Differential activation of basal and IL-7-induced PI3K/Akt/mTOR and JAK/STAT5 signaling distinguishes pediatric from adult acute lymphoblastic leukemia.

Not available.

Identification of patients with epilepsy using automated electronic health records phenotyping.

OBJECTIVE: Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy. METHODS: The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping. RESULTS: Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set. SIGNIFICANCE: A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.

Identifying inpatient hospitalizations with continuous electroencephalogram monitoring from administrative data.

BACKGROUND: Continuous electroencephalography (cEEG) is increasingly utilized in hospitalized patients to detect and treat seizures. Epidemiologic and observational studies using administrative datasets can provide insights into the comparative and cost effectiveness of cEEG utilization. Defining patient cohorts that underwent acute inpatient cEEG from administrative datasets is limited by the lack of validated codes differentiating elective epilepsy monitoring unit (EMU) admissions from acute inpatient hospitalization with cEEG utilization. Our aim was to develop hospital administrative data-based models to identify acute inpatient admissions with cEEG monitoring and distinguish them from EMU admissions. METHODS: This was a single center retrospective cohort study of adult (≥ 18 years old) inpatient admissions with a cEEG procedure (EMU or acute inpatient) between January 2016-April 2022. The gold standard for acute inpatient cEEG vs. EMU was obtained from the local EEG recording platform. An extreme gradient boosting model was trained to classify admissions as acute inpatient cEEG vs. EMU using administrative data including demographics, diagnostic and procedure codes, and medications. RESULTS: There were 9,523 patients in our cohort with 10,783 hospital admissions (8.5% EMU, 91.5% acute inpatient cEEG); with average age of 59 (SD 18.2) years; 46.2% were female. The model achieved an area under the receiver operating curve of 0.92 (95% CI [0.91-0.94]) and area under the precision-recall curve of 0.99 [0.98-0.99] for classification of acute inpatient cEEG. CONCLUSIONS: Our model has the potential to identify cEEG monitoring admissions in larger cohorts and can serve as a tool to enable large-scale, administrative data-based studies of EEG utilization.

New Approaches in Early-Stage NSCL Management: Potential Use of PARP Inhibitors and Immunotherapy Combination.

Lung cancer is the second most common cancer in the world, being the first cause of cancer-related mortality. Surgery remains the only potentially curative treatment for Non-Small Cell Lung Cancer (NSCLC), but the recurrence risk remains high (30-55%) and Overall Survival (OS) is still lower than desirable (63% at 5 years), even with adjuvant treatment. Neoadjuvant treatment can be helpful and new therapies and pharmacologic associations are being studied. Immune Checkpoint Inhibitors (ICI) and PARP inhibitors (PARPi) are two pharmacological classes already in use to treat several cancers. Some pre-clinical studies have shown that its association can be synergic and this is being studied in different settings. Here, we review the PARPi and ICI strategies in cancer management and the information will be used to develop a clinical trial to evaluate the potential of PARPi association with ICI in early-stage neoadjuvant setting NSCLC.

Classification of neurologic outcomes from medical notes using natural language processing.

Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely 'good recovery', 'moderate disability', 'severe disability', and 'death' and on the Modified Rankin Scale (mRS), with 7 classes, namely 'no symptoms', 'no significant disability', 'slight disability', 'moderate disability', 'moderately severe disability', 'severe disability', and 'death'. For 428 patients' notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93-0.95) and 0.77 (0.75-0.80) for GOS, and 0.90 (0.89-0.91) and 0.59 (0.57-0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review.

When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n = 12), decision tree methods (n = 11), hybrid models (n = 8), or latent variable models (n = 6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

Machine Learning Models with Preoperative Risk Factors and Intraoperative Hypotension Parameters Predict Mortality After Cardiac Surgery.

OBJECTIVES: Machine learning models used to predict postoperative mortality rarely include intraoperative factors. Several intraoperative factors like hypotension (IOH), vasopressor-inotropes, and cardiopulmonary bypass (CPB) time are significantly associated with postoperative outcomes. The authors explored the ability of machine learning models incorporating intraoperative risk factors to predict mortality after cardiac surgery. DESIGN: Retrospective study. SETTING: Tertiary hospital. PARTICIPANTS: A total of 5,015 adults who underwent cardiac surgery from 2008 to 2016. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: The intraoperative phase was divided into the following: (1) CPB, (2) outside CPB, and (3) total surgery for quantifying IOH only. Phase-specific IOH parameters (area under the curve for mean arterial pressure <65 mmHg), vasopressor-inotropes (norepinephrine equivalents), duration, and cross-clamp time, along with preoperative risk factors ,were incorporated into the models. The primary outcome was mortality. The following 5 models were applied to 3 intraoperative phases separately: (1) logistic regression, (2) random forests, (3) neural networks, (4) support vector machines, and (5) extreme gradient boosting (XGB). Mortality was predicted using area under the receiver operating characteristic curve. Of 5,015 patients included, 112 (2.2%) died. XGB model from the outside-CPB phase predicted mortality better with area under the receiver operating characteristic curve, 95% confidence interval (CI): 0.88(0.83-0.94); positive predictive value, 0.10(0.06-0.15); specificity 0.85 (0.83-0.87) and sensitivity 0.75 (0.57-0.90). CONCLUSION: XGB machine learning model from IOH outside the CPB phase seemed to offer a better discrimination, sensitivity, specificity, and positive predictive value compared with other models. Machine learning models incorporating intraoperative adverse factors might offer better predictive ability for risk stratification and triaging of patients after cardiac surgery.

Evaluation of an Integrated Health Promotion Program for a low-income urban population: Findings and lessons learned.

OBJECTIVES: To evaluate a multicomponent pilot program for low-income individuals with, or at risk for, hypertension, diabetes, and/or overweight. DESIGN: Pre-post evaluation including baseline and follow-up assessments, satisfaction surveys, program utilization data, and focus groups. SAMPLE: The evaluation included 138 participants. The majority were Latinx (88%), female (82%), born outside the United States (80%), and had not graduated from high school (52%). The most common health conditions were hypertension (59%), overweight or obesity (55%), high cholesterol (53%), and diabetes (34%). MEASUREMENTS: Engagement in program activities, health indicators (e.g., blood pressure), and behavior change. Qualitative data focused on perceptions of the program and its impacts. INTERVENTION: The program offered a number of health promotion services, including consultation with a nurse and a community health worker (CHW), health and nutrition talks, subsidized farm shares, cooking classes, exercise classes, and home visits. RESULTS: There were improvements in general health, blood pressure, and knowledge and behavior related to disease management and healthy eating. CONCLUSIONS: Program success was attributed to the wide range of complementary program components. The staffing model was also a strength: the CHW/nurse collaboration combined clinical expertise with cultural, language, and community knowledge to create a program that was accessible and empowering.

Perceived social support before and after bariatric surgery: association with depression, problematic eating behaviors, and weight outcomes.

PURPOSE: Engaging in a healthy lifestyle after bariatric surgery is essential to optimize and sustain weight loss in the long term. There is promising evidence that social support of patients who undergo bariatric surgery plays an important role in promoting a better quality of life and adherence to the required behavioral changes and medical appointments. This study sought to investigate: (a) if post-operative patients experience different levels of perceived social support compared to pre-operative patients; (b) correlations between perceived social support, depression, disordered eating, and weight outcomes; (c) if social support is a moderator between psychological distress, and disordered eating behavior and weight outcomes. METHODS: A group of 65 patients assessed pre-surgery and another group of 65 patients assessed post-surgery (M = 26.12; SD 7.97 months since surgery) responded to a set of self-report measures assessing social support, eating disorder psychopathology, disordered eating, and depression. RESULTS: Greater social support was associated with lower depression, emotional eating, weight and shape concerns, and greater weight loss in pre- and post-surgery groups. Social support was found to be a moderator between different psychological/weight variables but only for the post-surgery group: the relation between depression and eating disorder psychopathology or weight loss was significant for patients scoring medium to high level is social support; the relation between grazing and weight regain was significant for patients scoring medium to low levels of social support. CONCLUSIONS: The associations found between perceived social support and depression, disordered eating and weight outcomes highlight the importance of considering and working with the social support network of patients undergoing bariatric surgery to optimize treatment outcomes. Level of Evidence  Level III: case-control study.

Optimization of bacterial nanocellulose fermentation using recycled paper sludge and development of novel composites.

In this work, recycled paper sludge (RPS), composed of non-recyclable fibres, was used as a carbon source for bacterial nanocellulose (BNC) production. The biomass was enzymatically hydrolysed with Cellic CTec 2 to produce a sugar syrup with 45.40 g/L glucose, 1.69 g/L cellobiose and 2.89 g/L xylose. This hydrolysate was used for the optimization of BNC fermentation by static culture, using Komagataeibacter xylinus ATCC 700178, through response surface methodology (RSM). After analysis and validation of the model, a maximum BNC yield (5.69 g/L, dry basis) was obtained using 1.50% m/v RPS hydrolysate, 1.0% v/v ethanol and 1.45% m/v yeast extract/peptone (YE/P). Further, the BNC obtained was used to produce composites. A mixture of an amino-PolyDiMethylSiloxane-based softener, polyethyleneglycol (PEG) 400 and acrylated epoxidized soybean oil (AESO), was incorporated into the BNC membranes through an exhaustion process. The results show that BNC composites with distinct performances can be easily designed by simply varying the polymers percentage contents. This strategy represents a simple approach towards the production of BNC and BNC-based composites.

Smuggling gold nanoparticles across cell types - A new role for exosomes in gene silencing.

Once released to the extracellular space, exosomes enable the transfer of proteins, lipids and RNA between different cells, being able to modulate the recipient cells' phenotypes. Members of the Rab small GTP-binding protein family, such as RAB27A, are responsible for the coordination of several steps in vesicle trafficking, including budding, mobility, docking and fusion. The use of gold nanoparticles (AuNPs) for gene silencing is considered a cutting-edge technology. Here, AuNPs were functionalized with thiolated oligonucleotides anti-RAB27A (AuNP@PEG@anti-RAB27A) for selective silencing of the gene with a consequent decrease of exosomes´ release by MCF-7 and MDA-MB-453 cells. Furthermore, communication between tumor and normal cells was observed both in terms of alterations in c-Myc gene expression and transportation of the AuNPs, mediating gene silencing in secondary cells.

Application of the therapeutic relationship to people with common mental disorder. / Aplicação do relacionamento terapêutico a pessoas com transtorno mental comum.

OBJECTIVE: To report the results from applying a therapeutic relationship to people with common mental disorder. METHOD: Quantitative, descriptive, before and after study, conducted with 112 records accessed from an extension project in mental health in primary health care in Santo André, São Paulo. Screening was performed using a Self-Reporting Questionnaire. Data was collected in October 2014 and analyzed using simple frequency measures. RESULTS: The entry score ranged between 7 and18 points, and the higher the frequency, the more relationship sessions were necessary. At the end of the relationship process, 55% had a negative score and 45% passed the score ≤3. CONCLUSION: Therapeutic relationship as nursing care in mental health transcends the area of specialty. Continuous review and process redirecting led users to enlarge their view of the suffering triggers, build coping strategies and exercise changes in daily life.

Cancer diagnosis disclosure from Portuguese physicians.

OBJECTIVE: Doctor-patient communication in oncology, particularly concerning diagnostic disclosure, is a crucial factor related to the quality of the doctor-patient relationship and the psychological state of the patient. The aims of our study were to investigate physicians' opinions and practice with respect to disclosure of a cancer diagnosis and to explore potential related factors. METHOD: A self-report questionnaire developed for our study was responded to by 120 physicians from Coimbra University Hospital Centre and its primary healthcare units. RESULTS: Some 91.7% of physician respondents generally disclosed a diagnosis, and 94.2% were of the opinion that the patient knowing the truth about a diagnosis had a positive effect on the doctor-patient relationship. A need for training about communicating with oncology patients was reported by 85.8% of participants. The main factors determining what information to provide to patients were: (1) patient intellectual and cultural level, (2) patient desire to know the truth, and (3) the existence of family. SIGNIFICANCE OF RESULTS: Our results point to a paradigm shift in communication with cancer patients where disclosure of the diagnosis should be made part of general clinical practice. Nevertheless, physicians still experience difficulties in revealing cancer diagnoses to patients and often lack the skills to deal with a patient's emotional responses, which suggests that more attention needs to be focused on communication skills training programs.

Parents' experience of children with acquired brain injury undergoing neurorehabilitation: a systematic review.

BACKGROUND: This systematic review aims to synthesise the qualitative evidence exploring parents' experiences of children with acquired brain injury (ABI) undergoing neurorehabilitation during the first year post-injury. METHODS: A systematic review of qualitative research was conducted using thematic synthesis with Thomas and Harden's approach. The population, exposure and outcome model was used for the search strategy. The electronic databases Ovid Embase, Ovid MEDLINE, CINAHL, Scopus and PsycINFO were searched from 2009 to 2023. The review included qualitative and mixed-method studies published in English only. Grey literature was excluded. There were no geographical restrictions. Reporting within the review followed the Enhancing Transparency in Reporting the Synthesis of Qualitative Research guideline. The studies' quality was appraised using the Critical Appraisal Skills Programme tool. RESULTS: Three studies met the inclusion criteria and were included in the synthesis, representing the experiences of 30 parents. The quality assessment showed that the three included studies met most quality indicators. Following thematic synthesis, four analytical themes were identified: school unpreparedness, parents as advocates and navigators, parents as monitors, and parents recognising the impact of ABI on their child. The reviewers proposed a group of recommendations for services reviewing their parental support. CONCLUSION: This review highlights some challenges parents of children diagnosed with ABI experience during their child's neurorehabilitation journey. This review has suggested potential improvements that could be made in paediatric neurorehabilitation services when reviewing their parental support and care pathways. These will ultimately influence parents' and children's experience of paediatric neurorehabilitation services.

Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing.

BACKGROUND: Multi-center electronic health records (EHR) can support quality improvement initiatives and comparative effectiveness research in stroke care. However, limitations of EHR-based research include challenges in abstracting key clinical variables from non-structured data at scale. This is further compounded by missing data. Here we develop a natural language processing (NLP) model that automatically reads EHR notes to determine the NIH stroke scale (NIHSS) score of patients with acute stroke. METHODS: The study included notes from acute stroke patients (>= 18 years) admitted to the Massachusetts General Hospital (MGH) (2015-2022). The MGH data were divided into training (70%) and hold-out test (30%) sets. A two-stage model was developed to predict the admission NIHSS. A linear model with the least absolute shrinkage and selection operator (LASSO) was trained within the training set. For notes in the test set where the NIHSS was documented, the scores were extracted using regular expressions (stage 1), for notes where NIHSS was not documented, LASSO was used for prediction (stage 2). The reference standard for NIHSS was obtained from Get With The Guidelines Stroke Registry. The two-stage model was tested on the hold-out test set and validated in the MIMIC-III dataset (Medical Information Mart for Intensive Care-MIMIC III 2001-2012) v1.4, using root mean squared error (RMSE) and Spearman correlation (SC). RESULTS: We included 4,163 patients (MGH = 3,876; MIMIC = 287); average age of 69 [SD 15] years; 53% male, and 72% white. 90% patients had ischemic stroke and 10% hemorrhagic stroke. The two-stage model achieved a RMSE [95% CI] of 3.13 [2.86-3.41] (SC = 0.90 [0.88-0. 91]) in the MGH hold-out test set and 2.01 [1.58-2.38] (SC = 0.96 [0.94-0.97]) in the MIMIC validation set. CONCLUSIONS: The automatic NLP-based model can enable large-scale stroke severity phenotyping from EHR and therefore support real-world quality improvement and comparative effectiveness studies in stroke.

Predictors of follow-up care for critically-ill patients with seizures and epileptiform abnormalities on EEG monitoring.

OBJECTIVE: Post-hospitalization follow-up visits are crucial for preventing long-term complications. Patients with electrographic epileptiform abnormalities (EA) including seizures and periodic and rhythmic patterns are especially in need of follow-up for long-term seizure risk stratification and medication management. We sought to identify predictors of follow-up. METHODS: This is a retrospective cohort study of all patients (age ≥ 18 years) admitted to intensive care units that underwent continuous EEG (cEEG) monitoring at a single center between 01/2016-12/2019. Patients with EAs were included. Clinical and demographic variables were recorded. Follow-up status was determined using visit records 6-month post discharge, and visits were stratified as outpatient follow-up, neurology follow-up, and inpatient readmission. Lasso feature selection analysis was performed. RESULTS: 723 patients (53 % female, mean (std) age of 62.3 (16.4) years) were identified from cEEG records with 575 (79 %) surviving to discharge. Of those discharged, 450 (78 %) had outpatient follow-up, 316 (55 %) had a neurology follow-up, and 288 (50 %) were readmitted during the 6-month period. Discharge on antiseizure medications (ASM), younger age, admission to neurosurgery, and proximity to the hospital were predictors of neurology follow-up visits. Discharge on ASMs, along with longer length of stay, younger age, emergency admissions, and higher illness severity were predictors of readmission. SIGNIFICANCE: ASMs at discharge, demographics (age, address), hospital care teams, and illness severity determine probability of follow-up. Parameters identified in this study may help healthcare systems develop interventions to improve care transitions for critically-ill patients with seizures and other EA.

Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study.

Background and Objectives: Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes. Methods: This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk. Results: There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort. Discussion: The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.

IL-7 and IL-7R in health and disease: An update through COVID times.

The role of IL-7 and IL-7R for normal lymphoid development and an adequately functioning immune system has been recognized for long, with severe immune deficiency and lymphoid leukemia as extreme examples of the consequences of deregulation of the IL-7-IL-7R axis. In this review, we provide an update (focusing on the past couple of years) on IL-7 and IL-7R in health and disease. We highlight the findings on IL-7/IL-7R signaling mechanisms and the, sometimes controversial, impact of IL-7 and its receptor on leukocyte biology, COVID-19, acute lymphoblastic leukemia, and different solid tumors, as well as their relevance as therapeutic tools or targets.

Parents' experience of children with acquired brain injury undergoing neuro-rehabilitation: thematic synthesis protocol.

INTRODUCTION: Children with moderate to severe acquired brain injury frequently require a period of demanding medical and rehabilitative care to optimise their long-term capabilities and quality of life. Usually, the initial acute care is provided in tertiary centres and can last up to 12 months following the original injury. Parents of children with acquired brain injury share that experience with their child and face many different challenges encountered as their child's long-term needs become apparent. Parents are essential partners in care, hence there is a need to better understand their experiences to support them as they face those challenges and adapt to the needs of their child. We aim to synthesise the qualitative evidence exploring parents' experiences of children undergoing neuro-rehabilitative care. METHODS AND ANALYSIS: The Enhancing Transparency in Reporting the Synthesis of Qualitative Research guideline was used in the design of this protocol. The Population, Exposure and Outcome model was used to define inclusion and exclusion criteria and refine search terms. The databases Ovid Embase, Ovid MEDLINE, CINAHL, Scopus and PsychINFO will be searched from 2009 to 2022. Two independent reviewers will review studies, assess quality using the Critical Appraisal Skills Programme and scrutinise and extract the data. Disagreements will be resolved after discussion with the third reviewer. Thematic synthesis using Thomas and Harden's approach will be undertaken to provide the evidence to develop a model for parental support during the first year of their child's neuro-rehabilitation. ETHICS AND DISSEMINATION: Ethical committee approval will not be required as no new data will be collected. The findings will be disseminated through presentations at professional conferences, publications in peer-reviewed journals and shared with the public through relevant charities and local family support groups and networks. PROSPERO REGISTRATION NUMBER: CRD42022333182.