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Prediction of multiple infections after severe burn trauma: a prospective cohort study.

ABSTRACT: To develop predictive models for early triage of burn patients based on hypersusceptibility to repeated infections.Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking.Secondary analysis of 459 burn patients (?16 years old) with 20% or more total body surface area burns recruited from 6 US burn centers. We compared blood transcriptomes with a 180-hour cutoff on the injury-to-transcriptome interval of 47 patients (?1 infection episode) to those of 66 hypersusceptible patients [multiple (?2) infection episodes (MIE)]. We used LASSO regression to select biomarkers and multivariate logistic regression to built models, accuracy of which were assessed by area under receiver operating characteristic curve (AUROC) and cross-validation.Three predictive models were developed using covariates of (1) clinical characteristics; (2) expression profiles of 14 genomic probes; (3) combining (1) and (2). The genomic and clinical models were highly predictive of MIE status [AUROCGenomic = 0.946 (95% CI: 0.906-0.986); AUROCClinical = 0.864 (CI: 0.794-0.933); AUROCGenomic/AUROCClinical P = 0.044]. Combined model has an increased AUROCCombined of 0.967 (CI: 0.940-0.993) compared with the individual models (AUROCCombined/AUROCClinical P = 0.0069). Hypersusceptible patients show early alterations in immune-related signaling pathways, epigenetic modulation, and chromatin remodeling.Early triage of burn patients more susceptible to infections can be made using clinical characteristics and/or genomic signatures. Genomic signature suggests new insights into the pathophysiology of hypersusceptibility to infection may lead to novel potential therapeutic or prophylactic targets.

SUBMITTER: Yan S

PROVIDER: S-EPMC4284150 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Prediction of multiple infections after severe burn trauma: a prospective cohort study.

Yan Shuangchun S Tsurumi Amy A Que Yok-Ai YA Ryan Colleen M CM Bandyopadhaya Arunava A Morgan Alexander A AA Flaherty Patrick J PJ Tompkins Ronald G RG Rahme Laurence G LG

Annals of surgery 20150401 4

<h4>Objective</h4>To develop predictive models for early triage of burn patients based on hypersusceptibility to repeated infections.<h4>Background</h4>Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking.<h4>Methods</h4>Secondary analysis of 459 burn patients (≥16 years old) with 20% or more total body surface area burns recruited from 6 US burn centers. We compared blood transc ...[more]

PMID: 24950278

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Project description:BackgroundThrough improvements in trauma care there has been a decline in injury mortality, as more people survive severe trauma. Patients who survive severe trauma are at risk of long-term disabilities which may place a high economic burden on society. The purpose of this study was to estimate the health care and productivity costs of severe trauma patients up to 24 months after sustaining the injury. Furthermore, we investigated the impact of injury severity level on health care utilization and costs and determined predictors for health care and productivity costs.MethodsThis prospective cohort study included adult trauma patients with severe injury (ISS≥16). Data on in-hospital health care use, 24-month post-hospital health care use and productivity loss were obtained from hospital registry data and collected with the iMTA Medical Consumption and Productivity Cost Questionnaire. The questionnaires were completed 1 week and 1, 3, 6, 12 and 24 months after injury. Log-linked gamma generalized linear models were used to investigate the drivers of health care and productivity costs.ResultsIn total, 174 severe injury patients were included in this study. The median age of participants was 55 years and the majority were male (66.1%). The mean hospital stay was 14.2 (SD = 13.5) days. Patients with paid employment returned to work 21 weeks after injury. In total, the mean costs per patient were €24,760 with in-hospital costs of €11,930, post-hospital costs of €7,770 and productivity costs of €8,800. Having an ISS ≥25 and lower health status were predictors of high health care costs and male sex was associated with higher productivity costs.ConclusionsBoth health care and productivity costs increased with injury severity, although large differences were observed between patients. It is important for decision-makers to consider not only in-hospital health care utilization but also the long-term consequences and associated costs related to rehabilitation and productivity loss.

Project description:The purpose of the study was to assess the patterns of global gene expression in peripheral blood cells and uncover the complex dynamics of host response to ARIs such as pandemic H1N1. To understand the molecular bases and network orchestration of host responses, we prospectively enrolled 1610 healthy adults in the fall of 2009 and 2010, followed the subjects with influenza-like illness (N=133) for 3 weeks, and examined changes in their peripheral blood gene expression. We discovered distinct phases of the host response spanning 6 days after infection, and identified genes that differentiate influenza from non-influenza virus infection. We examined pre- and post-infection anti-influenza antibody titers defining novel gene expression correlates. Healthy adults were invited to enroll to be followed for acute respiratory illness (ARI) through two consecutive influenza seasons 2009-2010 and 2010-2011. After subjects provided consent, baseline blood specimens were obtained during enrollment. Subjects were given thermometers and instructions to call and report for evaluation within 48 hours of ARI onset. Those persons who had a new ARI were seen within 48 hours of onset (day 0) and 2, 4, and 6 days later for repeat evaluation, blood specimen collections, and medical care (including the antiviral zanamivir if indicated) and 21 days later for collection of convalescent specimens. Nasal wash samples were collected for virus detection by RT-PCR on day 0 and day 2. Surveillance for influenza was terminated after 5.5 months; all subjects were asked to return for specimen collection and to provide a medical and ARI history in spring of next year. Serum specimens obtained at baseline, day 0 and day 21 visits for illnesses, and the terminal visit were tested simultaneously using hemagglutination-inhibition (HAI) antibody assay for Influenza H1N1, H3N2, and Influenza B strains. Peripheral blood RNA (PaxGene) obtained from blood specimens at each visit were analyzed using Illumina Human HT-12 v4. The study was repeated 2010-2011. A total of 880 arrays, corresponding to 133 individuals, passed quality control and are included in this data set.

Project description:Objective: To assess the predictive value of symptoms, sociodemographic characteristics, and SARS-CoV-2 exposure in household, school, and community setting for SARS-CoV-2 seropositivity in Swiss schoolchildren at two time points in 2020. Design: Serological testing of children in primary and secondary schools (aged 6-13 and 12-16 years, respectively) took place in June-July (T1) and October-November (T2) 2020, as part of the longitudinal, school-based study Ciao Corona in the canton of Zurich, Switzerland. Information on sociodemographic characteristics and clinical history was collected with questionnaires to parents; information on school-level SARS-CoV-2 infections was collected with questionnaires to school principals. Community-level cumulative incidence was obtained from official statistics. We used logistic regression to identify individual predictors of seropositivity and assessed the predictive performance of symptom- and exposure-based prediction models. Results: A total of 2,496 children (74 seropositive) at T1 and 2,152 children (109 seropositive) at T2 were included. Except for anosmia (odds ratio 15.4, 95% confidence interval [3.4-70.7]) and headache (2.0 [1.03-3.9]) at T2, none of the individual symptoms were significantly predictive of seropositivity at either time point. Of all the exposure variables, a reported SARS-CoV-2 case in the household was the strongest predictor for seropositivity at T1 (12.4 [5.8-26.7]) and T2 (10.8 [4.5-25.8]). At both time points, area under the receiver operating characteristic curve was greater for exposure-based (T1, 0.69; T2, 0.64) than symptom-based prediction models (T1, 0.59; T2, 0.57). Conclusions: In children, retrospective identification of past SARS-CoV-2 infections based on symptoms is imprecise. SARS-CoV-2 seropositivity is better predicted by factors of SARS-CoV-2 exposure, especially reported SARS-CoV-2 cases in the household. Predicting SARS-CoV-2 seropositivity in children in general is challenging, as few reliable predictors could be identified. For an accurate retrospective identification of SARS-CoV-2 infections in children, serological tests are likely indispensable. Trial registration number: NCT04448717.

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Prediction of multiple infections after severe burn trauma: a prospective cohort study.

Publications

Prediction of multiple infections after severe burn trauma: a prospective cohort study.

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