Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal clinical research environment for such development because they collect many clinical data and are highly computerized environments. We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36 % of total) and 3503 negative cases classified by two independent physicians using a standardized approach. The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89 % accuracy, 88 % recall, and 89 % precision. Furthermore, a generative autoencoder learning algorithm was proposed to leverage the sparsity reduction that achieved 91% accuracy, 91% recall, and 91% precision. This study successfully applied learning representation and machine learning algorithms to detect heart failure from clinical natural language in a single French institution. Further work is needed to use the same methodology in other institutions and other languages.Comment: Submitting to IEEE Transactions on Biomedical Engineering. arXiv admin note: text overlap with arXiv:2104.0393

Executive Summary of the Second International Guidelines for the Diagnosis and Management of Pediatric Acute Respiratory Distress Syndrome (PALICC-2)

OBJECTIVES: We sought to update our 2015 work in the Second Pediatric Acute Lung Injury Consensus Conference (PALICC-2) guidelines for the diagnosis and management of pediatric acute respiratory distress syndrome (PARDS), considering new evidence and topic areas that were not previously addressed. DESIGN: International consensus conference series involving 52 multidisciplinary international content experts in PARDS and four methodology experts from 15 countries, using consensus conference methodology, and implementation science. SETTING: Not applicable. PATIENTS: Patients with or at risk for PARDS. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Eleven subgroups conducted systematic or scoping reviews addressing 11 topic areas: 1) definition, incidence, and epidemiology; 2) pathobiology, severity, and risk stratification; 3) ventilatory support; 4) pulmonary-specific ancillary treatment; 5) nonpulmonary treatment; 6) monitoring; 7) noninvasive respiratory support; 8) extracorporeal support; 9) morbidity and long-term outcomes; 10) clinical informatics and data science; and 11) resource-limited settings. The search included MEDLINE, EMBASE, and CINAHL Complete (EBSCOhost) and was updated in March 2022. Grading of Recommendations, Assessment, Development, and Evaluation methodology was used to summarize evidence and develop the recommendations, which were discussed and voted on by all PALICC-2 experts. There were 146 recommendations and statements, including: 34 recommendations for clinical practice; 112 consensus-based statements with 18 on PARDS definition, 55 on good practice, seven on policy, and 32 on research. All recommendations and statements had agreement greater than 80%. CONCLUSIONS: PALICC-2 recommendations and consensus-based statements should facilitate the implementation and adherence to the best clinical practice in patients with PARDS. These results will also inform the development of future programs of research that are crucially needed to provide stronger evidence to guide the pediatric critical care teams managing these patients.</p

Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning

When dealing with clinical text classification on a small dataset, recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, including deep learning ones. To increase the performance of the neural network classifier, feature selection for the learning representation can effectively be used. However, most feature selection methods only estimate the degree of linear dependency between variables and select the best features based on univariate statistical tests. Furthermore, the sparsity of the feature space involved in the learning representation is ignored. Goal: Our aim is, therefore, to access an alternative approach to tackle the sparsity by compressing the clinical representation feature space, where limited French clinical notes can also be dealt with effectively. Methods: This study proposed an autoencoder learning algorithm to take advantage of sparsity reduction in clinical note representation. The motivation was to determine how to compress sparse, high-dimensional data by reducing the dimension of the clinical note representation feature space. The classification performance of the classifiers was then evaluated in the trained and compressed feature space. Results: The proposed approach provided overall performance gains of up to 3&#x0025; for each test set evaluation. Finally, the classifier achieved 92&#x0025; accuracy, 91&#x0025; recall, 91&#x0025; precision, and 91&#x0025; f1-score in detecting the patient&#x2019;s condition. Furthermore, the compression working mechanism and the autoencoder prediction process were demonstrated by applying the theoretic information bottleneck framework. Clinical and Translational Impact Statement&#x2014; An autoencoder learning algorithm effectively tackles the problem of sparsity in the representation feature space from a small clinical narrative dataset. Significantly, it can learn the best representation of the training data because of its lossless compression capacity compared to other approaches. Consequently, its downstream classification ability can be significantly improved, which cannot be done using deep learning models

Sevoflurane Sedation with AnaConDa-S Device for a Child Undergoing Extracorporeal Membrane Oxygenation

International audienceBackground: Deep sedation in critically ill children undergoing extracorporeal membrane oxygenation (ECMO) can be challenging. Volatile anesthetics like sevoflurane can be a good alternative for patients hospitalized in pediatric intensive care units, in whom adequate sedation is difficult to obtain.Case description: We report here the first pediatric case of a patient under extracorporeal membrane oxygenation receiving sedation by sevoflurane using the AnaConDa-S device. This 2-year-old girl, suffering from congenital diaphragmatic hernia, was put on extracorporeal membrane oxygenation due to a persistent pulmonary hypertension following metapneumovirus infection. Despite high doses of drugs, neither satisfactory sedation nor analgesia could be reached. Sevoflurane allowed her to be released and we were able to wean her from certain drugs. Her physiological parameters and the indicators of pain and sedation improved.Conclusion: Anesthesia using sevoflurane with the AnaConDa-S device is efficient for children under ECMO.Clinical significance: This is the first pediatric report on anesthesia with sevoflurane under ECMO

Clinical Decision Support System to Detect the Occurrence of Ventilator-Associated Pneumonia in Pediatric Intensive Care

Objectives: Ventilator-associated pneumonia (VAP) is a severe care-related disease. The Centers for Disease Control defined the diagnosis criteria; however, the pediatric criteria are mainly subjective and retrospective. Clinical decision support systems have recently been developed in healthcare to help the physician to be more accurate for the early detection of severe pathology. We aimed at developing a predictive model to provide early diagnosis of VAP at the bedside in a pediatric intensive care unit (PICU). Methods: We performed a retrospective single-center study at a tertiary-care pediatric teaching hospital. All patients treated by invasive mechanical ventilation between September 2013 and October 2019 were included. Data were collected in the PICU electronic medical record and high-resolution research database. Development of the clinical decision support was then performed using open-access R software (Version 3.6.1®). Measurements and main results: In total, 2077 children were mechanically ventilated. We identified 827 episodes with almost 48 h of mechanical invasive ventilation and 77 patients who suffered from at least one VAP event. We split our database at the patient level in a training set of 461 patients free of VAP and 45 patients with VAP and in a testing set of 199 patients free of VAP and 20 patients with VAP. The Imbalanced Random Forest model was considered as the best fit with an area under the ROC curve from fitting the Imbalanced Random Forest model on the testing set being 0.82 (95% CI: (0.71, 0.93)). An optimal threshold of 0.41 gave a sensitivity of 79.7% and a specificity of 72.7%, with a positive predictive value (PPV) of 9% and a negative predictive value of 99%, and with an accuracy of 79.5% (95% CI: (0.77, 0.82)). Conclusions: Using machine learning, we developed a clinical predictive algorithm based on clinical data stored prospectively in a database. The next step will be to implement the algorithm in PICUs to provide early, automatic detection of ventilator-associated pneumonia

Hidden Harlequin syndrome in neonatal and pediatric VA-ECMO

International audienc

Nusinersen in patients older than 7 months with spinal muscular atrophy type 1: A cohort study.

OBJECTIVE: To evaluate the safety and clinical efficacy of nusinersen in patients older than 7 months with spinal muscular atrophy type 1 (SMA1). METHODS: Patients with SMA1 were treated with nusinersen by intrathecal injections as a part of the Expanded Access Program (EAP; NCT02865109). We evaluated patients before treatment initiation (M0) and at 2 months (M2) and 6 months (M6) after treatment initiation. Survival, respiratory, and nutritional data were collected. Motor function was assessed with the modified Hammersmith Infant Neurologic Examination Part 2 (HINE-2) and physiotherapist scales adjusted to patient age (Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders and the Motor Function Measure 20 or 32). RESULTS: We treated 33 children ranging in age from 8.3 to 113.1 months between December 2016 and May 2017. All patients were alive and were continuing treatment at M6. Median progress on the modified HINE-2 score was 1.5 points after 6 months of treatment (p < 0.001). The need for respiratory support significantly increased over time. There were no statistically significant differences between patients presenting with 2 and those presenting with 3 copies of the survival motor neuron 2 (SMN2) gene. CONCLUSIONS: Our results are in line with the phase 3 study for nusinersen in patients with SMA1 treated before 7 months of age and indicate that patients benefit from nusinersen even at a later stage of the disease. CLINICALTRIALSGOV IDENTIFIER: NCT02865109. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that for patients with SMA1 who are older than 7 months, nusinersen is beneficial

GPR56-related bilateral frontoparietal polymicrogyria:further evidence for an overlap with the cobblestone complex

International audienceGPR56 mutations cause an autosomal recessive polymicrogyria syndrome that has distinctive radiological features combining bilateral frontoparietal polymicrogyria, white matter abnormalities and cerebellar hypoplasia. Recent investigations of a GPR56 knockout mouse model suggest that bilateral bifrontoparietal polymicrogyria shares some features of the cobblestone brain malformation and demonstrate that loss of GPR56 leads to a dysregulation of the maintenance of the pial basement membrane integrity in the forebrain and the rostral cerebellum. In light of these findings and other data in the literature, this study aimed to refine the clinical features with the first description of a foetopathological case and to define the range of cobblestone-like features in GPR56 bilateral bifrontoparietal polymicrogyria in a sample of 14 patients