Joint Impact of Clinical and Behavioral Variables on the Risk of Unplanned Readmission and Death after a Heart Failure Hospitalization

Most current methods for modeling rehospitalization events in heart failure patients make use of only clinical and medications data that is available in the electronic health records. However, information about patient-reported functional limitations, behavioral variables and socio-economic background of patients may also play an important role in predicting the risk of readmission in heart failure patients. We developed methods for predicting the risk of rehospitalization in heart failure patients using models that integrate clinical characteristics with patient-reported functional limitations, behavioral and socio-economic characteristics. Our goal was to estimate the predictive accuracy of the joint model and compare it with models that make use of clinical data alone or behavioral and socio-economic characteristics alone, using real patient data. We collected data about the occurrence of hospital readmissions from a cohort of 789 heart failure patients for whom a range of clinical and behavioral characteristics data is also available. We applied the Cox model, four different variants of the Cox proportional hazards framework as well as an alternative non-parametric approach and determined the predictive accuracy for different categories of variables. The concordance index obtained from the joint prediction model including all types of variables was significantly higher than the accuracy obtained from using only clinical factors or using only behavioral, socioeconomic background and functional limitations in patients as predictors. Collecting information on behavior, patient-reported estimates of physical limitations and frailty and socio-economic data has significant value in the predicting the risk of readmissions with regards to heart failure events and can lead to substantially more accurate events prediction models.     

A Model for Censored Survival Data When There is an Intervening Event

A model is discussed for incorporating information from a time-dependent covariable (an intervening event) and covariables independent of time into the analysis of survival data. In the model, it is assumed that individuals are potentially subject to two paths to failure, one including the intervening event and the other not. Additional assumptions are that failure times associated with the two paths are independent and that the time to failure subsequent to the intervening event is dependent on the intervening event time. Allowing the underlying hazard rates for the model to follow a WEIBULL form, use of the model and methods for fitting and hypothesis testing are illustrated by application to a follow-up study involving industrial workers where disability retirement was the intervening event. Extensions of the model to accommodate grouped survival data are presented.     

A Simple,Flexible Failure Model

A new failure model is introduced in the form of a four-parameter nonlinear differential equation, with failure probability as the dependent variable and failure time as the independent variable. The first parameter characterizes the location, the second the scale, and the other two the shape of the model. The type of the accompanying hazard function is immediately read off the shape parameters. The new model approximates the classical failure models with rather high precision, but also models cases where the failure density is skewed to the left. It can be used to analyze survival data objectively, based on the shape of the failure distribution. The computation of quantiles and moments is easy and fast. Nonlinear regression methods are used to estimate parameter values.     

Mechanical characterization of liver capsule through uniaxial quasi-static tensile tests until failure

Accidentology data showed that liver is often injured in car crashes; three types of injuries occur: hematoma, laceration and vessel failure. This paper focuses on surface laceration, which involves liver capsule and hepatic parenchyma. Liver capsule behavior has been studied but its failure properties are still unclear, particularly on a local point of view. In the present study, tensile quasi-static tests are run on parenchyma and capsule samples until failure to characterize capsule failure. Normalized load as well as failure properties—ultimate load per width unit and ultimate strain—are determined. Digital image correlation is used to measure the full local strain field on the capsule. Mean values of failure characteristics for hepatic capsule are 47±29% for the ultimate local strain and 0.3±0.3 N/mm for the ultimate load per width unit. A comparison between human and porcine tissues is conducted based on Mann–Whitney statistical test; it reveals that capsule characteristics are close between these two species; however, freezing preservation significantly affects porcine capsule failure properties. Therefore using porcine instead of human tissue to determine failure characteristics of liver capsule seems satisfactory only on fresh tissues.     

Prognosis Analysis of Heart Failure Based on Recurrent Attention Model

ObjectivesHeart failure is a group of complex clinical syndromes that lead to ventricular filling or impaired ejection ability due to abnormal heart structure or function. Difficult treatment, poor prognosis and high mortality are the main characteristics of heart failure. According to admission data and past medical use, the 30-day mortality rate of patients with heart failure was obtained and the main characteristics affecting the 30-day mortality of patients with heart failure were determined.Material and methodsBased on the data of April 2016 to July 2018 of Shanxi Acadeny of Medical Sciences, and we chose 4,682 information on heart failure patients, of which 539 died in the hospital by screening. We built a 30-day mortality prediction model for patients with heart failure. The model can fuse clinical data and text data through multiple kernel learning, and input the fused data into the recurrent attention model. It can not only predict the 30-day mortality of patients with heart failure, but also the influencing factors of prognosis of patients with heart failure were also obtained.ResultsThe prediction accuracy of the recurrent attention network is obviously higher than that of other machine learning models, and the accuracy rate reaches 93.4%. The AUC value of the area under the ROC curve of the model reaches 87%, which is obviously higher than that of the traditional machine learning models such as decision tree, naive Bayesian and support vector machine. In addition, the model can also reach a conclusion that New York heart function classification, age, NT—ProBNP, LVEF, β-blockers, ventricular arrhythmia, high blood pressure, coronary heart disease (CHD) and bronchitis were independent risk factors for death. And patients with revascularization, ACEI/ARB drugs, β-blockers, spironolactone have a better prognosis than non-users. This provides an important reference for doctors to better treat and manage patients with heart failure.ConclusionExperiments show that the prognostic effect of the recurrent attention model is significantly higher than that of other traditional machine learning models. Because the model increases the attention mechanism, the important features affecting the prognostic results are obtained, which enables doctors to prescribe drugs according to the symptoms, take timely precautions and help patients to treat in time.     

Numerical investigations of rib fracture failure models in different dynamic loading conditions

Rib fracture is one of the most common thoracic injuries in vehicle traffic accidents that can result in fatalities associated with seriously injured internal organs. A failure model is critical when modelling rib fracture to predict such injuries. Different rib failure models have been proposed in prediction of thorax injuries. However, the biofidelity of the fracture failure models when varying the loading conditions and the effects of a rib fracture failure model on prediction of thoracic injuries have been studied only to a limited extent. Therefore, this study aimed to investigate the effects of three rib failure models on prediction of thoracic injuries using a previously validated finite element model of the human thorax. The performance and biofidelity of each rib failure model were first evaluated by modelling rib responses to different loading conditions in two experimental configurations: (1) the three-point bending on the specimen taken from rib and (2) the anterior–posterior dynamic loading to an entire bony part of the rib. Furthermore, the simulation of the rib failure behaviour in the frontal impact to an entire thorax was conducted at varying velocities and the effects of the failure models were analysed with respect to the severity of rib cage damages. Simulation results demonstrated that the responses of the thorax model are similar to the general trends of the rib fracture responses reported in the experimental literature. However, they also indicated that the accuracy of the rib fracture prediction using a given failure model varies for different loading conditions.     

Measures of Departure from Constant Failure Rate Models and Proportional Hazards Rate Models for Grouped Data

Two measures are proposed to represent the degree of departure from the constant failure rate model of a system when data are grouped. Two measures are also proposed to represent the degree of departure from the proportional hazards rate model when two systems are present and grouped data are considered. In each case one measure is based on the Kullback-Leibler discrepancy and the other is based on the Pearson χ² type discrepancy using the failure rates. The usefulness of the proposed measures are discussed with applications. A simulation study shows that the proposed measures perform no worse than the goodness-of-fit tests when testing for the constant failure rate model.     

Host-parasitoid dynamics of a generalized Thompson model

A discrete-time host-parasitoid model including host-density dependence and a generalized Thompson escape function is analyzed. This model assumes that parasitoids are egg-limited but not search-limited, and is proven to exhibit five types of dynamics: host failure in which the host goes extinct in the parasitoid's presence or absence, unconditional parasitoid failure in which the parasitoid always goes extinct while the host persists, conditional parasitoid failure in the host and the parasitoid go extinct or coexist depending on the initial host-parasitoid ratio, parasitoid driven extinction in which the parasitoid invariably drives the host to extinction, and coexistence in which the host and parasitoid coexist about a global attractor. The latter two dynamics only occur when the parasitoid's maximal rate of growth exceeds the host's maximal rate of growth. Moreover, coexistence requires parasitism events to be sufficiently aggregated. Small additive noise is proven to alter the dynamical outcomes in two ways. The addition of noise to parasitoid driven extinction results in random outbreaks of the host and parasitoid with varying intensity. Additive noise converts conditional parasitoid failure to unconditional parasitoid failure. Implications for classical biological control are discussed.     

Semiparametric regression analysis of longitudinal data with informative drop-outs

Informative drop-out arises in longitudinal studies when the subject's follow-up time depends on the unobserved values of the response variable. We specify a semiparametric linear regression model for the repeatedly measured response variable and an accelerated failure time model for the time to informative drop-out. The error terms from the two models are assumed to have a common, but completely arbitrary joint distribution. Using a rank-based estimator for the accelerated failure time model and an artificial censoring device, we construct an asymptotically unbiased estimating function for the linear regression model. The resultant estimator is shown to be consistent and asymptotically normal. A resampling scheme is developed to estimate the limiting covariance matrix. Extensive simulation studies demonstrate that the proposed methods are suitable for practical use. Illustrations with data taken from two AIDS clinical trials are provided.     

Prediction of femoral fracture load using finite element models: an examination of stress- and strain-based failure theories

Finite element (FE) models are often used to model bone failure. However, no failure theory for bone has been validated at this time. In this study, we examined the performance of nine stress- and strain-based failure theories, six of which could account for differences in tensile and compressive material strengths. The distortion energy, Hoffman and a strain-based Hoffman analog, maximum normal stress, maximum normal strain, maximum shear strain, maximum shear stress (tau(max)), Coulomb-Mohr, and modified Mohr failure theories were evaluated using automatically generated, computed tomographic scan-based FE models of the femur. Eighteen matched pairs of proximal femora were examined in two load configurations, one approximating joint loading during single-limb stance and one simulating impact from a fall. Mechanical testing was performed to assess model and failure theory performance in the context of predicting femoral fracture load. Measured and FE-computed fracture load were significantly correlated for both loading conditions and all failure criteria (p < or = 0.001). The distortion energy and tau(max) failure theories were the most robust of those examined, providing the most consistently strong FE model performance for two very different loading conditions. The more complex failure theories and the strain-based theories examined did not improve performance over the simpler distortion energy and tau(max) theories, and often degraded performance, even when differences between tensile and compressive failure properties were represented. The relatively strong performance of the distortion energy and tau(max) theories supports the hypothesis that shear/distortion is an important failure mode during femoral fracture.     

Ephaptic coupling of cardiac cells through the junctional electric potential

Cardiac cells are electrically coupled through gap junction channels, which allow ionic current to spread intercellularly from one cell to the next. In addition, it is possible that cardiac cells are coupled through the electric potential in the junctional cleft space between neighboring cells. We develop and analyze a mathematical model of two cells coupled through a common junctional cleft potential. Consistent with more detailed models, we find that the coupling mechanism is highly parameter dependent. Analysis of our model reveals that there are two time scales involved, and the dynamics of the slow subsystem provide new mathematical insight into how the coupling mechanism works. We find that there are two distinct types of propagation failure and we are able to characterize parameter space into regions of propagation success and the two different types of propagation failure.     

Estimates of a Treatment Effect Based on Censored Data Rank Tests

Linear rank test statistics are applied to the problem of estimating a treatment effect if two sets of censored failure time data are compared and the distributions of the log-failure times of the two samples are assumed to differ only in location. Rank tests for this accelerated failure time model are reviewed and Hodges-Lehmann type estimates for the shift parameter are proposed. Properties of these estimates are investigated, computational aspects are discussed and an example is presented.     

During sideways falls proximal femur fractures initiate in the superolateral cortex: Evidence from high-speed video of simulated fractures

Results of recent imaging studies and theoretical models suggest that the superior femoral neck is a location of local weakness due to an age-related thinning of the cortex, and thus the site of hip fracture initiation. The purpose of this study was to experimentally determine the spatial and temporal characteristics of the macroscopic failure process during a simulated hip fracture that would occur as a result of a sideways fall. Twelve fresh frozen human cadaveric femora were used in this study. The femora were fractured in an apparatus designed to simulate a fall on the greater trochanter. Image sequences of the surface events related to the fractures were captured using two high-speed video cameras at 9111 Hz. The videos were analyzed with respect to time and load to determine the location and sequence of these events occurring in the proximal femur. The mean failure load was 4032 N (SD 370 N). The first surface events were identified in the superior femoral neck in eleven of the twelve specimens. Nine of these specimens fractured in a clear two-step process that initiated with a failure in the superior femoral neck, followed by a failure in the inferior femoral neck. This cadaveric model of hip fracture empirically confirms hypotheses that suggested that hip fractures initiate with a failure in the superior femoral neck where stresses are primarily compressive during a sideways fall impact, followed by a failure in the inferior neck where stresses are primarily tensile. Our results confirm the superolateral neck of the femur as an important region of interest for future hip fracture screening, prevention and treatment research.     

On testing dependence between time to failure and cause of failure when causes of failure are missing

The hypothesis of independence between the failure time and the cause of failure is studied by using the conditional probabilities of failure due to a specific cause given that there is no failure up to certain fixed time. In practice, there are situations when the failure times are available for all units but the causes of failures might be missing for some units. We propose tests based on U-statistics to test for independence of the failure time and the cause of failure in the competing risks model when all the causes of failure cannot be observed. The asymptotic distribution is normal in each case. Simulation studies look at power comparisons for the proposed tests for two families of distributions. The one-sided and the two-sided tests based on Kendall type statistic perform exceedingly well in detecting departures from independence.     

A failure model for ligaments

We propose a failure model for ligament which assumes that sequential uncrimping and stretching of collagen fibers is responsible for the mechanical response of ligament. We further assume that the fibers rupture sequentially and in a brittle, strain-limited manner. The model was fit to stress strain curves obtained from medial collateral ligaments of New Zealand White rabbits from two age groups (4 and 7 months). The model indicated that collagen modulus values ranged from 300 to 680 MPa and that fiber failure strains ranged from 6 to 22%. The model provides a convenient means of describing the elastic and failure response of ligament using four structurally based parameters.     

Morphometric analysis of atrial natriuretic peptide-containing granules in atriocytes of rats with experimental congestive heart failure

The morphometric characteristics of atrial natriuretic peptide-containing granules were studied in atrial myoendocrine cells of rats with aorto-caval fistula, an experimental model of congestive heart failure. A total of 6680 granules of control and aorto-caval rats were analyzed by a computerized image analysis system that evaluated the number and sectioned surface area of granules and their subcellular location. Compared with control animals, rats with congestive heart failure displayed a slight increase in the number of peripheral granules, adjacent to the sarcolemma, but not centrally located in the Golgi areas. The mean sectioned surface area of granules in rats with congestive heart failure was about 50% of that in controls, both in the right and left atria. Rats with aortocaval fistula had a higher percent of small granules and lower percent of large granules compared with controls. The data demonstrate different morphometric characteristics in atrial natriuretic peptide-containing granules in atriocytes in rats with experimental congestive heart failure; this may reflect the enhanced synthesis and release of atrial natriuretic peptide in heart failure.     

慢性肾衰竭大鼠模型的建立

目的 建立两种慢性肾衰竭大鼠模型,观察瘦素蛋白在大鼠组织、器官中的表达.方法 建立两种慢性肾衰竭CRF动物模型:(1)大鼠肾大部分切除诱发肾衰(Platt法).(2)腺嘌呤诱发大鼠慢性肾衰竭的动物模型(Yokozawa法).分别测定血清中血尿素氮(BUN),血肌酐(Scr)Ca2+、P5+等含量.取肾脏组织,HE染色,行免疫荧光,检测瘦素蛋白在两种慢性肾衰竭大鼠模型中的表达情况.结果 模型组大鼠血清中血尿素氮(BUN),血肌酐(Scr)等含量明显升高,免疫荧光检测显示两种模型大鼠肾脏组织瘦素蛋白的表达.结论 成功建立两种慢性肾衰竭CRF动物模型,显示不同模型组织部位的瘦素蛋白的表达.为进一步探讨瘦素蛋白在动物体内的生物学作用提供实验基础.     

Estimating regression parameters and degree of dependence for multivariate failure time data

Multivariate failure time data are frequently encountered in longitudinal studies when subjects may experience several events or when there is a grouping of individuals into a cluster. To take into account the dependence of the failure times within the unit (the individual or the cluster) as well as censoring, two multivariate generalizations of the Cox proportional hazards model are commonly used. The marginal hazard model is used when the purpose is to estimate mean regression parameters, while the frailty model is retained when the purpose is to assess the degree of dependence within the unit. We propose a new approach based on the combination of the two aforementioned models to estimate both these quantities. This two-step estimation procedure is quicker and more simple to implement than the EM algorithm used in frailty models estimation. Simulation results are provided to illustrate robustness, consistency, and large-sample properties of estimators. Finally, this method is exemplified on a diabetic retinopathy study in order to assess the effect of photocoagulation in delaying the onset of blindness as well as the dependence between the two eyes blindness times of a patient.     

肾大部切除大鼠肾衰模型制备方法

目的探讨一次手术建立大鼠肾大部切除肾衰模型的可行性。方法取雄性SD大鼠40只随机平均分为两组构建大鼠肾大部切除肾衰模型,A组采用传统的两次手术造模,B组采用改良的一次手术的方法;比较两组模型的死亡率、成功率。结果两组间各指标无统计学差异（P〉0.05）。结论一次手术建立大鼠肾大部切除肾衰模型是简便易行,是值得推荐的一种肾大部切除术式。     

An experimental model of scalp expansion: a method of optimizing flap size and shape

An experimental model has been developed for testing and comparing the placement and characteristics of expanders for soft-tissue advancement. Expanders are fixed to model heads and covered with a stockinet sleeve, and the sleeve is impregnated with prevulcanized latex. The resulting stockinet-latex has many of the physical properties of skin and can be manipulated to ensure optimal covering and suture length prior to surgery. The system is rapid and reliable. Several types of scalp flaps were tested using the model. The models can be used in teaching and prior to surgery. Their use ensures an à la carte expansion that minimizes the risk of expansion failure.