A systematic review on risk factors associated with sepsis in patients admitted to intensive care units

ANGPTL8 deficiency attenuates lipopolysaccharide-induced liver injury by improving lipid metabolic dysregulation

Liver injury is closely related to poor outcomes in sepsis patients. Current studies indicate that sepsis is accompanied by metabolic disorders, especially those related to lipid metabolism. It is highly important to explore the mechanism of abnormal liver lipid metabolism during sepsis. As a key regulator of glucose and lipid metabolism, angiopoietin-like 8 (ANGPTL8) is involved in the regulation of multiple chronic metabolic diseases. In the present study, severe liver lipid deposition and lipid peroxidation were observed in the early stages of lipopolysaccharide (LPS) induced liver injury. LPS promotes the expression of ANGPTL8 both in vivo and in vitro. Knockout of ANGPTL8 reduced hepatic lipid accumulation and lipid peroxidation, improved fatty acid oxidation and liver function, and increased the survival rate of septic mice by activating the PGC1α/PPARα pathway. We also found that the expression of ANGPTL8 induced by LPS depends on TNF-α, and that inhibiting the TNF-α pathway reduces LPS-induced hepatic lipid deposition and lipid peroxidation. However, knocking out ANGPTL8 improved the survival rate of septic mice better than inhibiting the TNF-α pathway. Taken together, the results of our study suggest that ANGPTL8 functions as a novel cytokine in LPS-induced liver injury by suppressing the PGC1α/PPARα signaling pathway. Therefore, targeting ANGPTL8 to improve liver lipid metabolism represents an attractive strategy for the management of sepsis patients.

Risk factors for colonisation by Multidrug-Resistant bacteria in critical care units

INTRODUCTION

Antimicrobial resistance is a major public health challenge recognised by the WHO as an urgent global healthcare concern. Patients in Intensive Care Units (ICUs) are particularly prone to colonisation and/or infection by multidrug-resistant organisms (MDROs).

OBJECTIVES

Delineate the epidemiological characteristics and risk factors for MDROs colonisation in mixed ICUs and Resuscitation Units by focusing on initial and nosocomial colonisation.

MATERIAL AND METHODS

A descriptive observational study with analytical elements. It uses the Zero-Resistance register from the Preventive Medicine Service of the Albacete General University Hospital (Spain) from April 2016 to December 2021. It identifies the risk factors for MDROs colonisation.

RESULTS

Of 7,541 cases, 61.0 % with initial colonisation had risk factors for MDROs versus 34.0 % not colonised upon hospitalisation (p < 0.001). Significant risk factors for initial colonisation included hospitalisation for ≥ 5 days within the last 3 months, prior MDROs colonisation/infection and institutionalization. No significant risk factor differences were found for nosocomial colonisation. An association between longer ICU stays and nosocomial colonisation (p < 0.001) was noted.

CONCLUSIONS

Significant risk factors for initial MDROs colonisation were hospitalisation for ≥ 5 days in the last 3 months, prior MDROs colonisation/infection and institutionalisation. Longer ICU stays increased the nosocomial colonisation risk.

IMPLICATIONS FOR CLINICAL PRACTICE

This study underscores the importance to early identify and manage patients at risk for MDROs colonisation in ICUs. By recognising factors (i.e. previous hospitalisations, existing colonisation or infection, impact of prolonged ICU stay), healthcare providers can implement targeted strategies to mitigate the spread of MDROs; e.g. enhanced surveillance, stringent infection control measures and judicious antibiotics use. Our findings highlight the need for a comprehensive approach to manage antimicrobial resistance in critical care settings to ultimately improve patient outcomes and reduce MDROs burden in hospitals.

Vital D: A modifiable occupational risk factor of UK healthcare workers

BACKGROUND

The role of Vitamin D in immune function is well reported with a growing evidence base linking low levels to poorer outcomes from infectious disease. Vitamin D deficiency and insufficiency are prevalent worldwide with healthcare workers identified as a known at-risk group. Here we aim to investigate serum Vitamin D levels in a UK population of front line healthcare workers and to promote the occupational risk.

METHODS

A cross-sectional study of 639 volunteers was conducted to identify the prevalence of Vitamin D deficiency and insufficiency amongst a population of front-line health care workers in the UK. Participant demographics and co-morbid factors were collected at the time of serum sampling for multivariate analysis.

RESULTS

Only 18.8% of the population had a normal vitamin D level greater than or equal to 75nmol/L. This is compared to Public Health England's (PHE) stipulated normal levels of 60% during winter. 81.2% had a level less than 75nmol/L, with 51.2% less than 50nmol/L and 6.6% less than 25nmol/L. For serum levels less than 25nmol/L, Asian ethnicity was more likely to have a vitamin D deficiency than non-asian (OR (95%CI): 3.81 (1.73-8.39), p = 0.001), whereas white ethnicity was less likely to have a vitamin D deficiency compared to non-white (OR (95%CI: 0.43 (0.20-0.83), p = 0.03). Other factors that contributed to a higher likelihood of lower-than-normal levels within this population included male sex, decreased age and not taking supplementation.

CONCLUSION

It is concluded that our population of healthcare workers have higher rates of abnormal vitamin D levels in comparison with the general UK population reported prevalence. Furthermore, Asian ethnicity and age 30 years and below are more at risk of vitamin D insufficiency and deficiency. This highlights an occupational risk factor for the healthcare community to consider.

Obesity and septic patient outcomes: Shaping the puzzle through age and sex perspectives

BACKGROUND & AIMS

While obesity has been reported as a protective factor in septic patients, little is known about the potential modifying effects of age and sex. The objective of this study is to investigate age and sex-specific associations between obesity and the prognosis of septic patients.

METHODS

A retrospective analysis was conducted on a cohort of 15,464 septic patients, categorized by body mass index (BMI) into four groups: underweight (<18.5 kg/m2, n = 483), normal (18.5-24.9 kg/m2, n = 4344), overweight (25-29.9 kg/m2, n = 4949) and obese (≥30 kg/m2, n = 5688). Multivariable logistic regression and inverse probability weighting were employed to robustly confirm the protective effect of a higher BMI on 28-day mortality, with normal weight serving as the reference category. Subgroup analyses based on age (young: 18-39, middle-aged: 40-64 and elderly: ≥65) and sex were performed.

RESULTS

The findings demonstrate that high BMI independently confers a protective effect against 28-day mortality in septic patients. However, the relationship between BMI and 28-day mortality exhibits a non-linear trend, with a BMI of 34.5 kg/m2 displaying the lowest odds ratio. Notably, the survival benefits associated with a high BMI were not observed in the young group. Moreover, being underweight emerges as an independent risk factor for middle-aged and elderly female patients, while in males it is only a risk factor in the elderly group. Interestingly, being overweight and obese were identified as independent protective factors in middle-aged and elderly male patients, but not in females.

CONCLUSIONS

The effect of BMI on mortality in septic patients varies according to age and sex. Elderly individuals with sepsis may derive more prognostic benefits from obesity.

The Association between the rs3747406 Polymorphism in the Glucocorticoid-Induced Leucine Zipper Gene and Sepsis Survivals Depends on the SOFA Score

The variability in mortality in sepsis could be a consequence of genetic variability. The glucocorticoid system and the intermediate TSC22D3 gene product-glucocorticoid-induced leucine zipper-are clinically relevant in sepsis, which is why this study aimed to clarify whether TSC22D3 gene polymorphisms contribute to the variance in sepsis mortality. Blood samples for DNA extraction were obtained from 455 patients with a sepsis diagnosis according to the Sepsis-III criteria and from 73 control subjects. A SNP TaqMan assay was used to detect single-nucleotide polymorphisms (SNPs) in the TSC22D3 gene. Statistical and graphical analyses were performed using the SPSS Statistics and GraphPad Prism software. C-allele carriers of rs3747406 have a 2.07-fold higher mortality rate when the sequential organ failure assessment (SOFA) score is higher than eight. In a multivariate COX regression model, the SNP rs3747406 with a SOFA score ≥ 8 was found to be an independent risk factor for 30-day survival in sepsis. The HR was calculated to be 2.12, with a p-value of 0.011. The wild-type allele was present in four out of six SNPs in our cohort. The promoter of TSC22D3 was found to be highly conserved. However, we discovered that the C-allele of rs3747406 poses a risk for sepsis mortality for SOFA Scores higher than 6.

The causal effects of circulating cytokines on sepsis: a Mendelian randomization study

Background

In observational studies, sepsis and circulating levels of cytokines have been associated with unclear causality. This study used Mendelian randomization (MR) to identify the causal direction between circulating cytokines and sepsis in a two-sample study.

Methods

An MR analysis was performed to estimate the causal effect of 41 cytokines on sepsis risk. The inverse-variance weighted random-effects method, the weighted median-based method, and MR-Egger were used to analyze the data. Heterogeneity and pleiotropy were assessed using MR-Egger regression and Cochran's Q statistic.

Results

Genetically predicted beta-nerve growth factor (OR = 1.12, 95% CI [1.037-1.211], P = 0.004) increased the risk of sepsis, while RANTES (OR = 0.92, 95% CI [0.849-0.997], P = 0.041) and fibroblast growth factor (OR = 0.869, 95% CI [0.766-0.986], P = 0.029) reduced the risk of sepsis. These findings were robust in extensive sensitivity analyses. There was no clear association between the other cytokines and sepsis risk.

Conclusion

The findings of this study demonstrate that beta-nerve growth factor, RANTES, and fibroblast growth factor contribute to sepsis risk. Investigations into potential mechanisms are warranted.

Changes in Early T-Cell Subsets and Their Impact on Prognosis in Patients with Sepsis: A Single-Center Retrospective Study

Objective

To analyze the early changes in CD3+, CD4+, and CD8+T-cell subset counts in patients with sepsis and their correlation with prognosis to provide a feasible basis for clinical immunomodulation in sepsis.

Methods

This is a single-center retrospective study. The study enrolled sepsis patients (meeting SEPSIS 3.0 definition) who were admitted to the Department of Intensive Care Unit at the First Hospital of Jilin University from July 5th, 2018 to December 5th, 2019 and were aged 18 years or above. In addition, these patients underwent cellular immune testing (CD3+, CD4+, CD8+ T lymphocyte counts, and CD4+/CD8+ ratio) within 24 hours of ICU admission. Patient's clinical data including age, gender, infection site, APACHE II score, SOFA score, length of ICU stay, mechanical ventilation time, ICU mortality, 28-day mortality, and 3-year survival status were collected. The prognostic indicators and survival of the decreased and nondecreased groups of different subsets of T lymphocyte counts and CD4+/CD8+ ratio were compared.

Results

A total of 206 patients were enrolled, with 76.7% having a decrease in CD3+ T lymphocyte count, 76.7% having a decrease in CD4+ T lymphocyte count, and 63.6% having a decrease in CD8+ T lymphocyte count. Furthermore, 21.8% had a lower CD4+/CD8+ ratio. Analysis showed that the CD3+ T lymphocyte count decreased group had a longer length of ICU stay [11 d (4, 21) vs. 7 d (4, 17), P=0.03], increased percentage of mechanical ventilation (67.5% vs. 51.0%, P=0.04), and extended mechanical ventilation time [144 h (48, 360) vs. 96 h (48, 144), P=0.04] compared to the nondecreased group. The 28-day mortality was higher in the decreased group of CD4+/CD8+ ratio compared to the nondecreased group (33.3% vs. 25.5%, P=0.29); however, the difference did not reach statistical significance. Logistic regression analysis revealed no significant correlation between the decrease in CD4+/CD8+ ratio and 28-day mortality (P=0.11). The 3-year follow-up revealed that the CD4+/CD8+ decreased group had a lower survival rate than the nondecreased group (33.3% vs. 53.4%, P=0.01).

Conclusions

In the early stage of sepsis, most patients showed a decrease in CD3+, CD4+, and CD8+T-cell subsets, as well as in the CD4+/CD8+ ratio. The decrease in CD3+ and CD4+/CD8+ was related to some poor prognosis.

Four Assessment Tools for Predicting Mortality and Adverse Events in Surgical Patients With Sepsis and Septic Shock: A Comparative Study

BACKGROUND

The mortality rate for sepsis and septic shock in surgical patients is approximately 36%, which is higher than that of other medical patients. Predisposition, infection/injury, response, and organ dysfunction (PIRO) is currently the most widely used tool for assessing patients with surgical sepsis. However, it is not a standardized assessment tool for surgical patients in general.

PURPOSE

The purposes of this study were to (a) create a modified PIRO (mPIRO) that adds a count of platelets and does not include a body temperature reading; (b) test the sensitivity and specificity of the mPIRO for predicting mortality and adverse events among patients with surgical sepsis; and (c) compare the predictive accuracy of the mPIRO, sequential organ failure assessment (SOFA), quick SOFA, and PIRO tools.

METHODS

A retrospective observational cohort study was conducted. Two thousand fifty-five patient medical records were reviewed, with 103 identified as meeting the inclusion criteria.

RESULTS

Compared with the other tools, mPIRO ≥ 4 achieved better sensitivity (90.5%) in predicting mortality and high sensitivity (72%) and specificity (80%) in predicting adverse events. mPIRO was the most accurate predictor of mortality (area under the receiver operating characteristic curve [AUC] = 0.83) among the tools considered. SOFA and mPIRO were the first and second most accurate predictor of adverse events, respectively, with respective AUC values of 0.86 and 0.82.

CONCLUSIONS/IMPLICATIONS FOR PRACTICE

mPIRO, which employs an easy-to-use scoring system, is a valid assessment tool with good sensitivity and AUC for predicting both mortality and adverse events in patients with surgical sepsis. We recommend using mPIRO ≥ 3 as an indicator of potential adverse events.

The TIGIT+ T regulatory cells subset associates with nosocomial infection and fatal outcome in COVID-19 patients under mechanical ventilation

The TIGIT+FOXP3+Treg subset (TIGIT+Tregs) exerts robust suppressive activity on cellular immunity and predisposes septic individuals to opportunistic infection. We hypothesized that TIGIT+Tregs could play an important role in intensifying the COVID-19 severity and hampering the defense against nosocomial infections during hospitalization. Herein we aimed to verify the association between the levels of the TIGIT+Tregs with the mechanical ventilation requirement, fatal outcome, and bacteremia during hospitalization. TIGIT+Tregs were immunophenotyped by flow cytometry from the peripheral blood of 72 unvaccinated hospitalized COVID-19 patients at admission from May 29th to August 6th, 2020. The patients were stratified during hospitalization according to their mechanical ventilation requirement and fatal outcome. COVID-19 resulted in a high prevalence of the TIGIT+Tregs at admission, which progressively increased in patients with mechanical ventilation needs and fatal outcomes. The prevalence of TIGIT+Tregs positively correlated with poor pulmonary function and higher plasma levels of LDH, HMGB1, FGL2, and TNF. The non-survivors presented higher plasma levels of IL-33, HMGB1, FGL2, IL-10, IL-6, and 5.54 times more bacteremia than survivors. Conclusions: The expansion of the TIGIT+Tregs in COVID-19 patients was associated with inflammation, lung dysfunction, bacteremia, and fatal outcome.

Annexin A5 Inhibits Endothelial Inflammation Induced by Lipopolysaccharide-Activated Platelets and Microvesicles via Phosphatidylserine Binding

Sepsis is caused by a dysregulated immune response to infection and is a leading cause of mortality globally. To date, no specific therapeutics are available to treat the underlying septic response. We and others have shown that recombinant human annexin A5 (Anx5) treatment inhibits pro-inflammatory cytokine production and improves survival in rodent sepsis models. During sepsis, activated platelets release microvesicles (MVs) with externalization of phosphatidylserine to which Anx5 binds with high affinity. We hypothesized that recombinant human Anx5 blocks the pro-inflammatory response induced by activated platelets and MVs in vascular endothelial cells under septic conditions via phosphatidylserine binding. Our data show that treatment with wildtype Anx5 reduced the expression of inflammatory cytokines and adhesion molecules induced by lipopolysaccharide (LPS)-activated platelets or MVs in endothelial cells (p < 0.01), which was not observed with Anx5 mutant deficient in phosphatidylserine binding. In addition, wildtype Anx5 treatment, but not Anx5 mutant, improved trans-endothelial electrical resistance (p < 0.05) and reduced monocyte (p < 0.001) and platelet (p < 0.001) adhesion to vascular endothelial cells in septic conditions. In conclusion, recombinant human Anx5 inhibits endothelial inflammation induced by activated platelets and MVs in septic conditions via phosphatidylserine binding, which may contribute to its anti-inflammatory effects in the treatment of sepsis.

The association between PCSK9 inhibitor use and sepsis - A systematic review and meta-analysis of 20 double-blind, randomized, placebo-controlled trials

AIMS

To determine the impact of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor use on incident sepsis and other severe infections.

METHODS

We searched PubMed, EMBASE, CENTRAL and ClinicalTrial.gov up to September 14, 2021 for double-blind, placebo-controlled randomized trials of alirocumab, evolocumab, or inclisiran with >100 participants in each arm and report of serious adverse events related to infection. Data were synthesized with the fixed-effect Mantel-Haenszel model to generate risk ratios (RRs) with 95% confidence intervals (CIs) of each outcome for PCSK9 inhibitor versus placebo. Main outcome was sepsis. Other outcomes were total severe infections, severe bacterial and viral infections, and severe organ system-specific infections including respiratory tract, gastrointestinal, and genitourinary tract infections.

RESULTS

Twenty studies of 64,984 participants were included (alirocumab: n=7; evolocumab: n=9; inclisiran: n=4). Sepsis was reported in 292 (0.51%) participants from 11 trials (PCSK9 inhibitor 0.47%; placebo 0.56%). PCSK9 inhibitor use was not associated with risk of sepsis compared with placebo (Summary RR: 0.85, 95%CI: 0.67-1.07, P=.16); nor was it associated with any severe infection (0.96, 95% CI 0.89-1.03), severe bacterial (0.96, 95%CI 0.81-1.14) and viral infections (1.03, 95%CI 0.78-1.37); nor with any severe organ system-specific infection (all P values >0.05). The between-study heterogeneity in all analyses was small.

CONCLUSION

There was neither a beneficial nor a harmful association between PCSK9 inhibitors and risk of sepsis or severe infections. These findings provide reassurance regarding the safety of PCSK9 inhibitors in patients who are concerned about potential drug side effects related to infections.

Identification of biomarkers and the mechanisms of multiple trauma complicated with sepsis using metabolomics

Sepsis after trauma increases the risk of mortality rate for patients in intensive care unit (ICUs). Currently, it is difficult to predict outcomes in individual patients with sepsis due to the complexity of causative pathogens and the lack of specific treatment. This study aimed to identify metabolomic biomarkers in patients with multiple trauma and those with multiple trauma accompanied with sepsis. Therefore, the metabolic profiles of healthy persons designated as normal controls (NC), multiple trauma patients (MT), and multiple trauma complicated with sepsis (MTS) (30 cases in each group) were analyzed with ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS)-based untargeted plasma metabolomics using collected plasma samples. The differential metabolites were enriched in amino acid metabolism, lipid metabolism, glycometabolism and nucleotide metabolism. Then, nine potential biomarkers, namely, acrylic acid, 5-amino-3-oxohexanoate, 3b-hydroxy-5-cholenoic acid, cytidine, succinic acid semialdehyde, PE [P-18:1(9Z)/16:1(9Z)], sphinganine, uracil, and uridine, were found to be correlated with clinical variables and validated using receiver operating characteristic (ROC) curves. Finally, the three potential biomarkers succinic acid semialdehyde, uracil and uridine were validated and can be applied in the clinical diagnosis of multiple traumas complicated with sepsis.

Acute glycemic variability and risk of mortality in patients with sepsis: a meta-analysis

BACKGROUND

Acute glycemic variability (GV) has been correlated with the severity of sepsis. The aim of the study was to evaluate the potential association between acute GV and mortality risk in patients with sepsis.

METHODS

Cohort studies comparing the risk of death within 3 months between septic patients with higher versus lower acute GV were retrieved by systematic search of Medline, Embase, Web of Science, Wanfang and CNKI databases. We used a random-effect model to pool the data by incorporating the between-study heterogeneity. Sensitivity analyses were performed to evaluate the stability of the findings.

RESULTS

Ten studies including 4296 patients were available for the meta-analysis. Pooled results showed that septic patients with higher acute GV had significantly increased mortality risk compared to those with lower acute GV, as evidenced by results using different parameters including standard deviation of blood glucose (SDBG, risk ratio [RR]: 1.74, 95% confidence interval [CI] 1.36-2.24, p < 0.001; I² = 0%), coefficient of variation of blood glucose (RR: 1.91, 95% CI 1.57-2.31, p < 0.001; I² = 0%), mean amplitude of glycemic excursion (RR: 1.81. 95% CI 1.36-2.40, p < 0.001; I² = 0%), and glycemic lability index (RR: 2.52, 95% CI 1.72-3.68, p < 0.001; I² = 0%). Sensitivity analyses by excluding one study at a time did not significantly affect the results (p all < 0.05).

CONCLUSIONS

Higher acute GV may be a predictor of mortality risk in patients with sepsis.

SAPS III is superior to SOFA for predicting 28-day mortality in sepsis patients based on Sepsis 3.0 criteria

INTRODUCTION

The discrimination and calibration accuracy of prediction models tends to become poor over time. The performance of predictive models should be reevaluated periodically. The aim of this study was to reassess the discrimination of the six commonly used models for predicting 28-day mortality in patients with sepsis based on the Sepsis 3.0 criteria.

METHODS

Patient data were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC IV) database. The systemic inflammatory response syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Logistic Organ Dysfunction System (LODS), and Simplified Acute Physiology Score II (SAPS II) and III (SAPS III) scores were calculated and collected. The area under the receiver operating characteristic curve (AUROC) was used to compare the discrimination abilities of the models using non-parametric Wilcoxon statistics. The Delong method was used to perform pairwise comparisons of the AUROCs of the models. Multiple subgroup analyses for age, body mass index, and sex were performed with regard to the 28-day mortality prediction of the models.

RESULTS

A total of 12 691 patients were included. The mean age of the patients was 65.97 ± 15.77 years; 7673 patients (60.50%) were male. The mean SIRS, SOFA, OASIS, SAPS II, LODS, and SAPS III scores were higher in the non-survivor group than in the survivor group. The discrimination for 28-day mortality with the SAPS III (AUROC 0.812, 95% confidence interval (CI) 0.802-0.822) and LODS (AUROC 0.804, 95% CI 0.743-0.765) models was superior to that of the SIRS (AUROC 0.575, 95% CI 0.562-0.589), SOFA (AUROC 0.612, 95% CI 0.598-0.626), OASIS (AUROC 0.753, 95% CI 0.742-0.764), and SAPS II (AUROC 0.754, 95% CI 0.743-0.765) models. The Youden index of the SAPS III model was 0.484, which was the highest among the models. Subgroup analyses showed similar results to the overall results.

CONCLUSIONS

The discrimination for 28-day mortality with the SAPS III and LODS models was superior to that of the SIRS, SOFA, OASIS, and SAPS II models. The SAPS III model showed the best discrimination capacity for 28-day mortality compared with the other models.

The optimal oral body temperature cutoff and other factors predictive of sepsis diagnosis in elderly patients

INTRODUCTION:

METHODS:

RESULTS:

CONCLUSION:

The effect of dobutamine in sepsis: a propensity score matched analysis

BACKGROUND

The use of dobutamine in patients with sepsis is questionable currently. As the benefit of dobutamine in septic patients is unclear, we aimed to evaluate whether the use of dobutamine was associated with decreased hospital mortality in sepsis patients.

METHODS

Based on the analysis of MIMIC III public database, we performed a big-data, real world study. According to the use of dobutamine or not, patients were categorized as the dobutamine group or non dobutamine group.We used propensity score matched (PSM) analysis to adjust for confoundings. The primary outcome was hospital mortality.

RESULTS

In the present study, after screening 38,605 patients, 2826 patients with sepsis were included. 121 patients were in dobutamine group and 2165 patients were in non dobutamine group. Compared with patients in non-dobutamine group, patients in dobutamine group had a lower MAP, higher HR, higher RR, higher severity of illness scores. 72 of 121 patients (59.5%) in the dobutamine group and 754 of 2165 patients (34.8%) in the non-dobutamine group died in the hospital, which resulted in a significant between-group difference (OR 1.56, 95% CI 1.01-2.40; P = 0.000). For the secondary outcomes, patients in dobutamine group received more MV use, more renal replacement therapy use, had longer ICU stay durations and more cardiac arrhythmias than those in non-dobutamine group. After adjusting for confoundings between groups by PSM analysis, hospital mortality was consistently higher in dobutamine group than that in non-dobutamine group (60.2% vs. 49.4%, OR 1.55, 95% CI 1.01-2.37; P = 0.044).

CONCLUSIONS

Among patients with sepsis, our study showed that the use of dobutamine was not associated with decreased hospital mortality. Further large scale, randomized controlled studies are warrented to confirm our findings.

Incidence, prognosis, and risk factors of sepsis-induced cardiomyopathy

BACKGROUND

At present, large-scale studies on the clinical characteristics of sepsis-induced cardiomyopathy (SIC) are lacking.

AIM

To investigate the clinical characteristics of SIC.

METHODS

Based on the analysis of the MIMIC-III public database, we performed a large-scale retrospective study involving sepsis patients who were admitted to the intensive care unit (ICU) and had no concomitant cardiac disease. We used propensity score matching analysis and multivariate logistic regression to ensure the robustness of the results. The primary outcome was hospital mortality, and the secondary outcomes included the number of patients who received mechanical ventilation or renal replacement therapy during their hospital stay, the number of patients administered with vasopressors, the length of ICU stay, and the length of hospital stay.

RESULTS

In the present study, after screening 38605 patients, 3530 patients with sepsis were included. A total of 997 patients met the SIC diagnostic criteria, and the incidence of SIC was 28.20% (95% confidence interval [CI]: 26.80%-29.70%). Compared to patients in the non-SIC group, patients in the SIC group were of older age and had a higher Simplified Acute Physiology Score (SAPS)-I score, SAPS-II score, and Elixhauser comorbidity index (ECI). A total of 367 (36.8%) of 997 patients in the SIC group and 818 (32.3%) of 2533 patients in the non-SIC group died in the hospital, which resulted in a significant between-group difference (odds ratios = 1.22, 95%CI: 1.05-1.42; P = 0.011). For the secondary outcomes, more patients in the SIC group received mechanical ventilation and vasopressors. Multivariate logistic regression analysis showed that age, male sex, ECI, hemoglobin level, diabetes, and mechanical ventilation use on the first day of ICU admission were risk factors for SIC.

CONCLUSION

Compared with non-SIC patients, hospital mortality is higher in SIC patients.

The effect of dobutamine vs milrinone in sepsis: A big data, real-world study

BACKGROUND

The use of dobutamine in patients with sepsis is questionable. Some studies reported milrinone was used as an alternative inotropic agent. We aim to evaluate whether milrinone is better than dobutamine in patients with sepsis.

METHODS

Based on the analysis of MIMIC III public database, we performed a big data, real-world study. According to the use of dobutamine or milrinone, patients were categorised as the dobutamine group or milrinone group. We used propensity score matched (PSM) analysis to adjust for confoundings. The primary outcome was hospital mortality.

RESULTS

In this study, after screening 38 605 patients, 235 patients with sepsis were included. One hundred and eighty-three patients were in the dobutamine group and 52 patients were in the milrinone group. For the primary outcome of hospital mortality, there was no significant between-group difference (73/183 in dobutamine group vs 23/52 in milrinone group, OR 0.84, 95% CI 0.45-1.56; P = .574). After adjusting for confoundings between groups by PSM analysis, hospital mortality was consistent with the overall result (50% vs 41.3%, OR 1.42, 95% CI 0.68-2.97; P = .349). For the secondary outcomes, more patients in milrinone group received RRT use (46.2% vs 22.4%, P = .001), had longer length of ICU stay (20.97 ± 22.84 days vs 11.10 ± 11.54 days, P = .004) and hospital stay (26.14 ± 25.13 days vs 14.51 ± 13.11 days, P = .002) than those in dobutamine group.

CONCLUSIONS

Compared with dobutamine, the use of milrinone did not decrease hospital mortality in patients with sepsis. Furthermore, milrinone was associated with more RRT therapy, longer length of ICU stay and hospital stay than dobutamine.

A prognostic model for 1-month mortality in the postoperative intensive care unit

PURPOSES

Recognizing which patients admitted postsurgically to the intensive care unit (ICU) are at greater risk of mortality assists medical staff to identify who will benefit most from the care. We developed a prediction model for the 1-month mortality of postsurgical ICU patients.

METHODS

From May, 2019 to May, 2020, we conducted a prospective cohort study in the postsurgical ICU of a teaching hospital affiliated with our University of Medical Sciences. The outcome was death within 1 month of admission and the predictors were a variety of anthropometric and clinical features. The subjects of this analysis were 805 consecutive adult postsurgical patients with a mean (SD) age of 54.8 (18.9) years.

RESULTS

Overall, the resulted logistic model was well-fitted [χ² (26) = 772.097, p < 0.001, Nagelkerke R² = 0.814] accurate (88%), and specific (92%). The adjusted odds ratio for body temperature was 0.51, p < 0.001. Patients with comorbidities and those undergoing multiple operations were at a greater risk of mortality, odds = 10.00 and 10.65 (both p < 0.001).

CONCLUSIONS

Higher body temperature at the time of postoperative ICU admission is a protective factor against 1-month mortality. Our study found that patients with several comorbidities and those who have undergone multiple operations are at a greater risk of a poor outcome.

The effects of Alzheimer's and Parkinson's disease on 28-day mortality of COVID-19

We compared the prognosis of inpatients with a known diagnosis of Alzheimer's or Parkinson's disease who have COVID-19 infection with other hospitalized patients with COVID-19. Our cohort study started in October 2020 and ended in May 2021 and included inpatients with COVID-19 infection who were admitted to hospitals. From a total of 67,871 patients with a confirmed diagnosis of COVID-19, a sample of 3732 individuals were selected of which 363 had Alzheimer's, and 259 had Parkinson's disease. All patients had both positive RT-PCR test and positive chest CT for COVID-19. The outcome was dead within 28 days of admission and the predictors were a large number of demographic and clinical features, and comorbidities recorded at patients’ bedside. Mortality were 37.5%, 35.1%, and 29.5% in patients with Alzheimer's disease, Parkinson's disease; and in other patients, respectively. The hazard ratio for Alzheimer's disease was 1.27 (95% CI, 1.06–1.53, p = 0.010) and for Parkinson's disease was 1.17 (95% CI, 0.94–1.46, p = 0.171). Age was a predictor of mortality, hazard ratio = 1.04 (95% CI, 1.03–1.05, p < 0.001). Patients with Alzheimer's disease and COVID-19 infection were older and more likely to have a loss of consciousness on admission (both p ≤ 0.001). We concluded that inpatients with Alzheimer's disease have an increased risk for 28-day mortality from COVID-19 and healthcare settings should be ready to provide critical care for them such as early intubation and immediate O₂ therapy. However, Parkinson's disease does not significantly predict higher mortality of COVID-19.

Development and internal validation of a simple prognostic score for early sepsis risk stratification in the emergency department

OBJECTIVES

No validated, simple, powerful and continuously monitorable risk prediction tools are available for patients with sepsis during the early phases in the emergency department (ED). We sought to derive a novel Simple Sepsis Early Prognostic Score (SSEPS) composed of physiological indicators that do not depend on laboratory tests and that can be used by emergency clinicians in predicting outcomes in patients with sepsis.

DESIGN

Retrospective cohort analysis of a collected data source.

PARTICIPANTS

Patients with sepsis admitted to the ED of the West China Hospital of Sichuan University between July 2015 and June 2016 were included. We excluded patients who were pregnant, those with cardiac or respiratory arrest, and those using vasoactive drugs before admission to the ED.

PRIMARY OUTCOME MEASURES

28-day all-cause mortality.

RESULTS

The SSEPS consisted of age, heart rate, respiratory rate and altered consciousness. Patients in the development cohort with higher SSEPS had a significantly higher mortality (first tertile vs second tertile vs third tertile: 12.5% vs 28.6% vs 53.5%, p<0.001). The area under the receiver operating characteristic curve for SSEPS was 0.762 (95% CI 0.686 to 0.838), which was similar to Sequential Organ Failure Assessment (SOFA) (area under the curve: 0.745, 95% CI 0.692 to 0.798) and Acute Physiology and Chronic Health Evaluation (APACHE II) (area under the curve: 0.750, 95% CI 0.681 to 0.819). Moreover, the decision curve analysis showed that the net benefit of SSEPS was higher than SOFA and APACHE II at any probability threshold.

CONCLUSION

The SSEPS is simple and useful for clinicians in stratifying high-risk patients with sepsis at the early phase of ED admission.

De Ritis Ratio as a Significant Prognostic Factor in Patients with Sepsis: A Retrospective Analysis

PURPOSE

This study was performed to investigate the relationship between the aspartate transaminase and/or alanine transaminase ratio (DRR) and long-term mortality of patients diagnosed with sepsis or septic shock.

MATERIALS AND METHODS

We conducted a retrospective study among adult septic patients who were admitted to the surgical intensive care unit (ICU) of the Chinese People's Liberation Army (PLA) General Hospital from January 2014 to December 2018. Baseline characteristics were compared between survivors and non survivors. We performed univariate and multivariate Cox regression analyses to evaluate the relation of DRR with 180-day mortality. The potential prognostic value of DRR in predicting mortality rate was assessed by receiver operating characteristic (ROC) curve analysis. In addition, we conducted subgroup analysis by the optimal DRR cutoff value.

RESULTS

We included a total of 183 patients in the current study, and 44 (24%) patients died within 180 days of hospitalization. Univariate and multivariate Cox analyses revealed that DRR was an independent predictor of 180-day mortality (hazard ratio [HR] 1.421, 95% confidence interval [CI] 1.073-1.883, P = 0.014). The predictive accuracy of DRR for 180-day mortality was presented as an ROC curve, which had an area under the curve (AUC) of 0.708 (95% CI 0.629-0.786, P < 0.001). After we stratified all enrolled patients into two groups by using the optimal cutoff value of 1.29, we observed a significantly higher mortality in patients with a relatively high DRR.

CONCLUSIONS

An elevated DRR was associated with higher 180-day mortality among septic patients, and DRR might be an optimal marker for predicting the long-term mortality of sepsis. More prospective and randomized trials are needed to confirm the prognostic value of DRR.

Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review

BACKGROUND AND OBJECTIVES

Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsis. This systematic review aims to identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis.

METHODS

This systematic review was registered in PROSPERO database (CRD42020158685). We conducted a systematic literature review across 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase. Quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adults in all care settings were eligible for inclusion.

RESULTS

Seventeen articles met our inclusion criteria. We identified 194 predictors that were used to train machine learning algorithms, with 13 predictors used on average across all included studies. The most prevalent predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60 mL/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30 %. All included studies used artificial intelligence techniques, with average sensitivity 75.7 ± 17.88, and average specificity 63.08 ± 22.01.

CONCLUSION

The type of predictors influenced the predictive power and predictive timeframe of the developed machine learning algorithm. Predicting the likelihood of sepsis through artificial intelligence can help concentrate finite resources to those patients who are most at risk. Future studies should focus on developing more sensitive and specific algorithms.

The Impact of Prophylactic Lacosamide on LPS-Induced Neuroinflammation in Aged Rats

Sepsis-induced central nervous system damage is called sepsis-associated encephalopathy (SAE). In addition to neuroinflammation, oxidative stress and apoptosis act in the development of SAE. In the current study, we evaluated the protective effects of lacosamide (LCM) on neuroinflammation induced by lipopolysaccharide (LPS). Twenty-four Wistar albino rats were divided into 3 groups as controls, LPS group (5 mg/kg i.p.), and LPS plus LCM group (5 mg/kg i.p and 40 mg/kg i.p, respectively). In the rat brain, LPS-induced tissue damage was revealed histopathologically as hyperemia and microhemorrhages. LCM pretreatment ameliorated these histopathological changes. LPS decreased brain TAS levels and significantly increased MDA, CRP, HSP, IL-1β, and TNF-α expressions in the cortex, hippocampus, and cerebellum. Western analysis revealed increased brain tissue levels of TNF-α, NF-Kβ, and caspase-3 following LPS. Prophylactic LCM treatment reversed these parameters including oxidative stress, inflammation, and apoptosis in the cortex, hippocampus, and cerebellum.

Risk prediction models for intensive care unit readmission: A systematic review of methodology and applicability

OBJECTIVE

We conducted a systematic review of primary models to predict intensive care unit (ICU) readmission.

REVIEW METHODS

We searched MEDLINE, PubMed, Scopus, and Embase for studies on the development of ICU readmission prediction models that are published until January 2017. Data were extracted on the source of data, participants, outcomes, candidate predictors, sample size, missing data, methods for model development, and measures of model performance and model evaluation. The quality and applicability of the included studies were assessed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.

RESULTS

We identified five studies describing the development of the primary prediction models of ICU readmission. Studies ranged in size from 343 to 704,963 patients with the mean age of 58.0-68.9 years. The proportion of readmission ranged from 2.5% to 9.6%. The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.66-0.81. None of the studies performed external validations. The quality scores ranged from 42 to 54 out of 62, and the applicability scores from 24 to 32 out of 38.

CONCLUSION

We identified five prediction models for ICU readmission. However, owing to the numerous methodological and reporting deficiencies in the included studies, physicians using these models should interpret the predictions with precautions until an external validation study shows the acceptable level of calibration and accuracy of these models.