Who Receives Home-Based Perinatal Palliative Care: Experience from Poland

The current literature suggests that perinatal palliative care (PPC) programs should be comprehensive, initiated early, and integrative. So far there have been very few publications on the subject of home-based PC of newborns and neonates. Most publications focus on hospital-based care, mainly in the neonatal intensive care units. Objective. To describe the neonates and infants who received home-based palliative care in Lodz Region between 2005 and 2011. Methods. A retrospective review ofmedical records. Results. 53 neonates and infants were admitted to a home hospice in Lodz Region between 2005 and 2011. In general, they are a growing group of patients referred to palliative care. Congenital diseases (41%) were the primary diagnoses; out of 53 patients 16 died, 20 were discharged home, and 17 stayed under hospice care until 2011. The most common cause of death (56%) was cardiac insufficiency. Neurological symptoms (72%) and dysphagia (58%) were the most common clinical problems.The majority of children (45%) had a feeding tube inserted and were oxygen dependent (45%); 39 families received psychological care and 31 social supports. Conclusions. For terminally ill neonates and infants, perinatal palliative care is an option which improves the quality of their lives and provides the family with an opportunity to say goodby

Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243

A Cross-Sectional Study of People with Epilepsy and Neurocysticercosis in Tanzania: Clinical Characteristics and Diagnostic Approaches.

Neurocysticercosis (NCC) is a major cause of epilepsy in regions where pigs are free-ranging and hygiene is poor. Pork production is expected to increase in the next decade in sub-Saharan Africa, hence NCC will likely become more prevalent. In this study, people with epilepsy (PWE, n=212) were followed up 28.6 months after diagnosis of epilepsy. CT scans were performed, and serum and cerebrospinal fluid (CSF) of selected PWE were analysed. We compared the demographic data, clinical characteristics, and associated risk factors of PWE with and without NCC. PWE with NCC (n=35) were more likely to be older at first seizure (24.3 vs. 16.3 years, p=0.097), consumed more pork (97.1% vs. 73.6%, p=0.001), and were more often a member of the Iraqw tribe (94.3% vs. 67.8%, p=0.005) than PWE without NCC (n=177). PWE and NCC who were compliant with anti-epileptic medications had a significantly higher reduction of seizures (98.6% vs. 89.2%, p=0.046). Other characteristics such as gender, seizure frequency, compliance, past medical history, close contact with pigs, use of latrines and family history of seizures did not differ significantly between the two groups. The number of NCC lesions and active NCC lesions were significantly associated with a positive antibody result. The electroimmunotransfer blot, developed by the Centers for Disease Control and Prevention, was more sensitive than a commercial western blot, especially in PWE and cerebral calcifications. This is the first study to systematically compare the clinical characteristics of PWE due to NCC or other causes and to explore the utility of two different antibody tests for diagnosis of NCC in sub-Saharan Africa

Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: A retrospective cohort study

Background: The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event. Methods: This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models. Findings: The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0·826 [95% CI 0·817-0·835], AUC 0·897 [95% CI 0·875-0·913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0·739 [95% CI 0·738-0·741], AUROC 0·846 [95% CI 0·826-0·861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0·650 [95% CI 0·643-0·657], AUC 0·694 [95% CI 0·685-0·705], XGBoost: F1-score 0·679 [0·676-0·683], AUC 0·725 [0·717-0·734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0·596 [0·590-0·601], AUC 0·670 [0·664-0·675], XGBoost: F1-score 0·678 [0·668-0·687], AUC 0·710 [0·703-0·714]). Interpretation: Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts.</p

Models of collaboration between psychologist and family doctor: a systematic review of primary care psychology

open2noThe prevalence of psychological suffering is greater than the actual request for clinical consultation in Europe (Alonso et al., 2004). In Italy, no more than 5.5% of the population requested psychological assistance during lifetime (Miglioretti et al., 2008). There are different obstacles that prevent the access to mental health services, such as economic restrictions (Mulder et al., 2011), cultural prejudice (Kim et al., 2010), and lack of knowledge about the service providers that can answer to the patient’s psychological needs (Molinari et al., 2012). Therefore, the psychologist is often consulted as a last resort, only after everything else has failed, when problems have become severe, and thus requiring longer, more intensive, and expensive treatments. The introduction of the Primary Care Psychologist, a professional who works together with the family doctor, allows to overcome the above-mentioned problems and intercept unexpressed needs for psychological assistance. This professional role is operating in many countries since several years. In this study, current literature concerning different models of collaboration between physician and psychologist, in Europe and in Italy, was reviewed. A systematic search of Web of Science (ISI), Pubmed, Scopus, and PsychINFO was conducted using the initial search terms Primary Care Psychologist, Family Doctor, Primary Care, Collaborative Practice, and several relevant papers were identified. The review has shown the improved quality of care when mental health care is integrated into primary. Analyzing how different programs are implemented, results indicated that the more efficacious models of Primary Care Psychology are those tailored on the environment’s needs.The results of our systematic review stress the importance of the Primary Care Psychologist implementation also in Italy, to intercept unexpressed psychological needs and enhance clients’ quality of life.openFrancesca, Bianco; Enrico, BenelliBianco, Francesca; Benelli, Enric