New course to help navigate adverse patient outcomes
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Pregnancy outcomes among patients with complex congenital heart disease
Patients with complex congenital heart disease (CCHD) may pose a serious threat to the mother-infant safety. This study intends to explore the influencing factors for adverse pregnancy outcomes in the CCHD population. Totally 108 CCHD patients who terminated pregnancy from January 2013 to January 2023 were recruited. We collected clinical data during the pregnancy from electronic medical records. Among them, 45 patients had adverse pregnancy outcomes (41.7%) and no patient died. 5 patients with no newborn. The incidence rate of adverse pregnancy outcomes was significantly higher in patients with brain natriuretic peptide (BNP) > 100 pg/mL (OR: 2.736; 95%CI: 1.001–7.481, p = 0.049) and hypoxemia (OR: 15.46; 95%CI: 1.689–141.512, p = 0.015) and without undergoing cardiac surgical correction (OR: 3.226; 95%CI: 1.121–9.259, p = 0.03). It was confirmed by propensity score matching that no cardiac surgical correction was an independent risk factor. Maternal patients without undergoing cardiac surgical correction had poorer NYHA cardiac function (p = 0.000) and were more prone to heart failure (p = 0.027), hypoxemia (p = 0.042), pulmonary arterial hypertension (p = 0.038) and postpartum hemorrhage (p = 0.016). Moreover, these patients had prolonged Surgical Intensive Care Unit (SICU) stay (p = 0.000) and significantly higher risk of premature delivery (p = 0.005), low birth weight (p = 0.018), infection and asphyxia (p = 0.043). Corrective cardiac surgery in patients with CCHD before pregnancy significantly reduces the incidence of adverse pregnancy outcomes.
Opportunities and challenges for patient-derived models of brain tumors in functional precision medicine
Here, we review a growing paradigm shift from genomics-based precision medicine toward functional precision medicine, which evaluates therapeutic efficacy by directly treating living patient tumors ex vivo to better predict patient-specific responses to treatment. We discuss several classes of patient-derived models of central nervous system tumors, highlighting unique features of each. Each class of models holds promise to improve treatment selection, prolong survival, and enhance patient outcomes.
Mitochondrial dysfunction drives a neuronal exhaustion phenotype in methylmalonic aciduria
Methylmalonic aciduria (MMA) is an inborn error of metabolism resulting in loss of function of the enzyme methylmalonyl-CoA mutase (MMUT). Despite acute and persistent neurological symptoms, the pathogenesis of MMA in the central nervous system is poorly understood, which has contributed to a dearth of effective brain specific treatments. Here we utilised patient-derived induced pluripotent stem cells and in vitro differentiation to generate a human neuronal model of MMA. We reveal strong evidence of mitochondrial dysfunction caused by deficiency of MMUT in patient neurons. By employing patch-clamp electrophysiology, targeted metabolomics, and bulk transcriptomics, we expose an altered state of excitability, which is exacerbated by application of dimethyl-2-oxoglutarate, and we suggest may be connected to metabolic rewiring. Our work provides first evidence of mitochondrial driven neuronal dysfunction in MMA, which through our comprehensive characterisation of this paradigmatic model, enables first steps to identifying effective therapies.
Tackling antibiotic resistance—insights from eHealthResp’s educational interventions
Antibiotic resistance (AR) poses a significant challenging issue in public health worldwide. This phenomenon led to the emergence of antibiotic-resistant bacterial strains, making the treatment of respiratory infections increasingly difficult. Educational interventions targeting healthcare professionals are important to improve prescription practices and promote responsible antibiotic use. Digital tools, including clinical decision support systems and mobile applications, have proven to effectively enhance educational interventions and clinical decision-making. The eHealthResp project is one such initiative that includes an online course and a mobile app designed to improve antibiotic use for upper respiratory tract infections (URTIs). The online course provides clinical information and case studies, whereas the mobile app acts as a clinical decision support system for URTIs diagnosis. The purpose of this study is to analyse the utilization patterns of eHealthResp digital tools among primary care physicians and community pharmacists. Results showed that both physicians and pharmacists (n = 35) had favorable progress and high grades when completing the online course assessment. The mobile app data indicated a diverse range of searched cases with different respiratory symptoms, with the most common being acute nasal discharge and pain when swallowing. Most observations presented mild symptoms for less than seven days, suggesting the occurrence of acute self-limited infections. Despite limitations, digital tools show promise in enhancing patient care outcomes for managing URTIs. Future efforts should focus on expanding participation among health professionals and enhancing educational interventions to promote responsible antibiotic use.
Prediction of individual treatment allocation between electroconvulsive therapy or ketamine using the Personalized Advantage Index
Electroconvulsive therapy (ECT) and ketamine are effective treatments for depression; however, evidence-based guidelines are needed to inform individual treatment selection. We adapted the Personalized Advantage Index (PAI) using machine learning to predict optimal treatment assignment to ECT or ketamine using EHR data on 2506 ECT and 196 ketamine patients. Depressive symptoms were evaluated using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Propensity score matching across treatments was used to address confounding by indication, yielding a sample of 392 patients (n = 196 per treatment). Models predicted differential minimum QIDS scores (min-QIDS) over acute treatment using pretreatment EHR measures and SHAP values identified prescriptive predictors. Patients with large PAI scores who received a predicted optimal had significantly lower min-QIDS compared to the non-optimal treatment group (mean difference = 1.19 [95% CI: 0.32, ∞], t = 2.25, q < 0.05, d = 0.26). Our model identified candidate pretreatment factors to provide actionable, effective antidepressant treatment selection guidelines.
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