The role of cardiovascular comorbidities in ovarian cancer survival

Objective. One facet of cancer care that often goes ignored is comorbidities, or diseases that exist in concert with cancer. Comorbid conditions may affect survival by influencing treatment decisions and prognosis. The purpose of this secondary data analysis was to identify whether a history of cardiovascular comorbidities among ovarian cancer patients influenced survival time at the University of Texas M. D. Anderson Cancer Center. The parent study, Project Peace, has a longitudinal design with an embedded randomized efficacy study which seeks to improve detection of depressive disorders in ovarian, peritoneal, and fallopian tube cancers. ^ Methods. Survival time was calculated for the 249 ovarian cancer patients abstracted by Project Peace staff. Cardiovascular comorbidities were documented as present, based upon information from medical records in addition to self reported comorbidities in a baseline study questionnaire. Kaplan-Meier survival curves were used to compare survival time among patients with a presence or absence of particular cardiovascular comorbidities. Cox Regression proportional models accounted for multivariable factors such as age, staging, family history of cardiovascular comorbidities, and treatment. ^ Results. Among our patient population, there was a statistically significant relationship between shorter survival time and a history of thrombosis, pericardial disease/tamponade, or COPD/pulmonary hypertension. Ovarian cancer patients with a history of thrombosis lived approximately half as long as patients without thrombosis (58.06 months vs. 121.55 months; p=.001). In addition, patients who suffered from pericardial disease/tamponade had poorer survival than those without a history of pericardial disease/tamponade (48 months vs. 80.07 months; p=.002). Ovarian cancer patients with a history of COPD or pulmonary hypertension had a median survival of 60.2 months, while the median survival for patients without these comorbidities was 80.2 months (p=.014). ^ Conclusion. Especially because of its relatively lower survival rate, greater emphasis needs to be placed on the potential influence of cardiovascular comorbid conditions in ovarian cancer.^

Disentangling the impact of seroconversion age and set-point viral load on ART-free HIV survival

Thesis (Master's)--University of Washington, 2016-06

An R-package for the Estimation and Testing of Multiple Covariates and Biomarker Interactions for Survival Data Based on Local Partial Likelihood

When we study the variables that a ffect survival time, we usually estimate their eff ects by the Cox regression model. In biomedical research, e ffects of the covariates are often modi ed by a biomarker variable. This leads to covariates-biomarker interactions. Here biomarker is an objective measurement of the patient characteristics at baseline. Liu et al. (2015) has built up a local partial likelihood bootstrap model to estimate and test this interaction e ffect of covariates and biomarker, but the R code developed by Liu et al. (2015) can only handle one variable and one interaction term and can not t the model with adjustment to nuisance variables. In this project, we expand the model to allow adjustment to nuisance variables, expand the R code to take any chosen interaction terms, and we set up many parameters for users to customize their research. We also build up an R package called "lplb" to integrate the complex computations into a simple interface. We conduct numerical simulation to show that the new method has excellent fi nite sample properties under both the null and alternative hypothesis. We also applied the method to analyze data from a prostate cancer clinical trial with acid phosphatase (AP) biomarker.

Asset health prediction using condition indicators and operating environment indicators : a case study

Prognostics and asset life prediction is one of research potentials in engineering asset health management. We previously developed the Explicit Hazard Model (EHM) to effectively and explicitly predict asset life using three types of information: population characteristics; condition indicators; and operating environment indicators. We have formerly studied the application of both the semi-parametric EHM and non-parametric EHM to the survival probability estimation in the reliability field. The survival time in these models is dependent not only upon the age of the asset monitored, but also upon the condition and operating environment information obtained. This paper is a further study of the semi-parametric and non-parametric EHMs to the hazard and residual life prediction of a set of resistance elements. The resistance elements were used as corrosion sensors for measuring the atmospheric corrosion rate in a laboratory experiment. In this paper, the estimated hazard of the resistance element using the semi-parametric EHM and the non-parametric EHM is compared to the traditional Weibull model and the Aalen Linear Regression Model (ALRM), respectively. Due to assuming a Weibull distribution in the baseline hazard of the semi-parametric EHM, the estimated hazard using this model is compared to the traditional Weibull model. The estimated hazard using the non-parametric EHM is compared to ALRM which is a well-known non-parametric covariate-based hazard model. At last, the predicted residual life of the resistance element using both EHMs is compared to the actual life data.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

A phase III trial of docetaxel/carboplatin versus mitomycin C/ifosfamide/ cisplatin (MIC) or mitomycin C/vinblastine/cisplatin (MVP) in patients with advanced non-small-cell lung cancer : a randomised multicentre trial of the British Thoracic Oncology Group (BTOG1)

Background: Phase III studies suggest that non-small-cell lung cancer (NSCLC) patients treated with cisplatin-docetaxel may have higher response rates and better survival compared with other platinum-based regimens. We report the final results of a randomised phase III study of docetaxel and carboplatin versus MIC or MVP in patients with advanced NSCLC. Patients and methods: Patients with biopsy proven stage III-IV NSCLC not suitable for curative surgery or radiotherapy were randomised to receive four cycles of either DCb (docetaxel 75 mg/m 2, carboplatin AUC 6), or MIC/MVP (mitomycin 6 mg/m 2, ifosfamide 3 g/m 2 and cisplatin 50 mg/m 2 or mitomycin 6 mg/ m 2, vinblastine 6 mg/m 2 and cisplatin 50 mg/m 2, respectively), 3 weekly. The primary end point was survival, secondary end points included response rates, toxicity and quality of life. Results: The median follow-up was 17.4 months. Overall response rate was 32% for both arms (partial response = 31%, complete response = 1%); 32% of MIC/MVP and 26% of DCb patients had stable disease. One-year survival was 39% and 35% for DCb and MIC/MVP, respectively. Two-year survival was 13% with both arms. Grade 3/4 neutropenia (74% versus 43%, P < 0.005), infection (18% versus 9%, P = 0.01) and mucositis (5% versus 1%, P = 0.02) were more common with DCb than MIC/MVP. The MIC/MVP arm had significant worsening in overall EORTC score and global health status whereas the DCb arm showed no significant change. Conclusions: The combination of DCb had similar efficacy to MIC/MVP but quality of life was better maintained. © 2006 European Society for Medical Oncology.

Bone matters in lung cancer

Background: Bone metastases are a significant and undertreated clinical problem in patients with advanced lung cancer. Design: We reviewed the incidence of bone metastases and skeletal-related events (SREs) in patients with lung cancer and examined the burden on patients' lives and on health care systems. Available therapies to improve survival and lessen the impact of SREs on quality of life (QoL) were also investigated. Results: Bone metastases are common in lung cancer; however, owing to short survival times, data on the incidences of SREs are limited. As with other cancers, the costs associated with treating SREs in lung cancer are substantial. Bisphosphonates reduce the frequency of SREs and improve measures of pain and QoL in patients with lung cancer; however, nephrotoxicity is a common complication of therapy. Denosumab, a recently approved bone-targeted therapy, is superior to zoledronic acid in increasing the time to first on-study SRE in patients with solid tumours, including lung cancer. Additional roles of bone-targeted therapies beyond the prevention of SREs are under investigation. Conclusions: With increasing awareness of the consequences of SREs, bone-targeted therapies may play a greater role in the management of patients with lung cancer, with the aim of delaying disease progression and preserving QoL. © The Author 2012. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Prostacyclin synthase expression and epigenetic regulation in nonsmall cell lung cancer

BACKGROUND: Prostacyclin synthase (PGIS) metabolizes prostaglandin H(2), into prostacyclin. This study aimed to determine the expression profile of PGIS in nonsmall cell lung cancer (NSCLC) and examine potential mechanisms involved in PGIS regulation. METHODS: PGIS expression was examined in human NSCLC and matched controls by reverse transcriptase polymerase chain reaction (RT-PCR), Western analysis, and immunohistochemistry. A 204-patient NSCLC tissue microarray was stained for PGIS and cyclooxygenase 2 (COX2) expression. Staining intensity was correlated with clinical parameters. Epigenetic mechanisms underpinning PGIS promoter expression were examined using RT-PCR, methylation-specific PCR, and chromatin immunoprecipitation analysis. RESULTS: PGIS expression was reduced/absent in human NSCLC protein samples (P <.0001), but not mRNA relative to matched controls. PGIS tissue expression was higher in squamous cell carcinoma (P =.004) and in male patients (P <.05). No significant correlation of PGIS or COX2 expression with overall patient survival was observed, although COX2 was prognostic for short-term (2-year) survival (P <.001). PGIS mRNA expression was regulated by DNA CpG methylation and histone acetylation in NSCLC cell lines, with chromatin remodeling taking place directly at the PGIS gene. PGIS mRNA expression was increased by both demethylation agents and histone deacetylase inhibitors. Protein levels were unaffected by demethylation agents, whereas PGIS protein stability was negatively affected by histone deacetylase inhibitors. CONCLUSIONS: PGIS protein expression is reduced in NSCLC, and does not correlate with overall patient survival. PGIS expression is regulated through epigenetic mechanisms. Differences in expression patterns between mRNA and protein levels suggest that PGIS expression and protein stability are regulated post-translationally. PGIS protein stability may have an important therapeutic role in NSCLC. © 2011 American Cancer Society.

Platinum-based chemotherapy in metastatic breast cancer : the Leicester (UK) experience

Aims: After failure of anthracycline- and taxane-based chemotherapy in metastatic breast cancer, treatment options until recently were limited. Until the introduction of capecitabine and vinorelbine, no standard regimen was available. We conducted a retrospective study to determine the efficacy and toxicity of platinum-based chemotherapy in metastatic breast cancer. Materials and methods: Forty-two women with metastatic breast cancer previously treated with anthracyclines (93%) and/or taxanes (36%) received mitomycin-vinblastine-cisplatin (MVP) (n = 23), or cisplatin-etoposide (PE) (n = 19), as first-, second- and third-line treatment at a tertiary referral centre between 1997 and 2002. Chemotherapy was given every 3 weeks as follows: mitomycin-C (8 mg/m 2) (cycles 1, 2, 4, 6), vinblastine (6 mg/m 2), and cisplatin (50 mg/m 2) all on day 1; and cisplatin (75 mg/m 2) and etoposide (100 mg/m 2) on day 1 and (100 mg/m 2) orally twice a day on days 2-3. Results: The response rate for 40 evaluable patients (MVP: n = 23; PE: n = 17) was 18% (95% confidence interval [CI]: 9-32%). The response rate to MVP was 13% (95% CI: 5-32%, one complete and two partial responses) and to PE 24% (10-47%, four partial responses). Disease stabilised in 43% (26-63%) and 47% (26-69%) of women treated with MVP and PE, respectively. After a median follow-up of 18 months, 37 women (MVP: n = 19; PE: n = 18) died from their disease. Median (range) progression-free survival and overall survival were 6 months (0.4-18.7) and 9.9 months (1.3-40.8), respectively. Median progression-free survival for the MVP and PE groups was 5.5 and 6.2 months (Log-rank, P = 0.82), and median overall survival was 10.2 and 9.4 months (Log-rank, P = 0.46), respectively. The main toxicity was myelosuppression. Grades 3-4 neutropenia was more common in women treated with PE than in women treated with MVP (74% vs 30%; P = 0.012), but the incidence of neutropenic sepsis, relative to the number of chemotherapy cycles, was low (7% overall). The toxicity-related hospitalisation rate was 1.2 admissions per six cycles of chemotherapy. No treatment-related deaths occurred. MVP and PE chemotherapy have modest activity and are safe in women with metastatic breast cancer. © 2005 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Advances in the systemic therapy of malignant pleural mesothelioma

Malignant pleural mesothelioma is an aggressive thoracic malignancy associated with exposure to asbestos, and its incidence is anticipated to increase during the first half of this century. Chemotherapy is the mainstay of treatment, yet sufficiently robust evidence to substantiate the current standard of care has emerged only in the past 5 years. This Review summarizes the evidence supporting the clinical activity of chemotherapy, discusses the use of end points for its assessment and examines the influence of clinical and biochemical prognostic factors on the natural history of malignant pleural mesothelioma. Early-phase clinical trials of second-line and novel agents are emerging from an increased understanding of mesothelioma cell biology. Coupled with high-quality translational research, such developments have real potential to improve the outlook of patients at a time of increasing incidence.

A role for IGF-1R-targeted therapies in small-cell lung cancer?

Background: Small-cell lung cancer (SCLC) is an aggressive disease with a poor prognosis. The insulin-like growth factor-1 receptor (IGF-1R) is an autocrine growth factor and an attractive therapeutic target in many solid tumors, but particularly in lung cancer. Patients and Methods: This study examined tumor samples from 23 patients diagnosed with SCLC, 11 resected specimens and 12 nodal biopsies obtained by mediastinoscopy, for expression of IGF-1R using the monoclonal rabbit anti-IGF-1R (clone G11, Ventana Medical Systems, Tucson, AZ) and standard immunohistochemistry (IHC). Results: All 23 tumor samples expressed IGF-1R with a range of stain intensity from weak (1+) to strong (3+). Ten tumors had a score of 3+, 7 tumors 2+, and 6 tumors 1+. Patient survival data were available for all 23 patients. Two patients died < 30 days post biopsy, therefore, the intensity of anti-IGF-1R immunostaining for 21 patients was correlated to survival. Patients with 3+ immunostaining had a poorer prognosis (P = .003). The overall survival of patients who underwent surgical resection was significantly better (median survival not reached) than patients who were not resected (median survival, 7.4 months) (P = .006). Conclusion: IGF-1R targeted therapies may have a role in the treatment of SCLC in combination with chemotherapy or as maintenance therapy. Further studies on the clinical benefit of targeting IGF-1R in SCLC are needed.