Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables.

OBJECTIVE: Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS: In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS: The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION: The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Comparison for the diagnostic performance of early diagnostic methods for biliary atresia: a systematic review and network meta-analysis.

BACKGROUND: Biliary atresia (BA), a progressive condition affecting canalicular-bile duct function/anatomy, requires prompt surgical intervention for favorable outcomes. Therefore, we conducted a network meta-analysis of common diagnostic methods to assess their performance and provide evidence-based support for clinical decision-making. METHODS: We reviewed literature in PubMed, EMBASE, and Cochrane for BA diagnostics. The search included gamma-glutamyl transferase (GGT), direct/combined bilirubin, matrix metalloproteinase 7 (MMP-7), ultrasonic triangular cord sign (TCS), hepatic scintigraphy (HS), and percutaneous cholangiocholangiography/percutaneous transhepatic cholecysto-cholangiography (PCC/PTCC). QUADAS-2 assessed study quality. Heterogeneity and threshold effect were evaluated using I2 and Spearman's correlation. We combined effect estimates, constructed SROC models, and conducted a network meta-analysis based on the ANOVA model, along with meta-regression and subgroup analysis, to obtain precise diagnostic performance assessments for BA. RESULTS: A total of 40 studies were included in our analysis. GGT demonstrated high diagnostic accuracy for BA with a sensitivity of 81.5% (95% CI 0.792-0.836) and specificity of 72.1% (95% CI 0.693-0.748). Direct bilirubin/conjugated bilirubin showed a sensitivity of 87.6% (95% CI 0.833-0.911) but lower specificity of 59.4% (95% CI 0.549-0.638). MMP-7 exhibited a total sensitivity of 91.5% (95% CI 0.893-0.934) and a specificity of 84.3% (95% CI 0.820-0.863). TCS exhibited a sensitivity of 58.1% (95% CI 0.549-0.613) and high specificity of 92.9% (95% CI 0.911-0.944). HS had a high sensitivity of 98.4% (95% CI 0.968-0.994) and moderate specificity of 79.0% (95% CI 0.762-0.816). PCC/PTCC exhibited excellent diagnostic performance with a sensitivity of 100% (95% CI 0.900-1.000) and specificity of 87.0% (95% CI 0.767-0.939). Based on the ANOVA model, the network meta-analysis revealed that MMP-7 ranked second overall, with PCC/PTCC ranking first, both exhibiting superior diagnostic accuracy compared to other techniques. Our analysis showed no significant bias in most methodologies, but MMP-7 and hepatobiliary scintigraphy exhibited biases, with p values of 0.023 and 0.002, respectively. CONCLUSION: MMP-7 and ultrasound-guided PCC/PTCC show diagnostic potential in the early diagnosis of BA, but their clinical application is restricted due to practical limitations. Currently, the cutoff value of MMP-7 is unclear, and further evidence-based medical research is needed to firmly establish its diagnostic value. Until more evidence is available, MMP-7 is not suitable for widespread diagnostic use. Therefore, considering cost and operational simplicity, liver function tests combined with ultrasound remain the most clinically valuable non-invasive diagnostic methods for BA.

FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data-Study protocol of a multicenter trial.

INTRODUCTION: Endometriosis is a chronic disease that affects up to 190 million women and those assigned female at birth and remains unresolved mainly in terms of etiology and optimal therapy. It is defined by the presence of endometrium-like tissue outside the uterine cavity and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) intended to replace the need for invasive surgery, the time to diagnosis remains in the range of 4 to 11 years. AIMS: This study aims to create a large prospective data bank using the Lucy mobile health application (Lucy app) and analyze patient profiles and structured clinical data. In addition, we will investigate the association of removed or restricted dietary components with quality of life, pain, and central pain sensitization. METHODS: A baseline and a longitudinal questionnaire in the Lucy app collects real-world, self-reported information on symptoms of endometriosis, socio-demographics, mental and physical health, economic factors, nutritional, and other lifestyle factors. 5,000 women with confirmed endometriosis and 5,000 women without diagnosed endometriosis in a control group will be enrolled and followed up for one year. With this information, any connections between recorded symptoms and endometriosis will be analyzed using machine learning. CONCLUSIONS: We aim to develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, we may identify dietary components that worsen the quality of life and pain in women with endometriosis, upon which we can create real-world data-based nutritional recommendations.

Serum bta-miRNA-375 as a potential biomarker for the early diagnosis of enzootic bovine leukosis.

To identify a biomarker for the early diagnosis of enzootic bovine leukosis (EBL) caused by bovine leukemia virus (BLV), we investigated the expression of a microRNA, bta-miR-375, in cattle serum. Using quantitative reverse-transcriptase PCR analysis, we measured bta-miR-375 levels in 27 samples from cattle with EBL (EBL cattle), 45 samples from animals infected with BLV but showing no clinical signs (NS cattle), and 30 samples from cattle uninfected with BLV (BLV negative cattle). In this study, we also compared the kinetics of bta-miR-375 with those of the conventional biomarkers of proviral load (PVL), lactate dehydrogenase (LDH), and thymidine kinase (TK) from the no-clinical-sign phase until EBL onset in three BLV-infected Japanese black (JB) cattle. Bta-miR-375 expression was higher in NS cattle than in BLV negative cattle (P < 0.05) and greater in EBL cattle than in BLV negative and NS cattle (P < 0.0001 for both comparisons). Receiver operating characteristic curves demonstrated that bta-miR-375 levels distinguished EBL cattle from NS cattle with high sensitivity and specificity. In NS cattle, bta-miR-375 expression was increased as early as at 2 months before EBL onset-earlier than the expression of PVL, TK, or LDH isoenzymes 2 and 3. These results suggest that serum miR-375 is a promising biomarker for the early diagnosis of EBL.

Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning.

OBJECTIVE: The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics. METHODS: We retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation. RESULTS: Among all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5-25%), normal (25-75%), high (75-95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE. CONCLUSION: Machine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.

Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke.

BACKGROUND: Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. METHODS: We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. RESULTS: The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. CONCLUSION: This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.

Access to early diagnosis for attention-deficit/hyperactivity disorder among children and adolescents in Mexico City at specialized mental health services.

BACKGROUND: In Mexico, this pioneering research was undertaken to assess the accessibility of timely diagnosis of Dyads [Children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD) and their primary caregivers] at specialized mental health services. The study was conducted in two phases. The first phase involved designing an "Access Pathway" aimed to identify barriers and facilitators for ADHD diagnosis; several barriers, with only the teacher being identified as a facilitator. In the second phase, the study aimed to determine the time taken for dyads, to obtain a timely diagnosis at each stage of the Access Pathway. As well as identify any disparities based on gender and socioeconomic factors that might affect the age at which children can access a timely diagnosis. METHOD: In a retrospective cohort study, 177 dyads participated. To collect data, the Acceda Survey was used, based on the robust Conceptual Model Levesque, 2013. The survey consisted of 48 questions that were both dichotomous and polytomous allowing the creation of an Access Pathway that included five stages: the age of perception, the age of search, the age of first contact with a mental health professional, the age of arrival at the host hospital, and the age of diagnosis. The data was meticulously analyzed using a comprehensive descriptive approach and a nonparametric multivariate approach by sex, followed by post-hoc Mann-Whitney's U tests. Demographic factors were evaluated using univariable and multivariable Cox regression analyses. RESULTS: 71% of dyads experienced a late, significantly late, or highly late diagnosis of ADHD. Girls were detected one year later than boys. Both boys and girls took a year to seek specialized mental health care and an additional year to receive a formal specialized diagnosis. Children with more siblings had longer delays in diagnosis, while caregivers with formal employment were found to help obtain timely diagnoses. CONCLUSIONS: Our findings suggest starting the Access Pathway where signs and symptoms of ADHD are detected, particularly at school, to prevent children from suffering consequences. Mental health school-based service models have been successfully tested in other latitudes, making them a viable option to shorten the time to obtain a timely diagnosis.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

A study on the application of radiomics based on cardiac MR non-enhanced cine sequence in the early diagnosis of hypertensive heart disease.

BACKGROUND: The prevalence of hypertensive heart disease (HHD) is high and there is currently no easy way to detect early HHD. Explore the application of radiomics using cardiac magnetic resonance (CMR) non-enhanced cine sequences in diagnosing HHD and latent cardiac changes caused by hypertension. METHODS: 132 patients who underwent CMR scanning were divided into groups: HHD (42), hypertension with normal cardiac structure and function (HWN) group (46), and normal control (NOR) group (44). Myocardial regions of the end-diastolic (ED) and end-systolic (ES) phases of the CMR short-axis cine sequence images were segmented into regions of interest (ROI). Three feature subsets (ED, ES, and ED combined with ES) were established after radiomic least absolute shrinkage and selection operator feature selection. Nine radiomic models were built using random forest (RF), support vector machine (SVM), and naive Bayes. Model performance was analyzed using receiver operating characteristic curves, and metrics like accuracy, area under the curve (AUC), precision, recall, and specificity. RESULTS: The feature subsets included first-order, shape, and texture features. SVM of ED combined with ES achieved the highest accuracy (0.833), with a macro-average AUC of 0.941. AUCs for HHD, HWN, and NOR identification were 0.967, 0.876, and 0.963, respectively. Precisions were 0.972, 0.740, and 0.826; recalls were 0.833, 0.804, and 0.863, respectively; and specificities were 0.989, 0.863, and 0.909, respectively. CONCLUSIONS: Radiomics technology using CMR non-enhanced cine sequences can detect early cardiac changes due to hypertension. It holds promise for future use in screening for latent cardiac damage in early HHD.

[The importance of early recognition of Prader-Willi syndrome]. / Het Prader-Willi-syndroom.

Due to its rare nature and subtle dysmorphisms, Prader-Willi syndrome can be challenging to recognize and diagnose in the neonatal period. Feeding difficulties and hypotonia ('floppy infant') are the most striking characteristics. Prader-Willi syndrome requires specific follow-up and treatment, emphasizing the importance of early recognition.We encountered an infant of three months old with severe hypotonia. The hypotonia ameliorated spontaneously over time, although feeding per nasogastric tube was necessary. There were no apparent dysmorphisms. Extensive genetic investigations showed a maternal uniparental disomy of chromosome 15, fitting with Prader-Willi syndrome explaining all symptoms. After excluding contraindications, treatment with growth hormone therapy was started. Parents were educated regarding medical emergencies specific for Prader-Willi syndrome ('medical alerts'). Although Prader-Willi syndrome is rare, it should always be considered in cases of neonatal hypotonia. Early recognition is paramount as specific recommendations and treatment are warranted.

[CLINICAL MANIFESTATIONS OF AUTOIMMUNE DISEASES].

INTRODUCTION: Ocular inflammation, uveitis, represents over 40 distinct diseases, caused by infectious or non-infectious etiologies. Non-infectious uveitis may be related to systemic autoimmune diseases. Most uveitis patients are of working age, and prolonged disease may affect their independence and ability to work. Uveitis has various clinical manifestations and may result in the development of ocular complications and vision loss. Uveitis accounts for 10-15% of blindness in the developed world. Autoimmune diseases are increasing globally and often involve the eyes. Most cases occur in young active people and therefore any ocular changes have a longer effect. Symptoms may be mild but they might be severe, even blindness. It accounts for 10% to 15% of all causes of blindness among people of working age in the developed world. OBJECTIVES: To describe the ocular manifestation of uveitis related to systemic autoimmune diseases. We will describe ocular signs related to the disease and discuss the treatment approach to prevent the development of ocular complications and vision loss. METHODS: Review of clinical findings and treatment approach to non-infectious uveitis. CONCLUSIONS: Ocular involvement is commonly found in many autoimmune diseases. The severity of ocular disease varies between cases and complications may result in vision loss. Early diagnosis and treatment may prevent the development of ocular complications, maintaining visual acuity and patient independence.

A novel ESIPT fluorescent probe for early detection and assessment of ferroptosis-mediated acute kidney injury via peroxynitrite fluctuation.

BACKGROUND: Acute kidney injury (AKI) poses a severe risk to public health, mostly manifested by damage and death of renal tubular epithelial cells. However, routine blood examination, a conventional approach for clinical detection of AKI, is not available for identifying early-stage AKI. Plenty of reported methods were lack of early biomarkers and real time evaluation tools, which resulted in a vital challenge for early diagnosis of AKI. Therefore, developing novel probes for early detection and assessment of AKI is exceedingly crucial. RESULTS: Based on ESIPT mechanism, a new fluorescent probe (MEO-NO) with 2-(2'-hydroxyphenyl) benzothiazole (HBT) derivatives as fluorophore has been synthesized for dynamic imaging peroxynitrite (ONOO-) levels in ferroptosis-mediated AKI. Upon the addition of ONOO-, MEO-NO exhibited obvious fluorescence changes, a significant Stokes shift (130 nm) and rapid response (approximately 45 s), and featured exceptional sensitivity (LOD = 7.28 nM) as well as high selectivity from the competitive species at physiological pH. In addition, MEO-NO was conducive to the biological depth imaging ONOO- in cells, zebrafish, and mice. Importantly, MEO-NO could monitor ONOO- levels during sorafenib-induced ferroptosis and CP-induced AKI. With the assistance of MEO-NO, we successfully visualized and tracked ONOO- variations for early detection and assessment of ferroptosis-mediated AKI in cells, zebrafish and mice models. SIGNIFICANCE AND NOVELTY: Benefiting from the superior performance of MEO-NO, experimental results further demonstrated that the levels of ONOO- was overexpressed during ferroptosis-mediated AKI in cells, zebrafish, and mice models. The developed novel probe MEO-NO provided a strong visualization tool for imagining ONOO-, which might be a potential method for the prevention, diagnosis, and treatment of ferroptosis-mediated AKI.

Xpert HIV-1 qual point-of-care testing for HIV early infant diagnosis in Tanzania: experiences and perceptions of health care workers in a 2016 study.

BACKGROUND: HIV early infant diagnosis (HEID) at the centralized laboratory faces many challenges that impact the cascade of timely HEID. Point of Care (PoC) HEID has shown to reduce test turnaround times, allow for task shifting and has the potential to reduce infant mortality. We aimed at assessing the feasibility of nurse based PoC-HEID in five facilities of Mbeya region. METHODS: We analysed data from healthcare workers at five obstetric health facilities that participated in the BABY study which enrolled mothers living with HIV and their HIV exposed infants who were followed up until 6 weeks post-delivery. Nurses and laboratory personnel were trained and performed HEID procedures using the Xpert HIV-1 Qual PoC systems. Involved personnel were interviewed on feasibility, knowledge and competency of procedures and overall impression of the use of HIV-1 Qual PoC system in clinical settings. RESULTS: A total of 28 health care workers (HCWs) who participated in the study between 2014 and 2016 were interviewed, 23 being nurses, 1 clinical officer, 1 lab scientist and 3 lab technicians The median age was 39.5 years. Majority of the nurses (22/24) and all lab staff were confident using Gene Xpert PoC test after being trained. None of them rated Gene Xpert handling as too complicated despite minor challenges. Five HCWs (5/24) reported power cut as the most often occurring problem. As an overall impression, all interviewees agreed on PoC HEID to be used in clinical settings however, about half of them (11/24) indicated that the PoC-HEID procedures add a burden onto their routine workload. CONCLUSION: Overall, health care workers in our study demonstrated very good perceptions and experiences of using PoC HEID. Efforts should be invested on quality training, targeted task distribution at the clinics, continual supportive supervision and power back up mechanisms to make the wide-scale adoption of nurse based PoC HEID testing a possibility.

Personalized Intervention Based on Early Detection of Atherosclerosis: JACC State-of-the-Art Review.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide and challenges the capacity of health care systems globally. Atherosclerosis is the underlying pathophysiological entity in two-thirds of patients with CVD. When considering that atherosclerosis develops over decades, there is potentially great opportunity for prevention of associated events such as myocardial infarction and stroke. Subclinical atherosclerosis has been identified in its early stages in young individuals; however, there is no consensus on how to prevent progression to symptomatic disease. Given the growing burden of CVD, a paradigm shift is required-moving from late management of atherosclerotic CVD to earlier detection during the subclinical phase with the goal of potential cure or prevention of events. Studies must focus on how precision medicine using imaging and circulating biomarkers may identify atherosclerosis earlier and determine whether such a paradigm shift would lead to overall cost savings for global health.