Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database

Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database.

OBJECTIVE: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. METHOD: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. RESULTS: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. CONCLUSIONS: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

MicroRNA-124 negatively regulates STAT3 to alleviate hypoxic-ischemic brain damage by inhibiting oxidative stress.

MicroRNA-124 (miR-124) is implicated in various neurological diseases; however, its significance in hypoxic-ischaemic brain damage (HIBD) remains unclear. This study aimed to elucidate the underlying pathophysiological mechanisms of miR-124 in HIBD. In our study performed on oxygen-glucose deprivation followed by reperfusion (OGD)/R-induced primary cortical neurons, a substantial reduction in miR-124 was observed. Furthermore, the upregulation of miR-124 significantly mitigated oxidative stress, apoptosis, and mitochondrial impairment. We demonstrated that miR-124 interacts with the signal transducer and activator of transcription 3 (STAT3) to exert its biological function using the dual-luciferase reporter gene assay. As the duration of OGD increased, miR-124 exhibited a negative correlation with STAT3. STAT3 overexpression notably attenuated the protective effects of miR-124 mimics, while knockdown of STAT3 reversed the adverse effects of the miR-124 inhibitor. Subsequently, we conducted an HIBD model in rats. In vivo experiments, miR-124 overexpression attenuated cerebral infarction volume, cerebral edema, apoptosis, oxidative stress, and improved neurological function recovery in HIBD rats. In summary, the neuroprotective effects of the miR-124/STAT3 axis were confirmed in the HIBD model. MiR-124 may serve as a potential biomarker with significant therapeutic implications for HIBD.