Challenges In Clinical Decision-making Research Articles

Modelling chemotherapy-induced cardiotoxicity with live myocardial slices: from bedside to bench and back again

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Netherlands Heart Institute & Dutch Heart Foundation (Dekker Senior Clinical Scientist 2019) Introduction Cancer therapy related cardiac dysfunction (CTRCD) is a common side-effect of many oncological regimens. The prevention, diagnosis and treatment of CTRCD requires a well-orchestrated management by a multidisciplinary team composed by oncologists, cardiologists, radiologists, but less often translational scientists. Purpose In this work, we investigated the translational potential of live myocardial slices derived from a patient presenting with acute biventricular failure, with no history of cardiovascular disease, who had previously received neo-adjuvant chemotherapy containing doxorubicin (240 mg/m2) and trastuzumab, for the treatment of a breast carcinoma. In contrast to anthracycline cardiotoxicity, which is often permanent, cardiac dysfunction caused by trastuzumab is commonly reversible after cessation of treatment. Herein, we provide a novel side-by-side comparison of myocardial function ex vivo and clinical outcomes in the patient. Methods After several days on extracorporeal life support and inotropic therapy, the patient’s cardiac function did not improve. The patient was ultimately implanted with a left- and right-ventricular assist device (LVAD and RVAD, respectively). Live myocardial slices were produced from the apical biopsy obtained during LVAD implantation and cultured in a biomimetic system under physiological mechanical and electrical conditions. Slices were subsequently exposed to doxorubicin, trastuzumab or vehicle (0.1% DMSO) for a continued period of 7 days. Contractile force was recorded continuously, and a custom-made stimulation protocol applied every 24h. To evaluate responsiveness to inotropes, slices were incubated with milrinone before culture termination. Calcium transients and action potentials were determined optically using calcium and voltage-sensitive dyes. Results Continued exposure to either doxorubicin or trastuzumab limited the recovery of contractility in myocardial slices. In contrast, vehicle controls showed a robust recovery and development of stronger contractile forces. Doxorubicin-treated slices were less responsive to stimulation, displayed increased stimulation threshold and were less able to follow pacing at higher frequencies. Optical mapping revealed that exposure to doxorubicin led to the development of abnormal calcium handling. In parallel to the ex vivo findings, RVAD/LVAD-mediated cardiac unloading and the absence of any chemotherapy drug, resulted in a significant recovery of right-ventricular function, enabling RVAD removal within 2 weeks. Conclusions The complex nature of chemotherapy-induced cardiotoxicity imposes numerous challenges in clinical decision-making. In this unique case, patient care could be paralleled by concomitant state-of-the-art ex vivo tissue culture. We observed that myocardial tissue slices could partially recapitulate clinical observations, highlightings the promising synergy between translational science and personalized patient care.

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies.

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

An interpretable RL framework for pre-deployment modeling in ICU hypotension management

Computational methods from reinforcement learning have shown promise in inferring treatment strategies for hypotension management and other clinical decision-making challenges. Unfortunately, the resulting models are often difficult for clinicians to interpret, making clinical inspection and validation of these computationally derived strategies challenging in advance of deployment. In this work, we develop a general framework for identifying succinct sets of clinical contexts in which clinicians make very different treatment choices, tracing the effects of those choices, and inferring a set of recommendations for those specific contexts. By focusing on these few key decision points, our framework produces succinct, interpretable treatment strategies that can each be easily visualized and verified by clinical experts. This interrogation process allows clinicians to leverage the model’s use of historical data in tandem with their own expertise to determine which recommendations are worth investigating further e.g. at the bedside. We demonstrate the value of this approach via application to hypotension management in the ICU, an area with critical implications for patient outcomes that lacks data-driven individualized treatment strategies; that said, our framework has broad implications on how to use computational methods to assist with decision-making challenges on a wide range of clinical domains.

MO482: End-Stage Renal Disease in the Elderly: Dialysis or Conservative Management?

Abstract BACKGROUND AND AIMS Nowadays, end-stage renal disease (ESRD) is overrepresented in elderly patients. The presence of medical comorbidities, limited life expectancy, frailty and poor functional status in these patients poses substantial challenges in clinical decision-making and provision of optimal care. The decision to start dialysis or not should always be based on shared decision-making. The objective of our study was to compare clinical outcomes for patients who had chosen either maximum conservative management (MCM) or renal replacement therapy (RRT). METHOD This is an observational study of a single-center cohort in Tunisia that includes 152 elderly patients (>80 years), with chronic kidney disease (CKD) who consulted our center during the period from 1 January 2015 to 31 December 2016. Demographic and comorbidity data were collected on all patients. Then, we evaluated the management of patients who had reached the end stage of CKD during the study period (dialysis or conservative treatment), and we compared the survival of the two groups. RESULTS Of 152 patients, 54.6% were women. The mean age was 82 ± 2.6 years. High blood pressure, diabetes and dyslipidemia were the most common comorbidities. During follow-up, 65 patients have reached stage 5 of CKD. A total of 46.1% started dialysis and the rest received conservative treatment. Patients choosing dialysis were younger compared with those opting for conservative management and were less functionally impaired. Survival was better in those receiving conservative treatment: The mean survival was 24 months for RRT patients and 30 months for maximum conservative management (MCM) patients (P < .01). A total of 1- and 2-year survival rates were 76.3% and 22% in the dialysis group (n = 30) and 75.3% and 45% in the conservative group (n = 35), respectively. CONCLUSION When caring for older adults with advanced CKD who are contemplating dialysis therapy vs. conservative management, efforts must focus on promoting patient values and preferences, shared decision-making, and symptom burden alleviation.

A guide to immunotherapy for COVID-19.

Immune dysregulation is an important component of the pathophysiology of COVID-19. A large body of literature has reported the effect of immune-based therapies in patients with COVID-19, with some remarkable successes such as the use of steroids or anti-cytokine therapies. However, challenges in clinical decision-making arise from the complexity of the disease phenotypes and patient heterogeneity, as well as the variable quality of evidence from immunotherapy studies. This Review aims to support clinical decision-making by providing an overview of the evidence generated by major clinical trials of host-directed therapy. We discuss patient stratification and propose an algorithm to guide the use of immunotherapy strategies in the clinic. This will not only help guide treatment decisions, but may also help to design future trials that investigate immunotherapy in other severe infections.

Predicting cancer outcomes with radiomics and artificial intelligence in radiology.

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.

Variation in LOD Across SARS-CoV-2 Assay Systems: Need for Standardization

Multiple SARS-CoV-2 emergency use authorization (EUA) tests are being used for clinical testing across various clinical testing laboratories for meeting the diagnostic challenges of the ongoing pandemic. However, cross-assay variations in performance characteristics need to be recognized. A better understanding is needed of the clinical implications of cross-assay variation in performance characteristics, particularly in the limit of detection (LOD) of the SARS-CoV-2 assays used for clinical testing. Herein, a snapshot of the diversity of SARS-CoV-2 EUA analytical assay systems including methodologies, assay designs, and technology platforms is presented. Factors affecting the variations in LOD are discussed. Potential measures that may standardize across the various assay systems are suggested. Development of international standards and reference materials for the establishment of performance characteristics may substantially alleviate potential clinical decision-making challenges. Finally, cross-assay variation in LODs among the diverse SARS-CoV-2 diagnostic assays impacts clinical decision-making with multiple assay systems in use and lack of standardization across platforms. International standards in parallel with continued cross-platform studies and collaborative efforts across pertinent healthcare entities will help mitigate some of the clinical decision-making challenges.

ENDOCRINOLOGY IN THE TIME OF COVID-19: Remodelling diabetes services and emerging innovation.

The COVID-19 pandemic is a major international emergency leading to unprecedented medical, economic and societal challenges. Countries around the globe are facing challenges with diabetes care and are similarly adapting care delivery, with local cultural nuances. People with diabetes suffer disproportionately from acute COVID-19 with higher rates of serious complications and death. In-patient services need specialist support to appropriately manage glycaemia in people with known and undiagnosed diabetes presenting with COVID-19. Due to the restrictions imposed by the pandemic, people with diabetes may suffer longer-term harm caused by inadequate clinical support and less frequent monitoring of their condition and diabetes-related complications. Outpatient management need to be reorganised to maintain remote advice and support services, focusing on proactive care for the highest risk, and using telehealth and digital services for consultations, self-management and remote monitoring, where appropriate. Stratification of patients for face-to-face or remote follow-up should be based on a balanced risk assessment. Public health and national organisations have generally responded rapidly with guidance on care management, but the pandemic has created a tension around prioritisation of communicable vs non-communicable disease. Resulting challenges in clinical decision-making are compounded by a reduced clinical workforce. For many years, increasing diabetes mellitus incidence has been mirrored by rising preventable morbidity and mortality due to complications, yet innovation in service delivery has been slow. While the current focus is on limiting the terrible harm caused by the pandemic, it is possible that a positive lasting legacy of COVID-19 might include accelerated innovation in chronic disease management.

Predominant Polarity in Bipolar Affective Disorder: A Scoping Review of Its Relationship with Clinical Variables and Its Implications.

Background:Bipolar affective disorder (BPAD) is an episodic psychiatric disorder that is associated with considerable morbidity. Psychiatrists have found it difficult to treat the disorder owing to the variety of presentation and variety of challenges in clinical decision-making. To guide the clinicians, the concept of predominant polarity (PP) in BPAD has become important. This review was conducted to understand the definition, epidemiology, relationship with sociodemographic and clinical parameters, and implications of PP in BPAD.Methodology:The review was conducted after selecting 17 original research studies from PubMed using appropriate search terms.Results:There is no consensus definition of PP. Epidemiological data showed varied results, although most common PP demonstrated in most studies was depressive polarity. The relation between sociodemographic and clinical parameters also lacked uniformity, although certain patterns could be identified in their relationships. The implications of PP in diagnostics, treatment, and classificatory system are discussed.Conclusion:PP in BPAD conveys clinically important information that aids a clinician in decision-making. Further studies are required so that we can understand the neurobiological underpinning of the concept.

Challenges in clinical decision-making on concomitant drug therapies in patients with atrial fibrillation taking oral anticoagulants.

Challenges in clinical decision-making on concomitant drug therapies in patients with atrial fibrillation taking oral anticoagulants.

CDSS-RM: a clinical decision support system reference model

Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the ‘Historical Decision’ bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges.

Feasibility of Antimicrobial Stewardship (AMS) in Critical Care Settings: A Multidisciplinary Approach Strategy.

Antimicrobial resistance is escalating and triggers clinical decision-making challenges when treating infections in patients admitted to intensive care units (ICU). Antimicrobial stewardship (AMS) may help combat this problem, but it can be difficult to implement in critical care settings. The implementation of multidisciplinary AMS in ICUs could be more challenging than what is currently suggested in the literature. Our main goal was to analyze the reduction in duration of treatment (DOT) for the most commonly used antibacterial and antifungal agents during the first six months of 2014, and during the same period two years later (2016). A total of 426 and 424 patient encounters, respectively, were documented and collected from the intensive care unit’s electronic patient record system. Daily multidisciplinary ward rounds were conducted for approximately 30–40 min, with the goal of optimizing antimicrobial therapy in order to analyze the feasibility of implementing AMS. The only antimicrobial agent which showed a significant reduction in the number of prescriptions and in the duration of treatment during the second audit was vancomycin, while linezolid showed an increase in the number of prescriptions with no significant prolongation of the duration of treatment. A trend of reduction was also seen in the DOT for co-amoxiclavulanate and in the number of prescriptions of anidulafungin without any corresponding increases being observed for other broad-spectrum anti-infective agents (p-values of 0.07 and 0.05, respectively).

Cognitive Continuum: Areas of Controversy with Cognitive Enhancers

Acetylcholinesterase inhibitors and N-methyl-D-aspartate antagonists are the only drugs approved by the US Food and Drug Administration for the treatment of Alzheimer's dementia. These drugs have demonstrated only modest efficacy for cognitive improvement and progression delay. Given the lack of alternative therapies and the evidence for early pathophysiologic disruptions in cholinergic neurotransmission, cognitive enhancer use in intermediate states (such as mild cognitive impairment) has become an area of study. Further along the cognitive continuum, clinicians face clinical decision-making challenges when using these medications for even mild or moderate stages of dementia, where one must weigh the potentially modest return in benefit versus the significant adverse risks. In addition, ongoing controversy exists regarding continuation or discontinuation of cognitive enhancers in late or end-stage dementia. These medications have also demonstrated some efficacy for use outside of Alzheimer's dementia, and they may have a clinically beneficial effect in treating other forms of dementia. [ Psychiatr Ann . 2016;46(2):110–117.]

Ethical challenges in clinical decision-making in the era of new technologies: Experiences from low income countries

New technologies have remarkably changed the healthcare field over the last three decades by improving the functionality and efficiency of health care systems and patient outcomes. However, if not well managed, new technologies present ethical challenges to clinicians globally but more so in low income countries (LICs) where there is lack of resources and the technologies may serve to increase existing health inequities. There is paucity of literature exploring the ethical challenges faced by clinicians in LICs in this era of new technologies. ObjectiveTo discuss the ethical (and other) challenges clinicians in LICs face in this new technology era. Methods(i) The author’s personal experiences (As a clinician and researcher working in LICs), (ii) empirical research findings from interviews with 44 doctors and 16 nurses working in a LIC hospital,and 2 policy makers and (iii) relevant literature.Patient related challenges included; inappropriate demand, ability to make informed decisions and balancing self-interest vs. public interest. Clinician related challenges included; the use of unfair criteria, lack of explicit criteria and processes, lack of evidence, potential for abuse, physicians’ dual role, autonomy and moral distress. Most of these were directly influenced by the extreme lack of resources, and decisions made at the meso- macro and global levels with regards to research and development and investing in new technologies for LICs. Hence addressing these challenges require concerted efforts at all levels, ensuring that decisions are guided by ethical values; and not only the clinicians’ duty.

Assessing The Medication Use Among Diabetic Patients With Comorbid Diseases.

Type 2 Diabetes is on the rise in many Asian countries with growing economic prosperity. Treatment patterns of diabetic patients in South East Asia have rarely been studied using administrative data. Our objective was to understand medication use patterns among Malaysian patients with diabetes with comorbidities in the primary health care setting. This may be useful for understanding treatment strategies in subgroups of patients with diabetes who tend to pose more complex challenges in clinical decision-making. Data was obtained from diabetic patients who were audited in the NDR in 2012. Descriptive analysis was used to describe medication usage for patients with various comorbidities. Medications of interest were antiplatelet medications, lipid-lowering agents, anti-hypertensive and various classes of anti-diabetic medications including insulin. A total of 130,270 patients were identified with comorbidities. Mean age of patients was 59.8 years old and 60.2% were female. A majority of patients were hypertensive (70.3%), more than half were dyslipidemic (55.1%). Other comorbidities such as retinopathy, ischemic heart disease (IHD), cerebrovascular disease, nephropathy, diabetic foot ulcer, amputation, hypertension and dyslipidemia were recorded among less than 10% of patients. Among non-diabetes medications, statins was the most commonly used ranging from 64.6% to 86.3% for various comorbidities. This was followed by ACE-inhibitors and aspirin. Among anti-diabetic medications, metformin was the most commonly prescribed followed by sulfonylureas and insulins. The registry data was able to shed light on patterns of medication usage among diabetic patients with comorbidities. Perhaps in the future, this data can be used to evaluate long-term outcomes among patients on various treatment plans and finally to inform more evidence-based clinical management strategies relating to medication use.

The detection and role of circulating antibodies in rejection

In this review, we first discuss the evolution and current controversies in antibody detection methodology for transplant candidates. Then, we summarize how immunologists and transplant clinicians integrate new evidence with their understanding of how recipient alloantibodies influence patient management and posttransplant outcomes. New advances in solid-phase assays have allowed a more accurate and discriminate appraisal of preformed antibodies. As a result, sensitized patients who are awaiting suitable heart donors can now be better risk-stratified and screened by virtual crossmatch. Yet, the progress in the field also brings to light some new contentious issues in need of further exploration and consideration. Some of these issues are explored in this review: are the new solid-phase assays too sensitive? What are the reasons for and the clinical implications of inconsistencies in results between the different techniques? Which antibodies from a growing list are clinically pertinent? What is the role of the virtual crossmatch? And how do antibodies impact posttransplant outcomes? Serologic detection of preformed or de-novo donor anti-human leukocyte antigen (HLA) antibodies has been closely linked to allograft rejection and poor outcomes. Although welcome, new histocompatibility testing methods also pose new challenges in clinical decision-making. Increased interaction between the clinicians and the histocompatibility laboratory is paramount to better understand the significance of antibodies, to maximize safe donor organ use, and to improve the overall posttransplant outcomes.

Current Clinical Practices in Aphasia Therapy in Finland: Challenges in Moving towards National Best Practice

Aims: The objective of this article is to discover and document the state of clinical practices for aphasia therapy in Finland and to gather information for developing national best practice. Methods: Two surveys were administered in Finland that explored current clinical practices in aphasia rehabilitation and the resources available to speech and language therapists (SLTs). We integrated and compared the results of these surveys. The results are based on the responses of the 88 (45 + 43) returned questionnaires from SLTs. Results: Four principle themes were identified: planning the aphasia therapy, measures and assessment methods, current therapy service provision, and development suggestions and barriers to change. The results of this study showed considerable consistency in clinical practices among the respondents to the surveys. However, we noticed that there are some discrepancies between the recent research findings and present clinical practices. Conclusions: The findings from this study indicate that there are many challenges in clinical decision-making at the moment in Finland. The article helps clinicians to evaluate the practices they use and to execute justified modifications in order to implement more effective models of practice. It is evident that national best practice guidelines for aphasia therapy would support SLTs in clinical decision-making.