Principles for high-quality, high-value testing

Hereditary angioedema diagnosis evaluation score (HADES): A new clinical scoring system for predicting hereditary angioedema with C1 inhibitor deficiency

Background

Diagnosis of hereditary angioedema (HAE) poses challenges because of its rarity and its overlapping symptoms with allergic and gastrointestinal conditions, resulting in misdiagnosis.

Objective

We developed a predictive score using clinical variables for suspected HAE patients with C1 inhibitor deficiency (HAE-C1INH) to increase suspicion of HAE and thus improve diagnosis.

Methods

The HADES (HAE diagnosis evaluation score) study used a nationwide retrospective cohort of individuals with suspected HAE-C1INH in Argentina. A questionnaire was designed to collect relevant clinical information on possible predictors for HAE. Blood samples were analyzed for C1-INH/C1q levels and C1-INH function. A predictive score was developed from the odds ratios derived from multivariate logistic regression analysis.

Results

The study included 2423 individuals (1642 suspected index cases and 781 family cases). Only patients with confirmed HAE types I or II (n = 499) were included in the final analysis; acquired angioedema/F12 gene variants were excluded. Eight clinical variables were identified as independent predictors of HAE: age at onset ≤20 years, recurrent limb edema, abdominal pain, vomiting, trauma as a trigger, absence of wheals, family history of angioedema, and recurrent edema lasting ≥24 hours. The predictive score demonstrated favorable performance in identifying HAE cases within the index population (range, 0-18.5), with low scores (1.5-6.5) associated with high sensitivity (100%) and negative predictive value (100%), and high scores (≥15) associated with high specificity (99.4%) and positive predictive value (75.0%).

Conclusions

The predictive HADES offers a simple and efficient method for improving testing for suspicion of HAE by using clinical parameters. Further validation studies are required to confirm its reliability and accuracy.

Practicality meets precision: Wearable vest with integrated multi-channel PCG sensors for effective coronary artery disease pre-screening

The leading cause of mortality and morbidity worldwide is cardiovascular disease (CVD), with coronary artery disease (CAD) being the largest sub-category. Unfortunately, myocardial infarction or stroke can manifest as the first symptom of CAD, underscoring the crucial importance of early disease detection. Hence, there is a global need for a cost-effective, non-invasive, reliable, and easy-to-use system to pre-screen CAD. Previous studies have explored weak murmurs arising from CAD for classification using phonocardiogram (PCG) signals. However, these studies often involve tedious and inconvenient data collection methods, requiring precise subject preparation and environmental conditions. This study proposes using a novel data acquisition system (DAQS) designed for simplicity and convenience. The DAQS incorporates multi-channel PCG sensors into a wearable vest. The entire signal acquisition process can be completed in under two minutes, from fitting the vest to recording signals and removing it, requiring no specialist training. This exemplifies the potential for mass screening, which is impractical with current state-of-the-art protocols. Seven PCG signals are acquired, six from the chest and one from the subject's back, marking a novel approach. Our classification approach, which utilizes linear-frequency cepstral coefficients (LFCC) as features and employs a support vector machine (SVM) to distinguish between normal and CAD-affected heartbeats, outperformed alternative low-computational methods suitable for portable applications. Utilizing feature-level fusion, multiple channels are combined, and the optimal combination yields the highest subject-level accuracy and F1-score of 80.44% and 81.00%, respectively, representing a 7% improvement over the best-performing single channel. The proposed system's performance metrics have been demonstrated to be clinically significant, making the DAQS suitable for practical use. Moreover, the system shows promise in post-procedural monitoring for subjects undergoing percutaneous transluminal coronary angioplasty (PTCA) or coronary artery bypass grafting (CABG), effectively identifying cases of restenosis following intervention.

Efficacy of a deep learning system for automatic analysis of the comprehensive spatial relationship between the mandibular third molar and inferior alveolar canal on panoramic radiographs

OBJECTIVE

To develop and evaluate a deep learning (DL) system for predicting the contact and relative position relationships between the mandibular third molar (M3) and inferior alveolar canal (IAC) using panoramic radiographs (PRs) for preoperative assessment of patients for M3 surgery.

STUDY DESIGN

In total, 279 PRs with 441 M3s from individuals aged 18-32 years were collected, with one PR and cone beam computed tomography (CBCT) scan per individual. Six DL models were compared using 5-fold cross-validation. Model performance was evaluated using accuracy, precision, recall, specificity, F1-score, and area under the receiver operating characteristic (AUROC) curve. System performance was compared to that of experienced dentists. The diagnostic performance was investigated based on the reference standard for contact and relative position between M3 and IAC as determined by CBCT.

RESULTS

ResNet50 exhibited the best performance among all models tested. For contact prediction, ResNet50 achieved an accuracy of 0.748, F1-score of 0.759, and AUROC of 0.811. For relative position relationship prediction, ResNet50 yielded an accuracy of 0.611, F1-score of 0.548, and AUROC of 0.731. The DL system demonstrated advantages over experienced dentists in diagnostic outcomes.

CONCLUSIONS

The developed DL system shows broad application potential for comprehensive spatial relationship recognition between M3 and IAC. This system can assist dentists in treatment decision-making for M3 surgery and improve dentist training efficiency.

Deep learning and radiomics-based vascular calcification characterization in dental cone beam computed tomography as a predictive tool for cardiovascular disease: a proof-of-concept study

OBJECTIVES

This study evaluated an automated deep learning method for detecting calcifications in the extracranial and intracranial carotid arteries and vertebral arteries in cone beam computed tomography (CBCT) scans. Additionally, a model utilizing CBCT-derived radiomics imaging biomarkers was evaluated to predict the cardiovascular diseases (CVD) of stroke and heart attack.

METHODS

Models were trained using the nn-UNet architecture to identify three locations of arterial calcifications: extracranial carotid calcification (ECC), intracranial carotid calcification (ICC), and vertebral artery calcification (VAC). In total, 148 scans were used for model training and validation. Radiomics features extracted from 135 calcification regions were used to characterize arterial calcifications for predicting CVD.

RESULTS

The models demonstrated acceptable performance for detecting regions of calcification for ECC and ICC with bounding box accuracies of 0.71 ± 0.06 and 0.78 ± 0.12 respectively, although VAC performance was lower at 0.53 ± 0.17. Combining clinical data with radiomics for ICC improved stroke predictions, yielding an area under the curve derived from receiver operating characteristic analysis (AUC-ROC) of 0.94 ± 0.09, and combining data for ECC and ICC improved heart attack predictions, with AUC-ROC values of 0.88 ± 0.04 and 0.84 ± 0.16, respectively.

CONCLUSION

Automated, quantifiable methods have potential for detecting ECC and ICC and predicting the incidence of cardiovascular disease based on arterial calcification detection in dental CBCT scans.

Diagnostic validity of drinking behaviour for identifying alcohol use disorder: Findings from a representative sample of community adults and an inpatient clinical sample

BACKGROUND AND AIMS

Alcohol consumption is an inherent feature of alcohol use disorder (AUD), and drinking patterns may be diagnostically informative. This study had three aims: (1) to examine the classification accuracy of several individually analysed drinking behavior measures in a large sample of US community adults; (2) to extend the findings to an adult clinical sample; and (3) to examine potential sex differences.

DESIGN

In cross-sectional epidemiological and clinical datasets, receiver operating characteristic (ROC) curves were used to evaluate diagnostic classification using area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

SETTING AND PARTICIPANTS

Two samples were examined: a large random sample of US community adults who reported past-year drinking (n = 25 773, AUD = 20%) and a clinical sample from a Canadian inpatient addiction treatment centre (n = 1341, AUD = 82%).

MEASUREMENTS

Classifiers included measures of quantity/frequency (e.g. drinks/drinking day, largest drinks/drinking day, number of drinking days and heavy drinking frequency). The clinical criterion (reference standard) was AUD diagnostic status per structured clinical interview (community sample) or a symptom checklist (clinical sample).

FINDINGS

All drinking indicators were statistically significant classifiers of AUD (AUCs = 0.60-0.92, Ps<0.0001). Heavy drinking frequency indicators performed optimally in both the community (AUCs = 0.78-0.87; accuracy = 0.72-0.80) and clinical (AUCs = 0.85-0.92; accuracy = 0.77-0.89) samples. Collectively, the most discriminating drinking behaviours were number of heavy drinking episodes and frequency of exceeding drinking low-risk guidelines. No substantive sex differences were observed across drinking metrics.

CONCLUSIONS

Quantitative drinking indices appear to perform well at classifying alcohol use disorder (AUD) in both a large community adult and inpatient sample, robustly identifying AUD at rates much better than chance and above accepted clinical classification benchmarks, with limited differences by sex. These findings broadly support the potential clinical utility of quantitative drinking indicators in routine patient assessment.

Validation of Medication Proxies for the Identification of Hospitalizations for Major Adverse Cerebro-Cardiovascular Events

Purpose

To date, complete and long-term registrations of diseases and events are not available in every situation. As a useful proxy, medication usage data is very promising. For instance, real-world dispensing data from pharmacies are attractive because of the high validity of drug dispensing data, large sample sizes, and long-term registration. However, before application as a proxy, validity must be assessed. Therefore, in this study, we aim to assess the validity of various medicines used as a proxy for major adverse cardio-cerebrovascular events (MACCE), that is, to identify an incident or previous hospitalization for a MACCE.

Patients and Methods

Using the claims database of a large Dutch healthcare insurer, we estimated the concordance between hospitalization claims for MACCE and specific claims for dispensings to treat MACCE in a cohort of patients on primary preventive antihypertensive and/or antihyperlipidemic therapy between 2013 and 2020.

Results

In a cohort of more than 110,000 patients, a dispensing of either vitamin K antagonists, platelet aggregation inhibitors, or nitrates was predictive of an incident hospitalization for a MACCE between 2013 and 2020, with a sensitivity of 71.5% (95% CI: 70.4-72.5%) and specificity of 93.2% (95% CI 91.1-93.4%), and any history of hospitalization for a MACCE (prevalence) with a sensitivity of 86.9% (95% CI: 86.5-87.3%) and specificity of 81.9 (956% CI: 81.6-82.1%), while positive predicted value remains low. Sensitivity analyses across age, sex, and patients with asthma/COPD or diabetes showed a similarly good performance.

Conclusion

Claims for the dispensings of vitamin K antagonists, platelet aggregation inhibitors, and/or nitrates can be validly used as a proxy for new and previous hospitalization for MACCE in patients on primary preventive therapy. Further study is required to assess the validity of such dispensing claims for non-hospitalized cerebro-cardiovascular events and whether the results are generalizable in non-Dutch cohorts.

Low Stability and Specificity of Polygenic Risk Scores for Major Psychiatric Disorders Limit their Clinical Utility

BACKGROUND

There has been little examination of the stability and validity of polygenic risk scores (PRS) i.e., whether individuals identified as high-risk for a disorder with one PRS are identified as high-risk with another PRS, and whether high-risk individuals have the disorder.

METHODS

The UK Biobank recruited 502,534 individuals aged 37-73 in the UK between 2006-2010. PRS were calculated for 408,853 white British individuals. PRS-CS, which uses SNP effect sizes under continuous shrinkage, was used to calculate three different PRS for major depressive disorder (MDD), alcohol use disorder (AUD), and type 2 diabetes (T2D), and two different PRS for schizophrenia (SCZ). PRS stability was measured using correlations between different PRS for the same disorder, and percentage of individuals consistently identified as high-risk (top 5% PRS). Sensitivity and specificity were used to measure PRS validity.

RESULTS

Correlations between PRS ranged from low to high (SCZ: r=0.78; MDD: r=0.16-0.78; AUD: r=0.13-0.90; T2D r=0.29-0.77). Percentage of individuals consistently identified as high-risk (top 5% PRS) for schizophrenia with different SCZ PRS was 47.7%, i.e., less than half of individuals identified as high-risk with one PRS were identified as high-risk with another PRS. Percentages of individuals consistently identified as high-risk were 9.5-47.0% for MDD, 8.3-63.5% for AUD, and 14.1-45.2% for T2D. Sensitivity of PRS was moderate for MDD (66.1-74.4%) and AUD (72.3-74.2%), moderate/good for T2D (77.3-96.3%), and good for SCZ (90.2-93.3%). Specificity was low for all PRS (50.7-56.4%).

CONCLUSIONS

Limited stability and specificity of PRS highlight their lack of clinical utility in psychiatry.

DNA Methylation in Urine and Feces Indicative of Eight Major Human Cancer Types Globally

Toxic chemicals and epigenetic biomarkers associated with cancer have been used successfully in clinical diagnostic screening of feces and urine from individuals, but they have been underutilized in a global setting. We analyzed peer-reviewed literature to achieve the following: (i) compile epigenetic biomarkers of disease, (ii) explore whether research locations are geographically aligned with disease hotspots, and (iii) determine the potential for tracking disease-associated epigenetic biomarkers. Studies (n = 1145) of epigenetic biomarkers (n = 146) in urine and feces from individuals have established notable diagnostic potential for detecting and tracking primarily gastric and urinary cancers. Panels with the highest sensitivity and specificity reported more than once were SEPT9 (78% and 93%, respectively) and the binary biomarker combinations GDF15, TMEFF2, and VIM (93% and 95%), NDRG4 and BMP3 (98% and 90%), and TWIST1 and NID2 (76% and 79%). Screening for epigenetic biomarkers has focused on biospecimens from the U.S., Europe, and East Asia, whereas data are limited in regions with similar/higher disease incidence rates (i.e., data for New Zealand, Japan, and Australia for colorectal cancer). The epigenetic markers discussed here may aid in the future monitoring of multiple cancers from individual- to population-level scales by leveraging the emerging science of wastewater-based epidemiology (WBE).

Reflux-Related Abnormalities at Distal oesophagus, Gastric Pouch and Anastomotic Site 4 Years After OAGB: Diagnostic Accuracies of Endoscopy Compared to Biopsy and of Symptoms Compared to Both

BACKGROUND

The purpose of the current study is to appraise the diagnostic accuracy of upper endoscopy (UE) vs histopathological assessment of patients after one-anastomosis gastric bypass (OAGB), and the presence/absence of symptoms vs these two diagnostic modalities.

METHODS

Retrospective study of 50 consecutive patients who underwent OAGB during April 2019-April 2020 and consented to participate. Symptoms (symptoms score questionnaire), macroscopic and microscopic data were collected 4 years later to assess distal oesophageal, gastric pouch and anastomotic site changes. Diagnostic accuracies (sensitivity, specificity, positive/negative predictive values) of UE vs biopsy and symptoms vs both were assessed.

RESULTS

Mean age was 48.6 ± 13.3 years; 66% were females. At 4 years, 54% had symptoms (symptom score ≥ 4). There were no dysplasia or cancer among this series. UE abnormalities included non-erosive gastritis (44%) and ulcer/s or erosive gastritis (16% each); histopathology abnormalities included chronic gastritis (80%) and Barrett's oesophagus (14%). For UE compared to biopsy, highest sensitivity (76.5%) was at the level of distal oesophagus and highest specificity (100%) at anastomotic site. Pertaining to symptoms compared to investigative modality, highest sensitivity (81.5%) was in relation to symptoms vs UE, while highest specificity (82.6%) was for symptoms vs biopsy.

CONCLUSIONS

It is generally not recommended that (a) UE be used to forecast biopsy abnormalities or lack thereof, except at the anastomotic site, and (b) symptoms or lack thereof be used to forecast the findings of investigative modalities, except with caution, to forecast UE findings in identifying healthy individuals, or to forecast biopsy findings in identifying diseased individuals. Long-term routine follow-up is needed post-OAGB regardless of whether patients are symptomatic or otherwise to rule in or out possible macroscopic/microscopic pathologies. Further research on UE and biopsy findings post-OAGB and their relationships with each other and with symptoms/lack thereof are required to strengthen the thin evidence base.

Assessment of the Sensitivity of a Smartphone App to Assist Patients in the Identification of Stroke and Myocardial Infarction: Cross-Sectional Study

Background

Most people do not recognize symptoms of neurological and cardiac emergencies in a timely manner. This leads to delays in hospital arrival and reduced access to therapies that can open arteries. We created a smartphone app to help patients and families evaluate if symptoms may be high risk for stroke or heart attack (myocardial infarction, MI). The ECHAS (Emergency Call for Heart Attack and Stroke) app guides users to assess their risk through evidence-based questions and a test of weakness in one arm by evaluating finger-tapping on the smartphone.

Objective

This study is an initial step in the accuracy evaluation of the app focused on sensitivity. We evaluated whether the app provides appropriate triage advice for patients with known stroke or MI symptoms in the Emergency Department. We designed this study to evaluate the sensitivity of the app, since the most dangerous output of the app would be failure to recognize the need for emergency evaluation. Specificity is also important, but the consequences of low specificity are less dangerous than those of low sensitivity.

Methods

In this single-center cross-sectional study, we enrolled patients presenting with symptoms of possible stroke or MI. The ECHAS app assessment consisted of a series of evidence-based questions regarding symptoms and a test of finger-tapping speed and accuracy on the phone's screen to detect unilateral arm weakness. The primary outcome was the sensitivity of the ECHAS app in detecting the need for ED evaluation. The secondary outcome was the sensitivity of the ECHAS app in detecting the need for hospital admission. Two independent and blinded board-certified physicians reviewed the medical record and adjudicated the appropriateness of the ED visit based on a 5-point score (ground truth). Finally, we asked patients semistructured questions about the app's ease of use, drawbacks, and benefits.

Results

We enrolled 202 patients (57 with stroke and 145 with MI). The ECHAS score was strongly correlated with the ground truth appropriateness score (Spearman correlation 0.41, P<.001). The ECHAS app had a sensitivity of 0.98 for identifying patients in whom ED evaluation was appropriate. The app had a sensitivity of 1.0 for identifying patients who were admitted to the hospital because of their ED evaluation. Patients completed an app session in an average of 111 (SD 60) seconds for the stroke pathway and 60 (SD 33) seconds for the MI pathway. Patients reported that the app was easy to use and valuable for personal emergency situations at home.

Conclusions

The ECHAS app demonstrated a high sensitivity for the detection of patients who required emergency evaluation for symptoms of stroke or MI. This study supports the need for a study of specificity of the app, and then a prospective trial of the app in patients at increased risk of MI and stroke.

The diagnostic characteristics and reliability of radiological methods used in the assessment of scaphoid fracture union : a systematic review

Aims

To evaluate the diagnostic characteristics and reliability of radiological methods used to assess scaphoid fracture union through a systematic review and meta-analysis.

Methods

MEDLINE, Embase, and the Cochrane Library were searched from inception to June 2022. Any study reporting data on the diagnostic characteristics and/or the reliability of radiological methods assessing scaphoid union was included. Data were extracted and checked for accuracy and completeness by pairs of reviewers. Methodological quality was assessed using the QUADAS-2 tool.

Results

A total of 13 studies were included, which were three assessed radiographs alone, six CT alone, and four radiographs + CT. Diagnostic sensitivity was assessed by CT in three studies (0.78, 0.78, and 0.73) and by radiographs in two studies (0.65, 0.75). Diagnostic specificity was assessed by CT in three studies (0.96, 0.8, 0.4) and by radiographs in two studies (0.67, 0.4). Interobserver reliability was assessed for radiographs by seven studies (two fair, four moderate, and one substantial) and for CT in nine studies (one fair, one moderate, six substantial, and one almost perfect).

Conclusion

There is evidence to support both the use of CT and radiographs in assessing scaphoid fracture union. Although CT appears superior in terms of both its diagnostic characteristics and reliability, further research is necessary to better define the optimal clinical pathways for patients.

Predicting craniofacial fibrous dysplasia growth status: an exploratory study of a hybrid radiomics and deep learning model based on computed tomography images

OBJECTIVE

This study aimed to develop 3 models based on computed tomography (CT) images of patients with craniofacial fibrous dysplasia (CFD): a radiomics model (Model Rad), a deep learning (DL) model (Model DL), and a hybrid radiomics and DL model (Model Rad+DL), and evaluate the ability of these models to distinguish between adolescents with active lesion progression and adults with stable lesion progression.

METHODS

We retrospectively analyzed preoperative CT scans from 148 CFD patients treated at Shanghai Ninth People's Hospital. The images were processed using 3D-Slicer software to segment and extract regions of interest for radiomics and DL analysis. Feature selection was performed using t-tests, mutual information, correlation tests, and the least absolute shrinkage and selection operator algorithm to develop the 3 models. Model accuracy was evaluated using measurements including the area under the curve (AUC) derived from receiver operating characteristic analysis, sensitivity, specificity, and F1 score. Decision curve analysis (DCA) was conducted to evaluate clinical benefits.

RESULTS

In total, 1,130 radiomics features and 512 DL features were successfully extracted. Model Rad+DL demonstrated superior AUC values compared to Model Rad and Model DL in the training and validation sets. DCA revealed that Model Rad+DL offered excellent clinical benefits when the threshold probability exceeded 20%.

CONCLUSIONS

Model Rad+DL exhibits superior potential in evaluating CFD progression, determining the optimal surgical timing for adult CFD patients.

Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire

Leprosy is a dermatoneurological disease and can cause irreversible nerve damage. In addition to being able to mimic different rheumatological, neurological and dermatological diseases, leprosy is underdiagnosed because several professionals present lack of training. The World Health Organization instituted active search for new leprosy cases as one of the four pillars of the zero-leprosy strategy. The Leprosy Suspicion Questionnaire (LSQ) was created aiming to be a screening tool to actively detect new cases; it is composed of 14 simple yes/no questions that can be answered with the help of a health professional or by the very patient themselves. During its development, it was noticed that the combination of marked questions was related to new case detections. To better encapsulate and being able to expand its use, we developed MaLeSQs, a Machine Learning tool whose output may be LSQ Positive when the subject is indicated for being further clinically evaluated or LSQ Negative when the subject does not present any evidence that justify being further evaluated for leprosy. To achieve a reasonable product, we trained four classifiers with different learning paradigms, Support Vectors Machine, Logistic Regression, Random Forest and XGBoost. We compared them based on sensitivity, specificity, positive predicted value, negative predicted value, and area under the ROC curve. After the training process, the Support Vectors Machine was the classifier with the most balanced metrics of 85.7% sensitivity, 69.2% specificity, 18.6% precision, 98.3% negative predicted values and an area under the ROC curve of 0.775, and it was chosen as the MaLeSQs. With Shapley values, we were able to evaluate variable importance and nerve symptoms were considered important to differentiate between subjects that potentially had leprosy from those who did not.

New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding

Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) models to calculate the risk of in-hospital mortality in patients admitted due to overt GIB. We analyzed the prospective, multicenter Hungarian GIB Registry's data. The predictive performance of XGBoost and CatBoost machine-learning algorithms with the Glasgow-Blatchford (GBS), pre-endoscopic Rockall and ABC scores were compared. We evaluated our models using five-fold cross-validation, and performance was measured by area under receiver operating characteristic curve (AUC) analysis with 95% confidence intervals (CI). Overall, we included 1,021 patients in the analysis. In-hospital death occurred in 108 cases. The XGBoost and the CatBoost model identified patients who died with an AUC of 0.84 (CI:0.76-0.90; 0.77-0.90; respectively) in the internal validation set, whereas the GBS and pre-endoscopic Rockall clinical scoring system's performance was significantly lower, AUC values of 0.68 (CI:0.62-0.74) and 0.62 (CI:0.56-0.67), respectively. ABC score had an AUC of 0.77 (CI:0.71-0.83). The XGBoost model had a specificity of 0.96 (CI:0.92-0.98) at a sensitivity of 0.25 (CI:0.10-0.43) compared with the CatBoost model, which had a specificity of 0.74 (CI:0.66-0.83) at a sensitivity of 0.78 (CI:0.57-0.95). XGBoost and the CatBoost models evaluate the mortality risk of acute GI bleeding better, than the conventional risk assessment tools.

Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach

Inter-individual cognitive variability, influenced by genetic and environmental factors, is crucial for understanding typical cognition and identifying early cognitive disorders. This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. Differentially expressed proteins (DEPs) were identified using label-free proteomics and analyzed with the Linear Model for Microarray Data. We discovered 213 DEPs between lower and higher cognition groups, with 155 upregulated in the lower cognition group and enriched in the IL-17 signaling pathway. Subsequent bioinformatic analysis linked these DEPs to neuroinflammation-related cognitive impairment. A random forest model classified cognitive ability groups with an accuracy of 81.5%, sensitivity of 65%, specificity of 85.9%, and an AUC of 0.79. By targeting a specific Thai cohort, this research provides novel insights into the link between neuroinflammation and cognitive performance, advancing our understanding of cognitive variability, highlighting the role of biological markers in cognitive function, and contributing to developing more accurate machine learning models for diverse populations.

Significance of tibial-tubercle trochlear groove distance and adjunctive radiological parameters in patients with recurrent patellar instability

PURPOSE

Magnetic resonance imaging (MRI) is often used to evaluate patients with patellar dislocations to facilitate diagnosis and management strategies. Many radiological parameters have been described in the literature. The aim of this study was to assess the significance of tibial-tubercle trochlear groove distance (TT-TG) distance and other MRI measurements in patients with and without patellar instability.

METHODS

This case-control study included 41 patients with recurrent patellar instability and 50 patients with stable knees, all of whom underwent MRI scans. A total of 19 radiological parameters were measured in both groups.

RESULTS

All measured MRI parameters had statistically significant differences between both groups (p < 0.05) apart from trochlear cartilage length. TT-TG distance. 20 mm had the strongest association with patellar instability (OR 53.3, p = 0.006, 95%CI [3.1- 927.4]) and the highest specificity (100%) but had the lowest sensitivity (34%) out of all the measured parameters. TT-TG. 13 mm had a higher sensitivity (68%) but lower specificity (72%) and weaker association with patellar instability (OR 5.5, p < 0.001, 95%CI [2.2. 13.7]). TT-TG/trochlear articular cartilage width ratio also had a strong association with patellar instability (OR 14.7, p < 0.001, 95%CI [4.5. 48.5]) with high specificity (92%) but lower sensitivity (56%).

CONCLUSION

The cut-off values for TT-TG distance at 13 mm and 20 mm both had advantages and disadvantages which supports the concept of using patient-individualised ratios. Patellar instability is associated with many radiological abnormalities demonstrated on MRI scans. Selection of the most appropriate measurement is dependent on the philosophy and preference of the treating clinician.

Reliability and validity of instruments containing reported sleep measures among children from birth to <5 years of age: A systematic review

Valid and reliable sleep measures during the early years are crucial for practitioners and researchers seeking accurate evaluation methods. The authors in this review systematically examined the psychometric properties of instruments containing reported sleep measures in children from birth to <5 years of age. The search was conducted using several electronic databases, including MEDLINE, EMBASE, PsycINFO, Web of Science, CINAHL, SPORTDiscus, Scopus and HaPI, with the most recent update on August 30, 2022. The quality of the included studies was assessed using the COSMIN methodology. A total of 79 studies were analyzed. However, none of these measures had undergone a comprehensive evaluation of all psychometric properties. Unfortunately, suitable reported sleep measures for children aged 0-4.99 years could not be identified through this review. Further research is needed to develop and validate psychometrically robust sleep assessment tools for this specific age group.

The Diagnostic Utility of Host RNA Biosignatures in Adult Patients With Sepsis: A Systematic Review and Meta-Analysis

OBJECTIVES

Sepsis is a life-threatening medical emergency, with a profound healthcare burden globally. Its pathophysiology is complex, heterogeneous and temporally dynamic, making diagnosis challenging. Medical management is predicated on early diagnosis and timely intervention. Transcriptomics is one of the novel "-omics" technologies being evaluated for recognition of sepsis. Our objective was to evaluate the performance of host gene expression biosignatures for the diagnosis of all-cause sepsis in adults.

DATA SOURCES

PubMed/Ovid Medline, Ovid Embase, and Cochrane databases from inception to June 2023.

STUDY SELECTION

We included studies evaluating the performance of host gene expression biosignatures in adults who were diagnosed with sepsis using existing clinical definitions. Controls where applicable were patients without clinical sepsis.

DATA EXTRACTION

Data including population demographics, sample size, study design, tissue specimen, type of transcriptome, health status of comparator group, and performance of transcriptomic biomarkers were independently extracted by at least two reviewers.

DATA SYNTHESIS

Meta-analysis to describe the performance of host gene expression biosignatures for the diagnosis of sepsis in adult patients was performed using the random-effects model. Risk of bias was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A total of 117 studies (n = 17,469), comprising 132 separate patient datasets, were included in our final analysis. Performance of transcriptomics for the diagnosis of sepsis against pooled controls showed area under the receiver operating characteristic curve (AUC, 0.86; 95% CI, 0.84-0.88). Studies using healthy controls showed AUC 0.87 (95% CI, 0.84-0.89), while studies using controls with systemic inflammatory response syndrome (SIRS) had AUC 0.84 (95% CI, 0.78-0.90). Transcripts with excellent discrimination against SIRS controls include UrSepsisModel, a 210 differentially expressed genes biosignature, microRNA-143, and Septicyte laboratory.

CONCLUSIONS

Transcriptomics is a promising approach for the accurate diagnosis of sepsis in adults and demonstrates good discriminatory ability against both healthy and SIRS control subjects.

The Cambridge Knee Injury Tool (CamKIT): a clinical prediction tool for acute soft tissue knee injuries

Background/aim

This study focuses on the development of the Cambridge Knee Injury Tool (CamKIT), a clinical prediction tool developed as a 12-point scoring tool based on a modified e-Delphi study.

Methods

A retrospective cohort evaluation was conducted involving 229 patients presenting to a Major Trauma Centre with acute knee pain over 3 months. The evaluation extracted data on the 12 scoring tool variables as well as diagnostic and management pathway outcomes. CamKIT scores for the injured and non-injured cohorts were then calculated and evaluated.

Results

The CamKIT yielded a median score of 7.5 (IQR: 6-9) in the injured cohort, compared with a median score of 2 (IQR: 1-4) in the non-injured cohort, with a statistically significant difference (p<0.0001). When constructed as a three-tier risk stratification tool, the CamKIT produces a sensitivity of 100%, a specificity of 94.3%, a positive predictive value of 89% and a negative predictive value of 100% for diagnosing clinically significant soft tissue knee injuries.

Conclusion

The CamKIT provides a non-invasive tool that has the potential to streamline the diagnostic process and empower healthcare workers in resource-stretched settings by instilling confidence and promoting accuracy in clinical decision-making. The CamKIT also has the potential to support efficiency in the secondary healthcare setting by enabling more targeted and timely use of specialist resources. This research contributes to the ongoing efforts to enhance patient outcomes and the overall quality of care in managing acute knee injuries.

External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study

OBJECTIVES

Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative screening tools at the community level. This pilot study aimed first, to evaluate the accuracy of GumAI, a new mHealth tool that uses AI and smartphones to detect gingivitis, and the user acceptance of personalized oral hygiene instructions provided through the new tool, among older adults in day-care community centers.

METHODS

Participants were invited from 3 day-care community centers. Intraoral photographs were captured and assessed by both GumAI (test) and a panel consisting of 2 calibrated periodontists and a dentist (benchmark). Mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score were calculated to determine GumAI's diagnostic performance in comparison to the benchmark. User acceptance with this tool was assessed using 2 Rasch Theory-based 5-point Likert-type questions.

RESULTS

44 participants were recruited out of 80 invited older adults. GumAI demonstrated a sensitivity of 0.93 and specificity of 0.50 compared to the panel's assessments, with a PPV of 0.90 and NPV of 0.56. The accuracy and F1 scores were 0.85 and 0.91, respectively. All participants expressed high acceptance of the process.

CONCLUSION

GumAI demonstrates high sensitivity, PPV, accuracy, and F1 score compared to the panel's assessments but falls relatively short in specificity and NPV. Despite this, the tool was highly accepted by older adults, indicating its potential to enhance gingivitis detection and oral hygiene management in community settings. Further refinements are necessary to improve specificity and validate usability measures.

CLINICAL RELEVANCE

This study may pave the way for broader applications of mHealth systems in community settings, enabling greater health coverage and addressing oral health disparities.

Criterion validity of five open-source app-based cognitive and sensory tasks in an Australian adult life course sample aged 18 to 82: Labs without walls

With recent technical advances, many cognitive and sensory tasks have been adapted for smartphone testing. This study aimed to assess the criterion validity of a subset of self-administered, open-source app-based cognitive and sensory tasks by comparing test performance to lab-based alternatives. An in-person baseline was completed by 43 participants (aged 21 to 82) from the larger Labs without Walls project (Brady et al., 2023) to compare the self-administered, app-based tasks with researcher-administered equivalents. 4 preset tasks sourced from Apple's ResearchKit (Spatial Memory, Trail Making Test, Stroop Test, and dBHL Tone Audiometry) and 1 custom-built task (Ishihara Color Deficiency Test) were compared. All tasks except the Spatial Memory task demonstrated high comparability to the researcher-administered version. Specifically, the Trail Making Tests were strongly correlated (.77 and .78 for parts A and B, respectively), Stroop correlations ranged from .77 to .89 and the Ishihara tasks were moderately correlated (r = .69). ICCs for the Audiometry task ranged from .56 to .96 (Moderate to Excellent) with 83% sensitivity and 100% specificity. Bland-Altman plots revealed a mean bias between -5.35 to 9.67 dB for each ear and frequency with an overall bias of 3.02 and 1.98 for the left and right ears, respectively, within the minimum testing interval. Furthermore, all app-based tasks were significantly correlated with age. These results offer preliminary evidence of the validity of four open-source cognitive and sensory tasks with implications for effective remote testing in non-lab settings.

Performance of Noninvasive Indices for Discrimination of Metabolic Dysfunction-Associated Steatotic Liver Disease in Young Adults

Background/Aims

Although numerous noninvasive steatosis indices have been developed to assess hepatic steatosis, whether they can be applied to young adults in the evaluation of metabolic dysfunction-associated steatotic liver disease (MASLD) remains uncertain.

Methods

Data from patients under 35 years of age who visited the Liver Health Clinic at the Armed Forces Goyang Hospital between July 2022 and January 2024 were retrospectively collected. Steatosis was diagnosed on the basis of a controlled attenuation parameter score ≥250 dB/m. MASLD was defined as the presence of steatosis in patients with at least one cardiometabolic risk factor.

Results

Among the 1,382 study participants, 901 were diagnosed with MASLD. All eight indices for diagnosing steatosis differed significantly between the MASLD and non-MASLD groups (p<0.001). Regarding the predictive performance, the hepatic steatosis index (HSI), fatty liver index (FLI), Framingham steatosis index, Dallas steatosis index, Zhejiang University index, lipid accumulation product, visceral adiposity index, and triglyceride glucose-body mass index exhibited an area under the curve of 0.898, 0.907, 0.899, 0.893, 0.915, 0.869, 0.791, and 0.898, respectively. The cutoff values for the FLI and HSI were re-examined, indicating a need for alternative cutoff values for the HSI, with a rule-in value of 42 and a rule-out value of 36 in this population.

Conclusions

This study presents novel findings regarding the predictive performance of established steatosis markers in young adults. Alternative cutoff values for the HSI in this population have been proposed and warrant further validation.

Development and Validation of Child Observation Checklist for Early Screening of Young Children with Special Needs

Background: Families with young children with developmental disabilities often express concerns about delays in the identification of these and extended waiting times for obtaining assessments, learning support, and rehabilitation services. The identification process must and can be enhanced if preschool teachers have instrumental measures to detect early-stage developmental disabilities and adjustment difficulties in preschool children and, if necessary, to provide these children with prompt and effective support services. Aims: The aim with this study was to develop and validate a screening checklist for teachers to detect developmental delays and adjustment difficulties in Chinese preschool children in Hong Kong. Methods: The participants included 1085 children aged two to six years (including 365 children who were receiving rehabilitation services at the time of the study) and their preschool teachers. The teachers were requested to complete the screening checklist and the Strengths and Difficulties Questionnaire. Results: The results of the Rasch and Confirmatory Factor Analyses supported the unidimensionality of the checklist, with the validated version consisting of fifteen items and five factors. The checklist score was negatively correlated with children's behavioral problems and effectively differentiated children of different ages and between children receiving and not receiving rehabilitation services. The reliability estimates (internal consistency and test-retest) of this revised checklist were above 0.70. Conclusions: The 15-item checklist is a promising screening instrument for the identification of developmental disabilities and adjustment problems among Chinese preschool children. The use of the checklist has accelerated access to rehabilitation services for children and family caregivers.

Predictive value of an unsupervised web-based assessment of the neuropsychological function

Neuropsychological assessment has to consider the subjective and objective functional deficits of help-seeking individuals in several cognitive domains. Due to time constraints in clinical practice, several web-based approaches have been developed. The current study examined whether functional deficits in the mnestic and attentive domain can be predicted based on an unsupervised self-administered online assessment neuropsychological online screening (NOS): This screening includes self-reports and psychometric memory tests (face-name association, visual short-term memory). Data of help-seeking individuals (n = 213, mean age: 48.2 years) running an extensive in-person assessment were analyzed. A functional deficit in at least one cognitive domain was detected in 48 individuals. This classification was supported by the preceding online screening (sensitivity = 0.75, specificity = 0.80), and a linear regression model identified two significant predictors (free recall performance, form discrimination in visual short-term memory). The predictive quality was enhanced for individuals with selective deficits in the mnestic domain (n = 23: sensitivity = 0.78 and specificity = 0.78) as compared to the attentive domain (n = 25: sensitivity = 0.68 and specificity = 0.75). Our results show that a neuropsychological in-person assessment cannot be replaced by an unsupervised self-administered online test. However, a specific pattern of results in the online test might indicate the necessity of an extensive assessment in help-seeking individuals.

The utility of brief instruments for depression screening in dialysis patients

Background

Depression is a frequent but often underdiagnosed comorbid disorder in dialysis patients. The Beck Depression Inventory-Second Edition (BDI-II) is a reliable and valid instrument for depression screening but is relatively long for repeated use in clinical practice. The aim of this study was to compare the BDI-II with the shorter questionnaires Beck Depression Inventory-FastScreen (BDI-FS), the depression subscale of the Hospital Anxiety Depression Scale (HADS-D), the Mental Health (MH) scale of the 36-item Short Form Health Survey (SF-36) and two items of the MH ('So down in the dumps that nothing could cheer you up' and 'Downhearted and blue') to determine the most efficient instruments for screening depressive symptoms in dialysis patients.

Methods

A cross-sectional study was conducted involving patients from 14 health centres undergoing in-centre haemodialysis or peritoneal dialysis. All patients completed the BDI-II, HADS-D and MH scale. The sensitivity, specificity and positive and negative predictive values for each brief instrument were assessed relative to BDI-II ≥16.

Results

Of the 145 patients included in the study (mean age 62 years; 66% male), 24.8% had depressive symptoms (BDI ≥16). The cut-off points with the highest sensitivity and negative predictive value for BDI-FS were ≥3 (91.7% and 96.1%, respectively) and ≥4 (80.6% and 92.4%, respectively) and for the HADS-D these were ≥4 (91.7% and 95.8%, respectively) and ≥5 (83.3% and 92.6%, respectively). The cut-off points for the total MH and the two items (considered separately or together) resulted in lower sensitivity (<80%) and lower negative predictive values (<90%).

Conclusions

Both the BDI-FS and HADS-D are adequate screening tools for depression in the dialysis population. As the BDI-FS is easier to complete and score and enables identification of patients at risk of suicide, it may be the best alternative to the BDI-II for depression screening in dialysis patients in clinical settings.

Clinical validation of artificial intelligence-based single-subject morphometry without normative reference database

BACKGROUND

Single-subject voxel-based morphometry (VBM) is a powerful technique for reader-independent detection of brain atrophy in structural magnetic resonance imaging (MRI) to support the (differential) diagnosis and staging of neurodegenerative diseases in individual patients. However, VBM is sensitive to the MRI scanner platform and details of the acquisition sequence. To mitigate this limitation, we recently proposed and technically validated a convolutional neural network (CNN)-based VBM which does not rely on a normative reference database.

OBJECTIVE

Clinical validation of CNN-based VBM.

METHODS

CNN-based VBM was compared with conventional VBM based on a mixed-scanner normative database in 227 consecutive patients (66.0 ± 9.6 years, 53.3% female) with suspected dementing neurodegenerative disease. VBM maps were interpreted visually by two experienced readers, first with respect to the presence of any neurodegenerative disease, then for the differentiation between Alzheimer's disease (AD)-typical and non-AD atrophy patterns. A Likert 6-score was used for both tasks. Simultaneously acquired positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) served as reference standard.

RESULTS

Repeated-measures ANOVA revealed a significant impact of the VBM method on the visual detection of any neurodegenerative disease (p < 0.001). Balanced accuracy/sensitivity/specificity were 80.4/86.3/74.5% for CNN-based VBM versus 75.7/79.5/71.8% for conventional VBM. Differentiation between AD and non-AD typical atrophy patterns did not differ between both VBM methods (p = 0.871).

CONCLUSIONS

CNN-based VBM provides clinically useful accuracy for the detection of neurodegeneration-suspect atrophy with higher sensitivity than conventional VBM with a mixed-scanner normative reference database and without compromising specificity.

Identification of youth at clinical high-risk for psychosis: A community-based study from India

AIM

A two-stage process, wherein self-report screening precedes the structured interview, is suggested for identifying individuals at clinical high-risk for psychosis (CHR-P) in community samples. Aim of this study was to screen a community youth sample from India for CHR-P using the two-stage method. Specific objectives were to assess concordant validity of the self-report measure and predictive validity of the two-stage method.

METHODS

Based on probability sampling, 2025 youth aged 15-24 years were recruited from one rural and one urban area of Telangana, a Telugu-speaking state in India. Telugu version of the PRIME Screen-Revised (PS-R) and structured interview for psychosis-risk syndromes (SIPS) were used. CHR-P positive and negative cohorts were followed-up for transition to psychosis at 3-monthly intervals.

RESULTS

One hundred ten individuals screened positive on PS-R. SIPS conducted on 67 out of 110 individuals confirmed 62 (92.54%) to be CHR-P positive. PS-R showed 98.41% sensitivity and 90.74% specificity. Among CHR-P positive, three participants transitioned to psychosis in 15 months. The hazard ratio for psychosis transition was 11.4.

CONCLUSIONS

Screening accuracy of PS-R in the community youth sample in Telangana is optimum. The hazard ratio for psychosis transition in the community identified CHR-P indicates good predictive validity for the two-stage method.

The Minimally Invasive 1-Hour Esophageal String Test Monitors Therapeutic Changes in Mucosal Inflammation in Eosinophilic Esophagitis

INTRODUCTION

Endoscopy, standard-of-care for monitoring eosinophilic esophagitis (EoE), assesses mucosal inflammation. The Esophageal String Test (EST), a minimally invasive swallowed capsule and immunoassays, quantifies EoE inflammation. We determined whether the EST/EoEScore can monitor disease in patients undergoing treatment.

METHODS

Thirty-three samples from 14 patients with EoE (7 children, 7 adults) who underwent repeat endoscopies and ESTs were studied. Biopsies were analyzed for peak eosinophil counts; ESTs were analyzed for EoEScores.

RESULTS

Eosinophil counts and EoEScores significantly correlated during treatment, distinguishing patients with active EoE from treatment-associated remissions for 93.9% of ESTs performed.

DISCUSSION

The EST can be used to longitudinally monitor responses to treatment in EoE.

Development and Validation of a Self-Administered Online Hearing Test

We describe the development and validation of a self-administered online hearing test, which screens for hearing loss and provides an estimated audiogram. The hearing test computes test results from age, self-reported hearing abilities, and self-assessed pure-tone thresholds. It relies on regression, Bayesian and binary classification, leveraging probabilistic effects of age as well as interfrequency and interaural relationships in audiograms. The test was devised based on development data, collected prospectively in an online experiment from a purposive convenience sample of 251 adult American, Australian, Canadian, and Swiss participants, 58% of whom had hearing loss. Later, we externally validated the hearing test. Validation data were collected prospectively from a representative sample of 156 adult Belgian participants, 15% of whom had hearing loss. Participants completed the hearing test and audiometric assessments at home. The results for the primary screening outcome showed that the hearing test screened for mild hearing losses with a sensitivity of 0.83 [95%-confidence interval (CI): 0.65, 0.96], specificity of 0.94 [CI: 0.89, 0.98], positive predictive value of 0.70 [CI: 0.57, 0.87], and negative predictive value of 0.97 [CI: 0.94, 0.99]. Results for the secondary audiogram estimation outcome showed mean differences between estimated and gold standard hearing thresholds ranging from 2.1 to 12.4 dB, with an average standard deviation of the differences of 14.8 dB. In conclusion, the hearing test performed comparably to state-of-the-art hearing screeners. This test, therefore, is a validated alternative to existing screening tools, and, additionally, it provides an estimated audiogram.

Factors affecting survival of foals with pneumonia in a referral hospital

BACKGROUND

Pneumonia is a common condition in ailing neonatal foals, and it remains an important cause of morbidity and mortality in this veterinary patient group. Factors affecting the survival of young foals with pneumonia have not been thoroughly investigated. The aim of this study was to explore the potential prognostic factors associated with survival of these foals. Fifty foals under one month of age with pneumonia were included in this retrospective clinical study. The foals were divided into groups based on survival (survived to discharge or died/euthanised during hospitalisation). Multiple clinical and laboratory variables were investigated as risk factors with univariate logistic regression analyses and subsequently with multivariate analyses. If a variable showed prediction potential in regression analysis, a receiver operating characteristic (ROC) analysis was conducted.

RESULTS

In univariate analysis, odds (OR, 95% CI) of non-survival were associated with higher respiratory rate (RR) on the first day after admission to hospital (D1) (1.32, 1.07-1.62, P = 0.009, for each 5-unit increase) and positive bacterial blood culture (12.08, 1.88-77.67, P = 0.009). Odds of non-survival were decreased for Standardbred breed (0.11, 0.01-0.96, P = 0.046) and for foals with longer hospitalisation, with each additional day in hospital further reducing the odds (0.59, 0.40-0.86, P = 0.006). In multivariate analysis, odds of non-survival were associated only with higher RR on D1 (1.36, 1.07-1.71, P = 0.011, for each 5-unit increase). In ROC analysis, optimal cut-off value for RR was ≥ 55/min with sensitivity 75.0% and specificity 76.3%. Based on predictive values, RR < 55/min on D1 favoured survival.

CONCLUSIONS

Higher RR on D1 is a predictor of non-survival in foals with pneumonia in this study, increasing the odds of death by 36% for each 5-unit increase in RR. Respiratory rate below 55/min on D1 favours survival. These findings could assist in early identification of foals that are at increased risk of mortality, thereby aiding in treatment decisions.

Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging

Malnutrition is a commonly observed side effect in cancer patients, with a 30-85% worldwide prevalence in this population. Existing malnutrition screening tools miss ~ 20% of at-risk patients at initial screening and do not capture the abnormal body composition phenotype. Meanwhile, the gold-standard clinical criteria to diagnose malnutrition use changes in body composition as key parameters, particularly body fat and skeletal muscle mass loss. Diagnostic imaging, such as computed tomography (CT), is the gold-standard in analyzing body composition and typically accessible to cancer patients as part of the standard of care. In this study, we developed a deep learning-based body composition analysis approach over a diverse dataset of 200 abdominal/pelvic CT scans from cancer patients. The proposed approach segments adipose tissue and skeletal muscle using Swin UNEt TRansformers (Swin UNETR) at the third lumbar vertebrae (L3) level and automatically localizes L3 before segmentation. The proposed approach involves the first transformer-based deep learning model for body composition analysis and heatmap regression-based vertebra localization in cancer patients. Swin UNETR attained 0.92 Dice score in adipose tissue and 0.87 Dice score in skeletal muscle segmentation, significantly outperforming convolutional benchmarks including the 2D U-Net by 2-12% Dice score (p-values < 0.033). Moreover, Swin UNETR predictions showed high agreement with ground-truth areas of skeletal muscle and adipose tissue by 0.7-0.93 R2, highlighting its potential for accurate body composition analysis. We have presented an accurate body composition analysis based on CT imaging, which can enable the early detection of malnutrition in cancer patients and support timely interventions.

Predicting faller status in persons with multiple sclerosis using the Multiple Sclerosis Walking Scale-12

BACKGROUND

Persons with multiple sclerosis (PwMS) are at an increased risk for falling, making it necessary to identify useful screening tools. The aims of this study were to 1) determine a cut-off score for the 12-item Multiple Sclerosis Walking Scale (MSWS-12) for identifying PwMS as fallers and 2) evaluate its predictive ability of faller status after controlling for other potential contributing factors.

METHODS

Participant characteristics, MSWS-12, and falls in the last six months were collected on PwMS (n = 171) during a single session. Fallers (53.8 %; n = 92) were individuals reporting ≥ 1 fall in the past six months. A receiver-operating-characteristic (ROC) curve was performed to estimate the classification accuracy (area under the curve; AUC) of the MSWS-12 at detecting fallers. Optimal cut-off scores were calculated using the Youden Index and Index of Union methods. The dichotomized MSWS-12 cut-off score was then entered into a logistic regression, with faller status as the outcome, and age, gender, body mass index, disease duration, and fatigue as covariates.

RESULTS

The MSWS-12 had a fair classification accuracy for identifying fallers (AUC = 0.74), with the cut-off score of ≥ 46 % having 76.1 % sensitivity and 64.6 % specificity. The MSWS-12 cut-off score remained a significant predictor of faller status in the adjusted model (adjusted odds ratio [aOR]: 3.77, 95 % CI: 1.75, 8.15, P = .001), along with higher fatigue (aOR: 1.11, 95 % CI: 1.02, 1.20, P = .015).

CONCLUSION

PwMS with MSWS-12 scores ≥ 46 % were more likely to be fallers than those with lower scores. When used in conjunction with a clinician's judgement and other assessments, the MSWS-12 may be a useful screening tool for identifying PwMS who are fallers.

The screening accuracy of the Edinburgh Postnatal Depression Scale (EPDS) to detect perinatal depression with and without the self-harm item in pregnant and postpartum women

BACKGROUND

This study aims to examine whether the Edinburgh Postnatal Depression Scale (EPDS), excluding the self-harm item (EPDS-9), performs as effectively as the full EPDS in identifying depression among perinatal women.

METHODS

A total of 3571 pregnant women and 3850 postpartum women participated in this observational study. Participants who scored ≥ 9 on the EPDS underwent further diagnostic evaluations by a clinical psychologist and/or psychiatrist.

RESULTS

The EPDS-9 and full EPDS demonstrated a near-perfect correlation in both the antepartum (r = 0.996) and postpartum (r = 0.998) cohorts. EPDS-9 showed exceptional precision in identifying depression as screened by the full EPDS at cutoff points ranging 9-14, with areas under the curve ≥0.998. The sensitivity of EPDS-9 and full EPDS to detect depression that requires psychotropic medications was poor. The highest accuracy for both versions was at a cutoff score of 9: sensitivity of 0.579 for the full EPDS and 0.526 for the EPDS-9. At the cutoff point of 9, EPDS-9 performed adequately in predicting the response of the participants to the self-harm item.

CONCLUSION

The EPDS-9 represents a solid and effective replacement for the full EPDS in clinical settings. If the presence of suicidal thoughts needs to be assessed, specialized scales should be used.

An Automated Online Measure for Misophonia: The Sussex Misophonia Scale for Adults

Misophonia is a sound sensitivity disorder characterized by a strong aversion to specific sounds (e.g., chewing). Here we present the Sussex Misophonia Scale for Adults (SMS-Adult), within an online open-access portal, with automated scoring and results that can be shared ethically with users and professionals. Receiver operator characteristics show our questionnaire to be "excellent" and "good-to-excellent" at classifying misophonia, both when dividing our n = 501 adult participants by recruitment stream (self-declared misophonics vs. general population), and again when dividing them with by a prior measure of misophonia (as misophonics vs. non-misophonics). Factor analyses identified a five-factor structure in our 39 Likert-type items, and these were Feelings/Isolation, Life consequences, Intersocial reactivity, Avoidance/Repulsion, and Pain. Our measure also elicits misophonia triggers, each rated for their commonness in misophonia. We offer our open-access online tool for wider use (www.misophonia-hub.org), embedded within a well-stocked library of resources for misophonics, researchers, and clinicians.

Potential urinary volatile organic compounds as screening markers in cancer - a review

Early detection of cancer typically facilitates improved patient outcomes; however, many cancers are not easily diagnosed at an early stage. One potential route for developing new, non-invasive methods of cancer detection is by testing for cancer-related volatile organic compounds (VOCs) biomarkers in patients' urine. In this review, 44 studies covering the use and/or identification of cancer-related VOCs were examined, including studies which examined multiple types of cancer simultaneously, as well as diverse study designs. Among these studies the most studied cancers included prostate cancer (29% of papers), lung cancer (22%), breast cancer (20%), and bladder cancer (18%), with a smaller number of studies focused on colorectal cancer, cervical cancer, skin, liver cancer and others. Importantly, most studies which produced a VOC-based model of cancer detection observed a combined sensitivity and specificity above 150%, indicating that urine-based methods of cancer detection show considerable promise as a diagnostic tool. Mass spectrometry (MS) and electronic noses (eNose) were the most employed tools used in the detection of VOCs, while animal-based models were less common. In terms of VOCs of interest, 47 chemical species identified as correlated with various types of cancer in at least two unrelated papers, some of which were consistently up- or down-regulated in cancer patients, and which may represent useful targets for future studies investing urinary VOC biomarkers of cancer. Overall, it was concluded that research in this field has shown promising results, but more work may be needed before the widespread adoption of these techniques takes place.

Prevalent Atherosclerotic Cardiovascular Disease Among Veterans by Sexual Orientation

BACKGROUND

Seven million lesbian, gay, and bisexual (LGB) adults will be aged >50 years by 2030; assessing and addressing their risk for cardiovascular disease is critical.

METHODS AND RESULTS

We analyzed a nationwide cohort using the Veterans Health Administration data. Sexual orientation (SO) was classified via a validated natural language processing algorithm. Prevalent atherosclerotic cardiovascular disease (ASCVD) (history of acute myocardial infarction, ischemic stroke, or revascularization) was identified via International Classification of Diseases, Ninth and Tenth Revision (ICD-9 and ICD-10) codes. The index date was the date of the first primary care appointment on or after October 1, 2009. We ascertained covariates and prevalent ASCVD in the year following the index date; the baseline date was 1 year after the index date. We calculated sample statistics by sex and SO and used logistic regression analyses to assess associations between SO and prevalent ASCVD. Of 1 102 193 veterans with natural language processing-defined SO data, 170 861 were classified as LGB. Prevalent ASCVD was present among 25 031 (4105 LGB). Adjusting for age, sex, race, and Hispanic ethnicity, LGB veterans had 1.24 [1.19-1.28] greater odds of prevalent ASCVD versus non-LGB identified veterans. This association remained significant upon additional adjustment for the ASCVD risk factors substance use, anxiety, and depression (odds ratio [OR],1.17 [95% CI, 1.13-1.21]). Among a subset with self-reported SO, findings were consistent (OR, 1.53 [95% CI, 1.20-1.95]).

CONCLUSIONS

This is one of the first studies to examine cardiovascular risk factors and disease of the veteran population stratified by natural language processing-defined SO. Future research must explore psychological, behavioral, and physiological mechanisms that result in poorer cardiovascular health among LGB veterans.

ResNet-Transformer deep learning model-aided detection of dens evaginatus

BACKGROUND

Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical disease. Consequently, early detection and intervention of dens evaginatus are significant to preserve vital pulp.

AIM

This study aimed to develop a deep learning model to assist dentists in early diagnosing dens evaginatus, thereby supporting early intervention and mitigating the risk of severe consequences.

DESIGN

In this study, a deep learning model was developed utilizing panoramic radiograph images sourced from 1410 patients aged 3-16 years, with high-quality annotations to enable the automatic detection of dens evaginatus. Model performance and model's efficacy in aiding dentists were evaluated.

RESULTS

The findings indicated that the current deep learning model demonstrated commendable sensitivity (0.8600) and specificity (0.9200), outperforming dentists in detecting dens evaginatus with an F1-score of 0.8866 compared to their average F1-score of 0.8780, indicating that the model could detect dens evaginatus with greater precision. Furthermore, with its support, young dentists heightened their focus on dens evaginatus in tooth germs and achieved improved diagnostic accuracy.

CONCLUSION

Based on these results, the integration of deep learning for dens evaginatus detection holds significance and can augment dentists' proficiency in identifying such anomaly.

Combining different metal artifact reduction levels with sharpening filters and slice thickness for the visualization of mandibular canals perforated by implants

OBJECTIVES

To investigate the influence of combining levels of metal artifact reduction (MAR) tool with different filters and slice-thickness in the detection of mandibular canals perforated by implants on CBCT scans and to objectively assess the impact of the aforementioned combinations.

MATERIALS AND METHODS

Implants were placed above (8 implants) and inside (10 implants) mandibular canals of dried-mandibles. CBCT scans were obtained with the Eagle 3D unit (85 kVp, 8 mA, 5 × 5 cm FOV, 130 μm voxel size, and off/medium/high MAR levels). Examiners evaluated the scans under each MAR level and across different conditions: no filter, Sharpen 1×, Sharpen 2×; 0 mm, 1 mm, and 2 mm slice-thickness. The gray values on axial reconstructions were assessed. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, mean and standard deviation (SD) for gray values were calculated and compared by multi-way Analysis of Variance.

RESULTS

Overall, the AUC and sensitivity decreased with high-level MAR (p < 0.0001), regardless other variables tested. Enabling MAR tool decreased mean gray values (p < 0.01) and increasing MAR levels reduced the SD values on scans with Sharpen 2× (p < 0.012).

CONCLUSIONS

High-level MAR impairs the visualization of mandibular canals perforated by implants. Moreover, enabling MAR tool decreases the mean gray values.

CLINICAL RELEVANCE

MAR tool, filters, and slice thickness influence the image quality of CBCT scans. Therefore, it is important to evaluate the impact of these parameters on the diagnosis of mandibular canals perforated by implants.

Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis

BACKGROUND/OBJECTIVE

Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice.

METHODS

We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics.

RESULTS

The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling.

CONCLUSIONS

While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility.

Application of a validated prognostic plasma protein biomarker test for renal decline in type 2 diabetes to type 1 diabetes: the Fremantle Diabetes Study Phase II

BACKGROUND

There are scant data relating to prognostic biomarkers for chronic kidney disease (CKD) complicating type 1 diabetes. The aim of this study was to assess the performance of the plasma protein biomarker-based PromarkerD test developed and validated for predicting renal decline in type 2 diabetes in the context of type 1 diabetes.

METHODS

The baseline PromarkerD test score was determined in 91 community-based individuals (mean age 46.2 years, 56.5% males) with confirmed type 1 diabetes recruited to the longitudinal observational Fremantle Diabetes Study Phase II. The performance of the PromarkerD test in predicting the risk of incident CKD (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 in people without CKD at baseline) or an eGFR decline of ≥ 30% over the next four years was determined. The score can range from 0 to 100%, and is categorized as representing low (< 10%), moderate (10% to < 20%) or high (≥ 20%) risk.

RESULTS

The area under the receiver operating characteristic curve was 0.93 (95% confidence interval 0.87-0.99) for the composite renal endpoint, indicating strong predictive accuracy. The positive and negative predictive values at moderate (10% to < 20%) and high (≥ 20%) risk PromarkerD cut-offs were 46.7-50.0% and ≥ 92.0%, respectively.

CONCLUSIONS

These preliminary data suggest that PromarkerD is at least as good a prognostic test for renal decline in type 1 as type 2 diabetes.

Risk for Suicide Attempts Assessed Using the Patient Health Questionnaire-9 Modified for Teens

Importance

Suicide is a leading cause of death in US youths.

Objective

To assess whether screening with supplemental items 10 to 13 on the Patient Health Questionnaire-9 modified for teens (PHQ-9M) improves prediction of youth suicide attempts beyond the information provided by the first 9 items alone (the PHQ-9).

Design, Setting, and Participants

This retrospective cohort study used a retrospective cohort of adolescents aged 12 to 17 years who were screened for depression in outpatient facilities within a pediatric health care system between January 1, 2016, and December 31, 2022, with up to 1 year of follow-up to assess the occurrence of suicidal behavior. Follow-up was completed on December 31, 2023.

Exposure

Screening with the PHQ-9M.

Main Outcomes and Measures

This study developed and compared prediction using 3 Cox proportional hazards regression models (CR-9, CR-13, and CR-3) of subsequent suicide attempts, determined by the hospital's electronic health records up to 1 year following the last PHQ-9M screening. The CR-9 model used the PHQ-9 and the CR-13 model used all 13 items of PHQ-9M. The CR-3 model used the 3 most impactful variables selected from the 13 PHQ-9M items and PHQ-9 total score. All models were evaluated across 4 prediction horizons (30, 90, 180, and 365 days) following PHQ-9M screenings. Evaluation metrics were the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC).

Results

Of 130 028 outpatients (65 520 [50.4%] male) with 272 402 PHQ-9M screenings, 549 (0.4%) had subsequent suicide attempts within 1 year following the PHQ-9M screening. The AUROC of the CR-9 model in the 365-day horizon was 0.77 (95% CI, 0.75-0.79); of the CR-13 model, 0.80 (95% CI, 0.78-0.82); and of the CR-3 model, 0.79 (95% CI, 0.76-0.81); the AUPRC of the CR-9 model was 0.02 (95% CI, 0.02-0.03); of the CR-13 model, 0.03 (95% CI, 0.02-0.03); and of the CR-3 model, 0.02 (95% CI, 0.02-0.03). The 3 most impactful items using adjusted hazard ratios were supplemental item 13 (lifetime suicide attempts; 3.06 [95% CI, 2.47-3.80]), supplemental item 10 (depressed mood severity in the past year; 2.99 [95% CI, 2.32-3.86]), and supplemental item 12 (serious suicidal ideation in the past month; 1.63 [95% CI, 1.25-2.12]). All of the models achieved higher AUROCs as prediction horizons shortened.

Conclusions and Relevance

In this cohort study of adolescent PHQ-9M screenings, the supplemental items on PHQ-9M screening improved prediction of youth suicide attempts compared with screening using the PHQ-9 across all prediction horizons, suggesting that PHQ-9M screening should be considered during outpatient visits to improve prediction of suicide attempts.

A cycle generative adversarial network for generating synthetic contrast-enhanced computed tomographic images from non-contrast images in the internal jugular lymph node-bearing area

The objectives of this study were to create a mutual conversion system between contrast-enhanced computed tomography (CECT) and non-CECT images using a cycle generative adversarial network (cycleGAN) for the internal jugular region. Image patches were cropped from CT images in 25 patients who underwent both CECT and non-CECT imaging. Using a cycleGAN, synthetic CECT and non-CECT images were generated from original non-CECT and CECT images, respectively. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were calculated. Visual Turing tests were used to determine whether oral and maxillofacial radiologists could tell the difference between synthetic versus original images, and receiver operating characteristic (ROC) analyses were used to assess the radiologists' performances in discriminating lymph nodes from blood vessels. The PSNR of non-CECT images was higher than that of CECT images, while the SSIM was higher in CECT images. The Visual Turing test showed a higher perceptual quality in CECT images. The area under the ROC curve showed almost perfect performances in synthetic as well as original CECT images. In conclusion, synthetic CECT images created by cycleGAN appeared to have the potential to provide effective information in patients who could not receive contrast enhancement.

The utilisation of teledentistry in Australia: A systematic review and meta-analysis

BACKGROUND

Teledentistry is the usage of information-based technologies to deliver healthcare services remotely. It is used to deliver care in regional, rural and remote regions and was particularly useful to deliver care during the COVID-19 pandemic.

OBJECTIVE

This systematic review and meta-analysis aimed to determine teledentistry utilisation in Australia.

METHODS

The databases PubMed, Google Scholar, EMBASE and Web of Science were searched from inception to June-2024. The phrases "Dental" AND "Telehealth" AND "Australia" and "Teledentistry" AND "Australia" were used. Two authors completed the study selection and data extraction. The Joanna Briggs Institute Critical Appraisal Tools were used to assess quality and bias.

RESULTS

Eighteen articles met the inclusion criteria. There were six diagnostic tests, six cross-sectional studies, 4 economic evaluations, one qualitative study and one expert opinion. Teledentistry was accurate for screening caries (average sensitivity=69.7 %, average specificity=97.4 %). There also appeared to be a non-significant negative correlation between specificity and sensitivity (r = 0.432). Opinions regarding teledentistry were mixed from clinicians but positive from patients. Teledentistry may also lead to savings for patients and healthcare providers.

CONCLUSION

Teledentistry increases healthcare access especially for people in regional, rural and remote areas. It is an effective screening tool for caries. Whilst the opinions of clinicians were mixed, potential implementation barriers were identified which could improve opinions of clinicians and increase implementation.

CLINICAL IMPORTANCE

This study demonstrates teledentistry as a satisfactory tool for screening caries. This could be beneficial to those with difficulties visiting dentists in-person, particularly if they live in regional, rural or remote areas.

An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.8B. With an aging population, the rate of osteoporotic VCFs and their associated burdens are expected to rise. Those burdens include pain, functional impairment, and increased medical expenditure. Therefore, it is of utmost importance to develop an analytical tool to aid in the identification of VCFs. Computed Tomography (CT) imaging is commonly used to detect occult injuries. Unlike the existing VCF detection approaches based on CT, the standard clinical criteria for determining VCF relies on the shape of vertebrae, such as loss of vertebral body height. We developed a novel automated vertebrae localization, segmentation, and osteoporotic VCF detection pipeline for CT scans using state-of-the-art deep learning models to bridge this gap. To do so, we employed a publicly available dataset of spine CT scans with 325 scans annotated for segmentation, 126 of which also graded for VCF (81 with VCFs and 45 without VCFs). Our approach attained 96% sensitivity and 81% specificity in detecting VCF at the vertebral-level, and 100% accuracy at the subject-level, outperforming deep learning counterparts tested for VCF detection without segmentation. Crucially, we showed that adding predicted vertebrae segments as inputs significantly improved VCF detection at both vertebral and subject levels by up to 14% Sensitivity and 20% Specificity (p-value = 0.028).

Development and validation of the premenstrual dysphoric disorder scale in Indian females

Background

Premenstrual dysphoric disorder is a mood disorder that has been recognized in the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition (DSM-5) as a subcategory of depressive disorders. The disorder has a high prevalence rate, and yet no uniform measure is standardized on the Indian population.

Aim

This study aimed to develop a scale to assess premenstrual dysphoric disorder that can be used for screening and for clinical use and that is standardized on the Indian population.

Methods

This study used cross-sectional research design and collected data through social media platforms from a sample of 300 females, out of which 38 were excluded for pregnancy, mental health conditions, substance use, and oral contraceptive use.

Results

The scale was analyzed using SPSS 21.0 for descriptive statistics, internal consistency, and convergent, content (factor analysis), and divergent validity. Results showed a high reliability of 0.90 and a concurrent validity of 0.76 and 0.64 against PSTT and BDI. Factor analysis revealed four factors, namely, affective and cognitive functioning, daily life and occupational functioning, somatic symptoms, and interpersonal anger. The sensitivity and specificity of the scale were found to be 86 (86%) and 66 (66%), respectively.

Conclusion

The scale developed in this study was found reliable and valid for use on the Indian population. It has high sensitivity and can be used as a screening measure for premenstrual dysphoric disorder.

Diagnostic Accuracy of Subjective Features and Physical Examination Tests for Morton Neuroma: A Systematic Review

Background

Morton neuroma (MN) is a common pathology with many reported subjective features and physical examination tests. The objective of this systematic review was to examine the diagnostic accuracy of subjective features and physical examination tests for MN.

Methods

CINAHL, CENTRAL, EMBASE, MEDLINE, PubMed, Opengrey, PEDro, PsycINFO, Scopus and Trials register were searched in January 2021. Two reviewers independently screened studies for inclusion using the following criteria: (1) prospective or retrospective cohort studies, (2) participants aged ≥18 years with suspected MN, (3) primary data allowing construction of 2 × 2 diagnostic table or reported sensitivity and specificity figures, (4) diagnosis of MN using magnetic resonance imaging, ultrasonography, surgery, positive response to steroid and/or anesthetic injection, and (5) in English or translatable. Quality of included studies was assessed using the Quality Assessment for Diagnostic Accuracy Studies version-2 (QUADAS-2) tool.

Results

The search yielded 1170 results, of which 9 were included in this systematic review. Narrative synthesis revealed that subjective clicking reported by a patient was highly specific (0.96) and had a high positive likelihood ratio (13.14). The modified webspace tenderness test (thumb index finger squeeze test) was highly sensitive (0.96) with a low negative likelihood ratio (0.04). The commonly reported feeling of "walking on a pebble" and "burning pain" had sensitivities of 43% to 53% and 54% to 57% and associated specificities of 52% and 48%, respectively.Only 1 study had low risk of bias. The review was limited by the number of studies that included few or no patients without MN, and the impact this had on the ability to calculate diagnostic accuracy.

Conclusion

There is strong evidence that clicking reported by a patient rules in MN and that the modified webspace tenderness test rules out MN when negative.

A First-in-Class High-Throughput Screen to Discover Modulators of the Alternative Lengthening of Telomeres (ALT) Pathway

Telomeres are a protective cap that prevents chromosome ends from being recognized as double-stranded breaks. In somatic cells, telomeres shorten with each cell division due to the end replication problem, which eventually leads to senescence, a checkpoint proposed to prevent uncontrolled cell growth. Tumor cells avoid telomere shortening by activating one of two telomere maintenance mechanisms (TMMs): telomerase reactivation or alternative lengthening of telomeres (ALT). TMMs are a viable target for cancer treatment as they are not active in normal, differentiated cells. Whereas there is a telomerase inhibitor currently undergoing clinical trials, there are no known ALT inhibitors in development, partially because the complex ALT pathway is still poorly understood. For cancers such as neuroblastoma and osteosarcoma, the ALT-positive status is associated with an aggressive phenotype and few therapeutic options. Thus, methods that characterize the key biological pathways driving ALT will provide important mechanistic insight. We have developed a first-in-class phenotypic high-throughput screen to identify small-molecule inhibitors of ALT. Our screen measures relative C-circle level, an ALT-specific biomarker, to detect changes in ALT activity induced by compound treatment. To investigate epigenetic mechanisms that contribute to ALT, we screened osteosarcoma and neuroblastoma cells against an epigenetic-targeted compound library. Hits included compounds that target chromatin-regulating proteins and DNA damage repair pathways. Overall, the high-throughput C-circle assay will help expand the repertoire of potential ALT-specific therapeutic targets and increase our understanding of ALT biology.

Combination of metal artifact reduction and sharpening filter application for horizontal root fracture diagnosis in teeth adjacent to a zirconia implant

Purpose

This study examined the influence of metal artifact reduction (MAR), the application of sharpening filters, and their combination on the diagnosis of horizontal root fracture (HRF) in teeth adjacent to a zirconia implant on cone-beam computed tomography (CBCT) examinations.

Materials and Methods

Nineteen single-rooted teeth (9 with HRF and 10 without) were individually positioned in the right central incisor socket of a dry human maxilla. A zirconia implant was placed adjacent to each tooth. Imaging was performed using an OP300 Maxio CBCT (Instrumentarium, Tuusula, Finland) unit with the following settings: a current of 8 mA, both MAR modes (enabled and disabled), a 5×5 cm field of view, a voxel size of 0.085 mm, and a peak kilovoltage of 90 kVp. Four oral and maxillofacial radiologists independently evaluated the CBCT scans under both MAR conditions and across 3 levels of sharpening filter application (none, Sharpen 1×, and Sharpen 2×). Diagnostic metrics were calculated and compared using 2-way analysis of variance (α=5%). The weighted kappa test was used to assess intra- and inter-examiner reliability in the diagnosis of HRF.

Results

MAR tool activation, sharpening filter use, and their combination did not significantly impact the area under the receiver operating characteristic curve, sensitivity, or specificity of HRF diagnosis (P>0.05). Intra- and inter-examiner agreement ranged from fair to substantial.

Conclusion

The diagnosis of HRF in a tooth adjacent to a zirconia implant is not affected by the activation of MAR, the application of a sharpening filter, or the combination of these tools.

Development and validation of supervised machine learning multivariable prediction models for the diagnosis of Pneumocystis jirovecii pneumonia using nasopharyngeal swab PCR in adults in a low-HIV prevalence setting

BACKGROUND

The global burden of the opportunistic fungal disease Pneumocystis jirovecii pneumonia (PJP) remains substantial. Polymerase chain reaction (PCR) on nasopharyngeal swabs (NPS) has high specificity and may be a viable alternative to the gold standard diagnostic of PCR on invasively collected lower respiratory tract specimens, but has low sensitivity. Sensitivity may be improved by incorporating NPS PCR results into machine learning models.

METHODS

Three supervised multivariable diagnostic models (random forest, logistic regression and extreme gradient boosting) were constructed and validated using a 111-person Australian dataset. The predictors were age, gender, immunosuppression type and NPS PCR result. Model performance metrics such as accuracy, sensitivity, specificity and predictive values were compared to select the best-performing model.

RESULTS

The logistic regression model performed best, with 80% accuracy, improving sensitivity to 86% and maintaining acceptable specificity of 70%. Using this model, positive and negative NPS PCR results indicated post-test probabilities of 84% (likely PJP) and 26% (unlikely PJP), respectively.

CONCLUSIONS

The logistic regression model should be externally validated in a wider range of settings. As the predictors are simple, routinely collected patient variables, this model may represent a diagnostic advance suitable for settings where collection of lower respiratory tract specimens is difficult but PCR is available.