Related Experiment Video
Updated: Feb 10, 2026

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Prognostic factors and scoring model of hematological malignancies patients with bloodstream infections
Yishu Tang1, Qian Cheng1, Qing Yang2
1Department of Hematology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
Purpose:
Patients with hematological malignancies (HMs) are at a higher risk for bloodstream infections (BSIs), which pose significant burden on morbidity and mortality. Better risk stratification helps in medical decision making, increasing efficiency and reducing economic burden. The aim of this study was to develop and validate a reliable prediction model which can be used to identify HM patients at higher risk for BSIs.
Methods:
We conducted a retrospective cohort study in three university-affiliated hospitals in Hunan Province, China, from January 2010 to April 2015. A total of 521 HMs patients with BSIs were finally included in this study and were divided into the derivation set and validation set. Survivors and non-survivors were compared to identify the predictors of 30-day mortality.
Results:
The multivariate analysis yielded the following significant mortality-related risk factors: age > 60 years (95% CI 1.047-5.474), relapsed or uncontrolled malignancy (95% CI 2.043-14.029), Pitt bacteremia score > 3 (95% CI 1.614-6.35), prolonged neutropenia (95% CI 1.181-5.824), use of vasopressors (95% CI 3.009-12.210), acute respiratory failure (95% CI 3.061-14.911), fungemia (95% CI 1.334-12.121), inadequate antibiotic treatment (95% CI 1.682-7.591), albumin < 30 g/L (95% CI 1.030-3.446), TBil > 34.2 µmol/L (95% CI 1.109-5.438). In both derivation and validation sets, our model showed reliable prediction value with areas under the receiver operating curve of 0.876 and 0.873.
Conclusions:
The risk factors in this study have the ability to identify patients with HMs and BSIs at high risk for mortality. Our model provides an excellent foundation for predicting 30-day morality in HM patients suffering from BSI and helps target high-risk patients for management decision making.
More Related Videos
Related Concept Videos
Factors Affecting the Risk of Infection
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
Factors Affecting Protein-Drug Binding: Patient-Related Factors
Age stands as a key determinant in protein-drug binding. Neonates, characterized by low albumin content, experience heightened concentrations of unbound drugs such as phenytoin and...
Introduction to z Scores
z scores...
Introduction to z Scores
z scores...
z Scores and Area Under the Curve
z Scores and Unusual Values
This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...

