Effects of marital status on overall and cancer-specific survival in laryngeal cancer patients: a population-based study

The impact of malignancy on death anxiety and psychological well-being in middle-aged and older patients undergoing abdominal surgery: a quasi-experimental study

BACKGROUND

Malignancy is a critical factor affecting death anxiety and psychological well-being. This study examined the impact of malignancy on death anxiety and psychological well-being in middle-aged and older adult patients undergoing abdominal surgery and projected it along with sociodemographic profiles.

METHODS

This study is quasi-experimental in design. It was conducted with patients undergoing abdominal surgery at a university hospital due to suspected malignancy. A total of 93 patients participated in the study: 57 patients diagnosed with malignancy were included in the experimental group, while 36 patients without a malignancy diagnosis were included in the control group. Data were collected using a questionnaire developed by the researchers based on the literature, the 'Turkish Death Anxiety Scale', and the 'Psychological Well-being Scale'. The analyses revealed that the data were normally distributed, and parametric tests were used to analyze differences between demographic variables and groups. Cohen's d and r values were examined to assess the effect size in the tests.

RESULTS

The study found a significant difference in death anxiety between the experimental and control groups (P < 0.05), with higher mean scores in the experimental group. Malignancy had a large effect on death anxiety (d = 1.42). Additionally, the control group had significantly higher mean psychological well-being scores compared to the experimental group (P < 0.05).

CONCLUSIONS

The study found that malignancy significantly affects death anxiety and psychological well-being. Increased death anxiety in patients with malignancy was associated with reduced psychological well-being. Thus, assessing death anxiety and providing targeted nursing care are essential for supporting the psychological well-being of these patients.

Early death in supraglottic laryngeal squamous cell carcinoma: A population-based study

BACKGROUND

Supraglottic laryngeal squamous cell carcinoma (LSCC) is the second most common type of laryngeal cancer with a poor prognosis. Current population-based estimates of the early death rate and associated factors for early death of supraglottic LSCC are lacking. The purpose of this study was to assess the early death rate and related factors for early death in patients with supraglottic LSCC.

METHODS

We identified 3733 adult patients diagnosed with supraglottic LSCC between 2010 and 2017 for whom the vital status at 3 months was known from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were staged according to the seventh edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system. The early death (survival time ≤ 3 months) rate was calculated. Univariate and multivariate logistic regression analyses were performed to identify the risk factors associated with the early death rate.

RESULTS

313 (8.38%) of the 3733 patients died within 3 months of diagnosis of supraglottic LSCC. Of these, 225 patients died from cancer-specific causes. Multivariate logistic regression analyses confirmed that advanced age, male sex, advanced T stage, advanced N stage, advanced M stage, and not undergoing treatment (surgery, radiotherapy, and chemotherapy) had significant correlations with all-cause early death in supraglottic LSCC. In addition, advanced age, advanced T stage, advanced N stage, advanced M stage, and not undergoing treatment (surgery, radiotherapy, and chemotherapy) were significantly correlated with cancer specificity in supraglottic LSCC.

CONCLUSION

When a tumor is newly diagnosed, we should pay close attention to sex, age, unmarried status and AJCC TNM staging to quickly detect supraglottic LSCC patients with a tendency toward early death. These findings have implications for precise prognosis prediction and individualized and personalized patient counseling and therapy.

Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods

Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical foundation when predicting patient survival. This study aimed to analyze the impact of marital status on survival outcomes of PDAC patients using propensity score matching and machine learning. The goal was to develop a prognosis prediction model specific to married patients with PDAC. We extracted a total of 206,968 patient records of pancreatic cancer from the SEER database. To ensure the baseline characteristics of married and unmarried individuals were balanced, we used a 1:1 propensity matching score. We then conducted Kaplan-Meier analysis and Cox proportional-hazards regression to examine the impact of marital status on PDAC survival before and after matching. Additionally, we developed machine learning models to predict 5-year CSS and OS for married patients with PDAC specifically. In total, 24,044 PDAC patients were included in this study. After 1:1 propensity matching, 8043 married patients and 8,043 unmarried patients were successfully enrolled. Multivariate analysis and the Kaplan-Meier curves demonstrated that unmarried individuals had a poorer survival rate than their married counterparts. Among the algorithms tested, the random forest performed the best, with 0.734 5-year CSS and 0.795 5-year OS AUC. This study found a significant association between marital status and survival in PDAC patients. Married patients had the best prognosis, while widowed patients had the worst. The random forest is a reliable model for predicting survival in married patients with PDAC.

Epidemiology and prognostic nomogram for chronic eosinophilic leukemia: a population-based study using the SEER database

Chronic Eosinophilic Leukemia (CEL), a rare and intricate hematological disorder characterized by uncontrolled eosinophilic proliferation, presents clinical challenges owing to its infrequency. This study aimed to investigate epidemiology and develop a prognostic nomogram for CEL patients. Utilizing the Surveillance, Epidemiology and End Results database, CEL cases diagnosed between 2001 and 2020 were analyzed for incidence rates, clinical profiles, and survival outcomes. Patients were randomly divided into training and validation cohorts (7:3 ratio). LASSO regression analysis and Cox regression analysis were performed to screen the prognostic factors for overall survival. A nomogram was then constructed and validated to predict the 3- and 5-year overall survival probability of CEL patients by incorporating these factors. The incidence rate of CEL was very low, with an average of 0.033 per 100,000 person-years from 2001 to 2020. The incidence rate significantly increased with age and was higher in males than females. The mean age at diagnosis was 57 years. Prognostic analysis identified advanced age, specific marital statuses, and secondary CEL as independent and adverse predictors of overall survival. To facilitate personalized prognostication, a nomogram was developed incorporating these factors, demonstrating good calibration and discrimination. Risk stratification using the nomogram effectively differentiated patients into low- and high-risk groups. This study enhances our understanding of CEL, offering novel insights into its epidemiology, demographics, and prognostic determinants, while providing a possible prognostication tool for clinical use. However, further research is warranted to elucidate molecular mechanisms and optimize therapeutic strategies for CEL.

Interpretable machine learning model for prediction of overall survival in laryngeal cancer

Background: The mortality rates of laryngeal squamous cell carcinoma cancer (LSCC) have not significantly decreased in the last decades.Objectives: We primarily aimed to compare the predictive performance of DeepTables with the state-of-the-art machine learning (ML) algorithms (Voting ensemble, Stack ensemble, and XGBoost) to stratify patients with LSCC into chance of overall survival (OS). In addition, we complemented the developed model by providing interpretability using both global and local model-agnostic techniques.Methods: A total of 2792 patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with LSCC were reviewed. The global model-agnostic interpretability was examined using SHapley Additive exPlanations (SHAP) technique. Likewise, individual interpretation of the prediction was made using Local Interpretable Model Agnostic Explanations (LIME).Results: The state-of-the-art ML ensemble algorithms outperformed DeepTables. Specifically, the examined ensemble algorithms showed comparable weighted area under receiving curve of 76.9, 76.8, and 76.1 with an accuracy of 71.2%, 70.2%, and 71.8%, respectively. The global methods of interpretability (SHAP) demonstrated that the age of the patient at diagnosis, N-stage, T-stage, tumor grade, and marital status are among the prominent parameters.Conclusions: A ML model for OS prediction may serve as an ancillary tool for treatment planning of LSCC patients.

Investigating Disparities in Hypopharyngeal/Laryngeal Cancer Survival in Florida With Geospatial Mapping Analysis

OBJECTIVE

Identify predictors of overall survival (OS) after hypopharyngeal/laryngeal cancer in Florida.

MATERIAL AND METHODS

We conducted a retrospective cohort study using data from the Florida Cancer Data System (FCDS) on patients diagnosed with hypopharyngeal or laryngeal cancer from 2010-2017. Primary outcome was OS. Hazard ratios (HRs) were estimated from univariable and multivariable Cox regression models for OS. Data was analyzed from November 1, 2022, to June 30, 2023.

RESULTS

We analyzed 6771 patients, who were primarily male (81.2%), White non-Hispanic (WNH) (78.2%), publicly insured (70.1%), married (51.8%), and residents of urban counties (73.6%). Black patients were more likely to be younger at diagnosis (38.9%), single (43.4%), to have distant SEER stage disease (25.6%). Median OS were lowest among patients who were uninsured (34 months), with hypopharyngeal site disease (18 months), and a smoking history (current: 34 months, former: 46 months, no smoking: 63 months). Multivariable Cox regression analysis showed worse OS for single/unmarried vs married (HR 1.47 [95%CI: 1.36-1.59], P < .001), history of tobacco use (current: HR 1.62 [95%CI: 1.440-1.817], P < .001; former smokers: (HR 1.28 [95%CI: 1.139-1.437], P < .001) vs no history). Improved OS was observed among White Hispanics (WH) vs WNH (HR .73 [95%CI: .655-.817], P < .001) and women vs men (HR .88 [95%CI: .807-.954], P = .002). Geographical mapping showed that mortality rates were highest in census tracts with low income and education.

CONCLUSION

Our findings suggest that sociodemographic and clinical factors impact OS from hypopharyngeal/laryngeal cancer in Florida and vary geographically within the state. These results will help guide future public health interventions.

Demographic characteristics and prognosis of HHV8-positive diffuse large B-cell lymphoma, not otherwise specified: Insights from a population-based study with a 10-year follow-up

HHV8-positive diffuse large B-cell lymphoma, not otherwise specified (HDN) is a subtype of lymphoma that usually arises in association with HHV8-positive multicentric Castleman disease. However, the epidemiology, treatment patterns, and survival outcomes of HDN are poorly understood. A retrospective analysis was performed for 67 patients with HDN diagnosed from 2011 to 2020 using the SEER database. The demographic characteristics, treatment modalities, and survival outcomes of HDN patients were evaluated. Kaplan-Meier analysis and Cox regression analysis were employed to identify prognostic factors for overall survival (OS) and disease-specific survival (DSS). The age-adjusted incidence rate of HDN was 0.010 per 100,000 person-years. The median age at diagnosis was 51.8 years with male predominance. The primary site distribution was mainly nodal (79.1%), while the extranodal sites were rarely involved (20.9%). The majority of patients were white (65.7%). Only 3.0% of patients received radiotherapy, while 55.2% received chemotherapy. The 1-year, 3-year, and 5-year OS was 67.4%, 65.6%, 58.4%, and 56.3%, respectively, and the corresponding DSS was 73.1%, 73.1%, and 67.8%, respectively. The diagnosis year group of 2016-2020 had a significantly worse OS than the diagnosis year group of 2011-2015 (P = .040), but not for DSS (P = .074). No significant survival improvement was observed in patients underwent chemotherapy. Age and marital status were independent prognostic factors for OS, and age was an independent prognostic factor for DSS. In conclusion, HDN is a rare and aggressive disease, our study provides a comprehensive overview of the epidemiology, treatment patterns and survival outcomes of HDN patients for the first time. We revealed that older age and marital status of single were associated with worse survival of HDN, while chemotherapy was not associated with improved survival outcomes in HDN patients.

Epidemiology and survival of primary extraosseous plasmacytoma: insights from a population-based study with a 20-year follow-up

Primary extraosseous plasmacytoma (PEP) is a rare and localized form of plasmacytoma that is not well understood. This study aimed to investigate the clinical features and prognostic factors associated with PEP. Using the Surveillance, Epidemiology, and End Results (SEER) database, a total of 1044 patients diagnosed with PEP between 2000 and 2019 were identified. The average age was 60.3 ± 15.2 years, with 64.3% being male (male: female = 1.8:1) and 53.8% being over 60-year old. The survival outcome of patients with PEP depends on several factors including age, race, marital status, and treatment options such as chemotherapy, radiotherapy, and surgery, which were also identified as independent predictors of overall survival for PEP. Patients who were younger, Asian or Pacific Islander, American Indian or Native American, and received radiotherapy or surgery had a more favorable prognosis, while those who underwent chemotherapy had poorer outcomes.

Competing risk analysis of cardiovascular-specific mortality in typical carcinoid neoplasms of the lung: A SEER database analysis

Cardiovascular mortality (CVM) is a growing concern for cancer survivors. This study aimed to investigate the mortality patterns of individuals with typical carcinoid (TC) tumors, identify independent predictors of CVM, and compare these risk variables with those associated with TC deaths. The Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2019 was utilized for obtaining data on patients with TC. Standardized mortality rates were employed to evaluate the risk of CVM while multivariate competing risk models were used to determine the association between patient characteristics and the probability of CVM or TC-related deaths. Our findings show that TC patients had an increased risk of CVM, with an standardized mortality rates of 1.12 (95% CI:1.01-1.25). Furthermore, we discovered that age at diagnosis, marital status, year of diagnosis, SEER stage, site, year of diagnosis, surgery, radiotherapy, and chemotherapy all contributed independently to the risk of CVM in patients with TC, whereas age at diagnosis, sex, race, SEER stage, site, year of diagnosis, surgery, radiotherapy, and chemotherapy all contributed significantly to TC mortality. Compared to the general population in the United States, patients with TC are significantly more likely to acquire CVM. Timely introduction of cardioprotective treatments is critical for preventing CVM in patients with TC.

A Random Forest Model for Post-Treatment Survival Prediction in Patients with Non-Squamous Cell Carcinoma of the Head and Neck

BACKGROUND

Compared to squamous cell carcinoma, head and neck non-squamous cell carcinoma (HNnSCC) is rarer. Integrated survival prediction tools are lacking.

METHODS

4458 patients of HNnSCC were collected from the SEER database. The endpoints were overall survivals (OSs) and disease-specific survivals (DSSs) of 3 and 5 years. Cases were stratified-randomly divided into the train & validation (70%) and test cohorts (30%). Tenfold cross validation was used in establishment of the model. The performance was evaluated with the test cohort by the receiver operating characteristic, calibration, and decision curves.

RESULTS

The prognostic factors found with multivariate analyses were used to establish the prediction model. The area under the curve (AUC) is 0.866 (95%CI: 0.844-0.888) for 3-year OS, 0.862 (95%CI: 0.842-0.882) for 5-year OS, 0.902 (95%CI: 0.888-0.916) for 3-year DSS, and 0.903 (95%CI: 0.881-0.925) for 5-year DSS. The net benefit of this model is greater than that of the traditional prediction methods. Among predictors, pathology, involved cervical nodes level, and tumor size are found contributing the most variance to the prediction. The model was then deployed online for easy use.

CONCLUSIONS

The present study incorporated the clinical, pathological, and therapeutic features comprehensively and established a clinically effective survival prediction model for post-treatment HNnSCC patients.

Primary blastic plasmacytoid dendritic cell neoplasm: a US population-based study

Background

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and poorly understood hematopoietic malignancy. This study aimed to investigate the clinical characteristics and prognostic factors in patients with primary BPDCN.

Methods

Patients diagnosed with primary BPDCN from 2001 to 2019 were extracted from the Surveillance, Epidemiology and End Results (SEER) database. Survival outcome was analysed with Kaplan-Meier method. Prognostic factors were evaluated based on the univariate and multivariate accelerated failure time (AFT) regression analysis.

Results

A total of 340 primary BPDCN patients were included in this study. The average age was 53.7 ± 19.4 years, with 71.5% being male. The mostly affected sites were lymph nodes (31.8%). Most patients (82.1%) received chemotherapy, while 14.7% received radiation therapy. For all the patients, the 1-year, 3-year, 5-year, and 10-year overall survival (OS) were 68.7%, 49.8%, 43.9%, and 39.2%, respectively, and the corresponding disease-specific survival (DSS) were 73.6%, 56.0%, 50.2%, and 48.1%, respectively. Univariate AFT analysis showed that older age, marital status of divorced, widowed and separated at diagnosis, primary BPDCN only, treatment delay for 3-6 months and without radiation therapy were significantly associated with poor prognosis of primary BPDCN patients. But multivariate AFT analysis indicated that older age was independently associated with worse survival, while second primary malignancies (SPMs) and radiation therapy were independently associated with extended survival.

Conclusions

Primary BPDCN is a rare disease with poor prognosis. Advanced age was linked independently to poorer survival, while SPMs and radiation therapy were linked independently to prolonged survival.

Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL

BACKGROUND

In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e., external validation).

OBJECTIVES

This study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared.

METHODS

We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

The ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated.

CONCLUSION

The performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice.

Depression, anxiety and stress among metastatic breast cancer patients on chemotherapy in China

OBJECTIVE

This study aimed to assess the level of depression, anxiety and stress among metastatic breast cancer (MBC) patients undergoing chemotherapy (CT) in Beijing, China.

METHODS

A cross-sectional study was conducted on 176 MBC women receiving CT, selected by purposive sampling. Data were collected using self-administered questionnaires that included participants' socio-demographic status, DASS-21 and Brief COPE. Data were analyzed using descriptive statistics and general linear regression analysis.

RESULTS

The incidence of depression, anxiety and stress among MBC women were 52.3%, 60.2% and 36.9%, respectively. General linear regression showed that age, marital status, monthly income, physical functioning, emotional functioning, pain, dyspnea, and appetite loss were associated with depression. All variance determined the depression (R²) was 35.6%. Marital status, self-blame and behavioral disengagement were the predictors of stress and accounted for a 35.4% stress variance in MBC women.

CONCLUSION

Our study demonstrated depression, anxiety, and stress prevalence are high in MBC women. Assessment of psychological distress (depression, anxiety, and stress) is important to recognise MBC patients who need help and further medical and mental help support. This study's findings can increasingly highlight that depression, anxiety, and stress are substantial problems in MBC patients. Therefore, psychological interventions are needed to reduce depression, anxiety, and stress for MBC patients.

Gleason score, surgical and distant metastasis are associated with cancer-specific survival and overall survival in middle aged high-risk prostate cancer: A population-based study

Objective

According to statistics, patients with high-risk prostate cancer (PC) account for about 15% of prostate cancer diagnoses, and high-risk patients usually have a poor prognosis due to metastasis and recurrence and have a high mortality rate. Therefore, the accurate prediction of prognostic-related risk factors in middle-aged high-risk PC patients between 50 and 65 can help reduce patient mortality. We aimed to construct new nomograms for predicting cancer-specific survival (CSS) and Overall survival (OS) in middle-aged high-risk PC patients.

Methods

Data for patients aged between 50 and 65 years old and diagnosed with high-risk PC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for CSS and OS in patients. Nomograms predicting CSS and OS were developed based on multivariate Cox regression models. The concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and the calibration curve are used to detect the accuracy and discrimination of the model. Decision curve analysis (DCA) is used to detect the potential clinical value of this model.

Results

Between 2010 and 2018, 1,651 patients diagnosed with high-risk PC and aged 50-65 years were included. In this study, the training group (n = 1,146) and the validation group (n = 505) were randomly assigned in a ratio of 7:3. The results showed that M stage, Gleason (GS) and surgical mode were independent risk factors for CSS; marital status, T stage, M stage, surgical mode, and GS were independent risk factors for OS. The C-index for predicting CSS in the training and validation groups are 0.84 and 0.811, respectively; the C-index for predicting OS in the training and validation groups are 0.824 and 0.784, respectively. The AUC and the calibration curves also showed good accuracy and discrimination.

Conclusions

We constructed new nomograms to predict CSS and OS in middle-aged high-risk PC patients. The prediction tools showed good accuracy and reliability, which can help clinicians and patients to make better clinical decisions.

Clinical features and prognostic indicators in upper-tract urothelial carcinoma with bone metastasis

Purpose

With the gradual increase in the incidence of upper-tract urothelial carcinoma (UTUC), its metastatic disease has attracted much attention. The prognosis of UTUC patients with bone metastasis is worse than that of UTUC patients with other metastases. Therefore, the current study is performed to analyze the clinicopathologic features and survival predictors among UTUC patients with bone metastasis.

Patients and methods

We reviewed the Surveillance, Epidemiology, and End Results (SEER) database to select cases diagnosed with UTUC and bone metastasis at present from 2010 to 2016. Overall survival (OS) and cancer-specific survival (CSS) were first performed by applying univariate Cox regression analysis. Then we performed multivariate Cox analysis to determine independent predictors of survival. Forest plots were drawn by GraphPad 8.0.1 and used to visually display the results of multivariate analysis. Kaplan-Meier method was applied to intuitively show the prognosis difference of each independent risk factor.

Results

We finally identified 380 UTUC patients with bone metastasis for survival analysis, of which 230 males (60.5%) and 150 females (39.5%). The mean and median age at diagnosis were 71 and 72 years, respectively. Simultaneous lung metastasis (33.4%) and liver metastasis (31.1%) were more common in UTUC patients with bone metastasis. The 1-year OS and CSS rates of this research population were 23.8% and 26.6%, respectively. Multivariate Cox proportional hazards modeling controlling for surgery, chemotherapy, brain metastasis, liver metastasis, lung metastasis, and marital status, revealed that surgery, chemotherapy, no liver metastasis, no lung metastasis, and married status predicted for better OS and CSS.

Conclusion

Surgery and chemotherapy are optimal management of UTUC patients with bone metastasis. Active treatments on lung and liver metastases should be performed. The prognosis of patients with unmarried status or others should be further improved.

Factors associated with loss to follow-up after radiation therapy for head and neck cancer

OBJECTIVES

Head and neck cancer (HNC) patients are at high risk for late occurring radiation-related morbidity and recurrence, necessitating close long-term medical surveillance. This study identified factors associated with becoming lost to follow-up (LTFU) at a comprehensive cancer center.

MATERIALS AND METHODS

Patients were drawn from survivors who received radiation for HNC at a single institution between 2001 and 2018. LTFU was defined as living patients without a clinical encounter within 2 years of the data query.

RESULTS

In total, 537 patients met the inclusion criteria and 57 (10.6%) were identified as LTFU. Individual comparisons identified time since completing radiation, non-White race and being unmarried as associated with LTFU. Multiple regression identified time since treatment and being unmarried as factors associated with LTFU. A decision tree correctly sorted 89.4% using time, distance, and marital status.

CONCLUSION

Time since radiation, distance to clinic, and being unmarried were factors associated with becoming LTFU.

Marital Adjustment in Patients with Cancer: Association with Psychological Distress, Quality of Life, and Sleep Problems

Marital adjustment plays a key role in the physical and psychosocial wellbeing. We conducted a cross-sectional study to evaluate marital adjustment and its association with psychological distress, suicidal ideation, sleep problems, and quality of life in patients with cancer. We collected demographic and clinical information using a structured survey. We assessed marital adjustment, quality of life, psychological distress profile, and sleep problems of participants using validated instruments: the Locke and Wallace Marital Adjustment Test (LWMAT), the Short-Form Health Survey-12, the Beck’s Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), and the Insomnia Severity Index (ISI). Suicidal ideation was assessed with item nine of the BDI. Of the 130 patients (52.3% females, mean age 57.9 ± 12.4 years) enrolled, 20 (15%) were classified as experiencing poor marital adjustment. Moderate to severe depression, anxiety, and insomnia were found in 25.4%, 34.6%, and 24.7% of participants, respectively. Positive suicidal ideation was documented in 13.8% of participants. We found a significant association between poor marital adjustment and depression, anxiety, suicidal ideation, and poor sleep. Our study confirms the relevance of marital adjustment in relation to the psychological wellbeing of patients with cancer. Depression, anxiety, and poor sleep were found to be significantly associated with poor marital adjustment.