
==== Front
Hum Vaccin Immunother
Hum Vaccin Immunother
Human Vaccines & Immunotherapeutics
2164-5515
2164-554X
Taylor & Francis

39267589
10.1080/21645515.2024.2398309
2398309
Version of Record
Research Article
Immunotherapy - Cancer
Predictive value of near-term prediction models for severe immune-related adverse events in malignant tumor PD-1 inhibitor therapy
Y. DU ET AL.
HUMAN VACCINES & IMMUNOTHERAPEUTICS
Du Yunyi a *
Zhang Ying a *
Zhao Wenqi b *
Zhang Yuexiang c
Su Fei d
Zhang Xiaoling c
Li Weiling e
Hu Wenqing f
Li Yongai g h
https://orcid.org/0000-0001-5022-9215
Zhao Jun c
a Department of Respiratory, Pengzhou People’s Hospital , Chengdu, Sichuan, China
b Department of Statistics, University of Auckland , Auckland
c Department of Oncology, Changzhi People’s Hospital Affiliated to Changzhi Medical College , Changzhi, Shanxi, China
d Department of Oncology, Graduate of School of Changzhi Medical College , Changzhi, Shanxi, China
e Department of Oncology, The People’s Hospital of Jianyang City , Chengdu, Sichuan, China
f Department of Gastrointestinal Surgery, Changzhi People’s Hospital Affiliated to Changzhi Medical College , Changzhi, Shanxi, China
g Department of Radiology, Changzhi People’s Hospital , Changzhi, Shanxi, China
h Department of Radiology, Jinshan Hospital, Fudan University , Shanghai, China
CONTACT Jun Zhao zhaojun380@outlook.com Department of Oncology, Changzhi People’s Hospital Affiliated to Changzhi Medical College, No. 502 Changxing Middle Road, Luzhou District, Changzhi, Shanxi 046000, China.
* These authors contributed equally to this work and should be considered as co-author.

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© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.
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https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

ABSTRACT

Immune-related adverse events (irAEs) impact outcomes, with most research focusing on early prediction (baseline data), rather than near-term prediction (one cycle before the occurrence of irAEs and the current cycle). We aimed to explore the near-term predictive value of neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), absolute eosinophil count (AEC) for severe irAEs induced by PD-1 inhibitors. Data were collected from tumor patients treated with PD-1 inhibitors. NLR, PLR, and AEC data were obtained from both the previous and the current cycles of irAEs occurrence. A predictive model was developed using elastic net logistic regression Cutoff values were determined using Youden’s Index. The predicted results were compared with actual data using Bayesian survival analysis. A total of 138 patients were included, of whom 47 experienced grade 1–2 irAEs and 18 experienced grade 3–5 irAEs. The predictive model identified optimal α and λ through 10-fold cross-validation. The Shapiro-Wilk test, Kruskal-Wallis test and logistic regression showed that only current cycle data were meaningful. The NLR was statistically significant in predicting irAEs in the previous cycle. Both NLR and AEC were significant predictors of irAEs in the current cycle. The model achieved an area under the ROC curve (AUC) of 0.783, with a sensitivity of 77.8% and a specificity of 80.8%. A probability ≥ 0.1345 predicted severe irAEs. The model comprising NLR, AEC, and sex may predict the irAEs classification in the current cycle, offering a near-term predictive advantage over baseline models and potentially extending the duration of immunotherapy for patients.

KEYWORDS

Immune-related adverse events (irAEs)
PD-1 inhibitors
neutrophil/lymphocyte ratio (NLR)
platelet/lymphocyte ratio (PLR)
absolute eosinophil count (AEC)
prediction model
near-term prediction
The author(s) reported there is no funding associated with the work featured in this article.
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pmcIntroduction

According to statistics, cancer is still one of the major causes of death.1,2 The emergence of immune checkpoint inhibitors (ICIs) has brought new hope to cancer patients, but the incidence of irAEs is relatively high and can affect any organ or system in the body, such as the skin, lungs, heart, liver, kidneys, nervous and endocrine system.3 Some of the damage caused by irAEs is irreversible, and severe cases can be life-threatening.4

Research on identifying biomarkers for irAEs is increasing, such as blood cell analysis, chemokines/cytokines, autoantibodies and genetic susceptibility factors, immune cell subsets, T cell bank, fecal microbiome, tumor genomics, and radiological characteristics.5 In clinical work, traditional laboratory markers are commonly used, such as blood cell analysis, it is an easily accessible and inexpensive test, so our focus is mainly on this. Several clinical trial results have shown that NLR, PLR, and AEC are associated with the occurrence of irAEs.6–20 Increased NLR is associated with an increased risk of grade 3–4 pulmonary and gastrointestinal irAEs in PD-1 inhibitor treatment.21 Low NLR and PLR at baseline are significantly correlated with the occurrence of irAEs, and multivariate analysis confirms PLR as an independent risk factor for irAEs.12 Increased AEC at baseline and at 1 month is associated with an increased risk of grade 2 endocrine and cutaneous irAEs in PD-1 inhibitor treatment.22 These studies mainly focus on baseline data, and there is a lack of near-term prediction models. The occurrence of irAEs is random and can happen at any time after treatment, usually within 2–16 weeks of starting immunosuppressive therapy. The risk of first episode of irAEs in the first 4 weeks before treatment is three times higher than that between 4 weeks and the end of treatment.23 Baseline data can only predict the likelihood of irAEs occurring in patients after immunosuppressive therapy, but cannot predict when irAEs may occur and the severity of irAEs occurrence. In clinical practice, the ability to predict severe irAEs is of greater value. We assume that NLR, PLR, and AEC before the occurrence of irAEs can predict the occurrence of severe irAEs. However, in current research, the relationship between NLR, PLR, AEC and severe irAEs in the previous and current cycles of irAEs has not been studied.

Therefore, this study aims to explore the relationship between NLR, PLR, and AEC in patients with tumors who received PD-1 inhibitors and the occurrence of severe irAEs in the previous cycle and current cycle. Additionally, a prediction model was constructed to assess its predictive value for the occurrence of severe irAEs in the next cycle.

Materials and methods

Case data

Patient information

All solid tumor patients who received any ICIs treatment in our hospital from July 2019 to May 2022 were identified through the database. Then, a retrospective review of available medical records was conducted using a central database and standardized electronic medical record forms in our hospital. Patients who were excluded include those with blood malignancies and patients who received cellular or therapeutic immune therapy, patients who were not receiving PD-1 inhibitor therapy for the first time, patients who had toxic side effects from previous anti-tumor treatments before PD-1 inhibitor therapy, patients who did not adhere to regular medication (excluding those who discontinued due to irAEs), and patients who had conditions affecting study parameters in the previous and current cycles before the occurrence of irAEs (non-irAEs factors), such as infections, surgeries, burns, use of corticosteroids, various intoxications (acidosis, uremia, heavy metal poisoning), infectious diseases (typhoid fever, measles, rubella), hematological disorders (aplastic anemia, granulocytopenia, leukemia, myelodysplastic syndrome, primary immune thrombocytopenia, hemolytic anemia), hypersensitivity disorders (asthma, drug allergies, allergic purpura), rheumatic diseases, splenic hyperfunction, disseminated intravascular coagulation, and organ transplantation. Baseline characteristics of patients were collected, including age, sex, smoking status, ECOG score, relevant comorbidities of interest (hypertension, diabetes, coronary heart disease, stroke), and tumor type. Histological subtype, PD-L1 expression, treatment status (radiotherapy, chemotherapy, anti-angiogenic therapy), and laboratory test reports were also reviewed. For patients who underwent PD-L1 expression testing, PD-L1 detection and nature were determined according to the standards for each anti-PD1/PD-L1 agent. Survival outcomes were collected as well. Overall survival (OS) was defined as the time from the start date of PD-1 inhibitor therapy to death or the last follow-up. Patient survival status was verified. The occurrence of irAEs was observed between week 2 and week 16 (within 3 months), with a follow-up duration of at least 3 months. The last follow-up date was May 5, 2023. The study obtained approval from the Institutional Ethics Committee (EC). All procedures in this study comply with the ethical standards of the institutional research committee and the Helsinki Declaration, as well as its subsequent revisions or similar ethical standards.

This study included eligible patients who received any immune checkpoint inhibitor therapy in clinical trials, compassionate use, and clinical practice settings. The evaluated ICIs in this study only included PD-1 inhibitors and did not include PD-L1 and CTLA-4 inhibitors. The dosage and regimen of each PD-1 inhibitor in this study were determined according to the recommended dosage in clinical trials, compassionate use programs, or standard recommended dosage in clinical practice. The PD-1 inhibitors used in this study were camrelizumab (200 mg every three weeks), sintilimab (200 mg every three weeks), nivolumab (3 mg/kg every two weeks), and pembrolizumab (2 mg/kg every three weeks). However, the dosage and regimen of each drug may be adjusted in patients receiving PD-1 inhibitor therapy in clinical practice settings under the cautious management of the treating physician. Treatment adjustments followed the NCCN guidelines. In this study, irAEs were defined as all immune-related toxicities occurring at any time after receiving PD-1 inhibitor therapy. The grading of irAEs was assessed according to the CTCAE version 5.0. The evaluation and management of irAEs followed the NCCN guidelines for immune therapy-related toxicities.

Data collection

Peripheral blood samples from patients were rapidly centrifuged within 2 hours and analyzed using the SYSMEX XN20-A2 automated hematology analyzer to measure peripheral blood cell counts. The NLR and PLR were calculated. Data from patients who experienced irAEs were collected for one cycle prior to the occurrence of irAEs (median cycle number was the 2nd cycle) and the first day of the current cycle (median cycle number was the 3rd cycle). Data from patients who did not experience irAEs were collected based on the median cycle number of patients who did experience irAEs. See Figure 1 for the flowchart. Figure 1. Flowchart.

Statistical analysis

The primary objective of this study was to evaluate the incidence, characteristics, treatment, and outcomes of irAEs in patients receiving PD-1 inhibitor therapy, as well as to assess the impact of irAEs on survival. Descriptive analysis was used to describe baseline demographic data, with irAEs categorized into G0-G2 and G3-G5 groups. Pearson’s chi-square test and Fisher’s exact test were employed to analyze the association between categorical variables and the binary classification of irAEs. The Shapiro-Wilk test was used to assess the normality of continuous variables, and the Kruskal-Wallis test was applied to analyze differences in continuous variables between the binary classification of irAEs. Variables associated with the occurrence of irAEs were included in the predictive model. The optimal α and λ parameters were selected for variable selection in the elastic net logistic regression using a 10-fold cross-validation technique. The dataset was divided into training and validation sets to evaluate model performance. Sensitivity analysis was conducted, followed by Bayesian survival analysis and the Kaplan-Meier method to estimate overall survival (OS). The log-rank test was used to compare survival between the G0-G2 and G3-G5 groups after irAE classification. A p-value of < 0.05 was considered statistically significant. Statistical analyses were performed using RStudio version 4.3.0.

Ethics approval

This study involves human participants and was approved by the Human Ethics Committee of Changzhi People’s Hospital, Shanxi Province, China. (ID 2021K032).

Results

Patient characteristics

A total of 138 cancer patients who were newly treated with PD-1 inhibitors were included, including 91 males (65.9%) and 47 females (34.1%). The median age was 64.9 years (range: 56–69 years). The ECOG scores were all 1. The tumor types included esophageal cancer (23.9%), gastroesophageal junction cancer (14.5%), gastric cancer (21.0%), lung cancer (17.4%), and others (23.2%). Among the included patients, 23.2% did not receive chemotherapy or radiation therapy, 10.1% received chemotherapy only, 61.6% received radiation therapy only, and 5.1% received both. Among the patients, 23.9% were treated with anti-angiogenic therapy, while 76.1% were not. The anti-angiogenic drugs used included bevacizumab, anlotinib, and apatinib. The types of PD-1 inhibitors used in combination therapy were camrelizumab (200 mg every three weeks), sintilimab (200 mg every three weeks), nivolumab (3 mg/kg every two weeks), and pembrolizumab (2 mg/kg every three weeks). The histological subtype recorded were squamous cell carcinoma, adenocarcinoma, and others. Tumor stage was categorized as stage I to stage IV. Among the 138 patients, 73 did not experience irAEs, 47 experienced grade 1–2 irAEs, 16 experienced grade 3–4 irAEs, and 2 experienced grade 5 irAEs.

irAEs

The specific classification of patients with irAEs is shown in Table 1. Out of 138 patients, 65 patients (47.1%) experienced at least one type of irAEs, while 19 patients (13.8%) experienced two or more types of irAEs. There was a total of 47 patients with Grade 1–2 irAEs, 16 patients with Grade 3–4 irAEs, and 2 patients with Grade 5 irAEs. Reactive cutaneous capillary endothelial proliferation (RCCEP) occurred in 25 patients (29.4%), skin toxicities (itchiness, rash, and bullous dermatitis) in 14 patients (16.5%), endocrine toxicities (hyperthyroidism with decreased serum TSH and increased free T4 or total T3, hypothyroidism with increased serum TSH and decreased free T4, pituitary inflammation with abnormalities in ACTH, TSH, FT4, FT3, LH, FSH, PRL, and type 1 diabetes with ketosis) in 13 patients (15.3%), lung toxicities (symptoms and chest CT showing ground-glass opacities or patchy nodular infiltrations) in 11 patients (12.9%), liver toxicities (elevated AST, ALT, and bilirubin caused by PD-1 inhibitors) in 11 patients (12.9%), muscle toxicities (myositis with elevated creatine kinase) in 2 patients (2.4%), gastrointestinal toxicities (colitis) in 3 patients (3.5%), cardiac toxicities (myocarditis with elevated cardiac injury biomarkers and multi-organ dysfunction, atrial fibrillation and complete atrioventricular block on electrocardiogram) in 3 patients (3.5%), renal toxicities (elevated creatinine) in 2 patients (2.4%), and ocular toxicities (uveitis) in 1 patient (1.2%).Table 1. Classification of irAEs patients.

 	irAEs(n)	G1-2(n)	G3-4(n)	G5(n)	
 	65	47	16	2	
RCCEP	25	24	1	 	
Skin toxicities	14	11	3	 	
itchiness	2	2	 	 	
rash	9	7	2	 	
bullous dermatitis	3	2	1	 	
Endocrine toxicities	13	8	5	 	
hyperthyroidism	1	1	 	 	
hyperthyroidism	7	7	 	 	
pituitary inflammation	3	 	3	 	
type 1 diabetes	2	 	2	 	
Lung toxicities	11	6	4	1	
Liver toxicities	11	5	6	 	
Myositis	2	1	1	 	
Colitis	3	3	 	 	
Cardiac toxicities	3	1	1	1	
myocarditis	1	 	 	1	
atrial fibrillation	1	1	 	 	
complete atrioventricular block	1	 	1	 	
Renal toxicities	2	2	 	 	
Ocular toxicities	1	1	 	 	
irAEs: immune-related adverse events; G1–2: grade 1 and grade 2; G3–4: grade 3 and grade 4; G5: grade 5; RCCEP: reactive cutaneous capillary endothelial proliferation

Relationship between categorical variables and binary classification of irAEs

The main purpose of this study was to explore relevant indicators and predictive models for recent prediction of severe irAEs, therefore the patients were divided into two groups as follows: those who did not experience irAEs or experienced grade 1–2 irAEs were classified as the low-risk irAEs group (G0–2 group), and those who experienced grade 3 or higher irAEs were classified as the severe irAEs group (G3–5 group).

The categorical variables in this study included sex, number of comorbidities (hypertension, diabetes, coronary heart disease, and stroke, categorized as no disease, 1 disease, 2 diseases, 3 or more diseases), tumor type, histological subtype, tumor stage, status of chemotherapy and radiation therapy, type of PD-1 inhibitor, and whether combined with anti-angiogenic therapy. The comparison of all categorical variables with the binary classification of irAEs is shown in Table 2.Table 2. Comparison of the characteristics for categorical variables between the low-risk i and the severe irAEs groups.

 	Total (N = 138)	Low-risk irAEs group (n = 73)	Severe irAEs group (n = 65)	p	
Sex	 	 	 	.737	
male	91	78	13	 	
female	47	42	5	 	
Age	 	 	 	.524	
<65	71	63	8	 	
≥65	67	57	10	 	
Tumor type	 	 	 	.406	
esophageal cancer	33	32	1	 	
gastroesophageal junction cancer	20	17	3	 	
gastric cancer	29	23	6	 	
lung cancer	24	20	4	 	
others*	32	28	4	 	
Histological subtype	 	 	 	.381	
squamous cell carcinoma	50	46	4	 	
adenocarcinoma	61	52	9	 	
others	27	22	5	 	
Tumor stage	 	 	 	.913	
stage I	15	14	1	 	
stage II	13	11	2	 	
stage III	34	30	4	 	
stage IV	76	65	11	 	
Number of comorbidities#	 	 	 	.645	
0	80	70	10	 	
1	38	31	7	 	
2	16	15	1	 	
≥3	4	4	0	 	
Treatment scenario	 	 	 	.877	
neoadjuvant/adjuvant therapy	7	7	0	 	
first-line treatment	59	51	8	 	
second-line treatment	47	41	6	 	
≥third line treatment	25	21	4	 	
Radiotherapy and chemotherapy status	 	 	 	.535	
no radiotherapy or chemotherapy	32	28	4	 	
chemotherapy only	85	75	10	 	
radiotherapy only	14	12	2	 	
concurrent chemoradiotherapy	7	5	2	 	
PD-1 inhibitors	 	 	 	.220	
Camrelizumab	67	61	6	 	
Sintilimab	40	33	7	 	
Nivolumab	16	12	4	 	
Pembrolizumab	15	14	1	 	
Combined with anti-angiogenic therapy	 	 	 	.897	
Yes	33	29	4	 	
No	105	91	14	 	
irAEs: immune-related adverse events; *: liver cancer, cervical cancer, colon cancer, and renal cancer; #: hypertension, diabetes, coronary heart disease, and stroke.

Pearson’s chi-square test and Fisher’s exact test were used for categorical variables. The results indicated that there was no significant association between the following variables and the categorized irAEs: sex (p = .7367, Pearson’s chi-square test), number of comorbidities (p = .6447, Fisher’s exact test), tumor type (p = .4062, Fisher’s exact test), histological subtype (p = .3811, Fisher’s exact test), tumor stage (p = .9126, Fisher’s exact test), radiotherapy and chemotherapy status (p = .5346, Fisher’s exact test), PD-1 inhibitor type (p = .2204, Fisher’s exact test), and whether combined with anti-angiogenic therapy (p = .8972, Fisher’s exact test). This suggested that using categorical variables alone cannot obtain a predictive model.

Relationship between continuous variables and binary categorized irAEs

In Section 3.3, the relationship between continuous variables and binary-categorized irAEs was analyzed. The continuous variables included NLR, PLR, AEC, and age in both the previous and current cycles.

The Shapiro-Wilk normality test revealed that these variables were not normally distributed, with p-values for NLR, PLR, and AEC in the previous cycle, as well as for NLR, PLR, AEC, and age in the current cycle, all significantly less than 0.05.

The Kruskal-Wallis test was then employed to examine the relationship between these continuous variables and the binary-categorized irAEs (Table 3). A higher chi-squared value from the Kruskal-Wallis test suggests a greater difference between the categorized irAEs. Statistical significance was determined at p < .05, indicating a meaningful difference between groups. In the previous cycle, only NLR showed a statistically significant relationship with the categorized irAEs. In the current cycle, both NLR and AEC demonstrated statistical significance with the categorized irAEs. Additionally, the Pearson correlation coefficient between NLR and AEC in the current cycle was 0.0024, indicating that these two variables are independent of each other.Table 3. The differences in medians for continuous variables between the low-risk and the severe irAEs groups.

 	F-Statistic	p	
The previous cycle	
Age	0.295	.588	
NLR	7.72	.006	
PLR	2.457	.119	
AEC	0.004	.947	
The current cycle	
Age	0.295	.588	
NLR	18.04	.000	
PLR	1.986	.161	
AEC	11.44	.001	
irAEs: immune-related adverse events; NLR: neutrophil/lymphocyte ratio; PLR: platelet/lymphocyte ratio; AEC: absolute eosinophil count.

Predictive model

Since in the previous cycle, only NLR and post-classification irAEs were correlated. Logistic regression was used, the NLR variable was not significant (p = .0679), and the prediction was not meaningful. In the current cycle, NLR and AEC were correlated with post-classification irAEs, so a recent prediction model was only constructed for irAEs in the current cycle. Binary logistic regression was used to predict the patient classification of irAEs in the current cycle. The results showed that the variables NLR (p = .0190), AEC (p = .0425), female sex (p = .0138), and male sex (p = .0145) were statistically significant.

Age, sex (male: sex = 1; female: sex = 0), number of comorbidities (Ch: none: Ch = 0, 1 type: Ch = 1; 2 types: Ch = 2; 3 types: Ch = 3), tumor stage (S: S1=Stage I, S2=Stage II, S3=Stage III, S4=Stage IV), Histological subtype (Cl: squamous carcinoma: Cl = 1, adenocarcinoma: Cl = 2, others: Cl = 0), combined with anti-angiogenic therapy (An: yes: An = 1, no: An = 0), radiotherapy and chemotherapy status (Cr: no radiotherapy or chemotherapy: Cr = 0; chemotherapy only: Cr = 1, radiotherapy only: Cr = 2; concurrent chemoradiotherapy: Cr = 3), tumor type (Ct), PD-1 inhibitors, NLR, PLR, and AEC were used as variables to predict the grading of irAEs in current cycle (G0–2 = 1, G3–5 = 2). Direct application of iteratively reweighted least squares fitting in logistic regression showed that only the variables NLR (p = .0190), AEC (p = .0425), female sex (p = .0138), and male sex (p = .0145) were statistically significant.

Elastic net was applied to logistic regression for variable selection, automatically controlling the correlations between variables. Sex was recoded as a dummy variable, where male was coded as 1 and female as 0. To determine the best α, we selected it based on the minimum cross-validated binomial deviance by defining a sequence of α values and using 10-fold cross-validation. This method allows for precise control over the allocation of observations into fixed folds. In each iteration, one group served as the test set while the remaining nine groups were used for training. This process was repeated 10 times, ensuring each group had an opportunity to act as the test data. Figure 2a displays the binomial residuals for α values ranging from 0 to 1 (in increments of 0.1). For comparison, Figure 2b shows that α = 0 (ridge regression) yielded the smallest relative binomial deviance, with λ = 0.1075 minimizing the mean squared error (MSE). Figure 2c visualizes the coefficients, with the x-axis representing the default log(λ) sequence. The numbers at the top of the figure indicate the number of non-zero coefficients in the model, and the orange vertical line represents the optimal log(λ) value. Figure 2. a. The cross-validation plot illustrates α values ranging from 0 to 1 (in increments of 0.1). On the x-axis, we display the default log(λ) values, which decrease along the solution path, encompassing 100 values of the regularization parameter λ evenly spaced on a log scale, starting from the largest λ where all coefficients are zero. The y-axis represents the binomial deviance. Numbers at the top of the plot indicate the number of non-zero coefficients for each model. Error bars around each point reflect the standard error of the cross-validated binomial deviance; larger error bars suggest greater variability and lower confidence in the results for those λ values. The orange vertical line marks the λ value that minimizes the cross-validated error, while the blue vertical line indicates the λ value where the cross-validated error is within one standard error of this minimum. The black number on the plot denotes the minimum binomial deviance value. b. Comparison of cross-validated binomial deviance from α = 0 to α = 1. When α = 0 (ridge regression), it yielded the smallest relative binomial deviance, and λ = 0.1075 minimized the mean squared error. c. Visualization of coefficients for α = 0 in elastic net. The plot visualizes the coefficients with the x-axis representing the default log(λ) sequence. The numbers at the top of the figure indicate the number of non-zero coefficients for the model, and the orange vertical line represents the optimal log(λ) value. d. ROC curve of the elastic net regression model. The predictive model achieved an AUC of 0.783, with a sensitivity of 77.8% and specificity of 80.8%. A probability of ≥ 0.1345 was indicative of severe irAes.

The response variable for grading patients’ irAEs in current cycle was in G={1,2}. Pr(G = 1|X=x_i)=p_i (where i = 1,2 … ,138), the prediction model is as follows.

log(p_i/1-p_i)=logit(p_i) = −2.8911 + 0.1997*NLR_i + 1.4135*AEC_i + 0.1662*I(sex_i).

When the patient’s sex is male, I (sex) = 1, and when the patient’s sex is female, I (sex) = 0.

We generated bootstrap samples, fitted the model, and calculated the standard errors to determine the p-values of the coefficients. These p-values were then adjusted using three methods: Bonferroni, Holm-Bonferroni, and Benjamini-Hochberg. The results, shown in Table 4, demonstrate that all model coefficients remain statistically significant after these adjustments.Table 4. P-values from the prediction model adjusted for statistical significance.

Variables	p	Bonferroni adjusted p-value	Holm-Bonferroni adjusted p-value	Benjamini-Hochberg adjusted p-value	
Intercept	.066	0.264	0.264	0.264	
Sex	.534	1.000	0.828	0.534	
NLR	.287	1.000	0.828	0.383	
AEC	.276	1.000	0.828	0.383	

The optimal cutoff value was determined to be 0.1345 using Youden’s index, maximizing the sum of model sensitivity and specificity, with a sensitivity of 77.8% and specificity of 80.8%. When the predicted probability exceeds 0.1345, the data are classified as G={2} (irAEs in the G3-G5 group). Figure 2d showed the ROC curve for this model.

We also employed stepwise model selection based on the Akaike Information Criterion (AIC). As shown in Table 5, the model incorporating NLR, AEC, and sex exhibited the highest robustness and generalizability. Additionally, we used a random forest with 100 trees to validate the model. The Out-Of-Bag (OOB) error rate estimate was 13.77%. Despite a low class error of 0.0083 for irAEs in the G0-G2 group, the class error for irAEs in the G3-G5 group was nearly 1.00. This indicates that the random forest struggles with imbalanced datasets, leading us to select the elastic net regression model instead.To further validate the accuracy of the model, two methods were used as follows, and the results showed high accuracy and low errors. Method 1, randomly select 69 data points as the training set to fit the model, and the remaining 69 data points as the test set, repeated 100 times. The average prediction accuracy for G={1} (irAEs in the G0-G2 groups) was 0.9184; the average prediction accuracy for G={2} (irAEs in the G3-G5 group) was 0.2731. Method 2, generate a performance measures list on the 69 randomly selected test data sets, repeated 100 times. The average misclassification error was 0.1319, and the average AUC was 0.7533.Table 5. The AICs for predictive variables.

Variables	AIC	
NLR + PLR + AEC + age + sex + S + Ct + Cl + Ch + Cr + PD-1+ An	127.84	
NLR + PLR + AEC + age + sex + S + Cl + Ch + Cr + PD-1+ An	119.89	
NLR + PLR + AEC + age + sex + S + Cl + Ch + Cr + An	114.69	
NLR + PLR + AEC + age + sex + Cl + Ch + Cr + An	110.38	
NLR + PLR + AEC + age + sex + Cl + Ch + An	107.76	
NLR + PLR + AEC + age + sex + Cl + An	105.75	
NLR + PLR + AEC + age + sex + An	103.97	
NLR + PLR + AEC + age + sex	101.91	
NLR + PLR + AEC + sex	100.76	
NLR + AEC + sex	100.32	
irAEs: immune-related adverse events; NLR: neutrophil/lymphocyte ratio; PLR: platelet/lymphocyte ratio; AEC: absolute eosinophil count; Ch: number of comorbidities; S: tumor stage; CI: Histological subtype; An: combined with anti-angiogenic therapy; Cr: radiotherapy and chemotherapy status; Ct: tumor type; PD-1: the type of PD-1 inhibitor.

Sensitivity analysis

To verify the influence of different types of PD-1 inhibitors on the analysis results, a sensitivity analysis was further conducted. The results showed that removing any one type of PD-1 inhibitor had no effect on irAEs or the prediction model.

Removing camrelizumab analyzing. The data by removing camrelizumab from the PD-1 inhibitor variable showed no association between PD-1 inhibitor type and the occurrence of irAEs in current cycle (p = .4396, Fisher’s exact test). Directly fitting the logistic regression with iteratively reweighted least squares showed that the p-values for the PD-1 inhibitor were 0.6193 (pembrolizumab) and 0.4301 (nivolumab), confirming that PD-1 inhibitor type was not relevant to the predictive model.

Removing nivolumab analyzing. The data by removing nivolumab from the PD-1 inhibitor variable showed no association between PD-1 inhibitor type and the occurrence of irAEs in current cycle (p = .3679, Fisher’s exact test). Directly fitting the logistic regression with iteratively reweighted least squares showed that the p-values for the PD-1 inhibitor were 0.6482 (camrelizumab) and 0.9299 (pembrolizumab), confirming that PD-1 inhibitor type was not relevant to the predictive model.

Removing both camrelizumab and nivolumab analyzing. The data by removing both camrelizumab and nivolumab from the PD-1 inhibitor variable showed no association between PD-1 inhibitor type and the occurrence of irAEs in current cycle (p = .4231, Fisher’s exact test). Directly fitting the logistic regression with iteratively reweighted least squares showed that the p-value for the PD-1 inhibitor (pembrolizumab) was very close to 1, further confirming that PD-1 inhibitor type was not relevant to the predictive model.

Survival analysis

Bayesian survival analysis was conducted by specifying a prior distribution for the logistic regression coefficients. We developed the likelihood function based on the survival data and applied Bayes’ theorem to combine this prior with the likelihood function, yielding the posterior distributions for the logistic regression model. To estimate these distributions, we set up four Markov Chain Monte Carlo (MCMC) chains, each with 1000 iterations.

Comparison of Bayesian survival analysis between the predicted model and the actual model. Figure 3a shows the survival plot of patients graded for irAEs in the current cycle in the actual data. Using the log-rank test, there is a statistically significant difference in survival rate between groups G0-G2 and G3-G5 (p = .040). Figure 3b shows the survival plot of patients graded for irAEs in the current cycle in the predicted model data. Using the log-rank test, there is a statistically significant difference in survival rate between groups G0-G2 and G3-G5 (p = .035). Horizontal and vertical dotted lines represent the median survival probability for each group. Figure 3c compares the survival plots of patients graded for irAEs in the actual data and predicted model data in the same graph, and it can be observed that the accuracy of the G0-G2 group prediction is higher. Figure 3. a. Survival plot of patients graded for irAEs in the current cycle in the actual data. b. Survival plot of patients graded for irAEs in the predicted model data for this period. c. Comparison of survival plots of patients graded for irAEs in the current cycle in the actual dataset and predicted model dataset.

Discussion

The use of PD-1 inhibitors has greatly improved the prognosis of tumor patients. With the widespread application of PD-1 inhibitors, the reports of irAEs are increasing, and rare and fatal irAEs cannot be ignored. The mechanism of irAEs is not completely understood and may be triggered by common antigens of tumors and inflammatory organs.24,25 It may also be related to the composition of the intestinal microbiota26–28 and independent autoimmune toxicity mechanisms that exist separately from anti-tumor responses.29,30 PD-1 inhibitors can enhance the effector function of T cells and may lead to pathogenic T cells, excessive release of cytokines, changes in the number/function of B cells, and increased autoantibodies, leading to inflammation and autoimmunity.31

NLR and PLR are new systemic inflammation markers.32 In recent years, NLR and PLR have been extensively studied in autoimmune diseases and malignant tumor prognosis.33–36 Eosinophils are also important cells in immune response and allergic reactions, and eosinophil infiltration has been observed in the lung tissue of some patients with immune-related pneumonia37 and in the pancreatic islets of rats before the onset of type 1 diabetes,38,39 suggesting a possible association between irAEs and eosinophils. Previous studies have also examined the relationship between NLR, PLR, AEC, and irAEs, but most of these studies were based on baseline data and the results varied. Some baseline studies have shown a correlation between a lower NLR6,8,12–15,40/PLR,12–15 and the occurrence of irAEs. There have also been reports of a correlation between a higher baseline NLR/PLR and irAEs.7,41 On the other hand, there are reports that baseline NLR/PLR is not associated with irAEs.11,40 Further analysis of data from the third week of treatment by Egami et al. showed no correlation with the occurrence of irAEs.41 Studies have shown that a higher baseline AEC is associated with the occurrence of irAEs.16,17 Chu et al.18 found that a higher baseline AEC is more likely to develop immune-related pneumonia. Yan et al.19 found that a higher baseline AEC is associated with a lower incidence of irAEs. Yun et al.20 found that AEC is independently correlated with grade 3–5 irAEs. On the other hand, Yushi et al.42 found no correlation between baseline AEC and irAEs, but they continuously monitored the dynamic changes in AEC during medication and found that the maximum AEC in the group that experienced irAEs was significantly higher than that in the group that did not experience irAEs.The differences in these results may be due to various factors affecting the NLR, PLR, and AEC of patients, such as age, sex, genetics, environment, lifestyle, and drugs.

In this study, we conducted a retrospective analysis of the relationship between NLR, PLR, AEC and grade 3 or lower irAEs, as well as grade 3 or higher irAEs, in patients with tumors treated with PD-1 inhibitors. Our results showed that the NLR in the previous cycle was a risk factor for irAEs after categorization, and the NLR and AEC in this cycle were risk factors for irAEs after categorization.

We further constructed a near-term prediction model for predicting the occurrence of irAEs and grade 3 or higher irAEs, which has not been reported in previous studies. The model we built has high specificity and sensitivity. The model can predict the likelihood of grade 3 or higher irAEs occurring in the current cycle, with higher AUC (0.783), specificity (80.8%), and sensitivity (77.8%). These data indicate that the model effectively predicts the occurrence of grade 3 or above irAEs in tumor patients receiving PD-1 inhibitors during the current cycle. NLR and AEC values were measured in patients’ blood on the first day of each cycle, and the predicted values are obtained by inputting these measurements into the model. This helps clinical practitioners identify patients at risk of severe irAEs in advance. If the predicted value exceeds the cutoff threshold, clinicians can make corresponding response measures, such as adjusting treatment plans, reducing drug dosage or even discontinuing medication, thereby reducing or avoiding the occurrence of severe irAEs. In survival analysis, the model has high accuracy in predicting grade 3 or lower irAEs. Baseline-related studies only analyze data before patients receive treatment, which may determine the likelihood of irAEs occurring after using PD-1 inhibitors, but cannot predict when and the severity of irAEs. The occurrence of some irAEs may be related to better treatment efficacy. In clinical practice, if patients are not allowed to use PD-1 inhibitors due to the possibility of irAEs (without knowing the severity of occurrence), it will deprive them of the opportunity for clinical benefit. Therefore, predicting severe and life-threatening irAEs is crucial. The results of peripheral blood cell analysis are in a dynamic state, and analyzing only the correlation between baseline data and irAEs may have some bias. However, a dynamic combined analysis of multiple indicators can better reflect the true status of patients after using PD-1 inhibitors. When the model predicts a high probability of severe irAEs in patients, clinicians can choose to temporarily discontinue PD-1 inhibitors, observe changes in blood routine after symptomatic supportive treatment, and continue medication after improvement in NLR, PLR, and AEC. This can effectively avoid the occurrence of severe irAEs, which will have greater significance for clinical work. The use of steroids may lead to an increase in NLR, potentially affecting the model’s results. Therefore, it should be evaluated considering the specific situation of the patient. NLR, PLR, and AEC are inexpensive and easily accessible biomarkers, and we recommend dynamically observing changes in peripheral blood cell analysis to determine the current situation of patients.

From the occurrence of irAEs, the incidence rates of RCCEP, skin toxicity, pneumonia, and liver toxicity recorded in this study are significantly different from previous studies, while the incidence rates of other irAEs are similar to previous studies.4 According to the CameL study,43 the incidence rate of RCCEP in patients receiving combination chemotherapy with carilizumab reached 78%, which is significantly higher than that in our study. In patients using carilizumab, there are more patients receiving combination anti-angiogenic drugs, which reduces the incidence rate of RCCEP. This may be the reason for the large difference in RCCEP incidence rates. The incidence rate of skin toxicity in our study is also low, which may be because some patients had mild skin toxicity that did not receive much attention from the patients themselves or was not recorded in the medical record system, resulting in a reduced incidence rate of skin toxicity in the retrospective analysis. The incidence of pneumonia and hepatitis is higher among our patients. On one hand, this could be due to the increasing use of PD-1 inhibitors, leading to a higher incidence in the population. On the other hand, the increasing reports of irAEs have raised awareness among clinicians, resulting in a higher diagnosis rate of irAEs.

There are several limitations and strengths of this study. Our study is limited by its retrospective, single-center, and small sample size design. Despite the small sample size, we employed three methods to ensure the significance and credibility of our findings: 1) elastic net with 10-fold cross-validation: we used 10-fold cross-validation to divide the dataset into 10 groups. Each group served as the test set once while the remaining nine groups were used for training, allowing for a robust evaluation of alpha selection. 2) repeated random splits: to validate the fitted model, we randomly divided the data into two groups, compared predicted outcomes with actual data, and repeated this process 100 times. This resulted in an average AUC of 0.7533. 3) Bayesian survival analysis: we applied Bayesian survival analysis to integrate survival analysis techniques with Bayesian methods. This approach provides more robust parameter estimates and mitigates the risk of overfitting by incorporating prior information, particularly valuable with smaller sample sizes. In the future, it is important to increase the sample size for analysis. The study also lacks sufficient control over confounding factors and concurrent treatment regimens. Thus, in future trial designs, it is important to collect as many confounding factors as possible and control treatment plans to minimize the impact on the trial results. However, our study has several strengths. To our knowledge, this is the first study to explore the relationship between the NLR, PLR, AEC, and grade 3 or above irAEs in the previous cycle and the current cycle. We further developed a prediction model which showed high specificity and sensitivity in predicting severe or life-threatening irAEs. This has significant clinical implications and the method is simple and convenient, but it is necessary to exclude patients with factors that may cause non-irAEs, such as infection, surgery, poisoning, infectious diseases, hematological diseases, hypersensitivity diseases, rheumatic diseases, and immune deficiency diseases. The study population included patients with different types of tumors, making the results applicable to a variety of cancer patients, which is closer to real-life situations. In the future, large-scale multicenter prospective studies are needed to validate these findings.

Overall, our study found that NLR, AEC, and sex can predict irAEs after categorization in the current cycle. Dynamic monitoring of NLR, PLR, and AEC can better reflect the true condition of patients, provide early warnings for the occurrence of grade 3 or higher irAEs, and reduce or avoid the occurrence of severe irAEs. The inexpensive and simple nature of the NLR, AEC, and sex makes them suitable for clinical application, and our study provides valuable reference for clinical practice.

Acknowledgments

The abstract of this paper was presented at 2023 European Society for Medical Oncology entitled “Predictive value of a near-term prediction model for severe irAEs in cancer treatment with ICIs”. The abstract was published in “Abstracts (1212P)” in Annals of Oncology (Volume 34, Supplement 2, S713) https://www.annalsofoncology.org/article/S0923-7534(23)03138-1/fulltext. We thank Dr. Qiao Bin’s for his editing assistance.

Jun Zhao medical doctor, chief physician, and master’s supervisor. Executive Director of Cancer Center of Changzhi People’s Hospital. Director of Oncology Department of Changzhi People’s Hospital. Dean of Shanxi iHope College. Chairman of Changzhi Cancer Prevention Association. Backbone elite talent of “Hundred Thousand Million Health Talents Training Project” of the Shanxi Health Commission, top-notch elite talent of the “Sanjin Talent” support plan in Shanxi Province. Winner of the Shanxi Province May Day Labor Medal. The 10th “Excellent Science and Technology Worker” in Shanxi Province. Corresponding Editor of the 8th Chinese Journal of Oncology.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Contributors

YD, Ying Zhang, and JZ conceived and designed the research. YD, Ying Zhang, XZ, Yuexiang Zhang, FS, and WL collected the dates. JZ and WH supervised the study. YingZhang and WZ participated in the data analysis. Ying Zhang, WZ, and JZ reviewed and edited the manuscript. All the authors contributed to the review of the manuscript.

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.
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