Stephanie A Adaikalam1,2, Nara S Higano2,3,4,5,6, Erik B Hysinger2,3,4,5, Alister J Bates2,3,4,5,6, Robert J Fleck2,6,7, Andrew H Schapiro2,6,7, Melissa A House1,2,5, Amy T Nathan1,2,5, Shawn K Ahlfeld1,2,5, Jennifer M Brady1,2,5, Jason C Woods2,3,4,5,6,7, Paul S Kingma1,2,3,5. 1. Department of Pediatrics, The Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 2. Department of Pediatrics, Cincinnati Bronchopulmonary Dysplasia Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 3. Division of Pulmonary Medicine and Department of Radiology, Center for Pulmonary Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 4. Department of Pediatrics, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 5. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA. 6. Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 7. Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
Abstract
RATIONALE: Clinical management of neonatal bronchopulmonary dysplasia (BPD) is often imprecise and can vary widely between different institutions and providers, due to limited objective measurements of disease pathology severity. There is critical need to improve guidance on the application and timing of interventional treatments, such as tracheostomy. OBJECTIVES: To generate an imaging-based clinical tool for early identification of those patients with BPD who are likely to require later tracheostomy and long-term mechanical ventilation. METHODS: We conducted a prospective cohort study of n = 61 infants (55 BPD, 6 preterm non-BPD). Magnetic resonance imaging (MRI) scores of lung parenchymal disease were used to create a binomial logistic regression model for predicting tracheostomy requirement. This model was further investigated using clinical variables and MRI-quantified tracheomalacia (TM). MEASUREMENTS AND MAIN RESULTS: A model for predicting tracheostomy requirement was created using MRI parenchymal score. This model had 89% accuracy, 100% positive predictive value (PPV), and 85% negative predictive value (NPV), compared with 84%, 60%, and 83%, respectively, when using only relevant clinical variables. In a subset of patients with airway MRI (n = 36), a model including lung and TM measurements had 83% accuracy, 92% PPV, and 78% NPV. CONCLUSIONS: MRI-based measurements of parenchymal disease and TM can be used to predict need for tracheostomy in infants with BPD, more accurately than clinical factors alone. This prediction model has strong potential as a clinical tool for physicians and families for early determination of tracheostomy requirement.
RATIONALE: Clinical management of neonatal bronchopulmonary dysplasia (BPD) is often imprecise and can vary widely between different institutions and providers, due to limited objective measurements of disease pathology severity. There is critical need to improve guidance on the application and timing of interventional treatments, such as tracheostomy. OBJECTIVES: To generate an imaging-based clinical tool for early identification of those patients with BPD who are likely to require later tracheostomy and long-term mechanical ventilation. METHODS: We conducted a prospective cohort study of n = 61 infants (55 BPD, 6 preterm non-BPD). Magnetic resonance imaging (MRI) scores of lung parenchymal disease were used to create a binomial logistic regression model for predicting tracheostomy requirement. This model was further investigated using clinical variables and MRI-quantified tracheomalacia (TM). MEASUREMENTS AND MAIN RESULTS: A model for predicting tracheostomy requirement was created using MRI parenchymal score. This model had 89% accuracy, 100% positive predictive value (PPV), and 85% negative predictive value (NPV), compared with 84%, 60%, and 83%, respectively, when using only relevant clinical variables. In a subset of patients with airway MRI (n = 36), a model including lung and TM measurements had 83% accuracy, 92% PPV, and 78% NPV. CONCLUSIONS: MRI-based measurements of parenchymal disease and TM can be used to predict need for tracheostomy in infants with BPD, more accurately than clinical factors alone. This prediction model has strong potential as a clinical tool for physicians and families for early determination of tracheostomy requirement.
Authors: K Murthy; R C Savani; J M Lagatta; I Zaniletti; R Wadhawan; W Truog; T R Grover; H Zhang; J M Asselin; D J Durand; B L Short; E K Pallotto; M A Padula; F D Dykes; K M Reber; J R Evans Journal: J Perinatol Date: 2014-03-20 Impact factor: 2.521
Authors: Jean A Tkach; Noah H Hillman; Alan H Jobe; Wolfgang Loew; Ron G Pratt; Barret R Daniels; Suhas G Kallapur; Beth M Kline-Fath; Stephanie L Merhar; Randy O Giaquinto; Patrick M Winter; Yu Li; Machiko Ikegami; Jeffrey A Whitsett; Charles L Dumoulin Journal: Pediatr Radiol Date: 2012-06-27
Authors: Laura L Walkup; Jean A Tkach; Nara S Higano; Robert P Thomen; Sean B Fain; Stephanie L Merhar; Robert J Fleck; Raouf S Amin; Jason C Woods Journal: Am J Respir Crit Care Med Date: 2015-11-15 Impact factor: 21.405
Authors: Erik B Hysinger; Alister J Bates; Nara S Higano; Dan Benscoter; Robert J Fleck; Catherine K Hart; Gregory Burg; Alessandro De Alarcon; Paul S Kingma; Jason C Woods Journal: Chest Date: 2019-12-17 Impact factor: 9.410