Diagnosis最新文献

Diagnostic errors in health care are a global threat to patient safety. Researchers have traditionally focused diagnostic safety efforts on identifying errors and their causes with the goal of reducing diagnostic error rates. More recently, complementary approaches to diagnostic errors have focused on improving diagnostic performance drawn from the safety sciences. These approaches have been called Safety-II and Safety-III, which apply resilience engineering and system safety principles, respectively. This review explores the safety science paradigms and their implications for analyzing diagnostic errors, highlighting their distinct yet complementary perspectives. The integration of Safety-I, Safety-II, and Safety-III paradigms presents a promising pathway for improving diagnosis. Diagnostic researchers not yet familiar with the various approaches and potential paradigm shift in diagnostic safety research may use this review as a starting point for considering Safety-I, Safety-II, and Safety-III in their efforts to both reduce diagnostic errors and improve diagnostic performance.

Objectives: Medical errors and complications pose a major threat to the safety of healthcare systems worldwide. This article was hence aimed at determining the current burden of complications of medical and surgical care in the US.

Methods: We searched the latest version of the US Centers for Disease Control and Prevention (CDC) WONDER online database (years 2018-2022) using ICD-10 codes Y40-Y84 (complications of medical and surgical care).

Results: The age-adjusted death rate for complications of medical and surgical care increased from 1.17 × 100,000 in 2018 to 1.49 × 100,000 in 2021, but then declined to 0.85 × 100,000 in 2022. The gender-specific analysis showed a similar trend, with the age-adjusted death rate values always being higher in men than in women. A clear age-dependent relationship was also found in the crude mortality rate for complications of medical and surgical care, as higher death rates were observed in older patients.

Conclusions: This analysis reveals that the burden of complications of medical and surgical care has increased over time, especially during the COVID-19 pandemic, but has then considerably declined in 2022. However, root cause analysis and actions are still needed for preventing the still noticeable consequences of medical complications.

Objectives: Proper coding of heart failure (HF) in electronic health records (EHRs) is an important prerequisite for adequate care and research towards this vulnerable patient population. We set out to evaluate the accuracy of registration of HF diagnoses in primary care EHRs.

Methods: In a routine primary care database covering the Amsterdam Metropolitan Area, we identified all episodes of care with International Classification of Primary Care (ICPC) codes K77 (decompensatio cordis) or K84.03 (cardiomyopathy) up to 31/12/2021. We also performed two text-based searches to identify HF episodes without an appropriate ICPC-code. An expert panel evaluated all ICPC and text matches for congruence between the assigned codes and notes.

Results: From a database of 968,433 records we identified 19,106 patients (2.0 %) with a total of 24,011 ICPC-coded HF episodes. Removal of 1,324 episodes found to concern other or uncertain diagnoses and inclusion of 4,582 validated HF episodes identified through text search led to exclusion of 909 (overregistration: 4.8 %) and inclusion of 2,266 additional patients (underregistration: 11.1 %). The inclusion of miscoded HF episodes advanced the first known date of HF diagnosis in 3.9 % of records, with a median shift of 3.45 years. Episode-level underregistration decreased significantly over time, from 23.8 % in 2006 to 10.0 % in 2021.

Conclusions: While there is improvement over time, there are still substantial levels of over- and underregistration of HF, emphasizing the need for cautious interpretation of ICPC-coded data. The findings contribute to the understanding of HF registration issues in primary care and provide insights for improving registration practices.

Objectives: Analytical interferences, caused by antibodies, often go unnoticed and require a deep understanding of analyzer principles in the correct clinical context.

Methods: A case report details a 56-year-old man with symptoms of hyperviscosity syndrome (HVS) due to multiple myeloma.

Results: The DxH 900 analyzer revealed abnormalities in the nucleated red blood cell (nRBC) graph, attributed to a high concentration of IgA kappa. Immediate plasmapheresis successfully treated HVS, reducing the monoclonal component and eliminating the aberrant green signal.

Conclusions: In the appropriate clinical context, the recognition of analytical interferences is necessary for accurate clinical interpretation, and it is only possible with knowledge of the analytical principles of the instruments. In this case, the high concentration of IgA kappa generated an aberrant green signal in the VCSm.

Atrial fibrillation (AF) is the most frequent type of cardiac arrhythmia that affects over six million individuals in Europe. The incidence and prevalence of AF rises with age, and often occurs after cardiac surgery. Other risk factors correlated with AF comprise high blood pressure, diabetes mellitus, left atrial enlargement, ischemic heart disease, and congestive heart failure. Considering the high prevalence of AF in aging societies, strategies to prevent serious complications, such as stroke or heart failure, are important because they are correlated with high morbidity and mortality. The supplementation of sea-derived n-3 polyunsaturated fatty acids (PUFA) is widely discussed in this context, but the results of experimental and observational studies are in contrast to randomized placebo-controlled intervention trials (RCTs). Specifically, larger placebo-controlled n-3 PUFA supplementation studies with long follow-up showed a dose-dependent rise in incident AF. Daily n-3 PUFA doses of ≥1 g/d are correlated with a 50 % increase in AF risk, whereas a daily intake of <1 g/d causes AF in only 12 %. Individuals with a high cardiovascular risk (CVD) risk and high plasma-triglycerides seem particularly prone to develop AF upon n-3 PUFA supplementation. Therefore, we should exercise caution with n-3 PUFA supplementation especially in patients with higher age, CVD, hypertriglyceridemia or diabetes. In summary, existing data argue against the additive intake of n-3 PUFA for preventative purposes because of an incremental AF risk and lacking CVD benefits. However, more clinical studies are required to disentangle the discrepancy between n-3 PUFA RCTs and observational studies showing a lower CVD risk in individuals who regularly consume n-3 PUFA-rich fish.

Objectives: Validate the diagnostic accuracy of the Artificial Intelligence Large Language Model ChatGPT4 by comparing diagnosis lists produced by ChatGPT4 to Isabel Pro.

Methods: This study used 201 cases, comparing ChatGPT4 to Isabel Pro. Systems inputs were identical. Mean Reciprocal Rank (MRR) compares the correct diagnosis's rank between systems. Isabel Pro ranks by the frequency with which the symptoms appear in the reference dataset. The mechanism ChatGPT4 uses to rank the diagnoses is unknown. A Wilcoxon Signed Rank Sum test failed to reject the null hypothesis.

Results: Both systems produced comprehensive differential diagnosis lists. Isabel Pro's list appears immediately upon submission, while ChatGPT4 takes several minutes. Isabel Pro produced 175 (87.1 %) correct diagnoses and ChatGPT4 165 (82.1 %). The MRR for ChatGPT4 was 0.428 (rank 2.31), and Isabel Pro was 0.389 (rank 2.57), an average rank of three for each. ChatGPT4 outperformed on Recall at Rank 1, 5, and 10, with Isabel Pro outperforming at 20, 30, and 40. The Wilcoxon Signed Rank Sum Test confirmed that the sample size was inadequate to conclude that the systems are equivalent. ChatGPT4 fabricated citations and DOIs, producing 145 correct references (87.9 %) but only 52 correct DOIs (31.5 %).

Conclusions: This study validates the promise of Clinical Diagnostic Decision Support Systems, including the Large Language Model form of artificial intelligence (AI). Until the issue of hallucination of references and, perhaps diagnoses, is resolved in favor of absolute accuracy, clinicians will make cautious use of Large Language Model systems in diagnosis, if at all.

Objectives: Diagnostic errors are the leading cause of preventable harm in clinical practice. Implementable tools to quantify and target this problem are needed. To address this gap, we aimed to generalize the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE) framework by developing its computable phenotype and then demonstrated how that schema could be applied in multiple clinical contexts.

Methods: We created an information model for the SPADE processes, then mapped data fields from electronic health records (EHR) and claims data in use to that model to create the SPADE information model (intention) and the SPADE computable phenotype (extension). Later we validated the computable phenotype and tested it in four case studies in three different health systems to demonstrate its utility.

Results: We mapped and tested the SPADE computable phenotype in three different sites using four different case studies. We showed that data fields to compute an SPADE base measure are fully available in the EHR Data Warehouse for extraction and can operationalize the SPADE framework from provider and/or insurer perspective, and they could be implemented on numerous health systems for future work in monitor misdiagnosis-related harms.

Conclusions: Data for the SPADE base measure is readily available in EHR and administrative claims. The method of data extraction is potentially universally applicable, and the data extracted is conveniently available within a network system. Further study is needed to validate the computable phenotype across different settings with different data infrastructures.

Objectives: It has been recognized that shortened activated partial thromboplastin time (aPTT) may be caused by various preanalytical conditions. As coagulation Factor VIII is included in the in vitro intrinsic coagulation cascade measured by aPTT, we hypothesized that the shortened aPTT could be a result of elevated FVIII activity. We aimed to inspect the connection of elevated FVIII with shortened aPTT, and the possible effect inflammation has on routine laboratory parameters.

Methods: 40 patients from various hospital departments with aPTT measurement below the lower limit of the reference interval (<23.0 s) were included in the study. To compare the obtained results with aPTT measurements in the non-inflammatory state, samples from 25 volunteers (laboratory personnel) were collected. White blood cell count, C-reactive protein, aPTT, and FVIII values were measured in the control group.

Results: Only two samples among 40 patients with shortened aPTT (5 %) were clotted. Out of the remaining 38, 26 had FVIII activity above 150 % (upper limit of a reference interval), median value of 194 % (IQR: 143-243 %). Seven samples in the control group had shortened aPTT results (36 %). However, all coagulation samples were clot and hemolysis-free. Multiple regression identified only FVIII activity as an independent variable in predicting aPTT values (p=0.001).

Conclusions: Our results support the thesis that shortened aPTT is rarely a consequence of preanalytical problems. Elevated FVIII activity causes shortened aPTT, not only in the inflammatory state but also in individuals with concentration of inflammatory markers within reference intervals.

Objectives: This study evaluates the comparative diagnostic accuracy of dental students and artificial intelligence (AI), specifically a modified ChatGPT 4, in endodontic assessments related to pulpal and apical conditions. The findings are intended to offer insights into the potential role of AI in augmenting dental education.

Methods: Involving 109 dental students divided into junior (54) and senior (55) groups, the study compared their diagnostic accuracy against ChatGPT's across seven clinical scenarios. Juniors had the American Association of Endodontists (AEE) terminology assistance, while seniors relied on prior knowledge. Accuracy was measured against a gold standard by experienced endodontists, using statistical analysis including Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests.

Results: ChatGPT achieved significantly higher accuracy (99.0 %) compared to seniors (79.7 %) and juniors (77.0 %). Median accuracy was 100.0 % for ChatGPT, 85.7 % for seniors, and 82.1 % for juniors. Statistical tests indicated significant differences between ChatGPT and both student groups (p<0.001), with no notable difference between the student cohorts.

Conclusions: The study reveals AI's capability to outperform dental students in diagnostic accuracy regarding endodontic assessments. This underscores AIs potential as a reference tool that students could utilize to enhance their understanding and diagnostic skills. Nevertheless, the potential for overreliance on AI, which may affect the development of critical analytical and decision-making abilities, necessitates a balanced integration of AI with human expertise and clinical judgement in dental education. Future research is essential to navigate the ethical and legal frameworks for incorporating AI tools such as ChatGPT into dental education and clinical practices effectively.