BACKGROUND: Community-acquired pneumonia (CAP) accounts for 1.5 million emergency department (ED) patient visits in the United States each year. OBJECTIVES: To derive an algorithm for the ED triage setting that facilitates rapid and accurate ordering of chest radiography (CXR) for CAP. METHODS: The authors conducted an ED-based retrospective matched case-control study using 100 radiographic confirmed CAP cases and 100 radiographic confirmed influenzalike illness (ILI) controls. Sensitivities and specificities of characteristics assessed in the triage setting were measured to discriminate CAP from ILI. The authors then used classification tree analysis to derive an algorithm that maximizes sensitivity and specificity for detecting patients with CAP in the ED triage setting. RESULTS: Temperature greater than 100.4 degrees F (likelihood ratio = 4.39, 95% confidence interval [CI] = 2.04 to 9.45), heart rate greater than 110 beats/minute (likelihood ratio = 3.59, 95% CI = 1.82 to 7.10), and pulse oximetry less than 96% (likelihood ratio = 2.36, 95% CI = 1.32 to 4.20) were the strongest predictors of CAP. However, no single characteristic was adequately sensitive and specific to accurately discriminate CAP from ILI. A three-step algorithm (using optimum cut points for elevated temperature, tachycardia, and hypoxemia on room air pulse oximetry) was derived that is 70.8% sensitive (95% CI = 60.7% to 79.7%) and 79.1% specific (95% CI = 69.3% to 86.9%). CONCLUSIONS: No single characteristic adequately discriminates CAP from ILI, but a derived clinical algorithm may detect most radiographic confirmed CAP patients in the triage setting. Prospective assessment of this algorithm will be needed to determine its effects on the care of ED patients with suspected pneumonia.
BACKGROUND: Community-acquired pneumonia (CAP) accounts for 1.5 million emergency department (ED) patient visits in the United States each year. OBJECTIVES: To derive an algorithm for the ED triage setting that facilitates rapid and accurate ordering of chest radiography (CXR) for CAP. METHODS: The authors conducted an ED-based retrospective matched case-control study using 100 radiographic confirmed CAP cases and 100 radiographic confirmed influenzalike illness (ILI) controls. Sensitivities and specificities of characteristics assessed in the triage setting were measured to discriminate CAP from ILI. The authors then used classification tree analysis to derive an algorithm that maximizes sensitivity and specificity for detecting patients with CAP in the ED triage setting. RESULTS: Temperature greater than 100.4 degrees F (likelihood ratio = 4.39, 95% confidence interval [CI] = 2.04 to 9.45), heart rate greater than 110 beats/minute (likelihood ratio = 3.59, 95% CI = 1.82 to 7.10), and pulse oximetry less than 96% (likelihood ratio = 2.36, 95% CI = 1.32 to 4.20) were the strongest predictors of CAP. However, no single characteristic was adequately sensitive and specific to accurately discriminate CAP from ILI. A three-step algorithm (using optimum cut points for elevated temperature, tachycardia, and hypoxemia on room air pulse oximetry) was derived that is 70.8% sensitive (95% CI = 60.7% to 79.7%) and 79.1% specific (95% CI = 69.3% to 86.9%). CONCLUSIONS: No single characteristic adequately discriminates CAP from ILI, but a derived clinical algorithm may detect most radiographic confirmed CAP patients in the triage setting. Prospective assessment of this algorithm will be needed to determine its effects on the care of ED patients with suspected pneumonia.
Authors: Dheeraj Gupta; Ritesh Agarwal; Ashutosh Nath Aggarwal; Navneet Singh; Narayan Mishra; G C Khilnani; J K Samaria; S N Gaur; S K Jindal Journal: Lung India Date: 2012-07
Authors: Gang Luo; Bryan L Stone; Michael D Johnson; Peter Tarczy-Hornoch; Adam B Wilcox; Sean D Mooney; Xiaoming Sheng; Peter J Haug; Flory L Nkoy Journal: JMIR Res Protoc Date: 2017-08-29