A Transcriptomic Severity Metric that Predicts Clinical Outcomes in Critically Ill Surgical Sepsis Patients

Brakenridge, Scott C.; Starostik, Petr; Ghita, Gabriella; Midic, Uros; Darden, Dijoia B; Fenner, Brittany; Wacker, James; Efron, Philip A.; Liesenfeld, Oliver; Sweeney, Timothy E.; Moldawer, Lyle L.

doi:10.1101/2021.05.28.21258014

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…EAS electronic alert system, SD standard deviation. The p-value was calculated using the indicated statistics: www.nature.com/scientificreports/ Many challenges and limitations can be encountered during electronic alert development, including differences in adult and pediatric parameters, the presentation timing, and the variable phenotypes of sepsis and the underpinning genomics subgroups 33,[36][37][38][39] . For instance, compared to adults, pediatric sepsis recognition is challenging due to the low specificity of tachycardia and tachypnea and the relatively late manifestation of hypotension 38 .…”

Section: Discussionmentioning

confidence: 99%

Impact of an electronic alert system for pediatric sepsis screening a tertiary hospital experience

Alturki

Al‐Eyadhy

Alfayez

et al. 2022

Sci Rep

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This study aimed to assess the potential impact of implementing an electronic alert system (EAS) for systemic inflammatory syndrome (SIRS) and sepsis in pediatric patients mortality. This retrospective study had a pre and post design. We enrolled patients aged ≤ 14 years who were diagnosed with sepsis/severe sepsis upon admission to the pediatric intensive care unit (PICU) of our tertiary hospital from January 2014 to December 2018. We implemented an EAS for the patients with SIRS/sepsis. The patients who met the inclusion criteria pre-EAS implementation comprised the control group, and the group post-EAS implementation was the experimental group. Mortality was the primary outcome, while length of stay (LOS) and mechanical ventilation in the first hour were the secondary outcomes. Of the 308 enrolled patients, 147 were in the pre-EAS group and 161 in the post-EAS group. In terms of mortality, 44 patients in the pre-EAS group and 28 in the post-EAS group died (p 0.011). The average LOS in the PICU was 7.9 days for the pre-EAS group and 6.8 days for the post-EAS group (p 0.442). Considering the EAS initiation time as the “zero time”, early recognition of SIRS and sepsis via the EAS led to faster treatment interventions in post-EAS group, which included fluid boluses with median (25th, 75th percentile) time of 107 (37, 218) min vs. 30 (11,112) min, p < 0.001) and time to initiate antimicrobial therapy median (25th, 75th percentile) of 170.5 (66,320) min vs. 131 (53,279) min, p 0.042). The difference in mechanical ventilation in the first hour of admission was not significant between the groups (25.17% vs. 24.22%, p 0.895). The implementation of the EAS resulted in a statistically significant reduction in the mortality rate among the patients admitted to the PICU in our study. An EAS can play an important role in saving lives and subsequent reduction in healthcare costs. Further enhancement of systematic screening is therefore highly recommended to improve the prognosis of pediatric SIRS and sepsis. The implementation of the EAS, warrants further validation in multicenter or national studies.

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Section: Discussionmentioning

confidence: 99%

Impact of an electronic alert system for pediatric sepsis screening a tertiary hospital experience

Alturki

Al‐Eyadhy

Alfayez

et al. 2022

Sci Rep

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“…Recent studies have also demonstrated the potential of measuring expression of targeted mRNA transcripts from patient blood to diagnose a range of infectious diseases [38, 42, 1, 2]. Development of these host response signatures requires first identifying those genes strongly associated with a condition of interest in one or multiple studies.…”

Section: Introductionmentioning

confidence: 99%

Towards Equitable Patient Subgroup Performance by Gene-Expression-Based Diagnostic Classifiers of Acute Infection

Mayhew¹,

Midic²,

Choi³

et al. 2022

Preprint

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Host-response gene expression measurements may carry confounding associations with patient demographic characteristics that can induce bias in downstream classifiers. Assessment of deployed machine learning systems in other domains has revealed the presence of such biases and exposed the potential of these systems to cause harm. Such an assessment of a gene-expression-based classifier has not been carried out and collation of requisite patient subgroup data has not been undertaken. Here, we present data resources and an auditing framework for patient subgroup analysis of diagnostic classifiers of acute infection. Our dataset comprises demographic characteristics of nearly 6500 patients across 49 studies. We leverage these data to detect differences across patient subgroups in terms of gene-expression-based host response and performance with both our candidate pre-market diagnostic classifier and a standard-of-care biomarker of acute infection. We find evidence of variable representation with respect to patient covariates in our multi-cohort datasets as well as differences in host-response marker expression across patient subgroups. We also detect differences in performance of multiple host-response-based diagnostics for acute infection. This analysis marks an important first step in our ongoing efforts to characterize and mitigate potential bias in machine learning-based host-response diagnostics, highlighting the importance of accounting for such bias in developing diagnostic tests that generalize well across diverse patient populations.

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“…Although there are several validated tools such as Acute Physiology and Chronic Health Evaluation (APACHE) IV and Sequential Organ Failure Assessment (SOFA) that have been used to predict sepsis mortality, there is no equivalent that predicts disease progression to more severe forms of infection among hospitalized patients without regard to mortality (4). Previous studies have attempted to use clinical methods to develop predictive models for sepsis progression (5)(6)(7)(8)(9)(10). Although there have been encouraging initial results in these studies, predictive models have not yet been demonstrated to be sufficiently generalizable to have clinical utility.…”

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confidence: 99%

Host Gene Expression to Predict Sepsis Progression*

Fiorino

Liu

Henao

et al. 2022

Critical Care Medicine

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OBJECTIVES: Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality. DESIGN:Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data. PATIENTS:Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study. MEASUREMENTS AND MAIN RESULTS:Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures. CONCLUSIONS:Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility.

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A Transcriptomic Severity Metric that Predicts Clinical Outcomes in Critically Ill Surgical Sepsis Patients

Cited by 3 publications

References 33 publications

Impact of an electronic alert system for pediatric sepsis screening a tertiary hospital experience

Impact of an electronic alert system for pediatric sepsis screening a tertiary hospital experience

Towards Equitable Patient Subgroup Performance by Gene-Expression-Based Diagnostic Classifiers of Acute Infection

Host Gene Expression to Predict Sepsis Progression*

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