Pediatric post COVID-19 condition: an umbrella review of the most common symptoms and associated factors.

BACKGROUND: Although the long-term consequences of the Coronavirus Disease-2019 (COVID-19) pandemic are yet to be fully comprehended, a syndrome symptomatically akin to the COVID-19 disease has been defined, for children and adolescents, in February 2023 by the World Health Organization (WHO) as 'post COVID-19 condition' (PCC). Potential consequences of COVID-19 that affect developmental milestones in children and adolescents should be comprehended in their magnitude and duration. The aim is to investigate the most common symptoms and predictors or risk factors for pediatric PCC. METHODS: In this umbrella review, the population of interest was defined as children and adolescents from 0 to 19 years old presenting PCC symptoms as defined by the WHO in the International Classification of Diseases. The intervention considered was general follow-up activity to monitor the patients' recovery status. No comparator was chosen, and the outcomes were symptoms of PCC and predictors or risk factors of developing PCC. Methodological quality, risk of bias and the level of overlap between studies were assessed. A random-effects meta-analytic synthesis of respective estimates with inverse variance study weighting was carried out, for the primary studies included by the reviews retrieved, regarding predictors or risk factors reported. RESULTS: We identified six eligible systematic reviews, five with meta-analyses, from three databases. The most common symptoms reported were fatigue and respiratory difficulties; female sex and older age were the most reported factors associated with the development of pediatric PCC. CONCLUSIONS: A deeper understanding of pediatric PCC requires well-designed and clearly defined prospective studies, symptom differentiation, and adequate follow-up.

Cost-Effectiveness of Whole-Genome vs Whole-Exome Sequencing Among Children With Suspected Genetic Disorders.

Importance: The diagnosis of rare diseases and other genetic conditions can be daunting due to vague or poorly defined clinical features that are not recognized even by experienced clinicians. Next-generation sequencing technologies, such as whole-genome sequencing (WGS) and whole-exome sequencing (WES), have greatly enhanced the diagnosis of genetic diseases by expanding the ability to sequence a large part of the genome, rendering a cost-effectiveness comparison between them necessary. Objective: To assess the cost-effectiveness of WGS compared with WES and conventional testing in children with suspected genetic disorders. Design, Setting, and Participants: In this economic evaluation, a bayesian Markov model was implemented from January 1 to June 30, 2023. The model was developed using data from a cohort of 870 pediatric patients with suspected genetic disorders who were enrolled and underwent testing in the Ospedale Pediatrico Bambino Gesù, Rome, Italy, from January 1, 2015, to December 31, 2022. The robustness of the model was assessed through probabilistic sensitivity analysis and value of information analysis. Main Outcomes and Measures: Overall costs, number of definitive diagnoses, and incremental cost-effectiveness ratios per diagnosis were measured. The cost-effectiveness analyses involved 4 comparisons: first-tier WGS with standard of care; first-tier WGS with first-tier WES; first-tier WGS with second-tier WES; and first-tier WGS with second-tier WGS. Results: The ages of the 870 participants ranged from 0 to 18 years (539 [62%] girls). The results of the analysis suggested that adopting WGS as a first-tier strategy would be cost-effective compared with all other explored options. For all threshold levels above €29 800 (US $32 408) per diagnosis that were tested up to €50 000 (US $54 375) per diagnosis, first-line WGS vs second-line WES strategy (ie, 54.6%) had the highest probability of being cost-effective, followed by first-line vs second-line WGS (ie, 54.3%), first-line WGS vs the standard of care alternative (ie, 53.2%), and first-line WGS vs first-line WES (ie, 51.1%). Based on sensitivity analyses, these estimates remained robust to assumptions and parameter uncertainty. Conclusions and Relevance: The findings of this economic evaluation encourage the development of policy changes at various levels (ie, macro, meso, and micro) of international health systems to ensure an efficient adoption of WGS in clinical practice and its equitable access.

Bayesian cost-effectiveness analysis of Whole genome sequencing versus Whole exome sequencing in a pediatric population with suspected genetic disorders.

Genetic diseases are medical conditions caused by sequence or structural changes in an individual's genome. Whole exome sequencing (WES) and whole genome sequencing (WGS) are increasingly used for diagnosing suspected genetic conditions in children to reduce the diagnostic delay and accelerating the implementation of appropriate treatments. While more information is becoming available on clinical efficacy and economic sustainability of WES, the broad implementation of WGS is still hindered by higher complexity and economic issues. The aim of this study is to estimate the cost-effectiveness of WGS versus WES and standard testing for pediatric patients with suspected genetic disorders. A Bayesian decision tree model was set up. Model parameters were retrieved both from hospital administrative datasets and scientific literature. The analysis considered a lifetime time frame and adopted the perspective of the Italian National Health Service (NHS). Bayesian inference was performed using the Markov Chain Monte Carlo simulation method. Uncertainty was explored through a probabilistic sensitivity analysis (PSA) and a value of information analysis (VOI). The present analysis showed that implementing first-line WGS would be a cost-effective strategy, against the majority of the other tested alternatives at a threshold of €30,000-50,000, for diagnosing outpatient pediatric patients with suspected genetic disorders. According to the sensitivity analyses, the findings were robust to most assumption and parameter uncertainty. Lessons learnt from this modeling study reinforces the adoption of first-line WGS, as a cost-effective strategy, depending on actual difficulties for the NHS to properly allocate limited resources.

Organizational Aspects of the Implementation and Use of Whole Genome Sequencing and Whole Exome Sequencing in the Pediatric Population in Italy: Results of a Survey.

This study explores the organizational aspects of whole genome sequencing (WGS) implementation for pediatric patients with suspected genetic disorders in Italy, comparing it with whole exome sequencing (WES). Health professionals' opinions were collected through an internet-based survey and analyzed using a qualitative summative content analysis methodology. Among the 16 respondents, most were clinical geneticists performing only WES, while 5 also used WGS. The key differences identified include higher needs for analyzing genome rearrangements following WES, greater data storage and security requirements for WGS, and WGS only being performed in specific research studies. No difference was detected in centralization and decentralization issues. The main cost factors included genetic consultations, library preparation and sequencing, bioinformatic analysis, interpretation and confirmation, data storage, and complementary diagnostic investigations. Both WES and WGS decreased the need for additional diagnostic analyses when not used as last-resort tests. Organizational aspects were similar for WGS and WES, but economic evidence gaps may exist for WGS in clinical settings. As sequencing costs decline, WGS will likely replace WES and traditional genetic testing. Tailored genomic policies and cost-effectiveness analyses are needed for WGS implementation in health systems. WGS shows promise for enhancing genetics knowledge and expediting diagnoses for pediatric patients with genetic disorders.

Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review.

OBJECTIVES: The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type. METHODS: A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials. RESULTS: Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety. CONCLUSION: This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.