
==== Front
BMC Infect Dis
BMC Infect Dis
BMC Infectious Diseases
1471-2334
BioMed Central London

39251931
9742
10.1186/s12879-024-09742-x
Research
Increased plasma AACT level as an indicator of poor prognosis in patients hospitalised with community-acquired pneumonia: a multicentre prospective cohort study
Zhao Lili 1
Xi Wen 1
Shang Ying 1
Gao Wenjun 1
Bian Wenjie 1
Chen Xi 1
Xue Jianbo 1
Xu Yu 2
Gong Pihua gardenia1978@163.com

1
Guo Shuming kyzx@linfench.com

3
Gao Zhancheng zcgao@bjmu.edu.cn

1
1 https://ror.org/035adwg89 grid.411634.5 0000 0004 0632 4559 Department of Respiratory and Critical Care Medicine, Peking University People’s Hospital, No. 11, Xizhimen South Street, Beijing, 100044 China
2 https://ror.org/035t17984 grid.414360.4 0000 0004 0605 7104 Department of Respiratory and Critical Care Medicine, Beijing Jishuitan Hospital, No. 31 Xinjiekou East Street, Beijing, 100035 China
3 Linfen Clinical Medicine Research Center, Linfen Central Hospital, No. 17, Jiefang West Road, Linfen, Shanxi 041000 China
9 9 2024
9 9 2024
2024
24 94618 8 2023
8 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background and objective

Community-acquired pneumonia (CAP) is a common respiratory disease that frequently requires hospitalisation, and is a significant cause of death worldwide. This study aimed to evaluate the usefulness of alpha-1-antichymotrypsin (AACT) as a diagnostic and prognostic biomarker of CAP.

Methods

We conducted a multicentre prospective cohort study in patients hospitalised with CAP. Plasma AACT levels were measured using a quantitative enzyme-linked immunosorbent assay. Receiver-operating characteristic (ROC) curves and Cox proportional hazards regression were used to assess the association between plasma AACT levels and CAP diagnosis and prognosis.

Results

A total of 274 patients with CAP were enrolled in the study. AACT levels were elevated in patients with CAP, especially those with severe CAP and non-survivors. The area under the curve (AUC) of AACT and CRP for diagnosing CAP was 0.755 and 0.843. Cox regression showed that CURB-65 and AACT levels were independent predictors of 30-day mortality. ROC curves showed that plasma AACT levels had the highest accuracy for predicting acute respiratory distress syndrome (ARDS), with an AUC of 0.862. Combining AACT with Pneumonia Severity Index and CURB-65 significantly improved their predictive accuracy for predicting 30-day mortality.

Conclusion

Plasma AACT levels are elevated in patients with CAP, but plasma AACT level is inferior to the C-reactive protein level for diagnosing CAP. The AACT level can reliably predict the occurrence of ARDS and 30-day mortality in patients with CAP.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-024-09742-x.

Keywords

Community-acquired pneumonia
Alpha-1-antichymotrypsin
Prognosis
Mortality
Diagnosis
Prospective cohort study
Chinese Science and Technology Key Project2017ZX10103004-006 National and Provincial Key Clinical Specialty Capacity Building Project 2020issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

Community-acquired pneumonia (CAP) is a common respiratory infectious disease globally. It frequently necessitates hospitalisation, causes death, and poses a major burden on healthcare resources. The annual morbidity of CAP ranges from 1.76 to 7.03 per 1000 people [1]. The mortality levels are approximately 4–20% of hospitalised patients [2, 3]. Hospital mortality among patients with severe CAP (SCAP) is up to 50% [4, 5]. It is challenging for physicians of early diagnosis and risk stratification of CAP, which can reduce morbidity and mortality from SCAP [6, 7].

The CURB-65 and Pneumonia Severity Index (PSI) scores are widely used for risk stratification [8, 9]. The clinical application of PSI is limited owing to the requirement of many indices and a longer period of time for its calculation. CURB-65 is concise and more convenient to apply clinically; however, it is less sensitive than the PSI for predicting mortality. In addition, neither score considers the host inflammatory response, which may be meaningful for assessing the prognosis of CAP.

Exploration of new molecular biomarkers for CAP diagnosis and prognosis is also an important aspect of the current research. Currently, C-reactive protein (CRP) and procalcitonin (PCT) are the most widely used biomarkers in the clinical setting [10–12]. Some studies have identified novel indicators that could be used as biomarkers for CAP. Progranulin [13] and pro-adrenomedullin [14] levels are useful biomarkers in patients with CAP. Several studies have found that some small metabolic molecules, including lipids, can also be used for CAP diagnosis and risk stratification [15, 16]. Additionally, some studies have investigated the use of circular RNAs for CAP diagnosis [17]. Although these novel biomarkers of CAP are not currently used in clinical practice, they have potential for future use [18].

Alpha-1-antichymotrypsin (AACT) is a glycosylated protein, also known as serpin family A member 3 (SERPINA3). It is synthesised mainly in the liver and astrocytes, then secreted into the surrounding tissue or blood, where it has important functions [19]. AACT can bind mast cell chymase and neutrophil cathepsin G to protect cells and tissues from damage. AACT levels in the peripheral blood are elevated in cases of fibromyalgia, multiple sclerosis, chronic heart failure, Alzheimer’s disease, and some cancers, and can be used as a diagnostic or prognostic biomarker [20–22]. AACT is a potentially useful biomarker for CAP diagnosis and prognosis; however, there have been few previous clinical studies on plasma AACT levels in patients with CAP.

Based on previous studies, we hypothesised that plasma AACT levels may be associated with the diagnosis and prognosis of CAP. Thus, the aim of this study was to assess the effectiveness of AACT as a diagnostic indicator, and predictor of survival in patients with CAP.

Methods

Study population

We conducted a multicenter prospective cohort study of patients with CAP. The participants were patients hospitalised in Tibet Autonomous Region People’s Hospital, Peking University People’s Hospital, Shanghai Pulmonary Hospital, Jilin University Second Hospital, Fujian Provincial Hospital, Linfen Central Hospital and West China Hospital, from March 2017 to December 2018. The study protocol was registered in 28/03/2017 with ClinicalTrials.gov (ClinicalTrials.gov ID, NCT03093220), and was approved by the medical ethics committee of Peking University People’s Hospital (approval no.: 2016PHB202-01). Informed consent was obtained from all participants.

Eligible patients were more than 18 years in age, and met the following criteria specified for diagnosis of CAP in adults [23, 24]: (1) onset in community; (2) chest radiograph showing new patchy infiltrates, lobar or segmental consolidation, ground-glass opacities, or interstitial changes, with or without pleural effusion; (3) relevant clinical manifestations of pneumonia such as: (a) new onset of cough or expectoration, or aggravation of existing symptoms of respiratory tract diseases, with or without purulent sputum, chest pain, dyspnoea, or haemoptysis; (b) fever; (c) signs of pulmonary consolidation and/or moist rales; (d) peripheral white blood cell (WBC) count > 10 × 109/L or < 4 × 109/L. Clinical diagnosis can be established if a patient satisfies criterion (1), criterion (2) and any one condition of criterion (3) and meanwhile, tuberculosis, pulmonary tumour, non-infectious interstitial lung disease (ILD), pulmonary edema, atelectasis, pulmonary embolism, pulmonary eosinophilia and pulmonary vasculitis are all excluded. Pregnant women with CAP were excluded in this study.

Data collection

Clinical characteristics including sex, age, comorbidities (diabetes mellitus, high blood pressure, and chronic liver, renal, and cardiac disease), and laboratory findings including WBC count, blood biochemistry, CRP, and PCT were recorded within 24 h of admission. Concurrently, CURB-65 and PSI scores were calculated for all patients with CAP. Complications after admission, drug treatment, SCAP, acute respiratory distress syndrome (ARDS), requirement for mechanical ventilation, mortality, and survival time were also recorded. In patients with CAP, pathogens were identified in respiratory specimens using loop-mediated isothermal amplification (LAMP). Severe CAP (SCAP) was defined according to a standard definition described in the clinical practice guidelines of the Chinese Thoracic Society and American Thoracic Society [23, 24]. The definition of SCAP requires the presence of at least one major criterion or at least three minor criteria. The major criteria: (1) Septic shock with need for vasopressors; (2) Respiratory failure requiring mechanical ventilation. The minor criteria: (1) Respiratory rate ≥ 30 breaths/min; (2) PaO2/FiO2 ratio ≤ 250; (3) Multilobar infiltrates; (4) Confusion/disorientation; (5) Uremia (blood urea nitrogen level ≥ 20 mg/dl); (6) Leukopenia* (white blood cell count < 4,000 cells/µl); (7) Thrombocytopenia (platelet count < 100,000/µl); (8) Hypothermia (core temperature < 36 °C); (9) Hypotension requiring aggressive fluid resuscitation. The ARDS was defined according to the Berlin Definition [25].

Measurement of AACT levels

Within 24 h of admission, and within 24 h before discharge (including recovery or death), blood samples were collected in anticoagulant tubes and centrifuged immediately. Plasma samples were preserved at − 80 °C for further analysis. Plasma AACT levels were measured using quantitative enzyme-linked immunosorbent assay (ELISA) kits (ab217779, Abcam, Cambridge, UK), according to the manufacturer’s instructions. The absorbance of plasma samples and standard at 450 nm was measured using a Multiskan FC system (Thermo Scientific, Waltham, MA, USA) with the correction wavelength set at 570 nm. Four-parameter logistic curve fit was applied to create a standard curve using a standard sample, and plasma AACT levels were calculated based on the curve created.

Statistical analysis

Normally distributed continuous variables were reported as the mean ± standard deviation and assessed using independent samples t-tests, analysis of variance (ANOVA), or Mann-Whitney U tests, as appropriate. Non-normally distributed continuous variables were reported as the median (interquartile range) and groups were compared using Mann-Whitney U tests. Categorical variables were reported as frequencies and percentages, and were analysed using either chi-squared, correction for continuity chi-squared, or Fisher’s exact tests, as appropriate. Kaplan–Meier analysis was used to plot 30-day survival curves, and the log-rank test was used to compare the differences in survival between groups. Receiver-operating characteristic (ROC) curves were used to evaluate the diagnostic accuracy of different variables. Cox proportional hazards regression and ROC curves were used to analyse the effects of variables on 30-day survival in patients with CAP. Correlations between several variables were assessed using the Pearson or Spearman’s rho test, as appropriate.

The analyses were performed using SPSS version 20.0 (IBM Corp., Armonk, NY, USA), MedCalc statistical software v. 15.2.2 (MedCalc Software Ltd., Ostend, Belgium), or GraphPad Prism version 8 software (GraphPad Software, La Jolla, CA, USA). Two-sided P values < 0.05 were considered statistically significant.

Sample size and power calculations

In this study, the sample size and power for the survival analysis using Cox proportional hazards regression was calculated as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:N=\frac{({{Z}_{1-\alpha\:/2}+{Z}_{1-\beta\:})}^{2}}{P\left(1-{R}^{2}\right){\sigma\:}^{2}{B}^{2}}.$$\end{document}

According to the above formula, with a sample size of 274, we set the two-sided significance level to α = 0.05. A 30-day mortality (P) of 0.0657, an R2 value of AACT with CURB-65 of 0.0396, a standard deviation of AACT (σ) of 585.780, and log hazard ratio of AACT (B) of 0.0011, the estimated power was 0.779.

Results

Demographic and clinical characteristics of participants

A flowchart of the participant enrolment is shown in Fig. 1. A total of 274 patients with CAP and 107 controls, including 35 healthy controls (HCs), 30 lung cancer patients with possible checkpoint inhibitor pneumonitis (LC), and ILD, were included. Demographic characteristics and laboratory findings of all participants are shown in Supplementary Table S1. There were no significant differences in sex, or the prevalence of high blood pressure, liver disease and chronic renal disease, or blood glucose and blood urea nitrogen (BUN) levels observed between groups. The age of HC group was younger than that of other groups. Laboratory analyses revealed that participants in the HC group had lower WBC counts and higher haemoglobin levels, platelet counts, and albumin (ALB) levels. CRP levels in the CAP group were higher than those in the LC and ILD groups.

Fig. 1 Flow chart

The characteristics of patients with CAP are shown in Table 1. Thirty-day mortality in patients with CAP was 6.57% (n = 18). A total of 274 patients with CAP were divided into survivor and non-survivor groups. There were no significant differences between the groups in sex, age, comorbidities, WBC count, haemoglobin level, platelet count, glucose level, or antibiotic use. Patients with CAP in the non-survivor group had lower ALB levels, and higher levels of BUN, CRP and PCT than survivors. Apart from pleural effusion, more complications were detected in the non-survivor group, (all P < .001). No statistically significant differences were seen regarding bacterial, viral, or fungal detection rates between the two groups using the LAMP combined pathogen culture method. During hospitalisation, non-survivors were more likely than survivors to receive corticosteroids, antiviral drugs, and mechanical ventilation (P = .003, P < .0001, and P < .0001, respectively). Patients with SCAP and those admitted to the intensive care unit (ICU) were more likely to not survive. The CURB-65 and PSI scores of the survivor group were significantly lower than those of the non-survivor group (P < .0001 and P = .002, respectively; Table 1).

Table 1 Clinical characteristics and laboratory findings of survivors and non-survivors with CAP

	CAP
n = 274	Survivors (I)
n = 256 (93.4%)	Non-Survivors (II)
n = 18 (6.6%)	P value
(I vs. II)	
Male sex (%)	167 (60.9%)	155 (60.5%)	12 (66.7%)	0.607	
Age (years)	67 (54–77)	66 (54–77)	72(66–80)	0.076	
Comorbidities, n (%)					
 Heart disfunction	23 (8.4%)	23 (9.0%)	0 (0.00%)	0.374	
 Chronic renal disease	15 (5.5%)	15 (5.9%)	0 (0.00%)	0.608	
 Liver disease	11(4.0%)	11 (4.3%)	0 (0.00%)	1.000	
 Diabetes mellitus	60 (21.9%)	58 (22.7%)	2 (11.1%)	0.395	
 High pressure	94 (34.3%)	89 (34.8%)	5 (27.8%)	0.546	
Laboratory findings					
  WBC count (×103/mm3)	6.8 (4.9–10.6)	6.8 (5.0-10.5)	11.6 (4.4–17.9)	0.067	
  Hemoglobin level g/dL)	129.0 (114.5–140.0)	128.0 (115.0-140.0)	132.5 (110.5-149.3)	0.330	
  Platelet count (×103/mm3)	206.0 (146.0-278.0)	206.0 (147.0-274.0)	221.0 (107.5–313.0)	0.810	
 Glucose (mmol/L)	5.6 (4.8–7.2)	5.6 (4.8-7.0)	7.0 (5.2–8.8)	0.057	
  Albumin (g/L)	35.6 (31.0–39.0)	36.0 (32.00–39.0)	28.6 (24.2–33.6)	0.0002	
  Blood urea nitrogen (mmol/L)	5.0 (3.8–6.5)	4.9 (3.7–6.4)	6.5 (4.9–10.9)	0.005	
  CRP (mg/L)	41.7 (9.0-115.8)	37.1 (8.1–110.0)	138.0 (92.9-190.3)	< 0.0001	
  PCT (µg/L)	0.12 (0.05–0.68)	0.10 (0.05–0.51)	1.42 (0.30–4.21)	0.0003	
Complications, n (%)					
 Septicopyemia	31 (11.3%)	19 (7.4%)	12 (66.7%)	< 0.0001	
 Pleural effusion	96 (35.0%)	86 (33.6%)	10 (55.6%)	0.059	
 ARDS	14 (5.1%)	4 (1.6%)	10(55.6%)	< 0.0001	
 Confusion	14 (5.1%)	10 (3.9%)	4 (22.2%)	0.009	
Non-invasive ventilation, n (%)	139 (50.7%)	138 (53.9%)	1 (5.6%)	< 0.0001	
Invasive ventilation, n (%)	32 (11.7%)	15 (5.9%)	17 (94.4%)	< 0.0001	
ICU admission, n (%)	45 (16.4%)	29 (11.3%)	16 (88.9%)	< 0.0001	
Pathogens n (%)					
 Bacteria	62 (22.6%)	59 (23.0%)	3 (16.7%)	0.738	
 Virus	79 (28.8%)	75 (29.3%)	4 (22.2%)	0.522	
 Fungus	20 (7.3%)	19 (7.4%)	1 (5.6%)	1.000	
 Mixed	87 (31.8%)	79 (30.9%)	8 (44.4%)	0.231	
Drug treatment, n (%)					
 Antibiotics	269 (98.2%)	251 (98.0%)	18 (100.0%)	1.000	
 Antiviral drugs	41 (15.0%)	29 (11.3%)	12 (66.7%)	< 0.0001	
 Corticosteroids	45 (16.4%)	37 (14.5%)	8 (44.4%)	0.003	
CURB-65	1 (0–1)	1 (0–1)	2 (1–2)	< 0.0001	
PSI	78 (59–97)	77 (58–96)	96 (86–122)	0.0002	
Severe CAP, n (%)	51 (18.6%)	33 (12.9%)	18 (100.0%)	< 0.0001	
Data are presented as means ± standard deviation from the mean, or median (interquartile range) or n (%). WBC, White blood cell; CRP, C-reactive protein; PCT, procalcitonin; ARDS, Acute Respiratory Distress Syndrome; PSI, Pneumonia Severity Index; CURB-65 confusion, urea > 7 mmol/L, respiratory rate ≥ 30 breaths/min, low blood pressure, and age ≥ 65 years

Comparisons of plasma AACT levels

The mean plasma AACT level in patients with CAP was 915.82 ± 585.78 mg/L, which was significantly higher than that of the HC (297.17 ± 80.08 mg/L), LC (541.93 ± 262.31 mg/L), and ILD (481.79 ± 234.31 mg/L) groups (P < .0001 for each comparison; Fig. 2A). A variety of different pathogens were detected in respiratory specimens from patients with CAP, including viral, bacterial, fungal, mixed, and unknown types. The AACT levels in patients with CAP infected with different pathogens were significantly higher than those in HCs (P < .0001 for each comparison). ANOVA showed that in patients with CAP, the AACT levels were higher in patients with mixed causative pathogens than those in patients with a single causative pathogen or unknown type of pathogen (P < .05; Fig. 2B).

CAP severity was assessed using various metrics. The mean AACT level in the non-survivor group (1633.05 ± 905.29 mg/L) was notably higher than that of the survivor group (865.39 ± 523.31 mg/L, P < .0001; Fig. 2C). Patients with SCAP had a significantly higher mean AACT level (1459.56 ± 782.16 mg/L) than those of patients with non-severe CAP (791.46 ± 448.02 mg/L; P < .001) and HCs (297.17 ± 80.08 mg/L; P < .001). Levels of AACT in the healthy HC, non-SCAP, and SCAP groups tended to increase (Fig. 2D).

Fig. 2 Levels of plasma alpha-1-antichymotrypsin (AACT) in groups of participants considered. (A) Levels of AACT in patients with community-acquired pneumonia (CAP), lung cancer, and Interstitial lung disease, and healthy individuals. (B) Comparison of AACT levels in CAP patients with different disease etiologies. (C) Levels of AACT among CAP survivors and non-survivors. (D) Levels of AACT in patients with and without severe CAP (SCAP). (* P < .05, **** P < .0001)

Diagnostic capabilities of plasma AACT level in patients with CAP

Several inflammatory markers, including the WBC count, CRP, and AACT were used to assess their diagnostic capabilities for distinguishing patients with CAP from patients with LC and ILD. ROC curves of the WBC count, and CRP and AACT levels were generated and produced independently. ROC curves showed that plasma CRP levels had the highest accuracy for predicting the occurrence of CAP in patients, and was better than that of the WBC count (P < .0001) and AACT (P = .0002). Among the three variables, plasma AACT levels had a moderate ability to distinguish patients with CAP from those with LC and ILD, which was better than that of the WBC count, but worse than CRP (P < .0001 and P < .05; Supplementary Fig. S1). The optimal cut-off value of AACT for diagnosis of CAP was determined to be 576 mg/L (Supplementary Table S2).

Prognostic power of plasma AACT level in patients with CAP

Univariate Cox proportional hazards regression analyses were used to investigate predictors of 30-day survival. Results revealed that 30-day survival was not significantly associated with sex, age, haemoglobin level, platelet count, or glucose, BUN, or PCT levels, whereas WBC count, PSI and CURB-65 scores, and ALB, CRP, and AACT levels were significantly associated with survival. After inclusion of these significant variables in a multivariable Cox proportional hazards regression model, only AACT (HR: 1.001, 95% confidence interval [CI]: 1.001–1.002; P = .0001) and CURB-65 (hazard ratio [HR]: 2.212, 95% CI: 1.354–3.616; P = .002) were independent predictors of the 30-day mortality; Table 2).

Table 2 Cox proportional hazards regression analysis of risk factors associated with 30-day mortality

	Univariate analysis		Multivariate analysis		
Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value	
Male sex	1.298 (0.487–3.457)	0.602			
Age	1.031 (0.997–1.065)	0.072			
WBC	1.075 (1.025–1.128)	0.003			
Hemoglobin level	1.005 (0.985–1.024)	0.650			
Platelet count	1.001 (0.997–1.006)	0.566			
Glucose	1.077 (0.953–1.217)	0.233			
Albumin	0.873 (0.811–0.940)	0.0003			
Blood urea nitrogen	1.003 (0.988–1.019)	0.696			
CRP	1.010 (1.006–1.014)	< 0.0001			
PCT	1.008 (0.950–1.069)	0.794			
PSI	1.026 (1.013–1.040)	0.0001			
CURB-65	2.831 (1.814–4.421)	< 0.0001	2.212 (1.354–3.616)	0.002	
AACT	1.001 (1.001–1.002)	< 0.0001	1.001 (1.001–1.002)	0.0001	
CI, Confidence interval; WBC, White blood cell; CRP, C-reactive protein; PCT, procalcitonin; PSI, Pneumonia Severity Index; CURB-65, confusion, urea > 7 mmol/L, respiratory rate ≥ 30 breaths/min, low blood pressure and age ≥ 65 years; AACT, Alpha-1-antichymotrypsin

ROC curves showed that the AUC of plasma AACT levels was 0.787 (95% CI: 0.734–0.834) for predicting the 30-day mortality, and did not differ significantly from the AUCs of the other indicators assessed (Fig. 3A). The optimal cut-off value of AACT for predicting the 30-day mortality was 972 mg/L determined using Youden’s index of the ROC (Supplementary Table S3). Combining AACT with PSI and CURB-65 significantly improved their predictive accuracy (Fig. 3B, C; Supplementary Table S3).

Additionally, we analysed the capability of several inflammatory markers to predict the occurrence of acute respiratory distress syndrome (ARDS). ROC curves showed that the plasma AACT level had a significantly higher accuracy for predicting ARDS than those of WBC count, or CRP and PCT levels, with an AUC of 0.862 (95% CI: 0.818–0.902; Fig. 3D; Supplementary Table S4).

Fig. 3 Receiver-operating characteristic (ROC) analysis of alpha-1-antichymotrypsin (AACT) level for prognosis. (A) Prediction of the 30-day mortality. (B) A comparison of the use of a combination of AACT and PSI scores versus PSI score used alone is shown. (C) A comparison of use of a combination of AACT levels and CURB-65 versus CURB-65 alone is shown. (D) Prediction of acute respiratory distress syndrome occurrence (* P < .05)

Associations between predictor levels

Relationships between clinical parameters and plasma AACT levels were investigated using Pearson or Spearman’s correlation analyses, as appropriate. The resultant correlation matrix is shown in Fig. 4A. AACT was found to be strong positively correlated with CRP (R > .7, P < .0001), moderately positively correlated with PCT and PSI (0.3 < R ≤ .7; P < .0001), and weak positively correlated with the WBC count and CURB-65 (R ≤ .3; P < .01). AACT levels were moderately negatively correlated with ALB (R = − .466; P < .0001).

Kaplan–Meier survival analysis of AACT in CAP patients

Patients were divided into two groups (≤ 972 mg/L and AACT > 972 mg/L), according to the AACT cut-off determined by the ROC curve analysis. Kaplan–Meier curves were used to assess the difference between the two groups for predicting the 30-day mortality rate of patients with CAP (Fig. 4B). The risk of death in patients with AACT levels > 972 mg/L was significantly higher than that in patients with AACT levels ≤ 972 mg/L (P < .0001).

Fig. 4 Correlation and survival analysis. (A) Alpha-1-antichymotrypsin (AACT) level versus multiple clinical predictors of community-acquired pneumonia (CAP). (B) A Kaplan–Meier analysis of the 30-day mortality in CAP patients is shown. (** P < .01, **** P < .0001)

Comparison of plasma AACT levels at discharge and admission

We collected paired plasma of 69 patients with CAP within 24 h before discharge (including recovery or death). The plasma AACT levels are shown in Fig. 5. The mean plasma AACT level before discharge in the non-survival group was 1521 ± 392 mg/L, which was significantly higher than that of survival group (581 ± 238 mg/L) (P < .0001; Fig. 5A). Six of the seven patients (86%) who died, had plasma AACT levels above the threshold of 972 mg/L before death, whereas two of the 62 surviving patients (3%) had plasma AACT levels above the threshold (P < .0001; Fig. 5B). The plasma AACT levels tended to increase in non-survivors, whereas they tended to decrease in survivors; however, the mean plasma AACT level in the survivors with CAP was still higher than that in the HCs (P < .0001, data not showed).

Fig. 5 Levels of paired plasma alpha-1-antichymotrypsin (AACT) in 69 patients with community-acquired pneumonia (CAP) at admission and discharge; (A) Levels of AACT among CAP survivors and non-survivors at discharge. (B) A comparison of AACT levels in 69 paired patients at admission and discharge. Patients with CAP who died within 30 days were represented by red dots, while survivors were represented by black dots (**** P < .0001)

Discussion

In this prospective study, 274 patients with CAP and 107 controls were enrolled, and the 30-day mortality of patients with CAP was 6.57%. The major findings were: (1) Plasma AACT levels were higher in patients with CAP than in the HC, LC, and ILD groups, especially in non-survivors and those with SCAP. (2) Levels of AACT in patients with CAP infected with mixed causative pathogens were higher than those infected with a single causative pathogen. (3) Plasma AACT levels were less accurate than those of CRP for diagnosing CAP. Conversely, AACT was more accurate than CRP for predicting ARDS. (4) The plasma AACT level was an independent predictor of the 30-day mortality. (5) CURB-65 or PSI combined with plasma AACT significantly improved their accuracy for predicting 30-day mortality. (6) Plasma AACT levels were strong positively correlated with CRP. (7) The mortality rate was significantly higher in patients with AACT levels > 972 mg/L. (8) Plasma AACT levels continued to increase during the progression of CAP and patients with a plasma AACT level > 972 mg/L, were at high risk of death. Therefore, the study findings show that the plasma AACT level is an effective predictor of the 30-day mortality and ARDS in patients with CAP.

Immune checkpoint inhibitor-associated pneumonia (CIP) occurs in patients with LC undergoing immune checkpoint inhibitor therapy. The lung X-ray computed tomography imaging of patients with CIP and ILD has similarities with that of CAP, and biomarkers may play an important role in distinguishing between diseases. AACT has been shown to be elevated in many inflammatory diseases; however, clinical studies on CAP are scarce. Studies have confirmed that the AACT level increases in non-small cell LC (NSCLC) and is as a biomarker for early diagnosis of NSCLC [22, 26]. Urinary AACT levels in patients with NSCLC are also elevated [27]; lung adenocarcinoma tissue also has higher AACT expression [28]. Patients with ILD have diffuse cutaneous systemic sclerosis; which is associated with elevated AACT levels on serum protein profiles [21]. To our knowledge, this is the first study to compare plasma AACT levels in healthy people, and patients with LC, ILD, and CAP. The results show that AACT levels in patients with CAP are considerably higher than those in patients with LC and ILD; and that the plasma AACT levels in patients with any of the three diseases are higher than those of HCs, which is consistent with previous studies. Our study confirmed that plasma AACT levels are significantly increased in patients with CAP.

Multiple cohort studies have studied CRP as a biomarker for identifying ILD [29, 30]. CRP levels increase during the acute phase of CIP and decrease gradually in the subacute and chronic phases [31]. In NSCLC, an elevated CRP level is a predictor of serious adverse events induced by immune checkpoint inhibitors [32]. CRP was markedly increased in patients with CAP [33]. We compared the role of CRP, AACT, and the WBC count in distinguishing patients with CAP from patients without CAP (including patients with LC and ILD). The ROC curve showed that CRP can better distinguish CAP than AACT.

Cox proportional hazards regression showed that AACT was an independent predictor of 30-day mortality in hospitalised patients with CAP after adjusting for other factors. ROC curves revealed that combining AACT level with PSI and CURB-65 scores significantly improved the accuracy of the 30-day mortality rate prediction. ARDS was a serious complication of CAP, and the mortality rate of patients with ARDS was up to 50% [34]. Efficient prediction of the occurrence of ARDS can guide therapy of CAP. Our study shows that AACT was a useful biomarker for predicting the occurrence of ARDS, and is a significantly better predictor than the WBC count, and CRP and PCT levels. The plasma AACT levels tended to be higher in non-survivors. These results show that AACT is a useful biomarker for identification of patients at high risk of ARDS and death. AACT was significantly and positively associated with the CRP level, which is consistent with the findings of a previous study [35].

Chymotrypsin-like proteases are released from immune cells, and cathepsin G is produced in inflammatory areas. They can play pro-inflammatory roles by activating inflammatory cytokines, tissue destruction, and remodelling. AACT inhibits the destructive effects by binding proteases [36]. This binding helps to control inflammation but further investigation is required to assess whether it contributes to persistent chronic inflammation. Our study showed that patients with higher plasma AACT levels had higher mortality rates. Additionally, AACT plays other roles, such as in DNA binding and regulation of lipid metabolites [19, 37, 38]. Considering the multiple roles of AACT in multiple diseases, the role of AACT in patients with CAP and its potential relevance as a biomarker deserves further exploration.

A major limitation of this study is that only a limited number of study participants had paired plasma AACT levels measured on admission and discharge.

Conclusion

our results showed that plasma AACT levels in patients with CAP, particularly in non-survivors and those with SCAP, were higher than those in HCs and patients with LC and ILD. The AACT level could be used to accurately predict the occurrence of ARDS in patients with CAP. Considering the AACT level in combination with the CURB-65 and PSI scores improves their prognostic accuracy for 30-day mortality. Early and continuous measurement of plasma AACT levels provides an effective aid in identifying high-risk patients with CAP.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

We would like to thank the staff at the following hospitals for their efforts and dedication in enrolling study participants: Fujian Provincial Hospital, Linfen Central Hospital, Tibet Autonomous Region People’s Hospital, West China Hospital, Shanghai Pulmonary Hospital, Second Hospital of Jilin University. We would like to thank Editage (www.editage.cn) for English language editing.

Author contributions

Lili Zhao, Wen Xi, Pihua Gong and, Shuming Guo, Zhancheng Gao designed the study. Lili Zhao performed ELISA experiment. Ying Shang, Wenjun Gao and Wenjie Bian acquired clinical data. Lili Zhao, Xi Chen, Jianbo Xue, Pihua Gong and Shuming Guo performed data analysis and plotting. Lili Zhao and Wen Xi drafted the manuscript. Yu Xu and Zhancheng Gao obtained research funding. All authors read and approved the final manuscript.

Funding

The study was funded by the National and Provincial Key Clinical Specialty Capacity Building Project 2020 and the Chinese Science and Technology Key Project (2017ZX10103004-006) The funder had no role in the study design, data analysis, or outcome assessment.

Data availability

The datasets generated and analysed during the current study are not publicly available due to health privacy concerns, but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Ethics approval and consent to participate All subjects provided informed consent. This study was approved by the medical ethics committee of Peking University People’s Hospital.

Competing interests

The authors declare no competing interests.

Abbreviations

CAP Community-acquired pneumonia

ROC Receiver operating characteristic

AUC Areas under the curve

CI Confidence interval

PSI Pneumonia Severity Index

CURB-65 Confusion, urea &gt; 7 mmol/L, respiratory rate ≥ 30breaths/min, low blood pressure, and age ≥ 65 years

ARDS Acute Respiratory Distress Syndrome

CRP C-reactive protein

PCT Procalcitonin

WBC White blood cell

AACT Alpha-1-antichymotrypsin

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lili Zhao and Wen Xi contributed equally to this work.
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References

1. Aliberti S Dela Cruz CS Amati F Sotgiu G Restrepo MI Community-acquired pneumonia Lancet (London England) 2021 398 10303 906 19 10.1016/s0140-6736(21)00630-9 34481570
Aliberti S, Dela Cruz CS, Amati F, Sotgiu G, Restrepo MI. Community-acquired pneumonia. Lancet (London England). 2021;398(10303):906–19. 10.1016/s0140-6736(21)00630-9.34481570 10.1016/s0140-6736(21)00630-9
2. Prina E Ranzani OT Torres A Community-acquired pneumonia Lancet (London England) 2015 386 9998 1097 108 10.1016/s0140-6736(15)60733-4 26277247
Prina E, Ranzani OT, Torres A. Community-acquired pneumonia. Lancet (London England). 2015;386(9998):1097–108. 10.1016/s0140-6736(15)60733-4.26277247 10.1016/s0140-6736(15)60733-4
3. Song JH Oh WS Kang CI Chung DR Peck KR Ko KS Epidemiology and clinical outcomes of community-acquired pneumonia in adult patients in Asian countries: a prospective study by the Asian network for surveillance of resistant pathogens Int J Antimicrob Agents 2008 31 2 107 14 10.1016/j.ijantimicag.2007.09.014 18162378
Song JH, Oh WS, Kang CI, Chung DR, Peck KR, Ko KS, et al. Epidemiology and clinical outcomes of community-acquired pneumonia in adult patients in Asian countries: a prospective study by the Asian network for surveillance of resistant pathogens. Int J Antimicrob Agents. 2008;31(2):107–14. 10.1016/j.ijantimicag.2007.09.014.18162378 10.1016/j.ijantimicag.2007.09.014
4. Nair GB Niederman MS Updates on community acquired pneumonia management in the ICU Pharmacol Ther 2021 217 107663 10.1016/j.pharmthera.2020.107663 32805298
Nair GB, Niederman MS. Updates on community acquired pneumonia management in the ICU. Pharmacol Ther. 2021;217:107663. 10.1016/j.pharmthera.2020.107663.32805298 10.1016/j.pharmthera.2020.107663
5. Welte T Torres A Nathwani D Clinical and economic burden of community-acquired pneumonia among adults in Europe Thorax 2012 67 1 71 9 10.1136/thx.2009.129502 20729232
Welte T, Torres A, Nathwani D. Clinical and economic burden of community-acquired pneumonia among adults in Europe. Thorax. 2012;67(1):71–9. 10.1136/thx.2009.129502.20729232 10.1136/thx.2009.129502
6. Chalmers JD Identifying severe community-acquired pneumonia: moving beyond mortality Thorax 2015 70 6 515 6 10.1136/thoraxjnl-2015-207090 25877217
Chalmers JD. Identifying severe community-acquired pneumonia: moving beyond mortality. Thorax. 2015;70(6):515–6. 10.1136/thoraxjnl-2015-207090.25877217 10.1136/thoraxjnl-2015-207090
7. Blasi F Stolz D Piffer F Biomarkers in lower respiratory tract infections Pulm Pharmacol Ther 2010 23 6 501 7 10.1016/j.pupt.2010.04.007 20434579
Blasi F, Stolz D, Piffer F. Biomarkers in lower respiratory tract infections. Pulm Pharmacol Ther. 2010;23(6):501–7. 10.1016/j.pupt.2010.04.007.20434579 10.1016/j.pupt.2010.04.007
8. Fine MJ Auble TE Yealy DM Hanusa BH Weissfeld LA Singer DE A prediction rule to identify low-risk patients with community-acquired pneumonia N Engl J Med 1997 336 4 243 50 10.1056/nejm199701233360402 8995086
Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, Singer DE, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336(4):243–50. 10.1056/nejm199701233360402.8995086 10.1056/nejm199701233360402
9. Lim WS van der Eerden MM Laing R Boersma WG Karalus N Town GI Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study Thorax 2003 58 5 377 82 10.1136/thorax.58.5.377 12728155
Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58(5):377–82. 10.1136/thorax.58.5.377.12728155 10.1136/thorax.58.5.377
10. Song Y Sun W Dai D Liu Y Li Z Tian Z Prediction value of procalcitonin combining CURB-65 for 90-day mortality in community-acquired pneumonia Expert Rev Respir Med 2021 15 5 689 96 10.1080/17476348.2021.1865810 33336607
Song Y, Sun W, Dai D, Liu Y, Li Z, Tian Z, et al. Prediction value of procalcitonin combining CURB-65 for 90-day mortality in community-acquired pneumonia. Expert Rev Respir Med. 2021;15(5):689–96. 10.1080/17476348.2021.1865810.33336607 10.1080/17476348.2021.1865810
11. Self WH Balk RA Grijalva CG Williams DJ Zhu Y Anderson EJ Procalcitonin as a marker of etiology in adults hospitalized with community-acquired pneumonia Clin Infect Diseases: Official Publication Infect Dis Soc Am 2017 65 2 183 90 10.1093/cid/cix317
Self WH, Balk RA, Grijalva CG, Williams DJ, Zhu Y, Anderson EJ, et al. Procalcitonin as a marker of etiology in adults hospitalized with community-acquired pneumonia. Clin Infect Diseases: Official Publication Infect Dis Soc Am. 2017;65(2):183–90. 10.1093/cid/cix317.10.1093/cid/cix317
12. Guo S Mao X Liang M The moderate predictive value of serial serum CRP and PCT levels for the prognosis of hospitalized community-acquired pneumonia Respir Res 2018 19 1 193 10.1186/s12931-018-0877-x 30285748
Guo S, Mao X, Liang M. The moderate predictive value of serial serum CRP and PCT levels for the prognosis of hospitalized community-acquired pneumonia. Respir Res. 2018;19(1):193. 10.1186/s12931-018-0877-x.30285748 10.1186/s12931-018-0877-x
13. Luo Q He X Zheng Y Ning P Xu Y Yang D Elevated progranulin as a novel biomarker to predict poor prognosis in community-acquired pneumonia J Infect 2020 80 2 167 73 10.1016/j.jinf.2019.12.004 31837341
Luo Q, He X, Zheng Y, Ning P, Xu Y, Yang D, et al. Elevated progranulin as a novel biomarker to predict poor prognosis in community-acquired pneumonia. J Infect. 2020;80(2):167–73. 10.1016/j.jinf.2019.12.004.31837341 10.1016/j.jinf.2019.12.004
14. Viasus D Del Rio-Pertuz G Simonetti AF Garcia-Vidal C Acosta-Reyes J Garavito A Biomarkers for predicting short-term mortality in community-acquired pneumonia: a systematic review and meta-analysis J Infect 2016 72 3 273 82 10.1016/j.jinf.2016.01.002 26777314
Viasus D, Del Rio-Pertuz G, Simonetti AF, Garcia-Vidal C, Acosta-Reyes J, Garavito A, et al. Biomarkers for predicting short-term mortality in community-acquired pneumonia: a systematic review and meta-analysis. J Infect. 2016;72(3):273–82. 10.1016/j.jinf.2016.01.002.26777314 10.1016/j.jinf.2016.01.002
15. Ning P Zheng Y Luo Q Liu X Kang Y Zhang Y Metabolic profiles in community-acquired pneumonia: developing assessment tools for disease severity Crit Care (London England) 2018 22 1 130 10.1186/s13054-018-2049-2
Ning P, Zheng Y, Luo Q, Liu X, Kang Y, Zhang Y, et al. Metabolic profiles in community-acquired pneumonia: developing assessment tools for disease severity. Crit Care (London England). 2018;22(1):130. 10.1186/s13054-018-2049-2.10.1186/s13054-018-2049-2
16. Banoei MM, Vogel HJ, Weljie AM, Yende S, Angus DC, Winston BW. Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP). Critical care (London, England). 2020;24(1):461. 10.1186/s13054-020-03147-3
17. Zhao T Zheng Y Hao D Jin X Luo Q Guo Y Blood circRNAs as biomarkers for the diagnosis of community-acquired pneumonia J Cell Biochem 2019 120 10 16483 94 10.1002/jcb.28863 31286543
Zhao T, Zheng Y, Hao D, Jin X, Luo Q, Guo Y, et al. Blood circRNAs as biomarkers for the diagnosis of community-acquired pneumonia. J Cell Biochem. 2019;120(10):16483–94. 10.1002/jcb.28863.31286543 10.1002/jcb.28863
18. Ito A Ishida T Diagnostic markers for community-acquired pneumonia Annals Translational Med 2020 8 9 609 10.21037/atm.2020.02.182
Ito A, Ishida T. Diagnostic markers for community-acquired pneumonia. Annals Translational Med. 2020;8(9):609. 10.21037/atm.2020.02.182.10.21037/atm.2020.02.182
19. Soman A, Asha Nair S. Unfolding the cascade of SERPINA3: inflammation to cancer. Biochimica et biophysica acta reviews on cancer. 2022;1877(5):188760. 10.1016/j.bbcan.2022.188760
20. Jin Y Wang W Wang Q Zhang Y Zahid KR Raza U Alpha-1-antichymotrypsin as a novel biomarker for diagnosis, prognosis, and therapy prediction in human diseases Cancer Cell Int 2022 22 1 156 10.1186/s12935-022-02572-4 35439996
Jin Y, Wang W, Wang Q, Zhang Y, Zahid KR, Raza U, et al. Alpha-1-antichymotrypsin as a novel biomarker for diagnosis, prognosis, and therapy prediction in human diseases. Cancer Cell Int. 2022;22(1):156. 10.1186/s12935-022-02572-4.35439996 10.1186/s12935-022-02572-4
21. Bellocchi C, Ying J, Goldmuntz EA, Keyes-Elstein L, Varga J, Hinchcliff ME et al. Large-scale characterization of systemic sclerosis serum protein Profile: comparison to peripheral blood cell transcriptome and correlations with Skin/Lung fibrosis. Arthritis & rheumatology (Hoboken, NJ). 2021;73(4):660–70. 10.1002/art.41570
22. Jin Y Wang J Ye X Su Y Yu G Yang Q Identification of GlcNAcylated alpha-1-antichymotrypsin as an early biomarker in human non-small-cell lung cancer by quantitative proteomic analysis with two lectins Br J Cancer 2016 114 5 532 44 10.1038/bjc.2015.348 26908325
Jin Y, Wang J, Ye X, Su Y, Yu G, Yang Q, et al. Identification of GlcNAcylated alpha-1-antichymotrypsin as an early biomarker in human non-small-cell lung cancer by quantitative proteomic analysis with two lectins. Br J Cancer. 2016;114(5):532–44. 10.1038/bjc.2015.348.26908325 10.1038/bjc.2015.348
23. Cao B Huang Y She DY Cheng QJ Fan H Tian XL Diagnosis and treatment of community-acquired pneumonia in adults: 2016 clinical practice guidelines by the Chinese Thoracic Society, Chinese Medical Association Clin Respir J 2018 12 4 1320 60 10.1111/crj.12674 28756639
Cao B, Huang Y, She DY, Cheng QJ, Fan H, Tian XL, et al. Diagnosis and treatment of community-acquired pneumonia in adults: 2016 clinical practice guidelines by the Chinese Thoracic Society, Chinese Medical Association. Clin Respir J. 2018;12(4):1320–60. 10.1111/crj.12674.28756639 10.1111/crj.12674
24. Metlay JP Waterer GW Long AC Anzueto A Brozek J Crothers K Diagnosis and treatment of adults with community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America Am J Respir Crit Care Med 2019 200 7 e45 67 10.1164/rccm.201908-1581ST 31573350
Metlay JP, Waterer GW, Long AC, Anzueto A, Brozek J, Crothers K, et al. Diagnosis and treatment of adults with community-acquired Pneumonia. An official clinical practice guideline of the American thoracic society and infectious diseases society of America. Am J Respir Crit Care Med. 2019;200(7):e45–67. 10.1164/rccm.201908-1581ST.31573350 10.1164/rccm.201908-1581ST
25. Ranieri VM Rubenfeld GD Thompson BT Ferguson ND Caldwell E Fan E Acute respiratory distress syndrome: the Berlin definition JAMA 2012 307 23 2526 33 10.1001/jama.2012.5669 22797452
Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, et al. Acute respiratory distress syndrome: the Berlin definition. JAMA. 2012;307(23):2526–33. 10.1001/jama.2012.5669.22797452 10.1001/jama.2012.5669
26. Jin Y Yang Y Su Y Ye X Liu W Yang Q Identification a novel clinical biomarker in early diagnosis of human non-small cell lung cancer Glycoconj J 2019 36 1 57 68 10.1007/s10719-018-09853-z 30607521
Jin Y, Yang Y, Su Y, Ye X, Liu W, Yang Q, et al. Identification a novel clinical biomarker in early diagnosis of human non-small cell lung cancer. Glycoconj J. 2019;36(1):57–68. 10.1007/s10719-018-09853-z.30607521 10.1007/s10719-018-09853-z
27. Zhang Y Li Y Qiu F Qiu Z Comparative analysis of the human urinary proteome by 1D SDS-PAGE and chip-HPLC-MS/MS identification of the AACT putative urinary biomarker J Chromatogr B Anal Technol Biomedical life Sci 2010 878 32 3395 401 10.1016/j.jchromb.2010.10.026
Zhang Y, Li Y, Qiu F, Qiu Z. Comparative analysis of the human urinary proteome by 1D SDS-PAGE and chip-HPLC-MS/MS identification of the AACT putative urinary biomarker. J Chromatogr B Anal Technol Biomedical life Sci. 2010;878(32):3395–401. 10.1016/j.jchromb.2010.10.026.10.1016/j.jchromb.2010.10.026
28. Higashiyama M Doi O Yokouchi H Kodama K Nakamori S Tateishi R Alpha-1-antichymotrypsin expression in lung adenocarcinoma and its possible association with tumor progression Cancer 1995 76 8 1368 76 10.1002/1097-0142(19951015)76:8<1368::aid-cncr2820760812>3.0.co;2-n 8620411
Higashiyama M, Doi O, Yokouchi H, Kodama K, Nakamori S, Tateishi R. Alpha-1-antichymotrypsin expression in lung adenocarcinoma and its possible association with tumor progression. Cancer. 1995;76(8):1368–76. 10.1002/1097-0142(19951015)76:8%3C1368::aid-cncr2820760812%3E3.0.co;2-n.8620411 10.1002/1097-0142(19951015)76:8<1368::aid-cncr2820760812>3.0.co;2-n
29. Xu L You H Wang L Lv C Yuan F Li J Identification of three different phenotypes in anti-MDA5 antibody-positive dermatomyositis patients: implications for rapidly progressive interstitial lung disease prediction 2022 Hoboken, NJ Arthritis & rheumatology
Xu L, You H, Wang L, Lv C, Yuan F, Li J, et al. Identification of three different phenotypes in anti-MDA5 antibody-positive dermatomyositis patients: implications for rapidly progressive interstitial lung disease prediction. Hoboken, NJ: Arthritis & rheumatology; 2022. 10.1002/art.42308.
30. Xue J Hu W Wu S Wang J Chi S Liu X Development of a risk Nomogram Model for identifying interstitial lung disease in patients with rheumatoid arthritis Front Immunol 2022 13 823669 10.3389/fimmu.2022.823669 35784288
Xue J, Hu W, Wu S, Wang J, Chi S, Liu X. Development of a risk nomogram model for identifying interstitial lung disease in patients with rheumatoid arthritis. Front Immunol. 2022;13:823669. 10.3389/fimmu.2022.823669.35784288 10.3389/fimmu.2022.823669
31. Zhou C Yang Y Lin X Fang N Chen L Jiang J Proposed clinical phases for the improvement of personalized treatment of checkpoint inhibitor-related pneumonitis Front Immunol 2022 13 935779 10.3389/fimmu.2022.935779 35967342
Zhou C, Yang Y, Lin X, Fang N, Chen L, Jiang J, et al. Proposed clinical phases for the improvement of personalized treatment of checkpoint inhibitor-related pneumonitis. Front Immunol. 2022;13:935779. 10.3389/fimmu.2022.935779.35967342 10.3389/fimmu.2022.935779
32. Suazo-Zepeda E Bokern M Vinke PC Hiltermann TJN de Bock GH Sidorenkov G Risk factors for adverse events induced by immune checkpoint inhibitors in patients with non-small-cell lung cancer: a systematic review and meta-analysis Cancer Immunol Immunother 2021 70 11 3069 80 10.1007/s00262-021-02996-3 34195862
Suazo-Zepeda E, Bokern M, Vinke PC, Hiltermann TJN, de Bock GH, Sidorenkov G. Risk factors for adverse events induced by immune checkpoint inhibitors in patients with non-small-cell lung cancer: a systematic review and meta-analysis. Cancer Immunol Immunother. 2021;70(11):3069–80. 10.1007/s00262-021-02996-3.34195862 10.1007/s00262-021-02996-3
33. Hsu CW Suk CW Hsu YP Chang JH Liu CT Huang SK Sphingosine-1-phosphate and CRP as potential combination biomarkers in discrimination of COPD with community-acquired pneumonia and acute exacerbation of COPD Respir Res 2022 23 1 63 10.1186/s12931-022-01991-1 35307030
Hsu CW, Suk CW, Hsu YP, Chang JH, Liu CT, Huang SK, et al. Sphingosine-1-phosphate and CRP as potential combination biomarkers in discrimination of COPD with community-acquired pneumonia and acute exacerbation of COPD. Respir Res. 2022;23(1):63. 10.1186/s12931-022-01991-1.35307030 10.1186/s12931-022-01991-1
34. Bellani G Laffey JG Pham T Fan E Brochard L Esteban A Epidemiology, patterns of Care, and mortality for patients with Acute Respiratory Distress Syndrome in Intensive Care Units in 50 countries JAMA 2016 315 8 788 800 10.1001/jama.2016.0291 26903337
Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315(8):788–800. 10.1001/jama.2016.0291.26903337 10.1001/jama.2016.0291
35. Chard MD Calvin J Price CP Cawston TE Hazleman BL Serum alpha 1 antichymotrypsin concentration as a marker of disease activity in rheumatoid arthritis Ann Rheum Dis 1988 47 8 665 71 10.1136/ard.47.8.665 3261967
Chard MD, Calvin J, Price CP, Cawston TE, Hazleman BL. Serum alpha 1 antichymotrypsin concentration as a marker of disease activity in rheumatoid arthritis. Ann Rheum Dis. 1988;47(8):665–71. 10.1136/ard.47.8.665.3261967 10.1136/ard.47.8.665
36. Beatty K Bieth J Travis J Kinetics of association of serine proteinases with native and oxidized alpha-1-proteinase inhibitor and alpha-1-antichymotrypsin J Biol Chem 1980 255 9 3931 4 10.1016/S0021-9258(19)85615-6 6989830
Beatty K, Bieth J, Travis J. Kinetics of association of serine proteinases with native and oxidized alpha-1-proteinase inhibitor and alpha-1-antichymotrypsin. J Biol Chem. 1980;255(9):3931–4.6989830 10.1016/S0021-9258(19)85615-6
37. Sun YX Wright HT Janciauskiene S Alpha1-antichymotrypsin/Alzheimer’s peptide abeta(1–42) complex perturbs lipid metabolism and activates transcription factors PPARgamma and NFkappaB in human neuroblastoma (Kelly) cells J Neurosci Res 2002 67 4 511 22 10.1002/jnr.10144 11835318
Sun YX, Wright HT, Janciauskiene S. Alpha1-antichymotrypsin/Alzheimer’s peptide abeta(1–42) complex perturbs lipid metabolism and activates transcription factors PPARgamma and NFkappaB in human neuroblastoma (Kelly) cells. J Neurosci Res. 2002;67(4):511–22. 10.1002/jnr.10144.11835318 10.1002/jnr.10144
38. Siddiqui AA Hughes AE Davies RJ Hill JA The isolation and identification of alpha 1-antichymotrypsin as a DNA-binding protein from human serum Biochem Biophys Res Commun 1980 95 4 1737 42 10.1016/s0006-291x(80)80099-4 6893414
Siddiqui AA, Hughes AE, Davies RJ, Hill JA. The isolation and identification of alpha 1-antichymotrypsin as a DNA-binding protein from human serum. Biochem Biophys Res Commun. 1980;95(4):1737–42. 10.1016/s0006-291x(80)80099-4.6893414 10.1016/s0006-291x(80)80099-4
