Acute respiratory distress syndrome screening: Difference between revisions
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Several clinical algorithms have been proposed and validated for early recognition of ARDS. No single biomarker is currently specific or sensitive enough to be incorporated into routine clinical practice. | Several clinical algorithms have been proposed and validated for early recognition of ARDS. No single biomarker is currently specific or sensitive enough to be incorporated into routine clinical practice. | ||
Trillo-Alvarez et al. devised the Lung Injury Prediction Study (LIPS) score to | Trillo-Alvarez et al. devised the Lung Injury Prediction Study (LIPS) score to identify patients at high risk for acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) before ICU admission by utilizing variables that are clearly defined and routinely available in the medical record.<ref>Trillo-Alvarez, C., R. Cartin-Ceba, D. J. Kor, M. Kojicic, R. Kashyap, S. Thakur, L. Thakur, V. Herasevich, M. Malinchoc, and O. Gajic. “Acute Lung Injury Prediction Score: Derivation and Validation in a Population-Based Sample.” European Respiratory Journal 37, no. 3 (March 1, 2011): 604–9. doi:10.1183/09031936.00036810.</ref> Covariates used in model derivation include predisposing conditions ([[trauma]], high-risk [[surgery]], [[sepsis]], [[shock]], [[pneumonia]], [[aspiration]], and [[pancreatitis]]) and risk-modifiers ([[tachypnea]], [[alcohol abuse]], [[hypoalbuminemia]], [[oxygen]] supplementation, [[chemotherapy]], [[diabetes mellitus]], and [[smoking]] history). The LIPS score efficiently discriminated patients who developed ALI from those who did not in both the retrospective derivation cohort and prospective validation cohort with an area under the ROC curve (AUC) of 0.84. The performance of the LIPS score was consistent in a multicenter cohort study with an AUC of 0.80 while maintaining an appropriate sensitivity for a screening tool (negative predictive value of 0.97).<ref>Gajic, Ognjen, Ousama Dabbagh, Pauline K. Park, Adebola Adesanya, Steven Y. Chang, Peter Hou, Harry Anderson, et al. “Early Identification of Patients at Risk of Acute Lung Injury: Evaluation of Lung Injury Prediction Score in a Multicenter Cohort Study.” American Journal of Respiratory and Critical Care Medicine 183, no. 4 (February 15, 2011): 462–70. doi:10.1164/rccm.201004-0549OC.</ref> | ||
Thakur et al. developed and validated an ALI screening tool based on the American-European Consensus Conference definition using an electronic medical record that facilitates | |||
early recognition of specific criteria.<ref>Herasevich, Vitaly, Murat Yilmaz, Hasrat Khan, Rolf D. Hubmayr, and Ognjen Gajic. “Validation of an Electronic Surveillance System for Acute Lung Injury.” Intensive Care Medicine 35, no. 6 (June 2009): 1018–23. doi:10.1007/s00134-009-1460-1.</ref> The tool demonstrated a sensitivity of 96.3% and a specificity of 89.4%, with a positive predictive value of 46.0% and a negative predictive value of 99.6%. | |||
==References== | ==References== | ||
Revision as of 21:18, 14 July 2016
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Acute respiratory distress syndrome Microchapters |
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Differentiating Acute respiratory distress syndrome from other Diseases |
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Diagnosis |
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Treatment |
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Case Studies |
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Acute respiratory distress syndrome screening On the Web |
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American Roentgen Ray Society Images of Acute respiratory distress syndrome screening |
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Directions to Hospitals Treating Acute respiratory distress syndrome |
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Risk calculators and risk factors for Acute respiratory distress syndrome screening |
Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1]; Associate Editor(s)-in-Chief: Brian Shaller, M.D. [2]
Overview
There are no screening tools for ARDS. The best way to make an early diagnosis of ARDS is to apply the diagnostic criteria to any patient with bilateral pulmonary infiltrates on chest x ray, and new/worsening hypoxemia with an increasing supplemental oxygen requirement in whom a potential cause or risk factor for ARDS exists.
Screening
Several clinical algorithms have been proposed and validated for early recognition of ARDS. No single biomarker is currently specific or sensitive enough to be incorporated into routine clinical practice.
Trillo-Alvarez et al. devised the Lung Injury Prediction Study (LIPS) score to identify patients at high risk for acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) before ICU admission by utilizing variables that are clearly defined and routinely available in the medical record.[1] Covariates used in model derivation include predisposing conditions (trauma, high-risk surgery, sepsis, shock, pneumonia, aspiration, and pancreatitis) and risk-modifiers (tachypnea, alcohol abuse, hypoalbuminemia, oxygen supplementation, chemotherapy, diabetes mellitus, and smoking history). The LIPS score efficiently discriminated patients who developed ALI from those who did not in both the retrospective derivation cohort and prospective validation cohort with an area under the ROC curve (AUC) of 0.84. The performance of the LIPS score was consistent in a multicenter cohort study with an AUC of 0.80 while maintaining an appropriate sensitivity for a screening tool (negative predictive value of 0.97).[2]
Thakur et al. developed and validated an ALI screening tool based on the American-European Consensus Conference definition using an electronic medical record that facilitates early recognition of specific criteria.[3] The tool demonstrated a sensitivity of 96.3% and a specificity of 89.4%, with a positive predictive value of 46.0% and a negative predictive value of 99.6%.
References
- ↑ Trillo-Alvarez, C., R. Cartin-Ceba, D. J. Kor, M. Kojicic, R. Kashyap, S. Thakur, L. Thakur, V. Herasevich, M. Malinchoc, and O. Gajic. “Acute Lung Injury Prediction Score: Derivation and Validation in a Population-Based Sample.” European Respiratory Journal 37, no. 3 (March 1, 2011): 604–9. doi:10.1183/09031936.00036810.
- ↑ Gajic, Ognjen, Ousama Dabbagh, Pauline K. Park, Adebola Adesanya, Steven Y. Chang, Peter Hou, Harry Anderson, et al. “Early Identification of Patients at Risk of Acute Lung Injury: Evaluation of Lung Injury Prediction Score in a Multicenter Cohort Study.” American Journal of Respiratory and Critical Care Medicine 183, no. 4 (February 15, 2011): 462–70. doi:10.1164/rccm.201004-0549OC.
- ↑ Herasevich, Vitaly, Murat Yilmaz, Hasrat Khan, Rolf D. Hubmayr, and Ognjen Gajic. “Validation of an Electronic Surveillance System for Acute Lung Injury.” Intensive Care Medicine 35, no. 6 (June 2009): 1018–23. doi:10.1007/s00134-009-1460-1.