Chronic kidney disease (CKD) arises due to uncontrolled hypertension (HTN). HTN significantly increases the risk of complications in vital organs, mainly the kidneys. If hypertensive individuals receive early intervention, the majority of these complications and deaths from CKD can be avoided. Having a clinically applicable tool to predict the future risk of those complications can prevent early disability and premature mortality. However, to this day, there is a lack of a validated risk prediction model specifically designed for CKD of hypertensive patients in Ethiopia. We aimed to develop a risk prediction model for CKD among hypertensive patients at the University of Gondar Comprehensive Specialised Hospital (UoGCSH), Ethiopia.

A retrospective follow-up study was conducted from 1 January 2012 to 30 December 2021. The Least Absolute Shrinkage and Selection Operator regression methods were used to select predictors. The performance of the models was assessed using the Area Under the Curve and calibration plots. The internal validity of the model was evaluated using bootstrapping methods, and the model was presented as a nomogram. Decision curve analysis was conducted to assess the net benefit of the prediction model in clinical and public health contexts.

Data from patients’ medical records were collected via the Kobo Toolbox in the UoGCSH.

We followed a total of 1120 Patients diagnosed with HTN.

The incidence of CKD among adult hypertensive patients was 19.82% (95% CI 17.59% to 22.26%). In the multivariable logistic regression analysis, age, residency, baseline blood pressure status, type of HTN, family history of HTN, baseline serum creatinine levels, proteinuria at baseline and dyslipidaemia were identified as statistically significant predictors of CKD. The nomogram demonstrated a discriminatory power of 91.98% (95% CI 90.09% to 93.88%) and a calibration p value of 0.327. The sensitivity and specificity of the prediction model were 80.63% (95% CI 74.81% to 85.61%) and 87.97% (95% CI 85.66% to 90.03%), respectively. The developed nomogram has a greater net benefit than using the treat-all or treat-none strategies when the threshold probability of the patient is increased.

The nomogram demonstrated excellent discrimination and calibration in identifying hypertensive patients at high risk of CKD. This predictive model offers clinicians a valuable tool for early identification of high-risk individuals, enabling timely interventions, personalised counselling and optimised management through close monitoring to prevent disease progression.

To characterise the information needs and experiences of receiving COVID-19 vaccine information by youth with mental health concerns.

Thematic analysis of semistructured interview transcripts.

Semistructured interviews via WebEx video conferencing or by telephone.

46 youth aged 16–29 with one or more self-reported mental health concerns and six family members of youth.

Our analysis generated four main themes: (1) information content and characteristics; (2) critical appraisal; (3) modulators of information-seeking behaviour; and (4) unmet information needs.

Our findings suggest that youth with mental health concerns have unique information needs and processing patterns influenced by their environments and experiences with mental health concerns. Participants identified barriers to receiving reliable health information and suggested ways to improve this process.

Children with medical complexity (CMC) are a subset of children with special healthcare needs, defined by high healthcare utilisation, severe single or multisystem organ dysfunction, and in many cases, reliance on medical technology. In the emergency care setting, known challenges for this population include poor quality of care, avoidable admissions and high caregiver and provider burden. While experts and professional societies recommend emergency care planning tools to address these concerns, evidence to support effectiveness and implementation of such tools is lacking. Through a human-centred design approach, we recently engaged key partners to create and optimise an emergency care action plan (ECAP) for infants with medical complexity. Here, we describe the protocol for a pilot type 1 hybrid effectiveness-implementation randomised controlled trial (RCT) for infants with medical complexity aimed to evaluate ECAP effectiveness and implementation.

Infants with medical complexity and their caregivers will be randomly assigned to the intervention group (ECAP) or control group (standard care) in a pilot type 1 hybrid effectiveness-implementation RCT. The primary outcome is number of inpatient hospital days for infant participants. Additional effectiveness outcomes include perceived avoidance of emergency department (ED) visits, healthcare costs, caregiver stress and self-efficacy. Preliminary implementation outcomes include acceptability, feasibility, appropriateness and usability, as well as contextual barriers and facilitators to reach, adoption and implementation. Key partners, including caregivers of CMC and healthcare providers, will be engaged throughout the implementation of the ECAP and execution of the trial.

This study was approved by the University of Vermont Institutional Review Board (STUDY00002937). Findings will be disseminated through peer-reviewed publications, conference presentations, and focus groups and interviews with key stakeholders.

NCT06444282.