Clinical Prediction
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2021 ◽  
Yin-Hong Geng ◽  
Zhe Zhang ◽  
Jun-Jun Zhang ◽  
Bo Huang ◽  
Zui-Shuang Guo ◽  

Abstract Objective. To construct a novel nomogram model that predicts the risk of hyperuricemia incidence in IgA nephropathy (IgAN) . Methods. Demographic and clinicopathological characteristics of 1184 IgAN patients in the First Affiliated Hospital of Zhengzhou University Hospital were collected. Univariate analysis and multivariate logistic regression were used to screen out hyperuricemia risk factors. The risk factors were used to establish a predictive nomogram model. The performance of the nomogram model was evaluated using an area under the receiver operating characteristic curve (AUC), calibration plots, and a decision curve analysis. Results. Independent predictors for hyperuricemia incidence risk included sex, hypoalbuminemia, hypertriglyceridemia, blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), 24-hour urinaryprotein (24h TP), Gross and tubular atrophy/interstitial fibrosis (T). The nomogram model exhibited moderate prediction ability with an AUC of 0.834 ((95% CI 0.804–0.864)). The AUC from validation reached 0.787 (95% CI 0.736-0.839). The decision curve analysis displayed that the hyperuricemia risk nomogram was clinically applicable.Conclusion. Our novel and simple nomogram containing 8 factors may be useful in predicting hyperuricemia incidence risk in IgAN.

2021 ◽  
David M. Kent ◽  
Jason Nelson ◽  
Jenica N. Upshaw ◽  
Gaurav Gulati ◽  
Riley Brazil ◽  

2021 ◽  
Vol 21 (1) ◽  
Masaaki Sakuraya ◽  
Takuo Yoshida ◽  
Yusuke Sasabuchi ◽  
Shodai Yoshihiro ◽  
Shigehiko Uchino

Abstract Purpose This study sought to describe the epidemiology of anticoagulation therapy for critically ill patients with new-onset atrial fibrillation (NOAF) according to CHA2DS2-VASc and HAS-BLED scores and to assess the efficacy of early anticoagulation therapy. Method Adult patients who developed NOAF during intensive care unit stay were included. We compared the patients who were treated with and without anticoagulation therapy within 48 h from AF onset. The primary outcome was a composite outcome that included mortality and ischemic stroke during the period until hospital discharge. Results In total, 308 patients were included in this analysis. Anticoagulants were administered to 95 and 33 patients within 48 h and after 48 h from NOAF onset, respectively. After grouping the patients into four according to their CHA2DS2-VASc and HAS-BLED bleeding scores, we found that the proportion of anticoagulation therapy administered was similar among all groups. After adjustment using a multivariable Cox regression model, we noted that early anticoagulation therapy did not decrease the composite outcome (adjusted hazard ratio [HR] 0.77; 95% confidence interval [CI] 0.47‒1.23). However, in patients without rhythm control drugs, early anticoagulation was significantly associated with better outcomes (adjusted HR 0.46; 95% CI; 0.22‒0.87, P = 0.041). Conclusions We found that clinical prediction scores were supposedly not used in the decision to implement anticoagulation therapy and that early anticoagulation therapy did not improve clinical outcomes in critically ill patients with NOAF. Trial registration UMIN-CTR UMIN000026401. Registered 5 March 2017.

2021 ◽  
Amelie T. Ven ◽  
Jessika Johannsen ◽  
Fanny Kortüm ◽  
Matias Wagner ◽  
Konstantinos Tsiakas ◽  

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii28-ii28
X Xue ◽  
Q Gao

Abstract OBJECTIVE WHO grade II glioma has the characteristics of heterogeneity, and this disease progresses rapidly in some patients, in whom the malignant degree is equivalent to that of high-grade glioma. In order to accurately predict the prognosis of patients, an effective clinical prediction model based on relevant risk factors is needed which could provide a theoretical basis for optimization of clinical individualized treatment. METHODS According to the inclusion and exclusion criteria, eligible patients from January 2010 to December 2018 in our hospital were selected, and those who met the criteria were randomly assigned 4:1 to the training group and the validation group, respectively. The predictors were screened by univariate and multivariate Cox regression analysis, the prediction model was established, and the model was verified and evaluated. RESULTS A total of 258 patients with WHO grade II glioma were recruited, including 208 patients as the training group and 50 patients as the validation group. Six independent risk factors, including patient age, preoperative Karnofsky performance status (KPS) score, preoperative seizure symptoms, surgical resection range, tumor size and IDH status, were selected and included into the prediction model by univariate and multivariate Cox regression analysis, and were visualized in the form of Nomogram. The concordance index (C index) was used to evaluate the predictive ability of the model. Results showed that the C-index was 0.832 in the training group and 0.853 in the validation group, respectively, indicating good performance for the prediction model. The calibration charts were drawn in both groups respectively, which showed that the calibration lines were in good agreement with the standard lines, indicating good consistency between the two groups. CONCLUSIONS In this study, a clinical prediction model for WHO grade II glioma was established, and it was verified that the model has good predictive ability, which may be beneficial for clinical work.

2021 ◽  
Vol 28 (1) ◽  
pp. e100267
Keerthi Harish ◽  
Ben Zhang ◽  
Peter Stella ◽  
Kevin Hauck ◽  
Marwa M Moussa ◽  

ObjectivesPredictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data.MethodsWe performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020.ResultsMost models failed validation when applied to our institution’s data. Included studies reported an average validation area under the receiver–operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies’ reported AUROC values.DiscussionPublished and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations.ConclusionsClinicians should employ caution when applying models for clinical prediction without careful validation on local data.

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Xiaobin Liu ◽  
Jing Xue ◽  
Xiaoyi Guo ◽  
Yijie Ding ◽  
Yi Zhang ◽  

Background. Known as an autoimmune glomerular disease, idiopathic membranous nephropathy (IMN) is considered to be associated with phospholipase A2 receptor (PLA2R) in terms of the main pathogenesis. The quantitative detection of serum PLA2R-IgG and PLA2R-IgG4 antibodies by time-resolved fluoroimmunoassay (TRFIA) was determined, and the value of them, both in the clinical prediction of risk stratification in IMN, was observed in this study. Methods. 95 patients with IMN proved by renal biopsy were enrolled, who had tested positive for serum PLA2R antibodies by ELISA, and the quantitative detection of serum PLA2R-IgG and PLA2R-IgG4 antibodies was achieved by TRFIA. All the patients were divided into low-, medium-, and high-risk groups, respectively, which were set as dependent variables, according to proteinuria and renal function. Random forest (RF) was used to estimate the value of serum PLA2R-IgG and PLA2R-IgG4 in predicting the risk stratification of progression in IMN. Results. Out-of-bag estimates of variable importance in RF were employed to evaluate the impact of each input variable on the final classification accuracy. The variable of albumin, PLA2R-IgG, and PLA2R-IgG4 had high values (>0.3) of 0.3156, 0.3981, and 0.7682, respectively, which meant that these three were more important for the risk stratification of progression in IMN. In order to further assess the contribution of PLA2R-IgG and PLA2R-IgG4 to the model, we built four different models and found that PLA2R-IgG4 played an important role in improving the predictive ability of the model. Conclusions. In this study, we established a random forest model to evaluate the value of serum PLA2R-IgG4 antibodies in predicting risk stratification of IMN. Compared with PLA2R-IgG, PLA2R-IgG4 is a more efficient biomarker in predicting the risk of progression in IMN.

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