Bootstrap Internal Validation Research Articles

Development and validation of a prediction model for estimating patency rates after treatment of haemodialysis access dysfunction.

This study aims to develop a risk assessment model for predicting haemodialysis access dysfunction and to construct a nomogram. The clinical data of patients with haemodialysis access dysfunction treated at our hospital from October 2020 to January 2024 were retrospectively analysed. The least absolute shrinkage and selection operator regression method was used to filter variables and select predictors, while Cox regression was applied to filter variables and construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). Calibration was evaluated using bootstrap internal validation and the Hosmer-Lemeshow test. The clinical utility and applicability of the model were assessed through decision curve analysis (DCA) and the clinical impact curve (CIC). Subgroup analysis of risk factors for haemodialysis access dysfunction was performed using Kaplan-Meier survival curves. The study included 423 patients, and seven variables were used to construct the risk prediction model and nomogram for haemodialysis access dysfunction. The C-index of the prediction model was 0.783, and the time-dependent AUC (>0.8) at 6, 12, 18 and 24 months post-surgery indicated strong discriminatory ability. The calibration curve and Hosmer-Lemeshow test demonstrated good agreement between the prediction of the nomogram and the observed values. The DCA and CIC curves further confirmed the clinical practicability of the model. A risk assessment model and nomogram for haemodialysis access dysfunction based on seven variables were successfully constructed. This model demonstrates good discrimination and calibration, offering valuable guidance for clinical decision-making and significant clinical utility.

Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients

ObjectiveThe objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy.MethodsWe applied the LASSO regression model to identify the independent risk factors associated with POD. Subsequently, we utilized R software to develop and validate a nomogram model capable of accurately predicting the incidence of POD.ResultsThe final variables selected by the LASSO method were: Ramelteon, Age, Sex, Alcohol, Viral status, Cardiovascular disease, ASA class, Total bilirubin, Prothrombin time, Laparoscopic approach, and Blood transfusion. The performance of the nomogram was measured using ROC curve analysis, with an AUC of 0.854 (95% CI: 0.794–0.914) for the model. At the optimal cutoff value, the model demonstrated a sensitivity of 91.9% and a specificity of 68.8%. Model validation was performed using internal bootstrap validation to further verify the regression analysis. The ROC curve was generated by repeating the bootstrapping process 500 times, resulting in an AUC of 0.848 (95% CI: 0.786–0.904) for the model. The DCA curve representing the net benefit demonstrated the strong clinical validity of the model in predicting postoperative delirium.ConclusionOur results demonstrated that LASSO-based regression effectively constructed a nomogram model for predicting post-hepatectomy delirium.

Estimation of the Harvey Bradshaw Index from the Patient-Reported Outcome 2 in Crohn's Disease: Results Based on a Large Scale Randomized Controlled Trial.

Many registrational trials in Crohn's disease assess treatment efficacy with the 2-item Patient-Reported Outcome (PRO2), while the Harvey-Bradshaw Index (HBI) is prominent in pragmatic trials and clinical practice. The translation between PRO2 and HBI has not been established. Data from a Phase 3 trial of vedolizumab in Crohn's disease were used to determine the Pearson correlation between PRO2 and HBI. Linear regression was used to fit equations that estimate between indices; 95% prediction intervals were determined for HBI scores corresponding to PRO2 thresholds for disease activity. Internal validation of the conversion equations was performed using the bootstrap methods. PRO2 and HBI were highly correlated at baseline (r = 0.75 95% confidence interval (CI) 0.73-0.78; P < .001), induction (r = 0.87; 95% CI, 0.85-0.88; P < .001), and maintenance (r = 0.88; 95% CI, 0.85-0.90; P < .001). PRO2 and HBI change scores were moderately correlated (r = 0.72; 95% CI 0.69-0.75; P < .001) in induction and more strongly correlated during maintenance (r = 0.81; 95% CI 0.78- 0.84; P < .001). Regression equations for conversion of PRO2 to HBI from all cohorts (induction, maintenance, randomized, open-label) support an approximate conversion where HBI = 0.5 PRO2. As expected from the imperfect correlation between scores, the prediction intervals were generally wide. No evidence of overfitting was seen in bootstrap internal validation. PRO2and HBI correlate strongly and conversion between them is possible. These findings facilitate the practical application of trial results and clinical guidelines.

A prognostic nomogram for patients with III-IV nasopharyngeal carcinoma based on dynamic changes in the inflammatory and nutrition index.

The purpose of the study was to explore the value of dynamic changes in inflammatory and nutritional index after comprehensive treatment in patients with stage III-IVA nasopharyngeal carcinoma (NPC). A prognostic model was also established and validated for progression-free survival (PFS) of patients. We retrospectively selected 279 NPC patients with stage III-IVA. Their general clinical data and hematological index were collected and then calculated the changes during treatment. X-tile software was used to determine the optimal cut-off value. COX regression, Lasso method, and Boruta method were used to variable selection and model establishment. Using the bootstrap internal validation method, concordance index (C-index), calibration plot, and Kaplan-Meier curves were used to evaluate the model. To test the prognostic value of the model, we have also evaluated the performance of the nomogram against a conventional tumor metastasis staging system (TNM). Multivariable COX regression analysis demonstrated that clinical staging, delta lymphocyte, delta monocyte, delta albumin, delta platelet-to-lymphocyte ratio and delta systemic immune inflammation index were related to the PFS of NPC patients. The C-index of the model was 0.712, and the calibration curve indicated that the model had good consistency. The C-index of the TNM staging system was 0.597, which was considerably lower compared to our model (P = 0.015). We demonstrated the predictive value of dynamic changes in inflammatory and nutritional indices for the prognosis of NPC by successfully establishing and validating a prognostic model for predicting 1- and 3-year PFS after comprehensive treatment in patients with stage III-IVA NPC.

Utilizing Saliva Metabolomics for Diagnosing Gastric Cancer and Exploring the Changes in Saliva Metabolites After Surgery.

Gastric cancer (GC) is a disease with high prevalence and mortality, but we lack convenient and accurate methods to screen for this disease. Thus, we aimed to search for some salivary biomarkers and explore changes in metabolites in patients' saliva after radical gastrectomy. A total of 152 subjects were divided into three groups (healthy group, GC group, and one-week postoperative group). After simple processing, saliva samples were analyzed by liquid chromatography-mass spectrometry. First, we used total ion chromatography and principal component analysis to determine the metabolite profiles. Next, t-test, partial least squares discriminant analysis, support vector machine, and receiver operating characteristics curve analysis were performed to identify biomarkers. Then, Fisher discriminant analysis and hierarchical clustering analysis were performed to determine the discriminating ability of biomarkers. Finally, we established a generalized linear model to predict GC based on biomarkers, and used bootstrapping for internal validation. Between the healthy and GC groups, we identified four biomarkers: lactic acid, kynurenic acid, 3-hydroxystachydrine, and S-(1,2,2-trichlorovinyl)-L-cysteine. We used stepwise regression to select five metabolites and develop a model with areas under the curve equal to 0.973 in the training dataset and 0.924 in the validation dataset. Between the GC and one-week postoperative groups, we found two differential metabolites: 19-hydroxyprostaglandin E2 and DG (14:0/0:0/18:2n6). Differential metabolites were observed among the three groups. GC could be initially diagnosed on the basis of detection of these biomarkers. Moreover, changes in salivary metabolites in postoperative patients could provide important insights for basic studies.

Whole blood transcriptional profiles and the pathogenesis of tuberculous meningitis.

Mortality and morbidity from tuberculous meningitis (TBM) are common, primarily due to inflammatory response to Mycobacterium tuberculosis infection, yet the underlying mechanisms remain poorly understood. We aimed to uncover genes and pathways associated with TBM pathogenesis and mortality, and determine the best predictors of death, utilizing whole-blood RNA sequencing from 281 Vietnamese adults with TBM, 295 pulmonary tuberculosis (PTB), and 30 healthy controls. Through weighted gene co-expression network analysis, we identified hub genes and pathways linked to TBM severity and mortality, with a consensus analysis revealing distinct patterns between HIV-positive and HIV-negative individuals. We employed multivariate elastic-net Cox regression to select candidate predictors of death, then logistic regression and internal bootstrap validation to choose best predictors. Increased neutrophil activation and decreased T and B cell activation pathways were associated with TBM mortality. Among HIV-positive individuals, mortality associated with increased angiogenesis, while HIV-negative individuals exhibited elevated TNF signaling and impaired extracellular matrix organization. Four hub genes-MCEMP1, NELL2, ZNF354C, and CD4-were strong TBM mortality predictors. These findings indicate that TBM induces a systemic inflammatory response similar to PTB, highlighting critical genes and pathways related to death, offering insights for potential therapeutic targets alongside a novel four-gene biomarker for predicting outcomes.

Whole blood transcriptional profiles and the pathogenesis of tuberculous meningitis

Mortality and morbidity from tuberculous meningitis (TBM) are common, primarily due to inflammatory response to Mycobacterium tuberculosis infection, yet the underlying mechanisms remain poorly understood. We aimed to uncover genes and pathways associated with TBM pathogenesis and mortality, and determine the best predictors of death, utilizing whole-blood RNA sequencing from 281 Vietnamese adults with TBM, 295 pulmonary tuberculosis (PTB), and 30 healthy controls. Through weighted gene co-expression network analysis, we identified hub genes and pathways linked to TBM severity and mortality, with a consensus analysis revealing distinct patterns between HIV-positive and HIV-negative individuals. We employed multivariate elastic-net Cox regression to select candidate predictors of death, then logistic regression and internal bootstrap validation to choose best predictors. Increased neutrophil activation and decreased T and B cell activation pathways were associated with TBM mortality. Among HIV-positive individuals, mortality associated with increased angiogenesis, while HIV-negative individuals exhibited elevated TNF signaling and impaired extracellular matrix organization. Four hub genes—MCEMP1, NELL2, ZNF354C, and CD4—were strong TBM mortality predictors. These findings indicate that TBM induces a systemic inflammatory response similar to PTB, highlighting critical genes and pathways related to death, offering insights for potential therapeutic targets alongside a novel four-gene biomarker for predicting outcomes.

Development and validation of a predicting model for risk of rehospitalization within 30 days in heart failure patients with preserved ejection fraction

Abstract Objective This study aimed to develop and validate a predicting model for risk of rehospitalization within 30 days post discharge in patients diagnosed with Heart Failure with Preserved Ejection Fraction (HFpEF). Methods Clinical data of 9555 HFpEF patients registered in the heart failure registry from 2014 to 2022 at our hospital were analyzed. A total of 8160 patients (from 2014-2021) were designated as the training set, and 1395 patients (from 2022) as the validation set. A predictive model was established based on risk factors associated with 30-day rehospitalization due to heart failure identified by the Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression analysis. The model's stability was ensured through Bootstrap internal validation and Temporal validation external validation. Results LASSO and logistic regression analyses identified eight risk factors associated with increased risk of 30-day rehospitalization, including New York Heart Association (NYHA) classification, albumin, c-reactive protein, N-terminal pro b-type natriuretic peptide (NT-proBNP) divided by 100, anemia, pleural effusion, atrial fibrillation, and pulmonary hypertension. The established predictive model demonstrated good discriminative ability in the internal validation with an Area Under the Curve (AUC) value of 0.714, and a 95% confidence interval (CI) of 0.693-0.736 after 500 Bootstrap replicates. In the Temporal validation external validation, the model exhibited robust stability with an AUC value of 0.740 and a 95% CI of 0.693-0.781. When compared to analogous models from prior studies, present model showcased superior predictive accuracy in that both Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) values were positive and within their respective 95% confidence intervals (P-values &amp;lt;0.05). Conclusion This study successfully developed and validated a predictive model for 30-day rehospitalization due to heart failure in patients diagnosed with HFpEF. This model might allow the timely identification of high-risk HFpEF patients for 30-day rehospitalization due to heart failure and optimizes the patient management accordingly.

Machine learning to predict incident atrial fibrillation in patients with acute stroke: FIND-AF STROKE

Abstract Background Atrial fibrillation (AF)-related strokes have a high rate of recurrence, and are associated with substantial morbidity, disability, healthcare expenditure, and mortality.(1-3) Detection of AF promises opportunity to prevent recurrent stroke through conversion of antiplatelet to anticoagulant therapy,(4,5) but use of extended monitoring has cost implications.(6) Thus accurately identifying patients with stroke at high risk for AF is important but previous previous models have been developed in small, unrepresentative cohorts.(7) Purpose To develop and validate a simple novel decision support tool for prediction of incident AF after hospitalisation with stroke through machine learning. Methods We developed the model (FIND-AF STROKE) in UK secondary care and death certificate-linked primary care health record data from individuals aged ≥30 years without known AF hospitalized with a stroke or transient ischaemic attack (Jan 2, 1998 to Feb 28, 2022), randomly divided into training (80%) and testing (20%) datasets. We evaluated logistic regression and random forest models for prediction of 2-year AF risk in the testing dataset with internal bootstrap validation, and compared against the CHA2DS2VASC score. Youden index was used to set a threshold for higher and lower risk. Results Of 35 530 UK individuals (mean age 72.1 (SD 12.7) years, 50.9% women), 1742 (4.8%) had developed incident AF at 2 years. Using feature importance a parsimonious version of the model was created requiring only age, sex, ethnicity (white versus other) and five comorbidities. In the testing dataset prediction performance for the random forest algorithm (area under the receiver operating characteristic (AUROC) 0.710, 95% CI 0.685 – 0.736) was superior to the CHA2DS2VASC score (0.645, 95% CI 0.617-0.672) (Figure 1). The threshold for higher risk using the Youden index was 4.81% which resulted in a negative predictive value of 97.6%, sensitivity 0.712, specificity 0.583. Of the post-stroke patients 44% were labelled at higher risk of AF, who were older, had a higher prevalence of comorbidities, and a 3.3-fold higher incidence of AF post-stroke, than those identified as lower risk (Table 1). Conclusions The machine learning FIND-AF STROKE decision support tool can accurately identify individuals at risk of incident AF after stroke to enable a targeted and cost-effective approach to extended monitoring. Validation within cohorts that have undergone extended monitoring post-stroke will give a more accurate estimation of yield at different risk thresholds.Figure 1

Machine learning for prediction of incident heart failure and heart failure hospitalisation: multi-cohort development, validation and association with clinical phenotypes

Abstract Background Delayed heart failure (HF) diagnosis is associated with adverse outcomes and costs of hospitalisation.(1,2) Whilst previous tools have been developed to predict incident HF,(3) risk of HF incidence may correlate poorly with risk of HF hospitalisation, which may be more important in targeting diagnostics. Purpose To develop and validate a novel decision support tool for prediction of incident HF and HF hospitalisation, and investigate association of score with HF with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). Methods We developed the models (FIND-HF) in UK primary care health record data from individuals aged ≥40 years without known HF (Jan 2, 1998 to Feb 28, 2022), randomly divided into training (80%) and testing (20%) datasets. We evaluated logistic regression and supervised machine learning models for prediction of 1- and 5- year HF and HF hospitalisation risk in the testing dataset with internal bootstrap validation. Models were externally validated for HF hospitalisation in data from a Taiwanese university hospital. Association of FIND-HF score with HF phenotypes of HFpEF and HFrEF was assessed in two prospective cohorts, the MATCH registry of patients diagnosed with HFrEF and the FIND-AF cohort of individuals at higher predicted risk of atrial fibrillation who underwent echocardiography. Results Of 565 284 UK individuals (mean age 68.9 (SD 12.2) years, 49.2% women), incidence of HF and HF hospitalisation was 1.1% and 0.5% at 1-year, and 6.6% and 3.4% at 5-years, respectively. Prediction performance was superior for machine learning algorithms compared with logistic regression, with best performance for XGBoost (area under receiver operating characteristic curve (AUROC) at 1 and 5-years: 0.755 and 0.723 for incident HF, and 0.738 and 0.711 for incident HF hospitalization). On external validation in 106,026 individuals in Taiwan (mean age 64.7 (SD 10.6) years, 52.8 % women), HF hospitalisation incidence was 0.71%, and 2.46% at 1- and 5- years, respectively, and discrimination performance was excellent (AUROC at 1- and 5-years: 0.834 and 0.843). When the FIND-HF score was applied to individuals in the MATCH cohort (n = 133, mean age 69.0 (SD 11.7) years, 38.3% women) and FIND-AF cohort (n = 82, mean age 71.6 (SD 7.5) years, 50.0% women), the distribution of score to clinical phenotypes demonstrated a binomial distribution (Figure 1), and likelihood of HFpEF diagnosis correlated with the numeric score (Figure 2). Conclusions The machine learning FIND-HF decision support tool can accurately identify individuals at risk of incident HF and HF hospitalisation, to enable new approaches for early detection and prevention. The variation of score amongst high risk individuals with different HF clinical phenotypes emphasizes the distinct pathophysiological pathways but shared adverse outcome profile across the HF syndrome.Figure 1Figure 2

Nomogram Predictive Model for Vaginal Birth after One Prior Cesarean Section: A Retrospective Study

Background: Repeat cesareans pose significant maternal risks, whereas vaginal birth after cesarean (VBAC) offers better outcomes, with a complication rate of 2.4% compared with 3.6% for elective repeat cesareans and 14.1% for failed trial of labor after cesarean (TOLAC). TOLAC rates are low in China, and success rates vary between 60% and 80%. This study aims to develop a nomogram-based predictive model for VBAC in China and improve existing, less rigorous scoring models. Methods: This retrospective cohort study was conducted at Hangzhou Women's Hospital from February 2015 to March 2020, and included 159 parturient attempts at labor after one prior low transverse cesarean section. The participants were divided into two groups based on their mode of delivery for comparison: the VBAC and the TOLAC failure group. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors for VBAC and develop a nomogram predictive model to estimate the success rate of TOLAC. The bootstrap method was used for internal validation of the models. Three different VBAC prediction models were evaluated by plotting receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: A total of 127 women had VBAC and 32 women failed TOLAC, with a success rate of 79.9%. Three independent factors affecting the VBAC were identified: gestational age at delivery, Bishop score and newborn birth weight. A predictive nomogram model for the VBAC was constructed that incorporates these three factors. The model showed a good fit (χ2 = 11.94, p = 0.154) with an overall prediction accuracy of 81.1%. The area under the ROC curve was 0.83 (95% CI (confidence interval), 0.76–0.90) (p &lt; 0.001) and the optimal cut-off value was 83.4%. The bootstrap internal validation showed that our predictive model maintained high overall accuracy and specificity, but exhibited low sensitivity and a low Kappa coefficient. Compared to the Grobman model and Jiaming Rao et al. model, our developed prediction model possesses the strongest discriminatory ability and the highest net benefit, followed by the model by Jiaming Rao et al. All three models demonstrate a high degree of fit. Conclusions: Shorter gestational age at delivery, lower newborn birth weight and higher cervical Bishop score are favorable factors for VBAC. The predictive nomogram model for the VBAC after a single cesarean section, constructed with these three factors, has good predictive efficacy. The model is simple to calculate and has practical value in the clinical selection of suitable candidates for TOLAC after a single cesarean section.

The prognostic value of the 8th American Joint Committee on cancer anatomic and prognostic stage groups for penile cancer: A multicenter collaboration study

ObjectiveThe aim of this study was to investigate the value of 8th American Joint Committee on Cancer (AJCC) anatomic and prognostic stage groups for penile cancer patients and explore whether there is room for improvement. MethodsThe clinical and histopathologic data from 16 centers between 2000 and 2021 were assessed according to the 8th AJCC anatomic and prognostic stage groups. Kaplan–Meier plots were used to estimate the disease-specific survival (DSS) of the patients. The accuracy of the staging systems was investigated using the Harrell's concordance index (C-index). ResultsThe 5-year DSS rates for patients with stages 0is/a, I, IIA, IIB, IIIA, IIIB, and IV (stage 0 to stage IV) disease were 100 %, 98.9 %, 85.5 %, 81.1 %, 65.8 %, 34.4 %, and 23.2 %, respectively (p0is/a–I=0.798, pI–IIA<0.001, pIIA–IIB=0.486, pIIB–IIIA<0.001, pIIIA–IIIB<0.001, pIIIB–IV=0.004, ptotal<0.001). According to the modified model 1 system, the 5-year DSS rates without survivorship overlap for patients with stages 0is/a, I, II, IIIA, IIIB, and IV disease were 100 %, 98.9 %, 87.8 %, 65.8 %, 34.4 %, and 23.2 %, respectively (p0is/a–I=0.798, pI–II<0.001, pII–IIIA=0.002, pIIIA–IIIB<0.001, pIIIB–IV=0.004, ptotal<0.001). Similarly, according to the modified model 2 system, the 5-year DSS rates without survivorship overlap for patients with stages 0is/a, I, II, IIIA, IIIB, and IV disease were 100 %, 99.0 %, 85.8 %, 65.8 %, 34.4 %, and 23.2 %, respectively (p0is/a–I=0.757, pI–II<0.001, pII–IIIA=0.008, pIIIA–IIIB<0.001, pIIIB–IV=0.004, ptotal<0.001). The C-index of the simple modified staging systems were not inferior to those of the AJCC anatomic and prognostic stage groups. These results were confirmed by the bootstrap internal validation. ConclusionThere is still room for improvement about 8th AJCC anatomic and prognostic stage groups. The improved models, which are more concise and convenient, had a similar predictive value.

Development of a Clinical Prediction Rule to Determine Walking Independence in Older Adults With Hip Fractures.

Accurate determination of walking independence in older adults after hip fracture surgery is crucial for selecting appropriate walking strategies and providing fall prevention guidance. We developed and validated a clinical prediction rule (CPR) to determine level-surface walking independence and community walking independence in older adults with hip fractures. In a multicenter, cross-sectional study, we investigated 289 older inpatients with hip fractures. A backward stepwise logistic regression analysis was performed to develop a CPR for determining level-surface walking independence and community walking independence. The independent variables incorporated the patients' walking and balance evaluations at discharge, including the Berg Balance Scale (BBS), the Timed Up and Go (TUG) test, and maximum walking speed (MWS), as well as age and cognitive function evaluations. We used a bootstrap internal validation for the CPR's internal validation. At their hospital discharge, 171 patients had achieved level-surface walking independence, and 51 had achieved community walking independence. As the level of walking independence decreased, patients tended to be older, with lower BBS scores and slower TUG times and walking speeds. As diagnostic factors, the level-surface walking model included age, BBS, and cognitive decline; the community walking model included BBS, TUG, and MWS. The diagnostic accuracy, represented by the area under the curve (confidence interval), was 0.88 (0.84-0.92) for the level-surface walking model and 0.81 (0.74-0.87) for the community walking model. Internal validation confirmed that the models' discrimination accuracy was good, and no model overfitting was observed. We developed a moderately accurate CPR to determine walking independence in hip fracture patients. CPR can be a useful indicator for determining an individual's walking independence at present, but external validations with other samples are necessary.

Combination of serological and cytokine release assays for improved diagnosis of childhood tuberculosis in Zambia (PROMISE-TB)

ObjectivesThe diagnostic gaps for childhood tuberculosis (TB) remain considerable in settings with high TB incidence and resource constraints. We established and evaluated the performance of a scoring system based on a combination of serological tests and T-cell cytokine release assays, chosen for their ability to detect immune responses indicative of TB, in a context of high prevalence of pediatric HIV infection. MethodsWe enrolled 628 consecutive children aged ≤15 years, admitted for TB suspicion. Multiple cytokine levels in QuantiFERON Gold In-Tube supernatants and antigen 85B (Ag85B) antibodies were assessed in children who tested positive with either Xpert TB or mycobacterial culture. The results were compared with those of control children. ResultsAmong the biomarkers most strongly associated with TB, random forest classification analysis selected Ag85B antibodies, interleukin-2/interferon-γ ratio, and monokine induced by interferon-γ for the scoring system. The receiver operating characteristic curve derived from our scoring system showed an area under the curve of 0.95 (0.91-0.99), yielding 91% sensitivity and 88% specificity. The internal bootstrap validation gave the following 95% confidence intervals for the score performance: sensitivity 71%-97% and specificity 79%-99%. ConclusionsThis study suggests that supplementing the QuantiFERON assay with a combination of serological and T-cell markers could enhance childhood TB screening regardless of HIV status and age. Further validation among the target population is necessary to confirm the performance of this scoring system.

The potential of a nomogram risk assessment model for the diagnosis of abdominal aortic aneurysm: a multicenter retrospective study

The incidence of abdominal aortic aneurysm (AAA) is very high, but there is no risk assessment model for early identification of AAA in clinic. The aim of this study was to develop a nomogram risk assessment model for predicting AAA. The data of 280 patients diagnosed as AAA and 385 controls in The Affiliated Suzhou Hospital of Nanjing Medical University were retrospectively reviewed. The LASSO regression method was applied to filter variables, and multivariate logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). The calibration capability of the model is evaluated by using bootstrap (resampling = 1000) internal validation and Hosmer–Lemeshow test. The clinical utility and clinical application value were evaluated by decision curve analysis (DCA) and clinical impact curve (CIC). In addition, a retrospective review of 133 AAA patients and 262 controls from The First Affiliated Hospital of Soochow University was performed as an external validation cohort. Eight variables are selected to construct the nomogram of AAA risk assessment model. The nomogram predicted AAA with AUC values of 0.928 (95%CI, 0.907–0.950) in the training cohort, and 0.902 (95%CI, 0.865–0.940) in the external validation cohort, the risk prediction model has excellent discriminative ability. The calibration curve and Hosmer–Lemeshow test proved that the nomogram predicted outcomes were close to the ideal curve, the predicted outcomes were consistent with the real outcomes, the DCA curve and CIC curve showed that patients could benefit. This finding was also confirmed in the external validation cohort. In this study, a nomogram was constructed that incorporated eight demographic and clinical characteristics of AAA patients, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors.

Development and internal validation of a predictive model for prolonged intensive care unit stays in patients with psychotropic drug poisoning

BackgroundSome patients with psychotropic drug poisoning need intensive care unit (ICU) admission, but risk prediction models for prolonged ICU stays are lacking. ObjectivesDevelop and evaluate a prediction model for prolonged ICU stays in patients with psychotropic drug poisoning. MethodsThe clinical data of patients with psychotropic drug poisoning were collected from the Medical Information Mart for Intensive Care (MIMIC)-Ⅳ 2.2 database. Patients were grouped by their ICU length of stay: non-prolonged (<2 days) and prolonged (≥2 days).Variable selection methods included LASSO and logistic regression. The selected variables were used to construct the model, which was subsequently evaluated for discrimination, calibration, and clinical utility. ResultsThe cohort included 413 patients with psychotropic drug poisoning, 49.4 % male, with a median age of 41 years. The variables stepwise selected for model construction through LASSO and logistic regression include sepsis, SAPS Ⅱ, heart rate, respiratory rate, and mechanical ventilation. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.785 (95 % CI: 0.736–0.833) and was validated well with bootstrap internal validation (AUC: 0.792, 95 % CI: 0.745–0.839). Calibration curves indicated good fit (χ2 = 4.148, P = 0.844), aligning observed and predicted rates of prolonged ICU stays. Decision curve analysis (DCA) showed positive net benefits across a threshold probability range of 0.07–0.85. ConclusionsThe model developed in this study may help predict the risk of prolonged ICU stays for patients with psychotropic drug poisoning.

Predictive utility of emotional regulation abilities for assessing cognitive improvement in depression

ObjectiveTo construct a predictive model for the improvement of cognitive function in patients with depressive disorder treated with SNRIs, based on emotional regulation abilities, and to provide personalized treatment for depressed patients. MethodsClinical data from 170 patients with depressive disorder treated with SNRIs at Tongji Hospital, Shanghai, from December 2017 to May 2023 were collected. Based on whether the MoCA-B total score at 3–6 months post-treatment was at least 2 points higher than at baseline, patients were divided into the cognitive function improved group (n = 80) and the cognitive function not improved group (n = 90). Stepwise logistic regression and LASSO regression were used to select predictive factors, and logistic regression analysis was applied to construct predictive models solely based on emotional regulation abilities, combined with executive functions and HAMD scores. The models were further validated through Bootstrap internal validation, calibration curve plotting, and C-index calculation, and a comparison between the two models was performed. ResultsAn ER model with an area under the ROC curve of 0.817was established using four emotional regulation ability indicators: the valence of reappraised images, the arousal of negative images, the arousal of neutral images, and the success of reappraisal (arousal). Internal validation using Bootstrap showed a C index of 0.817, and clinical decision curves indicated that this model has a significant net benefit with a probability of improved cognitive function ranging from about 20 to 85%. Additionally, an EREH model including emotional regulation ability, executive function, and HAMD score as predictors was constructed using Lasso and logistic regression methods. This model reached an area under the ROC curve of 0.859and clinical decision curves showed high net benefits with probabilities of improved cognitive function ranging from 10 to 100%. The calibration curves of both models coincided well with the actual curves, with the latter having a higher AUC and significant statistical differences between the two models. ConclusionThis study suggests that emotional regulation ability may serve as a predictor for the improvement of cognitive functions in patients with depression depressive disorder treated with SNRIs. However, it is important to note that there may be other factors not covered or included in this study.The predictive model that includes executive functions and HAMD scores offers better differentiation and consistency and is more feasible in clinical practice.

Procalcitonin for safe reduction of unnecessary blood cultures in the emergency department: Development and validation of a prediction model

Blood cultures (BCs) are commonly ordered in emergency departments (EDs), while a minority yields a relevant pathogen. Diagnostic stewardship is needed to safely reduce unnecessary BCs. We aimed to develop and validate a bacteremia prediction model for ED patients, with specific focus on the benefit of incorporating procalcitonin. We included adult patients with suspected bacteremia from a Dutch ED for a one-year period. We defined 23 candidate predictors for a "full model", of which nine were used for an automatable "basic model". Variations of both models with C-reactive protein and procalcitonin were constructed using LASSO regression, with bootstrapping for internal validation. External validation was done in an independent cohort of patients with confirmed infection from 71 Spanish EDs. We assessed discriminative performance using the C-statistic and calibration with calibration curves. Clinical usefulness was evaluated by sensitivity, specificity, saved BCs, and Net Benefit. Among 2111 patients in the derivation cohort (mean age 63 years, 46% male), 273 (13%) had bacteremia, versus 896 (20%) in the external cohort (n=4436). Adding procalcitonin substantially improved performance for all models. The basic model with procalcitonin showed most promise, with a C-statistic of 0.87 (0.86-0.88) upon external validation. At a 5% risk threshold, it showed a sensitivity of 99% and could have saved 29% of BCs while only missing 10 out of 896 (1.1%) bacteremia patients. Procalcitonin-based bacteremia prediction models can safely reduce unnecessary BCs at the ED. Further validation is needed across a broader range of healthcare settings.

Amniotic fluid proteomic analysis identifies IL1RL1, APOE, and NECTIN4 as new biomarkers for preterm birth

BackgroundDespite extensive research, the identification of effective biomarkers for early prediction of preterm birth (PTB) continues to be a challenging endeavor. This study aims to identify amniotic fluid (AF) protein biomarkers useful for the early diagnosis of PTB.MethodsWe initially identified the protein expression profiles in the AF of women with PTB (n = 22) and full-term birth (FTB, n = 22), from the First People’s Hospital of Yunnan Province who underwent amniocentesis from November 2019 to February 2020, using mass spectrometry employing the data-independent acquisition (DIA) technique, and then analyzed differentially expressed proteins (DEPs). Subsequently, the least absolute shrinkage and selection operator (LASSO) and random forest analysis were employed to further screen the key proteins for PTB biomarker identification. The receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analyses (DCA) were utilized to assess the discrimination and calibration of the key biomarkers.ResultsA total of 25 DEPs were identified between the PTB and FTB groups, comprising 13 up-regulated and 12 down-regulated proteins. Three key protein biomarkers for early PTB diagnosis were identified: IL1RL1 (interleukin-1 receptor-like 1), APOE (apolipoprotein E), and NECTIN4 (nectin cell adhesion molecule 4). The results of the ROC analysis showed that the area under the curve (AUC) of the three proteins combined as a biomarker for early diagnosis of PTB was 0.913 (95% CI: 0.823-1.000), with a sensitivity of 0.864 and a specificity of 0.955, both superior to those of the individual biomarkers. Bootstrap internal validation revealed a concordance index (C-index) of 0.878, with a sensitivity of 0.812 and a specificity of 0.773, indicating the robust predictive performance of these biomarkers.ConclusionsWe identified three previously unexplored yet potentially useful protein biomarkers in AF for early PTB diagnosis: IL1RL1, APOE, and NECTIN4.

Development and validation of a risk prediction model for short-term progression of carotid atherosclerosis among early middle age adults

AimWe aimed at creating and validating a prognostic model incorporating easily accessible clinical and laboratory parameters to forecast the likelihood of short-term progression of carotid atherosclerosis. MethodsA prediction model was developed and validated for carotid plaque progression within 2 years in an early middle-age population in China. Progression was defined as the new appearance of carotid plaque or stenosis among participants who had normal carotid status at baseline. Leveraging data from a health check-up chain, predictors were identified using statistical methods including stepwise logistic regression, Markov Chain Monte Carlo (MCMC) simulation, random forest analysis and least absolute shrinkage selection operator (Lasso). Model performance was assessed. Bootstrap internal validation, validation on another check-up population and subgroup analysis were also conducted. ResultsAmong 7765 participants, predictors including age, diastolic blood pressure, uric acid levels, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were identified for carotid plaque progression in 2 years. The developed prediction model demonstrated good discrimination (AUC = 0.755, 95%CI:0.736–0.774) and calibration ability (slope = 0.922 and interception = 0.007) among development data, as well as among validation data (AUC = 0.759, 95%CI:0.674–0.770; slope = 1.076 and intercept = −0.014). Internal validation using bootstrap method yielded an adjusted AUC of 0.753. The model's performance remained consistent across different subgroups. ConclusionsOur study presents a validated risk prediction model for carotid plaque progression in an early middle age population, offering a valuable tool for early identification and monitoring of cardiovascular risks. The model's robustness and applicability across different subgroups highlight its potential utility in preemptive cerebrovascular and cardiovascular disease management.