Reliability estimation and remaining useful lifetime prediction for bearing based on proportional hazard model

As the central component of rotating machine,the performance reliability assessment and remaining useful lifetime prediction of bearing are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability.A prognostic algorithm to assess the reliability and forecast the remaining useful lifetime(RUL) of bearings was proposed,consisting of three phases.Online vibration and temperature signals of bearings in normal state were measured during the manufacturing process and the most useful time-dependent features of vibration signals were extracted based on correlation analysis(feature selection step).Time series analysis based on neural network,as an identification model,was used to predict the features of bearing vibration signals at any horizons(feature prediction step).Furthermore,according to the features,degradation factor was defined.The proportional hazard model was generated to estimate the survival function and forecast the RUL of the bearing(RUL prediction step).The positive results show that the plausibility and effectiveness of the proposed approach can facilitate bearing reliability estimation and RUL prediction.

Cox Proportional Hazard Model for Survival Time of Neonatal Mortality in Neonatal Intensive Care Unit of Hospitals in River Nile State-Sudan

Cox Proportional Hazard model is a popular statistical technique for exploring the relationship between the survival time of neonates and several explanatory variables. It provides an estimate of the study variables’ effect on survival after adjustment for other explanatory variables, and allows us to estimate the hazard (or risk) of death of newborn in NICU of hospitals in River Nile State-Sudan for the period (2018-2020). Study Data represented (neonate gender, mode of delivery, birth type, neonate weight, resident type, gestational age, and survival time). Kaplan-Meier method is used to estimate survival and hazard function for survival times of newborns that have not completed their first month. Of 700 neonates in the study area, 25% of them died during 2018-2020. Variables of interest that had a significant effect on neonatal death by Cox Proportional Hazard Model analysis were neonate weight, resident type, and gestational age. In Cox Proportional Hazard Model analysis all the variables of interest had an effect on neonatal death, but the variables with a significant effect included, weight of neonate, resident type and gestational age.

Influence of donor age on liver transplantation outcomes: A multivariate analysis and comparative study

BACKGROUND The growing disparity between the rising demand for liver transplantation(LT)and the limited availability of donor organs has prompted a greater reliance on older liver grafts.Traditionally,utilizing livers from elderly donors has been associated with outcomes inferior to those achieved with grafts from younger donors.By accounting for additional risk factors,we hypothesize that the utili-zation of older liver grafts has a relatively minor impact on both patient survival and graft viability.AIM To evaluate the impact of donor age on LT outcomes using multivariate analysis and comparing young and elderly donor groups.METHODS In the period from April 2013 to December 2018,656 adult liver transplants were performed at the University Hospital Merkur.Several multivariate Cox propor-tional hazards models were developed to independently assess the significance of donor age.Donor age was treated as a continuous variable.The approach involved univariate and multivariate analysis,including variable selection and assessment of interactions and transformations.Additionally,to exemplify the similarity of using young and old donor liver grafts,the group of 87 recipients of elderly donor liver grafts(≥75 years)was compared to a group of 124 recipients of young liver grafts(≤45 years)from the dataset.Survival rates of the two groups were estimated using the Kaplan-Meier method and the log-rank test was used to test the differences between groups.RESULTS Using multivariate Cox analysis,we found no statistical significance in the role of donor age within the constructed models.Even when retained during the entire model development,the donor age's impact on survival remained insignificant and transformations and interactions yielded no substantial effects on survival.Consistent insigni-ficance and low coefficient values suggest that donor age does not impact patient survival in our dataset.Notably,there was no statistical evidence that the five developed models did not adhere to the proportional hazards assumption.When comparing donor age groups,transplantation using elderly grafts showed similar early graft function,similar graft(P=0.92),and patient survival rates(P=0.86),and no significant difference in the incidence of postoperative complications.CONCLUSION Our center's experience indicates that donor age does not play a significant role in patient survival,with elderly livers performing comparably to younger grafts when accounting for other risk factors.

Risk prediction model for cutaneous squamous cell carcinoma in adult cardiac allograft recipients

BACKGROUND Heart transplant recipients are at higher risk of developing skin cancer than the general population due to the long-term immunosuppression treatment.Cancer has been reported as one of the major causes of morbidity and mortality for patients after heart transplantation.Among different types of skin cancers,cutaneous squamous cell carcinoma(cSCC)is the most common one,which requires timely screening and better management.AIM To identify risk factors and predict the incidence of cSCC for heart transplant recipients.METHODS We retrospectively analyzed adult heart transplant recipients between 2000 and 2015 extracted from the United Network for Organ Sharing registry.The whole dataset was randomly divided into a derivation set(80%)and a validation set(20%).Uni-and multivariate Cox regression were done to identify significant risk factors associated with the development of cSCC.Receiver operating characteristics curves were generated and area under the curve(AUC)was calculated to assess the accuracy of the prediction model.Based on the selected risk factors,a risk scoring system was developed to stratify patients into different risk groups.A cumulative cSCC-free survival curve was generated using the Kaplan-Meier method for each group,and the log-rank test was done to compare the intergroup cSCC rates.RESULTS There were 23736 heart-transplant recipients during the study period,and 1827 of them have been reported with cSCC.Significant predictors of post-transplant cSCC were older age,male sex,white race,recipient and donor human leukocyte antigen(HLA)mismatch level,malignancy at listing,diagnosis with restrictive myopathy or hypertrophic myopathy,heart re-transplant,and induction therapy with OKT3 or daclizumab.The multivariate model was used to predict the 5-,8-and 10-year incidence of cSCC and respectively provided AUC of 0.79,0.78 and 0.77 in the derivation set and 0.80,0.78 and 0.77 in the validation set.The risk scoring system assigned each patient with a risk score within the range of 0-11,based on which they were stratified into 4 different risk groups.The predicted and observed 5-year probability of developing cSCC match well among different risk groups.In addition,the log-rank test indicated significantly different cSCCfree survival across different groups.CONCLUSION A risk prediction model for cSCC among heart-transplant recipients has been generated for the first time.It offers a c-statistic of≥0.77 in both derivation and validation sets.

Analysis of Length of Stay (LOS) Data from the Medical Records of Tertiary Care Hospital in Saudi Arabia for Five Diagnosis Related Groups: Application of Cox Prediction Model

Background: One of the main objectives of hospital managements is to control the length of stay (LOS). Successful control of LOS of inpatients will result in reduction in the cost of care, decrease in nosocomial infections, medication side effects, and better management of the limited number of available patients’ beds. The length of stay (LOS) is an important indicator of the efficiency of hospital management by improving the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to model the distribution of LOS as a function of patient’s age, and the Diagnosis Related Groups (DRG), based on electronic medical records of a large tertiary care hospital. Materials and Methods: Information related to the research subjects were retrieved from a database of patients admitted to King Faisal Specialist Hospital and Research Center hospital in Riyadh, Saudi Arabia between January 2014 and December 2016. Subjects’ confidential information was masked from the investigators. The data analyses were reported visually, descriptively, and analytically using Cox proportional hazard regression model to predict the risk of long-stay when patients’ age and the DRG are considered as antecedent risk factors. Results: Predicting the risk of long stay depends significantly on the age at admission, and the DRG to which a patient belongs to. We demonstrated the validity of the Cox regression model for the available data as the proportionality assumption is shown to be satisfied. Two examples were presented to demonstrate the utility of the Cox model in this regard.

Prediction models for development of hepatocellular carcinoma in chronic hepatitis B patients

Chronic hepatitis B(CHB)-related hepatocellular carcinoma(HCC)is a major health problem in Asian-Pacific regions.Antiviral therapy reduces,but does not completely prevent,HCC development.Thus,there is a need for accurate risk prediction to assist prognostication and decisions on the need for antiviral therapy and HCC surveillance.A few risk scores have been developed to predict the occurrence of HCC in CHB patients.Initially,the scores were derived from untreated CHB patients.With the development and extensive clinical application of nucleos(t)ide analog(s)(NA),the number of risk scores based on treated CHB patients has increased gradually.The components included in risk scores may be categorized into host factors and hepatitis B virus factors.Hepatitis activities,hepatitis B virus factors,and even liver fibrosis or cirrhosis are relatively controlled by antiviral therapy.Therefore,variables that are more dynamic during antiviral therapy have since been included in risk scores.However,host factors are more difficult to modify.Most existing scores derived from Asian populations have been confirmed to be accurate in predicting HCC development in CHB patients from Asia,while these scores have not offered excellent predictability in Caucasian patients.These findings support that more relevant variables should be considered to provide individualized predictions that are easily applied to CHB patients of different ethnicities.CHB patients should receive different intensities of HCC surveillance according to their risk category.

Transformation Models for Survival Data Analysis with Applications

When the event of interest never occurs for a proportion of subjects during the study period, survival models with a cure fraction are more appropriate in analyzing this type of data. Considering the non-linear relationship between response variable and covariates, we propose a class of generalized transformation models motivated by Zeng et al. [1] transformed proportional time cure model, in which fractional polynomials are used instead of the simple linear combination of the covariates. Statistical properties of the proposed models are investigated, including identifiability of the parameters, asymptotic consistency, and asymptotic normality of the estimated regression coefficients. A simulation study is carried out to examine the performance of the power selection procedure. The generalized transformation cure rate models are applied to the First National Health and Nutrition Examination Survey Epidemiologic Follow-up Study (NHANES1) for the purpose of examining the relationship between survival time of patients and several risk factors.

Fibrinogen-to-albumin ratio predicts overall survival of hepatocellular carcinoma

BACKGROUND Fibrinogen-to-albumin ratio(FAR)has been found to be of prognostic significance for several types of malignant tumors.However,less is known about the association between FAR and survival outcomes in hepatocellular carcinoma(HCC)patients.AIM To explore the association between FAR and prognosis and survival in patients with HCC.METHODS A total of 366 histologically confirmed HCC patients diagnosed between 2013 and 2018 in a provincial cancer hospital in southwestern China were retrospectively selected.Relevant data were extracted from the hospital information system.The optimal cutoff for baseline serum FAR measured upon disease diagnosis was established using the receiver operating characteristic(ROC)curve.Univariate and multivariate Cox proportional hazards models were used to determine the crude and adjusted associations between FAR and the overall survival(OS)of the HCC patients while controlling for various covariates.The restricted cubic spline(RCS)was applied to estimate the dose-response trend in the FAR-OS association.RESULTS The optimal cutoff value for baseline FAR determined by the ROC was 0.081.Multivariate Cox proportional hazards model revealed that a lower baseline serum FAR level was associated with an adjusted hazard ratio of 2.43(95%confidence interval:1.87–3.15)in the OS of HCC patients,with identifiable dose-response trend in the RCS.Subgroup analysis showed that this FAR-OS association was more prominent in HCC patients with a lower baseline serum aspartate aminotransferase or carbohydrate antigen 125 level.CONCLUSION Serum FAR is a prominent prognostic indicator for HCC.Intervention measures aimed at reducing FAR might result in survival benefit for HCC patients.

Locally R-optimal designs for a class of nonlinear multiple regression models

This paper concerns with optimal designs for a wide class of nonlinear models with informa-tion driven by the linear predictor.The aim of this study is to generate an R-optimal design which minimizes the product of the main diagonal entries of the inverse of the Fisher informa tion matrix at certain values of the parameters.An equivalence theorem for the locally R optimal designs is provided in terms of the intensity function.Analytic solutions for the locally saturated R-optimal designs are derived for the models having linear predictors with and without intercept,respectively.The particle swarm optimization method has been employed to generate locally non-saturated R-optimal designs.Numerical examples are presented for ilustration of the locally R-optimal designs for Poisson regression models and proportional hazards regression models.

Regression Analysis of Misclassified Current Status Data with Informative Observation Times

Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity.For the situation,another issue that may occur is that the observation time may be correlated with the interested failure time,which is often referred to as informative censoring or observation times.It is well-known that in the presence of informative censoring,the analysis that ignores it could yield biased or even misleading results.In this paper,the authors consider such data and propose a frailty-based inference procedure.In particular,an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established.The numerical results show that the proposed method works well in practice and an application to a set of real data is provided.

Applications of survival functions to continuous semi-Markov processes for measuring reliability of power transformers

The reliability of power transformers is subject to service age and health condition.This paper proposes a practical model for the evaluation of two reliability indices:survival function(SF)and mean residual life(MRL).In the proposed model,the periodical modeling of power transformers are considered for collecting the information on health conditions.The corresponding health condition is assumed to follow a continuous semi-Markov process for representing a state transition.The proportional hazard model(PHM)is introduced to incorporate service age and health condition into hazard rate.In addition,the proposed model derives the analytical formulas for and offers the analytical evaluation of SF and MRL.SF and MRL are calculated for new components and old components,respectively.In both cases,the proposed model offers rational results which are compared with those obtained from comparative models.The results obtained by the contrast of the proposed analytical method and the Monte Carlo method.The impact of differentmodel parameters and the coefficient of variation(CV)on reliability indices are discussed in the case studies.

Clinical characteristics of COVID-19 hospitalized patients associated with mortality:A cohort study in Spain

Background:The heterogeneity of patients with COVID-19 may explain the wide variation of mortality rate due to the population characteristics,presence of comorbidities and clinical manifestations.Methods:In this study,we analyzed 5342 patients’recordings and selected a cohort of 177 hospitalized patients with a poor prognosis at an early stage.We assessed during 6 months their symptomatology,coexisting health conditions,clinical measures and health assistance related to mortality.Multiple Cox proportional hazards models were built to identify the associated factors with mortality risk.Results:We observed that cough and kidney failure triplicate the mortality risk and both bilirubin levels and oncologic condition are shown as the most associated with the demise,increasing in four and ten times the risk,respectively.Other clinical characteristics such as fever,diabetes mellitus,breathing frequency,neutrophil-lymphocyte ratio,oxygen saturation,and troponin levels,were also related to mortality risk of in-hospital death.Conclusions:The present study shows that some symptomatology,comorbidities and clinical measures could be the target of prevention tools to improve survival rates.

An evaluation of treatments and survival rates for pancreatic adenocarcinoma through survival analysis with inverse probability of treatment weighting:a population-based study

Objective:This study conducted inverse probability of treatment weighting(IPTW)survival analysis to examine survival in pancreatic adenocarcinoma patients.Methods:In this population-based study,data from the Surveillance,Epidemiology,and End Results program of the United States were analyzed to identify patients diagnosed with adenocarcinoma of the pancreas 2004 to 2014.Differences in survival rates were examined among patients who underwent pancreatectomy alone,radiotherapy alone,and those who had pancreatectomy plus adjuvant radiotherapy.Kaplan-Meier estimates and Cox proportional hazards models with the IPTW were performed to determine the effect of different treatments on overall and cancer-specific survival.This study was approved by the Ethics Review Board of Weifang Medical University.Results:A total of 8191 patients were included,with 3409 taking pancreatectomy only,2865 taking radiotherapy only,and 1917 taking pancreatectomy plus adjuvant radiotherapy.Patients who received surgery plus adjuvant radiotherapy had statistically a higher survival rate than those who received the other 2 treatments.Survival analysis with the IPTW for the 3 different groups showed that the difference in median overall survival time among these patient groups was significant.Conclusion:Using IPTW survival analysis,the present study shows that surgery with adjuvant radiotherapy is significantly associated with improved overall and cancer-specific survival among patients with pancreatic adenocarcinoma.