Hyperhydration to Improve Kidney Outcomes in Children with Shiga Toxin-Producing E. coli Infection: a multinational embedded cluster crossover randomized trial (the HIKO STEC trial).

BACKGROUND: Shiga toxin-producing E. coli (STEC) infections affect children and adults worldwide, and treatment remain solely supportive. Up to 15-20% of children infected by high-risk STEC (i.e., E. coli that produce Shiga toxin 2) develop hemolytic anemia, thrombocytopenia, and kidney failure (i.e., hemolytic uremic syndrome (HUS)), over half of whom require acute dialysis and 3% die. Although no therapy is widely accepted as being able to prevent the development of HUS and its complications, several observational studies suggest that intravascular volume expansion (hyperhydration) may prevent end organ damage. A randomized trial is needed to confirm or refute this hypothesis. METHODS: We will conduct a pragmatic, embedded, cluster-randomized, crossover trial in 26 pediatric institutions to determine if hyperhydration, compared to conservative fluid management, improves outcomes in 1040 children with high-risk STEC infections. The primary outcome is major adverse kidney events within 30 days (MAKE30), a composite measure that includes death, initiation of new renal replacement therapy, or persistent kidney dysfunction. Secondary outcomes include life-threatening, extrarenal complications, and development of HUS. Pathway eligible children will be treated per institutional allocation to each pathway. In the hyperhydration pathway, all eligible children are hospitalized and administered 200% maintenance balanced crystalloid fluids up to targets of 10% weight gain and 20% reduction in hematocrit. Sites in the conservative fluid management pathway manage children as in- or outpatients, based on clinician preference, with the pathway focused on close laboratory monitoring, and maintenance of euvolemia. Based on historical data, we estimate that 10% of children in our conservative fluid management pathway will experience the primary outcome. With 26 clusters enrolling a mean of 40 patients each with an intraclass correlation coefficient of 0.11, we will have 90% power to detect a 5% absolute risk reduction. DISCUSSION: HUS is a devastating illness with no treatment options. This pragmatic study will determine if hyperhydration can reduce morbidity associated with HUS in children with high-risk STEC infection. TRIAL REGISTRATION: ClinicalTrials.gov NCT05219110 . Registered on February 1, 2022.

GARDE: a standards-based clinical decision support platform for identifying population health management cohorts.

Population health management (PHM) is an important approach to promote wellness and deliver health care to targeted individuals who meet criteria for preventive measures or treatment. A critical component for any PHM program is a data analytics platform that can target those eligible individuals. OBJECTIVE: The aim of this study was to design and implement a scalable standards-based clinical decision support (CDS) approach to identify patient cohorts for PHM and maximize opportunities for multi-site dissemination. MATERIALS AND METHODS: An architecture was established to support bidirectional data exchanges between heterogeneous electronic health record (EHR) data sources, PHM systems, and CDS components. HL7 Fast Healthcare Interoperability Resources and CDS Hooks were used to facilitate interoperability and dissemination. The approach was validated by deploying the platform at multiple sites to identify patients who meet the criteria for genetic evaluation of familial cancer. RESULTS: The Genetic Cancer Risk Detector (GARDE) platform was created and is comprised of four components: (1) an open-source CDS Hooks server for computing patient eligibility for PHM cohorts, (2) an open-source Population Coordinator that processes GARDE requests and communicates results to a PHM system, (3) an EHR Patient Data Repository, and (4) EHR PHM Tools to manage patients and perform outreach functions. Site-specific deployments were performed on onsite virtual machines and cloud-based Amazon Web Services. DISCUSSION: GARDE's component architecture establishes generalizable standards-based methods for computing PHM cohorts. Replicating deployments using one of the established deployment methods requires minimal local customization. Most of the deployment effort was related to obtaining site-specific information technology governance approvals.

Patient Interactions With an Automated Conversational Agent Delivering Pretest Genetics Education: Descriptive Study.

BACKGROUND: Cancer genetic testing to assess an individual's cancer risk and to enable genomics-informed cancer treatment has grown exponentially in the past decade. Because of this continued growth and a shortage of health care workers, there is a need for automated strategies that provide high-quality genetics services to patients to reduce the clinical demand for genetics providers. Conversational agents have shown promise in managing mental health, pain, and other chronic conditions and are increasingly being used in cancer genetic services. However, research on how patients interact with these agents to satisfy their information needs is limited. OBJECTIVE: Our primary aim is to assess user interactions with a conversational agent for pretest genetics education. METHODS: We conducted a feasibility study of user interactions with a conversational agent who delivers pretest genetics education to primary care patients without cancer who are eligible for cancer genetic evaluation. The conversational agent provided scripted content similar to that delivered in a pretest genetic counseling visit for cancer genetic testing. Outside of a core set of information delivered to all patients, users were able to navigate within the chat to request additional content in their areas of interest. An artificial intelligence-based preprogrammed library was also established to allow users to ask open-ended questions to the conversational agent. Transcripts of the interactions were recorded. Here, we describe the information selected, time spent to complete the chat, and use of the open-ended question feature. Descriptive statistics were used for quantitative measures, and thematic analyses were used for qualitative responses. RESULTS: We invited 103 patients to participate, of which 88.3% (91/103) were offered access to the conversational agent, 39% (36/91) started the chat, and 32% (30/91) completed the chat. Most users who completed the chat indicated that they wanted to continue with genetic testing (21/30, 70%), few were unsure (9/30, 30%), and no patient declined to move forward with testing. Those who decided to test spent an average of 10 (SD 2.57) minutes on the chat, selected an average of 1.87 (SD 1.2) additional pieces of information, and generally did not ask open-ended questions. Those who were unsure spent 4 more minutes on average (mean 14.1, SD 7.41; P=.03) on the chat, selected an average of 3.67 (SD 2.9) additional pieces of information, and asked at least one open-ended question. CONCLUSIONS: The pretest chat provided enough information for most patients to decide on cancer genetic testing, as indicated by the small number of open-ended questions. A subset of participants were still unsure about receiving genetic testing and may require additional education or interpersonal support before making a testing decision. Conversational agents have the potential to become a scalable alternative for pretest genetics education, reducing the clinical demand on genetics providers.

Comparing models of delivery for cancer genetics services among patients receiving primary care who meet criteria for genetic evaluation in two healthcare systems: BRIDGE randomized controlled trial.

BACKGROUND: Advances in genetics and sequencing technologies are enabling the identification of more individuals with inherited cancer susceptibility who could benefit from tailored screening and prevention recommendations. While cancer family history information is used in primary care settings to identify unaffected patients who could benefit from a cancer genetics evaluation, this information is underutilized. System-level population health management strategies are needed to assist health care systems in identifying patients who may benefit from genetic services. In addition, because of the limited number of trained genetics specialists and increasing patient volume, the development of innovative and sustainable approaches to delivering cancer genetic services is essential. METHODS: We are conducting a randomized controlled trial, entitled Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE), to address these needs. The trial is comparing uptake of genetic counseling, uptake of genetic testing, and patient adherence to management recommendations for automated, patient-directed versus enhanced standard of care cancer genetics services delivery models. An algorithm-based system that utilizes structured cancer family history data available in the electronic health record (EHR) is used to identify unaffected patients who receive primary care at the study sites and meet current guidelines for cancer genetic testing. We are enrolling eligible patients at two healthcare systems (University of Utah Health and New York University Langone Health) through outreach to a randomly selected sample of 2780 eligible patients in the two sites, with 1:1 randomization to the genetic services delivery arms within sites. Study outcomes are assessed through genetics clinic records, EHR, and two follow-up questionnaires at 4 weeks and 12 months after last genetic counseling contactpre-test genetic counseling. DISCUSSION: BRIDGE is being conducted in two healthcare systems with different clinical structures and patient populations. Innovative aspects of the trial include a randomized comparison of a chatbot-based genetic services delivery model to standard of care, as well as identification of at-risk individuals through a sustainable EHR-based system. The findings from the BRIDGE trial will advance the state of the science in identification of unaffected patients with inherited cancer susceptibility and delivery of genetic services to those patients. TRIAL REGISTRATION: BRIDGE is registered as NCT03985852 . The trial was registered on June 6, 2019 at clinicaltrials.gov .

Mycobacterium tuberculosis reactivates latent HIV-1 in T cells in vitro.

Following proviral integration into the host cell genome and establishment of a latent state, the human immunodeficiency virus type 1 (HIV-1) can reenter a productive life cycle in response to various stimuli. HIV-1 reactivation occurs when transcription factors, such as nuclear factor-κB (NF-κB), nuclear factor of activated T cells (NFAT), and activator protein -1 (AP-1), bind cognate sites within the long terminal repeat (LTR) region of the HIV-1 provirus to promote transcription. Interestingly, pattern recognition receptors (PRRs) that recognize pathogen-associated molecular patterns (PAMPs) can reactivate latent HIV-1 through activation of the transcription factor NF-κB. Some PRRs are expressed on central memory CD4+ T cells (TCM), which in HIV-1 patients constitute the main reservoir of latent HIV-1. Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), interacts with PRRs through membrane components. However, the ability of Mtb to reactivate latent HIV-1 has not been extensively studied. Here we show that phosphatidylinositol mannoside 6 (PIM6), a component of the Mtb membrane, in addition to whole bacteria in co-culture, can reactivate HIV-1 in a primary TCM cell model of latency. Using a JLAT model of HIV-1 latency, we found this interaction to be mediated through Toll-like receptor-2 (TLR-2). Thus, we describe a mechanism by which Mtb can exacerbate HIV-1 infection. We hypothesize that chronic Mtb infection can drive HIV-1 reactivation. The phenomenon described here could explain, in part, the poor prognosis that characterizes HIV-1/Mtb co-infection.