Geographic and socioeconomic factors in patients with malignant pleural mesothelioma in New South Wales and their impact upon clinical outcomes

Influencing factors and prediction methods of radiotherapy and chemotherapy in patients with lung cancer based on logistic regression analysis

Logistic regression analysis has widespread applications in clinical disease diagnosis, but it has not yet been applied to assess the acceptance of radiotherapy and chemotherapy in patients with lung cancer. A prediction model was established to investigate the influencing factors of radiotherapy and chemotherapy in lung cancer patients in order to provide useful information for clinicians to develop targeted and effective treatment. A sample was admitted of lung cancer patients to Binzhou Medical University Hospital stays from January 2020 to June 2021. After investigating doctors, nurses, patients, managers and conducting expert demonstration, the questionnaire was formed. The questionnaire was filled out by the patient or the patient's family members. The factors in the questionnaire data of patients accepting and not accepting radiotherapy and chemotherapy were compared for univariate analysis, and the significantly different single factor were analyzed by multifactor logistic regression analysis, explored the influencing factors of radiotherapy and chemotherapy in lung cancer patients established a predictive model and drew the receiver operating characteristic curve (ROC curve). The factors of two groups had statistically significant differences or no statistically significant differences. After multifactor logistic regression analysis was conducted, own personality, self-care ability, disease course classification, own attitude towards disease treatment, and family attitude towards disease treatment were included in the influencing factors of radiotherapy and chemotherapy in patients with lung cancer. Then, a predictive model was established. The area under the ROC curve of the predicted model was 0.973, the 95% confidence interval was 0.952-0.995, the optimal critical value was 0.832, the sensitivity was 91.84%, the specificity was 89.09%, and the accuracy was 90.85%. Based on logistic regression analysis, the prediction model could predict the extent of accepting radiotherapy and chemotherapy in patients with lung cancer. Understanding the factors related to patients with lung cancer accepting radiotherapy and chemotherapy could provide useful information for the targeted and effective treatment by clinicians.

The Impact of Donor Type on Outcomes and Cost of Allogeneic Hematopoietic Cell Transplantation for Pediatric Leukemia: A Merged Center for International Blood and Marrow Transplant Research and Pediatric Health Information System Analysis

Allogeneic hematopoietic stem cell transplantation (alloHCT) may be associated with significant morbidity and mortality, resulting in increased healthcare utilization (HCU). To date, no multicenter comparative cost analyses have specifically evaluated alloHCT in children with acute leukemia. In this retrospective cohort study, we examined the relationship between survival and HCU while investigating the hypothesis that matched sibling donor (MSD) alloHCT has significantly lower inpatient HCU with unrelated donor (URD) alloHCT, and that among URDs, umbilical cord blood (UCB) alloHCT will have higher initial utilization but lower long-term utilization. Clinical and transplantation outcomes data from the Center for International Blood and Marrow Transplant Research (CIBMTR) were merged with inpatient cost data from the Pediatric Health Information System (PHIS) database using a probabilistic merge methodology. The merged dataset comprised US patients age 1 to 21 years who underwent alloHCT for acute leukemia between 2004 and 2011 with comprehensive CIBMTR data at a PHIS hospital. AlloHCT was analyzed by donor type, with specific analysis of utilization and costs using PHIS claims data. The primary outcomes of overall survival (OS), leukemia-free survival (LFS), and inpatient costs were evaluated using Kaplan-Meier curves and Cox and Poisson models. A total of 632 patients were identified in both the CIBMTR and PHIS data. The 5-year LFS was 60% for MSD alloHCT, 47% for well-matched matched unrelated donor bone marrow (MUD) alloHCT, 48% for mismatched unrelated donor alloHCT, and 45% for UCB alloHCT (P = .09). Total adjusted costs were significantly lower for MSD alloHCT versus MUD alloHCT by day 100 (adjusted cost ratio [ACR], .73; 95% confidence interval [CI], .62 to .86; P < .001), and higher for UCB alloHCT versus MUD alloHCT (ACR, 1.27; 95% CI, 1.11 to 1.45; P < .001). By 2 years, total adjusted costs remained significantly lower for MSD alloHCT compared with MUD alloHCT (ACR, .67; 95% CI, .56 to .81; P < .001) and higher for UCB alloHCT compared with MUD alloHCT (ACR, 1.25; 95% CI, 1.02 to 1.52; P = .0280). Our data show that UCB and MUD alloHCT provide similar survival outcomes; however, MUD alloHCT has a significant advantage in cost by day 100 and 2 years. More research is needed to determine whether the cost difference among URD alloHCT approaches remains significant with a larger sample size and/or beyond 2 years post-alloHCT.

Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors

Introduction

Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models.

Methods

Data regarding 269 patients with MPM were allocated to balanced training (n=169) and validation sets (n=100). Prognostic signatures (minimal length best performing multivariate trained models) were generated by least absolute shrinkage and selection operator regression for overall survival (OS), OS <6 months and OS <12 months. OS prediction was quantified using Somers D_XY statistic, which varies from 0 to 1, with increasing concordance between observed and predicted outcomes. 6-month survival and 12-month survival were described by area under the curve (AUC) scores.

Results

Median OS was 270 (IQR 140-450) days. The primary OS model assigned high weights to four predictors: age, performance status, white cell count and serum albumin, and after cross-validation performed significantly better than would be expected by chance (mean D_XY0.332 (±0.019)). However, validation set D_XY was only 0.221 (0.0935-0.346), equating to a 22% improvement in survival prediction than would be expected by chance. The 6-month and 12-month OS signatures included the same four predictors, in addition to epithelioid histology plus platelets and epithelioid histology plus C-reactive protein (mean AUC 0.758 (±0.022) and 0.737 (±0.012), respectively). The <6-month OS model demonstrated 74% sensitivity and 68% specificity. The <12-month OS model demonstrated 63% sensitivity and 79% specificity. Model content and performance were generally comparable with previous studies.

Conclusions

The prognostic value of the basic clinical information contained in these, and previously published models, is fundamentally of limited value in accurately predicting MPM prognosis. The methods described are suitable for expansion using emerging predictors, including tumour genomics and volumetric staging.