Rationale: The preterm lung is susceptible to injury during transition to air breathing at birth. It remains unclear whether rapid or gradual lung aeration at birth causes less lung injury. Objectives: To examine the effect of gradual and rapid aeration at birth on: 1) the spatiotemporal volume conditions of the lung; and 2) resultant regional lung injury. Methods: Preterm lambs (125 6 1 d gestation) were randomized at birth to receive: 1) tidal ventilation without an intentional recruitment (no-recruitment maneuver [No-RM]; n = 19); 2) sustained inflation (SI) until full aeration (n = 26); or 3) tidal ventilation with an initial escalating/de-escalating (dynamic) positive end-expiratory pressure (DynPEEP; n = 26). Ventilation thereafter continued for 90 minutes at standardized settings, including PEEP of 8 cm H 2 O. Lung mechanics and regional aeration and ventilation (electrical impedance tomography) were measured throughout and correlated with histological and gene markers of early lung injury. Measurements and Main Results: DynPEEP significantly improved dynamic compliance (P , 0.0001). An SI, but not DynPEEP or NoRM , resulted in preferential nondependent lung aeration that became less uniform with time (P = 0.0006). The nondependent lung was preferential ventilated by 5 minutes in all groups, with ventilation only becoming uniform with time in the NoRM and DynPEEP groups. All strategies generated similar nondependent lung injury patterns. Only an SI caused greater upregulation of dependent lung gene markers compared with unventilated fetal controls (P , 0.05). Conclusions: Rapidly aerating the preterm lung at birth creates heterogeneous volume states, producing distinct regional injury patterns that affect subsequent tidal ventilation. Gradual aeration with tidal ventilation and PEEP produced the least lung injury.

BackgroundBronchopulmonary dysplasia (BPD) is a common complication of preterm birth. Very different models using clinical parameters at an early postnatal age to predict BPD have been developed with little extensive quantitative validation. The objective of this study is to review and validate clinical prediction models for BPD.MethodsWe searched the main electronic databases and abstracts from annual meetings. The STROBE instrument was used to assess the methodological quality. External validation of the retrieved models was performed using an individual patient dataset of 3229 patients at risk for BPD. Receiver operating characteristic curves were used to assess discrimination for each model by calculating the area under the curve (AUC). Calibration was assessed for the best discriminating models by visually comparing predicted and observed BPD probabilities.ResultsWe identified 26 clinical prediction models for BPD. Although the STROBE instrument judged the quality from moderate to excellent, only four models utilised external validation and none presented calibration of the predictive value. For 19 prediction models with variables matched to our dataset, the AUCs ranged from 0.50 to 0.76 for the outcome BPD. Only two of the five best discriminating models showed good calibration.ConclusionsExternal validation demonstrates that, except for two promising models, most existing clinical prediction models are poor to moderate predictors for BPD. To improve the predictive accuracy and identify preterm infants for future intervention studies aiming to reduce the risk of BPD, additional variables are required. Subsequently, that model should be externally validated using a proper impact analysis before its clinical implementation.

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