A Novel Risk Predictive Scoring Model for Predicting Subsequent Infection After Carbapenem-Resistant Gram-Negative Bacteria Colonization in Hematological Malignancy Patients.

Abstract

Background:

This study investigated the high-risk factors associated with the increased vulnerability for subsequent clinical CR-GNB infection in carbapenem-resistant Gram-negative bacteria (CR-GNB)-colonized hematological malignancy (HM) patients and built a statistical model to predict subsequent infection.

Method:

All adult HM patients with positive rectoanal swabs culture for CR-GNB between January 2018 and June 2020 were prospectively followed to assess for any subsequent CR-GNB infections and to investigate the risk factors and clinical features of subsequent infection.

Results:

A total of 392 HM patients were enrolled. Of them, 46.7% developed a subsequent clinical CR-GNB infection, with 42 (10.7%) cases of confirmed infection and 141 (36%) cases of clinically diagnosed infection. Klebsiella pneumoniae was the dominant species. The overall mortality rate of patients colonized and infected with CR-GNB was 8.6% and 43.7%. A multivariate analysis showed that remission induction chemotherapy and the duration of agranulocytosis, mucositis, and hypoalbuminemia were significant predictors of subsequent infection after CR-GNB colonization. According to our novel risk-predictive scoring model, the high-risk group were >3 times more likely to develop a subsequent infection in comparison with the low-risk group.

Conclusion:

Our risk-predictive scoring model can early and accurately predict a subsequent CR-GNB infection in HM patients with CR-GNB colonization. The early administration of CR-GNB-targeted empirical therapy in the high-risk group is strongly recommended to decrease their mortality.

• Authors: Qiuling Wu, Chenjing Qian, Hua Yin, Fang Liu, Yaohui Wu, Weiming Li, Linghui Xia, Ling Ma, Mei Hong
• Journal: Frontiers in oncology (Front Oncol) Vol. 12 Pg. 897479 ( 2022) ISSN: 2234-943X [Print] Switzerland
• PMID: 35651791 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Wu, Qian, Yin, Liu, Wu, Li, Xia, Ma and Hong.