Development and validation of a new tool to estimate early mortality in patients with advanced cancer treated with immunotherapy

Development and validation of a new tool to estimate early mortality in patients with advanced cancer treated with immunotherapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a new tool to estimate early mortality in patients with advanced cancer treated with immunotherapy Andrea De Giglio, Alessandro Leonetti, Francesca Comito, Daria Maria Filippini, and 22 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4574786/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Oct, 2024 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Immune checkpoint inhibitors (ICIs) are standard treatments for advanced solid cancers. Resistance to ICIs, both primary and secondary, poses challenges, with early mortality (EM) within 30–90 days indicating a lack of benefit. Prognostic factors for EM, including the Lung Immune Prognostic Index (LIPI), remain underexplored. Methods: We performed a retrospective, observational study including patients affected by advanced solid tumors, treated with ICI as single-agent or combined with other agents. Logistic regression models identified factors associated with EM and 90-day progression risks. A nomogram for predicting 90-day mortality was built and validated within an external cohort . Results: 637 patients received ICIs (single-agent or in combination with other drugs) for advanced solid tumors. Most patients were male (61.9%), with NSCLC as the prevalent tumor (61.8%). Within the cohort, 21.3% died within 90 days, 8.4% died within 30 days, and 34.5% experienced early progression. Factors independently associated with 90-day mortality included ECOG PS 2 and a high/intermediate LIPI score. For 30-day mortality, lung metastasis and a high/intermediate LIPI score were independent risk factors. Regarding early progression, high/intermediate LIPI score was independently associated. A predictive nomogram for 90-day mortality combining LIPI and ECOG PS achieved an AUC of 0.76 (95% CI, 0.71–0.81). The discrimination ability of the nomogram was confirmed in the external validation cohort (n = 255) (AUC 0.72,95% CI, 0.64–0.80). Conclusion: LIPI and ECOG PS independently were able to estimate 90-day mortality, with LIPI also demonstrating prognostic validity for 30-day mortality and early progression. Figures Figure 1 Figure 2 Introduction Immune checkpoint inhibitors (ICIs) are currently the standard of care for many advanced solid cancers, either as a single agent or in combination with chemotherapy or molecular-targeted agents. A considerable proportion of patients exhibit primary or secondary resistance to ICIs. Primary resistance is characterized by the lack of clinical or radiological benefit following at least six weeks of treatment [ 1 ]. Secondary resistance, on the other hand, is defined as clinical or radiological progression in a patient who had previously demonstrated a response to treatment or remained stable for longer than six months [ 1 ]. Different definitions have been provided to include the speed of progression, mainly derived from retrospective experiences. In this context, fast progression (FP) refers to a condition with an increase of at least 50% in the sum of the longest diameter of target lesions within six weeks from the starting point [ 2 ]. The concept of hyperprogressive (HPD) disease, which entails the dynamic evaluation of tumor growth, remains controversial owing to the lack of a unanimous consensus on its definition and prevalence [ 2 , 3 ]. Despite the plethora of definitions regarding the patterns of progressive disease (PD), early mortality (EM) stands for death due to disease progression within 30–90 days from the treatment initiation [ 3 ]. According to findings from a large cohort study, patients with solid cancer treated with ICIs were observed to have a mortality rate of 7% within 30 days from treatment start, 15% within 60 days, and 22% within 90 days [ 3 ]. Evidently, these patients do not derive any benefit from immunotherapy and, if identified upfront, should ideally be spared this form of treatment since, in this case, immunotherapy would be associated only with useless costs and toxicity and, in addition, a possible detrimental effect on survival cannot be excluded. Several potential prognostic factors have been investigated as predictors of ICI-related EM in different cancers, including age, primary tumor site (lung, head and neck), baseline laboratory values (hemoglobin, white blood cells, platelet count, neutrophil to lymphocyte ratio [NLR], lactate dehydrogenase [LDH], albumin, and Eastern Cooperative Oncology Group performance status [ECOG PS]) [ 4 – 6 ]. The lung immune prognostic index (LIPI), a score incorporating the derived-NLR (dNLR) and serum LDH levels, demonstrated its prognostic value first in non-small cell lung cancer (NSCLC) [ 7 ]. Subsequent studies showed its association with disease progression and mortality risk in other tumor types, such as genito-urinary[ 8 , 9 ], breast[ 10 ], melanoma[ 9 ], and head and neck cancers[ 11 ], suggesting its agnostic applicability. No studies explored the short-term prognostic validity of the LIPI score. The present study investigated clinical and laboratory factors, including LIPI score, associated with EM and early progression under ICI-based treatments. Moreover, we developed a nomogram to predict 90-day mortality with an external validation. Methods We performed a single-center, retrospective, observational study including patients affected by advanced solid tumors, treated with ICI as single-agent or combined with other agents (chemotherapy, ICI [ICI doublets], targeted therapy) between August 2015 and December 2023 at the IRCCS Azienda Ospedaliero-Universitaria of Bologna, Italy. We collected data for a validation cohort of patients with the same clinical characteristics and treated within the same window of time at the Azienda Ospedaliero-Universitaria of Parma, Italy. This study was conducted in accordance with the Declaration of Helsinki (1964) after obtaining approval from the local Ethics Committee. Data were manually collected from electronic and paper-based medical records. The LIPI score was collected at baseline for each patient, when available. This score considers two factors: the dNLR [neutrophils / (leukocytes minus neutrophils)] and serum LDH levels. A dLNR value greater than 3 or LDH levels above the upper limit of normal count for 1 point each. Based on the values of these two variables, patients are categorized into three prognostic groups: low-risk (0 points), intermediate-risk (1 point), and high-risk (2 points). The primary objective of this study was to identify clinical and laboratory prognostic factors, including the LIPI score, associated with early 90-day mortality to ICI-based treatments. By integrating independent prognostic factors derived from a multivariable logistic regression model multivariate analysis, a nomogram was developed for 90-day mortality prediction within the development cohort. The training cohort was used to validate the nomogram’s performance. The secondary objectives were to investigate prognostic factors of 30-day mortality and early disease progression (≤ 90 days from treatment initiation). The decision to use a 90-day cut-off for the primary and secondary objectives was based on literature data and clinical practice, where the initial radiological assessment is generally conducted after 3–4 cycles of treatment (12 weeks). Statistical methods Clinical and laboratory findings were analyzed as continuous or categorical variables, with median values and proportions reported, as appropriate. The normality of the distribution was verified using the Shapiro test. To compare means and proportions, T-test (ANOVA, Pearson correlation test if needed) and chi2-test (or Fisher's exact test, if needed) were performed. Overall survival (OS) was defined as the time between the start of treatment and death from any cause. Progression-free survival (OS) was defined as the time from treatment initiation to the first clinical or radiographical evidence of disease progression or death from any cause. The ROC curve analysis was performed to determine the area under the curve (AUC) for the score obtained by nomogram analysis to differentiate between patients who survived and those who died within 90 days. A multivariable logistic regression model was employed to investigate the factors associated with EM, and subsequently, the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Patients alive with a follow-up inferior to 30 or 90 days were excluded from the logistic regression analyses. A statistically significant p-value was considered when < 0.05. Statistical analyses were accomplished with R-Studio free software, version 2023.06.2. Results Baseline characteristics and survival outcomes A total of 637 patients were included in the training cohort. Most patients were males (61.9%) and had an ECOG PS of 0–1 (86.5%). NSCLC was the most frequent tumor type (61.8%), followed by melanoma (17.3%), head-neck (11.3%), genitourinary (6.4%), and gastrointestinal (3.1%) tumors. 68.4% of patients received ICI as single-agent and were treated in first-line (67%). Baseline characteristics are summarized in Table 1 . The median OS in the training cohort was 11.7 months (95% CI, 9.6–15.0), and the median follow-up time was 26.1 months (IQR 11.7–41.4). The median PFS in the training cohort was 5.6 months (95% CI, 4.8–6.5). The COX-regression analyses for death and progression risk were summarized in Table 1 S and Table 2 S. Table 1 Baseline characteristics of the training cohort. Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; NSCLC, non-small cell lung cancer; Num., number; met., metastasis; ICI, immune-checkpoint inhibitor; CT, chemotherapy; TKI, tyrosine-kinase inhibitor; dNLR, derived neutrophil-to-lymphocyte ratio; LIPI, lung immune-prognostic index. Overall (N = 636) Age ≤ 65 238 (37.4%) > 65 398 (62.6%) Sex Female 242 (38.1%) Male 394 (61.9%) ECOG PS 0–1 511 (80.3%) 2 78 (12.3%) Missing 47 (7.4%) Smoking status current smoker 112 (17.6%) former smoker 274 (43.1%) never smoker 69 (10.8%) Missing 181 (28.5%) Histology Gastrointestinal 20 (3.1%) Genitourinary 41 (6.4%) Head-neck 72 (11.3%) Melanoma 110 (17.3%) NSCLC 393 (61.8%) Num. of metastatic sites ≤ 3 391 (61.5%) > 3 76 (11.9%) Missing 169 (26.6%) Lung met. no 291 (45.8%) yes 339 (53.3%) Missing 6 (0.9%) Brain met. no 526 (82.7%) yes 105 (16.5%) Missing 5 (0.8%) Liver met. no 514 (80.8%) yes 116 (18.2%) Missing 6 (0.9%) Line of treatment First 426 (67.0%) Subsequent 210 (33.0%) Type of treatment CT-ICI 171 (26.9%) ICI-ICI 13 (2.0%) ICI 435 (68.4%) immuno-TKI 17 (2.7%) dLNR Mean (SD) 2.99 (2.28) Median [Min, Max] 2.36 [0.0318, 20.4] Missing 35 (5.5%) LIPI high 71 (11.2%) intermediate 188 (29.6%) low 230 (36.2%) Missing 147 (23.1%) Early mortality and progression risk One hundred and thirty-six patients (21.3%) died within 90 days. The distribution of baseline features according to 90-day mortality is reported in Table 2 . Table 2 Baseline characteristics according to 90-day mortality in the training cohort. Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; NSCLC, non-small cell lung cancer; Num., number; met., metastasis; ICI, immune-checkpoint inhibitor; CT, chemotherapy; TKI, tyrosine-kinase inhibitor; dNLR, derived neutrophil-to-lymphocyte ratio; LIPI, lung immune-prognostic index. 90-day mortality No (%) 90-day mortality Yes (%) Total (%) p value Age > 65 293 (61.9) 92 (67.6) 385 (63.2) 0.265 ≤ 65 180 (38.1) 44 (32.4) 224 (36.8) Sex Female 185 (39.1) 48 (35.6) 233 (38.3) 0.516 Male 288 (60.9) 87 (64.4) 375 (61.7) ECOG PS 0–1 398 (91.1) 88 (70.4) 486 (86.5) < 0.001 2 39 (8.9) 37 (29.6) 76 (13.5) Smoking history current smoker 82 (25.4) 26 (22.8) 108 (24.7) 0.854 former smoker 193 (59.8) 71 (62.3) 264 (60.4) never smoker 48 (14.9) 17 (14.9) 65 (14.9) Histology Gastrointestinal 18 (3.8) 1 (0.7) 19 (3.1) 0.001 Genitourinary 29 (6.1) 4 (2.9) 33 (5.4) Head-neck 51 (10.8) 12 (8.8) 63 (10.3) Melanoma 97 (20.5) 13 (9.6) 110 (18.1) NSCLC 278 (58.8) 106 (77.9) 384 (63.1) Num. of metastatic sites > 3 51 (14.1) 22 (27.8) 73 (16.6) 0.005 ≤ 3 311 (85.9) 57 (72.2) 368 (83.4) Lung met. no 225 (47.9) 46 (34.6) 271 (44.9) 0.009 yes 245 (52.1) 87 (65.4) 332 (55.1) Brain met. no 403 (85.6) 101 (75.9) 504 (83.4) 0.012 yes 68 (14.4) 32 (24.1) 100 (16.6) Liver met. no 388 (82.6) 100 (75.2) 488 (80.9) 0.074 yes 82 (17.4) 33 (24.8) 115 (19.1) Line of treatment First 329 (69.6) 74 (54.4) 403 (66.2) 0.001 Subsequent 144 (30.4) 62 (45.6) 206 (33.8) Type of treatment CT-ICI 125 (26.4) 32 (23.5) 157 (25.8) 0.191 ICI-ICI 11 (2.3) 2 (1.5) 13 (2.1) ICI 325 (68.7) 102 (75.0) 427 (70.1) immuno-TKI 12 (2.5) 12 (2.0) dLNR Mean (SD) 2.6 (2.0) 4.2 (2.8) 3.0 (2.3) < 0.001 LIPI 0 203 (55.6) 14 (14.1) 217 (46.8) < 0.001 1 130 (35.6) 51 (51.5) 181 (39.0) 2 32 (8.8) 34 (34.3) 66 (14.2) Fifty-four patients (8.4%) died within 30 days. The distribution of baseline features according to 90-day mortality is reported in Table 3 S. 220 patients (34.5%) had disease progression or death within 90 days. The distribution of baseline features according to early progression is reported in Table 4S. At univariable analyses, patients with ECOG PS 2, high/intermediate LIPI score, > 3 metastatic sites, brain and lung metastasis, and those treated with a subsequent line of treatment presented an increased risk of 90-day mortality (Table 3 ). At multivariable analysis, ECOG PS 2 (OR 2.70, p 0.019), high (OR 11.47, p < 0.001), and intermediate LIPI score (OR 4.97, p < 0.001) were independently associated with an increased risk of 90-day mortality (Table 3 ). Table 3 Univariate and multivariate logistic regression analyses for 90-day mortality in the training cohort. Abbreviations: CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group performance status; LIPI, lung immune-prognostic index; NSCLC, non-small cell lung cancer; ICI, immune-checkpoint inhibitor; met., metastasis. 90-day mortality Univariate Multivariate Predictors Odds Ratios CI (95%) p value Odds Ratios CI (95%) p value Intercept 0.01 0.00–0.14 0.001 Age > 65 1.29 0.86–1.93 0.219 0.86 0.44–1.67 0.658 ECOG PS 2 4.28 2.58–7.10 < 0.001 2.70 1.18–6.18 0.019 High LIPI 15.33 7.42–31.67 < 0.001 11.47 4.34–30.31 < 0.001 Intermediate LIPI 5.66 3.01–10.64 < 0.001 4.97 2.16–11.41 < 0.001 Genitourinary cancers 2.34 0.24–22.73 0.462 1.41 0.10–20.08 0.799 Head-neck cancers 4.00 0.48–33.08 0.198 4.71 0.31–70.47 0.262 Melanoma 2.28 0.28–18.57 0.442 1.51 0.13–17.88 0.742 NSCLC 6.48 0.85–49.31 0.071 7.65 0.53–110.94 0.136 ICI 1.37 0.89–2.12 0.155 1.89 0.88–4.09 0.104 Subsequent line of treatment 1.91 1.29–2.82 0.001 4.43 0.85–23.02 0.077 > 3 metastatic sites 2.35 1.32–4.17 0.004 0.85 0.34–2.16 0.735 Brain met. 1.87 1.17–3.01 0.009 1.80 0.77–4.21 0.172 Liver met. 1.56 0.98–2.47 0.059 1.00 0.41–2.45 0.995 Lung met. 1.73 1.16–2.58 0.007 1.79 0.88–3.66 0.108 At univariable analyses, patients with ECOG PS 2, high/intermediate LIPI score, lung and liver metastasis, and those treated with a subsequent line of treatment presented an increased risk of 30-day mortality (Table 5S). At multivariable analysis, lung metastasis (OR 2.66, p = 0.048), high (OR 8.09, p = 0.006) and intermediate LIPI score (OR 8.62, p = 0.001) were independently associated with increased risk of 30-day mortality (Table 5S). At univariable analyses, patients with ECOG PS 2, high LIPI score, NSCLC histology, > 3 metastatic sites, lung metastasis, and those treated with ICI single-agent and a subsequent line of treatment presented an increased risk of early progression (Table 6S). At multivariable analysis, high (OR 8.11, p < 0.001) and intermediate (OR 2.63, p = 0.002) LIPI scores were independently associated with increased risk of early progression (Table 6S). Nomogram for 90-day mortality Next, we sought to build a nomogram to predict 90-day mortality using the variables that were significantly associated with increased risk of death at 90 days in the multivariable model. Among 637 patients in the training cohort, 212 were excluded because of missing ECOG PS or LIPI data or because they were alive with a follow-up < 3 months, for a total of 425 patients included in the analysis. Based on the multivariable assessment, the produced score confers 37 points for ECOG PS 2, 64 points for an intermediate LIPI score, and 100 points for a high LIPI score. Patients with the maximum score (137) had a 70% risk of death within 90 days from treatment start. A nomogram representing the model is provided in Fig. 1 . The Area Under the ROC for the score was 0.76 (95% CI, 0.71–0.81) for 90-day mortality prediction (Fig. 2 ) with a concordance index of 0.76. The same analysis was performed within each histology group. The AUC was 0.73 (95%CI, 0.67–0.79) for NSCLC, 0.85 (95%CI, 0.73–0.96) for melanoma, 0.78 (95%CI, 0.67–0.89) for other tumor types (GU, GI, Head-neck). Validation cohort A total of 255 patients were included in the validation cohort. Most patients were males (66.7%) and had an ECOG PS of 0–1 (92.2%). NSCLC was the most frequent tumor type (67.5%), followed by genitourinary (23.5%) and melanoma (9%). CT-ICI combination and ICI monotherapy were the most frequently used regimens (43.9% and 40%, respectively) and the majority of patients (74.9%) were treated in first-line (Table 7S). Of them, 37.3% were in the low-risk group (n = 95), 41.6% in the intermediate-risk group (n = 106), and 21.2% in the high-risk group (n = 54). After a median follow-up of 27.8 months (95%CI, 23.9–31.3), 106 patients were alive (41.6%), and the median OS was 15.5 months (95% CI, 12.5–22.3). Overall, 46 patients (18%) died within 90 days. The baseline characteristics according to 90-day mortality are reported in Table 8S. The multivariable regression analysis for 90-day mortality risk was resumed in Table 9S. When the score for 90-day mortality was applied, the Area Under the ROC was 0.72 (95% CI, 0.64–0.80), p < 0.001. Discussion We conducted a study on a cohort of 637 patients with advanced solid tumors treated with ICI, either as single-agent or in combination with chemotherapy or other drugs. Our findings showed that 21.4% and 8.8% of patients died within 90 days and 30 days from treatment start, respectively. Furthermore, 35.3% of patients experienced early progression (≤ 90 days) of their disease. We also analyzed the laboratory and clinical factors that contributed to EM and found that LIPI score and ECOG PS were independent predictors of 90-day mortality as a primary objective. Results from the study population were externally validated in 255 patients, and the prognostic role of intermediate-high LIPI score was confirmed. Based on our results, we developed a novel scoring system that can predict 90-day mortality with a good degree of accuracy (AUC of 0.76), which was further confirmed in the external validation cohort (AUC of 0.72). The definition of EM ranges from 30 to 90 days after the start of treatment, with a prevalence of 20 to 35%, depending on the type of treatment and disease burden [ 12 , 13 ]. According to a recent meta-analysis of 56 randomized controlled trials involving over 40,000 patients with various solid cancers, the rate of early death (≤ 90 days) was higher with single-agent ICI treatment compared to other ICI treatments (14.2% vs. 6.7%) [ 14 ]. Our findings from a real-world context evidenced a slightly increased early death rate (21.4%) compared with their results, with no difference according to treatment received. A large cohort study investigated the cause of EM, defined as within 60 days from treatment initiation among 7126 patients affected by solid cancers treated with ICI [ 4 ]. NSCLC was the predominant tumor type (58.1%), followed by melanoma (23.3%) and other tumors, reflecting the epidemiology of our cohort [ 4 ]. Noteworthy, only 37.7% of patients had a stage IV disease at diagnosis, receiving predominantly ICI alone (57.8%). Patients treated at tertiary centers, those admitted to the Hospital and treated with prior radiation therapy or chemotherapy had the greater adjusted probability of 60-day mortality, as well as those who presented higher Edmonton Symptom Assessment System (ESAS) scores, anemia, and leukocytosis [ 4 ]. Conversely, patients presenting low NLR or higher BMI, and those receiving ICI + ICI had a lower risk of 60-day mortality [ 4 ]. Interestingly, this large study evidenced a prognostic role of clinical conditions and laboratory tests, suggesting an external validity of our findings relative to LIPI and ECOG PS values. Prescribing immunotherapy to frail patients may be influenced by an overestimation of the potential benefits of novel therapies or inadequate evaluation of deteriorating clinical conditions, even if for treatment-naïve or young patients. Remarkably, individuals with a baseline ECOG PS 2 or higher were associated with reduced survival rates and a higher probability of receiving ICI during the last month of life [ 15 – 17 ]. Furthermore, no efficacy differences were found between ECOG PS 0 or 1 in solid cancer patients under ICI-based regimens in a comprehensive meta-analysis [ 18 ], confirming the discriminative importance of ECOG PS 2. On the other hand, ECOG PS may not be informative enough and be connected to comorbidities or to specific cancer-related symptoms that may benefit from anti-cancer treatments themselves. Notwithstanding efficacy reduction, prospective trials on NSCLC patients confirmed that single-agent ICI may exhibit an acceptable toxicity profile for frail patients, paving the way for prescription [ 19 , 20 ] The use of laboratory values may be useful in this setting to further select patients. In this context, the LIPI score, as previously described, combines dLNR and LDH levels with an established prognostic validity under immunotherapy regardless of the setting of treatment and disease type, reflecting an inflammatory status of the organism [ 7 , 9 ]. Indeed, altered LNR and LDH have been associated with EM [ 4 , 6 ] or HPD [ 21 , 22 ] in several experiences. It should be noted that these findings may not be generalizable due to the lack of data on other types than NSCLC and the limitation of analyses focusing on EM. In an observational work performed by our research team, the short-term prognostic value of the LIPI score was investigated for the first time among advanced NSCLC patients treated with single-agent immunotherapy [ 23 ]. An intermediate-high LIPI score was independently associated with increased 90-day mortality risk. Notably, we confirmed the superiority of a combined clinical-laboratory test score, such as a modified PaP score that includes performance status, pivotal clinical symptoms (dyspnea, anorexia), and total leukocyte and lymphocyte counts [ 23 ]. After an internal validation of the prognostic relevance of ECOG PS and LIPI scores regardless of malignancies and type of treatment, we developed and externally validated a 90-day prognostic score with a good capability of early mortality risk assessment in the present work. In addition, we confirmed that an intermediate-high LIPI score was an independent risk factor for 30-day mortality and early progression (≤ 90 days). However, it is crucial to note that our study has some limitations. Firstly, it is a retrospective analysis, and further larger prospective studies are required to validate our findings. The rate of missing data for certain variables, such as LIPI, was consistent, which could lead to selection bias. Secondly, our study population consisted primarily of patients with lung cancer, which may limit the generalizability of our results to other cancer types. Finally, no central revision of radiological imaging has been assessed, limiting the findings about radiological progression. Despite these limitations, our study highlights the importance of EM prediction and personalized treatment strategies for advanced cancer patients. Moreover, this is the first study investigating the short-term prognostic value of LIPI, including patients of multiple malignancies (NSCLC, melanoma, head-neck, others) treated with single-agent ICIs but also ICI-combinations (chemotherapy, other ICI, TKI), and the score developed is easily performable with a good performance in an external cohort. Conclusion In conclusion, our study emphasizes that the LIPI score and ECOG PS are independent predictors of 90-day mortality in the internal cohort. Importantly, the LIPI score also demonstrates significant prognostic value for 30-day mortality and early progression, making it a valuable tool for stratifying patients in clinical research and daily practice. Furthermore, the short-term prognostic significance of the LIPI score remains consistent in the validation cohort, underscoring its broad applicability in clinical practice regardless of the type of ICI-based regimen used. Our nomogram can assist clinicians in identifying patients at high risk of EM. Declarations Data statement The data underlying this article cannot be shared due to the privacy of individuals who participated in the study, as stated by the local Ethics Committee (approval no. 2381/2019). Additional aggregated data analyses and the underlying analytic R code are available from the authors upon request. Author contributions Andrea De Giglio, MD (Conceptualization; Data curation; Formal Analysis; Investigation; Methodology; Visualization; Writing-original draft; Writing-review and editing), Alessandro Leonetti (Data curation; Formal Analysis; Visualization; Writing-original draft; Writing-review and editing), Francesca Comito, MD (Data curation; Writing-review and editing), Daria Maria Filippini, MD (Data curation; Writing—review and editing), Veronica Mollica, MD (Data Curation; Methodology; Writing—review and editing), Karim Rihawi, MD, (Data Curation; Methodology; Writing-review and editing), Marianna Peroni, MD(Data curation; Writing—review and editing), Giulia Mazzaschi, MD (Data curation; Writing—review and editing),Ilaria Ricciotti, MD (Data curation; writing-review and editing), Francesca Carosi, MD (Data curation; writing-review and editing), Andrea Marchetti, MD (Data curation; writing-review and editing), Matteo Rosellini, MD (Data curation; writing-review and editing), Elisabetta Nobili, MD (Writing-review and editing), Francesco Gelsomino, MD (Writing-review and editing), Barbara Melotti, MD (Writing-review and editing), Paola Valeria Marchese, MD (Writing-review and editing), Francesca Sperandi, MD (Writing-review and editing), Alessandro Di Federico, (Methodology; Writing-review and editing), Sebastiano Buti, MD, (Writing-review and editing), Fabiana Perrone, MD, (Writing-review and editing),Francesco Massari, MD (Writing-review and editing), Maria Abbondanza Pantaleo, MD (Funding, Supervision; Validation; Writing-review and editing),Marcello Tiseo, MD, (Supervision; Validation; Writing-review and editing), Andrea Ardizzoni, MD, (Conceptualization; Project administration; Supervision; Validation; Writing-review and editing). Funding The research leading to these results has received funding from the European Union - NextGenerationEU through the Italian Ministry of University and Research under PNRR - M4C2-I1.3 Project PE_00000019 "HEAL ITALIA" to Andrea De Giglio, CUP J33C22002920006. The views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them Conflicts of interest ADG, FC, DM, VM, KR, IR,FC, VF, AG, GM, SB, FP, AM,MR,EN,BM,PVM,FS,MAP declare no conflicts of interests. FG has received personal fees or Advisory roles from Eli-Lilly, Novartis, AstraZeneca, MSD ADF has received honoraria from Society for Immunotherapy of Cancer and Advisory role from Hansen-Wade. FM has received research support and/or honoraria from Astellas, BMS, Janssen, Ipsen, MSD and Pfizer outside the submitted work. AA has received Honoraria for the participation to advisory boards and/or for lectures from BMS, Eli-Lilly, MSD, AZ, Roche, Takeda, Janssen, Sanofi, Novartis, AbbVie, Daiichi AL has received speakers’ fee for Astra-Zeneca, MSD, Takeda and Sanofi. AL has been on advisory board for BeiGene, Sanofi, Novartis, Astra-Zeneca. AL has attended editorial activities sponsored by Roche and Eli Lilly. AL has received travel support from MSD and Novartis. MT received speakers’ and consultants’ fee from Astra-Zeneca, Pfizer, Eli-Lilly, BMS, Novartis, Roche, MSD, Boehringer Ingelheim, Otsuka, Takeda, Pierre Fabre, Amgen, Merck, Sanofi. MT received institutional research grants from Astra-Zeneca, Boehringer Ingelheim. Acknowledgements No acknowledgements References Kluger HM, Tawbi HA, Ascierto ML et al (2020) Defining tumor resistance to PD-1 pathway blockade: recommendations from the first meeting of the SITC Immunotherapy Resistance Taskforce. 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J Clin Oncol Takeuchi E, Kondo K, Okano Y et al (2023) Early mortality factors in immune checkpoint inhibitor monotherapy for advanced or metastatic non-small cell lung cancer. J Cancer Res Clin Oncol 149(7):3139–3147. 10.1007/s00432-022-04215-7 Mezquita L, Auclin E, Ferrara R et al (2018) Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer. JAMA Oncol 4(3):351–357. 10.1001/jamaoncol.2017.4771 Parent P, Auclin E, Patrikidou A et al (2023) Prognostic Value of the Lung Immune Prognosis Index Score for Patients Treated with Immune Checkpoint Inhibitors for Advanced or Metastatic Urinary Tract Carcinoma. Cancers (Basel) 15(4):1066 Published 2023 Feb 7. 10.3390/cancers15041066 Meyers DE, Stukalin I, Vallerand IA et al (2019) The Lung Immune Prognostic Index Discriminates Survival Outcomes in Patients with Solid Tumors Treated with Immune Checkpoint Inhibitors. Cancers (Basel) 11(11):1713 Published 2019 Nov 2. 10.3390/cancers11111713 Vozy A, Simonaggio A, Auclin E et al (2018) Applicability of the lung immune prognostic index (LIPI) to metastatic triple negative breast cancer (mTNBC) patients treated with immune checkpoint targeted monoclonal antibodies (ICT mAbs). Ann Oncol 29:viii94. 10.1093/annonc/mdy272.286 Gomez RGH, Mezquita L, Auclin E et al (2019) The head and neck lung immune prognostic index (HN-LIPI): A prognostic score for immune checkpoint inhibitors (ICI) in recurrent or metastatic squamous cell carcinoma of the head and neck (R/M SCCHN) patients. Ann Oncol 30:v469–v470. 10.1093/annonc/mdz252.051 Globus O, Sagie S, Lavine N, Barchana DI, Urban D (2023) Early death after a diagnosis of metastatic solid cancer-raising awareness and identifying risk factors from the SEER database. PLoS ONE 18(9):e0281561 Published 2023 Sep 26. 10.1371/journal.pone.0281561 Leonetti A, Peroni M, Agnetti V et al (2023) Thirty-day mortality in hospitalised patients with lung cancer: incidence and predictors. BMJ Support Palliat Care Published online September 4. 10.1136/spcare-2023-004558 Viscardi G, Tralongo AC, Massari F et al (2022) Comparative assessment of early mortality risk upon immune checkpoint inhibitors alone or in combination with other agents across solid malignancies: a systematic review and meta-analysis. Eur J Cancer 177:175–185. 10.1016/j.ejca.2022.09.031 Krishnan M, Kasinath P, High R, Yu F, Teply BA (2022) Impact of Performance Status on Response and Survival Among Patients Receiving Checkpoint Inhibitors for Advanced Solid Tumors. JCO Oncol Pract 18(1):e175–e182. 10.1200/OP.20.01055 Khaki AR, Li A, Diamantopoulos LN et al (2020) Impact of performance status on treatment outcomes: A real-world study of advanced urothelial cancer treated with immune checkpoint inhibitors. Cancer 126(6):1208–1216. 10.1002/cncr.32645 Meyers DE, Pasternak M, Dolter S et al (2023) Impact of Performance Status on Survival Outcomes and Health Care Utilization in Patients With Advanced NSCLC Treated With Immune Checkpoint Inhibitors. JTO Clin Res Rep. ;4(4):100482. Published 2023 Feb 24. 10.1016/j.jtocrr.2023.100482 Mollica V, Rizzo A, Marchetti A et al (2023) The impact of ECOG performance status on efficacy of immunotherapy and immune-based combinations in cancer patients: the MOUSEION-06 study. Clin Exp Med Published online August 3. 10.1007/s10238-023-01159-1 Felip E, Ardizzoni A, Ciuleanu T et al (2020) CheckMate 171: a phase 2 trial of nivolumab in patients with previously treated advanced squamous non-small cell lung cancer, including ECOG PS 2 and elderly populations. Eur J Cancer 127:160–172. https://doi.org/10.1016/j.ejca.2019.11.019 Lee SM, Schulz C, Prabhash K et al (2023) First-line atezolizumab monotherapy versus single-agent chemotherapy in patients with non-small-cell lung cancer ineligible for treatment with a platinum-containing regimen (IPSOS): a phase 3, global, multicentre, open-label, randomised controlled study [published correction appears in Lancet. ;402(10400):450]. Lancet. 2023;402(10400):451–463. 10.1016/S0140-6736(23)00774-2 Chen S, Gou M, Yan H et al (2021) Hyperprogressive Disease Caused by PD-1 Inhibitors for the Treatment of Pan-Cancer. Dis Markers 2021:6639366 Published 2021 Jun 22. 10.1155/2021/6639366 Chen Y, Hu J, Bu F, Zhang H, Fei K, Zhang P (2020) Clinical characteristics of hyperprogressive disease in NSCLC after treatment with immune checkpoint inhibitor: a systematic review and meta-analysis. BMC Cancer. ;20(1):707. Published 2020 Jul 29. 10.1186/s12885-020-07206-4 De Giglio A, Tassinari E, Zappi A et al (2022) The Palliative Prognostic (PaP) Score without Clinical Evaluation Predicts Early Mortality among Advanced NSCLC Patients Treated with Immunotherapy. Cancers (Basel) 14(23):5845 Published 2022 Nov 27. 10.3390/cancers14235845 Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 03 Oct, 2024 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Revision requested 13 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviews received at journal 01 Jul, 2024 Reviews received at journal 30 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 23 Jun, 2024 Reviewers invited by journal 22 Jun, 2024 Editor assigned by journal 14 Jun, 2024 Submission checks completed at journal 14 Jun, 2024 First submitted to journal 13 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4574786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321471197,"identity":"a22f019a-bf03-4964-9498-d7767343f2dc","order_by":0,"name":"Andrea De 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Bologna","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Massari","suffix":""},{"id":321471223,"identity":"ec27a903-155f-4850-8cfe-c0c5e3e86765","order_by":23,"name":"Maria Abbondanza Pantaleo","email":"","orcid":"","institution":"Alma Mater Studiorum University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Abbondanza","lastName":"Pantaleo","suffix":""},{"id":321471224,"identity":"a90af21c-ce79-4c04-9229-2167accd6783","order_by":24,"name":"Marcello Tiseo","email":"","orcid":"","institution":"University Hospital of Parma","correspondingAuthor":false,"prefix":"","firstName":"Marcello","middleName":"","lastName":"Tiseo","suffix":""},{"id":321471225,"identity":"86b731a3-6f3d-4f9e-97af-fd487549cd62","order_by":25,"name":"Andrea Ardizzoni","email":"","orcid":"","institution":"Alma Mater Studiorum University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Ardizzoni","suffix":""}],"badges":[],"createdAt":"2024-06-13 08:48:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4574786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4574786/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00262-024-03836-w","type":"published","date":"2024-10-03T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60436996,"identity":"64053de5-166d-40cf-b462-b9a9be2ba75f","added_by":"auto","created_at":"2024-07-16 17:37:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79887,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for 90-day mortality prediction. Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; LIPI, lung immune-prognostic index\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4574786/v1/f59d42c164acca901479117b.png"},{"id":60436998,"identity":"98d445b0-8c6b-476e-9a63-13bc6b931e2b","added_by":"auto","created_at":"2024-07-16 17:37:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30596,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve for 90-day mortality prediction\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4574786/v1/b9f851b1cdcbb5f513359be5.png"},{"id":66097001,"identity":"8bb8edb5-4113-4982-97e1-e1da9d5df752","added_by":"auto","created_at":"2024-10-07 16:12:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":938691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4574786/v1/259e85de-eba0-4d7e-b378-611593693142.pdf"},{"id":60437529,"identity":"caf1fc26-d9c4-44b5-aeae-c9e3cf2328d3","added_by":"auto","created_at":"2024-07-16 17:45:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48048,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4574786/v1/011cf98d3889c39b2253d0a4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a new tool to estimate early mortality in patients with advanced cancer treated with immunotherapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImmune checkpoint inhibitors (ICIs) are currently the standard of care for many advanced solid cancers, either as a single agent or in combination with chemotherapy or molecular-targeted agents.\u003c/p\u003e \u003cp\u003eA considerable proportion of patients exhibit primary or secondary resistance to ICIs. Primary resistance is characterized by the lack of clinical or radiological benefit following at least six weeks of treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Secondary resistance, on the other hand, is defined as clinical or radiological progression in a patient who had previously demonstrated a response to treatment or remained stable for longer than six months [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferent definitions have been provided to include the speed of progression, mainly derived from retrospective experiences. In this context, fast progression (FP) refers to a condition with an increase of at least 50% in the sum of the longest diameter of target lesions within six weeks from the starting point [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The concept of hyperprogressive (HPD) disease, which entails the dynamic evaluation of tumor growth, remains controversial owing to the lack of a unanimous consensus on its definition and prevalence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the plethora of definitions regarding the patterns of progressive disease (PD), early mortality (EM) stands for death due to disease progression within 30\u0026ndash;90 days from the treatment initiation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to findings from a large cohort study, patients with solid cancer treated with ICIs were observed to have a mortality rate of 7% within 30 days from treatment start, 15% within 60 days, and 22% within 90 days [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Evidently, these patients do not derive any benefit from immunotherapy and, if identified upfront, should ideally be spared this form of treatment since, in this case, immunotherapy would be associated only with useless costs and toxicity and, in addition, a possible detrimental effect on survival cannot be excluded.\u003c/p\u003e \u003cp\u003eSeveral potential prognostic factors have been investigated as predictors of ICI-related EM in different cancers, including age, primary tumor site (lung, head and neck), baseline laboratory values (hemoglobin, white blood cells, platelet count, neutrophil to lymphocyte ratio [NLR], lactate dehydrogenase [LDH], albumin, and Eastern Cooperative Oncology Group performance status [ECOG PS]) [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe lung immune prognostic index (LIPI), a score incorporating the derived-NLR (dNLR) and serum LDH levels, demonstrated its prognostic value first in non-small cell lung cancer (NSCLC) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Subsequent studies showed its association with disease progression and mortality risk in other tumor types, such as genito-urinary[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], breast[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], melanoma[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and head and neck cancers[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], suggesting its agnostic applicability. No studies explored the short-term prognostic validity of the LIPI score.\u003c/p\u003e \u003cp\u003eThe present study investigated clinical and laboratory factors, including LIPI score, associated with EM and early progression under ICI-based treatments. Moreover, we developed a nomogram to predict 90-day mortality with an external validation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe performed a single-center, retrospective, observational study including patients affected by advanced solid tumors, treated with ICI as single-agent or combined with other agents (chemotherapy, ICI [ICI doublets], targeted therapy) between August 2015 and December 2023 at the IRCCS Azienda Ospedaliero-Universitaria of Bologna, Italy. We collected data for a validation cohort of patients with the same clinical characteristics and treated within the same window of time at the Azienda Ospedaliero-Universitaria of Parma, Italy.\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki (1964) after obtaining approval from the local Ethics Committee. Data were manually collected from electronic and paper-based medical records. The LIPI score was collected at baseline for each patient, when available. This score considers two factors: the dNLR [neutrophils / (leukocytes minus neutrophils)] and serum LDH levels. A dLNR value greater than 3 or LDH levels above the upper limit of normal count for 1 point each. Based on the values of these two variables, patients are categorized into three prognostic groups: low-risk (0 points), intermediate-risk (1 point), and high-risk (2 points).\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to identify clinical and laboratory prognostic factors, including the LIPI score, associated with early 90-day mortality to ICI-based treatments.\u003c/p\u003e \u003cp\u003eBy integrating independent prognostic factors derived from a multivariable logistic regression model multivariate analysis, a nomogram was developed for 90-day mortality prediction within the development cohort. The training cohort was used to validate the nomogram\u0026rsquo;s performance.\u003c/p\u003e \u003cp\u003eThe secondary objectives were to investigate prognostic factors of 30-day mortality and early disease progression (\u0026le;\u0026thinsp;90 days from treatment initiation). The decision to use a 90-day cut-off for the primary and secondary objectives was based on literature data and clinical practice, where the initial radiological assessment is generally conducted after 3\u0026ndash;4 cycles of treatment (12 weeks).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eClinical and laboratory findings were analyzed as continuous or categorical variables, with median values and proportions reported, as appropriate. The normality of the distribution was verified using the Shapiro test. To compare means and proportions, T-test (ANOVA, Pearson correlation test if needed) and chi2-test (or Fisher's exact test, if needed) were performed. Overall survival (OS) was defined as the time between the start of treatment and death from any cause. Progression-free survival (OS) was defined as the time from treatment initiation to the first clinical or radiographical evidence of disease progression or death from any cause. The ROC curve analysis was performed to determine the area under the curve (AUC) for the score obtained by nomogram analysis to differentiate between patients who survived and those who died within 90 days. A multivariable logistic regression model was employed to investigate the factors associated with EM, and subsequently, the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Patients alive with a follow-up inferior to 30 or 90 days were excluded from the logistic regression analyses. A statistically significant p-value was considered when \u0026lt;\u0026thinsp;0.05. Statistical analyses were accomplished with R-Studio free software, version 2023.06.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics and survival outcomes\u003c/h2\u003e \u003cp\u003eA total of 637 patients were included in the training cohort. Most patients were males (61.9%) and had an ECOG PS of 0\u0026ndash;1 (86.5%). NSCLC was the most frequent tumor type (61.8%), followed by melanoma (17.3%), head-neck (11.3%), genitourinary (6.4%), and gastrointestinal (3.1%) tumors. 68.4% of patients received ICI as single-agent and were treated in first-line (67%). Baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median OS in the training cohort was 11.7 months (95% CI, 9.6\u0026ndash;15.0), and the median follow-up time was 26.1 months (IQR 11.7\u0026ndash;41.4). The median PFS in the training cohort was 5.6 months (95% CI, 4.8\u0026ndash;6.5). The COX-regression analyses for death and progression risk were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eS and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003eS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the training cohort. Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; NSCLC, non-small cell lung cancer; Num., number; met., metastasis; ICI, immune-checkpoint inhibitor; CT, chemotherapy; TKI, tyrosine-kinase inhibitor; dNLR, derived neutrophil-to-lymphocyte ratio; LIPI, lung immune-prognostic index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;636)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e238 (37.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e398 (62.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e394 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eECOG PS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e511 (80.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e274 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenitourinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead-neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e393 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNum. of metastatic\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003esites\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung met.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e291 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e339 (53.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrain met.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e526 (82.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver met.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e514 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLine of treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e426 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubsequent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT-ICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICI-ICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e435 (68.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimmuno-TKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003edLNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.99 (2.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.36 [0.0318, 20.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLIPI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e230 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEarly mortality and progression risk\u003c/h2\u003e \u003cp\u003eOne hundred and thirty-six patients (21.3%) died within 90 days. The distribution of baseline features according to 90-day mortality is reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics according to 90-day mortality in the training cohort. Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; NSCLC, non-small cell lung cancer; Num., number; met., metastasis; ICI, immune-checkpoint inhibitor; CT, chemotherapy; TKI, tyrosine-kinase inhibitor; dNLR, derived neutrophil-to-lymphocyte ratio; LIPI, lung immune-prognostic index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90-day mortality No\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90-day mortality Yes\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e385 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e224 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e233 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e288 (60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e375 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e398 (91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e486 (86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eformer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193 (59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71 (62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e264 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGastrointestinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenitourinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead-neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e278 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e384 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNum. of metastatic sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311 (85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e368 (83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung met.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e271 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e332 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain met.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e403 (85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101 (75.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e504 (83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver met.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e388 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100 (75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e488 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e329 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e403 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubsequent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e206 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT-ICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e157 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICI-ICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e325 (68.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e427 (70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eimmuno-TKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edLNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.6 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e217 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e181 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFifty-four patients (8.4%) died within 30 days. The distribution of baseline features according to 90-day mortality is reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eS.\u003c/p\u003e \u003cp\u003e220 patients (34.5%) had disease progression or death within 90 days. The distribution of baseline features according to early progression is reported in Table\u0026nbsp;4S.\u003c/p\u003e \u003cp\u003eAt univariable analyses, patients with ECOG PS 2, high/intermediate LIPI score, \u0026gt;\u0026thinsp;3 metastatic sites, brain and lung metastasis, and those treated with a subsequent line of treatment presented an increased risk of 90-day mortality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At multivariable analysis, ECOG PS 2 (OR 2.70, p 0.019), high (OR 11.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and intermediate LIPI score (OR 4.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independently associated with an increased risk of 90-day mortality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses for 90-day mortality in the training cohort. Abbreviations: CI, confidence interval; ECOG PS, Eastern Cooperative Oncology Group performance status; LIPI, lung immune-prognostic index; NSCLC, non-small cell lung cancer; ICI, immune-checkpoint inhibitor; met., metastasis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e90-day mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOdds\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eRatios\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCI (95%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOdds\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eRatios\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCI (95%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u0026ndash;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u0026ndash;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u0026ndash;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.58\u0026ndash;7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.18\u0026ndash;6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh LIPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.42\u0026ndash;31.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.34\u0026ndash;30.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate LIPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.01\u0026ndash;10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.16\u0026ndash;11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenitourinary cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u0026ndash;22.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u0026ndash;20.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead-neck cancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u0026ndash;33.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u0026ndash;70.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u0026ndash;18.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.13\u0026ndash;17.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026ndash;49.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u0026ndash;110.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u0026ndash;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u0026ndash;4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubsequent line of treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u0026ndash;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85\u0026ndash;23.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3 metastatic sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u0026ndash;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u0026ndash;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain met.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026ndash;3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.77\u0026ndash;4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver met.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026ndash;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u0026ndash;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung met.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u0026ndash;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.88\u0026ndash;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt univariable analyses, patients with ECOG PS 2, high/intermediate LIPI score, lung and liver metastasis, and those treated with a subsequent line of treatment presented an increased risk of 30-day mortality (Table\u0026nbsp;5S). At multivariable analysis, lung metastasis (OR 2.66, p\u0026thinsp;=\u0026thinsp;0.048), high (OR 8.09, p\u0026thinsp;=\u0026thinsp;0.006) and intermediate LIPI score (OR 8.62, p\u0026thinsp;=\u0026thinsp;0.001) were independently associated with increased risk of 30-day mortality (Table\u0026nbsp;5S).\u003c/p\u003e \u003cp\u003eAt univariable analyses, patients with ECOG PS 2, high LIPI score, NSCLC histology, \u0026gt;\u0026thinsp;3 metastatic sites, lung metastasis, and those treated with ICI single-agent and a subsequent line of treatment presented an increased risk of early progression (Table\u0026nbsp;6S). At multivariable analysis, high (OR 8.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and intermediate (OR 2.63, p\u0026thinsp;=\u0026thinsp;0.002) LIPI scores were independently associated with increased risk of early progression (Table\u0026nbsp;6S).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNomogram for 90-day mortality\u003c/h2\u003e \u003cp\u003eNext, we sought to build a nomogram to predict 90-day mortality using the variables that were significantly associated with increased risk of death at 90 days in the multivariable model. Among 637 patients in the training cohort, 212 were excluded because of missing ECOG PS or LIPI data or because they were alive with a follow-up \u0026lt;\u0026thinsp;3 months, for a total of 425 patients included in the analysis.\u003c/p\u003e \u003cp\u003eBased on the multivariable assessment, the produced score confers 37 points for ECOG PS 2, 64 points for an intermediate LIPI score, and 100 points for a high LIPI score. Patients with the maximum score (137) had a 70% risk of death within 90 days from treatment start. A nomogram representing the model is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Area Under the ROC for the score was 0.76 (95% CI, 0.71\u0026ndash;0.81) for 90-day mortality prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with a concordance index of 0.76. The same analysis was performed within each histology group. The AUC was 0.73 (95%CI, 0.67\u0026ndash;0.79) for NSCLC, 0.85 (95%CI, 0.73\u0026ndash;0.96) for melanoma, 0.78 (95%CI, 0.67\u0026ndash;0.89) for other tumor types (GU, GI, Head-neck).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation cohort\u003c/h2\u003e \u003cp\u003eA total of 255 patients were included in the validation cohort. Most patients were males (66.7%) and had an ECOG PS of 0\u0026ndash;1 (92.2%). NSCLC was the most frequent tumor type (67.5%), followed by genitourinary (23.5%) and melanoma (9%). CT-ICI combination and ICI monotherapy were the most frequently used regimens (43.9% and 40%, respectively) and the majority of patients (74.9%) were treated in first-line (Table\u0026nbsp;7S). Of them, 37.3% were in the low-risk group (n\u0026thinsp;=\u0026thinsp;95), 41.6% in the intermediate-risk group (n\u0026thinsp;=\u0026thinsp;106), and 21.2% in the high-risk group (n\u0026thinsp;=\u0026thinsp;54). After a median follow-up of 27.8 months (95%CI, 23.9\u0026ndash;31.3), 106 patients were alive (41.6%), and the median OS was 15.5 months (95% CI, 12.5\u0026ndash;22.3).\u003c/p\u003e \u003cp\u003eOverall, 46 patients (18%) died within 90 days. The baseline characteristics according to 90-day mortality are reported in Table\u0026nbsp;8S. The multivariable regression analysis for 90-day mortality risk was resumed in Table\u0026nbsp;9S.\u003c/p\u003e \u003cp\u003eWhen the score for 90-day mortality was applied, the Area Under the ROC was 0.72 (95% CI, 0.64\u0026ndash;0.80), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a study on a cohort of 637 patients with advanced solid tumors treated with ICI, either as single-agent or in combination with chemotherapy or other drugs. Our findings showed that 21.4% and 8.8% of patients died within 90 days and 30 days from treatment start, respectively. Furthermore, 35.3% of patients experienced early progression (\u0026le;\u0026thinsp;90 days) of their disease. We also analyzed the laboratory and clinical factors that contributed to EM and found that LIPI score and ECOG PS were independent predictors of 90-day mortality as a primary objective. Results from the study population were externally validated in 255 patients, and the prognostic role of intermediate-high LIPI score was confirmed.\u003c/p\u003e \u003cp\u003eBased on our results, we developed a novel scoring system that can predict 90-day mortality with a good degree of accuracy (AUC of 0.76), which was further confirmed in the external validation cohort (AUC of 0.72).\u003c/p\u003e \u003cp\u003eThe definition of EM ranges from 30 to 90 days after the start of treatment, with a prevalence of 20 to 35%, depending on the type of treatment and disease burden [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to a recent meta-analysis of 56 randomized controlled trials involving over 40,000 patients with various solid cancers, the rate of early death (\u0026le;\u0026thinsp;90 days) was higher with single-agent ICI treatment compared to other ICI treatments (14.2% vs. 6.7%) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our findings from a real-world context evidenced a slightly increased early death rate (21.4%) compared with their results, with no difference according to treatment received.\u003c/p\u003e \u003cp\u003eA large cohort study investigated the cause of EM, defined as within 60 days from treatment initiation among 7126 patients affected by solid cancers treated with ICI [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. NSCLC was the predominant tumor type (58.1%), followed by melanoma (23.3%) and other tumors, reflecting the epidemiology of our cohort [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Noteworthy, only 37.7% of patients had a stage IV disease at diagnosis, receiving predominantly ICI alone (57.8%). Patients treated at tertiary centers, those admitted to the Hospital and treated with prior radiation therapy or chemotherapy had the greater adjusted probability of 60-day mortality, as well as those who presented higher Edmonton Symptom Assessment System (ESAS) scores, anemia, and leukocytosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Conversely, patients presenting low NLR or higher BMI, and those receiving ICI\u0026thinsp;+\u0026thinsp;ICI had a lower risk of 60-day mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Interestingly, this large study evidenced a prognostic role of clinical conditions and laboratory tests, suggesting an external validity of our findings relative to LIPI and ECOG PS values.\u003c/p\u003e \u003cp\u003ePrescribing immunotherapy to frail patients may be influenced by an overestimation of the potential benefits of novel therapies or inadequate evaluation of deteriorating clinical conditions, even if for treatment-na\u0026iuml;ve or young patients. Remarkably, individuals with a baseline ECOG PS 2 or higher were associated with reduced survival rates and a higher probability of receiving ICI during the last month of life [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, no efficacy differences were found between ECOG PS 0 or 1 in solid cancer patients under ICI-based regimens in a comprehensive meta-analysis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], confirming the discriminative importance of ECOG PS 2.\u003c/p\u003e \u003cp\u003eOn the other hand, ECOG PS may not be informative enough and be connected to comorbidities or to specific cancer-related symptoms that may benefit from anti-cancer treatments themselves. Notwithstanding efficacy reduction, prospective trials on NSCLC patients confirmed that single-agent ICI may exhibit an acceptable toxicity profile for frail patients, paving the way for prescription [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe use of laboratory values may be useful in this setting to further select patients. In this context, the LIPI score, as previously described, combines dLNR and LDH levels with an established prognostic validity under immunotherapy regardless of the setting of treatment and disease type, reflecting an inflammatory status of the organism [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Indeed, altered LNR and LDH have been associated with EM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] or HPD [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] in several experiences. It should be noted that these findings may not be generalizable due to the lack of data on other types than NSCLC and the limitation of analyses focusing on EM.\u003c/p\u003e \u003cp\u003eIn an observational work performed by our research team, the short-term prognostic value of the LIPI score was investigated for the first time among advanced NSCLC patients treated with single-agent immunotherapy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. An intermediate-high LIPI score was independently associated with increased 90-day mortality risk. Notably, we confirmed the superiority of a combined clinical-laboratory test score, such as a modified PaP score that includes performance status, pivotal clinical symptoms (dyspnea, anorexia), and total leukocyte and lymphocyte counts [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter an internal validation of the prognostic relevance of ECOG PS and LIPI scores regardless of malignancies and type of treatment, we developed and externally validated a 90-day prognostic score with a good capability of early mortality risk assessment in the present work.\u003c/p\u003e \u003cp\u003eIn addition, we confirmed that an intermediate-high LIPI score was an independent risk factor for 30-day mortality and early progression (\u0026le;\u0026thinsp;90 days).\u003c/p\u003e \u003cp\u003eHowever, it is crucial to note that our study has some limitations. Firstly, it is a retrospective analysis, and further larger prospective studies are required to validate our findings. The rate of missing data for certain variables, such as LIPI, was consistent, which could lead to selection bias.\u003c/p\u003e \u003cp\u003eSecondly, our study population consisted primarily of patients with lung cancer, which may limit the generalizability of our results to other cancer types. Finally, no central revision of radiological imaging has been assessed, limiting the findings about radiological progression.\u003c/p\u003e \u003cp\u003eDespite these limitations, our study highlights the importance of EM prediction and personalized treatment strategies for advanced cancer patients. Moreover, this is the first study investigating the short-term prognostic value of LIPI, including patients of multiple malignancies (NSCLC, melanoma, head-neck, others) treated with single-agent ICIs but also ICI-combinations (chemotherapy, other ICI, TKI), and the score developed is easily performable with a good performance in an external cohort.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study emphasizes that the LIPI score and ECOG PS are independent predictors of 90-day mortality in the internal cohort. Importantly, the LIPI score also demonstrates significant prognostic value for 30-day mortality and early progression, making it a valuable tool for stratifying patients in clinical research and daily practice. Furthermore, the short-term prognostic significance of the LIPI score remains consistent in the validation cohort, underscoring its broad applicability in clinical practice regardless of the type of ICI-based regimen used. Our nomogram can assist clinicians in identifying patients at high risk of EM.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article cannot be shared due to the privacy of individuals who participated in the study, as stated by the local Ethics Committee (approval no. 2381/2019). Additional aggregated data analyses and the underlying analytic R code are available from the authors upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAndrea De Giglio, MD (Conceptualization; Data curation; Formal Analysis; Investigation; Methodology; Visualization; Writing-original draft; Writing-review and editing), Alessandro Leonetti (Data curation; Formal Analysis; Visualization; Writing-original draft; Writing-review and editing), \u0026nbsp;Francesca Comito, MD (Data curation; Writing-review and editing), Daria Maria Filippini, MD (Data curation; Writing\u0026mdash;review and editing), Veronica Mollica, MD (Data Curation; Methodology; Writing\u0026mdash;review and editing), Karim Rihawi, MD, (Data Curation; Methodology; Writing-review and editing), Marianna Peroni, MD(Data curation; Writing\u0026mdash;review and editing), Giulia Mazzaschi, MD (Data curation; Writing\u0026mdash;review and editing),Ilaria Ricciotti, MD (Data curation; writing-review and editing), Francesca Carosi, MD (Data curation; writing-review and editing), Andrea Marchetti, MD (Data curation; writing-review and editing), Matteo Rosellini, MD (Data curation; writing-review and editing), Elisabetta Nobili, MD (Writing-review and editing), Francesco Gelsomino, MD (Writing-review and editing), Barbara Melotti, MD (Writing-review and editing), Paola Valeria Marchese, MD (Writing-review and editing), Francesca Sperandi, MD (Writing-review and editing), Alessandro Di Federico, (Methodology; Writing-review and editing), Sebastiano Buti, MD, (Writing-review and editing), Fabiana Perrone, MD, (Writing-review and editing),Francesco Massari, MD (Writing-review and editing), \u0026nbsp;Maria Abbondanza Pantaleo, MD (Funding, Supervision; Validation; Writing-review and editing),Marcello Tiseo, MD, (Supervision; Validation; Writing-review and editing), \u0026nbsp;Andrea Ardizzoni, MD, (Conceptualization; Project administration; Supervision; Validation; Writing-review and editing).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research leading to these results has received funding from the European Union - NextGenerationEU through the Italian Ministry of University and Research under PNRR - M4C2-I1.3 Project PE_00000019 \u0026quot;HEAL ITALIA\u0026quot; to Andrea De Giglio, CUP J33C22002920006. The views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eADG, FC, DM, VM, KR, IR,FC, VF, AG, GM, SB, FP, AM,MR,EN,BM,PVM,FS,MAP declare no conflicts of interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFG has received personal fees or Advisory roles from Eli-Lilly, Novartis, AstraZeneca,\u0026nbsp;MSD\u003c/p\u003e\n\u003cp\u003eADF has received honoraria from Society for Immunotherapy of Cancer and Advisory role from Hansen-Wade.\u003c/p\u003e\n\u003cp\u003eFM has received research support and/or honoraria from Astellas, BMS, Janssen, Ipsen, MSD and Pfizer outside the\u0026nbsp;submitted\u0026nbsp;work.\u003c/p\u003e\n\u003cp\u003eAA has received Honoraria for the participation to advisory boards and/or for lectures from BMS, Eli-Lilly, MSD, AZ, Roche, Takeda, Janssen, Sanofi, Novartis, AbbVie, Daiichi\u003c/p\u003e\n\u003cp\u003eAL has received speakers\u0026rsquo; fee for Astra-Zeneca, MSD, Takeda and Sanofi. AL has been on advisory board for BeiGene, Sanofi, Novartis, Astra-Zeneca. AL has attended editorial activities sponsored by Roche and Eli Lilly. AL has received travel support from MSD and Novartis.\u003c/p\u003e\n\u003cp\u003eMT received speakers\u0026rsquo; and consultants\u0026rsquo; fee from Astra-Zeneca, Pfizer, Eli-Lilly, BMS, Novartis, Roche, MSD, Boehringer Ingelheim, Otsuka, Takeda, Pierre Fabre, Amgen, Merck, Sanofi. MT received institutional research grants from Astra-Zeneca, Boehringer Ingelheim.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo acknowledgements\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKluger HM, Tawbi HA, Ascierto ML et al (2020) Defining tumor resistance to PD-1 pathway blockade: recommendations from the first meeting of the SITC Immunotherapy Resistance Taskforce. 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Cancers (Basel) 14(23):5845 Published 2022 Nov 27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers14235845\u003c/span\u003e\u003cspan address=\"10.3390/cancers14235845\" 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":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4574786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4574786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eImmune checkpoint inhibitors (ICIs) are standard treatments for advanced solid cancers. Resistance to ICIs, both primary and secondary, poses challenges, with early mortality (EM) within 30\u0026ndash;90 days indicating a lack of benefit. Prognostic factors for EM, including the Lung Immune Prognostic Index (LIPI), remain underexplored.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe performed a retrospective, observational study including patients affected by advanced solid tumors, treated with ICI as single-agent or combined with other agents. Logistic regression models identified factors associated with EM and 90-day progression risks. A nomogram for predicting 90-day mortality was built and validated within an external cohort .\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003e637 patients received ICIs (single-agent or in combination with other drugs) for advanced solid tumors. Most patients were male (61.9%), with NSCLC as the prevalent tumor (61.8%). Within the cohort, 21.3% died within 90 days, 8.4% died within 30 days, and 34.5% experienced early progression. Factors independently associated with 90-day mortality included ECOG PS 2 and a high/intermediate LIPI score. For 30-day mortality, lung metastasis and a high/intermediate LIPI score were independent risk factors. Regarding early progression, high/intermediate LIPI score was independently associated. A predictive nomogram for 90-day mortality combining LIPI and ECOG PS achieved an AUC of 0.76 (95% CI, 0.71\u0026ndash;0.81). The discrimination ability of the nomogram was confirmed in the external validation cohort (n\u0026thinsp;=\u0026thinsp;255) (AUC 0.72,95% CI, 0.64\u0026ndash;0.80).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eLIPI and ECOG PS independently were able to estimate 90-day mortality, with LIPI also demonstrating prognostic validity for 30-day mortality and early progression.\u003c/p\u003e","manuscriptTitle":"Development and validation of a new tool to estimate early mortality in patients with advanced cancer treated with immunotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 17:36:56","doi":"10.21203/rs.3.rs-4574786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-14T03:24:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-10T11:31:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-01T18:55:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-30T06:23:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230489140974336229189124158459898480476","date":"2024-06-25T03:03:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336020650893270323862738146440563010504","date":"2024-06-25T02:22:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27418093842970650466788660876593723865","date":"2024-06-23T13:26:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-23T02:06:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-14T08:01:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-14T08:00:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2024-06-13T08:47:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4ea00a4d-a11e-4b41-8286-e3a2f9efb9c9","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T16:05:15+00:00","versionOfRecord":{"articleIdentity":"rs-4574786","link":"https://doi.org/10.1007/s00262-024-03836-w","journal":{"identity":"cancer-immunology-immunotherapy","isVorOnly":false,"title":"Cancer Immunology, Immunotherapy"},"publishedOn":"2024-10-03 15:58:16","publishedOnDateReadable":"October 3rd, 2024"},"versionCreatedAt":"2024-07-16 17:36:56","video":"","vorDoi":"10.1007/s00262-024-03836-w","vorDoiUrl":"https://doi.org/10.1007/s00262-024-03836-w","workflowStages":[]},"version":"v1","identity":"rs-4574786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4574786","identity":"rs-4574786","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}