In over 50 years since the genetic counseling (GC) profession began, a systematic study of GC communication skills and patient-reported outcomes in actual sessions across multiple clinical specialties has never been conducted. To optimize GC quality and improve efficiency of care, the field must first be able to comprehensively measure GC skills and determine which skills are most critical to achieving positive patient experiences and outcomes. This study aims to characterise GC communication skills using a novel and pragmatic measure and link variations in communication skills to patient-reported outcomes, across clinical specialties and with patients from diverse backgrounds in the USA. Our community-engagement and provider-engagement approach is crucial to develop recommendations for quality, culturally informed GC care, which are greatly needed to improve GC practice.

A mixed methods, sequential explanatory design will be used to collect and analyze: audio-recorded GC sessions in cancer, cardiac, and prenatal/reproductive genetic indications; pre-visit and post-visit quantitative surveys capturing patient experiences and outcomes and post-visit qualitative interview data. A novel, practical checklist will measure GC communication skills. Coincidence analysis will identify patterns of GC skills that are consistent with high scores on patient-reported measures. Two-level, multilevel models will be used to evaluate how GC communication skills and other session/patient characteristics predict patient-reported outcomes. Four community advisory boards (CABs) and a genetic counselor advisory board will inform the study design and analysis.

This study has been approved by the single Institutional Review Board of the University of Minnesota. This research poses no greater than minimal risk to participants. Results from this study will be shared through national and international conferences and through community-based dissemination as guided by the study’s CABs. A lay summary will also be disseminated to all participants.

Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration.

We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU.

A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a ‘static’ score of the patient’s condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient’s risk of deterioration throughout their hospital stay.

Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model.

A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort.

Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653).

We showed that a scoring system incorporating data from a patient’s stay in the ICU has better performance than commonly used EWS systems based on vital signs alone.

ISRCTN32008295.

Stroke is a major cause of death and disability worldwide, frequently resulting in persistent cognitive deficits among survivors. These deficits negatively impact recovery and therapy engagement, and their treatment is consistently rated as high priority by stakeholders and clinicians. Although clinical guidelines endorse cognitive screening for poststroke management, there is currently no gold-standard approach for identifying cognitive deficits after stroke, and clinical stroke services lack the capacity for long-term cognitive monitoring and care. Currently, available assessment tools are either not stroke-specific, not in-depth or lack scalability, leading to heterogeneity in patient assessments.

To address these challenges, a cost-effective, scalable and comprehensive screening tool is needed to provide a stroke-specific assessment of cognition. The current study presents such a novel digital tool, the Imperial Comprehensive Cognitive Assessment in Cerebrovascular Disease (IC3), designed to detect both domain-general and domain-specific cognitive deficits in patients after stroke with minimal input from a health professional. To ensure its reliability, we will use multiple validation approaches, and aim to recruit a large normative sample of age-matched, gender-matched and education-matched UK-based controls. Moreover, the IC3 assessment will be integrated within a larger prospective observational longitudinal clinical trial, where poststroke cognition will be examined in tandem with brain imaging and blood biomarkers to identify novel multimodal biomarkers of recovery after stroke. This study will enable deeper cognitive phenotyping of patients at a large scale, while identifying those with highest risk of progressive cognitive decline, as well as those with greatest potential for recovery.

This study has been approved by South West—Frenchay Research Ethics Committee (IRAS 299333) and authorised by the UK’s Health Research Authority. Results from the study will be disseminated at conferences and within peer-reviewed journals.

NCT05885295. Stage: Pre-results.