Prevalence of diabetes mellitus in Spanish primary care setting and its association with cardiovascular risk factors and cardiovascular diseases. SIMETAP-DM study

Ruiz‐García, Antonio; Arranz-Martínez, Ezequiel; García-Álvarez, Juan Carlos; García-Fernández, María Eugenia; Palacios-Martínez, D.; Montero-Costa, Alejandra; Ciria-de-Pablo, Cristina; López-Uriarte, Beatriz; García-Pliego, Rosa A.; Chao-Escuer, Paula; Zafra-Urango, Concepción; Alcaraz-Bethencourt, A.; Redondo-de-Pedro, Sonia; Escamilla-Guijarro, N.; Pascual-Val, Teresa; Vieira-Pascual, María Carmen; Martínez-Irazusta, Juncal; Martínez-Cid-de-Rivera, Eva; Rodríguez-de-Cossío, A.; de-Prado-Prieto, Lidia; Adrián-Sanz, Mercedes; Minguela-Puras, María Esther; Blanco-Canseco, José Manuel; Rubio-Villar, Mercedes; Berbil-Bautista, María Luisa; Hueso-Quesada, Rosa; Plata-Barajas, María Teresa; Redondo-Sánchez, Mercedes; Durán-Tejada, María Rosario; García-Redondo, María Rosa; Sánchez-Herráiz, Miriam; Rey-López, Ana María; García-García-Alcañiz, María Prado; Abad-Schilling, C.; Hidalgo-Calleja, Yolanda; Rivera-Teijido, Montserrat

doi:10.1016/j.artere.2019.03.007

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…The presence of comorbidities, such as diabetes mellitus, is a common concern for nurses in both groups. In Spain, this disease has a prevalence of 12.5% in adults, mostly affecting older adults [59]. Other records referred to the assessment and control of pain, a symptom that is usually associated with rehabilitation processes [56,60].…”

Section: Principal Findingsmentioning

confidence: 99%

Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study

Cuenca-Zaldívar¹,

Torrente²,

Martín-Losada³

et al. 2022

JMIR Med Inform

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Background The COVID-19 pandemic has changed the usual working of many hospitalization units (or wards). Few studies have used electronic nursing clinical notes (ENCN) and their unstructured text to identify alterations in patients' feelings and therapeutic procedures of interest. Objective This study aimed to analyze positive or negative sentiments through inspection of the free text of the ENCN, compare sentiments of ENCN with or without hospitalized patients with COVID-19, carry out temporal analysis of the sentiments of the patients during the start of the first wave of the COVID-19 pandemic, and identify the topics in ENCN. Methods This is a descriptive study with analysis of the text content of ENCN. All ENCNs between January and June 2020 at Guadarrama Hospital (Madrid, Spain) extracted from the CGM Selene Electronic Health Records System were included. Two groups of ENCNs were analyzed: one from hospitalized patients in post–intensive care units for COVID-19 and a second group from hospitalized patients without COVID-19. A sentiment analysis was performed on the lemmatized text, using the National Research Council of Canada, Affin, and Bing dictionaries. A polarity analysis of the sentences was performed using the Bing dictionary, SO Dictionaries V1.11, and Spa dictionary as amplifiers and decrementators. Machine learning techniques were applied to evaluate the presence of significant differences in the ENCN in groups of patients with and those without COVID-19. Finally, a structural analysis of thematic models was performed to study the abstract topics that occur in the ENCN, using Latent Dirichlet Allocation topic modeling. Results A total of 37,564 electronic health records were analyzed. Sentiment analysis in ENCN showed that patients with subacute COVID-19 have a higher proportion of positive sentiments than those without COVID-19. Also, there are significant differences in polarity between both groups (Z=5.532, P<.001) with a polarity of 0.108 (SD 0.299) in patients with COVID-19 versus that of 0.09 (SD 0.301) in those without COVID-19. Machine learning modeling reported that despite all models presenting high values, it is the neural network that presents the best indicators (>0.8) and with significant P values between both groups. Through Structural Topic Modeling analysis, the final model containing 10 topics was selected. High correlations were noted among topics 2, 5, and 8 (pressure ulcer and pharmacotherapy treatment), topics 1, 4, 7, and 9 (incidences related to fever and well-being state, and baseline oxygen saturation) and topics 3 and 10 (blood glucose level and pain). Conclusions The ENCN may help in the development and implementation of more effective programs, which allows patients with COVID-19 to adopt to their prepandemic lifestyle faster. Topic modeling could help identify specific clinical problems in patients and better target the care they receive.

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Section: Principal Findingsmentioning

confidence: 99%

Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study

Cuenca-Zaldívar¹,

Torrente²,

Martín-Losada³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

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“…The prevalence figure is double that in other age ranges. It has been estimated that diabetes has a RR of 1.39 (95% CI: 1.17-1.66) for the development of AD [35,36].…”

Section: Cardiovascular Risk Factorsmentioning

confidence: 99%

Analysis of Risk Factors in Dementia Through Machine Learning

Balea-Fernández

Martinez-Vega

Ortega

et al. 2021

JAD

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Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

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“…In Spain, 12.5% of the population 18 years or older have diabetes mellitus (DM) (1.0% DM type 1 and 11.5% DM type 2) [ 12 ]. Moreover, 5.7% of patients with DM type 1 and 6.4% of patients with DM type 2 present with DMO [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Burden of Disease Study of Patients with Diabetic Macular Oedema in Spain

Ruiz-Moreno,

Gámez Lechuga,

Calvo

et al. 2024

Ophthalmol Ther

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Introduction Diabetic macular oedema (DMO) is a complication of diabetic retinopathy that can result in vision loss. The disease can impact different spheres of a patient’s life, including physical and psychological health, work, and activities of daily living, entailing an important use of healthcare and non-healthcare resources. This study aimed to estimate the socio-economic burden of DMO in Spain. Methods The burden of DMO was estimated from a societal perspective, per patient, year of treatment since diagnosis, and type of treatment. Four categories were considered: direct healthcare costs (DHC), direct non-healthcare costs (DNHC), labour productivity losses (LPL), and intangible costs (IC) associated with loss of quality of life. Average annual costs were calculated by multiplying the resources used per patient by their corresponding unit price (or financial proxy). For a more accurate estimation, differences in resource use between treatments (intravitreal anti-vascular endothelial growth factor injections of ranibizumab or aflibercept, and intravitreal dexamethasone implants) and year since diagnosis (first, second, and third year or beyond) were considered and presented separately. The reference year for costs was 2021. Results The average annual costs of DMO in the first year of treatment after diagnosis was estimated at €18,774, €17,512, and €16,188 per patient treated with ranibizumab, aflibercept, and dexamethasone, respectively. This burden would be reduced to €15,783, €15,701, and €12,233 in the second year, and to €15,119, €15,043, and €12,790 in the third year, respectively. Diagnosis of DMO entails an additional one-off cost of €485. DHC accounted for the greatest proportion of total annual costs per patient, independent of the year, with LPL also making an important contribution to total costs. Conclusions The socio-economic impact of DMO on patients, the healthcare system, and society at large is substantial. The constant increase in its prevalence accentuates the need for planning and implementation of healthcare strategies to prevent vision loss and reduce the socio-economic burden of the disease. Supplementary Information The online version contains supplementary material available at 10.1007/s40123-024-00959-2.

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Prevalence of diabetes mellitus in Spanish primary care setting and its association with cardiovascular risk factors and cardiovascular diseases. SIMETAP-DM study

Cited by 8 publications

References 30 publications

Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study

Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study

Analysis of Risk Factors in Dementia Through Machine Learning

Burden of Disease Study of Patients with Diabetic Macular Oedema in Spain

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