Long-term outcomes of laparoscopic cholecystectomy: a prospective piecewise linear regression analysis

Shi, Hon-Yi; Lee, Hao-Hsien; Tsai, Mee-Sun; Chiu, Chong‐Chi; Uen, Yih-Huei; Lee, King‐Teh

doi:10.1007/s00464-010-1508-x

Cited by 15 publications

(12 citation statements)

References 16 publications

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“…Additionally, a recent study indicates that, compared to patients in early stages of a disease, patients in advanced stages not only tend to have more co-morbidities, they also tend to have less social support [2]. Notably, the CCI score in the current study was inversely related to QOL, which is consistent with the reported association between increased comorbidity and poor postcholecystectomy QOL [28]–[30].…”

Section: Discussionsupporting

confidence: 89%

“…Throughout this two-year follow-up study, the best single predictor of QOL subscale scores was preoperative functional status, which is consistent with reports that preoperative functional scores are the best predictors of postoperative QOL [2], [13]. Therefore, effective counseling is essential for apprising patients of expected post-surgery impairments.…”

Section: Discussionsupporting

confidence: 86%

“…Each of its thirty-six items is scored from 0 to 4 with a higher score indicating better health status, and the total GIQLI score ranges from 0 to 144. A Chinese version of the GIQLI has demonstrated validity [2], [13].…”

Section: Methodsmentioning

confidence: 99%

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Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

et al. 2012

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BackgroundFew studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR.Methodology/Principal FindingsA total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC.Conclusions/SignificanceCompared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 86%

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Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

et al. 2012

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“…Three non-comparative prospective cohort studies concerned QoL after LC [260][261][262]. Gastrointestinal symptoms were evaluated by either the GIQLI or the GISS scores, the SF-36 was adopted for generic QoL.…”

Section: Qol After Laparoscopic Cholecystectomy: Is the Operation Effmentioning

confidence: 99%

Laparoscopic cholecystectomy: consensus conference-based guidelines

Agresta

Campanile

Vettoretto³

et al. 2015

Langenbecks Arch Surg

101

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“…For example, it is used in the field of econometrics [1], geophysics [2], health [3], and ecology [4]. In the field of econometrics, piecewise linear regression models used to model the commission.…”

mentioning

confidence: 99%

Bayesian Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm

Suparman¹

2015

CTA

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Abstract:Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC (Marcov Chain Monte Carlo) algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.

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Long-term outcomes of laparoscopic cholecystectomy: a prospective piecewise linear regression analysis

Cited by 15 publications

References 16 publications

Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

Laparoscopic cholecystectomy: consensus conference-based guidelines

Bayesian Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm

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