Kairos in diagnostics.

Kairos has been a key concept in medicine for millennia and is frequently understood as "the right time" in relation to treatment. In this study we scrutinize kairos in the context of diagnostics. This has become highly topical as technological developments have caused diagnostics to be performed ever earlier in the disease development. Detecting risk factors, precursors, and predictors of disease (in biomarkers, pre-disease, and pre-pre-disease) has resulted in too early diagnoses, i.e., overdiagnoses. Nonetheless, despite vast advances in science and technology, diagnoses also come too late. Accordingly, timing diagnostics right is crucial. In this article we start with giving a brief overview of the etymology and general use of the concepts of kairos and diagnosis. Then we delimit kairos in diagnostics by analysing "too early" and "too late" diagnosis and by scrutinizing various phases of diagnostics. This leads us to define kairos of diagnostics as the time when there is potential for sufficient information for making a diagnosis that is most helpful for the person. It allows us to conclude that kairos is as important in diagnostics as in therapeutics.

[The multi-professional alliance in the diagnostic process]. / L'alliance pluriprofessionnelle dans la démarche diagnostique.

Professional support for elderly people suffering from cognitive impairment needs to be comprehensive, and requires the active involvement of a wide range of professionals. Two clinical cases show how the combined support of a therapeutic day-care center and medical specialists can improve the quality of life of patients and their families, by helping to refine diagnoses or adjust treatment.

Artificial Intelligence vs Clinician Performance in Estimating Probabilities of Diagnoses Before and After Testing.

This diagnostic study compares the performance of artificial intelligence (AI) with that of human clinicians in estimating the probability of diagnoses before and after testing.

The Psychosomatic Traits of "People with the Five Elements in Traditional Chinese Medicine": A Qualitative Study.

Objective: To identify the representative attributes of the five elements of a person with a qualitative methodology and provide the basis for the clinical diagnosis and treatment of "people with the five elements in traditional Chinese medicine (TCM)." Methods: Data collected from the literature review, two sessions of brainstorming of experts with related experience in "people with the five elements in TCM" from October 2020 to December 2020, and six rounds of in-depth interviews with 30 participants who had various attributes of the five elements from March 2021 to October 2021 were analyzed. Triangulation was used in this study, and theming and synthesizing were used to analyze the data. Results: A total of 31 experts and 30 interviewees participated in this study. The median age of the experts and interviewees were 48.0 and 38.5 years, respectively; 51.66% and 54.8% of experts and interviewees, respectively, were men. The descriptors of facial diagrams of "people with the five elements in TCM" were complexion, shape, distribution state of facial bones, convergence trend of facial muscles, and facial expression. A theoretical model of "people with the five elements in TCM" was shaped based on these findings. Conclusion: The study suggests a possibility for bridging the gap between personality and bodily state, identifying an avenue for personality research from the perspective of TCM.

Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study.

Importance: Artificial intelligence (AI) could support clinicians when diagnosing hospitalized patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to mitigate errors made by models, but the effectiveness of this strategy has not been established. Objectives: To evaluate the impact of systematically biased AI on clinician diagnostic accuracy and to determine if image-based AI model explanations can mitigate model errors. Design, Setting, and Participants: Randomized clinical vignette survey study administered between April 2022 and January 2023 across 13 US states involving hospitalist physicians, nurse practitioners, and physician assistants. Interventions: Clinicians were shown 9 clinical vignettes of patients hospitalized with acute respiratory failure, including their presenting symptoms, physical examination, laboratory results, and chest radiographs. Clinicians were then asked to determine the likelihood of pneumonia, heart failure, or chronic obstructive pulmonary disease as the underlying cause(s) of each patient's acute respiratory failure. To establish baseline diagnostic accuracy, clinicians were shown 2 vignettes without AI model input. Clinicians were then randomized to see 6 vignettes with AI model input with or without AI model explanations. Among these 6 vignettes, 3 vignettes included standard-model predictions, and 3 vignettes included systematically biased model predictions. Main Outcomes and Measures: Clinician diagnostic accuracy for pneumonia, heart failure, and chronic obstructive pulmonary disease. Results: Median participant age was 34 years (IQR, 31-39) and 241 (57.7%) were female. Four hundred fifty-seven clinicians were randomized and completed at least 1 vignette, with 231 randomized to AI model predictions without explanations, and 226 randomized to AI model predictions with explanations. Clinicians' baseline diagnostic accuracy was 73.0% (95% CI, 68.3% to 77.8%) for the 3 diagnoses. When shown a standard AI model without explanations, clinician accuracy increased over baseline by 2.9 percentage points (95% CI, 0.5 to 5.2) and by 4.4 percentage points (95% CI, 2.0 to 6.9) when clinicians were also shown AI model explanations. Systematically biased AI model predictions decreased clinician accuracy by 11.3 percentage points (95% CI, 7.2 to 15.5) compared with baseline and providing biased AI model predictions with explanations decreased clinician accuracy by 9.1 percentage points (95% CI, 4.9 to 13.2) compared with baseline, representing a nonsignificant improvement of 2.3 percentage points (95% CI, -2.7 to 7.2) compared with the systematically biased AI model. Conclusions and Relevance: Although standard AI models improve diagnostic accuracy, systematically biased AI models reduced diagnostic accuracy, and commonly used image-based AI model explanations did not mitigate this harmful effect. Trial Registration: ClinicalTrials.gov Identifier: NCT06098950.

The Promise and Pitfalls of AI in the Complex World of Diagnosis, Treatment, and Disease Management.

In this Medical News article, Ida Sim, MD, PhD, a primary care physician and computational precision health expert at the University of California, San Francisco, discusses the ramifications of using AI technologies in patient care.

Recyclable and Robust Optical Nanoprobes with Engineered Enzymes for Sustainable Serodiagnostics.

Recyclable fluorescence assays that can be stored at room temperature would greatly benefit biomedical diagnostics by bringing sustainability and cost-efficiency, especially for point-of-care serodiagnostics in developing regions. Here, a general strategy is proposed to generate recyclable fluorescent probes by using engineered enzymes with enhanced thermo-/chemo-stability, which maintains an outstanding serodiagnostic performance (accuracy >95%) after 10 times of recycling as well as after storage at elevated temperatures (37 °C for 10 days). With these three outstanding properties, recyclable fluorescent probes can be designed to detect various biomarkers of clinical importance by using different enzymes.

Missing values and inconclusive results in diagnostic studies - A scoping review of methods.

Most diagnostic studies exclude missing values and inconclusive results from the analysis or apply simple methods resulting in biased accuracy estimates. This may be due to the lack of availability or awareness of appropriate methods. This scoping review aimed to provide an overview of strategies to handle missing values and inconclusive results in the reference standard or index test in diagnostic accuracy studies. Conducting a systematic literature search in MEDLINE, Cochrane Library, and Web of Science, we could identify many articles proposing methods for addressing missing values in the reference standard. There are also several articles describing methods regarding missing values or inconclusive results in the index test. The latter encompass imputation, frequentist and Bayesian likelihood, model-based, and latent class methods. While methods for missing values in the reference standard are regularly applied in practice, this is not true for methods addressing missing values and inconclusive results in the index test. Our comprehensive overview and description of available methods may raise further awareness of these methods and will enhance their application. Future research is needed to compare the performance of these methods under different conditions to give valid and robust recommendations for their usage in various diagnostic accuracy research scenarios.

The Mighty Metabolomics: Emerging Tool for Disease Diagnostics.

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A universal diagnosis syntax.

BACKGROUND: Diagnoses are crucial assets of clinical work and provide the foundation for treatment and follow up. They should be informative and customized to the patient's problem. Common prefixes, morphemes, and suffixes may aid the implementation of expressions that generate diagnoses. RESULTS: Apt choices of symbols plays a major role in science. In this study, the variables e, o, and p are assigned to names of an etiological agent, a disorder, and a pathogenetic mechanism, respectively. The suffix -itis designates infections, allergies, inflammation, and/or immune reactions. Diagnoses (d) are generated by the formula d:= e&o&p where '&' means concatenation and ':= ' means assignment. Thus, with e:= 'Staphylococcus aureus ', o:= 'endocard', and p:= 'itis', d:= e&o&p generates the diagnosis d = 'Staphylococcus aureus endocarditis'. Diagnoses formed this way comply with common clinical diagnoses. Certain extensions generate complete, systematic medical diagnoses that are applicable to all medical specialties. For example, common medical prefixes, morphemes, and suffixes give rise to o = 'hypothyroidism', o = 'tachycardia', and o = 'hypophagocytosis'. The formula scales well with the developments in clinical medicine, systems biology, molecular biology, and microbiology. The diagnosis generating formula d:= e&o&p requires meticulous analysis of the components of diagnoses plus the introduction of appropriate variables and terms. Terms partition on established clinical categories and adhere to established clinical nomenclature. The syntax generates universal medical diagnoses. CONCLUSIONS: The present study concerns a universal diagnosis syntax (UDS) that generates diagnoses using the formula d:= e&o&p with several extensions described in the study. The formula is easy to learn and covers diagnoses in all medical specialties. The present work succeeded in creating diagnoses from the formula. The fundamental insight is that no matter how complicated a diagnosis is it can be generated by a systematic process, which adds terms one by one. UDS may have implications for medical education and classifications. The formula lays a foundation for structured clinical decision-making. Formulas are hallmarks of hard science. So, d:= e&o&p anticipates a scientific medical revolution.

Development and evaluation of a customized checklist to assess the quality control of disease registry systems of Tehran, the capital of Iran in 2021.

BACKGROUND: Clinical registries facilitate medical research by providing 'real data'. In the past decade, an increasing number of disease registry systems (DRS) have been initiated in Iran. Here, we assessed the quality control (QC) of the data recorded in the DRS established by Shahid Beheshti University of Medical Sciences in Tehran, the capital city of Iran, in 2021. METHODS: The present study was conducted in two consecutive qualitative and quantitative phases and employed a mixed-method design. A checklist containing 23 questions was developed based on a consensus reached following several panel group discussions, whose face content and construct validities were confirmed. Cronbach's alpha was calculated to verify the tool's internal consistency. Overall, the QC of 49 DRS was assessed in six dimensions, including completeness, timeliness, accessibility, validity, comparability, and interpretability. The seventy percent of the mean score was considered a cut-point for desirable domains. RESULTS: The total content validity index (CVI) was obtained as 0.79, which is a reasonable level. Cronbach's alpha coefficients obtained showed acceptable internal consistency for all of the six QC domains. The data recorded in the registries included different aspects of diagnosis/treatment (81.6%) and treatment quality requirements outcomes (12.2%). According to the acceptable quality cut-point, out of 49 evaluated registries, 48(98%), 46(94%), 41(84%), and 38(77.5%), fulfilled desirable quality scores in terms of interpretability, accessibility, completeness, and comparability, however, 36(73.5%) and 32(65.3%) of registries obtained the quality requirement for timeliness and validity, respectively. CONCLUSION: The checklist developed here, containing customized questions to assess six QC domains of DRSs, provided a valid and reliable tool that could be considered as a proof-of-concept for future investigations. The clinical data available in the studied DRSs fulfilled desirable levels in terms of interpretability, accessibility, comparability, and completeness; however, timeliness and validity of these registries needed to be improved.

The first diagnostic excellence conference in Japan.

In February 2023, the first national conference on Diagnostic Excellence was held in Japan. As the conference covered a wide range of academic areas, we placed particular emphasis on topics related to excellence in physicians' clinical reasoning. This focus reflected the culture of Japanese medical professionals, especially of physicians, who have shown passion for clinical diagnosis for decades, having held non-profit voluntary multi-institutional conferences during off-duty hours. Of the over 1,400 participants who attended the two-day conference, 80% were generalist physicians and residents, 10% were medical students, and the remainder were healthcare professionals and participants from other academic areas. Given the background of Japanese physicians' passion for clinical diagnosis, the conference organizers believed that focusing on the diagnostic thinking of physicians as part of the Diagnostic Excellence concept would have strong appeal to participants. Simultaneously, the organizers believed that it was important to target participants in their 20s-40s, and made efforts to utilize social networking services and advertising strategies, including creating individual posters with the help of professional designers. On reflection after the conference, consideration of the local characteristics of the population, particularly their interest in the target population, and the expansion of the conference's focus to include younger participants may have served as drivers of the success of the conference. This outcome of the conference in Asia is an intriguing step in the world deployment strategy of Diagnostic Excellence, and is expected to promote cooperation among Asia and the United States, Europe, and Oceania.

Establishment and application of the performance appraisal system for hierarchical diagnosis and treatment in China: A case study in Fujian province.

Purpose: Hierarchical diagnosis and treatment, as an important measure and direction for China's medical reform, are conducive to improving the capacity of medical services and the national level of health. In this study, a hierarchical diagnosis and treatment performance evaluation index system is established to identify the effects of different influencing factors on developing hierarchical diagnosis and treatment. Methods: In this study, samples collected from 23 representative integrated medical institutions in nine Fujian cities from 2018 to 2020 are taken as subjects. A hierarchical diagnosis and treatment performance appraisal system is established based on the mechanism of research on the operation of hierarchical diagnosis and treatment. This is combined with the evaluation index system established by the Health Development Research Center, the National Health Commission of the People's Republic of China for the evaluation of hierarchical diagnosis and treatment and the construction of the medical treatment alliance, including studies carried out by related scholars. The weight of each evaluation index is determined with the CRITIC method, and the hierarchical diagnosis and treatment effects on 23 subjects are quantitatively evaluated by the Gray correlation method based on the weight of each index. Results: The hierarchical diagnosis and treatment performance evaluation index system is established from three aspects, namely allocation of hierarchical diagnosis and treatment resources, establishment of the hierarchical diagnosis and treatment management system, and hierarchical diagnosis and treatment implementation effect; 27 tertiary indexes are formed in total. The Gray correlation of each year in Fujian exceeds 0.5, but <0.53. Conclusion: Gray correlation of each year in Fujian has gradually increased. But there is still room for improvement. The government departments must improve the investment in medical resources with measures adjusted according to local conditions, promote a balanced allocation of resources for hierarchical diagnosis and treatment, increase communication and interaction between upper and lower medical institutions, and optimize the allocation of resources for hierarchical diagnosis and treatment. Then determine the types of disease to be treated, expand the coverage of chronic disease management, establish standardized chronic disease health management, and strengthen training of health management staff.

How to Critically Appraise and Interpret Systematic Reviews and Meta-Analyses of Diagnostic Accuracy: A User Guide.

Systematic reviews of diagnostic accuracy studies can provide the best available evidence to inform decisions regarding the use of a diagnostic test. In this guide, the authors provide a practical approach for clinicians to appraise diagnostic accuracy systematic reviews and apply their results to patient care. The first step is to identify an appropriate systematic review with a research question matching the clinical scenario. The user should evaluate the rigor of the review methods to evaluate its credibility (Did the review use clearly defined eligibility criteria, a comprehensive search strategy, structured data collection, risk of bias and applicability appraisal, and appropriate meta-analysis methods?). If the review is credible, the next step is to decide whether the diagnostic performance is adequate for clinical use (Do sensitivity and specificity estimates exceed the threshold that makes them useful in clinical practice? Are these estimates sufficiently precise? Is variability in the estimates of diagnostic accuracy across studies explained?). Diagnostic accuracy systematic reviews that are judged to be credible and provide diagnostic accuracy estimates with sufficient certainty and relevance are the most useful to inform patient care. This review discusses comparative, noncomparative, and emerging approaches to systematic reviews of diagnostic accuracy using a clinical scenario and examples based on recent publications.

Label-Free Electrochemical Methods for Disease Detection.

Label-free electrochemical biosensing leverages the advantages of label-free techniques, low cost, and fewer user steps, with the sensitivity and portability of electrochemical analysis. In this review, we identify four label-free electrochemical biosensing mechanisms: (a) blocking the electrode surface, (b) allowing greater access to the electrode surface, (c) changing the intercalation or electrostatic affinity of a redox probe to a biorecognition unit, and (d) modulating ion or electron transport properties due to conformational and surface charge changes. Each mechanism is described, recent advancements are summarized, and relative advantages and disadvantages of the techniques are discussed. Furthermore, two avenues for gaining further diagnostic information from label-free electrochemical biosensors, through multiplex analysis and incorporating machine learning, are examined.

Past, present and imaginary: Pathography in all its forms.

Diagnosis is a profoundly social phenomenon which, while putatively identifying disease entities, also provides insights into how societies understand and explain health, illness and deviance. In this paper, we explore how diagnosis becomes part of popular culture through its use in many non-clinical settings. From historical diagnosis of long-deceased public personalities to media diagnoses of prominent politicians and even diagnostic analysis of fictitious characters, the diagnosis does meaningful social work, explaining diversity and legitimising deviance in the popular imagination. We discuss a range of diagnostic approaches from paleopathography to fictopathography, which all take place outside of the clinic. Through pathography, diagnosis creeps into widespread and everyday domains it has not occupied previously, performing medicalisation through popularisation. We describe how these pathographies capture, not the disorders of historical or fictitious figures, rather, the anxieties of a contemporary society, eager to explain deviance in ways that helps to make sense of the world, past, present and imaginary.