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Your search keyword '"Candela Toha, Á."' showing total 14 results
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14 results on '"Candela Toha, Á."'

1. Effect of colchicine on perioperative atrial fibrillation and myocardial injury after non-cardiac surgery in patients undergoing major thoracic surgery (COP-AF): an international randomised trial

Author

Conen, David, Devereaux, PJ, Vincent, Jessica, Wang, Michael Ke, Wells, Jennifer R., Healey, Jeff S., Landoni, Giovanni, McIntyre, William F., Popova, Ekaterine, Sessler, Daniel I., Srinathan, Sadeesh K., Amir, Mohammed, Bangdiwala, Shrikant I., Bossard, Matthias, Chan, Matthew TV, Eikelboom, John W., Fleischmann, Edith, Jolly, Sanjit, Montes, Felix Ramón, Reimer, Cara, Schmartz, Denis, Wang, Chew Yin, Ofori, Sandra N., Blum, Steffen, Alvarez-Garcia, Jesus, Lo Bianco, Giuliana, Traquair, Hugh, Guerrero-Pinedo, Fernando Andres, Oleynick, Christopher, Meyre, Pascal B., Méndez-Zurita, Francisco, Chiarella-Redfern, Helene, Marcucci, Maura, Donati, Francesco, Gonzalez-Osuna, Aranzazu, Minervini, Fabrizio, Moreno Weidmann, Zoraida, Guerra-Ramos, Jose M., Dion, Gabriel, Ramos-Pachón, Anna, Borges, Flavia K., de Sa Boasquevisque, Danielle, Mosconi, Maria Giulia, Amarenco, Pierre, Mitchell, L. Brent, Wyse, George, Cheng, Davy, McAlister, Finlay A., Wells, George A., Baskaran, Geethan, Burns, Andrew T., Gennaccaro, Julia, Howe, Rosemary, Mastrangelo, Louise, Pettit, Shirley, Popovic, Michelle, Shahbaz, Subana, Tosh, Makayla, Zucchetto, Simona J., Heenan, Laura, Lee, Shun Fu, Li, Zhuoru, Xu, Lizhen, Kabon, Barbara, Hoetzenecker, Konrad, Schweiger, Thomas, Reiterer, Christian, Zotti, Oliver, Bsuchner, Paul, Hochreiter, Beatrix, Taschner, Alexander, Adamowitsch, Nikolas, Horvath, Katharina, Hantáková, Nicole, Bidgoli, Javad, Huybrechts, Isabelle, Cappeliez, Serge, Neary, John D., Shargall, Yaron, Tandon, Vikas, Finley, Christian, Agzarian, John, Hanna, Waël, Abdulrahman, Muammar, Lawrence, Kelly, Gregus, Krysten, Quraishi, Faraaz, Wikkerink, Spencer, Wallace, Christine, Prine, Merissa, Gregus, Emily, Hare, Jacqueline, Lombardo, Kristen, Fezia, Behashta, Columbus, Teresa, Rushton, Jacob, DuMerton, Deborah, Reid, Ken, Parlow, Joel, Chung, Wiley, Karizhenskaia, Maria, Malik, Aftab, Tanzola, Rob, Giles, Andrew, McLean, Sean R., McGuire, Anna, Lohser, Jens, Lim, Shirley, Grey, Rebecca, Yee, John, Grant, Kyle, Lee, Alex L., Choi, James J., Dewar, Leith R., Durkin, Christopher, Schisler, Travis, Hecht, Patrick, Hughes, Bevan, Kidane, Biniam, Liu, Richard, Tan, Lawrence, Gowing, Stephen, Buduhan, Gordon, Enns, Stephanie, Poole, Emma, Graham, Kristin, Dubik, Niina, Chin, Alex, Maziak, Donna E., Seely, Andrew JE, Gilbert, Sebastien, Villeneuve, P. James, Sundaresan, Sudhir, Gingrich, Molly, Fazekas, Anna, Bucciero, Kirby, Malthaner, Richard A., Lewis, Deb, Fortin, Dalilah, Qiabi, Mehdi, Nayak, Rahul, Plourde, Madelaine Marie, Haider, Tehmina, Murphy, Rowan, Sellers, Daniel, Donahoe, Laura, Lefebvre, Marco, Lanthier, Luc, Ko, Michael Augustine, Parente, David, Cheung, Victoria, Schieman, Colin, Bessissow, Amal, Wong, Randolph HL, Joynt, Gavin M., Lam, Carmen KM, Lau, Rainbow WH, Wan, Innes YP, Underwood, Malcolm J., Wu, William KK, Wong, Wai Tat, Choi, Gordon YS, Lee, Eva, Hui, Ka Yan, Fung, Beaker, Chan, Chee Sam, Ng, Fung Kei, Thung, Kin Hoi, Gutiérrez-Soriano, Laura, Castañeda, Laura Carmenza, Téllez, Luis Jaime, Ortiz-Ramirez, Lina Marcela, Baiardo-Redaelli, Martina, Belletti, Alessandro, Dieci, Elisa, Monaco, Fabrizio, Muriana, Piergiorgio, Nakhnoukh, Cristina, Novellis, Pierluigi, Turi, Stefano, Viscardi, Stefano, Veronesi, Giulia, Voltolini, Luca, Bongiolatti, Stefano, Salvicchi, Alberto, Gatteschi, Lavinia, Indino, Rossella, Tombelli, Simone, Ravasin, Alice, Salimbene, Ottavia, Rosboch, Giulio Luca, Balzani, Eleonora, Ceraolo, Edoardo, Neitzert, Luca, Brazzi, Luca, Londero, Francesco, Grossi, William, Massullo, Domenico, Fiorelli, Silvia, Margaritora, Stefano, Hashim, Shahrul Amry, Krishnasamy, Sivakumar, See, Woan Shiang, Nawaz, Mohammed Asghar, Bilal, Haris, Trujillo Reyes, Juan Carlos, Martínez-Téllez, Elisabeth, Belda Sanchis, Josep, Planas Canovas, Georgina, Libreros Niño, Alejandra, Parera Ruiz, Ana, Cladellas Gutierrez, Esther, Guarino, Mauro, Urrutia Cuchi, Gerard, Argilaga Nogues, Marta, Rovira Juan, Anna, Medina-Aedo, Melixa, Turró Castillejo, Mª Asunción, Gil Sanchez, Josep Mª, Araúz-Sarmiento, Ibell, Herranz Perez, Gracia, Chavarria Murillo, Stephania, Garcia-Osuna, Alvaro, Rodriguez-Arias, Ainhoa, Berga Garrote, Nuria, Martín Grande, Ascension, Parise Roux, Diego, Gajate Martín, Luis, De Pablo Pajares, Angélica, Candela Toha, Angel Manuel, Moreno Mata, Nicolás, Muñoz Molina, Gema, Caballero Silva, Usue, Cabañero, Alberto, Fra Fernandez, Sara, Cavestany García-Matres, Cristina, Simón, Luisa, Montenegro, Cristina, Pozo, Sergio, Gonzàlez-Tallada, Anna, González Suarez, Susana, Ribas Ball, Montserrat, De Nadal Clanchet, Miriam, Pérez Vélez, Javier, Cruz, Patrícia, Sánchez-Pedrosa, Guillermo, Duque, Patricia, González, Guillermo, Huerta, Luis, Rodríguez, Laura, Garutti, Ignacio, Ruiz-Villa, Laura, Martí-Ejarque, Maria del Mar, Gili-Bueno, Mireia, Maddio Vieyra, Ricardo Matias, Fibla, Juan J., Durán, Núria, de Temple Pla, Marina, Rodríguez-Fuster, Alberto, Bermejo-Martínez, Silvia, Carramiñana, Albert, Opitz, Isabelle, Etienne, Harry, Cata, Juan P., Guerra-Londono, Juan Jose, Rajaram, Ravi, Corrales, German, Vaporciyan, Ara, Mehran, Reza, Sepesi, Boris, Walsh, Garrett, Rice, David, Cukierman, Daniel S., Swisher, Stephen, Marchant, Bryan E., Harris, Lynne C., Cusson, Bruce D., Miller, Scott A., Ayad, Sabry, Araujo, Jorge, Marquez-Roa, Leonardo, Hofstra, Richard L., Wudel, Jr., Leonard J., Minear, Steven C., Teixeira, Camila, Pimentel, Mario, Popoff, Andrew M., Marsack, Kelly, Meehan, Sarah, Chen, Tzonghuei H., Essandoh, Michael, Poppers, Jeremy S., Ke Wang, Michael, Chan, Matthew T V, Cata, Juan P, McLean, Sean R, Srinathan, Sadeesh K, Reyes, Juan Carlos Trujillo, Grande, Ascension Martín, Tallada, Anna Gonzalez, Sessler, Daniel I, Maziak, Donna E, McIntyre, William F, Wong, Randolph H L, McGuire, Anna L, Roux, Diego Parise, Wells, Jennifer R, Ofori, Sandra N, Eikelboom, John W, Jolly, Sanjit S, Healey, Jeff S, and Devereaux, P J

Published
2023
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12. Protocolization of Analgesia and Sedation Through Smart Technology in Intensive Care: Improving Patient Safety.

Author

Ojeda IM, Sánchez-Cuervo M, Candela-Toha Á, Serrano-López DR, Bermejo-Vicedo T, and Alcaide-López-de-Lerma JM

Subjects
Humans, Medication Errors prevention & control, Intensive Care Units, Critical Care, Infusion Pumps, Infusions, Intravenous, Patient Safety, Analgesia
Abstract

Background: The risk of medication errors in intensive care units is high, primarily in the drug administration phase., Local Problem: Management of high-alert medications within intensive care units in the study institution varied widely. The aim of this quality improvement project was to protocolize and centralize the management of high-alert medications in acute care settings and to implement smart intravenous infusion pump technology in intensive care units., Methods: The project was conducted in 4 phases: (1) protocolization and standardization of intravenous mixtures, (2) centralization of intravenous mixture preparation in the Pharmacy Department, (3) programming of the smart pumps, and (4) dissemination and staged implementation of intravenous mixture protocols. Smart pumps (Alaris, CareFusion) were used to deliver the medicines, and the manufacturer's software (Alaris Guardrails, CareFusion) was used to analyze data regarding adherence to the drug library and the number of programming errors detected., Results: Morphine, remifentanil, fentanyl, midazolam, dexmedetomidine, and propofol were included. After implementation of the smart pumps, 3283 infusions were started; of these, 2198 were programmed through the drug library, indicating 67% compliance with the safety software. The pumps intercepted 398 infusion-related programming errors that led to cancellation or reprogramming of drug infusions., Conclusions: Protocolization and centralization of the preparation of high-alert sedative and analgesic medications for critically ill patients and the administration of these drugs using smart pump technology decrease variability of clinical practice and intercept potentially serious medication errors., (©2023 American Association of Critical-Care Nurses.)

Published
2023
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13. Impact of COVID-19 on Short- and Medium-Term Prescription of Enteral Nutrition in the General Population vs. Older People in the Community of Madrid, Spain.

Author

Luque Calvo C, Mataix Sanjuan ÁL, Candela Toha Á, Martínez Castro N, Pintor Recuenco MR, Calleja López JL, Botella-Carretero JI, and Arrieta Blanco F

Subjects
Aged, Energy Intake, Humans, Prescriptions, Spain epidemiology, COVID-19 epidemiology, COVID-19 therapy, Enteral Nutrition
Abstract

We aimed to analyse the impact of COVID-19 during 2020 and 2021 on the prescription of enteral nutritional support and its expenditure in the Community of Madrid, Spain, compared to pre-pandemic data from 2016 in the general population vs. elderly. We analysed official electronic prescriptions of all public hospitals of the Community of Madrid. The population over 75 years of age have the higher prescription of nutritional supplements (p < 0.001 vs. other age groups), with no differences between the 45−64 age group compared to the 65−74 age group (χ2 = 3.259, p = 0.196). The first wave of COVID-19 or the first time there was a real awareness of the virus in Spain is similar in a way to the first peak of prescription of enteral nutrition in March 2020. The second peak of prescription was observed in the over 75 age group in July 2020, being more pronounced in December 2020 and March−April of the following year (F = 7.863, p = 0.041). The last peaks correspond to summer 2021 and autumn of the same year (p = 0.031—year 2021 vs. 2020, p = 0.011—year 2021 vs. 2019), where a relationship between increased prescription of enteral nutrition and COVID-19 cases is observed. High-protein and high-calorie dietary therapies were the most prescribed in patients with or without diabetes. All of this entailed higher cost for the Community of Madrid. In conclusion, COVID-19 significantly affected the prescription of nutritional support, especially in the population over 75 years of age.

Published
2022
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14. Preoperative clinical model to predict myocardial injury after non-cardiac surgery: a retrospective analysis from the MANAGE cohort in a Spanish hospital.

Author

Serrano AB, Gomez-Rojo M, Ureta E, Nuñez M, Fernández Félix B, Velasco E, Burgos J, Popova E, Urrutia G, Gomez V, Del Rey JM, Sanjuanbenito A, Zamora J, Monteagudo JM, Pestaña D, de la Torre B, and Candela-Toha Á

Subjects
Cohort Studies, Humans, Retrospective Studies, Risk Factors, Hospitals, Nomograms
Abstract

Objectives: To determine preoperative factors associated to myocardial injury after non-cardiac surgery (MINS) and to develop a prediction model of MINS., Design: Retrospective analysis., Setting: Tertiary hospital in Spain., Participants: Patients aged ≥45 years undergoing major non-cardiac surgery and with at least two measures of troponin levels within the first 3 days of the postoperative period. All patients were screened for the MANAGE trial., Primary and Secondary Outcome Measures: We used multivariable logistic regression analysis to study risk factors associated with MINS and created a score predicting the preoperative risk for MINS and a nomogram to facilitate bed-side use. We used Least Absolute Shrinkage and Selection Operator method to choose the factors included in the predictive model with MINS as dependent variable. The predictive ability of the model was evaluated. Discrimination was assessed with the area under the receiver operating characteristic curve (AUC) and calibration was visually assessed using calibration plots representing deciles of predicted probability of MINS against the observed rate in each risk group and the calibration-in-the-large (CITL) and the calibration slope. We created a nomogram to facilitate obtaining risk estimates for patients at pre-anaesthesia evaluation., Results: Our cohort included 3633 patients recruited from 9 September 2014 to 17 July 2017. The incidence of MINS was 9%. Preoperative risk factors that increased the risk of MINS were age, American Status Anaesthesiology classification and vascular surgery. The predictive model showed good performance in terms of discrimination (AUC=0.720; 95% CI: 0.69 to 0.75) and calibration slope=1.043 (95% CI: 0.90 to 1.18) and CITL=0.00 (95% CI: -0.12 to 0.12)., Conclusions: Our predictive model based on routinely preoperative information is highly affordable and might be a useful tool to identify moderate-high risk patients before surgery. However, external validation is needed before implementation., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2021
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