AI Article Synopsis
- Researchers aimed to improve mortality risk stratification in septic shock patients using a multibiomarker approach, addressing issues in current clinical trials where risk assessment is often unequal or unclear.
- The study utilized previous findings to analyze plasma samples from 881 adults in various ICUs and developed a decision tree model that combined both biomarkers and clinical data for predicting 28-day mortality.
- The decision tree showed high sensitivity and reasonably good specificity for predicting outcomes, validating its effectiveness across several cohorts, thus offering a potential tool for better risk assessment in septic shock patients.
Article Abstract
Objectives: Clinical trials in septic shock continue to fail due, in part, to inequitable and sometimes unknown distribution of baseline mortality risk between study arms. Investigators advocate that interventional trials in septic shock require effective outcome risk stratification. We derived and tested a multibiomarker-based approach to estimate mortality risk in adults with septic shock.
Design: Previous genome-wide expression studies identified 12 plasma proteins as candidates for biomarker-based risk stratification. The current analysis used banked plasma samples and clinical data from existing studies. Biomarkers were assayed in plasma samples obtained from 341 subjects with septic shock within 24 hours of admission to the ICU. Classification and regression tree analysis was used to generate a decision tree predicting 28-day mortality based on a combination of both biomarkers and clinical variables. The derived tree was first tested in an independent cohort of 331 subjects, then calibrated using all subjects (n = 672), and subsequently validated in another independent cohort (n = 209).
Setting: Multiple ICUs in Canada, Finland, and the United States.
Subjects: Eight hundred eighty-one adults with septic shock or severe sepsis.
Intervention: None.
Measurements And Main Results: The derived decision tree included five candidate biomarkers, admission lactate concentration, age, and chronic disease burden. In the derivation cohort, sensitivity for mortality was 94% (95% CI, 87-97), specificity was 56% (50-63), positive predictive value was 50% (43-57), and negative predictive value was 95% (89-98). Performance was comparable in the test cohort. The calibrated decision tree had the following test characteristics in the validation cohort: sensitivity 85% (76-92), specificity 60% (51-69), positive predictive value 61% (52-70), and negative predictive value 85% (75-91).
Conclusions: We have derived, tested, calibrated, and validated a risk stratification tool and found that it reliably estimates the probability of mortality in adults with septic shock.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4620515 | PMC |
| http://dx.doi.org/10.1097/CCM.0000000000000106 | DOI Listing |
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