Comparison of Cox Regression and Parametric Models: Application for Assessment of Survival of Pediatric Cases of Acute Leukemia in Southern Iran

Advanced statistical methods for hazard modeling in cardiothoracic surgery: a comprehensive review of techniques and approaches

Hazard modeling in cardiothoracic surgery, crucial for understanding patient outcomes, utilizes survival analysis like the Cox proportional hazards model. Kaplan-Meier curves are employed in survival analysis to represent the probability of survival over time. While Cox assumes proportional hazards, the Fine-Gray model deals with competing risks. Parametric models (e.g., Weibull) specify survival distributions, unlike Cox. Bayesian analysis integrates prior knowledge with data. Machine learning, including decision trees and support vector machines, enhances risk prediction by analyzing extensive datasets. However, it is important to note that whatever new approaches one may adopt will enhance the quality of risk assessment and not the risk assessment as such. Preprocessing is vital for data quality in complex cardiovascular datasets, alongside robust validation methods like cross-validation for model reliability across patient cohorts.

Can spirometry improve the performance of cardiovascular risk model in high-risk Eastern European countries?

Aims

Impaired lung function has been strongly associated with cardiovascular disease (CVD) events. We aimed to assess the additive prognostic value of spirometry indices to the risk estimation of CVD events in Eastern European populations in this study.

Methods

We randomly selected 14,061 individuals with a mean age of 59 ± 7.3 years without a previous history of cardiovascular and pulmonary diseases from population registers in the Czechia, Poland, and Lithuania. Predictive values of standardised Z-scores of forced expiratory volume measured in 1 s (FEV1), forced vital capacity (FVC), and FEV1 divided by height cubed (FEV1/ht3) were tested. Cox proportional hazards models were used to estimate hazard ratios (HRs) of CVD events of various spirometry indices over the Framingham Risk Score (FRS) model. The model performance was evaluated using Harrell's C-statistics, likelihood ratio tests, and Bayesian information criterion.

Results

All spirometry indices had a strong linear relation with the incidence of CVD events (HR ranged from 1.10 to 1.12 between indices). The model stratified by FEV1/ht3 tertiles had a stronger link with CVD events than FEV1 and FVC. The risk of CVD event for the lowest vs. highest FEV1/ht3 tertile among people with low FRS was higher (HR: 2.35; 95% confidence interval: 1.96-2.81) than among those with high FRS. The addition of spirometry indices showed a small but statistically significant improvement of the FRS model.

Conclusions

The addition of spirometry indices might improve the prediction of incident CVD events particularly in the low-risk group. FEV1/ht3 is a more sensitive predictor compared to other spirometry indices.

Retention on antiretroviral therapy in person with HIV and viral hepatitis coinfection in Ethiopia: a retrospective cohort study

Background

HIV coinfection with viral hepatitis B (HBV) or viral hepatitis C (HCV) is not uncommon in Ethiopia. Although the coinfections are presumed to interfere with antiretroviral treatment (ART), this is not widely studied in Sub-Saharan African settings. This study was conducted to determine ART retention in persons coinfected with HIV + HBV or HIV + HCV.

Methods

We reviewed the medical records of HIV-positive adults who initiated ART between 2011 to 2018 in four high-burden hospitals of Addis Ababa. Retention in care was the primary outcome of the study, which was compared between HIV and either HBV or HCV coinfected persons, and HIV-monoinfected persons. A parametric Gompertz regression model was used to compare retention between the coinfected and monoinfected groups.

Results

A total of 132 coinfected persons and 514 HIV-monoinfected individuals who initiated ART in 2011–2018 were compared. At 12-months of follow-up, 81.06% [95% CI: 73.3–86.9%] of the coinfected and 86.96% [95% CI: 83.7–89.6%] of the monoinfected were still on ART care. Cumulative retention in the coinfected group was 68.93% [60.4–76.3%] versus 80.35% [76.6–83.5%, p = 0.0048] in the monoinfected group. The cumulative retention was lower (61.25, 95% CI: 49.9–71.4%) in male coinfected patients than male monoinfected patients (77.77, 95% CI: 71.8–82.7%, p = 0.0041). In contrast, cumulative retention was similar in females in the coinfected group (80.76, 95% CI:67.3–89.5%) versus the monoinfected group (82.29, 95% CI:77.4–86.3%, p = 0.792). Overall, HIV-positive with viral hepatitis coinfection were 24 and 31% less likely to still be on ART care than the monoinfected group at 12 months and overall, with sub-distribution adjusted hazard ratio (AHR) of 0.76(95% CI:0.61–0.96, p = 0.021) and 0.69(95% CI:0.54–0.87, p = 0.002) respectively.

Conclusions

We observed that coinfected individuals are less likely to stay on ART than HIV monoinfected individuals. The low retention in the coinfected group from this study may affect the success of survival gained in people living with HIV (PLHIV) in the long term. More concerted efforts need to be made to retain coinfected individuals at least at the level of monoinfected persons on long-term ART care. Future studies are needed to better understand the difference in retention, preferable in a prospective manner.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13025-y.

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Spatial models are becoming more popular in time-to-event data analysis. Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue. This paper aimed to extend these two models, to evaluate the suitability of these models for estimation and prediction of the length of stay, compare different spatial priors, and determine factors that significantly affect the duration of hospital stay for dengue fever patients in the case study location, namely Wahidin hospital in Makassar, Indonesia. We compared two different models with three different spatial priors with respect to goodness of fit and generalisability. For all models considered, the Leroux prior was preferred over the intrinsic conditional autoregressive and independent priors, but Cox and Weibull versions had similar predictive performance, model fit, and results. Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of dengue patients at this hospital. Using appropriate Bayesian spatial survival models enables identification of factors that substantively affect the length of stay.

Association of ARID5B Genetic Variants with Risk of Childhood B Cell Precursor Acute Lymphoblastic Leukaemia in Latvia

Background: Acute lymphoblastic leukaemia (ALL) is the most common malignancy in childhood. Despite numerous investigations very little is still known about its aetiology. However, in one genome wide association study conducted to identify the possible genetic risk factors, two allelic variations rs10821936 and rs10994982 in the 3rd intron of the ARID5B gene were identified as possible ALL risk alleles. Association between ARID5B gene variants and ALL risk was also been confirmed for different ethnic groups. Materials and Methods: Eight genetic variants in the gene ARID5B were genotyped - rs10994982, rs7908445, rs7923074, rs10821936, rs10821937, rs7896246, rs10821938 and rs7089424 in 77 ALL patients in remission and in 122 age and gender matched controls; parental samples were also genotyped in 50 cases. Results: Six out of the eight (rs7908445, rs7923074, rs10821936, rs10821937, rs7896246 and rs7089424) analysed allelic variations were identified in the case-control analysis as statistically significant risk alleles for ALL development. In the family study and using hybrid analysis, all allelic variations were significantly associated with ALL. During the study, risk haplotype was identified rs10994982/rs7908445/rs7923074/ rs10821936/ rs10821937/rs7896246/rs10821938/rs7089424 – ATACCAAG – with a frequency in cases of 0.17 and in the control group at 0.29 (chi square = 6.69, p value = 0.009). In the family association study the same haplotype showed statistical significance (chi squared = 10.3, p value = 0.001). Conclusions: Results of the study replicate and extend previously published findings for ARID5B localized allelic variants, but do not explain the mechanism of action related to the pathogenesis of ALL.

Cox Regression and Parametric Models: Comparison of How They Determine Factors Influencing Survival of Patients with Non-Small Cell Lung Carcinoma

Background and objectives: The present study of survival rate of patients with non-small cell carcinoma (NSCLC) compared the efficiency of Cox semi-parametric vs. parametric models in determination of influencing factors. Methods: In this retrospective cohort study, data were gathered from 190 patients with a confirmed diagnosis of NSCLC referred to Shahid Sadoughi and Shohadaye Kargar Hospitals in Yazd, Iran during 2005 to 2014. To identify and compare factors influencing the survival rate, a Cox semi-parametric model was fitted to the data. Data analysis was performed using the R software version R3.3.1, and the significance level was set at 0.05. Results: The average age was 64.5 years. About 40% of patients had stage 4 disease. The median survival was 8 months. After comparing the models, the more efficient was the log-normal distribution (AIC=889.3829), with which disease stage, type of therapy, and age were significant factors. Among the different types of therapy, chemotherapy and radiotherapy yielded higher survival rates, and increased age was associated with lower survival. Conclusion: The most efficient model was a log-normal model. Implementation of optimal therapies at early stages can improve the survival of patients.