Development and validation of novel nomograms for predicting the survival of patients after surgical resection of pancreatic ductal adenocarcinoma

A nomogram proposal for early prediction of intensive care unit admission in patients with acute antipsychotic poisoning

Background

Early identification of antipsychotic poisoned patients who may have a potential risk for intensive care unit (ICU) admission is crucial especially when resources are limited. Nomograms were previously used as a practical tool to predict prognosis and planning the treatment of some diseases including some poisoning conditions. However, they were not previously investigated in antipsychotic poisoning.

Aim

The current study aimed to construct a nomogram to predict the need for ICU admission in acute antipsychotic poisoning. Patients and methods: This 2-year study included 140 patients acutely intoxicated with antipsychotics and admitted at Tanta University Poison Control Centre throughout July 2019 to June 2021. Personal and toxicological data, findings of clinical examination and electrocardiography, as well as, results of laboratory investigations at time of admission were recorded. According to the outcome, patients were divided into ICU-admitted and ICU-not admitted groups.

Results

The results of this study provided a proposed nomogram that included five significant independent predictors for ICU admission in acute antipsychotic intoxications; the presence of seizures (OR: 31132.26[108.97-Inf]), corrected QT interval (OR: 1.04[1.01-1.09]), mean arterial blood pressure (OR: 0.83[0.70-0.92]), oxygen saturation (OR: 0.62[0.40 to 0.83)], and Glasgow Coma Scale (OR: 0.25 [0.06-0.56]).

Conclusion

It could be concluded that the developed nomogram is a promising tool for easy and rapid decision making to predict the need for ICU admission in acute antipsychotic poisoning.

A novel nomogram for predicting overall survival in peripheral T cell lymphoma patients.. [DOI: 10.21203/rs.3.rs-2823604/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background The prognosis of peripheral T cell lymphomas (PTCLs) varies greatly. This study aimed at generating a prognostic nomogram based on differentially expressed genes (DEGs).Methods Firstly, we collected RNA transcripts from Gene Expression Omnibus and identified DEGs. Secondly we used univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) to screen the independent risk factors to construct nomogram in the training cohort. Thirdly, we evaluate its prediction accuracy via decision curves analysis (DCA), receiver operating characteristic (ROC) and calibration rate to confirm its performance on survival in training and validation cohort. Then we carried out subgroup analysis in training and validation to eliminate the effects of age, gender, and pathological subtype. Lastly, to verify feasibility of nomogram in practice, we applied immunohistochemistry to clinical samples and analyzed the relationship between IHC scores and prognosis.Results The 702 DEGs between 40 PTCLs and 20 non-tumor patients were identified. Then ANGPTL2, CPSF4, CLIC4 and OTUD6B were screened out as independent risk factors via univariate Cox regression and LASSO. The DCA, ROC, Harrell’s concordance index (c-index) and calibration rate showed nomogram predicting more accurately than any single specific transcript. The results showed PTCLs with higher nomogram-score had a longer survival, regardless of age, gender and pathological subtype. Finally, the high expression level of ANGPTL2, CPSF4 and OTUD6B related to poor prognosis. Higher expression of CLIC4 related to longer survival.Conclusion This nomogram showed the favorable clinical applicability, regardless of age, gender and pathological subtype.

Robotic pancreaticoduodenectomy for pancreatic ductal adenocarcinoma: Analysis of surgical outcomes and long-term prognosis in a high-volume center

BACKGROUND

Robotic pancreaticoduodenectomy (RPD) has been reported to be safe and feasible for patients with pancreatic ductal adenocarcinoma (PDAC) of the pancreatic head. This study aimed to analyze the surgical outcomes and risk factors for poor long-term prognosis of these patients.

METHODS

Data from patients who underwent RPD for PDAC of pancreatic head were retrospectively analyzed. Multivariate Cox regression analysis was used to seek the independent prognostic factors for overall survival (OS), and an online nomogram calculator was developed based on the independent prognostic factors.

RESULTS

Of the 273 patients who met the inclusion criteria, the median operative time was 280.0 minutes, the estimated blood loss was 100.0 mL, the median OS was 23.6 months, and the median recurrence-free survival (RFS) was 14.4 months. Multivariate analysis showed that preoperative carbohydrate antigen 19-9 (CA19-9) [hazard ratio (HR) = 2.607, 95% confidence interval (CI): 1.560-4.354, P < 0.001], lymph node metastasis (HR = 1.429, 95% CI: 1.005-2.034, P = 0.047), tumor moderately (HR = 3.190, 95% CI: 1.813-5.614, P < 0.001) or poorly differentiated (HR = 5.114, 95% CI: 2.839-9.212, P < 0.001), and Clavien-Dindo grade ≥ III (HR = 1.657, 95% CI: 1.079-2.546, P = 0.021) were independent prognostic factors for OS. The concordance index (C-index) of the nomogram constructed based on the above four independent prognostic factors was 0.685 (95% CI: 0.640-0.729), which was significantly higher than that of the AJCC staging (8th edition): 0.541 (95% CI: 0.493-0.589) (P < 0.001).

CONCLUSIONS

This large-scale study indicated that RPD was feasible for PDAC of pancreatic head. Preoperative CA19-9, lymph node metastasis, tumor poorly differentiated, and Clavien-Dindo grade ≥ III were independent prognostic factors for OS. The online nomogram calculator could predict the OS of these patients in a simple and convenient manner.

Survival analysis and individualized prediction of survival benefit for pancreatic signet ring cell carcinoma: a population study based on the SEER database

OBJECTIVES

This study aimed to compare the incidence, clinicopathological characteristics and survival results of pancreatic signet ring cell carcinoma (PSRCC) and pancreatic adenocarcinomas (PDAC), as well as to analyze the clinical characteristics related to the overall survival (OS) of PSRCC, and to establish an effective prognostic nomogram to predict the risks associated with patient outcomes.

METHODS

A total of 85,288 eligible patients including 425 PSRCC and 84,863 PDAC cases were retrieved from the Surveillance, Epidemiology, and End Results database. The survival curve was calculated using the Kaplan-Meier method and differences in them were measured by Log-rank tests. The Cox proportional hazards regression model was used to identify independent predictors of OS in patients with PSRCC. A nomogram was constructed to predict 1-, 3-, and 5-year OS. The performance of the nomogram was measured by C-index, receiver operating characteristic (ROC) curve, decision curve analysis (DCA).

RESULTS

The incidence of PSRCC is much lower than that of PDAC (10.798 V.S. 0.349 per millions). PSRCC is an independent predictor of pancreatic cancer with a poorer histological grade, a higher rate of lymph node and distant metastasis, and a poorer prognosis. We identified four independent prognostic factors including grade, American Joint Committee on Cancer Tumor-Node-Metastasis (TNM) stage, surgery and chemotherapy based on the Cox regression model. The C-index and DCA curves showed better performance of the nomogram than TNM stage. ROC curve analysis also showed that the nomogram had good discrimination, with an area under the curve of 0.840, 0.896, and 0.923 for 1-, 3-, and 5-year survival. The calibration curves showed good agreement between the prediction by the nomogram and actual observations.

CONCLUSION

PSRCC is a rare but fatal subtype of pancreatic cancer. The constructed nomogram in this study accurately predicted the prognosis of PSRCC, performed better than the TNM stage.

Risk factors, prognostic predictors, and nomograms for pancreatic cancer patients with initially diagnosed synchronous liver metastasis

BACKGROUND

Liver metastasis (LM) remains a major cause of cancer-related death in patients with pancreatic cancer (PC) and is associated with a poor prognosis. Therefore, identifying the risk and prognostic factors in PC patients with LM (PCLM) is essential as it may aid in providing timely medical interventions to improve the prognosis of these patients. However, there are limited data on risk and prognostic factors in PCLM patients.

AIM

To investigate the risk and prognostic factors of PCLM and develop corresponding diagnostic and prognostic nomograms.

METHODS

Patients with primary PC diagnosed between 2010 and 2015 were reviewed from the Surveillance, Epidemiology, and Results Database. Risk factors were identified using multivariate logistic regression analysis to develop the diagnostic mode. The least absolute shrinkage and selection operator Cox regression model was used to determine the prognostic factors needed to develop the prognostic model. The performance of the two nomogram models was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and risk subgroup classification. The Kaplan-Meier method with a log-rank test was used for survival analysis.

RESULTS

We enrolled 33459 patients with PC in this study. Of them, 11458 (34.2%) patients had LM at initial diagnosis. Age at diagnosis, primary site, lymph node metastasis, pathological type, tumor size, and pathological grade were identified as independent risk factors for LM in patients with PC. Age > 70 years, adenocarcinoma, poor or anaplastic differentiation, lung metastases, no surgery, and no chemotherapy were the independently associated risk factors for poor prognosis in patients with PCLM. The C- index of diagnostic and prognostic nomograms were 0.731 and 0.753, respectively. The two nomograms could accurately predict the occurrence and prognosis of patients with PCLM based on the observed analysis results of ROC curves, calibration plots, and DCA curves. The prognostic nomogram could stratify patients into prognostic groups and perform well in internal validation.

CONCLUSION

Our study identified the risk and prognostic factors in patients with PCLM and developed corresponding diagnostic and prognostic nomograms to help clinicians in subsequent clinical evaluation and intervention. External validation is required to confirm these results.

A Prognostic Model Based on Nutritional Risk Index in Operative Breast Cancer

Background: The nutritional risk index (NRI) is an independent prognostic factor for overall survival in various cancers, but its prognostic value in breast cancer remains unclear. This study aimed to explore the relationship between the NRI and overall survival (OS) in breast cancer and to develop a predictive nomogram. Methods: We retrospectively enrolled 1347 breast cancer patients who underwent mastectomy or lumpectomy between January 2011 and November 2012. Using a cutoff value of 110.59, patients were divided into a high-NRI group and a low-NRI group. OS was compared between the two groups. Clinicopathological factors independently associated with survival were used to construct a predictive nomogram. Results: Of the 1347 patients, 534 patients were classified as high NRI and 813 as low NRI. OS was significantly shorter in low-NRI patients. The 3- and 5-year OS rates were 87.3% and 73.4%, respectively, in the high-NRI group whereas they were 83.0% and 67.2%, respectively, in the low-NRI group. Cox regression analysis found that histopathological type, tumor size, lymph node status, progesterone receptor (PR) status, Ki-67, and NRI were independently associated with OS. Conclusions: NRI is an independent prognostic factor of OS in breast cancer patients. The proposed nomogram model may be a useful tool for individualized survival prediction.

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

Survival analysis is a branch of statistics that deals with both the tracking of time and the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarize the survival time and perform a classification analysis.

Here, we develop a benchmarking design, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics data sets. SurvBenchmark not only focuses on classical approaches such as the Cox model but also evaluates state-of-the-art machine learning survival models. All approaches were assessed using multiple performance metrics; these include model predictability, stability, flexibility, and computational issues. Our systematic comparison design with 320 comparisons (20 methods over 16 data sets) shows that the performances of survival models vary in practice over real-world data sets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies.

A nomogram for predicting paradoxical immune reconstitution inflammatory syndrome associated with cryptococcal meningitis among HIV-infected individuals in China

Background

Cryptococcal meningitis (CM) associated immune reconstitution inflammatory syndrome (CM-IRIS) is the second most common complication in HIV-infected individuals with cryptococcal meningitis, with a reported mortality rate ranging from 8 to 30%. Given the devastating consequences of CM-IRIS related intracranial neuroinflammation and its challenging in diagnosis, we conducted a study to explore the risk factors and the occurrence of paradoxical CM-IRIS in HIV-infected patients, which is of great value for prevention and clinical management.

Methods

We conducted a retrospective cohort study to identify the indicators associated with paradoxical CM-IRIS among 86 HIV-infected patients with CM using univariate and multivariate cox analysis. A nomogram was constructed using selected variables to evaluate the occurrence of paradoxical CM-IRIS at 6 months and 12 months after ART initiation. The discrimination and calibration of the nomogram were assessed by concordance index (C-index) and calibration plots. Decision curves analysis (DCA) were used to evaluate clinical effectiveness of the nomogram. Subsequently, to help clinicians recognize patients at high risk faster, patients were divided into high-risk and low-risk groups according to the best cutoff point identified by X-tile.

Results

Of 86 AIDS patients with CM, 22.1% experienced paradoxical CM-IRIS at a median of 32 days after antiretroviral therapy (ART) initiation. The occurrence of paradoxical CM-IRIS was associated with age, ART initiation within 4 weeks of antifungal treatment, a four-fold increase in CD4 T cell counts, C-reactive protein levels, and hemoglobin levels independently. These five variables were further used to construct a predictive nomogram. The C-index (0.876) showed the favorable discriminative ability of the nomogram. The calibration plot revealed a high consistency between the predicted and actual observations. DCA showed that the nomogram was clinically useful. Risk stratification based on the total score of the nomogram showed well-differentiated in the high-risk and low-risk groups. Clinicians should pay attention to patients with total points high than 273.

Conclusions

We identified the predictive factors of paradoxical CM-IRIS and constructed a nomogram to evaluate the occurrence of paradoxical CM-IRIS in 6 months and 12 months. The nomogram represents satisfactory performance and might be applied clinically to the screening and management of high-risk patients.

Effects of Total Pancreatectomy on Survival of Patients With Pancreatic Ductal Adenocarcinoma: A Population-Based Study

Background: Total pancreatectomy (TP) seems to be experiencing a renaissance in recent years. In this study, we aimed to determine the long-term survival of pancreatic ductal adenocarcinoma (PDAC) patients who underwent TP by comparing with pancreaticoduodenectomy (PD), and formulate a nomogram to predict overall survival (OS) for PDAC individuals following TP.

Methods: Patients who were diagnosed with PDAC and received PD (n = 5,619) or TP (n = 1,248) between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. OS and cancer-specific survival (CSS) of the PD and TP groups were compared using Kaplan-Meier method and log-rank test. Furthermore, Patients receiving TP were randomly divided into the training and validation cohorts. Univariate and multivariate Cox regression were applied to identify the independent factors affecting OS to construct the nomogram. The performance of the nomogram was measured according to concordance index (C-index), calibration plots, and decision curve analysis (DCA).

Results: There were no significant differences in OS and CSS between TP and PD groups. Age, differentiation, AJCC T stage, radiotherapy, chemotherapy, and lymph node ratio (LNR) were identified as independent prognostic indicators to construct the nomogram. The C-indexes were 0.67 and 0.69 in the training and validation cohorts, while 0.59 and 0.60 of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system. The calibration curves showed good uniformity between the nomogram prediction and actual observation. DCA curves indicated the nomogram was preferable to the AJCC staging system in terms of the clinical utility. A new risk stratification system was constructed which could distinguish patients with different survival risks.

Conclusions: For PDAC patients following TP, the OS and CSS are similar to those who following PD. We developed a practical nomogram to predict the prognosis of PDAC patients treated with TP, which showed superiority over the conventional AJCC staging system.

Prognostic Nomogram for Patients With Pancreatic Ductal Adenocarcinoma of Pancreatic Head After Pancreaticoduodenectomy

Background:

The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) of pancreatic head remains poor, even after potentially curative R0 resection. The aim of this study was to develop an accurate model to predict patients’ prognosis for PDAC of pancreatic head following pancreaticoduodenectomy.

Methods:

We retrospectively reviewed 112 patients with PDAC of pancreatic head after pancreaticoduodenectomy in Guangdong Provincial People’s Hospital between 2014 and 2018.

Results:

Five prognostic factors were identified using univariate Cox regression analysis, including age, histologic grade, American Joint Committee on Cancer (AJCC) Stage 8th, total bilirubin (TBIL), CA19-9. Using all subset analysis and multivariate Cox regression analysis, we developed a nomogram consisted of age, AJCC Stage 8th, perineural invasion, TBIL, and CA19-9, which had higher C-indexes for OS (0.73) and RFS (0.69) compared with AJCC Stage 8th alone (OS: 0.66; RFS: 0.67). The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve for the nomogram for OS and RFS were significantly higher than other single parameter, which are AJCC Stage 8th, age, perineural invasion, TBIL, and CA19-9. Importantly, our nomogram displayed higher C-index for OS than previous reported models, indicating a better predictive value of our model.

Conclusions:

A simple and practical nomogram for patient prognosis in PDAC of pancreatic head following pancreaticoduodenectomy was established, which shows satisfactory predictive efficacy and deserves further evaluation in the future.

Construction and Validation of Novel Nomograms for Predicting Prognosis of Pancreatic Ductal Adenocarcinoma After Surgery According to Different Primary Cancer Locations

Background/Aims

Pancreatic ductal adenocarcinoma (PDAC) can occur in different parts of the pancreas. This study aimed to identify clinicopathological characteristics independently correlated with the prognosis of PDAC of the pancreatic head/uncinate (PHC) or body-tail (PBTC), and to develop novel nomograms for predicting cancer-specific survival (CSS) according to different primary cancer locations.

Methods

1160 PDAC patients were retrospectively enrolled and assigned to training and test sets with each set divided into PHC and PBTC groups. Comparative analysis of clinicopathologic characteristics, survival analysis, and multivariate analysis were performed. Independent factors were identified and used for constructing nomograms. The performance of the nomograms was validated in the test set.

Results

Primary tumor location was an independent risk factor for prognosis of PDAC after surgery. Specially, gender, fasting blood glucose, and preoperative cancer antigen 19-9 were significantly associated with prognosis of PHC, whereas age, body mass index, and lymph nodes were significantly correlated with the prognosis of PBTC. A significant difference in prognosis was found between PHC and PBTC in stage Ia and stage III. Three nomograms were established for predicting the prognosis for PDAC, PHC, and PBTC. Notably, these nomograms were calibrated modestly (c-indexes of 0.690 for PDAC, 0.669 for PHC, and 0.704 for PBTC), presented better accuracy and reliability than the 8^th AJCC staging system, and achieved clinical validity.

Conclusions

PHC and PBTC share the differential clinical-pathological characteristics and survival. The nomograms show good performance for predicting prognosis in PHC and PBTC. Therefore, these nomograms hold potential as novel approaches for predicting survival of PHC and PBTC patients after surgery.

Development and Validation of a Nomogram for Assessing Survival in Patients With COVID-19 Pneumonia

Background

The outbreak of coronavirus disease (COVID-19) in 2019 has spread worldwide and continues to cause great threat to peoples’ health as well as put pressure on the accessibility of medical systems. Early prediction of survival of hospitalized patients will help the clinical management of COVID-19, but such a prediction model which is reliable and valid is still lacking.

Methods

We retrospectively enrolled 628 confirmed cases of COVID-19 using positive RT-PCR tests for SARS-CoV-2 in Tongji Hospital in Wuhan, China. These patients were randomly grouped into a training cohort (60%) and a validation cohort (40%). In the training cohort, least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis were utilized to identify prognostic factors for in-hospital survival of patients with COVID-19. A nomogram based on the three variables was built for clinical use. Areas under the ROC curves (AUC), concordance index (C-index) and calibration curve were used to evaluate the efficiency of the nomogram in both the training and validation cohorts.

Results

Hypertension, higher neutrophil-to-lymphocyte ratio and increased NT-proBNP value were found to be significantly associated with poorer prognosis in hospitalized patients with COVID-19. The three predictors were further used to build a prediction nomogram. The C-index of the nomogram in the training and validation cohorts was 0.901 and 0.892, respectively. The AUC in the training cohort was 0.922 for 14- day and 0.919 for 21-day probability of in-hospital survival, while in the validation cohort was 0.922 and 0.881, respectively. Moreover, the calibration curve for 14- day and 21-day survival also showed high coherence between the predicted and actual probability of survival.

Conclusion

We managed to build a predictive model and constructed a nomogram for predicting in-hospital survival of patients with COVID-19. This model represents good performance and might be utilized clinically in the management of COVID-19.

Development and validation of novel nomograms for predicting the survival of patients after surgical resection of pancreatic ductal adenocarcinoma

Background/Aims

Pancreatic ductal adenocarcinoma (PDAC) is associated with high mortality, even after surgical resection. The existing predictive models for survival have limitations. This study aimed to develop better nomograms for predicting overall survival (OS) and cancer‐specific survival (CSS) in PDAC patients after surgery.

Methods

A total of 6323 PDAC patients were retrospectively recruited from the Surveillance, Epidemiology, and End Results (SEER) database and randomly allocated into training, validation, and test cohorts. Multivariate Cox regression analysis was conducted to identify significant independent factors for OS and CSS, which were used for construction of nomograms. The performance was evaluated, validated, and compared with that of the 8th edition AJCC staging system.

Results

Ten independent factors were significantly correlated with OS and CSS. The 1‐, 3‐, and 5‐year OS rates were 40%, 20%, and 15%, and 1‐, 3‐, and 5‐year CSS rates were 45%, 24%, and 19%, respectively. The nomograms were calibrated well, with c‐indexes of 0.640 for OS and 0.643 for CSS, respectively. Notably, relative to the 8th edition AJCC staging system, the nomograms were able to stratify each AJCC stage into three prognostic subgroups for more robust risk stratification. Furthermore, the nomograms achieved significant clinical validity, exhibiting wide threshold probabilities and high net benefit. Performance assessment also showed high predictive accuracy and reliability.

Conclusions

The predictive ability and reliability of the established nomograms have been validated, and therefore, these nomograms hold potential as novel approaches to predicting survival and assessing survival risks for PDAC patients after surgery.