scholarly journals Development of a Novel Nomogram for Predicting Premature Rupture of Membrane in Pregnant Women With Vulvovaginal Candidiasis

2021 ◽  
Vol 8 ◽  
Author(s):  
Lilin Yang ◽  
Haikuan Wang ◽  
Yanfang Li ◽  
Cheng Zeng ◽  
Xi Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Pregnant Women ◽  
Curve Analysis ◽  
Vulvovaginal Candidiasis ◽  
Training Dataset ◽  
Premature Rupture ◽  
Decision Curve Analysis ◽  
History Of ◽  
Selection Operator

Objective: The aim of this study was to develop a nomogram to predict the risk of premature rupture of membrane (PROM) in pregnant women with vulvovaginal candidiasis (VVC).Patients and methods: We developed a prediction model based on a training dataset of 417 gravidas with VVC, the data were collected from January 2013 to December 2020. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using bootstrapping validation.Results: Predictors contained in the prediction nomogram included age, regular perinatal visits, history of VVC before pregnancy, symptoms with VVC, cured of VVC during pregnancy, and bacterial vaginitis. The model displayed discrimination with a C-index of 0.684 (95% confidence interval: 0.631–0.737). Decision curve analysis showed that the PROM nomogram was clinically useful when intervention was decided at a PROM possibility threshold of 13%.Conclusion: This novel PROM nomogram incorporating age, regular perinatal visits, history of VVC before pregnancy, symptoms with VVC, cured of VVC during pregnancy, and bacterial vaginitis could be conveniently used to facilitate PROM risk prediction in gravidas.

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 ◽  
Xiaolong Chen ◽  
Li Xu
Keyword(s):  
Diabetic Nephropathy ◽  
Regression Model ◽  
Curve Analysis ◽  
Training Dataset ◽  
Calibration Plot ◽  
Multivariable Logistic Regression Analysis ◽  
Predicting Model ◽  
Decision Curve Analysis ◽  
Anti Vegf ◽  
Selection Operator

Abstract Background: The aim of this study was to develop and internally validate a postoperative NVG risk nomogram in a Chinese population of patients with PDR.Methods: We developed a prediction model based on a training dataset 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. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.Results: Predictors contained in the prediction nomogram included HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy. The model displayed good discrimination with a C-index of 0.852 (95% CI: 0.740–0.964) and good calibration. High C-index value of 0.849 could still be reached in the interval validation. Decision curve analysis showed that the NVG nomogram was clinically useful when intervention was decided at the NVG possibility threshold of 2%.Conclusion: This novel NVG nomogram incorporating HbAlc level, presence of diabetic nephropathy and anti-VEGF therapy could be conveniently used to facilitate the postoperative NVG risk prediction in PDR patients.

scholarly journals Development and Validation of a Risk Score to Predict Low Birthweight Using Characteristics of the Mother: Analysis from BUNMAP Cohort in Ethiopia

2020 ◽  
Vol 9 (5) ◽  
pp. 1587
Author(s):  
Hamid Y. Hassen ◽  
Seifu H. Gebreyesus ◽  
Bilal S. Endris ◽  
Meselech A. Roro ◽  
Jean-Pierre Van Geertruyden
Keyword(s):  
Prediction Model ◽  
Pregnant Women ◽  
Risk Score ◽  
Low Income ◽  
Low Birthweight ◽  
Multivariable Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Maternal Characteristics ◽  
Trained Manpower

At least one ultrasound is recommended to predict fetal growth restriction and low birthweight earlier in pregnancy. However, in low-income countries, imaging equipment and trained manpower are scarce. Hence, we developed and validated a model and risk score to predict low birthweight using maternal characteristics during pregnancy, for use in resource limited settings. We developed the model using a prospective cohort of 379 pregnant women in South Ethiopia. A stepwise multivariable analysis was done to develop the prediction model. To improve the clinical utility, we developed a simplified risk score to classify pregnant women at high- or low-risk of low birthweight. The accuracy of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration plot. All accuracy measures were internally validated using the bootstrapping technique. We evaluated the clinical impact of the model using a decision curve analysis across various threshold probabilities. Age at pregnancy, underweight, anemia, height, gravidity, and presence of comorbidity remained in the final multivariable prediction model. The AUC of the model was 0.83 (95% confidence interval: 0.78 to 0.88). The decision curve analysis indicated the model provides a higher net benefit across ranges of threshold probabilities. In general, this study showed the possibility of predicting low birthweight using maternal characteristics during pregnancy. The model could help to identify pregnant women at higher risk of having a low birthweight baby. This feasible prediction model would offer an opportunity to reduce obstetric-related complications, thus improving the overall maternal and child healthcare in low- and middle-income countries.

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 Dosimetry of Submandibular Glands on Xerostomia for Nasopharyngeal Carcinoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Xin-Bin Pan ◽  
Yang Liu ◽  
Shi-Ting Huang ◽  
Su Pei ◽  
Kai-Hua Chen ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Regression Model ◽  
Curve Analysis ◽  
Calibration Plot ◽  
Calibration Model ◽  
Predicting Model ◽  
Decision Curve Analysis ◽  
Submandibular Glands ◽  
The Mean ◽  
Selection Operator

PurposeTo investigate dosimetry of submandibular glands on xerostomia after intensity-modulated radiotherapy for nasopharyngeal carcinoma (NPC).MethodsFrom September 2015 to March 2016, 195 NPC patients were investigated. Xerostomia was evaluated at 12 months after treatment via the RTOG/EORTC system. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for grades 2–3 xerostomia. 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. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis.ResultsThe V30 of the parotid glands was selected based on the least absolute shrinkage and selection operator regression. The nomogram displayed good discrimination with a C-index of 0.698 (95% confidence interval [CI]: 0.626–0.771) and good calibration (model 1). Addition of the dosimetric parameters including the mean dose to the submandibular glands, V50 of the submandibular glands, and volume of the submandibular glands to the model 1 failed to show incremental prognostic value (model 2). The model 2 showed a C-index of 0.704 (95% CI: 0.632–0.776). Decision curve analysis demonstrated that the model 1 was clinically useful when intervention was decided at the possibility threshold of > 20%. Within this range, net benefit was comparable between the model 1 and model 2.ConclusionPGv30 was a major predictive factor of grades 2–3 xerostomia for NPC. In contrast, the mean dose to the submandibular glands, V50 of the submandibular glands, and volume of the submandibular glands were not independent predictive factors.

scholarly journals Prediction Model of in-Hospital Venous Thromboembolism in Chinese Adult Patients after Hernia Surgery: The CHAT Score

2021 ◽  
Vol 27 ◽  
pp. 107602962110517
Author(s):  
Zhi-Chun Gu ◽  
Chi Zhang ◽  
Ya Yang ◽  
Ming-Gang Wang ◽  
Hang-Yu Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Prediction Model ◽  
Curve Analysis ◽  
Chinese Patients ◽  
Brier Score ◽  
Hernia Surgery ◽  
Decision Curve Analysis ◽  
History Of

Background Venous thromboembolism (VTE) events after hernia surgery influence prognosis and life quality and may be preventable. This study aimed to develop a useful model for predicting in-hospital VTE in Chinese patients after hernia surgery. Methods Patients after hernia surgery were retrospectively recruited from 58 institutions (n = 14 322). Totally, 36 potential predictors were involved in the regression analysis. Weighted points were assigned to the predictors of in-hospital VTE identified in the multivariate logistic regression analysis and a prediction model was established. Decision curve analysis was performed to evaluate the net clinical benefit between the established and Caprini models. Results A total of 11 707 patients were included and five variables were explored as predictors related to in-hospital VTE: varicose veins of lower extremity, history of VTE, family history of thrombosis, interruption of antithrombotic agents, and reducible hernia. The prediction model (the CHAT score) revealed a good performance metrics (c-statistic, 0.81 [95% CI, 0.80 to 0.81]; Nagelkerke R2, 0.27 [95% CI, 0.26 to 0.30]; Brier score, 0.16 [95% CI, 0.13 to 0.23]). The rate of in-hospital VTE after hernia surgery at low-risk (−4 points), intermediate-risk (0-1 points), high-risk (4 points) and very high-risk (≥5 points) were 0.05%, 0.39%, 0.73% and 8.62%, respectively. The CHAT score identified a considerable variability (from 0.05% to 8.62%) for in-hospital VTE among the overall population after hernia surgery. Decision curve analysis found a superior net benefit of the established model than the Caprini score. Conclusions The CHAT score is likely to be a practical 5-item supporting tool to identify patients at high risk of in-hospital VTE after hernia surgery that might assist in decision making and VTE prevention. Further validated study will strengthen this finding.

RUPTURE OF AMNIOTIC MEMBRANES IN PRETERM, AS A FACTOR IN THE DEVELOPMENT OF OBSTETRICS COMPLICATIONS

2020 ◽  
Vol 93 (1) ◽  
pp. 76-79
Author(s):  
Iroda Tosheva ◽  
◽  
N. Ashurova ◽  
Gulchekhra Ikhtiyarova
Keyword(s):  
Retrospective Study ◽  
Pregnant Women ◽  
Significant Role ◽  
Perinatal Complications ◽  
Premature Rupture ◽  
Perinatal Center ◽  
History Of ◽  
Amniotic Membranes

This article presents the results of the retrospective study of the childbirth history of 106 pregnant women in whom labor was complicated by premature rupture of the membranes, delivery in the Bukhara regional perinatal center for the period 2017-2019 years. The results show the significant role of premature rupture of the membranes in the development of obstetrics and perinatal complications, especially in women with a history of somatic and gynecological anamnesis

scholarly journals Association of polymorphisms in C1orf106, IL1RN, and IL10 with post-induction infliximab trough level in Crohn’s disease patients

2019 ◽  
Vol 8 (5) ◽  
pp. 367-373
Author(s):  
Jian Tang ◽  
Cai-Bin Zhang ◽  
Kun-Sheng Lyu ◽  
Zhong-Ming Jin ◽  
Shao-Xing Guan ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Prediction Model ◽  
Regression Model ◽  
Trough Level ◽  
Single Gene ◽  
Training Dataset ◽  
Multivariate Logistic Regression Model ◽  
Post Induction ◽  
Multivariate Prediction

Abstract Background Trough levels of the post-induction serum infliximab (IFX) are associated with short-term and long-term responses of Crohn’s disease patients to IFX, but the inter-individual differences are large. We aimed to elucidate whether single gene polymorphisms (SNPs) within FCGR3A, ATG16L1, C1orf106, OSM, OSMR, NF-κB1, IL1RN, and IL10 partially account for these differences and employed a multivariate regression model to predict patients’ post-induction IFX levels. Methods The retrospective study included 189 Crohn’s disease patients undergoing IFX therapy. Post-induction IFX levels were measured and 41 tag SNPs within eight genes were genotyped. Associations between SNPs and IFX levels were analysed. Then, a multivariate logistic-regression model was developed to predict whether the patients’ IFX levels achieved the threshold of therapy (3 μg/mL). Results Six SNPs (rs7587051, rs143063741, rs442905, rs59457695, rs3213448, and rs3021094) were significantly associated with the post-induction IFX trough level (P = 0.015, P < 0.001, P = 0.046, P = 0.022, P = 0.011, P = 0.013, respectively). A multivariate prediction model of the IFX level was established by baseline albumin (P = 0.002), rs442905 (P = 0.025), rs59457695 (P = 0.049), rs3213448 (P = 0.056), and rs3021094 (P = 0.047). The area under the receiver operating characteristic curve (AUROC) of this prediction model in a representative training dataset was 0.758. This result was verified in a representative testing dataset, with an AUROC of 0.733. Conclusions Polymorphisms in C1orf106, IL1RN, and IL10 play an important role in the variability of IFX post-induction levels, as indicated in this multivariate prediction model of IFX levels with fair performance.

scholarly journals Prediction and clinical utility of a contralateral breast cancer risk model

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniele Giardiello ◽  
Ewout W. Steyerberg ◽  
Michael Hauptmann ◽  
Muriel A. Adank ◽  
Delal Akdeniz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Prediction Model ◽  
Germline Mutation ◽  
Clinical Utility ◽  
Contralateral Breast Cancer ◽  
Contralateral Breast ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Mutation Status

Abstract Background Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Methods We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Results In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52–0.74; at 10 years, 0.53–0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62–1.37), and the calibration slope was 0.90 (95% PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4–10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

Utility of PCA3 and TMPRSS2:ERG urinary biomarkers in African American men undergoing prostate biopsy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 126-126
Author(s):  
Allison H. Feibus ◽  
A. Oliver Sartor ◽  
Krishnarao Moparty ◽  
Michael W. Kattan ◽  
Kevin M. Chagin ◽  
...  
Keyword(s):  
Prostate Biopsy ◽  
Prediction Models ◽  
African American Men ◽  
Standard Of Care ◽  
Curve Analysis ◽  
High Grade ◽  
Net Benefit ◽  
Racially Diverse ◽  
Decision Curve Analysis ◽  
History Of

126 Background: To determine the performance characteristics of urinary PCA3 andTMPRSS2:ERG (T2:ERG) in a racially diverse group of men. Methods: Following IRB approval, from 2013-2015, post digital rectal exam (DRE) urine was prospectively collected in patients without known prostate cancer (PCa), prior to biopsy. PCA3 and T2:ERG RNA copies were quantified and normalized to PSA mRNA copies using Progensa assay (Hologic, San Diego, CA). Prediction models for PCa and high-grade PCa were created using standard of care (SOC) variables (age, race, family history of PCa, prior prostate biopsy and abnormal DRE) plus PSA. Decision Curve Analysis was performed to compare the net benefit of using SOC, plus PSA, with the addition of PCA3 and T2:ERG. Results: Of 304 patients, 182 (60%) were AA; 139(46%) were diagnosed with PCa (69% AA). PCA3 and T2:ERG scores were greater in men with PCa, ≥ 3 cores, ≥ 33.3% cores, > 50% involvement of greatest biopsy core and Epstein significant PCa (p-values < 0.04). PCA3 added to the SOC plus PSA model for the detection of any PCa in the overall cohort (0.747 vs 0.677; p < 0.0001), in AA only (0.711 vs 0.638; p = 0.0002) and non-AA (0.781 vs 0.732; p = 0.0016). PCA3 added to the model for the prediction of high-grade PCa for the overall cohort (0.804 vs 0.78; p = 0.0002) and AA only (0.759 vs 0.717; p = 0.0003) but not non-AA. Decision curve analysis demonstrated significant net benefit with the addition of PCA3 compared with SOC plus PSA. For AA, T2:ERG did not improve concordance statistics for the detection any or high-grade PCa. Conclusions: For AA, urinary PCA3 improves the ability to predict the presence of any and high-grade PCa. However for this population, T2:ERG urinary assay does not add significantly to standard detection and risk stratification tools.

scholarly journals Predicting the risk of sarcopenia in elderly patients with patellar fracture: Development and assessment of a new predictive nomogram

2019 ◽  
Author(s):  
Chen Yisheng ◽  
Tao Jie
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Elderly Patients ◽  
Prediction Model ◽  
Predictive Model ◽  
Body Mass ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Low Body Mass Index ◽  
Patellar Fractures

AbstractPurposeThis study was aimed at developing a risk prediction model for postoperative dysplasia in elderly patients with patellar fractures in China.Patients and methodsWe conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (Lasso analysis). The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We conducted internal sampling methods for qualitative assessment.ResultWe recruited 61 participants (males: 20, mean age: 68.1 years). Various risk factors were assessed, and low body mass index and diabetes mellitus were identified as the most important risk factors (P<0.05). The model showed a good prediction rate (C-index: 0.909; 95% confidence interval: 0.81–1.00) and good correction effect. The C-index remained high (0.828) even after internal sample verification. Decision curve analysis showed that the risk of sarcopenia was 8.3–80.0%, suggesting good clinical practicability.ConclusionOur prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, longer postoperative hospital stay, no higher education, no postoperative rehabilitation, removal of internal fixation, and less outdoor exercise.

Sign in / Sign up

Export Citation Format

Share Document