Aim: Multiple parameters are available to predict the outcome of critically sick neonates admitted in neonatal intensive care unit (NICU). Main aim of the study is to validate the role of TOPS, especially the post-transport TOPS score as a simplified assessment of neonatal acute physiology in predicting mortality among transported neonates admitted at level III NICU. Also, to compare the efficiency of post transport TOPS score with SNAP II PE in predicting mortality.
Methods: A prospective study carried out with 85 neonates transported from various primary health care centres to level III NICU. Physiological status of the neonates was assessed with the help of pre and post transport TOPS scores. Post-transport TOPS score was recorded immediately after the admission and SNAP II PE within 24 h of admission at level III NICU. Receiver operating characteristics analysis was performed to observe the mortality prediction efficiency of TOPS score and was compared with SNAP II PE.
Results: 64 neonates were died due to asphyxia and preterm birth (32%) related complications. Strong significant association with the mortality rate was found between the total post transport TOPS score (0.001) and SNAP II PE (0.003). The AUC, sensitivity and specificity of post transport TOPS score for a cut-off value ≤7 were 0.900, 87.5% and 80% and significant (<0.001) and for SNAP II PE for a cut-off value >12 were 0.913, 75.5% and 100% and is significant (<0.001).
Conclusion: TOPS score, especially the post transport TOPS score has an equally good prediction capacity of mortality similar like SNAP II PE among mobilised critically ill neonates. Hence, the TOPS score can be used as a simple and effective method to predict mortality risk among transported neonates immediately after admission at level III NICU.
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http://dx.doi.org/10.1016/j.heliyon.2022.e10165 | DOI Listing |
EBioMedicine
January 2025
Princess Máxima Center for Paediatric Oncology, Utrecht, the Netherlands. Electronic address:
Background: With many rare tumour types, acquiring the correct diagnosis is a challenging but crucial process in paediatric oncology. Historically, this is done based on histology and morphology of the disease. However, advances in genome wide profiling techniques such as RNA sequencing now allow the development of molecular classification tools.
View Article and Find Full Text PDFFront Bioeng Biotechnol
October 2024
National Clinical Research Center for Oral Diseases, National Center for Stomatology, Shanghai, China.
Introduction: The study aims to predict tooth extraction decision based on four machine learning methods and analyze the feature contribution, so as to shed light on the important basis for experts of tooth extraction planning, providing reference for orthodontic treatment planning.
Methods: This study collected clinical information of 192 patients with malocclusion diagnosis and treatment plans. This study used four machine learning strategies, including decision tree, random forest, support vector machine (SVM) and multilayer perceptron (MLP) to predict orthodontic extraction decisions on clinical examination data acquired during initial consultant containing Angle classification, skeletal classification, maxillary and mandibular crowding, overjet, overbite, upper and lower incisor inclination, vertical growth pattern, lateral facial profile.
Res Pract Thromb Haemost
August 2024
Department of Medicine-Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, The Netherlands.
[This corrects the article DOI: 10.1016/j.rpth.
View Article and Find Full Text PDFRes Pract Thromb Haemost
May 2024
Department of Medicine-Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, The Netherlands.
Background: Implantation of a left ventricular assist device (LVAD) is a crucial therapeutic option for selected end-stage heart failure patients. However, major bleeding (MB) complications postimplantation are a significant concern.
Objectives: We evaluated current risk scores' predictive accuracy for MB in LVAD recipients.
Ear Hear
October 2024
Department of Otorhinolaryngology and Head & Neck surgery, Leiden University Medical Center, Leiden, the Netherlands.
Objectives: We investigated whether listening effort is dependent on task difficulty for cochlear implant (CI) users when using the Matrix speech-in-noise test. To this end, we measured peak pupil dilation (PPD) at a wide range of signal to noise ratios (SNR) by systematically changing the noise level at a constant speech level, and vice versa.
Design: A group of mostly elderly CI users performed the Dutch/Flemish Matrix test in quiet and in multitalker babble at different SNRs.
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