Evidence map›Paper›PMID 34302546›Full record

ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2022

A TTP-incorporated scoring model for predicting mortality of solid tumor patients with bloodstream infection caused by Escherichia coli.

Qing Zhang, Hao-Yang Gao, Ding Li, Chang-Sen Bai, Zheng Li, Shan Zheng, Wen-Fang Zhang, Yun-Li Zhou, Si-He Zhang

Open access · hybridAbstract read
In one paragraph

Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 53% of its field
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed, 1 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Qing ZhangMedical Laboratory Department, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Hao-Yang GaoMedical Laboratory Department, Affiliated Hospital of Gansu University of Chinese Medicine, Gansu, China.
Ding LiMedical Laboratory Department, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Chang-Sen BaiMedical Laboratory Department, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Zheng LiMedical Laboratory Department, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Shan ZhengMedical Laboratory Department, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Wen-Fang ZhangMedical Laboratory Department, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Yun-Li ZhouMedical Laboratory Department, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Si-He ZhangDepartment of Cell Biology, School of Medicine, Nankai University, 94 Weijin Road, Nankai District, Tianjin, 300071, People's Republic of China. sihezhang@nankai.edu.cn.ORCID http://orcid.org/0000-0002-8923-1993
Tianjin Medical University Cancer Institute and Hospital · CNGansu University of Traditional Chinese Medicine · CNNankai University · CN

Funding

Key Research and Development Program of Tianjin Municipal Science and Technology Commission 20YFZCSY00450Nankai-Cangzhou NCC Fund Project NCC2020PY20Youth Innovation Promotion Association of the Chinese Academy of Sciences (CN) CPAYLJ202003
6 · The paper itself

Abstract

backgroundFew mortality-scoring models are available for solid tumor patients who are predisposed to develop Escherichia coli-caused bloodstream infection (ECBSI). We aimed to develop a mortality-scoring model by using information from blood culture time to positivity (TTP) and other clinical variables.

methodsA cohort of solid tumor patients who were admitted to hospital with ECBSI and received empirical antimicrobial therapy was enrolled. Survivors and non-survivors were compared to identify the risk factors of in-hospital mortality. Univariable and multivariable regression analyses were adopted to identify the mortality-associated predictors. Risk scores were assigned by weighting the regression coefficients with corresponding natural logarithm of the odds ratio for each predictor.

resultsSolid tumor patients with ECBSI were distributed in the development and validation groups, respectively. Six mortality-associated predictors were identified and included in the scoring model: acute respiratory distress (ARDS), TTP ≤ 8 h, inappropriate antibiotic therapy, blood transfusion, fever ≥ 39 °C, and metastasis. Prognostic scores were categorized into three groups that predicted mortality: low risk (< 10% mortality, 0-1 points), medium risk (10-20% mortality, 2 points), and high risk (> 20% mortality, ≥ 3 points). The TTP-incorporated scoring model showed excellent discrimination and calibration for both groups, with AUC being 0.833 vs 0.844, respectively, and no significant difference in the Hosmer-Lemeshow test (6.709, P = 0.48) and the chi-square test (6.993, P = 0.46). Youden index showed the best cutoff value of ≥ 3 with 76.11% sensitivity and 79.29% specificity. TTP-incorporated scoring model had higher AUC than no TTP-incorporated model (0.837 vs 0.817, P < 0.01).

conclusionsOur TTP-incorporated scoring model was associated with improving capability in predicting ECBSI-related mortality. It can be a practical tool for clinicians to identify and manage bacteremic solid tumor patients with high risk of mortality.

Indexed as

NeoplasmsSepsisEscherichia coliHospital MortalityHumansPrognosisRetrospective StudiesRisk FactorsBacteremiaRisk factorScoring modelSolid tumorsTime to positivity

Identifiers

PMID34302546
PMCPMC8636427
OpenAlexW3183531031

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.