Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6046-6046
Author(s):  
Sik-Kwan Chan ◽  
Cheng Lin ◽  
Shao Hui Huang ◽  
Tin Ching Chau ◽  
Qiaojuan Guo ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Nasopharyngeal Carcinoma ◽  
Bone Metastases ◽  
Validation Cohort ◽  
De Novo ◽  
Multivariable Analysis ◽  
Term Survival ◽  
Training Cohort ◽  
Study Results ◽  
Multivariable Analyses

6046 Background: The eighth edition TNM (TNM-8) classified de novo metastatic (metastatic disease at presentation) nasopharyngeal carcinoma (NPC) as M1 without further subdivision. However, survival heterogeneity exists and long-term survival has been observed in a subset of this population. We hypothesize that certain metastatic characteristics could further segregate survival for de novo M1 NPC. Methods: Patients with previously untreated de novo M1 NPC prospectively treated in two academic institutions (The University of Hong Kong [n = 69] and Provincial Clinical College of Fujian Medical University [n = 114] between 2007 and 2016 were recruited and re-staged based on TNM-8 in this study. They were randomized in 2:1 ratio to generate a training cohort (n = 120) and validation cohort (n = 63) respectively. Univariable and multivariable analyses (MVA) were performed for the training cohort to identify the anatomic prognostic factors of overall survival (OS). We then performed recursive partitioning analysis (RPA) which incorporated the anatomic prognostic factors identified in multivariable analyses and derived a new set of RPA stage groups (Anatomic-RPA groups) which predicted OS in the training cohort. The significance of Anatomic-RPA groups in the training cohort was then validated in the validation cohort. UVA and MVA were performed again on the validation cohorts to identify significant OS prognosticators. Results: The training and the validation cohorts had a median follow-up of 27.2 months and 30.2 months, respectively, with the 3-year OS of 51.6% and 51.1%, respectively. Univariable analysis (UVA) and multivariable analysis (MVA) revealed that co-existing liver and bone metastases was the only factor prognostic of OS. Anatomic-RPA groups based on the anatomic prognostic factors identified in UVA and MVA yielded good segregation (M1a: no co-existing liver and bone metastases and M1b: co-existing both liver and bone metastases; median OS 39.5 and 23.7 months respectively; P =.004). RPA for the validation set also confirmed good segregation with co-existing liver and bone metastases (M1a: no co-existing liver and bone metastases and M1b: co-existing liver and bone metastases), with median OS 47.7 and 16.0 months, respectively; P =.008). It was also the only prognostic factor in UVA and MVA in the validation cohort. Conclusions: Our Anatomic-RPA M1 stage groups with anatomical factors provided better subgroup segregation for de novo M1 NPC. The study results provide a robust justification to refine M1 categories in future editions of TNM staging classification.

Prognostic nomogram for pediatric nasopharyngeal carcinoma in the intensity-modulated radiotherapy era.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18036-e18036
Author(s):  
Yizhuo Zhang ◽  
Ye Hong ◽  
Huimou Chen ◽  
Yanpeng Wu ◽  
Jia Zhu ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Nasopharyngeal Carcinoma ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Intensity Modulated Radiotherapy ◽  
Serum Lactate Dehydrogenase ◽  
Training Cohort ◽  
Intensity Modulated ◽  
Mri Features ◽  
Actual Observation

e18036 Background: There was no prognostic nomogram model exclusive for pediatric nasopharyngeal carcinoma (NPC). This study aimed to establish a nomogram, incorporating clinical characteristics and MRI features, for predicting progression-free survival (PFS) and overall survival (OS) in pediatric NPC. Methods: Children with nonmetastatic NPC treated with intensity-modulated radiotherapy (IMRT) in Sun Yat-sen University cancer center in China between 2004 to 2018 were enrolled and separated in a 3:1 ratio into a training cohort and a validation cohort. The data of the training cohort were analyzed to identify the variables independently associated with PFS and OS, and these variables were used to construct the prognostic nomogram. The predictive accuracy and discriminative capability of the nomogram were evaluated by the calibration curve and the concordance index (C-index). The nomogram was validated in the validation cohort. Results: A total of 200 patients were enrolled: 152 in the training cohort and 48 in the validation cohort. Age, serum lactate dehydrogenase level, and invasion of the clivus were the independent prognostic factors for PFS in the training cohort. The C-index of the nomogram constructed using these variables was 0.86. The calibration curve showed consistency between the nomogram prediction and the actual observation for 5-, 6- and 7-year PFS. In the validation cohort, the C-index of the nomogram was 0.64, and the calibration curve showed good consistency between the nomogram and actual observation for 5-, 6- and 7-year PFS. For OS, no independent prognostic factors were identified. Conclusions: The prognostic nomogram could be a useful tool for predicting PFS in pediatric NPC patients treated with IMRT.

scholarly journals Development and validation of a prognostic nomogram for the pre-treatment prediction of early metachronous metastasis in endemic nasopharyngeal carcinoma: a big-data intelligence platform-based analysis

2020 ◽  
Vol 12 ◽  
pp. 175883592097813
Author(s):  
Lu-Lu Zhang ◽  
Fei Xu ◽  
Wen-Ting He ◽  
Meng-Yao Huang ◽  
Di Song ◽  
...  
Keyword(s):  
Big Data ◽  
Nasopharyngeal Carcinoma ◽  
Validation Cohort ◽  
Risk Groups ◽  
Discriminative Performance ◽  
Term Survival ◽  
Training Cohort ◽  
Metachronous Metastasis ◽  
Calibration Curves ◽  
Pre Treatment

Background: Early failure of cancer treatment generally indicates a poor prognosis. Here, we aim to develop and validate a pre-treatment nomogram to predict early metachronous metastasis (EMM) in nasopharyngeal carcinoma (NPC). Methods: From 2009 to 2015, a total of 9461 patients with NPC (training cohort: n = 7096; validation cohort: n = 2365) were identified from an institutional big-data research platform. EMM was defined as time to metastasis within 2 years after treatment. Early metachronous distant metastasis-free survival (EM-DMFS) was the primary endpoint. A nomogram was established with the significant prognostic factors for EM-DMFS determined by multivariate Cox regression analyses in the training cohort. The Harrell Concordance Index (C-index), area under the receiver operator characteristic curve (AUC), and calibration curves were applied to evaluate this model. Results: EMM account for 73.5% of the total metachronous metastasis rate and is associated with poor long-term survival in NPC. The final nomogram, which included six clinical variables, achieved satisfactory discriminative performance and significantly outperformed the traditional tumor–node–metastasis (TNM) classification for predicting EM-DMFS: C-index: 0.721 versus 0.638, p < 0.001; AUC: 0.730 versus 0.644, p < 0.001. The calibration curves showed excellent agreement between the predicted and actual EM-DMFS. The nomogram can stratify patients into three risk groups with distinct EM-DMFS (2-year DMFS: 96.8% versus 90.1% versus 80.3%, p < 0.001). A validation cohort supported the results. The three identified risk groups are correlated with the efficacy of different treatment regimens. Conclusion: Our established nomogram can reliably predict EMM in patients with NPC and might aid in formulating risk-adapted treatment decisions and personalized patient follow-up strategies.

Early Bone Metastases are Associated with Worse Outcomes in Metastatic Urothelial Carcinoma

2021 ◽  
pp. 1-10
Author(s):  
Ariel A. Nelson ◽  
Robert J. Cronk ◽  
Emily A. Lemke ◽  
Aniko Szabo ◽  
Ali R. Khaki ◽  
...  
Keyword(s):  
Bone Metastases ◽  
Urothelial Carcinoma ◽  
Clinical Outcomes ◽  
De Novo ◽  
Opioid Analgesic ◽  
Multivariable Analysis ◽  
Unmet Need ◽  
Risk Of Death ◽  
Platinum Based Chemotherapy ◽  
Metastatic Urothelial Carcinoma

BACKGROUND: Outcomes of patients with metastatic urothelial carcinoma (mUC) with early bone metastases (eBM) vs no early bone metastases (nBM) have not thoroughly been described in the age of immuno-oncology. OBJECTIVE: To compare survival and other clinical outcomes in patients with eBM and nBM. METHODS: We used a multi-institutional database of patients with mUC treated with systemic therapy. Demographic, metastatic site, treatment patterns, and clinical outcomes were recorded. Wilcoxon rank-sum, chi-square tests were performed. Survival was estimated by Kaplan-Meier method; multivariable Cox analysis was performed. RESULTS: We identified 270 pts, 67%men, mean age 69±11 years. At metastatic diagnosis, 27%had≥1 eBM and were more likely to have de novo vs. recurrent metastases (42%vs 19%, p <  0.001). Patients with eBM had shorter overall survival (OS) vs. those with nBM, (6.1 vs 13.7 months, p <  0.0001). On multivariable analysis, eBM independently associated with higher risk of death, HR = 2.52 (95%CI: 1.75–3.63, p <  0.0001). OS was shorter for patients with eBM who received initial immune checkpoint inhibitor vs platinum-based chemotherapy, (1.6 vs 9.1 months, p = 0.02). Patients with eBM received higher opioid analgesic doses compared to patients with nBM and received quantitatively more palliative radiation. CONCLUSIONS: Patients with mUC and eBM have poorer outcomes, may benefit less from anti-PD-1/PD-L1 therapy and represent an unmet need for novel therapeutic interventions. Dedicated clinical trials, biomarker validation to assist in patient selection, as well as consensus on reporting of non-measurable disease are required.

Construction and Validation of a Nomogram for Predicting Progression-Free Survival in Patients with Early-Stage Testicular Germ Cell Tumor

2021 ◽  
Vol 16 ◽  
Author(s):  
Jin-Guo Chen ◽  
Jing-Quan Wang ◽  
Tian-Wen Peng ◽  
Zhe-Sheng Chen ◽  
Shan-Chao Zhao
Keyword(s):  
Prognostic Factors ◽  
Validation Cohort ◽  
Early Stage ◽  
Predictive Ability ◽  
Testicular Germ Cell Tumor ◽  
Progression Free Survival ◽  
Cell Tumor ◽  
Testicular Germ Cell ◽  
Free Survival ◽  
Training Cohort

Background: Testicular Germ Cell Tumor (TGCT) is the most common malignant tumor in young men, but there is a lack of prediction model to evaluate prognosis of patients with TGCT. Objective: To explore the prognostic factors for Progression-Free Survival (PFS) and construct a nomogram model for patients with early-stage TGCT after radical orchiectomy. Methods: Patients with TGCT from The Cancer Genome Atlas (TCGA) database were used as the training cohort; univariate and multivariate cox analysis were performed. A nomogram was constructed based on the independent prognostic factors. Patients from the Nanfang Hospital affiliated with Southern Medical University were used as the cohort to validate the predictive ability using the nomogram model. Harrell's concordance index (C-index) and calibration plots were used to evaluate the nomogram. Results: A total of 110 and 62 patients with TGCT were included in training cohort and validation cohort, respectively. Lymphatic Vascular Invasion (LVI), American Joint Committee on Cancer (AJCC) stage and adjuvant therapy were independent prognostic factors in multivariate regression analyses and were included to establish a nomogram. The C-index in the training cohort for 1-, 3-, and 5-year PFS were 0.768, 0.74 and 0.689, respectively. While the C-index for 1-, 3-, and 5-year PFS in the external validation cohort were 0.853, 0.663 and 0.609, respectively. The calibration plots for 1-, 3-, and 5-year PFS in the training and validation cohort showed satisfactory consistency between predicted and actual outcomes. The nomogram revealed a better predictive ability for PFS than AJCC staging system. Conclusion: The nomogram as a simple and visual tool to predict individual PFS in patients with TGCT could guide clinicians and clinical pharmacists in therapeutic strategy.

scholarly journals Prognostic Nutritional Index as an independent prognostic factor in locoregionally advanced squamous cell head and neck cancer

ESMO Open ◽  
2018 ◽  
Vol 3 (6) ◽  
pp. e000425 ◽  
Author(s):  
Gema Bruixola ◽  
Javier Caballero ◽  
Federica Papaccio ◽  
Angelica Petrillo ◽  
Aina Iranzo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Prognostic Factors ◽  
Prognostic Factor ◽  
Head And Neck ◽  
Squamous Cell ◽  
Validation Cohort ◽  
Prognostic Nutritional Index ◽  
Training Cohort ◽  
Lymphocyte Ratio ◽  
Nutritional Index

BackgroundLocally advanced head and neck squamous cell carcinoma (LAHNSCC) is a heterogeneous disease in which better predictive and prognostic factors are needed. Apart from TNM stage, both systemic inflammation and poor nutritional status have a negative impact on survival.MethodsWe retrospectively analysed two independent cohorts of a total of 145 patients with LAHNSCC treated with induction chemotherapy followed by concurrent chemoradiotherapy at two different academic institutions. Full clinical data, including the Prognostic Nutritional Index (PNI), neutrophil to lymphocyte ratio and derived neutrophil to lymphocyte ratio, were analysed in a training cohort of 50 patients. Receiver operating characteristic curve analysis was used to establish optimal cut-off. Univariate and multivariate analyses of prognostic factors for overall survival (OS) were performed. Independent predictors of OS identified in multivariate analysis were confirmed in a validation cohort of 95 patients.ResultsIn the univariate analysis, low PNI (PNI<45) (p=0.001), large primary tumour (T4) (p=0.044) and advanced lymph node disease (N2b-N3) (p=0.025) were significantly associated with poorer OS in the validation cohort. The independent prognostic factors in the multivariate analysis for OS identified in the training cohort were dRNL (p=0.030) and PNI (p=0.042). In the validation cohort, only the PNI remained as independent prognostic factor (p=0.007).ConclusionsPNI is a readily available, independent prognostic biomarker for OS in LAHNSCC. Adding PNI to tumour staging could improve individual risk stratification of patients with LAHNSCC in future clinical trials.

scholarly journals Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xianghong Zhou ◽  
Shi Qiu ◽  
Kun Jin ◽  
Qiming Yuan ◽  
Di Jin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Radical Prostatectomy ◽  
Risk Stratification ◽  
Marital Status ◽  
Cumulative Incidence ◽  
Validation Cohort ◽  
Cumulative Incidence Function ◽  
Competing Risk ◽  
Training Cohort

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.

scholarly journals Proteomics Analysis Identified ASNS as a Novel Biomarker for Predicting Recurrence of Skull Base Chordoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yutao Shen ◽  
Mingxuan Li ◽  
Yujia Xiong ◽  
Songbai Gui ◽  
Jiwei Bai ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Prognostic Factor ◽  
Skull Base ◽  
Validation Cohort ◽  
High Expression ◽  
Migration And Invasion ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
Skull Base Chordoma

BackgroundThe prognostic factors of skull base chordoma associated with outcomes of patients after surgery remain inadequately identified. This study was designed to identify a novel prognostic factor for patients with skull base chordoma.MethodUsing a proteomic technique, the tumor biomarkers that were upregulated in the rapid-recurrence group of chordoma were screened and then narrowed down by bioinformatic analysis. Finally one potential biomarker was chosen for validation by immunohistochemistry using tissue microarray (TMA). A total of 187 patients included in TMA were randomly divided into two cohorts, the training cohort included 93 patients and the validation cohort included 94 patients. Kaplan-Meier survival analysis was used to assess the patients’ survival. Univariable and multivariable Cox regression analysis were used to identify prognostic factors predicting recurrence-free survival (RFS). CCK-8 assay, clonal formation assay and transwell assay were used to test the effect of asparagine synthetase (ASNS) on the proliferation, migration and invasion in chordoma cell lines.ResultsAmong 146 upregulated proteins, ASNS was chosen as a potential prognostic biomarker after bioinformatics analysis. The H-scores of ASNS ranged from 106.27 to 239.58 in TMA. High expression of ASNS was correlated with shorter RFS in both the training cohort (p = 0.0093) and validation cohort (p &lt; 0.001). Knockdown of ASNS by small interfering RNA (siRNA) inhibited the growth, colony formation, migration and invasion of chordoma cells in vitro.ConclusionThis study indicates that high expression of ASNS is correlated with poor prognosis of patients with skull base chordoma. ASNS may be a useful prognostic factor for patients with skull base chordoma.

scholarly journals Adenocarcinoma with mixed subtypes in the early and advanced gastric cancer.

2021 ◽  
Author(s):  
Xixian Zhao ◽  
Yizhang Li ◽  
Zhenwei Yang ◽  
Hailin Zhang ◽  
Hongling Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Advanced Gastric Cancer ◽  
Validation Cohort ◽  
Signet Ring Cell Carcinoma ◽  
Signet Ring Cell ◽  
Cancer Specific Survival ◽  
Term Survival ◽  
Training Cohort ◽  
Clinical Benefits ◽  
Similar Survival

Abstract Background Based on the WHO classification, adenocarcinoma with mixed subtypes (AM) is a histological classification. We aimed to compare the prognosis among AM, classic adenocarcinoma (CA), mucinous adenocarcinoma (MAC), and signet-ring cell carcinoma (SRCC) in early and advanced gastric cancer (EGC, AGC), respectively. Methods We compared the clinicopathologic features and prognosis between AM and other histologic subtypes of CA, SRCC and MAC in ECG and ACG, respectively. A nomogram was established to predict the cancer-specific survival (CSS) of gastric cancer (GC) patients with AM. C-index, calibration curves, Receiver Operating Characteristic (ROC) and Decision Curve Analysis (DCA) curves were applied to examine the accuracy and clinical benefits. Results In the prognosis among these four histological subtypes in EGC patients, there are no differences. For AGC patients, AM had a significantly poorer prognosis compared with CA and MAC (P = 0.003, 0.029), but similar prognosis to SRCC. A nomogram based on race, T stage, N stage, M stage and surgical modalities were proposed to predict 1- and 3- year CSS for GC patients with AM (C-index: training cohort: 0.804, validation cohort: 0.748. 1-, 3-year CSS AUC: training cohort: 0.871, 0.914, validation cohort: 0.810, 0.798). 1- and 3-year CSS DCA curves showed good net benefits. Conclusions EGC patients with AM had similar survival to those with CA, MAC and SRCC. AM was an independent predictor of poor prognosis in AGC. A nomogram for predicting the prognosis of GC patients with AM was proposed to quantitatively assess the long-term survival.

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

2021 ◽  
Vol 8 ◽  
Author(s):  
Haisheng You ◽  
Mengmeng Teng ◽  
Chun Xia Gao ◽  
Bo Yang ◽  
Sasa Hu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prognostic Factors ◽  
Elderly Patients ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Validation Cohort ◽  
Discrimination Performance ◽  
Seer Database ◽  
Histologic Subtype ◽  
Training Cohort

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

scholarly journals An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage

2021 ◽  
Vol 12 ◽  
Author(s):  
Ping Hu ◽  
Yang Xu ◽  
Yangfan Liu ◽  
Yuntao Li ◽  
Liguo Ye ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prognostic Factors ◽  
Subarachnoid Hemorrhage ◽  
Validation Cohort ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
External Validation ◽  
Brier Score ◽  
Web Based ◽  
Training Cohort ◽  
Multivariable Logistic Regression

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH.Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer–Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram.Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer–Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities.Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

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