Humanitas SpA

Introduction High-flow oxygen therapy is increasingly used to treat acute respiratory failure (ARF). It can reduce intubation rates without increasing mortality. Moreover, it is better tolerated than other non-invasive respiratory support therapies. However, patients with ARF often make strong breathing efforts, which may cause several harm. They can fatigue the respiratory muscles. They can increase whole-body oxygen consumption and carbon dioxide production. They can cause pulmonary edema. They might also injure the diaphragm and the lungs. For all these reasons, strong breathing efforts should be recognized and treated.
During spontaneous breathing, inspiratory muscle contractions produce parallel deflections of the pleural and esophageal pressure (ΔPes), which reflect the magnitude of the effort. Unlike changes in pleural pressure, ΔPes can be easily measured at the bedside using a catheter similar to a typical nasogastric tube. In healthy subjects, ΔPes is only a few cmH2O during quiet breathing but >10-15 cmH2O during vigorous exercise or carbon dioxide inhalation. In patients with ARF, the upper limit for a "safe" ΔPes is unknown. Nonetheless, according to experts, breathing efforts with a ΔPes >10-15 cmH2O are probably too strong to be tolerated for a long time.
However, esophageal manometry is not widely available. Estimating breathing efforts without it is complex, especially in non-intubated patients. Doctors mostly rely on their gestalt or overall impression. Therefore, it is unsurprising that they may disagree when rating their patients' breathing efforts or debating whether to proceed to intubation.
The investigators have recently developed two clinical prediction models for estimating the breathing effort of patients with ARF from a few variables readily available at the bedside. The first, "linear", model estimates the continuous value of ΔPes (in cmH2O) from the presence or absence of COVID-19, arterial base excess concentration (BEa) (in mmol/L), respiratory rate (in bpm), the ratio of the arterial tension to the inspiratory fraction of oxygen (PaO2:FiO2) (in mmHg), and the product term between COVID-19 and PaO2:FiO2. The calibration slope was 1, and the adjusted R2 was 0.39. The second, "logistic", model estimates the probability of ΔPes being >10 cmH2O (dichotomous outcome) from BEa (in mmol/L), respiratory rate (in bpm), and PaO2:FiO2 (in mmHg). When this model was tested on the same data set used to develop it (apparent performance), the area under the ROC curve (AUROC) was 0.79 (95% CI, 0.73-0.85). At internal validation (optimism-corrected performance), the AUROC was 0.76 (0.71-0.81). The investigators called these models BREF, which stands for BReathing EFfort, but also to the three main predictors: BEa (B), respiratory rate (RE), and PaO2:FiO2 (F).
Study aims The main goal of this study is to evaluate the BREF models' predictive performance in a new population. This process is known as "external validation". Additionally, there are two other secondary objectives. The investigators aim to update the BREF models by adding more variables unavailable in the development dataset (method "extension") and compare the accuracy of the BREF models with that of doctors who do not use them in assessing their patients' breathing efforts.
Study population
Inclusion criteria:
* adult (≥18 years of age) patients in the ICU
* treated with high-flow oxygen delivered via nasal cannula
* equipped with an esophageal balloon as per local clinical practice.
Exclusion criteria:
* history of chronic lung disease
* cardiogenic pulmonary edema
* >96 hours from admission to the participating unit.
Methods First, participants will record all the variables needed to estimate ΔPes using the original version of the BREF models. These are the presence or absence of COVID-19, BEa, respiratory rate, PaO2:FiO2, and the product term between COVID-19 and PaO2:FiO2. Moreover, participants will record other variables that may help predict ΔPes based on scientific reasoning. Next, the attending doctor (blinded to the actual ΔPes) will assess the patient's breathing effort based on clinical judgment. Third, the actual ΔPes will be measured with esophageal manometry. Finally, the participants will record the type of respiratory support provided to the patient in the 72 hours following the study, the length of stay in the unit, and the vital status of the patient (dead or alive) at discharge from the unit.
Sample size calculation This study aims to enroll 250 patients in approximately two years.

The goal of this clinical trial is to demonstrate the efficacy of multi domain robotic and technological rehabilitation on everyday life and satisfaction in children with neuromotor conditions. The main questions it aims to answer are:
* Will we have significant changes using the COPM in children taking part in the sperimental group?
* The children who will undergo the multidomain treatment will also experience functional improvements in individual areas?
* Thy type of treatment will improve motivation, active participation and satisfaction in children?
Researcher will compare the multidomain treatment to standard care proposed by the included clinical centers: single domain rehabilitation with one innovative device. Both groups will undergo also conventional multidomain therapy.
This will be done to see if using a holistic approach, and including every need of the child in the rehabilitation plan, can lead to greater improvements in quality of life compared to the control group.
Participant will:
* Be evaluated 3 times with clinical outcome measures
* Take part in a rehabilitation program, at least 20 x 2hours sessions treatment, organized in:
* 4 weeks, 5 times per week
* 7 weeks, 3 times per week

Cerebral palsy (CP) is the most common chronic disability in childhood, burden by motor, sensation, cognition, feeding and communication impairment. A serious concern in children with CP is bone/muscle health deterioration, which negatively impacts the already reduced quality of life (QoL). Children with CP show low bone density, vitamin D deficiency, sarcopenia and high risk of fragility fractures, with heavy effects on what is already limited home, school and community life. The causes for muscle-bone impairment are low weight-bearing deambulation during skeletal formation with low bone mineralization, poor nutrition and low calcium intake, low sun exposure, use of anticonvulsant medications with a negative profile on bone. Understanding the causes affecting bone quality and setting up interventions to reduce the impact of physical disability are essential in young subjects with CP.
This project combines complementary expertise and resources in the fields of Endocrinological Biochemistry, Paediatric Neurological Disorders and Neuroimaging, to allow an innovative, technology-assisted workup for bone/muscle health evaluation in young subjects with CP, which could drive novel therapeutics, nutritional and rehabilitation programs.
The first aim of this project is to evaluate bone/muscle health in young subjects with CP compared with sex-age matched healthy subjects, providing i) serum biomarkers of mineral metabolism and the metabolome of Vitamin D, assessed with last generation Mass Spectrometry, ii) muscle sarcopenia markers like Irisin and other myokines, depicting the response of the muscle to exercise iii) neuronal damage and inflammatory markers, iv) densitometric data by the low-cost and safe Quantitative ultrasound (QUS) at phalanges of the hand, plus the novel and very promising Radiofrequency Echographic Multi Spectrometry (REMS) served by the software for fragility fractures risk.
We also aim to correlate the previous mentioned markers of bone/muscle health with a combination of demographic, clinical, cognitive and technological parameters, the last obtained by an innovative use of wearable sensors or actigraphs, positioned at the wrists, which depicts movements, physical activity (PA), energy expenditures (EE) and, together with heart-rate monitors, metabolic data during a normal like week in subjects with CP and healthy controls. Finally, a newly validated scoring for brain lesions in subjects with CP and the production of imaging "biomarkers" of neuronal damage, will be correlated with their bone/muscle health data, PA and EE to understand the impact of brain damage on functional performance and bone metabolism.