Clinical correlates of a high cardiorespiratory risk score for very low birth weight infants.

Background: A pulse oximetry warning system (POWS) to analyze heart rate and oxygen saturation data and predict risk of sepsis was developed for very low birth weight (VLBW) infants.

Methods: We determined the clinical correlates and positive predictive value (PPV) of a high POWS score in VLBW infants. In a two-NICU retrospective study, we identified times when POWS increased above 6 (POWS spike).

Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards.

. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts.

Examining the economic impacts of caregiving among families of children of medical complexity: A qualitative study to inform inclusive economic models.

Background: Children with medical complexity (CMC) represent a heterogeneous group of children with multiple, chronic healthcare conditions. Caregivers of CMC experience a high intensity of caregiving that is often variable, extends across several networks of care, and often lasts for the entirety of the child's life. The economic impacts of caregiving are yet understudied in the CMC context.

Designing for caregiving networks: a case study of primary caregivers of children with medical complexity.

Objective: The study aimed to characterize the experiences of primary caregivers of children with medical complexity (CMC) in engaging with other members of the child's caregiving network, thereby informing the design of health information technology (IT) for the caregiving network. Caregiving networks include friends, family, community members, and other trusted individuals who provide resources, information, health, or childcare.

Materials And Methods: We performed a secondary analysis of two qualitative studies.

External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial.

Background: Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.

The Use of Medical Crowdfunding to Mitigate the Personal Costs of Serious Chronic Illness: Scoping Review.

Background: Persons diagnosed with serious chronic illnesses and their caretakers experience multiple types of financial costs that strain their income and generate financial distress. Many turn to medical crowdfunding (MCF) to mitigate the harms of these costs on their health and quality of life.

Objective: This scoping review aims to summarize the research on MCF for persons diagnosed with serious chronic illness regarding study designs and methods; the responsible conduct of research practices; and study foci as they relate to stress, stress appraisals, and the coping processes.

Disseminating for impact: creating curriculum activities for translational dissemination for the clinical and translational research workforce.

There has been an increased focus on the practices associated with dissemination for the translation of research to clinical practice and ultimately, policy. Simultaneously, there has been attention placed on the role of the clinical research workforce in supporting optimal dissemination efforts for impact and societal benefit. Curriculums focused on education opportunities for dissemination for translational scientists have been under-reported.

Establishing the Need for Anticipatory Symptom Guidance and Networked Models of Disease in Adaptive Family Management Among Children With Medical Complexity: Qualitative Study.

Background: Caregivers of children with medical complexity navigate complex family management tasks for their child both in the hospital and home-based setting. The roles and relationships of members of their social network and the dynamic evolution of these family management tasks have been underexamined.

Objective: The purpose of this study was to explore the structures and processes of family management among caregivers of children with medical complexity, with a focus on the underlying dynamic nature of family management practices and the role of members of their social network.

Ryan White HIV/AIDS Part B and AIDS Drug Assistance Programs during COVID-19: safety net public health programs' challenges and innovations.

Introduction: We characterized the challenges and innovations of states' Ryan White HIV/AIDS Program (RWHAP) Part B programs, including AIDS Drug Assistance Programs (ADAPs), during the COVID-19 pandemic. In the United States, these are important safety net programs for HIV healthcare, providing essential medical and support services, and medications, to people with HIV with low incomes who are uninsured/underinsured.

Methods: Data were collected via the 2021-2022 NASTAD National RWHAP Part B and ADAP Monitoring Project Report, a cross-sectional survey of state, district, and territorial programs through a mixed method study design.

Cancer Screening and Treatment Delays During the COVID-19 Pandemic and the Role of Health Literacy in Care Re-engagement: Findings from an NCI-Designated Comprehensive Cancer Center sample.

The COVID-19 pandemic has led to numerous delays in cancer-related care and cancer-specific screening, but the extent is not fully understood. For those that experience a delay or disruption in care, health related self-management is required to re-engage in care pathways and the role of health literacy in this pathway has not been explored. The purpose of this analysis is to (1) report the frequency of self-reported delays in cancer treatment and preventative screening services at an academic, NCI-designated center during the COVID-19 pandemic and (2) investigate cancer-related care and screening delays among those with adequate and limited health literacy.

Evaluating the cost-effectiveness of pediatric concurrent versus standard hospice care.

Using a sample of 18,152 pediatric hospice patients, this study assessed the cost-effectiveness of concurrent care over standard hospice care. Analysis of incremental cost-effectiveness ratios with bootstrapping simulations showed that concurrent care was more effective but at a higher cost.

Data Science and Precision Oncology Nursing: Creating an Analytic Ecosystem to Support Personalized Supportive Care across the Trajectory of Illness.

Objectives: The authors' objective is to present an overarching framework of an analytic ecosystem using diverse data domains and data science approaches that can be used and implemented across the cancer continuum. Analytic ecosystems can improve quality practices and offer enhanced anticipatory guidance in the era of precision oncology nursing.

Data Sources: Published scientific articles supporting the development of a novel framework with a case exemplar to provide applied examples of current barriers in data integration and use.

A Comparison of Hospice Care Utilization Between Rural and Urban Children in Appalachia: A Geographic Information Systems Analysis.

Long driving times from hospice providers to patients lead to poor quality of care, which may exacerbate in rural and highly isolated areas of Appalachia. This study aimed to investigate geographic patterns of pediatric hospice care across Appalachia. Using person-level Medicaid claims of 1,788 pediatric hospice enrollees who resided in the Appalachian Region between 2011 and 2013.

Decision Analytic Modeling for Global Clinical Trial Planning: A Case for HIV-Positive Patients at High Risk for Sepsis in Uganda.

Sepsis is a significant cause of mortality among people living with human immunodeficiency virus (HIV) in sub-Saharan Africa. In the planning period prior to the start of a large multi-country clinical trial studying the efficacy of the immediate empiric addition of anti-tuberculosis therapy to standard-of-care antibiotics for sepsis in people living with HIV, we used decision analysis to assess the costs and potential health outcome impacts of the clinical trial design based on preliminary data and epidemiological parameter estimates. The purpose of this analysis was to highlight this approach as a case example where decision analysis can estimate the cost effectiveness of a proposed clinical trial design.

Application of Instrumental Variable Analysis in Pediatric End-of-Life Research: A Case Study.

Instrumental variable analysis (IVA) has been widely used in many fields, including health care, to determine the comparative effectiveness of a treatment, intervention, or policy. However, its application in pediatric end-of-life care research has been limited. This article provides a brief overview of IVA and its assumptions.

Geographic Information Systems Utilization in Pediatric End-of-Life Research: A Scoping Review.

Currently, little is known about how geographic information systems (GIS) has been utilized to study end-of-life care in pediatric populations. The purpose of this review was to collect and examine the existing evidence on how GIS methods have been used in pediatric end-of-life research over the last 20 years. Scoping review method was used to summarize existing evidence and inform research methods and clinical practice was used.

Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit.

Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model.

Beyond prediction: Off-target uses of artificial intelligence-based predictive analytics in a learning health system.

Introduction: Artificial-intelligence (AI)-based predictive analytics provide new opportunities to leverage rich sources of continuous data to improve patient care through early warning of the risk of clinical deterioration and improved situational awareness.Part of the success of predictive analytic implementation relies on integration of the analytic within complex clinical workflows. Pharmaceutical interventions have off-target uses where a drug indication has not been formally studied for a different indication but has potential for clinical benefit.

Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study.

Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation.

Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration.

The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.

Conceptualizing the Value of Pediatric Concurrent Hospice Care.

Given that pediatric concurrent hospice care has been available for more than a decade, it is appropriate to seek an understanding of the value of this care delivery approach. Value is the cost associated with achieving beneficial health outcomes. In pursuit of this goal, the current literature on pediatric concurrent hospice care was synthesized and used to develop a model to explain its value.

Economic benefits of the United States' AIDS drug assistance Program: A systematic review of cost analyses to guide research and policy priorities.

As part of the Ryan White HIV/AIDs Program, the federally-funded, state-administered AIDS Drug Assistance Program (ADAP) provides prescription drug medications, including antiretroviral therapy, for people with HIV (PWH) who are uninsured/underinsured and have a low income. ADAP expenditures are ∼$2.4 billion annually, but there is a dearth of formal economic analysis supporting the societal perspective.

Incremental cost analysis of pediatric hospice care in rural and urban Appalachia.

Purpose: Considering growing disparities in health outcomes between rural and urban areas of Appalachia, this study compared the incremental Medicaid costs of pediatric concurrent care (implemented by the Patient Protection and Affordable Care Act) versus standard hospice care.

Methods: Data on 1,788 pediatric hospice patients, from the Appalachian region, collected between 2011 and 2013, were obtained from the Centers for Medicare and Medicaid Services. Incremental per-patient-per-month (PPM) costs of enrollment in concurrent versus standard hospice care were analyzed using multilevel generalized linear models.