An algorithm for the early diagnosis and correct approach to dementia management: results of a multiprofessional team.

BACKGROUNG: The early identification of cognitive disorder is a primary scope, because it could reduce the rate of severe cognitive impairment and thus contribute to reduce healthcare costs in the next future. AIMS: The present paper aimed to build a virtuous diagnostic path of cognitive impairment, highlighting all the professionalism that can serve this purpose. METHODS: The Delphi method was used by the experts, who reviewed the information available during each meeting related to the following topics: early diagnosis of cognitive impairment, definition of Mild Cognitive Impairment, unmet needs in post-stroke patients, critical decision-making nodes in complex patients, risk factors, neuropsychological, imaging diagnosis, blood tests, the criteria for differential diagnosis and the possible treatments. RESULTS: The discussion panels analyzed and discussed the available evidences on these topics and the related items. At each meeting, the activities aimed at the creation of a diagnostic-welfare flow chart derived from the proposal of the board and the suggestions of the respondents. Subsequently, the conclusions of each panel were written, and the study group reviewed them until a global consensus was reached. Once this process was completed, the preparation of the final document was carried out. CONCLUSIONS: Eventually, we built an algorithm for the early diagnosis and treatment, the risk factors, with the possible differences among the different kinds of dementia.

Evaluating lumbar disc degeneration by MRI quantitative metabolic indicators: the perspective of factor analysis.

PURPOSE: This study aimed to investigate an early diagnostic method for lumbar disc degeneration (LDD) and improve its diagnostic accuracy. METHODS: Quantitative biomarkers of the lumbar body (LB) and lumbar discs (LDs) were obtained using nuclear magnetic resonance (NMR) detection technology. The diagnostic weights of each biological metabolism indicator were screened using the factor analysis method. RESULTS: Through factor analysis, common factors such as the LB fat fraction, fat content, and T2* value of LDs were identified as covariates for the diagnostic model for the evaluation of LDD. This model can optimize the accuracy and reliability of LDD diagnosis. CONCLUSION: The application of biomarker quantification methods based on NMR detection technology combined with factor analysis provides an effective means for the early diagnosis of LDD, thereby improving diagnostic accuracy and reliability.

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.

Case report: Initial atypical skeletal symptoms and dental anomalies as first signs of Gardner syndrome: the importance of genetic analysis in the early diagnosis.

Background: Gardner syndrome is a rare genetic cancer predisposition disorder characterized by intestinal polyposis, multiple osteomas, and soft and hard tissue tumors. Dental anomalies are present in approximately 30%-70% of patients with Gardner syndrome and can be discovered during routine dental examinations. However, sometimes the diagnosis is challenging due to the high clinical variability and incomplete clinical picture. Herein, we report a family with various dental and bone anomalies, in which the definitive diagnosis was established with the help of a comprehensive genetic analysis based on state-of-the-art next-generation sequencing technology. Case presentation: A 17-year-old female index patient presented with dental (caries, impacted, retained and anteriorly located teeth) and atypical bone anomalies not resembling Gardner syndrome. She was first referred to our Genetic Counselling Unit at the age of 11 due to an atypical bone abnormality identified by a panoramic X-ray. Tooth 3.6 was surgically removed and the histopathology report revealed a Paget's disease-like bone metabolic disorder with mixed osteoblastic and osteoclastic activity of the mandible. A small lumbar subcutaneous tumor was discovered by physical examination. Ultrasound examination of the tumor raised the possibility of a soft tissue propagation of chondromatosis. Her sister, 2 years younger at the age of 14, had some benign tumors (multiple exostoses, odontomas, epidermoid cysts) and impacted teeth. Their mother had also skeletal symptoms. Her lower teeth did not develop, the 9th-10th ribs were fused, and she complained of intermittent jaw pain. A cranial CT scan showed fibrous dysplasia on the cranial bones. Whole exome sequencing identified a heterozygous pathogenic nonsense mutation (c.4700C>G; p.Ser1567*) in the APC gene in the index patient's DNA. Targeted sequencing revealed the same variant in the DNA of the other affected family members (the sister and the mother). Conclusion: Early diagnosis of this rare, genetically determined syndrome is very important, because of the potentially high malignant transformation of intestinal polyps. Dentists should be familiar with the typical maxillofacial features of this disorder, to be able to refer patients to genetic counseling. Dental anomalies often precede the intestinal polyposis and facilitate the early diagnosis, thereby increasing the patients' chances of survival. Genetic analysis may be necessary in patients with atypical phenotypic signs.

The potential of virtual triage AI to improve early detection, care acuity alignment, and emergent care referral of life-threatening conditions.

Objective: To evaluate the extent to which patient-users reporting symptoms of five severe/acute conditions requiring emergency care to an AI-based virtual triage (VT) engine had no intention to get such care, and whose acuity perception was misaligned or decoupled from actual risk of life-threatening symptoms. Methods: A dataset of 3,022,882 VT interviews conducted over 16 months was evaluated to quantify and describe patient-users reporting symptoms of five potentially life-threatening conditions whose pre-triage healthcare intention was other than seeking urgent care, including myocardial infarction, stroke, asthma exacerbation, pneumonia, and pulmonary embolism. Results: Healthcare intent data was obtained for 12,101 VT patient-user interviews. Across all five conditions a weighted mean of 38.5% of individuals whose VT indicated a condition requiring emergency care had no pre-triage intent to consult a physician. Furthermore, 61.5% intending to possibly consult a physician had no intent to seek emergency medical care. After adjustment for 13% VT safety over-triage/referral to ED, a weighted mean of 33.5% of patient-users had no intent to seek professional care, and 53.5% had no intent to seek emergency care. Conclusion: AI-based VT may offer a vehicle for early detection and care acuity alignment of severe evolving pathology by engaging patients who believe their symptoms are not serious, and for accelerating care referral and delivery for life-threatening conditions where patient misunderstanding of risk, or indecision, causes care delay. A next step will be clinical confirmation that when decoupling of patient care intent from emergent care need occurs, VT can influence patient behavior to accelerate care engagement and/or emergency care dispatch and treatment to improve clinical outcomes.

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.

[Children and adolescents in a neglectful environment Early detection as the first response to their needs]. / Enfants et adolescent-e-s dans un environnement négligent. La détection précoce comme première réponse à leurs besoins.

Neglect of children and adolescents is the most common form of abuse and occurs when their basic needs are not met. The negative impact on physical and mental health can be significant. Early detection by primary care physicians and support for parents and the community, in collaboration with the social and health network, are essential to ensure that minors have an environment conducive to their healthy development. Recognizing the needs of children and teenagers is an important issue in social and preventive medicine, as is defending their interests and rights.

La négligence envers les enfants et les adolescent-e-s est la forme la plus fréquente de maltraitance et survient lorsque leurs besoins fondamentaux ne sont pas pourvus. Les impacts négatifs sur la santé physique et psychique peuvent être importants. La détection précoce par les médecins de premier recours ainsi qu'un accompagnement des parents et de la communauté en collaboration avec le réseau socio-sanitaire sont essentiels pour garantir aux mineur-e-s un environnement propice à leur bon développement. La reconnaissance des besoins des enfants et adolescent-e-s est un enjeu important de médecine sociale et préventive qui s'inscrit dans la défense de leurs intérêts et de leurs droits.

[Multifocal electroretinography in the diagnosis and monitoring of early and intermediate stages of age-related macular degeneration]. / Mul'tifokal'naya elektroretinografiya v diagnostike i monitoringe rannei i promezhutochnoi stadii vozrastnoi makulyarnoi degeneratsii.

Multifocal electroretinography is a valuable diagnostic method for the objective localization and quantitative assessment of functional disorders of the central retina in age-related macular degeneration. It is used to detect early changes, monitor the course of the disease and treatment outcomes. In many cases, multifocal electroretinography is a more sensitive method for detecting functional disorders at the early/intermediate stage of age-related macular degeneration compared to morphological (optical coherence tomography) and subjective (visual acuity, perimetry) testing methods.

A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.

Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient's outcome. Data on machine-learning models to predict BSI in pediatric patients are limited and neither study included time series data. We aimed to develop a machine learning model to predict an early diagnosis of BSI in patients admitted to the PICU. This was a retrospective cohort study of patients who had at least one positive blood culture result during stay at a PICU of a tertiary-care university hospital, from January 1st to December 31st 2019. Patients with positive blood culture results with growth of contaminants and those with incomplete data were excluded. Models were developed using demographic, clinical and laboratory data collected from the electronic medical record. Laboratory data (complete blood cell counts with differential and C-reactive protein) and vital signs (heart rate, respiratory rate, blood pressure, temperature, oxygen saturation) were obtained 72 hours before and on the day of blood culture collection. A total of 8816 data from 76 patients were processed by the models. The machine committee was the best-performing model, showing accuracy of 99.33%, precision of 98.89%, sensitivity of 100% and specificity of 98.46%. Hence, we developed a model using demographic, clinical and laboratory data collected on a routine basis that was able to detect BSI with excellent accuracy and precision, and high sensitivity and specificity. The inclusion of vital signs and laboratory data variation over time allowed the model to identify temporal changes that could be suggestive of the diagnosis of BSI. Our model might help the medical team in clinical-decision making by creating an alert in the electronic medical record, which may allow early antimicrobial initiation and better outcomes.

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.

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.

Rapid antiretroviral therapy initiation following rollout of point-of-care early infant diagnosis testing, Uganda, 2018-2021.

BACKGROUND: Uganda Ministry of Health (MOH) recommends a first HIV DNA-PCR test at 4-6 weeks for early infant diagnosis (EID) of HIV-exposed infants (HEI) and immediate return of results. WHO recommends initiating antiretroviral therapy (ART) ≤ 7 days from HIV diagnosis. In 2019, MOH introduced point-of-care (POC) whole-blood EID testing in 33 health facilities and scaled up to 130 facilities in 2020. We assessed results turnaround time and ART linkage pre-POC and during POC testing. METHODS: We evaluated EID register data for HEI at 10 health facilities with POC and EID testing volume of ≥ 12 infants/month from 2018 to 2021. We abstracted data for 12 months before and after POC testing rollout and compared time to sample collection, results receipt, and ART initiation between periods using medians, Wilcoxon, and log-rank tests. RESULTS: Data for 4.004 HEI were abstracted, of which 1.685 (42%) were from the pre-POC period and 2.319 (58%) were from the period during POC; 3.773 (94%) had a first EID test (pre-POC: 1.649 [44%]; during POC: 2.124 [56%]). Median age at sample collection was 44 (IQR 38-51) days pre-POC and 42 (IQR 33-50) days during POC (p < 0.001). Among 3.773 HEI tested, 3.678 (97%) had test results. HIV-positive infants' (n = 69) median age at sample collection was 94 (IQR 43-124) days pre-POC and 125 (IQR 74-206) days during POC (p = 0.04). HIV positivity rate was 1.6% (27/1.617) pre-POC and 2.0% (42/2.061) during POC (p = 0.43). For all infants, median days from sample collection to results receipt by infants' caregivers was 28 (IQR 14-52) pre-POC and 1 (IQR 0-25) during POC (p < 0.001); among HIV-positive infants, median days were 23 (IQR 7-30) pre-POC and 0 (0-3) during POC (p < 0.001). Pre-POC, 4% (1/23) HIV-positive infants started ART on the sample collection day compared to 33% (12/37) during POC (p < 0.001); ART linkage ≤ 7 days from HIV diagnosis was 74% (17/23) pre-POC and 95% (35/37) during POC (p < 0.001). CONCLUSION: POC testing improved EID results turnaround time and ART initiation for HIV-positive infants. While POC testing expansion could further improve ART linkage and loss to follow-up, there is need to explore barriers around same-day ART initiation for infants receiving POC testing.