Prospective Validation Study Research Articles

Malnutrition prevalence according to GLIM and its feasibility in geriatric patients: a prospective cross-sectional study.

In 2019, the Global Leadership Initiative on Malnutrition (GLIM) suggested a 2-step diagnostic format for malnutrition including screening and diagnosis. Prospective validation and feasibility studies, using the complete set of the five GLIM criteria, are needed. The aims of this study were to determine the prevalence of malnutrition, and investigate how the prevalence varied with mode of screening. Furthermore, we assessed the feasibility of GLIM in geriatric patients. Consecutive patients from two acute geriatric wards were included. For screening risk of malnutrition, the Mini Nutritional Assessment-Short Form (MNA-SF) or Malnutrition Screening Tool (MST) were used. In accordance with GLIM, a combination of phenotypic and etiologic criteria were required for the diagnosis of malnutrition. Feasibility was determined based on % data completeness, and above 80% completeness was considered feasible. One hundred patients (mean age 82years, 58% women) were included. After screening with MNA-SF malnutrition was confirmed by GLIM in 51%, as compared with 35% after screening with MST (p = 0.039). Corresponding prevalence was 58% with no prior screening. Using hand grip strength as a supportive measure for reduced muscle mass, 69% of the patients were malnourished. Feasibility varied between 70 and 100% for the different GLIM criteria, with calf circumference as a proxy for reduced muscle mass having the lowest feasibility. In acute geriatric patients, the prevalence of malnutrition according to GLIM varied depending on the screening tool used. In this setting, GLIM appears feasible, besides for the criterion of reduced muscle mass.

Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review

This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification. Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting. This review highlights the need for transparency to facilitate the critical appraisal of models prior to clinical implementation, to minimize bias and inappropriate use. Transparent reporting can improve effective and equitable use in clinical settings. Eligible articles (as of January 2022) from PubMed, Embase, Web of Science, and CINAHL were independently screened by 2 reviewers. All observational and clinical trial studies evaluating the performance of an AI model for disease classification of ophthalmic conditions were included. Studies were evaluated for reporting of parameters derived from reporting guidelines (CONSORT-AI, MI-CLAIM) and our previously published editorial on model cards. The reporting of these factors, which included basic model and dataset details (source, demographics), and prospective validation outcomes, were summarized. Thirty-seven prospective validation studies were included in the scoping review. Eleven additional associated training and/or retrospective validation studies were included if this information could not be determined from the primary articles. These 37 studies validated 27 unique AI models; multiple studies evaluated the same algorithms (EyeArt, IDx-DR, and Medios AI). Details of model development were variably reported; 18 of 27 models described training dataset annotation and 10 of 27 studies reported training data distribution. Demographic information of training data was rarely reported; 7 of the 27 unique models reported age and gender and only 2 reported race and/or ethnicity. At the level of prospective clinical validation, age and gender of populations was more consistently reported (29 and 28 of 37 studies, respectively), but only 9 studies reported race and/or ethnicity data. Scope of use was difficult to discern for the majority of models. Fifteen studies did not state or imply primary users. Our scoping review demonstrates variable reporting of information related to both model development and validation. The intention of our study was not to assess the quality of the factors we examined, but to characterize what information is, and is not, regularly reported. Our results suggest the need for greater transparency in the reporting of information necessary to determine the appropriateness and fairness of these tools prior to clinical use. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Ophthalmic artery Doppler at 36 weeks' gestation in prediction of pre-eclampsia: validation and update of previous model.

To validate and extend a model incorporating maternal ophthalmic artery Doppler at 35-37 weeks' gestation in the prediction of subsequent development of pre-eclampsia (PE). This was a prospective validation study of screening for PE (defined according to the 2019 American College of Obstetricians and Gynecologists criteria) by maternal ophthalmic artery peak systolic velocity (PSV) ratio in 6746 singleton pregnancies undergoing routine care at 35 + 0 to 36 + 6 weeks' gestation (validation dataset). Additionally, the data from the validation dataset were combined with those of 2287 pregnancies that were previously used for development of the model (training dataset), and the combined data were used to update the original model parameters. The competing-risks model was used to estimate the individual patient-specific risk of delivery with PE at any time and within 3 weeks from assessment by a combination of maternal demographic characteristics and medical history with PSV ratio alone and in combination with the established PE biomarkers of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), serum placental growth factor (PlGF) and serum soluble fms-like tyrosine kinase-1 (sFlt-1). We evaluated the predictive performance of the model by examining, first, the ability to discriminate between the PE and non-PE groups using the area under the receiver-operating-characteristics curve and the detection rate (DR) at fixed screen-positive (SPR) and false-positive rates of 10% and, second, calibration by measuring the calibration slope and calibration-in-the-large. McNemar's test was used to compare the performance of screening by a biophysical test (maternal factors, MAP, UtA-PI and PSV ratio) vs a biochemical test (maternal factors, PlGF and sFlt-1), low PlGF concentration (< 10th percentile) or high sFlt-1/PlGF concentration ratio (> 90th percentile). In the validation dataset, the performance of screening by maternal factors and PSV ratio for delivery with PE within 3 weeks and at any time after assessment was consistent with that in the training dataset, and there was good agreement between the predicted and observed incidence of PE. In the combined data from the training and validation datasets, good prediction for PE was achieved in screening by a combination of maternal factors, MAP, UtA-PI, PlGF, sFlt-1 and PSV ratio, with a DR, at a 10% SPR, of 85.0% (95% CI, 76.5-91.4%) for delivery with PE within 3 weeks and 65.7% (95% CI, 59.2-71.7%) for delivery with PE at any time after assessment. The performance of a biophysical test was superior to that of screening by low PlGF concentration or high sFlt-1/PlGF concentration ratio but not significantly different from the performance of a biochemical test combining maternal factors with PlGF and sFlt-1 for both PE within 3 weeks and PE at any time after assessment. Maternal ophthalmic artery PSV ratio at 35-37 weeks' gestation in combination with other biomarkers provides effective prediction of subsequent development of PE. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Translating the potential of the urine steroid metabolome to stage NAFLD (TrUSt-NAFLD): study protocol for a multicentre, prospective validation study

IntroductionNon-alcoholic fatty liver disease (NAFLD) affects approximately one in four individuals and its prevalence continues to rise. The advanced stages of NAFLD with significant liver fibrosis are associated with adverse...

Pulmonary embolism risk stratification: external validation of the 4-level Clinical Pretest Probability Score (4PEPS)

BackgroundThe 4-level clinical pretest probability score (4PEPS) was recently introduced as a clinical decision rule for the diagnosis of pulmonary embolism (PE). Based on the score, patients are classified into clinical pretest probability categories (c-PTP). The “very low” category aims at excluding PE without further testing; “low” and “moderate” categories require D-dimer testing with specific thresholds, while patients with a “high” pretest directly proceed to imaging. ObjectivesTo provide further external validation of the 4PEPS model. MethodsThe 4PEPS was applied to a previously collected prospective database of 756 patients with clinically suspected PE enrolled from European emergency departments in 2002 to 2003. The safety threshold for the failure rate in our study was calculated at 1.95% based on a 26% prevalence of PE in our study, as per the International Society on Thrombosis and Haemostasis Scientific and Standardization Committee guidance. ResultsPatients were classified as follows: 90 (12%) in the very low c-PTP group, of whom 5 (5.6%; 95% CI, 2.4%-12.4%) had PE; 363 (49%) in the low c-PTP group, of whom 34 had PE (9.4%); 246 (34%) in the moderate c-PTP group, of whom 124 (50%) had PE; and 35 (5%) in the high c-PTP group of whom 30 (86%) had PE. Overall, the failure rate of the 4PEPS was 9/734 (1.2%; 95% CI, 0.59%-2.23%) Overall, 9 out of 734 patients (1.2%; 95% CI, 0.59%-2.23%) were diagnosed with PE despite a negative 4PEPS rule; 5 (5.6%) from the very low c-PTP group, 3 (1.4%) in the low c-PTP group, and 1 (3.2%) in the moderate c-PTP group. ConclusionWe provide external validation data of the 4PEPS. In this high-prevalence cohort (26% prevalence), PE prevalence in the very low-risk group was higher than expected. A prospective validation study is needed before implementing the 4PEPS model in routine clinical practice.

Assessing insomnia after stroke: a diagnostic validation of the Sleep Condition Indicator in self-reported stroke survivors.

Insomnia is common after stroke and is associated with poorer recovery and greater risk of subsequent strokes. Yet, no insomnia measures have been validated in English-speaking individuals affected by stroke. This prospective diagnostic validation study investigated the discriminatory validity and optimal diagnostic cut-off of the Sleep Condition Indicator when screening for Diagnostic and Statistical Manual of Mental Disorders-fifth edition (DSM-5) insomnia disorder post-stroke. A convenience sample of 180 (60.0% women, mean age=49.61 ± 12.41 years) community-based, adult (≥18 years) self-reported stroke survivors completed an online questionnaire. Diagnosis of DSM-5 insomnia disorder was based on analysis of a detailed sleep history questionnaire. Statistical analyses explored discriminant validity, convergent validity, relationships with demographic and mood variables, and internal consistency. Receiver operating characteristic curves were plotted to assess diagnostic accuracy. Data from the sleep history questionnaire suggested that 75 participants (41.67%) met criteria for DSM-5 insomnia disorder, 33 (18.33%) exhibited symptoms of insomnia but did not meet diagnostic criteria, and 72 (40.0%) had no insomnia symptoms at the time of assessment. The Sleep Condition Indicator (SCI) demonstrated 'excellent' diagnostic accuracy in the detection of insomnia post-stroke, with an area under the curve of 0.86 (95% CI (0.81, 0.91)). The optimal cut-off was determined as being ≤13, yielding a sensitivity of 88.0% and a specificity of 71.43%. The findings of this study demonstrate the SCI to be a valid and reliable method with which to diagnose DSM-5 insomnia disorder and symptoms post-stroke. However, a lower threshold than is used in the general population may be necessary after stroke.

Algorithm Development and Early Performance Evaluation of a Next-Generation Multitarget Stool DNA Screening Test for Colorectal Cancer

Background and AimsThe multitarget stool DNA (mt-sDNA) assay is a non-invasive average-risk colorectal cancer (CRC) screening test. A new biomarker panel was developed for a next-generation test to improve specificity while maintaining/increasing sensitivity. We aimed first to establish an algorithm and cutoff for the next-generation mt-sDNA test and then to validate it using archived samples from the pivotal DeeP-C study (NCT01397747) of the first-generation test. MethodsAlgorithm development and cross-validation included 3011 samples from 2 specimen collection studies (NCT03821948, NCT03789162). The algorithm and cutoff were locked before validation. Validation test set samples included 57 CRC, 583 advanced precancerous lesions (APLs), and 7022 samples negative for CRC or APLs from the DeeP-C study, which prospectively enrolled average-risk, asymptomatic adults aged 50 to 84 years before screening colonoscopy. Next-generation biomarkers included methylated DNA markers LASS4, LRRC4, PPP2R5C, and ZDHHC1 (reference marker), and fecal hemoglobin. Primary validation endpoints were CRC sensitivity and specificity for the absence of advanced neoplasia. Secondary endpoints included APL sensitivity and specificity for non-neoplastic findings or negative colonoscopy. ResultsCross-validation and best-fit results from algorithm development closely matched, confirming algorithm reliability and reproducibility. For the test set, next-generation mt-sDNA test sensitivity was 93.0% (95% CI, 83.0%-98.1%) for CRC and 48.4% (95% CI, 44.2%-52.5%) for APLs. Specificity was 88.5% (95% CI, 87.7%-89.2%) for the absence of advanced neoplasia and 90.4% (95% CI, 89.5%-91.2%) for the combination of non-neoplastic findings or negative colonoscopy. ConclusionsBased on archived samples, the next-generation mt-sDNA test demonstrated promising CRC screening performance characteristics that will be further assessed in a prospective clinical validation study (BLUE-C; NCT04144738).

Role of C-Reactive Proteins at Early Gestational Age in Predicting Pre-Eclampsia in Pregnant Females

Background: Pre-eclampsia remains a significant concern in obstetrics due to its potential complications for both mother and fetus. Early detection of pre-eclampsia (PE0 is crucial for timely intervention and management. C-Reactive Protein (CRP), an inflammatory marker, has shown promise as a potential predictor of pre-eclampsia when assessed during early gestational age. Objective: To evaluate the diagnostic accuracy of raised serum CRP levels in early gestation to predict the risk of pre-eclampsia in pregnant females. Study Design: Prospective validation Study. Settings: Outdoor Department of obstetrics and gynecology, Lady Atchison Hospital Lahore. Duration: 02-September 2021 to 01-March-2022. Methods: A total of 130 pregnant females having gestational age 16 to 20 weeks and presenting in the hospital for regular antenatal visits were included. A five ml venous blood sample was drawn and was sent to the central hospital laboratory for determination of CRP levels from each case. Patients having CRP ≥5.0 mg/L were labelled as high risk of pre-eclampsia. All patients followed till delivery to determine the development of pre-eclampsia. Results: The mean age of the patients was found to be 28.16±4.74 years, with a mean BMI of 26.27±5.05 kg/m². Among the 130 patients, 12 (9.23%) were identified as having elevated CRP levels, while the remaining 118 (90.77%) showed normal CRP levels. Additionally, pre-eclampsia was diagnosed in 11 (8.46%) of the patients. Evaluation of the diagnostic accuracy of CRP levels revealed a sensitivity of 81.8% and specificity of 97.5%. The positive predictive value (PPV) was calculated at 75%, while the negative predictive value (NPV) stood at 98.3%. Conclusion: The study observed that elevated CRP levels in early pregnancy serve as a significant predictor of pre-eclampsia, with sensitivity of 81.8%, indicating its ability to correctly identify those with pre-eclampsia, while the specificity was 97.5%, suggesting its accuracy in ruling out those without the condition.

Optically Guided High-Frequency Ultrasound Shows Superior Efficacy for Preoperative Estimation of Breslow Thickness in Comparison with Multispectral Imaging: A Single-Center Prospective Validation Study.

Melanoma is the most aggressive form of skin cancer that is known for its metastatic potential and has an increasing incidence worldwide. Breslow thickness, which determines the staging and surgical margin of the tumor, is unavailable at initial diagnosis. Novel imaging techniques for assessing Breslow thickness lack comparative data. This study evaluates optically guided high-frequency ultrasound (OG-HFUS) and multispectral imaging (MSI) for preoperative estimation of Breslow thickness and staging. We enrolled 101 patients with histologically confirmed primary melanoma and categorized them based on tumor thickness. Optically guided 33 MHz HFUS and MSI were utilized for the assessment. Our MSI-based algorithm categorized melanomas into three subgroups with a sensitivity of 62.6%, specificity of 81.3%, and fair agreement (κ = 0.440, CI: 0.298-0.583). In contrast, OG-HFUS demonstrated a sensitivity of 91.8%, specificity of 96.0%, and almost perfect agreement (κ = 0.858, CI: 0.763-0.952). OG-HFUS performed better than MSI in estimating Breslow thickness, emphasizing its potential as a valuable tool for melanoma diagnosis and patient management. OG-HFUS holds promise for enhancing preoperative staging and treatment decision-making in melanoma.

Adding a Coefficient for Race to the 4Kscore Improves Calibration for Black Men.

Black men face a higher incidence of high-risk prostate cancer (PCa) compared with non-Black men. While the 4Kscore is a widely utilized commercial test for PCa risk assessment, it does not currently account for racial differences. The aim of this study is to describe and validate a prespecified race coefficient for the 4Kscore with the goal of improving the accuracy of this test for Black men. Using data from 85 Black men from the initial US prospective validation study, a race coefficient of 0.6 on the log-odds scale was prespecified. We calculated discrimination, calibration, and clinical utility of the 4Kscore with and without this coefficient for Black race in our primary analysis cohort of 205 Black men undergoing biopsy for PCa in a Veterans Affairs (VA) institution. We performed a sensitivity analysis using a combined cohort from the US prospective validation and the VA studies. The mean probability of high-grade PCa from the 4Kscore in the primary cohort increased from 25% to 37% with race coefficient addition. Incorporating the race coefficient improved 4Kscore's calibration in Black men, with consequent improvements in clinical utility based on decision curve analysis. Model discrimination was maintained (AUC 0.825 vs 0.828, P = .14) in the combined cohort of Black and non-Black men from the US prospective and VA studies and the calibration remained largely unchanged. Incorporating a prespecified coefficient for Black race improved calibration and clinical utility of the 4Kscore among Black men and should be added to the 4Kscore.

Organ damage is a major determinant of work productivity impairment in Behçet’s syndrome: a post hoc analysis of the BODI validation study

Abstract Objectives To evaluate the prevalence, magnitude and potential determinants of work productivity impairment in patients with Behçet’s syndrome (BS), focusing on the role of irreversible organ damage. Methods A post hoc analysis of the BS Overall Damage Index (BODI) prospective validation study was performed. Demographics and clinical features were recorded in all patients. The Work Productivity and Activity Impairment: General Health (WPAI:GH) questionnaire was administered to assess the work limitation and the BODI to measure organ damage. The independent effect of BS features on WPAI:GH outcomes was evaluated by regression analysis. Results Of 148 patients, 34.5% were unemployed, with age [odds ratio (OR) 1.035] and BODI score (OR 1.313 for a 1-unit increase) as the only factors significantly (P &amp;lt; 0.05) associated with the unemployment state. Overall work impairment was reported in ≈64.2% of the employed patients. Indeed, 22.7% reported missing work hours due to their health (absenteeism), with a mean time loss of 34.4%, whereas 60.2% declared reduced performance at work because of their health (presenteeism), with a mean productivity impairment of 45.4%. Ocular damage was associated with absenteeism (β = 0.225); female sex (β = 0.260), physician global assessment of disease activity (β = 0.502) and an increased BODI score (β = 0.166 for 1-point increase) with presenteeism; and fibromyalgia (β = 0.246), Physician Global Assessment (β = 0.469) and musculoskeletal damage (β = 0.325) with overall work impairment. Conclusions Disease activity and organ damage accrual remarkably affect work productivity in BS patients. Achieving remission and preventing damage accrual are crucial and complementary objectives.

Comparison between ultrasonography and computed tomography for measuring skeletal muscle mass in critically ill patients with different body mass index

Among critical patients, there is an early onset of changes in both the quantity and quality of muscle mass. It is essential to find tools that promptly identify this muscle mass loss. The aim of this study was to compare the ultrasonography of the quadriceps femoris to the gold standard, thigh computed tomography (CT) for assessing the musculature of critically ill patients with different body mass index who have suffered traumatic brain injury. This is a prospective validation study in an Intensive Care Unit (ICU) specialized in trauma care, located at a tertiary teaching hospital. Our study involved a convenience sample of patients. Sequential ultrasound and CT scans were performed at three distinct time intervals: upon admission, between 24 and 96h' post-admission, and finally, between 96 and 168h' post-admission. For all ultrasound measurements, we conducted simultaneous quadriceps CT measurements. The correlation between measurements obtained by ultrasound and computed tomography at three different times and in three BMI ranges was analyzed, in individuals with normal weight, overweight and obese. Results: We analyzed 252 images in 49 patients in time 1, 40 patients in time 2, and 37 in time 3 to compare the thickness quadriceps muscle using US and CT. Of these, 18 patients had a BMI ≤ 24.9kg/m2 (normal weight), 18 patients from 25 to 29.9kg/m2 (overweight), and 8 patients had a BMI ≥ 30kg/m2 (obese). The mean age was 37 years, the majority (94%) were male and the main comorbidities were: hypertension 12%, diabetes 4% and 14% smoking. The results revealed minor discrepancies between measurements obtained through the two methods, these changes were not influenced by the body mass index, with these variations being practically insignificant in the context of clinical application. Thus, the correlation and concordance between the values obtained found a strong positive correlation with good limits of agreement. The Spearman's correlation coefficients obtained were r=0.89, 0.91 and 0.88, p<0.01at T1, T2 and T3 respectively for normal weight, r=0.91, 0.80 and 0.81, p<0.01at T1, T2 and T3 respectively for overweight and r=0.89, 0.94 and 0.84, p<0.01at T1, T2 and T3 respectively for obesity. In addition to a positive correlation, we observed a high agreement between the methods. The Bland & Altman analysis at time 1 showed, respectively, the bias of 1.46, 2.03 and 0.76. At time 2, the bias was 0.42, 3.11 and 2.12. At time 3, the bias was 2.26, 3.38 and 2.11mm. Our findings suggest that measure femoral quadriceps muscle thickness ultrasound-based exhibits a comparable performance to thigh CT. This conclusion stems from the excellent correlation and good agreement observed between ultrasound and CT, which is considered the gold standard for muscle assessment in critically ill patients. This clinical trial is registered at REBEC https://ensaiosclinicos.gov.br/ identifier: RBR-2bzspnz. The protocol was approved, on July 30, 2019, by the Research Ethics Committee of the Hospital das Clínicas, Faculdade de Medicina de Ribeirão Preto - Trial Registration Number: 3,475,851.

DeltaScan for the Assessment of Acute Encephalopathy and Delirium in ICU and non-ICU Patients, a Prospective Cross-Sectional Multicenter Validation Study

To measure the diagnostic accuracy of DeltaScan: a portable real-time brain state monitor for identifying delirium, a manifestation of acute encephalopathy (AE) detectable by polymorphic delta activity (PDA) in single-channel electroencephalograms (EEGs). Prospective cross-sectional study. Six Intensive Care Units (ICU's) and 17 non-ICU departments, including a psychiatric department across 10 Dutch hospitals. 494 patients, median age 75 (IQR:64-87), 53% male, 46% in ICUs, 29% delirious. DeltaScan recorded 4-minute EEGs, using an algorithm to select the first 96 seconds of artifact-free data for PDA detection. This algorithm was trained and calibrated on two independent datasets. Initial validation of the algorithm for AE involved comparing its output with an expert EEG panel's visual inspection. The primary objective was to assess DeltaScan's accuracy in identifying delirium against a delirium expert panel's consensus. DeltaScan had a 99% success rate, rejecting 6 of the 494 EEG's due to artifacts. Performance showed and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.86 (95% CI: 0.83-0.90) for AE (sensitivity: 0.75, 95%CI=0.68-0.81, specificity: 0.87 95%CI=0.83-0.91. The AUC was 0.71 for delirium (95%CI=0.66-0.75, sensitivity: 0.61 95%CI=0.52-0.69, specificity: 72, 95%CI=0.67-0.77). Our validation aim was an NPV for delirium above 0.80 which proved to be 0.82 (95%CI: 0.77-0.86). Among 84 non-delirious psychiatric patients, DeltaScan differentiated delirium from other disorders with a 94% (95%CI: 87-98%) specificity. DeltaScan can diagnose AE at bedside and shows a clear relationship with clinical delirium. Further research is required to explore its role in predicting delirium-related outcomes.

Comparison of Video Nasopharyngoscopy and Tympanometry in Suspected Eustachian Tube Dysfunction - A Prospective Study.

Objective Tympanometry is widely used for diagnosing Eustachian tube dysfunction (ETD). However, in clinical practice, it is not uncommon to come across patients with symptoms of ETD with a normal tympanogram. Dynamic slow motion video endoscopy (DSMVE) of Eustachian tube (ET) and Eustachian tube dysfunction questionnaire-7 (ETDQ-7) are other tools to diagnose ETD. Primary aim of the study was to compare DSMVE and tympanometry in diagnosing ETD. Secondary objective was to find an association between DSMVE and ETDQ-7. It was a prospective diagnostic validity study in the department of Otorhinolaryngology at a tertiary care centre. Patients with symptoms suggestive of ETD with intact tympanic membrane (TM) and aged above 12 years were evaluated. The study duration was 17 months. The parameters assessed were tympanic membrane(TM) retraction by otoscopy, DSMVE, tympanometry and ETDQ-7 questionnaire. To test the significance of association and difference between tympanogram and nasopharyngoscopy, Chi-Square t test and McNemar's tests were applied. Out of 107 ears, DSMVE, tympanometry, ETDQ- 7 and otoscopy detected ETD in 51, 31,70 and 57 ears respectively. DSMVE and tympanometry together detected ETD in 29 ears and did not detect in 54 ears. Both these diagnostic methods were not comparable statistically (moderate agreement- Kappa value- 0.542). Comparison of DSMVE with ETDQ-7 was statistically significant (p-value- 0.004). Video nasopharyngoscopy cannot be a stand- alone diagnostic aid for ETD. ETDQ-7 questionnaire showed higher concordance with nasopharyngoscopy than tympanometry. Diagnostic accuracy was more when all four parameters were assessed together.

Feasibility of ADappt: towards personalized communication and prediction in early Alzheimer’s disease in memory clinics

AbstractBackgroundADappt is a Web‐based toolkit (www.ADappt.health) to support personalized communication and prediction in memory clinics. In the context of an iterative development process with professionals, patients, and care partners, we assessed feasibility, satisfaction, and usability of ADappt in the clinical workflow.MethodWe conducted a feasibility pilot in four Dutch memory clinics. Three neurologists, one geriatrician, two specialized nurses and two medical doctors (12±7y clinical experience) participated and recruited 21 patients (69±8yrs, 19%F) and 21 care partners (60±15yrs, 81%F). For professionals, ADappt‐modules comprised a 1) topic list, 2) shared decision‐making conversation guide, 3) overview of diagnostic tests, and 4) risk calculation tool. Prior to their visit, patients and care partners were informed about ADappt‐patient tools: 1) educational video‐animations, and 2) question prompt list. All participants completed questionnaires on feasibility, satisfaction, and usability after the first (n = 17) and/or the disclosure (n = 7) consultation. Professionals were interviewed afterward.ResultProfessionals reported good usability (68±14, range 50‐83, System Usability Scale, 0‐100) and high satisfaction (71±10, range 50‐85, scale 0‐100). They most often used the overview of diagnostic tests (67%) and reported this module as most helpful (3.8±0.4, range 3‐4, scale 1‐5). Patients and care partners were highly satisfied with the clinician‐patient consultations in general (81±14, range 50‐100, Patient Satisfaction Questionnaire, scale 0‐100). Of those who reported to know and have used the patient tools (54%), 97% would recommend them, whereby most used the question prompt list (84%) and a smaller proportion the video‐animations (64%). Recommendations from the interviews to improve feasibility include: 1) the development of more concrete instructions for professionals on how to use ADappt, and 2) a better alignment of patient tools with the patient journey and general information provided by that particular memory clinic.ConclusionStakeholders reported good usability and high satisfaction regarding ADappt. Points of attention to foster feasibility in the clinical workflow are: 1) improved training for professionals and 2) improving the accessibility of patient tools. We take these matters into account while preparing for the next step: a prospective validation study in the memory clinic setting to study the influence of ADappt on patient satisfaction.

ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) trial study protocol: a prospective multicenter observational study for validation of a deep learning-based 12-lead electrocardiogram analysis model for detecting acute myocardial infarction in patients visiting the emergency department.

Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department. Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board. This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments. Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022.

Predicting recurrence and survival in patients with non-metastatic renal-cell carcinoma after nephrectomy: a prospective population-based study with multicenter validation.

Accurate prognostication of oncological outcomes is crucial for the optimal management of patients with renal cell carcinoma (RCC) after surgery. Previous prediction models were developed mainly based on retrospective data in the Western populations, and their predicting accuracy remains limited in contemporary, prospective validation. We aimed to develop contemporary RCC prognostic models for recurrence and overall survival (OS) using prospective population-based patient cohorts and compare their performance with existing, mostly utilized ones. In this prospective analysis and external validation study, the development set included 11 128 consecutive patients with non-metastatic RCC treated at a tertiary urology center in China between 2006 and 2022, and the validation set included 853 patients treated at 13 medical centers in the USA between 1996 and 2013. The primary outcome was progression-free survival (PFS), and the secondary outcome was OS. Multivariable Cox regression was used for variable selection and model development. Model performance was assessed by discrimination [Harrell's C-index and time-dependent areas under the curve (AUC)] and calibration (calibration plots). Models were validated internally by bootstrapping and externally by examining their performance in the validation set. The predictive accuracy of the models was compared with validated models commonly used in clinical trial designs and with recently developed models without extensive validation. Of the 11 128 patients included in the development set, 633 PFS and 588 OS events occurred over a median follow-up of 4.3 years [interquartile range (IQR) 1.7-7.8]. Six common clinicopathologic variables (tumor necrosis, size, grade, thrombus, nodal involvement, and perinephric or renal sinus fat invasion) were included in each model. The models demonstrated similar C-indices in the development set (0.790 [95% CI 0.773-0.806] for PFS and 0.793 [95% CI 0.773-0.811] for OS) and in the external validation set (0.773 [0.731-0.816] and 0.723 [0.731-0.816]). A relatively stable predictive ability of the models was observed in the development set (PFS: time-dependent AUC 0.832 at 1 year to 0.760 at 9 years; OS: 0.828 at 1 year to 0.794 at 9 years). The models were well calibrated and their predictions correlated with the observed outcome at 3, 5, and 7 years in both development and validation sets. In comparison to existing prognostic models, the present models showed superior performance, as indicated by C-indices ranging from 0.722 to 0.755 (all P <0.0001) for PFS and from 0.680 to 0.744 (all P <0.0001) for OS. The predictive accuracy of the current models was robust in patients with clear-cell and non-clear-cell RCC. Based on a prospective population-based patient cohort, the newly developed prognostic models were externally validated and outperformed the currently available models for predicting recurrence and survival in patients with non-metastatic RCC after surgery. The current models have the potential to aid in clinical trial design and facilitate clinical decision-making for both clear-cell and non-clear-cell RCC patients at varying risk of recurrence and survival.

High-Sensitivity Cardiac Troponin I Enhances Preeclampsia Prediction Beyond Maternal Factors and the sFlt-1/PlGF Ratio.

Preeclampsia shares numerous risk factors with cardiovascular diseases. Here, we aimed to assess the potential utility of high-sensitivity cardiac troponin I (hs-cTnI) values during pregnancy in predicting preeclampsia occurrence. This study measured hs-cTnI levels in 3721 blood samples of 2245 pregnant women from 4 international, prospective cohorts. Three analytical approaches were used: (1) a cross-sectional analysis of all women using a single blood sample, (2) a longitudinal analysis of hs-cTnI trajectories in women with multiple samples, and (3) analyses of prediction models incorporating hs-cTnI, maternal factors, and the sFlt-1 (soluble fms-like tyrosine kinase 1)/PlGF (placental growth factor) ratio. Women with hs-cTnI levels in the upper quarter had higher odds ratios for preeclampsia occurrence compared with women with levels in the lower quarter. Associations were driven by preterm preeclampsia (odds ratio, 5.78 [95% CI, 2.73-12.26]) and remained significant when using hs-cTnI as a continuous variable adjusted for confounders. Between-trimester hs-cTnI trajectories were independent of subsequent preeclampsia occurrence. A prediction model incorporating a practical hs-cTnI level of detection cutoff (≥1.9 pg/mL) alongside maternal factors provided comparable performance with the sFlt-1/PlGF ratio. A comprehensive model including sFlt-1/PlGF, maternal factors, and hs-cTnI provided added value (cross-validated area under the receiver operator characteristic, 0.78 [95% CI, 0.73-0.82]) above the sFlt-1/PlGF ratio alone (cross-validated area under the receiver operator characteristic, 0.70 [95% CI, 0.65-0.76]; P=0.027). As assessed by likelihood ratio tests, the addition of hs-cTnI to each prediction model significantly improved the respective prediction model not incorporating hs-cTnI, particularly for preterm preeclampsia. Net reclassification improvement analyses indicated that incorporating hs-cTnI improved risk prediction predominantly by correctly reclassifying women with subsequent preeclampsia occurrence. These exploratory findings uncover a potential role for hs-cTnI as a complementary biomarker in the prediction of preeclampsia. After validation in prospective studies, hs-cTnI, alongside maternal factors, may either be considered as a substitute for angiogenic biomarkers in health care systems where they are sparce or unavailable, or as an enhancement to established prediction models using angiogenic markers.

Natural language processing and expert follow-up establishes tachycardia association with CDKL5 deficiency disorder

PurposeCDKL5 deficiency disorder (CDD) is a developmental and epileptic encephalopathy with multisystemic comorbidities. Cardiovascular involvement in CDD was shown in animal models but is yet poorly described in CDD cohorts. MethodsWe identified 38 individuals with genetically confirmed CDD through the Cleveland Clinic CDD specialty clinic and matched 190 individuals with non-genetic epilepsy to them as a comparison group. Natural language processing was applied to yield Human Phenotype Ontology (HPO) terms from medical records. We conducted HPO association testing and manual chart review to explore cardiovascular comorbidities associated with CDD. ResultsWe extracted 243,541 HPO terms from 30,512 medical encounters. Phenome-wide analysis confirmed well-established CDD phenotypes and identified association of tachycardia with CDD (Odds ratio 4.2, 95% confidence interval (CI) 1.75-9.93, Padj < .001). We found a 99.6-fold enrichment of supraventricular tachycardia (SVT) in CDD encounter notes (Padj < .001), which led to identification of 2 cases of fetal/neonatal onset SVT previously undescribed in CDD. Tachycardia in CDD individuals was associated with the presence of other autonomic symptoms (Odds ratio 5.63, 95% CI 1.08-40.3, P = .038). ConclusionCDD is associated with tachycardia, potentially including early-onset SVT. Alongside prospective validation studies, semiautomated genotype-phenotype analysis with matched controls is a scalable, rapid, and efficient approach for validating known and identifying novel phenotype associations.

An automated reduced order CT-FFR model for diagnosis of hemodynamically significant coronary artery disease- a prospective validation study

Abstract Background/Introduction CT derived fractional flow reserve (CT-FFR) is introduced in current guidelines as a gatekeeper for invasive coronary angiography (ICA) in symptomatic patients with intermediate stenoses on coronary CT angiography (CCTA). Traditionally, CT-FFR is validated with knowledge of the precise location of invasive FFR measurements to ensure a direct comparison. However, in a clinical setting, this information will not be available and may affect the diagnostic performance of CT-FFR. Purpose To validate the diagnostic performance of a novel physics augmented machine learning algorithm based on reduced order modelling for CT-FFR calculations, with an automated method for defining the site of CT-FFR evaluations. Methods In this prospective validation study, 108 consecutive patients with stable coronary artery disease (CAD) and at least one suspected obstructive stenosis of questionable hemodynamic significance on CCTA were included. All patients underwent a clinically indicated invasive coronary angiography. The diagnostic performance of the CT-FFR algorithm was assessed with invasive FFR at a threshold of ≤ 0,80 as reference standard. CT-FFR at the location of invasive FFR measurements (CT-FFRatloc) was compared with CCTA, CT-FFR at automatically selected locations (CT-FFRauto) and CT-FFR predictions at the distal end of the vessel (CT-FFRdistal). Results CT-FFRatloc showed good correlation with invasive FFR measurements (r=0,67) and demonstrated improved diagnostic performance compared to CCTA diameter stenosis ≥50%. alone both at lesion level (AUC= 0,85 vs 0,63, p&amp;lt;0,01) and at patient level (AUC=0,87 vs 0,74, p&amp;lt;0,01). Both specificity (84% vs 40%, p=0,01) and accuracy (81% vs 63%, p&amp;lt;0,01) of CT-FFRatloc was significantly increased compared to CCTA. CT-FFRauto performed equally well as CT-FFRatloc, both at lesion level (AUC= 0,82 vs AUC= 0,85, p=0,27) and patient level (AUC= 0,86 vs AUC= 0,87(p=0,67). When comparing CT-FFRdistal to CT-FFRatloc the overall diagnostic performance was poorer (AUC=0,79 vs 0,85, p&amp;lt;0,01). Conclusions Compared to CCTA, CT-FFRatloc showed improved diagnostic performance, including accuracy and specificity for the detection of hemodynamically significant coronary artery disease in a symptomatic population. Predictions using an automated location algorithm, CT-FFRauto, was comparable to evaluations at the location of invasive FFR measurements, CT-FFRatloc.Figure 1Figure 2