Nomogram Model Research Articles

Development and Validation of a Predictive Model for Early Identification of Cognitive Impairment Risk in Community-Based Hypertensive Patients.

Objective: To investigate the risk factors for the development of mild cognitive dysfunction in hypertensive patients in the community and to develop a risk prediction model. Method: The data used in this study were obtained from two sources: the China Health and Retirement Longitudinal Study (CHARLS) and the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 1121 participants from CHARLS were randomly allocated into a training set and a validation set, following a 70:30 ratio. Meanwhile, an additional 4016 participants from CLHLS were employed for external validation of the model. The patients in this study were divided into two groups: those with mild cognitive impairment and those without. General information, employment status, pension, health insurance, and presence of depressive symptoms were compared between the two groups. LASSO regression analysis was employed to identify the most predictive variables for the model, utilizing 14-fold cross-validation. The risk prediction model for cognitive impairment in hypertensive populations was developed using generalized linear models. The model's discriminatory power was evaluated through the area under the receiver operating characteristic (ROC) curve and calibration curves. Results: In the modeling group, eight variables such as gender, age, residence, education, alcohol use, depression, employment status, and health insurance were ultimately selected from an initial pool of 21 potential predictors to construct the risk prediction model. The area under the curve (AUC) values for the training, internal, and external validation sets were 0.777, 0.785, and 0.782, respectively. All exceeded the threshold of 0.7, suggesting that the model effectively predicts the incidence of mild cognitive dysfunction in community-based hypertensive patients. A risk prediction model was developed using a generalized linear model in conjunction with Lasso regression. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve. Hosmer-Lemeshow test values yielded p = .346 and p = .626, both of which exceeded the 0.05 threshold. Calibration curves demonstrated a significant agreement between the nomogram model and observed outcomes, serving as an effective tool for evaluating the model's predictive performance. Discussion: The predictive model developed in this study serves as a promising and efficient tool for evaluating cognitive impairment in hypertensive patients, aiding community healthcare workers in identifying at-risk populations.

Empathy in undergraduate medical students: a multi-center cross-sectional study in China

BackgroundFostering empathy has been continuously emphasized in the global medical education. Empathy is crucial to enhance patient-physician relationships, and is associated with medical students’ academic and clinical performance. However, empathy level of medical students in China and related influencing factors are not clear.MethodsThis was a cross-sectional study among medical students in 11 universities. We used the Jefferson Scale of Empathy Student-version of Chinese version to measure empathy level of medical students. Factors associated with empathy were identified by the univariate and multivariate logistic regression analyses. Based on the variables identified above, the nomogram was established to predict high empathy probability of medical students. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate the discrimination, calibration and educational utility of the model.ResultsWe received 10,901 samples, but a total of 10,576 samples could be used for further analysis (effective response rate of 97.02%). The mean empathy score of undergraduate medical students was 67.38 (standard deviation = 9.39). Six variables including gender, university category, only child or not, self-perception doctor-patient relationship in hospitals, interest of medicine, Kolb learning style showed statistical significance with empathy of medical students (P < 0.05). Then, the nomogram was established based on six variables. The validation suggested the nomogram model was well calibrated and had good utility in education, as well as area under the curve of model prediction was 0.65.ConclusionsWe identify factors influencing empathy of undergraduate medical students. Moreover, increasing manifest and hidden curriculums on cultivating empathy of medical students may be needed among medical universities or schools in China.

A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study.

We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making. This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models. Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model. The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients. The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS. Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.

A nomogram based on pretreatment radiomics and dosiomics features for predicting overall survival associated with esophageal squamous cell cancer

ObjectivesTo propose a nomogram-based survival prediction model for esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy using pretreatment computed tomography (CT), positron emission tomography (PET) radiomics and dosiomics features, and common clinical factors. MethodsRadiomics and dosiomics features were extracted from CT and PET images and dose distribution from 2 institutions. The least absolute shrinkage and selection operator (LASSO) with logistic regression was used to select radiomics and dosiomics features by calculating the radiomics and dosiomics scores (Rad-score and Dos-score), respectively, in the training model. The model was trained in 81 patients and validated in 35 patients at Center 1 using 10-fold cross validation. The model was externally tested in 26 patients at Center 2. The predictive clinical factors, Rad-score, and Dos-score were identified to develop a nomogram model. ResultsUsing LASSO Cox regression, 13, 11, and 19 CT, PET-based radiomics, and dosiomics features, respectively, were selected. The clinical factors T-stage, N-stage, and clinical stage were selected as significant prognostic factors by univariate Cox regression. In the external validation cohort, the C-index of the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were 0.74, 0.82, and 0.92, respectively. Significant differences in overall survival (OS) in the combined model of CT-based radiomics, PET-based radiomics, and dosiomics features with clinical factors were observed between the high- and low-risk groups (P = 0.019, 0.038, and 0.014, respectively). ConclusionThe dosiomics features have a better predicter for OS than CT- and PET-based radiomics features in ESCC treated with radiotherapy. Clinical relevance statementThe current study predicted the overall survival for esophageal squamous cell carcinoma patients treated with definitive chemoradiotherapy. The dosiomics features have a better predicter for overall survival than CT- and PET-based radiomics features.

Nomogram for Predicting Survival in Locally Advanced Cervical Cancer with Concurrent Chemoradiotherapy plus or Not Adjuvant Chemotherapy: A Retrospective Analysis Based on 2018 FIGO Staging.

Background: The comprehensive treatment mode of combining concurrent chemoradiotherapy (CCRT) with adjuvant chemotherapy (AC) is a commonly used mainstream model in the clinical practice of locally advanced cervical cancer (LACC). However, the necessity for AC after CCRT lacks sufficient evidence-based medical support. This study constructs a predictive model for the survival time dependence of CCRT ± AC for LACC based on the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging with internal validation, the prognosis was assessed with intensity-modulated radiotherapy (IMRT) and concurrent cisplatin, and provides guidance for future stratified treatment. Materials and Methods: The retrospective analysis included 482 patients with LACC who CCRT from January 2016 to January 2023. Patients who used the 2009 FIGO staging were all standardized for the 2018 FIGO staging. The 482 patients with LACC were divided into a training set (n = 290) and a validation set (n = 192) at a ratio of 6:4. COX multivariate regression model and LASSO regression were used to screen for independent prognostic factors affecting progression-free survival (PFS) and overall survival (OS), and a nomogram clinical prediction model was constructed based on these factors. Evaluate the effectiveness of the model through the receiver operating characteristic curve, calibration curve, decision curve, risk heat map, and survival curves for risk stratification. Results: The PFS and OS independent prognostic risk factors affecting the 2018 FIGO staging of LACC during CCRT were validated to be similar to the 2009 FIGO staging prediction model reported in previous literature. In the training cohort, area under the curve (AUC) values at 1, 3, and 5 years were 0.941, 0.882, and 0.885 for PFS, and 0.946, 0.946, and 0.969 for OS, respectively. When applied to a test cohort, the model also showed accurate prediction result (AUC at 1, 3, and 5 years were 0.869, 0.891, and 0.899 for PFS, and 0.891, 0.941 and 0.878 for OS, respectively). Subgroup analysis suggests that patients with LACC, adenocarcinoma, stage IVA, pelvic lymph node metastasis, pretreatment hemoglobin ≤100 g/l and residual tumor diameter >2 cm, who received CCRT in the 2018 FIGO stage, may benefit more from adjuvant chemtherapy. Conclusions: Based on the 2018 FIGO staging, a nomogram prediction model for PFS and OS in patients with LACC undergoing CCRT was developed. The model, established by combining weighted clinical and pathological factors, can provide more personalized treatment predictions in clinical practice. For patients with high-risk factors such as residual tumor diameter > 2 cm after CCRT for LACC, AC may bring benefits.

Predicting Kawasaki disease shock syndrome in children.

Kawasaki disease shock syndrome (KDSS) is a critical manifestation of Kawasaki disease (KD). In recent years, a logistic regression prediction model has been widely used to predict the occurrence probability of various diseases. This study aimed to investigate the clinical characteristics of children with KD and develop and validate an individualized logistic regression model for predicting KDSS among children with KD. The clinical data of children diagnosed with KDSS and hospitalized between January 2021 and December 2023 were retrospectively analyzed. The best predictors were selected by logistic regression and lasso regression analyses. A logistic regression model was built of the training set (n = 162) to predict the occurrence of KDSS. The model prediction was further performed by logistic regression. A receiver operating characteristic curve was used to evaluate the performance of the logistic regression model. We built a nomogram model by visualizing the calibration curve using a 1000 bootstrap resampling program. The model was validated using an independent validation set (n = 68). In the univariate analysis, among the 24 variables that differed significantly between the KDSS and KD groups, further logistic and Lasso regression analyses found that five variables were independently related to KDSS: rash, brain natriuretic peptide, serum Na, serum P, and aspartate aminotransferase. A logistic regression model was established of the training set (area under the receiver operating characteristic curve, 0.979; sensitivity=96.2%; specificity=97.2%). The calibration curve showed good consistency between the predicted values of the logistic regression model and the actual observed values in the training and validation sets. Here we established a feasible and highly accurate logistic regression model to predict the occurrence of KDSS, which will enable its early identification.

Nomogram model of serum thymidine kinase 1 combined with ultrasonography for prediction of central lymph node metastasis risk in patients with papillary thyroid carcinoma pre-surgery.

The aim of this study was to develop a nomogram, using serum thymidine kinase 1 protein (STK1p) combined with ultrasonography parameters, to early predict central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) pre-surgery. Patients with PTC pre-surgery in January 2021 to February 2023 were divided into three cohorts: the observation cohort (CLNM, n = 140), the control cohort (NCLNM, n = 128), and the external verification cohort (CLNM, n = 50; NCLNM, n = 50). STK1p was detected by an enzyme immunodot-blot chemiluminescence analyzer and clinical parameters were evaluated by ultrasonography. A suitable risk threshold value for STK1p of 1.7 pmol/L was selected for predicting CLNM risk by receiver operating characteristic (ROC) curve analysis. Multivariate analysis identified the following six independent risk factors for CLNM: maximum tumor size >1 cm [odds ratio (OR) = 2.406, 95% confidence interval (CI) (1.279-4.526), p = 0.006]; capsule invasion [OR = 2.664, 95% CI (1.324-5.360), p = 0.006]; irregular margin [OR = 2.922; 95% CI (1.397-6.111), p=0.004]; CLN flow signal [OR = 3.618, 95% CI (1.631-8.027), p = 0.002]; tumor-foci number ≥2 [OR = 4.064, 95% CI (2.102-7.859), p < 0.001]; and STK1p≥1.7 pmol/L [OR = 7.514, 95% CI (3.852-14.660), p < 0.001]. The constructed nomogram showed that the area under the ROC curve for the main dataset was 0.867 and that for the validation dataset was 0.830, exhibiting effectivity, and was recalculated to a total score of approximately 383. Through monitoring the response post-surgery, all patients were assessed as tumor-free at 12 months post-surgery, which was significantly associated with a reduction in STK1p to disease-free levels. We demonstrate for the first time that a novel nomogram including STK1p combined with ultrasonography can assist in the clinical prevention of CLNM, by facilitating timely, individualized prophylactic CLNM dissection, thereby reducing the risk of secondary surgery and the probability of recurrence.

Construction and validation of a nomogram for predicting the prognosis of patients with septic shock in department of emergency medicine

To construct a nomogram model for predicting the 28-day mortality of patients with septic shock in the emergency medicine department and to validate the predictive efficacy. Based on the database of the emergency medicine department of Chu Hsien-I Memorial Hospital of Tianjin Medical University, Tianjin Medical University General Hospital and the Second Hospital of Tianjin Medical University, the data of 913 patients with septic shock admitted to the emergency medicine department from January 2017 to October 2020 were collected, including baseline demographic information and clinical characteristics, laboratory indices, and the main endpoints (28-day mortality). The patients were divided into a training set and a validation set based on simple random sampling. All significant variables from the one-way binary Logistic regression analysis of the training set were included in the multivariate Logistic regression analysis to analyze the risk factors for 28-day mortality in patients with septic shock and to construct a column-line graphical model. The predictive efficacy of the nomogram model was assessed using calibration curves and receiver operator characteristic curve (ROC curve). A total of 860 patients with septic shock meeting the criteria were finally enrolled, including 472 in the training set and 388 in the validation set. The 28-day mortalities were 52.5% (248/472) and 54.1% (210/388) for the training and validation sets, respectively. In the training set, age, respiratory rate (RR), the levels of C-reactive protein (CRP), D-dimer, white blood cell count (WBC), neutrophil count (NEU), neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), mean platelet volume (MPV), and platelet count (PLT) in the death group were significantly higher than those in the survival group, and the levels of base remaining (BE), lymphocyte count (LYM), hemoglobin (Hb) and the proportion of chronic obstructive pulmonary diseases (COPD) were significantly lower than those in the survival group (all P < 0.05). Multifactorial Logistic regression analysis showed that NLR [odds ratio (OR) = 0.023 0, 95% confidence interval (95%CI) was -0.204 4 to 0.113 0], MPV (OR = 0.179 8, 95%CI was -0.877 6 to 0.172 7), Hb (OR = 0.007 8, 95%CI was 0.010 3 to 0.040 8), procalcitonin (PCT; OR = 1.957 0, 95%CI was 1.243 0 to 3.081 0), and D-dimer (OR = 0.000 1, 95%CI was -0.000 4 to 0.000 1) were independent predictors of 28-day mortality in patients with septic shock in the emergency department (all P < 0.05). A column-line graph model was established based on the above variables, and the ROC curves showed that the area under the ROC curve (AUC) of the nomogram model in the training set and validation set for predicting the 28-day mortality of patients with septic shock was 0.907 (95%CI was 0.864 to 0.940) and 0.822 (95%CI was 0.781 to 0.863), respectively. The calibration curves showed good agreement between the predicted and observed results for both the training and validation sets. The nomogram model constructed based on NLR, MPV, Hb, PCT and D-dimer has significant clinical value in predicting the 28-day mortality of patients with septic shock in the emergency medicine department.

Development and Validation of an In-Hospital Mortality Prediction Model for Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

Patients with chronic obstructive pulmonary disease (COPD) often face unknown risks during acute exacerbation of the disease (AECOPD), which could potentially result in mortality. This study aimed to develop and validate a nomogram model for predicting the risk of in-hospital mortality in AECOPD patients. Clinical data of patients hospitalized at The Second People's Hospital of Wuhu City for AECOPD between January 2013 and December 2022 were retrospectively collected. Variables underwent selection through LASSO regression and multivariable logistic regression to develop a nomogram model. The model's predictive performance was assessed using the concordance index, calibration curve, and decision curve analysis (DCA), with internal validation conducted using the bootstrap method. A total of 1224 patients were included in this study, with 98 (8%) deaths occurring during hospitalization. LASSO regression identified 11 variables, used to construct model A. Further multivariable logistic regression was conducted to select variables with P < 0.05 to establish model B. model B was selected as the final model based on discrimination, calibration, and clinical utility, encompassing variables including acute respiratory failure, lung cancer, heart rate, hemoglobin, absolute neutrophil count, serum albumin, blood urea nitrogen, and serum chloride. The nomogram model achieved a concordance index of 0.858. Internal validation of the model was conducted using the bootstrap method with 500 repetitions, resulting in a concordance index of 0.851 (95% CI: 0.805, 0.893). The calibration curve demonstrated a good fit, with a Hosmer-Lemeshow goodness-of-fit test P-value of 0.520. Moreover, DCA findings suggested patient benefit within a threshold probability range of 0.02 to 0.73, with a maximum net benefit of 0.07. The model constructed in this study has good predictive performance, which helps clinical doctors identify patients at high risk of death early.

A novel nomogram to predict the recurrence of hepatocellular carcinoma after liver transplantation using extended selection criteria

BackgroundLiver transplantations (LTs) with extended criteria have produced surgical results comparable to those obtained with traditional standards. However, it is not sufficient to predict hepatocellular carcinoma (HCC) recurrence after LT according to morphological criteria alone. The present study aimed to construct a nomogram for predicting HCC recurrence after LT using extended selection criteria. MethodsRetrospective data on patients with HCC, including pathology, serological markers and follow-up data, were collected from January 2015 to April 2020 at Huashan Hospital, Fudan University, Shanghai, China. Logistic least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to identify and construct the prognostic nomogram. Receiver operating characteristic (ROC) curves, Kaplan-Meier curves, decision curve analyses (DCAs), calibration diagrams, net reclassification indices (NRIs) and integrated discrimination improvement (IDI) values were used to assess the prognostic capacity of the nomogram. ResultsA total of 301 patients with HCC who underwent LT were enrolled in the study. The nomogram was constructed, and the ROC curve showed good performance in predicting survival in both the development set (2/3) and the validation set (1/3) (the area under the curve reached 0.748 and 0.716, respectively). According to the median value of the risk score, the patients were categorized into the high- and low-risk groups, which had significantly different recurrence-free survival (RFS) rates (P < 0.01). Compared with the Milan criteria and University of California San Francisco (UCSF) criteria, DCA revealed that the new nomogram model had the best net benefit in predicting 1-, 3- and 5-year RFS. The nomogram performed well for calibration, NRI and IDI improvement. ConclusionsThe nomogram, based on the Milan criteria and serological markers, showed good accuracy in predicting the recurrence of HCC after LT using extended selection criteria.

Prognostic factors for patients with breast cancer brain metastasis after stereotactic radiotherapy.

e13109 Background: Breast cancer (BC) is the most common cancer in females and BC brain metastasis (BCBM) is considered as the second most frequent brain metastasis. Numerous prognostic factors are associated with survival for patients with BCBMs. Here we investigated the clinical factors associated with overall survival (OS) and distant intracranial control (DIC) after stereotactic radiotherapy (SRT) for BCBM. Methods: The records of BCBM patients treated with SRT were collected from electronic database in Cancer Center, Union Hospital, Tongji medical college, Huazhong University of Science and Technology Hospital. Based on the univariate and multivariate Cox regression analysis, the prognostic significance of available variables was assessed. The nomogram was created from Cox models and internally validated by use of bootstrap and cross-validation. Model discrimination was measured by calibration plots and the concordance index, and the clinical benefits were evaluated through clinical decision curve analysis (DCA). Results: 101 BCBM patients receiving SRT were analyzed in this study, and 5 patients were excluded with other primary malignancies or missing data. Median observation time was 15.0 months, and median OS was 22.0 months. OS rate was 71.5% in 1 year, 45.8% in 2 years, and 32.8% in 3 years. DIC was 49.8% in 1 year, 26.6% in 2 years, and 18.3% in 3 years. Multivariable analysis identified prognostic factors for OS including number of the brain metastasis, molecular subtypes type, brain metastasis as the first metastasis site, Karnofsky performance status (KPS), and receiving systemic therapy after SRT. KPS&lt;80, triple-negative subtype, the existence of extracranial metastases, the existence of lung metastases were associated with decreased DIC. Selected by the Akaike information criterion (AIC), the nomogram model contains age, KPS, histological subtype, number of brain metastases, brain metastasis as first metastasis site, PTV size, liver metastases, albumin and neutrophils. The calibration curve for probability of survival showed high consistencies between prediction by nomogram and actual observation. The C-index of the nomogram for predicting survival was 0.823, and the bias-corrected C-index generated by bootstrap validation with 1,000 resamples was 0.772, which was statistically higher than the C-index values of the other systems: Recursive partitioning analysis (RPA) (0.627), Graded prognostic assessment (GPA) (0.637), Breast-specific Graded Prognostic Assessment (breast-GPA) (0.699). BCBM patients receiving SRT were divided into four risk groups: I group (risk score &lt; 150), II group (150≤risk score&lt; 220), III group (220≤risk score&lt;290) and IV group (risk score≥290). The Kaplan‐Meier OS curves showed great discrimination among the four risk groups. Conclusions: The proposed nomogram resulted in a more-accurate prognostic prediction for BCBM patients receiving SRT.

The combined use of serum Raman spectroscopy and D dimer testing for the early diagnosis of acute aortic dissection

ObjectivesAcute aortic dissection (AAD) is an extremely life-threatening medical emergency, often misdiagnosed in its early stages, resulting in prolonged wait times for rescue. This study aims to identify potential serum biomarkers that can assist in the accurate diagnosis of AAD and effectively differentiate it from other conditions causing severe chest pain. MethodsA total of 122 patients with AAD and 129 patients with other severe chest pain disorders were included in the study. Serum samples were analyzed by measuring the peak intensities of Raman spectra. For each measurement, the Raman spectrum was accumulated by two accumulations (3 s per acquisition). Logistic regression and nomogram models were developed using these peak intensities as well as D-dimer levels to predict the occurrence of AAD. The clinical utilities of these models were assessed through receiver operating characteristics (ROC) curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) in both training and internal test cohorts. ResultsThe D-dimer levels of AAD patients were significantly increased, as well as higher intensities at specific Raman peaks, including 505 cm−1, 842 cm−1, 947 cm−1, 1254 cm−1, 1448 cm−1, and 1655 cm−1 when compared to non-AAD patients. Conversely, decreased intensities were observed at Raman peaks such as 750 cm−1, 1004 cm−1, 1153 cm−1, 1208 cm−1, and 1514 cm−1 in AAD patients. Least absolute shrinkage and selection operator regression analysis on the training cohort identified eight potential predictors: D-dimer along with intensity measurements at peaks such as 505 cm−1, 750 cm−1, 1153 cm−1, 1208 cm−1, 1254 cm−1, 1448 cm−1, and 1655 cm−1. The combination of these eight potential predictors demonstrated a good discriminatory performance, with an area under the curve (AUC) value of 0.928 in the training cohort and an AUC of 0.936 in the internal test cohort, outperforming the use of D-dimer alone. Furthermore, DCA curve analysis revealed that leveraging this combination of eight potential predictors would provide substantial net benefits for clinical application. Moreover, this combination significantly augmented discrimination power, as evidenced by a continuous NRI of 39.8 % and IDI of 9.95 % in the training cohort, as well as a continuous NRI of 27.1 % and IDI of 9.95 % in the internal test cohort. ConclusionsThe employment of this combination of eight potential predictors effectively rules out AAD to a greater extent. This study presents a promising diagnostic strategy for early detection using optical diagnostic techniques such as Raman spectroscopy.

A novel signature of seven aging-related genes for risk stratification, prognosis prediction and benefit evaluation of chemotherapy, and immunotherapy in elderly patients with lung adenocarcinoma

BackgroundAging, a multifaceted biological process, is thought to be associated with lung adenocarcinoma (LUAD) development and progression. However, it is unclear whether aging-related genes (ARGs) can predict tumor risk, chemotherapy and immunotherapy benefits, and prognosis in LUAD patients at different ages. MethodsGene expression datasets and clinical information of LUAD patients were downloaded from TCGA and GEO database. Univariate and multivariate Cox regression, and lasso algorithm were employed to identify the ARG signatures. Patients were stratified into high-risk and low-risk groups to evaluate the predictive accuracy using Kaplan-Meier curves, ROC curves, and time-dependent AUC. A nomogram was established to predict the survival probability. GSEA revealed potential pathways, and CIBERSORT indicated different immunologic status. TIDE score was used to predict the potential tumor response to immune checkpoint inhibitors, and GDSC was employed to evaluate the sensitivity of chemotherapeutic drugs. The correlation of TIDE score and patient age, as well as that of ARGs and patient age was investigated. And cell Culture and RT-qPCR for external validation for key gene. ResultsA novel gene signature based on seven ARGs was established, including BMP15, CD79A, CDKN3, CDX2, COL1A1, DKK1, and GRIK2. Our model demonstrated exceptional prediction accuracy for elderly LUAD patients of 71–90 years old. A nomogram model was constructed to predict the survival probability, and the C-index value was 0.737, indicating our prognostic nomogram model has high accuracy. Through external RT-qPCR validation, we found that CD79A expression in H1299 was higher than that of BEAS-2B. And novel immunotherapy and chemotherapy regimens were accordingly proposed for the elderly LUAD patients. ConclusionWe identified a novel gene signature based on seven ARGs for risk stratification, prognosis prediction and benefit evaluation of immunotherapy and chemotherapy in elderly LUAD patients.

Accurate identification of lung cancer in the early phase by serum exosomal miR-1271-5p.

e20029 Background: Exosomal miRNAs are associated with the proliferation, migration, and invasion of lung cancer cells. We aim to investigate the clinical significance of serum exosomal miR-1271-5p in early-stage lung cancer (ESLC) mainly characterized by pulmonary nodules to push forward the prevention and treatment of lung cancer. Methods: The serum exosome (sEVs) miR-1271-5p was obtained from a total of 134 subjects from the patients with ESLC and the benign nodule (BLN) and healthy controls, and the expression level of miR-1271-5p were detected by RT-qPCR. Through analysis the clinical feature and CT (Computed Tomography) imaging finding of the subjects confirmed by pathology, independent risk factors for ESLC were obtained according to univariate and multivariate logistic regression analysis. R software was incorporated to build a mathematical prediction model of nomogram, and the clinical significance of sEVs-miR-1271-5p in the prediction and diagnosis of ESLC was verified by ROC curve. Results: The relative expression of sEVs-miR-1271-5p was lower in the patients with ESLC than the patients with BLN and healthy controls. The AUC value of the ESLC predicted by sEVs-miR-1271-5p was 0.9845 (95%CI: 0.9649-1.000, P &lt; 0.0001). The results of multi-factor logistic regression analysis showed that gender (OR = 0.213, p = 0.046), nodule diameter (OR = 0.794, p = 0.031), lobulation (OR = 0.013, p &lt; 0.0001), and sEVs-miR-1271-5p (OR = 0.014, p = 0.037) were independent risk factors for ESLC. Based on these independent risk factors, a prediction model for ESLC was established, with an AUC value of 0.8810 (95% CI: 0.8076-0.9553, P &lt; 0.0001), the sensitivity, specificity, positive and negative predictive value and accuracy of the model were 85%, 80.43%, 79.07%, 86.05% and 82.56% respectively. Conclusions: Serum exosomal miR-1271-5p may break through the early diagnosis of lung cancer and provide important therapeutic targets.

Development of a prediction model for lung metastasis by using postoperative circulating tumor DNA binding clinicopathological risk factors in patients with colorectal cancer.

e15035 Background: Lung metastasis is common in patients with colorectal cancer (CRC). However, diagnosing lung metastasis in CRC is challenging due to slow-growing pulmonary lesions. Circulating tumor DNA (ctDNA) testing provides a direct indication of the presence of minimal residual disease (MRD) which contributing to cancer recurrence. Yet, there are no reports on the potential indications of ctDNA for the site of metastasis. The objective of this study is to develop a predictive model for lung metastasis in CRC patients who have received adjuvant therapy by integrating ctDNA with clinicopathological factors. Methods: Two independent cohorts' series were evaluated including 299 patients from China as training cohort and 134 patients from Spain as validation cohort from 2012-2020. We specifically focused on the detection of ctDNA in these patients. We employed LASSO regression for optimal feature selection and built a predictive model by multivariable logistic regression. Results: In training cohort, 59/299(19.7%) patients have detectable ctDNA. In validation cohort, 24/134 (18.6%) patients have detectable ctDNA. Patients who tested ctDNA-positive had a significantly higher incidence of lung metastasis (33.9% vs. 11.2%, P&lt;0.001) compared to ctDNA-negative patients. A predictive model for lung metastasis was developed by multivariable logistic regression analysis. The model incorporated factors such as ctDNA status, preoperative carcinoembryonic antigen (CEA) level, presence of cancerous nodes, and the mutation status of Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA) in plasma. The lung metastasis prediction nomogram demonstrated a favorable ability to distinguish with a concordance index of 0.775 (95% CI: 0.706-0.775) during internal validation. The Hosmer-Lemeshow test, with p-value of 0.967, indicated that prediction nomogram model had a good fit. In addition, the prediction model of validation cohort demonstrated a discriminative performance with a concordance index of 0.724(95% CI: 0.698-0.724). Furthermore, the DCA (Decision Curve Analysis) and CIC (Clinical Impact Curve) in both cohorts indicated that the lung metastasis nomogram exhibited a favorable clinical net benefit. Finally, through multi-model comparison, it was found that the predictive performance of the model, which includes ctDNA and blood PIK3CA mutation status, was significantly superior to all clinical prediction models. Conclusions: A lung metastasis prediction nomogram was developed by integrating postoperative ctDNA and clinicopathological risk factors. The purpose of this nomogram is to assist clinicians in predicting the probability of lung metastasis in patients who have undergone surgery for CRC, with the aim to facilitate prompt intervention.

A novel nomogram model for lung adenocarcinoma subtypes based on RNA-modification regulatory genes

BackgroundIn non-small cell lung cancer (NSCLC), lung adenocarcinoma (LUAD) is the most common subtype. RNA modification has become the frontier and hotspot of current tumor research. ResultsIn this study, 109 genes that regulate RNA modifications were identified according to The Cancer Genome Atlas (TCGA). A differential gene expression analysis identified 46 differentially expressed RNA modification regulatory genes (DERRGs). LUAD samples were stratified into two distinct clusters based on the expression of these DERRGs. A significant correlation was observed between these clusters and patient survival rates, as well as clinical features. Furthermore, a four-DERRG signature (EIF3B, HNRNPC, IGF2BP1, and METTL3) developed using LASSO regression. According to the calculated risk scores from this signature, LUAD patients were categorized into high-risk and low-risk groups. Patients in the low-risk group exhibited a more favorable prognosis. A prognostic nomogram was crafted, integrating the four-DERRGs signature with clinical parameters. The nomogram was revealed that OS, age, clinical stage, immune cell infiltration, and immune checkpoint molecule expression were significantly linked to the OS of LUAD. GSEA analysis found that the DERRGs were primarily regulated immune pathways. ConclusionsThis study developed four DERRGs signatures and formulated a nomogram model for precise prognosis estimation in LUAD patients. The study's insights are instrumental for advancing diagnosis, prognosis, and therapeutic strategies for LUAD.

Exploration and prognostic analysis of two types of high-risk ovarian cancers: clear cell vs. serous carcinoma: a population-based study

BackgroundOvarian clear cell carcinoma (OCCC) is a rare pathological histotype in ovarian cancer, while the survival rate of advanced OCCC (Stage III-IV) is substantially lower than that of the advanced serous ovarian cancer (OSC), which is the most common histotype. The goal of this study was to identify high-risk OCCC by comparing OSC and OCCC, with investigating potential risk and prognosis markers.MethodsPatients diagnosed with ovarian cancer from 2009 to 2018 were identified from the Surveillance, Epidemiology, and End Results (SEER) Program. Logistic and Cox regression models were used to identify risk and prognostic factors in high-risk OCCC patients. Cancer-specific survival (CSS) and overall survival (OS) were assessed using Kaplan-Meier curves. Furthermore, Cox analysis was employed to build a nomogram model. The performance evaluation results were displayed using the C-index, calibration plots, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Immunohistochemically approach was used to identify the expression of the novel target (GPC3).ResultsIn the Cox analysis for advanced OCCC, age (45–65 years), tumor numbers (total number of in situ/malignant tumors for patient), T3-stage, bilateral tumors, and liver metastases could be defined as prognostic variables. Nomogram showed good predictive power and clinical practicality. Compared with OSC, liver metastases had a stronger impact on the prognosis of patients with OCCC. T3-stage, positive distant lymph nodes metastases, and lung metastases were risk factors for developing liver metastases. Chemotherapy was an independent prognostic factor for patient with advanced OCCC, but had no effect on CSS in patients with liver metastases (p = 0.0656), while surgery was significantly related with better CSS in these patients (p < 0.0001) (p = 0.0041). GPC3 expression was detected in all tissue sections, and GPC3 staining was predominantly found in the cytoplasm and membranes.ConclusionAdvanced OCCC and OCCC with liver metastases are two types of high-risk OCCC. The constructed nomogram exhibited a satisfactory survival prediction for patients with advanced OCCC. GPC3 immunohistochemistry is expected to accumulate preclinical evidence to support the inclusion of GPC3 in OCCC targeted therapy.

NFKBIA deletions reshape the epigenome antithetical to the IDH mutation and indication of prognosis in diffuse gliomas.

2083 Background: Genetic alterations help predict the clinical behavior of diffuse gliomas, but some variability remains uncorrelated. The NFKBIA gene at 14q13.2 encodes IkBa, a protein that regulates the activity of NF-kB in the cytoplasm. Evidence that, in chromatin, nuclear NFKBIA dynamically interacts with histones H2A and H4 to regulate polycomb-dependent transcriptional repression and, thus, stem cell maturation and lineage specification and our characterization of NFKBIA as a tumor suppressor in glioblastoma prompted our investigation of the relationship of NFKBIA deletions to other genetic markers, alterations in the methylome, and the clinical course of gliomas. Methods: We analyzed the genetic profiles of 2,343 WHO grade 2 to 4 diffuse gliomas in multiple clinically well-characterized patient populations, including national randomized phase III consortium trial RTOG 9802, to determine whether incorporation of NFKBIA deletions could enhance the prognostic value of current molecular descriptions. Results: We demonstrate that haploinsufficient deletions of NFKBIA display distinct patterns of occurrence in relation to other genetic markers, including driver mutations IDH, TERT, ATRX, and PTEN, 1p19q codeletion, and CDKN2A/B deletion, and are disproportionally present at recurrence. NFKBIA haploinsufficiency is associated with unfavorable patient outcomes, independent of genetic and clinicopathologic predictors. NFKBIA deletions reshape the DNA and histone methylome antipodal to the IDH mutation. The methylome changes facilitated through NFKBIA deletions engender a transcriptome landscape partly reminiscent of H3K27M mutant pediatric gliomas and are highly enriched for neural stem cell and neurogenesis genes bearing repressive H3K27 marks. Our findings imply that the NFKBIA deletion in adult gliomas and the H3K27M mutation in pediatric diffuse midline gliomas define aggressive disease subtypes that may share an epigenetic state reflecting a common PRC2 loss-of-function phenotype antipodal to the quasi PRC2 gain-of-function phenotype of IDH mutant gliomas and their favorable clinical course. Lower-grade gliomas harboring NFKBIA deletions behave much like high-grade gliomas. In IDH mutant gliomas, NFKBIA deletions are common in tumors with a clinical course similar to that of IDH wildtype tumors. A sparse, easily interpretable, and externally validated nomogram model for estimating individual patient survival in IDH mutant gliomas, incorporating NFKBIA deletion and current best-established genetic and clinicopathologic factors, confirms that NFKBIA deletions predict comparatively brief survival. Conclusions: NFKBIA haploinsufficiency aligns with distinct epigenome changes, portends a poor prognosis, and should be incorporated into clinical models predicting the disease fate of diffuse gliomas.

Construction and validation of a nomogram prediction model for the progression to septic shock in elderly patients with urosepsis

BackgroundSeptic shock is a clinical syndrome characterized by the progression of sepsis to a severe stage. Elderly patients with urosepsis in the intensive care unit (ICU) are more likely to progress to septic shock. This study aimed to establish and validate a nomogram model for predicting the risk of progression to septic shock in elderly patients with urosepsis. MethodsWe extracted data from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV dataset was split into a training set for model development and an internal validation set to assess model performance. Further external validation was performed using a distinct dataset sourced from the eICU-CRD. Predictors were screened using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. The evaluation of model performance included discrimination, calibration, and clinical usefulness. ResultsThe study demonstrated that the Glasgow Coma Scale (GCS), white blood count (WBC), platelet, blood urea nitrogen (BUN), calcium, albumin, congestive heart failure (CHF), and invasive ventilation were closely associated with septic shock in the training cohort. Nomogram prediction, utilizing eight parameters, demonstrated strong predictive accuracy with area under the curve (AUC) values of 0.809 (95 % CI 0.786–0.834), 0.794 (95 % CI 0.756–0.831), and 0.723 (95 % CI 0.647–0.801) in the training, internal validation, and external validation sets, respectively. Additionally, the nomogram demonstrated a promising calibration performance and significant clinical usefulness in both the training and validation sets. ConclusionThe constructed nomogram is a reliable and practical tool for predicting the risk of progression to septic shock in elderly patients with urosepsis. Its implementation in clinical practice may enhance the early identification of high-risk patients, facilitate timely and targeted interventions to mitigate the risk of septic shock, and improve patient outcomes.

Prognostic nomogram for overall survival in patients with stage IV non-small cell lung cancer treated with chemotherapy combined with high-dose thoracic radiotherapy.

e20607 Background: Although the targeted therapy and immunotherapy have become the first line options for selected stage IV non-small cell lung cancer (NSCLC) patients. However, there are instances where certain patients are not administered targeted therapy or immunotherapy due to a variety of factors, chemotherapy and radiotherapy remain the main way to treat such patients. This study aimed to construct a prognostic nomogram for patients with stage IV NSCLC treated with chemotherapy combined with high-dose (≥ 60 Gy) thoracic radiotherapy. Methods: The nomogram was based on a retrospective study of 335 patients with stage IV NSCLC who treated with chemotherapy combined with high-dose (≥ 60 Gy) thoracic radiotherapy were hospitalized between January 2010 and January 2018(patient did not receive TKI therapy or immunotherapy). The patients were randomly assigned to either a primary cohort (n = 234) or a validation cohort (n = 101) at a ratio of 7 : 3. In the primary cohort, a univariate analysis of the clinical or therapeutic factors that may predict OS was performed using a Cox regression model, with P &lt; 0.05 indicating statistical significance. The variables from the univariate analysis that affected OS significantly were included in the Cox regression model for the multivariate analysis. A nomogram was then created based on the results of multivariate analysis and using the “rms,” “foreign,” and “survival” R version 4.0.3 software packages. The discriminatory ability of the nomogram was measured using the area under the curve (AUC) of a receiver operating curve (ROC). Results: Multivariate logistic regression analysis revealed that malignant pericardial effusion, adrenal metastasis, elevated lactate dehydrogenase levels, and excessive planning target volume were independent risk factors for poorer 1–3 year overall survival (OS), while a higher radiotherapy dose for the primary lesion was associated with better OS. Nomogram prediction models were constructed using these five variables. The AUC of the ROC for 1-year, 2-year, and 3-year OS in the primary cohort were 0.685, 0.729, and 0.816, respectively. In the validation cohort, the AUC of the ROC for 1-year, 2-year, and 3-year OS were 0.706, 0.749, and 0.797, respectively. The Kaplan-Meier survival analysis showed that differentiation between the high-risk group and low-risk group using the prediction model had a direct, significant effect on survival in both the primary cohort ( P &lt; 0.001) and validation cohort ( P = 0.004). Conclusions: The nomogram model constructed in this study may be used to objectively predict the prognosis and OS of patients with stage IV NSCLC treated with chemotherapy and high-dose thoracic radiotherapy. Identify patients who may not benefit from high-dose thoracic radiotherapy prior to treatment to avoid overtreatment.