Interclass Correlation Research Articles

Qualitative and quantitative analysis of 18F-GP1 positron emission tomography in thrombotic cardiovascular disease.

18F-GP1 is a novel highly specific radiotracer that binds to activated platelets and thrombus. We aimed to establish the observer repeatability of coronary, carotid and cerebral 18F-GP1 uptake in patients presenting with acute myocardial infarction or ischaemic stroke. Forty-three patients presenting with acute myocardial infarction or ischaemic stroke underwent hybrid positron emission tomography (PET) and computed tomography (CT) angiography. Qualitative and quantitative assessment of 18F-GP1 uptake was performed on coronary arteries, carotid arteries and brain parenchyma. Qualitative uptake of 18F-GP1 had excellent intraobserver and interobserver agreement, with complete agreement for the presence or absence of visual 18F-GP1 uptake. For quantitative analysis, there were excellent intraclass correlation coefficients for intraobserver repeatability for coronary artery, carotid artery and brain parenchymal SUVmax and TBRmax measurements (all ≥ 0.92). Coronary artery and brain parenchymal analyses showed the strongest agreement in SUVmax values with mean biases of - 0.04 (limits of agreement - 0.21 to 0.20) and 0.02 (limits of agreement - 0.29 to 0.32) respectively. There was good interclass correlation coefficients for interobserver repeatability for coronary artery, carotid artery and brain parenchymal SUVmax and TBRmax measurements (all ≥ 0.89). The strongest interobserver agreement was seen with brain parenchymal SUVmax (mean SUVmax 1.95 ± 0.94) and TBRmax (mean TBRmax 9.55 ± 6.56) with mean biases of - 0.05 (limits of agreement - 0.37 to 0.27) and 0.04 (limits of agreement - 0.59 to 0.52) respectively. Visual qualitative and quantitative 18F-GP1 PET-CT image analyses provide robust and repeatable measurements of activated platelets and thrombi within the coronary arteries, carotid arteries and brain parenchyma.

Enhanced Preoperative Planning for Intracranial Aneurysms Through Multimodal Image Fusion of Silent/Time-of-Magnetic Resonance Angiography and Computed Tomography Using 3DSlicer: A Comparative Efficacy Analysis With Computed Tomography Angiography.

This study evaluates the effectiveness of multimodal image fusion (MIF) using silent and time-of-flight (TOF) magnetic resonance angiography (MRA) and computed tomography (CT) for preoperative planning in patients with intracranial aneurysms who have contraindications to contrast media. A retrospective study included 40 patients with intracranial aneurysms, diagnosed using three-dimensional computed tomography angiography (CTA). These patients underwent both Silent and TOF MRA scans, followed by a CTA scan. The multi-image fusion (MIF) technique, applied using 3DSlicer software, integrated the silent/TOF-MRA with CT images for preoperative assessment. This study compared the image quality, aneurysm detection sensitivity, and anatomic accuracy of the MIF images with those of three-dimensional CTA. Silent-MRA-CT fusion images demonstrated higher sensitivity (95.5%) and lower false negative rates (4.5%) compared with TOF-MRA-CT. Furthermore, silent-MRA-CT fusion images outperformed TOF-MRA-CT in terms of signal homogeneity, venous signal interference suppression, and aneurysm visibility (all P < 0.05). The interclass correlation coefficient and kappa values for aneurysm morphology and shape indicated superior measurement consistency and shape concordance of silent-MRA-CT with CTA compared with TOF-MRA-CT (all P < 0.01). This study supports the use of silent/TOF-MRA-CT fusion imaging as a reliable alternative to CTA, noting that silent-MRA-CT closely mirrors CTA. Contrast-free MRA-CT fusion images have the potential to be used for preoperative planning in patients with intracranial aneurysms who have contraindications to contrast.

Tumour Load in Advanced Ovarian Cancer Patients and Its Validation by Radiological Peritoneal Cancer Index (PCI) Score.

To compare the radiological peritoneal cancer index (PCI) score to the surgical PCI score for validating it as a non-invasive method to predict surgical outcomes. A descriptive study. Place and Duration of the Study: Department ofObstetrics and Gynaecology of the Aga Khan University Hospital, Karachi, Pakistan from September 2021 to May 2022. All successive patients diagnosed with advanced-stageovarian cancer were enrolled in the research. Prior to surgery, the severity of ovarian cancer was evaluated using the Sugarbaker Peritoneal Cancer Index score derived from radiological imaging. The score was compared to the surgical PCI score determined during the surgery. The correlation between the scores and residual tumour status was confirmed. The study included a total of 26 patients. The mean age of patients was 50.17 years, with a standard deviation of 11.04. Five (19.2%) patients underwent surgery first, whereas 21(80.8%) needed interval debulking surgery after receiving chemotherapy in the neoadjuvant setting. The interclass correlation value among radiological and surgical (PCI) was 0.52, with a 95% confidence interval ranging from 0.17 to 0.75. The Bland-Altman plot displays the agreement amongst the PCI scores, indicating a bias of 1.115 with a 95% confidence interval of 4.61. Surgical exploration revealed zero residual disease in 90% of persons with a PCI score <10. Patients with a PCI score <10 had significantly brief operative time and reduced blood loss compared to those with a score >10. Patients with a PCI score below 10 had also fewer complications. PCI is an efficient means for anticipating the success of surgery and the existence of residual disease without invasive measures. This can be very helpful in deciding the best time for surgery. Peritoneal cancer index, Advanced ovarian cancer, Carcinomatosis, Prognosis, Tumour load.

Assessing the Quality and Accuracy of Syphilis-related Content on TikTok and YouTube: A Comprehensive Analysis.

Sexually transmitted infections (STIs), including syphilis, pose a significant public health challenge. The advent of social media platforms has revolutionized health information dissemination, with YouTube and TikTok emerging as prominent sources. However, concerns persist regarding the reliability of syphilis-related content on these platforms. This study aimed to evaluate the quality and accuracy of syphilis-related content on TikTok and YouTube, employing established tools such as DISCERN, Accuracy in Digital-health Instrument (ANDI), and Global Quality Scale (GQS). We conducted a thorough search on TikTok and YouTube on November 26, 2023, using the keyword "syphilis." Inclusion criteria comprised videos in English, less than 20 minutes in duration, and relevance to syphilis. Two dermatologists independently rated 98 eligible videos using DISCERN, ANDI, and GQS. Statistical analyses included Chi-square tests, mean comparisons, and interclass correlation. TikTok videos exhibited higher mean views (222,519 ± 412,746) compared to YouTube (127,527 ± 223,622). However, TikTok videos had lower mean GQS (2.3 ± 0.9), ANDI (2.19 ± 0.99), and DISCERN (28.7 ± 6.56) scores compared to YouTube (GQS: 2.9 ± 1.1, ANDI: 2.90 ± 0.97, DISCERN: 38.8 ± 9). Non-professional uploaders were 40.8% on TikTok, while YouTube were (53.1%). This study reveals disparities in the quality and accuracy of syphilis-related content on TikTok and YouTube. Despite higher popularity on TikTok, content quality, as assessed by DISCERN, ANDI, and GQS, was generally lower compared to YouTube. Targeted interventions are needed to improve the reliability of syphilis-related information on social media platforms.

Fully automated CT analysis with a deep-learning based algorithm for pre-procedural planning in transcatheter aortic valve implantation

Abstract Background Pre-procedural cardiac computed tomography analysis has emerged to an essential tool in transcatheter aortic valve implantation (TAVI). It facilitates the valve choice and allows for optimal vascular access planning. For this purpose, software programs have been specifically developed to assist a fast and precise analysis of the generated data set in high quality. However, these software programs only assist the evaluator and are not able to perform a complete analysis on their own. Purpose We report the performance of a fully-automated, deep-learning based algorithm for pre-procedural CT analysis as compared to the current clinical standard. Methods Ninety-eight patients with symptomatic severe aortic stenosis undergoing TAVI were retrospectively enrolled. The pre-procedural data set was analyzed in two ways: - conventionally with a commercially available TAVI CT-analysis software by experiences users . - by a fully-automated CT-analysis platform with a deep-learning based algorithm. The current beta-version of the platform allows the determination of the annulus diameter, -area, -perimeter and the distance measurements of the coronary arteries to the aortic annulus. Hence, analyzable parameters were limited to the aforementioned, to ensure comparability. Statistical analysis: Mean, standard deviation, mean of difference and the interclass-correlation coefficient (ICC) were calculated and Bland-Altman plots created. Results The mean annulus diameter was 24.5±2.5mm (conventional) vs. 24.1±2.4 mm (deep learning). The mean of difference was 0.42±0.82mm (Bland-Altmann diagram. Figure 1). The ICC was 0.964 (0.925; 0.980), showing an excellent correlation between the two values. The mean annulus perimeter was 77.7±7.4mm (conventional) vs. 75.6±9.3mm (deep learning), showing a very good correlation of the means [mean of difference: 2.11±5.75mm; ICC: 0.852 (0.759; 0.906)]. The distance from the annulus to the left coronary artery was 14.0±3.2mm (conventional) vs. 12.6±2.8mm (deep learning). We depicted a good correlation between the two values [mean of difference: 1.45±2.52mm; ICC 0.736 (IQR 0.483; 0.850)]. The distance from the annulus to the right coronary artery was 17.1±2.7mm (conventional) vs. 16.5±3.2mm (deep learning), respectively. A very good correlation between the two values could be shown [mean of difference 0.57±2.2mm; ICC 0.822 (0.730; 0.882)], as well. Additionally, both methods visualize the measurements and allow manual adjustments (Figure 2). Conclusion In this retrospective analysis, deep-learning based analysis of peri-procedural derived CT data sets showed an very good correlation with conventional assessment. If results can be sustained in a bigger patient cohort and extended to more complex measurements, this artificial intelligence based fully-automated CT-analysis could emerge to a valuable alternative to conventional CT assessment in the pre-procedural planning for TAVI.Figure 1 - Bland-Altman plotFigure 2- Measurements by both methods

CT-perfusion quantitation of cardiac infarct size - a novel technique

Abstract Introduction Cardiac infarct size is associated with cardiac prognosis after AMI. Cardiac CT is able to delineate both coronary anatomy and myocardial pefusion. Thus far, CT identification of infarction has been qualitative. We describe a novel technique for quantitation of myocardial infarct size in CT-perfusion imaging. Methods Patient Population comprised 51 patients that were previously clinically diagnosed with myocardial infarction on the basis of clinical presentation, ECG findings and elevated cardiac enzymes. CTP Imaging All patients had undergone dynamic rest, vasodilator-stress, rest, delayed enhancement CT perfusion imaging performed on a dual-source, third generation scanner (FORCE, Siemens Healthcare). Image Analysis 2 trained investigators independently quantified the infarct volume in 51 patient studies using Siemens SmartSimulator software. Mean ± SD HU were taken from a 1cm² circular ROI of normal myocardium used in a thresholding method. The epicardium and endocardium of infarcted areas were manually contoured from apex to base. Volumetric measurements were obtained of the infarct and total myocardium. Interpolation of contours for segmentation objects was performed. Subtraction of left ventricle cavity from total left ventricle allowed volumetric measurements of total left ventricular myocardium. ROIs of the infarct were estimated using SDs above mean HU of normal myocardium (0.5, 1, 1.5 and 2 SDs) in delayed imaging. Selection of optimal threshold HU was based on visual assessment that best mapped the hyperenhanced infarct area. This allowed volumetric asessment of infarct and total myocardium. Inter-investigator variability was evaluated with Bland-Altman and Intra-Class Correlation Coefficient (ICC). Results Patient characteristics are follow: Age 60.20±11.35 years, 41(89%)male, BMI 25.41±4.17kg/m², Hypertension 23(50%), diabetes 10(22%), hyperlipidemia 40(87%), smoking 7(15%), family history of premature CAD 4(8.7%), previous PCI 45(97.8%). In 51 patients, 23(41%), 24 (46%) &amp; 5(10%) of the infarctions were quantitated as being medium-sized (10%-20%), large (20% -30%) and very large (&amp;gt;30%). Thirty-seven (70%), 6 (11%) &amp; 10(19%) involved the LAD, LCx and RCA territories respectively. 49 (94%) of infarctions were transmural. Identical thresholding was selected by both investigators for each SD above mean (mean 1.42; SD 0.46). 93% of the total cases were within a set threshold of 1 to 2 SDs above the mean HU for both. Interclass correlation coefficient was 0.95, CI range 0.90 – 0.97. Bland-Altman analysis did not demonstrate systemic variations or proportional bias from the plot. Conclusion The study suggests that a method of quantifying cardiac infarct size using a threshold technique and delayed hyperenhancement images with dynamic perfusion imaging is clinically feasible and consistent.Rest-Stress-Delayed Enhancement infarct

Are muscle and fat loss predictive of clinical events in pancreatic cancer? The importance of precision metrics

BackgroundMuscle and fat loss from cancer may have prognostic significance. Skeletal muscle and fat areas measured at L3 on a CT scan correlate with body muscle and fat mass. We wished to know if reduced skeletal muscle area or fat on diagnostic CT scans or changes from initial CT scans in patients with pancreatic cancer who died in 2018 and 2019 predicted mortality. MethodeEectronic records of 112 patients with locally advanced or metastatic pancreatic cancer were used to extract stage, age, gender, comorbidities, weight, and height at the time of the first CT scan. Survival (in days) was defined from the first CT scan to the death date. Patients had at least one CT scan of the abdomen. I. Two trained medical students read scans independently using TeraRecon software (Durham, NC). Results were averaged, and the differences determined precision. Interclass correlation coefficient (ICC), coefficient of variation, and least significant change determined the precision between readers. Independent prognostic modeling included age and BMI. ResultsAn evaluable sample of 104 with an average age of 67, 56 were male. Nearly half had a TNM Stage of IV (45%). The average Charlson Comorbidity index is 7.2. In those undergoing repeat scans, most were in the timeframe of 60-120 days. Changes in visceral fat in men in the unadjusted Cox proportional hazard model and reduced skeletal muscle area in the age-adjusted model of men predicted mortality. In contrast, myosteatosis in women marginally predicted improved survival. ICC's precision between readers was adequate but by least significant change would have missed subtle, clinically important changes. DiscussionMuscle loss during chemotherapy in men predicted mortality in men but not women. Precision is an important metric when measuring body composition. ConclusionMuscle loss in men during chemotherapy of pancreatic cancer predicts mortality.

Repeatability and reproducibility of artificial intelligence-acquired fetal brain measurements (SonoCNS) in the second and third trimesters of pregnancy

Artificial Intelligence (AI)-based algorithms are increasingly entering clinical practice, aiding in the assessment of fetal anatomy and biometry. One such tool for evaluating the fetal head and central nervous system structures is SonoCNS™, which delineates appropriate planes for measuring head circumference (HC), biparietal diameter (BPD), occipitofrontal diameter (OFD), transcerebellar diameter (TCD), width of the posterior horn of the lateral ventricle (Vp), and cisterna magna (CM) based on a 3D volume acquired at the level of the fetal head’s thalamic plane. This study aimed to evaluate the intra- and interobserver variability of measurements obtained using this software. The study included 381 patients, 270 in their second trimester of pregnancy (70%) and 111 in the third trimester. Each patient underwent manual biometric measurements of the aforementioned structures and twice using the SonoCNS software. We calculated the intraobserver variability between the manual measurements and the average of the automated measurements, as well as the interobserver variability for automated measurements. We also compared the median examination time for manual and automated measurements. The interclass correlation coefficients (ICC) for interobserver and intraobserver variability for parameters BPD, HC, and OFD ranged from good to excellent reproducibility in the general population and subgroups (> 0.75). CM and Vp measurements, both in the general population and subgroups, fell into the category of moderate (0.5–0.75) and poor reproducibility (< 0.5). TCD measurements showed moderate (> 0.5) to good reproducibility (0.75–0.9), and OFD showed good and excellent reproducibility. The assessment of the biometry of fetal head structures using SonoCNS took an average of 63 s compared to 14 s for manual measurement (p < 0.001). The SonoCNS™ software is characterized by good to excellent reproducibility and repeatability in the measurement of fetal skull biometry (BPD, HC, and OFD), with poorer performance in measurements of intracranial structures (CM, Vp, TCD). Apart from biometric parameters, the software is useful in clinical practice for delineating appropriate planes from the acquired volume of the fetal head and shortening examination time.

Automated volumetric analysis of the inner ear fluid space from hydrops magnetic resonance imaging using 3D neural networks

Due to the development of magnetic resonance (MR) imaging processing technology, image-based identification of endolymphatic hydrops (EH) has played an important role in understanding inner ear illnesses, such as Meniere’s disease or fluctuating sensorineural hearing loss. We segmented the inner ear, consisting of the cochlea, vestibule, and semicircular canals, using a 3D-based deep neural network model for accurate and automated EH volume ratio calculations. We built a dataset of MR cisternography (MRC) and HYDROPS-Mi2 stacks labeled with the segmentation of the perilymph fluid space and endolymph fluid space of the inner ear to devise a 3D segmentation deep neural network model. End-to-end learning was used to segment the perilymph fluid and the endolymph fluid spaces simultaneously using aligned pair data of the MRC and HYDROPS-Mi2 stacks. Consequently, the segmentation performance of the total fluid space and endolymph fluid space had Dice similarity coefficients of 0.9574 and 0.9186, respectively. In addition, the EH volume ratio calculated by experienced otologists and the EH volume ratio value predicted by the proposed deep learning model showed high agreement according to the interclass correlation coefficient (ICC) and Bland–Altman plot analysis.

Assessment of left atrial function and left atrioventricular coupling via cardiac magnetic resonance in individuals with prediabetes and diabetes.

Assessment of left atrial (LA) function and the left atrioventricular coupling index (LACI) have recently been increasingly recognized as important indices for cardiovascular diseases associated with the presence of prediabetes and diabetes. We aimed to evaluate LA function and the LACI in patients with prediabetes and diabetes via cardiac magnetic resonance (CMR). In this retrospective study, we included 35 patients with prediabetes, 32 patients with diabetes, and 84 healthy control participants. The LACI and LA total, passive, and active emptying fractions (LATEmF, LAPEmF, and LAAEmF, respectively) were calculated. The LA reservoir, conduit, and booster pump strains (εs, εe, and εa), and peak positive, peak early negative, and peak late negative strain rates (SRs, SRe, and SRa) were obtained via CMR-feature tracking (CMR-FT). For the statistical analyses, one-way analysis of variance, the Kruskal-Wallis test, and linear regression were conducted, and Pearson's and interclass correlation coefficients were calculated. Compared with healthy control participants, patients with prediabetes or diabetes presented lower εs and εe values and a relatively preserved LACI. Patients with diabetes presented considerably reduced SRs, SRe, and LAPEmF. Elevated glycated haemoglobin (HbA1c) levels were independently associated with decreased magnitudes of εs, SRs, εe, and SRe. No significant associations were found between the LACI and the HbA1c or LA deformation parameters. We observed significant correlations between LATEmF and εs, LAPEmF and εe and between LAAEmF and εa. CMR-FT provides a potential noninvasive approach for the early detection of alterations in the LA reservoir and conduit function in individuals with prediabetes and diabetes.

Advancing health coaching: A comparative study of large language model and health coaches

ObjectiveRecent advances in large language models (LLM) offer opportunities to automate health coaching. With zero-shot learning ability, LLMs could revolutionize health coaching by providing better accessibility, scalability, and customization. The aim of this study is to compare the quality of responses to clients' sleep-related questions provided by health coaches and an LLM. Design, setting, and participantsFrom a de-identified dataset of coaching conversations from a pilot randomized controlled trial, we extracted 100 question-answer pairs comprising client questions and corresponding health coach responses. These questions were entered into a retrieval-augmented generation (RAG)-enabled open-source LLM (LLaMa-2-7b-chat) to generate LLM responses. Out of 100 question-answer pairs, 90 were taken out and assigned to three groups of evaluators: experts, lay-users, and GPT-4. Each group conducted two evaluation tasks: (Task 1) a single-response quality assessment spanning five criteria—accuracy, readability, helpfulness, empathy, and likelihood of harm—rated on a five-point Likert scale, and (Task 2) a pairwise comparison to choose the superior response between pairs. A suite of inferential statistical methods, including the paired and independent sample t-tests, Pearson correlation, and chi-square tests, were utilized to answer the study objective. Recognizing potential biases in human judgment, the remaining 10 question-answer pairs were used to assess inter-evaluator reliability among the human evaluators, quantified using the interclass correlation coefficient and percentage agreement metrics. ResultsUpon exclusion of incomplete data, the analysis included 178 single-response evaluations (Task 1) and 83 pairwise comparisons (Task 2). Expert and GPT-4 assessments revealed no discernible disparities in health coach and LLM responses across the five metrics. Contrarily, lay-users deemed LLM responses significantly more helpful than that of human coaches (p < 0.05). LLM responses were preferred in the majority (62.25 %, n = 155) of the aggregate 249 assessments, with all three evaluator groups favoring LLM over health coach inputs. While GPT-4 rated both health coach and LLM responses significantly higher than experts in terms of readability, helpfulness, and empathy, its ratings on accuracy and likelihood of harm aligned with those of experts. Response length positively correlated with accuracy and empathy scores, but negatively affected readability across all evaluator groups. Expert and lay-user evaluators demonstrated moderate to high inter-evaluator reliability. ConclusionOur study showed encouraging findings by demonstrating that RAG-enabled LLM has comparable performance to health coaches in the domain tested. Serving as an initial step towards the creation of more sophisticated, adaptive, round-the-clock automated health coaching systems, our findings call for more extensive evaluation which could assist in the development of the model that could in the future lead to potential clinical implementation.

Analyzing women’s knowledge of pregnancy complications in Ethiopia through a multilevel approach

Pregnancy complications can’t always be predicted. However, pregnant women must be aware of these pregnancy complications to avoid serious complications and begin treatment as soon as possible. Maternal mortality can be decreased by offering high-quality maternity services and educating women about the early warning signs of pregnancy. Therefore, the current study aimed to assess individual and community-level factors associated with women’s knowledge about pregnancy complications in Ethiopia based on the 2019 Ethiopian mini-demographic health survey data (EMDHS). The study analyzed data from the 2019 EMDHS, including a weighted sample of 1,655 reproductive-age women. Multilevel binary logistic regression analysis was used to fit the associated variables. Using the interclass correlation (ICC), deviance, proportional change variance (PVC), and median odds ratio (MOR), the comparison and fit of the models were evaluated. The significant variables associated with knowledge about complications during pregnancy were identified using an adjusted odds ratio (AOR) and a 95% confidence interval (CI). The proportion of mothers with good knowledge of pregnancy complications was 44.8% (CI 42.4%–47.2%). The multi-level analysis revealed that secondary education (AOR = 1.54, 95% CI = 1.04–2.29), a higher education level (AOR = 1.74, 95% CI = 1.11–2.72), four and above ANC Visits (AOR = 0.74, 95% CI = 0.49–0.98), women who lived in Amhara (AOR = 2.10, 95% CI = 1.24–3.55), and SNNPR (AOR = 3.92, 95% CI = 2.10–7.31) were positively associated with knowledge about pregnancy complications while, women residing in Harari (AOR = 0.20, 95% CI = 0.98–0.44) and Dire-Dawa (AOR = 0.48, 95% CI = 0.24–0.95) were negatively associated with knowledge about pregnancy complications. This study found that nearly half (44.8%) of the study participants demonstrated knowledge about pregnancy complications. This suggests a significant gap in awareness that could potentially impact access to obstetric care for women experiencing complications during pregnancy. Therefore, prioritizing enhancements in antenatal counseling services and community health education regarding pregnancy complications is crucial.

Comparing cadence-based energy expenditure and oxygen cost indices in able-bodied male people.

Walking is a fundamental aspect of daily life and exercise, with clinical benefits for cardiovascular health and muscle strength. However, accurately measuring energy efficiency during walking poses challenges due to equipment and spatial constraints. In this study, we proposed the cadence-based energy expenditure index (cEEI) and analyzed its correlation with the previously proposed index for measuring energy expenditure under various gait conditions. We enrolled 15 healthy participants and conducted an experimental protocol on a treadmill to measure the following energy expenditure-related indices: oxygen cost index (OCI), energy expenditure index (EEI), and cEEI. The participants underwent stages of walking at different speeds and inclinations that comply with the modified Bruce protocol while their heart rate, oxygen uptake, and cadence were recorded. Participants showed significant increases in heart rate, oxygen uptake, and cadence with higher walking speeds and inclinations. Correlation analysis revealed strong associations between cEEI and OCI, especially during walking conditions. Bland-Altman plots and interclass correlation coefficient analysis demonstrated a favorable agreement between cEEI and OCI, outperforming EEI. In conclusion, this study proposes cEEI as a reliable metric for estimating energy expenditure during walking by proving a strong correlation and agreement with OCI across various gait conditions. This suggests the potential for cEEI to provide real-time, individualized feedback on energy expenditure during walking, facilitating more personalized exercise prescriptions.

Acceptability, validity and responsiveness of inertial measurement units for assessing motor recovery after gene therapy in infants with early onset spinal muscular atrophy: a prospective cohort study

BackgroundOnasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT.MethodsClinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors.Results23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values < 0.001). ||A||_95 for the foot, the wrist, the elbow sensors and ||A||_CPM for the foot, the wrist, the elbow sensors increased significantly between baseline and the 12 months follow-up visit (respective p-values: 0.004, < 0.001, < 0.001, 0.006, < 0.001, < 0.001).ConclusionIMUs were well accepted, consistent, concurrently valid, responsive and associated with unaided sitting acquisition especially for the elbow sensors. This study is the first reporting a large set of inertial sensor derived data after GT in SMA patients and paves the way for IMU-based follow-up of SMA patients after treatment.

EzySCR: A free and easy tool for scoring event-related skin conductance responses in the first, second, and third interval latency windows.

Skin conductance is a commonly used physiological measure during psychology experiments, such as during fear conditioning. Methods for scoring skin conductance responses (SCRs) are highly heterogeneous, though most researchers agree that manually inspected scores provide the highest quality data when compared to most available fully automated scoring methods. However, manual scoring is extremely time-consuming. We developed a semi-automated scoring program that reduces the time required to process SCR data at a level of quality akin to manual scoring. In contrast to all previous scoring programs, our program enables scoring of first interval response (FIR), second interval response (SIR), and third interval response (TIR) SCRs. Using interclass correlation coefficients (ICCs), Bland-Altman plots and Pareto analysis, we show here that our method is highly reliable and produces data that are almost identical to data that are manually scored and scored using LEDALAB. This software is very easy to use and is freely available to download and modify. We expect that this software will be helpful in reducing the time required to produce high quality FIR, SIR, and TIR skin conductance data for psychology researchers around the world.

Comparison of Diffusion-weighted MRI using Singe-Shot Echo-planar Imaging (SS-EPI) and Split Acquisition of Fast Spin Echo Signal (SPLICE) Imaging, a non-EPI technique, in Tumors of the Head and Neck.

Diffusion-weighted imaging (DWI) using single-shot echo planar imaging (DW-EPI) is susceptible to distortions around air-filled cavities and dental fillings, typical for the head and neck area. Non-EPI, Split acquisition of fast spin echo signals for diffusion imaging (DWSPLICE) could reduce these distortions and enhance image quality, thereby potentially improving recurrence assessment in squamous cell carcinoma (SCC) of the head and neck region. This study evaluated whether DW-SPLICE is a viable alternative to DW-EPI through quantitative and qualitative analyses. The DW-SPLICE sequence was incorporated into the standard 3.0T head and neck MRI protocol with DW-EPI. Retrospective analysis was conducted on two subgroups: firstly benign or malignant lesions, and secondly post-treatment SCC recurrence. In both subgroups Image quality and distortion were scored by two independent radiologists, blinded for DW-technique, and evaluated using mixed-effect linear models. Lesion apparent diffusion coefficient (ADC) values were assessed with inter-class correlation (ICC) and Bland-Altman analyses. DWI's delineation geometric similarity to T1-weighted post-contrast (T1Wc) MRI was evaluated using the Dice Similarity Coefficient (DSC) before and after registration. Recurrence in post-treatment SCC scans was evaluated by the same two radiologists blinded for DW-technique. Recurrence detection rates were then compared between DW-SPLICE and DW-EPI using mixed logistic regression at six months and at one-year post-scan follow-up data. From August 2020 to January 2022, 55 benign or malignant lesion scans (55 patients) and 74 post-treatment SCC scans (66 patients) were analyzed. DW-SPLICE scored better on image quality and showed less overall distortion than DW-EPI (0.04<p<0.001). There was high ADC measurement reliability (ICC=0.93, p<0.001), though a proportional bias was also observed (β=0.11, p=0.03), indicating the bias increases as ADC values rise. DWSPLICE exhibited greater geometric similarity to T1Wc before registration (DSC 0.63 vs 0.47, p<0.001) and outperformed DW-EPI by more accurately identifying recurrences after one year (OR=0.96, p=0.05) but not after six months (OR=0.72, p=0.13). DW-SPLICE surpasses DW-EPI on image distortion and quality and improves diagnostic reliability for detecting recurrent or residual SCC on 3T MRI of the HN. Consistent use of one method for follow-up is advised, as ADC values are not completely interchangeable. Integrating DW-SPLICE can significantly improve tumor assessments in clinical practice. ANTs = Advanced Normalization Tools; DSC = Dice Similarity Coefficient; DW-EPI = Diffusion-weighted single-shot echo planar imaging; DW-MS-EPI = Diffusion-weighted multi-shot echo planar imaging; DW-SPLICE = Diffusion-weighted split acquisition of fast spin echo signals for diffusion imaging; DW-TSE = Diffusion-weighted Turbo Spin Echo; ICC = Intraclass Correlation Coefficient; ROC = Receiver Operating Characteristic; SCC = Squamous cell carcinoma; T1WIc = T1 weighted imaging with gadolinium contrast.

Workplace dignity scale in Iranian nurses: translation and psychometric evaluation

BackgroundWorkplace dignity is defined as a person’s perception of respect and trust, equal and fair treatment, valuing the person, independence, freedom of expression, and decision-making enjoyed by the employee in the workplace. Therefore, this study aimed to develop the Workplace Dignity Scale (WDS) and evaluate its psychometric properties.MethodsIn this methodological study, WDS was translated based on Beaton et al. Guideline. After assessing face and content validity (using CVI, CVR, modified kappa, and item impact), 250 nurses were selected by random sampling from Kashan hospitals in 2023. Then, WDS was assessed through expletory factor analysis (EFA), known-groups comparison, internal consistency, and stability. The ceiling and floor effects were also examined. Data were analyzed using SPSS v16 by Kruskal–Wallis test, Cronbach’s alpha, McDonald’s Omega, interclass correlation coefficient (ICC), standard error of measurement (SEM), and minimum detectable changes (MDC).ResultsThe scale’s CVI, CVR, and modified kappa were above 0.79. Item impact was higher than 1.5. EFA extracted three factors, including “general dignity, respect, and indignity”, that could explain 70.6% of the total variance in scale. WDS could differentiate between the three groups in terms of occupation satisfaction status. Cronbach’s alpha, McDonald’s Omega, ICC, SEM, and STC of scale were 0.949, 0.950, 0.970, 2.793, and 7.742, respectively.ConclusionThe Persian version of the WDS has shown validity and reliability for measuring workplace dignity among nurses in the Iranian context.

Proposed Classification of Syndesmotic Soft Tissue Impingement and Its Association With Lateral Talar Cartilage Lesions.

Syndesmotic impingement syndrome has been described to explain persistent lateral or anterolateral ankle pain. This condition is thought to often result from a rotational ankle injury. This study aimed to identify the type of syndesmotic impingement tissue according to arthroscopic and histologic findings and to analyze its association with lateral talar cartilage lesions. This retrospective study included 107 ankles in 105 patients who underwent arthroscopic surgery for syndesmotic impingement syndrome from January 2017 to August 2019. Histologic findings of biopsied impingement tissue were obtained in 59 ankles (55.1%) and compared with gross findings on arthroscopy. Cartilage lesions were evaluated using the International Cartilage Repair Society grading system. Risk factors associated with lateral talar cartilage injury were evaluated using multivariate logistic regression analysis. Based on the histologic results, the most common pathology was fibrosis (24 ankles [22.4%]), followed by fatty degeneration (23 ankles [21.5%]), synovial hyperplasia (8 ankles [7.5%]), and inflammatory synovium (4 ankles [3.7%]). Interclass correlation coefficient was 0.841 (P = .001) between histologic results and arthroscopic findings. Patients with fibrotic impingement tissue had on average a significantly longer duration of symptoms (12 vs 48 months, P = .021). A lateral talar dome cartilage lesion was detected in 25 ankles (23.4%). Fibrotic impingement tissue was the only factor significantly associated with lateral talar dome lesion on multivariate logistic regression analysis (odds ratio 10.04; 95% CI 1.84-54.9; P = .008). Syndesmotic impingement with fibrotic tissue was often associated with cartilage lesions at the lateral talar dome. Patients with prolonged anterolateral or lateral ankle pain after a rotational ankle injury warrant careful assessment for possible syndesmotic impingement by fibrotic tissue and associated lateral talar cartilage injury.

Preoperative Planning for Shoulder Arthroplasty is Feasible with Computed Tomography at Lower-Than-Conventional Radiation Doses

Computed tomography (CT) offers a detailed assessment of the shoulder for preoperative shoulder arthroplasty planning; however, this technique exposes the patient to ionizing radiation. The purpose of this study was to prospectively evaluate the practicality of reducing the CT radiation dose compared to conventional dose levels for manual and preoperative planning software measurements for shoulder arthroplasty. A total of 10 shoulder CT examinations were performed for preoperative planning purposes on a dual x-ray source CT scanner. A specialized dose-split scan technique was utilized to reconstruct CT images corresponding to 100%, 70%, and 30% radiation dose relative to our institution's standard of care imaging protocol. Glenoid version, inclination, and humeral head subluxation were measured manually by three authors and by commercially available software platforms. These measurements were analyzed for agreement between the 100%, 70%, and 30% dose levels for each patient. Tolerances of 5° of glenoid version, 5° of glenoid inclination, and 10% humeral head subluxation were used as equivalent for preoperative planning. Automated measurements of 70% dose images were within 5° of version, 5° of inclination, and 10% subluxation in 95.0% of cases. Manual measurements of 70% RD images were within 5° of version for 90.0% of cases, 5° of inclination in 86.7% of cases, and 10% subluxation in 100% of cases. Automated measurements from the 30% dose images were within 5° of version, 5° of inclination, and 10% subluxation for 100% of cases. Manual measurements from the 30% dose images were within 5° of version for 86.7% of cases, 5° of inclination in 76.7% of cases, and 10% subluxation in 100% of cases. The mean absolute difference in software measurement of glenoid version (p = 0.96), glenoid inclination (p = 0.64), or humeral head subluxation (p = 0.09) or in aggregated manual mean absolute difference of version (p = 0.22), inclination (p = 0.31), or humeral head subluxation (p = 0.56) was not significant. Good to excellent reliability was determined by interclass correlation coefficients among the manual observers and automatic software platforms for measurements at all doses (P<0.001) CONCLUSIONS: The results indicate that both preoperative planning software platforms and human observers produced similar measurements of glenoid version, inclination, and humeral head subluxation from reduced-dose images compared to standard of care doses. By implementing reduced dose techniques in preoperative shoulder CT, the potential risks associated with radiation exposure could be reduced for patients undergoing shoulder arthroplasty.

Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device.

Freezing of Gait (FOG) is prevalent in people with Parkinson's disease (PD) and severely disrupts mobility. Detecting the exact boundaries of FOG episodes may facilitate new technologies in "breaking" FOG in real-time. This study investigates the performance of automatic device-based FOG detection. Eight machine-learning classifiers (including Neural Networks, Ensemble methods, and Support Vector Machines) were developed using (i) accelerometer and (ii) combined accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed mobility tasks designed to elicit FOG. Two clinicians independently annotated exact FOG episodes using synchronized video and a flowchart algorithm based on international guidelines. Device-detected FOG episodes were compared to annotated episodes using 10-fold cross-validation and Interclass Correlation Coefficients (ICC) for agreement. Development used 50,962 windows of data and annotated activities (>10 hours). Strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity, and ICC1,1 = 0.97 for total FOG duration). Device performance varied by method, complexity, and cost matrix. The Neural Network using 67 accelerometer features achieved high sensitivity to FOG (89% sensitivity, 81% specificity, and ICC1,1 = 0.83) and stability (validation loss 5%). The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The strong clinician agreement indicates room for improvement in future device-based FOG detection. This study may enhance PD care by reducing reliance on visual FOG inspection, demonstrating that high sensitivity in automatic FOG detection is achievable.