Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery

Stenberg, Erik; Cao, Yang; Szabó, Éva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

doi:10.1007/s11695-017-3099-2

Cited by 46 publications

(38 citation statements)

References 32 publications

(36 reference statements)

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“…The HFHL-RPS is convenient for use by managers, or it can be used for self-checks. There are some successful examples of prediction models and risk score prediction used in clinical medicine, such as the risk score prediction model used for assessing dementia in patients with type 2 diabetes [33] , the risk prediction model used for severe postoperative [34] , and the LIPID heart failure risk-prediction model et al [35] . In addition to Pentti Kuronen et al [36] built the model of NIHL for military pilots, but this model mainly focused on a military pilot group.…”

Section: Discussionmentioning

confidence: 99%

The prediction and risk point score (RPS) of high frequency hearing loss in noise exposed workers

Sun

Shang

Cao³

et al. 2020

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Background: Occupational hearing loss is a significant occupational health concern in many countries, and high frequency hearing loss (HFHL) is an early symptom. Based on realistic demands, we aimed to build a prediction risk model of HFHL and developed the related risk point score (RPS). The results of this study are expected to provide technological support for interventions and management to enhance application-oriented research of HFHL.Methods: A total of 32121 participants who were noise exposed workers were enrolled. The datasets from the National key occupational diseases survey (NKODS) performed from 2014 to 2017 in Sichuan Province in China were utilized. The sociodemographic and occupational characteristics were assessed by standardized questionnaires, and the level of HFHL were collected by audiometric testing and was defined as a binaural high frequency threshold average (BHFTA) over 40 dB in the right and left ears. The risk prediction models were generated by linear logistic regression, and based on the models, the risk point score (RPS) of HFHL were calculated. Results: Of the 32121 participants in the study, 9.97% (n=4029) of workers had HFHL (BHFTA ≥ 40 dB). Age (OR=1.08, 95% CI: 1.071–1.083), sex (OR=3.34, 95% CI: 2.880–3.636), noise exposure time (OR=1.01, 95% CI: 1.008–1.018), manufacturing industry (OR=1.46, 95% CI: 1.302–1.647), construction industry (OR=2.14, 95% CI: 1.488–3.069), mining industry (OR=2.57, 95% CI: 2.225–2.957), foreign enterprise (OR=0.94, 95% CI: 0.781–1.122), and private enterprise (OR=1.32, 95% CI: 1.200–1.442) were predictors of HFHL (P<0.05). By comparing the two risk prediction models, the 40 dB HL criterion model was found to be more effective than the 25 dB HL criterion model (AUC=0.637). Verification of the two models revealed that the 25 dB HL criterion model was more stable than the 40 dB HL criterion.Conclusion: The study found that the prevalence of HFHL was moderate in Sichuan Province. Sex, age, noise exposure years, and employment in the manufacturing industry, construction industry, mining industry, foreign enterprise, or private enterprise were predictors of HFHL, and the development of the RPS of HFHL is necessary for application-oriented research on HFHL.

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Section: Discussionmentioning

confidence: 99%

The prediction and risk point score (RPS) of high frequency hearing loss in noise exposed workers

Sun

Shang

Cao³

et al. 2020

Preprint

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“…En conclusion, les auteurs recommandent qu'une cholécystectomie soit réalisée en préopératoire du bypass en cas de lithiase vésiculaire. Stenberg E, et al (2018) Obes surg [9] Background: Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently.…”

Section: Morbidity Of Cholecystectomy and Gastric Bypass In A Nationamentioning

confidence: 99%

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The fraction of cancer attributable to modifiable risk factors in England, Wales, Scotland, Northern Ireland, and the United Kingdom in 2015 Brown KF et al (2018) Br J Cancer [1] Background: Changing population-level exposure to modifiable risk factors is a key driver of changing cancer incidence. Understanding these changes is therefore vital when prioritising risk-reduction policies, in order to have the biggest impact on reducing cancer incidence. UK figures on the number of risk factor-attributable cancers are updated here to reflect changing behaviour as assessed in representative national surveys, and new epidemiological evidence. Figures are also presented by UK constituent country because prevalence of risk factor exposure varies between them.Methods: Population attributable fractions (PAFs) were calculated for combinations of risk factor and cancer type with sufficient/convincing evidence of a causal association. Relative risks (RRs) were drawn from meta-analyses of cohort studies where possible. Prevalence of exposure Réferences

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“…In the test data, the statistically significant differences were found for age, waist 85 circumference (WC), HbA1c, dyslipidaemia, and revisional surgery (Table 2). Lemshow goodness of fit 17.91 (p=0.056) and Nagelkerke R 2 0.013 (Stenberg et al, 2018).…”

Section: Introduction 33mentioning

confidence: 98%

“…The SOReg is a national quality 68 and research register, covering virtually all bariatric surgical procedures performed in Sweden 69 since 2010. The register has been described in detail elsewhere (Hedenbro et al, 2015; 70 Stenberg et al, 2014), and a prediction model based on logistic regression for the same group 71 of patients has been described previously (Stenberg et al, 2018). The aim of the current study 72 was to find an algorithm or algorithms that perform well not only on the training data but also 73 on the test data that were not used to train the algorithms.…”

Section: Introduction 33mentioning

confidence: 99%

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A comparative study of machine learning algorithms in predicting severe complications after bariatric surgery

Cao

Fang

Ottosson

et al. 2018

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Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. Traditional statistical methods have so far failed to produce high accuracy. To find a useful algorithm to predict the risk for severe complication after bariatric surgery, we trained and compared 29 supervised machine learning (ML) algorithms using information from 37,811 patients operated with a bariatric surgical procedure between 2010 and 2014 in Sweden. The algorithms were then tested on 6,250 patients operated in 2015. Most ML algorithms showed high accuracy (>90%) and specificity (>0.9) in both the training and test data. However, none achieved an acceptable sensitivity in the test data. ML methods may improve accuracy of prediction but we did not yet identify one with a high enough sensitivity that can be used in clinical praxis in bariatric surgery. Further investigation on deeper neural network algorithms is needed.

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Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery

Cited by 46 publications

References 32 publications

The prediction and risk point score (RPS) of high frequency hearing loss in noise exposed workers

The prediction and risk point score (RPS) of high frequency hearing loss in noise exposed workers

Revue de presse

A comparative study of machine learning algorithms in predicting severe complications after bariatric surgery

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