Development and validation of BLOOMY prediction scores for 14-day and 6-month mortality in hospitalised adults with bloodstream infections: a multicentre, prospective, cohort study.

医学 前瞻性队列研究 队列 逻辑回归 比例危险模型 队列研究 儿科 血流感染 内科学
作者
Evelina Tacconelli,Siri Göpel,Beryl P Gladstone,Simone Eisenbeis,Florian Hölzl,Michael Buhl,Anna Górska,Chiara Cattaneo,Alexander Mischnik,Siegbert Rieg,Anna M Rohde,Britta Kohlmorgen,Jane Falgenhauer,Janina Trauth,Nadja Käding,Evelyn Kramme,Lena M Biehl,Sarah V Walker,Silke Peter,Petra Gastmeier,Trinad Chakraborty,Maria Jgt Vehreschild,Harald Seifert,Jan Rupp,Winfried V Kern
出处
期刊:Lancet Infectious Diseases [Elsevier]
卷期号:22 (5): 731-741
标识
DOI:10.1016/s1473-3099(21)00587-9
摘要

The burden of bloodstream infections remains high worldwide and cannot be confined to short-term in-hospital mortality. We aimed to develop scores to predict short-term and long-term mortality in patients with bloodstream infections.The Bloodstream Infection due to Multidrug-resistant Organisms: Multicenter Study on Risk Factors and Clinical Outcomes (BLOOMY) study is a prospective, multicentre cohort study at six German tertiary care university hospitals to develop and validate two scores assessing 14-day and 6-month mortality in patients with bloodstream infections. We excluded patients younger than 18 years or who were admitted to an ophthalmology or psychiatry ward. Microbiological, clinical, laboratory, treatment, and survival data were prospectively collected on day 0 and day 3 and then from day 7 onwards, weekly. Participants were followed up for 6 months. All patients in the derivation cohort who were alive on day 3 were included in the analysis. Predictive scores were developed using logistic regression and Cox proportional hazards models with a machine-learning approach. Validation was completed using the C statistic and predictive accuracy was assessed using sensitivity, specificity, and predictive values.Between Feb 1, 2017, and Jan 31, 2019, 2568 (61·5%) of 4179 eligible patients were recruited into the derivation cohort. The in-hospital mortality rate was 23·75% (95% CI 22·15-25·44; 610 of 2568 patients) and the 6-month mortality rate was 41·55% (39·54-43·59; 949 of 2284). The model predictors for 14-day mortality (C statistic 0·873, 95% CI 0·849-0·896) and 6-month mortality (0·807, 0·784-0·831) included age, body-mass index, platelet and leukocyte counts, C-reactive protein concentrations, malignancy (ie, comorbidity), in-hospital acquisition, and pathogen. Additional predictors were, for 14-day mortality, mental status, hypotension, and the need for mechanical ventilation on day 3 and, for 6-month mortality, focus of infection, in-hospital complications, and glomerular filtration rate at the end of treatment. The scores were validated in a cohort of 1023 patients with bloodstream infections, recruited between Oct 9, 2019, and Dec 31, 2020. The BLOOMY 14-day score showed a sensitivity of 61·32% (95% CI 51·81-70·04), a specificity of 86·36% (83·80-88·58), a positive predictive value (PPV) of 37·57% (30·70-44·99), and a negative predictive value (NPV) of 94·35% (92·42-95·80). The BLOOMY 6-month score showed a sensitivity of 69·93% (61·97-76·84), a specificity of 66·44% (61·86-70·73), a PPV of 40·82% (34·85-47·07), and a NPV of 86·97% (82·91-90·18).The BLOOMY scores showed good discrimination and predictive values and could support the development of protocols to manage bloodstream infections and also help to estimate the short-term and long-term burdens of bloodstream infections.DZIF German Center for Infection Research.For the German translation of the abstract see Supplementary Materials section.

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