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1.
J Patient Rep Outcomes ; 6(1): 61, 2022 Jun 06.
Artigo em Inglês | MEDLINE | ID: mdl-35666405

RESUMO

BACKGROUND: Long-term treatment adherence is a worldwide concern, with nonadherence resulting from a complex interplay of behaviors and health beliefs. Determining an individual's risk of nonadherence and identifying the drivers of that risk are crucial for the development of successful interventions for improving adherence. Here, we describe the development of a new tool assessing a comprehensive set of characteristics predictive of patients' treatment adherence based on the Social, Psychological, Usage and Rational (SPUR) adherence framework. Concepts from existing self-reporting tools of adherence-related behaviors were identified following a targeted MEDLINE literature review and a subset of these concepts were then selected for inclusion in the new tool. SPUR tool items, simultaneously generated in US English and in French, were tested iteratively through two rounds of cognitive interviews with US and French patients taking long-term treatments for chronic diseases. The pilot SPUR tool, resulting from the qualitative analysis of patients' responses, was then adapted to other cultural settings (China and the UK) and subjected to further rounds of cognitive testing. RESULTS: The literature review identified 27 relevant instruments, from which 49 concepts were included in the SPUR tool (Social: 6, Psychological: 13, Usage: 11, Rational: 19). Feedback from US and French patients suffering from diabetes, multiple sclerosis, or breast cancer (n = 14 for the first round; n = 16 for the second round) indicated that the SPUR tool was well accepted and consistently understood. Minor modifications were implemented, resulting in the retention of 45 items (Social: 5, Psychological: 14, Usage: 10, Rational: 16). Results from the cognitive interviews conducted in China (15 patients per round suffering from diabetes, breast cancer or chronic obstructive pulmonary disease) and the UK (15 patients suffering from diabetes) confirmed the validity of the tool content, with no notable differences being identified across countries or chronic conditions. CONCLUSION: Our qualitative analyses indicated that the pilot SPUR tool is a promising model that may help clinicians and health systems to predict patient treatment behavior. Further steps using quantitative methods are needed to confirm its predictive validity and other psychometric properties.

2.
Fam Syst Health ; 2022 Jun 02.
Artigo em Inglês | MEDLINE | ID: mdl-35653740

RESUMO

INTRODUCTION: The aim of the current study was to determine whether parents of pediatric patients and health care providers (i.e., physicians and nurse practitioners) have different preferences for shared decision making (SDM) and whether these preferences vary across medical situations. METHOD: Participants consisted of parents of children presenting to pediatric clinics (n = 164) and their matched pediatric health care providers (n = 18). Parents and providers completed measures of preferred autonomy for decision-making in general and across specific medical scenarios. RESULTS: Preferences for autonomy were not uniform and varied across situations among providers and among parents. Further, parents and their providers differed from one another in their autonomy preferences across most scenarios, but not in general preferences. DISCUSSION: The results of this study provide evidence of the complex nature of the provider-parent relationship in pediatric practice. This study highlights the need for providers to consider contextual factors that impact parents' preferences for autonomy when making shared medical decisions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

3.
Circulation ; : 101161CIRCULATIONAHA121058143, 2022 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-35708014

RESUMO

BACKGROUND: There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional, and arrhythmic disease features. METHODS: UK Biobank participants who had undergone whole exome sequencing, ECG, and cardiovascular magnetic resonance imaging were selected for study. Three variant-calling strategies (1 primary and 2 secondary) were used to identify participants with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded DCM (clinical or cardiovascular magnetic resonance diagnosis); early DCM features, including arrhythmia or conduction disease, isolated ventricular dilation, and hypokinetic nondilated cardiomyopathy; or phenotype-negative. RESULTS: Among 18 665 individuals included in the study, 1463 (7.8%) possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and cardiovascular magnetic resonance analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in an additional 15.9% of individuals with putative pathogenic variants. Arrhythmias or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic nondilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all 3 variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes as compared with those with variants in moderate/limited evidence genes. CONCLUSIONS: In the UK Biobank, ≈1 of 6 of adults with putative pathogenic variants in DCM genes exhibited early DCM features potentially associated with DCM genotype, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation or dysfunction.

4.
Am J Ophthalmol ; 2022 May 31.
Artigo em Inglês | MEDLINE | ID: mdl-35660421

RESUMO

PURPOSE: To evaluate the utility of nanopore sequencing for identification of potential causative pathogens in endophthalmitis, comparing culture results against full-length 16S rRNA nanopore sequencing (16S Nanopore), whole genome nanopore sequencing (Nanopore WGS) and Illumina (Illumina WGS) DESIGN: : Cross-sectional diagnostic comparison METHODS: : Patients with clinically suspected endophthalmitis underwent intraocular vitreous biopsy as per standard-care. Clinical samples were cultured by conventional methods, together with full length 16S rRNA and WGS using nanopore and Illumina sequencing platforms. RESULTS: Of twenty-three patients (median age 68.5[range 47-88] years; 14[61%] male), 18 cases were culture-positive. Nanopore sequencing identified the same cultured organism as in all of the culture-positive cases and identified potential pathogens in 2(40%) of culture-negative cases. Nanopore WGS was able to additionally detect the presence of bacteriophages in three samples. The agreement at genus level between culture and 16S Nanopore, Nanopore WGS and Illumina WGS were 75%, 100% and 78% respectively. CONCLUSIONS: WGS has higher sensitivity and provides a viable alternative to culture and 16S sequencing for detection of potential pathogens in endophthalmitis. Moreover, WGS has the ability to detect other potential pathogens in culture-negative cases. Whilst Nanopore and Illumina WGS provide comparable data, nanopore sequencing provides potential for cost-effective point-of-care diagnostics.

6.
Case Rep Hematol ; 2022: 2802680, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35515507

RESUMO

Acute myeloid leukemia (AML) is associated with particularly poor outcomes in the elderly population, in whom the disease is most prevalent. BCL-2 has been identified as an antiapoptotic protein and promotes survival of leukemia stem cells. Recently, the United States FDA has approved venetoclax, a selective oral BCL-2 inhibitor, for use in conjunction with hypomethylating agents (azacitidine or decitabine) or low-dose cytarabine as a first-line treatment option for those AML patients ineligible for standard induction chemotherapy. However, there are nuances and challenges when using this regimen in the extremely elderly AML patients. Given the widespread adoption of this regimen and increasing prevalence of patients who are well into their 80 s, it is important to evaluate and understand how to safely use this regimen in this so-called "extremely elderly" population. We present here 3 case studies involving AML patients >85 years of age who were treated with venetoclax plus HMA and provide clinical knowledge on how this population should be appropriately managed.

8.
Transl Vis Sci Technol ; 11(4): 16, 2022 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-35435921

RESUMO

Purpose: Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis-regulatory variants. Methods: We used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence, putative cis-regulatory elements. Eighty percent of these elements were used to train a machine learning model utilizing a gapped k-mer support vector machine-based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all potential single nucleotide variants within cis-regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay data. Results: We generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants were scored. Variants with negative impact scores correlated with higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively parallel reporter expression. Conclusions: We demonstrated the utility of human retinal epigenomic data to train a machine learning model for the purpose of predicting the impact of non-coding regulatory sequence variants. Our model accurately scored sequences and predicted putative transcription factor binding motifs. This approach has the potential to expedite the characterization of pathogenic non-coding sequence variants in the context of unexplained retinal disease. Translational Relevance: This workflow and resulting dataset serve as a promising genomic tool to facilitate the clinical prioritization of functionally disruptive non-coding mutations in the retina.


Assuntos
Aprendizado de Máquina , Doenças Retinianas , Humanos , Nucleotídeos , Filogenia , Retina , Doenças Retinianas/genética , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo
9.
Biomed Opt Express ; 13(3): 1328-1343, 2022 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-35414972

RESUMO

A deep learning algorithm was developed to automatically identify, segment, and quantify geographic atrophy (GA) based on optical attenuation coefficients (OACs) calculated from optical coherence tomography (OCT) datasets. Normal eyes and eyes with GA secondary to age-related macular degeneration were imaged with swept-source OCT using 6 × 6 mm scanning patterns. OACs calculated from OCT scans were used to generate customized composite en face OAC images. GA lesions were identified and measured using customized en face sub-retinal pigment epithelium (subRPE) OCT images. Two deep learning models with the same U-Net architecture were trained using OAC images and subRPE OCT images. Model performance was evaluated using DICE similarity coefficients (DSCs). The GA areas were calculated and compared with manual segmentations using Pearson's correlation and Bland-Altman plots. In total, 80 GA eyes and 60 normal eyes were included in this study, out of which, 16 GA eyes and 12 normal eyes were used to test the models. Both models identified GA with 100% sensitivity and specificity on the subject level. With the GA eyes, the model trained with OAC images achieved significantly higher DSCs, stronger correlation to manual results and smaller mean bias than the model trained with subRPE OCT images (0.940 ± 0.032 vs 0.889 ± 0.056, p = 0.03, paired t-test, r = 0.995 vs r = 0.959, mean bias = 0.011 mm vs mean bias = 0.117 mm). In summary, the proposed deep learning model using composite OAC images effectively and accurately identified, segmented, and quantified GA using OCT scans.

10.
Pan Afr Med J ; 41: 108, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35432704

RESUMO

Introduction: to achieve the sustainable development goal for child survival, we must better understand the socioeconomic characteristics, household behaviors and access to community health services which predict care utilization for children. This study assessed predictors of health care utilization for children under five in Migori County, Kenya. Methods: we used multivariable logistic regression in the context of an integrated health intervention which employed paid, trained, and supervised community health workers (CHWs), inclusive of traditional birth attendants (TBAs). The intervention was delivered with Ministry of Health in one of five geographies included in the study. Results: community health workers (CHW) home visits were associated with a two-fold increase in care seeking for children with respiratory symptoms. Following implementation of a CHW-led malaria intervention, the use of malaria rapid diagnostic tests increased, while fever prevalence decreased. Households in the intervention area were three times more likely to seek care for their child´s fever. Increased care utilization for children with fever was positively associated with male partner attendance at antenatal care visits and negatively associated with skilled delivery and recognition of warning signs. Care utilization for respiratory symptoms was positively associated with caregiver education and negatively associated with household size. Care utilization for diarrhea was positively associated with having a recent under-five death in the household. Conclusion: the study suggests that trained and motivated CHWs may be an effective tool for improving care utilization for children. Further, the study builds on evidence of male partner involvement and caregiver education as predictors of child care utilization.


Assuntos
Malária , População Rural , Agentes Comunitários de Saúde , Estudos Transversais , Feminino , Febre/epidemiologia , Febre/terapia , Humanos , Quênia/epidemiologia , Malária/epidemiologia , Malária/terapia , Masculino , Aceitação pelo Paciente de Cuidados de Saúde , Gravidez
11.
Clin Transl Sci ; 15(6): 1332-1339, 2022 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-35319833

RESUMO

Technological advancements are dramatically changing the landscape of therapeutic development. The convergence of advances in computing power, analytical methods, artificial intelligence, novel digital health tools, and cloud-based platforms has the potential to power an exponential acceleration of evidence generation. For regulatory agencies responsible for evidence evaluation and oversight of medical products, these advances present both promises and challenges. Ultimately, realizing the translation and impact of these innovations that could potentially enhance therapeutic development and improve the health of individuals and the public will require a nimble and responsive regulatory approach. Supporting an adaptive policy-making infrastructure that is poised to address novel regulatory considerations, creating a workforce to ensure relevant expertise, and fostering more diverse collaborations with a broader group of stakeholders are steps toward the goal of modernizing the regulatory ecosystem. This article outlines approaches that can help provide the flexibility and tools needed to foster innovation, while ensuring the safety and effectiveness of medical products.


Assuntos
Inteligência Artificial , Ecossistema , Humanos , Formulação de Políticas
14.
Sci Rep ; 12(1): 2391, 2022 02 14.
Artigo em Inglês | MEDLINE | ID: mdl-35165324

RESUMO

Although having been the subject of intense research over the years, cardiac function quantification from MRI is still not a fully automatic process in the clinical practice. This is partly due to the shortage of training data covering all relevant cardiovascular disease phenotypes. We propose to synthetically generate short axis CINE MRI using a generative adversarial model to expand the available data sets that consist of predominantly healthy subjects to include more cases with reduced ejection fraction. We introduce a deep learning convolutional neural network (CNN) to predict the end-diastolic volume, end-systolic volume, and implicitly the ejection fraction from cardiac MRI without explicit segmentation. The left ventricle volume predictions were compared to the ground truth values, showing superior accuracy compared to state-of-the-art segmentation methods. We show that using synthetic data generated for pre-training a CNN significantly improves the prediction compared to only using the limited amount of available data, when the training set is imbalanced.


Assuntos
Aprendizado Profundo , Ventrículos do Coração/diagnóstico por imagem , Ventrículos do Coração/fisiopatologia , Humanos , Processamento de Imagem Assistida por Computador , Imagem Cinética por Ressonância Magnética , Redes Neurais de Computação , Volume Sistólico , Função Ventricular Esquerda
15.
Sci Rep ; 12(1): 1716, 2022 02 02.
Artigo em Inglês | MEDLINE | ID: mdl-35110593

RESUMO

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.


Assuntos
COVID-19/diagnóstico , COVID-19/virologia , Aprendizado Profundo , SARS-CoV-2 , Tórax/diagnóstico por imagem , Tórax/patologia , Tomografia Computadorizada por Raios X , Algoritmos , COVID-19/mortalidade , Bases de Dados Genéticas , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos , Prognóstico , Tomografia Computadorizada por Raios X/métodos , Tomografia Computadorizada por Raios X/normas
16.
Front Cardiovasc Med ; 9: 822269, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35155637

RESUMO

OBJECTIVES: Cardiac computed tomography (CCT) is a common pre-operative imaging modality to evaluate pulmonary vein anatomy and left atrial appendage thrombus in patients undergoing catheter ablation (CA) for atrial fibrillation (AF). These images also allow for full volumetric left atrium (LA) measurement for recurrence risk stratification, as larger LA volume (LAV) is associated with higher recurrence rates. Our objective is to apply deep learning (DL) techniques to fully automate the computation of LAV and assess the quality of the computed LAV values. METHODS: Using a dataset of 85,477 CCT images from 337 patients, we proposed a framework that consists of several processes that perform a combination of tasks including the selection of images with LA from all other images using a ResNet50 classification model, the segmentation of images with LA using a UNet image segmentation model, the assessment of the quality of the image segmentation task, the estimation of LAV, and quality control (QC) assessment. RESULTS: Overall, the proposed LAV estimation framework achieved accuracies of 98% (precision, recall, and F1 score metrics) in the image classification task, 88.5% (mean dice score) in the image segmentation task, 82% (mean dice score) in the segmentation quality prediction task, and R 2 (the coefficient of determination) value of 0.968 in the volume estimation task. It correctly identified 9 out of 10 poor LAV estimations from a total of 337 patients as poor-quality estimates. CONCLUSIONS: We proposed a generalizable framework that consists of DL models and computational methods for LAV estimation. The framework provides an efficient and robust strategy for QC assessment of the accuracy for DL-based image segmentation and volume estimation tasks, allowing high-throughput extraction of reproducible LAV measurements to be possible.

17.
Ophthalmology ; 2022 Feb 21.
Artigo em Inglês | MEDLINE | ID: mdl-35202616

RESUMO

PURPOSE: To develop and validate a deep learning (DL) system for predicting each point on visual fields (VFs) from disc and OCT imaging and derive a structure-function mapping. DESIGN: Retrospective, cross-sectional database study. PARTICIPANTS: A total of 6437 patients undergoing routine care for glaucoma in 3 clinical sites in the United Kingdom. METHODS: OCT and infrared reflectance (IR) optic disc imaging were paired with the closest VF within 7 days. EfficientNet B2 was used to train 2 single-modality DL models to predict each of the 52 sensitivity points on the 24-2 VF pattern. A policy DL model was designed and trained to fuse the 2 model predictions. MAIN OUTCOME MEASURES: Pointwise mean absolute error (PMAE). RESULTS: A total of 5078 imaging scans to VF pairs were used as a held-out test set to measure the final performance. The improvement in PMAE with the policy model was 0.485 (0.438, 0.533) decibels (dB) compared with the IR image of the disc alone and 0.060 (0.047, 0.073) dB with to the OCT alone. The improvement with the policy fusion model was statistically significant (P < 0.0001). Occlusion masking shows that the DL models learned the correct structure-function mapping in a data-driven, feature agnostic fashion. CONCLUSIONS: The multimodal, policy DL model performed the best; it provided explainable maps of its confidence in fusing data from single modalities and provides a pathway for probing the structure-function relationship in glaucoma.

18.
Ophthalmology ; 129(5): e43-e59, 2022 05.
Artigo em Inglês | MEDLINE | ID: mdl-35016892

RESUMO

OBJECTIVE: Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD. PURPOSE: To delineate the state of AI for AMD, including current data, standards, achievements, and challenges. METHODS: Members of the Collaborative Community on Ophthalmic Imaging Working Group for AI in AMD attended an inaugural meeting on September 7, 2020, to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at a consensus. RESULTS: Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization. CONCLUSIONS: Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.


Assuntos
Oftalmopatias , Degeneração Macular , Oftalmologia , Inteligência Artificial , Técnicas de Diagnóstico Oftalmológico , Oftalmopatias/diagnóstico , Humanos , Degeneração Macular/diagnóstico por imagem , Estados Unidos
19.
Transl Vis Sci Technol ; 11(1): 2, 2022 01 03.
Artigo em Inglês | MEDLINE | ID: mdl-34978561

RESUMO

Purpose: This article describes the Humphrey field analyzer (HFA) dataset from the Department of Ophthalmology at the University of Washington. Methods: Pointwise sensitivities were extracted from HFA 24-2, stimulus III visual fields (VF). Total deviation (TD), mean TD (MTD), pattern deviation, and pattern standard deviation (PSD) were calculated. Progression analysis was performed with simple linear regression on global, regional, and pointwise values for VF series with greater than four tests spanning at least four months. VF data were extracted independently of clinical information except for patient age, gender, and laterality. Results: This dataset includes 28,943 VFs from 7248 eyes of 3871 patients. Progression was calculated for 2985 eyes from 1579 patients. Median [interquartile range] age was 64 years [54, 73], and follow-up was 2.49 years [1.11, 5.03]. Baseline MTD was -4.51 dB [-8.01, -2.65], and baseline PSD was 2.41 dB [1.7, 5.34]. Conclusion: MTD was found to decrease by -0.10 dB/yr [-0.40, 0.11] in eyes for which progression analysis was able to be performed. VFs with deep localized defects, PSD > 12 dB and MTD -15 dB to -25 dB, were plotted, visually inspected, and found to be consistent with neurologic or glaucomatous VFs from patients. For a small number of tests, extracted sensitivity values were compared to corresponding printouts and confirmed to match. Translational Relevance: This open access pointwise VF dataset serves as a source of raw data for investigation such as VF behavior, clinical comparisons to trials, and development of new machine learning algorithms.


Assuntos
Pressão Intraocular , Testes de Campo Visual , Progressão da Doença , Humanos , Pessoa de Meia-Idade , Estudos Retrospectivos , Campos Visuais
20.
Curr Med Res Opin ; 38(2): 171-179, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-34878967

RESUMO

OBJECTIVE: The SPUR (Social, Psychological, Usage, and Rational) Adherence Profiling Tool is a recently developed adaptive instrument for measuring key patient-level risk factors for adherence problems. This study describes the SPUR questionnaire's psychometric refinement and evaluation. METHODS: Data were collected through an online survey among individuals with type 2 diabetes in the United States. 501 participants completed multiple questionnaires, including SPUR and several validated adherence measures. A Partial Credit Model (PCM) analysis was performed to evaluate the structure of the SPUR tool and verify the assumption of a single underlying latent variable reflecting adherence. Partial least-squares discriminant analyses (PLS-DA) were conducted to identify which hierarchically-defined items within each dimension needed to be answered by a given patient. Lastly, correlations were calculated between the latent trait of SPUR adherence and other patient-reported adherence measures. RESULTS: Of the 45 candidate SPUR items, 39 proved to fit well to the PCM confirming that SPUR responses reflected one underlying latent trait hypothesized as non-adherence. Correlations between the latent trait of the SPUR tool and other adherence measures were positive, statistically significant, and ranged from 0.32 to 0.48 (p-values < .0001). The person-item map showed that the items reflected well the range of adherence behaviors from perfect adherence to high levels of non-adherence. The PLS-DA results confirmed the relevance of using four meta-items as filters to open or close subsequent items from their corresponding SPUR dimensions. CONCLUSIONS: The SPUR tool represents a promising new adaptive instrument for measuring adherence accurately and efficiently using the digital behavioral diagnostic tool.


Assuntos
Diabetes Mellitus Tipo 2 , Algoritmos , Humanos , Psicometria/métodos , Reprodutibilidade dos Testes , Inquéritos e Questionários
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