1
|
Peri F, De Nardi L, Canuto A, Gaiero A, Noli S, Ferretti M, Vergine G, Falcioni A, Copponi E, Tagliabue B, Massart F, Fabiani E, Stringhi C, Rubini M, Zamagni G, Amaddeo A, Genovese MR, Norbedo S. Drowning in Children and Predictive Parameters: A 15-Year Multicenter Retrospective Analysis. Pediatr Emerg Care 2023; 39:516-523. [PMID: 37335544 DOI: 10.1097/pec.0000000000002987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
BACKGROUND Drowning is a serious and underestimated public health problem, with the highest morbidity and mortality reported among children. Data regarding pediatric outcomes of drowning are often inadequate, and data collection is poorly standardized among centers. This study aims to provide an overview of a drowning pediatric population in pediatric emergency department, focusing on its main characteristics and management and evaluating prognostic factors. METHODS This is a retrospective multicenter study involving eight Italian Pediatric Emergency Departments. Data about patients between 0 to 16 years of age who drowned between 2006 and 2021 were collected and analyzed according to the Utstein-style guidelines for drowning. RESULTS One hundred thirty-five patients (60.9% males, median age at the event 5; interquartile range, 3-10) were recruited and only those with known outcome were retained for the analysis (133). Nearly 10% had a preexisting medical conditions with epilepsy being the most common comorbidity. One third were hospitalized in the intensive care unit (ICU) and younger males had a higher rate of ICU admission than female peers. Thirty-five patients (26.3%) were hospitalized in a medical ward while 19 (14.3%) were discharged from the emergency department and 11 (8.3%) were discharged after a brief medical observation less than 24 hours. Six patients died (4.5%). Medium stay in the ED was approximately 40 hours. No difference in terms of ICU admission was found between cardiopulmonary resuscitation performed by bystanders or trained medical personnel ( P = 0.388 vs 0.390). CONCLUSIONS This study offers several perspectives on ED victims who drowned. One of the major finding is that no difference in outcomes was seen in patients who received cardiopulmonary resuscitation performed by bystanders or medical services, highlighting the importance of a prompt intervention.
Collapse
Affiliation(s)
- Francesca Peri
- From the Department of Medicine, Surgery, and Health Sciences, University of Trieste, Trieste, Italy
| | - Laura De Nardi
- From the Department of Medicine, Surgery, and Health Sciences, University of Trieste, Trieste, Italy
| | - Arianna Canuto
- From the Department of Medicine, Surgery, and Health Sciences, University of Trieste, Trieste, Italy
| | - Alberto Gaiero
- Pediatric and Neonatology Unit, Ospedale San Paolo Savona, Savona, Italy
| | - Serena Noli
- Department of Pediatrics, IRCCS Istituto Giannina Gaslini Institute, University of Genova, Genova, Italy
| | - Marta Ferretti
- Department of Pediatrics, IRCCS Istituto Giannina Gaslini Institute, University of Genova, Genova, Italy
| | - Gianluca Vergine
- Department of Pediatrics, Infermi Hospital Rimini, ASL Romagna, Italy
| | - Alice Falcioni
- Department of Pediatrics, Infermi Hospital Rimini, ASL Romagna, Italy
| | | | - Bruna Tagliabue
- Department of Pediatrics, University of Brescia, Brescia, Italy
| | - Francesco Massart
- Pediatric Unit, Maternal and Infant Department, Santa Chiara's University Hospital of Pisa, Pisa, Italy
| | - Elisabetta Fabiani
- Department of Pediatric Emergency, Gaspare Salesi Hospital, Azienda Ospedaliera Ospedali Riuniti, Ancona, Italy
| | | | - Monica Rubini
- Department of Pediatric Emergency, Parma Children's Hospital, Parma, Italy
| | - Giulia Zamagni
- Clinical Epidemiology and Public Health Research Unit, Institute for Maternal and Child Health-IRCCS "Burlo Garofolo," Trieste, Italy
| | - Alessandro Amaddeo
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy
| | - Maria Rita Genovese
- From the Department of Medicine, Surgery, and Health Sciences, University of Trieste, Trieste, Italy
| | - Stefania Norbedo
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy
| |
Collapse
|
2
|
Xie X, Li Z, Xu H, Peng D, Yin L, Meng R, Wu W, Ma W, Chen Q. Non-Fatal Drowning Risk Prediction Based on Stacking Ensemble Algorithm. CHILDREN 2022; 9:children9091383. [PMID: 36138692 PMCID: PMC9498184 DOI: 10.3390/children9091383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/06/2022] [Accepted: 09/10/2022] [Indexed: 11/16/2022]
Abstract
Drowning is a major public health problem and a leading cause of death in children living in developing countries. We seek better machine learning (ML) algorithms to provide a novel risk-assessment insight on non-fatal drowning prediction. The data on non-fatal drowning were collected in Qingyuan city, Guangdong Province, China. We developed four ML models to predict the non-fatal drowning risk, including a logistic regression model (LR), random forest model (RF), support vector machine model (SVM), and stacking-based model, on three primary learners (LR, RF, SVM). The area under the curve (AUC), F1 value, accuracy, sensitivity, and specificity were calculated to evaluate the predictive ability of the different learning algorithms. This study included a total of 8390 children. Of those, 12.07% (1013) had experienced non-fatal drowning. We found the following risk factors are closely associated with the risk of non-fatal drowning: the frequency of swimming in open water, distance between the school and the surrounding open waters, swimming skills, personality (introvert) and relationality with family members. Compared to the other three base models, the stacking generalization model achieved a superior performance in the non-fatal drowning dataset (AUC = 0.741, sensitivity = 0.625, F1 value = 0.359, accuracy = 0.739 and specificity = 0.754). This study indicates that applying stacking ensemble algorithms in the non-fatal drowning dataset may outperform other ML models.
Collapse
Affiliation(s)
- Xinshan Xie
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510200, China
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Zhixing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
- Department of Public Health, School of Medicine, Jinan University, Guangzhou 510630, China
| | - Haofeng Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Dandan Peng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Lihua Yin
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Ruilin Meng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Wei Wu
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510200, China
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
- Correspondence:
| | - Wenjun Ma
- Department of Public Health, School of Medicine, Jinan University, Guangzhou 510630, China
| | - Qingsong Chen
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510200, China
| |
Collapse
|