Search Results (22)

Background: Despite significant advancements in diabetes care, many individuals with type 2 diabetes (T2D) do not receive optimal care and treatment. Digital interventions promoting behavioral changes have shown promising long-term results in supporting healthier lifestyles but are not implemented in most healthcare offerings, maybe due to lack of general practice support and collaboration. This study evaluates the efficacy of the Digital, Individualized, and Collaborative Treatment of T2D in General Practice Based on Decision Aid (DICTA), a randomized controlled trial integrating a patient-centered smartphone application for lifestyle support in conjunction with a clinical decision support (CDS) tool to assist general practitioners (GPs) in optimizing antidiabetic treatment. Methods: The present randomized controlled trial aims to recruit 400 individuals with T2D from approximately 70 GP clinics (GPCs) in Denmark. The GPCs will be cluster-randomized in a 2:3 ratio to intervention or control groups. The intervention group will receive one year of individualized eHealth lifestyle coaching via a smartphone application, guided by patient-reported outcomes (PROs). Alongside this, the GPCs will have access to the CDS tool to optimize pharmacological decision-making through electronic health records. The control group will receive usual care for one year, followed by the same intervention in the second year. Results: The primary outcome is the one-year change in estimated ten-year cardiovascular risk, assessed by SCORE2-Diabetes calculated from age, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, age at diabetes diagnosis, HbA1c, and eGFR. Conclusions: If effective, DICTA could offer a scalable, digital-first approach for improving T2D management in primary care by combining patient-centered lifestyle coaching with real-time pharmacological clinical decision support. Full article

Background/Objectives: Carbohydrate antigen 19-9 (CA19-9) is a clinically established biomarker primarily used for monitoring disease progression and recurrence in pancreatic and gastrointestinal cancers. Accurate and continuous quantification of CA19-9 in patient samples is critical for effective clinical management. This study aimed to develop dual-emitting molecularly imprinted nanopolymers (dual@nanoMIPs) for ratiometric and reliable detection of CA19-9 in serum. Methods: Dual-emitting nanoMIPs were synthesized via a one-step molecular imprinting process, incorporating both blue-emitting carbon dots (b-CDs) as internal reference fluorophores and yellow-emitting quantum dots (y-QDs) as responsive probes. The CA19-9 template was embedded into the polymer matrix to create specific recognition sites. Fluorescence measurements were carried out under 365 nm excitation in 1% human serum diluted in phosphate-buffered saline (PBS). Results: The dual@nanoMIPs exhibited a ratiometric fluorescence response upon CA19-9 binding, characterized by the emission quenching of the y-QDs at 575 nm, while the b-CDs emission remained stable at 467 nm. The fluorescence shift observed in the RGB coordinates from yellow to green in the concentration range of CA19-9 tested, improved quantification accuracy by compensating for matrix effects in serum. A linear detection range was achieved from 4.98 × 10⁻³ to 8.39 × 10² U mL⁻¹ in serum samples, with high specificity and reproducibility. Conclusions: The dual@nanoMIPs developed in this work enable a stable, sensitive, and specific detection of CA19-9 in minimally processed serum, offering a promising tool for longitudinal monitoring of cancer patients. Its ratiometric fluorescence design enhances reliability, supporting clinical decision-making in the follow-up of pancreatic cancer. Full article

Celiac disease (CD) is a chronic immune-mediated enteropathy that requires strict adherence to a gluten-free diet (GFD) to prevent intestinal damage. Traditional methods for monitoring GFD adherence, such as serology and dietary assessments, often poorly correlate with histological findings and typically involve a waiting period before results are available, limiting their usefulness for immediate clinical decision-making. This cross-sectional case study reports on a 45-year-old mother and her 11-year-old twin daughters, all diagnosed with CD and following a GFD for over two years. Despite being asymptomatic and showing negative anti-tTG serology, the mother continued to present Marsh 1 histological lesions, suggesting ongoing subclinical inflammation. Point-of-care testing (POCT) for gluten immunogenic peptides (GIP) in urine revealed positive results for all three individuals, indicating recent gluten exposure despite reported dietary adherence. A follow-up GIP test after dietary review and reinforcement yielded negative results, confirming improved adherence. Additionally, a psychological assessment using the Hospital Anxiety and Depression Scale (HADS) revealed anxiety symptoms in the mother and one of the daughters, which may have influenced adherence to the GFD. These findings underscore the clinical value of urinary GIP POCT as a rapid, non-invasive tool for detecting hidden gluten exposure, even when traditional monitoring appears normal. Integrating GIP testing and psychological screening into routine clinical practice may enhance management and support timely, personalized interventions in patients with CD. Full article

Tuberculosis (TB) is an infectious disease that has been declared a global emergency by the World Health Organization and remains one of the top ten causes of death worldwide. TB diagnosis is particularly challenging in developing countries, where limited infrastructure for detection and treatment complicates efforts to control the disease. These resource constraints are especially critical in remote areas with few mechanisms for timely diagnosis, which is essential for effective patient management. Artificial intelligence (AI) has emerged as a valuable tool in supporting health professionals by enhancing diagnostic processes. This paper explores the use of natural language processing (NLP) techniques and machine learning (ML) models to facilitate TB diagnosis in settings where robust data infrastructure is unavailable. Two distinct data sources were analyzed: text extracted from electronic medical records (EMRs) and patient clinical data (CD). Four different ML-based approaches were implemented: two models using each data source independently and two data fusion models combining both sources. The relevance of these strategies was assessed in collaboration with physicians to ensure their practical applicability in clinical decision-making. The results of the data fusion models were compared to determine which source provided more valuable diagnostic information. The best-performing model, which relied solely on CD, achieved a sensitivity of $73\%$, outperforming smear microscopy, which typically ranges from $40\%$ to $60\%$. These findings underscore the importance of analyzing physicians’ reports and assessing the availability of such information alongside structured clinical data. This approach is particularly beneficial in resource-limited settings, where access to comprehensive clinical data may be restricted. Full article

In a rapidly changing technology landscape, “Clinical Decision Support” (CDS) has become an important tool to improve patient management. CDS systems offer medical professionals new insights to improve diagnostic accuracy, therapy planning, and personalized treatment. In addition, CDS systems provide cost-effective options to augment conventional screening for secondary prevention. This review aims to (i) describe the purpose and mechanisms of CDS systems, (ii) discuss different entities of algorithms, (iii) highlight quality features, and (iv) discuss challenges and limitations of CDS in clinical practice. Furthermore, we (v) describe contemporary algorithms in oncology, acute care, cardiology, and nephrology. In particular, we consolidate research on algorithms across diseases that imply a significant disease and economic burden, such as lung cancer, colorectal cancer, hepatocellular cancer, coronary artery disease, traumatic brain injury, sepsis, and chronic kidney disease. Full article

Multiple myeloma is biologically and clinically a complex and heterogeneous disease which develops late in life, with the median age at the time of initial diagnosis being 66 years. In 1975, Durie and Salmon developed the first broadly adopted staging system in multiple myeloma, and in the ensuing decades, the risk stratification tools have improved and now incorporate different parameters to better predict the prognosis and to guide the treatment decisions. The International Staging System (ISS) was initially developed in 2005, revised in 2015 (R-ISS), and again in 2022 (R2-ISS). Tremendous progress has been achieved in multiple myeloma therapy over the past 25 years with the approval of immunomodulatory drugs, proteasome inhibitors, and anti-CD38 monoclonal antibodies, resulting in a major paradigm shift. The dysfunction of the innate and adaptive immune system, especially in the T cell repertoire, represents a hallmark of multiple myeloma evolution over time, supporting the need for additional therapeutic approaches to activate the host’s immune system and to overcome the immunosuppressive tumor microenvironment. Novel T cell-directed therapies include chimeric antigen receptor (CAR) T cell therapies and bispecific antibodies that leverage the immune system’s T cells to recognize and attack the tumor cells. Second-generation anti-BCMA CAR T cell therapies and bispecific antibodies that bind the tumor antigen BCMA or GPRC5D onto myeloma cells and CD3 on the T cell’s surface are currently available for the treatment of relapsed/refractory multiple myeloma. Despite impressive results obtained with currently approved treatments, multiple myeloma remains incurable, and almost all patients eventually relapse. Moreover, patients with extramedullary disease and plasma cell leukemia represent an unmet medical need that require additional strategies to improve the outcome. In this review, we provide an overview of the evolution of risk stratification and the treatment of multiple myeloma. Full article

Thromboprophylaxis of hospitalized patients at risk of venous thromboembolism (VTE) presents challenges owing to patient heterogeneity and lack of adoption of evidence-based methods. Intuitive practices for thromboprophylaxis have resulted in many patients being inappropriately prophylaxed. We conducted a narrative review summarizing system-wide thromboprophylaxis interventions in hospitalized patients. Multiple interventions for thromboprophylaxis have been tested, including multifaceted approaches such as national VTE prevention programs with audits, pre-printed order entry, passive alerts (either human or electronic), and more recently, the use of active clinical decision support (CDS) tools incorporated into electronic health records (EHRs). Multifaceted health-system and order entry interventions have shown mixed results in their ability to increase appropriate thromboprophylaxis and reduce VTE unless mandated through a national VTE prevention program, though the latter approach is potentially costly and effort- and time-dependent. Studies utilizing passive human or electronic alerts have also shown mixed results in increasing appropriate thromboprophylaxis and reducing VTE. Recently, a universal cloud-based and EHR-agnostic CDS VTE tool incorporating a validated VTE risk score revealed high adoption and effectiveness in increasing appropriate thromboprophylaxis and reducing major thromboembolism. Active CDS tools hold promise in improving appropriate thromboprophylaxis, especially with further refinement and widespread implementation within various EHRs and clinical workflows. Full article

Practical and affordable tools to screen intestinal dysbiosis are needed to support clinical decision making. Our study aimed to design a new subjective screening tool for the risk of intestinal dysbiosis from a previously described nonvalidated questionnaire (DYS/FQM) and based on subjective and objective data. A total of 219 individuals comprised the chronic diseases (CD; n = 167) and healthy control (HC; 52 subjects) groups. Sociodemographic, anthropometric, body composition, lifestyle, past history, intestinal health, and dietary data were collected. The gut microbiota (GM) profile was assessed from fecal samples using the 16S rRNA sequencing. Scores for the new tool (Dys-R Questionnaire) were assigned using discrete optimization techniques. The association between Dys-R scores and dysbiosis risk was assessed through correlation, simple linear models, sensitivity, specificity, as well as positive and negative predictive values. We found significant differences in the Chao1 Index between CD and HC groups (adjusted p-value = 0.029), highlighting lower GM richness as the primary marker for intestinal dysbiosis. DYS/FQM showed poor performance in identifying poor GM richness. Dys-R exhibited a 42% sensitivity, 82% specificity, 79% positive predictive value (PPV), and 55% negative predictive value (NPV) to identify poor GM richness. The new Dys-R questionnaire showed good performance in ruling out dysbiosis. Full article

Familial Hypercholesterolemia (FH) is underdiagnosed in the United States. Clinical decision support (CDS) could increase FH detection once implemented in clinical workflows. We deployed CDS for FH at an academic medical center and sought clinician insights using an implementation survey. In November 2020, the FH CDS was deployed in the electronic health record at all Mayo Clinic sites in two formats: a best practice advisory (BPA) and an in-basket alert. Over three months, 104 clinicians participated in the survey (response rate 11.1%). Most clinicians (81%) agreed that CDS implementation was a good option for identifying FH patients; 78% recognized the importance of implementing the tool in practice, and 72% agreed it would improve early diagnosis of FH. In comparing the two alert formats, clinicians found the in-basket alert more acceptable (p = 0.036) and more feasible (p = 0.042) than the BPA. Overall, clinicians favored implementing the FH CDS in clinical practice and provided feedback that led to iterative refinement of the tool. Such a tool can potentially increase FH detection and optimize patient management. Full article

Lynch syndrome (LS) is a hereditary cancer susceptibility condition associated with varying cancer risks depending on which of the five causative genes harbors a pathogenic variant; however, lifestyle and medical interventions provide options to lower those risks. We developed MyLynch, a patient-facing clinical decision support (CDS) web application that applies genetically-guided personalized medicine (GPM) for individuals with LS. The tool was developed in R Shiny through a patient-focused iterative design process. The knowledge base used to estimate patient-specific risk leveraged a rigorously curated literature review. MyLynch informs LS patients of their personal cancer risks, educates patients on relevant interventions, and provides patients with adjusted risk estimates, depending on the interventions they choose to pursue. MyLynch can improve risk communication between patients and providers while also encouraging communication among relatives with the goal of increasing cascade testing. As genetic panel testing becomes more widely available, GPM will play an increasingly important role in patient care, and CDS tools offer patients and providers tailored information to inform decision-making. MyLynch provides personalized cancer risk estimates and interventions to lower these risks for patients with LS. Full article

This article describes the rationale for the development of the MSProDiscuss™ clinical decision support (CDS) tool, its development, and insights into how it can help neurologists improve care for patients with multiple sclerosis (MS). MS is a progressive disease characterized by heterogeneous symptoms and variable disease course. There is growing consensus that MS exists on a continuum, with overlap between relapsing–remitting and secondary progressive phenotypes. Evidence demonstrates that neuroaxonal loss occurs from the outset, that progression can occur independent of relapse activity, and that continuous underlying pathological processes may not be reflected by inflammatory activity indicative of the patient’s immune response. Early intervention can benefit patients, and there is a need for a tool that assists physicians in rapidly identifying subtle signs of MS progression. MSProDiscuss, developed with physicians and patients, facilitates a structured approach to patient consultations. It analyzes multidimensional data via an algorithm to estimate the likelihood of progression (the MSProDiscuss score), the contribution of various symptoms, and the impact of symptoms on daily living, enabling a more personalized approach to treatment and disease management. Data from CDS tools such as MSProDiscuss offer new insights into disease course and facilitate informed decision-making and a holistic approach to MS patient care. Full article

Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues. Full article

Molecular biology combined with genomics can be a powerful tool for developing potential intervention strategies for improving outcomes in children with autism spectrum disorders (ASD). Monogenic etiologies rarely cause autism. Instead, ASD is more frequently due to many polygenic contributing factors interacting with each other, combined with the epigenetic effects of diet, lifestyle, and environment. One limitation of genomics has been identifying ways of responding to each identified gene variant to translate the information to something clinically useful. This paper will illustrate how understanding the function of a gene and the effects of a reported variant on a molecular level can be used to develop actionable and targeted potential interventions for a gene variant or combinations of variants. For illustrative purposes, this communication highlights a specific genomic variant, SHANK3. The steps involved in developing molecularly genomically targeted actionable interventions will be demonstrated. Cases will be shared to support the efficacy of this strategy and to show how clinicians utilized these targeted interventions to improve ASD-related symptoms significantly. The presented approach demonstrates the utility of genomics as a part of clinical decision-making. Full article

A formal assessment of pharmacogenomics clinical decision support (PGx-CDS) by providers is lacking in the literature. The objective of this study was to evaluate the usability of PGx-CDS tools that have been implemented in a healthcare setting. We enrolled ten prescribing healthcare providers and had them complete a 60-min usability session, which included interacting with two PGx-CDS scenarios using the “Think Aloud” technique, as well as completing the Computer System Usability Questionnaire (CSUQ). Providers reported positive comments, negative comments, and suggestions for the two PGx-CDS during the usability testing. Most provider comments were in favor of the current PGx-CDS design, with the exception of how the genotype and phenotype information is displayed. The mean CSUQ score for the PGx-CDS overall satisfaction was 6.3 ± 0.95, with seven strongly agreeing and one strongly disagreeing for overall satisfaction. The implemented PGx-CDS at our institution was well received by prescribing healthcare providers. The feedback collected from the session will guide future PGx-CDS designs for our healthcare system and provide a framework for other institutions implementing PGx-CDS. Full article

In 2018, a clinical decision support (CDS) tool was implemented as part of a “daily checklist” for frontline pharmacists to review patients on antibiotics with procalcitonin (PCT) <0.25 mcg/L. This study used a retrospective cohort design to assess change in antibiotic use from pharmacist interventions after this PCT alert in patients on antibiotics for lower respiratory tract infections (LRTI). The secondary outcome was antibiotic days of therapy (DOT), with a subgroup analysis examining antibiotic use and the length of stay (LOS) in patients with a pharmacist intervention. From 1/2019 to 11/2019, there were 165 alerts in 116 unique patients on antibiotics for LRTI. Pharmacists attempted interventions after 34 (20.6%) of these alerts, with narrowing spectrum or converting to oral being the most common interventions. Pharmacist interventions prevented 125 DOT in the hospital. Vancomycin was the most commonly discontinued antibiotic with an 85.3% use reduction in patients with interventions compared to a 27.4% discontinuation in patients without documented intervention (p = 0.0156). The LOS was similar in both groups (median 6.4 days vs. 7 days, p = 0.81). In conclusion, interventions driven by a CDS tool for pharmacist-driven antimicrobial stewardship in patients with a normal PCT resulted in fewer DOT and significantly higher rates of vancomycin discontinuation. Full article