Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis.

BACKGROUND: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. METHODS: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. RESULTS: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. CONCLUSIONS: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

Effectiveness of Platform-Based Robot-Assisted Rehabilitation for Musculoskeletal or Neurologic Injuries: A Systematic Review.

During the last ten years the use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. This systematic review aims to evaluate the effectiveness of platform-based robotic rehabilitation for individuals with musculoskeletal or neurological injuries. Thirty-eight studies met the inclusion criteria and evaluated the efficacy of platform-based rehabilitation robots. Our findings showed that rehabilitation with platform-based robots produced some encouraging results. Among the platform-based robots studied, the VR-based Rutgers Ankle and the Hunova were found to be the most effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson's disease) and various musculoskeletal ankle injuries. Our results were drawn mainly from studies with low-level evidence, and we think that our conclusions should be taken with caution to some extent and that further studies are needed to better evaluate the effectiveness of platform-based robotic rehabilitation devices.

Effectiveness and feasibility of smoking counselling: a randomized controlled trial in an Italian emergency department.

BACKGROUND: 5A's counselling is recommended for screening and treating patients with smoking addiction. The emergency department (ED) setting might be a suitable environment for conducting interventions for smoking cessation. The present study aims to determine the feasibility and effectiveness on smoking cessation of 5A's counselling administered to ED patients by nurses. METHODS: Parallel group randomized trial assessing 5A's counselling for smoking cessation vs. usual care at a University Hospital in the North of Italy. The primary end-point was prevalence of tobacco-free patients. The secondary outcomes at 6- and 12-month follow-up were (i) consecutive past 30-day smoking abstinence; (ii) past 7-day 50%, or more, decrease in daily tobacco consumption over baseline; and (iii) number of attempts to quit smoking. RESULTS: A total of 480 patients were randomized to intervention (n = 262) or usual care (n = 218). Intention to treat analysis displayed no differences in primary and secondary outcomes between groups. A slight but not statistically significant enhancement in cessation was recorded in the intervention group [relative risk (RR) = 1.04, 95% confidence interval (CI) = 0.58-1.87] at 6 months, whereas a reversed observation at 12 months (RR = 0.86, 95% CI = 0.50-1.47). Similar results were obtained for the secondary outcomes. Per protocol analysis increased the size of the results. Of the 126 smokers receiving counselling, 18 were visited and treated at the local smoking cessation centre, with 12 of them successfully completing the treatment. CONCLUSION: The results of this study indicate that the ED is not a suited environment for 5A's counselling.

Access to medicine among asylum seekers, refugees and undocumented migrants across the migratory cycle: a scoping review protocol.

INTRODUCTION: Migration creates new health vulnerabilities and exacerbates pre-existing medical conditions. Migrants often face legal, system-related, administrative, language and financial barriers to healthcare, but little is known about factors that specifically influence migrants' access to medicines and vaccines. This scoping review aims to map existing evidence on access to essential medicines and vaccines among asylum seekers, refugees and undocumented migrants who aim to reach Europe. We will consolidate existing information and analyse the barriers that limit access at the different stages of the migratory phases, as well as policies and practices undertaken to address them. METHODS: We follow the Arksey and O'Malley framework for knowledge synthesis of research, as updated by Levac et al. For reporting the results of our search and to synthetise evidence, we will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extended reporting guideline for scoping reviews. This scoping review consists of five iterative stages. Bibliographic databases (PubMed, CINAHL, Cochrane Database of Systematic Reviews and Scopus) and grey literature databases (Open Grey, Grey Literature Report and Google Scholar, Web of Science Conference Proceedings, non-governmental organisations and United Nations agency websites) will be searched for relevant studies. DISSEMINATION AND ETHICS: This review will be disseminated through a peer-reviewed article in a scientific open-access journal and conference presentations. Furthermore, findings will be shared at workshops of research and operational stakeholders for facilitating translation into research and operational practices. Since it consists of reviewing and collecting data from publicly available materials, this scoping review does not require ethics approval.

Development of a Model Care Pathway for Myasthenia Gravis.

Myasthenia Gravis (MG) is a chronic, life-lasting condition that requires high coordination among different professionals and disciplines. The diagnosis of MG is often delayed and sometimes misdiagnosed. The goal of the care pathway (CP) is to add value to healthcare reducing unnecessary variations. The quality of the care received by patients affected with MG could benefit from the use of CP. We conducted a study aimed to define an inclusive, comprehensive, and multidisciplinary CP for the diagnosis, treatment, and care of MG. The development of the model CP, key interventions, and process indicators is based on the literature review and 85 international MG experts were involved in their evaluation, expressing a judgment of relevance through the Delphi study. 60 activities are included in the model CP and evaluated by the MG experts were valid and feasible. The 60 activities were then translated into 14 key interventions and 24 process indicators. We believe that the developed model CP will help for MG patients to have a timely diagnosis and high-quality, accessible, and cost-effective treatments and care. We also believe that the development of model CPs for other rare diseases is feasible and could aid in the integration of evidence-based knowledge into clinical practice.

Physical exercise and fall prevention: A systematic review and meta-analysis of experimental studies included in Cochrane reviews.

The aim of this systematic review (SR) and meta-analysis was to assess what type of exercise is associated with fall risk reduction among apparently healthy adults aged 50 and older. We conducted a SR by searching for randomized controlled trials (RCTs) included in Cochrane SRs published until October 2019. Five SRs that compared exercise versus any type of control included 32 RCTs. The outcomes examined were falls, fallers, fractures, and fear of falling. A random effects-based meta-analysis by type of exercise was performed. Almost all the interventions were effective for fall rate reduction, with a major effect for three-dimensional exercise, strength/resistance exercises, and mixed exercises. The number of fallers was reduced by three-dimensional exercise and mixed exercises. Fall-related fractures were generally reduced by all types of exercises considered all together, but none singly resulted in statistically effective fracture prevention. Fear of falling was slightly decreased with endurance exercises.

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review.

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Lymphocyte doubling time in chronic lymphocytic leukemia modern era: a real-life study in 848 unselected patients.

The prognostic significance of lymphocyte doubling time (LDT) in chronic lymphocytic leukemia (CLL) was identified when the biology of the disease was poorly understood and therapy was not effective. We assessed the clinical and biological significance of LDT in 848 CLL patients in a real-life setting and the context of new biomarkers and effective therapy. A short LDT (≤12 months) was enriched for adverse biomarkers. Patients with a rapid LDT did need therapy shortly after diagnosis (median 23 months vs. not reached; p < 0.001) and had a poorer overall survival (median 95 months vs. not reached p < 0.001). LDT, IGHV mutational status, Beta-2 microglobulin, and Rai clinical stage were independent predictors for time to first treatment in the whole series and in Binet stage A patients. No correlation was observed between LDT and response to chemoimmunotherapy. However, a short LDT along with age ≥65 years, high-risk FISH (del(17p), del(11q)), unmutated IGHV, increased Beta-2 microglobulin, and TP53 mutations predicted short survival. Moreover, the prognostic significance of LDT was independent of the CLL-IPI and the Barcelona/Brno prognostic model. LDT remains an important outcome marker in the modern CLL era and should be incorporated into the clinical assessment and stratification of CLL patients.