Evaluation of a Previously Developed Predictive Model for Infective Endocarditis in 320 Patients Presenting with Fever at 4 Centers in Japan Between January 2018 and December 2020.

BACKGROUND In our previous single-center study, we developed an infective endocarditis (IE) prediction model among patients with undiagnosed fever (UF) based on 5 factors that can be obtained on admission: ambulance transfer, presence of cardiac murmur or pleural effusion, blood neutrophil percentage, and platelet count. The present study aimed to retrospectively evaluate the prediction model for IE in 320 patients presenting with fever at 4 university hospitals in Japan from January 2018 to December 2020. MATERIAL AND METHODS Patients aged ≥20 years admitted to 4 hospitals with I-330 (IE) or R-50-9 (UF) according to the International Statistical Classification of Diseases and Related Health Problems-10 were enrolled. More than 2 physicians at each hospital reviewed the patient diagnoses using the modified Duke criteria, allocating "definite IE" to IE group (n=119) and "non-definite IE" to UF group (n=201). Five factors on admission were analyzed by multivariate logistic regression. The discriminative ability and calibration of the model were evaluated using the area under the curve (AUC) and the shrinkage coefficient, respectively. RESULTS A total of 320 patients were enrolled. The odds ratios (95% confidence intervals) were as follows: ambulance transfer 1.81 (0.91-3.55); cardiac murmur 13.13 (6.69-27.36); pleural effusion 2.34 (0.62-2.42); blood neutrophil percentage 1.09 (1.06-1.14); and platelet count 0.96 (0.93-0.99). The AUC was 0.783 (0.732-0.834) with a shrinkage coefficient of 0.961. CONCLUSIONS The IE prediction model is useful to estimate the probability of IE immediately after admission for fever in patients aged ≥20 years.

Eosinopenia as a diagnostic marker of bloodstream infection in a general internal medicine setting: a cohort study.

BACKGROUND: Little is known about the potential use of the eosinophil count as a predictive marker of bloodstream infection. In this study, we aimed to assess the reliability of eosinopenia as a predictive marker of bloodstream infection. METHODS: This retrospective cohort study was performed in the outpatient department and general internal medicine department of a tertiary university hospital in Japan. A total of 189 adult patients with at least 2 sets of blood cultures obtained during the period January 1-December 31, 2018, were included; those with the use of antibiotic therapy within 2 weeks prior to blood culture, steroid therapy, or a history of haematological cancer were excluded. The diagnostic accuracies of each univariate variable and the multivariable logistic regression models were assessed by calculating the areas under the receiver operating characteristic curves (AUROCs). The primary outcome was a positive blood culture indicating bloodstream infection. RESULTS: Severe eosinopenia (< 24.4 cells/mm3) alone yielded small but statistically significant overall predictive ability (AUROC: 0.648, 95% confidence interval (CI): 0.547-0.748, P < 0.05), and only moderate sensitivity (68, 95% CI: 46-85%) and specificity (62, 95% CI: 54-69%). The model comprising baseline variables (age, sex), the C-reactive protein level, and neutrophil count yielded an AUROC of 0.729, and further addition of eosinopenia yielded a slight improvement, with an AUROC of 0.758 (P < 0.05) and a statistically significant net reclassification improvement (NRI) (P = 0.003). However, the integrated discrimination index (IDI) (P = 0.284) remained non-significant. CONCLUSIONS: Severe eosinopenia can be considered an inexpensive marker of bloodstream infection, although of limited diagnostic accuracy, in a general internal medicine setting.

Five-year follow-up of polymer-free sirolimus- and probucol-eluting stents versus new generation zotarolimus-eluting stents in patients presenting with st-elevation myocardial infarction.

BACKGROUND: Patients with ST-segment elevation myocardial infarction (STEMI) undergoing drug-eluting stent (DES) implantation are at increased risk of late adverse events, partly explained by an exaggerated inflammatory reaction to durable-polymer stent coatings. OBJECTIVES: We sought to investigate whether implantation of polymer-free DES would reduce this risk. METHODS: In the ISAR-TEST 5 (the Intracoronary Stenting and Angiographic Results: Test Efficacy of Sirolimus- and Probucol- and Zotarolimus-Eluting Stents) trial, patients were randomly allocated to receive a polymer-free sirolimus- and probucol-eluting stent or a new generation durable-polymer zotarolimus-eluting stent. We analyzed late clinical outcomes in the subgroup of patients presenting with STEMI. The primary endpoint was the combined incidence of cardiac death, target vessel-related myocardial infarction or target lesion revascularization at 5 years. RESULTS: 311 patients with STEMI were randomized to receive sirolimus- and probucol-eluting stents (n = 215) or zotarolimus-eluting stents (n = 96). At 5 years, there was no difference in the incidence of the primary endpoint in patients treated with sirolimus- and probucol-eluting stents versus zotarolimus-eluting stents (18.3% versus 20.1% respectively, hazard ratio = 0.87, 95% CI, 0.50-1.51; P = 0.62). Rates of the individual components of the primary endpoint were also comparable in both groups. The incidence of definite/probable stent thrombosis was 1.4% versus 1.0% respectively (hazard ratio = 1.35, 95% CI, 0.14-12.94, P = 0.80). CONCLUSIONS: Long-term outcomes of patients with STEMI treated with polymer-free sirolimus- and probucol-eluting stents versus durable-polymer zotarolimus-eluting stents were similar. Stent thrombosis rates were low and comparable in both treatment groups, with no events beyond 12 months. CLINICAL TRIAL REGISTRATION: Registered at ClinicalTrials.gov (Identifier NCT 00598533) © 2016 Wiley Periodicals, Inc.

A pan-coronary artery angiographic study of the association between diabetes mellitus and progression or regression of coronary atherosclerosis.

The frequency and pattern of progression or regression of coronary atherosclerosis in contemporary patients with diabetes remain unknown. This study included 605 patients with coronary artery disease (CAD). Two coronary angiographic examinations at baseline and after 2 years were performed. The analysis focused on non-stented segments with diameter stenosis ≥25 %. Atherosclerosis progression (or regression) was defined as a decrease (or increase) in the mean minimal lumen diameter (MLD) in the 2-year angiogram compared to mean MLD in the baseline angiogram of >0.2 mm. Statins were prescribed in 576 patients (95.2 %). The primary outcome was atherosclerosis progression or regression in the 2-year angiogram. One hundred sixty-nine patients (28 %) had diabetes. Diabetic patients had greater reduction of mean MLD in the 24 angiogram compared to baseline angiogram than nondiabetic patients (0.11 ± 0.18 vs. -0.08 ± 0.15 mm, P < 0.001). Atherosclerosis progression was observed in 37 patients with diabetes and 16 nondiabetic patients (21.9 vs. 3.7 %; P < 0.001). Atherosclerosis regression was observed in two diabetic patients and 78 nondiabetic patients (1.2 vs. 17.9 %; P < 0.001). A progression pattern across all coronary segments was observed in 70 patients (41.4 %) with diabetes and 60 patients (13.8 %) without diabetes (P < 0.001). Diabetic patients with a low-density lipoprotein cholesterol ≥70 mg/dl showed more atherosclerosis progression than diabetic patients with LDL cholesterol <70 mg/dl (delta-MLD: 0.12 ± 0.19 vs. 0.08 ± 0.16 mm; P = 0.04). In conclusion, in contemporary patients with CAD treated with moderate-intensity statin therapy, diabetes is associated with the increased risk of progression and decreased probability of regression of coronary atherosclerosis.

Five-year clinical outcomes in patients with diabetes mellitus treated with polymer-free sirolimus- and probucol-eluting stents versus second-generation zotarolimus-eluting stents: a subgroup analysis of a randomized controlled trial.

BACKGROUND: Improved outcomes in patients with diabetes mellitus undergoing percutaneous coronary intervention remain an unmet clinical need. We assessed the long-term efficacy and safety of novel polymer-free sirolimus- and probucol-eluting stent in diabetic patients enrolled in intracoronary stenting and angiographic results: test efficacy of sirolimus- and probucol-eluting versus zotarolimus-eluting stents 5 trial. METHODS: In a pre-specified subgroup analysis, outcomes of diabetic patients treated with a sirolimus- and probucol-eluting stent or a second-generation zotarolimus-eluting stent were compared. The primary endpoint was a device-oriented composite outcome comprising cardiac death, target vessel-related myocardial infarction (MI), or target lesion revascularization (TLR) at 5-year follow-up. Event-free survival was assessed using the Kaplan-Meier method. Hazard ratios (HR) and 95 % confidence intervals (CI) were estimated from univariate Cox proportional hazards models. RESULTS: A total of 870 patients with diabetes mellitus were treated with either a sirolimus- and probucol-eluting stent (n = 575) or a second-generation zotarolimus-eluting stent (n = 295). At 5 years, the rate of device-oriented composite endpoint was comparable between the sirolimus- and probucol-eluting stent and the second-generation zotarolimus-eluting stent (32.9 versus 33.4 %, HR 0.88, 95 % CI 0.76-1.26). No significant differences were observed between the sirolimus- and probucol-eluting stent and the second-generation zotarolimus-eluting stent groups in the incidence of cardiac death (15.6 versus 16.7 % HR 0.92, 95 % CI 0.63-1.32), target-vessel MI (4.6 versus 6.6 %, HR 0.73, 95 % CI 0.40-1.34), and TLR (18.6 versus 18.8 %, HR 1.00, 95 % CI, 0.72-1.41). The rate of definite or probable stent thrombosis was low and similar in both groups (2.5 versus 2.6 %, HR 1.02, 95 % CI, 0.41-2.52). CONCLUSIONS: In patients with diabetes the long-term efficacy and safety of a polymer-free sirolimus- and probucol-eluting stent were comparable to a second-generation durable polymer zotarolimus-eluting stent. Trial registration ClinicalTrials.gov NCT00598533. Registered 10 January 2008.

Prevalence of atypical presentations among outpatients and associations with diagnostic error.

OBJECTIVES: This study aimed to assess the prevalence of atypical presentations and their association with diagnostic errors in various diseases. METHODS: This retrospective observational study was conducted using cohort data between January 1 and December 31, 2019. Consecutive outpatients consulted by physicians from the Department of Diagnostic and Generalist Medicine at a university hospital in Japan were included. Patients for whom the final diagnosis was not confirmed were excluded. Primary outcomes were the prevalence of atypical presentations, and the prevalence of diagnostic errors in groups with typical and atypical presentations. Diagnostic errors and atypical presentations were assessed using the Revised Safer Dx Instrument. We performed primary analyses using a criterion; the average score of less than five to item 12 of two independent reviewers was an atypical presentation (liberal criterion). We also performed additional analyses using another criterion; the average score of three or less to item 12 was an atypical presentation (conservative criterion). RESULTS: A total of 930 patients were included out of a total of 2022 eligible. The prevalence of atypical presentation was 21.7 and 6.7 % when using liberal and conservative criteria for atypical presentation, respectively. Diagnostic errors (2.8 %) were most commonly observed in the cases with slight to moderate atypical presentation. Atypical presentation was associated with diagnostic errors with the liberal criterion for atypical presentation; however, this diminished with the conservative criterion. CONCLUSIONS: An atypical presentation was observed in up to 20 % of outpatients with a confirmed diagnosis, and slight to moderate atypical presentation may be the highest risk population for diagnostic errors.

Can ChatGPT-4 evaluate whether a differential diagnosis list contains the correct diagnosis as accurately as a physician?

OBJECTIVES: The potential of artificial intelligence (AI) chatbots, particularly the fourth-generation chat generative pretrained transformer (ChatGPT-4), in assisting with medical diagnosis is an emerging research area. While there has been significant emphasis on creating lists of differential diagnoses, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in these lists. This short communication aimed to assess the accuracy of ChatGPT-4 in evaluating lists of differential diagnosis compared to medical professionals' assessments. METHODS: We used ChatGPT-4 to evaluate whether the final diagnosis was included in the top 10 differential diagnosis lists created by physicians, ChatGPT-3, and ChatGPT-4, using clinical vignettes. Eighty-two clinical vignettes were used, comprising 52 complex case reports published by the authors from the department and 30 mock cases of common diseases created by physicians from the same department. We compared the agreement between ChatGPT-4 and the physicians on whether the final diagnosis was included in the top 10 differential diagnosis lists using the kappa coefficient. RESULTS: Three sets of differential diagnoses were evaluated for each of the 82 cases, resulting in a total of 246 lists. The agreement rate between ChatGPT-4 and physicians was 236 out of 246 (95.9 %), with a kappa coefficient of 0.86, indicating very good agreement. CONCLUSIONS: ChatGPT-4 demonstrated very good agreement with physicians in evaluating whether the final diagnosis should be included in the differential diagnosis lists.

Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study.

BACKGROUND: Artificial intelligence (AI) symptom checker models should be trained using real-world patient data to improve their diagnostic accuracy. Given that AI-based symptom checkers are currently used in clinical practice, their performance should improve over time. However, longitudinal evaluations of the diagnostic accuracy of these symptom checkers are limited. OBJECTIVE: This study aimed to assess the longitudinal changes in the accuracy of differential diagnosis lists created by an AI-based symptom checker used in the real world. METHODS: This was a single-center, retrospective, observational study. Patients who visited an outpatient clinic without an appointment between May 1, 2019, and April 30, 2022, and who were admitted to a community hospital in Japan within 30 days of their index visit were considered eligible. We only included patients who underwent an AI-based symptom checkup at the index visit, and the diagnosis was finally confirmed during follow-up. Final diagnoses were categorized as common or uncommon, and all cases were categorized as typical or atypical. The primary outcome measure was the accuracy of the differential diagnosis list created by the AI-based symptom checker, defined as the final diagnosis in a list of 10 differential diagnoses created by the symptom checker. To assess the change in the symptom checker's diagnostic accuracy over 3 years, we used a chi-square test to compare the primary outcome over 3 periods: from May 1, 2019, to April 30, 2020 (first year); from May 1, 2020, to April 30, 2021 (second year); and from May 1, 2021, to April 30, 2022 (third year). RESULTS: A total of 381 patients were included. Common diseases comprised 257 (67.5%) cases, and typical presentations were observed in 298 (78.2%) cases. Overall, the accuracy of the differential diagnosis list created by the AI-based symptom checker was 172 (45.1%), which did not differ across the 3 years (first year: 97/219, 44.3%; second year: 32/72, 44.4%; and third year: 43/90, 47.7%; P=.85). The accuracy of the differential diagnosis list created by the symptom checker was low in those with uncommon diseases (30/124, 24.2%) and atypical presentations (12/83, 14.5%). In the multivariate logistic regression model, common disease (P<.001; odds ratio 4.13, 95% CI 2.50-6.98) and typical presentation (P<.001; odds ratio 6.92, 95% CI 3.62-14.2) were significantly associated with the accuracy of the differential diagnosis list created by the symptom checker. CONCLUSIONS: A 3-year longitudinal survey of the diagnostic accuracy of differential diagnosis lists developed by an AI-based symptom checker, which has been implemented in real-world clinical practice settings, showed no improvement over time. Uncommon diseases and atypical presentations were independently associated with a lower diagnostic accuracy. In the future, symptom checkers should be trained to recognize uncommon conditions.