Reimagining How We Synthesize Information to Impact Clinical Care, Policy, and Research Priorities in Real Time: Examples and Lessons Learned from COVID-19.

Real-time clinical care, policy, and research decisions need real-time evidence synthesis. However, as we found during the COVID-19 pandemic, it is challenging to rapidly address key clinical and policy questions through rigorous, relevant, and usable evidence. Our objective is to present three exemplar cases of rapid evidence synthesis products from the Veterans Healthcare Administration Evidence Synthesis Program (ESP) and, in the context of these examples, outline ESP products, challenges, and lessons learned. We faced challenges in (1) balancing scientific rigor with the speed in which evidence synthesis was needed, (2) sorting through rapidly evolving large bodies of evidence, and (3) assessing the impact of evidence synthesis products on clinical care, policy, and research. We found solutions in (1) engaging stakeholders early, (2) utilizing artificial intelligence capabilities, (3) building infrastructure to establish living reviews, and (4) planning for dissemination to maximize impact.

Risk prediction models for diabetic foot ulcer development or amputation: a review of reviews.

BACKGROUND: In adults with diabetes, diabetic foot ulcer (DFU) and amputation are common and associated with significant morbidity and mortality. PURPOSE: Identify tools predicting risk of DFU or amputation that are prognostically accurate and clinically feasible. METHODS: We searched for systematic reviews (SRs) of tools predicting DFU or amputation published in multiple databases from initiation to January, 2023. We assessed risk of bias (ROB) and provided a narrative review of reviews describing performance characteristics (calibration and discrimination) of prognostically accurate tools. For such tools, we additionally reviewed original studies to ascertain clinical applicability and usability (variables included, score calculation, and risk categorization). RESULTS: We identified 3 eligible SRs predicting DFU or amputation risk. Two recent SRs (2020 and 2021) were rated as moderate and low ROB respectively. Four risk prediction models - Boyko, Martins-Mendes (simplified), Martins-Mendes (original), and PODUS 2020 had good prognostic accuracy for predicting DFU or amputation over time horizons ranging from 1- to 5-years. PODUS 2020 predicts absolute average risk (e.g., 6% risk of DFU at 2 years) and consists of 3-binary variables with a simple, summative scoring (0-4) making it feasible for clinic use. The other 3 models categorize risk subjectively (e.g., high-risk for DFU at 3 years), include 2-7 variables, and require a calculation device. No data exist to inform rescreening intervals. Furthermore, the effectiveness of targeted interventions in decreasing incidence of DFU or amputation in response to prediction scores is unknown. CONCLUSIONS: In this review of reviews, we identified 4 prognostically accurate models that predict DFU or amputation in persons with diabetes. The PODUS 2020 model, predicting absolute average DFU risk at 2 years, has the most favorable prognostic accuracy and is clinically feasible. Rescreening intervals and effectiveness of intervention based on prediction score are uncertain.

Nutrition as prevention for improved cancer health outcomes: a systematic literature review.

BACKGROUND: Among adults with cancer, malnutrition is associated with decreased treatment completion, more treatment harms and use of health care, and worse short-term survival. To inform the National Institutes of Health Pathways to Prevention workshop, "Nutrition as Prevention for Improved Cancer Health Outcomes," this systematic review examined the evidence for the effectiveness of providing nutrition interventions before or during cancer therapy to improve outcomes of cancer treatment. METHODS: We identified randomized controlled trials enrolling at least 50 participants published from 2000 through July 2022. We provide a detailed evidence map for included studies and grouped studies by broad intervention and cancer types. We conducted risk of bias (RoB) and qualitative descriptions of outcomes for intervention and cancer types with a larger volume of literature. RESULTS: From 9798 unique references, 206 randomized controlled trials from 219 publications met the inclusion criteria. Studies primarily focused on nonvitamin or mineral dietary supplements, nutrition support, and route or timing of inpatient nutrition interventions for gastrointestinal or head and neck cancers. Most studies evaluated changes in body weight or composition, adverse events from cancer treatment, length of hospital stay, or quality of life. Few studies were conducted within the United States. Among intervention and cancer types with a high volume of literature (n = 114), 49% (n = 56) were assessed as high RoB. Higher-quality studies (low or medium RoB) reported mixed results on the effect of nutrition interventions across cancer and treatment-related outcomes. CONCLUSIONS: Methodological limitations of nutrition intervention studies surrounding cancer treatment impair translation of findings into clinical practice or guidelines.