Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years

[EN] Objective To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. Materials and methods Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. Results Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. Discussion TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities¿ relocation and increment of citizens (findings 1, 3¿4), the impact of strategies (findings 2¿3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. Conclusions The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.F.J.P.B, C.S., J.M.G.G. and J.A.C. were funded Universitat Politecnica de Valencia, project "ANALISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MEDICOS". www.upv.es. J.M.G.G.is also partially supported by: Ministerio de Economia y Competitividad of Spain through MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); and European Commission projects H2020-SC1-2016-CNECT Project (No. 727560) and H2020-SC1-BHC-2018-2020 (No. 825750). The funders did not play any role in the study design, data collection and analysis, decision to publish, nor preparation of the manuscript.Perez-Benito, FJ.; Sáez Silvestre, C.; Conejero, JA.; Tortajada, S.; Valdivieso, B.; Garcia-Gomez, JM. (2019). Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years. PLoS ONE. 14(8):1-19. https://doi.org/10.1371/journal.pone.0220369S119148Aguilar-Savén, R. S. (2004). Business process modelling: Review and framework. International Journal of Production Economics, 90(2), 129-149. doi:10.1016/s0925-5273(03)00102-6Poulymenopoulou, M. 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Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

[EN] Background The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage. Methods A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients. Results The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208. Conclusion Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2018/1, and by Carlos III Institute of Health under the project DTS15/00080Pérez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Pollán, M.; Perez-Gómez, B.; Salas-Trejo, D.; Casals, M.... (2019). Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts. Computer Methods and Programs in Biomedicine. 177:123-132. https://doi.org/10.1016/j.cmpb.2019.05.022S12313217

Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks

[EN] The a priori probability of a dataset is usually used as a baseline for comparing a particular algorithm's accuracy in a given binary classification task. ZeroR is the simplest algorithm for this, predicting the majority class for all examples. However, this is an extremely simple approach that has no predictive power and does not describe other dataset features that could lead to a more demanding baseline. In this paper, we present the Extended A Priori Probability (EAPP), a novel semi-supervised baseline metric for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset as well as other features that could provide a relatively trivial separability of the target classes. The approach is based on the area under the ROC curve (AUC ROC), known to be quite insensitive to class imbalance. The procedure involves multiobjective feature extraction and a clustering stage in the input space with autoencoders and a subsequent combinatory weighted assignation from clusters to classes depending on the distance to nearest clusters for each class. Class labels are then assigned to establish the combination that maximizes AUC ROC for each number of clusters considered. To avoid overfit in the combined feature extraction and clustering method, a cross-validation scheme is performed in each case. EAPP is defined for different numbers of clusters, starting from the inverse of the minority class proportion, which is useful for a fair comparison among diversely imbalanced datasets. A high EAPP usually relates to an easy binary classification task, but it also may be due to a significant coarse-grained bias in the dataset, when the task is previously known to be difficult. This metric represents a baseline beyond the a priori probability to assess the actual capabilities of binary classification models.This work was supported in part by the Generalitat Valenciana through the Valencian Institute of Business Competitiveness (IVACE) Distributed Nominatively to Valencian Technological Innovation Centers under Project IMAMCN/2021/1, in part by the Cervera Network of Excellence Project in Data-Based Enabling Technologies (AI4ES) Co-Funded by the Centre for Industrial and Technological Development¿E. P. E. (CDTI), and in part by the European Union through the Next Generation EU Fund within the Cervera Aids Program for Technological Centers under Project CER-20211030.Ortiz, V.; Pérez-Benito, FJ.; Del Tejo Catalá, O.; Salvador Igual, I.; Llobet Azpitarte, R.; Perez-Cortes, J. (2022). Extended a Priori Probability (EAPP): A Data-Driven Approach for Machine Learning Binary Classification Tasks. IEEE Access. 10:120074-120085. https://doi.org/10.1109/ACCESS.2022.32219361200741200851

Breast Dense Tissue Segmentation with Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach

Breast density assessed from digital mammograms is a known biomarker related to a higher risk of developing breast cancer. Supervised learning algorithms have been implemented to determine this. However, the performance of these algorithms depends on the quality of the ground-truth information, which expert readers usually provide. These expert labels are noisy approximations to the ground truth, as there is both intra- and inter-observer variability among them. Thus, it is crucial to provide a reliable method to measure breast density from mammograms. This paper presents a fully automated method based on deep learning to estimate breast density, including breast detection, pectoral muscle exclusion, and dense tissue segmentation. We propose a novel confusion matrix (CM)-YNet model for the segmentation step. This architecture includes networks to model each radiologist's noisy label and gives the estimated ground-truth segmentation as well as two parameters that allow interaction with a threshold-based labeling tool. A multi-center study involving 1785 women whose "for presentation" mammograms were obtained from 11 different medical facilities was performed. A total of 2496 mammograms were used as the training corpus, and 844 formed the testing corpus. Additionally, we included a totally independent dataset from a different center, composed of 381 women with one image per patient. Each mammogram was labeled independently by two expert radiologists using a threshold-based tool. The implemented CM-Ynet model achieved the highest DICE score averaged over both test datasets (0.82±0.14) when compared to the closest dense-tissue segmentation assessment from both radiologists. The level of concordance between the two radiologists showed a DICE score of 0.76±0.17. An automatic breast density estimator based on deep learning exhibited higher performance when compared with two experienced radiologists. This suggests that modeling each radiologist's label allows for better estimation of the unknown ground-truth segmentation. The advantage of the proposed model is that it also provides the threshold parameters that enable user interaction with a threshold-based tool.This research was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed by nomination to Valencian technological innovation centres under project expedient IMDEEA/2021/100. It was also supported by grants from Instituto de Salud Carlos III FEDER (PI17/00047).S

A deep learning framework to classify breast density with noisy labels regularization

Background and objective: Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. Methods: A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. Results: The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. Conclusions: The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.This work was partially funded by Generalitat Valenciana through IVACE (Valencian Institute of Business Competitiveness) distributed nominatively to Valencian technological innovation centres under project expedient IMAMCN/2021/1.S

A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

[EN] Background and Objective: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer. It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard. This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. Methods: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work.This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2019/1, and by Carlos III Institute of Health under the project DTS15/00080.Perez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Fuster Bagetto, A.; Pollan, M.; Pérez-Gómez, B.; Salas-Trejo, D.... (2020). A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Computer Methods and Programs in Biomedicine. 195:123-132. https://doi.org/10.1016/j.cmpb.2020.105668S123132195Kuhl, C. K. (2015). The Changing World of Breast Cancer. 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Cardiac monitoring for patients with palpitations

Monitoreo cardíaco; Grabadora de bucle; PalpitaciónCardiac monitoring; Loop recorder; PalpitationMonitorització cardíaca; Gravadora de bucle; PalpitacióPalpitations are one of the most common reasons for medical consultation. They tend to worry patients and can affect their quality of life. They are often a symptom associated with cardiac rhythm disorders, although there are other etiologies. For diagnosis, it is essential to be able to reliably correlate the symptoms with an electrocardiographic record allowing the identification or ruling out of a possible rhythm disorder. However, reaching a diagnosis is not always simple, given that they tend to be transitory symptoms and the patient is frequently asymptomatic at the time of assessment. In recent years, electrocardiographic monitoring systems have incorporated many technical improvements that solve several of the 24-h Holter monitor limitations. The objective of this review is to provide an update on the different monitoring methods currently available, remarking their indications and limitations, to help healthcare professionals to appropriately select and use them in the work-up of patients with palpitations

Blood stasis imaging predicts cerebral microembolism during acute myocardial infarction

Background: Cardioembolic stroke is a major source of mortality and disability worldwide. The authors hypothesized that quantitative characterization of intracardiac blood stasis may be useful to determine cardioembolic risk in order to personalize anticoagulation therapy. The aim of this study was to assess the relationship between image-based metrics of blood stasis in the left ventricle and brain microembolism, a surrogate marker of cardiac embolism, in a controlled animal experimental model of acute myocardial infarction (AMI). -- Methods: Intraventricular blood stasis maps were derived from conventional color Doppler echocardiography in 10 pigs during anterior AMI induced by sequential ligation of the mid and proximal left anterior descending coronary artery (AMI-1 and AMI-2 phases). From these maps, indices of global and local blood stasis were calculated, such as the average residence time and the size and ratio of contact with the endocardium of blood regions with long residence times. The incidence of brain microemboli (high-intensity transient signals [HITS]) was monitored using carotid Doppler ultrasound. -- Results: HITS were detected in 0%, 50%, and 90% of the animals at baseline and during AMI-1 and AMI-2 phases, respectively. The average residence time of blood in the left ventricle increased in parallel. The residence time performed well to predict microemboli (C-index = 0.89, 95% CI, 0.75–1.00) and closely correlated with the number of HITS (R = 0.87, P < .001). Multivariate and mediation analyses demonstrated that the number of HITS during AMI phases was best explained by stasis. Among conventional echocardiographic variables, only apical wall motion score weakly correlated with the number of HITS (R = 0.3, P = .04). Mural thrombosis in the left ventricle was ruled out in all animals. -- Conclusions: The degree of stasis of blood in the left ventricle caused by AMI is closely related to the incidence of brain microembolism. Therefore, stasis imaging is a promising tool for a patient-specific assessment of cardioembolic risk.This study was supported by grant PI15/02211, Rio Hortega (CM17/00144), and Juan Rodés fellowships (JR15/00039) from Instituto de Salud Carlos III; grant DPI2016-75706-P and a Juan de la Cierva fellowship (IJCI-2014-19507) from Ministerio de Economía y Competitividad; synergy grant Y2018/BIO-4858-PREFI-CM from Comunidad Autónoma de Madrid; the European Union - European Regional Development Fund; by the Spanish Society of Cardiology (ISBI-DCM); by the University of California,San Diego, CTRI Galvanizing Engineering and Medicine Program; American Heart Association grant 16GRNT27250262; and National Institutes of Health UC CAI grant CII4560. P.M.-L. was also funded by CIBERCV. P.M.-L., L.R., J.C.A., and J.B. are inventors of a method for quantifying intracardiac stasis from imaging data under a Patent Cooperation Treaty patent application (WO2017091746A1)

A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain

Background: Sense of Coherence (SOC) is defined as a tendency to perceive life experiences as comprehensible, manageable and meaningful. The construct is split in three major domains: Comprehensibility, Manageability, and Meaningfulness. SOC has been associated with successful coping strategies in the face of illness and traumatic events and is a predictor of self-reported and objective health in a variety of contexts. In the present study we aim to evaluate the association of SOC with disability and dependence in Spanish elders. Methods: A total of 377 participants aged 75 years or over from nine locations across Spain participated in the study (Mean age: 80.9 years; 65.3% women). SOC levels were considered independent variables in two ordinal logistic models on disability and dependence, respectively. Disability was established with the World health Organization-Disability Assessment Schedule 2.0 (36-item version), while dependence was measured with the Extended Katz Index on personal and instrumental activities of daily living. The models included personal (sex, age, social contacts, availability of an intimate confidant), environmental (municipality size, access to social resources) and health-related covariates (morbidity). Results: High Meaningfulness was a strong protective factor against both disability (Odds Ratio [OR] = 0.50; 95% Confidence Interval [CI] = 0.29-0.87) and dependence (OR = 0.33; 95% CI = 0.19-0.58) while moderate and high Comprehensibility was protective for disability (OR = 0.40; 95% CI = 0.22-0.70 and OR = 0.39; 95% CI = 0.21-0.74), but not for dependence. Easy access to social and health resources was also highly protective against both disability and dependence. Conclusions: Our results are consistent with the view that high levels of SOC are protective against disability and dependence in the elderly. Elderly individuals with limited access to social and health resources and with low SOC may be a group at risk for dependence and disability in Spain

A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain

Background Sense of Coherence (SOC) is defined as a tendency to perceive life experiences as comprehensible, manageable and meaningful. The construct is split in three major domains: Comprehensibility, Manageability, and Meaningfulness. SOC has been associated with successful coping strategies in the face of illness and traumatic events and is a predictor of self-reported and objective health in a variety of contexts. In the present study we aim to evaluate the association of SOC with disability and dependence in Spanish elders. Methods A total of 377 participants aged 75 years or over from nine locations across Spain participated in the study (Mean age: 80.9 years; 65.3% women). SOC levels were considered independent variables in two ordinal logistic models on disability and dependence, respectively. Disability was established with the World health Organization-Disability Assessment Schedule 2.0 (36-item version), while dependence was measured with the Extended Katz Index on personal and instrumental activities of daily living. The models included personal (sex, age, social contacts, availability of an intimate confidant), environmental (municipality size, access to social resources) and health-related covariates (morbidity). Results High Meaningfulness was a strong protective factor against both disability (Odds Ratio [OR] = 0.50; 95% Confidence Interval [CI] = 0.29–0.87) and dependence (OR = 0.33; 95% CI = 0.19–0.58) while moderate and high Comprehensibility was protective for disability (OR = 0.40; 95% CI = 0.22–0.70 and OR = 0.39; 95%CI = 0.21–0.74), but not for dependence. Easy access to social and health resources was also highly protective against both disability and dependence. Conclusions Our results are consistent with the view that high levels of SOC are protective against disability and dependence in the elderly. Elderly individuals with limited access to social and health resources and with low SOC may be a group at risk for dependence and disability in Spain.This project was partially funded by a research contract in support of the project “Epidemiological Study of Dementia in Spain” signed by the Pfizer Foundation and Carlos III Institute of HealthS