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

2020 ◽  
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.

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

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.

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

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhong Zhang ◽  
Juan Pu ◽  
Haijun Zhang
Keyword(s):  
Risk Factors ◽  
Pancreatic Adenocarcinoma ◽  
Validation Cohort ◽  
External Validation ◽  
Significant Risk ◽  
Early Death ◽  
Seer Database ◽  
Data Set ◽  
Internal Validation ◽  
Chinese Cohort

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.

scholarly journals A Competing Risk Nomogram for Predicting Cancer-Specific Death of Patients With Maxillary Sinus Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Mingbin Hu ◽  
Xiancai Li ◽  
Weiguo Gu ◽  
Jinhong Mei ◽  
Dewu Liu ◽  
...  
Keyword(s):  
Maxillary Sinus ◽  
Cumulative Incidence ◽  
External Validation ◽  
Competing Risk ◽  
Validation Data ◽  
Data Set ◽  
Internal Validation ◽  
Data Resource ◽  
Seer Data ◽  
Risk Of Cancer

ObjectivesHerein, we purposed to establish and verify a competing risk nomogram for estimating the risk of cancer-specific death (CSD) in Maxillary Sinus Carcinoma (MSC) patients.MethodsThe data of individuals with MSC used in this study was abstracted from the (SEER) Surveillance, Epidemiology, and End Results data resource as well as from the First Affiliated Hospital of Nanchang University (China). The risk predictors linked to CSD were identified using the CIF (cumulative incidence function) along with the Fine-Gray proportional hazards model on the basis of univariate analysis coupled with multivariate analysis implemented in the R-software. After that, a nomogram was created and verified to estimate the three- and five-year CSD probability.ResultsOverall, 478 individuals with MSC were enrolled from the SEER data resource, with a 3- and 5-year cumulative incidence of CSD after diagnosis of 42.1% and 44.3%, respectively. The Fine-Gray analysis illustrated that age, histological type, N stage, grade, surgery, and T stage were independent predictors linked to CSD in the SEER-training data set (n = 343). These variables were incorporated in the prediction nomogram. The nomogram was well calibrated and it demonstrated a remarkable estimation accuracy in the internal validation data set (n = 135) abstracted from the SEER data resource and the external validation data set (n = 200). The nomograms were well-calibrated and had a good discriminative ability with concordance indexes (c-indexes) of 0.810, 0.761, and 0.755 for the 3- and 5-year prognosis prediction of MSC-specific mortality in the training cohort, internal validation, and external validation cohort, respectively.ConclusionsThe competing risk nomogram constructed herein proved to be an optimal assistant tool for estimating CSD in individuals with MSC.

scholarly journals Nomogram predicting the risk of three-year chronic kidney disease adverse outcomes among East Asian patients with CKD

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Huizhen Ye ◽  
Youyuan Chen ◽  
Peiyi Ye ◽  
Yu Zhang ◽  
Xiaoyi Liu ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
East Asian ◽  
External Validation ◽  
Adverse Outcomes ◽  
R Software ◽  
Validation Data ◽  
Data Set ◽  
Internal Validation ◽  
Asian Patients ◽  
East Asian Patients

Abstract Background Chronic kidney disease (CKD) is a common health challenge. There are some risk models predicting CKD adverse outcomes, but seldom focus on the Mongoloid population in East Asian. So, we developed a simple but intuitive nomogram model to predict 3-year CKD adverse outcomes for East Asian patients with CKD. Methods The development and internal validation of prediction models used data from the CKD-ROUTE study in Japan, while the external validation set used data collected at the First People’s Hospital of Foshan in southern China from January 2013 to December 2018. Models were developed using the cox proportional hazards model and nomogram with SPSS and R software. Finally, the model discrimination, calibration and clinical value were tested by R software. Results The development and internal validation data-sets included 797 patients (191 with progression [23.96%]) and 341 patients (89 with progression [26.10%]), respectively, while 297 patients (108 with progression [36.36%]) were included in the external validation data set. The nomogram model was developed with age, eGFR, haemoglobin, blood albumin and dipstick proteinuria to predict three-year adverse-outcome-free probability. The C-statistics of this nomogram were 0.90(95% CI, 0.89–0.92) for the development data set, 0.91(95% CI, 0.89–0.94) for the internal validation data set and 0.83(95% CI, 0.78–0.88) for the external validation data-set. The calibration and decision curve analyses were good in this model. Conclusion This visualized predictive nomogram model could accurately predict CKD three-year adverse outcomes for East Asian patients with CKD, providing an easy-to-use and widely applicable tool for clinical practitioners.

scholarly journals Development and external validation of a logistic regression derived algorithm to estimate a 12-month post open defecation free slippage risk

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Warren Mukelabai Simangolwa
Keyword(s):  
Logistic Regression ◽  
External Validation ◽  
Data Sets ◽  
Nonparametric Bootstrap ◽  
Open Defecation ◽  
Validation Data ◽  
Data Set ◽  
C Statistic ◽  
Sanitation And Hygiene ◽  
Development And Validation

Appropriate open defecation free (ODF) sustainability interventions are key to further mobilise communities to consume sanitation and hygiene products and services that enhance household’s quality of life and embed household behavioural change for heathier communities. This study aims to develop a logistic regression derived risk algorithm to estimate a 12-month ODF slippage risk and externally validate the model in an independent data set. ODF slippage occurs when one or more toilet adequacy parameters are no longer present for one or more toilets in a community. Data in the Zambia district health information software for water sanitation and hygiene management information system for Chungu and Chabula chiefdoms was used for the study. The data was retrieved from the date of chief Chungu and Chabula chiefdoms' attainment of ODF status in October 2016 for 12 months until September 2017 for the development and validation data sets respectively. Data was assumed to be missing completely at random and the complete case analysis approach was used. The events per variables were satisfactory for both the development and validation data sets. Multivariable regression with a backwards selection procedure was used to decide candidate predictor variables with p < 0.05 meriting inclusion. To correct for optimism, the study compared amount of heuristic shrinkage by comparing the model’s apparent C-statistic to the C- statistic computed by nonparametric bootstrap resampling. In the resulting model, an increase in the covariates ‘months after ODF attainment’, ‘village population’ and ‘latrine built after CLTS’, were all associated with a higher probability of ODF slippage. Conversely, an increase in the covariate ‘presence of a handwashing station with soap’, was associated with reduced probability of ODF slippage. The predictive performance of the model was improved by the heuristic shrinkage factor of 0.988. The external validation confirmed good prediction performance with an area under the receiver operating characteristic curve of 0.85 and no significant lack of fit (Hosmer-Lemeshow test: p = 0.246). The results must be interpreted with caution in regions where the ODF definitions, culture and other factors are different from those asserted in the study.

scholarly journals Development and prospective external validation of a tool to predict poor recovery at 9 months after acute ankle sprain in UK emergency departments: the SPRAINED prognostic model

BMJ Open ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. e022802 ◽  
Author(s):  
Michael M Schlussel ◽  
David J Keene ◽  
Gary S Collins ◽  
Jennifer Bostock ◽  
Christopher Byrne ◽  
...  
Keyword(s):  
Ankle Sprain ◽  
Emergency Departments ◽  
Prognostic Model ◽  
External Validation ◽  
Data Sets ◽  
Baseline Model ◽  
Validation Data ◽  
Data Set ◽  
Poor Recovery ◽  
C Statistic

ObjectivesTo develop and externally validate a prognostic model for poor recovery after ankle sprain.Setting and participantsModel development used secondary data analysis of 584 participants from a UK multicentre randomised clinical trial. External validation used data from 682 participants recruited in 10 UK emergency departments for a prospective observational cohort.Outcome and analysisPoor recovery was defined as presence of pain, functional difficulty or lack of confidence in the ankle at 9 months after injury. Twenty-three baseline candidate predictors were included together in a multivariable logistic regression model to identify the best predictors of poor recovery. Relationships between continuous variables and the outcome were modelled using fractional polynomials. Regression parameters were combined over 50 imputed data sets using Rubin’s rule. To minimise overfitting, regression coefficients were multiplied by a heuristic shrinkage factor and the intercept re-estimated. Incremental value of candidate predictors assessed at 4 weeks after injury was explored using decision curve analysis and the baseline model updated. The final models included predictors selected based on the Akaike information criterion (p<0.157). Model performance was assessed by calibration and discrimination.ResultsOutcome rate was lower in the development (6.7%) than in the external validation data set (19.9%). Mean age (29.9 and 33.6 years), body mass index (BMI; 26.3 and 27.1 kg/m2), pain when resting (37.8 and 38.5 points) or bearing weight on the ankle (75.4 and 71.3 points) were similar in both data sets. Age, BMI, pain when resting, pain bearing weight, ability to bear weight, days from injury until assessment and injury recurrence were the selected predictors. The baseline model had fair discriminatory ability (C-statistic 0.72; 95% CI 0.66 to 0.79) but poor calibration. The updated model presented better discrimination (C-statistic 0.78; 95% CI 0.72 to 0.84), but equivalent calibration.ConclusionsThe models include predictors easy to assess clinically and show benefit when compared with not using any model.Trial registration numberISRCTN12726986; Results.

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

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/.

scholarly journals Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e040778
Author(s):  
Vineet Kumar Kamal ◽  
Ravindra Mohan Pandey ◽  
Deepak Agrawal
Keyword(s):  
Hospital Mortality ◽  
External Validation ◽  
Trauma Centre ◽  
Unfavourable Outcome ◽  
Motor Score ◽  
Validation Data ◽  
Data Set ◽  
Development Data ◽  
Level 1 ◽  
Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

scholarly journals INeo-Epp: A novel T-cell HLA class-I immunogenicity or neoantigenic epitope prediction method based on sequence related amino acid features

2019 ◽  
Author(s):  
Guangzhi Wang ◽  
Huihui Wan ◽  
Xingxing Jian ◽  
Yuyu Li ◽  
Jian Ouyang ◽  
...  
Keyword(s):  
Amino Acid ◽  
T Cell ◽  
Antigen Processing ◽  
External Validation ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Validation Data ◽  
Data Set ◽  
Immunogenic Peptide ◽  
Related Amino Acid

AbstractIn silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g. proteasomal cleavage, transporter associated with antigen processing (TAP) and major histocompatibility complex (MHC) combination. To date, however, the mechanism for immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including amino acid physicochemical property of epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score and frequency score for immunogenic peptide. Subsequently, a random forest classifier for T cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC=0.81, external validation data set AUC=0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called ‘neoantigens’. Based on mutation information and sequence related amino acid characteristics, a prediction model of neoantigen was established as well (the optimal AUC=0.78). Further, an easy-to-use web-based tool ‘INeo-Epp’ was developed (available at http://www.biostatistics.online/INeo-Epp/neoantigen.php)for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

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

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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