| 1. |
|
|
| 2. |
- Edberg, Niklas J. T., et al.
(författare)
-
Spatial distribution of low-energy plasma around comet 67P/CG from Rosetta measurements
- 2015
-
Ingår i: Geophysical Research Letters. - 0094-8276 .- 1944-8007. ; 42:11, s. 4263-4269
-
Tidskriftsartikel (refereegranskat)abstract
- We use measurements from the Rosetta plasma consortium Langmuir probe and mutual impedance probe to study the spatial distribution of low-energy plasma in the near-nucleus coma of comet 67P/Churyumov-Gerasimenko. The spatial distribution is highly structured with the highest density in the summer hemisphere and above the region connecting the two main lobes of the comet, i.e., the neck region. There is a clear correlation with the neutral density and the plasma to neutral density ratio is found to be approximate to 1-210(-6), at a cometocentric distance of 10km and at 3.1AU from the Sun. A clear 6.2h modulation of the plasma is seen as the neck is exposed twice per rotation. The electron density of the collisionless plasma within 260km from the nucleus falls off with radial distance as approximate to 1/r. The spatial structure indicates that local ionization of neutral gas is the dominant source of low-energy plasma around the comet.
|
|
| 3. |
|
|
| 4. |
- Anna Karin, Hedström, et al.
(författare)
-
The impact of bariatric surgery on disease activity and progression of multiple sclerosis : A nationwide matched cohort study
- 2022
-
Ingår i: Multiple Sclerosis Journal. - : Sage Publications. - 1352-4585 .- 1477-0970. ; 28:13, s. 2099-2105
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Surgical outcomes in patients with multiple sclerosis (MS) following metabolic surgery appear to be similar compared to those of the general bariatric population.OBJECTIVE: To study the impact of metabolic surgery on the clinical course of MS.METHODS: Using data from the Scandinavian Obesity Surgery Registry and the Swedish Multiple Sclerosis register, we compared disease outcomes in 122 cases of MS who had undergone metabolic surgery with those of 122 cases of MS without surgery, matched by a two-staged Propensity score match, including age at disease onset, sex, MS phenotype, body mass index, and preoperative severity of MS as measured by the Expanded Disability Status Scale.RESULTS: The time to 6-month confirmed disability progression during the first five years postbaseline was shorter among the surgical patients (hazard ratio (HR) = 2.31, 95% confidence interval (CI) = 1.09-4.90; p = 0.03). No differences were observed regarding postoperative annual relapse rate (p = 0.24) or time to first postoperative relapse (p = 0.52).CONCLUSION: Although metabolic surgery appears to be a safe and efficient treatment of obesity in patients with MS, the clinical course of the disease might be negatively affected. Long-term nutritional follow-up after surgery and supplementation maintenance are crucial, particularly among those with preoperative deficits.
|
|
| 5. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
-
A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery
- 2019
-
Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 8:5
-
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.
|
|
| 6. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
-
Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery : Analysis of Scandinavian Obesity Surgery Registry Data
- 2020
-
Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:5
-
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.
|
|
| 7. |
- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
-
Using a Convolutional Neural Network to Predict Remission of Diabetes After Gastric Bypass Surgery : Machine Learning Study From the Scandinavian Obesity Surgery Register
- 2021
-
Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 9:8
-
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.
|
|
| 8. |
- Dahlberg, Karuna, 1979-, et al.
(författare)
-
Incident self-harm after bariatric surgery : A nationwide registry-based matched cohort study
- 2023
-
Ingår i: Clinical Obesity. - : John Wiley & Sons. - 1758-8103 .- 1758-8111. ; 13:3
-
Tidskriftsartikel (refereegranskat)abstract
- The aims of this study were to evaluate the longitudinal risk of self-harm and the risk factors for self-harm after bariatric surgery in patients and control subjects without prior self-harm. This observational cohort study was based on prospectively registered data. Patients 18–70 years at time of surgery, body mass index (BMI) > 30 kg/m2, who underwent a primary Roux-en-Y gastric bypass (RYGB) procedure or a primary sleeve gastrectomy between 2007 and 2019 were considered for inclusion. All patients who met the inclusion criteria were matched 1:10 to the general population in Sweden (69 492 patients vs. 694 920 controls). After excluding patients and controls with previous self-harm, a self-harm event occurred in 1408 patients in the surgical group (incidence rate (IR) 3.54/1000 person-years, 95% confidence interval (CI) 3.36–3.73) versus in 3162 patients in the control group (IR 0.81/1000 person-years, 95% CI 0.78–0.84), with a hazard ratio (HR) of 4.38 (95% CI 4.11–4.66, p < .001). Median follow-up time was 6.1 years. Risk factors were younger age, lower BMI, cardiovascular, and chronic obstructive pulmonary disease, all aspects of psychiatric comorbidities (except neuropsychiatric disorder), lower socioeconomic status, RYGB, lower health-related quality of life, lower postoperative weight loss, and not attending postoperative follow-up visits. Self-harm is clearly higher after bariatric surgery than in the general population. A qualitative follow-up may be particularly important for patients at increased risk.
|
|
| 9. |
- Gryth, Karin, et al.
(författare)
-
The Influence of Socioeconomic Factors on Quality-of-Life After Laparoscopic Gastric Bypass Surgery
- 2019
-
Ingår i: Obesity Surgery. - : Springer. - 0960-8923 .- 1708-0428. ; 29:11, s. 3569-3576
-
Tidskriftsartikel (refereegranskat)abstract
- Introduction: Patients with low socioeconomic status have been reported to experience poorer outcome after several types of surgery. The influence of socioeconomic factors on health-related quality-of-life (HRQoL) after bariatric surgery is unclear.Materials and Methods: Patients operated with a primary laparoscopic gastric bypass procedure in Sweden between 2007 and 2015 were identified in the Scandinavian Obesity Surgery Register. Patients with a completed assessment of health-related quality-of-life based on the Obesity-related Problem Scale (OP Scale) were included in the study. Socioeconomic status was based on data from Statistics Sweden.Results: A total of 13,723 patients (32% of the 43,096 operated during the same period), with complete OP scores at baseline and two years after surgery, were included in the study. Age, lower preoperative BMI, male gender, higher education, professional status and disposable income as well as not receiving social benefits (not including retirement pension), and not a first- or second-generation immigrant, were associated with a higher postoperative HRQoL. Patients aged 30-60 years, with lower BMI, higher socioeconomic status, women and those born in Sweden by Swedish parents experienced a higher degree of improvement in HRQoL. Postoperative weight-loss was associated with higher HRQoL (unadjusted B 16.3, 95%CI 14.72-17.93, p < 0.0001).Conclusion: At 2 years, a strong association between weight loss and improvement in HRQoL was seen, though several factors influenced the degree of improvement. Age, sex, preoperative BMI and socioeconomic status all influence the postoperative HRQoL as well as the improvement in HRQoL after laparoscopic gastric bypass surgery.
|
|
| 10. |
- Hedberg, Suzanne, et al.
(författare)
-
Comparison of Sleeve Gastrectomy vs Roux-en-Y Gastric Bypass : A Randomized Clinical Trial
- 2024
-
Ingår i: JAMA Network Open. - : American Medical Association (AMA). - 2574-3805. ; 7:1
-
Tidskriftsartikel (refereegranskat)abstract
- IMPORTANCE: Laparoscopic sleeve gastrectomy (SG) and laparoscopic Roux-en-Y gastric bypass (RYGB) are widely used bariatric procedures for which comparative efficacy and safety remain unclear.OBJECTIVE: To compare perioperative outcomes in SG and RYGB.DESIGN, SETTING, AND PARTICIPANTS: In this registry-based, multicenter randomized clinical trial (Bypass Equipoise Sleeve Trial), baseline and perioperative data for patients undergoing bariatric surgery from October 6, 2015, to March 31, 2022, were analyzed. Patients were from university, regional, county, and private hospitals in Sweden (n = 20) and Norway (n = 3). Adults (aged ≥18 years) eligible for bariatric surgery with body mass indexes (BMIs; calculated as weight in kilograms divided by height in meters squared) of 35 to 50 were studied.INTERVENTIONS: Laparoscopic SG or RYGB.MAIN OUTCOMES AND MEASURES: Perioperative complications were analyzed as all adverse events and serious adverse events (Clavien-Dindo grade >IIIb). Ninety-day mortality was also assessed.RESULTS: A total of 1735 of 14 182 eligible patients (12%; 1282 [73.9%] female; mean (SD) age, 42.9 [11.1] years; mean [SD] BMI, 40.8 [3.7]) were included in the study. Patients were randomized and underwent SG (n = 878) or RYGB (n = 857). The mean (SD) operating time was shorter in those undergoing SG vs RYGB (47 [18] vs 68 [25] minutes; P < .001). The median (IQR) postoperative hospital stay was 1 (1-1) day in both groups. The 30-day readmission rate was 3.1% after SG and 4.0% after RYGB (P = .33). There was no 90-day mortality. The 30-day incidence of any adverse event was 40 (4.6%) and 54 (6.3%) in the SG and RYGB groups, respectively (odds ratio, 0.71; 95% CI, 0.47-1.08; P = .11). Corresponding figures for serious adverse events were 15 (1.7%) for the SG group and 23 (2.7%) for the RYGB group (odds ratio, 0.63; 95% CI, 0.33-1.22; P = .19).CONCLUSIONS AND RELEVANCE: This randomized clinical trial of 1735 patients undergoing primary bariatric surgery found that both SG and RYGB were performed with a low perioperative risk without clinically significant differences between groups.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02767505.
|
|
