Marital status is an independent prognostic factor for cervical adenocarcinoma: A population-based study

The interaction between social determinants of health and cervical cancer survival: A systematic review

OBJECTIVE

This systematic review aimed to investigate what are the most relevant social determinants of health (SDH), how they are measured, how they interact among themselves and what is their impact on the outcomes of cervical cancer patients.

METHODS

Search was performed in PubMed, Scopus, Web of Science, Embase, Cochrane, and Google Scholar databases from January 2001 to September 2022. The protocol was registered at PROSPERO (CRD42022346854). We followed the PICOS strategy: Population- Patients treated for cervical cancer in the United States; Intervention - Any SDH; Comparison- None; Outcome measures- Cancer treatment outcomes related to the survival of the patients; Types of studies- Observational studies. Two reviewers extracted the data following the PRISMA guidelines. Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies was used for risk of bias (ROB) assessment.

RESULTS

Twenty-four studies were included (22 had low and 2 had moderate ROB). Most manuscripts analyzed data from public registries (83.3%) and only one SDH (54.17%). The SDH category of Neighborhood was not included in any study. Although the SDH were measured differently across the studies, not being married, receiving treatment at a low-volume hospital, and having public insurance (Medicaid or Medicare) or not being insured was associated with shorter survival of cervical cancer patients in most studies.

CONCLUSIONS

There is a deficit in the number of studies comprehensively assessing the impact of SDH on cervical cancer treatment-related outcomes. Marital status, hospital volume and health insurance status are potential predictors of worse outcome.

A prognostic nomogram and risk classification system of elderly patients with extraosseous plasmacytoma: a SEER database analysis

BACKGROUND

The survival trends and prognostic factors of patients with extraosseous plasmacytoma (EOP) or extramedullary plasmacytoma (EMP) have not been reported in recent years. The objective of this study was to develop a novel nomogram and risk stratification system for predicting the overall survival (OS) of elderly patients with EOP based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

The demographic characteristics of 900 patients aged 60 years and above, diagnosed with EOP between 2000 and 2019, were extracted from the SEER database. The patient population was randomly divided into a training cohort and an internal validation cohort in a ratio of 7:3. Univariate and multivariate Cox regression analyses were conducted to identify independent predictors of prognosis in elderly EOP patients, followed by developing a nomogram for prognostic assessment. The performance of the model was evaluated through receiver-operating characteristic (ROC) curves, C-index, calibration curves for calibration accuracy assessment, and decision curve analysis (DCA) to assess its clinical utility. All elderly EOP patients were stratified into three risk subgroups by cutoff value utilizing X-tile software based on their total OS scores for comparative analysis purposes. Kaplan-Meier (K-M) survival curve analysis was employed to validate any observed differences in OS among these three risk groups.

RESULTS

Six factors including age, year of diagnosis, marital status, primary site, surgery, and prior tumor history were identified to be independently predictive of the OS of elderly patients with EOP, and these predictors were included in the construction of the nomogram. The 1-, 3-, and 5-year area under the curves (AUCs) for OS were 0.717, 0.754, and 0.734 in the training cohort and 0.740, 0.730, and 0.765 in the validation cohort, respectively. The C-index values in the two cohorts were 0.695 and 0.690. The calibration curves and DCA exhibit commendable consistency and validity, respectively, thereby demonstrating their robust performance. The training set was stratified into low-, medium-, and high-risk subgroups based on the optimal cutoff points (167.8 and 264.8) identified. The K-M curve and cumulative risk curve exhibited statistically significant disparities in survival rates among the groups.

CONCLUSIONS

We developed a nomogram and risk classification system, which can serve as an intuitive and effective tool for clinicians to enhance the prediction of OS in elderly EOP patients, thereby facilitating the formulation of more rational and personalized treatment strategies.