Diagnostic accuracy of time to first positivity of blood cultures for predicting severe clinical outcomes in children with pneumonia-related bacteremia

Biomarker-based score for predicting in-hospital mortality of children admitted to the intensive care unit

This study aims to establish a new scoring system based on biomarkers for predicting in-hospital mortality of children admitted to the pediatric intensive care unit (PICU). The biomarkers were chosen using the least absolute shrinkage and selection operator (LASSO)-logistic regression in this observational case-control study. The performance of the new predictive model was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration plot was established to validate the new score accompanied by the Hosmer-Lemeshow test. There were 8818 patients included in this study. Finally, six predictors were included in the LASSO-regression model. Albumin <40 g/L, lactate dehydrogenase >452 U/L, lactate >3.2 mmol/L, urea >5.6 mmol/L, arterial PH <7.3 and glucose >6.9 mmol/L were treated as risk factors for higher mortality. The new score ranged from 1 to 6 among all the included patients. In the training set, the AUC of the probability of in-hospital mortality for the new predictive model was 0.81 (95% CI 0.79 to 0.84), which is larger than for the Pediatric Critical Illness Score (PCIS) (0.69, 95% CI 0.66 to 0.72). Similarly, in the validating set, the AUC of the probability of in-hospital mortality was larger for the new score (0.80, 95% CI 0.77 to 0.84) than for PCIS (0.67, 95% CI 0.63 to 0.72). The calibration plot and Hosmer-Lemeshow test showed excellent calibration. The calculated ORs showed a trend that higher scores indicated higher risk of death (p value for trend <0.001). In summary, this study develops and validates a totally biomarker-based new score to predict in-hospital mortality for pediatric patients admitted to PICU. More attention and more positive care and treatment should be given to children with a higher score.

Comparison of a new predictive model with other critical scores for predicting in-hospital mortality among children with pneumonia-related bacteremia

Prediction of mortality in children with pneumonia-related bacteremia is necessary for providing timely care and treatment. This study aims to develop and validate a nomogram and compare it with Pediatric Risk of Mortality III (PRISM III), Brighton Pediatric Early Warning Score (Brighton PEWS) and Pediatric Critical Illness Score (PCIS), which are widely used in predicting in-hospital mortality in children with pneumonia-related bacteremia. This retrospective study collected clinical data of hospitalized children with pneumonia-related bacteremia in Chongqing, China (January 2013-May 2019). The nomogram was built using multivariate logistic regression analysis. The nomogram was compared with PRISM III, PEWS and PCIS in accuracy and clinical benefits in predicting in-hospital mortality in children with pneumonia-related bacteremia. A total of 242 children were included. The nomogram including time to first positivity of blood cultures (TTFP), serum albumin (ALB) and lactate dehydrogenase (LDH) was established. The area under the receiver operating characteristic curve of the nomogram was 0.84 (95% CI 0.77 to 0.91) in the training set and 0.82 (95% CI 0.71 to 0.93) in the validating set. Good consistency was observed between the predictions and the actual observations, and the decision curve analysis showed that the nomogram was clinically useful. The results showed that the nomogram significantly performed better than the three critical scores. In conclusion, a nomogram-illustrated model incorporating TTFP, ALB and LDH for predicting in-hospital mortality in children with pneumonia-related bacteremia at the early stage was established and validated. It performed better than PRISM III, PEWS and PCIS.

Hypoalbuminemia as Surrogate and Culprit of Infections

Hypoalbuminemia is associated with the acquisition and severity of infectious diseases, and intact innate and adaptive immune responses depend on albumin. Albumin oxidation and breakdown affect interactions with bioactive lipid mediators that play important roles in antimicrobial defense and repair. There is bio-mechanistic plausibility for a causal link between hypoalbuminemia and increased risks of primary and secondary infections. Serum albumin levels have prognostic value for complications in viral, bacterial and fungal infections, and for infectious complications of non-infective chronic conditions. Hypoalbuminemia predicts the development of healthcare-associated infections, particularly with Clostridium difficile. In coronavirus disease 2019, hypoalbuminemia correlates with viral load and degree of acute lung injury and organ dysfunction. Non-oncotic properties of albumin affect the pharmacokinetics and pharmacodynamics of antimicrobials. Low serum albumin is associated with inadequate antimicrobial treatment. Infusion of human albumin solution (HAS) supplements endogenous albumin in patients with cirrhosis of the liver and effectively supported antimicrobial therapy in randomized controlled trials (RCTs). Evidence of the beneficial effects of HAS on infections in hypoalbuminemic patients without cirrhosis is largely observational. Prospective RCTs are underway and, if hypotheses are confirmed, could lead to changes in clinical practice for the management of hypoalbuminemic patients with infections or at risk of infectious complications.

Analysis of Risk Factors and Clinical Indicators in Bloodstream Infections Among Patients with Hematological Malignancy

Purpose

The incidence of bloodstream infection (BSI) is more common in patients with hematological malignancy. It is important to distinguish infectious episodes from noninfectious episodes. The present study was aimed to describe the epidemiology, clinical indexes, and antibiotic use for in-hospital bloodstream infections of hematological malignancy patients.

Patients and Methods

Single-center retrospective research was performed on hematological malignancy patients admitted to our hospital from July 2015 to March 2018. Laboratory and clinical information from 322 febrile patients were acquired. These episodes were divided by blood culture results into two groups: 1) blood culture positive-group, 2) blood culture negative-group.

Results

In the 322 febrile cases, 81 (25.2%) patients were blood culture positive, and among them, Gram-negative (G-) bacteria (51.9%) were more isolated than Gram-positive (G+) bacteria (32.1%) and fungi (7.4%). Gram-negative bacteria were more likely to have drug resistance than G+ bacteria. Independent risk factors revealed that patients with complications, high levels of procalcitonin (PCT), glucose, interleukin-6 (IL-6), and d-dimer (D-D), and low concentration of albumin were correlated with the occurrence of BSI. PCT, IL-6 and D-D performed well in differentiating the positive group from the negative group. Moreover, IL-6 and D-D showed excellent performance in differentiating G- and G+ groups, with the areas under the curve all above 0.8.

Conclusion

We analyzed the risk factors for BSI in patients with hematological malignancy, the distribution of bacteria, antibiotic resistance, and the changes in clinical parameters. This single-center retrospective study may provide clinicians insight into the diagnosis and treatment of BSI.