scholarly journals A Validation Study Comparing Risk Prediction Models of IgA Nephropathy

2021 ◽  
Vol 12 ◽  
Author(s):  
Yan Ouyang ◽  
Zhanzheng Zhao ◽  
Guisen Li ◽  
Huimin Luo ◽  
Feifei Xu ◽  
...  
Keyword(s):  
Prediction Models ◽  
Risk Model ◽  
Collaborative Study ◽  
Low Risk ◽  
Composite Outcome ◽  
Risk Models ◽  
Clinical Indicators ◽  
Clinical Model ◽  
Risk Patients

We aimed to validate three IgAN risk models proposed by an international collaborative study and another CKD risk model generated by an extended CKD cohort with our multicenter Chinese IgAN cohort. Biopsy-proven IgAN patients with an eGFR ≥15 ml/min/1.73 m2 at baseline and a minimum follow-up of 6 months were enrolled. The primary outcomes were a composite outcome (50% decline in eGFR or ESRD) and ESRD. The performance of those models was assessed using discrimination, calibration, and reclassification. A total of 2,300 eligible cases were enrolled. Of them, 288 (12.5%) patients reached composite outcome and 214 (9.3%) patients reached ESRD during a median follow-up period of 30 months. Using the composite outcome for analysis, the Clinical, Limited, Full, and CKD models had relatively good performance with similar C statistics (0.81, 0.81, 0.82, and 0.82, respectively). While using ESRD as the end point, the four prediction models had better performance (all C statistics > 0.9). Furthermore, subgroup analysis showed that the models containing clinical and pathological variables (Full model and Limited model) had better discriminatory abilities than the models including only clinical indicators (Clinical model and CKD model) in low-risk patients characterized by higher baseline eGFR (≥60 ml/min/1.73 m2). In conclusion, we validated recently reported IgAN and CKD risk models in our Chinese IgAN cohort. Compared to pure clinical models, adding pathological variables will increase performance in predicting ESRD in low-risk IgAN patients with baseline eGFR ≥60 ml/min/1.73 m2.

scholarly journals P0343RISK PREDICTION MODELS FOR PREDICTING PROGRESSION OF IGA NEPHROLOGY:A VALIDATION STUDY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Jingyuan Xie
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Collaborative Study ◽  
Clinical Parameters ◽  
Full Model ◽  
Risk Prediction Models ◽  
Clinical Model ◽  
C Statistic ◽  
Model C

Abstract Background and Aims Risk prediction models for IgA nephropathy (IgAN) containing clinical variables (clinical model) or clinical plus pathological variables (full model) have been established based on a large international collaborative study recently, but external validation of these models are still required before clinical application. The aim of this study is to externally validate previously reported risk prediction models based on our multi-center IgAN cohort. Method Biopsy-proven IgAN patients with eGFR ≥15 ml/min/1.73 m2 at baseline and a minimum follow-up of 6 months were enrolled. Primary outcome was defined as end-stage kidney disease (ESRD). Cox proportional hazards models were built to validate risk models. R2, Akaike information criterion (AIC) and C statistic were calculated to evaluate model accuracy. Results A total of 2300 IgAN patients with a median follow-up of 30 months were enrolled, and 214(9.3%) ESRD occurred during the follow-up period. The median age was 35(interquartile range, 28-44) years, and 1106 cases (48.1%) were men. Our cohort successfully validated the clinical model and the full model based on C statistic (0.90 and 0.91) and R2 (0.32 and 0.32). Our results showed limited improvement in model performance after adding the Oxford classification parameters to clinical parameters. However, both two models performed better than the model consisting only pathological parameters(C statistic 0.83, R2 0.24). We also validated other risk prediction models, including CLIN model (C statistic 0.90, R2 0.32) and CLINPATH model (C statistic 0.91, R2 0.31) derived from Chinese IgAN patients or CKD model (C statistic 0.90, R2 0.32) derived from Canadian CKD patients. It was found that clinical models based on different combinations of clinical parameters performed similarly. Conclusion In summary, we successfully validated a recently reported IgAN risk model and we found that clinical parameters alone could accurately predict ESRD risk in IgAN patients.

scholarly journals Anticoagulant Therapy in Initially Low‐Risk Patients With Nonvalvular Atrial Fibrillation Who Develop Risk Factors

2020 ◽  
Vol 9 (16) ◽  
Author(s):  
Sun Young Choi ◽  
Moo Hyun Kim ◽  
Kwang Min Lee ◽  
Young‐Rak Cho ◽  
Jong Sung Park ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Stroke Risk ◽  
Oral Anticoagulants ◽  
Low Risk ◽  
Composite Outcome ◽  
Stroke Risk Factor ◽  
Stroke Risk Factors ◽  
Risk Patients

Background The CHA 2 DS 2 ‐VASc score has been validated for stroke risk prediction in patients with atrial fibrillation (AF). Antithrombotic therapy is not recommended for low‐risk patients with AF (CHA 2 DS 2 ‐VASc 0 [male] or 1 [female]). We studied a cohort of initially low‐risk patients with AF in relation to their development of incident comorbidities and their treatment on oral anticoagulation therapy. Methods and Results We assessed data from 14 441 low‐risk patients with AF (CHA 2 DS 2 ‐VASc score of 0 [male] or 1 [female]) using the Korean National Health Insurance Service database, in relation to their development of incident stroke risk factors and adverse outcomes. The clinical end point was the occurrence of ischemic stroke, major bleeding, all‐cause death, or the composite outcome (ischemic stroke + major bleeding + all‐cause death). In our cohort, 2615 (29.1%) male and 1650 (30.3%) female patients acquired at least 1 new stroke risk factor during a mean follow‐up of 2.0 years. Among the patients with an increasing CHA 2 DS 2 ‐VASc score ≥1, male and female patients treated with oral anticoagulants had a significantly lower risk of ischemic stroke (male: hazard ratio [HR], 0.62 [95% CI, 0.44–0.82; P =0.003]; female: HR, 0.65 [95% CI, 0.47–0.84; P =0.007]), all‐cause death (male: HR, 0.67 [95% CI, 0.49–0.88; P =0.009]; female: HR, 0.82 [95% CI, 0.63–1.02; P =0.185]), and composite outcomes (male: HR, 0.78 [95% CI, 0.61–0.95; P =0.042]; female: HR, 0.79 [95% CI, 0.62–0.96; P =0.045]) than patients not treated with oral anticoagulants. Conclusions Approximately 30% of patients acquired ≥1 stroke risk factor over a 2‐year follow‐up period. Low‐risk patients with AF should be regularly reassessed to adequately identify those with incident stroke risk factors that would merit thromboprophylaxis for the prevention of stroke and the composite outcome.

scholarly journals Ki67 Predicts Progression in Early CIN: Validation of a Multivariate Progression-Risk Model

2004 ◽  
Vol 26 (1-2) ◽  
pp. 13-20
Author(s):  
Arnold-Jan Kruse ◽  
Jan P. A. Baak ◽  
Emiel A. Janssen ◽  
Kjell-Henning Kjellevold ◽  
Bent Fianec ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Value ◽  
Risk Model ◽  
Subjective Evaluation ◽  
Low Risk ◽  
Subjective Analysis ◽  
Risk Patients ◽  
Progression Risk ◽  
Quantitative Assessments

This study of early CIN biopsies (25 CIN1 and 65 CIN2) with long follow-up was done to validate, in a new group of patients, the value of Ki67 immuno-quantitative features to predict high CIN grade in a follow-up biopsy (often denoted to as “progression”), as described in a previous study. Each biopsy in the present study was classified with the previously described Ki67-model (consisting of the stratification index and the % positive nuclei in the middle third layer of the epithelium) as “low-risk” or “high-risk”, and matched with the follow-up outcome (progression-or-not). Furthermore, it was studied whether subjective evaluation of the Ki67 sections by experienced pathologists, who were aware of the prognostic quantitative Ki67 features, could also predict the outcome. Thirdly, the reproducibility of routine use of the quantitative Ki67-model was assessed. Fifteen cases progressed (17%) to CIN3, 2/25 CIN1 (8%) and 13/65 CIN2 (20%), indicating that CIN grade (as CIN1 or CIN2) is prognostic and that the percentage of CIN1 and CIN2 cases with progression in the present study is comparable to many previous studies. However, the quantitative Ki67 model had stronger prognostic value than CIN grade as none of the 40 “Ki67-model low-risk” patients progressed, in contrast to 15 (30%) of the 50 “Ki67-model high-risk” patients (p < 0.001). In multivariate analysis, neither CIN grade nor any of the other quantitative Ki67 features added to the abovementioned prognostic Ki67-model. Subjective analysis of the Ki67 features was also prognostic, although quantitative assessments gave better results. Routine application of the quantitative Ki67-model in CIN1 and CIN2 was well reproducible. In conclusion, the results confirm that quantitative Ki67 features have strong prognostic value for progression in early CIN lesions.

Prospective 12-Year Follow-up Study of Clinical and Hemodynamic Sequelae After Deep Vein Thrombosis in Low-Risk Patients (Zürich Study)

Circulation ◽  
1996 ◽  
Vol 93 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Ulrich K. Franzeck ◽  
Ilse Schalch ◽  
Kurt A. Jäger ◽  
Ernst Schneider ◽  
Jörg Grimm ◽  
...  
Keyword(s):  
Deep Vein Thrombosis ◽  
Low Risk ◽  
Vein Thrombosis ◽  
Follow Up Study ◽  
Risk Patients ◽  
Deep Vein

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

scholarly journals Accuracy of upper endoscopies with random biopsies to identify patients with gastric premalignant lesions who can safely be exempt from surveillance

2021 ◽  
Vol 24 (3) ◽  
pp. 680-690
Author(s):  
Michiel C. Mommersteeg ◽  
Stella A. V. Nieuwenburg ◽  
Wouter J. den Hollander ◽  
Lisanne Holster ◽  
Caroline M. den Hoed ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Low Risk ◽  
Premalignant Lesions ◽  
High Risk Patients ◽  
European Guidelines ◽  
Progression Of Disease ◽  
Risk Patients ◽  
Progression Risk

Abstract Introduction Guidelines recommend endoscopy with biopsies to stratify patients with gastric premalignant lesions (GPL) to high and low progression risk. High-risk patients are recommended to undergo surveillance. We aimed to assess the accuracy of guideline recommendations to identify low-risk patients, who can safely be discharged from surveillance. Methods This study includes patients with GPL. Patients underwent at least two endoscopies with an interval of 1–6 years. Patients were defined ‘low risk’ if they fulfilled requirements for discharge, and ‘high risk’ if they fulfilled requirements for surveillance, according to European guidelines (MAPS-2012, updated MAPS-2019, BSG). Patients defined ‘low risk’ with progression of disease during follow-up (FU) were considered ‘misclassified’ as low risk. Results 334 patients (median age 60 years IQR11; 48.7% male) were included and followed for a median of 48 months. At baseline, 181/334 (54%) patients were defined low risk. Of these, 32.6% were ‘misclassified’, showing progression of disease during FU. If MAPS-2019 were followed, 169/334 (51%) patients were defined low risk, of which 32.5% were ‘misclassified’. If BSG were followed, 174/334 (51%) patients were defined low risk, of which 32.2% were ‘misclassified’. Seven patients developed gastric cancer (GC) or dysplasia, four patients were ‘misclassified’ based on MAPS-2012 and three on MAPS-2019 and BSG. By performing one additional endoscopy 72.9% (95% CI 62.4–83.3) of high-risk patients and all patients who developed GC or dysplasia were identified. Conclusion One-third of patients that would have been discharged from GC surveillance, appeared to be ‘misclassified’ as low risk. One additional endoscopy will reduce this risk by 70%.

scholarly journals A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

2020 ◽  
Author(s):  
Yi Ding ◽  
Tian Li ◽  
Min Li ◽  
Tuersong Tayier ◽  
MeiLin Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Clinical Information ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Positive Regulation ◽  
Risk Patients ◽  
Cox Analysis

Abstract Background: Autophagy and long non-coding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.Methods: We downloaded RNA-sequencing data and clinical information of melanoma from The Cancer Genome Atlas. The co-expression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate COX regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.Results: According to the results of the univariate COX analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate COX analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p<0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.Conclusion: The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNAs risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

Algorithms for Individual Prediction of COVID-19 infection and Hospitalization among Symptomatic patients based on health data (Preprint)

2021 ◽  
Author(s):  
Rossella Murtas ◽  
Nuccia Morici ◽  
Chiara Cogliati ◽  
Massimo Puoti ◽  
Barbara Omazzi ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Health Data ◽  
Low Risk ◽  
Risk Prediction Models ◽  
Predictive Algorithms ◽  
Medium Risk ◽  
Risk Patients ◽  
At Home

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has generated a huge strain on the health care system worldwide. The metropolitan area of Milan, Italy was one of the most hit area in the world. OBJECTIVE Robust risk prediction models are needed to stratify individual patient risk for public health purposes METHODS Two predictive algorithms were implemented in order to foresee the probability of being a COVID-19 patient and the risk of being hospitalized. The predictive model for COVID-19 positivity was developed in 61.956 symptomatic patients, whereas the model for COVID-19 hospitalization was developed in 36.834 COVID-19 positive patients. Exposures considered were age, gender, comorbidities and symptoms associated with COVID-19 (vomiting, cough, fever, diarrhoea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnoea). RESULTS The predictive models showed a good fit for predicting COVID-19 disease [AUC 72.6% (95% CI 71.6%-73.5%)] and hospitalization [AUC 79.8% (95% CI 78.6%-81%)]. Using these results, 118,804 patients with COVID-19 from October 25 to December 11, 2020 were stratified into low, medium and high risk for COVID-19 severity. Among the overall population, 67.030 (56%) were classified as low-risk, 43.886 (37%) medium-risk, and 7.888 (7%) high-risk, with 89% of the overall population being assisted at home, 9% hospitalized, and 2% dead. Among those assisted at home, most people (60%) were classified as low risk, whereas only 4% were classified at high risk. According to ordinal logistic regression, the OR of being hospitalised or dead was 5.0 (95% CI 4.6-5.4) in high-risk patients and 2.7 (95% CI 2.6-2.9) in medium-risk patients, as compared to low-risk patients. CONCLUSIONS A simple monitoring system, based on primary care datasets with linkage to COVID-19 testing results, hospital admissions data and death records may assist in proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

scholarly journals Mortality in low-risk patients with aortic stenosis undergoing transcatheter or surgical aortic valve replacement: a reconstructed individual patient data meta-analysis

2020 ◽  
Vol 31 (5) ◽  
pp. 587-594
Author(s):  
Mevlüt Çelik ◽  
Milan M Milojevic ◽  
Andras P Durko ◽  
Frans B S Oei ◽  
Ad J J C Bogers ◽  
...  
Keyword(s):  
Data Analysis ◽  
Aortic Stenosis ◽  
Individual Patient Data ◽  
Severe Aortic Stenosis ◽  
Mortality Rates ◽  
Patient Data ◽  
Low Risk ◽  
All Cause Mortality ◽  
Risk Patients

Abstract OBJECTIVES Although the standard of care for patients with severe aortic stenosis at low-surgical risk has included surgical aortic valve replacement (SAVR) since the mid-1960s, many clinical studies have investigated whether transcatheter aortic valve implantation (TAVI) can be a better approach in these patients. As no individual study has been performed to detect the difference in mortality between these 2 treatment strategies, we did a reconstructive individual patient data analysis to study the long-term difference in all-cause mortality. METHODS Randomized clinical trials and propensity score-matched studies that included low-risk adult patients with severe aortic stenosis undergoing either SAVR or TAVI and with reports on the mortality rates during the follow-up period were considered. The primary outcome was all-cause mortality of up to 5 years. RESULTS In the reconstructed individual patient data analysis, there was no statistically significant difference in all-cause mortality between TAVI and SAVR at 5 years of follow-up [30.7% vs 21.4%, hazard ratio (HR) 1.19, 95% confidence interval (CI) 0.96–1.48; P = 0.104]. However, landmark analyses in patients surviving up to 1 year of follow-up showed significantly higher all-cause mortality at 5 years of follow-up (27.5% vs 17.3%, HR 1.77, 95% CI 1.29–2.43; P &lt; 0.001) in patients undergoing TAVI compared to patients undergoing SAVR, respectively. CONCLUSIONS This reconstructed individual patient data analysis in low-risk patients with severe aortic stenosis demonstrates that the 5-year all-cause mortality rates are higher after TAVI than after SAVR, driven by markedly higher mortality rates between 1 and 5 years of follow-up in the TAVI group. The present results call for caution in expanding the TAVI procedure as the treatment of choice for the majority of all low-risk patients until long-term data from contemporary randomized clinical trials are available.

P9 Novel use of CHA2DS2VASC score to select patients to undergo repeat atrial fibrillation screening

2020 ◽  
Vol 41 (Supplement_1) ◽  
Author(s):  
W Sun ◽  
B P Y Yan
Keyword(s):  
Atrial Fibrillation ◽  
High Risk ◽  
Clinical Outcomes ◽  
Cross Validation ◽  
Risk Group ◽  
Curve Analysis ◽  
Low Risk ◽  
Decision Curve Analysis ◽  
Risk Patients

Abstract Background We have previously demonstrated unselected screening for atrial fibrillation (AF) in patients ≥65 years old in an out-patient setting yielded 1-2% new AF each time screen-negative patients underwent repeated screening at 12 to 18 month interval. Selection criteria to identify high-risk patients for repeated AF screening may be more efficient than repeat screening on all patients. Aims This study aimed to validate CHA2DS2VASC score as a predictive model to select target population for repeat AF screening. Methods 17,745 consecutive patients underwent 24,363 index AF screening (26.9% patients underwent repeated screening) using a handheld single-lead ECG (AliveCor) from Dec 2014 to Dec 2017 (NCT02409654). Adverse clinical outcomes to be predicted included (i) new AF detection by repeated screening; (ii) new AF clinically diagnosed during follow-up and (ii) ischemic stroke/transient ischemic attack (TIA) during follow-up. Performance evaluation and validation of CHA2DS2VASC score as a prediction model was based on 15,732 subjects, 35,643 person-years of follow-up and 765 outcomes. Internal validation was conducted by method of k-fold cross-validation (k = n = 15,732, i.e., Leave-One-Out cross-validation). Performance measures included c-index for discriminatory ability and decision curve analysis for clinical utility. Risk groups were defined as ≤1, 2-3, or ≥4 for CHA2DS2VASC scores. Calibration was assessed by comparing proportions of actual observed events. Results CHA2DS2VASC scores achieved acceptable discrimination with c-index of 0.762 (95%CI: 0.746-0.777) for derivation and 0.703 for cross-validation. Decision curve analysis showed the use of CHA2DS2VASC to select patients for rescreening was superior to rescreening all or no patients in terms of net benefit across all reasonable threshold probability (Figure 1, left). Predicted and observed probabilities of adverse clinical outcomes progressively increased with increasing CHA2DS2VASC score (Figure 1, right): 0.7% outcome events in low-risk group (CHA2DS2VASC ≤1, predicted prob. ≤0.86%), 3.5% intermediate-risk group (CHA2DS2VASC 2-3, predicted prob. 2.62%-4.43%) and 11.3% in high-risk group (CHA2DS2VASC ≥4, predicted prob. ≥8.50%). The odds ratio for outcome events were 4.88 (95%CI: 3.43-6.96) for intermediate-versus-low risk group, and 17.37 (95%CI: 12.36-24.42) for high-versus-low risk group.  Conclusion Repeat AF screening on high-risk population may be more efficient than rescreening all screen-negative individuals. CHA2DS2VASC scores may be used as a selection tool to identify high-risk patients to undergo repeat AF screening. Abstract P9 Figure 1

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