Diagnosis最新文献

Algorithms are a ubiquitous part of modern life. Despite being a component of medicine since early efforts to deploy computers in medicine, clinicians' resistance to using decision support and use algorithms to address cognitive biases has been limited. This resistance is not just limited to the use of algorithmic clinical decision support, but also evidence and stochastic reasoning and the implications of the forcing function of the electronic medical record. Physician resistance to algorithmic support in clinical decision making is in stark contrast to their general acceptance of algorithmic support in other aspects of life.

Coronavirus disease 2019 (COVID-19) is frequently associated with thrombo inflammation, which can predispose to developing of life-threatening conditions in children such as the multisystem inflammatory syndrome (MIS-C) and Kawasaki disease. Because of the consistent overlap in pathogenesis and symptoms, identifying laboratory tests that may aid in the differential diagnosis of these pathologies becomes crucial. We performed an electronic search in PubMed, Web of Science and Scopus, without date or language restrictions, to identify all possible studies reporting D-dimer values in separate cohorts of children with MIS-C or Kawasaki disease. Three multicenter cohort studies were included in our analysis, totaling 487 patients (270 with MIS-C and 217 with Kawasaki disease). In this meta-analysis, significantly higher D-dimer values were found in MIS-C compared to Kawasaki disease in all three studies, yielding an SMD of 1.5 (95 % CI, 1.3-1.7) mg/L. Thus, very high D-dimer values early in the course of disease should raise the clinical suspicion of MIS-C rather than Kawasaki disease. Further studies should be planned to identify harmonized D-dimer diagnostic thresholds that may help discriminate these conditions.

Objectives: Limitations in human cognition commonly result in clinical reasoning failures that can lead to diagnostic errors. A metacognitive structured reflection on what clinical findings fit and/or do not fit with a diagnosis, as well as how discordance of data can help advance the reasoning process, may reduce such errors.

Case presentation: A 60-year-old woman with Hashimoto thyroiditis, diabetes, and generalized anxiety disorder presented with diffuse arthralgias and myalgias. She had been evaluated by physicians of various specialties and undergone multiple modalities of imaging, as well as a electromyography/nerve conduction study (EMG/NCS), leading to diagnoses of fibromyalgia, osteoarthritis, and lumbosacral plexopathy. Despite treatment for these conditions, she experienced persistent functional decline. The only definitive alleviation of her symptoms identified was in the few days following intra-articular steroid injections for osteoarthritis. On presentation to our institution, she appeared fit with a normal BMI. She was a long-time athlete and had been training consistently until her symptoms began. Prediabetes had been diagnosed the year prior and her A_1c progressed despite lifestyle modifications and 10 pounds of intentional weight loss. She reported fatigue, intermittent nausea without emesis, and reduced appetite. Examination revealed intact strength and range of motion in both the shoulders and hips, though testing elicited pain. She had symmetric hyperreflexia as well as a slowed, rigid gait. Autoantibody testing revealed strongly positive serum GAD-65 antibodies which were confirmed in the CSF. A diagnosis of stiff-person syndrome was made. She had an incomplete response to first-line therapy with high-dose benzodiazepines. IVIg was initiated with excellent response and symptom resolution.

Conclusions: Through integrated commentary on the diagnostic reasoning process from clinical reasoning experts, this case underscores the importance of frequent assessment of fit along with explicit explanation of dissonant features in order to avoid misdiagnosis and halt diagnostic inertia. A fishbone diagram is provided to visually demonstrate the major factors that contributed to the diagnostic error. The case discussant demonstrates the power of iterative reasoning, case progression without commitment to a single diagnosis, and the dangers of both explicit and implicit bias. Finally, this case provides clinical teaching points in addition to a pitfall, myth, and pearl specific to overcoming diagnostic inertia.

Objectives: Practice-based learning and improvement (PBLI) is an ACGME (Accreditation Council for Graduate Medical Education) core competency. Learning and reflecting on patients through follow-up is one method to help achieve this competency. We therefore designed a study evaluating a structured patient follow-up intervention for senior internal medicine (IM) residents at the University of Colorado Hospital (UCH).

Methods: Trainees completed structured reflections after performing chart review of prior patients during protected educational time. Two-month follow-up surveys evaluated the exercise's potential influence on clinical and reflective practices.

Results: Forty out of 108 (37 %) eligible residents participated in the exercise. Despite 62.5 % of participants lacking specific questions about patient outcomes before chart review, 81.2 % found the exercise at least moderately helpful. 48.4 % of participants believed that the review would change their practice, and 60.9 % felt it reinforced their existing clinical practices. In our qualitative data, residents learned lessons related to challenging clinical decisions, improving transitions of care, the significance of early goals of care conversations, and diagnostic errors/strategies.

Conclusions: Our results indicate that IM residents found a structured patient follow-up intervention educational, even when they lacked specific patient outcomes questions. Our results underscore the importance of structured self-reflection in the continuous learning process of trainees and suggest the benefit of dedicated educational time for this process.

Clinicians employ two main cognitive approaches for diagnoses, depending on their expertise. Novices typically use linear hypothetico-deductive methods, while experts rely more on intuitive pattern recognition. These closely correspond to System 1 and System 2 thinking described in behavioral economics. We propose that complex cases additionally require the cognitive skill of synthesis, to visualize and understand the connections between various elements. To illustrate the concept, we describe a 60-year-old individual with a 6 h history of chest pain, fever, cough, accompanying chronic heart failure, atrial fibrillation, COPD, thyrotoxicosis, and ischemic heart disease. Faced with such a scenario, a bedside approach adapted by clinicians is to generate a list of individual diagnoses or pathways of pathogenesis, and address them individually. For example, this cluster could include: smoking causing COPD, IHD leading to chest pain and heart failure, and thyrotoxicosis causing atrial fibrillation (AF). However, other interconnections across pathways could be considered: smoking contributing to IHD; COPD exacerbating heart failure; IHD and pneumonia triggering atrial fibrillation; thyrotoxicosis and AF, independently worsening heart failure; COPD causing hypoxemia and worsening ventricular function. The second cluster of explanation offers a richer network of relationships and connections across disorders and pathways of pathogenesis. This cognitive process of creatively identifying these relationships is synthesis, described in Bloom's taxonomy of the cognitive domain. It is a crucial skill required for visualizing a comprehensive and holistic view of a patient. The concept of synthesis as a cognitive skill in clinical reasoning warrants further exploration.

Objectives: Perform a pilot study of online game-based learning (GBL) using natural frequencies and feedback to teach diagnostic reasoning.

Methods: We conducted a multicenter randomized-controlled trial of computer-based training. We enrolled medical students, residents, practicing physicians and nurse practitioners. The intervention was a 45 min online GBL training vs. control education with a primary outcome of score on a scale of diagnostic accuracy (composed of 10 realistic case vignettes, requesting estimates of probability of disease after a test result, 0-100 points total).

Results: Of 90 participants there were 30 students, 30 residents and 30 practicing clinicians. Of these 62 % (56/90) were female and 52 % (47/90) were white. Sixty were randomized to GBL intervention and 30 to control. The primary outcome of diagnostic accuracy immediately after training was better in GBL (mean accuracy score 59.4) vs. control (37.6), p=0.0005. The GBL group was then split evenly (30, 30) into no further intervention or weekly emails with case studies. Both GBL groups performed better than control at one-month and some continued effect at three-month follow up. Scores at one-month GBL (59.2) GBL plus emails (54.2) vs. control (33.9), p=0.024; three-months GBL (56.2), GBL plus emails (42.9) vs. control (35.1), p=0.076. Most participants would recommend GBL to colleagues (73 %), believed it was enjoyable (92 %) and believed it improves test interpretation (95 %).

Conclusions: In this pilot study, a single session with GBL nearly doubled score on a scale of diagnostic accuracy in medical trainees and practicing clinicians. The impact of GBL persisted after three months.

Objectives: Diagnostic management teams (DMT) are groups of experts with specialized knowledge to guide test selection and interpretation of results. They have been active in institutions over the past 20 years. To date, there are limited data on whether the presence of experts to advise healthcare providers on appropriate laboratory test selection and interpretation of complex test results positively impacts patient care.

Methods: A retrospective study at a regional healthcare system with 257,000 patient encounters between 2011 and 2022 reviewing test interpretations provided by clinical laboratory experts on a diagnostic management team.

Results: Cases reviewed by the coagulation DMT were 6 times more likely to have an established, scientifically based diagnosis compared to those without a DMT. Patients who have a coagulation DMT review were twice as likely to receive a diagnosis vs. having no diagnosis.

Conclusions: This study demonstrates that for several objective clinical outcomes, specifically diagnostic conclusions and length of stay, a DMT of coagulation experts assessing patients' test results has had a major impact on outcomes and delivery of care.

Objectives: Cognitive load is postulated to be a significant factor in clinical reasoning performance. Monitoring physiologic measures, such as heart rate variability (HRV) may serve as a way to monitor changes in cognitive load. The pathophysiology of why HRV has a relationship to cognitive load is unclear, but it may be related to blood pressure changes that occur in a response to mental stress.

Methods: Fourteen residents and ten attendings from Internal Medicine wore Holter monitors and watched a video depicting a medical encounter before completing a post encounter form used to evaluate their clinical reasoning and standard psychometric measures of cognitive load. Blood pressure was obtained before and after the encounter. Correlation analysis was used to investigate the relationship between HRV, blood pressure, self-reported cognitive load measures, clinical reasoning performance scores, and experience level.

Results: Strong positive correlations were found between increasing HRV and increasing mean arterial pressure (MAP) (p=0.01, Cohen's d=1.41). There was a strong positive correlation with increasing MAP and increasing cognitive load (Pearson correlation 0.763; 95 % CI [; 95 % CI [-0.364, 0.983]). Clinical reasoning performance was negatively correlated with increasing MAP (Pearson correlation -0.446; 95 % CI [-0.720, -0.052]). Subjects with increased HRV, MAP and cognitive load were more likely to be a resident (Pearson correlation -0.845; 95 % CI [-0.990, 0.147]).

Conclusions: Evaluating HRV and MAP can help us to understand cognitive load and its implications on trainee and physician clinical reasoning performance, with the intent to utilize this information to improve patient care.

Objectives: Troponin testing is indicated in the diagnostic work-up of acute coronary syndrome (ACS) and incorporated in risk stratification pathways. This study aims to gain insights on the use, outcomes, and diagnostic accuracy of troponin testing in routine primary care; a setting that is understudied.

Methods: Routine data were used from the academic primary care network in the Amsterdam metropolitan area (968,433 patient records). The study population included adult patients who underwent high-sensitivity troponin I or T (hs-TnI/T) testing between 2011 and 2021. The primary outcome was the reported diagnosis and the secondary outcome was the diagnostic accuracy measured by death or ACS at 30 days.

Results: 3,184 patients underwent hs-troponin testing, either with hsTNT (n=2,333) or hsTNI (n=851). Median patients' age was 55 (44-65) years, and 62.3 % were female. Predominant symptoms were chest pain and dyspnea (56.7 %). Additional diagnostic laboratory tests were commonly performed (CRP: 47.7 %, natriuretic peptides: 25.6 %, d-dimer: 21.5 %). Most common diagnoses were musculoskeletal symptoms (21.6 %) and coronary heart disease (7.1 %; 1.1 % ACS). Troponin testing showed sensitivity and specificity of 77.8 % (60.9-89.9) and 94.3 % (93.5-95.1), respectively. Negative and positive predictive values were 99.7 (99.5-99.9) and 13.5 (11.1-16.4), and positive and negative likelihood ratios were 13.7 (10.9-17.1) and 0.24 (0.13-0.43).

Conclusions: GPs occasionally use troponin testing in very low-risk patients, often as part of a multi-marker rule-out strategy. The diagnostic characteristics of troponin tests, while promising, warrant prospective validation and implementation to facilitate appropriate use.