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Infection

, Volume 46, Issue 4, pp 513–521 | Cite as

Prognostic factors and scoring model of hematological malignancies patients with bloodstream infections

  • Yishu Tang
  • Qian Cheng
  • Qing Yang
  • Jing Liu
  • Di Zhang
  • Wei Cao
  • Qingxia Liu
  • Tianyi Zhou
  • Huiqi Zeng
  • Li Zhou
  • QinJin Wang
  • Huan Wei
  • Xin Li
Original Paper
  • 102 Downloads

Abstract

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.

Keywords

Hematological malignancies Bloodstream infection Prognostic factors Scoring model 

Notes

Acknowledgements

We thank all those who helped us in this study; in particular, the Department of Hematology and the Department of Clinical Laboratory for making this study possible. We thank Qing Yang for checking the English version of the manuscript and Di Zhang, Wei Cao, Qingxia Liu for providing patients to the study.

Funding

This work was supported by the Fundamental Research Funds for the Central Universities of Central South University No.2016zzts151 and No.2016zzts567, and the Education Reform Program for Central South University No. 2016JGB20.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest. All authors meet the ICMJE authorship criteria.

Ethical approval

This retrospective chart review study was evaluated by the Ethics Committee of the Central South University and deemed exempt from a formal review as no personally identifiable information would be collected.

Informed consent

The requirement for informed consent from patients was also waived.

Supplementary material

15010_2018_1151_MOESM1_ESM.doc (67 kb)
Supplementary material 1 (DOC 67 KB)
15010_2018_1151_MOESM2_ESM.doc (76 kb)
Supplementary material 2 (DOC 75 KB)
15010_2018_1151_MOESM3_ESM.doc (302 kb)
Supplementary material 3 (DOC 302 KB)
15010_2018_1151_MOESM4_ESM.doc (239 kb)
Supplementary material 4 (DOC 239 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yishu Tang
    • 1
  • Qian Cheng
    • 1
  • Qing Yang
    • 2
  • Jing Liu
    • 1
  • Di Zhang
    • 3
  • Wei Cao
    • 4
  • Qingxia Liu
    • 5
  • Tianyi Zhou
    • 1
  • Huiqi Zeng
    • 1
  • Li Zhou
    • 3
  • QinJin Wang
    • 3
  • Huan Wei
    • 3
  • Xin Li
    • 1
  1. 1.Department of Hematology, The Third Xiangya HospitalCentral South UniversityChangshaChina
  2. 2.Department of MedicineYale New Haven HospitalNew HavenUSA
  3. 3.Department of Clinical Laboratory, The Third Xiangya HospitalCentral South UniversityChangshaChina
  4. 4.Department of Clinical Laboratory, The Second Xiangya HospitalCentral South UniversityChangshaChina
  5. 5.Department of Clinical Laboratory, Xiangya HospitalCentral South UniversityChangshaChina

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