| 11. |
- Bruze, Gustaf, et al.
(författare)
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Hospital admission after gastric bypass : a nationwide cohort study with up to 6 years follow-up.
- 2017
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Ingår i: Surgery for Obesity and Related Diseases. - : Elsevier. - 1550-7289 .- 1878-7533. ; 13:6, s. 962-969
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Several studies have addressed short-term admission rates after bariatric surgery. However, studies on long-term admission rates are few and population based studies are even scarcer.OBJECTIVE: The aim of this study was to assess short- and long-term admission rates for gastrointestinal surgery after gastric bypass in Sweden compared with admission rates in the general population.SETTING: Swedish healthcare system.METHODS: The surgery cohort consisted of adults with body mass index≥35 identified in the Scandinavian Obesity Surgery Registry (n = 28,331; mean age 41 years; 76% women; Roux-en-Y gastric bypass performed 2007-2012). For each individual, up to 10 comparators from the general population were matched on birth year, sex, and place of residence (n = 274,513). The primary outcome was inpatient admissions due to gastrointestinal surgery retrieved from the National Patient Register through December 31, 2014. Conditional hazard ratios (HR) were estimated using Cox regression.RESULTS: All-cause admission rates were 6.5%, 21.4%, and 65.9% during 30 days, 1 year, and 6 years after surgery, respectively. The corresponding rates for gastrointestinal surgery were 1.8%, 6.8%, and 24.4%. Compared with that of the general population, there was an increased risk of all-cause hospital admission at 1 year (HR 2.6 [2.5-2.6]) and 6 years (HR 2.7 [2.6-2.7]). The risk of hospital admission for any gastrointestinal surgical procedure was greatly increased throughout the study period (HR 8.6 [8.4-8.9]). Female sex, psychiatric disease, and low education were risk factors.CONCLUSION: We found a significant risk of admission to hospital over>6 years after gastric bypass surgery.
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| 12. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery
- 2019
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- Background: Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. So far, traditional statistical methods have failed to produce high accuracy. We aimed to find a useful machine learning (ML) algorithm to predict the risk for severe complication after bariatric surgery.Methods: We trained and compared 29 supervised ML algorithms using information from 37,811 patients that operated with a bariatric surgical procedure between 2010 and 2014 in Sweden. The algorithms were then tested on 6250 patients operated in 2015. We performed the synthetic minority oversampling technique tackling the issue that only 3% of patients experienced severe complications.Results: Most of the ML algorithms showed high accuracy (>90%) and specificity (>90%) in both the training and test data. However, none of the algorithms achieved an acceptable sensitivity in the test data. We also tried to tune the hyperparameters of the algorithms to maximize sensitivity, but did not yet identify one with a high enough sensitivity that can be used in clinical praxis in bariatric surgery. However, a minor, but perceptible, improvement in deep neural network (NN) ML was found.Conclusion: In predicting the severe postoperative complication among the bariatric surgery patients, ensemble algorithms outperform base algorithms. When compared to other ML algorithms, deep NN has the potential to improve the accuracy and it deserves further investigation. The oversampling technique should be considered in the context of imbalanced data where the number of the interested outcome is relatively small.
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| 13. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery : Analysis of Scandinavian Obesity Surgery Registry Data
- 2020
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:5
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Obesity is one of today's most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients.OBJECTIVE: This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods.METHODS: Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve.RESULTS: In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively.CONCLUSIONS: MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information.
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| 14. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network : A Study Based on the Scandinavian Obesity Surgery Registry
- 2019
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 8:12
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Tidskriftsartikel (refereegranskat)abstract
- Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.
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| 15. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery : Machine Learning Study From the Scandinavian Obesity Surgery Register
- 2021
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 9:8
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Prediction of diabetes remission is an important topic in the evaluation of patients with type 2 diabetes (T2D) before bariatric surgery. Several high-quality predictive indices are available, but artificial intelligence algorithms offer the potential for higher predictive capability.OBJECTIVE: This study aimed to construct and validate an artificial intelligence prediction model for diabetes remission after Roux-en-Y gastric bypass surgery.METHODS: Patients who underwent surgery from 2007 to 2017 were included in the study, with collection of individual data from the Scandinavian Obesity Surgery Registry (SOReg), the Swedish National Patients Register, the Swedish Prescribed Drugs Register, and Statistics Sweden. A 7-layer convolution neural network (CNN) model was developed using 80% (6446/8057) of patients randomly selected from SOReg and 20% (1611/8057) of patients for external testing. The predictive capability of the CNN model and currently used scores (DiaRem, Ad-DiaRem, DiaBetter, and individualized metabolic surgery) were compared.RESULTS: In total, 8057 patients with T2D were included in the study. At 2 years after surgery, 77.09% achieved pharmacological remission (n=6211), while 63.07% (4004/6348) achieved complete remission. The CNN model showed high accuracy for cessation of antidiabetic drugs and complete remission of T2D after gastric bypass surgery. The area under the receiver operating characteristic curve (AUC) for the CNN model for pharmacological remission was 0.85 (95% CI 0.83-0.86) during validation and 0.83 for the final test, which was 9%-12% better than the traditional predictive indices. The AUC for complete remission was 0.83 (95% CI 0.81-0.85) during validation and 0.82 for the final test, which was 9%-11% better than the traditional predictive indices.CONCLUSIONS: The CNN method had better predictive capability compared to traditional indices for diabetes remission. However, further validation is needed in other countries to evaluate its external generalizability.
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| 16. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery : A Study Based on the Scandinavian Obesity Surgery Registry
- 2020
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 9:6
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Tidskriftsartikel (refereegranskat)abstract
- Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, we examined performance of the Bayesian networks (BN) method in predicting long-term postoperative HRQoL and compared it with the convolution neural network (CNN) and multivariable logistic regression (MLR). The patients registered in the Scandinavian Obesity Surgery Registry (SOReg) were used for the current study. In total, 6542 patients registered in the SOReg between 2008 and 2012 with complete demographic and preoperative comorbidity information, and preoperative and postoperative 5-year HROoL scores and comorbidities were included in the study. HRQoL was measured using the RAND-SF-36 and the obesity-related problems scale. Thirty-five variables were used for analyses, including 19 predictors and 16 outcome variables. The Gaussian BN (GBN), CNN, and a traditional linear regression model were used for predicting 5-year HRQoL scores, and multinomial discrete BN (DBN) and MLR were used for 5-year comorbidities. Eighty percent of the patients were randomly selected as a training dataset and 20% as a validation dataset. The GBN presented a better performance than the CNN and the linear regression model; it had smaller mean squared errors (MSEs) than those from the CNN and the linear regression model. The MSE of the summary physical scale was only 0.0196 for GBN compared to the 0.0333 seen in the CNN. The DBN showed excellent predictive ability for 5-year type 2 diabetes and dyslipidemia (area under curve (AUC) = 0.942 and 0.917, respectively), good ability for 5-year hypertension and sleep apnea syndrome (AUC = 0.891 and 0.834, respectively), and fair ability for 5-year depression (AUC = 0.750). Bayesian networks provide useful tools for predicting long-term HRQoL and comorbidities in patients after bariatric surgery. The hybrid network that may involve variables from different probability distribution families deserves investigation in the future.
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| 17. |
- Coulman, Karen D., et al.
(författare)
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Development of a Bariatric Surgery Core Data Set for an International Registry
- 2023
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Ingår i: Obesity Surgery. - : Springer. - 0960-8923 .- 1708-0428. ; 33:5, s. 1463-1475
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: Bariatric and metabolic surgery is an effective treatment for severe and complex obesity; however, robust long-term data comparing operations is lacking. Clinical registries complement clinical trials in contributing to this evidence base. Agreement on standard data for bariatric registries is needed to facilitate comparisons. This study developed a Core Registry Set (CRS) - core data to include in bariatric surgery registries globally.MATERIALS AND METHODS: Relevant items were identified from a bariatric surgery research core outcome set, a registry data dictionary project, systematic literature searches, and a patient advisory group. This comprehensive list informed a questionnaire for a two-round Delphi survey with international health professionals. Participants rated each item's importance and received anonymized feedback in round 2. Using pre-defined criteria, items were then categorized for voting at a consensus meeting to agree the CRS.RESULTS: Items identified from all sources were grouped into 97 questionnaire items. Professionals (n = 272) from 56 countries participated in the round 1 survey of which 45% responded to round 2. Twenty-four professionals from 13 countries participated in the consensus meeting. Twelve items were voted into the CRS including demographic and bariatric procedure information, effectiveness, and safety outcomes.CONCLUSION: This CRS is the first step towards unifying bariatric surgery registries internationally. We recommend the CRS is included as a minimum dataset in all bariatric registries worldwide. Adoption of the CRS will enable meaningful international comparisons of bariatric operations. Future work will agree definitions and measures for the CRS including incorporating quality-of-life measures defined in a parallel project.
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| 18. |
- de Vries, Claire E. E., et al.
(författare)
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Outcomes of the first global multidisciplinary consensus meeting including persons living with obesity to standardize patient-reported outcome measurement in obesity treatment research
- 2022
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Ingår i: Obesity Reviews. - : John Wiley & Sons. - 1467-7881 .- 1467-789X. ; 23:8
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Tidskriftsartikel (refereegranskat)abstract
- Quality of life is a key outcome that is not rigorously measured in obesity treatment research due to the lack of standardization of patient-reported outcomes (PROs) and PRO measures (PROMs). The S.Q.O.T. initiative was founded to Standardize Quality of life measurement in Obesity Treatment. A first face-to-face, international, multidisciplinary consensus meeting was conducted to identify the key PROs and preferred PROMs for obesity treatment research. It comprised of 35 people living with obesity (PLWO) and healthcare providers (HCPs). Formal presentations, nominal group techniques, and modified Delphi exercises were used to develop consensus-based recommendations. The following eight PROs were considered important: self-esteem, physical health/functioning, mental/psychological health, social health, eating, stigma, body image, and excess skin. Self-esteem was considered the most important PRO, particularly for PLWO, while physical health was perceived to be the most important among HCPs. For each PRO, one or more PROMs were selected, except for stigma. This consensus meeting was a first step toward standardizing PROs (what to measure) and PROMs (how to measure) in obesity treatment research. It provides an overview of the key PROs and a first selection of the PROMs that can be used to evaluate these PROs.
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| 19. |
- Droeser, R. A., et al.
(författare)
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Hypoparathyroidism after total thyroidectomy in patients with previous gastric bypass
- 2017
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Ingår i: Langenbecks Archives of Surgery. - : Springer Science and Business Media LLC. - 1435-2443 .- 1435-2451. ; 402:2, s. 273-280
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Tidskriftsartikel (refereegranskat)abstract
- Purpose Case reports suggest that patients with previous gastric bypass have an increased risk of severe hypocalcemia after total thyroidectomy, but there are no population-based studies. The prevalence of gastric bypass before thyroidectomy and the risk of hypocalcemia after thyroidectomy in patients with previous gastric bypass were investigated. Methods By cross-linking The Scandinavian Quality Registry for Thyroid, Parathyroid and Adrenal Surgery with the Scandinavian Obesity Surgery Registry patients operated with total thyroidectomy without concurrent or previous surgery for hyperparathyroidism were identified and grouped according to previous gastric bypass. The risk of treatment with intravenous calcium during hospital stay, and with oral calcium and vitamin D at 6 weeks and 6 months postoperatively was calculated by using multiple logistic regression in the overall cohort and in a 1:1 nested case-control analysis. Results We identified 6115 patients treated with total thyroidectomy. Out of these, 25 (0.4 %) had undergone previous gastric bypass surgery. In logistic regression, previous gastric bypass was not associated with treatment with i.v. calcium (OR 2.05, 95 % CI 0.48-8.74), or calcium and/or vitamin D at 6 weeks (1.14 (0.39-3.35), 1.31 (0.39-4.42)) or 6 months after total thyroidectomy (1.71 (0.40-7.32), 2.28 (0.53-9.75)). In the nested case-control analysis, rates of treatment for hypocalcemia were similar in patients with and without previous gastric bypass. Conclusion Previous gastric bypass surgery was infrequent in patients undergoing total thyroidectomy and was not associated with an increased risk of postoperative hypocalcemia.
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| 20. |
- Granstam, Elisabet, et al.
(författare)
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Gastric bypass surgery reduced the risk for diabetic retinopathy in patients with type 2 diabetes : A nationwide observational study
- 2019
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Konferensbidrag (refereegranskat)abstract
- Purpose: Diverging results have been reported with regards to the occurrence and progression of diabetic retinopathy following gastric bypass surgery (GBP) in patients with diabetes. We aimed to investigate the incidence of diabetic ocular complications in a nationwide study in Sweden in obese patients with type 2 diabetes mellitus (T2DM) following GBP and compared to a matched cohort of patients with T2DM not subjected to GBP surgery.Setting: Nationwide registry study in Sweden.Methods: We used data from two nationwide registers in Sweden: the Scandinavian Obesity Surgery Registry (SOReg) and the National Diabetes Registry (NDR). Patients with T2DM who had undergone GBP 2007-2013 reported to the SOReg were matched (1:1) with patients with T2DM from the NDR who had not had GBP surgery for obesity, based on sex, age, body mass index (BMI) and calender time (year). Follow-up data were obtained until December 31, 2015. The main outcome was occurrence of new diabetic retinopathy and was assessed with Cox proportional-hazards regression model. The importance of potential risk factors was assessed using a machine learning approach.Results: The study population consisted of 5321 patients who had undergone GBP and 5321 matched controls in NDR, and was followed up for a mean of 4.5 years. Mean age was 49.0 (SD 9.5) in the GBP and 47.1 (11.5) years in the control patients, respectively. BMI and HbA1c at baseline were 42.0 (5.7) and 60.0 (16.8) in the GBP group and 40.9 (7.3) kg/m2 and 58.5 (16.9) mmol/mol in the control group. Duration of diabetes was approximately 6 years in both groups. The risk for new diabetic retinopathy was reduced in the GBP patients (hazard ratio [HR] 0·62, 95% CI 0·49–0·78; p<0.001). The most important risk factors for development of diabetic retinopathy were diabetes duration, HbA1c, glomerular filtration rate (GFR), use of insulin and BMI. There was no evidence of increased risk for development of sight-threatening or treatment-requiring diabetic ocular complications such as diabetic macular edema, proliferative diabetic retinopathy, need for intravitreal drug administration, panretinal photocoagulation or vitrectomy.Conclusions: In this nationwide large cohort study of patients with type 2 diabetes we found a beneficial effect of GBP surgery on the risk for development of diabetic retinopathy. Furthermore, there were no indications for increased occurrence of sight-threatening or treatment-requiring diabetic retinopathy. These data provide support that, besides standard screening for diabetic retinopathy, there is no need for extended ophthalmological surveillance of patients with type 2 diabetes undergoing GBP surgery.
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