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

Abstract

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.

Comparisons of Prediction Models of Myofascial Pain Control after Dry Needling: A Prospective Study

“…Throughout this one-year follow-up study, the best single predictor of BPI scores was pretreatment pain function, which is consistent with reports that pretreatment functional scores are the best predictors of posttreatment QOL [2, 9]. Therefore, effective counseling is essential for apprising patients of expected posttreatment impairments.…”

“…In this respect, ANNs avoid the dimensionality problem and can achieve a predictive accuracy superior to those of SVM and MLR. To ensure a sufficiently robust basis for network training, the present study used a large and homogeneous dataset comprising all demographic and clinical variables shown to affect patient-reported BPI scores in previous linear regression models [9]. …”

“…The MSE, which is computed between the desired and predicted values and then averaged across all data, is used as an indicator of goodness of fit. The MAPE indicates the average deviation from the desired value and is usually expressed as a percentage [9, 19]. The prediction accuracy of a model is considered excellent if its MAPE value is lower than 10%.…”

“…The prediction accuracy of a model is considered excellent if its MAPE value is lower than 10%. Values between 10% and 20%, between 20% and 50%, and higher than 50% are considered indicators of high, average, and low prediction accuracy, respectively [9, 19]. The formulas for calculating MSE and MAPE are $\begin{array}{c}\text{MSE}=\frac{\mathrm{1}}{n}{\sum }_{i=\mathrm{normal1}}^n{\left(Y_i-{\widehat{Y}}_i\right)}^{\mathrm{2}},\\ \text{MAPE}=\frac{\mathrm{1}}{n}{\sum }_{i=\mathrm{normal1}}^n\frac{\left|Y_i-{\widehat{Y}}_i\right|}{Y_i}\times \mathrm{100}\%,\end{array}$ where n is the number of observations, Y i is the desired (target) value of the i th observation, and ${\widehat{Y}}_i$ is the actual output value of the i th observation.…”

Variants of postcholecystectomy syndrome clinical progression in concomitant diseases of the abdominal cavity

“…Так, по данным отечественной и зарубежной литературы, одни авторы склонны рассматривать ПХЭС как функциональный синдром, обусловлен-ный выпадением функций удаленного желчного пузыря [7][8][9]; другие считают оправданным вклю-чение в это понятие органических процессов, свя- Variants of postcholecystectomy syndrome clinical progression in concomitant diseases of the abdominal cavity занных с техническими погрешностями операций, а также заболеваниями, развившимися у больных хроническим калькулезным холециститом еще до ХЭ как его осложнение [10,11].…”