Complications following the Fontan procedure include prolonged pleural drainage and readmission for effusions. To address these complications, a post-Fontan management pathway was implemented with primary goals of reducing chest tube duration/reinsertion rates and decreasing hospital length of stay and readmissions.

Fontan patients were identified by retrospective chart review (2017–2019) to obtain baseline data for chest tube duration/reinsertion rates, hospital length of stay, and readmission rates for effusion. A post-Fontan management pathway was implemented (2020–2021) utilising post-operative vasopressin, nasal cannula oxygen until chest tube removal, and discharge regimen of three times daily diuretics, sildenafil, and afterload reducing medications. Patients were followed to evaluate primary outcomes.

The pre- and post-pathway groups were similar in single ventricle morphology, demographics, and pre-operative haemodynamics. Forty-three and 36 patients were included in the pre- and post-pathway cohorts, respectively. There were statistically significant reductions in chest tube duration (8 vs. 5 days, p ≤ 0.001), chest tube output on post-operative day 4 (20.4 vs. 9.9 mL/kg/day, p = 0.003), and hospital readmission rates for effusion (13[30%] vs. 3[8%], p = 0.02) compared to baseline. There was an absolute reduction in hospital length of stay (11 vs. 9.5 days, p = 0.052). When combining average cost savings for the Fontan hospitalisations, readmissions for effusion, and cardiac catheterisations within 6 months of Fontan completion, there was a $325,144 total cost savings for 36 patients following pathway implementation.

Implementation of a post-Fontan management pathway resulted in significant reductions in chest tube duration and output, and readmission rates for effusion in the perioperative period.

The cause of most CHD is unknown and considered complex, implicating genetic and environmental factors in disease causation. The Kids Heart BioBank was established in 2003 to accelerate genetic investigations into CHD.

Recruitment includes patients undergoing interventions for CHD at The Children’s Hospital at Westmead. Informed consent is obtained from parents/guardians, and blood is collected at the time of cardiac intervention from which DNA is extracted and stored. Associated detailed clinical information and a family history are stored in the purpose-designed database.

To date, the Kids Heart BioBank contains biospecimens and associated clinical information from over 4,900 patients with CHD and their families. Two-thirds (64.1%) of probands have been included in research studies with 28.9% of participants who underwent genomic sequencing receiving a molecular diagnosis with direct clinical utility. The value of this resource to patients and families is highlighted by the high consent rate (94.6%) and the low withdrawal of consent rate (0.4%). The Kids Heart BioBank has supported many large national and international collaborations and contributed significantly to CHD research.

The Kids Heart BioBank is an invaluable resource and, together with other similar resources, the resulting research has paved the way for clinical genetic testing options for CHD patients, previously not possible. With research in the field moving away from diagnosing monogenic disease, the Kids Heart BioBank is ideally placed to support the next chapter of research efforts into complex disease mechanisms, requiring large patient cohorts with detailed phenotypic information.

Despite strong evidence of efficacy of electroconvulsive therapy (ECT) in the treatment of depression, no sensitive and specific predictors of ECT response have been identified. Previous meta-analyses have suggested some pre-treatment associations with response at a population level.

Using 10 years (2009–2018) of routinely collected Scottish data of people with moderate to severe depression (n = 2074) receiving ECT we tested two hypotheses: (a) that there were significant group-level associations between post-ECT clinical outcomes and pre-ECT clinical variables and (b) that it was possible to develop a method for predicting illness remission for individual patients using machine learning.

Data were analysed on a group level using descriptive statistics and association analyses as well as using individual patient prediction with machine learning methodologies, including cross-validation.

ECT is highly effective for moderate to severe depression, with a response rate of 73% and remission rate of 51%. ECT response is associated with older age, psychotic symptoms, necessity for urgent intervention, severe distress, psychomotor retardation, previous good response, lack of medication resistance, and consent status. Remission has the same associations except for necessity for urgent intervention and, in addition, history of recurrent depression and low suicide risk. It is possible to predict remission with ECT with an accuracy of 61%.

Pre-ECT clinical variables are associated with both response and remission and can help predict individual response to ECT. This predictive tool could inform shared decision-making, prevent the unnecessary use of ECT when it is unlikely to be beneficial and ensure prompt use of ECT when it is likely to be effective.

Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform.

Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts.

The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns.

Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.

To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.

We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model.

We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.

We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.

Patients receiving hematopoietic stem cell transplants (HSCT) are at increased risk for Clostridioides difficile infection (CDI). The purpose of this study was to assess the effectiveness of oral vancomycin prophylaxis (OVP) for CDI in HSCT patients.

Single-center, retrospective cohort.

Tertiary care academic medical center in New Jersey.

Patients ≥18 years old during admission for the HSCT were included. Patients who were admitted <72 hours or who had an active CDI prior to HSCT day 0 were excluded.

Medical records of patients admitted between January 2015 and August 2022 to undergo an allogeneic or autologous HSCT were reviewed. The primary end point was the incidence of in-hospital CDI. Secondary end points included the incidence of vancomycin-resistant enterococci (VRE) bloodstream infections, VRE isolated from any clinical culture, gram-negative bloodstream infections, hospital survival, and hospital length of stay. Exploratory end points, including 1-year survival, relapse, and incidence of graft-versus-host disease, were also collected.

A total of 156 HSCT patients were included. There was 1 case of CDI (1 of 81, 1.23%) in the OVP group compared to 8 CDI cases (8 of 75, 10.67%) in the no OVP group (P = .0147). There were no significant (P > .05) between-group differences in incidence of gram-negative bloodstream infections, hospital survival, and length of stay. There were zero clinical cultures positive for VRE.

In-hospital incidence of CDI in HSCT patients was significantly decreased with OVP. Randomized controlled trials are needed in this high-risk population to assess the efficacy and risks of OVP for CDI.