Predictability of cerebral palsy in a high-risk NICU population.
OBJECTIVE
This study aims to create a predictive model for the assessment of the individual risk of developing cerebral palsy in a large cohort of selected high-risk infants.


METHODS
1099 NICU-admitted high-risk infants were assessed up to the corrected age of at least 12 months. CP was categorized relative to subtype, distribution and severity. Several perinatal characteristics (gender, gestational age, multiple gestation, small for gestational age, perinatal asphyxia and duration of mechanical ventilation), besides neonatal cerebral ultrasound data were used in the logistic regression model for the risk of CP.


RESULTS
Perinatal asphyxia, mechanical ventilation>7 days, white matter disease except for transient echodensities<7 days, intraventricular haemorrhage grades III and IV, cerebral infarction and deep grey matter lesions were recognized as independent predictors for the development of CP. 95% of all children with CP were correctly identified at or above the cut-off value of 4.5% probability of CP development. Higher gestational age, perinatal asphyxia and deep grey matter lesion are independent predictors for non-spastic versus spastic CP (OR=1.1, 3.6, and 7.5, respectively). Independent risk factors for prediction of unilateral versus bilateral spastic CP are higher gestational age, cerebral infarction and parenchymal haemorrhagic infarction (OR=1.2, 31, and 17.6, respectively). Perinatal asphyxia is the only significant variable retained for the prediction of severe CP versus mild or moderate CP.


CONCLUSIONS
The presented model based on perinatal characteristics and neonatal US-detected brain injuries is a useful tool in identifying specific infants at risk for developing CP.
Himpens
E
E
Ghent University, Belgium. eveline.himpens@ugent.be
Oostra
A
A
Franki
I
I
Vansteelandt
S
S
Vanhaesebrouck
P
P
den Broeck
C Van
CV
D002547:Cerebral Palsy / Q000175:diagnosis* / Q000000981:diagnostic imaging; D015331:Cohort Studies; D004453:Echoencephalography; D005260:Female; D005865:Gestational Age; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D016015:Logistic Models; D008297:Male; D011247:Pregnancy; D011272:Pregnancy, Multiple; D012044:Regression Analysis; D012121:Respiration, Artificial / Q000009:adverse effects; D018570:Risk Assessment; D012307:Risk Factors