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Improving risk classification of critical illness with biomarkers: a simulation study.

ABSTRACT: Optimal triage of patients at risk for critical illness requires accurate risk prediction, yet few data on the performance criteria required of a potential biomarker to be clinically useful exists.We studied an adult cohort of nonarrest, nontrauma emergency medical services encounters transported to a hospital from 2002 to 2006. We simulated hypothetical biomarkers increasingly associated with critical illness during hospitalization and determined the biomarker strength and sample size necessary to improve risk classification beyond a best clinical model.Of 57,647 encounters, 3121 (5.4%) were hospitalized with critical illness and 54,526 (94.6%) without critical illness. The addition of a moderate-strength biomarker (odds ratio, 3.0, for critical illness) to a clinical model improved discrimination (c statistic, 0.85 vs 0.8; P<.01) and reclassification (net reclassification improvement, 0.15; 95% confidence interval, 0.13-0.18) and increased the proportion of cases in the highest-risk category by +8.6% (95% confidence interval, 7.5%-10.8%). Introducing correlation between the biomarker and physiological variables in the clinical risk score did not modify the results. Statistically significant changes in net reclassification required a sample size of at least 1000 subjects.Clinical models for triage of critical illness could be significantly improved by incorporating biomarkers, yet substantial sample sizes and biomarker strength may be required.

SUBMITTER: Seymour CW

PROVIDER: S-EPMC3707977 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Improving risk classification of critical illness with biomarkers: a simulation study.

Seymour Christopher W CW Cooke Colin R CR Wang Zheyu Z Kerr Kathleen F KF Yealy Donald M DM Angus Derek C DC Rea Thomas D TD Kahn Jeremy M JM Pepe Margaret S MS

Journal of critical care 20130406 5

<h4>Purpose</h4>Optimal triage of patients at risk for critical illness requires accurate risk prediction, yet few data on the performance criteria required of a potential biomarker to be clinically useful exists.<h4>Materials and methods</h4>We studied an adult cohort of nonarrest, nontrauma emergency medical services encounters transported to a hospital from 2002 to 2006. We simulated hypothetical biomarkers increasingly associated with critical illness during hospitalization and determined th ...[more]

PMID: 23566734

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Project description:BackgroundEndothelial activation plays a role in organ dysfunction in the systemic inflammatory response syndrome (SIRS). Angiopoietin-1 (Ang-1) promotes vascular quiescence while angiopoietin-2 (Ang-2) mediates microvascular leak. Circulating levels of Ang-1 and Ang-2 in patients with SIRS could provide insight on risks for organ dysfunction and death distinct from inflammatory proteins. In this study, we determined if biomarkers of endothelial activation and inflammation exhibit independent associations with poor outcomes in SIRS.MethodsWe studied 943 critically ill patients with SIRS admitted to an Intensive Care Unit (ICU) of an academic medical center. We measured plasma levels of endothelial markers (Ang-1, Ang-2, soluble vascular cell adhesion molecule-1 (sVCAM-1)) and inflammatory markers (interleukin-6 (IL-6), interleukin-8 (IL-8), granulocyte-colony stimulating factor (G-CSF), soluble tumor necrosis factor receptor-1 (sTNFR-1)) within 24 hours of enrollment. We tested for associations between each marker and 28 day mortality, shock, and day 3 sequential organ failure assessment (SOFA) score. For 28 day mortality, we performed sensitivity analysis for those subjects with sepsis and those with sterile inflammation. We used multivariate models to adjust for clinical covariates and determine if associations identified with endothelial activation markers were independent of those observed with inflammatory markers.ResultsHigher levels of all biomarkers were associated with increased 28 day mortality except levels of Ang-1 which were associated with lower mortality. After adjustment for comorbidities and sTNFR-1 concentration, a doubling of Ang-1 concentration was associated with lower 28 day mortality (Odds ratio (OR) = 0.81; p<0.01), shock (OR = 0.82; p<0.001), and SOFA score (? = -0.50; p<0.001), while Ang-2 concentration was associated with increased mortality (OR = 1.55; p<0.001), shock (OR = 1.51; p<0.001), and SOFA score (? = +0.63; p<0.001). sVCAM-1 was not independently associated with SIRS outcomes.ConclusionsIn critically ill patients with SIRS, early measurements of Ang-1 and Ang-2 are associated with death and organ dysfunction independently of simultaneously-measured markers of inflammation.

Project description:BackgroundRisk for acute kidney injury (AKI) in older adults has not been evaluated systematically. We sought to delineate the determinants of risk for AKI in older compared with younger adults.Study designRetrospective analysis of patients hospitalized in July 2000 to September 2008.Setting & participantsWe identified all adult patients admitted to an intensive care unit (n=45,655) in a large tertiary-care university hospital system. We excluded patients receiving dialysis or a kidney transplant prior to hospital admission and patients with baseline creatinine levels ≥ 4mg/dL, liver transplantation, indeterminate AKI status, or unknown age, leaving 39,938 patients.PredictorWe collected data for multiple susceptibilities and exposures, including age, sex, race, body mass, comorbid conditions, severity of illness, baseline kidney function, sepsis, and shock.OutcomesWe defined AKI according to KDIGO (Kidney Disease: Improving Global Outcomes) criteria. We examined susceptibilities and exposures across age strata for impact on the development of AKI.MeasurementsWe calculated area under the receiver operating characteristic curve (AUC) for prediction of AKI across age groups.Results25,230 (63.2%) patients were 55 years or older. Overall, 25,120 (62.9%) patients developed AKI (69.2% aged ≥55 years). Examples of risk factors for AKI in the oldest age category (≥75 years) were drugs (vancomycin, aminoglycosides, and nonsteroidal anti-inflammatories), history of hypertension (OR, 1.13; 95% CI, 1.02-1.25), and sepsis (OR, 2.12; 95% CI, 1.68-2.67). Fewer variables remained predictive of AKI as age increased and the model for older patients was less predictive (P<0.001). For the age categories 18 to 54, 55 to 64, 65 to 74, and 75 years or older, AUCs were 0.744 (95% CI, 0.735-0.752), 0.714 (95% CI, 0.702-0.726), 0.706 (95% CI, 0.693-0.718), and 0.673 (95% CI, 0.661-0.685), respectively.LimitationsAnalysis may not apply to non-intensive care unit patients.ConclusionsThe likelihood of developing AKI increases with age; however, the same variables are less predictive for AKI as age increases. Efforts to quantify risk for AKI may be more difficult in older adults.

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Improving risk classification of critical illness with biomarkers: a simulation study.

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Improving risk classification of critical illness with biomarkers: a simulation study.

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