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Objective: This study assessed characteristics and outcomes of younger (18-65) versus older (>65) recipients of simultaneous heart–kidney (SHK) transplantation with varying functional dependence. Methods: This study retrospectively analyzed 1398 patients from the United Network for Organ Sharing database who received SHK between 2010 and 2021. Patients who were 65 years (n = 236), baseline characteristics were similar and well-balanced between the 2 cohorts. Between matched cohorts, older recipients did not have increased posttransplant mortality compared with younger recipients (90-day survival, P = .85; 7-year survival, P = .61). Multivariable Cox regression analysis found that age (hazard ratio [HR], 1.039 [0.975-1.106], P = .2415) and pretransplant functional status with interaction term for age (some assistance, HR, 0.965 [0.902-1.033], P = .3079; total assistance, HR, 0.976 [0.914-1.041], P = .4610) were not significant risk factors for 7-year post-SHK transplantation mortality. Conclusions: Older and more functionally dependent recipients in this study did not have increased post-SHK transplantation mortality. These findings have important implications for organ allocation among elderly patients, as they support the need for thorough assessment of SHK candidates in terms of comorbidities, rather than exclusion solely based on age and functional dependence.

Objective: Neurodevelopmental impairment has been realized as the most common complication in children with congenital heart disease undergoing cardiac surgery during the past 30 years. But little attention has been paid to this problem in China. The potential risk factors for adverse outcomes include demographic, perioperative, and socioeconomic factors, which are vastly different in China compared with the developed countries in previous reports. Methods: Four hundred twenty-six patients (aged 35.9 ± 18.6 months) at about 1- to 3-year follow-up after cardiac surgery were prospectively enrolled from March 2019 to February 2022. Griffiths Mental Development Scales-Chinese was used to evaluate the quotients of overall development and 5 subscales of the child's locomotor, language, personal-social, eye-hand coordination, and performance skills. Demographic, perioperative, socioeconomic, and feeding type during the first year of life (breastfeeding, mixed, or never breastfeeding) were examined to identify the risk factors for adverse neurodevelopmental outcomes. Results: Mean scores were 90.0 ± 15.5 for development quotient, 92.3 ± 19.4 for locomotor, 89.6 ± 19.2 for personal-social, 85.5 ± 21.7 for language, 90.3 ± 17.2 for eye-hand coordination, and 92 ± 17.1 for performance subscales. For the entire cohort, the impairment in at least 1 subscale was found in 76.1% of the cohort (>1 SD below population mean) with 50.1% being severe (>2 SDs below the mean). The significant risk factors included prolonged hospital stay, peak level of postoperative C-reactive protein, socioeconomic status, and never breastfeeding or mixed feeding. Conclusions: Neurodevelopmental impairment is substantial in terms of incidence and severity in children with congenital heart disease undergoing cardiac surgery in China. Risk factors contributing to the adverse outcomes included prolonged hospital stay, early postoperative inflammatory response, socioeconomic status, and never breastfeeding or mixed feeding. There is an urgent need for standardized follow-up and neurodevelopmental assessment in this special group of children in China.

In order to overcome the shortage of the current costly DVT diagnosis and reduce the waste of valuable healthcare resources, we proposed a new diagnostic approach based on machine learning pre-test prediction models using EHRs. We examined the sociodemographic and clinical factors in the prediction of DVT with 518 NICU admitted patients, including 189 patients who eventually developed DVT. We used cross-validation on the training data to determine the optimal parameters, and finally, the applied ROC analysis is adopted to evaluate the predictive strength of each model. Two models (GLM and SVM) with the strongest ROC were selected for DVT prediction, based on which, we optimized the current intervention and diagnostic process of DVT and examined the performance of the proposed approach through simulations. The use of machine learning based pre-test prediction models can simplify and improve the intervention and diagnostic process of patients in NICU with suspected DVT, and reduce the valuable healthcare resource occupation/usage and medical costs.