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Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome.

ABSTRACT: The Intubation-Surfactant-Extubation (INSURE) procedure is used worldwide to treat pre-term newborn infants suffering from respiratory distress syndrome, which is caused by an insufficient amount of the chemical surfactant in the lungs. With INSURE, the infant is intubated, surfactant is administered via the tube to the trachea, and at completion the infant is extubated. This improves the infant's ability to breathe and thus decreases the risk of long term neurological or motor disabilities. To perform the intubation safely, the newborn infant first must be sedated. Despite extensive experience with INSURE, there is no consensus on what sedative dose is best. This paper describes a Bayesian sequentially adaptive design for a multi-institution clinical trial to optimize the sedative dose given to pre-term infants undergoing the INSURE procedure. The design is based on three clinical outcomes, two efficacy and one adverse, using elicited numerical utilities of the eight possible elementary outcomes. A flexible Bayesian parametric trivariate dose-outcome model is assumed, with the prior derived from elicited mean outcome probabilities. Doses are chosen adaptively for successive cohorts of infants using posterior mean utilities, subject to safety and efficacy constraints. A computer simulation study of the design is presented.

SUBMITTER: Thall PF

PROVIDER: S-EPMC4215739 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome.

Thall Peter F PF Nguyen Hoang Q HQ Zohar Sarah S Maton Pierre P

Journal of the American Statistical Association 20140901 507

The Intubation-Surfactant-Extubation (INSURE) procedure is used worldwide to treat pre-term newborn infants suffering from respiratory distress syndrome, which is caused by an insufficient amount of the chemical surfactant in the lungs. With INSURE, the infant is intubated, surfactant is administered via the tube to the trachea, and at completion the infant is extubated. This improves the infant's ability to breathe and thus decreases the risk of long term neurological or motor disabilities. To ...[more]

PMID: 25368435

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Project description:BackgroundNeonatal acute respiratory distress syndrome (ARDS) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of neonatal ARDS is critical. This study aimed to build a perinatal prediction nomogram for early prediction of neonatal ARDS.MethodsA prediction model was built including 243 late-preterm and full-term infants from Daping Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and Dec 31, 2019. 80 patients from the Children's Hospital in Chongqing, China, hospitalised between Jan 1, 2018 and June 30, 2018 were considered for external validation. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of neonatal ARDS. Both discrimination and calibration were assessed by bootstrapping with 1000 resamples.FindingsMultivariate logistic regression demonstrated that mother's education level (odds ratio [OR] 0·478, 95% confidence interval [CI] 0·324-0·704), premature rupture of membrane (OR 0·296, 95% CI 0·133-0·655), infectious disease within 7 days before delivery (OR 0·275, 95% CI 0·083-0·909), hospital level (OR 2·479, 95% CI 1·260-4·877), and Apgar 5-min score (OR 0·717, 95% CI 0·563-0·913) were independent predictors for neonatal ARDS in late-preterm and full-term infants, who experienced dyspnoea within 24 h after birth and required mechanical ventilation. The area under the curve and concordance index of the nomogram constructed from the above five factors were 0·760 and 0·757, respectively. The Hosmer-Lemeshow test showed that the model was a good fit (P = 0.320). The calibration curve of the nomogram was close to the ideal diagonal line. Furthermore, the decision curve analysis demonstrated significantly better net benefit in the model. The external validation proved the reliability of the prediction nomogram.InterpretationA nomogram based on perinatal factors was developed to predict the occurrence of neonatal ARDS in late-preterm and full-term infants who experienced dyspnoea within 24 h after birth and required mechanical ventilation. It provided clinicians with an accurate and effective tool for the early prediction and timely management of neonatal ARDS.FundingNo funding was associated with this study.

Dataset Information

Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome.

Publications

Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome.

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