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 Development and Validation of a Risk Score to Predict Low Birthweight Using Characteristics of the Mother: A Prospective Cohort Study

2020 ◽  
Author(s):  
Hamid Y. Hassen ◽  
Bilal S. Endris ◽  
Meselech A. Roro ◽  
Seifu H. Gebreyesus ◽  
Jean-Pierre Van Geertruyden
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Risk Score ◽  
Low Birthweight ◽  
Curve Analysis ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Decision Curve Analysis ◽  
Maternal Characteristics ◽  
Receiver Operating Characteristics Curve

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 conducted a prospective cohort study among 379 pregnant women in South Ethiopia. A step-wise multivariable analysis was done to develop the prediction model. To improve 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 characteristics curve (AUC) and calibration plot. 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 area under the receiver operating characteristics curve (AUC) of the model was 0.83 (95% confidence interval: 0.78 to 0.88). The decision curve analysis shows 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 those at higher risk of having a low birthweight baby. This feasible prediction model would offer an opportunity to reduce obstetric-related complications and thus improving the overall maternal and child healthcare in low- and middle-income countries.

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 Mortality risk prediction in lupus patients complicated with invasive infection in the emergency department: LUPHAS score

2019 ◽  
Vol 11 ◽  
pp. 1759720X1988555 ◽  
Author(s):  
Wanlong Wu ◽  
Jun Ma ◽  
Yuhong Zhou ◽  
Chao Tang ◽  
Feng Zhao ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Risk Score ◽  
Roc Curve ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Invasive Infection ◽  
Roc Curve Analysis ◽  
All Cause Mortality ◽  
Qsofa Score

Background: Infection remains a major cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). This study aimed to establish a clinical prediction model for the 3-month all-cause mortality of invasive infection events in patients with SLE in the emergency department. Methods: SLE patients complicated with invasive infection admitted into the emergency department were included in this study. Patient’s demographic, clinical, and laboratory characteristics on admission were retrospectively collected as baseline data and compared between the deceased and the survivors. Independent predictors were identified by multivariable logistic regression analysis. A prediction model for all-cause mortality was established and evaluated by receiver operating characteristic (ROC) curve analysis. Results: A total of 130 eligible patients were collected with a cumulative 38.5% 3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure Assessment (qSOFA) score, and age at baseline were independent predictors for all-cause mortality (LUPHAS). In contrast, a history of hydroxychloroquine use was protective. In a combined, odds ratio-weighted LUPHAS scoring system (score 3–22), patients were categorized to three groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk (score 16–22), with mortalities of 4.9% (2/41), 45.9% (28/61), and 78.3% (18/23) respectively. ROC curve analysis indicated that a LUPHAS score could effectively predict all-cause mortality [area under the curve (AUC) = 0.86, CI 95% 0.79–0.92]. In addition, LUPHAS score performed better than the qSOFA score alone (AUC = 0.69, CI 95% 0.59–0.78), or CURB-65 score (AUC = 0.69, CI 95% 0.59–0.80) in the subgroup of lung infections ( n = 108). Conclusions: Based on a large emergency cohort of lupus patients complicated with invasive infection, the LUPHAS score was established to predict the short-term all-cause mortality, which could be a promising applicable tool for risk stratification in clinical practice.

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.

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.

scholarly journals Prediction of vaginal birth after cesarean delivery in southeast China: A retrospective cohort study

2020 ◽  
Author(s):  
Hua-Le Zhang ◽  
Liang-Hui Zheng ◽  
Li-Chun Cheng ◽  
Zhao-Dong Liu ◽  
Lu Yu ◽  
...  
Keyword(s):  
Cesarean Section ◽  
Pregnant Women ◽  
Success Rate ◽  
Gestational Age ◽  
Curve Analysis ◽  
Inclusion Criteria ◽  
Vaginal Birth After Cesarean ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Validation Set

Abstract Objective To develop and validate a nomogram to better predict the vaginal birth after cesarean (VBAC) on the premise of clinical guide application. Methods We retrospectively identified hospitalised pregnant women who trial of labor after cesarean (TOLAC) between October 2015 and October 2017 using data from the Fujian Provincial Maternity and Children's Hospital. The inclusion criteria were as follows: Singleton pregnant women whose gestational age was above 37 weeks and underwent a primary cesarean section. Sociodemographic data and Clinical Characteristics were extracted. The samples were randomly divided into a training set and a validation set. Least absolute shrinkage and selection operator (LASSO) regression were used to select variables and construct of VBAC success rate in training set. The validation of the nomogram was performed using the concordance index (C-index), decision curve analysis (DCA), and calibration curves in the validation set. For comparison with published VBAC prediction models, the Grobman’s model was used. Results Among the 708 pregnant women included according to inclusion criteria, 586 (82.77%) patients were successfully for VBAC. In multivariate logistic regression models, Maternal height (OR, 1.11; 95% CI, 1.04 to 1.19), maternal BMI at delivery (OR, 0.89; 95% CI, 0.79 to 1.00), fundal height (OR, 0.71; 95% CI, 0.58 to 0.88), cervix Bishop score (OR, 3.27; 95% CI, 2.49 to 4.45), maternal age at delivery (OR, 0.90; 95% CI, 0.82 to 0.98), gestational age (OR, 0.33; 95% CI, 0.17 to 0.62) and history of vaginal delivery (OR, 2.92; 95% CI, 1.42 to 6.48) were independently associated with successful VBAC. The predictive model was constructed showed better discrimination in the validation series than Grobman’s model (c-index 0.906 VS 0.694, respectively). Decision curve analysis revealed that the new model resulted in a better clinical net benefit than the Grobman’s model. Conclusions The promotion of VBAC is helpful to reduce the cesarean section rate in China. On the basis of following the clinical practice guidelines, the TOLAC prediction model helps to improve the success rate of VBAC and has a potential contribution to the reduction of secondary cesarean section.

scholarly journals A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9890
Author(s):  
Xue-ran Kang ◽  
Bin Chen ◽  
Yi-sheng Chen ◽  
Bin Yi ◽  
Xiaojun Yan ◽  
...  
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Surgical Outcome ◽  
Middle Turbinate ◽  
Multivariate Logistic Regression Analysis ◽  
Preoperative Assessment ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Nasal Septoplasty ◽  
Snot 22

Background To create a nomogram prediction model for the efficacy of endoscopic nasal septoplasty, and the likelihood of patient benefiting from the operation. Methods A retrospective analysis of 155 patients with nasal septum deviation (NSD) was performed to develop a predictive model for the efficacy of endoscopic nasal septoplasty. Quality of life (QoL) data was collected before and after surgery using Sinonasal Outcome Test-22 (SNOT-22) scores to evaluate the surgical outcome. An effective surgical outcome was defined as a SNOT-22 score change ≥ 9 points after surgery. Multivariate logistic regression analysis was then used to establish a predictive model for the NSD treatment. The predictive quality and clinical utility of the predictive model were assessed by C-index, calibration plots, and decision curve analysis. Results The identified risk factors for inclusion in the predictive model were included. The model had a good predictive power, with a AUC of 0.920 in the training group and a C index of 0.911 in the overall sample. Decision curve analysis revealed that the prediction model had a good clinical applicability. Conclusions Our prediction model is efficient in predicting the efficacy of endoscopic surgery for NSD through evaluation of factors including: history of nasal surgery, preoperative SNOT-22 score, sinusitis, middle turbinate plasty, BMI, smoking, follow-up time, seasonal allergies, and advanced age. Therefore, it can be cost-effective for individualized preoperative assessment.

scholarly journals Construction of a novel mRNA-signature prediction model for prognosis of bladder cancer based on a statistical analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianpeng Li ◽  
Jinlong Cao ◽  
Pan Li ◽  
Zhiqiang Yao ◽  
Ran Deng ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Survival Analysis ◽  
Candidate Genes ◽  
Risk Score ◽  
Cox Regression ◽  
Curve Analysis ◽  
Functional Enrichment ◽  
Time Dependent ◽  
Stage Model ◽  
Decision Curve Analysis

Abstract Background Bladder cancer (BC) is a common malignancy neoplasm diagnosed in advanced stages in most cases. It is crucial to screen ideal biomarkers and construct a more accurate prognostic model than conventional clinical parameters. The aim of this research was to develop and validate an mRNA-based signature for predicting the prognosis of patients with bladder cancer. Methods The RNA-seq data was downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened in three datasets, and prognostic genes were identified from the training set of TCGA dataset. The common genes between DEGs and prognostic genes were narrowed down to six genes via Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox regression. Then the gene-based risk score was calculated via Cox coefficient. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic power of risk score. Multivariate Cox regression analysis was applied to construct a nomogram. Decision curve analysis (DCA), calibration curves, and time-dependent ROC were performed to assess the nomogram. Finally, functional enrichment of candidate genes was conducted to explore the potential biological pathways of candidate genes. Results SORBS2, GPC2, SETBP1, FGF11, APOL1, and H1–2 were screened to be correlated with the prognosis of BC patients. A nomogram was constructed based on the risk score, pathological stage, and age. Then, the calibration plots for the 1-, 3-, 5-year OS were predicted well in entire TCGA-BLCA patients. Decision curve analysis (DCA) indicated that the clinical value of the nomogram was higher than the stage model and TNM model in predicting overall survival analysis. The time-dependent ROC curves indicated that the nomogram had higher predictive accuracy than the stage model and risk score model. The AUC of nomogram time-dependent ROC was 0.763, 0.805, and 0.806 for 1-year, 3-year, and 5-year, respectively. Functional enrichment analysis of candidate genes suggested several pathways and mechanisms related to cancer. Conclusions In this research, we developed an mRNA-based signature that incorporated clinical prognostic parameters to predict BC patient prognosis well, which may provide a novel prognosis assessment tool for clinical practice and explore several potential novel biomarkers related to the prognosis of patients with BC.

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