scholarly journals Predicting bleeding risk in a Chinese immune thrombocytopenia (ITP) population: development and assessment of a new predictive nomogram

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mingjing Wang ◽  
Weiyi Liu ◽  
Yonggang Xu ◽  
Hongzhi Wang ◽  
Xiaoqing Guo ◽  
...  
Keyword(s):  
Feature Selection ◽  
Predictive Model ◽  
Risk Model ◽  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Bleeding Risk ◽  
Inflammatory Factors ◽  
Calibration Plot ◽  
Ifn Γ ◽  
Selection Operator

Abstract The aim of this study was to develop a model that could be used to forecast the bleeding risk of ITP based on proinflammatory and anti-inflammatory factors. One hundred ITP patients were recruited to build a new predictive nomogram, another eighty-eight ITP patients were enrolled as validation cohort, and data were collected from January 2016 to January 2019. Four demographic characteristics and fifteen clinical characteristics were taken into account. Eleven cytokines (IFN-γ, IL-1, IL-4, IL-6, IL-8, IL-10, IL-17A, IL-22, IL-23, TNF-α and TGF-β) were used to study and the levels of them were detected by using a cytometric bead array (CBA) human inflammation kit. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariate logistic regression analysis was applied to build a new predictive nomogram based on the results of the least absolute shrinkage and selection operator regress ion model. The application of C-index, ROC curve, calibration plot, and decision curve analyses were used to assess the discrimination, calibration, and clinical practicability of the predictive model. Bootstrapping validation was used for testing and verifying the predictive model. After feature selection, cytokines IL-1, IL-6, IL-8, IL-23 and TGF-β were excluded, cytokines IFN-γ, IL-4, IL-10, IL-17A, IL-22, TGF-β, the count of PLT and the length of time of ITP were used as predictive factors in the predictive nomogram. The model showed good discrimination with a C-index of 0.82 (95% confidence interval 0.73376–0.90 624) in training cohortn and 0.89 (95% CI 0.868, 0.902) in validation cohort, an AUC of 0.795 in training cohort, 0.94 in validation cohort and good calibration. A high C-index value of 0.66 was reached in the interval validation assessment. Decision curve analysis showed that the bleeding risk nomogram was clinically useful when intervention was decided at the possibility threshold of 16–84%. The bleeding risk model based on IFN-γ, IL-4, IL-10, IL-17A, IL-22, TGF-β, the count of PLT and the length of time of ITP could be conveniently used to predict the bleeding risk of ITP.

scholarly journals Development and validation of a nomogram for predicting severity in patients with hemorrhagic fever with renal syndrome: A retrospective study

Open Medicine ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. 944-954
Author(s):  
Zheng Yang ◽  
Qinming Hu ◽  
Zhipeng Feng ◽  
Yi Sun
Keyword(s):  
Retrospective Study ◽  
Risk Model ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Public Health Problem ◽  
Hemorrhagic Fever ◽  
Multiple Organ ◽  
Hantavirus Infection ◽  
Lasso Regression ◽  
Selection Operator

Abstract Background Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by hantavirus infection. Patients with severe HFRS may develop multiple organ failure or even death, which makes HFRS a serious public health problem. Methods In this retrospective study, we included a total of 155 consecutive patients who were diagnosed with HFRS, of whom 109 patients served as a training cohort and 46 patients as an independent verification cohort. In the training set, the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables of the risk model. Multivariate logistic regression analysis was used to construct a nomogram containing the characteristic variables selected in the LASSO regression model. Results The area under the receiver operating characteristic curve (AUC) of the nomogram indicated that the model had good discrimination. The calibration curve exhibited that the nomogram was in good agreement between the prediction and the actual observation. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility. Conclusion In this study, we established a simple and feasible model to predict severity in patients with HFRS, with which HFRS would be better identified and patients can be treated early.

scholarly journals Predicting Neovascular Glaucoma risk in a Chinese Proliferative Diabetic Retinopathy population: development and assessment of a new predictive nomogram

2020 ◽  
Author(s):  
Xinyue Zhang ◽  
li xu ◽  
Xiaolong Chen
Keyword(s):  
Diabetic Nephropathy ◽  
Regression Model ◽  
Clinical Data ◽  
Risk Model ◽  
Curve Analysis ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Anti Vegf ◽  
Selection Operator

Abstract Background: The aim of this study was to develop and evaluate a postoperative NVG risk nomogram based on the clinical data of a Chinese population of patients with PDR.Methods: A prediction model has been established based on the clinical data of 107 PDR patients who underwent vitrectomy from March,2017 to March,2018 in Shenyang Fourth People’s Hospital, and they were followed up for at least 12 months.The presence or absence of NVG were observed.The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the postoperative NVG risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. C-index, calibration plot, and decision curve analysis were also introduced to evaluate the model. The bootstrapping validation was also utilized to accomplish internal validation.Results: Risk factors screened out by the model included HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy. The model was testified with a satisfying C-index of 0.852 (95% CI: 0.740–0.964).Decision curve analysis showed that the NVG nomogram was clinically useful when intervention was adopted with the NVG possibility threshold of 2%.Conclusion: This novel nomogram revealed that a good control of HbAlc level, absence of diabetic nephropathy and anti-VEGF therapy are prophylactic factors of postoperative NVG in PDR patients.

scholarly journals A Nomogram for Predicting the Risk of Tuberculosis Infection

2021 ◽  
Author(s):  
Huanqing Liu ◽  
Tingting Li ◽  
Jun Lyn
Keyword(s):  
Liver Function ◽  
Regression Model ◽  
Predictive Model ◽  
Risk Model ◽  
Multivariate Logistic Regression Analysis ◽  
Infection Risk ◽  
Abnormal Liver Function ◽  
Smoking History ◽  
Training Dataset ◽  
Internal Validation

Abstract Background: Tuberculosis (TB) has become one of the main causes of deaths worldwide. Because of certain conditions prevent the early TB diagnosis and treatment to some extent. This study aimed to develop a tuberculosis (TB) infection risk model and validate the ability of nomogram to predict risk for TB infection in a Chinese population.Methods: A prediction model based on the training dataset of 272 patients was established. Minimum absolute shrinkage and selection operator regression model were adopted to optimize the feature selection of the TB infection risk model. Using multivariate logistic regression analysis, a predictive model combining the features selected in the minimum absolute shrinkage and the selected operator regression model was constructed. The ability of this predictive model to discriminate and calibrate TB infection risk and its utility in clinical settings were assessed via concordance index (C-index), calibration plot, area under time-dependent receiver operating characteristic curve (AUC), and decision curve analysis (DCA). The clinical practicality of nomogram was evaluated via net reclassification index (NRI) and integrated discrimination improvement (IDI). Bootstrapping validation allowed internal validation.Results: According to this predictive nomogram, the main predictors of TB infection risk were gender, age, smoking history, fever, hemoptysis, fatigue, emaciation, CD8, CD4/CD8, ESR, CRP, and abnormal liver function. The model exhibited superior risk calibration and discrimination with a C-index of 0.737 (95% CI: 0.685–0.789). The internal validation reached a C-index value of 0.688. The predictive model was able to produce an AUC of 0.729 (95% CI: 0.677–0.781). Analysis of the decision curve revealed the TB infection probability nomogram manifested its clinical usefulness on the condition that intervention was decided at the TB probability threshold of 13%. Moreover, results demonstrated that nomogram could be utilized as an effective prognostic tool according to NRI and IDI.Conclusion: The new TB probability nomogram for predicting TB infection risk developed herein that combines various factors, such gender, age, smoking history, fever, hemoptysis, fatigue, emaciation, CD8, CD4/CD8, ESR, CRP, and abnormal liver function is convenient and useful in predicting individual TB risks among patients.

scholarly journals Development and Validation of a Multivariable Predictive Model for Mortality of COVID-19 Patients Demanding High Oxygen Flow at Admission to ICU: AIDA Score

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Marija Zdravkovic ◽  
Viseslav Popadic ◽  
Slobodan Klasnja ◽  
Vedrana Pavlovic ◽  
Aleksandra Aleksic ◽  
...  
Keyword(s):  
Predictive Model ◽  
Predictive Value ◽  
Risk Model ◽  
Validation Cohort ◽  
External Validation ◽  
Health Resources ◽  
Severe Form ◽  
Derivation Cohort ◽  
Number Of Patients ◽  
Using Data

Introduction. Risk stratification is an important aspect of COVID-19 management, especially in patients admitted to ICU as it can provide more useful consumption of health resources, as well as prioritize critical care services in situations of overwhelming number of patients. Materials and Methods. A multivariable predictive model for mortality was developed using data solely from a derivation cohort of 160 COVID-19 patients with moderate to severe ARDS admitted to ICU. The regression coefficients from the final multivariate model of the derivation study were used to assign points for the risk model, consisted of all significant variables from the multivariate analysis and age as a known risk factor for COVID-19 patient mortality. The newly developed AIDA score was arrived at by assigning 5 points for serum albumin and 1 point for IL-6, D dimer, and age. The score was further validated on a cohort of 304 patients admitted to ICU due to the severe form of COVID-19. Results. The study population included 160 COVID-19 patients admitted to ICU in the derivation and 304 in the validation cohort. The mean patient age was 66.7 years (range, 20–93 years), with 68.1% men and 31.9% women. Most patients (76.8%) had comorbidities with hypertension (67.7%), diabetes (31.7), and coronary artery disease (19.3) as the most frequent. A total of 316 patients (68.3%) were treated with mechanical ventilation. Ninety-six (60.0%) in the derivation cohort and 221 (72.7%) patients in the validation cohort had a lethal outcome. The population was divided into the following risk categories for mortality based on the risk model score: low risk (score 0–1) and at-risk ( score > 1 ). In addition, patients were considered at high risk with a risk score > 2 . By applying the risk model to the validation cohort ( n = 304 ), the positive predictive value was 78.8% (95% CI 75.5% to 81.8%); the negative predictive value was 46.6% (95% CI 37.3% to 56.2%); the sensitivity was 82.4% (95% CI 76.7% to 87.1%), and the specificity was 41.0% (95% CI 30.3% to 52.3%). The C statistic was 0.863 (95% CI 0.805-0.921) and 0.665 (95% CI 0.598-0.732) in the derivation and validation cohorts, respectively, indicating a high discriminative value of the proposed score. Conclusion. In the present study, AIDA score showed a valuable significance in estimating the mortality risk in patients with the severe form of COVID-19 disease at admission to ICU. Further external validation on a larger group of patients is needed to provide more insights into the utility of this score in everyday practice.

Prediction of Poor Outcome in Intracerebral Hemorrhage Based on Computed Tomography Markers

2020 ◽  
Vol 49 (5) ◽  
pp. 556-562
Author(s):  
Chaonan Du ◽  
Boxue Liu ◽  
Mingfei Yang ◽  
Qiang Zhang ◽  
Qingfang Ma ◽  
...  
Keyword(s):  
Black Hole ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Calibration Plot ◽  
Midline Shift ◽  
Multivariate Logistic Regression ◽  
Poor Outcome

<b><i>Introduction:</i></b> Intracerebral hemorrhage (ICH) is the most fatal type of stroke worldwide. Herein, we aim to develop a predictive model based on computed tomography (CT) markers in an ICH cohort and validate it in another cohort. <b><i>Methods:</i></b> This retrospective observational cohort study was conducted in 3 medical centers in China. The values of CT markers, including hypodensities, hematoma density, blend sign, black hole sign, island sign, midline shift, baseline hematoma volume, and satellite sign, in predicting poor outcome were analyzed by logistic regression analysis. A nomogram was developed based on the results of multivariate logistic regression analysis in development cohort. Area under curve (AUC) and calibration plot were used to assess the accuracy of nomogram in this development cohort and validate in another cohort. <b><i>Results:</i></b> A total of 1,498 patients were included in this study. Multivariate logistic regression analysis indicated that hypodensities, black hole sign, island sign, midline shift, and baseline hematoma volume were independently associated with poor outcome in development cohort. The AUC was 0.75 (95% confidence interval [CI]: 0.73–0.76) in the internal validation with development cohort and 0.74 (95% CI: 0.72–0.75) in the external validation with validation cohort. The calibration plot in development and validation cohort indicated that the nomogram was well calibrated. <b><i>Conclusions:</i></b> CT markers of hypodensities, black hole sign, and island sign might predict poor outcome of ICH patients within 90 days.

scholarly journals Luteolin transforms the polarity of bone marrow-derived macrophages to regulate the cytokine storm

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Shuxia Wang ◽  
Shuhang Xu ◽  
Jing Zhou ◽  
Li Zhang ◽  
Xiaodong Mao ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Inflammatory Responses ◽  
Bone Marrow Cells ◽  
Membrane Surface ◽  
Inflammatory Factors ◽  
Mouse Bone Marrow ◽  
Macrophage Polarisation ◽  
Ifn Γ ◽  
Anti Inflammatory ◽  
M1 Type

Abstract Background Macrophages are indispensable regulators of inflammatory responses. Macrophage polarisation and their secreted inflammatory factors have an association with the outcome of inflammation. Luteolin, a flavonoid abundant in plants, has anti-inflammatory activity, but whether luteolin can manipulate M1/M2 polarisation of bone marrow-derived macrophages (BMDMs) to suppress inflammation is still unclear. This study aimed to observe the effects of luteolin on the polarity of BMDMs derived from C57BL/6 mice and the expression of inflammatory factors, to explore the mechanism by which luteolin regulates the BMDM polarity. Methods M1-polarised BMDMs were induced by lipopolysaccharide (LPS) + interferon (IFN)-γ and M2-polarisation were stimulated with interleukin (IL)-4. BMDM morphology and phagocytosis were observed by laser confocal microscopy; levels of BMDM differentiation and cluster of differentiation (CD)11c or CD206 on the membrane surface were assessed by flow cytometry (FCM); mRNA and protein levels of M1/M2-type inflammatory factors were performed by qPCR and ELISA, respectively; and the expression of p-STAT1 and p-STAT6 protein pathways was detected by Western-blotting. Results The isolated mouse bone marrow cells were successfully differentiated into BMDMs, LPS + IFN-γ induced BMDM M1-phenotype polarisation, and IL-4 induced M2-phenotype polarisation. After M1-polarised BMDMs were treated with luteolin, the phagocytosis of M1-polarized BMDMs was reduced, and the M1-type pro-inflammatory factors including IL-6, tumour necrosis factor (TNF)-α, inducible nitric oxide synthase (iNOS), and CD86 were downregulated while the M2-type anti-inflammatory factors including IL-10, IL-13, found in inflammatory zone (FIZZ)1, Arginase (Arg)1 and CD206 were upregulated. Additionally, the expression of M1-type surface marker CD11c decreased. Nevertheless, the M2-type marker CD206 increased; and the levels of inflammatory signalling proteins phosphorylated signal transducer and activator of transcription (p-STAT)1 and p-STAT6 were attenuated and enhanced, respectively. Conclusions Our study suggests that luteolin may transform BMDM polarity through p-STAT1/6 to regulate the expression of inflammatory mediators, thereby inhibiting inflammation. Naturally occurring luteolin holds promise as an anti-inflammatory and immunomodulatory agent.

Development and Validation of a Nomogram for Predicting Bladder Calculi Risk in Patients With Benign Prostatic Hyperplasia

2021 ◽  
Author(s):  
Euxu Xie ◽  
Xuelian Gu ◽  
Chen Ma ◽  
Li Guo ◽  
Man Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Benign Prostatic Hyperplasia ◽  
Characteristic Curve ◽  
Prostatic Hyperplasia ◽  
Calibration Plot ◽  
Lasso Regression ◽  
Net Benefit ◽  
Bladder Calculi ◽  
Validation Set ◽  
Selection Operator

Abstract Objective To develop and validate a nomogram for predicting bladder calculi risk in patients with benign prostatic hyperplasia (BPH).Methods A total of 368 patients who underwent transurethral resection of the prostate (TURP) and had histologically proven BPH from January 2018 to January 2021 were retrospectively collected. Eligible patients were randomly assigned to the training and validation datasets. Least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal risk factors. A prediction model was established based on the selected characteristics. The performance of the nomogram was assessed by calibration plots and the area under the receiver operating characteristic curve (AUROC). Furthermore, decision curve analysis (DCA) was used to determine the net benefit rate of of the nomogram. Results Among 368 patients who met the inclusion criteria, older age, a history of diabetes and hyperuricemia, longer intravesical prostatic protrusion (IPP)and larger prostatic urethral angulation (PUA) were independent risk factors for bladder calculi in patients with BPH. These factors were used to develop a nomogram, which had a good identification ability in predicting the risk of bladder calculi in patients, with AUROCs of 0.911 (95% CI: 0.876–0.945) in the training set and 0.884 (95% CI: 0.820–0.948) in the validation set. The calibration plot showed that the model had good calibration. Moreover, DCA indicated that the model had a goodclinical benefit. Conclusion We developed and internally validated the first nomogram to date to help physicians assess the risk of bladder calculi in patients with BPH, which may help physicians improve individual interventions and make better clinical decisions.

scholarly journals A novel procalcitonin-based score for detecting sepsis among critically ill patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245748
Author(s):  
Tung-Lin Tsui ◽  
Ya-Ting Huang ◽  
Wei-Chih Kan ◽  
Mao-Sheng Huang ◽  
Min-Yu Lai ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Sepsis Group ◽  
C Reactive Protein ◽  
Multivariate Logistic Regression ◽  
Reactive Protein

Background Procalcitonin (PCT) has been widely investigated as an infection biomarker. The study aimed to prove that serum PCT, combining with other relevant variables, has an even better sepsis-detecting ability in critically ill patients. Methods We conducted a retrospective cohort study in a regional teaching hospital enrolling eligible patients admitted to intensive care units (ICU) between July 1, 2016, and December 31, 2016, and followed them until March 31, 2017. The primary outcome measurement was the occurrence of sepsis. We used multivariate logistic regression analysis to determine the independent factors for sepsis and constructed a novel PCT-based score containing these factors. The area under the receiver operating characteristics curve (AUROC) was applied to evaluate sepsis-detecting abilities. Finally, we validated the score using a validation cohort. Results A total of 258 critically ill patients (70.9±16.3 years; 55.4% man) were enrolled in the derivation cohort and further subgrouped into the sepsis group (n = 115) and the non-sepsis group (n = 143). By using the multivariate logistic regression analysis, we disclosed five independent factors for detecting sepsis, namely, “serum PCT level,” “albumin level” and “neutrophil-lymphocyte ratio” at ICU admission, along with “diabetes mellitus,” and “with vasopressor.” We subsequently constructed a PCT-based score containing the five weighted factors. The PCT-based score performed well in detecting sepsis with the cut-points of 8 points (AUROC 0.80; 95% confidence interval (CI) 0.74–0.85; sensitivity 0.70; specificity 0.76), which was better than PCT alone, C-reactive protein and infection probability score. The findings were confirmed using an independent validation cohort (n = 72, 69.2±16.7 years, 62.5% men) (cut-point: 8 points; AUROC, 0.79; 95% CI 0.69–0.90; sensitivity 0.64; specificity 0.87). Conclusions We proposed a novel PCT-based score that performs better in detecting sepsis than serum PCT levels alone, C-reactive protein, and infection probability score.

scholarly journals Adverse pregnancy outcomes are associated with Plasmodium vivax malaria in a prospective cohort of women from the Brazilian Amazon

2021 ◽  
Vol 15 (4) ◽  
pp. e0009390
Author(s):  
Jamille Gregório Dombrowski ◽  
André Barateiro ◽  
Erika Paula Machado Peixoto ◽  
André Boler Cláudio da Silva Barros ◽  
Rodrigo Medeiros de Souza ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Pregnancy Outcomes ◽  
Multivariate Logistic Regression Analysis ◽  
Brazilian Amazon ◽  
First Trimester ◽  
Inflammatory Factors ◽  
Anthropometric Measures ◽  
First Trimester Of Pregnancy ◽  
Adverse Pregnancy ◽  
Angiopoietin 2

Background Malaria in Brazil represents one of the highest percentages of Latin America cases, where approximately 84% of infections are attributed to Plasmodium (P.) vivax. Despite the high incidence, many aspects of gestational malaria resulting from P. vivax infections remain poorly studied. As such, we aimed to evaluate the consequences of P. vivax infections during gestation on the health of mothers and their neonates in an endemic area of the Amazon. Methods and findings We have conducted an observational cohort study in Brazilian Amazon between January 2013 and April 2015. 600 pregnant women were enrolled and followed until delivery. After applying exclusion criteria, 329 mother-child pairs were included in the analysis. Clinical data regarding maternal infection, newborn’s anthropometric measures, placental histopathological characteristics, and angiogenic and inflammatory factors were evaluated. The presence of plasma IgG against the P. vivax (Pv) MSP119 protein was used as marker of exposure and possible associations with pregnancy outcomes were analyzed. Multivariate logistic regression analysis revealed that P. vivax infections during the first trimester of pregnancy are associated with adverse gestational outcomes such as premature birth (adjusted odds ratio [aOR] 8.12, 95% confidence interval [95%CI] 2.69–24.54, p < 0.0001) and reduced head circumference (aOR 3.58, 95%CI 1.29–9.97, p = 0.01). Histopathology analysis showed marked differences between placentas from P. vivax-infected and non-infected pregnant women, especially regarding placental monocytes infiltrate. Placental levels of vasomodulatory factors such as angiopoietin-2 (ANG-2) and complement proteins such as C5a were also altered at delivery. Plasma levels of anti-PvMSP119 IgG in infected pregnant women were shown to be a reliable exposure marker; yet, with no association with improved pregnancy outcomes. Conclusions This study indicates that P. vivax malaria during the first trimester of pregnancy represents a higher likelihood of subsequent poor pregnancy outcomes associated with marked placental histologic modification and angiogenic/inflammatory imbalance. Additionally, our findings support the idea that antibodies against PvMSP119 are not protective against poor pregnancy outcomes induced by P. vivax infections.

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