Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Application of comprehensive geriatric assessment in oncology nursing: A literature review on optimizing treatment decisions and patient outcomes

With the global population aging, the care of elderly cancer patients has become increasingly complex and significant. Comprehensive geriatric assessment (CGA), a multidimensional evaluation tool, has been widely implemented in oncology nursing to enhance the precision of treatment decisions and improve patient outcomes. This review examines the application of CGA in oncology nursing, drawing on literature published between 2010 and 2024 in major databases using keywords such as "Comprehensive Geriatric Assessment" and "Oncology Nursing". It highlights how CGA contributes to optimizing treatment selection, monitoring the treatment process, and improving patients' quality of life and long-term outcomes. CGA provides a comprehensive evaluation of elderly cancer patients, including physical, psychological, and social aspects, enabling the identification of high-risk patients and reducing treatment-related side effects and complications. It also offers a critical foundation for developing personalized care plans. The article discusses various practical examples of CGA implementation across different countries and regions, including multidisciplinary collaborative models in France, the United States, and Australia, demonstrating CGA's flexible application in diverse healthcare settings. Although significant progress has been made in applying CGA in oncology nursing, numerous challenges remain in its implementation, such as resource limitations and insufficient personnel training. Future research will focus on integrating CGA with emerging technologies, such as artificial intelligence and precision medicine, to further improve the quality of care and treatment outcomes for elderly cancer patients. By summarizing the current status and challenges of CGA in oncology nursing, this review provides guidance for future research and clinical practice, emphasizing the importance of advancing CGA application to meet the growing demands of elderly oncology care.

Comparative survival analysis of stage T1-T2N0M0 lung squamous cell carcinoma and adenocarcinoma using SEER data, and nomogram analysis for early-stage lung squamous cell carcinoma

Background

Lung cancer is one of the most common malignant tumors worldwide. It is of great significance to conduct in-depth research on early lung cancer with a better prognosis. This study aimed to use the Surveillance, Epidemiology, and End Results (SEER) database to compare the clinicopathological characteristics and survival between early squamous cell carcinoma (SQCC) and adenocarcinoma (AC) under the same treatment model, and develop a nomogram for early lung SQCC.

Methods

This study examined 40,325 cases of stage T1-T2N0M0 lung SQCC and AC from 2004 to 2019. Propensity score matching (PSM) was used to reduce bias. Kaplan-Meier curves and Cox proportional hazards models were used for assessing lung cancer-specific survival (LCSS) and overall survival (OS) under various treatments. A nomogram for early-stage SQCC was constructed and validated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA).

Results

In patients with T1-T2N0M0 non-small cell lung cancer (NSCLC), when only radiotherapy was performed, the LCSS of patients in the SQCC group was worse than that of the AC group [hazard ratio (HR) =1.20, 95% confidence interval (CI): 1.079-1.336, P<0.001], and same for 3-year LCSS (55.9% vs. 62.7%) and the 5-year LCSS (43.6% vs. 47.8%). The OS of patients in the SQCC group was worse than the AC group (HR =1.32, 95% CI: 1.215-1.429, P<0.001). When only surgical treatment was performed, no statistically significant difference was found in the LCSS between the two groups (HR =1.03, 95% CI: 0.965-1.092, P=0.41). The OS of patients in the SQCC group was worse than the AC group (HR =1.25, 95% CI: 1.200-1.309, P<0.001). Additionally, a nomogram was created to predict survival rates for early-stage lung SQCC patients.

Conclusions

The prognosis of patients with T1-T2N0M0 lung SQCC is worse than that of AC patients. Individualized treatment is recommended in the early stages.

Metastasis pattern and prognosis of large cell neuroendocrine carcinoma: a population-based study

PURPOSE

As a rare type of tumor, the metastasis pattern of large cell neuroendocrine carcinoma (LCNEC) is still unclear. Our aim was to investigate metastatic patterns and develop a predictive model of prognosis in patients with advanced LCNEC.

METHODS

Patients of LCNEC diagnosed between 2010-2015 from the Surveillance, Epidemiology and End Results (SEER) database were retrospectively included. Chi-square test was used for baseline characteristics analysis. Survival differences were assessed using Kaplan-Meier curves. Independent prognostic factors identified by multivariate Cox proportional risk model were used for the construction of nomogram.

RESULTS

557 eligible patients with metastasis LCNEC (median (IQR), 64 (56 to 72) years; 323 males) were included in this research. Among patients with isolated metastases, brain metastases had the highest incidence (29.4%), and multisite metastases had worse OS (HR: 2.020: 95% CI 1.413-2.888; P < 0.001) and LCSS (HR: 2.144, 95% CI 1.480-3.104; P < 0.001) in all age groups. Independent prognostic indicators including age, race, T stage, N stage, chemotherapy, radiotherapy and metastatic site were used for the construction of nomogram. Concordance index (C-index) and decision-curve analyses (DCAs) showed higher accuracy and net clinical benefit of nomogram compared to the 7th TNM staging system (OS: 0.692 vs 0.555; P < 0.001; LCSS: 0.693 vs 0.555; P < 0.001).

CONCLUSIONS

We firstly established a novel comprehensive nomogram to predict the prognosis of metastasis LCNEC. The prognostic model demonstrated excellent accuracy and predictive performance. Chemotherapy and metastasis pattern were the two strongest predictive variables. Close follow-up of patients with LCNEC is necessary to make individualized treatment decisions according to different metastasis patterns.

Development and validation of a nomogram for predicting overall survival in patients with stage III-N2 lung adenocarcinoma based on the SEER database

Background

There is variability in the prognosis of stage III-N2 lung adenocarcinoma (LUAD) patients. The current tumor-node-metastasis (TNM) staging is not sufficient to precisely estimate the prognosis of stage III-N2 LUAD patients. The Surveillance, Epidemiology, and End Results (SEER) database collected first-hand information from a large number of LUAD patients. Based on the SEER database, this study aimed to determine the prognostic factors that affect overall survival (OS) in stage III-N2 LUAD patients and then establish a nomogram for predicting OS in this type of cancer to identify the high-risk population that may require more frequent surveillance or intensive care.

Methods

Data for 1,844 stage III-N2 primary LUAD patients who were registered between 2010 and 2015 were obtained from the SEER database. These patients were randomly assigned to either training (n=1,290) or validation (n=554) cohorts at a 7:3 ratio. The univariate and multivariate Cox regression (UCR and MCR) analyses were performed to find the relevant independent prognostic factors. To predict the OS based on these prognostic factors, a nomogram was then developed. The performance of the nomogram was examined based on the calibration curves, and receiver operating characteristic (ROC) curves. The ability of nomogram to stratify patient risk was validated by Kaplan-Meier survival analysis.

Results

Age, gender, tumor location, T-stage and treatment modality (chemotherapy, radiation therapy, surgery and scope of lymph node dissection) of stage III-N2 LUAD patients were significantly associated with prognosis. The area under the curve (AUC) values of OS predicted by the nomogram constructed with these factors at 12-, 36- and 60-month were 0.784, 0.762 and 0.763 in the training cohort, whereas 0.707, 0.685 and 0.705 in the validation cohort, respectively. Additionally, calibration curves demonstrated concordance between predicted and observed outcomes. Nomogram risk stratification provides a meaningful distinction between patients with various survival risks.

Conclusions

A survival prediction model that may be useful for risk stratification and decision-making is developed and validated for stage III-N2 LUAD patients. A high-risk patient predicted by the prediction model may require more frequent surveillance or intensive care.

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004-2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell's concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/ ). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC.

Establishment and validation of nomograms for predicting survival of lung invasive adenocarcinoma based on the level of pathological differentiation: a SEER cohort-based analysis

BACKGROUND

The pathological differentiation of invasive adenocarcinoma (IAC) has been linked closely with epidemiological characteristics and clinical prognosis. However, the current models cannot accurately predict IAC outcomes and the role of pathological differentiation is confused. This study aimed to establish differentiation-specific nomograms to explore the effect of IAC pathological differentiation on overall survival (OS) and cancer-specific survival (CSS).

METHODS

The data of eligible IAC patients between 1975 and 2019 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided in a ratio of 7:3 into a training cohort and a validation cohort. The associations between pathological differentiation and other clinical characteristics were evaluated using chi-squared test. The OS and CSS analyses were performed using the Kaplan-Meier estimator, and the log-rank test was used for nonparametric group comparisons. Multivariate survival analysis was performed using a Cox proportional hazards regression model. The discrimination, calibration, and clinical performance of nomograms were assessed by area under receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS

A total of 4,418 IAC patients (1,001 high-differentiation, 1,866 moderate-differentiation, and 1,551 low-differentiation) were identified. Seven risk factors [age, sex, race, tumor-node-metastasis (TNM) stage, tumor size, marital status, and surgery] were screened to construct differentiation-specific nomograms. Subgroup analyses showed that disparate pathological differentiation played distinct roles in prognosis, especially in patients with older age, white race, and higher TNM stage. The AUC of nomograms for OS and CSS in the training cohort were 0.817 and 0.835, while in the validation cohort were 0.784 and 0.813. The calibration curves showed good conformity between the prediction of the nomograms and the actual observations. DCA results indicated that these nomogram models could be used as a supplement to the prediction of the TNM stage.

CONCLUSIONS

Pathological differentiation should be considered as an independent risk factor for OS and CSS of IAC. Differentiation-specific nomogram models with good discrimination and calibration capacity were developed in the study to predict the OS and CSS in 1-, 3- and 5-year, which could be used predict prognosis and select appropriate treatment options.

A nomogram for predicting postoperative overall survival of patients with lung squamous cell carcinoma: A SEER-based study

Background

Lung squamous cell carcinoma (LSCC) is a common subtype of non-small cell lung cancer. Our study aimed to construct and validate a nomogram for predicting overall survival (OS) for postoperative LSCC patients.

Methods

A total of 8,078 patients eligible for recruitment between 2010 and 2015 were selected from the Surveillance, Epidemiology, and End Results database. Study outcomes were 1-, 2- and 3-year OS. Analyses performed included univariate and multivariate Cox regression, receiver operating characteristic (ROC) curve construction, calibration plotting, decision curve analysis (DCA) and Kaplan-Meier survival plotting.

Results

Seven variables were selected to establish our predictive nomogram. Areas under the ROC curves were 0.658, 0.651 and 0.647 for the training cohort and 0.673, 0.667 and 0.658 for the validation cohort at 1-, 2- and 3-year time-points, respectively. Calibration curves confirmed satisfactory consistencies between nomogram-predicted and observed survival probabilities, while DCA confirmed significant clinical usefulness of our model. For risk stratification, patients were divided into three risk groups with significant differences in OS on Kaplan-Meier analysis (P < 0.001).

Conclusion

Here, we designed and validated a prognostic nomogram for OS in postoperative LSCC patients. Application of our model in the clinical setting may assist clinicians in evaluating patient prognosis and providing highly individualized therapy.

Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study

The prognosis of metastatic lung adenocarcinoma (MLUAD) varies greatly. At present, no studies have constructed a satisfactory prognostic model for MLUAD. We identified 44,878 patients with MLUAD. The patients were randomized into the training and validation cohorts. Cox regression models were performed to identify independent prognostic factors. Then, R software was employed to construct a new nomogram for predicting overall survival (OS) of patients with MLUAD. Accuracy was assessed by the concordance index (C-index), receiver operating characteristic curves and calibration plots. Finally, clinical practicability was examined via decision curve analysis. The OS time range for the included populations was 0 to 107 months, and the median OS was 7.00 months. Nineteen variables were significantly associated with the prognosis, and the top 5 prognostic factors were chemotherapy, grade, age, race and surgery. The nomogram has excellent predictive accuracy and clinical applicability compared to the TNM system (C-index: 0.723 vs 0.534). The C-index values were 0.723 (95% confidence interval: 0.719-0.726) and 0.723 (95% confidence interval: 0.718-0.729) in the training and validation cohorts, respectively. The area under the curve for 6-, 12-, and 18-month OS was 0.799, 0.764, and 0.750, respectively, in the training cohort and 0.799, 0.762, and 0.746, respectively, in the validation cohort. The calibration plots show good accuracy, and the decision curve analysis values indicate good clinical applicability and effectiveness. The nomogram model constructed with the above 19 prognostic factors is suitable for predicting the OS of MLUAD and has good predictive accuracy and clinical applicability.

Associations of Depression, Anxiety, and Life Events With the Risk of Obstructive Sleep Apnea Evaluated by Berlin Questionnaire

Background

Psychological problems are prevalent in the general population, and their impacts on sleep health deserve more attention. This study was to examine the associations of OSA risk with depression, anxiety, and life events in a Chinese population.

Methods

A total of 10,287 subjects were selected from the Guangzhou Heart Study. Berlin Questionnaire (BQ) was used to ascertain the OSA. The Center for Epidemiologic Studies Depression Scale (CES-D) and Zung's self-rating anxiety scale (SAS) were used to define depression and anxiety. A self-designed questionnaire was used to assess life events. Odds ratio (OR) with 95% confidence interval (95% CI) was calculated by using the logistic regression model.

Results

There were 1,366 subjects (13.28%) classified into the OSA group. After adjusting for potential confounders, subjects with anxiety (OR: 2.60, 95% CI: 1.63-4.04) and depression (OR: 1.91, 95% CI: 1.19-2.97) were more likely to have OSA. Subjects suffering from both anxiety and depression were associated with a 3.52-fold (95% CI: 1.88-6.31) risk of OSA. Every 1-unit increment of CES-D score and SAS index score was associated with 13% (95% CI: 1.11-1.15) and 4% (95% CI: 1.03-1.06) increased risk of OSA. Neither positive life events nor adverse life events were associated with OSA.

Conclusions

The results indicate that depression and anxiety, especially co-occurrence of both greatly, were associated with an increased risk of OSA. Neither adverse life events nor positive life events were associated with any risk of OSA. Screening for interventions to prevent and manage OSA should pay more attention to depression and anxiety.

Surgical Treatment is Still Recommended for Patients Over 75 Years with IA NSCLC: A Predictive Model Based on Surveillance, Epidemiology and End Results Database

Background

To determine the populations who suitable for surgical treatment in elderly patients (age ≥ 75 y) with IA stage.

Methods

The clinical data of NSCLC patients diagnosed from 2010 to 2015 were collected from the SEER database and divided into surgery group (SG) and no-surgery groups (NSG). The confounders were balanced and differences in survival were compared between groups using PSM (Propensity score matching, PSM). Cox regression analysis was used to screen the independent factors that affect the Cancer-specific survival (CSS). The surgery group was defined as the patients who surgery-benefit and surgery-no benefit according to the median CSS of the no-surgery group, and then randomly divided into training and validation groups. A surgical benefit prediction model was constructed in the training and validation group. Finally, the model is evaluated using a variety of methods.

Results

A total of 7297 patients were included. Before PSM (SG: n = 3630; NSG: n = 3665) and after PSM (SG: n = 1725, NSG: n = 1725) confirmed that the CSS of the surgery group was longer than the no-surgery group (before PSM: 82 vs. 31 months, P < .0001; after PSM: 55 vs. 39 months, P < .0001). Independent prognostic factors included age, gender, race, marrital, tumor grade, histology, and surgery. In the surgery cohort after PSM, 1005 patients (58.27%) who survived for more than 39 months were defined as surgery beneficiaries, and the 720 patients (41.73%) were defined surgery-no beneficiaries. The surgery group was divided into training group 1207 (70%) and validation group 518 (30%). Independent prognostic factors were used to construct a prediction model. In training group (AUC = .678) and validation group (AUC = .622). Calibration curve and decision curve prove that the model has better performance.

Conclusions

This predictive model can well identify elderly patients with stage IA NSCLC who would benefit from surgery, thus providing a basis for clinical treatment decisions.