Identification of fibronectin type III domain containing 3B as a potential prognostic and therapeutic target for pancreatic cancer: a preliminary analysis.

BACKGROUND: Fibronectin type III domain containing 3B (FNDC3B), a member of the fibronectin type III domain-containing protein family, has been indicated in various malignancies. However, the precise role of FNDC3B in the progression of pancreatic cancer (PC) still remains to be elucidated. METHODS: In this study, we integrated data from the National Center for Biotechnology Information, the Cancer Genome Atlas, Genotype-Tissue Expression database, and Gene Expression Omnibus datasets to analyze FNDC3B expression and its association with various clinicopathological parameters. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, along with Gene Set Enrichment Analysis (GSEA), single sample Gene Set Enrichment Analysis (ssGSEA) and estimate analysis were recruited to delve into the biological function and immune infiltration based on FNDC3B expression. Additionally, the prognostic estimation was conducted using Cox analysis and Kaplan-Meier analysis. Subsequently, a nomogram was constructed according to the result of Cox analysis to enhance the prognostic ability of FNDC3B. Finally, the preliminary biological function of FNDC3B in PC cells was explored. RESULTS: The study demonstrated a significantly higher expression of FNDC3B in tumor tissues compared to normal pancreatic tissues, and this expression was significantly associated with various clinicopathological parameters. GSEA revealed the involvement of FNDC3B in biological processes and signaling pathways related to integrin signaling pathway and cell adhesion. Additionally, ssGSEA analysis indicated a positive correlation between FNDC3B expression and infiltration of Th2 cells and neutrophils, while showing a negative correlation with plasmacytoid dendritic cells and Th17 cells infiltration. Kaplan-Meier analysis further supported that high FNDC3B expression in PC patients was linked to shorter overall survival, disease-specific survival, and progression-free interval. However, although univariate analysis demonstrated a significant correlation between FNDC3B expression and prognosis in PC patients, this association did not hold true in multivariate analysis. Finally, our findings highlight the crucial role of FNDC3B expression in regulating proliferation, migration, and invasion abilities of PC cells. CONCLUSION: Despite limitations, the findings of this study underscored the potential of FNDC3B as a prognostic biomarker and its pivotal role in driving the progression of PC, particularly in orchestrating immune responses.

Integrative single-cell analysis of LUAD: elucidating immune cell dynamics and prognostic modeling based on exhausted CD8+ T cells.

Background: The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD. Method: Utilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2. Results: Our study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD's microenvironment. We underscored pDCs' role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2's function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment. Conclusion: The creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage. METHODS: We first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis. RESULTS: The datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation. CONCLUSION: The new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients.

Characteristics of Postoperative Heart Failure in Older Hip Fractures Patients Combined with Coronary Heart Disease and Construction of a Prediction Model of Nomogram, a Retrospective Cohort Study.

Introduction: Older patients combined with coronary heart disease (CHD) develop acute heart failure (AHF) after hip fracture surgery is common, and this study aimed to investigate the risk factors of postoperative AHF in older hip fracture patients and to construct a nomogram prediction model. Methods: We retrospectively collected older hip fracture patients with CHD who underwent hip fracture surgery at the Third Hospital of Hebei Medical University from January 2017 to December 2021. We divided them into a training set and a validation set. We collected the demographic data, laboratory indicators and imaging examination results. We identified risk factors for postoperative AHF and used R language software to establish a nomogram prediction model, plot ROC curves, calibration curves and DCA decision curves. Results: We retrospectively collected 1288 older hip fractures patients with CHD. After excluding 214 patients who did not meet the criteria, 1074 patients were included in our research and we divided them into the training set and the validation set. In the training set, a total of 346 (42.8%) patients developing postoperative AHF. Through univariate and multivariate logistic regression analysis, we identified the risk factors for postoperative AHF and constructed a nomogram prediction model. The AUC of the prediction model is 0.778. The correction curve shows that the model has good consistency. The decision curve analysis shows that the model has good clinical practicality. Conclusion: There were 42.8% older patients combined with CHD develop postoperative AHF. Among them, fracture type, age, anemia at admission, combined with COPD, ASA ≥ 3, and preoperative waiting time >3 days are risk factors for postoperative AHF. We constructed a nomogram prediction model that can effectively predict the risk of postoperative AHF in older hip fracture patients combined with CHD.

Identification of diagnostic biomarkers and immune cell infiltration in coronary artery disease by machine learning, nomogram, and molecular docking.

Background: Coronary artery disease (CAD) is still a lethal disease worldwide. This study aims to identify clinically relevant diagnostic biomarker in CAD and explore the potential medications on CAD. Methods: GSE42148, GSE180081, and GSE12288 were downloaded as the training and validation cohorts to identify the candidate genes by constructing the weighted gene co-expression network analysis. Functional enrichment analysis was utilized to determine the functional roles of these genes. Machine learning algorithms determined the candidate biomarkers. Hub genes were then selected and validated by nomogram and the receiver operating curve. Using CIBERSORTx, the hub genes were further discovered in relation to immune cell infiltrability, and molecules associated with immune active families were analyzed by correlation analysis. Drug screening and molecular docking were used to determine medications that target the four genes. Results: There were 191 and 230 key genes respectively identified by the weighted gene co-expression network analysis in two modules. A total of 421 key genes found enriched pathways by functional enrichment analysis. Candidate immune-related genes were then screened and identified by the random forest model and the eXtreme Gradient Boosting algorithm. Finally, four hub genes, namely, CSF3R, EED, HSPA1B, and IL17RA, were obtained and used to establish the nomogram model. The receiver operating curve, the area under curve, and the calibration curve were all used to validate the accuracy and usefulness of the diagnostic model. Immune cell infiltrating was examined, and CAD patients were then divided into high- and low-expression groups for further gene set enrichment analysis. Through targeting the hub genes, we also found potential drugs for anti-CAD treatment by using the molecular docking method. Conclusions: CSF3R, EED, HSPA1B, and IL17RA are potential diagnostic biomarkers for CAD. CAD pathogenesis is greatly influenced by patterns of immune cell infiltration. Promising drugs offers new prospects for the development of CAD therapy.

Nomogram establishment for short-term survival prediction in ICU patients with aplastic anemia based on the MIMIC-IV database.

OBJECTIVE: To establish an efficient nomogram model to predict short-term survival in ICU patients with aplastic anemia (AA). METHODS: The data of AA patients in the MIMIC-IV database were obtained and randomly assigned to the training set and testing set in a ratio of 7:3. Independent prognosis factors were identified through univariate and multivariate Cox regression analyses. The variance inflation factor was calculated to detect the correlation between variables. A nomogram model was built based on independent prognostic factors and risk scores for factors were generated. Model performance was tested using C-index, receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and Kaplan-Meier curve. RESULTS: A total of 1,963 AA patients were included. A nomogram model with 7 variables was built, including SAPS II, chronic pulmonary obstructive disease, body temperature, red cell distribution width, saturation of peripheral oxygen, age and mechanical ventilation. The C-indexes in the training set and testing set were 0.642 and 0.643 respectively, indicating certain accuracy of the model. ROC curve showed favorable classification performance of nomogram. The calibration curve reflected that its probabilistic prediction was reliable. DCA revealed good clinical practicability of the model. Moreover, the Kaplan-Meier curve showed that receiving mechanical ventilation could improve the survival status of AA patients in the short term but did not in the later period. CONCLUSION: The nomogram model of the short-term survival rate of AA patients was built based on clinical characteristics, and early mechanical ventilation could help improve the short-term survival rate of patients.

A CT-based radiomics nomogram for predicting histologic grade and outcome in chondrosarcoma.

OBJECTIVE: The preoperative identification of tumor grade in chondrosarcoma (CS) is crucial for devising effective treatment strategies and predicting outcomes. The study aims to build and validate a CT-based radiomics nomogram (RN) for the preoperative identification of tumor grade in CS, and to evaluate the correlation between the RN-predicted tumor grade and postoperative outcome. METHODS: A total of 196 patients (139 in the training cohort and 57 in the external validation cohort) were derived from three different centers. A clinical model, radiomics signature (RS) and RN (which combines significant clinical factors and RS) were developed and validated to assess their ability to distinguish low-grade from high-grade CS with area under the curve (AUC). Additionally, Kaplan-Meier survival analysis was applied to examine the association between RN-predicted tumor grade and recurrence-free survival (RFS) of CS. The predictive accuracy of the RN was evaluated using Harrell's concordance index (C-index), hazard ratio (HR) and AUC. RESULTS: Size, endosteal scalloping and active periostitis were selected to build the clinical model. Three radiomics features, based on CT images, were selected to construct the RS. Both the RN (AUC, 0.842) and RS (AUC, 0.835) were superior to the clinical model (AUC, 0.776) in the validation set (P = 0.003, 0.040, respectively). A correlation between Nomogram score (Nomo-score, derived from RN) and RFS was observed through Kaplan-Meier survival analysis in the training and test cohorts (log-rank P < 0.050). Patients with high Nomo-score tumors were 2.669 times more likely to suffer recurrence than those with low Nomo-score tumors (HR, 2.669, P < 0.001). CONCLUSIONS: The CT-based RN performed well in predicting both the histologic grade and outcome of CS.

Predictors of cancer-specific survival and overall survival among patients aged ≥60 years with lung adenocarcinoma using the SEER database.

OBJECTIVE: We developed a simple, rapid predictive model to evaluate the prognosis of older patients with lung adenocarcinoma. METHODS: Demographic characteristics and clinical information of patients with lung adenocarcinoma aged ≥60 years were retrospectively analyzed using Surveillance, Epidemiology, and End Results (SEER) data. We built nomograms of overall survival and cancer-specific survival using Cox single-factor and multi-factor regression. We used the C-index, calibration curve, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to evaluate performance of the nomograms. RESULTS: We included 14,117 patients, divided into a training set and validation set. We used the chi-square test to compare baseline data between groups and found no significant differences. We used Cox regression analysis to screen out independent prognostic factors affecting survival time and used these factors to construct the nomogram. The ROC curve, calibration curve, C-index, and DCA curve were used to verify the model. The final results showed that our predictive model had good predictive ability, and showed better predictive ability compared with tumor-node-metastasis (TNM) staging. We also achieved good results using data of our center for external verification. CONCLUSION: The present nomogram could accurately predict prognosis in older patients with lung adenocarcinoma.

A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma.

BACKGROUND: To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model. METHODS: We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomogram were assessed utilizing calibration curve and the area under the receiver operating factor curve (AUC), and the clinical benefits were assessed utilizing decision curve analysis (DCA). RESULTS: We constructed three predictive models.The AUC values of the radiomics signature and hematology model reached 0.874 (95% CI: 0.819-0.928) and 0.772 (95% CI: 0.699-0.845), respectively. Tumor length, cN stage, the radScore, and the hemScore were found to be independent factors influencing pCR according to univariate and multivariate analyses (P < 0.05). A combined nomogram was constructed from these factors, and AUC reached 0.934 (95% CI: 0.896-0.972). DCA demonstrated that the clinical benefits brought by the nomogram for patients across an extensive range were greater than those of other individual models. CONCLUSIONS: By combining CT radiomics, hematological factors, and clinicopathological characteristics before treatment, we developed a nomogram model that effectively predicted whether ESCC patients would achieve pCR after nICT, thus identifying patients who are sensitive to nICT and assisting in clinical treatment decision-making.

A chest CT-based nomogram for predicting survival in acute myeloid leukemia.

BACKGROUND: The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). METHODS: 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. RESULTS: Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011). CONCLUSIONS: In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.

Evaluation of risk factors related to sleep disorders in patients undergoing hemodialysis using a nomogram model.

This study aimed to investigate the risk factors related to sleep disorders in patients undergoing hemodialysis using a nomogram model. A cross-sectional survey was conducted in a hospital in Zhejiang province, China from January 1, 2020, to November 31, 2022 among patients undergoing hemodialysis. Dietary intake was assessed applying a Food Frequency Questionnaire. Sleep quality was evaluated by the Pittsburgh Sleep Quality Index. Evaluation of risk factors related to sleep disorders in patients undergoing hemodialysis was using a nomogram model. This study included 201 patients and 87 individuals (43.3%, 87/201) exhibited sleep disorders. The average age of included patients was 51.1 ±â€…9.0 years, with males accounting for 55.7% (112/201). Results from nomogram model exhibited that potential risk factors for sleep disorders in patients undergoing hemodialysis included female, advanced age, increased creatinine and alanine aminotransferase levels, as well as higher red meat consumption. Inversely, protective factors against sleep disorders in these patients included higher consumption of poultry, fish, vegetables, and dietary fiber. The C-index demonstrated a high level of discriminative ability (0.922). This study found that age, sex, and dietary factors were associated with sleep disorders in hemodialysis patients. Hemodialysis patients with sleep disorders require urgent dietary guidance.

Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.

BACKGROUND: To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS: A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS: Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION: The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

Association between increased systemic immune-inflammation index and postoperative delirium in older intertrochanteric fracture patients.

BACKGROUND AND PURPOSE: The systemic immune-inflammation index (SII), a novel inflammation index derived from the counts of circulating platelets, neutrophils and lymphocytes, has been studied in the treatment of acute cancer and ischemic stroke (AIS). However, the clinical value of the SII in postoperative delirium patients has not been further investigated. The purpose of our research was to study the incidence and preoperative risk factors for postoperative delirium (POD) and verify whether the SII could serve as a potential marker for POD in older intertrochanteric fracture patients. Finally, we created a novel nomogram for predicting POD in older patients with intertrochanteric fractures. METHODS: We enrolled elderly patients with intertrochanteric fractures who underwent proximal femoral nail antirotation (PFNA) between February 2021 and April 2023. Univariate and multivariate logistic analyses were subsequently performed to confirm the risk factors and construct a nomogram model.Calibration curve and clinical decision curve analysis (DCA) were used to assess the model's fitting performance. The performance of the nomogram was evaluated for discrimination, calibration, and clinical utility. RESULTS: A total of 293 patients were eligible for inclusion in the study, 25.6% (75/293) of whom had POD. The POD patients had higher SII values than the non-POD patients. The SII was strongly correlated with POD in older intertrochanteric fracture patients, and the optimal cutoff value was 752.6 × 109. Multivariate analysis revealed that age, diabetes, total albumin, SII > 752.6 × 109 and a CRP > 20.25 mg/L were independent risk factors for POD patients. By incorporating these 5 factors, the model achieved a concordance index of 0.745 (95% CI, 0.683-0.808) and had a well-fitted calibration curve and good clinical application value. CONCLUSION: The SII is a simple and valuable biomarker for POD, and the new nomogram model can be used to accurately predict the occurrence of POD. They can be utilized in clinical practice to identify those at high risk of POD in older intertrochanteric fracture patients.

Developing and validating a nomogram for penile cancer survival: A comprehensive study based on SEER and Chinese data.

OBJECTIVE: The primary aim of this study was to create a nomogram for predicting survival outcomes in penile cancer patients, utilizing data from the Surveillance, Epidemiology, and End Results (SEER) and a Chinese organization. METHODS: Our study involved a cohort of 5744 patients diagnosed with penile cancer from the SEER database, spanning from 2004 to 2019. In addition, 103 patients with penile cancer from Sun Yat-sen Memorial Hospital of Sun Yat-sen University were included during the same period. Based on the results of regression analysis, a nomogram is constructed and validated internally and externally. The predictive performance of the model was evaluated by concordance index (c-index), area under the curve, decision curve analysis, and calibration curve, in internal and external datasets. Finally, the prediction efficiency is compared with the TNM staging model. RESULTS: A total of 3154 penile patients were randomly divided into the training group and the internal validation group at a ratio of 2:1. Nine independent risk factors were identified, including age, race, marital status, tumor grade, histology, TNM stage, and the surgical approach. Based on these factors, a nomogram was constructed to predict OS. The nomogram demonstrated relatively better consistency, predictive accuracy, and clinical relevance, with a c-index over 0.73 (in the training cohort, the validation cohort, and externally validation cohort.) These evaluation indexes are far better than the TNM staging system. CONCLUSION: Penile cancer, often overlooked in research, has lacked detailed investigative focus and guidelines. This study stands as the first to validate penile cancer prognosis using extensive data from the SEER database, supplemented by data from our own institution. Our findings equip surgeons with an essential tool to predict the prognosis of penile cancer better suited than TNM, thereby enhancing clinical decision-making processes.

Analysis of health risk factors for older adults living alone in China and establishment and evaluation of a nomogram prediction model.

Objective: To understand the health status of older adults living alone in China and analyze the influencing factors, so as to provide reference for improving the health status of older adults living alone. Methods: Based on CGSS data from China General Social Survey (2017), the influencing factors of health status of older adults living alone were analyzed by unconditional Logistic regression, and the R software was used to develop a nomogram for predicting the risk of self-assessed unhealthy adverse outcomes. Results: Gender, annual income, mandarin listening level and participation in medical insurance were the influencing factors of self-rated health of older adults living alone. Age and annual income are the influencing factors of physiological health. Annual income and Internet use were influential factors for mental health. C-Statistic of nomogram prediction model was 0.645. The calibration curve showed that goodness of fit test (χ2 = 58.09, p < 0.001), and the overall prediction ability of the model was good. Conclusion: The health status of older adults living alone in the home-based older adults care is worrying, and it is affected by various factors. We should pay more attention to older adults living alone, improve the ability of listening and distinguishing mandarin and the use of health information platforms for older adults living alone, and further implement medical insurance policies and health services. Announcing the solution to promote healthy home-based care for older adults living alone.

A time-averaged serum bicarbonate-based nomogram to predict the probability of residual kidney function preservation for patients undergoing peritoneal dialysis.

OBJECTIVE: Residual kidney function (RKF) is an important prognostic indicator in peritoneal dialysis (PD) patients. So far, there are no prediction tools available for RKF, and the association between serum bicarbonate and RKF has received little attention in patients with PD. We aimed to develop a nomogram for the preservation of RKF based on the time-averaged serum bicarbonate (TA-Bic) levels. PATIENTS AND METHODS: A prediction model was established by conducting a retrospective cohort study of 151 PD patients who had been treated at the First Affiliated Hospital of Anhui Medical University. The nomogram was developed using a multivariate Cox regression model. The discrimination, calibration, and clinical utility of the model were evaluated by the C-index, receiver operating curve (ROC) curve, calibration curve, and decision curve analysis. RESULTS: In the elderly PD onset, higher baseline values of residual glomerular filtration rate, total Kt/V and higher TA-Bic levels were identified as protective predictors of RKF loss. The nomogram was conducted on the basis of the minimum value of the Akaike Information Criterion and Bayesian Information Criterion with a reasonable C-index of 0.766, showing great discrimination, proper calibration, and high potential for clinical practice. Through the total score of the nomogram, the patients were classified into the high-risk group and low-risk group, and a higher cumulative incidence of complete RRF loss was found in the high-risk group compared with the patients in the low-risk group. CONCLUSIONS: The novel predictive nomogram model can predict the probability of RKF preservation in long-term PD patients with high accuracy. Future studies are needed to externally validate the current nomogram before clinical application.

Development and validation of a nomogram model for predicting distant metastasis of aged ≥50 patients with thyroid carcinoma: a SEER database analysis.

OBJECTIVE: This work aimed to construct and validate a model for predicting distant metastasis (DM) in thyroid carcinoma (TC) patients aged≥50. PATIENTS AND METHODS: The research data were collected from the Surveillance, Epidemiology, and End Results (SEER) program databases via SEER*Stat software (https://seer.cancer.gov/). Logistics regression was used to screen the independent risk factors for TC patients. The nomogram was constructed and validated based on the logistics regression results for predicting DM occurrence in TC patients. Moreover, the characteristic curves (ROC) were used to assess the predictive performance. The decision analysis curve (DCA) and the calibration curve were used to test this nomogram's accuracy and discrimination. Additionally, we analyzed survival and risk scores in TC patients with metastasis using the Kaplan-Meier (KM) method. RESULTS: A total of 11,166 TC patients were divided into a training set and a validation set. The results showed that topography (T), lymph node metastasis (N), and (grade) G were crucial risk factors for predicting DM. ROC analysis showed that the model had a good discriminative ability both in the training and validation set. The DCA curve showed greater net benefits across a range of DM risks for the nomogram in the training and validation set. Survival analyses showed that the metastasis cases with low-risk scores have shown a poorer prognosis in this study, both in the training and validation set. CONCLUSIONS: The nomogram model had excellent predictive performance and net benefit for predicting DM of TC patients aged ≥50. The model can help doctors develop treatment plans for their patients.

Risk factor analysis and nomogram construction in patients with distant metastatic prostate cancer at different PSA levels: a study based on the SEER database.

OBJECTIVE: Prostate cancer (PCa) is the most common malignant tumor in the male genitourinary system. Once PCa has metastasized, it is very difficult to cure. The purpose of this study was to investigate the prognostic risk factor analysis of patients with different prostate-specific antigen (PSA) levels in distant metastatic PCa. At the same time, we construct effective models for predicting the survival rate of prostate cancer patients. PATIENTS AND METHODS: Data on prostate cancer patients with the presence of distant metastases were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. PCa patients with distant metastases were categorized into two groups based on PSA levels, one with PSA <20 ng/mL and the other with PSA ≥20 ng/mL. Univariate and multivariate COX regression analyses were used to identify independent factors affecting the prognosis of the patients. A nomogram was constructed using the independent prognostic factors, and the results were evaluated using calibration curves, timeROC curves, and Kaplan-Meier curves. RESULTS: In the PSA <20 ng/mL group, there were a total of 1,832 patients. COX regression analysis showed that age, marital status, N stage, grade, Gleason score, and medical household income inflation were independent prognostic factors for overall survival (OS) in patients. In addition, we found that age, marital status, N stage, bone metastasis, grade, and Gleason score were independent prognostic factors for cancer-specific survival (CSS) in patients. In the PSA ≥20 ng/mL group, there were a total of 5,314 patients. It was found that age, ethnicity, marital status, bone metastasis, first malignant primary indicator, grade, Gleason score, and medical household income inflation were patients' independent prognostic factors for OS. For CSS, we found that age, ethnicity, marital status, T stage, radiotherapy, bone metastasis, Gleason score, and Median household income inflation were independent prognostic factors. Constructing a nomogram can accurately predict the prognosis of this group of patients. CONCLUSIONS: We found different independent prognostic factors for different PSA levels in patients with distant metastatic PCa. A new nomogram was constructed to predict OS and CSS in patients, which helps in clinical-assisted decision-making.

Analysis of risk factors affecting the prognosis of patients with sepsis and construction of nomogram prediction model.

OBJECTIVE: This study analyzed the clinical data of 200 sepsis patients, exploring the risk factors that affect patient prognosis and providing the basis for clinically targeted intervention to improve patient prognosis. PATIENTS AND METHODS: 200 septic patients were admitted to Yulin Second Hospital, and they were divided into a survival group of 151 patients and a death group of 49 patients, according to their clinical outcomes on admission. The relevant clinical parameters within 24 h of admission were collected, and the independent risk factors affecting the prognosis of septic patients were analyzed by multivariate Logistic regression. R language 4.21 software was used to construct a nomogram prediction model. The receiver operating characteristic curve was used to evaluate the discrimination of the nomogram model, and decline curve analysis was drawn to evaluate the effectiveness of the model. RESULTS: In the nomogram prediction model, age, the Acute Physiology and Chronic Health Scoring System Domain (APACHE II) score, the Sequential Organ Failure Assessment (SOFA) score, C-reactive protein (CRP), total bilirubin, albumin (Alb), urea nitrogen, creatinine, and lactate (Lac) were independent risk factors for death in septic patients. The area under the receiver operating characteristic (ROC) curve for predicting the prognosis of septic patients was 0.597-1.000, and the calibration curve tends to be the ideal curve. The model had good discrimination and calibration and had high accuracy in evaluating septic patients. The modeling curves in the decline curve analysis (DCA) were all above the two extreme curves, which had good clinical value. CONCLUSIONS: Nine clinical variables have been found to be independent risk factors for death in septic patients. The prediction model established based on this has good accuracy, discrimination, and consistency in predicting the prognosis of sepsis patients.

Development and validation of a nomogram for predicting lymph node metastasis in ductal carcinoma in situ with microinvasion: A SEER population-based study.

BACKGROUND: Ductal carcinoma in situ with microinvasion (DCIS-MI) is a special type of breast cancer. It is an invasive lesion less than 1.0 mm in size related to simple ductal carcinoma in situ (DCIS). Lymph node metastasis (LNM) in DCIS-MI often indicates a poor prognosis. Therefore, the management of lymph nodes plays a vital role in the treatment strategy of DCIS-MI. Since DCIS-MI is often diagnosed by postoperative paraffin section and immunohistochemical detection, to obtain the best clinical benefits for such patients, we aim to establish and verify a nomogram to predict the possibility of lymph node metastasis in DCIS-MI patients and help preoperative or intraoperative clinical decision-making. METHODS: A retrospective analysis of patients with DCIS-MI in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 was performed. The study cohort was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The risk factors were determined by univariate and multivariate logistic regression analyses in the training cohort, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. An independent data cohort was obtained from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) for external validation. RESULTS: This study included 3951 female patients from SEER with DCIS-MI, including 244 patients with regional lymph node metastasis, accounting for 6.18% of the total. An independent test set of 323 patients from SJTU-BCDB was used for external validation. According to the multifactorial logistic regression analysis results, age at diagnosis, ethnicity, grade, and surgical modality were included in the prediction model. The areas under the ROC curves (AUCs) were 0.739 (95% CI: 0.702~0.775), 0.732 (95% CI: 0.675~0.788), and 0.707 (95%CI: 0.607-0.807) in the training, validation and external test groups, suggesting that the column line graphs had excellent differentiation. The calibration curves slope was close to 1, and the model's predicted values were in good agreement with the actual values. The DCA curves showed good clinical utility. CONCLUSION: In this study, we constructed accurate and practical columnar maps with some clinical benefit to predict the likelihood of lymph node metastasis in patients with postoperatively diagnosed DCIS-MI and provide a reference value for specifying treatment strategies.