Person: Cuenca Zaldívar, Juan Nicolás
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First Name
Juan Nicolás
Last Name
Cuenca Zaldívar
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Ciencias de la Salud
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Item Radiofrecuencia en la cicatrización de heridas crónicas. Una revisión en hospital de media estancia.(Gerokomos, 2021) Barbas Monjo, Miguel Ángel; Velasco García Cuevas, Jara; Rodríguez Lastra, Jesús; Cuenca Zaldívar, Juan NicolásLas heridas crónicas son un problema de salud significativo. Parece que la estimulación eléctrica produce una reducción significativamente mayor en el área de superficie y cicatrización más completa de las úlceras de difícil cicatrización y de evolución tórpida en comparación con la terapia habitual, sin vendaje compresivo. Objetivos: Evaluar el efecto que la radiofrecuencia a baja intensidad y con efectos no térmicos tiene sobre los diferentes componentes del mecanismo del proceso de cicatrización. Metodología: Para el tratamiento, se utilizó un dispositivo de tecarterapia (CAPENERGY C200). Se aplicaron un total de 10 sesiones de radiofrecuencia con una periodicidad de 1 vez a la semana con una potencia del 60% y una frecuencia de 1,2 MHz durante 30 minutos. Resultados: La presencia de edema, observada en todos los pacientes en la región de la extremidad inferior, desapareció en 30 de los 36 pacientes (Wilcoxon p = 0,004). Este resultado fue confirmado por ultrasonido. El edema celular subcutáneo medio disminuyó en 1,73 cm (Friedman p = 0,000). La temperatura del área tomada antes y después del tratamiento se incrementó en un promedio de 1,4 °C. Estas diferencias son estadísticamente significativas (Wilcoxon p = 0,000). Conclusiones: La radiofrecuencia parece que puede reducir el largo proceso de cicatrización de las lesiones de evolución tórpida, y nos encontramos con unas diferencias significativas a lo largo del tratamiento y con una reducción progresiva en las mediciones de las lesiones y mayor rapidez en la cicatrización de las heridas complejas.Item Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study.(Journal of Medical Internet Research (JMIR), 2022) Cuenca Zaldívar, Juan Nicolás; Torrente, María; Martín-Losada, Laura; Fernández-De-Las-Peñas, César; Lima Florencio, Lidiane; Alexandre Sousa, Pedro; Palacios-Ceña, DomingoBackground: 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.Item The Role of Rehabilitative Ultrasound Imaging Technique in the Lumbopelvic Region as a Diagnosis and Treatment Tool in Physiotherapy: Systematic Review, Meta-Analysis and Meta-Regression.(Journal of Clinical Medicine, 2021) Fernández Carnero, Samuel; Martín Saborido, Carlos; ; Ferragut-Garcias, Alejandro; Cuenca Zaldívar, Juan Nicolás; Leal Quiñones, Alejandro; Calvo Lobo, César; Gallego Izquierdo, Tomás; Achalandabaso Ochoa, AlexanderRehabilitative ultrasound imaging (RUSI) technique seems to be a valid and reliable tool for diagnosis and treatment in physiotherapy and has been widely studied in the lumbopelvic region the last three decades. The aims for this utility in clinical settings must be review through a systematic review, meta-analysis and meta-regression. A systematic review was designed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines with PROSPERO registration and per review in all phases of the process using COVIDENCE, analysis of risk of bias and meta-analysis using REVMAN, and meta-regression calculation using STATA. Database screening provided 6544 references, out of which 321 reported narrative synthesis, and 21 reported quantitative synthesis, while only 7 of them provided comparable data to meta-analyze the variables pain and muscle thickness. In most cases, the forest plots showed considerable I2 heterogeneity indexes for multifidus muscle thickness (I2 = 95%), low back pain (I2 = 92%) and abdominal pain (I2 = 95%), not important for transversus abdominis muscle thickness (I2 = 22%), significant heterogenity (I2 = 69%) depending on the subgroup and not important internal oblique muscle thickness (I2 = 0%) and external oblique muscle thickness (I2 = 0%). Meta-regression did not provide significant data for the correlations between the variables analyzed and the intervention, age, and BMI (Body Mass Index). This review reveals that RUSI could contribute to a high reliability of the measurements in the lumbopelvic region with validity and reliability for the assessments, as well as showing promising results for diagnosis and intervention assessment in physiotherapy compared to the traditional model, allowing for future lines of research in this area.