臨床小児科: オープンアクセス

臨床小児科: オープンアクセス
オープンアクセス

ISSN: 2572-0775

概要

Developing a New Scoring System for Diagnosing Shock in Critically-Ill Neonates Admitted in NICU in a Resource Poor Country

Sunisha Arora, Yashwant Kumar Rao*, Tanu Midha, Neeraj Kumar Rao

Objective: To determine the clinical predictors of shock in critically ill neonates and design a shock score to diagnose neonatal shock.

Materials and methods: This was a cross-sectional study conducted from Dec 2015-July 2017 in Sick Newborn Care Unit, GSVM medical college, Kanpur. All sick neonates requiring intensive hemodynamic monitoring having umbilical venous catheter in-situ were enrolled in the study. Central Venous Pressure (CVP) was measured through the umbilical venous catheter. Clinical parameters including gestational age, birth weight, cyanosis, pallor, core to peripheral temperature difference, heart rate, capillary refill time, blood pressure and lactate levels were recorded. Shock was defined as central venous pressure less than 5 cm H2O or more than 8 cm H2O.

Results: 122 neonates were included in the study, 76 of which had shock. Core to peripheral temperature difference (sensitivity-96%) and prolonged capillary refill time (sensitivity-75%) were observed to be the most sensitive indicators of neonatal shock while the best predictors were tachycardia (positive predictive value-87%) and hypotension (positive predictive value-82%). A new shock score (0-23) was developed based on clinical parameters to diagnose shock. Shock score more than 17 predicted neonatal shock with 58% sensitivity, 86% specificity and 88% positive predictive value.

Conclusion: This study established that core to peripheral temperature difference was the most sensitive indicator and tachycardia was the best predictor of neonatal shock. A new shock scoring system has been designed to diagnose neonatal shock in a resource poor country like India where facilities for invasive procedures like central venous pressure monitoring are not available at grassroot level.

免責事項: この要約は人工知能ツールを使用して翻訳されたものであり、まだレビューまたは検証されていません。
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