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This study aims (1) to elucidate the potential relevance of patient and PICU-related characteristics for long-term adverse neurocognitive outcome after PICU admission for bronchiolitis, and (2) to perform a preliminary exploration of the potential of machine learning as compared to linear regression to improve neurocognitive outcome prediction in a relatively small sample of children after PICU admission.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This cross-sectional observational study investigated 65 children aged 6\u201312 years with previous PICU admission for bronchiolitis (age\u2009\u2264\u20091 year). They were compared to demographically comparable healthy peers (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200976) on neurocognitive functioning. Patient and PICU-related characteristics used for the prediction models were as follows: demographic characteristics, perinatal and disease parameters, laboratory results, and intervention characteristics, including hourly validated mechanical ventilation parameters. Neurocognitive outcome was measured by intelligence and computerized neurocognitive testing. Prediction models were developed for each of the neurocognitive outcomes using Regression Trees, k-Nearest Neighbors, and conventional linear regression analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The patient group had lower intelligence than the control group (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.001,\n                      <jats:italic>d<\/jats:italic>\n                      \u2009=\u2009\u22120.59) and poorer performance in neurocognitive functions, i.e., speed and attention (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u2009.03,\n                      <jats:italic>d<\/jats:italic>\n                      \u2009=\u2009\u22120.41) and verbal memory (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.001,\n                      <jats:italic>d<\/jats:italic>\n                      \u2009=\u2009\u22120.60). Lower intelligence was predicted by lower birth weight and lower socioeconomic status (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200925.9%). Poorer performance on the speed and attention domain was predicted by younger age at follow-up (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200953.5%). Poorer verbal memory was predicted by lower birth weight, younger age at follow-up, and greater exposure to acidotic events (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200950.6%). The machine learning models did not reveal added value in terms of model performance as compared to linear regression.\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>Conclusion<\/jats:italic>\n                      : The findings of this study suggest that in children with previous PICU admission for bronchiolitis, (1) lower birth weight, younger age at follow-up, and lower socioeconomic status are associated with poorer neurocognitive outcome; and (2) greater exposure to acidotic events during PICU admission is associated with poorer verbal memory outcome. The findings of this study provide no evidence for the added value of machine learning models as compared to linear regression analysis in the prediction of long-term neurocognitive outcome in a relatively small sample of children.\n                      <jats:table-wrap>\n                        <jats:table>\n                          <jats:tbody>\n                            <jats:tr>\n                              <jats:td colspan=\"2\">\n                                <jats:bold>What is Known:<\/jats:bold>\n                                <jats:italic>\u2022 Adverse neurocognitive outcomes are described in PICU survivors, which are known to interfere with development in other major domains of functioning, such as mental health, academic achievement, and socioeconomic success, highlighting neurocognition as an important outcome after PICU admission.<\/jats:italic>\n                                <jats:italic>\u2022 Machine learning is a rapidly growing field of artificial intelligence that is increasingly applied in health care settings, with great potential to capture the complexity of outcome prediction.<\/jats:italic>\n                              <\/jats:td>\n                            <\/jats:tr>\n                            <jats:tr>\n                              <jats:td colspan=\"2\">\n                                <jats:bold>What is New:<\/jats:bold>\n                                <jats:italic>\u2022 This study shows that lower birth weight, lower socioeconomic status, and greater exposure to acidotic events during PICU admission for bronchiolitis are associated with poorer long-term neurocognitive outcome after PICU admission. Results provide no evidence for the added value of machine learning models in a relatively small sample of children.<\/jats:italic>\n                                <jats:italic>\u2022 As bronchiolitis seldom manifests neurologically, the relation between acidotic events and neurocognitive outcome may reflect either potentially harmful effects of acidosis itself or related processes such as hypercapnia or hypoxic and\/or ischemic events during PICU admission. This study further highlights the importance of structured follow-up to monitor long-term outcome of children after PICU admission.<\/jats:italic>\n                              <\/jats:td>\n                            <\/jats:tr>\n                          <\/jats:tbody>\n                        <\/jats:table>\n                      <\/jats:table-wrap>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00431-023-05307-3","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T03:01:53Z","timestamp":1699239713000},"page":"471-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting long-term neurocognitive outcome after pediatric intensive care unit admission for bronchiolitis\u2014preliminary exploration of the potential of machine learning"],"prefix":"10.1007","volume":"183","author":[{"given":"Eleonore S. 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