Abstract
Background/Aims
This study delved into cirrhosis-related infections to unveil their epidemiology, risk factors, and implications for antimicrobial decisions.
Methods
We analyzed acutely decompensated cirrhosis patients (n = 971) from North India between 2013–2023 at a tertiary center. Microbiological and clinical features based on infection sites (EASL criteria) and patient outcomes were assessed.
Results
Median age was 45 years; 87% were males with 47% having alcoholic hepatitis. Of these, 675 (69.5%) had infections; 305 (45%) were culture-confirmed. Notably, 71% of confirmed cases were multi-drug resistant organisms (MDRO)-related, chiefly carbapenem-resistant (48%). MDRO prevalence was highest in pulmonary (80.5%) and skin-soft-tissue infections (76.5%). Site-specific distribution and antimicrobials were suggested. Predictive models identified prior hospitalization [OR:2.23 (CI:1.58–3.14)], norfloxacin prophylaxis [OR:2.26 (CI:1.44–3.55)], prior broad-spectrum antibiotic exposure [OR:1.61 (CI:1.12–2.30)], presence of systemic inflammatory response-SIRS [OR:1.75 (CI: 1.23–2.47)], procalcitonin [OR:4.64 (CI:3.36–6.40)], and HE grade [OR:1.41 (CI:1.04–1.90)], with an area under curve; AUC of 0.891 for infection prediction. For MDRO infection prediction, second infection [OR: 7.19 (CI: 4.11–12.56)], norfloxacin prophylaxis [OR: 2.76 (CI: 1.84–4.13)], CLIF-C OF [OR: 1.10 (CI: 1.01–1.20)], prior broad-spectrum antibiotic exposure [OR: 1.66 (CI: 1.07–2.55)], rifaximin [OR: 040 (0.22–0.74)] multisite [OR: 3.67 (CI: 1.07–12.56)], and polymicrobial infection [OR: 4.55 (CI: 1.45–14.17)] yielded an AUC of 0.779 and 93% specificity. Norfloxacin prophylaxis, multisite infection, mechanical ventilation, prior broad-spectrum antibiotic exposure, and infection as acute precipitant predicted carbapenem-resistant infection (AUC: 0.821). Infections (culture-proven or probable), MDROs, carbapenem/pan-drug resistance, and second infections independently linked with mortality (P < 0.001), adjusted for age, leucocytosis, and organ failures. A model incorporating age [HR:1.02 (CI: 1.01–1.03), infection [HR:1.52 (CI: 1.05–2.20)], prior hospitalization [HR:5.33 (CI: 3.75–7.57)], norfloxacin [HR:1.29 (CI: 1.01–1.65)], multisite infection [HR:1.47 (CI:1.06–2.04)], and chronic liver failure consortium-organ failure score; CLIF-C OF [HR:1.17 (CI: 1.11–1.23)] predicted mortality with C-statistics of 0.782 (P < 0.05).
Conclusion
High MDRO burden, especially carbapenem-resistant, necessitates urgent control measures in cirrhosis. Site-specific epidemiology and risk models can guide empirical antimicrobial choices in cirrhosis management.
Keywords: infections, epidemiology, risk factors, predictors, antimicrobials
Graphical abstract
Cirrhosis is the 11th leading cause of death and the 15th most common cause of disability-associated life years globally.1 It is a unique condition characterized by liver fibrosis, immune dysfunction, and gut dysbiosis.2 There exists an ineffective immune response and increased exposure to gut-derived pathogens due to portal hypertension, dysbiosis, and leaky gut.3 Immunoparesis, repeated hospitalizations, and interventions in cirrhosis predispose to infections, which are the most common cause of hospitalization and trigger of acute-on-chronic liver failure (ACLF) in these patients.4
Up to 60% of patients with cirrhosis have infections at or during admission, which is 4- to 5-fold higher than the general hospitalized population.5,6 About half of the infections are community-acquired and rest are healthcare-associated or nosocomial in origin.7 About one-fourth of patients develop secondary infections during hospitalization, which further increases mortality.7,8 Bacterial infections increase the risk of organ failures, and ACLF,9 and accounts for 50–70% of total deaths in cirrhosis.10 Repeated hospitalizations, multiple interventions, altered microbiome, and impaired immunity, further enhance the risk of multidrug-resistant organism (MDRO) infections in cirrhosis. Although the global prevalence of MDRO infections was 34%,7 73% of isolates were found to be MDR in India7 which is independently associated with high mortality (57–65%).
Indian studies describing local epidemiology of infections in patients with cirrhosis, the burden of MDRO infections, and their risk factors and predictors of outcomes are limited. Therefore, we aimed to study the prevalence, risk factors, and outcomes of bacterial infections in hospitalized patients with acute decompensation of cirrhosis.
METHODS
Design and Setting
This was a single center study including patients with acute decompensation (AD) of cirrhosis admitted at a tertiary care research institute. The patients were recruited ambispectively; retrospective data of hospitalized patients was collected from 2013 to 2018 from hospital records, while AD patients were prospectively enrolled between 2018 and 2023. We adhered to the ethical guidelines of the Declaration of Helsinki (1975) and Good Clinical Practices. Patients enrolled in it prospectively signed an informed consent and the study was approved by the Institutional Ethics Committee (NK/5760/Study/259).
Participants and Variables
Adult patients (>18 years) meeting the criteria of AD with or without ACLF were enrolled in the study. Patients with any hepatic or extra-hepatic malignancy, including solid tumors and hematologic disorders, on immunosuppressive drugs other than steroids for alcoholic steatohepatitis and autoimmune liver disease, with Human immunodeficiency virus (HIV), previous organ-transplantation, and known immunodeficiency state, with suspected infection with tuberculosis and viral infection and those refusing to give valid consent were excluded. For the retrospective data the parameters were extracted from the online database or in-hospital records, and all the prospectively enrolled patients were screened at the time of admission for the eligibility criteria. Patients were followed for a period of 90-days or until death or discharge. The outcomes of those patients discharged from hospital before 30-days or 90-days were noted down through follow-up. The demographic details, clinical parameters, laboratory data, radiological imaging, and sepsis workup, including microbial cultures from the suspected site of infection, were collected. Parameters including etiology of cirrhosis, acute precipitants, vitals, liver functions, renal functions, blood counts, presence of infection, type of infections, risk factors for infections, previous healthcare contact details, medication history, and procalcitonin were noted down. Variables were noted down at the diagnosis of infection (positive microbial cultures) for infected patients and at the matched time points for patients with culture-negative infections and non-infected patients.
Definitions
AD refers to the acute development of ascites, gastrointestinal bleed, encephalopathy, and/or bacterial infections. ACLF was defined as per EASL criteria as “acute deterioration of pre-existing chronic liver disease usually related to a precipitating event and associated with increased 3-month mortality”.4 Microbiologically proven infections (culture from normally sterile sites excluding the mucus membranes such as blood/ascitic fluid,pleural fluid,cerebrospinal fluid, etc.) were labelled as proven infection cases. Patients with negative culture reports, but meeting clinical criteria of infection were labelled as probable infection cases. These infections were defined as per EASL criteria.11 Briefly, we defined infections as spontaneous bacteremia in the presence of bacteria in blood without an evident source, spontaneous bacterial peritonitis as ascitic fluid neutrophil count >250/μL, lower respiratory tract infections as chest infiltrates with local and systemic signs and symptoms of infection, soft-tissue/skin Infection as fever with signs and symptoms of cellulitis, urinary tract infection as urine white blood cell ≥10/high-power field with either positive urine gram stain or culture. The criteria for infections in detailed in Supplementary Table 1.
Multidrug-resistant infections (MDR) were defined as non-susceptibility to at least one agent in at least three antimicrobial categories.12 Extensively drug-resistant (XDR) bacteria were defined as non-susceptibility to at least one agent in all but two or fewer antimicrobial categories (i.e., bacterial isolates remain susceptible to only one or two categories).12 Pan drug-resistant (PDR) bacteria were defined as non-susceptibility to all antimicrobial agents listed.12 The presence of ESBLs- Extended spectrum beta-lactamase producing organisms (Escherichia coli, Klebsiella pneumoniae, Enterobacter spp., or Citrobacter spp.), CRE- carbapenem-resistant Enterobacterales (E. coli and K. pneumoniae), CRAB- carbapenem-resistant Acinetobacter baumannii, CRPA- carbapenem-resistant Pseudomonas aeruginosa, MDR-EF- multidrug-resistant Enterococcus faecium, MDR-S- multidrug-resistant Staphylococcus aureus and VRE- vancomycin-resistant Enterococci sp. were noted as per literature.12
We used CLIF-C OF and Simple organ failure count (SOFC) criteria to define organ failures where hepatic failure was defined as serum bilirubin level ≥12 mg/dl, coagulation failure with International normalized ratio (INR) ≥ 2.5, renal failure with serum creatinine ≥2 mg/dl, cerebral failure with hepatic encephalopathy grade III/IV, respiratory failure with SPO2/FiO2<214 or use of ventilator, and circulatory failure with mean arterial pressure (MAP) < 65 mmHg or use of vasopressors.
Statistical Analysis
Kolmogorov–Smirnov and Shapiro–Wilk tests were used to check the normality of data. Data was represented as mean ± standard deviation (SD) for normally distributed numerical variables or as median (Interquartile range, IQR) for skewed data. Qualitative variables were presented as proportions with percentages. Student's t-test and Mann–Whitney U test were used to compare normally distributed and skewed numerical variables between two groups. Categorical variables were compared using chi-square or Fisher's exact test. Multivariable logistic regression analysis was performed to identify the risk factors of infections and MDRO infections. Survival analysis was done by the Kaplan–Meier method and the log-rank test was used for inter-group comparisons. Multivariable Cox-regression analysis was conducted to identify the predictors of mortality. We performed predictive modelling for choosing the final parameters in the multivariable analyses through incremental incorporation of clinical variables in several combinations using empirical approach and avoiding multicollinearity by keeping the variance inflation factor threshold of five. The best model was selected based on the optimum model fit (minimum Akaike infromation criterion (AIC) or Bayesian information criterion (BIC)) and performance measures (highest Area under the curve (AUC) or Harrell's c-index), as an appropriate. P-value of <0.05 was considered statistically significant and the analysis was carried out using Statistical Package for Social Sciences (SPSS) v.22.0 (SPSS Inc., Chicago, IL).
RESULTS
Nine hundred and seventy one patients of Acute decompensation (AD) were enrolled in the study, with a median age of 45 (36–52) years, mostly males (87%). Of the total cohort, 63% of the patients were diagnosed with ACLF defined as per EASL criteria. Alcoholic hepatitis (47%) was the most common acute precipitant and ethanol (67%) was the most common cause of cirrhosis, followed by viral hepatitis (13%), and Non-alcoholic steatohepatitis (NASH) (6%). The mean Model for End-Stage Liver Disease (MELD) score was 27.6 ± 7.2 and CLIF-C OF was 10 ± 2.1. The overall 30-day and 90-day mortality of the cohort was 34.5% and 40% respectively (Table 1).
Table 1.
Baseline Characteristics of Study Cohort.
| Descriptives | Overall cohort (n = 971) | Infected (n = 675) | Non-infected (n = 296) | P-value |
|---|---|---|---|---|
| Age (years)∗ | 45.0 (36.0–52.0) | 44.0 (36.0–52.0) | 45.0 (36.0–53.0) | 0.420 |
| Gender (males) (n, %) | 844 (86.9) | 599 (89) | 245 (83) | 0.011 |
| ACLF criteria (n, %) | ||||
| EASL criteria | <0.001 | |||
| AD | 363 (37.3) | 221 (32.7) | 142 (47.9) | |
| ACLF | 608 (62.6) | 454 (67.2) | 154 (52.0) | |
| APASL Criteria | ||||
| ACLF | 723 (74.4) | 470 (69.6) | 253 (85.4) | |
| Acute precipitant (n, %) | ||||
| Alcohol | 456 (47.0) | 295 (44) | 161 (54) | <0.001 |
| Infection | 160 (16.5) | 124 (18) | 36 (12) | |
| Alcohol + others | 97 (10.0) | 91 (13) | 6 (2.0) | |
| Autoimmune flare | 40 (4.1) | 24 (3.6) | 16 (5.4) | |
| UGIB | 22 (2.3) | 17 (2.5) | 5 (1.7) | |
| DILI | 41 (4.2) | 22 (3.3) | 19 (6.4) | |
| Viral | 116 (11.9) | 75 (11) | 41 (14) | |
| Others | 39 (4.0) | 27 (4.0) | 12 (4.1) | |
| Chronic insult (n, %) | ||||
| Alcohol | 653 (67.3) | 472 (70) | 181 (61) | <0.001 |
| NASH | 61 (6.3) | 47 (7) | 14 (4.7) | |
| Viral hepatitis (B or C) | 126 (13.0) | 73 (11) | 53 (18) | |
| BASH Cirrhosis | 22 (2.3) | 14 (2.1) | 8 (2.7) | |
| Autoimmune | 54 (5.6) | 32 (4.7) | 22 (7.4) | |
| Others | 42 (4.3) | 28 (4.1) | 14 (4.7) | |
| Alcohol + Viral hepatitis (B or C) | 13 (1.3) | 9 (1.3) | 4 (1.4) | |
| Acquisition of infection | ||||
| Nosocomial or health-care associated | 608 (90) | 608 (90) | – | |
| Community acquired | 67 (10) | 67 (10) | – | |
| Hepatic decompensations | ||||
| Hepatic encephalopathy (n, %) | ||||
| Grade I | 466 (48.0) | 281 (42) | 185 (62) | <0.001 |
| Grade II | 431 (44.4) | 327 (48) | 104 (35) | <0.001 |
| Grade III or IV | 67 (6.9) | 60 (8.8) | 7 (2.4) | <0.001 |
| Ascites (n, %) | ||||
| Grade I | 501 (51.6) | 334 (49) | 167 (56) | 0.006 |
| Grade II | 262 (27.0) | 204 (30) | 58 (20) | |
| Grade III | 184 (18.9) | 123 (18) | 61 (21) | |
| Risk factors (last 3 months) | ||||
| Previous hospitalization (n, %) | 551 (56.7) | 443 (65.6) | 108 (36.5) | <0.001 |
| Rifaximin prophylaxis (n, %) | 855 (88.1) | 615 (91.1) | 240 (81.1) | <0.001 |
| Norfloxacin prophylaxis (n, %) | 204 (21.0) | 168 (24.9) | 36 (12.2) | <0.001 |
| Exposure to broad spectrum antibiotics (n, %) | 577 (59.4) | 456 (67.6) | 121 (40.9) | <0.001 |
| Any invasive procedure (n, %) | 832 (85.7) | 591 (87.6) | 241 (81.4) | 0.016 |
| Vitals | ||||
| Pulse∗ (beats/minute) | 97.0 (87.0–108.0) | 98.0 (88.0–110.0) | 94.0 (85.8–101.0) | <0.001 |
| MAP∗ (mmHg) | 85.0 (78.0–93.0) | 83.0 (77.0–93.0) | 87.0 (81.0–95.0) | <0.001 |
| RR∗ (per minute) | 20.0 (20.0–22.0) | 20.0 (20.0–22.5) | 20.0 (20.0–22.0) | 0.004 |
| Inotrope support (n, %) | 224 (23.1) | 186 (27.6) | 38 (12.8) | <0.001 |
| Ventilator support (n, %) | 170 (17.5) | 155 (23%) | 15 (5.1%) | <0.001 |
| SIRS (n, %) | 513 (52.8) | 408 (60.4) | 105 (35.5) | <0.001 |
| Investigations | ||||
| Haemoglobin∗ (g/dL) | 9.6 (7.9–11.1) | 9.3 (7.7–10.9) | 10.0 (8.4–11.6) | <0.001 |
| WBC count∗ (cells/mm3) | 10.9 (7.7–15.9) | 12.3 (8.2–17.9) | 9.2 (6.8–12.0) | <0.001 |
| Platelet count∗ (cells/mm3) | 105 (67–161.5) | 107 (67–167) | 102 (68–150) | <0.001 |
| Sodium∗ (mmol/L) | 133.0 (129.0–137.0) | 133.0 (129.0–137.0) | 133.0 (129.0–136.0) | 0.204 |
| Creatinine∗ (mg/dL) | 1.1 (0.8–2.0) | 1.2 (0.8–2.3) | 1.0 (0.7–1.6) | <0.001 |
| Bilirubins (mg/dL) | 16.1 ± 10.6 | 16.5 ± 11.2 | 15.1 ± 8.9 | 0.051 |
| AST∗ (U/L) | 120.0 (76.0–192.5) | 119.0 (75.0–189.0) | 123.5 (79.8–195.0) | 0.51 |
| ALT∗ (U/L) | 55.0 (32.0–98.0) | 52.0 (30.0–95.0) | 62.0 (37.8–106.2) | 0.003 |
| ALP∗ (U/L) | 126.0 (93.0–177.5) | 125.0 (91.5–176.5) | 132.0 (98.0–182.0) | 0.172 |
| Albumin∗ (g/dL) | 2.7 (2.3–3.2) | 2.7 (2.2–3.1) | 2.7 (2.4–3.2) | 0.04 |
| INRჄ | 2.2 ± 0.8 | 2.2 ± 0.9 | 2.1 ± 0.6 | 0.145 |
| Procalcitonin∗ (ng/mL) | 0.9 (0.5–2.0) | 1.5 (0.8–2.8) | 0.5 (0.3–0.6) | <0.001 |
| Lactate∗ (mmol/L) | 2.1 (1.5–2.9) | 2.3 (1.6–3.1) | 1.8 (1.4–2.6) | <0.001 |
| Disease severity scoresჄ | ||||
| CTPჄ | 11.2 ± 1.4 | 11.3 ± 1.4 | 10.9 ± 1.4 | <0.001 |
| MELDჄ | 27.6 ± 7.2 | 28.1 ± 7.7 | 26.3 ± 5.8 | <0.001 |
| MELD-NaჄ | 29.4 ± 6.7 | 29.8 ± 7.1 | 28.4 ± 5.6 | <0.001 |
| CLIF-C OFჄ | 10 ± 2.1 | 10.4 ± 2.2 | 9.1 ± 1.7 | <0.001 |
| SOFCჄ | 1.7 ± 1.2 | 1.9 ± 1.3 | 1.3 ± 1.0 | <0.001 |
| Organ failures (n, %) | ||||
| Cardiovascular | 224 (23.1) | 186 (27.6) | 38 (12.8) | <0.001 |
| Respiratory | 270 (27.8) | 229 (33.9) | 41 (13.9) | <0.001 |
| Cerebral | 67 (6.9) | 60 (8.9) | 7 (2.4) | <0.001 |
| Renal | 250 (25.7) | 202 (30) | 48 (16) | <0.001 |
| Liver | 561 (57.8) | 390 (58) | 171 (58) | 0.99 |
| Coagulation | 260 (26.8) | 183 (27) | 77 (26) | 0.72 |
| 30-day mortality (n, %) | 335 (34.5) | 294 (44) | 41 (14) | <0.001 |
| 90-day mortality (n, %) | 386 (39.8) | 330 (49) | 56 (19) | <0.001 |
AST: aspartate aminotransferase; ALT: Alanine aminotransaminase, ALP: Alkaline phosphatase; BASH: both alcoholic and non-alcoholic steatohepatitis, CTP: Child Turcotte Pugh score, CLIF-C OF: Chronic Liver Failure Consortium Organ failure, DILI: drug induced liver injury, INR: International normalized ratio, LGIB: lower gastrointestinal bleed; MAP: mean arterial pressure, MELD: Model for end stage liver disease, MELD-Na: MELD-sodium, NASH: non-alcoholic steatohepatitis, RR: respiratory rate; SIRS: systemic inflammatory response syndrome; UGIB: upper gastrointestinal bleed.
Data distribution was analyzed using Shapiro–Wilk test. ∗- values are represented as median (IQR); Ⴤ -values represented as mean ± SD. Student's t-test for normally distributed data and Mann–Whitney U-test for non-parametric data was performed. chi-square test was performed on the categorical variables. P < 0.05 was considered as significant.
Epidemiology of Infections
675 patients (69.5%) out of the total cohort were infected, of which 305 cases (45%) were microbiologically proven bacterial infections with or without fungal infections. Overall, culture-proven infections were defined in 31% of the study population with isolation of 371 different bacterial pathogens. Of 305 patients, 68.5% were infected with gram-negative infections while 35.7% were infected with gram-positive infections (13 patients were infected with both gram-positive and gram-negative isolates); 4.6% of patients had concomitant culture-positive fungal infections, Figure 1A. Enterococcus sp. and Staphylococcus sp. were the most common gram-positive isolates, while Enterobacterales such as E. coli, K. pneumoniae were the most common gram-negative isolates, Figure 1B and C. Peritoneal infections were the most common site of infection (40%), followed by Bloodstream infection (BSI) (35.4%), and Urinary tract infection (UTI) (21.3%), Figure 2A. 13.8% of patients had a polymicrobial infection (>1 bacterial isolate) and 12.5% had a multisite infection (>1 site of infection). Among culture proven cases, Candida albicans (n = 10) and Candida tropicalis (n = 4) were the two identified isolates, and the most common site was bloodstream (n = 9), ascetic fluid (n = 5).
Figure 1.
Distribution of isolates. (A) Percentage of Gram-Positive bacteria, Gram-Negative bacteria, and Fungal infections in AD patients. (B-C) Major Gram-positive bacteria and Gram-negative bacteria were isolated in microbiology cultures. G+: Gram-positive bacteria, G-: Gram-negative bacteria.
Figure 2.
(A) Site-wise prevalence of infection. (B) Prevalence of MDR organisms. (C) MDRO subgroups among culture-proven infection. (D) Prevalence of site-wise MDRO infections. MDR - Multi-Drug resistant isolates, PDR - Pan drug-resistant isolates, XDR- Extensively drug-resistant isolates, ESBL - Extended spectrum beta-lactamase producing organisms, CRE - Carbapenem resistant enterobacterales, MDR-EF - Multi-drug resistant Enterococcus faecium, CRAB - Carbapenem-resistant Acinetobacter baumannii, VRE - Vancomycin-resistant enterococci, MDR-S - Multidrug-resistant Staphylococcus sp., CRPA - Carbapenem-resistant Pseudomonas aeruginosa, CR-bacteria - Carbapenem-resistant bacteria, BSI - bloodstream infections, UTI - urinary tract infections, SSTI - skin and soft tissue infection.
Prevalence of MDRO Infections
215 patients (71%) amongst culture-positive cases were infected with MDROs, of which 46% were MDR bacteria, 9% were XDR, 15% suspected PDR. The most common MDROs were Extended Spectrum Beta-Lactamase producing organisms (ESBL), CRE, and MDR-EF; the overall prevalence of carbapenem-resistant bacteria was 48%. On site-wise MDRO analysis, pulmonary infections had the highest prevalence of MDRO infections, followed by SSTI and UTI (Figure 2B–D).
Site-wise Infections
Of all microbiologically proven isolates (n = 371), 33% (n = 122) were isolated from ascitic fluid cultures, 31% (n = 116) were isolated from blood cultures, 18% (n = 67) from urine cultures, 12% (n = 45) from endotracheal aspirates, and 6% (n = 21) from wound and pus cultures. Gram-negative isolates were more common among all the sites, with the highest gram-negative to gram-positive ratio in pulmonary (9:1) and peritoneal (8:2) infections, Supplementary Figure 1.
Peritoneal Infections
122 bacteria were isolated from peritoneal infections, 24% (n = 29) were gram-positive, and 76% (n = 93) were gram-negative bacteria. Staphylococcus sp. among gram-positive and E. coli among gram-negative were the predominant isolates (Supplementary Figure 2A and B). 65% (n = 79) of isolates were MDROs, with ESBL (43%) being the most common followed by CRE (Supplementary Figure 2C and D).
Bloodstream Infections
Of 116 isolates from BSI, 43% (n = 50) were gram-positive bacteria, and 57% (n = 66) were gram-negative bacteria. Enterococci among gram-positive, and Acinetobacter sp. among the gram-negative bacteria were the most common isolates (Supplementary Figure 3A and B). 61% (n = 71) isolates from BSI were MDROs, with ESBL (22%) being the most common followed by CRE, CRAB, and MDR-S (Supplementary Figure 3C and D).
Urinary Tract Infections
67 bacteria were isolated from urine cultures, of which 46% were gram-positive bacteria (100% enterococci), and 54% were gram-negative bacteria (70% E. coli) (Supplementary Figure 4A and B). 66% (n = 44) of isolates were MDROs, ESBL (37%) and MDR-EF (34%) being the most common (Supplementary Figure 4C and D).
Pulmonary Infections
Of 45 isolates from pulmonary infections, 11% (n = 5) were gram-positive (60% Staphylococcus sp., 40% Enterococci sp.) and 89% were gram-negative bacteria of which K. pneumoniae (52.5%) and Acinetobacter sp. (20%) were common (Supplementary Figure 5A and B). 78% (n = 35) of isolates were MDROs, ESBL (60%), and CRE (42%) being the most common (Supplementary Figure 5C and D).
Skin and Soft Tissue Infections
21 bacteria were isolated from pus/wound cultures, 33% (n = 7) gram-positive, 67% (n = 14) gram-negative. Staphylococcus sp. (71%) among gram-positive and K. pneumoniae (67%) among gram-negative were the common isolates (Supplementary Figure 6A and B). Eighty-one percent (n = 17) of isolates were MDROs, ESBL (38%), and CRE (33%) being the most common MDROs followed by CRAB (24%) (Supplementary Figure 6C and D).
Risk Factors
Overall Infections
On multivariable regression, prior hospitalization [OR:2.23 (CI:1.58–3.14)], norfloxacin prophylaxis [OR:2.26 (CI:1.44–3.55)], exposure to broad-spectrum antibiotics [OR:1.61 (CI:1.12–2.30)] within last 3 months, presence of SIRS [OR:1.75 (CI: 1.23–2.47)], procalcitonin levels [OR:4.64 (CI:3.36–6.40)], and HE grade [OR:1.41 (CI:1.04–1.90)] were the independent risk factors of developing infections in AD patients (P < 0.05, each), Table 2, Supplementary Table 2. The prediction model for the infections had an AUC of 0.891.
Table 2.
Multivariable Prediction Model for Infections in Cirrhosis.
| Variable | Odds ratio (95% CI) | P value |
| I) Infections in overall cohort | ||
| Previous hospitalization | 2.23 (1.58–3.14) | <0.001 |
| Norfloxacin prophylaxis | 2.26 (1.44–3.55) | <0.001 |
| Previous antibiotic use | 1.61 (1.12–2.30) | 0.009 |
| Presence of SIRS | 1.75 (1.23–2.47) | 0.001 |
| Procalcitonin | 4.64 (3.36–6.40) | <0.001 |
| HE grades | 1.41 (1.04–1.90) | 0.023 |
| II) MDRO infections among infected cohort | ||
| Secondary infection | 7.19 (4.11–12.56) | <0.001 |
| Rifaximin prophylaxis | 0.40 (0.22–0.74) | 0.003 |
| Norfloxacin prophylaxis | 2.76 (1.84–4.13) | <0.001 |
| Previous antibiotic use | 1.66 (1.07–2.55) | 0.021 |
| CLIF-C OF | 1.10 (1.01–1.20) | 0.028 |
| Multisite infection | 3.67 (1.07–12.56) | 0.038 |
| Polymicrobial infection | 4.55 (1.45–14.17) | 0.009 |
| III) Carbapenem resistant bacterial infections in patients among culture-proven cases | ||
| Norfloxacin prophylaxis | 1.92 (1.19–3.11) | 0.007 |
| Rifaximin prophylaxis | 0.69 (0.32–1.50) | 0.356 |
| Multisite infection | 3.81 (1.76–8.24) | <0.001 |
| Ventilator support | 3.07 (1.92–4.90) | <0.001 |
| Exposure to broad spectrum antibiotics | 1.77 (1.08–2.90) | 0.023 |
| Infection as acute precipitant | 5.45 (3.46–8.58) | <0.001 |
Model fit measures and performance measures of various multivariable models:
I): AIC: 827, BIC: 876, AUC: 0.891.
II) AIC: 343.8, BIC: 373.5, AUC: 0.779.
III) AIC: 410, BIC: 436, AUC: 0.821.
CLIF-C OF: Chronic Liver Failure Consortium Organ failure; SIRS: systemic inflammatory response syndrome.
MDRO Infections
Among patients with infections, development of second infection [OR: 7.19 (CI: 4.11–12.56)], norfloxacin prophylaxis [OR: 2.76 (CI: 1.84–4.13)], prior exposure to broad-spectrum antibiotics [OR: 1.66 (CI: 1.07–2.55)], infection at multiple sites [OR: 3.67 (CI: 1.07–12.56)], polymicrobial infection (>1 isolate) [OR: 4.55 (CI: 1.45–14.17)] and CLIF-C OF [OR: 1.10 (CI: 1.01–1.20)] were found as independent risk factors of MDRO infections (P < 0.05, each) (Table 2, Supplementary Table 3). Rifaximin prophylaxis [OR: 0.44 (CI: 022–0.74)] was found to be protective against developing MDRO infections. The model was able to differentiate between MDRO and non-MDRO infections with an area under the curve (AUC) of 0.779. The probability of MDRO infection could be estimated from the following equation:
P = 1/1+e−Z, where z= (−2.0717 + 1.9727 ∗second infection + 0.5075 ∗prior use of antibiotic exposure in 3month + 1.0161 ∗ norfloxacin prophylaxis + 1.3002 ∗multisite infection + 1.5138 ∗polymicrobial infection + 0.0980 ∗ CLIF-C OF score—0.9064 ∗ rifaximin prophylaxis)
Carbapenem-resistant Bacteria Infections
Among patients with infections, norfloxacin prophylaxis [OR: 1.92 (CI:1.19–3.11)], multisite infection [OR: 3.81 (CI: 1.76–8.24)], requirement of mechanical ventilation [OR: 3.07 (CI: 1.92–4.90)], prior exposure to broad-spectrum antibiotics [OR: 1.77 (CI: 1.08–2.90)], and infection as an acute precipitant of AD [OR: 5.45 (CI: 3.46–8.58)] were independent risk factors of infections with carbapenem-resistant isolates (Table 2). The prediction model had an AUC of 0.821. The probability of carbapenem-resistant bacterial infection could be estimated from the following equation:
P = 1/1+e−Z, where z= (−2.844 + 1.697 ∗Infection as acute precipitant—0.362 ∗rifaximin prophylaxis + 0.657 ∗norfloxacin prophylaxis + 0.573 ∗prior use of broad-spectrum antibiotics in 3months + 1.340 ∗multisite infection + 1.122 ∗ventilator support)
The calculator for estimating the probability, based on the logistic regression formula is available in the supplementary data.
Impact of Infections on Outcomes
Overall Cohort
Patients with infection had a higher incidence of systemic inflammatory response syndrome (SIRS) (60% vs. 35%), WBC count (12.3/mm3 vs 9.2/mm3), procalcitonin levels (1.5 vs. 0.5 ng/mL), requirement of inotropic support (28% vs. 13%), mechanical ventilation (23% vs. 5%), number of organ failures viz., cardiovascular (28% vs 13%), respiratory (40% vs. 14%), cerebral failure (9% vs. 3%), renal (30% vs. 16%), poorer disease severity scores, P < 0.001 each (Table 1). Thirty-day survival was lower in infected group as compared to non-infected group (56% vs. 86%) (Figure 3A). Patients with ACLF and infections had the worst outcomes at 30-day follow-up (P < 0.001) (Figure 3B). Patients with culture-proven infections had the worst prognosis (59.7%) compared to those with probable infection (30.3%) or no infection (13.9%), (P < 0.001) (Figure 3C). During the study, 487 out of 675 patients developed infections during hospitalization follow-up, revealing a higher prevalence of ACLF in this group (82.3% vs. 64.9%, P < 0.001) and poorer 30-day outcomes (61.2% vs. 38.4%, P < 0.001) compared to those with infections at admission.
Figure 3.
Kaplan-Meier curve demonstrating the cumulative probability of 30-day survival in patients with acute decompensation of cirrhosis. (A) According to the presence or absence of infections. Kaplan-Meier survival curve shows that there was a significantly worse 30-day survival in infected patients compared to non-infected patients (P = <0.001) (B) According to the criteria of AD with and without ACLF and presence or absence of infection. Kaplan-Meier survival curve shows that their 30-day survival was worst in patients with ACLF and infections (P = <0.001). (C) According to the status of infection, culture-proven infections, culture-negative infections (probable infections), and non-infected group. Kaplan-Meier survival curve shows that patients with culture-proven infections had the worst 30-day survival than those with probable infections or without any infections (P <0.001).
MDRO Infections
MDRO infections were more prevalent in older patients (P = 0.036), with a higher incidence of ACLF (70% vs. 60%). Patients who developed MDRO infections had a significant history of previous hospitalization, norfloxacin prophylaxis, and prior exposure to broad-spectrum antibiotics, P < 0.001 each. MDRO group had a higher incidence of a second infection (33% vs. 3%), infection at more than one site (multisite) (15% vs. 5%), and polymicrobial infections (17% vs. 1%), P < 0.001 each. MDRO group had significantly lower haemoglobin levels, platelet count, and a heightened inflammatory response including raised heart rate, incidence of SIRS, WBC count, procalcitonin levels, a higher number of organ failures (P < 0.05 each), and 30-day mortality (61% vs. 27%), P < 0.001 (Supplementary Table 4, Supplementary Figure 7).
Factors Affecting Mortality in AD Patients
Compared to survivors, non-survivors were likely to be older, had a higher incidence of ACLF (76% vs. 56%), and infections (88% vs. 60%), higher prevalence of MDRO infections (39% vs. 13%), P < 0.001 each. Among patients with ACLF, the outcomes were poorer with increasing severity of ACLF within infected and non-infected subgroups (Supplementary Figure 8). Various risk factors associated with infections and disease severity such as prior history of hospitalization (89% vs. 40%), rifaximin prophylaxis (95% vs. 84%), norfloxacin prophylaxis (27% vs. 18%), exposure to broad-spectrum antibiotics (73% vs. 52%), invasive procedure (95% vs. 81%, P = 0.002), were higher in non-survivors, P < 0.001 each. Non-survivors presented with a heightened inflammatory response as raised WBC count, procalcitonin levels, SIRS components, a higher number of organ failures, and poorer disease severity scores, P < 0.001 each (Table 3).
Table 3.
Baseline Characteristics of Cohort Based on 30-day Outcomes.
| Variable | Non-survivors (n = 335) | Survivors (n = 636) | P-value |
|---|---|---|---|
| Age | 47.0 (38.0–54.0) | 43.0 (35.0–52.0) | <0.001 |
| Infection type (n, %) | |||
| Proven | 182 (54.3) | 123 (19.3) | <0.001 |
| Probable | 112 (33.4) | 258 (40.6) | |
| No infection | 41 (12.2) | 255 (40.1) | |
| MDRO infection | 131 (39.1) | 84 (13.2) | <0.001 |
| EASL criteria (n, %) | |||
| AD | 80 (23.9) | 283 (44.5) | <0.001 |
| ACLF | 255 (76.1) | 353 (55.5) | |
| Risk factors (previous 3 months) (n, %) | |||
| Previous hospitalization | 297 (88.7) | 254 (39.9) | <0.001 |
| Rifaximin prophylaxis | 319 (95) | 536 (84.3) | <0.001 |
| Norfloxacin prophylaxis | 90 (26.9) | 114 (17.9) | 0.002 |
| Prior exposure to broad spectrum antibiotics | 245 (73.1) | 332 (52.2) | <0.001 |
| Any invasive procedure | 318 (94.9) | 514 (80.8) | <0.001 |
| Vitals | |||
| PR (beats/minute) | 100 (87–112) | 96 (86–102.2) | <0.001 |
| MAP (mmHg) | 85.0 (77.5–93.0) | 86.0 (78.0–93.0) | 0.250 |
| RR (per minutes) | 21.0 (20.0–24.0) | 20.0 (20.0–22.0) | <0.001 |
| SIRS (%) | 221 (66.0) | 292 (45.9) | <0.001 |
| Haemoglobin (g/dL) | 8.8 (7.3–10.4) | 9.9 (8.3–11.4) | <0.001 |
| WBC count (cells/mm3) | 12.4 (8.5–17.9) | 10.4 (7.5–14.8) | <0.001 |
| Platelets (cells/mm3) | 100 (60–148.5) | 108.5 (68–168.2) | 0.014 |
| NLR | 7.7 (5.1–12.9) | 6.8 (4.3–9.7) | <0.001 |
| Sodium (mmol/L) | 133.0 (128.0–138.0) | 133.0 (129.0–136.0) | 0.200 |
| Potassium (mmol/L) | 4.1 (3.6–4.5) | 4.0 (3.5–4.5) | 0.136 |
| Bilirubin (mg/dL) | 15.2 (6.6–24.6) | 15.1 (7.1–23.3) | 0.699 |
| Creatinine (mg/dL) | 1.5 (0.9–2.5) | 1.0 (0.7–1.7) | <0.001 |
| Protein (g/dL) | 5.9 (5.2–6.5) | 5.9 (5.3–6.6) | 0.533 |
| INR | 2.1 (1.7–2.7) | 2.0 (1.7–2.4) | 0.001 |
| Procalcitonin (ng/mL) | 1.2 (0.6–2.7) | 0.8 (0.5–2.0) | <0.001 |
| Disease severity scores | |||
| CTP | 12.0 (11.0–13.0) | 11.0 (10.0–12.0) | <0.001 |
| MELD | 29.0 (25.0–35.0) | 26.0 (22.0–31.0) | <0.001 |
| MELD-Na | 31.0 (26.0–36.0) | 29.0 (25.0–33.0) | <0.001 |
| CLIF-C OF | 11.0 (9.0–12.0) | 9.0 (8.0–11.0) | <0.001 |
| SOFC | 2.0 (1.0–3.0) | 1.0 (1.0–2.0) | <0.001 |
| Organ failures (n, %) | |||
| Cardiovascular failure | 127 (37.9) | 97 (15.3) | <0.001 |
| Respiratory failure | 145 (43.3) | 125 (19.7) | <0.001 |
| Cerebral failure | 49 (14.6) | 18 (2.8) | <0.001 |
| Renal failure | 125 (37.3) | 125 (19.7) | <0.001 |
| Liver failure | 190 (56.7) | 371 (58.3) | 0.677 |
| Coagulation failure | 109 (32.5) | 151 (23.7) | 0.004 |
CLIF-C OF: Chronic Liver Failure Consortium Organ failure; CTP: Child-Turcotte-Pugh score; PR: pulse rate; INR: international-normalized ratio; MAP: mean-arterial pressure; MELD: Model for end stage liver disease; NLR: neutrophil-lymphocyte ratio; RR: respiratory rate; SIRS: systemic inflammatory response syndrome.
Data distribution was analyzed using Shapiro–Wilk test. All values are represented as median (IQR) and categorical variables as proportions. Mann–Whitney U-test was performed for skewed data and chi-square test was performed on the categorical variables. P < 0.05 was considered as significant.
Predictors of Outcomes
Overall Cohort
Univariable Cox-regression analysis was adjusted for age, leucocytosis, and organ failures revealed infection as an acute precipitant, incidence of infection, previous hospitalization, development of a second infection, MDRO infections as predictors of outcomes (Table 4). Univariable analysis and various multivariable models are depicted in Supplementary Table 5.
Table 4.
Multivariable Prediction Models for 30-day Mortality in Patients With AD.
| Variable | Hazard ratio (95% CI) | P value |
|---|---|---|
| I. Overall cohort | ||
| Infection (no infection/probable infection/proven infection) | 1.52 (1.05–2.20) | 0.028 |
| Previous hospitalization | 5.33 (3.75–7.57) | <0.001 |
| Norfloxacin prophylaxis | 1.29 (1.01–1.65) | 0.043 |
| Multisite infection | 1.47 (1.06–2.04) | 0.022 |
| CLIF-C OF | 1.17 (1.11–1.23) | <0.001 |
| II. Patients with infections | ||
| Previous hospitalization | 4.96 (3.37–7.30) | <0.001 |
| MDRO infection | 1.67 (1.31–2.12) | <0.001 |
| Infection as an acute precipitant | 1.32 (1.01–1.72) | 0.041 |
| CLIF-C OF | 1.16 (1.09–1.24) | <0.001 |
| III. Patients with culture-proven infections | ||
| NLR | 1.02 (1.00–1.04) | 0.015 |
| Previous hospitalization | 1.73(1.12–2.66) | 0.013 |
| Invasive procedure | 3.82 (2.12–6.85) | <0.001 |
| Pulmonary infection | 1.47 (0.99–2.16) | 0.055 |
| CLIF-C OF | 1.14 (1.06–1.23) | 0.001 |
| IV. Patients with MDRO infections | ||
| Previous hospitalization | 2.28 (1.33–3.92) | 0.003 |
| Ventilator support | 2.19 (1.51–3.19) | <0.001 |
| NLR | 1.03 (1.01–1.06) | 0.012 |
The prediction models were adjusted for age and MELD.
The best multivariable model was selected in various sub-group analysis based on best Harell's C-index value:
I) C = 0.782.
II) C = 0.741.
III) C = 0.721.
IV) C = 0.724.
CLIF-C OF: Chronic Liver Failure Consortium Organ failure; MDRO: multidrug resistant organisms; MELD: model for end stage liver disease; NLR: neutrophil-to-lymphocyte ratio; SIRS: systemic inflammatory response syndrome;.
On multivariable analysis, five different models were generated to predict the outcomes of the study population. Final model including infections, previous hospitalization, norfloxacin prophylaxis, Multisite infection, and CLIF-C OF as independent predictors of outcome in AD patients was selected, with a Harrell's C-statistics index of 0.782 (Table 4).
Infected Patients
On multivariable Cox regression analysis in patients with infections, adjusted for age and MELD, previous hospitalization, infection as an acute precipitant, MDRO infections, and CLIF-C OF were the independent predictors of outcomes (Harrell's C-statistics index: 0.741) (Table 4). Univariable and multivariable hazard's ratio are detailed in Supplementary Table 6A and B, respectively.
Culture-positive Infections
Among patients with culture-proven infections, non-survivors had a higher incidence of ACLF (76% vs 58%), SIRS (76% vs. 50%), requirement of inotropic (39% vs. 14%), and ventilator support (54% vs. 20%), P < 0.001 each (Supplementary Table 7). A heightened inflammatory response with a raised White blood cell count (WBC) count, Neutrophil-to-Lymphocyte Ratio (NLR), procalcitonin levels, poorer disease severity scores (P < 0.001 each), and higher number of organ failures (P < 0.05, each) were found in the non-survivor group than the survivors (Supplementary Table 7). Difference between patient’s nosocomial or healthcare-associated infections and community-acquired infections are presented in Supplementary Table 8.
Multivariable Cox regression analysis adjusted for age and MELD, in the subgroup of patients with culture-proven infections, revealed neutrophil-lymphocyte ratio, previous hospitalization, any invasive procedure, pulmonary infections, and CLIF-C OF as independent predictors of mortality (Harrell's C-statistics index: 0.721) (Table 4, Supplementary Table 9).
MDRO Infections
On multivariable Cox-regression analysis adjusted for age and MELD, in the subgroup of patients with MDRO infections, previous hospitalization, requirement of mechanical ventilation, and neutrophil-lymphocyte ratio (NLR) were found as independent predictors of mortality (Harrell's C-statistics index: 0.724) (Table 4, Supplementary Table 10).
Discussion
Infections in patients with cirrhosis are common and one of the major causes of acute decompensation, development of ACLF, and hospitalization, with 30–50% of patients presenting with infection at admission and 15–35% developing infection during the hospital stay. Infections significantly impair the prognosis in these patients and are associated with a high mortality rate. The incidence and microbial landscape of these infections exhibit geographic variation, a critical factor for steering empiric antimicrobial strategies and guiding antimicrobial stewardship programs. The main aim of our study was to address the local epidemiological profile of infection in hospitalized patients with acute decompensation of cirrhosis, with a focus on site-specific data on the incidence of infections and prevalence of MDRO infections, which is another major concern in Indian sub-continent with reported prevalence as high as 73%.
We reported a high rate of infections in 71% of patients with AD, of which 45% were microbiologically proven bacterial infections (31% of the whole cohort were culture-proven infections). Our findings align with the previously reported prevalence of infection (68%)13 and incidence of culture-positive infections (30%)14 from India. A large prospective global study on infections in cirrhosis reported a slightly higher rate of culture-positive infections (57%).7 Such high prevalence of infections in cirrhosis is explained by the immunocompromised state of AD patients, increased exposure to infectious pathogens owing to an intestinal dysbiosis, loss of integrity of the intestinal mucosal barrier, and sustained translocation of pathogen-associated molecular patterns (PAMPs). Additionally, the risk of nosocomial infections is increased due to frequent healthcare contact, multiple invasive procedures, and treatments.
Gram-negative infections are commonly prevalent in patients with cirrhosis.7 The multicentric global study reported 57% of culture-positive infections to as gram-negative bacteria, with E. coli and K. pneumoniae as predominant isolates, and 38% to be gram-positive isolates with Staphylococci and Enterococci as common isolates7 Kandasamy et al., reported more than half (66%) of culture-proven infections to be having gram-negative isolates with E. coli and K. pneumoniae as predominant isolates.14 Another Indian study reported the prevalence of gram-negative infections as high as 90% of patients with cirrhosis, 70% of which were reported to be E. coli and K. pneumoniae.13 In concordance with the previous studies, 69% of infections in our cohort were reported with gram-negative isolates with E. coli (36.4%) being the most common isolate, followed by K. pneumoniae (28.2%) and Acinetobacter sp. (23%). Enterococci sp. (62.4%) was the predominant isolate among gram-positive bacteria, while the rest were majorly Staphylococci sp. (42.2%).
The site of infection is often challenging to identify in our clinical practice. The commonest source of infection in our study was peritoneal infections (40%), followed by bloodstream infections (35.4%), and urinary tract infections (21.3%). Similar findings were reported by the multicentric global study with Spontaneous bacterial peritonitis (SBP) as the main focus of infection (27%), followed by UTI (22%), and pneumonia (19%).7 Gopinath et al., reported similar trend in the focus of infections with SBP as the most common site (37%), followed by UTI (30%), and Lower respiratory tract infection (LRTI) (15%).15 Another study by Bhattacharya et al. also reported 39% of infections in SBP, and 37% in UTI(13). Kandasamy et al., reported UTI as the main focus of infection (52%), followed by BSI (24%), and SBP (14%).14 Prevalence of skin and soft tissue infections were reported to be the lowest ranging between 3% and 7%.13,14
Prevalence of MDRO infections is daunting as reported by multiple studies. The global prevalence of Multidrug resistant (MDR) infections in patients with cirrhosis was 34% of all culture-proven infections; however, the data from Indian centers had MDR prevalence of 73%, with ESBL, Methicillin resistant Staphylococcus aureus (MRSA), and Vancomycin resistant Enterococci (VRE) being the most common subgroups of MDR bacteria.7 Single-center studies from the Indian sub-continent have reported infections with MDR organisms ranging from 49%13 to 60% (71% ESBL, 66% MR-CONS, 38% MRSA, 8% VRE).14 In our study, we also reported 71% of all culture-proven infections (31% of overall cohort) to be MDRO infections, 46% MDR, 9% XDR, and 15% suspected PDR. And 43.3% of MDRO isolates were ESBL, 32% CRE, and 16% were MDR-EF, while all carbapenem-resistant bacteria were 48%.
To the best of our knowledge, this is the first study to report the site-wise epidemiology and MDRO infections. Pulmonary infections had the highest prevalence of MDRO infections (81% of all patients with respiratory tract infections), followed by SSTI (76.5%), and UTI (66%). We reported a total of 371 isolates from 305 patients with culture-proven infections, of which SSTI (81%) and pulmonary infections (78%) had the highest prevalence of MDRO infections, with carbapenem-resistant bacteria and ESBL as the predominant subgroups in majority of the sites. These findings are important in guiding empiric treatment in hospitalized patients with AD, to suspect infections with carbapenem-resistant bacteria and ESBL-producing isolates, and ought to start broad-spectrum antimicrobial therapies to cover MDROs.
Second major aim of our study was to identify the risk factors of infections and MDRO infections in our setting. On multivariable regression analysis, we found prior hospitalization within the previous three months, history of recent norfloxacin prophylaxis, prior exposure to broad-spectrum antibiotics within the last three months, presence of SIRS, procalcitonin levels, and HE grade as the independent risk factors of developing infections in hospitalized AD patients. Among the subgroup of patients with infections, prior exposure to broad-spectrum antibiotics and norfloxacin prophylaxis within last three months, incidence of a second infection, infection at multiple sites, polymicrobial infection (>1 isolate), and CLIF-C OF were found as independent risk factors of developing MDRO infections. Rifaximin prophylaxis was surprisingly found to be protective against developing MDRO infections. The apparent protective effect of rifaximin against MDROs in our study resonates with the cumulative evidence revealing rifaximin's minimal influence on gut microbiome diversity and bacterial abundance.16 Furthermore, investigations specifically assessing drug-resistant genes in the gut microbiome before and after rifaximin prophylaxis found no statistically significant differences, indicative of stability during the treatment period. This supports the hypothesis that rifaximin's protective role against MDROs in our study may be attributed to its nuanced impact on the gut microbiota, however more studies are required to validate this finding.17, 18, 19 We were also interested in exploring the risk factors of infections with carbapenem-resistant bacteria, given their strikingly high prevalence in our setting. We found norfloxacin prophylaxis and prior exposure to broad-spectrum antibiotics within last three months, infection as acute precipitant of AD, multisite infection, and requirement of mechanical ventilation were independent risk factors of infections with carbapenem-resistant isolates. The multicentric global study had similar findings in terms of prior exposure to broad-spectrum antibiotics, invasive procedures, and previous healthcare contact to be the risk factors for MDR infections.7 However, this study did not find any significant association of quinolone (for SBP prophylaxis) with risk of MDR infections. Infections emerging from risk factors such as previous healthcare contact and invasive procedure suggest that precautionary measures such as limited contact and hand hygiene be promoted in all healthcare settings to limit the spread of infections to the patients and prevent the onset of secondary infections. Centers with a high-predisposed risk of infections/MDRO infections must go for active screening of infections and colonization of MDROs at admission by swab tests or rapid molecular diagnostic tests in addition to the conventional sepsis screening protocols. We have also shown that colonization by MDROs increased the risk of infection by MDROs at admission (OR: 8.5, P = 0.017) and follow-up (OR: 7.5, P < 0.001).20 The risk of infections due to prior use of broad-spectrum antibiotics stresses the need for timely antibiotic stewardship programs to be introduced in high-risk centers.
Another major aim of our study was to assess the burden of infections on outcomes and various predictors of mortality. As expected, patients with infections had a higher incidence of SIRS and an exacerbated inflammatory response. The number of organ failures (cardiovascular, respiratory, cerebral, and renal), disease severity scores (Child Turcotte Pugh (CTP), MELD, CLIF-C OF) were more and severe in infected patients. We showed that 30-day survival was significantly poor in infected patients, with worst in those with ACLF and infections. A higher incidence of ACLF, infections, and infections with MDRO were significantly associated with mortality. On regression analysis, we found previous hospitalization, norfloxacin prophylaxis, multisite infection, and CLIF-C OF to be the independent predictors of mortality in our study population. Previous hospitalization is directly linked to the disease severity and prolonged course of illness, which may further compromise the immune-paralysed state of AD patients. Additionally, it suggests infections to be the major killers in AD patients, as prior healthcare contact is an important risk factor for infections which complicates the course and worsens the prognosis in these patients.
MDRO infections were found to be prevalent in older patients, those having ACLF and a history of previous hospitalization. MDRO group had a higher incidence of a second infection, and a heightened inflammatory response including incidence of SIRS, incidence of shock, number of organ failures, and 30-day mortality. On Cox-regression analysis, we found age, MELD, previous hospitalization, requirement of mechanical ventilation, and neutrophil-lymphocyte ratio (NLR) were found as independent predictors of mortality in MDRO infections. Predictors of outcomes as assessed by the multicentric global study were also age, MELD, and components of systemic inflammation.7
In our study, our ultimate goal was to present a well-founded proposal for the most appropriate empiric antimicrobial therapy, taking into account the specific local epidemiology of infections at various sites. Table 5 summarizes the recommended empiric antimicrobial therapy, as outlined in practice guidelines, and justified explanations for selecting the proposed antimicrobials of choice. This comprehensive approach ensures that our proposed therapy aligns with current best practices and is tailored to effectively address the unique challenges posed by different infection sites.
Table 5.
Concise Summary of Recommended and Proposed Empiric Antimicrobial Decision Based on Local Epidemiologyf.
| Condition | Recommended antimicrobial decisions (EASL practice guidelinesg) | Proposed empiric antimicrobial decisions (Hospitalized patientsg) | Justification |
|---|---|---|---|
| Patients with shock | Piperacillin/tazobactam or Carbapenem with Gram-positive cover glycopeptide/lipopeptide | Carbapenem + glycopeptide/lipopeptide Tigecyclineb or Polymyxinsb (when high risk for CR-bacteria) Consider CAZ/AVI ± AZTa or Ampicillin-sulbactamb based on culture and sensitivity |
Rate of infection with:
|
| Site of infection | |||
| Spontaneous bacteremia | Piperacillin/tazobactam or meropenem ± vancomycin/daptomycin or linezolid | Meropenem or Imipenem-cilastatin (in low-risk patients for CR-bacterial infection) + glycopeptide/lipopeptide Tigecyclineb or Polymyxinsb (when high risk for CR-bacteria) Consider CAZ/AVI ± AZTa or Ampicillin-sulbactamb based on culture and sensitivity |
Rate of infection with:
|
| SBP | Piperacillin/tazobactam or meropenem ± vancomycin/daptomycin or linezolid | Piperacillin/tazobactam (first line) Meropenem + glycopeptide/lipopeptide (when high risk of MDR infection) Tigecyclineb or Polymyxinsb (when high risk for CR-bacteria) Consider CAZ/AVI ± AZTa or Ampicillin-sulbactamb based on culture and sensitivity |
Rate of infection with:
|
| UTI | Uncomplicated: Fosfomycin or Nitrofurantoin If complicated: Meropenem + Teicoplanin/Vancomycin |
Uncomplicated: Fosfomycinc or Nitrofurantoin Complicated: Piperacillin/tazobactam or Meropenem + Teicoplanin/Vancomycin Consider Colistin (when high risk for CR-bacteria) TMP-SMX or CAZ/AVI ± AZT (based on culture and sensitivity) |
Rate of infection with:
|
| Pneumonia | Piperacillin/tazobactam or meropenem/ceftazidime + ciprofloxacin/levofloxacin ± glycopeptides or linezolid should be added in patients with risk factors for MRSA | Meropenem or Imipenem-cilastatin (in low-risk patients for CR-bacterial infection) Empiric cover for gram-positive MDROs not recommended Polymyxinsb or Minocyclineb or Tigecyclineb (when high risk for CR-bacteria) Consider CAZ/AVI ± AZTa or Ampicillin-sulbactamb based on culture and sensitivity |
Rate of infection with:
|
| Skin and soft tissue infections (SSTI) | Third generation cephalosporin or piperacillin/tazobactam or Meropenem + glycopeptides or daptomycin or linezolid ± clindamycin |
Meropenem or Imipenem-cilastatin (in low-risk patients for CR-bacterial infection) + glycopeptide/lipopeptide/linezolidd ± clindamycine Tigecyclineb or Polymyxinsb (when high risk for CR-bacteria) Consider CAZ/AVI ± AZTa or Ampicillin-sulbactamb based on culture and sensitivity |
Rate of infection with:
|
Risk factors for MDRO infections: second infection, norfloxacin prophylaxis, prior use of broad-spectrum antibiotics in three months, CLIF-C OF score, multisite infection, polymicrobial infection.
Risk factors for CR-bacterial infections: norfloxacin prophylaxis, multisite infection, ventilator support, prior use of broad-spectrum antibiotics in three month, and infection as an acute precipitant.
AZT: Aztreonam; CAZ/AVI: Ceftazidime-avibactam; CRAB: carbapenem-resistant Acinetobacter baumannii; CRE-carbapenem-resistant Enterobacterales; CR-bacteria: carbapenem-resistant bacteria; ESBL: Extended spectrum beta-lactamase producing organisms; G+: gram-positive bacteria; G-: gram-negative bacteria; MDRO: multidrug resistant organisms; MDR-EF - multidrug-resistant Enterococcus faecium, VRE: vancomycinresistant enterococci; MDR-S: multidrugresistant Staphylococcus sp.; TMP-SMX: Trimethoprim/Sulfamethoxazole.
Suggested in areas with high risk of CRE infections.
Suggested in areas with high risk of CRAB infections with consideration of combination treatment.
Change if culture shows growth of G-bacteria other than E. coli.
Suggested in areas with high prevalence of VRE.
Suggested in patients with severe skin and soft tissue infections because of antitoxin activity.
The decisions should be individualized through risk stratification, likely pathogens, disease severity, site of infection, patient-specific issues (e.g. hypersensitivity, chronic kidney disease, finances), and drug-specific issues (availability, pharmacokinetics).
Mainly for healthcare or nosocomial infections.
However, acknowledging the inherent high baseline probability of infections in our cohort, and to address concerns related to injudicious antibiotic use potentially propagating multidrug-resistant organisms (MDROs), we provided two most relevant prediction algorithms for MDRO infections and infections with carbapenem resistant bacteria. For instance, a patient admitted with infection as acute precipitant, recent use of norfloxacin and broad spectrum antibiotics, multisite infection and ventilatory support would have a 90% probability of carrying carbapenem resistant bacterial infection. This information would guide empiric antimicrobials affective against carbapenem resistant bacteria for timely care of vulnerable patients.
Strengths of this study include details of the epidemiology of infections, particularly the site-wise distribution in the largest Indian cohort of AD patients detailing the risk factors of infections, MDRO infections, and infections with carbapenem-resistant bacteria, and predictors of outcomes in these patients. Limitations include that the data was sourced from a referral institute draining >8 states in India, thus the results are largely generalizable to the Indian population. Further, the information about cirrhosis outpatients, the reason for norfloxacin prophylaxis, beta-blocker use, and community-onset infections was limited. Moreover, precise data on few parameters that could affect outcomes, such as Proton pump inhibitors (PPI) or albumin use, sarcopenia or nutritional evaluation, antimicrobials, renal support, and cirrhotic cardiomyopathy was unavailable. Finally, the estimation of SIRS could have been confounded by factors such as anemia or prior beta-blocker use.
Bacterial infections are highly prevalent among hospitalized patients with cirrhosis and are responsible for poorer outcomes. A disturbingly high prevalence of MDRO infections; especially due to carbapenem-resistant bacteria observed in Indian setting emphasizes the urgent need for immediate nationwide control measures. Guiding antibiotic therapy based on local epidemiology and risk prediction models provided in our study would improve the prognosis for cirrhosis patients. While we strongly advocate a shift toward preventive measures over treatment, strategies such as reducing unnecessary admissions, optimizing antibiotic management, early extubating, and meticulous device care are crucial. Aligned with local epidemiology and supported by risk prediction models, these interventions not only enhance antibiotic stewardship but also contribute to improved outcomes for cirrhosis patients.
Credit authorship contribution statement
Conceptualization: NV.
Data Curation: PG, AV, VDR, JA, RC.
Formal Analysis: PG, NV.
Funding Acquisition: NV.
Investigation: AA, NT.
Methodology: NV.
Project Administration: NV.
Resources: NV.
Software: NV.
Validation: NV, PG, AA, NT.
Visualization: NV, PG.
Writing original draft: PG, NV.
Writing- Review & editing: PG, NV, NT, PK, SR, ADe, MPK, ST, AD.
Conflicts of interest
None.
Acknowledgements
We acknowledge the support of senior residents and staff of the Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh for assistance during data collection.
funding
The study was partly supported by the Indian Council of Medical Research (grant number 5/4/8-12/CD/NV/2021-NCD-II, awarded to Nipun Verma).
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jceh.2024.101352.
Appendix A. Supplementary data
The following are the Supplementary data to this article:
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