The Effect of Time-dependent Prognostic Factors on Survival of Non-Small Cell Lung Cancer using Bayesian Extended Cox Model

Background: Lung cancer is one of the most common cancers around the world. The aim of this study was to use Extended Cox Model (ECM) with Bayesian approach to survey the behavior of potential time-varying prognostic factors of Non-small cell lung cancer. Materials and Methods: Survival status of all 190 patients diagnosed with Non-Small Cell lung cancer referring to hospitals in Yazd were recorded from 2009 to 2013 by phone call. We fitted conventional Cox proportional hazards (Cox PH) as well as Bayesian ECM. Inference for estimated risk ratios was based on 90% credible intervals. Log pseudo marginal likelihood criteria (LMPL) was used for model comparison. Statistical computations were based on R language. Results: In this study, 190 patients with non-small cell lung cancer were followed, of whom 160 died because of the disease (84.2%). Median of survival time was 8 ± 0.076 month. After fitting the Cox PH Model, it was determined that the PH assumption was not satisfied for the type of treatment, the disease stage, and pathology status variables (p <0.001). LPML for Cox PH and Bayesian ECM was -431.593 and -401.01, respectively. Estimated hazard ratio curves based on Bayesian ECM showed that the risk ratio for these variables exhibited significant time varying behavior on hazard of lung cancer through follow up time. Conclusion: Based on LMPL, Bayesian ECM was found to have a better fit than Cox PH Model which declares, results from Cox PH should be interpreted with care. Especially, from beginning of the study to about 20 month after, very high risk ratio was estimated for variables whose PH was not satisfying for them.

The Effect of Neutropenia and Filgrastim (G-CSF) in Cancer Patients With COVID-19 Infection

Background: Neutropenia is commonly encountered in cancer patients, and recombinant human granulocyte colony-stimulating factor (G-CSF, filgrastim) is widely given to oncology patients to counteract neutropenia and prevent infection. G-CSF is both a growth factor and cytokine that initiates proliferation and differentiation of mature granulocytes. However, the clinical impact of neutropenia and G-CSF use in cancer patients, who are also afflicted with coronavirus disease 2019 (COVID-19), remains unknown. Methods: An observational cohort of 304 hospitalized patients with COVID-19 at Memorial Sloan Kettering Cancer Center was assembled to investigate links between concurrent neutropenia (N=55) and G-CSF administration (N=16) on COVID-19-associated respiratory failure and death. These factors were assessed as time-dependent predictors using an extended Cox model, controlling for age and underlying cancer diagnosis. To determine whether the degree of granulocyte response to G-CSF affected outcomes, a similar model was constructed with patients that received G-CSF, categorized into high- and low-response, based on the level of absolute neutrophil count (ANC) rise 24 hours after growth factor administration. Results: Neutropenia (ANC < 1 K/mcL) during COVID-19 course was not independently associated with severe respiratory failure or death (HR: 0.71, 95% Cl: 0.34-1.50, P value: 0.367) in hospitalized COVID-19 patients. When controlling for neutropenia, G-CSF administration was associated with increased need for high oxygen supplementation and death (HR: 2.97, 95% CI: 1.06-8.28, P value: 0.038). This effect was predominantly seen in patients that exhibited a high response to G-CSF based on their ANC increase post-G-CSF administration (HR: 5.18, 95% CI: 1.61-16.64, P value: 0.006). Conclusion: Possible risks versus benefits of G-CSF administration should be weighed in neutropenic cancer patients with COVID-19 infection, as G-CSF may lead to worsening clinical and respiratory status in this setting.

Bayesian Analysis of Extended Cox Model with Time-Varying Covariates Using Bootstrap Prior

A new Bayesian estimation procedure for extended cox model with time varying covariate was presented. The prior was determined using bootstrapping technique within the framework of parametric empirical Bayes. The efficiency of the proposed method was observed using Monte Carlo simulation of extended Cox model with time varying covariates under varying scenarios. Validity of the proposed method was also ascertained using real life data set of Stanford heart transplant. Comparison of the proposed method with its competitor established appreciable supremacy of the method.

Blood Pressure and Incident Atrial Fibrillation in Older Patients Initiating Hemodialysis

Background and objectivesWe examined the association of predialysis systolic and diastolic BP and intradialytic hypotension with incident atrial fibrillation in older patients initiating hemodialysis.Design, setting, participants, & measurementsWe used the US Renal Data System linked to the records of a large dialysis provider to identify patients aged ≥67 years initiating hemodialysis between January 2006 and October 2011. We examined quarterly average predialysis systolic BP, diastolic BP, and proportion of sessions with intradialytic hypotension (i.e., nadir systolic BP <90 mm Hg). We applied an extended Cox model to compute adjusted hazard ratios (HRs) of each exposure with incident atrial fibrillation.ResultsAmong 17,003 patients, 3785 developed atrial fibrillation. When comparing predialysis systolic BP to a fixed reference of 140 mm Hg, lower predialysis systolic BP was associated with a higher hazard of atrial fibrillation, whereas higher systolic BP was associated with a lower hazard of atrial fibrillation. When comparing across a range of systolic BP for two hypothetical patients with similar measured covariates, the association varied by mean systolic BP: at systolic BP 190 mm Hg, each 10 mm Hg lower systolic BP was associated with lower atrial fibrillation hazard (HR, 0.94; 95% confidence interval, 0.90 to 1.00), whereas at systolic BP 140 mm Hg, a 10 mm Hg lower systolic BP was associated with a higher atrial fibrillation hazard (HR, 1.12; 95% confidence interval, 1.10 to 1.14). Lower diastolic BP was associated with higher atrial fibrillation hazards. Intradialytic hypotension was weakly associated with atrial fibrillation.ConclusionsIn this observational study of older patients initiating hemodialysis, lower predialysis systolic BP and diastolic BP were associated with higher incidence of atrial fibrillation.

Model Regresi Cox dan Aplikasinya dalam Menganalisis Ketahanan Hidup Pasien Penderita Diabetes Mellitus di Rumah Sakit Bhayangkara Makassar

Abstrak. Analisis tahan hidup adalah salah satu prosedur statistik untuk melakukan analisa data berupa waktu tahan hidup dan variabel yang mempengaruhi waktu tahan hidup. Pada penelitian ini analisis tahan hidup diaplikasikan pada kasus diabetes mellitus di Rumah Sakit Bhayangkara Makassar pada tahun 2016. Salah satu metode analisis tahan hidup yang digunakan adalah model Regresi Cox Proporsional Hazard. Penggunaan model regresi cox proporsional hazard harus memenuhi asumsi proporsional hazard. Penelitian ini juga menggunakan distribusi eksponensial dua parameter untuk menentukan fungsi hazard dan metode Breslow dalam membentuk model cox terbaik. Dari hasil penelitian diperoleh faktor-faktor signifikan yang mempengaruhi waktu tahan hidup adalah umur dan kadar gula darah, namun faktor kadar gula darah tidak memenuhi asumsi proporsional hazard, sehingga digunakan Model Cox Extended untuk memperbaiki model cox proporsional hazard. Covariate yang tidak memenuhi asumsi proporsional hazard dalam model cox extended dinteraksikan dengan fungsi waktu . Model Cox Extended pada akhirnya memberikan informasi tentang faktor -faktor yang berpengaruh signifikan terhadap waktu tahan hidup yaitu umur dan kadar gula darah terikat waktu, dimana setiap individu yang berumur kurang dari 45 tahun memiliki resiko kegagalan 0,015 kali lebih kecil dibandingkan dengan pasien yang berumur lebih dari 45 tahun dan individu yang kadar gula darahnya tinggi memiliki resiko kegagalan sebesar 1,128 kali lebih besar dibandingkan dengan pasien yang memiliki kadar gula darah rendah dan normal.Kata Kunci: Analisis Tahan Hidup, Regresi Cox Proporsional Hazard, Diabetes Mellitus, Model Cox ExtendedAbstract. Survival analyze is one of the statistical procedures to analyze data survival time and variable that will affect the rate of recovery of patients. In this research, survival analyze was applicated by diabetes mellitus case in Bhayangkara Hospital Makassar 2016. One of the methods survival analyze used is cox regression model with proportional hazard. The use of cox regression model with proportional hazard must fulfill assumption of proportional hazard. This research also use 2-parameter exponential distribution to determine of hazard function and Breslow method to shaping the best of cox model. From the results of the research give conclusion that factors affecting of time recovery are age and blood sugar level. But the blood sugar level factor does not fulfill the proportional hazard assumptions. So that the extended cox model was used to improve the cox proportional hazard model. Variables that does not fulfill the proportional hazard assumption in the extended cox model are interacted with the time function . Finally, the extended cox model give information about the factors most affect the rate of recovery are age and time bound blood sugar level. Every individual less than 45 years old has a 0,015 times greater risk of failure than patients older than 45 years old and individuals with high blood sugar level had a risk of failure is 1,128 times greater than the low and normal blood sugar level Keywords: Survival Analyze, Cox Proportional Hazard Regression, Diabetes Mellitus, Extended Cox Model