Intensive Care Medicine

, Volume 43, Issue 8, pp 1123–1131 | Cite as

External validation of a biomarker and clinical prediction model for hospital mortality in acute respiratory distress syndrome

  • Zhiguo Zhao
  • Nancy Wickersham
  • Kirsten N. Kangelaris
  • Addison K. May
  • Gordon R. Bernard
  • Michael A. Matthay
  • Carolyn S. Calfee
  • Tatsuki Koyama
  • Lorraine B. Ware



Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort.


The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied.


The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70–0.79) in FACTT, compared to 0.72 (95% CI 0.67–0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70–0.76) when FACTT and VALID were combined.


We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion.


Validation Prediction Biomarker Hospital mortality ARDS 



American European Consensus Conference


Acute lung injury


Acute physiology and chronic health evaluation


Acute respiratory distress syndrome


Area under receiver operating characteristic curve


Confidence interval


Fluid and catheter treatment trial


Intensive care unit


Integrated discrimination improvement


National Heart, Lung, and Blood Institute


Net reclassification improvement


Positive end-expiratory pressure


Receiver operating characteristic curve


Risk ratio


Sivelestat trial in ALI patients requiring mechanical ventilation


Validating acute lung injury biomarkers for diagnosis


Ventilator-free days



This manuscript was prepared using FACTT Research Materials obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center and does not necessarily reflect the opinions or views of the FACTT investigators or the NHLBI. We would like to thank the NHLBI BioLINCC/Biorepository for providing clinical samples and clinical data from the FACTT clinical trial. We also thank Eli Lilly and Company for providing clinical data and plasma samples from the STRIVE study.

Compliance with ethical standards


This study was supported by NIH HL112656 (LBW), 1K23HL116800-01 (KK), HL51856 (MAM), HL110969 and HL131621 (CSC), and the NHLBI BioLINCC/Biorepository.

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study with the exception of some patients in the VALID study who were enrolled under an IRB-approved waiver of informed consent.

Supplementary material

134_2017_4854_MOESM1_ESM.pdf (76 kb)
Supplementary material 1 (PDF 75 kb)
134_2017_4854_MOESM2_ESM.tiff (27 mb)
Supplementary material 2 (TIFF 27685 kb)
134_2017_4854_MOESM3_ESM.doc (144 kb)
Supplementary material 3 (DOC 144 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg and ESICM 2017

Authors and Affiliations

  • Zhiguo Zhao
    • 1
    • 2
  • Nancy Wickersham
    • 3
  • Kirsten N. Kangelaris
    • 4
  • Addison K. May
    • 5
  • Gordon R. Bernard
    • 3
  • Michael A. Matthay
    • 6
    • 7
  • Carolyn S. Calfee
    • 6
    • 7
  • Tatsuki Koyama
    • 1
  • Lorraine B. Ware
    • 3
    • 8
  1. 1.Department of BiostatisticsVanderbilt UniversityNashvilleUSA
  2. 2.The Institution for Medicine and Public HealthVanderbilt UniversityNashvilleUSA
  3. 3.Division of Allergy, Pulmonary and Critical Care Medicine, Department of MedicineVanderbilt UniversityNashvilleUSA
  4. 4.Division of Hospital Medicine, Department of MedicineUniversity of California San FranciscoSan FranciscoUSA
  5. 5.Division of Trauma and Surgical Critical CareVanderbilt UniversityNashvilleUSA
  6. 6.Department of MedicineUniversity of California San FranciscoSan FranciscoUSA
  7. 7.Department of Anesthesia and Cardiovascular Research InstituteUniversity of California San FranciscoSan FranciscoUSA
  8. 8.Department of Pathology, Microbiology and ImmunologyVanderbilt UniversityNashvilleUSA

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