Fei Yin

Pretransplant Prediction of Posttransplant Survival for Liver Recipients with Benign End-Stage Liver Diseases: A Nonlinear Model

Background The scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients survival with benign end-stage liver diseases (BESLD) by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD) and a general-illness severity model (the sequential organ failure assessment score, or SOFA score). Methodology/Principal Findings With retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP) network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na+; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, with c-statistic of 0.91 (P<0.001) in one-year and 0.88 (P<0.001) in two-year prediction. The performances of SOFA and MELD were fairly poor in prognostic assessment, with c-statistics of 0.70 and 0.66, respectively, in one-year prediction, and 0.67 and 0.65 in two-year prediction. Conclusions/Significance The posttransplant prognosis is a multidimensional nonlinear problem, and the MLP can achieve significantly high accuracy than SOFA and MELD scores in posttransplant survival prediction. The pattern recognition methodologies like MLP hold promise for solving posttransplant outcome prediction.

Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model

Hand, foot and mouth disease (HFMD) is an infectious disease caused by enteroviruses, which usually occurs in children aged <5 years. In China, the HFMD situation is worsening, with increasing number of cases nationwide. Therefore, monitoring and predicting HFMD incidence are urgently needed to make control measures more effective. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast HFMD incidence in Sichuan province, China. HFMD infection data from January 2010 to June 2014 were used to fit the ARIMA model. The coefficient of determination (R 2), normalized Bayesian Information Criterion (BIC) and mean absolute percentage of error (MAPE) were used to evaluate the goodness-of-fit of the constructed models. The fitted ARIMA model was applied to forecast the incidence of HMFD from April to June 2014. The goodness-of-fit test generated the optimum general multiplicative seasonal ARIMA (1,0,1) × (0,1,0)12 model (R 2 = 0·692, MAPE = 15·982, BIC = 5·265), which also showed non-significant autocorrelations in the residuals of the model (P = 0·893). The forecast incidence values of the ARIMA (1,0,1) × (0,1,0)12 model from July to December 2014 were 4103-9987, which were proximate forecasts. The ARIMA model could be applied to forecast HMFD incidence trend and provide support for HMFD prevention and control. Further observations should be carried out continually into the time sequence, and the parameters of the models could be adjusted because HMFD incidence will not be absolutely stationary in the future.

Spatio-temporal clustering of hand, foot and mouth disease at the county level in Sichuan province, China, 2008-2013.

China has recently experienced a marked increase in the incidence of hand, foot and mouth disease (HFMD). Effective spatio-temporal monitoring of HFMD incidence is important for successful implementation of control and prevention measures. This study monitored county-level HFMD reported incidence rates for Sichuan province, China by examining spatio-temporal patterns. County-level data on HFMD daily cases between January 2008 and December 2013 were obtained from the China Information System for Disease Control and Prevention. We first conducted purely temporal and purely spatial descriptive analyses to characterize the distribution patterns of HFMD. Then, the global Morans I statistic and space-time scan statistic were used to detect the spatial autocorrelation and identify the high-risk clusters in each year, respectively. A total of 212267 HFMD cases were reported in Sichuan province during the study period (annual average incidence 43·65/100000), and the incidence seasonal peak was between April and July. Relatively high incidence rates appeared in the northeastern-southwestern belt. HFMD had positive spatial autocorrelation at the county level with global Morans I increasing from 0·27 to 0·52 (P < 0·001). Spatio-temporal cluster analysis detected six most-likely clusters and several secondary clusters from 2008 to 2013. The centres of the six most-likely clusters were all located in the provincial capital city Chengdu. Chengdu and its neighbouring cities had always been spatio-temporal clusters, which indicated the need for further intensive space-time surveillance. Allocating more resources to these areas at suitable times might help to reduce HFMD incidence more effectively.

The Association between Ambient Temperature and Childhood Hand, Foot, and Mouth Disease in Chengdu, China: A Distributed Lag Non-linear Analysis

Hand, foot and mouth disease (HFMD) has recently been recognized as a critical challenge to disease control and public health response in China. This study aimed to quantify the association between temperature and HFMD in Chengdu. Daily HFMD cases and meteorological variables in Chengdu between January 2010 and December 2013 were obtained to construct the time series. A distributed lag non-linear model was performed to investigate the temporal lagged association of daily temperature with age- and gender-specific HFMD. A total of 76,403 HFMD cases aged 0–14 years were reported in Chengdu during the study period, and a bimodal seasonal pattern was observed. The temperature-HFMD relationships were non-linear in all age and gender groups, with the first peak at 14.0–14.1u2009°C and the second peak at 23.1–23.2u2009°C. The high temperatures had acute and short-term effects and declined quickly over time, while the effects in low temperature ranges were persistent over longer lag periods. Males and children aged <1 year were more vulnerable to temperature variations. Temperature played an important role in HFMD incidence with non-linear and delayed effects. The success of HFMD intervention strategies could benefit from giving more consideration to local climatic conditions.

Spatio-Temporal Pattern and Socio-Economic Factors of Bacillary Dysentery at County Level in Sichuan Province, China.

Bacillary dysentery (BD) remains a big public health problem in China. Effective spatio-temporal monitoring of BD incidence is important for successful implementation of control and prevention measures. This study aimed to examine the spatio-temporal pattern of BD and analyze socio-economic factors that may affect BD incidence in Sichuan province, China. Firstly, we used space-time scan statistic to detect the high risk spatio-temporal clusters in each year. Then, bivariate spatial correlation and Bayesian spatio-temporal model were utilized to examine the associations between the socio-economic factors and BD incidence. Spatio-temporal clusters of BD were mainly located in the northern-southern belt of the midwest area of Sichuan province. The proportion of primary industry, the proportion of rural population and the rates of BD incidence show statistically significant positive correlation. The proportion of secondary industry, proportion of tertiary Industry, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons, per capital GDP and the rate of BD incidence show statistically significant negative correlation. The best fitting spatio-temporal model showed that medical and technical personnel per thousand persons and per capital GDP were significantly negative related to the risk of BD.

Mortality risk after liver transplantation in hepatocellular carcinoma recipients: a nonlinear predictive model.

BACKGROUNDnThe balanced application of a model for the estimate of outcomes of liver transplantation, in concert with assessment of disease severity, would not only improve transplant outcomes and maximize patient benefit from transplantation, but also facilitate informed decision making by patients and their relatives when considering transplantation. So far, however, linear discriminating methods have failed to attain sufficient power to predict post-transplant prognosis. Therefore, our aim was to develop a cancer-specific prognostic model by a nonlinear methodology based on pretransplant characteristics.nnnMETHODSnWith data collected retrospectively from 290 liver transplant recipients with HCC from February 1999 to August 2009, a multilayer perceptron (MLP) neural network was constructed to predict mortality risk after transplantation. Its predictive performances at posttransplant 1-, 2-, and 5-year intervals were evaluated using a receiver operating characteristic curve.nnnRESULTSnBy the forward stepwise selection in MLP network, donor age, donor body mass index, recipient hemoglobin, serum concentrations of total bilirubin, alkaline phosphatase, creatinine, aspartate aminotransferase, international normalized ratio of prothrombin time, and Na(+); alpha fetoprotein categorization, total diameter, number of tumor lesions, presence of imaging macrovascular invasion, and lobe distribution of the tumor were identified to be the optimal input features. The MLP, employing 24 inputs and 7 hidden neurons, yielded c-statistics of 0.909 (P < .001) in the 1-year, 0.888 (P < .001), in the 2-year, and 0.845 (P < .001) in the 5-year prediction.nnnCONCLUSIONnPost-transplant prognosis is a multidimensional, nonlinear problem, and the specific MLP can achieve high accuracy in the prediction of posttransplant mortality risk for HCC recipients. The pattern recognition methodologies like MLP hold promise for solving outcome prediction after liver transplantation.

Selection of the maximum spatial cluster size of the spatial scan statistic by using the maximum clustering set-proportion statistic

Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set–proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

The association between diurnal temperature range and childhood hand, foot, and mouth disease: a distributed lag non-linear analysis.

In recent years, hand, foot, and mouth disease (HFMD) has been increasingly recognized as a critical challenge to disease control and prevention in China. Previous studies have found that meteorological factors such as mean temperature and relative humidity were associated with HFMD. However, little is known about whether the diurnal temperature range (DTR) has any impact on HFMD. This study aimed to quantify the impact of DTR on childhood HFMD in 18 cities in Sichuan Province. A distributed lag non-linear model was adopted to explore the temporal lagged association of daily temperature with age-, gender- and pathogen-specific HFMD. A total of 290 123 HFMD cases aged 0-14 years were reported in the 18 cities in Sichuan Province. The DTR-HFMD relationships were non-linear in all subgroups. Children aged 6-14 years and male children were more vulnerable to the temperature changes. Large DTR had the higher risk estimates of HFMD incidence in cases of EV71 infection, while small DTR had the higher risk estimates of HFMD incidence in cases of CV-A16 infection. Our study suggested that DTR played an important role in the transmission of HFMD with non-linear and delayed effects.

Posttransplant mortality risk assessment for adult-to-adult right-lobe living donor liver recipients with benign end-stage liver disease

Abstract Objective. A model for living donor liver transplantation (LDLT) outcomes, in concert with pretransplant disease severity assessment, would facilitate informed decision-making on both sides considering donation and transplantation. So far, however, few of studies have focused on models specifically for adult-to-adult right-lobe LDLT recipients with benign end-stage liver diseases. Therefore, we aimed to develop such a prognostic model based on easily obtainable and objective pretransplant characteristics. Methods. With data retrospectively collected on 120 recipients, we used Cox proportional-hazards regression to analyze six donor characteristics and 33 pretransplant recipient variables for correlation with posttransplant mortality. In both a modeling set and a prospective validation set with 30 recipients, the performances of the new Cox model, MELD, and MELD-Na+ were assessed by measuring both calibration ability and discriminative power with the Hosmer–Lemeshow test and receiver operating characteristic analysis, respectively. Results. By univariate and multivariate analysis, donor age, serum total bilirubin, creatinine, and HBV-DNA level were significantly associated with posttransplant mortality. The Cox model, employing these four variables, yielded good calibration ability in the modeling set χ 2 = 2.465, p = 0.653) and the validation set χ 2 = 2.836, p = 0.586), and high discriminative power in the modeling set (c-statistic = 0.826, p = 0.001) and validation set (c-statistic = 0.816, p = 0.028). The calibration ability and discriminative power of MELD and MELD-Na+ in both sets were poor. Conclusions. The newly derived Cox model was valuable in posttransplant mortality risk assessment for adult-to-adult right-lobe LDLT recipients with benign end-stage liver diseases.