Bootstrap Resampling Approach Research Articles

Demand Side Perception on Success Factors for Implementing Public Road Construction Projects

Cost and schedule overruns, as well as substandard works, are common factors that fail organisations’ objectives within the construction industry. Particularly, these factors affect public road construction projects and cost taxpayers. Researchers continue scanning the environment to establish why construction projects are ever behind schedule and over budget with substandard works and contract variations to identify significant factors for successful project implementation. This study expands the debate by looking at demand side perception in establishing success factors for implementing public road construction projects motivated by governments’ high expenditure on construction projects without meeting objectives. A cross-sectional research design with a structured self-administered questionnaire was used to obtain views from three public entities representing the demand side. Results were analysed following partial least square structural modelling (PLS-SEM) in Smart-PLS3. The research design enabled statistical tests to be conducted on validity, reliability, normality, multicollinearity, correlations and regression. The PLS algorithm and bootstrapping resampling approach were employed to determine the relationship between variables by estimating path coefficients and significance. Path coefficients helped to determine strength, direction and significance, and examine the variance of dependent variables explained by combined independent variables. Results revealed that the professionalism of the staff, compliance with the public procurement regulatory framework, monitoring activities and contractors’ resistance to non-compliance are significant success factors in enhancing public road implementation. Hence, adopting these factors would be a game changer in implementing complex road construction projects. In addition, complex construction projects in a dynamic construction industry require continuous scanning to establish more factors and cope with industry dynamics.

Unbiased Proxy Calibration

The linear calibration model is a powerful statistical tool that can be utilized to predict an unknown response variable, Y, through observations of a proxy or predictor variable, X. Since calibration involves estimation of regression model parameters on the basis of a limited amount of noisy data, an unbiased calibration slope estimation is of utmost importance. This can be achieved by means of state-of-the-art, data-driven statistical techniques. The present paper shows that weighted least-squares for both variables estimation (WLSXY) is able to deliver unbiased slope estimations under heteroscedasticity. In the case of homoscedasticity, besides WLSXY, ordinary least-squares (OLS) estimation with bias correction (OLSBC) also performs well. For achieving unbiasedness, it is further necessary to take the correct regression direction (i.e., of Y on X) into account. The present paper introduces a pairwise moving block bootstrap resampling approach for obtaining accurate estimation confidence intervals (CIs) under real-world climate conditions (i.e., non-Gaussian distributional shapes and autocorrelations in the noise components). A Monte Carlo simulation experiment confirms the feasibility and validity of this approach. The parameter estimates and bootstrap replications serve to predict the response with CIs. The methodological approach to unbiased calibration is illustrated for a paired time series dataset of sea-surface temperature and coral oxygen isotopic composition. Fortran software with implementation of OLSBC and WLSXY accompanies this paper.

Nomogram for preoperative estimation of symptomatic subdural hygroma risk in pediatric intracranial arachnoid cysts.

The occurrence and predictors of symptomatic subdural hygroma (SSH) subsequent to the fenestration of pediatric intracranial arachnoid cysts (IACs) are unclear. In this study, the authors aimed to investigate the likelihood of an SSH following IAC fenestration and the impact on operative efficacy with the ultimate goal of constructing a nomogram. The medical records of 1782 consecutive patients who underwent surgical treatment at the Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were reviewed. Among these patients, a training cohort (n = 1214) underwent surgery during an earlier period and was used for the development of a nomogram. The remaining patients formed the validation cohort (n = 568) and were used to confirm the performance of the developed model. The development of the nomogram involved the use of potential predictors, while internal validation was conducted using a bootstrap-resampling approach. SSH was detected in 13.2% (160 of 1214) of patients in the training cohort and in 11.1% (63 of 568) of patients in the validation cohort. Through multivariate analysis, several factors including Galassi type, IAC distance to the basal cisterns, temporal bulge, midline shift, IAC shape in the coronal view, area of the stoma, and artery location near the stoma were identified as independent predictors of SSH. These 7 predictors were used to construct a nomogram, which exhibited a concordance statistic (C-statistic) of 0.826 and demonstrated good calibration. Following internal validation, the nomogram maintained good calibration and discrimination with a C-statistic of 0.799 (95% CI 0.665-0.841). Patients who had nomogram scores < 30 or ≥ 30 were considered to be at low and high risk of SSH occurrence, respectively. The predictive model and derived nomogram achieved satisfactory preoperative prediction of SSH. Using this nomogram, the risk for an individual patient can be estimated, and the appropriate surgery can be performed in high-risk patients.

Predictors on outcomes of cardiovascular disease of male patients in Malaysia using Bayesian network analysis

ObjectivesDespite extensive advances in medical and surgical treatment, cardiovascular disease (CVD) remains the leading cause of mortality worldwide. Identifying the significant predictors will help clinicians with the prognosis of the...

Estimating animal density using the Space‐to‐Event model and bootstrap resampling with motion‐triggered camera‐trap data

AbstractOver the past few decades, the use of camera‐traps has revolutionized our ability to monitor populations of wild terrestrial mammals. While methods to estimate abundance from individually‐identifiable animals are well‐established, they are mostly restricted to species with clear natural markings or else necessitate invasive and often costly animal tagging campaigns. Estimating abundance or density from unmarked animals remains challenging. Several models recently developed to deal with this issue are promising, but are not widely used by field ecologists. Here, we developed a framework for applying the Space‐To‐Event (STE) model—originally designed to be used with time‐lapse images—on motion‐triggered camera‐trap data. Our approach involves performing bootstrap resampling on the photographic dataset to generate multiple datasets that are then used as input to the STE model. We tested our approach on 29 datasets, including 17 ungulate species from eight sites, in six different countries and various ecosystems. Then, we conducted a regression analysis to evaluate how variations in ecological and sampling conditions across studies affected the bias and precision of our STE density estimates. Our study shows that with a bootstrap resampling approach and information on animal activity and effective detection distances to animals, the STE model can be used to analyze motion‐trigger datasets and provide population density estimates that are similar to those from other methods. We found that measuring the camera viewshed was critical to prevent major negative biases in density estimates. Moreover, using a 1‐s sampling window was important to avoid the positive bias that results from violating the instantaneous‐sampling assumption. We found that precision increased with greater sampling effort and higher density populations. Based on these results, we highlight several issues from past studies that have applied the original timelapse‐based STE to motion‐trigger datasets, issues that our bootstrap resampling approach addresses. We caution that the STE model, whether applied to timelapse or motion‐triggered datasets, relies on strict assumptions. Any violations of these assumptions, such as non‐instantaneous sampling or the application of angle and distance of detection provided by the camera manufacturer, can cause biases in multiple directions that may be difficult to differentiate.

Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data.

Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.

Prognostic utility of a novel risk prediction model of 1-year mortality in patients surviving to discharge after surgery for congenital or acquired heart disease

ObjectiveWe sought to develop a novel risk prediction model of 1-year mortality after congenital heart surgery that accounts for clinical, anatomic, echocardiographic, and socioeconomic factors. MethodsThis was a single-center, retrospective review of consecutive index operations for congenital or acquired heart disease, from January 2011 to January 2021, among patients with known survival status at 1 year after discharge from the index hospitalization. The primary outcome was postdischarge mortality at 1 year. Variables of interest included age, prematurity, noncardiac anomalies or syndromes, the Childhood Opportunity Index, primary procedure, major adverse postoperative complications, and the Residual Lesion Score. Logistic regression was used to develop a weighted risk score for the primary outcome. Internal validation using a bootstrap-resampling approach was performed. ResultsOf 10,412 consecutive operations for congenital or acquired heart disease, 8808 (84.6%) cases met entry criteria, including survival to discharge. There were 190 (2.2%) deaths at 1 year postdischarge. A weighted risk score was formulated on the basis of the variables in the final risk prediction model, which included all aforementioned risk factors of interest. This model had a C-statistic of 0.82 (95% confidence interval, 0.80-0.85). The median risk score was 6 (interquartile range, 4-8) points. Patients were categorized as low (score 0-5), medium (score 6-10), high (score 11-15), or very high (score 16-20) risk. The expected probability of mortality was 0.4% ± 0.2%, 2.0% ± 1.1%, 10.1% ± 5.0%, and 36.6% ± 9.6% for low-risk, medium-risk, high-risk, and very high-risk patients, respectively. ConclusionsA risk prediction model of 1-year mortality may guide prognostication and follow-up of patients after discharge after surgery for congenital or acquired heart disease.

A novel nomogram for predicting the decision to delayed extubation after thoracoscopic lung cancer surgery

Objective Delayed extubation was commonly associated with increased adverse outcomes. This study aimed to explore the incidence and predictors and to construct a nomogram for delayed extubation after thoracoscopic lung cancer surgery. Methods We reviewed medical records of 8716 consecutive patients undergoing this surgical treatment from January 2016 to December 2017. Using potential predictors to develop a nomogram and using a bootstrap-resampling approach to conduct internal validation. For external validation, we additionally pooled 3676 consecutive patients who underwent this procedure between January 2018 and June 2018. Extubation performed outside the operating room was defined as delayed extubation. Results The rate of delayed extubation was 1.60%. Multivariate analysis identified age, BMI, FEV1/FVC, lymph nodes calcification, thoracic paravertebral blockade (TPVB) usage, intraoperative transfusion, operative time and operation later than 6 p.m. as independent predictors for delayed extubation. Using these eight candidates to develop a nomogram, with a concordance statistic (C-statistic) value of 0.798 and good calibration. After internal validation, similarly good calibration and discrimination (C-statistic, 0.789; 95%CI, 0.748 to 0.830) were observed. The decision curve analysis (DCA) indicated the positive net benefit with the threshold risk range of 0 to 30%. Goodness-of-fit test and discrimination in the external validation were 0.113 and 0.785, respectively. Conclusion The proposed nomogram can reliably identify patients at high risk for the decision to delayed extubation after thoracoscopic lung cancer surgery. Optimizing four modifiable factors including BMI, FEV1/FVC, TPVB usage, and operation later than 6 p.m. may reduce the risk of delayed extubation. Key Messages: This study identified eight independent predictors for delayed extubation, among which lymph node calcification and anaesthesia type were not commonly reported. Using these eight candidates to develop a nomogram, we could reliably identify high-risk patients for the decision to delayed extubation. Optimizing four modifiable factors, including BMI, FEV1/FVC, TPVB usage, and operation later than 6 p.m. may reduce the risk of delayed extubation.

A model incorporating histopathology and preoperative gadoxetic acid-enhanced MRI to predict early recurrence of hepatocellular carcinoma without microvascular invasion after curative hepatectomy.

To assess the predictive value of preoperative gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) features and postoperative histopathological grading for early recurrence of hepatocellular carcinoma (HCC) without microvascular invasion (MVI) after curative hepatectomy. A total of 85 MVI-negative HCC cases were retrospectively analyzed. Cox analyses were used to identify the independent predictors of early recurrence (within a 24 months span). The clinical prediction Model-1 or Model-2 was established without or with postoperative pathological factor, respectively. Nomogram models were constructed and receiver operating characteristic (ROC) curve analysis was used to assess the models' predictive ability. Internal validation of the prediction models for early HCC recurrence was performed using a bootstrap re-sampling approach. In the multivariate cox regression analysis, Edmondson-Steiner grade, peritumoral hypointensity on hepatobiliary phase (HBP), and relative intensity ratio (RIR) in HBP were identified as independent variables associated with early recurrence. The C-index of the nomogram models and internal validation were both between 0.7 and 0.8, showing good model fitting and calibration effects. The area under the ROC curve (AUC) was 0.781 for Model-1 based on the two preoperative MRI factors. When a third factor, the Edmondson-Steiner grade, was included (Model-2), the AUC increased to 0.834, and the sensitivity increased from 71.4 to 96.4%. Edmondson-Steiner grade, peritumoral hypointensity on HBP, and RIR on HBP can help predict early recurrence of MVI-negative HCC. In comparison with Model-1 (only imaging features), Model-2 (imaging features + histopathological grades) increases the sensitivity in predicting early recurrence of HCC without MVI. Preoperative GA-enhanced MRI signs are of great value in predicting early postoperative recurrence of HCC without MVI, and a combined pathological model was established to evaluate the feasibility and effectiveness of this technique.

A novel nomogram for predicting respiratory adverse events during transport after interventional cardiac catheterization in children.

The rate and predictors of respiratory adverse events (RAEs) during transport discharged from operating room after interventional cardiac catheterization in children remain unclear. This study aimed to investigate the incidence and predictors, and to construct a nomogram for predicting RAEs during transport in this pediatric surgical treatment. This prospective cohort study enrolled 290 consecutive pediatric patients who underwent ventricular septal defects (VSD), atrial septal defects (ASD), and patent ductus arteriosus (PDA) between February 2019 and December 2020. Independent predictors were used to develop a nomogram, and a bootstrap resampling approach was used to conduct internal validation. Composite RAEs were defined as the occurrence of at least 1 complication regarding laryngospasm, bronchospasm, apnea, severe cough, airway secretions, airway obstruction, and oxygen desaturation. The rate of RAEs during transport was 23.1% (67 out of 290). Multivariate analysis identified age (vs. ≤3 years, adjusted odds ratio (aOR) = 0.507, 95% confidence interval (CI), 0.268-0.958, P = 0.036), preoperative upper respiratory tract infections (URI, aOR = 2.335, 95% CI, 1.223-4.460, P = 0.01), type of surgery (vs. VSD, for ASD, aOR = 2.856, 95% CI, 1.272-6.411, P = 0.011; for PDA, aOR = 5.518, 95% CI, 2.425-12.553, P < 0.001), morphine equivalent (vs. ≤0.153 mg/kg, aOR = 2.904, 95% CI, 1.371-6.150, P = 0.005), atropine usage (aOR = 0.463, 95% CI, 0.244-0.879, P = 0.019), and RAEs during extubation to transport (aOR = 5.004, 95% CI, 2.633-9.511, P < 0.001) as independent predictors of RAEs during transport. These six candidate predictors were used to develop a nomogram, which showed a C-statistic value of 0.809 and good calibration (P = 0.844). Internal validation revealed similarly good discrimination (C-statistic, 0.782; 95% CI, 0.726-0.837) and calibration. Decision curve analysis (DCA) also demonstrated the clinical usefulness of the nomogram. The high rate of RAEs during transport reminds us of the need for more medical care and attention. The proposed nomogram can reliably identify pediatric patients at high risk of RAEs during transport and guide clinicians to make proper transport plans. Our findings have important and meaningful implications for RAEs risk prediction, clinical intervention and healthcare quality control.

Heart failure in obesity: insights from proteomics in patients treated with or without weight-loss surgery

BackgroundObesity is associated with incident heart failure (HF), but the underlying mechanisms are unclear.MethodsWe performed a nested case-control study within the Swedish-Obese-Subjects study, by identifying 411 cases who developed HF and matched them with respect to age, sex, weight-loss-surgery and length of follow-up with 410 controls who did not develop HF. In analyses corrected for multiple testing, we studied 182 plasma proteins known to be related to cardiovascular disease to investigate whether they could add to the understanding of the processes underlying obesity-related HF.ResultsA total of 821 subjects were followed for 16 ± 6 years. Multivariable analysis adjusted for matching variables revealed that 32 proteins were significantly associated with HF. Twelve proteins were related to HF ≥ 80% of the time using a bootstrap resampling approach (false-discovery-rate [FDR] < 0.05): 11 were associated with increased HF-risk: TNFRSF10A*, ST6GAL1, PRCP, MMP12, TIMP1, CCL3, QPCT, ANG, C1QTNF1, SERPINA5 and GAL-9; and one was related to reduced HF-risk: LPL. An further 20 proteins were associated with onset of HF 50–80% of the time using bootstrap resampling (FDR < 0.05). A pathway analysis including all significant 32 proteins suggested that these biomarkers were related to inflammation, matrix remodeling, cardiometabolic hormones and hemostasis. Three proteins, C1QTNF1, FGF-21 and CST3, reflecting dyslipidemia and kidney disease, displayed a higher association with HF in patients who did not undergo weight-loss-surgery and maintained with obesity.ConclusionPathways associated with HF in obesity include inflammation, matrix remodeling, cardiometabolic hormones and hemostasis; three protein biomarkers predicting HF appeared to be obesity-specific.

Development and validation of a prognostic nomogram among patients with acute exacerbation of chronic obstructive pulmonary disease in intensive care unit

BackgroundAcute exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) contributes significantly to mortality among patients with COPD in Intensive care unit (ICU). This study aimed to develop a nomogram to predict 30-day mortality among AECOPD patients in ICU.MethodsIn this retrospective cohort study, we extracted AECOPD patients from Medical Information Mart for Intensive Care III (MIMIC-III) database. Multivariate logistic regression based on Akaike information criterion (AIC) was used to establish the nomogram. Internal validation was performed by a bootstrap resampling approach with 1000 replications. The discrimination and calibration of the nomogram were evaluated by Harrell’s concordance index (C-index) and Hosmer–Lemeshow (HL) goodness-of-fit test. Decision curve analysis (DCA) was performed to evaluate its clinical application.ResultsA total of 494 patients were finally included in the study with a mean age of 70.8 years old. 417 (84.4%) patients were in the survivor group and 77 (15.6%) patients were in the non-survivor group. Multivariate logistic regression analysis based on AIC included age, pO2, neutrophil-to-lymphocyte ratio (NLR), prognostic nutritional index (PNI), invasive mechanical ventilation and vasopressor use to construct the nomogram. The adjusted C-index was 0.745 (0.712, 0.778) with good calibration (HL test, P = 0.147). The Kaplan–Meier survival curves revealed a significantly lower survival probability in the high-risk group than that in the low-risk group (P < 0.001). DCA showed that nomogram was clinically useful.ConclusionThe nomogram developed in this study could help clinicians to stratify AECOPD patients and provide appropriate care in clinical setting.

A Left Ventricular Mechanical Dyssynchrony-Based Nomogram for Predicting Major Adverse Cardiac Events Risk in Patients With Ischemia and No Obstructive Coronary Artery Disease.

BackgroundThe risk stratification of patients with ischemia and no obstructive coronary artery disease (INOCA) remains suboptimal. This study aims to establish a left ventricular mechanical dyssynchrony (LVMD)-based nomogram to improve the present situation.MethodsPatients with suspected coronary artery disease (CAD) were retrospectively enrolled and divided into three groups: normal (stenosis <50%, without myocardial ischemia), INOCA (stenosis <50%, summed stress score >4, summed difference score ≥2), and obstructive CAD (stenosis ≥50%). LVMD was defined by ROC analysis. INOCA group were followed up for the occurrence of major adverse cardiac events (MACEs: cardiovascular death, non-fatal myocardial infarction, revascularization, stroke, heart failure, and hospitalization for unstable angina). Nomogram was established using multivariate Cox regression analysis.ResultsAmong 334 patients (118 [35.3%] INOCA), LVMD parameters were significantly higher in INOCA group versus normal group but they did not differ between obstructive CAD groups. In INOCA group, 27 (22.9%) MACEs occurred during a 26-month median follow-up. Proportion of LVMD was significantly higher with MACEs under both stress (63.0% vs. 22.0%, P < 0.001) and rest (51.9% vs. 20.9%, P = 0.002). Kaplan–Meier analysis revealed significantly higher rate of MACEs (stress log-rank: P = 0.002; rest log-rank: P < 0.001) in LVMD patients. Multivariate Cox regression analysis showed that stress LVMD (HR: 3.82; 95% CI: 1.30–11.20; P = 0.015) was an independent predictor of MACEs. The internal bootstrap resampling approach indicates that the C-index of nomogram was 0.80 (95% CI: 0.71–0.89) and the AUC values for 1 and 3 years of risk prediction were 0.68 (95% CI: 0.46–0.89) and 0.84 (95% CI: 0.72–0.95), respectively.ConclusionLVMD-based nomogram might provide incremental prognostic value and improve the risk stratification in INOCA patients.

Development and validation of a novel nomogram for postoperative pulmonary complications following minimally invasive esophageal cancer surgery.

Postoperative pulmonary complications (PPCs) are the most common complications following minimally invasive esophagectomy (MIE) and can be associated with adverse outcomes. This study aims to construct a nomogram based on clinical factors to predict PPCs and investigate related early outcomes. Clinical data of 969 consecutive patients receiving MIE were retrospectively collected. Univariate and multivariate analysis were performed to select independent predictors. Using independent predictors to develop a nomogram and using a bootstrap-resampling approach to conduct internal verification. Early outcomes of PPCs were analyzed. The incidence of PPCs following MIE was 39.6% (384 out of 969). In multivariate analysis, older age (Odds ratio (OR) 1.034, P < 0.001), higher body mass index (OR 0.993, P = 0.003), heavy smoking (OR 1.396, P = 0.027), FEV1/FVC < 105% (OR 1.958, P < 0.001), chemoradiotherapy (OR 0.653, P = 0.039), estimated blood loss ≥ 400mL (OR 2.582, P = 0.018), general anesthesia (vs Combined thoracic paravertebral blockade, OR 1.578, P = 0.014), operative time ≥ 240min (OR 1.388, P = 0.027), squamous cell carcinoma (OR 2.099, P = 0.036) and conversion to thoracotomy (OR 2.820, P = 0.026) were independent predictors for PPCs. These ten independent predictors were used to develop a nomogram, with concordance index (C index) value of 0.662 and good calibration. After internal validation, similarly good calibration and discrimination (C index, 0.654; 95% CI 0.614-0.690) were observed. Patients developing PPCs had higher rates of anastomotic leakage, reoperation, ICU and 30-day readmissions, and prolonged ICU and hospital stays (P < 0.05). Our study identified ten predictors for PPCs, which were associated with poor early outcomes. The proposed nomogram can be a useful tool to identify patients at high risk of PPCs after MIE.

Estimating the variance of a combined forecast: Bootstrap-based approach

This paper considers bootstrap inference in model averaging for predictive regressions. We first show that the standard pairwise bootstrap is not valid in the context of model averaging. This common bootstrap approach induces a bias-related term in the bootstrap variance of averaging estimators. We then propose and justify a fixed-design residual-based bootstrap resampling approach for model averaging. In a local asymptotic framework, we show the validity of the bootstrap in estimating the variance of a combined forecast and the asymptotic covariance matrix of a combined parameter vector with fixed weights. Our proposed method preserves non-parametrically the cross-sectional dependence between different models and the time series dependence in the errors simultaneously. The finite sample performance of these methods is assessed via Monte Carlo simulations. We illustrate our approach using an empirical study of the Taylor rule equation with 24 alternative specifications.

Candidates of Genomic Tests in HR+/HER2- Breast Cancer Patients With 1-2 Positive Sentinel Lymph Node Without Axillary Lymph Node Dissection: Analysis From Multicentric Cohorts.

BackgroundThe genomic tests such as the MammaPrint and Oncotype DX test are being gradually applied for hormone receptor positive/HER-2 negative (HR+/HER2-) breast cancer patients with up to three positive axillary lymph nodes (ALNs). The first results from RxPONDER trial suggested that Oncotype DX could be applied to patients with 1-2 positive sentinel lymph nodes (SLNs) without axillary lymph node dissection (ALND), which constituted 37.4% of the intent-to-treat population. However, there was no distinctive research on how to apply genomic tests precisely to HR+/HER2- patients with 1-2 positive SLNs without ALND. The purpose was to construct a nomogram using the multi-center retrospective data to predict precisely which HR+/HER2- candidates with 1-2 positive SLNs could be subjected to genomic tests (≤ 3 positive lymph nodes).MethodsWe conducted a retrospective analysis of 18,600 patients with stage I-III breast cancer patients treated with sentinel lymph node biopsy (SLNB) in Shandong Cancer Hospital, Fudan University Shanghai Cancer Center, and West China Hospital. The univariate and multivariate logistic regression analysis was conducted to identify the independent predictive factors of having ≤ 3 positive nodes among patients with 1-2 positive SLNs. A nomogram was developed based on variables in the final model with p<0.05. Calibration of the nomogram was carried out by internal validation using the bootstrap resampling approach and was displayed using a calibration curve. The discrimination of the model was evaluated using the ROC curve.ResultsBased on the database of the three institutions, a total of 18,600 breast cancer patients were identified undergoing SLNB between May 2010 and 2020. Among the 1817 HR+/HER2- patients with 1-2 positive SLNs undergoing ALND, 84.2% harbored ≤ 3 totals metastatic ALNs. The multivariate logistic regression analysis identified imaging abnormal nodes (OR=0.197, 95%CI: 0.082-0.472), the number of positive SLNs (OR=0.351, 95%CI: 0.266-0.464), the number of negative SLNs (OR=1.639, 95%CI: 1.465-1.833), pathological tumor stage (OR=0.730, 95%CI: 0.552-0.964), and lympho-vascular invasion (OR=0.287, 95%CI: 0.222-0.398) as independent predictors for the proportion of patients with ≤ 3 total metastatic ALNs (all p<0.05). These five predictors were used to create a predictive nomogram. The AUC value was 0.804 (95%CI: 0.681-0.812, p<0.001). The calibration curve showed a satisfactory fit between the predictive and actual observation based on internal validation with a bootstrap resampling frequency of 1000.ConclusionThe nomogram based on the multi-centric database showed a good accuracy and could assist the oncologist in determining precisely which HR+/HER2- candidates with 1-2 positive SLNs without ALND could perform genomic tests. In the era of SLNB and precision medicine, the combined application of genomic tests and SLNB could provide patients with a better strategy of dual de-escalation management, including the de-escalation of both surgery and systemic treatment.

The mediating effect of skeletal muscle index on the relationship between menarcheal age and bone mineral density in premenopausal women by race/ethnicity.

To examine the contribution of skeletal mass index (SMI) as a mediator in the relationship between menarcheal age and hip/spine bone mineral density (BMD) in premenopausal women by race/ethnicity. The data of 4,329 participants (age ≥ 18; mean age=35.7 ± 9.5) of Whites (n = 2,543), African Americans (n = 1,236), and Asians (n = 550) enrolled from October 2011 to January 2019 from the Louisiana Osteoporosis Study were analyzed. After adjustment for physiological and behavioral factors, multivariable linear regression analyses were conducted to evaluate each component of the proposed mediation models, and mediation was verified by the bootstrapping resampling approach. Premenopausal women with early menarcheal age tended to have higher SMI and BMD than women with normal menarcheal age among all races/ethnicities included. Women with late menarcheal age were, however, more likely to have a lower SMI than their counterparts with normal menarcheal age (r = -0.212, 95% CI = [-0.321 to -0.103] for White women; r = -0.181, 95% CI = [-0.410 to -0.008] for African-American women; r = -0.174, 95% CI = [-0.343 to -0.006] for Asian women). Similar results were found for both spine and hip BMD. SMI fully mediated the difference in BMD due to different menarcheal ages among Whites, African Americans, and Asian women with early menarcheal age; however, no mediating effects were observed for Asian women with late menarcheal age. SMI, as a full mediator, affected the relationship between menarcheal age and BMD among premenopausal women, and the mediating effects varied by race/ethnicity. To prevent or slow down the loss of hip/spine BMD and the development of osteoporosis, measures aiming at minimizing the risk for muscle mass loss should be recommended, especially for White and African-American women with late menarcheal age.

The survival nomograms for anti-neutrophil cytoplasmic antibody-associated glomerulonephritis patients with a follow-up of more than one year.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis with kidney injury, manifested as ANCA-associated glomerulonephritis (AAGN), often portends a poor prognosis of renal function and life survival in long term. A cohort of 339 AAGN patients were enrolled retrospectively. These patients survived and were followed up for at least 12 months after diagnosis in our centre. Multivariate Cox regression analysis and nomogram models were performed to determine the risk factors associated with renal survival and patient survival. The median follow-up time of all 339 patients was 65.2 (IQR 45.1, 91.3) months and the median age was 61(IQR 53, 69) years. In order to analyse the impact of the factors on renal survival, we divided the patients into 2 groups: non-dialysis group (204 patients without dialysis at the final visit) and dialysis group (135 patients with maintaining dialysis). The patients in dialysis group had lower haemoglobin level, lower eGFR level, lower platelets count, more daily urine protein, and higher Birmingham Vasculitis Activity Score (BVAS) at admission than those in non-dialysis group. Multivariate Cox regression revealed that low haemoglobin (HR=0.977, 95%CI 0.965-0.990, p<0.001), low eGFR (HR=0.957, 95%CI 0.941-0.973, p<0.001) and high proteinuria (HR=1.139, 95%CI 1.055-1.230, p=0.001) at admission were independent risk factors for developing maintaining dialysis. A nomogram was established based on the results of multivariate Cox analysis and the internal bootstrap resampling approach showed the C-index of the nomogram was 0.83. Then we divided all patients into death group (n=99) and survival group (n=240). The patients in death group had older age, more hypertension, more chronic lung disease, lower platelets count, lower serum albumin, higher BVAS and lower eGFR at admission than those in survival group. Multivariate Cox regression revealed that the status of maintaining dialysis (HR 3.51, 95% CI 1.91-6.47, p<0.001) and old age (HR 1.07, 95% CI 1.04-1.09, p<0.001) were independent risk factors for all-cause mortality. Again, a nomogram was established and the C-index was 0.74. We analysed the independent risk factors for maintaining dialysis and all-cause mortality in AAGN patients with a follow-up of more than 12 months. The two proposed nomograms were of predictive value.

Ranked simulated resampling: a more efficient and accurate resampling approximations for bootstrap inference

Since its invention, Efron’s bootstrap resampling approach has changed all the aspects of statistical inference, which has become the default framework whenever the classical inference approaches are not feasible. This paper introduces a new, more accurate, and efficient resampling approach, namely, the ranked simulated resampling approach. We show that, analytically and computationally, it is more efficient and precise than Efron’s uniform bootstrap resampling approach. We provide simulation studies and real data applications to support the comparison between the ranked simulated resampling approach and Efron’s uniform bootstrap resampling approach.

Internal and external validation of a tumour control probability model for non-small cell lung cancer

Tumour control probability (TCP) models are often used in radiation oncology to estimate local tumour control. However, validation of these models, while highly desirable, unfortunately remains very uncommon. Therefore, the purpose of this study is to validate a previously described TCP model for non-small cell lung cancer (NSCLC). To validate and, hence, measure the TCP model performance, two conventional approaches, internal and external validation, were used. 25 publications, which reported clinical outcomes of NSCLC, were employed in the validation procedure. The Bootstrap resampling approach was utilized to validate the TCP model internally. 1,000 bootstrap samples were randomly generated with replacement, each sample consisted of 25 data points. The TCP model outcomes were then evaluated in both the original and the bootstrap samples to determine the ‘optimism’ of the TCP model performance. For external validation, the patient cohorts were split, randomly, into a training set (70%) and a validation set (30%). Subsequently, the optimism in the model performances for both sets was determined. The bootstrap expected ‘optimism’ and ‘optimism-corrected’ performances in the coefficient of determination (R2) for two-year TCP were estimated to be 6% and 86%. Similarly, the expected ‘optimism’ and ‘optimism-corrected’ performances of the external validation samples in R2 were estimated to be 2% and 88%. These values agreed well with those observed in the internal validation samples. Nonetheless, both internal and external validation outcomes should, preferably, be the same. The TCP model in the current study demonstrates excellent performance, as suggested by the internal and external validation procedures.