Initial Data Research Articles

Predicting CDR status over 36 months with a recall-based digital cognitive biomarker.

Word-list recall tests are routinely used for cognitive assessment, and process scoring may improve their accuracy. We examined whether Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) derived, process-based digital cognitive biomarkers (DCBs) at baseline predicted Clinical Dementia Rating (CDR) longitudinally and compared them to standard metrics. Analyses were performed with Alzheimer's Disease Neuroimaging Initiative (ADNI) data from 330 participants (mean age=71.4±7.2). We conducted regression analyses predicting CDR at 36 months, controlling for demographics and genetic risk, with ADAS-Cog traditional scores and DCBs as predictors. The best predictor of CDR at 36 months was M, a DCB reflecting recall ability (area under the curve =0.84), outperforming traditional scores. Diagnostic results suggest that M may be particularly useful to identify individuals who are unlikely to decline. These results suggest that M outperforms ADAS-Cog traditional metrics and supports process scoring for word-list recall tests. More research is needed to determine further applicability with other tests and populations. Process scoring and latent modeling were more effective than traditional scoring. Latent recall ability (M) was the best predictor of Clinical Dementia Rating decline at 36 months. The top digital cognitive biomarker model had odds ≈ 90 times greater than the top Alzheimer's Disease Assessment Scale-Cognitive subscale model. Particularly high negative predictive value supports literature on cognitive testing as a useful screen. Consideration of both cognitive and pathological outcomes is needed.

Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.

Dynamic prediction models capable of retaining accuracy by evolving over time could play a significant role for monitoring disease progression in clinical practice. In biomedical studies with long-term follow up, participants are often monitored through periodic clinical visits with repeat measurements until an occurrence of the event of interest (e.g. disease onset) or the study end. Acknowledging the dynamic nature of disease risk and clinical information contained in the longitudinal markers, we propose an innovative concordance-assisted learning algorithm to derive a real-time risk stratification score. The proposed approach bypasses the need to fit regression models, such as joint models of the longitudinal markers and time-to-event outcome, and hence enjoys the desirable property of model robustness. Simulation studies confirmed that the proposed method has satisfactory performance in dynamically monitoring the risk of developing disease and differentiating high-risk and low-risk population over time. We apply the proposed method to the Alzheimer's Disease Neuroimaging Initiative data and develop a dynamic risk score of Alzheimer's Disease for patients with mild cognitive impairment using multiple longitudinal markers and baseline prognostic factors.

Implementing Patient-Centered Outcomes Research Institute Stakeholder Engagement Principles in Models of Palliative Care Delivery and Advance Care Planning Research.

Given the many challenges of conducting research that addresses the palliative and end-of-life care needs of patients with serious illnesses, stakeholder engagement starting from the moment of study conceptualization and design is critical to ensure successful participant recruitment, data collection, intervention delivery, data analysis, and dissemination. Guided by a conceptual model published by the Patient-Centered Outcomes Research Institute (PCORI) entitled, "Measuring What Matters for Advancing the Science and Practice of Engagement"14 and with the support of a PCORI Engagement Officer, representatives from 9 PCORI-funded study teams formed a working group to survey team members and review, outline, and describe key lessons learned and best practices for promoting stakeholder engagement in palliative care research. Almost all study teams engaged with patients/caregivers, clinicians, researchers, and health care system experts as stakeholder partners. About half the teams also included payers and training institutions as part of their stakeholder advisors as well as a range of content experts. Study teams relied on a variety of support structures and resources, and they employed 10 distinct methods for maintaining engagement. All engagement methods were generally considered to be effective by teams who used the method, though there was some variability in team-rated engagement quality of each method. Nine barriers to stakeholder engagement were identified across the 9 studies as well as 9 strategies (or facilitators) to overcome these barriers. We share examples of how stakeholder engagement impacted studies in all phases, including the preparatory phase, study initiation phase, execution phase, and data analysis/dissemination phase. Teams utilized a variety of resources and support structures as well as capitalized on multiple engagement methods for fostering stakeholder engagement, resulting in a high level of collaboration and integration.

Impact of Chronic Kidney Disease on Outcomes following Vascular Procedure in the Vascular Quality Initiative.

Chronic kidney disease (CKD) increases morbidity and mortality in most vascular procedures. However, a binary classification of estimated glomerular filtration rate (eGFR) <60mL/min/1.73 m2, which is often used in both modeling and clinical trials, may not be optimal for predicting clinical outcomes. Determine the optimal eGFR cutoff for use in risk stratification and prediction models. Vascular Quality Initiative (VQI) data for non-emergent, first-time OAR, EVAR, TEVAR, CEA, CAS, PVI, Supra- and infra-inguinal bypass were analyzed from to 2013-2023 and divided into cohorts based on eGFR (≥60, 45-59, 30-44, <30, and preoperative dialysis). χ2 and logistic regression were used to evaluate perioperative outcomes. Compared to patients with eGFR ≥60, those with eGFR 45-59 had similar odds of mortality following all procedures, except TEVAR. Driven by this group, the combined cohort showed a slight increase in the odds of mortality for eGFR 45-59 (0.6% vs. 0.7%, aOR 1.16, P=0.002). Those in the 30-44 group demonstrated increased odds of mortality both overall and in the individual procedure groups (0.6% vs. 1.2%, aOR 1.78, P<0.001). The odds of mortality continued to increase with worsening eGFR. The overall rate of new permanent dialysis was low for all eGFR cohorts, with a 0.02% difference between those with eGFR >60 and those in the 45-59 cohort (0.04% vs. 0.06%; a OR 1.65, P<0.001). The odds of permanent dialysis likewise continued to increase with decreasing eGFR. Rather than a binary eGFR cutoff of ≥60 and <60 to stratify patient risk, better risk stratification may be achieved by using five groups of ≥60, 45-59, 30-44, <30, and preoperative dialysis.

Fractures by race and ethnicity in a diverse sample of postmenopausal women: a current evaluation among Hispanic and Asian origin groups.

Using 1998-2022 Women's Health Initiative (WHI) data, our study provides contemporary fracture data by race and ethnicity, specifically focusing on Hispanic and Asian women. Fractures of interest included any clinical, hip, and major osteoporotic fractures (MOFs). We utilized the updated race and ethnicity information collected in 2003, which included seven Asian and five Hispanic origin groups. We computed crude and age-standardized fracture incidence rates per 10 000 woman-years across race and ethnic categories and by Asian and Hispanic origin. We used Cox proportional hazards model, adjusting for age and WHI clinical trial arm, to evaluate the risk of fracture (1) by race compared to White women, (2) Asian origin compared to White women, (3) Hispanic compared to non-Hispanic women, and (4) Asian and Hispanic origins compared the most prevalent origin group. Over a median (interquartile range) follow-up of 19.4 (9.2-24.2) years, 44.2% of the 160 824 women experienced any clinical fracture, including 36 278 MOFs and 8962 hip fractures. Compared to White women, Black, Pacific Islander, Asian, and multiracial women had significantly lower risk of any clinical and MOFs, while only Black and Asian women had significantly lower hip fracture risk. Within Asian women, Filipina women had 24% lower risk of any clinical fracture compared to Japanese women. Hispanic women had significantly lower risk of any clinical, hip, and MOF fractures compared to non-Hispanic women, with no differences in fracture risk observed within Hispanic origin groups. In this diverse sample of postmenopausal women, we confirmed racial and ethnic differences in fracture rates and risk, with novel findings among within Asian and Hispanic subgroups. These data can aid in future longitudinal studies evaluate contributors to racial and ethnic differences in fractures.

The Impact Bond Dataset: A Tool to Investigate Socially Motivated Cross-Sector Partnerships

Abstract The indigo Impact Bond Dataset is an open-access dataset that describes a specific form of impact-focused cross-sector partnership adopted worldwide since 2010. These partnerships are data-rich in principle, yet historically, little data is shared and re-used. The dataset is the result of an engaged, collaborative process where different organisations involved in impact bond projects share data with the indigo initiative data stewards so that practitioners and researchers can analyse and learn from these partnerships. This article introduces the dataset in terms of scope, data collection methods, and data model. The authors provide descriptive summaries of the current landscape and demonstrate a practical application of the dataset. In closing, they discuss future avenues for research and dataset development, as well as the limitations of working with a collaborative approach.

Outcomes Following Carotid Revascularization in Patients with Prior Ipsilateral Carotid Artery Stenting in the Vascular Quality Initiative

The outcomes of carotid revascularization in patients with prior carotid artery stenting (CAS) remain understudied. Prior research has not reported the outcomes after transcarotid artery revascularization (TCAR) in patients with previous CAS. In this study, we compared the peri-operative outcomes of TCAR, transfemoral CAS (tfCAS) and carotid endarterectomy (CEA) in patients with prior ipsilateral CAS using the Vascular Quality Iniatitive. Using Vascular Quality Initiative data from 2016 to 2023, we identified patients who underwent TCAR, tfCAS, or CEA after prior ipsilateral CAS. We included covariates such as age, race, sex, body mass index, comorbidities (hypertension, diabetes, prior coronary artery disease, prior coronary artery bypass grafting/percutaneous coronary intervention, congestive heart failure, renal dysfunction, smoking, chronic obstructive pulmonary disease, and anemia), symptom status, urgency, ipsilateral stenosis, and contralateral occlusion into a regression model to compute propensity scores for treatment assignment. We then used the propensity scores for inverse probability weighting and weighted logistic regression to compare in-hospital stroke, in-hospital death, stroke/death, postoperative myocardial infarction (MI), stroke/death/MI, 30-day mortality, and cranial nerve injury (CNI) after TCAR, tfCAS, and CEA. We also analyzed trends in the proportions of patients undergoing the three revascularization procedures over time using Cochrane-Armitage trend testing. We identified 2137 patients undergoing revascularization after prior ipsilateral carotid stenting: 668 TCAR patients (31%), 1128 tfCAS patients (53%), and 341 CEA patients (16%). In asymptomatic patients, TCAR was associated with a lower yet not statistically significant in-hospital stroke/death than tfCAS (TCAR vs tfCAS: 0.7% vs 2.0%; adjusted odds ratio [aOR], 0.33; 95% confidence interval [CI], 0.11-1.05; P= .06), and similar odds of stroke/death with CEA (TCAR vs CEA: 0.7% vs 0.9%; aOR, 0.80; 95% CI, 0.16-3.98; P= .8). Compared with CEA, TCAR was associated with lower odds of postoperative MI (0.1% vs 14%; aOR, 0.02; 95% CI, 0.00-0.10; P< .001), stroke/death/MI (0.8% vs 15%; aOR, 0.05; 95% CI, 0.01-0.25; P< .001), and CNI (0.1% vs 3.8%; aOR, 0.04; 95% CI, 0.00-0.30; P= .002) in this patient population. In symptomatic patients, TCAR had an unacceptably elevated in-hospital stroke/death rate of 5.1%, with lower rates of CNI than CEA. We also found an increasing trend in the proportion of patients undergoing TCAR following prior ipsilateral carotid stenting (2016 to 2023: 14% to 41%), with a relative decrease in proportions of tfCAS (61% to 45%) and CEA (25% to 14%) (P< .001). In asymptomatic patients with prior ipsilateral CAS, TCAR was associated with lower odds of in-hospital stroke/death compared with tfCAS, with comparable stroke/death but lower postoperative MI and CNI rates compared with CEA. In symptomatic patients, TCAR was associated with unacceptably higher in-hospital stroke/death rates. In line with the postprocedure outcomes, there has been a steady increase in the proportion of patients with prior ipsilateral stenting undergoing TCAR over time.

Revealing the significance of early detection of chronic obstructive pulmonary disease: insights from healthy lung initiative data at COP27 conference

BackgroundClimate change poses a significant threat to respiratory health, exacerbating conditions like asthma, Chronic Obstructive Pulmonary Disease (COPD), and respiratory infections increasing morbidity and mortality indirectly through exposure to greenhouse gases. During the 27th Climate Change Conference (COP27), the Healthy Lung Initiative (HLI) for the early detection of COPD among smokers was launched in Egypt.ObjectiveWe aimed to detect the prevalence and predictors of confirmed COPD among smokers and assess physicians’ adherence to prescribing pulmonary function tests (PFTs) among the COP27 conference attendees.MethodsThis study utilized smokers’ data from the HLI, employing a cross-sectional design with an interview-based questionnaire, supplemented by spirometry for lung function evaluation. Participants, comprising Egyptian and non-Egyptian residents in Sharm El-Sheikh city, were provided with educational materials and encouraged to seek further evaluation from a pulmonologist.ResultsThe HLI study, conducted from November 6th to 20th, 2022, involved 1133 participants, 90% (1047) of whom were smokers. Most smokers were males (96.3%) and Egyptians (98.6%), with half aged 30–39 and the majority starting smoking within the last 20 years. Despite common respiratory symptoms, 47% suffered from dyspnea on exertion, and only 4.3% have undergone lung function tests, suggesting a potential underdiagnosis of COPD. Most participants (82.6%) had an FEV1/FVC ratio > 70%, indicating no spirometry-confirmed COPD diagnosis, while 147 participants (17% of them) exhibited all three cardinal COPD symptoms simultaneously. Male gender and daily cigarette consumption were significant predictors for confirmed COPD, while age showed no significance in regression analyses.ConclusionThe ongoing HLI focuses on early detection and education to combat smoking-related risks, particularly among middle-aged males, while also highlighting the need for comprehensive strategies to address the intersection of smoking and climate change.

Pressure drop prediction for R407C fluid during flow evaporation in horizontal pipes using Kalman Filter

The estimation of pressure drop in systems involving two-phase fluids holds a substantial influence over system design, energy efficiency, heat transfer dynamics, and the overall system performance. Existing correlations in the current literature exhibit considerable errors, primarily attributable to the diverse characteristics of flow patterns inside the pipe. This work presents a discussion on the pressure drop of the R-407C fluid in horizontal pipes during two-phase flow, along with the application of the Kalman Filter to improve the estimations produced by well-known correlations. The initialization data used were obtained through equations created based on experimental data and considering the influence that diameter, mass velocity, and saturation pressure have on the pressure drop. The correlations used as a basis for the calculations were selected from the literature, considering the lowest percentage error observed in the pressure drop estimation. Experimental data of pressure drop where compared with the results the obtained by using the correlation alone and in combination with a Kalman Filter. For tubes with a diameter greater than 1.5 mm, applying the correlation together with the Kalman Filter resulted in a Mean Absolute Relative Deviation (MARD) of 15.71, whereas using the correlation alone yielded a MARD of 28.26. For tubes with diameters of 1.5 mm or less, the MARD values were 12.12 and 62.90, for the combination of correlation and the Kalman Filter and for the correlation alone, respectively. These results underscore the viability of the Kalman Filter as an effective tool for improving the accuracy of pressure drop calculations in horizontal tubes.

Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling.

This study investigates the heterogeneity of a biomarker's discriminative performance for predicting subsequent time-to-event outcomes across different patient subgroups. While the area under the curve (AUC) for the time-dependent receiver operating characteristic curve is commonly used to assess biomarker performance, the partial time-dependent AUC (PAUC) provides insights that are often more pertinent for population screening and diagnostic testing. To achieve this objective, we propose a regression model tailored for PAUC and develop two distinct estimation procedures for discrete and continuous covariates, employing a pseudo-partial likelihood method. Simulation studies are conducted to assess the performance of these procedures across various scenarios. We apply our model and inference procedure to the Alzheimer's Disease Neuroimaging Initiative data set to evaluate potential heterogeneities in the discriminative performance of biomarkers for early Alzheimer's disease diagnosis based on patients' characteristics.

Safety of Shunting Strategies During Carotid Endarterectomy: A Vascular Quality Initiative Data Analysis

This study aimed to evaluate in hospital outcomes after carotid endarterectomy (CEA) according to shunt usage, particularly in patients with contralateral carotid occlusion (CCO) or recent stroke. Data from CEAs registered in the Vascular Quality Initiative database between 2012 and 2020 were analysed, excluding surgeons with < 10 CEAs registered in the database, concomitant procedures, re-interventions, and incomplete data. Based on their rate of shunt use, participating surgeons were divided in three groups: non-shunters (< 5%), selective shunters (5 - 95%), and routine shunters (> 95%). Primary outcomes of in hospital stroke, death, and stroke and death rate (SDR) were analysed in symptomatic and asymptomatic patients. A total of 113 202 patients met the study criteria, of whom 31 147 were symptomatic and 82 055 were asymptomatic. Of the 1 645 surgeons included, 12.1% were non-shunters, 63.6% were selective shunters, and 24.3% were routine shunters, with 10 557, 71 160, and 31 579 procedures in each group, respectively. In the univariable analysis, in hospital stroke (2.0% vs. 1.9% vs. 1.6%; p = .17), death (0.5% vs. 0.4% vs. 0.4%; p = .71), and SDR (2.2% vs. 2.1% vs. 1.8%; p = .23) were not statistically significantly different among the three groups in the symptomatic cohort. The asymptomatic cohort also did not show a statistically significant difference for in hospital stroke (0.9% vs. 1.0% vs. 0.9%; p = .55), death (0.2% vs. 0.2% vs. 0.2%; p = .64), and SDR (1.0% vs. 1.1% vs. 1.0%; p = .43). The multivariate model did not show a statistically significant difference for the primary outcomes between the three shunting cohorts. On subgroup analysis, the SDRs were not statistically significantly different for patients with CCO (3.3% vs. 2.5% vs. 2.4%; p = .64) and those presenting with a recent stroke (2.9% vs. 3.4% vs. 3.1%; p = .60). No statistically significant differences were found between three shunting strategies for in hospital SDR, including in patients with CCO or recent stroke.

Analysis of Surgeon and Center Case Volume and Stroke or Death after Transcarotid Artery Revascularization.

It has been suggested that the annual hospital volume of cases may affect the number of adverse events following carotid endarterectomy (CEA). We aim to study the associations between hospital as well as surgeon volume and the risk of stroke/death following TCAR. Retrospective review of the Vascular Quality Initiative data of patients undergoing TCAR from 2016 to 2021. Surgeon and center volume were calculated based on the mean number of cases (MNC) performed yearly by each surgeon and center. The primary outcome was a composite endpoint of in-hospital stroke/death. A total of 22,624 cases were included. Surgeon volume was divided into three quantiles: low (MNC=4), medium (MNC=10), and high (MNC=26). Center volume was also divided into low (MNC=14), medium (MNC=32), and high (MNC=64). After adjusting for potential confounders, and when compared to high volume centers, low and medium center volume was not associated with any increased odds of in-hospital stroke/death, stroke, death, or stroke/TIA. Compared to high volume surgeons, low surgeons' volume was associated with a higher odd of stroke (OR: 1.5, 95%CI (1.1-2.04), P=0.008), and stroke/TIA (OR: 1.5, 95%CI (1.2-1.9), P=.002). However, medium surgeon volume was not associated with higher odds of stroke/death, stroke, and stroke/TIA. Neither low nor medium surgeon volume was associated with a difference in mortality compared to high surgeon volume. In this retrospective study, center volume was not associated with any differences in outcomes among patients undergoing TCAR. On the other hand, surgeons with low volume were associated with a higher risk of stroke/death/MI and stroke/TIA when compared to high surgeon volume. There was no difference in outcomes between medium and high surgeon volume.

Multi-Parameter Approach to Determine the Floods Causes in North Luwu, South Sulawesi.

Abstract Floods occur annually in North Luwu, South Sulawesi Province, Indonesia. The exact cause of the flooding is yet to be known; thus, this study aimed to find the main factors causing floods in North Luwu using a multi-parameter approach. The methods applied are hydrological, flood susceptibility, and land use changes in the analysis. The return period and intensity duration frequency of rain (1983-2021) was used for hydrological analysis. Flood susceptibility is obtained from the processing of slope, elevation, rainfall, stream density, soil type, and land use parameters. Land use change was analyzed using the Climate Change Initiative and Land Cover (CCI LC) data in 2000 and 2015. The results of the 1-year rainfall return period in North Luwu were relatively high, namely 83 mm/day, the 2-year return period of 119 mm/day, 5 years of 142 mm/day, 10 years of 155 mm/day, and 100 years 184 mm/day. The results of the IDF analysis obtained that the early rainy hours showed an intensity of 41-46 mm/hour with a total of 259 mm/day, which was considered extreme. There is unity between the results of the correlation of flood events in 2021 with recorded rainfall of 64-153 mm/day. Flood susceptibility analysis shows the dominant high vulnerability in the downstream area of 101,337 Ha, medium vulnerability (208,545 Ha) in the middle stream, and low vulnerability of 57,719 Ha upstream. The high flood susceptibility map results followed the flood events distribution from 2017-2021, while there is a lack of change in the land use analysis.

KURIKULUM MERDEKA: PERAN GURU PAI DALAM MEMBENTUK KARAKTER SISWA DI ERA DIGITAL

The freedom of teachers and students is the essence of self-oriented learning, capable of producing high-quality education, internationally recognised. In teaching learning activities, the role of the teacher is no longer the primary medium of instruction. Students can now quickly retrieve knowledge from a variety of media and learning resources that grow with technological developments, whether in terms of IT-based learning or other forms of communication. This research takes a qualitative approach, using library research methods to gather previous research data sources with correlation. Access to initiation data sources in Google Scholar, as well as other research related articles. The findings highlight the importance of PAI teacher competence in the independent curriculum learning process in the digital age. Given that Islamic education contains a large amount of material that is important to students, such as the Quran Hadith, Fiqih, and Akidah Akhlak, a PAI teacher should use digital media, social media, and search engines to find relevant material, as well as support the PAI learning process in an independent curriculum in the digital age.

On dynamical system modeling of learned primal-dual with a linear operator K : stability and convergence properties

Learned Primal-Dual (LPD) is a deep learning based method for composite optimization problems that is based on unrolling/unfolding the primal-dual hybrid gradient algorithm. While achieving great successes in applications, the mathematical interpretation of LPD as a truncated iterative scheme is not necessarily sufficient to fully understand its properties. In this paper, we study the LPD with a general linear operator. We model the forward propagation of LPD as a system of difference equations and a system of differential equations in discrete- and continuous-time settings (for primal and dual variables/trajectories), which are named discrete-time LPD and continuous-time LPD, respectively. Forward analyses such as stabilities and the convergence of the state variables of the discrete-time LPD to the solution of continuous-time LPD are given. Moreover, we analyze the learning problems with/without regularization terms of both discrete-time and continuous-time LPD from the optimal control viewpoint. We prove convergence results of their optimal solutions with respect to the network state initialization and training data, showing in some sense the topological stability of the learning problems. We also establish convergence from the solution of the discrete-time LPD learning problem to that of the continuous-time LPD learning problem through a piecewise linear extension, under some appropriate assumptions on the space of learnable parameters. This study demonstrates theoretically the robustness of the LPD structure and the associated training process, and can induce some future research and applications.

An Approach for Evaluation and Recognition of Facial Emotions Using EMG Signal

Background: Facial electromyography (fEMG) records muscular activities from the facial muscles, which provides details regarding facial muscle stimulation patterns in experimentation. Objectives: The Principal Component Analysis (PCA) is mostly implemented, whereas the actual or unprocessed initial fEMG data are rendered into low-spatial units with minimizing the level of data repetition. Methods: Facial EMG signal was acquired by using the instrument BIOPAC MP150. Four electrodes were fixed on the face of each participant for capturing the four different emotions like happiness, anger, sad and fear. Two electrodes were placed on arm for grounding purposes. Results: The aim of this research paper is to propagate the functioning of PCA in synchrony with the subjective fEMG analysis and to give a thorough apprehension of the advanced PCA in the areas of machine learning. It describes its arithmetical characteristics, while PCA is estimated by implying the covariance matrix. Datasets which are larger in size are progressively universal and their interpretation often becomes complex or tough. So, it is necessary to minimize the number of variables and elucidate linear compositions of the data to explicate it on a huge number of variables with a relevant approach. Therefore, Principal Component Analysis (PCA) is applied because it is an unsupervised training method that utilizes advanced statistical concept to minimize the dimensionality of huge datasets. Conclusion: This work is furthermore inclined toward the analysis of fEMG signals acquired for four different facial expressions using Analysis of Variance (ANOVA) to provide clarity on the variation of features.

Implementation of a vascular acute care surgery model is associated with improved surgeon efficiency and 2-year mortality after lower extremity intervention

ObjectiveVascular surgeons work long, unpredictable hours with repeated exposure to high-stress situations. Inspired by general surgery acute care surgery models, we sought to organize the care of vascular emergencies with the implementation of a vascular acute care surgery (VACS) model. Within this model, a surgeon is in-house without elective cases and assigned for consultations and urgent operative cases on a weekly basis. This study examined the impact of a VACS model on postoperative mortality and surgeon efficiency. MethodsThis was a retrospective cohort analysis of institutional Vascular Quality Initiative data from July 2014 to July 2023. Patients undergoing lower extremity bypass, peripheral vascular intervention, or amputation were included. There was a washout period from January 2020 to January 2022 to account for COVID-19 pandemic practice abnormalities. Patients were separated into pre- or post-VACS groups. The primary clinical outcomes were 30-day and 2-year mortality. Secondary clinical outcomes included 30-day complications and 30-day and 1-year major adverse limb events (MALE). Separate analyses of operating room data from July 2017 to February 2024 and fiscal data from fiscal year 2019 to fiscal year 2024 were conducted. A washout period from January 2020 to January 2022 was applied. Efficiency outcomes included monthly relative value units (RVUs) per clinical fraction full-time equivalent (cFTE) and daytime (0730-1700, Monday-Friday) operating room minutes. Patient factors and operative efficiency were compared using appropriate statistical tests. Regression modeling was performed for the primary outcomes. ResultsThere were 972 and 257 patients in the pre- and post-VACS groups, respectively. Pre-VACS patients were younger (66.8 ± 12.0 vs 68.7 ± 12.7 years; P = .03) with higher rates of coronary artery disease (34.6% vs 14.8%; P < .01), hypertension (88.4% vs 82.2%; P = .01), and tobacco history (84.4% vs 78.2%; P = .02). Thirty-day mortality (2.4% pre-vs 0.8% post-VACS; P = .18) and Kaplan-Meier estimation of 2-year mortality remained stable after VACS (P = .07). VACS implementation was not associated with 30-day mortality but was associated with lower 2-year mortality hazard on multivariable Cox regression (hazard ratio [HR], 0.5; 95% confidence interval [CI], 0.3-0.9; P = .01). Operative efficiency improved post-VACS (median, 850.0; interquartile range [IQR], 765.7-916.3 vs median, 918.0; IQR, 881.0-951.1 RVU/cFTE-month; P = .03). Daytime operating minutes increased (469.1 ± 287.5 vs 908.2 ± 386.2 minutes; P < .01), whereas non-daytime minutes (420.0; IQR, 266.0-654.0 vs 469.5; IQR, 242.0-738.3 minutes; P = .40) and weekend minutes (129.0; IQR, 0.0-298.0 vs 113.5; IQR, 0.0-279.5 minutes; P = .59) remained stable. ConclusionsA VACS model leads to improvement in surgeon operative efficiency while maintaining patient safety. The adoption of a vascular acute care model has a positive impact on the delivery of comprehensive vascular care.

Practice patterns in utilization of atherectomy and embolic protection devices in inpatient and outpatient treatment settings

ObjectiveThe frequency of atherectomy in lower extremity arterial disease has increased substantially over the past several years, specifically in the office-based laboratory (OBL) setting, yet the efficacy compared with other interventions and the consequences of distal embolization remain unknown. Embolic protection devices (EPDs) have been used at varying rates depending on physician and practice setting. Previous studies have described lesion characteristics to consider when weighing the benefits and drawbacks associated with device use. Our study focuses on the use of atherectomy and EPDs in femoropopliteal arterial disease to better characterize resource use trends and postoperative outcomes in the inpatient and OBL interventional settings. MethodsWe conducted a retrospective analysis on endovascular interventions performed for femoral-popliteal occlusive disease that were entered into the Vascular Quality Initiative data registry between 2017 and 2021. A one:one greedy match, adjusted analysis based on inpatient or OBL location of procedure was used to compare the groups. Hierarchical logistical regression with selective use of principal component analysis was used to further explore the differences in EPD use and immediate postoperative outcomes. A proportional hazard model was used to demonstrate differences in reintervention rates up to 2 years postoperatively between patients who underwent atherectomy in the inpatient vs OBL treatment setting. Results2849 matched pairs were inlcuded in the final analysis. In our cohort, there was 22% EPD use overall, 40% in the hospital setting and 4.4% in the OBL setting (P < .001). Among the patients with available follow-up information, OBL intervention setting increased probability of reintervention by 18% at 2 years postoperatively compared with the inpatient setting; however, there was no difference associated with EPD placement and rate of reintervention. ConclusionsUse of EPDs in the OBL setting compared with the hospital setting is dramatically decreased; however, no increased incidence of postoperative complications was seen compared to procedures performed in the hospital setting when controlling for patient and lesion characteristics. Patients with available follow-up data were more likely to undergo ipsilateral reintervention between 6 months and 2 years postoperatively if atherectomy was done in the OBL setting. Dedicated studies are encouraged to ensure patient safety, effective resource allocation, and long-term efficacy of OBL atherectomy as an ever-growing number of peripheral arterial procedures are transitioned to the OBL setting.

Characterization of clinical data for patient stratification in moderate osteoarthritis with support vector machines, regulatory network models, and verification against osteoarthritis Initiative data

Knee osteoarthritis (OA) diagnosis is based on symptoms, assessed through questionnaires such as the WOMAC. However, the inconsistency of pain recording and the discrepancy between joint phenotype and symptoms highlight the need for objective biomarkers in knee OA diagnosis. To this end, we study relationships among clinical and molecular data in a cohort of women (n = 51) with Kellgren–Lawrence grade 2–3 knee OA through a Support Vector Machine (SVM) and a regulation network model. Clinical descriptors (i.e., pain catastrophism, depression, functionality, joint pain, rigidity, sensitization and synovitis) are used to classify patients. A Youden’s test is performed for each classifier to determine optimal binarization thresholds for the descriptors. Thresholds are tested against patient stratification according to baseline WOMAC data from the Osteoarthritis Initiative, and the mean accuracy is 0.97. For our cohort, the data used as SVM inputs are knee OA descriptors, synovial fluid proteomic measurements (n = 25), and transcription factor activation obtained from regulatory network model stimulated with the synovial fluid measurements. The relative weights after classification reflect input importance. The performance of each classifier is evaluated through ROC-AUC analysis. The best classifier with clinical data is pain catastrophism (AUC = 0.9), highly influenced by funcionality and pain sensetization, suggesting that kinesophobia is involved in pain perception. With synovial fluid proteins used as input, leptin strongly influences every classifier, suggesting the importance of low-grade inflammation. When transcription factors are used, the mean AUC is limited to 0.608, which can be related to the pleomorphic behaviour of osteoarthritic chondrocytes. Nevertheless, funcionality has an AUC of 0.7 with a decisive importance of FOXO downregulation. Though larger and longitudinal cohorts are needed, this unique combination of SVM and regulatory network model shall help to stratify knee OA patients more objectively.

Dynamic Survival Prediction Using Sparse Longitudinal Images via Multi-Dimensional Functional Principal Component Analysis

Our work is motivated by predicting the progression of Alzheimer’s disease (AD) based on a series of longitudinally observed brain scan images. Existing works on dynamic prediction for AD focus primarily on extracting predictive information from multivariate longitudinal biomarker values or brain imaging data at the baseline; whereas in practice, the subject’s brain scan image represented by a multi-dimensional data matrix is collected at each follow-up visit. It is of great interest to predict the progression of AD directly from a series of longitudinally observed images. We propose a novel multi-dimensional functional principal component analysis based on alternating regression on tensor-product B-spline, which circumvents the computational difficulty of doing eigendecomposition, and offers the flexibility of accommodating sparsely and irregularly observed image series. We then use the functional principal component scores as features in the Cox proportional hazards model. We further develop a dynamic prediction framework to provide a personalized prediction that can be updated as new images are collected. Our method extracts visibly interpretable images of the functional principal components and offers an accurate prediction of the conversion to AD. We examine the effectiveness of our method via simulation studies and illustrate its application on the Alzheimer’s Disease Neuroimaging Initiative data. Supplementary materials for this article are available online.