Diagnosis最新文献

Decision curve analysis (DCA) bridges the gap between statistical accuracy and clinical usefulness - a distinction frequently overlooked in diagnostic research. Using a simulated cohort representing a real-world diagnostic scenario, this tutorial demonstrates how predictors with similar ROC-based performance can yield markedly different net benefit profiles when evaluated through DCA. Three tools were compared: a strong predictor (composite clinical score), a moderate biomarker (leukocytes), and a weak marker with modest AUC but limited practical value (serum sodium). Whereas ROC curves portray discrimination alone, decision curves situate performance within real clinical trade-offs, making explicit when a model adds value beyond default strategies such as treating all or none. The tutorial provides a step-by-step framework for interpretation, clarifies frequent misconceptions (thresholds, prevalence effects, calibration), and illustrates how DCA incorporates the consequences of decisions rather than just their statistical accuracy. Rather than adding 'just another metric', DCA reframes evaluation around a practical question: does using this model improve decisions across clinically reasonable thresholds?

While modern medicine emphasizes teamwork, expert performance in clinical reasoning may require periods of deliberate solitude to refine intuition, enhance diagnostic and/or management accuracy, and mitigate potential cognitive biases. Evidence from cognitive psychology, philosophy, and education suggests that cognitive withdrawal supports deep learning and problem-solving, yet its role in clinical reasoning learning and performance remains underexplored. Medicine often prioritizes speed and real-time collaboration, potentially limiting opportunities for independent time for thought. This article explores whether deliberate solitude could support the development and performance of clinical reasoning. Clinicians might consider engaging in diagnostic rehearsal, independent synthesis, and/or cognitive withdrawal during these solitary moments, but the specific opportunities and benefits remain uncertain. By drawing from research in other disciplines, we consider how solitude might help physicians refine their clinical reasoning, which, in turn, could potentially reduce errors. While no specific course of action can yet be made, this conceptual perspective suggests potential directions for future inquiry.

Objectives: To assess the pulmonary physical examination (PE) skills of internal medicine residents using real patients to inform pulmonary PE educational initiatives.

Methods: First year medicine residents (interns) from two large academic medical centers in Maryland examined the same patient with interstitial lung disease (ILD) as part of the Assessment of Physical Examination and Communication Skills (APECS). Interns were evaluated on five clinical domains: PE technique, identifying physical signs, generating a differential diagnosis, clinical judgment, and maintaining patient welfare. Spearman's correlation test described associations between clinical domains. Preceptor comments were examined to identify errors in PE technique and identifying physical signs.

Results: One-hundred and fifty-five interns examined the same patient with ILD across 33 APECS sessions. 111 interns (71.6 %) correctly identified the presence of crackles; 96 interns (61.9 %) included ILD on their differential diagnosis. There was a significant and positive correlation between PE technique and identification of PE findings (r=0.48, p<0.0001). PE technique (r=0.19, p=0.016) and identifying signs (r=0.42, p<0.0001) were both significantly associated with generating an appropriate differential diagnosis, which in turn was significantly associated with appropriate clinical management (r=0.46, p<0.0001). Common errors were not auscultating the entire lung (55 interns, 35.5 %), auscultating through the gown (20 interns, 12.9 %), not percussing the chest (15 interns, 9.7 %), and using incorrect technique for percussion (37 interns, 23.9 %).

Conclusions: Medicine interns had variable skills in performing the pulmonary PE. Improving PE skills would lead to increased identification of relevant pulmonary findings, inform clinical decision making, and improve overall patient care.

Background: Although the issue of injustice in healthcare has been extensively discussed-particularly in relation to access to treatments-diagnostic injustice remains insufficiently addressed. Therefore, it is crucial to clarify the concept of diagnostic injustice, identify its underlying sources, and explore potential solutions to mitigate its impact.

Content: An ethical analysis of diagnostics reveals that diagnostic injustice manifests in various traditional forms of injustice, including distributive, procedural, social, structural, systemic, and epistemic injustice. A subsequent narrative review identifies various sources of diagnostic injustice, such as unclear diagnostic criteria, arbitrary diagnostics, unfair taxonomic processes, biomedical (technological) dominance, uncertainty, prejudice, stereotypes, biases, as well as diagnostic hierarchies. Corresponding to these sources of diagnostic injustice, a range of measures are proposed to mitigate its effects.

Summary: Diagnostic injustice is pervasive and rooted in a complex array of sources tied to social and professional norms and values, making it challenging to effectively mitigate. By clarifying the concept, pinpointing its sources, and recommending measures to manage diagnostic injustice, this article highlights the importance of promoting diagnostic justice in healthcare.

Outlook: Diagnostic injustice is an understudied topic that deserves more attention. This study defines the concept, identifies its sources, and suggests measures to mitigate its effects. As such it is the first step to address diagnostic injustice and to enhance the equity in healthcare. Future work should focus on developing and implementing effective interventions that target the identified sources of injustice, ultimately striving for a more just healthcare system.

The lack of universal diagnostic criteria for multiple sclerosis (MS) delays diagnosis and care. Diagnostic guidelines widen existing gaps in neurological health depending on how they are applied by clinicians across the globe. The 2016 MAGNIMS and 2017 McDonald's criteria for MS exclude cerebrospinal fluid (CSF) findings, and optic neuritis (ON), respectively, opening the door for missed diagnosis, which, in under-resourced settings, could have devastating consequences. When guidelines prioritize precision over clinical inclusion, they risk excluding patients with a true diagnosis, and undermine the very utility they aim to standardize. Diagnostic frameworks must evolve not only to reflect statistical rigor but support health policy that demands the real-world consequences of underdiagnosis just as heavily as the cost of overdiagnosis. Precision with inclusivity and epidemiologic modeling should be part of that conversation. The burden of MS is immense; we need universal diagnostic criteria that cut across borders.

Objectives: Diagnostic reasoning often favors simplicity, with clinicians seeking a single unifying explanation for a patient's presentation. However, in certain cases, a narrow approach can lead to diagnostic oversight and delay. In this case, an early attribution of symptoms to leukocytoclastic vasculitis (LCV) postponed the recognition and treatment of an underlying varicella-zoster virus (VZV) infection.

Case presentation: An 88-year-old woman presented with palpable purpuric macules in a linear distribution along the right pretibial region, dorsum of the foot, and toes, accompanied by severe unilateral pain radiating from the foot to the buttock. The pain preceded the rash by one day, and no vesicles were noted by the patient or clinician. Clinically, the morphology was consistent with leukocytoclastic vasculitis, but the pain followed a dermatomal distribution, raising concern for varicella-zoster virus (VZV) infection. A biopsy ultimately confirmed both leukocytoclastic vasculitis and viral cytopathic changes. The patient improved with a combination of valacyclovir, prednisone, and gabapentin.

Conclusions: This case illustrates a diagnostically challenging presentation in which two distinct pathologies mimicked and masked one another. Anchoring on a unifying diagnosis led to premature closure, delaying antiviral therapy. Herpes zoster without vesicles is uncommon but documented, and VZV-induced vasculitis is rare. Together, they created a deceptive clinical picture. The case reinforces several key teaching points: the value of maintaining diagnostic flexibility, the importance of empiric antiviral treatment when herpes zoster is suspected, and the need to recognize cognitive biases that can distort clinical judgment. When findings are discordant or incomplete, clinicians should resist diagnostic simplification and remain open to diagnosing coexisting conditions.

Objectives: Serious adverse drug reactions (SADRs) require robust causality assessment methods. Identifying the culprit drug relies on clinical history, causality algorithms, and in vitro tests. The lymphocyte transformation test (LTT) is widely used, but flow cytometry has been proposed as an alternative due to LTT's limitations. This study evaluated the diagnostic performance of LTT and CD69 upregulation on T lymphocytes by flow cytometry.

Methods: A total of 148 patients (150 SADRs) with at least a "possible" algorithmic causality score (≥4) were assessed using LTT and flow cytometry. Twenty-five healthy individuals served as negative controls. Over 98 % of cases were organ-specific reactions, with drug-induced liver injury being most frequent (52.6 %), and systemic antibiotics the most implicated drug group (29.7 %).

Results: 400 suspected drugs were analyzed. LTT and flow cytometry were positive in 43 and 30 % of patients, respectively. For cases with an algorithmic score≥6, sensitivity increased to 50 % for LTT and 33 % for flow cytometry. Sensitivity varied by reaction type (LTT: 17-47 %; flow cytometry: 20-38 %) and drug group (LTT: 17-86 %; flow cytometry: 18-50 %). Combining both tests improved sensitivity to 50-63 % by reaction type and 50-100 % by drug group. An algorithmic score≥6 combined with both tests further increased overall sensitivity to 70 %.

Conclusions: These findings show the added value of integrating multiple in vitro diagnostic approaches with causality algorithms to improve culprit drug identification in organ-specific SADRs. Future studies are needed to validate these findings.

Diagnostic delays and errors are serious and costly, affecting approximately 5 % of US adults in the outpatient setting annually. Patients with difficult-to-diagnose conditions may spend months or years undergoing diagnostic evaluation in search of a correct diagnosis. Methods are needed to identify patients with diagnostically challenging conditions (DCCs) who are experiencing diagnostic odysseys and, as a result, potential missed opportunities in their diagnosis. Given the increasing availability of longitudinal EHR data to map a patient's journey, we propose a new framework to proactively identify patients with DCCs using electronic data. These patients are at risk for missed opportunities in diagnosis, and a timelier diagnosis can improve their outcomes. We propose criteria for identifying specific DCCs where the diagnostic process for that condition makes them amenable to detection using EHR-based algorithms. We discuss the application of the proposed framework to an exemplary case study of fibrotic interstitial lung disease and provide examples of algorithms that could be implemented in the future. This work can help identify patients earlier in their diagnostic journeys, resulting in adequate follow-up and fewer missed or delayed diagnoses. Our proposed framework can inform research and potential solutions that are more real-time to potentially mitigate and avoid delays in care and resulting harm.

Objectives: To develop and test feasibility of patient survey-based quality measures to assess timeliness of cancer diagnosis.

Methods: We developed a 39-item survey through review of literature and measures, input from experts, and cognitive testing. We field-tested it in an urban health system among patients with new cancer diagnoses (prior 3-9 months); surveys were administered by web and mail September-October 2023. We calculated top-box scores and conducted confirmatory factor analysis for evaluative items and assessed construct validity through correlations to the diagnostic interval, number of health care visits, and overall assessments of care quality.

Results: The overall response rate was 23.9 %. The 276 respondents primarily spoke English (89 %), were non-Hispanic white (66 %), age 65+ (68 %), and college graduates (67 %). More than a quarter believed their cancer could have been diagnosed much sooner. Better communication with health care professionals, timely receipt of usual and specialty care, and ease of receipt and follow-up of tests, X-rays, and scans were positively associated with patients reporting that health care professionals could not have diagnosed the cancer much sooner. Patients reporting it was always easy to receive needed tests, X-rays, and scans and that they always received follow-up results were more than three times more likely to report a diagnostic interval less than or equal to 30 days (p<0.001).

Conclusions: Patients can report on barriers to timely diagnosis associated with the length of their cancer diagnostic interval. Surveys are a promising source of information regarding opportunities to improve timeliness of cancer diagnosis.