RETRACTED ARTICLE: The diagnostic nomogram of platelet-based score models for
hepatic alveolar echinococcosis and atypical liver cancer

AbstractHepatic alveolar echinococcosis (HAE) and liver cancer had similarities in imaging results, clinical characteristics, and so on. And it is difficult for clinicians to distinguish them before operation. The aim of our study was to build a differential diagnosis nomogram based on platelet (PLT) score model and use internal validation to check the model. The predicting model was constructed by the retrospective database that included in 153 patients with HAE (66 cases) or liver cancer (87 cases), and all cases was confirmed by clinicopathology and collected from November 2011 to December 2018. Lasso regression analysis model was used to construct data dimensionality reduction, elements selection, and building prediction model based on the 9 PLT-based scores. A multi-factor regression analysis was performed to construct a simplified prediction model, and we added the selected PLT-based scores and relevant clinicopathologic features into the nomogram. Identification capability, calibration, and clinical serviceability of the simplified model were evaluated by the Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic curve (ROC), and decision curve. An internal validation was also evaluated by the bootstrap resampling. The simplified model, including in 4 selected factors, was significantly associated with differential diagnosis of HAE and liver cancer. Predictors of the simplified diagnosis nomogram consisted of the API index, the FIB-4 index, fibro-quotent (FibroQ), and fibrosis index constructed by King’s College Hospital (King’s score). The model presented a perfect identification capability, with a high C-index of 0.929 (0.919 through internal validation), and good calibration. The area under the curve (AUC) values of this simplified prediction nomogram was 0.929, and the result of ROC indicated that this nomogram had a good predictive value. Decision curve analysis showed that our differential diagnosis nomogram had clinically identification capability. In conclusion, the differential diagnosis nomogram could be feasibly performed to verify the preoperative individualized diagnosis of HAE and liver cancer.

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Retraction Note: The diagnostic nomogram of platelet-based score models for hepatic alveolar echinococcosis and atypical liver cancer

Scientific Reports ◽

10.1038/s41598-020-73758-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Qiancheng Du ◽

Yanyan Wang ◽

Shihao Guan ◽

Chenliang Hu ◽

Mengxuan Li ◽

...

Keyword(s):

Liver Cancer ◽

Alveolar Echinococcosis ◽

Link Type ◽

Hepatic Alveolar Echinococcosis

Editor's Note: this Article has been retracted; the Retraction Note is available at https://www.nature.com/articles/s41598-020-73758-x

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Differential diagnosis between hepatic alveolar echinococcosis and intrahepatic cholangiocarcinoma with conventional ultrasound and contrast-enhanced ultrasound

BMC Medical Imaging ◽

10.1186/s12880-020-00499-8 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Zeng-Cheng Wa ◽

Ting Du ◽

Xian-Feng Li ◽

Hui-Qing Xu ◽

Qiu-Cuo Suo-Ang ◽

...

Keyword(s):

Differential Diagnosis ◽

Intrahepatic Cholangiocarcinoma ◽

Alveolar Echinococcosis ◽

Contrast Enhanced Ultrasound ◽

Hepatic Alveolar Echinococcosis ◽

Contrast Enhanced ◽

Conventional Ultrasound

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A Sarcopenia-Based Prediction Model for Postoperative Complications of ex vivo Liver Resection and Autotransplantation to Treat End-Stage Hepatic Alveolar Echinococcosis

Infection and Drug Resistance ◽

10.2147/idr.s340478 ◽

2021 ◽

Vol Volume 14 ◽

pp. 4887-4901

Author(s):

Ting Sun ◽

Tao Wang ◽

Yiwen Qiu ◽

Shu Shen ◽

Xianwei Yang ◽

...

Keyword(s):

Liver Resection ◽

Postoperative Complications ◽

Prediction Model ◽

Alveolar Echinococcosis ◽

Ex Vivo ◽

Hepatic Alveolar Echinococcosis ◽

End Stage

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Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model

PLoS ONE ◽

10.1371/journal.pone.0254550 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254550

Author(s):

Matteo Luigi Giuseppe Leoni ◽

Luisa Lombardelli ◽

Davide Colombi ◽

Elena Giovanna Bignami ◽

Benedetta Pergolotti ◽

...

Keyword(s):

Prediction Model ◽

Critically Ill ◽

Critically Ill Patients ◽

Goodness Of Fit ◽

Cox Regression ◽

Prognostic Models ◽

Calibration Plot ◽

Internal Validation ◽

Icu Admission ◽

C Statistic

Background COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. Materials and methods We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model’s discriminatory ability was assessed with Harrell’s C-statistic and the goodness-of-fit was evaluated with calibration plot. Results 242 patients were included [median age, 64 years (56–71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6–18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). Conclusions We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

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A Nomogram for Preoperative Estimation of Microvascular Invasion Risk in Hepatocellular Carcinoma: Single-Center Analyses With Internal Validation

Frontiers in Oncology ◽

10.3389/fonc.2021.616976 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiarui Yang ◽

Shuguang Zhu ◽

Juanjuan Yong ◽

Long Xia ◽

Xiangjun Qian ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Liver Cancer ◽

Predictive Model ◽

Predictive Factors ◽

Microvascular Invasion ◽

Lasso Regression ◽

Internal Validation ◽

Treat All ◽

Upper Abdomen

BackgroundMicrovascular invasion (MVI) is highly associated with poor prognosis in patients with liver cancer. Predicting MVI before surgery is helpful for surgeons to better make surgical plan. In this study, we aim at establishing a nomogram to preoperatively predict the occurrence of microvascular invasion in liver cancer.MethodA total of 405 patients with postoperative pathological reports who underwent curative hepatocellular carcinoma resection in the Third Affiliated Hospital of Sun Yat-sen University from 2013 to 2015 were collected in this study. Among these patients, 290 were randomly assigned to the development group while others were assigned to the validation group. The MVI predictive factors were selected by Lasso regression analysis. Nomogram was established to preoperatively predict the MVI risk in HCC based on these predictive factors. The discrimination, calibration, and effectiveness of nomogram were evaluated by internal validation.ResultsLasso regression analysis revealed that discomfort of right upper abdomen, vascular invasion, lymph node metastases, unclear tumor boundary, tumor necrosis, tumor size, higher alkaline phosphatase were predictive MVI factors in HCC. The nomogram was established with the value of AUROC 0.757 (0.716–0.809) and 0.768 (0.703–0.814) in the development and the validation groups. Well-fitted calibration was in both development and validation groups. Decision curve analysis confirmed that the predictive model provided more benefit than treat all or none patients. The predictive model demonstrated sensitivity of 58.7%, specificity of 80.7% at the cut-off value of 0.312.ConclusionNomogram was established for predicting preoperative risk of MVI in HCC. Better treatment plans can be formulated according to the predicted results.

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The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML

Journal of Translational Medicine ◽

10.1186/s12967-021-02914-2 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Chao Guo ◽

Ya-yue Gao ◽

Qian-qian Ju ◽

Chun-xia Zhang ◽

Ming Gong ◽

...

Keyword(s):

Regression Analysis ◽

Prediction Model ◽

Hox Genes ◽

Expression Profiles ◽

Penalized Regression ◽

Diagnostic Utility ◽

Lasso Regression ◽

Hub Genes ◽

Npm1 Mutation ◽

Genetic Abnormalities

Abstract Background The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA. Methods We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. Results A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743–0.79). Conclusions We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

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Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

Healthcare ◽

10.3390/healthcare9070853 ◽

2021 ◽

Vol 9 (7) ◽

pp. 853

Author(s):

Jee-Yun Kim ◽

Jeong Yee ◽

Tae-Im Park ◽

So-Youn Shin ◽

Man-Ho Ha ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Linear Regression ◽

Parenteral Nutrition ◽

Prediction Model ◽

Scoring System ◽

Prediction Equation ◽

Icu Patients ◽

Risk Scoring ◽

Risk Scoring System

Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15–29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2–4, and 5–6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) − 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.

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Predicting Burn Mortality Using a Simple Novel Prediction Model

Indian Journal of Plastic Surgery ◽

10.1055/s-0040-1721867 ◽

2021 ◽

Author(s):

Sneha Sharma ◽

Raman Tandon

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Receiver Operating Characteristic ◽

Logistic Regression Analysis ◽

Operating Characteristic ◽

Binary Logistic Regression ◽

Apache Ii ◽

Apache Ii Score ◽

Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

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