0c51-4921-9510-3791ea0be7ce","added_by":"auto","created_at":"2026-04-24 00:42:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow Diagram of Patient Cohort Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlow diagram showing selection of ICI-treated patients with acute respiratory failure requiring ≥24 hours of invasive or noninvasive ventilatory support. Of 3,011 ICU or intermediate-care admissions, 197 patients met ARF criteria and comprised the analytic cohort, including 29 cases of CIP-related ARF and 168 non-CIP cases.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9096889/v1/452ebc03e5f93f1802a14a83.jpg"},{"id":107708064,"identity":"caa6bfe1-69b2-4f36-892a-cad6e513e367","added_by":"auto","created_at":"2026-04-24 09:21:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Acute Respiratory Failure Etiologies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar chart showing the number of patients by adjudicated cause of acute respiratory failure among immune checkpoint inhibitor–treated patients.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9096889/v1/643c8d26458c6917809ac5f2.jpg"},{"id":107675449,"identity":"0280772e-2cc0-4f0f-b83a-f9d210982337","added_by":"auto","created_at":"2026-04-24 00:42:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNinety-Day Outcomes After Acute Respiratory Failure by Checkpoint Inhibitor Pneumonitis (CIP) Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Kaplan–Meier estimates of 90-day survival following acute respiratory failure among patients with adjudicated CIP versus non-CIP etiologies. Numbers at risk are displayed below the x-axis.\u003c/p\u003e\n\u003cp\u003e(B) Cumulative incidence of discharge alive over 90 days, accounting for the competing risk of in-hospital death, stratified by CIP status.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9096889/v1/77f740a6ff7a53a383385e88.jpg"},{"id":107869637,"identity":"b0f940cc-57be-4ff5-a9a8-d1600e919a1d","added_by":"auto","created_at":"2026-04-27 07:37:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":582866,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9096889/v1/54a6603b-c9fd-4c21-898d-3fdfb47cf03b.pdf"},{"id":107675447,"identity":"e113bcf7-49af-483d-9b47-2533626503ae","added_by":"auto","created_at":"2026-04-24 00:42:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":873194,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9096889/v1/34464b5081a359034319d085.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Etiologies and Outcomes of Acute Respiratory Failure in Patients Treated with Immune Checkpoint Inhibitors","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eImmune checkpoint inhibitors (ICIs) are a class of cancer therapeutics that can generate durable responses in difficult-to-treat malignancies. In 2017, approximately 44% of patients with cancer in the United States were eligible for ICI therapy, and indications have continued to expand since that time and across a spectrum of strategies from curative to palliative intent.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e As ICIs are increasingly used across cancer types and in patients with multiple comorbidities, understanding the presentation and targeted management of treatment-related toxicities is an important clinical consideration.\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile pulmonary complications of ICI-therapy have been described, there is little known about which complications yield severe acute respiratory failure (ARF), which is generally likely to be life-threatening, and may have significant implications for survivors in regards to ongoing cancer care.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In oncologic patients broadly, ARF has many causes, including infections, pulmonary embolism, exacerbation of underlying lung disease, and progressive malignancy. In ICI-treated patients, ICI-related specific pulmonary toxicities include inflammatory lung injury, termed checkpoint inhibitor pneumonitis (CIP), in addition to other inflammatory-related sequelae such as sarcoid-like granulomatous disease, and serositis-related pleural effusions. In addition, non-pulmonary immune-related adverse events, including myocarditis and neuromuscular toxicity may precipitate ARF indirectly and further complicate diagnostic and management decisions in ICI treated patients presenting with ARF.\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e While, these etiologies of ARF differ substantially in diagnostic approach, management strategies, and implications for continuation of ICI therapy,\u003csup\u003e20\u003c/sup\u003e the etiologic distribution, and outcomes of severe ARF following ICI exposure remain incompletely characterized.\u003c/p\u003e \u003cp\u003eLimited data exist to inform whether outcomes differ when ARF is attributable to CIP versus alternative etiologies, particularly among patients with severe disease requiring ventilatory support.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e To address this gap, we conducted a multihospital retrospective cohort study of ICI-treated patients with severe ARF to describe ARF etiologies and to evaluate the association between CIP versus non-CIP ARF and survival. We hypothesized that CIP-related ARF is associated with better survival compared to non-CIP-related ARF.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Cohort\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study across two academic and three community hospitals within the Johns Hopkins Health System (JHHS). Data were extracted from an electronic health-record (EHR) registry of patients ever admitted to intensive care unit or intermediate care units in the JHHS and was housed on the Johns Hopkins Precision Medicine Analytics Platform (PMAP).\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e We included adults (\u0026ge;\u0026thinsp;18 years old) treated with ICIs in an inpatient or outpatient JHHS setting between July 1, 2017, and December 31, 2024, and who developed ARF within 2 years after first ICI exposure. This time window for ARF was selected based on prior reports of the range for CIP-related respiratory failure.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e We pragmatically defined ARF as receipt of advanced respiratory support, either invasive mechanical ventilation (IMV) or non-invasive ventilation (NIV), including non-invasive positive-pressure ventilation (NIPPV) or high-flow nasal oxygen (HFNO), for \u0026ge;\u0026thinsp;24 continuous hours. This threshold excludes brief procedural use and selects for clinically significant ARF as signified by persistent respiratory support requirements. For NIV, breaks of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;2 hours off NIV support were allowed to capture patients who are intermittently but persistently supported with NIV over a 24-hour period. All patients meeting initial inclusion criteria were manually chart reviewed by author R.S. (pulmonary and critical care physician), and patients supported with IMV for non-respiratory failure related reasons (e.g. post-procedural, altered mental status only) were excluded.\u003c/p\u003e \u003cp\u003eThe cause of ARF was adjudicated by manual chart review of clinical documentation, laboratory results, and imaging (author R.S). Although multiple etiologies could coexist, patients were classified into mutually exclusive groups according to the clinically-suspected primary clinical driver: aspiration, suspected CIP, neuromuscular weakness, pneumonia (defined as positive respiratory viral panel, and/or positive bacterial or fungal respiratory culture), progressive cancer, pulmonary embolism, sepsis-related respiratory failure (defined as positive non-respiratory culture with other signs of sepsis), or fluid overload.\u003c/p\u003e \u003cp\u003eCIP was diagnosed in accordance with national and international guidelines, including using a best practice of multi-disciplinary adjudication.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Patients were initially classified as having suspected CIP if the treating clinical team documented concern for immune checkpoint inhibitor-associated pneumonitis and initiated management for CIP (e.g. corticosteroid therapy and/or discontinuation of immune checkpoint inhibitor therapy). For each suspected CIP patient, we retrospectively determined CIP diagnosis via multidisciplinary adjudication by a panel including two board-certified pulmonologists, one oncologist, and one thoracic radiologist. A CIP diagnosis required compatible clinical features (new or worsening dyspnea, cough, or hypoxemia), characteristic radiologic patterns (ground-glass opacities, patchy consolidations, or organizing-pneumonia pattern), and exclusion of alternative etiologies through targeted microbiologic testing, including bronchoalveolar lavage studies when available, and comprehensive assessment for alternative etiologies including tumor progression, or other treatment-related lung injury (\u003cb\u003eAppendix A\u003c/b\u003e).\u003csup\u003e17, 25\u0026ndash;27\u003c/sup\u003e We evaluated inter-rater reliability among multidisciplinary reviewers using Cohen\u0026rsquo;s κ statistic.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003e We extracted covariates electronically from the EHR registry and via manual chart review. Extracted variables included demographic characteristics (age, sex, race, ethnicity, body mass index [BMI], smoking history, hospital type [academic/community]), and comorbidities (Elixhauser comorbidity count). Cancer-specific covariates included cancer type, stage, and presence of brain metastases. ICI-specific data included which ICI was administered, cumulative instances of any ICI administration, and timing of administrations. We also assessed for receipt of concurrent chemotherapy, thoracic radiation (palliative or definitive intent), or tyrosine kinase inhibitors (TKIs), as these have been linked to CIP in prior studies.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e We included the following acute-illness variables: type of respiratory support (NIV or IMV), presence of viral infection, lowest oxygen saturation-to-fraction-of-inspired-oxygen ratio (SpO₂/F\u003csub\u003eI\u003c/sub\u003eO₂ [S/F]) within the first 24 hours of ARF, non-respiratory Sequential Organ Failure Assessment (SOFA) score, vasopressor use, and presence of other non-pulmonary immune related adverse event (irAE) during admission. We additionally captured data related to other therapies received during hospitalization including administration of intravenous antibiotics for \u0026ge;\u0026thinsp;2 days after start of ARF, corticosteroid use and dosing (\u0026ge;\u0026thinsp;1 mg/kg methylprednisolone equivalent, which is the recommended dosing for treating CIP\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e), and use of additional immunosuppressive therapies including intravenous immunoglobulin (IVIG), infliximab, tocilizumab, and mycophenolate mofetil. For SOFA sub-scores (central nervous system, cardiovascular, liver, coagulation, and renal), we imputed missing values as normal, otherwise no missing data were imputed, and we performed complete-case analyses throughout (\u003cb\u003eTable E1\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eExposures and Outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary exposure was ARF from CIP versus non-CIP causes. The primary outcome was 90-day all-cause mortality following the onset of ARF. Vital status for patients discharged from the hospital alive before day 90 was determined by linkage to the national death records.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Secondary outcomes included in-hospital mortality, and time to hospital discharge accounting for competing risk of death. For time-to-event analyses, individuals entered risk sets at ARF onset and were followed until death or administrative censoring at 90 days for the primary outcome, until in-hospital death or discharge for in-hospital mortality, and until hospital discharge for length of stay, with death treated as a competing event using Fine-Gray sub-distribution hazard models.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe summarized and compared baseline characteristics and clinical outcomes between patients diagnosed with CIP vs non-CIP ARF using Wilcoxon rank sum, and χ\u0026sup2; or Fisher exact tests for continuous and categorical variables, respectively. Time to death and discharge for the CIP and non-CIP ARF groups were plotted using Kaplan-Meier curves and compared using a log-rank test.\u003c/p\u003e \u003cp\u003eWe used unadjusted and multivariable Cox proportional-hazards regression to evaluate the association between CIP status and 90-day mortality as well as in-hospital mortality. Covariates for adjusted models were pre-specified based on prior literature and expert study team consensus and were guided by a directed acyclic graph (\u003cb\u003eFigure E1\u003c/b\u003e). These included age, sex, Elixhauser comorbidity count, smoking status, presence of other immune-related adverse events, cancer type (lung vs. other), and cancer stage. The proportional hazards assumption was verified for all models. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs), where a HR\u0026thinsp;\u0026lt;\u0026thinsp;1.0 indicates a lower hazard of death (better survival). For the secondary outcome of time-to-hospital discharge alive, in-hospital death was treated as competing risk using a Fine-Gray sub-distribution model.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Competing risk model estimates are presented as sub-distribution hazard ratios (sHRs), in which a sHR\u0026thinsp;\u0026gt;\u0026thinsp;1.0 indicates faster time to discharge alive.\u003c/p\u003e \u003cp\u003eAs exploratory analyses, we evaluated patterns of ICI rechallenge among patients who survived to hospital discharge after the initial ARF episode. We stratified these analyses by CIP versus non-CIP adjudicated diagnosis, and by treatment for suspected CIP versus not during hospitalization. We also conducted several sensitivity analyses. These included analyses adding additional covariates for prior cancer-directed therapies to assess for potential treatment-related confounding, and included: concurrent chemotherapy with the ICI, definitive thoracic radiation (defined as curative-intent radiation to the thorax) prior to ARF event, TKI exposure prior to ARF, and ICI exposure duration (number of days from first to last ICI dose prior to ARF). We also repeated the primary analysis in a subset of patients with non-small cell lung cancer (NSCLC), the most common underlying malignancy in this cohort, to evaluate whether findings were consistent within this more homogeneous cancer population. Lastly, we evaluated the primary and secondary outcomes in models stratified by oxygenation status using a cutoff of SpO₂/FiO₂ \u0026le; 148 versus \u0026gt;\u0026thinsp;148, values which were selected for their correlation as they are the threshold for severe ARDS oxygenation in newly published criteria.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA two-sided p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Statistical analyses were conducted using Stata version 18.0 (StataCorp, College Station, TX). Reporting follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (\u003cb\u003eTable E1\u003c/b\u003e). This study was approved by the Johns Hopkins Medicine Institutional Review Board (IRB00513735) with a waiver of informed consent for secondary analysis of de-identified data.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAmong 3,011 patients in the registry who received an ICI in the JHHS, 197 (7%) met our definition of ARF requiring at least 24 hours of noninvasive or invasive ventilatory support for confirmed respiratory failure (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Fifty-three patients were treated for suspected CIP at the bedside and underwent multidisciplinary adjudication. Among patients treated for suspected CIP by their inpatient clinicians, 55% (29/53) were ultimately adjudicated as having CIP-related respiratory failure. Over the entire cohort, this yielded 15% (29/197) with CIP-related ARF, and 85% (168/197) with non-CIP ARF. Agreement between pulmonology consensus adjudication and oncology review was perfect (\u0026kappa; = 1.00), with similarly high agreement between pulmonology and radiology reviewers (\u0026kappa; = 0.91). Among those with non-CIP ARF, the etiologies were pneumonia (n=54), progressive cancer (n=37), fluid overload (n=29), aspiration (n=26), sepsis-related respiratory failure (n=9), pulmonary embolism (n=9), and neuromuscular causes (n=3) (\u003cstrong\u003eFigure 2\u003c/strong\u003e). The distribution of primary cancer sites for the cohort is shown in \u003cstrong\u003eFigure E2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1: Flow Diagram of Patient Cohort Selection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlow diagram showing selection of ICI-treated patients with acute respiratory failure requiring \u0026ge;24 hours of invasive or noninvasive ventilatory support. Of 3,011 ICU or intermediate-care admissions, 197 patients met ARF criteria and comprised the analytic cohort, including 29 cases of CIP-related ARF and 168 non-CIP cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2: Distribution of Acute Respiratory Failure Etiologies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar chart showing the number of patients by adjudicated cause of acute respiratory failure among immune checkpoint inhibitor\u0026ndash;treated patients.\u003c/p\u003e\n\u003cp\u003eThe median patient age was 66 years (interquartile range [IQR]: 59-74). Aside from lung cancer being more frequent in patients with CIP versus non-CIP (62 vs. 35% of patients), other baseline variables were similar between groups (\u003cstrong\u003eTable 1\u003c/strong\u003e). Patients in both groups had received a median of 3 ICI administrations prior to respiratory failure, and the distribution of initial ICI agents and classes, and total duration of ICI exposure were similar. The median time from first ICI exposure to ARF was 112 days in CIP (IQR: 56-209) and 138 days in non-CIP etiologies (IQR:54-332; p=0.44). CIP patients were more frequently managed with noninvasive ventilation alone (69% vs 45%), but more often experienced escalation from noninvasive to invasive ventilation compared with non-CIP patients (28% vs 11%; p\u0026lt;0.01). Although CIP patients had lower non-respiratory SOFA scores (median 2 vs 5; p\u0026lt;0.01), they presented with more severe hypoxemia, reflected by a lower nadir SpO₂/FiO₂ ratio in the first 24 hours (median of 120 vs 148; p=0.01). All CIP patients received systemic glucocorticoids compared with 68% of non-CIP patients (p\u0026lt;0.01), and among patients who received glucocorticoids, high-dose therapy (\u0026ge;1 mg/kg methylprednisolone equivalent) was more common in CIP vs. non-CIP (90% vs 67%; p=0.02). Intravenous immunoglobulin was administered to 14% of CIP patients and 2% of non-CIP patients (p\u0026lt;0.01); use of other immunosuppressive agents was rare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Clinical Characteristics of Acute Respiratory Failure Cases after Immune Checkpoint Inhibitor Therapy, Stratified by Etiology of Acute Respiratory Failure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n= 197)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCIP-related ARF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(n=29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-CIP ARF\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=168)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 630px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eAge at admission, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e66 (59-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e70 (64-76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e65 (58-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e67 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e8 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e59 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 582px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e125 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e17 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e108 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e55 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e10 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e45 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e12 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e11 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eHispanic Ethnicity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e17 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e25 (22-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25 (22-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e25 (22-29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eEver smoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e126 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e21 (73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e105 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eElixhauser comorbidity count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3 (1-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3 (2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (1-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eLung Cancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e77 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e59 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 582px;\"\u003e\n \u003cp\u003eCancer Stage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;I\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e11 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e9 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e33 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e28 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e140 (71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e19 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e121 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Incomplete/Unknown\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e9 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eBrain Mets\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e54 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e47 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 630px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmune Checkpoint Inhibitor Therapy Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eNumber of ICI Administrations Prior to Respiratory Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3 (2-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3 (2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (2-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 582px;\"\u003e\n \u003cp\u003eFirst ICI Administered\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Atezolizumab\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e15 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e12 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Cemiplimab\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e8 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e8 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Dostarlimab \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Durvalumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e15 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Ipilimumab-Nivolumab\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e20 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Nivolumab \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e34 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e29 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Nivolumab-Relatlimab \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Pembrolizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e101 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e89 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Tremelimumab-Durvalumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eTotal duration of ICI exposure (time from first ICI receipt to last ICI receipt occurring before respiratory failure) (days) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e52 (21-156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e43 (21-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e54 (21-182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eMedian time from first ICI to Respiratory Failure (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e133 (54-316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e112 (56-209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e138 (54-332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 630px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Cancer Therapies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eChemotherapy concurrent with ICI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e68 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e11 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e57 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 630px;\"\u003e\n \u003cp\u003eThoracic radiation therapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Palliative dose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e28 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e22 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Definitive dose\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e24 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e9 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e15 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003ePrior TKI therapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 630px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute Respiratory Failure Event Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eAdmitted to academic hospital\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e147 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e21 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e126 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eRespiratory support\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 348px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Non-invasive Ventilation Only (NIV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e96 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e20 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e76 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Invasive Ventilation Only (IMV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e75 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e74 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Both NIV and IMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e26 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e8 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e18 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eRespiratory virus positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e26 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e23 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Coronavirus 19 positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e13 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e12 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eLowest SpO2/FiO2 ratio in first 24 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e136 (115-168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e120 (110-139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e148 (116-182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eNon-respiratory SOFA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5 (2-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (1-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e5 (2-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eVasopressor use\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e62 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e58 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eOther iRAE (any organ)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e13 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e13 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 630px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Therapies Received During Hospitalization\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eIntravenous antibiotics for \u0026ge; 48 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e129 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e111 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eAny corticosteroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e141 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e29 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e114 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge; 1 mg/kg methylprednisolone equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e140 (71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e26 (90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e112 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eIntravenous Immunoglobulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e7 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eInfliximab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eTocilizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 282px;\"\u003e\n \u003cp\u003eMycophenolate mofetil\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are shown as median (interquartile range) for continuous variables and n (%) for categorical variables.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ARF, acute respiratory failure; CIP, checkpoint inhibitor pneumonitis; ICI, immune checkpoint inhibitor; NIV, non-invasive ventilation; IMV, invasive mechanical ventilation; SOFA, Sequential Organ Failure Assessment; SpO₂/FiO₂, peripheral oxygen saturation to fraction of inspired oxygen ratio; iRAE, immune-related adverse event; TKI, tyrosine kinase inhibitor; COVID-19, coronavirus disease 2019.\u003c/p\u003e\n\u003cp\u003eAll-cause mortality by day 90 occurred in 65% of patients with CIP-related ARF and 71% of patients with non-CIP ARF (\u003cstrong\u003eTable 2, Figure 3\u003c/strong\u003e). In multivariable Cox proportional hazards models, time to death within 90 days did not differ significantly between CIP and non-CIP ARF (adjusted HR [aHR] 0.66; 95% CI, 0.40-1.08). In-hospital mortality was 31% in patients with CIP and 49% in patients with non-CIP ARF and was associated with a lower hazard of in-hospital death compared with non-CIP ARF (aHR 0.43; 95% CI, 0.21-0.88). Accounting for the competing risk of in-hospital death, patients with CIP-related ARF had a shorter time to discharge alive compared to non-CIP (sHR:1.70; 95% CI, 1.02-2.84) \u003cstrong\u003e(Figure 3).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eTable 2: Association Between CIP-Related Respiratory Failure and Outcomes\u003c/strong\u003e\u003c/h2\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCIP-related ARF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-CIP-related ARF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable Analysis*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 798px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e90-day all-cause mortality (n=188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e(65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003cp\u003e(71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003cp\u003e(0.45-1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e(0.40-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 798px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary Outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eIn-hospital mortality\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003cp\u003e(49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003cp\u003e(0.26-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003cp\u003e(0.21-0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eMedian Days [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eMedian Days [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eTime to hospital discharge (n=188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e11 (9-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e12 (7-27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eSHR 1.70\u003c/p\u003e\n \u003cp\u003e(1.02-2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 798px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity/Exploratory Analyses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eAnalysis Including Pre-ARF Additional Cancer Therapeutics\u003c/p\u003e\n \u003cp\u003e(n=188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.32\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.75-2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eNSCLC only\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e(52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e52\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003cp\u003e(0.27-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e(0.31-1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eS/F \u0026le;148\u003c/p\u003e\n \u003cp\u003e(n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e(23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003cp\u003e(77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e(0.35-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003cp\u003e(0.31-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 186px;\"\u003e\n \u003cp\u003eS/F \u0026gt;148\u003c/p\u003e\n \u003cp\u003e(n=82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003cp\u003e(91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e(0.42-2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e(0.51-3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Adjusted for age, sex, Elixhauser comorbidity count, smoking status, presence of other immune-related adverse events, lung, and cancer stage.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ARF, acute respiratory failure; CIP, checkpoint inhibitor pneumonitis; NSCLC, non-small cell lung cancer; SHR, sub-hazard ratio; S/F, SpO2/FiO2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3: Ninety-Day Outcomes After Acute Respiratory Failure by Checkpoint Inhibitor Pneumonitis (CIP) Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Kaplan\u0026ndash;Meier estimates of 90-day survival following acute respiratory failure among patients with adjudicated CIP versus non-CIP etiologies. Numbers at risk are displayed below the x-axis.\u003c/p\u003e\n\u003cp\u003e(B) Cumulative incidence of discharge alive over 90 days, accounting for the competing risk of in-hospital death, stratified by CIP status.\u003c/p\u003e\n\u003cp\u003eIn patients who survived ARF hospitalization (n=106), a total of 14 patients (13.2%) restarted ICI therapy after recovery from ARF. Re-initiation of ICI occurred in 5.0% of adjudicated CIP cases who survived, and 16.9% of adjudicated non-CIP patients who were not treated for CIP (p=0.28). In patients in whom clinicians suspected and treated for CIP, but the multidisciplinary panel did not think was CIP, only 6.7% of survivors reinitiated ICI therapy. Among patients who restarted ICI therapy, the median time from recovery to rechallenge was 435 days.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSensitivity analyses adjusting for recent cancer-directed therapies, including concurrent chemotherapy, definitive thoracic radiation, recent tyrosine kinase inhibitor exposure, and total duration of ICI therapy prior to ARF, also resulted in a non-significant difference in 90-day survival for CIP versus non-CIP, but did suggest the possibility of treatment-related confounding with a point estimate favoring non-CIP 1.32 (95% CI, 0.75-2.31). In analyses restricted to patients with non-small cell lung cancer (n=67), the adjusted HR for 90-day mortality was 0.74 (95% CI, 0.31-1.74) (\u003cstrong\u003eTable 2,\u003c/strong\u003e \u003cstrong\u003eFigure E2)\u003c/strong\u003e. When stratified by hypoxemia severity (SpO₂/FiO₂ \u0026le;148 [n=96] vs \u0026gt;148 [n=82]), adjusted hazard ratios for 90-day mortality were 0.56 (95% CI, 0.30-1.03) and 1.34 (95% CI, 0.49-3.61) (\u003cstrong\u003eTable 2\u003c/strong\u003e, \u003cstrong\u003eFigure E3)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this retrospective cohort of patients who developed ARF within two years of initiating ICI therapy, CIP accounted for a minority of ARF cases. Ninety-day survival did not differ significantly between CIP and non-CIP ARF; however, patients with CIP experienced lower in-hospital mortality and shorter hospital length of stay. ICI therapy was infrequently reinitiated among survivors. Overall, the high morbidity and mortality observed in this cohort underscore the severity of acute respiratory failure in patients receiving immunotherapy.\u003c/p\u003e\n\u003cp\u003ePrior studies of CIP resulting in ARF, largely limited to NSCLC populations, have reported substantial in-hospital mortality and poor overall survival. In a recent cohort of patients with severe CIP, median hospital length of stay was 8 days, in-hospital mortality was 32%, and median overall survival was 4.4 months.\u003csup\u003e34\u003c/sup\u003e Another study similarly reported a 29% mortality rate among patients with grade 3-4 pneumonitis, with deaths occurring exclusively in severe cases.\u003csup\u003e35\u003c/sup\u003e In another real-world cohort, severe-grade CIP was associated with a median overall survival of 3.0 months and a CIP-related mortality rate of 22.7%.\u003csup\u003e36\u003c/sup\u003e The in-hospital mortality observed in our CIP-related ARF cohort (31%) is comparable to these prior reports. Importantly, however, our study included patients across cancer types and additionally evaluated 90-day mortality, demonstrating that nearly two-thirds of patients with CIP-related ARF died within 90 days, suggesting substantial intermediate-term mortality beyond the index hospitalization.\u003c/p\u003e\n\u003cp\u003eAlthough CIP-related ARF was associated with lower in-hospital mortality, this short-term advantage did not translate into improved intermediate-term outcomes. This may reflect the potentially reversible nature of immune-mediated lung injury compared with other causes of ARF in this population, such as progressive malignancy or aspiration. However, outcomes following ICU-level ARF in oncology patients likely reflect not only the mechanism of lung injury but also the broader burden of malignancy and critical illness.\u003csup\u003e\u0026nbsp;4 37\u0026nbsp;\u003c/sup\u003eNotably, ICI therapy was infrequently reinitiated following ARF, even among patients ultimately adjudicated as not having CIP. The occurrence of ARF itself—independent of confirmed immune-mediated pneumonitis—appears to function as a clinical inflection point, frequently leading to permanent discontinuation of immunotherapy.\u003c/p\u003e\n\u003cp\u003eDespite access to multidisciplinary immunotherapy toxicity expertise within our health system, diagnostic uncertainty was common. Among patients treated for suspected CIP, just over half (55%) were ultimately adjudicated as having CIP-related respiratory failure. This reflects the significant overlap in presenting features between CIP and alternative etiologies such as infection, tumor progression, and aspiration.\u003csup\u003e37, 38\u003c/sup\u003eAlthough multidisciplinary adjudication is increasingly recommended as a gold standard approach for CIP diagnosis, real-time access to such expertise remains limited in many settings.\u003csup\u003e17, 25-27\u003c/sup\u003e Structured diagnostic pathways incorporating radiology input, targeted microbiologic evaluation, and timely multidisciplinary review may help reduce misclassification, limit unnecessary immunosuppression, and preserve opportunities for ICI continuation when appropriate.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. The number of adjudicated CIP cases was modest, limiting statistical power and precision. As an observational study, residual confounding is possible despite multivariable adjustment, and unmeasured differences in underlying cancer burden, functional status, or frailty could have influenced both ARF etiology and mortality risk. Misclassification of CIP diagnosis remains possible despite multidisciplinary adjudication, reflecting the inherent diagnostic uncertainty of CIP in critically ill patients, and may have influenced observed associations between ARF etiology and outcomes. Finally, the single health-system design may limit generalizability to settings with different patient populations, ICU admission practices, or access to multidisciplinary immunotherapy toxicity expertise.\u003c/p\u003e\n\u003cp\u003eIn summary, ARF after ICI therapy arises from heterogeneous etiologies, with CIP accounting for a minority of cases. CIP-related ARF was not associated with differential 90-day survival but was associated with lower in-hospital mortality and lower subsequent ICI reinitiation. These findings underscore the clinical complexity of ARF in ICI-treated patients and highlight the importance of precise etiologic attribution to inform subsequent immunotherapy decision-making.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was approved by the Johns Hopkins University School of Medicine Institutional Review Board. The requirement for informed consent was waived due to the retrospective design of the study and use of de-identified electronic health record data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eRS was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under institutional training grant T32HL007534-41 and individual fellowship award F32HL182250-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRS conceptualized the study, performed data analysis, and drafted the manuscript. KJP contributed to study design and statistical methodology. AP, CTL, and MG contributed to data interpretation and clinical adjudication. DNH and SD contributed to study design and critical revision of the manuscript. KS and CHH supervised the study, contributed to interpretation of results, and critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to institutional privacy regulations but are available from the corresponding author on reasonable request and with appropriate Institutional Review Board approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHaslam A, Gill J, Prasad V. Estimation of the Percentage of US Patients With Cancer Who Are Eligible for Immune Checkpoint Inhibitor Drugs. 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Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis. Radiother Oncol. 2024;195:110266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.radonc.2024.110266\u003c/span\u003e\u003cspan address=\"10.1016/j.radonc.2024.110266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"immune checkpoint inhibitors, respiratory failure, checkpoint inhibitor pneumonitis","lastPublishedDoi":"10.21203/rs.3.rs-9096889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9096889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIndications for immune checkpoint inhibitors (ICIs) are expanding across cancer types and stages. While this carries promise for improved survival, increasing use has raised concern for respiratory complications in patients with multi-morbidities. Pulmonary complications such as checkpoint inhibitor pneumonitis (CIP), a recognized immune-related ICI toxicity, can lead to severe acute respiratory failure (ARF). However, not all ARF following ICI exposure is attributable to CIP, and while different causes imply distinct management and prognostic implications, the types of complications leading to ARF after ICI remain poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study of ICI-treated adults who developed ARF, defined as \u0026ge;\u0026thinsp;24 hours of invasive or noninvasive ventilatory support within 2 years of first ICI receipt, treated at five hospitals in a single health system (2017\u0026ndash;2024). ARF etiology was determined by manual chart review, and suspected CIP cases underwent multidisciplinary adjudication. We evaluated associations between CIP versus non-CIP ARF and 90-day all-cause mortality using multivariable Cox proportional hazards models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 197 ICI-treated patients with ARF, the median age was 66 years, and the most frequent cancer type was non-small cell lung cancer. Among these patients, 29 (15%) had CIP, and 168 (85%) had other etiologies of ARF (e.g., pneumonia, progressive cancer, aspiration, fluid overload). In the first 24 hours, CIP patients exhibited more severe hypoxemia but less multiorgan dysfunction than non-CIP patients. Ninety-day mortality was 66% for CIP and 71% for non-CIP ARF. In adjusted analyses, CIP vs. non-CIP ARF was not significantly associated with 90-day mortality (adjusted hazard ratio [aHR]\u0026thinsp;=\u0026thinsp;0.66; 95% CI: 0.40\u0026ndash;1.08) but was associated with decreased in-hospital mortality (aHR\u0026thinsp;=\u0026thinsp;0.43; 95% CI: 0.21\u0026ndash;0.88). Among ARF survivors, 13% restarted ICI therapy: 5% of adjudicated CIP vs 17% of adjudicated non-CIP (p\u0026thinsp;=\u0026thinsp;0.28); only 7% of suspected but non-adjudicated CIP cases restarted ICI.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this cohort of ICI-treated patients who developed acute respiratory failure, most ARF was not attributable to CIP. While CIP and non-CIP ARF had similar 90-day mortality, CIP-related respiratory failure was associated with lower in-hospital mortality and lower likelihood of ICI continuation, underscoring the importance of accurate etiologic attribution in this vulnerable group.\u003c/p\u003e","manuscriptTitle":"Etiologies and Outcomes of Acute Respiratory Failure in Patients Treated with Immune Checkpoint Inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 00:42:45","doi":"10.21203/rs.3.rs-9096889/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"823c0c96-e178-42a1-849d-a49db1088d85","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T09:21:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 00:42:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9096889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9096889","identity":"rs-9096889","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}