Systematically analysis of decompensated cirrhotic patients with spontaneous bacterial peritonitis to identify diagnostic and prognostic indexes

doi:10.21203/rs.3.rs-2976156/v1

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Research Article

Systematically analysis of decompensated cirrhotic patients with spontaneous bacterial peritonitis to identify diagnostic and prognostic indexes

https://doi.org/10.21203/rs.3.rs-2976156/v1

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Journal Publication

published 11 Nov, 2023

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Background

Spontaneous bacterial peritonitis (SBP) is a common complication in patients with cirrhosis, but the biomarkers for diagnosis have yet to be validated. This study aimed to identify reliable and easy-to-use biomarkers for the diagnosis and prognosis of cirrhotic patients with complicated SBP.

Methods

Cirrhosis data were collected retrospectively; The clinical features and laboratory test indices were analyzed. The indices related to the SBP of cirrhosis were analyzed. Two machine learning methods (Xgboost and LASSO algorithms) and a logistic regression analysis were adopted to screen and validate the indexed risk associated with SBP risk, then a predictive model was built and validated using the area under curve (AUC) estimated. The indices related to the survival of cirrhotic patients were also analyzed.

Results

A total of 413 cirrhotic patients were included, of whom 329 were decompensated and 84 were compensated. 52 patients complicated SPB and they were all decompensated patients. Patients with SBP have a poorer Child-Pugh score (P < 0.05) and the proportion of cancers was higher in patients with SBP than in those without SBP (P < 0.05). Most laboratory test indices differed significantly between patients with and without SBP (P < 0.05). Xgboost, LASSO algorithms, and logistic regression analysis identified that albumin, neutrophil-to-lymphocyte ratio (NLR), and ferritin-to-neutrophil ratio (FNR) were independently associated with SBP in uncompensated cirrhotic patients, and that established by the three indices model showed a high predictive value. with the AUC being 0.808. In addition, high neutrophils, ALP and CAR were associated with the shorter survival time of patients with decompensated cirrhosis, and the combination of these indices showed a better prognostic value for cirrhotic patients.

Conclusions

The present study identified FNR as a new index in the diagnosis of SBP in uncompensated patients with cirrhosis. A model constructed from neutrophils, ALP and CAR showed high performance in predicting the prognosis of patients with uncompensated cirrhosis.

cirrhosis

spontaneous bacterial peritonitis

laboratory test indexes

predictive value

prognosis analysis

Spontaneous bacterial peritonitis (SBP) refers to bacterial infection in the peritoneum with no apparent intra-abdominal source of infection [1, 2].SBP is a common complication in patients with cirrhosis, and the prevalence of SBP in patients with cirrhosis is estimated at 10–30%, and the outcomes of patients with cirrhosis SBP in patients with cirrhosis remain unfavorable despite the advancement of therapy[3, 4]. Multiple mechanisms have been shown to contribute to the occurrence of SBP in cirrhosis, including impaired humoral and cell-specific immunity caused by hypoalbuminemia[5], increased intestinal permeability caused by portal hypertension, and the impaired neutrophil and reticuloendothelial system [6, 7], all these factors lead to an increased susceptibility to infection.

Currently, blood indices have been shown to be potential biomarkers of SBP in cirrhosis. For example, the general laboratory test index of ascites calprotectin and lactoferrin [8], serum procalcitonin and C-reactive protein (CRP) [9], platelet and neutrophil to lymphocyte ratio (NLR) [10, 11]have been documented in the diagnosis of SBP in cirrhosis. In addition, some new biomarkers such as ascites endocane [12], IL-17 [13] and CD206 [14] have also been reported as biomarker candidates for the diagnosis of SBP in cirrhosis. However, these results have yet to be validated due to limitations, including sample size or confounding factors. Therefore, it remains necessary to find reliable biomarkers for the diagnosis and prognosis of cirrhosis.

In this study, we retrospectively analyzed the data from cirrhotic patients aiming to examine the blood biomarkers associated with SBP and constructed a model to diagnose SBP in decompensated cirrhosis. Finally, we attempted to identify the blood biomarkers associated with the survival of decompensated cirrhotic patients. Our results would shed light on the crucial role of blood biomarkers in the diagnostic and prognostic value of cirrhosis with SBP.

Data collection

We retrospectively enrolled cirrhotic patients hospitalized between March 2013 and July 2022. The inclusion criteria were as follows: (1) age over 18 years; (2) the diagnosis of liver cirrhosis with or without SBP was made by clinical, laboratory, or imaging tests; (3) availability of complete clinicopathological and follow-up data after discharge. Exclusion criteria were pregnancy, secondary peritonitis, peritoneal dialysis-associated peritonitis, liver malignancy, extrahepatic malignancy, and chronic liver disease without cirrhosis.

Clinical data collection and definitions

Clinicopathologic features and laboratory data were collected from the patient's electronic medical records, and laboratory data were selected within two days of hospital admission. Clinicopathologic data of cirrhosis included patients' age, sex, Child-Pugh score, cancers, infection status, and laboratory test indices. We selected only the laboratory test indices that were tested for the first time after the diagnosis of cirrhosis. The index was calculated as a formula: NLR = neutrophil count/ lymphocyte count ratio; PLR = platelet count /neutrophil count ratio; FAR = ferritin levels / albumin levels ratio; FNR = ferritin levels / neutrophil count ratio.

Follow-up

All the patients were followed for a maximum of one year after recruitment, and thereafter every three to six months at the discretion of the attending physician. The last follow-up was December 31, 2022.

Statistical analysis

Normal continuous data are presented as mean standard deviation and compared to Student's t-test, while nonnormal data are presented as median and interquartile range (IQR) and compared to Mann-Whitney's U-test. Categorical data are presented as absolute numbers (percent) and were compared using the chi-square test or Fisher's exact test for small samples. Xgboost algorithms and LASSO algorithms were used to verify key features related to the SBP. Logistic regression and Cox regression analysis were applied to identify key features related to the SBP and the constructed predictive model. The predicted value was calculated using receiver operating characteristic (ROC) and area under the curve (AUC) analyses. A Kaplan-Meier plot and log-rank test were used to analyze survival data using Kaplan-Meier plots. The risk score was calculated by multiplying the value and the coefficients of each index from the Cox regression model according to the previous described [15]. Statistical significance was defined as a P-value less than 0.05. All analyzes were performed with the R software (version 4.2.2).

Clinical characteristics of included data

Data were collected from a total of 413 cirrhotic patients with an average age of 54.4 to 12.3 years, 419 male and 89 female patients. 329 were decompensated and 84 were compensated. The etiology of cirrhosis included HBV infection (348 patients) and others (65 patients). 52 (12.6%) patients complicated SBP, 188 patients complicated cancer, most were HCC (175 patients). The laboratory tests showed that the inflammatory indices, liver function indices and nutritional indices were significantly different between compensated and uncompensated patients (P < 0.05). The median follow-up time was eight months. The details of the clinical features are given in Table 1.

Table 1

Clinical characteristics of included data
Features	Total	Compensated (N = 84)	Uncompensated (N = 329)	P value
Age	54.4 ± 12.3	54.5 ± 11.8	54.3 ± 12.5	0.911
Sex				0.498
Male	336 (81.4%)	71 (84.5%)	265 (80.5%)
Female	77 (18.6%)	13 (15.5%)	64 (19.5%)
Etiology				1.000
HBV	348 (84.3%)	71 (84.5%)	277 (84.2%)
Others	65 (15.7%)	13 (15.5%)	52 (15.8%)
Class				< 0.001
A	154 (37.3%)	84 (100%)	70 (21.3%)
B	168 (40.7%)	0 (0%)	168 (51.1%)
C	91 (22.0%)	0 (0%)	91 (27.7%)
SBP				< 0.001
Yes	52 (12.6%)	0 (0%)	52 (15.8%)
No	361 (87.4%)	84 (100%)	277 (84.2%)
Cancers	188 (45.5%)	42 (50%)	146 (44.4%)	0.423
WBC	6.2 (4.4–8.1)	6.4 (4.8–7.8)	6.2 (4.3–8.3)	0.725
Neutrophil	3.8 (2.4–5.6)	3.7 (2.2–5.2)	3.8 (2.5–5.7)	0.404
Lymphocyte	1.2 (0.8–1.6)	1.5 (1.1-2.0)	1.1 (0.8–1.5)	< 0.001
Platelet	131.6 (83.6-206.2)	179.8 (110.9-223.3)	122.0 (76.4–184.0)	< 0.001
TBil	25.6 (15.0-57.1)	14.1 (9.4–19.2)	33.4 (17.4–68.6)	< 0.001
ALB	30.9 (26.3–35.5)	36.5 ± 5.2	29.7 ± 6.2	< 0.001
AST	67.0 (39.0-130.0)	40.5 (28.5–69.0)	79.0 (44.0-148.0)	< 0.001
ALT	42.0 (25.0–72.0)	31.0 (20.0-49.5)	44.0 (27.0–86.0)	< 0.001
ALP	138.0 (97.0-203.0)	112.0 (84.0-171.5)	147.0 (101.0-213.0)	0.002
CRP	12.3 (3.8–37.0)	5.0 (1.7–12.6)	16.6 (5.3–40.2)	< 0.001
Ferritin	491.7 (143.9-1014.6)	417.8 (158.8-731.1)	555.3 (136.6-1123.5)	0.089
FAR	15.5 (4.8–34.7)	10.7 (4.5–20.0)	17.9 (4.9–42.1)	0.004
FNR	121.2 (40.0-306.1)	104.0 (36.2-253.8)	127.6 (41.7-318.9)	0.367
PLR	108.8 (75.8-172.5)	119.1 (83.9-166.2)	107.5 (72.2-173.6)	0.393
NLR	3.2 (2.1–5.2)	2.6 (1.6–4.1)	3.4 (2.2–5.4)	< 0.001
CAR	0.4 (0.1–1.3)	0.1 (0.0-0.4)	0.6 (0.2–1.5)	< 0.001

Comparison of uncompensated cirrhotic patient complicated with and without SBP

The uncompensated cirrhosis data were divided into two groups based on whether the patients complicated SBP. As shown in Table 2, we found that patients with SBP had a poorer Child-Pugh score (P < 0.05) and the proportion of cancers was higher in patients with SBP than in patients without SBP (P < 0, 05). Regarding the laboratory tests, the results showed that most of the inflammatory indices, liver function indices and nutritional indices were significantly different between patients with and without SBP, except lymphocytes, platelets, ALP and PLR (P < 0.05).

Table 2

**Comparison of uncompensated cirrhotic patients complicated with and without SBP**
Features	Non-SBP (N = 277)	SBP (N = 52)	P value
Age	54.6 ± 12.3	52.9 ± 13.4	0.379
Sex	58 (20.9%)	6 (11.5%)	0.167
Male	219 (79.1%)	46 (88.5%)
Female
Etiology	229 (82.7%)	48 (92.3%)	0.123
HBV	48 (17.3%)	4 (7.7%)
Others
Class			< 0.001
A	70 (25.3%)	0 (0%)
B	151 (54.5%)	17 (32.7%)
C	56 (20.2%)	35 (67.3%)
Cancers	144 (52%)	39 (75%)	0.004
WBC	6.0 (4.2–7.8)	7.5 (4.8–11.0)	0.003
Neutrophil	3.6 (2.3–5.3)	5.2 (2.9–8.2)	0.008
Lymphocyte	1.1 (0.8–1.5)	1.0 (0.8–1.4)	0.317
Platelet	125.0 (77.9-200.1)	107.0 (70.7-156.6)	0.133
TBil	30.5 (16.8–57.9)	66.8 (33.4-232.4)	< 0.001
ALB	30.5 ± 6.1	25.8 ± 5.0	< 0.001
AST	72.0 (43.0-144.0)	94.0 (59.0-259.0)	0.005
ALT	43.0 (26.0–81.0)	47.5 (33.0-137.5)	0.022
ALP	150.0 (100.0-222.0)	138.0 (111.5–176.0)	0.207
CRP	14.3 (4.6–37.7)	27.6 (11.5–78.1)	< 0.001
Ferritin	452.2 (123.2-997.8)	1021.3 (478.2-3262.3)	< 0.001
FAR	15.2 (4.1–33.5)	45.0 (18.1-117.8)	< 0.001
FNR	116.3 (37.6-262.4)	312.5 (84.0-622.5)	< 0.001
PLR	108.7 (75.8-176.8)	97.6 (58.8-170.5)	0.379
NLR	3.3 (2.2–5.2)	4.8 (2.4–10.2)	0.013
CAR	0.5 (0.1–1.3)	1.0 (0.5–3.2)	< 0.001

Effect of cancers on uncompensated cirrhotic patients with SBP

To examine the effect of cancer on cirrhosis with SBP, we performed a subgroup analysis for uncompensated cirrhotic patients with SBP with or without cancer. As shown in Table 3, the indices of laboratory tests of patients with SBP were strongly influenced by the cancer types in terms of WBC, Neutrophil, TBil, CRP, FNR, PLR, NLR and CAR. In the patients without SBP, more laboratory test indices were affected by cancer, but the effect was different between those with and without SBP in terms of lymphocytes, platelets, TBil, AST, ALT, ALP, ferritin, FAR and NLR, suggesting that cancer is occurring differential effect on these indices in uncompensated cirrhotic patients with SBP.

Table 3

Effect of cancers on uncompensated cirrhosis with SBP
Features	Without SBP (N = 277)			With SBP (N = 52)
	Cancer (N = 133)	No cancer (N = 144)	P value	Cancer (N = 13)	No cancer (N = 39)	P value
WBC	6.5 (5.1–8.9)	5.0 (3.8–7.2)	< 0.001	11.0 (7.5–13.6)	6.6 (4.6–8.9)	0.045
Neu	4.1 (3.0-6.2)	3.2 (2.1–4.9)	< 0.001	8.9 (5.7–11.6)	3.9 (2.8–6.8)	0.012
Lymphocyte	1.2 (0.9–1.5)	1.0 (0.7–1.5)	0.005	1.0 (0.6–1.1)	1.2 (0.8–1.4)	0.176
Platelet	163.2 (112.6-244.6)	90.0 (62.9-147.6)	< 0.001	136.3 (104.3–172.0)	102.9 (70.7-143.7)	0.078
TBil	32.2 (18.2–57.5)	26.1 (15.0-63.2)	0.329	39.2 (19.4–98.0)	90.1 (45.2-279.4)	0.018
ALB	30.9 ± 5.4	30.1 ± 6.7	0.292	24.4 (21.6–28.7)	25.3 (22.1–28.4)	0.634
AST	104.0 (65.0-174.0)	48.5 (30.5–91.0)	< 0.001	81.0 (64.0-127.0)	98.0 (59.0-310.5)	0.627
ALT	57.0 (34.0–92.0)	33.0 (24.0-60.5)	< 0.001	47.0 (34.0–84.0)	59.0 (30.5–236.0)	0.612
ALP	179.0 (126.0-278.0)	126.0 (89.0-180.5)	< 0.001	163.0 (111.0-184.0)	126.0 (114.0-166.5)	0.479
CRP	24.1 (9.4–46.9)	8.2 (2.7–25.2)	< 0.001	89.2 (48.1-131.8)	19.0 (10.8–41.7)	0.009
Ferritin	663.1 (238.7-1322.2)	335.8 (64.6-792.5)	< 0.001	579.8 (221.2-2876.3)	1187.8 (654.5-3537.5)	0.276
FAR	19.5 (8.4–37.4)	11.5 (2.3–31.1)	< 0.001	27.2 (10.9-112.8)	51.6 (24.9-130.1)	0.304
FNR	145.8 (66.5-262.4)	88.3 (24.8-263.2)	0.008	101.2 (59.2–349.0)	415.8 (93.3–737.0)	0.035
PLR	138.1 (88.4-198.8)	92.7 (64.7-145.3)	< 0.001	118.0 (93.5-293.8)	91.4 (53.7-144.7)	0.044
NLR	3.5 (2.4–5.1)	3.2 (1.9–5.3)	0.241	7.7 (5.9–14.5)	3.4 (1.9–7.1)	0.006
CAR	0.8 (0.3–1.6)	0.3 (0.1–0.8)	< 0.001	4.4 (2.0-5.3)	0.8 (0.5–1.6)	0.011

Screening risk factors associated with SBP in uncompensated cirrhotic patients

To determine the laboratory test indices related to SBP in uncompensated cirrhotic patients, two machine learning methods, Xgboost and LASSO algorithms, were adopted to study the risk factors related to SBP in uncompensated cirrhotic patients. The Xgboost and LASSO algorithms identified 10 and 14 indices related to SBP risk with an AUC value of 0.704 and 0.849, respectively (Fig. 1A-B). Then we overlapped these indices and identified six common indices including Neutrophils, Alb, ALP, NLR, CAR, FNR. Then we included these common indices in the multivariate logistic regression model and found that only ALB, NLR and FNR were independently associated with the SBP in uncompensated cirrhotic patients (Table 4). We used these three indices to create a diagnostic model and found that the diagnostic value was high, with an AUC value of 0.808 (Fig. 1C).

Table 4

Logistics regression for the index associated with SBP in cirrhotic patients
Features	OR (univariable)	P value	OR (multivariable)	P value
ALB	0.87 (0.82–0.92)	< 0.001	0.88 (0.83–0.94)	< 0.001
ALP	1.00 (0.99-1.00)	0.050	1.04 (0.96–1.12)	0. 309
CAR	1.43 (1.22–1.67)	< 0.001	1.16 (0.91–1.48)	0..218
Neu	1.17 (1.08–1.28)	< 0.001	1.10 (0.96–1.26)	0 .183
NLR	1.11 (1.05–1.16)	< 0.001	0.99 (0.99-1.00)	0.019
FNR	1.00 (1.00–1.00)	< 0.001	1.00 (1.00–1.00)	< 0.001

Identify risk factors related to the prognosis of uncompensated cirrhotic patients

We first estimated the association of the eight indices with the survival of uncompensated cirrhotic patients, using the optimal cut-off value obtained by Survminer algorithms, we found that all six indices (neutrophils, ALB, ALP, NLR, CAR, FNR) were ALP, NLR, CAR, FNR were associated with survival of uncompensated patients with cirrhosis and high neutrophil levels with a shorter survival time, while ALB was associated with a longer survival time (P < 0.05; Fig. 2). However, when focusing on the 52 SBP patients, the results did not show that Alb and ALT were associated with survival of SBP patients (P > 0.05; Fig. 3).

Next, a Cox regression analysis was applied to screen the risk factors associated with patient prognosis, as summarized in Table 5, CAR, neutrophils and ALP were associated with the prognosis of uncompensated patients with cirrhosis, while platelets, neutrophils, NLR, CAR and ALP were significantly associated with the prognosis of patients with SBP. These results suggested that CAR, neutrophils and ALP could predict the prognosis of uncompensated cirrhotic patients with or without SBP. Finally, we used CAR, neutrophils and ALP to construct a prognostic model for uncompensated cirrhotic patients and calculated the risk score from the Cox regression model and visualized it by a nomogram. We found that the risk score calculated from neutrophils, ALP and CAR shows a much better prognostic value in uncompensated patients with cirrhosis than other indices, regardless of the total number of patients or patients with SBP (Fig. 4).

Table 5

Cox regression analysis for the risk factors related to the prognosis of patients
Features	Cirrhotic patients		SBP only
	HR (univariable)	P value	HR (univariable)
ALB	0.97 (0.90–1.05)	0.451	0.98 (0.96-1.00)	0.055
NLR	1.01 (0.99–1.03)	0.237	1.01 (1.00-1.03)	0.021
CAR	1.23 (1.08–1.41)	0.002	1.16 (1.08–1.24)	< 0.001
Platelet	1.00 (1.00-1.01)	0.656	1.00 (1.00–1.00)	0.001
ALT	1.00 (1.00–1.00)	0.910	1.00 (1.00–1.00)	0.138
Neutrophils	1.09 (1.03–1.16)	0.005	1.10 (1.06–1.14)	< 0.001
ALP	1.01 (1.00-1.01)	0.016	1.00 (1.00–1.00)	0.001
FNR	1.00 (1.00–1.00)	0.309	1.00 (1.00–1.00)	0.849

In the present study, we retrospectively analyzed data from 413 cirrhosis patients and found that 58 (11.4%) patients with SBP experienced complications, a prevalence consistent with previous reports [3, 4]. We found that all SBP patients were uncompensated, suggesting that cirrhotic status was significantly associated with SBP. We also found that cirrhotic patients with cancer are more prone to SBP compared to non-cancer patients. The results of the laboratory test indices showed that most of the inflammatory indices, liver function indices and nutritional indices were significantly different between patients with and without SBP; In addition, we observed that laboratory test indices in uncompensated patients with cirrhosis were similar to those in patients with complete cirrhosis. Importantly, we found that cancer had differential effects on some laboratory test indices in cirrhotic patients without SBP, but had little effect in patients with SBP. Next, we examined the laboratory test indices associated with SBP in uncompensated cirrhotic patients using two machine learning methods and validated by a logistic regression model. The results showed that ALB, NLR and FNR were independently associated with the SBP in which FNR had not previously been reported. Finally, we identified neutrophils, ALP, and CAR, all of which were associated with survival in cirrhotic patients with or without SBP, and the prognostic value constructed by these three indices was high compared to other indices. Taken together, these results showed that several indices were associated with the occurrence of SBP in uncompensated cirrhotic patients and FNR was a new biomarker for the diagnosis of cirrhosis with SBP. We also identified three indices that are significantly associated with the prognosis of uncompensated cirrhotic patients.

SBP is a common complication in uncompensated cirrhotic patients. Currently, the diagnosis of SBP in cirrhotic patients relies on testing for ascites, and abdominal paracentesis is an invasive surgery that increases the risk of infection [16]. Therefore, it is helpful for cirrhotic patients to find a non-invasive approach to diagnosing SBP. Using laboratory test indices from blood samples to diagnose SBP or predict survival in cirrhotic patients is a viable approach in clinical practice. Previous studies have identified several common blood indices related to the SBP in cirrhosis, including CRP and NLR [11]. In the present study, we confirmed the diagnostic value of NLR in SBP of cirrhosis, indicating the robustness of this index. More importantly, as the diagnostic value of FNR in SBP in cirrhosis was reported for the first time, these results underscore the role of FNR in uncompensated patients with cirrhosis.

Serum ferritin reflects the body's iron stores and was increased in iron overload and decreased in patients with iron deficiency disorders. Elevated liver iron would promote increased ferritin synthesis [17], and ferritin activates production of collagen and liver fibrogenesis[18]. Therefore, serum ferritin could be a useful marker of ongoing fibrosis[19]. In this study, we found that serum ferritin levels were significantly different between cirrhotic patients with and without SBP, but as shown in Table 3, the change in ferritin levels was strongly influenced by the cancer, suggesting that the change in ferritin in cirrhotic patients with SBP were affected by cancer, so it could not be an independent index to diagnose SBP in cirrhosis. We therefore used a combination of ferritin with other blood indices. One study reported that FAR was able to determine mortality in critically ill COVID-19 patients treated in the ICU [20]. Our study showed that FAR was elevated in uncompensated cirrhotic patients and patients with SBP, but was not an independent index of SBP and was not associated with cirrhotic patient survival.

FNR was calculated from the ratio of ferritin and neutrophils. Neutrophil count is a sensitive index that reflects inflammation in the body. The combination of neutrophils with other indices has proven critical in diagnosing or predicting multiple diseases such as platelets to neutrophils ratio in a stroke [21], eosinophil-neutrophil ratio in tumors [22], platelet-to-neutrophil ratio in lupus nephritis [23]. This evidence showed that the combination of neutrophils with other indices had a more important value than the individual index. This study found that FNR was independently associated with risk of SBP and with worsening in patients with cirrhosis. We speculated that the mechanism underlying the value of FNR in cirrhotic patients with SBP might be that ferritin reflects the status of cirrhosis while neutrophils reflect infection of the peritoneum, and this combination might reflect the overall status of cirrhotic patients with SBP reflect better.

Regarding the prognostic value of laboratory test indices associated with cirrhotic patients, one study reported that serum CRP levels were associated with a higher mortality rate in patients with SBP[24], ascitic fluid lactate and NLR[25], and calprotectin[26] were associated with the mortality of patients with SBP. The present study showed that blood neutrophils, ALP and CAR levels were significantly associated with the prognosis of uncompensated cirrhotic patients regardless of the total number of patients or patients with SBP, and the prognostic value of the model constructed by these indices was better than the individual Index. These results indicate that the combination of certain laboratory tests indices could achieve a better prognostic value in cirrhotic patients.

There were several limitations in this study. First, although we included a large sample of cirrhotic patients, the number of SBP was relatively small, the survival analysis for cirrhotic patients with SBP could be performed. Second, since this study is retrospective, the chosen bias is inevitable and confounding factors could undermine the robustness of the results. Third, this study is a single center research, so our results should be interpreted with caution when extrapolating to other population regions. Therefore, a prospective multi-center design cohort is warranted to validate our results.

The present study systematically analyzed the risk factors associated with the occurrence and prognosis of SBP in uncompensated cirrhotic patients and identified FNR as a new index for the diagnosis of SBP in uncompensated cirrhotic patients. A model constructed from neutrophils, ALP and CAR showed high performance in predicting the prognosis of patients with uncompensated cirrhosis.

Ethics approval and consent to participate

This study was approved by the ethics board of First Affiliated Hospital of Guangxi Medical University, and was conducted according to the principles expressed in the Declaration of Helsinki.

The requirement for individual informed consent was waived by the institutional ethics board since this study used currently existing medical records collected during the course of routine medical care and did not pose any additional risks to the patients. All the data were anonymized prior to the analysis. All methods and experimental protocols in this study were conducted in accordance with the approved protocols and Ethics Committee’s existing guidelines.

Consent for publication

Not applicable

Availability of data and materials

The data used to support the findings of this study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare that they have no conflict of interest.

Funding

Authors’ contributions

Study concept and design: BY and WL. Collection and assembly of data: WL, Q-PL and J-HW. Performed the experiment: L-QY, Y-JF and FL. Data analysis and interpretation: WL, BY, S-YQ and H-XJ. Manuscript writing and review: All authors. All authors contributed to the article and approved the submitted version.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (81860116, 82260109 and 81960119); National Natural Science Foundation of Guangxi (2020GXNSFAA297116); National Natural Science Foundation of Cultivation Science Fund (GIPY2019005). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Popoiag RE, Fierbințeanu-Braticevici C. Spontaneous bacterial peritonitis: update on diagnosis and treatment. Romanian Journal of Internal Medicine = Revue Roumaine De Medecine Interne. 2021 Dec 1;59(4):345–50.
Dever JB, Sheikh MY. Review article: spontaneous bacterial peritonitis--bacteriology, diagnosis, treatment, risk factors and prevention. Aliment Pharm Ther. 2015 Jun;41(11):1116–31.
Marciano S, Díaz JM, Dirchwolf M, Gadano A. Spontaneous bacterial peritonitis in patients with cirrhosis: incidence, outcomes, and treatment strategies. Hepatic Medicine: Evidence and Research. 2019;11:13–22.
Alotaibi A, Almaghrabi M, Ahmed O, Rodrigues D, Iansavichene A, Puka K, et al. Incidence of spontaneous bacterial peritonitis among asymptomatic cirrhosis patients undergoing outpatient paracentesis: a systematic review and meta-analysis. Eur J Gastroen Hepat. 2021 Dec 1;33(1S Suppl 1):e851–7.
Bernardi M. Spontaneous bacterial peritonitis: from pathophysiology to prevention. Intern Emerg Med. 2010 Oct;5 Suppl 1:S37-44.
Bartoletti M, Giannella M, Caraceni P, Domenicali M, Ambretti S, Tedeschi S, et al. Epidemiology and outcomes of bloodstream infection in patients with cirrhosis. J Hepatol. 2014 Jul;61(1):51–8.
Alaniz C, Regal RE. Spontaneous bacterial peritonitis: a review of treatment options. P & T: A Peer-Reviewed Journal for Formulary Management. 2009 Apr;34(4):204–10.
Patel KP, Korbitz PM, Gallagher JP, Schmidt C, Ingviya T, Manatsathit W. Ascitic calprotectin and lactoferrin for detection of spontaneous bacterial peritonitis: a systematic review and meta-analysis. Translational Gastroenterology and Hepatology. 2022;7:37.
Verma R, Satapathy SK, Bilal M. Procalcitonin and C-reactive protein in the diagnosis of spontaneous bacterial peritonitis. Translational Gastroenterology and Hepatology. 2022;7:36.
Abdel Hammed MR, El-Amien HA, Asham MN, Elgendy SG. Can platelets indices and blood neutrophil to lymphocyte ratio be used as predictors for diagnosis of spontaneous bacterial peritonitis in decompensated post hepatitis liver cirrhosis? The Egyptian Journal of Immunology. 2022 Oct;29(4):12–24.
N M, T B, A AR, M H, Ag D, T S, et al. Can combined blood neutrophil to lymphocyte ratio and C-reactive protein be used for diagnosis of spontaneous bacterial peritonitis? British journal of biomedical science [Internet]. 2018 Apr [cited 2023 Apr 26];75(2). Available from: https://pubmed.ncbi.nlm.nih.gov/29452544/
Wu X, Luo J, Huang W, Jia B, Luo T. Role of ascitic endocan levels in the diagnosis of spontaneous bacterial peritonitis in decompensated cirrhosis. Biomarkers. 2020 Jun;25(4):360–6.
Keryakos HKH, Mohammed AA, Higazi AM, Mahmoud EAM, Saad ZM. Serum and ascitic fluid interleukin-17 in spontaneous bacterial peritonitis in Egyptian patients with HCV-related liver cirrhosis. Current Research in Translational Medicine. 2020 Nov;68(4):237–43.
Stengel S, Quickert S, Lutz P, Ibidapo-Obe O, Steube A, Köse-Vogel N, et al. Peritoneal Level of CD206 Associates With Mortality and an Inflammatory Macrophage Phenotype in Patients With Decompensated Cirrhosis and Spontaneous Bacterial Peritonitis. Gastroenterology. 2020 May;158(6):1745–61.
Guo XW, Li SQ, Lei RE, Ding Z, Hu BL, Lin R. Tumor-infiltrating immune cells based TMEscore and related gene signature is associated with the survival of CRC patients and response to fluoropyrimidine-based chemotherapy. Front Oncol. 2022;12:953321.
E S, C S, P G. Management of uninfected and infected ascites in cirrhosis. Liver international : official journal of the International Association for the Study of the Liver [Internet]. 2016 Jan [cited 2023 Apr 26];36 Suppl 1. Available from: https://pubmed.ncbi.nlm.nih.gov/26725907/
Tran TN, Eubanks SK, Schaffer KJ, Zhou CY, Linder MC. Secretion of ferritin by rat hepatoma cells and its regulation by inflammatory cytokines and iron. Blood. 1997 Dec 15;90(12):4979–86.
Wang W, Knovich MA, Coffman LG, Torti FM, Torti SV. Serum ferritin: Past, present and future. Biochimica Et Biophysica Acta. 2010 Aug;1800(8):760–9.
Butt AA, Yan P, Lo Re V, Rimland D, Goetz MB, Leaf D, et al. Liver fibrosis progression in hepatitis C virus infection after seroconversion. JAMA internal medicine. 2015 Feb;175(2):178–85.
Taşkin Ö, Yilmaz A, Soylu VG, Demir U, Çatan Inan F. Ferritin / albumin ratio could be a new indicator of COVID-19 disease mortality. Journal of Infection in Developing Countries. 2023 Jan 31;17(1):37–42.
Hu ZB, Zhong QQ, Lu ZX, Zhu F. Association of platelet-to-white blood cell ratio and platelet-to-neutrophil ratio with the risk of fatal stroke occurrence in middle-aged to older Chinese. BMC geriatrics. 2022 May 17;22(1):430.
Ma Y, Shang K, Wu S, Wang J, Cao B. The Prognostic Value of Albumin-Globulin Ratio and Eosinophil-Neutrophil Ratio in Patients with Advanced Tumors Undergoing Treatment with PD-1/PD-L1 Inhibitors. Nutr Cancer. 2022;74(8):2815–28.
Liu P, Li P, Peng Z, Xiang Y, Xia C, Wu J, et al. Predictive value of the neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-neutrophil ratio, and neutrophil-to-monocyte ratio in lupus nephritis. Lupus. 2020 Aug;29(9):1031–9.
Cho Y, Park SY, Lee JH, Lee DH, Lee M, Yoo JJ, et al. High-sensitivity C-reactive protein level is an independent predictor of poor prognosis in cirrhotic patients with spontaneous bacterial peritonitis. Journal of Clinical Gastroenterology. 2014;48(5):444–9.
Mani I, Alexopoulos T, Hadziyannis E, Tsiriga A, Vourli G, Alexopoulou A. An exploratory study of ascitic fluid lactate as prognostic factor of mortality in cirrhotic patients with spontaneous bacterial peritonitis. Eur J Gastroen Hepat. 2021 Dec 1;33(1S Suppl 1):e970–7.
Nasereslami M, Khamnian Z, Moaddab Y, Jalali Z. Diagnostic and prognostic role of ascitic fluid calprotectin level: six-month outcome findings in cirrhotic patients. Scand J Gastroenterol. 2020 Sep;55(9):1093–8.

No competing interests reported.

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Systematically analysis of decompensated cirrhotic patients with spontaneous bacterial peritonitis to identify diagnostic and prognostic indexes

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Journal Publication

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Abstract

Background

Methods

Results

Conclusions

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Introduction

Materials and Methods

Data collection

Clinical data collection and definitions

Follow-up

Statistical analysis

Results

Clinical characteristics of included data

Comparison of uncompensated cirrhotic patient complicated with and without SBP

Effect of cancers on uncompensated cirrhotic patients with SBP

Screening risk factors associated with SBP in uncompensated cirrhotic patients

Identify risk factors related to the prognosis of uncompensated cirrhotic patients

Discussion

Conclusions

Declarations

References

Additional Declarations

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Journal Publication

Version 1