Abstract
(1) Background: Spontaneous bacterial peritonitis (SBP) is a major and severe complication in cirrhosis patients with ascites. Over the years, advance in antibiotic treatment has led to changes in microbial patterns in some regions, including the emergence of extended-spectrum beta-lactamases resistant (ESBL)-producing bacteria and an increase in Gram-positive bacteria (GPC). In addition, three SBP types (classic SBP, culture-negative neutrophilic ascites (CNNA), and monomicrobial non-neutrocytic bacterascites (MNB)), may also have different prognoses. Therefore, the study aimed to investigate the microbial pattern and the predictors of short-term outcomes in patients with SBP. (2) Methods: Patients discharged with a diagnosis of the first episode of SBP between January 2006 and July 2017 were enrolled. Patients’ clinical, demographic, hematological, and biochemical data were obtained at diagnosis, and the model for end-stage liver disease (MELD)-based scores were calculated accordingly. Patients were followed up until February 2018 or until death. (3) Results: A total of 327 patients were analyzed. The prevalence of classic SBP was nearly equivalent to CNNA. As for the microbial pattern, Gram-negative bacillus (GNB) remained more prevalent than GPC (75 vs. 25%), with E. coli being the most common bacterial species, followed by K. Pneumoniae and then Staphylococcus. The percentage of ESBL strain in culture-positive patients was 10.9%. By univariable and multivariable logistic regression survival analysis, there was no significant difference in predicting short-term mortality among the three SBP types, neither between GNB vs. GPC nor between ESBL- and non-ESBL-producing bacteria. Only bacteremia (sepsis), hepatorenal syndrome (HRS), and serum creatinine (Cr) were independent predictors of in-hospital and 3-month mortality, whereas HRS and Cr were independent predictors of 6-month mortality. (4) Conclusions: SBP types, Gram stain result, and ESBL strain did not affect survival. Only bacteremia (sepsis), HRS, and serum Cr independently predicted the short-term mortality in patients with SBP.
Keywords: spontaneous bacterial peritonitis and types, liver cirrhosis, extended-spectrum beta-lactamases resistant (ESBL), Gram-positive bacteria (GPC), bacteremia or sepsis, hepatorenal syndrome (HRS)
1. Introduction
Cirrhotic patients have an impaired defense system against bacteria associated with reduced bacterial clearance [1,2]. This immune defect facilitates bacterial translocation induced by increased intestinal permeability and intestinal bacterial overgrowth observed in cirrhosis [3]. Bacterial infection is present at admission or develops during hospitalization in about 30% of patients with cirrhosis [4]. The most common infection was spontaneous bacterial peritonitis (SBP) [4].
SBP is a serious complication representing an advanced stage in patients with cirrhosis and ascites [5,6]. There are three types of SBP: (1) classic SBP; (2) culture-negative neutrophilic ascites (CNNA); and (3) monomicrobial non-neutrocytic bacterascites (MNB). The 2013 American Association for the Study of Liver Diseases (AASLD) guideline on the management of adult patients with ascites due to cirrhosis suggested empiric antibiotic therapy for patients with ascites fluid PMN counts ≧ 250 cells/mm3 or <250 cells/mm3 but with symptom/sign of infection [7]. This implies that all three types of SBP need prompt treatment when a symptom/sign of infection is present. In addition, when first reported, the in-hospital mortality of an episode of SBP exceeded 90%; however, the rate has been reduced to approximately 20% through early diagnosis and prompt antibiotic therapy [8]. Nevertheless, the prognosis of these three types of SBP is rarely compared with each other.
Bacterial translocation from the gastrointestinal tract is the most common source of SBP. Thus, two-thirds of SBP cases were caused by GNB, of which Escherichia coli is the most frequently isolated pathogen [4,8]. Therefore, the 2013 AASLD guideline and 2018 EASL guideline recommended that the first-line antibiotic treatment for SBP is third generation cephalosporins [7,9]. However, changes in the patterns and microbiology of SBP have been observed in some regions over the past few years, such as the increased prevalence of CNNA, ESBL-producing bacteria, increased resistance rate to first-line antibiotics [10], and higher frequency of Gram-positive organisms [11]. In a Netherlands report, a nonsignificant increase in the proportion of patients with SBP caused by Gram-positive bacteria and multidrug-antibiotic-resistant bacteria over 10 years was found [12], prompting our interest in studying the microbial pattern of SBP and whether it would influence the prognosis.
Therefore, the study aimed to investigate the incidence and prognosis of the three types of SBP, the microbial pattern, and to determine whether the form, the bacterial pattern, and the drug-resistant strain would influence the prognosis of SBP.
2. Materials and Methods
2.1. Patient Selection and Follow-Ups
With the approval of the ethical committees of Chang Gung Memorial Hospital (202000112B0), a list of patients with a discharge diagnosis of SBP and liver cirrhosis between January 2006 and July 2017 was obtained sequentially from the medical record management committee. A total of 327 patients who met the criteria for SBP and were diagnosed for the first time were included in the retrospective study. Patients’ clinical, demographic, hematological, and biochemical data were obtained at diagnosis, and the MELD-based scores were calculated accordingly. Patients were followed up until February 2018 or until death.
2.2. Diagnosis, Definition, and Management of Liver Cirrhosis and Spontaneous Bacterial Peritonitis
The diagnosis of liver cirrhosis was based mainly on the following criteria: (1) Typical sonographic diagnosis for liver cirrhosis [13]; (2) ascites were caused by liver cirrhosis (serum-ascites albumin gradient > 1.1 g/dL) [14]; (3) exclusion of other underlying diseases such as malignancy (HCC or metastasis), right-sided congestive heart failure, Budd–Chiari syndrome, post-sinusoidal obstruction syndrome, portal or splenic vein thrombosis, and the possibility of schistosomiasis. Management of liver cirrhosis was in accordance with the AASLD, Baveno VI, as well as the Asian Pacific Association for the Study of the Liver (APASL) guidelines [7,15,16].
SBP was diagnosed upon positive ascites culture and/or an absolute neutrophil count in ascites fluid of ≧250 cells/mm3, in the absence of a surgically treatable source of infection and other causes of elevated ascites neutrophil count, such as hemorrhage, pancreatitis, peritoneal tuberculosis, or carcinomatosis [17,18,19]. The treatment of SBP adhered to the recommendations of the International Ascites Club and AASLD guidelines [17].
There are three variants of SBP: (1) classic SBP (elevated PMN count >250/mm3 and positive culture); (2) culture-negative neutrophilic ascites (CNNA): ascites culture is negative and PMN cell count is >250/mm3 (3) monomicrobial non-neutrocytic bacterascites (MNB), in which PMN count < 250/mm3 but the culture was positive.
2.3. The Outcomes of SBP
Due to the high short-term mortality, the outcome or prognosis of SBP was defined as the in-hospital, 3-month (3 M), and 6-month (6 M) mortality.
2.4. The Diagnosis of Hepatorenal Syndrome and Hepatic Encephalopathy
The hepatorenal syndrome was diagnosed based on clinical criteria brought up by the AASLD, the International Club of Ascites (ICA), and Kidney Disease: Improving Global Outcomes (KDIGO) [20,21]. The prerequisites were the absence of any other apparent cause for the acute kidney injury, including shock, current or recent treatment with nephrotoxic drugs, and the absence of ultrasonographic evidence of obstruction or parenchymal kidney disease.
Hepatic encephalopathy (HE) was diagnosed according to the European Association for the Study of the Liver (EASL)/American Association for the Study of Liver Diseases (AASLD) guidelines [22,23] and by the exclusion of other causes of mental status changes. HE was graded by the West Haven Criteria [24].
2.5. The Diagnosis of Bacteremia (Sepsis)
Bacteremia was diagnosed when at least two serial sets of blood cultures yielded positive bacteria species. Sepsis was defined as two or more SIRS criteria with documented infection focus [25] (SBP in this study).
2.6. Calculations of Predicting Scores
The MELD score was 11.2 × ln (international normalized ratio (INR)) + 9.57 × ln (creatinine, mg/dL) + 3.78 × ln (bilirubin, mg/dL) + 6.43) with lower bound of one for all three variables and an upper bound of four for serum creatinine. The MELD-Na score was MELD score − Na − (0.025 × MELD score × (140 − Na)) + 140, in which Na was bounded at 125 and 140. The iMELD score was MELD score + (Age × 0.3) − (0.7 × Na + 100) [26].
2.7. Methods/Assays Used For Serum Biochemistry and Hemogram
The method/assays used for serum biochemistry were as follows: serum creatinine: colorimetry (reference value: F:0.44–1.03, M: 0.64–1.28 mg/dL); serum bilirubin: spectrophotometry (reference value:0.3–1.2 for <60 y/o, 0.2–1.1 for 60–90 y/o, 0.2–0.9 for >90 y/o mg/dL); serum AST/ALT: enzymatic method (reference value: AST: ≦34 U/L, ALT ≦ 36 U/L); serum Na: ion-selective sensor (reference value: 136–146 mEq/L); serum albumin: colorimetry (reference value: 3.5–4.5 g/dL), respectively.
The method/assays used for serum hemogram were as follows: serum INR: electrochemical method (reference value: 2.0–3.0); WBC and PLT: automated cell count (reference value: WBC: 3.9–10.6 103/μL; PLT: 150–400 103/μL); Hb: spectrophotometric method (reference value: M:13.5–17.5; F:12–16 g/dL), respectively.
2.8. Statistical Analysis
Continuous variables were expressed as mean ± standard deviation (SD) or median and interquartile range (IQR, 25–75 percentile), depending on their distribution. Non-parametric Kruskal Wallis test or Mann–Whitney U test was used to compare continuous variables among three groups, or between two groups respectively. Post-hoc tests including Bonferroni correction were used to adjust for the significance level for multiple pairwise comparisons and multiple testing correction. Categorical variables were reported as frequencies or counts with percentages. Their significance was calculated by Chi-square test first while Fisher’s exact test was performed instead when more than 20% of the cells have expected frequencies less than 5. Survival analyses were performed by univariable and/or multivariable logistic regression analysis.
Statistics were performed using SPSS software (SPSS Inc., Chicago, IL, USA, Version 22). A p-value of <0.05 was considered statistically significant.
3. Results
3.1. Flowchart
As delineated in Figure 1, a total of 327 patients diagnosed with the first episode of SBP were enrolled after inclusion and exclusion criteria.
Figure 1.
Flow chart.
3.2. Baseline Demographic Characteristics of 327 Patients
Patients’ baseline demographic characteristics are shown in Table 1. Patients were predominantly middle-aged (mean age, 57.1 ± 13.6 years) and male (72%). Evidence of chronic viral hepatitis infection was seen in 58.7% of patients. Various abnormal laboratory data could be interpreted as follows: renal function impairment (creatinine 1.2 (0.9–2.4) mg/dL), hyponatremia (sodium 135 (131–139) mg/dL), hyperbilirubinemia (bilirubin 4.1 (1.9–9.8) mg/dL), hypoalbuminemia (albumin 2.4 (2.2–2.8) g/dL), the prolonged international normalized ratio for the prothrombin time (INR 1.6 (1.4–2.2)), anemia (Hb 9.6 (8.4–11.0) g/dL), and thrombocytopenia (PLT 74.0 (48.0–122.0) × 1000/μL). The median WBC counts of blood were 7.9 (5.2–12.8) × 1000 per mL. Anti-viral agents included Entecavir 19.5% (64), Lamivudine 1.2% (4), Telbivudine 0.6% (2), and Tenofovir disoproxil fumarate 4.5% (15).
Table 1.
Demographic and baseline clinical characteristics of 327 patients with the first episode of SBP.
| Baseline Parameters | Values |
|---|---|
| Clinical parameters | |
| Age, mean ± SD | 57.1 ± 13.6 |
| Male No. (%) | 236 (72%) |
| Etiology No. (%) | |
| Alcohol HBV HCV Others |
94 (28.7) 118 (36.0) 74 (22.6) 74 (22.6) |
| Classic (Positive PMN and culture) | 141 (43.2) |
| CNNA | 143 (43.7) |
| MNB | 43 (13.1) |
| Blood culture positive | 42(12.8%) |
| Laboratory parametersMedian (IQR) | |
| iMELD score | 44.44 (37.58–52.94) |
| MELD score | 21.11 (15.39–28.36) |
| CTP score | 9(8–11) |
| INR | 1.64 (1.39–2.20) |
| WBC (103/μL) | 7.9 (5.20–12.80) |
| Hemoglobin (g/dL) | 9.6 (8.4–11.0) |
| PLT (103/μL) | 74.0 (48.0–122.0) |
| Creatinine (mg/dL) | 1.2 (0.86–2.41) |
| Bilirubin Total (mg/dL) | 4.1 (1.9–9.8) |
| AST (U/L) | 70 (46–129) |
| ALT (U/L) | 36 (23–60) |
| Sodium (mEq/L) | 135 (131–139) |
| Albumin (g/dL) | 2.4 (2.2–2.8) |
| Anti-viral agents, n, (%) | |
| Entecavir | 64(19.5) |
| Lamivudine | 4 (1.2) |
| Telbivudine | 2 (0.6) |
| Tenofovir disoproxil fumarate | 15 (4.5) |
CNNA: culture-negative neutrophilic ascites; MNB: monomicrobial non-neutrocytic bacterascites; iMELD: integrated MELD score.
3.3. Baseline Characteristics and Prognosis Comparison of the Three Types of SBP
There were three types of SBP as shown in Table 1 and Table 2. The number of patients with classic SBP was 141 (43.2%). A total of 143 patients (43.7%) were CNNA. Moreover, there were 43 patients (13.1%) with MNB. Furthermore, 42 (12.8%) patients showed both positive ascites and blood culture, defined as bacteremia at diagnosis (Table 1). There was no significant difference in age, sex, etiology, baseline MELD score, CTP score, INR, WBC, Hb, PLT, and serum bilirubin total among the three types of SBP. In contrast, there were significant differences in bacteremia rate, iMELD score, creatinine, serum sodium, and albumin among the three types of SBP.
Table 2.
Demographic and baseline clinical characteristics of three types of SBP.
| SBP Types(n) | Classic (141) | CNNA (143) | MNB (43) | p-Value |
|---|---|---|---|---|
| Clinical parameters | ||||
| Age, mean ± SD | 57.6 ± 14.1 | 57.3 ± 13.5 | 55.1 ± 12.8 | 0.578 |
| Male No. (%) | 104(73.8) | 101(70.6) | 31(72.1) | 0.841 |
| Etiology No. (%) | 0.157 | |||
| Alcohol HBV HCV Others |
43(30.5) 58(40.2) 36(25.5) 4(2.8) |
39(27.3) 65(45.5) 33(23.0) 6(4.2) |
10(23.3) 23(53.5) 9(20.9) 1(2.3) |
|
| Blood culture positive, n (%) | 39(27.7) | 0 | 3(7) | <0.001 |
| Laboratory parametersMedian (IQR) | ||||
| iMELD score | 44.8(30.7–55.0) | 44.6(37.4–53.2) | 40.2(33.9–47.5) | 0.015 |
| MELD score | 20.5(15.8–28.6) | 21.9(12.6–30.5) | 21.2(12.6–30.5) | 0.055 |
| CTP score | 9(8–10) | 10(8–11) | 9(8–11) | 0.196 |
| INR | 1.7(1.4–2.1) | 1.6(1.4–2.3) | 1.6(1.3–2.2) | 0.869 |
| WBC (103/μL) | 8.4(5.8–11.6) | 7.8(5.5–13.3) | 6.6(4.0–12.6) | 0.348 |
| Hemoglobin (g/dL) | 9.6(8.4–11.2) | 9.8(8.5–11.1) | 9.2(8.1–10.2) | 0.248 |
| PLT (103/μL) | 72(46–105) | 76(50–139) | 72(46–120) | 0.259 |
| Creatinine (mg/dL) | 1.5(1.0–2.7) | 1.0(0.7–1.8) | 1.0(0.7–2.0) | <0.001 |
| Bilirubin Total (mg/dL) | 4.0(2.1–8.8) | 4.2(2.0–11.0) | 3.0(1.4–7.8) | 0.457 |
| Sodium (mEq/L) | 135(130–138) | 134(131–139) | 137(135–141) | <0.001 |
| Albumin (g/dL) | 2.3(2.0–2.6) | 2.6(2.3–3.0) | 2.4(2.1–2.8) | <0.001 |
CNNA: culture-negative neutrophilic ascites; MNB: monomicrobial non-neutrocytic bacterascites; iMELD: integrated MELD score.
The mortality rate among the three types of SBP were further compared. There was no significant difference in the in-hospital, 3-month, and 6-month mortality rates among the three types of SBP (p-value 0.841, 0.461, 0.951, respectively).
3.4. The Bacteriology of SBP
In addition, the bacteriology of SBP was studied. First, ascites analysis revealed a median WBC of 1585 (IQR: 439–4996), median ascites PMN of 1394 (344–4203) cells/mm3, ascites total protein of 1.3 (1.3–1.7) g/dL, and ascites albumin of 0.5 (0.3–0.7) g/dL (Table 3). Second, as shown in Table 3 and Figure 2a, among those 184 patients with positive bacterial culture results, 138 patients (75.0%) yielded Gram-negative bacteria (GNB). Among them, 110 patients (79.7%) yielded Ceftriaxone-sensitive GNB while 28 patients (20.3%) yielded Ceftriaxone-resistant GNB in their cultures. On the other hand, 46 (25.0%) of the 184 culture-positive patients yielded Gram-positive coccus (GPC).
Table 3.
Bacterial culture results of 327 patients with first SBP episode.
| Ascites analysis, (Median (IQR)) | |
|---|---|
| WBC (103/μL) | 1585 (439–4996) |
| PMN (cells/mm3) | 1394 (344–4203) |
| Total protein (g/dL) | 1.3 (1.3–1.7) |
| Albumin (g/dL) | 0.5 (0.3–0.7) |
| Bacterial group, n, (%), total n = 184 | |
| Gram-negative bacteria (GNB) | 138 (75.0) |
| Ceftriaxone sensitive GNB | 110 (79.7) |
| Ceftriaxone resistant GNB | 28 (20.3) |
| Gram-positive coccus (GPC) | 46 (25.0) |
| Bacterial species, n, (%), total n = 184 | |
| Escherichia coli | 64 (34.8) |
| Escherichia coli, ESBL strain | 14/64 (21.8) |
| Klebsiella pneumoniae | 32 (17.4) |
| Klebsiella pneumoniae, ESBL strain | 1/32 (3.1) |
| Staphylococcus aureus | 23 (12.5) |
| ORSA | 5/23 (21.7) |
| Viridans streptococcus | 12 (6.5) |
ESBL: extended-spectrum beta-lactamases; ORSA: Oxacillin-resistant Staphylococcus aureus.
Figure 2.
Schematic demonstration of the bacteriology of SBP. (a) GNB (75%) vs. GPC (25%) (b) Overall, E. coli was the most common bacteria species. (c) The ESBL strain in all culture-positive patients was 10.9%.
Overall, the most common bacteria species was Escherichia coli (64 patients,34.8%), of which 14 patients (21.8%) had extended-spectrum beta-lactamases (ESBL) (Table 3 and Figure 2b). The second most common was Klebsiella pneumoniae (32 patients, 17.4%), of which only one (3.1%) had ESBL. Staphylococcus aureus was the most common GPC (23 patients, 12.5%), of which 5 patients (21.7%) were ORSA. Viridans streptococcus was the second most common GPC (12 patients, 6.5%). The total percentage of ESBL strain in culture-positive patients was 10.9% (20 over 184 patients), as revealed in Figure 2c.
There was no significant etiologic difference (etiology of cirrhosis) between the classic SBP and MNB (p = 0.065). However, there were significant differences in bacteriology between the classic SBP and MNB (Table 4).
Table 4.
Bacterial culture differences between classical SBP and MNB.
| SBP Types(n) | Classic (141) | MNB (43) | p-Value |
|---|---|---|---|
| GNB vs. GPC | 0.005 | ||
| GNB, No. (%) | 110(78) | 29(67.4) | |
| GPC, No. (%) | 34 (22) | 14(32.6) | |
| Ceftriaxone sensitive vs. resistant GNB | |||
| Ceftriaxone sensitive GNB | 91(82.7) | 19(65.5) | 0.008 |
| Ceftriaxone resistant GNB | 19(17.3) | 9(34.5) | 0.233 |
| ESBL strain, No. (%) | 15(13.6) | 0(0) | <0.001 |
3.5. Multivariable Logistic Regression Analysis to Predict Mortality
Furthermore, univariable then multivariable logistic regression analyses were performed to analyze independent factors that predict in-hospital, 3-month, and 6-month mortality.
3.5.1. In-Hospital Mortality
As shown in Table 5, univariable and multivariable logistic regression analysis for predicting in-hospital mortality confirmed that SBP type did not affect in-hospital mortality. Neither Gram stain types nor ESBL strain in bacterial culture affected the in-hospital mortality. Age, sex, etiology, hepatic encephalopathy (HE), serum albumin, CTP score, and MELD-based scores had no significant effect, either. In contrast, bacteremia, 3rd-generation cephalosporin (CRO)-resistant bacteria in ascites culture, hepatorenal syndrome, and serum creatinine were independent predictors of patients’ in-hospital mortality (Table 5).
Table 5.
Univariable and multivariable logistic regression analysis for predicting in-hospital mortality.
| Univariable Logistic Reg | Multivariable Logistic Reg | |||
|---|---|---|---|---|
| Variables | OR (95% CI) | p-Value | OR (95% CI) | p-Value |
| SBP types | ||||
| CNNA | Reference | |||
| MNB | 0.938 (0.459–1.914) | 0.859 | ||
| Classic | 1.373 (0.853–2.211) | 0.191 | ||
| Bacteremia | 2.286 (1.185–4.408) | 0.014 | 3.192 (1.419–7.176) | 0.005 |
| Gram stain | ||||
| 0 | Reference | |||
| GNB | 1.283 (0.796–2.066) | 0.306 | ||
| GPC | 1.391 (0.690–2.805) | 0.356 | ||
| ESBL strain | 2.366 (0.654–8.553) | 0.189 | ||
| CRO-resistant | ||||
| 0 | Reference | Reference | ||
| Ascites | 5.155 (1.623–16.373) | 0.005 | 6.493 (1.791–23.538) | 0.004 |
| Blood cult. | 1.145 (0.188–6.961) | 0.883 | 1.487 (0.232–9.515 | 0.676 |
| Ascites + Blood | 4.295 (0.82–22.514) | 0.085 | 0.803 (0.113–5.720) | 0.826 |
| Age | 1.018 (1.001–1.035) | 0.038 | 1.013 (0.992–1.034) | 0.242 |
| Sex | 0.875 (0.535–1.432) | 0.596 | ||
| Etiology | ||||
| Alcohol | Reference | |||
| HBV | 1.117 (0.659–1.891) | 0.682 | 1.196 (0.638–2.244) | 0.576 |
| HCV | 0.429 (0.221–0.833) | 0.012 | 0.582 (0.265–1.277) | 0.177 |
| Others | 0.762 (0.208–2.787) | 0.681 | 0.746 (0.175–3.337) | 0.720 |
| HE | ||||
| 0 | Reference | Reference | ||
| 1 | 1.538 (0.706–3.349) | 0.278 | 1.161 (0.436–3.094) | 0.765 |
| 2 | 2.766 (1.302–5.876) | 0.008 | 1.862 (0.764–4.538) | 0.171 |
| 3–4 | 3.786 (1.253–11.437) | 0.018 | 1.843 (0.537–6.330) | 0.331 |
| HRS | 9.744 (3.633–26.132) | <0.001 | 7.274 (2.497–21.192) | <0.001 |
| Cr | 1.397 (1.218–1.601) | <0.001 | 1.198 (1.035–1.388) | 0.015 |
| Albumin | 0.836 (0.536–1.305) | 0.431 | ||
| WBC | 1.031 (1.004–1.058) | 0.023 | 1.026 (1–1.052) | 0.049 |
| CTP score | 1.257 (1.091–1.448) | 0.002 | 1.118 (0.899–1.391) | 0.316 |
| MELD score | 1.05 (1.026–1.075) | <0.001 | 0.985 (0.926–1.047) | 0.622 |
| MELD-Na | 1.048 (1.025–1.071) | <0.001 | 1.04 (0.98–1.094) | 0.126 |
| iMELD score | 1.05 (1.028–1.073) | <0.001 | 1.009 (0.948–1.073) | 0.783 |
CNNA: culture-negative neutrophilic ascites; MNB: monomicrobial non-neutrocytic bacterascites; GNB: Gram-negative bacillus; GPC: Gram-positive coccus; ESBL: extended-spectrum beta-lactamases; HE: hepatic encephalopathy; HRS: hepatorenal syndrome; iMELD: integrated MELD score.
3.5.2. 3-Month Mortality
As shown in Table 6, univariable and multivariable logistic regression analysis for predicting 3-month mortality confirmed that SBP type did not affect in-hospital mortality. Gram stain types, ESBL strain, or CRO-resistant strain in bacterial culture did not affect the 3-month mortality. Age, sex, etiology, hepatic encephalopathy (HE), serum albumin, CTP score, and MELD-based scores had no significant effect, either. In contrast, bacteremia, hepatorenal syndrome, and serum creatinine were independent predictors of patients’ 3-month mortality (Table 6).
Table 6.
Univariable and multivariable logistic regression analysis for predicting 3-month mortality.
| Univariable Logistic Reg | Multivariable Logistic Reg | |||
|---|---|---|---|---|
| Variables | OR (95% CI) | p-Value | OR (95% CI) | p-Value |
| SBP types | ||||
| CNNA | Reference | |||
| MNB | 0.938 (0.459–1.914) | 0.859 | ||
| Classic | 1.328 (0.833–2.117) | 0.234 | ||
| Bacteremia | 2.343 (1.170–4.691) | 0.016 | 2.244 (1.077–4.673) | 0.031 |
| Gram stain | ||||
| 0 | Reference | |||
| GNB | 1.328 (0.834–2.115) | 0.232 | ||
| GPC | 1.422 (0.709–2.849) | 0.321 | ||
| ESBL strain | 2.261 (0.574–8.900) | 0.243 | ||
| CRO-resistant | ||||
| 0 | Reference | |||
| Ascites | 3.102 (0.978–9.837) | 0.055 | ||
| Blood cult. | 1.551 (0.255–9.416) | 0.883 | ||
| Ascites + Blood | 6.204 (0.738–52.16) | 0.093 | ||
| Age | 1.010 (0.994–1.026) | 0.24 | ||
| Sex | 1.113 (0.686–1.805) | 0.665 | ||
| Etiology | ||||
| Alcohol | Reference | |||
| HBV | 0.775 (0.456–1.315) | 0.344 | ||
| HCV | 0.452 (0.244–0.838) | 0.012 | ||
| Others | 0.571 (0.162–2.010) | 0.383 | ||
| HE | ||||
| 0 | Reference | |||
| 1 | 1.325 (0.611–2.874) | 0.476 | ||
| 2 | 2.369 (1.077–5.214) | 0.032 | ||
| 3–4 | 2.154 (0.715–6.487) | 0.173 | ||
| HRS | 7.420 (2.534–21.732) | <0.001 | 6.034 (1.977–18.416) | 0.002 |
| Cr | 1.283 (1.122–1.468) | <0.001 | 1.188 (1.024–1.378) | 0.023 |
| Albumin | 0.643 (0.414–1.000) | 0.431 | 0.711 (0.436–1.157) | 0.170 |
| WBC | 1.017 (0.994–1.041) | 0.158 | ||
| CTP Score | 1.169 (1.020–1.340) | 0.025 | 1.128 (0.929–1.370) | 0.225 |
| MELD score | 1.033 (1.010–1.056) | 0.005 | 0.968 (0.918–1.020) | 0.221 |
| MELD-Na | 1.029 (1.009–1.051) | 0.006 | 0.995 (0.953–1.039) | 0.811 |
| iMELD score | 1.035 (1.014–1.057) | 0.001 | 1.040 (0.991–1.092) | 0.107 |
CNNA: culture-negative neutrophilic ascites; MNB: monomicrobial non-neutrocytic bacterascites; GNB: Gram-negative bacillus; GPC: Gram-positive coccus; ESBL: extended-spectrum beta-lactamases; HE: hepatic encephalopathy; HRS: hepatorenal syndrome; iMELD: integrated MELD score.
3.5.3. 6-Month Mortality
As shown in Table 7, univariable and multivariable logistic regression analysis for predicting 6-month mortality confirmed that SBP type did not affect in-hospital mortality. Bacteremia, Gram stain types, ESBL strain, or CRO-resistant strain in bacterial culture did not affect the 6-month mortality. Age, sex, etiology, hepatic encephalopathy (HE), serum albumin, CTP score, and MELD-based scores had no significant effect, either. In contrast, hepatorenal syndrome and serum creatinine were independent predictors of patients’ 3-month mortality (Table 7).
Table 7.
Univariable and multivariable logistic regression analysis for predicting 6-month mortality.
| Univariable Logistic Reg | Multivariable Logistic Reg | |||
|---|---|---|---|---|
| Variables | OR (95% CI) | p-Value | OR (95% CI) | p-Value |
| SBP types | ||||
| CNNA | Reference | |||
| MNB | 1.118 (0.554–2.259) | 0.755 | ||
| Classic | 1.036 (0.644–1.668) | 0.883 | ||
| Bacteremia | 1.963 (0.948–4.061) | 0.069 | 1.797 (0.829–3.894) | 0.138 |
| Gram stain | ||||
| 0 | Reference | |||
| GNB | 0.982 (0.612–1.576) | 0.939 | ||
| GPC | 1.436 (0.689–2.993) | 0.334 | ||
| ESBL strain | 1.519 (0.386–5.985) | 0.550 | ||
| CRO-resistant | ||||
| 0 | Reference | |||
| Ascites | 2.039 (0.643–6.472) | 0.227 | ||
| Blood cult. | 1.020 (0.168–6.194) | 0.983 | ||
| Ascites + Blood | 4.079 (0.485–34.306) | 0.196 | ||
| Age | 1.015 (0.999–1.032) | 0.07 | 1.009 (0.989–1.030) | 0.388 |
| Sex | 1.239 (0.758–2.025) | 0.393 | ||
| Etiology | ||||
| Alcohol | Reference | |||
| HBV | 0.856 (0.497–1.474) | 0.576 | ||
| HCV | 0.633 (0.341–1.175) | 0.147 | ||
| Others | 1.446 (0.358–5.835) | 0.604 | ||
| HE | ||||
| 0 | Reference | |||
| 1 | 1.329 (0.593–2.978) | 0.490 | ||
| 2 | 1.538 (0.698–3.390) | 0.285 | ||
| 3–4 | 2.797 (0.769–10.169) | 0.118 | ||
| HRS | 4.866 (1.661–14.260) | 0.004 | 3.857 (1.248–11.919) | 0.019 |
| Cr | 1.343 (1.146–1.574) | <0.001 | 1.218 (1.030–1.441) | 0.021 |
| Albumin | 0.685 (0.438–1.070) | 0.097 | 0.731 (0.443–1.204) | 0.218 |
| WBC | 1.035 (1.002–1.069) | 0.035 | 1.029 (0.996–1.062) | 0.083 |
| CTP Score | 1.142 (0.993–1.313) | 0.062 | 1.126 (0.924–1.373) | 0.240 |
| MELD score | 1.030 (1.006–1.055) | 0.013 | 0.972 (0.918–1.028) | 0.316 |
| MELD-Na | 1.024 (1.003–1.045) | 0.024 | 0.982 (0.967–1.028) | 0.437 |
| iMELD score | 1.036 (1.014–1.058) | 0.001 | 1.048 (0.991–1.107) | 0.098 |
4. Discussion
In this study, we attempted to find better prognostic risk factors for cirrhotic patients with SBP, taking into account all clinical variables, including SBP types, bacteriology, and other cirrhotic complications such as HRS and HE. It was found that the prevalence of classic SBP was nearly equivalent to CNNA, followed by MNB. As for the microbial pattern, GNB was still more prevalent than GPC (75% vs. 25%), and E. coli were the most common bacteria species followed by K.P. and then Staphylococcus. The total percentage of ESBL strain in culture-positive patients was 10.9%. By univariable and multivariable logistic regression survival analysis, there was no significant difference in predicting short-term mortality among the three SBP types, neither between GNB vs. GPC, nor between ESBL- and non-ESBL- producing bacteria. Only bacteremia (sepsis), hepatorenal syndrome (HRS), and serum creatinine (Cr) were independent predictors of in-hospital and 3-month mortality, whereas HRS and Cr were independent predictors of 6-month mortality. The results could greatly help identify high-risk groups of patients with SBP, allowing more prompt and intensive management.
SBP has high short-term mortality. When first reported, the in-hospital mortality of an episode of SBP exceeded 90%; however, the rate has been reduced to approximately 20% through early diagnosis and prompt antibiotic therapy [27,28]. To improve the stratification of patient care, identifying the most robust predictors of mortality in cirrhotic patients with SBP is critical but often overlooked [29]. The MELD score has been shown to be more accurate in predicting 3-month survival than the Child−Turcotte−Pugh (CTP) classification for patients with cirrhosis awaiting liver transplantation in the United States [30]. However, the literature review showed limited information on whether they were applicable in subgroups of patients with liver cirrhotic-related complications such as SBP [31,32].
We have previously demonstrated that for patients with HBV-related liver cirrhosis and SBP, the iMELD score had the highest AUC among the MEDL-based models and significantly outperformed CTP and ALBI scores in predicting 3-month and 6-month mortalities [33]. However, baseline clinical parameters such as SBP types, bacteriology, HRS, and HE were not considered. In this study, univariable and multivariable logistic regression survival analyses were used to consider all these variables and MELD-based scores, including the iMELD score. The results showed that only HRS and serum Cr consistently predicted the in-hospital, 3-month, and 6-month mortalities. This corresponds to a meta-analysis that also demonstrated that renal dysfunction was the most important independent predictor of mortality in cirrhotic patients with SBP [29]. In fact, renal failure occurs in 30% to 40% of people with SBP and is the leading cause of death [34]. The risk of renal impairment as well as mortality may be decreased significantly (renal impairment 30.6% to 8.3%, mortality 35.4% to 16.0%) [35] with an infusion of intravenous 25% albumin solution [36]. Therefore, the up-to-date AASLD guideline has recommended albumin infusion in patients with SBP and renal dysfunction [20]. Our result strengthens this notion that early identification of renal dysfunction at baseline in patients with SBP is critical and potentially life-saving.
The study also demonstrated that bacteremia (sepsis) is an important prognostic factor in predicting in-hospital and 3-month mortalities. Our previous study also found that SBP was associated with high sepsis-related mortality [37]. This implies that aggressive treatment for sepsis in patients with SBP is of utmost importance [38]. Indeed, an important predictive scoring system designed to assess the severity of illness in patients with sepsis, the Chronic Liver Failure-Sequential Organ Failure Assessment (CLIF-SOFA) score, has been shown to be useful in determining the appropriate antibiotic regimen [39]. Patients with suspected SBP who are not critically ill (CLIF-SOFA score < 7) are typically treated with a third-generation cephalosporin. Conversely, for patients with a CLIF-SOFA score ≥ 7, empiric treatment with carbapenems is recommended [39].
Furthermore, in this study, the prevalence of classic SBP was almost comparable to that of CNNA, which corresponds to other studies [10,40]. The mortality rates among these classic, CNNA, MNB SBP types were not significantly different if appropriate antibiotics are given promptly. In addition, GNB vs. GPC, nor ESBL-producing vs. non-ESBL-producing bacterial species did not affect outcomes. This finding is reasonable since empirical 3rd generation cephalosporins could cover 79.7% of patients without ESBL-producing bacteria strain. The finding that third generation cephalosporin (CRO)-resistant bacteria in ascites culture independently predicted patients’ in-hospital mortality reminds us of the need for timely antibiotic adjustment based on the susceptibility results.
Thus, liver transplantation should be seriously considered for survivors of SBP who are otherwise good transplantation candidates [35].
5. Conclusions
The short-term mortality rate of SBP remains high. By multivariable logistic regression analysis, there was no significant difference in predicting short-term mortality among the three SBP types, neither between GNB vs. GPC nor between ESBL- and non-ESBL-producing bacteria. Only bacteremia (sepsis), hepatorenal syndrome (HRS), and serum creatinine (Cr) were independent predictors of in-hospital and 3-month mortality, whereas HRS and Cr were independent predictors of 6-month mortality. The results could greatly help identify high-risk groups of patients with SBP, allowing more prompt and intensive management such as albumin infusion or liver transplantation.
Author Contributions
Conceptualization, C.-H.H.; methodology, C.-H.H. and C.-H.L.; software, Y.-T.H.; validation, S.-F.W. and C.-H.H.; formal analysis, C.-H.H.; investigation, S.-F.W., Y.-M.W., C.C., and B.-H.C.; writing—original draft preparation, S.-F.W., and C.-H.H.; writing—review and editing, C.-H.H.; visualization, Y.-P.H. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Chang Gung Memorial Hospital (202000112B0; date of approval: 2020/02/05).
Informed Consent Statement
Patient consent was waived by the IRB due to it is a retrospective cohort study. The study contains only medical chart reviews without creating any patient intervention or revealing any personal information. Therefore, the IRB did not request patients’ signature.
Data Availability Statement
Data and study materials will be made available to other researchers by email request.
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research was funded by NMRPG3L0331: MOST 110-2314-B-182A-093-.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
References
- 1.Albillos A., Lario M., Alvarez-Mon M. Cirrhosis-associated immune dysfunction: Distinctive features and clinical relevance. J. Hepatol. 2014;61:1385–1396. doi: 10.1016/j.jhep.2014.08.010. [DOI] [PubMed] [Google Scholar]
- 2.Huang C.H., Jeng W.J., Ho Y.P., Teng W., Chen W.T., Chen Y.C., Lin S.M., Chiu C.T., Sheen I.S., Lin C.Y. Increased regulatory T cells in patients with liver cirrhosis correlated with hyperbilirubinemia and predict bacterial complications. J. Gastroenterol. Hepatol. 2015;30:775–783. doi: 10.1111/jgh.12781. [DOI] [PubMed] [Google Scholar]
- 3.Wiest R., Garcia-Tsao G. Bacterial translocation (BT) in cirrhosis. Hepatology. 2005;41:422–433. doi: 10.1002/hep.20632. [DOI] [PubMed] [Google Scholar]
- 4.Fernández J., Navasa M., Gómez J., Colmenero J., Vila J., Arroyo V., Rodés J. Bacterial infections in cirrhosis: Epidemiological changes with invasive procedures and norfloxacin prophylaxis. Hepatology. 2002;35:140–148. doi: 10.1053/jhep.2002.30082. [DOI] [PubMed] [Google Scholar]
- 5.Lata J., Stiburek O., Kopacova M. Spontaneous bacterial peritonitis: A severe complication of liver cirrhosis. World J. Gastroenterol. 2009;15:5505–5510. doi: 10.3748/wjg.15.5505. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Huang C.H., Tseng H.J., Amodio P., Chen Y.L., Wang S.F., Chang S.H., Hsieh S.Y., Lin C.Y. Hepatic Encephalopathy and Spontaneous Bacterial Peritonitis Improve Cirrhosis Outcome Prediction: A Modified Seven-Stage Model as a Clinical Alternative to MELD. J. Pers. Med. 2020;10:186. doi: 10.3390/jpm10040186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Runyon B.A., AASLD Introduction to the revised American Association for the Study of Liver Diseases Practice Guideline management of adult patients with ascites due to cirrhosis 2012. Hepatology. 2013;57:1651–1653. doi: 10.1002/hep.26359. [DOI] [PubMed] [Google Scholar]
- 8.Oladimeji A.A., Temi A.P., Adekunle A.E., Taiwo R.H., Ayokunle D.S. Prevalence of spontaneous bacterial peritonitis in liver cirrhosis with ascites. Pan Afr. Med. J. 2013;15:128. doi: 10.11604/pamj.2013.15.128.2702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.European Association for the Study of the Liver Electronic address, e.e.e.; European Association for the Study of the, L. EASL Clinical Practice Guidelines for the management of patients with decompensated cirrhosis. J. Hepatol. 2018;69:406–460. doi: 10.1016/j.jhep.2018.03.024. [DOI] [PubMed] [Google Scholar]
- 10.Al-Ghamdi H., Al-Harbi N., Mokhtar H., Daffallah M., Memon Y., Aljumah A.A., Sanai F.M. Changes in the patterns and microbiology of spontaneous bacterial peritonitis: Analysis of 200 cirrhotic patients. Acta Gastroenterol. Belg. 2019;82:261–266. [PubMed] [Google Scholar]
- 11.Almeida P.R.L., Leao G.S., Goncalves C.D.G., Picon R.V., Tovo C.V. Impact of Microbiological Changes on Spontaneous Bacterial Peritonitis in Three Different Periods over 17 Years. Arq. Gastroenterol. 2018;55:23–27. doi: 10.1590/s0004-2803.201800000-08. [DOI] [PubMed] [Google Scholar]
- 12.Oey R.C., de Man R.A., Erler N.S., Verbon A., van Buuren H.R. Microbiology and antibiotic susceptibility patterns in spontaneous bacterial peritonitis: A study of two Dutch cohorts at a 10-year interval. United Eur. Gastroenterol. J. 2018;6:614–621. doi: 10.1177/2050640617744456. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lin D.Y., Sheen I.S., Chiu C.T., Lin S.M., Kuo Y.C., Liaw Y.F. Ultrasonographic changes of early liver cirrhosis in chronic hepatitis B: A longitudinal study. J. Clin. Ultrasound. 1993;21:303–308. doi: 10.1002/jcu.1870210502. [DOI] [PubMed] [Google Scholar]
- 14.Runyon B.A., Montano A.A., Akriviadis E.A., Antillon M.R., Irving M.A., McHutchison J.G. The serum-ascites albumin gradient is superior to the exudate-transudate concept in the differential diagnosis of ascites. Ann. Intern. Med. 1992;117:215–220. doi: 10.7326/0003-4819-117-3-215. [DOI] [PubMed] [Google Scholar]
- 15.de Franchis R., Baveno V.I.F. Expanding consensus in portal hypertension: Report of the Baveno VI Consensus Workshop: Stratifying risk and individualizing care for portal hypertension. J. Hepatol. 2015;63:743–752. doi: 10.1016/j.jhep.2015.05.022. [DOI] [PubMed] [Google Scholar]
- 16.Liaw Y.F., Leung N., Guan R., Lau G.K., Merican I., McCaughan G., Gane E., Kao J.H., Omata M., Chronic Hepatitis B Guideline Working Party of the Asian-Pacific Association for the Study of the Liver Asian-Pacific consensus statement on the management of chronic hepatitis B: A 2005 update. Liver Int. 2005;25:472–489. doi: 10.1111/j.1478-3231.2005.01134.x. [DOI] [PubMed] [Google Scholar]
- 17.Rimola A., García-Tsao G., Navasa M., Piddock L.J., Planas R., Bernard B., Inadomi J.M. Diagnosis, treatment and prophylaxis of spontaneous bacterial peritonitis: A consensus document. J. Hepatol. 2000;32:142–153. doi: 10.1016/S0168-8278(00)80201-9. [DOI] [PubMed] [Google Scholar]
- 18.Akriviadis E.A., Runyon B.A. Utility of an algorithm in differentiating spontaneous from secondary bacterial peritonitis. Gastroenterology. 1990;98:127–133. doi: 10.1016/0016-5085(90)91300-U. [DOI] [PubMed] [Google Scholar]
- 19.Runyon B.A., Committee A.P.G. Management of adult patients with ascites due to cirrhosis: An update. Hepatology. 2009;49:2087–2107. doi: 10.1002/hep.22853. [DOI] [PubMed] [Google Scholar]
- 20.Biggins S.W., Angeli P., Garcia-Tsao G., Gines P., Ling S.C., Nadim M.K., Wong F., Kim W.R. Diagnosis, Evaluation, and Management of Ascites, Spontaneous Bacterial Peritonitis and Hepatorenal Syndrome: 2021 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2021;74:1014–1048. doi: 10.1002/hep.31884. [DOI] [PubMed] [Google Scholar]
- 21.Angeli P., Gines P., Wong F., Bernardi M., Boyer T.D., Gerbes A., Moreau R., Jalan R., Sarin S.K., Piano S., et al. Diagnosis and management of acute kidney injury in patients with cirrhosis: Revised consensus recommendations of the International Club of Ascites. J. Hepatol. 2015;62:968–974. doi: 10.1016/j.jhep.2014.12.029. [DOI] [PubMed] [Google Scholar]
- 22.Campagna F., Montagnese S., Ridola L., Senzolo M., Schiff S., De Rui M., Pasquale C., Nardelli S., Pentassuglio I., Merkel C., et al. The animal naming test: An easy tool for the assessment of hepatic encephalopathy. Hepatology. 2017;66:198–208. doi: 10.1002/hep.29146. [DOI] [PubMed] [Google Scholar]
- 23.American Association for the Study of Liver Diseases. European Association for the Study of the Liver Hepatic encephalopathy in chronic liver disease: 2014 practice guideline by the European Association for the Study of the Liver and the American Association for the Study of Liver Diseases. J. Hepatol. 2014;61:642–659. doi: 10.1016/j.jhep.2014.05.042. [DOI] [PubMed] [Google Scholar]
- 24.Vilstrup H., Amodio P., Bajaj J., Cordoba J., Ferenci P., Mullen K.D., Weissenborn K., Wong P. Hepatic encephalopathy in chronic liver disease: 2014 Practice Guideline by the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver. Hepatology. 2014;60:715–735. doi: 10.1002/hep.27210. [DOI] [PubMed] [Google Scholar]
- 25.Singer M., Deutschman C.S., Seymour C.W., Shankar-Hari M., Annane D., Bauer M., Bellomo R., Bernard G.R., Chiche J.D., Coopersmith C.M., et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) JAMA. 2016;315:801–810. doi: 10.1001/jama.2016.0287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Luca A., Angermayr B., Bertolini G., Koenig F., Vizzini G., Ploner M., Peck-Radosavljevic M., Gridelli B., Bosch J. An integrated MELD model including serum sodium and age improves the prediction of early mortality in patients with cirrhosis. Liver Transpl. 2007;13:1174–1180. doi: 10.1002/lt.21197. [DOI] [PubMed] [Google Scholar]
- 27.Garcia-Tsao G. Current management of the complications of cirrhosis and portal hypertension: Variceal hemorrhage, ascites, and spontaneous bacterial peritonitis. Gastroenterology. 2001;120:726–748. doi: 10.1053/gast.2001.22580. [DOI] [PubMed] [Google Scholar]
- 28.Hung T.H., Tsai C.C., Hsieh Y.H., Tsai C.C. The long-term mortality of spontaneous bacterial peritonitis in cirrhotic patients: A 3-year nationwide cohort study. Turk. J. Gastroenterol. 2015;26:159–162. doi: 10.5152/tjg.2015.4829. [DOI] [PubMed] [Google Scholar]
- 29.Tandon P., Garcia-Tsao G. Renal dysfunction is the most important independent predictor of mortality in cirrhotic patients with spontaneous bacterial peritonitis. Clin. Gastroenterol. Hepatol. 2011;9:260–265. doi: 10.1016/j.cgh.2010.11.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Wiesner R., Edwards E., Freeman R., Harper A., Kim R., Kamath P., Kremers W., Lake J., Howard T., Merion R.M., et al. Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology. 2003;124:91–96. doi: 10.1053/gast.2003.50016. [DOI] [PubMed] [Google Scholar]
- 31.Huo T.I., Lin H.C., Wu J.C., Hou M.C., Lee F.Y., Lee P.C., Chang F.Y., Lee S.D. Limitation of the model for end-stage liver disease for outcome prediction in patients with cirrhosis-related complications. Clin. Transplant. 2006;20:188–194. doi: 10.1111/j.1399-0012.2005.00463.x. [DOI] [PubMed] [Google Scholar]
- 32.Huo T.I., Lin H.C., Huo S.C., Lee P.C., Wu J.C., Lee F.Y., Hou M.C., Lee S.D. Comparison of four model for end-stage liver disease-based prognostic systems for cirrhosis. Liver Transpl. 2008;14:837–844. doi: 10.1002/lt.21439. [DOI] [PubMed] [Google Scholar]
- 33.Chen P.C., Chen B.H., Huang C.H., Jeng W.J., Hsieh Y.C., Teng W., Chen Y.C., Ho Y.P., Sheen I.S., Lin C.Y. Integrated model for end-stage liver disease maybe superior to some other model for end-stage liver disease-based systems in addition to Child-Turcotte-Pugh and albumin-bilirubin scores in patients with hepatitis B virus-related liver cirrhosis and spontaneous bacterial peritonitis. Eur. J. Gastroenterol. Hepatol. 2019;31:1256–1263. doi: 10.1097/MEG.0000000000001481. [DOI] [PubMed] [Google Scholar]
- 34.Follo A., Llovet J.M., Navasa M., Planas R., Forns X., Francitorra A., Rimola A., Gassull M.A., Arroyo V., Rodes J. Renal impairment after spontaneous bacterial peritonitis in cirrhosis: Incidence, clinical course, predictive factors and prognosis. Hepatology. 1994;20:1495–1501. doi: 10.1002/hep.1840200619. [DOI] [PubMed] [Google Scholar]
- 35.Salerno F., Navickis R.J., Wilkes M.M. Albumin infusion improves outcomes of patients with spontaneous bacterial peritonitis: A meta-analysis of randomized trials. Clin. Gastroenterol. Hepatol. 2013;11:123–130.e121. doi: 10.1016/j.cgh.2012.11.007. [DOI] [PubMed] [Google Scholar]
- 36.Sort P., Navasa M., Arroyo V., Aldeguer X., Planas R., Ruiz-del-Arbol L., Castells L., Vargas V., Soriano G., Guevara M., et al. Effect of intravenous albumin on renal impairment and mortality in patients with cirrhosis and spontaneous bacterial peritonitis. N. Engl. J. Med. 1999;341:403–409. doi: 10.1056/NEJM199908053410603. [DOI] [PubMed] [Google Scholar]
- 37.Huang C.H., Lin C.Y., Sheen I.S., Chen W.T., Lin T.N., Ho Y.P., Chiu C.T. Recurrence of spontaneous bacterial peritonitis in cirrhotic patients non-prophylactically treated with norfloxacin: Serum albumin as an easy but reliable predictive factor. Liver Int. 2011;31:184–191. doi: 10.1111/j.1478-3231.2010.02377.x. [DOI] [PubMed] [Google Scholar]
- 38.Dellinger R.P., Levy M.M., Carlet J.M., Bion J., Parker M.M., Jaeschke R., Reinhart K., Angus D.C., Brun-Buisson C., Beale R., et al. Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008. Crit. Care Med. 2008;36:296–327. doi: 10.1097/01.CCM.0000298158.12101.41. [DOI] [PubMed] [Google Scholar]
- 39.Moreau R., Jalan R., Gines P., Pavesi M., Angeli P., Cordoba J., Durand F., Gustot T., Saliba F., Domenicali M., et al. Acute-on-chronic liver failure is a distinct syndrome that develops in patients with acute decompensation of cirrhosis. Gastroenterology. 2013;144:1426–1437, 1437.e1–1437.e9. doi: 10.1053/j.gastro.2013.02.042. [DOI] [PubMed] [Google Scholar]
- 40.Na S.H., Kim E.J., Nam E.Y., Song K.H., Choe P.G., Park W.B., Bang J.H., Kim E.S., Park S.W., Kim H.B., et al. Comparison of clinical characteristics and outcomes of spontaneous bacterial peritonitis and culture negative neutrocytic ascites. Scand. J. Gastroenterol. 2017;52:199–203. doi: 10.1080/00365521.2016.1245776. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data and study materials will be made available to other researchers by email request.