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Semin Pediatr Surg. 2017 Jun;26(3):136-139. doi: 10.1053/j.sempedsurg.2017.04.010. Epub 2017 Apr 24.

Predicting outcomes in congenital diaphragmatic hernia.

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Department of Surgery, University of Calgary, Calgary, Alberta, Canada.
Department of Surgery, University of Calgary, Calgary, Alberta, Canada; Department of Community Health Sciences, Alberta Children׳s Hospital, University of Calgary, 2888 Shaganappi Trail NW, Calgary, Alberta, Canada T3B 6A8. Electronic address:


Identification of CDH infant populations at high risk for mortality postnatally may help to develop targeted care strategies, guide discussions surrounding palliation and contribute to standardizing reporting and benchmarking, so that care strategies at different centers can be compared. Clinical prediction rules are evidence-based tools that combine multiple predictors to estimate the probability that a particular outcome in an individual patient will occur. In CDH, a suitable clinical prediction rule can stratify high- and low-risk populations and provide the ability to tailor management strategies based on severity. The ideal prediction tool for infants born with CDH would be validated in a large population, generalizable, easily applied in a clinical setting and would clearly discriminate patients at the highest and lowest risk of death. To date, 4 postnatal major clinical prediction rules have been published and validated in the North American CDH population. These models contain variables such as birth weight, Apgar score, blood gases, as well as measures of pulmonary hypertension, and associated anomalies. In an era of standardized care plans and population-based strategies, the appropriate selection and application of a generalizable tool to provide an opportunity for benchmarking, policy creation, and centralizing the care of high-risk populations. A well-designed clinical prediction tool remains the most practical and expedient way to achieve these goals.


Congenital diaphragmatic hernia; Outcomes; Prediction rules; Regression modeling; Survival

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