Not all organ dysfunctions are created equal – Prevalence and mortality in sepsis

Capan, Müge; Hoover, Stephen; Ivy, Julie S.; Miller, Kristen; Arnold, Ryan; Collaborative, Scope

doi:10.1016/j.jcrc.2018.08.021

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…Applying the findings from this study regarding the organ system simultaneous dysfunctions and inflammatory biomarker has the potential to inform early detection of individuals at highest risk of inhospital death and facilitate the best opportunity for sepsis patients to receive timely care. This work is aligned with a recent study [37] showing that respiratory and renal system dysfunction caries a higher risk of death from sepsis than other organ systems. The use of non-traditional markers such as BUN as a measure for renal system dysfunction has shown great value when it occurs simultaneously with respiratory, cardiovascular or metabolic system dysfunction in differentiating sepsis non-survivors from survivors.…”

Section: Translation Of Findings Into Clinical Practicesupporting

confidence: 87%

“…Furthermore, BUN was the renal system failure biomarker with highest predictive value, and not the commonly described serum creatinine level. BUN biomarker as measure of renal system dysfunction within the sepsis context has been previously described [36,37], but has not yet been studied in temporal relationship to other responses.…”

Section: Value Of Quantifying Simultaneous Organ Dysfunction In Sepsismentioning

confidence: 99%

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Network-Based Modeling of Sepsis

Jazayeri

Capan

Yang

et al. 2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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It is shown that appropriate therapeutic management at early stages of sepsis are crucial for preventing further deterioration and irreversible organ damage. Although previous studies considered the cellular and physiological responses as the components of sepsis-related predictive models, temporal connections among the responses have not been widely studied. The objective of this study is to investigate simultaneous changes in cellular and physiological responses represented by 16 clinical variables contributing to seven organ system dysfunctions in patients with sepsis to predict in-hospital mortality. Organ dysfunctions were represented by undirected weighted network models composed of: i) nodes (i.e., 16 clinical variables and three biomarkers including procalcitonin, C-reactive protein, and sedimentation rate), ii) edges (i.e., connection between pair of nodes representing simultaneous dysfunctions), and iii) weights representing the persistence of the co-occurrence of two dysfunctions. Data was collected from 13,367 adult patients (corresponding to 17,953 visits) admitted to the study hospital from July 1, 2013, to December 31, 2015. The study population were categorized based on clinical criteria representing sepsis progression to identify different subpopulations. The findings quantify the optimal window for defining the simultaneity of two dysfunctions, the network properties corresponding to different subpopulations, the discriminatory patterns of simultaneous dysfunctions among subpopulations and in-hospital mortality prediction. The results show that the level of persistence of simultaneous dysfunctions are subpopulation-specific. Insights from this study regarding optimal thresholds of the persistence and combination of simultaneous organ dysfunctions can inform policies to personalize the in-hospital mortality prediction.

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Section: Translation Of Findings Into Clinical Practicesupporting

confidence: 87%

Section: Value Of Quantifying Simultaneous Organ Dysfunction In Sepsismentioning

confidence: 99%

Network-Based Modeling of Sepsis

Jazayeri

Capan

Yang

et al. 2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Self Cite

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“…The prevalence of organ failure identified in this study falls in between the prevalence discovered in a systematic review conducted in preparation to this study, 7%–23%, based on very diverse non-ICU studies 8–11 44. Earlier studies indicate that the prevalence of organ failure in the ED increases by selection, where studies on patients with suspected infection present a prevalence between 20% and 28%,4 45 46 and the prevalence is reported at almost 60% among patients with verified infection 47. It seems more unclear when looking at studies focusing at sepsis, before the third definition based on organ failure, where the prevalence of organ failure in the ED is described between 50% and 80% 48 49…”

Section: Discussionmentioning

confidence: 58%

Prevalence of organ failure and mortality among patients in the emergency department: a population-based cohort study

et al. 2019

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ObjectivesThe aim was to describe population-based incidence and emergency department-based prevalence and 1-year all-cause mortality of patients with new organ failure present at arrival.DesignThis was a population-based cohort study of all citizens in four municipalities (population of 230 000 adults).SettingEmergency department at Odense University Hospital, Denmark.ParticipantsWe included all adult patients who arrived from 1 April 2012 to 31 March 2015.Primary and secondary outcome measuresOrgan failure was defined as a modified Sequential Organ Failure Assessment score≥2 within six possible organ systems: cerebral, circulatory, renal, respiratory, hepatic and coagulation.The primary outcome was prevalence of organ failure, and secondary outcomes were 0–7 days, 8–30 days and 31–365 days all-cause mortality.ResultsWe identified in total 175 278 contacts, of which 70 399 contacts were further evaluated for organ failure. Fifty-two per cent of these were women, median age 62 (IQR 42–77) years. The incidence of new organ failure was 1342/100 000 person-years, corresponding to 5.2% of all emergency department contacts.The 0–7-day, 8–30-day and 31–365-day mortality was 11.0% (95% CI: 10.2% to 11.8%), 5.6% (95% CI: 5.1% to 6.2%) and 13.2% (95% CI: 12.3% to 14.1%), respectively, if the patient had one or more new organ failures at first contact in the observation period, compared with 1.4% (95% CI: 1.3% to 1.6%), 1.2% (95% CI: 1.1% to 1.3%) and 5.2% (95% CI: 5.0% to 5.4%) for patients without. Seven-day mortality ranged from hepatic failure, 6.5% (95% CI: 4.9% to 8.6%), to cerebral failure, 33.8% (95% CI: 31.0% to 36.8%), the 8–30-day mortality ranged from cerebral failure, 3.9% (95% CI: 2.8% to 5.3%), to hepatic failure, 8.6% (95% CI: 6.6% to 10.8%) and 31–365-day mortality ranged from cerebral failure, 9.3% (95% CI: 7.6% to 11.2%), to renal failure, 18.2% (95% CI: 15.5% to 21.1%).ConclusionsThe study revealed an incidence of new organ failure at 1342/100 000 person-years and a prevalence of 5.2% of all emergency department contacts. One-year all-cause mortality was 29.8% among organ failure patients.

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“…Variable selection is the most critical part of the analysis because most clinical results have lead time and cost associated with it. Also, Capan et al 31 has shown that not all organ failures are equally associated with sepsis mortality. Therefore, to keep our model simple, and at the same time maintain high performance, we need to establish the best subset of variables.…”

Section: Variable Selectionmentioning

confidence: 99%

Clinical decision support system to assess the risk of sepsis using Tree Augmented Bayesian networks and electronic medical record data

Gupta

Liu

Shepherd

2019

Health Informatics J

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Early and accurate diagnoses of sepsis enable practitioners to take timely preventive actions. The existing diagnostic criteria suffer from deficiencies, such as triggering false alarms or leaving conditions undiagnosed. This study aims to develop a clinical decision support system to predict the risk of sepsis using tree augmented naive Bayesian network by identifying the optimal set of biomarkers. The key feature of our approach is that we captured the dynamics among biomarkers. With an area under receiver operating characteristic of 0.84, the proposed model outperformed the competing diagnostic criteria (systemic inflammatory response syndrome = 0.59, quick sepsis-related organ failure assessment = 0.65, modified early warning system = 0.75, sepsis-related organ failure assessment = 0.80). The richness of our proposed model is measured not only by achieving high accuracy, but also by utilizing fewer biomarkers. We also propose a left-center-right imputation method suitable for electronic medical record data. This method uses the individual patient’s visit, instead of aggregated (mean or median) value, to impute the missing data.

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Not all organ dysfunctions are created equal – Prevalence and mortality in sepsis

Cited by 10 publications

References 22 publications

Network-Based Modeling of Sepsis

Network-Based Modeling of Sepsis

Prevalence of organ failure and mortality among patients in the emergency department: a population-based cohort study

Clinical decision support system to assess the risk of sepsis using Tree Augmented Bayesian networks and electronic medical record data

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