Probability Prediction in Multistate Survival Models for Patients with Chronic Myeloid Leukaemia

In order to find an appropriate model suitable for a multistate survival experiment, 634 patients with chronic myeloid leukaemia (CML) were selected to illustrate the method of analysis. After transplantation, there were 4 possible situations for a patient: disease free, relapse but still alive, death before relapse, and death after relapse. The last 3 events were considered as treatment failure. The results showed that the risk of death before relapse was higher than that of the relapse, especially in the first year after transplantation with competing-risk method. The result of patients with relapse time less than 12 months was much poor by the Kaplan-Meier method. And the multistate survival models were developed, which were detailed and informative based on the analysis of competing risks and Kaplan-Meier analysis. With the multistate survival models, a further analysis on conditional probability was made for patients who were disease free and still alive at month 12 after transplantation. It was concluded that it was possible for an individual patient to predict the 4 possible probabilities at any time. Also the prognoses for relapse either death or not and death either before or after relapse may be given. Furthermore, the conditional probabilities for patients who were disease free and still alive in a given time after transplantation can be predicted.

Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension

BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

Using a Collateral Damage Model to Explain Survival Data in West Nile Virus Infections

Simulation code for a model of the adaptive immune response seen in flavivirus infections is used to explain the immunopathological consequences seen in West Nile Virus virus (WNV) infections. We use a model that specifically handles the differences in how the virus infects resting cells, the G0 state, versus dividing cells, the G1 state, which includes vastly increased MHC-I upregulation for resting cells over dividing cells. The simulation suggests how the infection progresses in a one host model and the results shed insight into the unusual survival curve data obtained for this infection: there is an increase in health even though viral load has increased.

Study of University Dropout Reason Based on Survival Model

In this paper, we introduce the survival modelling methodology in order to identify some factors which may be influencing the university dropout. By using the data base provided by the Fundación Universidad Autónoma de Colombia and the semi parametric proportional hazard Cox model, we have been able to identify these risk factors.

Bile acid indices as biomarkers for liver diseases Ⅱ: The bile acid score survival prognostic model

BACKGROUND Cholestatic liver diseases are characterized by an accumulation of toxic bile acids(BA)in the liver,blood and other tissues which lead to progressive liver injury and poor prognosis in patients.AIM To discover and validate prognostic biomarkers of cholestatic liver diseases based on the urinary BA profile.METHODS We analyzed urine samples by liquid chromatography-tandem mass spectrometry and investigated the use of the urinary BA profile to develop survival models that can predict the prognosis of hepatobiliary diseases.The urinary BA profile,a set of non-BA parameters,and the adverse events of liver transplant and/or death were monitored in 257 patients with cholestatic liver diseases for up to 7 years.The BA profile was characterized by calculating BA indices,which quantify the composition,metabolism,hydrophilicity,formation of secondary BA,and toxicity of the BA profile.We have developed and validated the bile-acid score(BAS)model(a survival model based on BA indices)to predict the prognosis of cholestatic liver diseases.RESULTS We have developed and validated a survival model based on BA(the BAS model)indices to predict the prognosis of cholestatic liver diseases.Our results demonstrate that the BAS model is more accurate and results in higher truepositive and true-negative prediction of death compared to both non-BAS and model for end-stage liver disease(MELD)models.Both 5-and 3-year survival probabilities markedly decreased as a function of BAS.Moreover,patients with high BAS had a 4-fold higher rate of death and lived for an average of 11 mo shorter than subjects with low BAS.The increased risk of death with high vs low BAS was also 2-4-fold higher and the shortening of lifespan was 6-7-mo lower compared to MELD or non-BAS.Similarly,we have shown the use of BAS to predict the survival of patients with and without liver transplant(LT).Therefore,BAS could be used to define the most seriously ill patients,who need earlier intervention such as LT.This will help provide guidance for timely care for liver patients.CONCLUSION The BAS model is more accurate than MELD and non-BAS models in predicting the prognosis of cholestatic liver diseases.

Survival and Its Predictors among Tuberculosis Patients on Treatment in Selected Health Centers of Addis Ababa, Ethiopia: A Retrospective Cohort Study

Tuberculosis is one of the leading causes of morbidity and mortality globally. Although different strategies have been designed and implemented to combat it, it has continuously increased in the past five years, resulting in 10 million new cases and 1.6 million deaths. This study aims to estimate survival and predictors among tuberculosis patients on treatment in selected health centers in Addis Ababa, Ethiopia. The study employed a retrospective cohort design where data were collected by reviewing medical records of tuberculosis patients who were registered from May 2016 to May 2017 on treatment in 20 selected health centers in Addis Ababa. Independent predictors were identified, and the strength of association between dependent and independent predictors was determined using the Weibull regression model. Before computing Weibull regression analysis, Cox proportional assumption, model diagnosis, and fitness were checked. The hazard ratio was calculated to indicate the strength of association. Of 371 TB patients, about 136 (36.7%) died during the treatment period. Most TB deaths occurred during the intensive phase, and the overall estimated median survival time was 157 days. In the multivariable Weibull model, age (HR = 0.98), baseline weight (HR = 0.96, P = 0.03), tuberculosis treatment phase (continuation phase, HR = 0.48), and tuberculosis type (pulmonary negative TB, HR = 19.92) were found to be independent predictors of time to death of tuberculosis patients. Finally, the study concluded that the survival time to death of the patients is high. The health care providers should give special attention and follow up for pulmonary negative and underweight TB patients.

Associations between Lesion Locations and Stroke Recurrence in Survivors of First-ever Ischemic Stroke:A Prospective Cohort Study

Summary:Several studies have indicated that stroke survivors with multiple lesions or with larger lesion volumes have a higher risk of stroke recurrence.However,the relationship between lesion locations and stroke recurrence is unclear.We conducted a prospective cohort study of first-ever ischemic stroke survivors who were consecutively enrolled from January 2010 to December 2015.Stroke recurrence was assessed every 3 months after post-discharge via telephone interviews by trained interviewers.Lesion locations were obtained from hospital-based MRI or CT scans and classified using two classification systems that were based on cerebral hemisphere or vascular territory and brain anatomical structures.Flexible parametric survival models using the proportional hazards scale(PH model)were used to analyze the time-to-event data.Among 633 survivors,63.51%(n-402)had anterior circulation ischemia(ACI),and morc than half of all ACIs occurred in the subcortex.After a median follow-up of 2.5 years,117(18.48%)survivors developed a recurrent stroke.The results of the multivariate PH model showed that survivors with non-brain lesions were at higher risk of recurrence than those with right-side lesions(HR,2.79;95%CI,1.53,5.08;P-0.001).There was no increase in risk among survivors with left-side lesions(HR,0.97;95%CI,0.53,1.75;P=0.914)or both-side lesions(HR,1.24;95%CI,0.75,2.07;P-0.401)compared to those with right-side lesions.Additionally,there were no associations between stroke ecurrence and lesion locations that were classified based on vascular territory and brain anatomical structures.It was concluded that first-ever ischemic stroke survivors with non-brain lesion had higher recurrence risk than those with right-side lesion,although no significant associations were found when the lesion locations were classified by vascular territory and brain anatomical structures.

A novel AC turning on behavior model based on survival analysis

Occupant control behavior is a key factor affecting the energy consumption of building air-conditioners(ACs).The operating behavior of ACs and their models in office buildings have been investigated extensively.However,although the thermal sensation of occupants is affected by their previous thermal experience,few researchers have attempted to incorporate this effect quantitatively in models of AC turning on behavior.Not considering the cumulative effect may result in inaccurate predictions.Therefore,in this study,a survival model is proposed to describe AC turning on behavior in office buildings under the cumulative dimension of time.Based on a dataset containing environmental parameters and occupant behavior information,as well as considering occupants entering a room as the starting event and turning on an air-conditioner as the end event,the endurance time before an AC is turned on is investigated,and a survival model is used to predict the probability of the AC turning on due to environmental factors.Based on a switch curve,confusion matrix,and tolerance–time curve,the prediction results of the survival model are analyzed and validated.The results show that a tolerance temperature of 29℃and a tolerance duration setting of 1 h can effectively model the turning on behavior of the AC.In addition,based on comparison results of different models,the survival model presents a more stable switching curve,a higher F1 score,and a tolerance curve that is more similar to reality.Different tolerance durations,as well as static and dynamic tolerance temperature settings,are considered to optimize the model.Furthermore,the AC energy consumption is calculated under the survival model and the traditional Weibull model.Simulation results were compared with measurement,and the survival model verified the improvement effect of prediction accuracy by 8%than the Weibull model.By considering the time-transformed accumulation of physical environmental factors,the accuracy of AC turning on models can be improved,thus providing an effective reference for future building energy consumption simulations.

Analytical and computational modelling for wave energy systems：the example of oscillating wave surge converters

The development of new wave energy converters has shed light on a number of unanswered questions in fluid mechanics, but has also identified a number of new issues of importance for their future deployment. The main concerns relevant to the practical use of wave energy converters are sustainability, survivability, and maintainability. Of course,it is also necessary to maximize the capture per unit area of the structure as well as to minimize the cost. In this review, we consider some of the questions related to the topics of sustainability, survivability, and maintenance access, with respect to sea conditions, for generic wave energy converters with an emphasis on the oscillating wave surge converter. New analytical models that have been developed are a topic of particular discussion. It is also shown how existing numerical models have been pushed to their limits to provide answers to open questions relating to the operation and characteristics of wave energy converters.

Predicting survival and prognosis of postoperative breast cancer brain metastasis:a population-based retrospective analysis

Background:Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis.The study aimed to construct a novel predictive clinical model to evaluate the overall survival(OS)of patients with postoperative brain metastasis of breast cancer(BCBM)and validate its effectiveness.Methods:From 2010 to 2020,a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University,and they were randomly assigned to the training cohort and the validation cohort.Data of another 173 BCBM patients were collected from the Surveillance,Epidemiology,and End Results Program(SEER)database as an external validation cohort.In the training cohort,the least absolute shrinkage and selection operator(LASSO)Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS.The model capability was assessed using receiver operating characteristic,C-index,and calibration curves.Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model.The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.Results:LASSO Cox regression analysis revealed that lymph node metastasis,molecular subtype,tumor size,chemotherapy,radiotherapy,and lung metastasis were statistically significantly correlated with BCBM.The C-indexes of the survival nomogram in the training,validation,and SEER cohorts were 0.714,0.710,and 0.670,respectively,which showed good prediction capability.The calibration curves demonstrated that the nomogram had great forecast precision,and a dynamic diagram was drawn to increase the maneuverability of the results.The Risk Stratification System showed that the OS of lowrisk patients was considerably better than that of high-risk patients(P<0.001).Conclusion:The nomogram prediction model constructed in this study has a good predictive value,which can effectively evaluate the survival rate of patients with postoperative BCBM.

Dynamic Analysis for the Global Performance of An SPM-Feeder-Cage System Under Waves and Currents

In the present study, the dynamic response of a coupled SPM-feeder-cage system under irregular waves and shear currents is analyzed. A numerical model is developed by using the commercial software Orca Flex. Hydrodynamics coefficients of the vessel are calculated by using a 3D diffraction/radiation panel program. First- and second-order wave forces are included in the calculations. Morison equation is used to compute the drag force on line elements representing the net. Drag coefficients are determined at every time step in the simulation considering the relative normal velocity between the structural elements and the fluid flow. The dynamic response of the coupled system is analyzed for various environments and net materials. The results of the study show the effects of solidity ratio of the net and vertical positions of the cage on the overall dynamic response of the system, confirming the viability of this type of configuration for future development of offshore aquaculture in deep waters.

Cost-Utility Analysis of Nivolumab plus Chemotherapy in the First-Line Treatment of Upper Gastrointestinal Adenocarcinoma

Objective: To evaluate the cost-utility of nivolumab plus chemotherapy compared with chemotherapy alone as the first-line treatment for advanced gastric, gastro-oesophageal junction, and esophageal adenocarcinoma in China. Methods: Based on CheckMate649, a partitioned survival model was carried out with a circulation cycle of 6 weeks to simulate the patient’s lifetime. Sensitivity analysis were adopted to verify the robustness of the results. Results: The results of the base-case analysis showed that both the total cost and utility of the nivolumab group were higher, and the ICUR value was CNY 267498.67/QALY, more than 3 times the GDP per capita of China in 2020. The results of deterministic sensitivity analysis indicated that the three most influential factors were the utility value of PFS state, the cost of nivolumab and the discount rate. The results of probabilistic sensitivity analysis were consistent with those of base-case analysis, proving that the results were robust. The scenario analysis illustrated that economical price of nivolumab was CNY 3652.71. Conclusions: Under the willing-to-pay threshold of three times the GDP per capita of China in 2020, compared with chemotherapy alone, nivolumab plus chemotherapy is not a cost-effective option in China.

Cost-Effectiveness Analysis of Atezolizumab plus Pemetrexed and Platinum in First-Line Treatment of Non-Squamous Non-Small Cell Lung Cancer in China

Objective: To evaluate the cost-effectiveness of atezolizumab plus pemetrexed and platinum-based (APP) in the first-line treatment of non-squamous non- small cell lung cancer (NSCLC). Methods: A partitioned survival model (PSM) was constructed based on the IMpower132 clinical trial. Total cost, quality- adjusted life years (QALY), and incremental cost-effectiveness ratio (ICER) were the main outputs of the model. Deterministic sensitivity analysis and probabilistic sensitivity analysis were adopted to test the uncertainty of the parameters. Results: The results of the base-case analysis illustrated that compared with PP, the incremental cost of APP was CNY 591040.94, the incremental utility was 0.46 QALY, and the ICER was CNY 1291414.83/QALY. Deterministic sensitivity analysis results illustrated that atezolizumab and other parameters have a greater impact on ICER. Probabilistic sensitivity analysis results show that no matter how each parameter changes, under the willingness to pay threshold of 3-times Chinese per capita GDP, the probability of APP has cost-effectiveness is 0. Conclusion: From the perspective of the Chinese health system, APP is not cost-effective for first-line treatment of non-squamous non-small cell lung cancer without sensitizing EGFR or ALK genetic alterations.

GLOBAL ATTRACTIVITY OF THE MODEL FOR THE SURVIVAL OF RED BLOOD CEELS WITH SEVERAL DELAYS

In this paper, we consider the model for the survival of red blood ceels with several delaysdN(t)dt=-μ N(t)+m i=1 P ie -r iN(t-τ i) , t≥ 0 (*) and establish a sufficient condition under which the positive equilibrium N * of (*) is a global attractor. Our criteria generalize and improve the correspondent results obtained in .

Global Attractivity of Positive Periodic Solutions for a Survival Model of Red Blood Cells

In this paper, we deal with a model for the survival of red blood cells with periodic coefficients x＇（g）=-μ（t）x（t）＋P（t）e^-γ（t）x（t-τ（t））,t≥0.（＊）A new sufficient condition for global attractivity of positive periodic solutions of Eq. （＊） is obtained. Our criterion improves corresponding result obtained by Li and Wang in 2005.