Yishu Tang1, Qian Cheng1, Qing Yang2, Jing Liu1, Di Zhang3, Wei Cao4, Qingxia Liu5, Tianyi Zhou1, Huiqi Zeng1, Li Zhou3, QinJin Wang3, Huan Wei3, Xin Li6. 1. Department of Hematology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China. 2. Department of Medicine, Yale New Haven Hospital, New Haven, CT, USA. 3. Department of Clinical Laboratory, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China. 4. Department of Clinical Laboratory, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China. 5. Department of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha, Hunan, China. 6. Department of Hematology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China. lixiner1975@163.com.
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.
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.
Entities:
Keywords:
Bloodstream infection; Hematological malignancies; Prognostic factors; Scoring model
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