Cited by Reimagining apnea monitoring in the neonatal ICU

Reimagining apnea monitoring in the neonatal ICU DOI

et al.

Current Opinion in Pediatrics, Journal Year: 2024, Volume and Issue: 37(2), P. 173 - 181

Published: Dec. 11, 2024

This review outlines the prevalence and complications of apneas intermittent hypoxemic events in preterm infants, examines current monitoring limitations neonatal ICUs (NICUs), explores emerging technologies addressing these challenges. New evidence from Prematurity-Related Ventilatory Control (Pre-Vent) study, which analyzed cardiorespiratory data 717 extremely exposes varying frequency, duration, severity apneas, hypoxemia, bradycardias, periodic breathing during hospitalization, highlights negative impact hypoxemia on pulmonary outcomes at discharge. Although traditional methods cannot differentiate between apnea types quantify their burden, recent advancements sensor integration hold promise for improving real-time detection evaluation NICU. Notably, small wearable mechano-acoustic sensors could improve through continuous airflow respiratory efforts. Additionally, integrating bedside physiological with modalities such as near-infrared spectroscopy, diaphragmatic activity, electrical impedance tomography help predict adverse by regional oxygen saturation lung function relation to apneas. Enhancing our understanding overcoming advanced lead more personalized management improved infants.

Language: Английский

A Prediction Model for the Diagnosis of Sepsis Based on the Classification of Acute Gastrointestinal Injury DOI

Sun Yu,

Chunyang Xu,

Shun Wen

et al.

British Journal of Hospital Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: May 19, 2025

Aims/Background The existing screening approaches for sepsis demonstrate lower sensitivity, potentially resulting in misdiagnosis of septic conditions. gastrointestinal tract is the primary and most susceptible organ during sepsis. Therefore, this study aims to establish evaluate a predictive model based on classification acute injury (AGI), improve diagnostic sensitivity. Methods This retrospective included patients with confirmed infections or suspected who were admitted general ward Changshu Hospital Affiliated Soochow University (Changshu First People's Hospital, China) between April 2023 December 2023. Patients randomly divided into developing cohort (n = 1667) validation 712) 7:3 ratio. Furthermore, data collected various variables, including inflammatory factors, hemodynamic dysfunction tissue perfusion variables. Univariate analysis was used screen risk factors associated sepsis, logistic regression employed identify independent factors. nomogram constructed these Additionally, prediction significance evaluated using receiver operating characteristic (ROC) curves, calibration decision curve (DCA) across both cohorts. Results Out total 2379 study, rate 12.5%. incidence AGI 96.0%, 23.2% grade I, 52.3% II, 16.1% III, 4.4% IV. Factors like age (Odds Ratio (OR) 1.029, 95% Confidence Interval (CI) 1.015–1.043, p < 0.01), hypotension (OR 3.863, CI 2.372–6.290, oxygen saturation (SpO 2 ) 0.795, 0.751–0.840, thrombocytopenia 5.657, 2.835–11.289, 0.01) 7.151, 5.040–10.144, observed as predictors Based five (model B) developed. Model B achieved area under (AUC) 0.947 (95% 0.932–0.963) 0.962 0.945–0.978) cohorts, respectively, which significantly higher than AUC value quick Sequential Organ Failure Assessment (qSOFA) A). curves datasets close ideal model. Decision revealed that exhibited better net clinical benefit A. Conclusion developed validated novel could predict wards, helping decision-making.

Language: Английский

Citations

Machine Learning Models as Early Warning Systems for Neonatal Infection DOI

Brynne A. Sullivan, Robert W. Grundmeier

Clinics in Perinatology, Journal Year: 2024, Volume and Issue: 52(1), P. 167 - 183

Published: Nov. 27, 2024

Language: Английский

Citations

Heart rate analysis in neonatal sepsis: a complex equation DOI