Risk Factors for Sepsis Based on Sepsis-3 Criteria after Orthotopic Liver Transplantation

Bundle care approach to reduce device associated infections in post-living-donor-liver transplantation in a tertiary care hospital, Egypt

BACKGROUND

Device-associated infections (DAIs) are a significant cause of morbidity following living donor liver transplantation (LDLT). We aimed to assess the impact of bundled care on reducing rates of device-associated infections.

METHODS

We performed a before-and-after comparative study at a liver transplantation facility over a three-year period, spanning from January 2016 to December 2018. The study included a total of 57 patients who underwent LDLT. We investigated the implementation of a care bundle, which consists of multiple evidence-based procedures that are consistently performed as a unified unit. We divided our study into three phases and implemented a bundled care approach in the second phase. Rates of pneumonia related to ventilators [VAP], bloodstream infections associated with central line [CLABSI], and urinary tract infections associated with catheters [CAUTI] were assessed throughout the study period. Bacterial identification and antibiotic susceptibility testing were performed using the automated Vitek-2 system. The comparison between different phases was assessed using the chi-square test or the Fisher exact test for qualitative values and the Kruskal-Wallis H test for quantitative values with non-normal distribution.

RESULTS

In the baseline phase, the VAP rates were 73.5, the CAUTI rates were 47.2, and the CLABSI rates were 7.4 per one thousand device days (PDD). During the bundle care phase, the rates decreased to 33.3, 18.18, and 4.78. In the follow-up phase, the rates further decreased to 35.7%, 16.8%, and 2.7% PDD. The prevalence of Klebsiella pneumonia (37.5%) and Methicillin resistance Staph aureus (37.5%) in VAP were noted. The primary causative agent of CAUTI was Candida albicans, accounting for 33.3% of cases, whereas Coagulase-negative Staph was the predominant organism responsible for CLABSI, with a prevalence of 40%.

CONCLUSION

This study demonstrates the effectiveness of utilizing the care bundle approach to reduce DAI in LDLT, especially in low socioeconomic countries with limited resources. By implementing a comprehensive set of evidence-based interventions, healthcare systems can effectively reduce the burden of DAI, enhance infection prevention strategies and improve patient outcomes in resource-constrained settings.

Development and validation of a practical machine learning model to predict sepsis after liver transplantation

BACKGROUND

Postoperative sepsis is one of the main causes of mortality after liver transplantation (LT). Our study aimed to develop and validate a predictive model for postoperative sepsis within 7 d in LT recipients using machine learning (ML) technology.

METHODS

Data of 786 patients received LT from January 2015 to January 2020 was retrospectively extracted from the big data platform of Third Affiliated Hospital of Sun Yat-sen University. Seven ML models were developed to predict postoperative sepsis. The area under the receiver-operating curve (AUC), sensitivity, specificity, accuracy, and f1-score were evaluated as the model performances. The model with the best performance was validated in an independent dataset involving 118 adult LT cases from February 2020 to April 2021. The postoperative sepsis-associated outcomes were also explored in the study.

RESULTS

After excluding 109 patients according to the exclusion criteria, 677 patients underwent LT were finally included in the analysis. Among them, 216 (31.9%) were diagnosed with sepsis after LT, which were related to more perioperative complications, increased postoperative hospital stay and mortality after LT (all p < .05). Our results revealed that a larger volume of red blood cell infusion, ascitic removal, blood loss and gastric drainage, less volume of crystalloid infusion and urine, longer anesthesia time, higher level of preoperative TBIL were the top 8 important variables contributing to the prediction of post-LT sepsis. The Random Forest Classifier (RF) model showed the best overall performance to predict sepsis after LT among the seven ML models developed in the study, with an AUC of 0.731, an accuracy of 71.6%, the sensitivity of 62.1%, and specificity of 76.1% in the internal validation set, and a comparable AUC of 0.755 in the external validation set.

CONCLUSIONS

Our study enrolled eight pre- and intra-operative variables to develop an RF-based predictive model of post-LT sepsis to assist clinical decision-making procedure.

Are short-term complications associated with poor allograft and patient survival after liver transplantation? A systematic review of the literature and expert panel recommendations

BACKGROUND

Maximizing patient and allograft survival after liver transplant (LT) is important from both a patient care and organ utilization perspective. Although individual studies have addressed the effects of short-term post-LT complications on a limited scale, there has not been a systematic review of the literature formally assessing the potential effects of early complications on long-term outcomes.

OBJECTIVES

To identify whether short-term complications after LT affect allograft and overall survival, to identify short-term complications of particular clinical interest and significance, and to provide recommendations to improve post-LT graft and patient survival.

DATA SOURCES

Ovid MEDLINE, Embase, Scopus, Google Scholar, and Cochrane Central.

METHODS

A systematic review following PRISMA guidelines and recommendations using the GRADE approach derived from an international expert panel.

RESULTS

The literature review and analysis provided show that short-term complications have a large impact on allograft and patient survival after LT. The complications with the strongest effect on survival are acute kidney injury (AKI), biliary complications, and early allograft dysfunction (EAD).

CONCLUSION

This panel recommends taking measures to reduce the risk and incidence of short-term complications post-LT. Clinicians should pay particular attention to preventing or ameliorating AKI, biliary complications, and EAD (Quality of evidence; Moderate | Grade of Recommendation; Strong).

Tree-based, two-stage risk factor analysis for postoperative sepsis based on Sepsis-3 criteria in elderly patients: A retrospective cohort study

Background

Sepsis remains the leading cause of postoperative death in elderly patients and is defined as organ dysfunction with proven or suspected infection according to Sepsis-3 criteria. To better avoid potential non-linear associations between the risk factors, we firstly used a tree-based analytic methods to explore the putative risk factors of geriatric sepsis based on the criteria in the study.

Methods

Data of 7,302 surgical patients aged ≥ 65 years at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were collected. An analytic method that combined tree-based analysis with the method of Mantel-Haenszel and logistic regression was adopted to assess the association between 17 putative risk factors and postoperative sepsis defined by the Sepsis-3 guideline by controlling 16 potential confounding factors.

Results

Among the 16 potential covariates, six major confounders were statistically identified by the tree-based model, including cerebrovascular diseases, preoperative infusion of red blood cells, pneumonia, age ≥ 75, malignant tumor and diabetes. Our analysis indicated that emergency surgery increases the risk of postoperative sepsis in elderly patients by more than six times. The type of surgery is also a crucial risk factor for sepsis, particularly transplantation and neurosurgery. Other risk factors were duration of surgery > 120 min, administration of steroids, hypoalbuminemia, elevated creatinine, blood urea nitrogen, hematocrit, platelets, glucose, white blood cell count, abnormal neutrophil-to-lymphocyte ratio and elevated hsCRP-to-albumin ratio.

Conclusions

Our study uses an effective method to explore some risk factors for postoperative sepsis in elderly by adjusting many potential confounders and it can provide information for intervention design.

Hepatic pannexin-1 mediates ST2+ regulatory T cells promoting resolution of inflammation in lipopolysaccharide-induced endotoxemia

Sepsis remains the most lethal infectious disease and substantially impairs patient prognosis after liver transplantation (LT). Our previous study reported a role of the pannexin 1 (PANX1)–interleukin‐33 (IL‐33) axis in activating innate immunity to protect against methicillin‐resistant Staphylococcus aureus infection; however, the role of PANX1 in regulating adaptive immunity in sepsis and the underlying mechanism are unclear. In this study, we examined the role of the PANX1–IL‐33 axis in protecting against sepsis caused by a gram‐negative bacterial infection in an independent LT cohort. Next, in animal studies, we assessed the immunological state of Panx1^−/‐ mice with lipopolysaccharide (LPS)‐induced endotoxemia and then focused on the cytokine storm and regulatory T cells (Tregs), which are crucial for the resolution of inflammation. To generate liver‐specific Panx1‐deficient mice and mimic clinical LT procedures, a mouse LT model was established. We demonstrated that hepatic PANX1 deficiency exacerbated LPS‐induced endotoxemia and dysregulated the immune response in the mouse LT model. In hepatocytes, we confirmed that PANX1 positively regulated IL‐33 synthesis after LPS administration. We showed that the adenosine triphosphate‐P2X7 pathway regulated the hepatic PANX1–IL‐33 axis during endotoxemia in vitro and in vivo. Recombinant IL‐33 treatment rescued LPS‐induced endotoxemia by increasing the numbers of liver‐infiltrating ST2⁺ Tregs and attenuating the cytokine storm in hepatic PANX1‐deficient mice. In conclusion, our findings revealed that the hepatic PANX1–IL‐33 axis protects against endotoxemia and liver injury by targeting ST2⁺ Tregs and promoting the early resolution of hyperinflammation.

The impact of recency and adequacy of historical information on sepsis predictions using machine learning

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis. This study contributes to this literature by providing clear insights regarding the role of the recency and adequacy of historical information in predicting sepsis using ML. To this end, we implemented a deep learning model using a bidirectional long short-term memory (BiLSTM) algorithm and compared it with six other ML algorithms based on numerous combinations of the prediction horizons (to capture information recency) and observation windows (to capture information adequacy) using different measures of predictive performance. Our results indicated that the BiLSTM algorithm outperforms all other ML algorithms and provides a great separability of the predicted risk of sepsis among septic versus non-septic patients. Moreover, decreasing the prediction horizon (in favor of information recency) always boosts the predictive performance; however, the impact of expanding the observation window (in favor of information adequacy) depends on the prediction horizon and the purpose of prediction. More specifically, when the prediction is responsive to the positive label (i.e., Sepsis), increasing historical data improves the predictive performance when the prediction horizon is short-moderate.

Survival Outcome of Sepsis in Recipients of Solid Organ Transplant

Sepsis is a complex disease stemming from a dysregulated immune response toward an infectious agent. In transplantation, sepsis remains one of the leading causes of morbidity and mortality. Solid organ transplant recipients have impaired adaptive immunity due to immunosuppression required to prevent rejection. Immunosuppression has unintended consequences, such as increasing the risk of infections and sepsis. Due to its high morbidity and mortality, early detection of sepsis is paramount to start aggressive treatment. Several biomarkers or combination of biomarkers of sepsis have emerged in the last decade, but they are not dependable for early diagnosis or for outcome prognosis.

Comparison of pediatric scoring systems for mortality in septic patients and the impact of missing information on their predictive power: a retrospective analysis

Background

Scores can assess the severity and course of disease and predict outcome in an objective manner. This information is needed for proper risk assessment and stratification. Furthermore, scoring systems support optimal patient care, resource management and are gaining in importance in terms of artificial intelligence.

Objective

This study evaluated and compared the prognostic ability of various common pediatric scoring systems (PRISM, PRISM III, PRISM IV, PIM, PIM2, PIM3, PELOD, PELOD 2) in order to determine which is the most applicable score for pediatric sepsis patients in terms of timing of disease survey and insensitivity to missing data.

Methods

We retrospectively examined data from 398 patients under 18 years of age, who were diagnosed with sepsis. Scores were assessed at ICU admission and re-evaluated on the day of peak C-reactive protein. The scores were compared for their ability to predict mortality in this specific patient population and for their impairment due to missing data.

Results

PIM (AUC 0.76 (0.68-0.76)), PIM2 (AUC 0.78 (0.72-0.78)) and PIM3 (AUC 0.76 (0.68-0.76)) scores together with PRSIM III (AUC 0.75 (0.68-0.75)) and PELOD 2 (AUC 0.75 (0.66-0.75)) are the most suitable scores for determining patient prognosis at ICU admission. Once sepsis is pronounced, PELOD 2 (AUC 0.84 (0.77-0.91)) and PRISM IV (AUC 0.8 (0.72-0.88)) become significantly better in their performance and count among the best prognostic scores for use at this time together with PRISM III (AUC 0.81 (0.73-0.89)). PELOD 2 is good for monitoring and, like the PIM scores, is also largely insensitive to missing values.

Conclusion

Overall, PIM scores show comparatively good performance, are stable as far as timing of the disease survey is concerned, and they are also relatively stable in terms of missing parameters. PELOD 2 is best suitable for monitoring clinical course.

A 'real-world' analysis of risk factors for post liver transplant delirium and the effect on length of stay

BACKGROUND

The development of delirium has been previously demonstrated to be associated with an increased risk of mortality and length of stay post liver transplant (LTx) with multiple risk factors being identified in previous studies. In this study, we have aimed to identify the most important variables associated with the onset of post-LTx delirium and understand the effect on length of stay (LOS).

METHODS

All liver transplants for chronic liver disease between 1 August 2012 and 1 August 2017 were included (n = 793). Data were collected for analysis retrospectively from electronic patient records.

RESULTS

Delirium is associated with an overall increased hospital and ICU LOS but not one-year mortality. The risk of developing post-LTx delirium was the greatest among patients: with post-LTx sepsis, who required renal sparing immunosuppression, who received donation after cardiac death (DCD) grafts and who were older. Patients with autoimmune hepatitis, primary biliary cholangitis or primary sclerosing cholangitis seemed to be at lower risk of post-LTx delirium. However, global patient LOS was only prolonged in patients with sepsis and renal failure.

CONCLUSION

Many of the risk factors previously described to be associated with the development of post-LTx delirium were not demonstrated to be significant in this study. Sepsis, renal failure, older age and DCD use are associated with delirium post-LTx. It is unclear if this syndrome is an independent risk factor for increased LOS or if it is a symptom of well established syndromes associated with increased LOS. The role for prophylactic strategies to reduce the incidence of post-LTx delirium is therefore unclear.

A Cross-sectional Study of Group B Streptococcus-Associated Sepsis, Coinfections, and Antibiotic Susceptibility Profile in Neonates in Pakistan

BACKGROUND

Neonatal sepsis is a leading cause of morbidity and early-life mortality worldwide, and previous data have reported the highest neonatal mortality rate in Pakistan.

PURPOSE

The present study aimed to decipher the prevalence of group B Streptococcus (GBS)-associated sepsis, coinfections, and antibiotic susceptibility of isolated microbes in neonates.

METHODS

Blood samples of 100 cases of neonatal sepsis were subcultured on blood agar, GBS agar, and MacConkey agar for isolation of GBS and suspected microbes.

RESULTS

Of 100 neonatal blood samples, 85 blood samples were culture-positive, including mixed culture growth (n = 18), 25 samples as early-onset neonatal sepsis (EONS) and 60 as late-onset neonatal sepsis (LONS). Staphylococcus aureus showed high percent positivity (31.4%), followed by Candida sp (16.5%), GBS (14.1%), Klebsiella (14.1%), Staphylococcus epidermidis (11.8%), Pseudomonas (9.4%), Acinetobacter (9.4%), Esherichia coli (8.2%), and Enterococcus (5.9%). GBS was isolated more frequently from EONS than from LONS with 50% coinfections. Mode of delivery, gender, and respiratory distress in neonates were significantly associated with culture-positive sepsis. GBS isolates were highly (91.7%) susceptible to vancomycin, cefotaxime, and chloramphenicol, followed by penicillin (83.3%), ampicillin, and tetracycline (75%). GBS isolates were resistant to erythromycin, clindamycin, ciprofloxacin, and linezolid.

IMPLICATIONS FOR PRACTICE

Our findings evidenced GBS-associated risk factors and antibiotic susceptibility pattern of neonatal sepsis, which will help clinicians in management, control, and treatment of neonatal sepsis.

IMPLICATIONS FOR RESEARCH

The epidemiological evidence of GBS-associated neonatal sepsis, demographic characteristics, risk factor data analysis, and drug resistance pattern has disease prevention implications in neonates in low-income countries including Pakistan.

Bacterial and Fungal Infections After Liver Transplantation: Microbial Epidemiology, Risk Factors for Infection and Death with Infection

Background

Infections, especially bacterial and fungal infections, are the leading cause of high mortality after liver transplantation (LT). This research investigated the pathogenic spectrum, antimicrobial susceptibility results, and risk factors of infection and death with infection to better control such infections.

Material/Methods

A retrospective cohort study was performed, and 433 liver transplant recipients between January 2010 and December 2016 were analyzed.

Results

We found 290 isolates of bacteria and fungi in 170 infected liver transplant patients. Significant independent risk factors for bacterial and fungal infections were prolonged hospital stay (OR 1.034, 95% CI 1.013~1.056, p=0.002), mechanical ventilation (OR 3.806, 95% CI 1.567~9.248, p=0.003), and liver failure (OR 2.659, 95% CI 1.019~6.940, p=0.046). Furthermore, postoperative MELD scores (OR 1.120, 95% CI 1.020~1.230, p=0.017) and septic shock (OR 12.000, 95% CI 1.124~128.066, p=0.003) were independent risk factors for death with infection. CRAB infection is the main pathogenic bacteria of septic shock in LT patients.

Conclusions

We found that 39.3% of recipients had at least 1 bacterial or fungal infection after LT. Shortening the length of hospital stay and early withdrawal of mechanical ventilation will reduce the risk of infection after LT. Patients with liver failure should be more vigilant against postoperative infection. Once an infection occurs, immediate assessment of the postoperative MELD score, early diagnosis of septic shock, and active search for pathogenic evidence for precise treatment will help improve patient prognosis. Routine screening for CRAB colonization before surgery will facilitate empirical use of effective antibiotics.

Sepsis outcomes in solid organ transplant recipients

BACKGROUND

We present data on a cohort of patients diagnosed with sepsis over a 10-year period comparing outcomes in solid organ transplant (SOT) and non-solid organ transplant (non-SOT) recipients.

METHODS

This is a retrospective single-center study of patients with diagnosis of sepsis from 1/1/06 to 6/30/16. Cases and controls were matched by year of sepsis diagnosis with propensity score matching. Conditional logistic regression and repeated measurement models were performed for binary outcomes. Trends over time for in-hospital mortality were determined using the Cochran-Armitage test. A gamma-distributed model was performed on the continuous variables.

RESULTS

Overall, there were 18 632 admission encounters with a discharge diagnosis of sepsis in 14 780 unique patients. Of those admissions, 1689 were SOT recipients. After 1:1 matching by year, there were three thousand three hundred and forty patients (1670 cases; 1670 controls) diagnosed with sepsis. There was a decreasing trend for in-hospital mortality for sepsis over time in SOT patients and non-SOT patients (P < .05) due to early sepsis recognition and improved standard of care. Despite higher comorbidities in the SOT group, conditional logistic regression showed that in-hospital mortality for sepsis in SOT patients was similar compared with non-SOT patients (odds ratio [OR] =1.14 [95% confidence interval {CI}, 0.95-1.37], P = .161). However, heart and lung SOT subgroups had higher odds of dying compared with the non-SOT group (OR = 1.83 [95% CI, 1.30-2.57], P < .001 and OR = 1.77 [95% CI, 1.34-2.34], P < .001). On average, SOT patients had 2 days longer hospital length of stay compared with non-SOT admissions (17.00 ± 19.54 vs 15.23 ± 17.07, P < .05). Additionally, SOT patients had higher odds of hospital readmission within 30 days (OR = 1.25 [95% CI, 1.06-1.51], P = .020), and higher odds for DIC compared with non-SOT patients (OR = 1.76 [95% CI, 1.10-2.86], P = .021).

CONCLUSION

Sepsis in solid organ transplants and non-solid organ transplant patients have similar mortality; however, the subset of heart and lung transplant recipients with sepsis has a higher rate of mortality compared with the non-solid organ transplant recipients. SOT with sepsis as a group has a higher hospital readmission rate compared with non-transplant sepsis patients.