OBJECTIVE: To develop and validate a statistical prediction model spanning the severity range of neonatal outcomes in infants born at ≤ 30 weeks' gestation. METHODS: A national cohort of infants, born at 23 to 30 weeks' gestation and admitted to level III NICUs in Canada in 2010-2011, was identified from the Canadian Neonatal Network database. A multinomial logistic regression model was developed to predict survival without morbidities, mild morbidities, severe morbidities, or mortality, using maternal, obstetric, and infant characteristics available within the first day of NICU admission. Discrimination and calibration were assessed using a concordance C-statistic and the Cg goodness-of-fit test, respectively. Internal validation was performed using a bootstrap approach. RESULTS: Of 6106 eligible infants, 2280 (37%) survived without morbidities, 1964 (32%) and 1251 (21%) survived with mild and severe morbidities, respectively, and 611 (10%) died. Predictors in the model were gestational age, small (<10th percentile) for gestational age, gender, Score for Neonatal Acute Physiology version II >20, outborn status, use of antenatal corticosteroids, and receipt of surfactant and mechanical ventilation on the first day of admission. High model discrimination was confirmed by internal bootstrap validation (bias-corrected C-statistic = 0.899, 95% confidence interval = 0.894-0.903). Predicted probabilities were consistent with the observed outcomes (Cg P value = .96). CONCLUSIONS: Neonatal outcomes ranging from mortality to survival without morbidity in extremely preterm infants can be predicted on their first day in the NICU by using a multinomial model with good discrimination and calibration. The prediction model requires additional external validation.
OBJECTIVE: To develop and validate a statistical prediction model spanning the severity range of neonatal outcomes in infants born at ≤ 30 weeks' gestation. METHODS: A national cohort of infants, born at 23 to 30 weeks' gestation and admitted to level III NICUs in Canada in 2010-2011, was identified from the Canadian Neonatal Network database. A multinomial logistic regression model was developed to predict survival without morbidities, mild morbidities, severe morbidities, or mortality, using maternal, obstetric, and infant characteristics available within the first day of NICU admission. Discrimination and calibration were assessed using a concordance C-statistic and the Cg goodness-of-fit test, respectively. Internal validation was performed using a bootstrap approach. RESULTS: Of 6106 eligible infants, 2280 (37%) survived without morbidities, 1964 (32%) and 1251 (21%) survived with mild and severe morbidities, respectively, and 611 (10%) died. Predictors in the model were gestational age, small (<10th percentile) for gestational age, gender, Score for Neonatal Acute Physiology version II >20, outborn status, use of antenatal corticosteroids, and receipt of surfactant and mechanical ventilation on the first day of admission. High model discrimination was confirmed by internal bootstrap validation (bias-corrected C-statistic = 0.899, 95% confidence interval = 0.894-0.903). Predicted probabilities were consistent with the observed outcomes (Cg P value = .96). CONCLUSIONS: Neonatal outcomes ranging from mortality to survival without morbidity in extremely preterm infants can be predicted on their first day in the NICU by using a multinomial model with good discrimination and calibration. The prediction model requires additional external validation.
Authors: M A Steurer; J Anderson; R J Baer; S Oltman; L S Franck; M Kuppermann; L Rand; K K Ryckman; J C Partridge; L L Jelliffe-Pawlowski; E E Rogers Journal: J Perinatol Date: 2017-02-16 Impact factor: 2.521
Authors: Debra H Brandon; Susan G Silva; Jinhee Park; William Malcolm; Heba Kamhawy; Diane Holditch-Davis Journal: Res Nurs Health Date: 2017-04-21 Impact factor: 2.228
Authors: Germán Iñiguez; Juan José Castro; Mirna Garcia; Elena Kakarieka; M Cecilia Johnson; Fernando Cassorla; Verónica Mericq Journal: PLoS One Date: 2014-07-22 Impact factor: 3.240