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Relisten is an artificial intelligence (AI)-based software developed by Recog Analytics that improves patient care by facilitating more natural interactions between health care professionals and patients. This tool extracts relevant information from recorded conversations, structuring it in the medical record, and sending it to the Health Information System after the professional's approval. This approach allows professionals to focus on the patient without the need to perform clinical documentation tasks. This study aims to evaluate patient-reported satisfaction and perceived quality of care, assess health care professionals' satisfaction with the care provided, and measure the time spent on entering records into the electronic medical record using this AI-powered solution. This proof-of-concept (PoC) study is conducted as a multicenter trial with the participation of several health care professionals (nurses and physicians) in primary care centers (CAPs). The key outcome measures include (1) patient-reported quality of care (evaluated through anonymous surveys), (2) health care professionals' satisfaction with the care provided (assessed through surveys and structured interviews), and (3) time saved on clinical documentation (determined by comparing the time spent manually writing notes versus reviewing and correcting AI-generated notes). Statistical analyses will be performed for each objective, using independent sample comparison tests according to normality evaluated with the Kolmogorov-Smirnov test and Lilliefors correction. Stratified statistical tests will also be performed to consider the variance between professionals. The protocol has been developed using the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) checklist. Recruitment began in July 2024, and as of November 2024, a total of 318 patients have been enrolled. Recruitment is expected to be completed by March 2025. Data analysis will take place between April and May 2025, with results expected to be published in June 2025. We expect an improvement in the perceived quality of care reported by patients and a significant reduction in the time spent taking clinical notes, with a saving of at least 30 seconds per visit. Although a high quality of the notes generated is expected, it is uncertain whether a significant improvement over the control group, which is already expected to have high-quality notes, will be demonstrated. ClinicalTrials.gov NCT06618092; https://clinicaltrials.gov/study/NCT06618092. DERR1-10.2196/66232.

Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5–10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the architecture of neural circuitry and developing imaging biomarkers of poor neurodevelopment outcome in infants with prenatal diseases.

Background: Our aim was to determine the influence of breastfeeding and postnatal nutrition on cardiovascular remodeling induced by fetal growth restriction (FGR). Methods: A cohort study including 81 children with birthweight <10th centile (FGR) and 121 with adequate fetal growth for gestational age (AGA) was conducted. Cardiovascular endpoints were left ventricular sphericity index (LVSI), carotid intima-media thickness (cIMT), and blood pressure (BP) at 4–5 y of age. The combined effect of FGR and postnatal variables—including breastfeeding, fat dietary intake, and BMI—on cardiovascular endpoints was assessed by linear and robust regressions. Results: FGR was the strongest predictor of cardiovascular remodeling in childhood, leading to lower LVSI and increased cIMT and BP as compared with AGA. Breastfeeding >6 mo (coefficient: 0.0982) and healthy-fat dietary intake (coefficient: −0.0128) showed an independent beneficial effect on LVSI and cIMT, respectively. Overweight/obesity induced an additional increment of 1 SD on cIMT in FGR children (interaction coefficient: 0.0307) when compared with its effect in AGA. BMI increased systolic BP (coefficient: 0.7830) while weight catch-up increased diastolic BP (coefficient: 4.8929). Conclusions: Postnatal nutrition ameliorates cardiovascular remodeling induced by FGR. Breastfeeding and healthy-fat dietary intake improved while increased BMI worsened cardiovascular endpoints, which opens opportunities for targeted postnatal interventions from early life.

Objective: To assess the value of gestational age and cardiovascular Doppler indices in predicting perinatal mortality in a multicenter cohort of early-onset intrauterine growth-restricted (IUGR) fetuses. Methods: A multicenter prospective cohort study including 157 early-onset (<34 weeks) IUGR cases with abnormal umbilical artery (UA) Doppler was conducted. Cardiovascular assessment included the ductus venosus (DV), the aortic isthmus flow index (IFI), and the myocardial performance index (MPI). Isolated and combined values to predict the risk of perinatal death were evaluated by logistic regression and by decision tree analysis, where the gestational age at delivery, UA, and middle cerebral artery (MCA) were also included as covariates. Results: Perinatal mortality was 17% (27/157). All parameters were significantly associated with perinatal death, with individual odds ratios (OR) of 25.2 for gestational age below 28 weeks, 12.1 for absent/reversed DV atrial flow, 5.3 for MCA pulsatility index <5th centile, 4.6 for UA absent/reversed diastolic end-flow, 1.8 for IFI <5th centile, and 1.6 for MPI >95th centile. Decision tree analysis identified gestational age at birth as the best predictor of death (<26 weeks, 93% mortality; 26–28 weeks, 29% mortality, and >28 weeks, 3% mortality). Between 26 and 28 weeks, DV atrial flow allowed further stratification between high (60%) and low risk (18%) of mortality. Conclusions: Gestational age largely determines the risk of perinatal mortality in early-onset IUGR before 26 weeks and later than 28 weeks of gestation. The DV may improve clinical management by stratifying the probability of death between 26 and 28 weeks of gestation.