Prediction models and prognostic analysis of immune-related acute kidney injury in lung cancer patients

Prediction models and prognostic analysis of immune-related acute kidney injury in lung cancer patients | 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 Prediction models and prognostic analysis of immune-related acute kidney injury in lung cancer patients Suying Qian, Ningjie Xu, Yihui Qu, Rongrong Zhu, Minqiao Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5388659/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Immune checkpoint inhibitors (ICIs) are extensively utilized in lung cancer patients, with documented instances of ICIs-associated acute kidney injury (ICIs-AKI). This study aims to explore the incidence rates, clinical features, risk factors, and prognostic outcomes of ICIs-AKI, while developing a model for early recognition of ICIs-AKI. Methods: The study involved 413 adult lung cancer patients treated with ICIs at Ningbo No.2 Hospital between Sept. 1, 2021, and June 30, 2023. Patients were followed until death or Dec. 31, 2023, and categorized into ICIs-AKI or non-AKI groups. Prediction models for ICIs-AKI were developed using logistic regression and MLP neural networks. Cox proportional-hazards models assessed the association between ICIs-AKI and overall survival. Results: The study included 381 lung cancer patients receiving ICIs treatment after excluding 32 patients. ICIs-AKI occurred in 13.39% of cases, with a median onset time of [123 (63, 303)] days. Multivariable logistic analysis identified diabetes, proteinuria, extrarenal irAEs, diuretic use, and chemotherapy as significant risk factors (all P <0.05), while higher baseline eGFR levels were protective ( P <0.05). Two prediction models were developed: logistic regression (AUC=0.877, sensitivity=0.922, specificity=0.726) and MLP (AUC=0.950, accuracy=0.843, precision=0.847). Survival analysis showed no difference in overall survival between ICIs-AKI and non-AKI groups (HR=1.021, 95% CI=0.629-1.659, P =0.932; adjusted HR=0.950, 95% CI=0.558-1.616, P =0.849). AKI to CKD progression incidence was 58.82%, with no significant difference in overall survival between CKD and non-CKD groups ( P =0.157). Conclusion: This study offers detailed insights into ICIs-AKI, including its rate, onset timing, risk factors, and clinical features. Approximately half of the affected patients experienced spontaneous renal function recovery. Both logistic regression and MLP models effectively predicted ICIs-AKI. Importantly, neither ICIs-AKI incidence nor renal function restoration correlated with patient mortality. These findings improve understanding of ICIs-AKI and underscore the importance of early detection and management strategies. lung cancer immune checkpoint inhibitors acute kidney injury prediction models prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Lung cancer emerged as the second most commonly diagnosed cancer and the leading cause of cancer-related deaths in 2020, representing approximately one in 10 (11.4%) of all cancer diagnoses and one in 5 (18.0%) cancer-related deaths [ 1 ] . Oncologists face an urgent need to develop novel treatments that effectively target tumor cells to address this widespread challenge. Immune checkpoint inhibitors (ICIs) have emerged as promising interventions that enhance immune responses against tumors by activating T cells to eliminate malignant cells, leading to significant improvements in survival rates across various cancer types, particularly melanoma and lung cancer [ 2 ] . With a growing body of clinical evidence, treatment regimens based on ICIs have gained approval as frontline therapies for lung cancer [ 3 ] . Unfortunately, ICIs have been shown to trigger autoimmune toxicities, leading to immune-related adverse events (irAEs) that can affect nearly any organ [ 4 ] . The incidence rates of irAEs in patients undergoing ICIs treatment vary widely, ranging from 15–90%, with the most commonly observed affecting the skin, gastrointestinal tract, endocrine system, and liver [ 5 ] . Despite the relatively low frequency of renal-associated irAEs, the expanding literature on the topic has facilitated a deeper understanding and recognition of immune checkpoint inhibitor-related acute kidney injury (ICIs-AKI). Acute interstitial nephritis (AIN) has been identified as the predominant pathology associated with ICIs, accounting for approximately 90% of biopsied patients [ 6 ] . Additionally, cases of lupus nephritis, IgA nephropathy, thrombotic microangiopathy, minimal change nephrosis, membranous nephropathy, and focal segmental glomerulosclerosis have been reported [ 7 ] . Previous studies have examined the incidence and risk factors associated with ICIs-AKI. The study by Tan and Sprangers highlighted that exposure to proton pump inhibitors (PPIs) correlated with the emergence of clinically significant short- and long-term kidney-related adverse effects in ICIs-treated patients. However, establishing causality remains a challenge [ 8 ] . Additionally, exposure to PPIs and nonsteroidal anti-inflammatory drugs (NSAIDs) was associated with an increased odds ratio (OR) for the risk of ICIs-AKI [ 9 ] . While several authors have noted an association between low baseline estimated glomerular filtration rates (eGFR) and ICIs-AKI, this finding has not been consistent across studies [ 10 ] . Recently, some studies have described significantly higher risks of irAEs occurrence in patients who received pembrolizumab, did not have central nervous system metastases, had a history of autoimmune disorders, and underwent chemotherapy in combination with ICIs. Race, socioeconomic status, prior radiation therapy, and comorbidity burden were identified as factors associated with the development of specific types of irAEs [ 11 ] . Nevertheless, reliable biomarkers for predicting the occurrence of ICIs-AKI are still lacking. In summary, this study conducted a retrospective analysis of lung cancer patients undergoing ICIs treatment, aiming to elucidate the incidence and clinical characteristics of ICIs-AKI in real-world settings. The study seeks to identify effective predictors of ICIs-AKI and develop a predictive model for its occurrence. Additionally, long-term clinical outcomes and renal recovery after AKI were evaluated through follow-up assessments. 2 Materials and methods 2.1. Study subjects A total of 413 adult patients diagnosed with lung cancer who underwent ICIs treatment were recruited from Sept. 1, 2021 to June 30, 2023 at Ningbo No.2 Hospital. Inclusion criteria for this study were as follows: (1) age ≥ 18 years; (2) diagnosis of primary lung cancer according to the Guidelines for the Diagnosis and Treatment of Primary Lung Cancer in 2023 [ 3 ] ; (3) receipt of at least one course of ICIs treatment. Exclusion criteria were as follows: (1) presence of end-stage renal disease, renal transplant, or undergoing renal replacement therapy; (2) incomplete clinical data; and (3) acute kidney injury attributed to other causes such as hypovolemia, urinary obstruction, or other factors. The enrollment process is illustrated in Fig. 1 . This study was approved by the Ethics Committee of Ningbo No. 2 Hospital (YJ-NBEY-KY-2023-013-01). 2.2 Definition and diagnostic criteria AKI was defined in accordance with KDIGO guidelines [ 12 ] as meeting any of the following criteria (not graded): an increase in sCr by ≥ 0.3 mg/dl (≥ 26.5 µmol/L) within 48 hours; or an increase in sCr to ≥ 1.5 times the baseline, which is known or presumed to have occurred within the prior 7 days; or urine volume < 0.5 ml/kg/hour for 6 hours. Staging of AKI according to KDIGO [ 12 ] : (1) Stage 1: absolute increase in sCr ≥ 0.3 mg/dL (≥ 26.5 µmol/L) or ≥ 1.5 to 2.0 fold from baseline; (2) Stage 2: increase in sCr > 2.0 to 3.0 fold from baseline; (3) Stage 3: increase in sCr > 3 fold from baseline or increase of sCr to ≥ 4.0 mg/dL (≥ 354 µmol/L), or initiation of renal replacement therapy. In patients < 18 years old, a decrease in eGFR to < 35 ml/min/1.73 m 2 is considered indicative. Chronic kidney disease (CKD) was defined as renal structural or functional abnormalities persisting for ≥ 3 months due to various causes according to KDIGO [ 12 ] . Staging of CKD: (1) Stage 1: eGFR ≥ 90 ml/min/1.73m 2 ; (2) Stage 2: 60 ≤ eGFR < 90 ml/min/1.73m 2 ; (3) Stage 3: 30 ≤ eGFR < 60 ml/min/1.73m 2 ; (4) Stage 4: 15 ≤ eGFR < 30 ml/min/1.73m 2 ; (5) Stage 5: eGFR < 15 ml/min/1.73m 2 . 2.3 Follow up The patients were followed up until death or Dec. 31, 2023. Based on disease progression, participants were categorized into two groups: (1) ICIs-AKI group; (2) non-AKI group. The primary outcome assessed was overall survival, defined as the duration from the initiation of ICIs therapy to death from any cause. CKD staging was determined based on eGFR levels three months after AKI onset. Patients were classified into CKD stages 3–5 as one subgroup and CKD stages 1–2 as another subgroup, further divided into: (1) CKD group; (2) non-CKD group. 2.4 Data collection Basic information on study subjects was collected, including age, gender, height, weight, duration of ICIs treatment, history of hypertension, history of diabetes, and medication history such as PPIs, NSAIDs, antibiotics, chemotherapy, and radiation therapy. Laboratory data included white blood cell count (WBC), hemoglobin (Hb), lymphocyte ratio, triglycerides (TC), albumin (ALB), creatinine (Cr), blood urea nitrogen (BUN), uric acid (UA), and liver function parameters such as aspartate aminotransferase (AST) and alanine aminotransferase (ALT). Additionally, electrolyte levels, proteinuria, and medical history of extrarenal irAEs were also recorded. BMI was calculated as weight in kilograms divided by the square of height in meters. Residual renal function (RRF), represented by the glomerular filtration rate (GFR), was assessed using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation (CKD-EPI). For males with sCr ≤ 0.9 mg/dL, the equation is 141 × (sCr/0.9) −0.411 × 0.993 Age ; for sCr > 0.9 mg/dl, it is 141 × (sCr/0.9) −1.209 × 0.993 Age . For females with sCr ≤ 0.7 mg/dL, the equation is 144 × (sCr/0.7) −0.329 × 0.993 Age ; for sCr > 0.7 mg/dL, it is 144 × (sCr/0.7) −1.209 × 0.993 Age . IrAEs were defined as encephalitis, myocarditis, pneumonitis, hepatitis, thyroiditis, colitis, etc. Baseline laboratory tests were conducted at Ningbo No. 2 Hospital's laboratory within two weeks before initiating ICIs treatment to evaluate the occurrence of ICIs-AKI. 2.5 Construction and evaluation of MLP predictive models The Multilayer Perceptron (MLP) stands as a classic form of feedforward neural network, esteemed for its reliability, flexibility, nonlinearity, and widespread applicability in neural networks. Comprising multiple layers of neurons, including input, hidden, and output layers, the MLP operates by receiving raw data through its input layer, which is then processed through hidden layers, serving as abstract interfaces. Ultimately, the output layer generates predictions or class labels based on the given problem. Renowned for its self-learning and modeling capabilities, the MLP is adept at handling nonlinear and complex problem domains while exhibiting strong generalization abilities. Upon completion of training, the MLP model can discern correlations within unseen real-world data, rendering it invaluable for predictive analysis and adeptly managing large datasets. Through adjustments to network complexity and weight values, it can effectively navigates parameter complexity. The non-parametric nature of the MLP enables it to minimizing errors in parameter estimation. Furthermore, the utilization of the MLP classifier imposes no constraints on the overall distribution of input data, presenting a notable advantage in its application. After selecting pertinent factors from all independent variables, patients were partitioned into a 70% training set and a 30% testing set. The MLP was employed as the classifier in our research study. Initially, the classification model underwent training, followed by evaluation using the testing set, utilizing specific performance metrics such as accuracy rate, precision, recall, F1-score, and more. Model evaluation was conducted based on the receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC) metrics. ROC-AUC was utilized to characterize the overall classification performance of the model, while PR-AUC was employed to assess the predictive performance of the model. ROC-AUC assesses the overall classification performance of the model, whereas PR-AUC evaluates the predictive performance specifically concerning positive samples. 2.6 Statistical methods Descriptive statistics were utilized to outline the baseline characteristics of patients. Categorical variables were depicted as absolute values and percentages, while normally distributed continuous variables were represented as means ± standard deviations (SD). Continuous variables conforming to a normal distribution were reported as mean ± standard deviation (SD), and group comparisons were performed using independent sample t-tests. Conversely, variables not adhering to a normal distribution were predominantly described as median (Q1, Q3), with group comparisons conducted using the Mann-Whitney U test. Categorical variables were expressed as proportions or rates, and group comparisons were carried out using the chi-square test. Clinical data underwent correlation and regression analyses, including logistic regression models and MLP predictive models. The accuracy of predictive models was assessed using the area under the receiver operating characteristic curve (AUC). Survival curves were generated using the Kaplan-Meier method. Covariates with P < 0.1 in univariate regression analysis were included in multivariate regression analysis. Statistical significance was defined as P < 0.05. All analyses were conducted using SPSS 24.0 and Python 3.7.6. 3 Results 3.1 Baseline characteristic A total of 381 patients with lung cancer who underwent ICIs treatment were included in the study, following the exclusion of 32 patients. Among them, 51 patients were classified into the ICIs-AKI group, while 330 patients were in the non-AKI group. The incidence of ICIs-AKI was 13.39%. Among them, 32 patients (62.75%) experienced mild acute kidney injury (KDIGO stage 1), 10 patients (19.61%) experienced moderate acute kidney injury (KDIGO stage 2), and 9 patients experienced severe acute kidney injury (KDIGO stage 3). The median time to onset of ICIs-AKI was [123 (63, 303)] days, with 27.45% of patients experiencing AKI within 3 months of initiating ICIs and 41.18% within 6 months. The ICIs-AKI group demonstrated a higher prevalence of hypertension, diabetes, proteinuria, and extrarenal irAEs compared to the non-AKI group (all P < 0.05). Regarding combination therapy, the ICIs-AKI group had a higher frequency of diuretic, chemotherapy, and radiotherapy usage compared to the non-AKI group ( P < 0.05, respectively). Additionally, the ICIs-AKI group exhibited significantly lower concentrations of Hb and eGFR, along with higher concentrations of sCr, BUN, and uric acid compared to the non-AKI group ( P < 0.05, respectively). Detailed data are presented in Table 1 . Table 1 Baseline characteristics of subjects in the study Variable ICIs-AKI(n = 51) non-AKI (n = 330) χ 2 /t p value Gender [n(%)] 2.144 0.143 Men 42(82.35) 295(89.40) Female 9(17.65) 35(10.60) Age (year) 68.51 ± 7.85 67.46 ± 7.55 -0.919 0.359 < 60 5(9.80) 42(12.73) 60–69 22(43.14) 156(47.27) 70–79 21(41.18) 115(34.85) ≥ 80 3(5.88) 17(5.15) BMI (kg/m 2 ) 22.48 ± 2.99 22.13 ± 2.99 -0.776 0.438 Types of ICIs -1.065 0.288 Anti-PD-1 47(92.16) 317(96.06) Anti-PD-L1 4(7.84) 13(3.94) Chronic diseases history Hypertension[n (%)] 25(49.02) 91(27.58) 9.592 0.002 * Diabetes [n (%)] 12(23.53) 30(9.10) 9.389 0.002 * Combination therapy PPI 36(70.59) 202(61.21) 1.656 0.198 NSAIDs 10(19.60) 47(14.24) 0.498 0.480 Diuretic 13(25.50) 46(13.94) 4.503 0.034 * Antibiotic 9(17.65) 59(17.88) 0.002 0.968 Chemotherapy 46(90.20) 172(52.12) 26.160 < 0.001 * Radiotherapy 5(9.80) 11(3.33) 4.597 0.032 * Laboratory measurements eGFR [ml/min/1.73m 2 ] 83.10 ± 22.06 97.91 ± 14.13 6.387 < 0.001 * Hemoglobin (g/L) 121.45 ± 21.20 126.52 ± 17.23 2.513 0.046 WBC(×10 9 /L) 7.05 ± 2.66 6.60 ± 2.35 -1.246 0.213 Lymphocyte ratio 0.20 ± 0.10 0.23 ± 0.11 0.879 0.068 Triglycerides (mmol/L) 1.45 ± 0.59 1.58 ± 0.80 1.119 0.264 Albumin (g/L) 38.45 ± 5.86 38.84 ± 4.80 0.531 0.596 sCr(µmol/L) 80.88 ± 27.26 67.07 ± 13.78 -5.663 < 0.001 * BUN(mmol/L) 6.12 ± 2.37 5.44 ± 1.66 -2.553 0.011 * UA (µmol/L) 370.27 ± 95.22 335.24 ± 91.73 -2.525 0.012 * AST 28.52 ± 11.52 28.30 ± 18.30 -0.089 0.929 ALT 23.41 ± 17.75 23.66 ± 24.31 0.069 0.945 K + 4.12 ± 0.44 4.20 ± 0.39 1.338 0.182 Na + 139.64 ± 3.52 139.10 ± 7.69 -0.496 0.620 Cl − 103.87 ± 4.05 103.71 ± 3.45 -0.297 0.767 CO 2 25.45 ± 2.61 25.78 ± 3.10 0.739 0.461 Ca 2+ 2.25 ± 0.15 2.28 ± 0.14 1.366 0.173 Mg 2+ 0.83 ± 0.05 0.82 ± 0.07 -0.246 0.805 P 1.11 ± 0.16 1.11 ± 0.17 -0.072 0.943 CRP 20.65 ± 42.01 22.03 ± 33.82 0.262 0.793 Proteinuria [n (%)] 11(21.57) 7(2.12) 37.115 < 0.001 * Extrarenal irAEs [n (%)] 12(23.53) 21(6.36) 16.453 < 0.001 * AKI: acute kidney injury; BMI: body mass index; Anti-PD-1: programmed death1 inhibitors; Anti-PD-L1: programmed death⁃ligand 1; PPI: proton pump inhibitors; NSAIDs: nonsteroidal anti-inflammatory drugs; eGFR: estimated glomerular filtration rate; WBC: white blood cell; sCr: creatinine; BUN: blood urea nitrogen; UA: uric acid; AST: aspartate aminotransferase; ALT: Alanine aminotransferase; CRP: C-reactive protein *P < 0.05 3.2 Logistic regression In the univariable logistic analysis, significant associations were observed between the history of hypertension, concurrent proteinuria, concurrent extrarenal irAEs, diuretic usage, chemotherapy combination, and the occurrence of AKI in lung cancer patients undergoing ICIs therapy (all P < 0.05). Furthermore, the occurrence of ICIs-AKI demonstrated positive correlations with baseline blood urea nitrogen and uric acid levels, while exhibiting negative correlations with baseline hemoglobin and eGFR levels. Detailed results of this analysis are provided in Table 2 . Table 2 Univariable logistic regression analysis of factors associated with ICIs-AKI Variable β S.E Wald OR 95%CI P Gender -0.591 0.409 2.094 0.554 0.249 ~ 1.233 0.148 Age 0.019 0.020 0.846 1.019 0.979 ~ 1.060 0.358 BMI 0.039 0.050 0.603 1.040 0.943 ~ 1.146 0.437 Types of ICIs 0.232 0.553 0.177 1.262 0.427 ~ 3.730 0.674 Hypertension 0.926 0.306 9.165 2.525 1.386 ~ 4.600 0.002* Diabetes 0.716 0.368 3.779 2.047 0.994 ~ 4.213 0.052 PPI 0.419 0.327 1.639 1.521 0.801 ~ 2.889 0.200 NSAIDs 0.384 0.386 0.990 1.469 0.689 ~ 3.131 0.320 Diuretic 0.748 0.358 4.351 2.112 1.046 ~ 4.264 0.037* Antibiotic -0.016 0.394 0.002 0.984 0.454 ~ 2.132 0.968 Chemotherapy 0.486 0.486 7.799 3.886 1.499 ~ 10.075 0.005* Radiotherapy 0.522 0.524 0.990 1.685 0.603 ~ 4.709 0.320 eGFR -0.051 0.009 30.227 0.950 0.933 ~ 0.968 < 0.001* Hemoglobin -0.016 0.008 3.899 0.984 0.969 ~ 1.000 0.048* WBC 0.074 0.059 1.543 1.076 0.958 ~ 1.209 0.214 Lymphocyte ratio -2.904 1.593 3.332 0.055 0.002 ~ 1.246 0.068 Triglycerides -0.267 0.239 1.250 0.766 0.480 ~ 1.222 0.264 Albumin -0.016 0.030 0.283 0.984 0.928 ~ 1.044 0.595 sCr 0.044 0.009 23.185 1.045 1.026 ~ 1.063 < 0.001* BUN 0.192 0.077 6.196 1.212 1.042 ~ 1.410 0.013* UA 0.004 0.002 6.189 1.004 1.001 ~ 1.007 0.013* AST 0.001 0.008 0.008 1.001 0.984 ~ 1.017 0.929 ALT 0.001 0.007 0.005 1.000 0.987 ~ 1.013 0.945 K + -0.504 0.377 1.782 0.604 0.288 ~ 1.266 0.182 Na + 0.018 0.036 0.244 1.018 0.948 ~ 1.093 0.622 Cl − 0.013 0.043 0.089 1.013 0.930 ~ 1.103 0.766 CO 2 -0.035 0.048 0.546 0.965 0.880 ~ 1.060 0.460 Ca 2+ -1.508 1.101 1.878 0.221 0.026 ~ 1.913 0.171 Mg 2+ 0.552 2.235 0.061 1.737 0.022 ~ 138.746 0.805 P 0.065 0.908 0.005 1.067 0.180 ~ 6.328 0.943 CRP -0.001 0.005 0.069 0.999 0.990 ~ 1.008 0.793 Proteinuria 2.541 0.512 24.652 12.689 4.654 ~ 34.595 < 0.001* Extrarenal irAEs 1.510 0.400 14.269 4.527 2.068 ~ 9.912 < 0.001* BMI: body mass index; PPI: proton pump inhibitors; NSAIDs: nonsteroidal anti-inflammatory drugs; eGFR: estimated glomerular filtration rate; WBC: white blood cell; sCr: creatinine; BUN: blood urea nitrogen; UA: uric acid; AST: aspartate aminotransferase; ALT: Alanine aminotransferase; CRP: C-reactive protein *P < 0.05 The indicators that showed statistical significance in the univariable logistic analysis were utilized as covariates in a multivariable logistic analysis. The results of the multivariable logistic regression analysis indicated that a history of diabetes, concurrent proteinuria, concurrent extrarenal irAEs, diuretic use, and chemotherapy combination remained significantly associated with the occurrence of AKI in lung cancer patients undergoing ICIs therapy (all P < 0.05). Furthermore, higher eGFR was identified as a protective factor against ICIs-AKI, as demonstrated in Table 3 . Table 3 Multivariable logistic regression analysis of ICIs-AKI β S.E Wald OR 95%CI P Hypertension 0.177 0.393 0.203 1.194 0.552 ~ 2.581 0.653 Diabetes 0.967 0.490 3.892 2.630 1.006 ~ 6.874 0.049* Diuretic 0.996 0.433 5.293 2.706 1.159 ~ 6.320 0.021* Chemotherapy 1.419 0.555 6.545 4.134 1.394 ~ 12.265 0.011* eGFR -0.043 0.016 7.027 0.958 0.928 ~ 0.989 0.008* Hemoglobin -0.012 0.010 1.520 0.988 0.969 ~ 1.007 0.218 Lymphocyte ratio -2.201 1.964 1.256 0.111 0.002 ~ 5.194 0.262 Serum creatinine 0.020 0.017 1.352 1.020 0.986 ~ 1.056 0.245 Blood urea nitrogen -0.050 0.113 0.193 0.952 0.763 ~ 1.187 0.660 Uric acid 0.002 0.002 0.531 1.002 0.997 ~ 1.006 0.466 Proteinuria 2.638 0.618 18.207 13.980 4.162 ~ 46.956 < 0.001* Extrarenal irAEs 1.772 0.515 11.832 5.885 2.144 ~ 16.165 < 0.001* *P < 0.05 3.3 Predictive models 3.3.1 Logistic regression model After conducting comparative analyses of factors between the ICIs-AKI and non-AKI groups, the predictive factors identified from multifactor analysis were employed to develop a logistic regression model for predicting ICIs-AKI. These factors included hypertension, diabetes, diuretic use, chemotherapy, eGFR, hemoglobin levels, lymphocyte ratio, creatinine levels, blood urea nitrogen levels, uric acid levels, presence of proteinuria, and occurrence of extrarenal irAEs. The Hosmer-Lemeshow test resulted in χ 2 = 10.596, P = 0.226 > 0.05. The area under the ROC curve (AUC) was calculated as 0.877 [95% CI (0.831, 0.923)], with a sensitivity of 0.922 and a specificity of 0.726, demonstrating a high level of accuracy for the predictive model. These results are depicted in Fig. 2 . 3.3.2 MLP predictive model The dataset was divided into a training set comprising 70% and a validation set comprising 30%, utilizing an MLP classifier. The results demonstrated the model's accurate predictive performance, with an accuracy of 0.843, precision of 0.847, recall of 0.989, F1 score of 0.913, and AUC of 0.950. The area under the PR curve (AP) was calculated as 0.762, indicating good model performance. These findings suggest that the MLP model is well-suited for predicting the occurrence of ICIs-AKI, as detailed in Table 4 and depicted in Fig. 3 . Table 4 Confusion matrices of MLP model TP FP FN TN ACC Pre Recall F-Measure AUC MLP 94 17 1 3 0.843 0.847 0.989 0.913 0.950 MLP: multilayer perceptron model; TP: true positives; FP: false positives; FN: false negatives; TN: true negatives; ACC: accuracy; Pre: precision; Recall: recall rate; F-Measure: F1 score; AUC: area under the ROC curve 3.4 Survival analysis The unadjusted Cox regression analysis revealed no statistically significant difference in overall survival between the ICIs-AKI group and the non-AKI group [HR = 1.021, 95% CI (0.629, 1.659), P = 0.932]. Upon adjustment for diabetes, NSAIDs and antibiotic usage, eGFR levels, hemoglobin, lymphocyte ratio, albumin, serum creatinine, uric acid, Cl − , Mg 2+ and CRP concentration, the absence of a statistically significant difference in overall survival between the ICIs-AKI group and the non-AKI group persisted [HR = 0.950, 95% CI (0.558, 1.616), P = 0.849]. Notably, the independent association of NSAIDs usage, Cl − , and Mg 2+ concentration with mortality was observed. Detailed results of this analysis are provided in Table 5 and illustrated in Fig. 4 . Table 5 Unadjusted and adjusted cox regression for overall survival following ICIs treatment Unadjusted Adjusted Variable HR 95%CI P HR 95%CI P ICIs-AKI 1.021 0.629 ~ 1.659 0.932 0.950 0.558 ~ 1.616 0.849 Gender 1.289 0.785 ~ 2.116 0.316 Age 0.999 0.976 ~ 1.022 0.913 BMI 0.970 0.916 ~ 1.028 0.306 Types of ICIs 0.753 0.395 ~ 1.436 0.390 Hypertension 1.068 0.740 ~ 1.541 0.725 Diabetes 0.645 0.421 ~ 0.989 0.045* 0.721 0.462 ~ 1.126 0.151 PPI 0.786 0.552 ~ 1.119 0.182 NSAIDs 0.403 0.273 ~ 0.595 < 0.001* 0.614 0.391 ~ 0.965 0.034* Diuretic 0.874 0.562 ~ 1.358 0.548 Antibiotic 0.664 0.445 ~ 0.990 0.044* 1.061 0.651 ~ 1.729 0.811 Chemotherapy 1.178 0.818 ~ 1.699 0.379 Radiotherapy 0.776 0.419 ~ 1.437 0.420 eGFR 1.012 1.001 ~ 1.023 0.036* 1.007 0.991 ~ 1.024 0.374 Hemoglobin 0.988 0.980 ~ 0.997 0.006* 0.995 0.985 ~ 1.006 0.386 WBC 1.052 0.980 ~ 1.128 0.159 Lymphocyte ratio 0.066 0.011 ~ 0.405 0.003* 0.416 0.057 ~ 3.020 0.386 Triglycerides 1.005 0.818 ~ 1.235 0.959 Albumin 0.936 0.907 ~ 0.965 < 0.001* 0.956 0.913 ~ 1.001 0.055 sCr 0.983 0.971 ~ 0.994 0.003* 0.994 0.977 ~ 1.011 0.473 BUN 0.972 0.881 ~ 1.072 0.567 UA 0.998 0.996 ~ 1.000 0.034* 1.000 0.998 ~ 1.002 0.765 AST 1.003 0.995 ~ 1.012 0.476 ALT 0.993 0.983 ~ 1.004 0.209 K + 0.805 0.526 ~ 1.232 0.318 Na + 0.994 0.978 ~ 1.010 0.496 Cl − 0.918 0.880 ~ 0.958 < 0.001* 0.950 0.905 ~ 0.996 0.035* CO 2 0.995 0.942 ~ 1.050 0.847 Ca 2+ 0.710 0.206 ~ 2.446 0.587 Mg 2+ 0.026 0.002 ~ 0.280 0.003* 0.071 0.005 ~ 0.932 0.044* P 0.556 0.205 ~ 1.508 0.249 CRP 1.004 1.001 ~ 1.008 0.019* 0.997 0.991 ~ 1.002 0.227 Proteinuria 1.487 0.609 ~ 3.635 0.384 Extrarenal irAEs 1.237 0.650 ~ 2.356 0.517 BMI: body mass index; PPI: proton pump inhibitors; NSAIDs: nonsteroidal anti-inflammatory drugs; eGFR: estimated glomerular filtration rate; WBC: white blood cell; sCr: creatinine; BUN: blood urea nitrogen; UA: uric acid; AST: aspartate aminotransferase; ALT: Alanine aminotransferase; CRP: C-reactive protein *P < 0.05 3.5 The renal outcome Among the initial 51 patients diagnosed with ICIs-AKI, 21 patients ultimately experienced renal function recovery, resulting in a recovery rate of 41.18%. The incidence of AKI to CKD progression was observed to be 58.82%. In the CKD group, there were 25 males and 5 females, with a mean age of (70.87 ± 6.10) years and a median eGFR of [56.33 (46.0,78.19)] ml/min/1.73m 2 . Within this group, 28 patients were classified as CKD stage 3, while 1 patient each was categorized into CKD stage 4 and CKD stage 5. Moreover, no statistically significant difference in overall survival was found between the CKD group and the non-CKD group ( P = 0.157). These results are depicted in Fig. 5 . 4 Discussion This retrospective study revealed an incidence of ICIs-AKI at 13.39%. We identified several risk factors potentially contributing to ICIs-AKI, including diabetes, proteinuria, extrarenal irAEs, diuretic use, combination chemotherapy, and low baseline eGFR levels. A noteworthy aspect of this study was the development of prediction models using logistic regression and MLP classifier, demonstrating robust predictive capabilities. These models aid in early clinical diagnosis of ICIs-AKI, thereby potentially mitigating the occurrence of severe adverse reactions. Surprisingly, we found no association between the occurrence of ICIs-AKI and primary or renal outcomes. In this cohort of lung cancer patients treated with ICIs therapy, we observed an incidence of ICIs-AKI at 13.39%. This rate slightly surpasses findings from other studies, which typically report incidences ranging from 2–5% [ 2 , 5 , 11 , 13 ] . A single-center retrospective study conducted in China documented a 12.8% incidence of renal-associated irAEs [ 14 ] . Furthermore, projections suggest that the occurrence rate of renal-associated irAEs may rise to approximately 9.9%-19% in the future [ 15 ] . Discrepancies in AKI incidence rates among studies stem from variations in inclusion criteria for AKI diagnosis. Additionally, while renal biopsy serves as the gold standard for diagnosing ICIs-AKI, its utilization in clinical practice is limited among cancer patients experiencing renal dysfunction, resulting in a low detection rate of ICIs-AKI. In our study, we found that the median onset time for ICIs-AKI was [123 (63, 303)] days, with 27.45% of patients experiencing AKI within 3 months of initiating ICIs treatment and 41.18% within 6 months. These findings are generally consistent with results from several foreign studies [ 9 , 11 ] . The duration of onset for ICIs-AKI is typically influenced by multiple factors. Onset times vary among different irAEs, with ICIs-AKI often exhibiting a longer latent period compared to extrarenal irAEs. For example, dermatitis typically occurs within 4 weeks after initiation of ICIs treatment, colitis usually develops within 6 weeks, immune-related hypophysitis manifests as neurologic symptoms at 6 weeks post-ICIs treatment, while skin toxicity typically arises after 3.6 weeks [ 16 ] . The onset time of ICIs-AKI also depends on the type of ICIs used for treating malignant tumors. Furthermore, compared to the use of PD-1 inhibitors alone, the combination of CTLA-4 inhibitors and PD-1 inhibitors leads to a higher incidence of kidney-related irAEs [ 17 ] . The onset time for AKI with CTLA-4 inhibitors is typically 6–12 weeks, whereas with PD-1 inhibitors, it ranges from 6–8 months [ 18 ] . The significant risk factors identified in our study, including the presence of diabetes, proteinuria, extrarenal irAEs, and low baseline eGFR levels, emerged as independent risk factors for ICIs-AKI, consistent with findings from studies conducted by Seethapathy and Liu [ 17 , 19 ] . Additionally, some studies have reported associations of ICIs-AKI with certain medications, such as PPI and NSAIDs usage [ 8 , 20 , 21 ] . In our cohort, we observed that the use of diuretics increased the risk of AKI in lung cancer patients receiving ICIs therapy. This underscores the importance of vigilance regarding drug combinations with ICIs in clinical practice. In our cohort, combination chemotherapy emerged as an independent risk factor for ICIs-AKI. Similar and divergent findings have been reported. A meta-analysis encompassing all cancer types did find an association with combining ICIs and chemotherapy treatments [ 22 ] , aligning with our results. However, a recent large cohort study focusing on NSCLC did not find an increased risk with combining ICIs and other agents [ 23 ] . Nevertheless, the discordance observed in these findings may be attributed to challenges in diagnosis. Other pertinent findings in our study include a 41.18% complete kidney recovery rate at 90 days and an incidence of AKI to CKD progression of 58.82%. The majority of patients presented at CKD stage 3, which generally carries a favorable prognosis. Previous studies have indicated that renal function recovers in more than half of ICIs-AKI patients, which may be attributed to the mild to moderate injury observed in most cases of ICIs-AKI [ 24 ] . Moreover, the recovery of renal function has been associated with the early usage of corticosteroids [ 24 ] , with some cases achieving over 90% recovery. Thus, early recognition of ICIs-AKI is crucial for improving prognosis. In our study, two clinical prediction models were developed for the early recognition of ICIs-AKI using logistic regression and MLP classifiers. By integrating multidimensional indicators through multiple fitting, these models achieved good predictive sensitivity and specificity. Particularly, the MLP prediction model exhibited superior overall performance with an AUC of 0.950, accuracy of 0.843, precision of 0.847, recall of 0.990, and F1 score of 0.913. These results outperformed previous AKI prediction models reported in other studies. For instance, Yu et al. [ 25 ] employed a neural networks (NNs) model as an AKI prediction model in patients undergoing ICIs treatment, achieving a competitive AUC of 0.8167, accuracy of 0.7703, recall of 0.7, precision of 0.56, and F1 value of 0.6222. In future clinical practice, these prediction models may be utilized for baseline risk assessment of ICIs-AKI in patients, enabling evaluation of the risk, adjustment of treatment plans, or rigorous monitoring of ICIs-AKI, thereby holding significant clinical significance. There have been relatively few studies investigating the primary outcome of ICIs-AKI. In our study, we found no association between ICIs-AKI and increased mortality regardless of disease status, a finding supported by other reports [ 19 , 26 , 27 ] . Additionally, we observed no statistically significant difference in overall survival between the CKD group and the non-CKD group. This could be attributed to the relatively mild AKI observed in most patients, with many having recovered renal function or being at an early stage of chronic kidney disease. An alternative theory suggests that the occurrence of irAEs, including ICIs-AKI, may indicate an effective antitumor response and therefore could potentially improve survival [ 28 ] . Recent studies have indicated a positive correlation between the occurrence of irAEs and the efficacy of ICIs [ 29 ] , supporting this hypothesis. Another explanation could be that the follow-up period might not have been long enough to establish a link between CKD and mortality. The overall survival of ICIs-AKI appears to be closely linked to the malignant tumor status and varies depending on the underlying cause of AKI. Increased mortality risk was not observed in patients with ICIs-AKI, but rather in those with AKI attributed to other causes, where complications such as tumor progression or infections may contribute to a higher mortality rate [ 28 ] . In another study, the mortality rate of ICIs-AKI was reported to be as high as 93.2% [ 30 ] . Therefore, further studies with larger samples and longer follow-up durations may be warranted to elucidate the prognosis of ICIs-AKI. There were several limitations in this study. Firstly, as a single-center study, the sample size was relatively small, and external validation was lacking, thus the predictive efficacy of the models remains uncertain. Future steps should involve constructing prospective study cohorts for model validation. Secondly, the majority of patients included in this study were treated with PD-1 inhibitors, with a smaller portion receiving PD-L1 inhibitors, and data on CTLA-4 inhibitors were lacking. Thirdly, only a few patients underwent renal biopsy, which limited our ability to supplement information on renal pathology and investigate the mechanism of ICIs-AKI. 5 Conclusion In this retrospective study, we explored the rate and onset timing, risk factors, and clinical features associated with ICIs-AKI. Notably, renal function was observed to recover in about half of the patients with ICIs-AKI. Importantly, our logistic regression and MLP models demonstrated robust predictive capabilities for the early recognition of ICIs-AKI. Moreover, neither the occurrence of ICIs-AKI nor the recovery of renal function was correlated with patient mortality. Future studies incorporating longitudinal biospecimen collection are needed to provide additional insight into the prognosis and mechanisms of ICIs-AKI, and to aid clinicians in differentiating it from other causes of AKI. Abbreviations ICIs Immune checkpoint inhibitors ICIs-AKI ICIs-associated acute kidney injury IrAEs immune-related adverse events AIN Acute interstitial nephritis PPIs Proton pump inhibitors NSAIDs Nonsteroidal anti-inflammatory drugs OR Odds ratio eGFR estimated glomerular filtration rates CKD Chronic kidney disease WBC White blood cell count Hb Hemoglobin TC Triglycerides ALB Albumin Cr Creatinine BUN Blood urea nitrogen UA Uric acid AST Aspartate aminotransferase ALT Alanine aminotransferase RRF Residual renal function CKD-EPI Chronic Kidney Disease Epidemiology Collaboration creatinine equation MLP The Multilayer Perceptron ROC-AUC Receiver operating characteristic area under the curve PR-AUC Precision-recall area under the curve AUC Receiver operating characteristic curve NNs Neural networks Declarations Ethics approval and consent to participate This study adhered to the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of the Ningbo No. 2 Hospital (YJ-NBEY-KY-2023-013-01). Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This study was supported by Medical Scientific Research Foundation of Zhejiang Province, China (2020KY853, 2023KY283). Author contributions Concept and design: Suying qian, Ningjie Xu, Xiaoyan Lu and Kedan Cai. Extraction and collection of data:Ningjie Xu, Yihui Qu, Rongrong Zhu, Minqiao Zhang,and Kanan Chen. Statistical analysis: Ningjie Xu and Kedan Cai. Drafting and revision of manuscript: Suying qian, Ningjie Xu and Kedan Cai. Supervision:Kedan Cai. Final approval of manuscript:Suying Qian, Ningjie Xu, Yihui Qu, Rongrong Zhu, Minqiao Zhang, Kanan Chen, Jing Wang, Xiaoyan Lu, Kedan Cai. All authors contributed to the article and approved the final version. Acknowledgements Not applicable Statement Informed consent was waived by ethical committee (Ethics Committee of the Ningbo No. 2 Hospital) due to retrospective nature of the study. References Sung H, Ferlay J, Siegel RL et al. Global Cancer Statistics. 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71 (3): 209–249. Sprangers B, Leaf DE, Porta C, et al. Diagnosis and management of immune checkpoint inhibitor-associated acute kidney injury [J]. Nat Rev Nephrol. 2022;18(12):794–805. Oncology Society of Chinese Medical Association, House CMAP. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2023 edition) [J]. Natl Med J China. 2023;103(27):2037–74. Sullivan RJ, Weber JS. Immune-related toxicities of checkpoint inhibitors: mechanisms and mitigation strategies [J]. Nat Rev Drug Discov. 2022;21(7):495–508. Liu C, Wei W, Yang L, et al. Incidence and risk factors of acute kidney injury in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis [J]. Front Immunol. 2023;14:1173952. Liu K, Qin Z, Ge Y, et al. Acute kidney injury in advanced lung cancer patients treated with PD-1 inhibitors: a single center observational study [J]. J Cancer Res Clin Oncol. 2023;149(8):5061–70. Ramos-Casals M, Brahmer JR, Callahan MK, et al. Immune-related adverse events of checkpoint inhibitors [J]. Nat Rev Dis Primers. 2020;6(1):38. Tan HZ, Sprangers B. Proton pump inhibitors and adverse kidney outcomes during immune checkpoint blockade: time to sound the alarm? [J]. Clin Kidney J. 2023;16(11):1709–13. Chen JJ, Lee TH, Kuo G, et al. All-cause and immune checkpoint inhibitor-associated acute kidney injury in immune checkpoint inhibitor users: a meta-analysis of occurrence rate, risk factors and mortality [J]. Clin Kidney J. 2024;17(1):292. Abudayyeh A, Suo L, Lin H, et al. Pathologic Predictors of Response to Treatment of Immune Checkpoint Inhibitor-Induced Kidney Injury [J]. Cancers (Basel). 2022;14(21):5267. Rong Y, Bentley JP, Bhattacharya K, et al. Incidence and risk factors of immune-related adverse events induced by immune checkpoint inhibitors among older adults with non-small cell lung cancer [J]. Cancer Med. 2024;13(1):e6879. Group KDIGOKAKIW. KDIGO clinical practice guideline for acute kidney injury [J]. Kidney Int. 2012;2(1 Suppl):1–138. Cook S, Samuel V, Meyers DE, et al. Immune-Related Adverse Events and Survival Among Patients With Metastatic NSCLC Treated With Immune Checkpoint Inhibitors [J]. JAMA Netw Open. 2024;7(1):e2352302. Xu LY, Zhao HY, Yu XJ, et al. Clinicopathological Features of Kidney Injury Related to Immune Checkpoint Inhibitors: A Systematic Review [J]. J Clin Med. 2023;12(4):1349. Wanchoo R, Karam S, Uppal NN, et al. Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review [J]. Am J Nephrol. 2017;45(2):160–9. Weber JS, Kahler KC, Hauschild A. Management of immune-related adverse events and kinetics of response with ipilimumab [J]. J Clin Oncol. 2012;30(21):2691–7. Seethapathy H, Street S, Strohbehn I, et al. Immune-related adverse events and kidney function decline in patients with genitourinary cancers treated with immune checkpoint inhibitors [J]. Eur J Cancer. 2021;157:50–8. Liu K, Qin Z, Xu X, et al. Comparative Risk of Renal Adverse Events in Patients Receiving Immune Checkpoint Inhibitors: A Bayesian Network Meta-Analysis [J]. Front Oncol. 2021;11:662731. Meraz-Munoz A, Amir E, Ng P, et al. Acute kidney injury associated with immune checkpoint inhibitor therapy: incidence, risk factors and outcomes [J]. J Immunother Cancer. 2020;8(1):e000467. Qin Z, Liu K, Xu X, et al. Incidence, predictors and 6-month overall outcome of acute kidney injury in Chinese patients receiving PD-1 inhibitors [J]. Future Oncol. 2022;18(16):1951–62. Shirali AC, Perazella MA, Gettinger S. Association of Acute Interstitial Nephritis With Programmed Cell Death 1 Inhibitor Therapy in Lung Cancer Patients [J]. Am J Kidney Dis. 2016;68(2):287–91. Liu F, Wang Z, Li X, et al. Comparative risk of acute kidney injury among cancer patients treated with immune checkpoint inhibitors [J]. Cancer Commun (Lond). 2023;43(2):214–24. Gupta S, Strohbehn IA, Wang Q, et al. Acute kidney injury in patients receiving pembrolizumab combination therapy versus pembrolizumab monotherapy for advanced lung cancer [J]. Kidney Int. 2022;102(4):930–5. Gupta S, Short SAP, Sise ME, et al. Acute kidney injury in patients treated with immune checkpoint inhibitors [J]. J Immunother Cancer. 2021;9(10):e003467. Yu X, Wu R, Ji Y, et al. Identifying Patients at Risk of Acute Kidney Injury among Patients Receiving Immune Checkpoint Inhibitors: A Machine Learning Approach [J]. Diagnostics (Basel). 2022;12(12):3157. Lumlertgul N, Vassallo P, Tydeman F, et al. Acute kidney injury in patients receiving immune checkpoint inhibitors: a retrospective real-world study [J]. Eur J Cancer. 2023;191:112967. Stein C, Burtey S, Mancini J, et al. Acute kidney injury in patients treated with anti-programmed death receptor-1 for advanced melanoma: a real-life study in a single-centre cohort [J]. Nephrol Dial Transpl. 2021;36(9):1664–74. Koks MS, Ocak G, Suelmann BBM, et al. Immune checkpoint inhibitor-associated acute kidney injury and mortality: An observational study [J]. PLoS ONE. 2021;16(6):e0252978. Zheng LP, Yang J, Chen XW, et al. Correlation of preclinical and clinical biomarkers with efficacy and toxicity of cancer immunotherapy [J]. Ther Adv Med Oncol. 2023;15:17588359231163807. Ji MS, Wu R, Feng Z, et al. Incidence, risk factors and prognosis of acute kidney injury in patients treated with immune checkpoint inhibitors: a retrospective study [J]. Sci Rep. 2022;12(1):18752. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5388659","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378801998,"identity":"1ad4382d-5e67-40a3-b7a8-b7ec697db655","order_by":0,"name":"Suying Qian","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Suying","middleName":"","lastName":"Qian","suffix":""},{"id":378801999,"identity":"96297435-3952-44ba-ac95-6fda611b4013","order_by":1,"name":"Ningjie Xu","email":"","orcid":"","institution":"Ningbo No. 2 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13:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5388659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5388659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71876789,"identity":"48624fb0-9adc-4c66-a8cc-527b0c7779f8","added_by":"auto","created_at":"2024-12-19 11:02:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1210739,"visible":true,"origin":"","legend":"\u003cp\u003eEnrollment scheme in this study\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5388659/v1/aa055798bd1ec5bc236069e2.png"},{"id":71876790,"identity":"32df5605-faf5-4bbe-b1ef-316609c44705","added_by":"auto","created_at":"2024-12-19 11:02:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":382384,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve of multivariable for prediction of ICIs-AKI\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5388659/v1/1538aeedb6f7bb15279aee18.png"},{"id":71876108,"identity":"9a50e074-e9e5-4202-a72d-9d09f51300fb","added_by":"auto","created_at":"2024-12-19 10:54:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":637553,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC and PR curves of the validation sets for the MLP model\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5388659/v1/4f0329da534ec7aa37930699.png"},{"id":71876105,"identity":"c3691217-f4af-447e-8562-834c8757d1e2","added_by":"auto","created_at":"2024-12-19 10:54:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":992725,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Unadjusted Kaplan–Meier survival plot between ICIs-AKI group and non-AKI group; (B) Adjusted Kaplan–Meier survival plot between ICIs-AKI group and non-AKI group\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5388659/v1/8fddc5e6d56facebeba4dc5b.png"},{"id":71876107,"identity":"9ee9ded0-bc7f-41cd-aa95-92dd7936ddfa","added_by":"auto","created_at":"2024-12-19 10:54:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":741982,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival plot between CKD group and non-CKD group\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5388659/v1/ccffe9fa8bf0fd6b161753f0.png"},{"id":78098625,"identity":"494a142e-9c01-4dd9-97f9-e495727a4d88","added_by":"auto","created_at":"2025-03-10 00:46:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5361434,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5388659/v1/5a269f5c-b620-4bcf-b32c-a512cc125c2a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction models and prognostic analysis of immune-related acute kidney injury in lung cancer patients","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung cancer emerged as the second most commonly diagnosed cancer and the leading cause of cancer-related deaths in 2020, representing approximately one in 10 (11.4%) of all cancer diagnoses and one in 5 (18.0%) cancer-related deaths\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Oncologists face an urgent need to develop novel treatments that effectively target tumor cells to address this widespread challenge. Immune checkpoint inhibitors (ICIs) have emerged as promising interventions that enhance immune responses against tumors by activating T cells to eliminate malignant cells, leading to significant improvements in survival rates across various cancer types, particularly melanoma and lung cancer\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. With a growing body of clinical evidence, treatment regimens based on ICIs have gained approval as frontline therapies for lung cancer\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnfortunately, ICIs have been shown to trigger autoimmune toxicities, leading to immune-related adverse events (irAEs) that can affect nearly any organ\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The incidence rates of irAEs in patients undergoing ICIs treatment vary widely, ranging from 15\u0026ndash;90%, with the most commonly observed affecting the skin, gastrointestinal tract, endocrine system, and liver\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Despite the relatively low frequency of renal-associated irAEs, the expanding literature on the topic has facilitated a deeper understanding and recognition of immune checkpoint inhibitor-related acute kidney injury (ICIs-AKI). Acute interstitial nephritis (AIN) has been identified as the predominant pathology associated with ICIs, accounting for approximately 90% of biopsied patients\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Additionally, cases of lupus nephritis, IgA nephropathy, thrombotic microangiopathy, minimal change nephrosis, membranous nephropathy, and focal segmental glomerulosclerosis have been reported\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have examined the incidence and risk factors associated with ICIs-AKI. The study by Tan and Sprangers highlighted that exposure to proton pump inhibitors (PPIs) correlated with the emergence of clinically significant short- and long-term kidney-related adverse effects in ICIs-treated patients. However, establishing causality remains a challenge\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Additionally, exposure to PPIs and nonsteroidal anti-inflammatory drugs (NSAIDs) was associated with an increased odds ratio (OR) for the risk of ICIs-AKI\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. While several authors have noted an association between low baseline estimated glomerular filtration rates (eGFR) and ICIs-AKI, this finding has not been consistent across studies\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Recently, some studies have described significantly higher risks of irAEs occurrence in patients who received pembrolizumab, did not have central nervous system metastases, had a history of autoimmune disorders, and underwent chemotherapy in combination with ICIs. Race, socioeconomic status, prior radiation therapy, and comorbidity burden were identified as factors associated with the development of specific types of irAEs\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, reliable biomarkers for predicting the occurrence of ICIs-AKI are still lacking.\u003c/p\u003e \u003cp\u003eIn summary, this study conducted a retrospective analysis of lung cancer patients undergoing ICIs treatment, aiming to elucidate the incidence and clinical characteristics of ICIs-AKI in real-world settings. The study seeks to identify effective predictors of ICIs-AKI and develop a predictive model for its occurrence. Additionally, long-term clinical outcomes and renal recovery after AKI were evaluated through follow-up assessments.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study subjects\u003c/h2\u003e \u003cp\u003eA total of 413 adult patients diagnosed with lung cancer who underwent ICIs treatment were recruited from Sept. 1, 2021 to June 30, 2023 at Ningbo No.2 Hospital. Inclusion criteria for this study were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) diagnosis of primary lung cancer according to the Guidelines for the Diagnosis and Treatment of Primary Lung Cancer in 2023\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e; (3) receipt of at least one course of ICIs treatment. Exclusion criteria were as follows: (1) presence of end-stage renal disease, renal transplant, or undergoing renal replacement therapy; (2) incomplete clinical data; and (3) acute kidney injury attributed to other causes such as hypovolemia, urinary obstruction, or other factors. The enrollment process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study was approved by the Ethics Committee of Ningbo No. 2 Hospital (YJ-NBEY-KY-2023-013-01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition and diagnostic criteria\u003c/h2\u003e \u003cp\u003eAKI was defined in accordance with KDIGO guidelines\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e as meeting any of the following criteria (not graded): an increase in sCr by \u0026ge;\u0026thinsp;0.3 mg/dl (\u0026ge;\u0026thinsp;26.5 \u0026micro;mol/L) within 48 hours; or an increase in sCr to \u0026ge;\u0026thinsp;1.5 times the baseline, which is known or presumed to have occurred within the prior 7 days; or urine volume\u0026thinsp;\u0026lt;\u0026thinsp;0.5 ml/kg/hour for 6 hours.\u003c/p\u003e \u003cp\u003eStaging of AKI according to KDIGO\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e: (1) Stage 1: absolute increase in sCr\u0026thinsp;\u0026ge;\u0026thinsp;0.3 mg/dL (\u0026ge;\u0026thinsp;26.5 \u0026micro;mol/L) or \u0026ge;\u0026thinsp;1.5 to 2.0 fold from baseline; (2) Stage 2: increase in sCr\u0026thinsp;\u0026gt;\u0026thinsp;2.0 to 3.0 fold from baseline; (3) Stage 3: increase in sCr\u0026thinsp;\u0026gt;\u0026thinsp;3 fold from baseline or increase of sCr to \u0026ge;\u0026thinsp;4.0 mg/dL (\u0026ge;\u0026thinsp;354 \u0026micro;mol/L), or initiation of renal replacement therapy. In patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years old, a decrease in eGFR to \u0026lt;\u0026thinsp;35 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e is considered indicative.\u003c/p\u003e \u003cp\u003eChronic kidney disease (CKD) was defined as renal structural or functional abnormalities persisting for \u0026ge;\u0026thinsp;3 months due to various causes according to KDIGO\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStaging of CKD: (1) Stage 1: eGFR\u0026thinsp;\u0026ge;\u0026thinsp;90 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e; (2) Stage 2: 60\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;90 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e; (3) Stage 3: 30\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e; (4) Stage 4: 15\u0026thinsp;\u0026le;\u0026thinsp;eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e; (5) Stage 5: eGFR\u0026thinsp;\u0026lt;\u0026thinsp;15 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Follow up\u003c/h2\u003e \u003cp\u003eThe patients were followed up until death or Dec. 31, 2023. Based on disease progression, participants were categorized into two groups: (1) ICIs-AKI group; (2) non-AKI group. The primary outcome assessed was overall survival, defined as the duration from the initiation of ICIs therapy to death from any cause. CKD staging was determined based on eGFR levels three months after AKI onset. Patients were classified into CKD stages 3\u0026ndash;5 as one subgroup and CKD stages 1\u0026ndash;2 as another subgroup, further divided into: (1) CKD group; (2) non-CKD group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data collection\u003c/h2\u003e \u003cp\u003eBasic information on study subjects was collected, including age, gender, height, weight, duration of ICIs treatment, history of hypertension, history of diabetes, and medication history such as PPIs, NSAIDs, antibiotics, chemotherapy, and radiation therapy. Laboratory data included white blood cell count (WBC), hemoglobin (Hb), lymphocyte ratio, triglycerides (TC), albumin (ALB), creatinine (Cr), blood urea nitrogen (BUN), uric acid (UA), and liver function parameters such as aspartate aminotransferase (AST) and alanine aminotransferase (ALT). Additionally, electrolyte levels, proteinuria, and medical history of extrarenal irAEs were also recorded.\u003c/p\u003e \u003cp\u003eBMI was calculated as weight in kilograms divided by the square of height in meters. Residual renal function (RRF), represented by the glomerular filtration rate (GFR), was assessed using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation (CKD-EPI). For males with sCr\u0026thinsp;\u0026le;\u0026thinsp;0.9 mg/dL, the equation is 141 \u0026times; (sCr/0.9)\u003csup\u003e\u0026minus;0.411\u003c/sup\u003e \u0026times; 0.993\u003csup\u003eAge\u003c/sup\u003e; for sCr\u0026thinsp;\u0026gt;\u0026thinsp;0.9 mg/dl, it is 141 \u0026times; (sCr/0.9)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e \u0026times; 0.993\u003csup\u003eAge\u003c/sup\u003e. For females with sCr\u0026thinsp;\u0026le;\u0026thinsp;0.7 mg/dL, the equation is 144 \u0026times; (sCr/0.7)\u003csup\u003e\u0026minus;0.329\u003c/sup\u003e \u0026times; 0.993\u003csup\u003eAge\u003c/sup\u003e; for sCr\u0026thinsp;\u0026gt;\u0026thinsp;0.7 mg/dL, it is 144 \u0026times; (sCr/0.7)\u003csup\u003e\u0026minus;1.209\u003c/sup\u003e \u0026times; 0.993\u003csup\u003eAge\u003c/sup\u003e. IrAEs were defined as encephalitis, myocarditis, pneumonitis, hepatitis, thyroiditis, colitis, etc. Baseline laboratory tests were conducted at Ningbo No. 2 Hospital's laboratory within two weeks before initiating ICIs treatment to evaluate the occurrence of ICIs-AKI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Construction and evaluation of MLP predictive models\u003c/h2\u003e \u003cp\u003eThe Multilayer Perceptron (MLP) stands as a classic form of feedforward neural network, esteemed for its reliability, flexibility, nonlinearity, and widespread applicability in neural networks. Comprising multiple layers of neurons, including input, hidden, and output layers, the MLP operates by receiving raw data through its input layer, which is then processed through hidden layers, serving as abstract interfaces. Ultimately, the output layer generates predictions or class labels based on the given problem. Renowned for its self-learning and modeling capabilities, the MLP is adept at handling nonlinear and complex problem domains while exhibiting strong generalization abilities. Upon completion of training, the MLP model can discern correlations within unseen real-world data, rendering it invaluable for predictive analysis and adeptly managing large datasets. Through adjustments to network complexity and weight values, it can effectively navigates parameter complexity. The non-parametric nature of the MLP enables it to minimizing errors in parameter estimation. Furthermore, the utilization of the MLP classifier imposes no constraints on the overall distribution of input data, presenting a notable advantage in its application.\u003c/p\u003e \u003cp\u003eAfter selecting pertinent factors from all independent variables, patients were partitioned into a 70% training set and a 30% testing set. The MLP was employed as the classifier in our research study. Initially, the classification model underwent training, followed by evaluation using the testing set, utilizing specific performance metrics such as accuracy rate, precision, recall, F1-score, and more. Model evaluation was conducted based on the receiver operating characteristic area under the curve (ROC-AUC) and precision-recall area under the curve (PR-AUC) metrics. ROC-AUC was utilized to characterize the overall classification performance of the model, while PR-AUC was employed to assess the predictive performance of the model.\u003c/p\u003e \u003cp\u003eROC-AUC assesses the overall classification performance of the model, whereas PR-AUC evaluates the predictive performance specifically concerning positive samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical methods\u003c/h2\u003e \u003cp\u003eDescriptive statistics were utilized to outline the baseline characteristics of patients. Categorical variables were depicted as absolute values and percentages, while normally distributed continuous variables were represented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD). Continuous variables conforming to a normal distribution were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and group comparisons were performed using independent sample t-tests. Conversely, variables not adhering to a normal distribution were predominantly described as median (Q1, Q3), with group comparisons conducted using the Mann-Whitney U test. Categorical variables were expressed as proportions or rates, and group comparisons were carried out using the chi-square test.\u003c/p\u003e \u003cp\u003eClinical data underwent correlation and regression analyses, including logistic regression models and MLP predictive models. The accuracy of predictive models was assessed using the area under the receiver operating characteristic curve (AUC). Survival curves were generated using the Kaplan-Meier method. Covariates with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate regression analysis were included in multivariate regression analysis. Statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted using SPSS 24.0 and Python 3.7.6.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristic\u003c/h2\u003e \u003cp\u003eA total of 381 patients with lung cancer who underwent ICIs treatment were included in the study, following the exclusion of 32 patients. Among them, 51 patients were classified into the ICIs-AKI group, while 330 patients were in the non-AKI group. The incidence of ICIs-AKI was 13.39%. Among them, 32 patients (62.75%) experienced mild acute kidney injury (KDIGO stage 1), 10 patients (19.61%) experienced moderate acute kidney injury (KDIGO stage 2), and 9 patients experienced severe acute kidney injury (KDIGO stage 3). The median time to onset of ICIs-AKI was [123 (63, 303)] days, with 27.45% of patients experiencing AKI within 3 months of initiating ICIs and 41.18% within 6 months.\u003c/p\u003e \u003cp\u003eThe ICIs-AKI group demonstrated a higher prevalence of hypertension, diabetes, proteinuria, and extrarenal irAEs compared to the non-AKI group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding combination therapy, the ICIs-AKI group had a higher frequency of diuretic, chemotherapy, and radiotherapy usage compared to the non-AKI group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). Additionally, the ICIs-AKI group exhibited significantly lower concentrations of Hb and eGFR, along with higher concentrations of sCr, BUN, and uric acid compared to the non-AKI group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). Detailed data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of subjects in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICIs-AKI(n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-AKI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;330)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender [n(%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42(82.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e295(89.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e9(17.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35(10.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(9.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42(12.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22(43.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156(47.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21(41.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115(34.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of ICIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-PD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47(92.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e317(96.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-PD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4(7.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic diseases history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension[n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(49.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91(27.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12(23.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30(9.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombination therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36(70.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202(61.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10(19.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47(14.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(25.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46(13.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(17.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59(17.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46(90.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172(52.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(9.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11(3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory measurements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR [ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.10\u0026thinsp;\u0026plusmn;\u0026thinsp;22.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.91\u0026thinsp;\u0026plusmn;\u0026thinsp;14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.45\u0026thinsp;\u0026plusmn;\u0026thinsp;21.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126.52\u0026thinsp;\u0026plusmn;\u0026thinsp;17.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC(\u0026times;10\u003csup\u003e9\u003c/sup\u003e /L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esCr(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.88\u0026thinsp;\u0026plusmn;\u0026thinsp;27.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.07\u0026thinsp;\u0026plusmn;\u0026thinsp;13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e370.27\u0026thinsp;\u0026plusmn;\u0026thinsp;95.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e335.24\u0026thinsp;\u0026plusmn;\u0026thinsp;91.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.52\u0026thinsp;\u0026plusmn;\u0026thinsp;11.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.30\u0026thinsp;\u0026plusmn;\u0026thinsp;18.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.66\u0026thinsp;\u0026plusmn;\u0026thinsp;24.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139.10\u0026thinsp;\u0026plusmn;\u0026thinsp;7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.78\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.65\u0026thinsp;\u0026plusmn;\u0026thinsp;42.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.03\u0026thinsp;\u0026plusmn;\u0026thinsp;33.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(21.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrarenal irAEs [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12(23.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21(6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAKI: acute kidney injury; BMI: body mass index; Anti-PD-1: programmed death1 inhibitors; Anti-PD-L1: programmed death⁃ligand 1; PPI: proton pump inhibitors; NSAIDs: nonsteroidal anti-inflammatory drugs; eGFR: estimated glomerular filtration rate; WBC: white blood cell; sCr: creatinine; BUN: blood urea nitrogen; UA: uric acid; AST: aspartate aminotransferase; ALT: Alanine aminotransferase; CRP: C-reactive protein\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Logistic regression\u003c/h2\u003e \u003cp\u003eIn the univariable logistic analysis, significant associations were observed between the history of hypertension, concurrent proteinuria, concurrent extrarenal irAEs, diuretic usage, chemotherapy combination, and the occurrence of AKI in lung cancer patients undergoing ICIs therapy (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the occurrence of ICIs-AKI demonstrated positive correlations with baseline blood urea nitrogen and uric acid levels, while exhibiting negative correlations with baseline hemoglobin and eGFR levels. Detailed results of this analysis are provided 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\u003eUnivariable logistic regression analysis of factors associated with ICIs-AKI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.249\u0026thinsp;~\u0026thinsp;1.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.979\u0026thinsp;~\u0026thinsp;1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u0026thinsp;~\u0026thinsp;1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of ICIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.427\u0026thinsp;~\u0026thinsp;3.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.386\u0026thinsp;~\u0026thinsp;4.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u0026thinsp;~\u0026thinsp;4.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\u0026thinsp;~\u0026thinsp;2.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.689\u0026thinsp;~\u0026thinsp;3.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.046\u0026thinsp;~\u0026thinsp;4.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.454\u0026thinsp;~\u0026thinsp;2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.499\u0026thinsp;~\u0026thinsp;10.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.603\u0026thinsp;~\u0026thinsp;4.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.933\u0026thinsp;~\u0026thinsp;0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.969\u0026thinsp;~\u0026thinsp;1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.958\u0026thinsp;~\u0026thinsp;1.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u0026thinsp;~\u0026thinsp;1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.480\u0026thinsp;~\u0026thinsp;1.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.928\u0026thinsp;~\u0026thinsp;1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.026\u0026thinsp;~\u0026thinsp;1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.042\u0026thinsp;~\u0026thinsp;1.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.001\u0026thinsp;~\u0026thinsp;1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.984\u0026thinsp;~\u0026thinsp;1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.987\u0026thinsp;~\u0026thinsp;1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.288\u0026thinsp;~\u0026thinsp;1.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.948\u0026thinsp;~\u0026thinsp;1.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.930\u0026thinsp;~\u0026thinsp;1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880\u0026thinsp;~\u0026thinsp;1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u0026thinsp;~\u0026thinsp;1.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u0026thinsp;~\u0026thinsp;138.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.180\u0026thinsp;~\u0026thinsp;6.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.990\u0026thinsp;~\u0026thinsp;1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.654\u0026thinsp;~\u0026thinsp;34.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eExtrarenal irAEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.068\u0026thinsp;~\u0026thinsp;9.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI: body mass index; PPI: proton pump inhibitors; NSAIDs: nonsteroidal anti-inflammatory drugs; eGFR: estimated glomerular filtration rate; WBC: white blood cell; sCr: creatinine; BUN: blood urea nitrogen; UA: uric acid; AST: aspartate aminotransferase; ALT: Alanine aminotransferase; CRP: C-reactive protein\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e*P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe indicators that showed statistical significance in the univariable logistic analysis were utilized as covariates in a multivariable logistic analysis. The results of the multivariable logistic regression analysis indicated that a history of diabetes, concurrent proteinuria, concurrent extrarenal irAEs, diuretic use, and chemotherapy combination remained significantly associated with the occurrence of AKI in lung cancer patients undergoing ICIs therapy (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, higher eGFR was identified as a protective factor against ICIs-AKI, as demonstrated in 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\u003eMultivariable logistic regression analysis of ICIs-AKI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.552\u0026thinsp;~\u0026thinsp;2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.006\u0026thinsp;~\u0026thinsp;6.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.159\u0026thinsp;~\u0026thinsp;6.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.394\u0026thinsp;~\u0026thinsp;12.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.928\u0026thinsp;~\u0026thinsp;0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.969\u0026thinsp;~\u0026thinsp;1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u0026thinsp;~\u0026thinsp;5.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.986\u0026thinsp;~\u0026thinsp;1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.763\u0026thinsp;~\u0026thinsp;1.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997\u0026thinsp;~\u0026thinsp;1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.162\u0026thinsp;~\u0026thinsp;46.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eExtrarenal irAEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.144\u0026thinsp;~\u0026thinsp;16.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e*P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Predictive models\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Logistic regression model\u003c/h2\u003e \u003cp\u003eAfter conducting comparative analyses of factors between the ICIs-AKI and non-AKI groups, the predictive factors identified from multifactor analysis were employed to develop a logistic regression model for predicting ICIs-AKI. These factors included hypertension, diabetes, diuretic use, chemotherapy, eGFR, hemoglobin levels, lymphocyte ratio, creatinine levels, blood urea nitrogen levels, uric acid levels, presence of proteinuria, and occurrence of extrarenal irAEs.\u003c/p\u003e \u003cp\u003eThe Hosmer-Lemeshow test resulted in χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.596, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.226\u0026thinsp;\u0026gt;\u0026thinsp;0.05. The area under the ROC curve (AUC) was calculated as 0.877 [95% CI (0.831, 0.923)], with a sensitivity of 0.922 and a specificity of 0.726, demonstrating a high level of accuracy for the predictive model. These results are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 MLP predictive model\u003c/h2\u003e \u003cp\u003eThe dataset was divided into a training set comprising 70% and a validation set comprising 30%, utilizing an MLP classifier. The results demonstrated the model's accurate predictive performance, with an accuracy of 0.843, precision of 0.847, recall of 0.989, F1 score of 0.913, and AUC of 0.950. The area under the PR curve (AP) was calculated as 0.762, indicating good model performance. These findings suggest that the MLP model is well-suited for predicting the occurrence of ICIs-AKI, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfusion matrices of MLP model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePre\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRecall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF-Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eMLP: multilayer perceptron model; TP: true positives; FP: false positives; FN: false negatives; TN: true negatives; ACC: accuracy; Pre: precision; Recall: recall rate; F-Measure: F1 score; AUC: area under the ROC curve\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Survival analysis\u003c/h2\u003e \u003cp\u003eThe unadjusted Cox regression analysis revealed no statistically significant difference in overall survival between the ICIs-AKI group and the non-AKI group [HR\u0026thinsp;=\u0026thinsp;1.021, 95% CI (0.629, 1.659), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.932]. Upon adjustment for diabetes, NSAIDs and antibiotic usage, eGFR levels, hemoglobin, lymphocyte ratio, albumin, serum creatinine, uric acid, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e and CRP concentration, the absence of a statistically significant difference in overall survival between the ICIs-AKI group and the non-AKI group persisted [HR\u0026thinsp;=\u0026thinsp;0.950, 95% CI (0.558, 1.616), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.849]. Notably, the independent association of NSAIDs usage, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and Mg\u003csup\u003e2+\u003c/sup\u003e concentration with mortality was observed. Detailed results of this analysis are provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and adjusted cox regression for overall survival following ICIs treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICIs-AKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.629\u0026thinsp;~\u0026thinsp;1.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.558\u0026thinsp;~\u0026thinsp;1.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.785\u0026thinsp;~\u0026thinsp;2.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.976\u0026thinsp;~\u0026thinsp;1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.916\u0026thinsp;~\u0026thinsp;1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of ICIs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.395\u0026thinsp;~\u0026thinsp;1.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.740\u0026thinsp;~\u0026thinsp;1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.421\u0026thinsp;~\u0026thinsp;0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.462\u0026thinsp;~\u0026thinsp;1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.552\u0026thinsp;~\u0026thinsp;1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.273\u0026thinsp;~\u0026thinsp;0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.391\u0026thinsp;~\u0026thinsp;0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.562\u0026thinsp;~\u0026thinsp;1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.445\u0026thinsp;~\u0026thinsp;0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.651\u0026thinsp;~\u0026thinsp;1.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.818\u0026thinsp;~\u0026thinsp;1.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.419\u0026thinsp;~\u0026thinsp;1.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.001\u0026thinsp;~\u0026thinsp;1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.991\u0026thinsp;~\u0026thinsp;1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.980\u0026thinsp;~\u0026thinsp;0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.985\u0026thinsp;~\u0026thinsp;1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.980\u0026thinsp;~\u0026thinsp;1.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u0026thinsp;~\u0026thinsp;0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.057\u0026thinsp;~\u0026thinsp;3.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.818\u0026thinsp;~\u0026thinsp;1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.907\u0026thinsp;~\u0026thinsp;0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.913\u0026thinsp;~\u0026thinsp;1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.971\u0026thinsp;~\u0026thinsp;0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.977\u0026thinsp;~\u0026thinsp;1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.881\u0026thinsp;~\u0026thinsp;1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.996\u0026thinsp;~\u0026thinsp;1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.998\u0026thinsp;~\u0026thinsp;1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.995\u0026thinsp;~\u0026thinsp;1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.983\u0026thinsp;~\u0026thinsp;1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.526\u0026thinsp;~\u0026thinsp;1.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.978\u0026thinsp;~\u0026thinsp;1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.880\u0026thinsp;~\u0026thinsp;0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.905\u0026thinsp;~\u0026thinsp;0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942\u0026thinsp;~\u0026thinsp;1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.206\u0026thinsp;~\u0026thinsp;2.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u0026thinsp;~\u0026thinsp;0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u0026thinsp;~\u0026thinsp;0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.044*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.205\u0026thinsp;~\u0026thinsp;1.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.001\u0026thinsp;~\u0026thinsp;1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.991\u0026thinsp;~\u0026thinsp;1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.609\u0026thinsp;~\u0026thinsp;3.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrarenal irAEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.650\u0026thinsp;~\u0026thinsp;2.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI: body mass index; PPI: proton pump inhibitors; NSAIDs: nonsteroidal anti-inflammatory drugs; eGFR: estimated glomerular filtration rate; WBC: white blood cell; sCr: creatinine; BUN: blood urea nitrogen; UA: uric acid; AST: aspartate aminotransferase; ALT: Alanine aminotransferase; CRP: C-reactive protein\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e*P\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The renal outcome\u003c/h2\u003e \u003cp\u003eAmong the initial 51 patients diagnosed with ICIs-AKI, 21 patients ultimately experienced renal function recovery, resulting in a recovery rate of 41.18%. The incidence of AKI to CKD progression was observed to be 58.82%. In the CKD group, there were 25 males and 5 females, with a mean age of (70.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.10) years and a median eGFR of [56.33 (46.0,78.19)] ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e. Within this group, 28 patients were classified as CKD stage 3, while 1 patient each was categorized into CKD stage 4 and CKD stage 5. Moreover, no statistically significant difference in overall survival was found between the CKD group and the non-CKD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.157). These results are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis retrospective study revealed an incidence of ICIs-AKI at 13.39%. We identified several risk factors potentially contributing to ICIs-AKI, including diabetes, proteinuria, extrarenal irAEs, diuretic use, combination chemotherapy, and low baseline eGFR levels. A noteworthy aspect of this study was the development of prediction models using logistic regression and MLP classifier, demonstrating robust predictive capabilities. These models aid in early clinical diagnosis of ICIs-AKI, thereby potentially mitigating the occurrence of severe adverse reactions. Surprisingly, we found no association between the occurrence of ICIs-AKI and primary or renal outcomes.\u003c/p\u003e \u003cp\u003eIn this cohort of lung cancer patients treated with ICIs therapy, we observed an incidence of ICIs-AKI at 13.39%. This rate slightly surpasses findings from other studies, which typically report incidences ranging from 2\u0026ndash;5%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. A single-center retrospective study conducted in China documented a 12.8% incidence of renal-associated irAEs\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Furthermore, projections suggest that the occurrence rate of renal-associated irAEs may rise to approximately 9.9%-19% in the future\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Discrepancies in AKI incidence rates among studies stem from variations in inclusion criteria for AKI diagnosis. Additionally, while renal biopsy serves as the gold standard for diagnosing ICIs-AKI, its utilization in clinical practice is limited among cancer patients experiencing renal dysfunction, resulting in a low detection rate of ICIs-AKI.\u003c/p\u003e \u003cp\u003eIn our study, we found that the median onset time for ICIs-AKI was [123 (63, 303)] days, with 27.45% of patients experiencing AKI within 3 months of initiating ICIs treatment and 41.18% within 6 months. These findings are generally consistent with results from several foreign studies\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The duration of onset for ICIs-AKI is typically influenced by multiple factors. Onset times vary among different irAEs, with ICIs-AKI often exhibiting a longer latent period compared to extrarenal irAEs. For example, dermatitis typically occurs within 4 weeks after initiation of ICIs treatment, colitis usually develops within 6 weeks, immune-related hypophysitis manifests as neurologic symptoms at 6 weeks post-ICIs treatment, while skin toxicity typically arises after 3.6 weeks\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. The onset time of ICIs-AKI also depends on the type of ICIs used for treating malignant tumors. Furthermore, compared to the use of PD-1 inhibitors alone, the combination of CTLA-4 inhibitors and PD-1 inhibitors leads to a higher incidence of kidney-related irAEs\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The onset time for AKI with CTLA-4 inhibitors is typically 6\u0026ndash;12 weeks, whereas with PD-1 inhibitors, it ranges from 6\u0026ndash;8 months\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe significant risk factors identified in our study, including the presence of diabetes, proteinuria, extrarenal irAEs, and low baseline eGFR levels, emerged as independent risk factors for ICIs-AKI, consistent with findings from studies conducted by Seethapathy and Liu\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Additionally, some studies have reported associations of ICIs-AKI with certain medications, such as PPI and NSAIDs usage\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In our cohort, we observed that the use of diuretics increased the risk of AKI in lung cancer patients receiving ICIs therapy. This underscores the importance of vigilance regarding drug combinations with ICIs in clinical practice.\u003c/p\u003e \u003cp\u003eIn our cohort, combination chemotherapy emerged as an independent risk factor for ICIs-AKI. Similar and divergent findings have been reported. A meta-analysis encompassing all cancer types did find an association with combining ICIs and chemotherapy treatments\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, aligning with our results. However, a recent large cohort study focusing on NSCLC did not find an increased risk with combining ICIs and other agents\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the discordance observed in these findings may be attributed to challenges in diagnosis.\u003c/p\u003e \u003cp\u003eOther pertinent findings in our study include a 41.18% complete kidney recovery rate at 90 days and an incidence of AKI to CKD progression of 58.82%. The majority of patients presented at CKD stage 3, which generally carries a favorable prognosis. Previous studies have indicated that renal function recovers in more than half of ICIs-AKI patients, which may be attributed to the mild to moderate injury observed in most cases of ICIs-AKI\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Moreover, the recovery of renal function has been associated with the early usage of corticosteroids\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, with some cases achieving over 90% recovery. Thus, early recognition of ICIs-AKI is crucial for improving prognosis.\u003c/p\u003e \u003cp\u003eIn our study, two clinical prediction models were developed for the early recognition of ICIs-AKI using logistic regression and MLP classifiers. By integrating multidimensional indicators through multiple fitting, these models achieved good predictive sensitivity and specificity. Particularly, the MLP prediction model exhibited superior overall performance with an AUC of 0.950, accuracy of 0.843, precision of 0.847, recall of 0.990, and F1 score of 0.913. These results outperformed previous AKI prediction models reported in other studies. For instance, Yu et al.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e employed a neural networks (NNs) model as an AKI prediction model in patients undergoing ICIs treatment, achieving a competitive AUC of 0.8167, accuracy of 0.7703, recall of 0.7, precision of 0.56, and F1 value of 0.6222. In future clinical practice, these prediction models may be utilized for baseline risk assessment of ICIs-AKI in patients, enabling evaluation of the risk, adjustment of treatment plans, or rigorous monitoring of ICIs-AKI, thereby holding significant clinical significance.\u003c/p\u003e \u003cp\u003eThere have been relatively few studies investigating the primary outcome of ICIs-AKI. In our study, we found no association between ICIs-AKI and increased mortality regardless of disease status, a finding supported by other reports\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Additionally, we observed no statistically significant difference in overall survival between the CKD group and the non-CKD group. This could be attributed to the relatively mild AKI observed in most patients, with many having recovered renal function or being at an early stage of chronic kidney disease. An alternative theory suggests that the occurrence of irAEs, including ICIs-AKI, may indicate an effective antitumor response and therefore could potentially improve survival\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Recent studies have indicated a positive correlation between the occurrence of irAEs and the efficacy of ICIs\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, supporting this hypothesis. Another explanation could be that the follow-up period might not have been long enough to establish a link between CKD and mortality. The overall survival of ICIs-AKI appears to be closely linked to the malignant tumor status and varies depending on the underlying cause of AKI. Increased mortality risk was not observed in patients with ICIs-AKI, but rather in those with AKI attributed to other causes, where complications such as tumor progression or infections may contribute to a higher mortality rate\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In another study, the mortality rate of ICIs-AKI was reported to be as high as 93.2%\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Therefore, further studies with larger samples and longer follow-up durations may be warranted to elucidate the prognosis of ICIs-AKI.\u003c/p\u003e \u003cp\u003eThere were several limitations in this study. Firstly, as a single-center study, the sample size was relatively small, and external validation was lacking, thus the predictive efficacy of the models remains uncertain. Future steps should involve constructing prospective study cohorts for model validation. Secondly, the majority of patients included in this study were treated with PD-1 inhibitors, with a smaller portion receiving PD-L1 inhibitors, and data on CTLA-4 inhibitors were lacking. Thirdly, only a few patients underwent renal biopsy, which limited our ability to supplement information on renal pathology and investigate the mechanism of ICIs-AKI.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this retrospective study, we explored the rate and onset timing, risk factors, and clinical features associated with ICIs-AKI. Notably, renal function was observed to recover in about half of the patients with ICIs-AKI. Importantly, our logistic regression and MLP models demonstrated robust predictive capabilities for the early recognition of ICIs-AKI. Moreover, neither the occurrence of ICIs-AKI nor the recovery of renal function was correlated with patient mortality. Future studies incorporating longitudinal biospecimen collection are needed to provide additional insight into the prognosis and mechanisms of ICIs-AKI, and to aid clinicians in differentiating it from other causes of AKI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmune checkpoint inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICIs-AKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eICIs-associated acute kidney injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIrAEs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmune-related adverse events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute interstitial nephritis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProton pump inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSAIDs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNonsteroidal anti-inflammatory drugs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rates\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResidual renal function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD-EPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Kidney Disease Epidemiology Collaboration creatinine equation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Multilayer Perceptron\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC-AUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic area under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR-AUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrecision-recall area under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNNs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeural networks\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of the Ningbo No. 2 Hospital (YJ-NBEY-KY-2023-013-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Medical Scientific Research Foundation of Zhejiang Province, China (2020KY853, 2023KY283).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: Suying qian, Ningjie Xu, Xiaoyan Lu and Kedan Cai. Extraction and collection of data:Ningjie Xu, Yihui Qu, Rongrong Zhu, Minqiao Zhang,and Kanan Chen. Statistical analysis: Ningjie Xu and Kedan Cai. Drafting and revision of manuscript: Suying qian, Ningjie Xu and Kedan Cai. Supervision:Kedan Cai. Final approval of manuscript:Suying Qian, Ningjie Xu, Yihui Qu, Rongrong Zhu, Minqiao Zhang, Kanan Chen, Jing Wang, Xiaoyan Lu, Kedan Cai. All authors contributed to the article and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was waived by ethical committee (Ethics Committee of the Ningbo No. 2 Hospital) due to retrospective nature of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al. Global Cancer Statistics. 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71 (3): 209\u0026ndash;249.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSprangers B, Leaf DE, Porta C, et al. Diagnosis and management of immune checkpoint inhibitor-associated acute kidney injury [J]. Nat Rev Nephrol. 2022;18(12):794\u0026ndash;805.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOncology Society of Chinese Medical Association, House CMAP. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2023 edition) [J]. Natl Med J China. 2023;103(27):2037\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSullivan RJ, Weber JS. Immune-related toxicities of checkpoint inhibitors: mechanisms and mitigation strategies [J]. Nat Rev Drug Discov. 2022;21(7):495\u0026ndash;508.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu C, Wei W, Yang L, et al. Incidence and risk factors of acute kidney injury in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis [J]. Front Immunol. 2023;14:1173952.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu K, Qin Z, Ge Y, et al. Acute kidney injury in advanced lung cancer patients treated with PD-1 inhibitors: a single center observational study [J]. J Cancer Res Clin Oncol. 2023;149(8):5061\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos-Casals M, Brahmer JR, Callahan MK, et al. Immune-related adverse events of checkpoint inhibitors [J]. Nat Rev Dis Primers. 2020;6(1):38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan HZ, Sprangers B. Proton pump inhibitors and adverse kidney outcomes during immune checkpoint blockade: time to sound the alarm? [J]. Clin Kidney J. 2023;16(11):1709\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen JJ, Lee TH, Kuo G, et al. All-cause and immune checkpoint inhibitor-associated acute kidney injury in immune checkpoint inhibitor users: a meta-analysis of occurrence rate, risk factors and mortality [J]. Clin Kidney J. 2024;17(1):292.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbudayyeh A, Suo L, Lin H, et al. Pathologic Predictors of Response to Treatment of Immune Checkpoint Inhibitor-Induced Kidney Injury [J]. Cancers (Basel). 2022;14(21):5267.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRong Y, Bentley JP, Bhattacharya K, et al. Incidence and risk factors of immune-related adverse events induced by immune checkpoint inhibitors among older adults with non-small cell lung cancer [J]. Cancer Med. 2024;13(1):e6879.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroup KDIGOKAKIW. KDIGO clinical practice guideline for acute kidney injury [J]. Kidney Int. 2012;2(1 Suppl):1\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook S, Samuel V, Meyers DE, et al. Immune-Related Adverse Events and Survival Among Patients With Metastatic NSCLC Treated With Immune Checkpoint Inhibitors [J]. JAMA Netw Open. 2024;7(1):e2352302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu LY, Zhao HY, Yu XJ, et al. Clinicopathological Features of Kidney Injury Related to Immune Checkpoint Inhibitors: A Systematic Review [J]. J Clin Med. 2023;12(4):1349.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWanchoo R, Karam S, Uppal NN, et al. Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review [J]. Am J Nephrol. 2017;45(2):160\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeber JS, Kahler KC, Hauschild A. Management of immune-related adverse events and kinetics of response with ipilimumab [J]. J Clin Oncol. 2012;30(21):2691\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeethapathy H, Street S, Strohbehn I, et al. Immune-related adverse events and kidney function decline in patients with genitourinary cancers treated with immune checkpoint inhibitors [J]. Eur J Cancer. 2021;157:50\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu K, Qin Z, Xu X, et al. Comparative Risk of Renal Adverse Events in Patients Receiving Immune Checkpoint Inhibitors: A Bayesian Network Meta-Analysis [J]. Front Oncol. 2021;11:662731.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeraz-Munoz A, Amir E, Ng P, et al. Acute kidney injury associated with immune checkpoint inhibitor therapy: incidence, risk factors and outcomes [J]. J Immunother Cancer. 2020;8(1):e000467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin Z, Liu K, Xu X, et al. Incidence, predictors and 6-month overall outcome of acute kidney injury in Chinese patients receiving PD-1 inhibitors [J]. Future Oncol. 2022;18(16):1951\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirali AC, Perazella MA, Gettinger S. Association of Acute Interstitial Nephritis With Programmed Cell Death 1 Inhibitor Therapy in Lung Cancer Patients [J]. Am J Kidney Dis. 2016;68(2):287\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu F, Wang Z, Li X, et al. Comparative risk of acute kidney injury among cancer patients treated with immune checkpoint inhibitors [J]. Cancer Commun (Lond). 2023;43(2):214\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Strohbehn IA, Wang Q, et al. Acute kidney injury in patients receiving pembrolizumab combination therapy versus pembrolizumab monotherapy for advanced lung cancer [J]. Kidney Int. 2022;102(4):930\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Short SAP, Sise ME, et al. Acute kidney injury in patients treated with immune checkpoint inhibitors [J]. J Immunother Cancer. 2021;9(10):e003467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu X, Wu R, Ji Y, et al. Identifying Patients at Risk of Acute Kidney Injury among Patients Receiving Immune Checkpoint Inhibitors: A Machine Learning Approach [J]. Diagnostics (Basel). 2022;12(12):3157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLumlertgul N, Vassallo P, Tydeman F, et al. Acute kidney injury in patients receiving immune checkpoint inhibitors: a retrospective real-world study [J]. Eur J Cancer. 2023;191:112967.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStein C, Burtey S, Mancini J, et al. Acute kidney injury in patients treated with anti-programmed death receptor-1 for advanced melanoma: a real-life study in a single-centre cohort [J]. Nephrol Dial Transpl. 2021;36(9):1664\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoks MS, Ocak G, Suelmann BBM, et al. Immune checkpoint inhibitor-associated acute kidney injury and mortality: An observational study [J]. PLoS ONE. 2021;16(6):e0252978.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng LP, Yang J, Chen XW, et al. Correlation of preclinical and clinical biomarkers with efficacy and toxicity of cancer immunotherapy [J]. Ther Adv Med Oncol. 2023;15:17588359231163807.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi MS, Wu R, Feng Z, et al. Incidence, risk factors and prognosis of acute kidney injury in patients treated with immune checkpoint inhibitors: a retrospective study [J]. Sci Rep. 2022;12(1):18752.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung cancer, immune checkpoint inhibitors, acute kidney injury, prediction models, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-5388659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5388659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Immune checkpoint inhibitors (ICIs) are extensively utilized in lung cancer patients, with documented instances of ICIs-associated acute kidney injury (ICIs-AKI). This study aims to explore the incidence rates, clinical features, risk factors, and prognostic outcomes of ICIs-AKI, while developing a model for early recognition of ICIs-AKI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study involved 413 adult lung cancer patients treated with ICIs at Ningbo No.2 Hospital between Sept. 1, 2021, and June 30, 2023. Patients were followed until death or Dec. 31, 2023, and categorized into ICIs-AKI or non-AKI groups. Prediction models for ICIs-AKI were developed using logistic regression and MLP neural networks. Cox proportional-hazards models assessed the association between ICIs-AKI and overall survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study included 381 lung cancer patients receiving ICIs treatment after excluding 32 patients. ICIs-AKI occurred in 13.39% of cases, with a median onset time of [123 (63, 303)] days. Multivariable logistic analysis identified diabetes, proteinuria, extrarenal irAEs, diuretic use, and chemotherapy as significant risk factors (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), while higher baseline eGFR levels were protective (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Two prediction models were developed: logistic regression (AUC=0.877, sensitivity=0.922, specificity=0.726) and MLP (AUC=0.950, accuracy=0.843, precision=0.847). Survival analysis showed no difference in overall survival between ICIs-AKI and non-AKI groups (HR=1.021, 95% CI=0.629-1.659, \u003cem\u003eP\u003c/em\u003e=0.932; adjusted HR=0.950, 95% CI=0.558-1.616,\u003cem\u003e P\u003c/em\u003e=0.849). AKI to CKD progression incidence was 58.82%, with no significant difference in overall survival between CKD and non-CKD groups (\u003cem\u003eP\u003c/em\u003e=0.157).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study offers detailed insights into ICIs-AKI, including its rate, onset timing, risk factors, and clinical features. Approximately half of the affected patients experienced spontaneous renal function recovery. Both logistic regression and MLP models effectively predicted ICIs-AKI. Importantly, neither ICIs-AKI incidence nor renal function restoration correlated with patient mortality. These findings improve understanding of ICIs-AKI and underscore the importance of early detection and management strategies.\u003c/p\u003e","manuscriptTitle":"Prediction models and prognostic analysis of immune-related acute kidney injury in lung cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-19 10:54:14","doi":"10.21203/rs.3.rs-5388659/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8efb00c-beaa-40e1-b6ea-cd3c2a565d90","owner":[],"postedDate":"December 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T00:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-19 10:54:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5388659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5388659","identity":"rs-5388659","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}