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World Journal of Pediatrics 2012-Aug

Clinical predictors of hypoxemia in Indian children with acute respiratory tract infection presenting to pediatric emergency department.

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Yashwant Kumar Rao
Tanu Midha
Pankaj Kumar
Virendra Nath Tripathi
Om Prakash Rai

Słowa kluczowe

Abstrakcyjny

BACKGROUND

In developing countries, facilities for measuring arterial oxygen saturation are not available in most settings, which make it difficult for health providers to detect hypoxemia in children with acute respiratory tract infection (ARI). Most health providers rely on symptoms and signs to identify hypoxemia and start oxygen therapy. Therefore, this study was conducted to determine the clinical predictors of hypoxemia in children with ARI.

METHODS

It was a cross-sectional study carried out at the Pediatric Emergency Department of GSVM Medical College, Kanpur, India in children in the age group between 2 months and 5 years, presenting with ARI. All children with ARI attending the pediatric emergency department from April 2007 to September 2008 were included in the study. Clinical signs and symptoms including fever, cough, nasal flaring, inability to feed/drink, cyanosis, chest wall retraction, wheezing, grunting, tachypnea and crepitations were noted and oxygen saturation (SpO(2)) was measured. Hypoxemia was defined as SpO(2) <90%.

RESULTS

Of the 261 children included in the study, 62 (23.8%) had hypoxemia. Chest wall retraction (sensitivity=90%), crepitations (sensitivity=87%), nasal flaring (sensitivity=84%), tachypnea (sensitivity=81%) and inability to feed (sensitivity=81%) were observed to be the most sensitive indicators of hypoxemia while the best predictors were cyanosis [positive predictive value (PPV)=88%] and nasal flaring (PPV=53%).

CONCLUSIONS

Chest wall retraction was found to be the most sensitive indicator, and cyanosis was the most specific indicator for hypoxemia. Of all the clinical signs and symptoms of hypoxemia, none had all the attributes of being a good predictor. A new hypoxemia score has been designed using a combination of clinical signs and symptoms to predict the need for supplemental oxygen therapy.

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