Development and validation of a prognostic model of vertebral refracture after percutaneous kyphoplasty

Purpose: To investigate the factors influencing refracture after percutaneous kyphoplasty and to develop and validate a prognostic model.Method: We retrospectively collected clinical data in 392 patients with osteoporotic vertebral compression fractures who underwent percutaneous kyphoplasty at the Affiliated Hospital of Xuzhou Medical University from 1 January 2018 to 1 January 2020.Predictors significantly associated with refracture after PKP were selected based on last absolute shrinkage and selection operator regression. Then a prognostic model were developed and internal validated using enhanced Bootstrap validation.Results: Among the 392 patients who included in this study, there were 19 refracture after percutaneous kyphoplasty(4.8%). Four factors were selected by least absolute shrinkage and selection operator regression for significant association with refracture after percutaneous kyphoplasty, including body mass index, bone mineral density, unilateral puncture, and bone cement leakage. After enhance Bootstrap validation, the bias-corrected curve of the model fitted well with the apparent curve, with the area under ROC curve of 0.931 and 95% CI of (0.789,0.936).Conclusion: The prognostic model developed based on four clinical profiles: body mass index, bone mineral density, unilateral puncture, and bone cement leakage can be used to identify those at most risk of refracture after percutaneous kyphoplasty.

A CpGs Methylation Signature as Potential Marker for Early Diagnosis of Hepatocellular Carcinoma from HBV-Related Liver Disease using Multiplex Bisulfite Sequencing

Background: Aberrant methylation of CpG sites severed as epigenetic marker for building diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC). Methods:Using Illumina 450K and EPIC Beadchip, we identified 34 CpG sites in peripheral blood mononuclear cell(PBMC) DNA that were differentially methylatedin early HCCversusHBV-related liver diseases (HBVLD). We employed multiplex bisulfite sequencing (MBS) based onnext-generation sequencing (NGS) to measure methylation of 34 CpG sites in PBMC DNA from 654 patients that were divided into a training set (n = 442), a test set (n = 212). Using training set, we selected and built a six-CpG-scorer (including cg14171514, cg07721852, cg05166871, cg18087306, cg05213896, and cg18772205), applying least absolute shrinkage and selection operator (LASSO) regression. We performed multivariable analyses of four candidate risk predictors (including six-CpG-scorer, age, sex, AFP level), using 20 times imputation of missing data, non-linearly transformed and backwards feature selection with logistic regression. The final model’s regression coefficients were calculated according to “Rubin's Rules”. The diagnostic accuracy of model was internally validated with10000 bootstrap validation dataset, and then applied to thetest set for validation.Results:The area under the receiver operating characteristic curve (AUROC) of the model was 0.81(95%CI, 0.77-0.85) and it showed good calibration, decision curve analysis. Using enhanced bootstrap validation, adjusted C-statistics and adjusted brier score was 0.809 and 0.199, respectively. The model also showed AUROC value of 0.84 (95% CI 0.79-0.88) of diagnosis for early HCC in test set.Conclusions:Our model basing onsix-CpG-scorer was a reliable diagnosis tool for early HCC from HBVLD.The usage of MBS method can realize large-scale detection of CpGsites in clinical diagnosis of early HCCand benefit the majority of patients.

Development and validation of nomograms for epithelial ovarian cancer: a SEER population-based, real-world study

Aim: To develop and internally validate nomograms to predict the overall survival (OS) and the cancer-specific survival (CSS) of patients with epithelial ovarian cancer (EOC). Methods: A total of 9001 EOC patients diagnosed between 2010 and 2013 were randomly divided into the training (n = 6301) and validation (n = 2700) cohorts. Nomogram and bootstrap validation were used to assess the predictive values of the models, including discrimination, calibration and clinical benefit. Results: In the validation cohort, the concordance statistic values were 0.733 for OS and 0.747 for CSS. Calibration plots and decision curve analyses demonstrated moderate accuracy and clinical applicability. Conclusion: Nomograms were user-friendly tools for guiding clinical treatment and estimating prognosis.

Nomogram for Acute Bilirubin Encephalopathy Risk in Newborns With Extreme Hyperbilirubinemia

Background and Objectives: This work aimed to develop a predictive model of neonatal acute bilirubin encephalopathy.Methods: We retrospectively analyzed the data on extreme hyperbilirubinemia (EHB) newborns hospitalized in the First Hospital of Jilin University from January 1, 2012 to December 31, 2019. The demographic characteristics, pathological information, and admission examination results of newborns were collected to analyze the factors affecting acute bilirubin encephalopathy and to establish a predictive model.Results: A total of 517 newborns were included in this study, of which 102 (19.7%) had acute bilirubin encephalopathy. T1WI hyperintensity [18.819 (8.838–40.069)], mother's age > 35 years [2.618 (1.096–6.2530)], abnormal white blood cell (WBC) [6.503 (0.226–18.994)], TSB level [1.340 (1.242–1.445)], and albumin level [0.812 (0.726–0.907)] were independently associated with neonatal acute bilirubin encephalopathy (ABE). All independently associated risk factors were used to form an ABE risk estimation nomogram. The bootstrap validation method was used to internally validate the resulting model. The nomogram demonstrated good accuracy in predicting the risk of ABE, with an unadjusted C index of 0.943 (95% CI, 0.919–0.962) and a bootstrap-corrected C index of 0.900.Conclusion: A nomogram was constructed using five risk factors of ABE. This model can help clinicians determine the best treatment for neonatal hyperbilirubinemia.

IgG Glycosylation Profile and the Glycan Score Are Associated with Type 2 Diabetes in Independent Chinese Populations: A Case-Control Study

Background. The relationship between the IgG glycan panel and type 2 diabetes remains unclear in Chinese population. We aimed to investigate the association of the IgG glycan profile and glycan score with type 2 diabetes. Methods. In the discovery population, 162 individuals diagnosed with type 2 diabetes and 162 matched controls from Beijing health management cohort were included. We analyzed the IgG glycan profile and composed a glycan score for type 2 diabetes. Findings were validated in the replication population from Beijing Xuanwu community cohort (280 cases and 508 controls). Area under curve (AUC) using 10-fold and bootstrap validation, net reclassification index (NRI), and integrated discrimination index (IDI) were calculated for the glycan score. Results. In the discovery population, 5 initial IgG glycans and 7 derived traits were significantly associated with type 2 diabetes after Bonferroni correction and Lasso selection, which were validated in the replication population subsequently. The glycan score composed of these IgG glycans and traits showed a strong association with type 2 diabetes (combined odds ratio (OR): 3.78) and its risk factors. In the replication population, AUC of the model involving clinical traits improved from 0.74 to above 0.90, and the values of NRI and IDI were 0.35 and 0.42, respectively, with the glycan score added. Conclusions. IgG glycosylation profiles were associated with type 2 diabetes and the glycan score may be a novel indicator for diabetes which reflected a proinflammatory status.

GPU-enabled design of an adaptable pattern recognition system for discriminating squamous intraepithelial lesions of the cervix

The aim of the present study was to design an adaptable pattern recognition (PR) system to discriminate low- from high-grade squamous intraepithelial lesions (LSIL and HSIL, respectively) of the cervix using microscopy images of hematoxylin and eosin (H&E)-stained biopsy material from two different medical centers. Clinical material comprised H&E-stained biopsies of 66 patients diagnosed with LSIL (34 cases) or HSIL (32 cases). Regions of interest were selected from each patient’s digitized microscopy images. Seventy-seven features were generated, regarding the texture, morphology and spatial distribution of nuclei. The probabilistic neural network (PNN) classifier, the exhaustive search feature selection method, the leave-one-out (LOO) and the bootstrap validation methods were used to design the PR system and to assess its precision. Optimal PR system design and evaluation were made feasible by the employment of graphics processing unit (GPU) and Compute Unified Device Architecture (CUDA) technologies. The accuracy of the PR-system was 93% and 88.6% when using the LOO and bootstrap validation methods, respectively. The proposed PR system for discriminating LSIL from HSIL of the cervix was designed to operate in a clinical environment, having the capability of being redesigned when new verified cases are added to its repository and when data from other medical centers are included, following similar biopsy material preparation procedures.

Development of a prediction model for inpatient gout flares in people with comorbid gout

ObjectivesHospitalisation is a risk factor for flares in people with gout. However, the predictors of inpatient gout flare are not well understood. The aim of this study was to develop a prediction model for inpatient gout flare among people with comorbid gout.MethodsWe used data from a retrospective cohort of hospitalised patients with comorbid gout from Wellington, Aotearoa/New Zealand, in 2017 calendar year. For the development of a prediction model, we took three approaches: (A) a clinical knowledge-driven model, (B) a statistics-driven model and (C) a decision tree model. The final model was chosen based on practicality and performance, then validated using bootstrap procedure.ResultsThe cohort consisted of 625 hospitalised patients with comorbid gout, 87 of whom experienced inpatient gout flare. Model A yielded 9 predictors of inpatient gout flare, while model B and C produced 15 and 5, respectively. Model A was chosen for its simplicity and superior C-statistics (0.82) and calibration slope (0.93). The final nine-item set of predictors were pre-admission urate >0.36 mmol/L, tophus, no pre-admission urate-lowering therapy (ULT), no pre-admission gout prophylaxis, acute kidney injury, surgery, initiation or increase of gout prophylaxis, adjustment of ULT and diuretics prior to flare. Bootstrap validation of the final model showed adequate C-statistics and calibration slope (0.80 and 0.78, respectively).ConclusionWe propose a set of nine predictors of inpatient flare for people with comorbid gout. The predictors are simple, practical and are supported by existing clinical knowledge.

Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

Background Other studies have assessed nonadherence to proton pump inhibitors (PPIs), but none has developed a screening test for its detection. Objectives To construct and internally validate a predictive model for nonadherence to PPIs. Methods This prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count). Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP) of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC), was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android). Results The points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83–0.91], p < 0.001). The test yielded a sensitivity of 0.80 (95% CI [0.70–0.87]) and a specificity of 0.82 (95% CI [0.76–0.87]). The three parameters were very similar in the bootstrap validation. Conclusions A points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.