scholarly journals Nomogram to predict the progression of patients with primary membranous nephropathy and nephrotic syndrome

2021 ◽  
Author(s):  
Lili Liu ◽  
Haitao Wang ◽  
Ban Zhao ◽  
Xin Liu ◽  
Ying Sun ◽  
...  
Keyword(s):  
Risk Factors ◽  
Nephrotic Syndrome ◽  
Membranous Nephropathy ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Discriminative Ability

Abstract Background The outcome of patients with primary membranous nephropathy (pMN) who present with nephrotic syndrome (NS) is variable and difficult to predict. The goal of this study was to develop a nomogram to predict the risk of progression for specific individuals. Methods This retrospective study involved biopsy-proven patients with pMN and NS treated between January 2012 and June 2018. The primary outcome of our investigation was progression, defined as a reduction of estimated glomerular filtration rate (eGFR) that was equal to or over 20% compared with baseline at the end of follow-up or the onset of end-stage renal disease (ESRD). We used backwards stepwise logistic regression analysis to create a nomogram to predict prognosis. The model was validated internally using bootstrap resampling. Results A total of 111 patients were enrolled. After a median follow-up of 40.0 months (range 12–92 months), 18.9% (21/111) patients showed progression. Backwards stepwise selection using the Akaike information criterion (AIC) identified the following four variables as independent risk factors for progression, which were all used in the nomogram: age ≥ 65 years [odds ratio (OR) 7.004; 95% confidence interval (CI) 1.783–27.505; p = 0.005], Ln (sPLA2R-Ab) (OR 2.150; 95% CI 1.293–3.577; p = 0.003), Ln (proteinuria) (OR 5.939; 95% CI 1.055–33.436; p = 0.043) and Ln (Uα1m/Cr) (OR 2.808; 95% CI 1.035–7.619; p = 0.043). The discriminative ability and calibration of the nomogram revealed good predictive ability, as indicated by a C-index of 0.888 (95% CI 0.814–0.940) and a bootstrap-corrected C-index of 0.869; calibration curves were also well fitted. A receiver operating characteristic (ROC) curve for the nomogram score revealed significantly better discrimination than each of the three risk factors alone, including Ln (sPLA2R-Ab) [area under the curve (AUC) 0.769], Ln (proteinuria) (AUC 0.653) and Ln (Uα1m) (AUC 0.781) in the prediction of progression (p < 0.05). The optimal cutoff value of the nomogram score was 117.8 with a positive predictive value of 44.4% and a negative predictive value of 98.5%. Conclusion The nomogram successfully achieved good predictive ability of progression for patients with pMN who present with NS. It can therefore help clinicians to individualize treatment plans and improve the outcome of pMN.

scholarly journals Multi-modality biomarkers in the early prediction of ischaemic heart disease in middle-aged men during a 21-year follow-up

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maria Sakalaki ◽  
Per-Olof Hansson ◽  
Annika Rosengren ◽  
Erik Thunström ◽  
Aldina Pivodic ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
General Population ◽  
Predictive Value ◽  
Population Sample ◽  
Predictive Ability ◽  
High Sensitivity ◽  
Hs Crp

Abstract Background Ischaemic heart disease (IHD) often develops after decades of preceding subclinical coronary atherosclerosis. Biomarkers are useful prognostic predictors of IHD, but their long-term predictive value in a general population has not been adequately studied. Purpose To investigate the early predictive value of multi-modality biomarkers in addition to clinical risk factors in incident IHD in a random male general population sample followed from 50 to 71 years of age. Method “The Study of Men Born in 1943” is a longitudinal cohort study during follow-up. All the men underwent a baseline examination in 1993, where a panel of biomarkers were analysed and incident IHD was registered during 21-year follow-ups. Results Of 739 participants, 97 men (13.1%) developed an IHD event. For time to first occurrence of IHD, univariable analyses showed that elevated levels of high sensitivity troponin T (hs-TNT), high sensitivity-C reactive protein (hs-CRP) and interleukin-6 (IL-6) were significant predictors of IHD. In addition, a high number of biomarkers with elevated levels (hs-TNT > 10 ng/L, hs-CRP > 1 mg/L, IL-6 > 8 ng/L and N-terminal pro b-type natriuretic peptide (NT-proBNP) > 100 pg/mL) increased predictive ability. In univariable and multivariable analysis high-density lipoprotein-cholesterol (HDL-C) had the highest predictive ability. Hs-TNT provided better predictive ability than smoking, body mass index and glucose, and was an independent significant predictor when adjusted for HDL-C, total cholesterol and hypertension. Addition of biomarkers on top of clinical risk factors provided significantly better prediction as tested by likelihood ratio test (p = 0.033), but did not significantly enhance the model’s discriminative ability However, it appeared contributing to higher sensitivity in the late phase of follow-up. Conclusion In this random, middle-aged male population sample, the addition of biomarker hs-TNT was an independent significant predictor of IHD and significantly improved prediction, indicating the probability of a better prediction of long-term risk of IHD in a low-risk population. Trial registration: The study is registered at Clinical Trials.gov Identifier number: NCT03138122

Predicting Vaginal Delivery in Nulliparous Women Undergoing Induction of Labor at Term

2017 ◽  
Vol 35 (07) ◽  
pp. 660-668 ◽  
Author(s):  
U. M. Reddy ◽  
C. C. Huang ◽  
T. C. Auguste ◽  
D. Bauer ◽  
R. T. Overcash ◽  
...  
Keyword(s):  
Vaginal Delivery ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Induction Of Labor ◽  
Stepwise Logistic Regression ◽  
Chronic Hypertension ◽  
Stepwise Logistic Regression Analysis ◽  
Maternal Characteristics ◽  
Nulliparous Women

Objective We sought to develop a model to calculate the likelihood of vaginal delivery in nulliparous women undergoing induction at term. Study Design We obtained data from the Consortium on Safe Labor by including nulliparous women with term singleton pregnancies undergoing induction of labor at term. Women with contraindications for vaginal delivery were excluded. A stepwise logistic regression analysis was used to identify the predictors associated with vaginal delivery by considering maternal characteristics and comorbidities and fetal conditions. The receiver operating characteristic curve, with an area under the curve (AUC) was used to assess the accuracy of the model. Results Of 10,591 nulliparous women who underwent induction of labor, 8,202 (77.4%) women had vaginal delivery. Our model identified maternal age, gestational age at delivery, race, maternal height, prepregnancy weight, gestational weight gain, cervical exam on admission (dilation, effacement, and station), chronic hypertension, gestational diabetes, pregestational diabetes, and abruption as significant predictors for successful vaginal delivery. The overall predictive ability of the final model, as measured by the AUC was 0.759 (95% confidence interval, 0.749–0.770). Conclusion We identified independent risk factors that can be used to predict vaginal delivery among nulliparas undergoing induction at term. Our predictor provides women with additional information when considering induction.

scholarly journals New screening approach to detecting congenital syphilis in China: a retrospective cohort study

2020 ◽  
pp. archdischild-2020-320549
Author(s):  
Fang Hu ◽  
Shuai-Jun Guo ◽  
Jian-Jun Lu ◽  
Ning-Xuan Hua ◽  
Yan-Yan Song ◽  
...  
Keyword(s):  
Predictive Value ◽  
Congenital Syphilis ◽  
Area Under The Curve ◽  
Diagnostic Tools ◽  
Operating Characteristics ◽  
Loss To Follow Up ◽  
Sensitivity Specificity ◽  
Mother To Child ◽  
Rule Out

BackgroundDiagnosis of congenital syphilis (CS) is not straightforward and can be challenging. This study aimed to evaluate the validity of an algorithm using timing of maternal antisyphilis treatment and titres of non-treponemal antibody as predictors of CS.MethodsConfirmed CS cases and those where CS was excluded were obtained from the Guangzhou Prevention of Mother-to-Child Transmission of syphilis programme between 2011 and 2019. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using receiver operating characteristics (ROC) in two situations: (1) receiving antisyphilis treatment or no-treatment during pregnancy and (2) initiating treatment before 28 gestational weeks (GWs), initiating after 28 GWs or receiving no treatment for syphilis seropositive women.ResultsAmong 1558 syphilis-exposed children, 39 had confirmed CS. Area under the curve, sensitivity and specificity of maternal non-treponemal titres before treatment and treatment during pregnancy were 0.80, 76.9%, 78.7% and 0.79, 69.2%, 88.7%, respectively, for children with CS. For the algorithm, ROC results showed that PPV and NPV for predicting CS were 37.3% and 96.4% (non-treponemal titres cut-off value 1:8 and no antisyphilis treatment), 9.4% and 100% (non-treponemal titres cut-off value 1:16 and treatment after 28 GWs), 4.2% and 99.5% (non-treponemal titres cut-off value 1:32 and treatment before 28 GWs), respectively.ConclusionsAn algorithm using maternal non-treponemal titres and timing of treatment during pregnancy could be an effective strategy to diagnose or rule out CS, especially when the rate of loss to follow-up is high or there are no straightforward diagnostic tools.

Predictive value of short-term migration in determining long-term stable fixation in cemented and cementless total knee arthroplasties

2019 ◽  
Vol 101-B (7_Supple_C) ◽  
pp. 55-60 ◽  
Author(s):  
E. K. Laende ◽  
C. G. Richardson ◽  
M. J. Dunbar
Keyword(s):  
Predictive Value ◽  
Predictive Ability ◽  
Porous Metal ◽  
Short Term ◽  
Long Term Follow Up ◽  
Total Knee ◽  
Total Point ◽  
Cementless Components

Aims Early implant migration measured with radiostereometric analysis (RSA) has been proposed as a useful predictor of long-term fixation of tibial components in total knee arthroplasty. Evaluation of actual long-term fixation is of interest for cemented components, as well as for cementless fixation, which may offer long-term advantages once osseointegration has occurred. The objective of this study was to compare the long-term migration with one- and two-year migration to evaluate the predictive ability of short-term migration data and to compare migration and inducible displacement between cemented and cementless (porous metal monoblock) components at least ten years postoperatively. Patients and Methods Patients who had participated in RSA migration studies with two-year follow-up were recruited to return for a long-term follow-up, at least ten years from surgery. Two cemented tibial designs from two manufacturers and one porous metal monoblock cementless tibial design were studied. At the long-term follow-up, patients had supine RSA examinations to determine migration and loaded examinations (single leg stance) to determine inducible displacement. In total, 79 patients (54 female) returned, with mean time since surgery of 12 years (10 to 14). There were 58 cemented and 21 cementless tibial components. Results Migration at one year and two years was significantly correlated with long-term migration (p < 0.001). Median migration at the long-term follow-up was 0.6 mm (maximum total point motion; interquartile range (IQR) 0.4 to 0.9) for the cemented group and 0.6 mm (IQR 0.3 to 1.1) for the cementless group with no difference between groups (p = 0.99). Inducible displacement was significantly lower for the cementless components (p < 0.001). Conclusion Long-term migration was strongly correlated with two-year migration. Although long-term migration was not different for cemented or cementless tibial components, inducible displacement at the long-term visit was significantly lower for these cementless components, suggesting superior fixation. These findings support the predictive value of short-term migration in determining long-term fixation. Cite this article: Bone Joint J 2019;101-B(7 Supple C):55–60

scholarly journals Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zexin Li ◽  
Kaiji Yang ◽  
Lili Zhang ◽  
Chiju Wei ◽  
Peixuan Yang ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Area Under The Curve ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Discriminative Ability ◽  
Sparse Autoencoder ◽  
Immune Related Genes

Purpose. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Methods. Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder. Results. The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638–0.931], accuracy of 92.9% [92.7–93.0%], sensitivity of 98.6% [95.9–101.3%], specificity of 58.3% [30.4–86.2%], positive likelihood ratio of 2.367 [1.211–4.625], and negative likelihood ratio of 0.024 [0.003–0.177]. In the cancer prevalence range of 20–40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37–61%, and the range of positive predictive value was 98–99%. Conclusion. The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use.

scholarly journals Association between clinical risk scores and mortality in atrial fibrillation: Systematic review and network meta-regression of 669,000 patients

2018 ◽  
Vol 27 (6) ◽  
pp. 633-644 ◽  
Author(s):  
Marco Proietti ◽  
Alessio Farcomeni ◽  
Giulio Francesco Romiti ◽  
Arianna Di Rocco ◽  
Filippo Placentino ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Predictive Ability ◽  
Risk Scores ◽  
Clinical Risk ◽  
Increased Risk ◽  
High Negative Predictive Value ◽  
Meta Regression

Aims Many clinical scores for risk stratification in patients with atrial fibrillation have been proposed, and some have been useful in predicting all-cause mortality. We aim to analyse the relationship between clinical risk score and all-cause death occurrence in atrial fibrillation patients. Methods We performed a systematic search in PubMed and Scopus from inception to 22 July 2017. We considered the following scores: ATRIA-Stroke, ATRIA-Bleeding, CHADS2, CHA2DS2-VASc, HAS-BLED, HATCH and ORBIT. Papers reporting data about scores and all-cause death rates were considered. Results Fifty studies and 71 scores groups were included in the analysis, with 669,217 patients. Data on ATRIA-Bleeding, CHADS2, CHA2DS2-VASc and HAS-BLED were available. All the scores were significantly associated with an increased risk for all-cause death. All the scores showed modest predictive ability at five years (c-indexes (95% confidence interval) CHADS2: 0.64 (0.63–0.65), CHA2DS2-VASc: 0.62 (0.61–0.64), HAS-BLED: 0.62 (0.58–0.66)). Network meta-regression found no significant differences in predictive ability. CHA2DS2-VASc score had consistently high negative predictive value (≥94%) at one, three and five years of follow-up; conversely it showed the highest probability of being the best performing score (63% at one year, 60% at three years, 68% at five years). Conclusion In atrial fibrillation patients, contemporary clinical risk scores are associated with an increased risk of all-cause death. Use of these scores for death prediction in atrial fibrillation patients could be considered as part of holistic clinical assessment. The CHA2DS2-VASc score had consistently high negative predictive value during follow-up and the highest probability of being the best performing clinical score.

scholarly journals Allogeneic Red Blood Cell Transfusion Rate and Risk Factors in Elderly Patients With Femoral Neck Fracture After Hemiarthroplasty

2020 ◽  
Author(s):  
Rui Yue ◽  
Minghui Yang ◽  
Xiaohui Deng ◽  
Ping Zhang
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Femoral Neck ◽  
Elderly Patients ◽  
Blood Cell ◽  
Stepwise Logistic Regression ◽  
Stepwise Logistic Regression Analysis ◽  
Neck Fracture ◽  
Multivariate Stepwise Logistic Regression

Abstract Objective: To determine the rate and risk factors of allogeneic red blood cell transfusions (ABT) in elderly patients with femoral neck fracture (FNF) after hemiarthroplasty (HA). Methods: We conducted a study on elderly patients (≥65 years old) who were admitted to the geriatric trauma orthopedics ward of Beijing Jishuitan Hospital for HA treatment from March 2018 to February 2019 due to FNF. The perioperative data were collected retrospectively, and univariate and multivariate stepwise logistic regression analysis were performed to determine the postoperative ABT rate and its risk factors. Results: 343 patients were included in the study, of which 151 (44.0%) received ABT after surgery. Multivariate stepwise logistic regression analysis showed that preoperative low hemoglobin (Hb), high intraoperative blood loss (IBL), advanced age, and low body mass index (BMI) are independent risk factors of ABT after HA in elderly FNF patients. Conclusion: ABT after HA is a common phenomenon in elderly patients with FNF. Their postoperative ABT needs are related to preoperative low Hb, high IBL, advanced age, and low BMI. Therefore, ABT can be reduced from the above aspects.

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

Serum procalcitonin levels predict disseminated intravascular coagulation in patients with sepsis shock: A retrospective cohort study

2020 ◽  
Author(s):  
Guang Fu ◽  
Xi-si He ◽  
Hao-li Li ◽  
Hai-chao Zhan ◽  
Jun-fu Lu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Septic Shock ◽  
Cohort Study ◽  
Disseminated Intravascular Coagulation ◽  
Area Under The Curve ◽  
Multivariable Logistic Regression Model ◽  
Intravascular Coagulation ◽  
The Relationship ◽  
Conventional Risk Factors

Abstract Background Complication of disseminated intravascular coagulation (DIC) is a determinant of the prognosis in patients with sepsis shock. Procalcitonin (PCT) has been advocated as a marker of bacterial sepsis. The purpose of this study was to evaluate the relationship between serum PCT levels and DIC with sepsis shock Methods A cohort study was designed which included patients that admitted in intensive care unit (ICU) between January 1, 2015 and December 31, 2018 and the follow-up to discharge. 164 septic shock patients were divided into DIC and non-DIC groups according to international society of thrombosis and homeostasis (ISTH). PCT was measured at the admission to ICU, and all the participants received routine biochemical coagulation test subsequently. Results PCT levels were considerably higher in septic shock patients who developed DIC than those who did not (54.6[13.6–200]vs12.6[2.4–53.3]ng/ml), respectively, P < 0.001). Multivariable logistic regression model revealed that PCT level was significantly associated with risk of DIC independent of conventional risk factors. In addition, curve fitting showed a linear relationship between PCT and DIC score. The Receiver Operating characteristic(ROC) curve suggested that the optimal cut-off point for PCT to predicting DIC induced by septic shock was 42.0 ng/ml, and the area under the curve (AUC) was 0.701(95% CI [0.619–0.784], P < 0.001). More importantly, incorporating PCT with other risk factors into the prediction model significantly increased the AUC for prediction of DIC induced by sepsis shock (0.801vs 0.706; P = 0.012). Conclusions Our study suggests that PCT levels on admission is significantly and independently associated with DIC development subsequently with septic shock, combining PCT levels with other risk factors could significantly improve the prediction of DIC induced by sepsis shock.

scholarly journals Plasma lipids as risk factors in relapsing nephrotic syndrome

2016 ◽  
Vol 48 (6) ◽  
pp. 322
Author(s):  
Sitti Aizah Lawang ◽  
Syarifuddin Rauf ◽  
J. S. Lisal ◽  
Husein Albar ◽  
Dasril Daud
Keyword(s):  
Risk Factors ◽  
Nephrotic Syndrome ◽  
Plasma Lipids ◽  
Lipid Fraction ◽  
Plasma Concentrations ◽  
Increased Risk ◽  
Significant Difference ◽  
Remission Phase ◽  
Blood Specimens

Background Nephrotic syndrome is primarily a pediatric disorderand is 15 times more common in children than in adults.Relapse rate after corticosteroid discontinuation is 39 - 59%.Hyperlipidemia is an important characteristic of nephroticsyndrome. The plasma concentrations of cholesterol, triglyceride,LDL, and VLDL are increased. Persistent hyperlipidemia afterremission can be found in frequent relapse nephrotic syndrome.Objective To determine plasma lipids as risk factor for relapsingnephrotic syndrome.Methods Thirty children with nephrotic syndrome were includedin this cohort study from March 2005 until June 2007 at WahidinSudirohusodo Hospital, Makassar. Thirty children without renal diseasewere enrolled as control. Blood specimens were collected to determineplasma lipids (cholesterol, triglyceride, LDL, and HDL) levels and LDUHDL ratio. Plasma lipids were examined in the acute and remissionphases. Follow up was carried out six months after remission todetermine the occurrence of relapsing nephrotic syndrome.Results Of 30 nephrotic syndrome patients, 12 had relapsed.There were highly significant differences in total cholesterol, HDL,LDL, triglyceride, and LDL/HDL ratio between acute nephroticsyndrome and nephrotic syndrome in remission. There were nosignificant differences in cholesterol, LDL, triglyceride, LDL!HDL ratio between nephrotic syndrome in remission and control.There was also no significant difference in the incidence in relapsebetween first attack and nephrotic syndrome with more than twoattacks. Acute lipid fraction levels were not risk factors in relapsingmephrotic syndrome. Remission triglyceride level was a risk factorin relapsing nephrotic syndrome with the prevalence risk of 5.2 andCI 95% of 1.06 to 25.3.Conclusion Persistent hypertriglyceride in remission phase isassociated with an increased risk of relapse in children withnephrotic syndrome.

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