Development and Validation of a Simple-to-Use Nomogram to Predict Early Death in Metastatic Pancreatic Adenocarcinoma

BackgroundPancreatic adenocarcinoma (PCa) is a highly aggressive malignancy with high risk of early death (survival time ≤3 months). The present study aimed to identify associated risk factors and develop a simple-to-use nomogram to predict early death in metastatic PCa patients.MethodsPatients diagnosed with metastatic PCa between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were collected for model construction and internal validation. An independent data set was obtained from China for external validation. Independent risk variables contributed to early death were identified by logistic regression models, which were then used to construct a nomogram. Internal and external validation was performed to evaluate the nomogram using calibration curves and the receiver operating characteristic curves.ResultsA total of 19,464 patients in the SEER cohort and 67 patients in the Chinese cohort were included. Patients from the SEER database were randomly divided into the training cohort (n = 13,040) and internal validation cohort (n = 6,424). Patients in the Chinese cohort were selected for the external validation cohort. Overall, 10,484 patients experienced early death in the SEER cohort and 35 in the Chinese cohort. A reliable nomogram was constructed on the basis of 11 significant risk factors. Internal validation and external validation of the nomogram showed high accuracy in predicting early death. Decision curve analysis demonstrated that this predictive nomogram had excellent and potential clinical applicability.ConclusionThe nomogram provided a simple-to-use tool to distinguish early death in patients with metastatic PCa, assisting clinicians in implementing individualized treatment regimens.

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Development and Validation of Nomograms Predicting Overall and Cancer-specific Survival of Spinal and Pelvic Tumor Patients with Distant Metastasis

10.21203/rs.3.rs-18003/v1 ◽

2020 ◽

Author(s):

Hao Zhang ◽

Qihao Tu ◽

Chuanli Zhou ◽

Chao Wang ◽

Kai Zhu ◽

...

Keyword(s):

Risk Factors ◽

Bone Tumor ◽

Distant Metastasis ◽

External Validation ◽

Pelvic Bone ◽

Seer Database ◽

Tumor Patients ◽

Cancer Specific Survival ◽

Internal Validation ◽

Independent Risk Factors

Abstract Background: Primary spinal bone tumors with distant metastasis are a sign of advanced stage and are usually accompanied by poor prognosis. This study is to identify the risk factors and establish prognostic nomograms to predict 1- and 3-year overall survival (OS) and cancer-specific survival (CSS) rates for spinal and pelvic bone tumor patients with distant metastasis.Patients and methods: Spinal and pelvic bone tumor patients with distant metastasis between 1998 and 2016 were selected for this study from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms to predict 1- and 3-year OS and CCS rates were constructed based on independent risk factors identified by univariate and multivariate Cox analyses. Concordance indexes (C-indexes), receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) were used to assess the nomograms.Results: All patients (n=343) were randomly divided into a training cohort (n=243) and validation cohort (n=100). No significant differences were found in thedemographic data of all patients in the training and validation cohorts. Ultimately, only four independent risk factors (patient age, histology, grade and surgery) were identified as significantly associated with OS and CCS. The C-indices were 0.722 (95% CI, 0.685 to 0.759) and 0.686 (95% CI, 0.61 to 0.760) for the internal validation and external validation of the OS nomogram, respectively. Similarly, the C-indices based on the CCS nomogram were 0.717 (95% CI, 0.678 to 0.757) and 0.695 (95% CI, 0.619 to 0.771) for the internal validation and external validation, respectively. The calibration curves revealed that the predicted survival and actual survival were in concordance. DCA showed the clinical utility and benefits of the nomograms.Conclusion: The nomograms we constructed based on the SEER database can accurately predict individual patient survival.

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Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

10.1101/2020.04.03.20052068 ◽

2020 ◽

Cited By ~ 4

Author(s):

Zhiyi Wang ◽

Jie Weng ◽

Zhongwang Li ◽

Ruonan Hou ◽

Lebin Zhou ◽

...

Keyword(s):

Predictive Factors ◽

Validation Cohort ◽

External Validation ◽

Clinical Impact ◽

Validation Data ◽

Data Set ◽

Internal Validation ◽

C Statistic ◽

Prediction Probability ◽

Wbc Count

BackgroundThe COVID-19 virus is an emerging virus rapidly spread worldwide This study aimed to establish an effective diagnostic nomogram for suspected COVID-19 pneumonia patients.METHODSWe used the LASSO aggression and multivariable logistic regression methods to explore the predictive factors associated with COVID-19 pneumonia, and established the diagnostic nomogram for COVID-19 pneumonia using multivariable regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram.RESULTSThe predictive factors including the epidemiological history, wedge- shaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the COVID-19 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression. The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration. Moreover, decision curve and clinical impact curve were also beneficial for COVID- 19 pneumonia patients.CONCLUSIONOur nomogram can be used to predict COVID-19 pneumonia accurately and favourably.

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Development and Analysis of a Predictive Nomogram Assessing Cancer Risk in a Chinese Cohort of Patients Presenting with Pulmonary Nodules

10.21203/rs.3.rs-1119406/v1 ◽

2021 ◽

Author(s):

Zufang Liao ◽

Rongjiong Zheng ◽

Ni Li ◽

Guofeng Shao

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Pulmonary Nodule ◽

Validation Cohort ◽

External Validation ◽

Pulmonary Nodules ◽

Hypertension Status ◽

Internal Validation ◽

Nodule Size ◽

Chinese Cohort

Abstract Background: Lung cancer is a major global threat to public health for which a novel prognostic nomogram is urgently needed.Patients and methods: Here, we designed a novel prognostic nomogram using a training dataset consisting of 178 pulmonary nodules for design and 124nodules for external validation. The R ‘caret’ package was used to separate patients for design into two groups, including a training cohort (n=126) for model construction and an internal validation cohort (n=52). Optimal feature selection for this model was achieved using the least absolute shrinkage and selection operator regression (LASSO) model. C-index values, calibration plots, and decision curve analyses were used to gauge the discrimination, calibration, and clinical utility, respectively, of this predictive model. Validation was then performed with the validation cohort.Results: A predictive nomogram was successfully constructed incorporating hypertension status, plasma fibrinogen levels, serum uric acid (SUA) levels, triglyceride (TG) and high-density lipoprotein (HDL) levels, density, spicule sign, ground-glass opacity (GGO), and pulmonary nodule size. This model exhibited good discriminative ability, with a C-index value of 0.795 (95% CI: 0.720–0.870), and was well-calibrated. When we used the validation cohort to evaluate the model, the C-indexes were 0.886 (95% CI: 0.800–0.972) and 0.817 (95% CI: 0.747–0.897) for internal validation and external validation, respectively. Decision curve analyses indicated the clinical value of this predictive nomogram when used at a lung cancer possibility threshold of 9%.Conclusion: The nomogram constructed in this study, which incorporates hypertension status, plasma fibrinogen levels, SUA, TG, HDL, density, spicule sign, GGO status, and pulmonary nodule size was able to reliably predict lung cancer risk in this Chinese cohort of patients presenting with pulmonary nodules.

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Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

10.21203/rs.3.rs-22140/v1 ◽

2020 ◽

Author(s):

Zhiyi Wang ◽

Jie Weng ◽

Zhongwang Li ◽

Ruonan Hou ◽

Lebin Zhou ◽

...

Keyword(s):

Predictive Factors ◽

Validation Cohort ◽

External Validation ◽

Respiratory Illness ◽

Clinical Impact ◽

Validation Data ◽

Data Set ◽

Internal Validation ◽

C Statistic ◽

Prediction Probability

Abstract Background The COVID-19 virus is an emerging virus associated with severe respiratory illness first detected in December, 2019, and rapidly spread worldwide. The aim of this study was to establish an effective diagnostic nomogram for suspected COVID-19 pneumonia patients. Methods We used the LASSO aggression and multivariable logistic regression methods to explore the predictive factors associated with COVID-19 pneumonia, and established the diagnostic nomogram for COVID-19 pneumonia using multivariable regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram. Results The predictive factors including the epidemiological history, wedge-shaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the COVID-19 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression. The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration. Moreover, decision curve and clinical impact curve were also beneficial for COVID-19 pneumonia patients. Conclusion Our nomogram can be used to predict COVID-19 pneumonia accurately and favourably.

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Prediction of Poststroke Urinary Tract Infection Risk in Immobile Patients Using Machine Learning: Observational Cohort Study (Preprint)

10.2196/preprints.32893 ◽

2021 ◽

Author(s):

Chen Zhu ◽

Zidu Xu ◽

Yaowen Gu ◽

Si Zheng ◽

Xiangyu Sun ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Ensemble Learning ◽

Predictive Models ◽

Validation Cohort ◽

External Validation ◽

Learning Model ◽

Stroke Patients ◽

Internal Validation ◽

Derivation Cohort

BACKGROUND Poststroke immobility gets patients more vulnerable to stroke-relevant complications. Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it is still challenging to accurately estimate personal UTI risk due to unclear interaction of various factors and variability of individual characteristics. This calls for more precise and trust-worthy predictive models to assist with potential UTI identification. OBJECTIVE The aim of this study was to develop predictive models for UTI risk identification for immobile stroke patients. A prospective analysis was conducted to evaluate the effectiveness and clinical interpretability of the models. METHODS The data used in this study were collected from the Common Complications of Bedridden Patients and the Construction of Standardized Nursing Intervention Model (CCBPC). Derivation cohort included data of 3982 immobile stroke patients collected during CCBPC-I, from November 1, 2015 to June 30, 2016; external validation cohort included data of 3837 immobile stroke patients collected during CCBPC-II, from November 1, 2016 to July 30, 2017. 6 machine learning models and an ensemble learning model were derived based on 80% of derivation cohort and its effectiveness was evaluated with the remaining 20% of derivation cohort data. We further compared the effectiveness of predictive models in external validation cohort. The performance of logistic regression without regularization was used as a reference. We used Shapley additive explanation values to determine feature importance and examine the clinical significance of prediction models. Shapely values of the factors were calculated to represent the magnitude, prevalence, and direction of their effects, and were further visualized in a summary plot. RESULTS A total of 103(2.59%) patients were diagnosed with UTI in derivation cohort(N=3982); the internal validation cohort (N=797) shared the same incidence. The external validation cohort had a UTI incidence of 1.38% (N=53). Evaluation results showed that the ensemble learning model performed the best in area under the receiver operating characteristic (ROC) curve in internal validation, up to 82.2%; second best in external validation, 80.8%. In addition, the ensemble learning model performed the best sensitivity in both internal and external validation sets (80.9% and 81.1%, respectively). We also identified seven UTI risk factors (pneumonia, glucocorticoid use, female sex, mixed cerebrovascular disease, increased age, prolonged length of stay, and duration of catheterization) contributing most to the predictive model, thus demonstrating the clinical interpretability of model. CONCLUSIONS Our ensemble learning model demonstrated promising performance. Identifying UTI risk and detecting high risk factors among immobile stroke patients would allow more selective and effective use of preventive interventions, thus improving clinical outcomes. Future work should focus on developing a more concise scoring tool and prospectively examining the model in practical use.

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LUCAS: A highly accurate yet simple risk calculator that predicts survival of COVID-19 patients using rapid routine tests

10.1101/2021.04.27.21256196 ◽

2021 ◽

Author(s):

Surajit Ray ◽

Andrew Swift ◽

Joey W Fanstone ◽

Abhirup Banerjee ◽

Michail Mamalakis ◽

...

Keyword(s):

Hospital Admission ◽

Validation Cohort ◽

Point Of Care ◽

External Validation ◽

Fatal Outcome ◽

Image Data ◽

Discriminatory Power ◽

Validation Dataset ◽

Data Set ◽

Internal Validation

There is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission. Methods We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set. Findings The LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors. Interpretation This simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available at https://mdscore.net/.

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Age-adapted percentiles of measured glomerular filtration in healthy individuals: extrapolation to living kidney donors over 65 years

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2021-1011 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Pierre Delanaye ◽

François Gaillard ◽

Jessica van der Weijden ◽

Geir Mjøen ◽

Ingela Ferhman-Ekholm ◽

...

Keyword(s):

Decision Making ◽

Glomerular Filtration ◽

Validation Cohort ◽

External Validation ◽

Healthy Individuals ◽

Kidney Donors ◽

Internal Validation ◽

Living Kidney Donors ◽

Multi Center Study ◽

Elderly Living

Abstract Objectives Most data on glomerular filtration rate (GFR) originate from subjects <65 years old, complicating decision-making in elderly living kidney donors. In this retrospective multi-center study, we calculated percentiles of measured GFR (mGFR) in donors <65 years old and extrapolated these to donors ≥65 years old. Methods mGFR percentiles were calculated from a development cohort of French/Belgian living kidney donors <65 years (n=1,983), using quantiles modeled as cubic splines (two linear parts joining at 40 years). Percentiles were extrapolated and validated in an internal cohort of donors ≥65 years (n=147, France) and external cohort of donors and healthy subjects ≥65 years (n=329, Germany, Sweden, Norway, France, The Netherlands) by calculating percentages within the extrapolated 5th–95th percentile (P5–P95). Results Individuals in the development cohort had a higher mGFR (99.9 ± 16.4 vs. 86.4 ± 14 and 82.7 ± 15.5 mL/min/1.73 m2) compared to the individuals in the validation cohorts. In the internal validation cohort, none (0%) had mGFR below the extrapolated P5, 12 (8.2%) above P95 and 135 (91.8%) between P5–P95. In the external validation cohort, five subjects had mGFR below the extrapolated P5 (1.5%), 25 above P95 (7.6%) and 299 (90.9%) between P5–P95. Conclusions We demonstrate that extrapolation of mGFR from younger donors is possible and might aid with decision-making in elderly donors.

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Prognostic Nomogram Incorporating Immunohistochemical Results for the Overall Survival of Patients with Bladder Cancer.

10.21203/rs.3.rs-24644/v1 ◽

2020 ◽

Author(s):

Tianwei Wang ◽

Yunyan Wang

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Bladder Cancer ◽

Risk Model ◽

Cox Regression ◽

Validation Cohort ◽

Multivariable Analysis ◽

Clinical Information ◽

Internal Validation ◽

Cox Regression Analysis

Abstract Objectives: In this study, we want to combine GATA3, VEGF, EGFR and Ki67 with clinical information to develop and validate a prognostic nomogram for bladder cancer.Methods: A total of 188 patients with clinical information and immunohistochemistry were enrolled in this study, from 1996 to 2018. Univariable and multivariable cox regression analysis was applied to identify risk factors for nomogram of overall survival (OS). The calibration of the nomogram was performed and the Area Under Curve (AUC) was calculated to assess the performance of the nomogram. Internal validation was performed with the validation cohort., the calibration curve and the AUC were calculated simultaneously.Results: Univariable and multivariable analysis showed that age (HR: 2.229; 95% CI: 1.162-4.274; P=0.016), histology (HR: 0.320; 95% CI: 0.136-0.751; P=0.009), GATA3 (HR: 0.348; 95% CI: 0.171-0.709; P=0.004), VEGF (HR: 2.295; 95% CI: 1.225-4.301; P=0.010) and grade (HR: 4.938; 95% CI: 1.339-18.207; P=0.016) remained as independent risk factors for OS. The age, histology, grade, GATA3 and VEGF were included to build the nomogram. The accuracy of the risk model was further verified with the C-index. The C-index were 0.65 (95% CI, 0.58-0.72) and 0.58 (95% CI, 0.46-0.70) in the training and validation cohort respectively. Conclusions: A combination of clinical variables with immunohistochemical results based nomogram would predict the overall survival of patients with bladder cancer.

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A Modified TNM Classification for Primary Operable Colorectal Cancer

JNCI Cancer Spectrum ◽

10.1093/jncics/pkaa093 ◽

2020 ◽

Author(s):

Chundong Zhang ◽

Zubing Mei ◽

Junpeng Pei ◽

Masanobu Abe ◽

Xiantao Zeng ◽

...

Keyword(s):

Colorectal Cancer ◽

Validation Cohort ◽

Tnm Classification ◽

Characteristic Curve ◽

External Validation ◽

Model Fitting ◽

Information Criteria ◽

Stage I ◽

Internal Validation ◽

Clinical Benefits

Abstract Background The American Joint Committee on Cancer (AJCC) 8th tumor/node/metastasis (TNM) classification for colorectal cancer (CRC) has limited ability to predict prognosis. Methods We included 45,379 eligible stage I-III CRC patients from the Surveillance, Epidemiology, and End Results Program. Patients were randomly assigned individually to a training (N =31,772) or an internal validation cohort (N =13,607). External validation was performed in 10,902 additional patients. Patients were divided according to T and N stage permutations. Survival analyses were conducted by a Cox proportional hazard model and Kaplan-Meier analysis, with T1N0 as the reference. Area under receiver operating characteristic curve (AUC) and Akaike information criteria (AIC) were applied for prognostic discrimination and model-fitting, respectively. Clinical benefits were further assessed by decision curve analyses. Results We created a modified TNM (mTNM) classification: stages I (T1-2N0-1a), IIA (T1N1b, T2N1b, T3N0), IIB (T1-2N2a-2b, T3N1a-1b, T4aN0), IIC (T3N2a, T4aN1a-2a, T4bN0), IIIA (T3N2b, T4bN1a), IIIB (T4aN2b, T4bN1b), and IIIC (T4bN2a-2b). In the internal validation cohort, compared to the AJCC 8th TNM classification, the mTNM classification showed superior prognostic discrimination (AUC = 0.675 vs. 0.667, respectively; two-sided P <0.001) and better model-fitting (AIC = 70,937 vs. 71,238, respectively). Similar findings were obtained in the external validation cohort. Decision curve analyses revealed that the mTNM had superior net benefits over the AJCC 8th TNM classification in the internal and external validation cohorts. Conclusions The mTNM classification provides better prognostic discrimination than AJCC 8th TNM classification, with good applicability in various populations and settings, to help better stratify stage I-III CRC patients into prognostic groups.

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MALNUTRITION, SEPSIS AND TUMOR LYSIS SYNDROME ARE ASSOCIATED WITH INCREASED RATE OF ACUTE MORTALITY IN MATURE B CELL NON-HODGKIN LYMPHOMA IN PEDIATRIC POPULATION- STUDY FROM TERTIARY CARE HOSPITAL IN PAKISTAN

Mediterranean Journal of Hematology and Infectious Diseases ◽

10.4084/mjhid.2019.043 ◽

2019 ◽

Vol 11 (1) ◽

pp. 02019043

Author(s):

Raheela Mansoor

Keyword(s):

Risk Factors ◽

Fungal Infection ◽

B Cell ◽

Pediatric Population ◽

Tertiary Care ◽

Tumor Lysis Syndrome ◽

Significant Risk ◽

Early Death ◽

Care Hospital ◽

Tumor Lysis

Background: Outcomes of pediatric mature B cell NHL in resource challenged countries are negatively affected by increased rate of early and toxic deaths. Aim of this study is to assess rate of acute mortality and define significant risk factors present in children with mature B Cell NHL. Methods: A retrospective analysis was done of patients with B cell NHL from January 2012 till December 2016. Risk factors studied for acute mortality were malnutrition, stage, prior surgery with open laparotomy, LDH levels, tumor lysis syndrome, sepsis and fungal infection Results: Total 233 patients were enrolled in the study. Eighty five (36.4%) were below 15th percentile. Treatment was started in 226 patients. Eighty eight percent children showed 20% response after COP pre-phase. Tumor lysis syndrome was developed in 20.6 % (n = 48) children and 42.9% (n = 100) patients had sepsis, 71/100 patients had culture proven sepsis. 19.7% (n = 46) patients developed fungal infection. There was 19.7% (n = 46) acute mortality. Most common cause of death was sepsis (n = 22, 47.8%) followed by acute renal failure secondary to tumor lysis syndrome. On multivariate analysis, three independent variables found significant for early death are malnutrition, sepsis and tumor lysis syndrome. Conclusion: Rate of acute mortality in B cell NHL is high in our set up and significant risk factors are tumor lysis syndrome, sepsis and malnourishment at time of presentation.

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