The SEEN comprehensive clinical survey of adult obesity: Executive summary

Pomar, María Dolores Ballesteros; Vilarrasa, Núria; Herrera, Miguel Angel Rubio; Barahona, María José; Bueno, Marta; Caixàs, Assumpta; Continente, Alfonso Calañas; Ciudin, Andreea; Cordido, Fernando; Hollanda, Ana de; Díaz, María Jesús González; Flores, Lílliam; Luna, P. P. García; Pérez-Sevillano, Fernando García; Goday, Albert; Lecube, Albert; Gómez, Juan José López; Miñambres, Inka; Gorria, María José Morales; Morı́nigo, Rosa; Nicolau, Joana; Pellitero, Sílvia; Salvador, Javier; Valdés, Sergio; Lesmes, Irene Bretón

doi:10.1016/j.endien.2020.05.006

Cited by 12 publications

(14 citation statements)

References 37 publications

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“…Excess weight with a body mass index (BMI) greater than 40 or a BMI greater than 35 and associated medical problems. 30 2. Age over 18 years.…”

Section: Datasetmentioning

confidence: 99%

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study

Benítez-Andrades,

Prada-García,

García-Fernández

et al. 2024

DIGITAL HEALTH

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Objectives Metabolic bariatric surgery is a critical intervention for patients living with obesity and related health issues. Accurate classification and prediction of patient outcomes are vital for optimizing treatment strategies. This study presents a novel machine learning approach to classify patients in the context of metabolic bariatric surgery, providing insights into the efficacy of different models and variable types. Methods Various machine learning models, including Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, K-nearest neighbour with RandomOverSampler, and K-nearest neighbour with SMOTE, were applied to a dataset of 73 patients. The dataset, comprising psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study also explored the impact of different variable groupings and oversampling techniques. Results Experimental results indicate average accuracy values as high as 66.7% for the best model. Enhanced versions of K-nearest neighbour and Decision Tree, along with variations of K-nearest neighbour such as RandomOverSampler and SMOTE, yielded the best results. Conclusions The study unveils a promising avenue for classifying patients in the realm of metabolic bariatric surgery. The results underscore the importance of selecting appropriate variables and employing diverse approaches to achieve optimal performance. The developed system holds potential as a tool to assist healthcare professionals in decision-making, thereby enhancing metabolic bariatric surgery outcomes. These findings lay the groundwork for future collaboration between hospitals and healthcare entities to improve patient care through the utilization of machine learning algorithms. Moreover, the findings suggest room for improvement, potentially achievable with a larger dataset and careful parameter tuning.

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“…Excess weight with a body mass index (BMI) greater than 40 or a BMI greater than 35 and associated medical problems. 30 2. Age over 18 years.…”

Section: Datasetmentioning

confidence: 99%

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study

Benítez-Andrades,

Prada-García,

García-Fernández

et al. 2024

DIGITAL HEALTH

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“…• Initial clinical assessment and anthropometric data, according to actual recommendations [2,19]. • Identify complications related to obesity and indicate their severity.…”

Section: Pc Assessmentmentioning

confidence: 99%

Integrative and Collaborative Approach in the Chronic Management of Obesity in Primary and Tertiary Care Setting: Vall Hebron-SAP Muntanya Healthcare Route

et al. 2022

Self Cite

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Introduction: In the context of obesity pandemic, the health care providers involved in the primary care should have a significant role. Several guidelines for the management of obesity in primary care were proposed recently. In general lines, these guidelines include recommendation on the baseline assessment, therapy and algorithm for referral to specialized obesity clinic and follow-up. Nevertheless, at present there is no guideline or protocol that continuously and bidirectionally links the two settings: primary care and specialized obesity clinic. Methods: We present a model of continuous, bilateral and integrative interaction between primary care units and reference tertiary care setting in the chronic management of obesity that is already implemented in a public health system. Results: The novelty of our algorithm is that incorporates the support and continuous communication with the specialized obesity clinic of the tertiary care setting from the beginning in the management of a patient with obesity, in a bidirectional manner. Conclusion: This kind of bidirectional and continuous collaboration will help engage health care providers in the management of obesity, optimize efforts, shorten the time until proper intervention, personalize the approach and, finally, save costs for the health system.

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“…Obesity treatment is based on a multimodal approach depending on the degree of obesity, associated comorbidities, patient age and disease progression (2). Bariatric surgery has been shown in multiple studies to be one of the most effective treatments for severe obesity.…”

Section: Introductionmentioning

confidence: 99%

“…Bariatric surgery has been shown in multiple studies to be one of the most effective treatments for severe obesity. Patients who undergo this type of surgery manage to reduce not only their body fat percentage, but also the prevalence and severity of the comorbidities associated with obesity, especially type 2 DM (2,3). Indeed, several studies have shown that bariatric surgery in subjects with Body Mass Index (BMI) ≥ 35 kg/m2 results in better glycaemic control, understood as an improvement in fasting blood glucose, diabetes incidence and greater remission of preexisting diabetes, both in subjects with and without diabetes, compared to conventional medical treatments.…”

Section: Introductionmentioning

confidence: 99%

Results of a Multimodal Rehabilitation programme in bariatric surgery versus the classic care protocol

Ortega-Lucea,

Herrero-Martín,

Pascual-Bellosta

et al. 2024

Preprint

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Introduction: Obesity is a very frequent pathology in our society. Weight gain and its associated comorbidities constitute a major cause of preventable deaths. Bariatric surgery is considered the most effective treatment for reducing weight and comorbidities in obese patients. ERAS (Enhanced Recovery After Surgery) programmes are a set of perioperative practices aimed at reducing the metabolic and immune response triggered by surgical stress, to improve the results of the intervention and achieve a speedy recovery of the patient. Their implementation in bariatric surgery has been slow, since obese patients constitute a high-risk surgical group. Objective: To determine whether the application of an ERAS protocol in bariatric surgery leads to a reduction in hospital stay, as well as in the complications associated with the procedure. Material and methods: A prospective case-control study was performed including 52 patients undergoing bariatric surgery: 25 following the classic protocol and 27 following the ERAS protocol. Results were analysed using the bilateral hypothesis. Statistical significance of the difference was considered from p-values less than 0.05. Confidence intervals included were 95%. Results: There was a reduction in hospital stay (- 0.76 days, p=0.002579), minor complications (48% less), postoperative nausea and vomiting rate (p=0.00029) and postoperative pain (p=1.36e-05) in those patients who followed the ERAS protocol. Conclusions: The implementation of the ERAS protocol reduces hospital stay, without compromising patient safety by not increasing morbidity.

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The SEEN comprehensive clinical survey of adult obesity: Executive summary

Cited by 12 publications

References 37 publications

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study

Integrative and Collaborative Approach in the Chronic Management of Obesity in Primary and Tertiary Care Setting: Vall Hebron-SAP Muntanya Healthcare Route

Results of a Multimodal Rehabilitation programme in bariatric surgery versus the classic care protocol

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