Development of Urinary Diagnostic Biomarker for IgA Nephropathy by Lectin Microarray

<b><i>Introduction:</i></b> The pathogenic roles of aberrantly glycosylated IgA1 have been reported. However, it is unexplored whether the profiling of urinary glycans contributes to the diagnosis of IgAN. <b><i>Methods:</i></b> We conducted a retrospective study enrolling 493 patients who underwent renal biopsy at Okayama University Hospital between December 2010 and September 2017. We performed lectin microarray in urine samples and investigated whether c-statistics of the reference standard diagnosis model employing hematuria, proteinuria, and serum IgA were improved by adding the urinary glycan intensity. <b><i>Results:</i></b> Among 45 lectins, 3 lectins showed a significant improvement of the models: <i>Amaranthus caudatus</i> lectin (ACA) with the difference of c-statistics 0.038 (95% CI: 0.019–0.058, <i>p</i> &#x3c; 0.001), <i>Agaricus bisporus</i> lectin (ABA) 0.035 (95% CI: 0.015–0.055, <i>p</i> &#x3c; 0.001), and <i>Maackia amurensis</i> lectin (MAH) 0.035 (95% CI: 0.015–0.054, <i>p</i> &#x3c; 0.001). In 3 lectins, each signal plus reference standard showed good reclassification (category-free NRI and relative IDI) and good model fitting associated with the improvement of AIC and BIC. Stratified by eGFR, the discriminatory ability of ACA plus reference standard was maintained, suggesting the robust renal function-independent diagnostic performance of ACA. By decision curve analysis, there was a 3.45% net benefit by adding urinary glycan intensity of ACA to the reference standard at the predefined threshold probability of 40%. <b><i>Conclusions:</i></b> The reduction of Gal(β1-3)GalNAc (T-antigen), Sia(α2-3)Gal(β1-3)GalNAc (Sialyl T), and Sia(α2-3)Gal(β1-3)Sia(α2-6)GalNAc (disialyl-T) was suggested by binding specificities of 3 lectins. C1GALT1 and COSMC were responsible for the biosynthesis of these glycans, and they were known to be downregulated in IgAN. The urinary glycan analysis by ACA is a useful and robust noninvasive strategy for the diagnosis of IgAN.

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

BackgroundBone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.MethodsPatients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.ResultsA total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.ConclusionsThe comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.

Value of Radiomics Features From Adrenal Gland and Periadrenal Fat in CT Images for Predicting COVID-19 Prognosis

Background: Endocrine system plays an important role in infectious disease prognosis. Our goal is to assess the value of radiomics features extracted from adrenal gland and periadrenal fat CT images in predicting disease prognosis in patients with COVID-19. Methods: A total of 1,325 patients (765 moderate and 560 severe patients) from three centers were enrolled in the retrospective study. We proposed a 3D cascade V-Net to automatically segment adrenal glands in onset CT images. Periadrenal fat areas were obtained using inflation operations. Then, the radiomics features were automatically extracted. Five models were established to predict the disease prognosis in patients with COVID-19: a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], fusion of adrenal gland and periadrenal fat model [FM]), and a radiomics nomogram model (RN).Data from one center (1,183 patients) were utilized as training and validation sets. The remaining two (36 and 106 patients) were used as 2 independent test sets to evaluate the models’ performance. Results: The auto-segmentation framework achieved an average dice of 0.79 in the test set. CM, AM, PM, FM, and RN obtained AUCs of 0.716, 0.755, 0.796, 0.828, and 0.825, respectively in the training set, and the mean AUCs of 0.754, 0.709, 0.672, 0.706 and 0.778 for 2 independent test sets. Decision curve analysis showed that if the threshold probability was more than 0.3, 0.5, and 0.1 in the validation set, the independent-test set 1 and the independent-test set 2 could gain more net benefits using RN than FM and CM, respectively. Conclusion: Radiomics features extracted from CT images of adrenal glands and periadrenal fat are related to disease prognosis in patients with COVID-19 and have great potential for predicting its severity.

An online international comparison of palliative care identification in primary care using the Surprise Question

Background: The Surprise Question (‘Would I be surprised if this patient died within 12 months?’) identifies patients in the last year of life. It is unclear if ‘surprised’ means the same for each clinician, and whether their responses are internally consistent. Aim: To determine the consistency with which the Surprise Question is used. Design: A cross-sectional online study of participants located in Belgium, Germany, Italy, The Netherlands, Switzerland and UK. Participants completed 20 hypothetical patient summaries (‘vignettes’). Primary outcome measure: continuous estimate of probability of death within 12 months (0% [certain survival]–100% [certain death]). A threshold (probability estimate above which Surprise Question responses were consistently ‘no’) and an inconsistency range (range of probability estimates where respondents vacillated between responses) were calculated. Univariable and multivariable linear regression explored differences in consistency. Trial registration: NCT03697213. Setting/participants: Registered General Practitioners (GPs). Of the 307 GPs who started the study, 250 completed 15 or more vignettes. Results: Participants had a consistency threshold of 49.8% (SD 22.7) and inconsistency range of 17% (SD 22.4). Italy had a significantly higher threshold than other countries ( p = 0.002). There was also a difference in threshold levels depending on age of clinician, for every yearly increase, participants had a higher threshold. There was no difference in inconsistency between countries ( p = 0.53). Conclusions: There is variation between clinicians regarding the use of the Surprise Question. Over half of GPs were not internally consistent in their responses to the Surprise Question. Future research with standardised terms and real patients is warranted.

A Suggestion for Hip Replacement Surgery: Probabilistic Surveillance

Objective: Surgical site infection (SSI) surveillance is time-consuming and hard. Identifying high-risk patients and focusing on these patients will be cost and time effective. This study aims to develop a model to identify high-risk patients for the development of SSI after hip replacement surgery and to estimate the utility of the model. Methods: Logistic regression model was created to determine the risk of SSI development using the National Health Service Associated Surveillance Network (USHİİSA) data. The stability of the model was tested using the Bootstrap resampling method. The individual probability of developing SSI was determined for each patient by using the model. The threshold probability to be used in distinguishing high-risk patients was found 1.2% by ROC analysis. For hospitals with different SSI rates and surveillance sensitivity, the utility of the model has been estimated by various parameters. Results: Female gender (OR:1.52; 95% CI:1.22-1.88), being over 65 years of age (OR:2.06; 95% CI:1.63-2.62), procedure duration longer than 75th percentile (OR:1.32; 95% CI:1.07-1.63), ASA score over 3 (OR:2.10; 95% CI:1.48-2.99), and surgery performed in a hospital other than a private hospital (p<0.001) were found to be independent risk factors for the development of SSI. When focusing on high-risk patients, as the rate of SSI of a hospital increases, the number of patients that need to be focused on detecting one more SSI decreased, and the number of additional SSIs increased. As the surveillance sensitivity of the hospitals decreases, the new rate obtained differs more from the old rate. Conclusions: Focusing on high-risk patients identified using the model caused to eliminate approximately half of the patients, thus saving labor and time. Using this model can be particularly beneficial for hospitals with a high SSI burden and low surveillance capacity. The model can be integrated into the national surveillance system so that high-risk patients can be prioritized. Modeling may be considered for the other surgeries.

Comparison of PHI and PHI Density for Prostate Cancer Detection in a Large Retrospective Caucasian Cohort

<b><i>Background:</i></b> Beyond prostate-specific antigen (PSA), other biomarkers for prostate cancer (PCa) detection are available and need to be evaluated for clinical routine. <b><i>Objective:</i></b> The aim of the study was to evaluate the Prostate Health Index (PHI) density (PHID) in comparison with PHI in a large Caucasian group &#x3e;1,000 men. <b><i>Methods:</i></b> PHID values were used from available patient data with PSA, free PSA, and [−2]pro­PSA and prostate volume from 3 former surveys from 2002 to 2014. Those 1,446 patients from a single-center cohort included 701 men with PCa and 745 with no PCa. All patients received initial or repeat biopsies. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves comparing area under the ROC curves (AUCs), precision-recall approach, and decision curve analysis (DCA). <b><i>Results:</i></b> PHID medians differed almost 2-fold between PCa (1.12) and no PCa (0.62) in comparison to PHI (48.6 vs. 33; <i>p</i> always &#x3c;0.0001). However, PHID and PHI were equal regarding the AUC (0.737 vs. 0.749; <i>p</i> = 0.226), and the curves of the precision-recall analysis also overlapped in the sensitivity range between 70 and 100%. DCA had a maximum net benefit of only ∼5% for PHID versus PHI between 45 and 55% threshold probability. Contrary, in the 689 men with a prostate volume ≤40 cm³, PHI (AUC 0.732) showed a significant larger AUC than PHID (AUC 0.69, <i>p</i> = 0.014). <b><i>Conclusions:</i></b> Based on DCA, PHID had only a small advantage in comparison with PHI alone, while ROC analysis and precision-recall analysis showed similar results. In smaller prostates, PHI even outperformed PHID. The increment for PHID in this large Caucasian cohort is too small to justify a routine clinical use.

Novel nomogram for predicting the 3-year incidence risk of osteoporosis in a Chinese male population

Objective: To establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40years) based on data mining technology. Materials and methods: A total of 1,834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model. Results: The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95%CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1% and 100%, the nomogram had good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870. Conclusions: This novel nomogram can effectively predict the 3-year incidence risk of OP in the male population.

Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer

Objectives: To develop and validate a noninvasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy. Methods: In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T2WI (n = 788) and DWI (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic (ROC) curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature. Results: nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%. Conclusions: The multiparametric MRI-based radiomics signature can potentially serve as a noninvasive tool for distinguishing between indolent and aggressive PCa prior to therapy. Advances in knowledge: The multiparametric MRI-based radiomics signature has the potential to noninvasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.

Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma

The presence of microvascular invasion (MVI) is a critical determinant of early hepatocellular carcinoma (HCC) recurrence and prognosis. We developed a nomogram model integrating clinical laboratory examinations and radiological imaging results from our clinical database to predict microvascular invasion presence at preoperation in HCC patients. 242 patients with pathologically confirmed HCC at the Ningbo Medical Centre Lihuili Hospital from September 2015 to January 2021 were included in this study. Baseline clinical laboratory examinations and radiological imaging results were collected from our clinical database. LASSO regression analysis model was used to construct data dimensionality reduction and elements selection. Multivariate logistic regression analysis was performed to identify the independent risk factors associated with MVI and finally a nomogram for predicting MVI presence of HCC was established. Nomogram performance was assessed via internal validation and calibration curve statistics. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the nomogram model by quantifying the net benefits along with the increase in threshold probabilities. Survival analysis indicated that the probability of overall survival (OS) and recurrence-free survival (RFS) were significantly different between patients with MVI and without MVI (P < 0.05). Histopathologically identified MVI was found in 117 of 242 patients (48.3%). The preoperative factors associated with MVI were large tumor diameter (OR = 1.271, 95%CI: 1.137–1.420, P < 0.001), AFP level greater than 20 ng/mL (20–400 vs. ≤ 20, OR = 2.025, 95%CI: 1.056–3.885, P = 0.034; > 400 vs. ≤ 20, OR = 3.281, 95%CI: 1.661–6.480, P = 0.001), total bilirubin level greater than 23 umol/l (OR = 2.247, 95%CI: 1.037–4.868, P = 0.040). Incorporating tumor diameter, AFP and TB, the nomogram achieved a better concordance index of 0.725 (95%CI: 0.661–0.788) in predicting MVI presence. Nomogram analysis showed that the total factor score ranged from 0 to 160, and the corresponding risk rate ranged from 0.20 to 0.90. The DCA showed that if the threshold probability was > 5%, using the nomogram to diagnose MVI could acquire much more benefit. And the net benefit of the nomogram model was higher than single variable within 0.3–0.8 of threshold probability. In summary, the presence of MVI is an independent prognostic risk factor for RFS. The nomogram detailed here can preoperatively predict MVI presence in HCC patients. Using the nomogram model may constitute a usefully clinical tool to guide a rational and personalized subsequent therapeutic choice.

Connector-Breaker Games on Random Boards

By now, the Maker-Breaker connectivity game on a complete graph $K_n$ or on a random graph $G\sim G_{n,p}$ is well studied. Recently, London and Pluhár suggested a variant in which Maker always needs to choose her edges in such a way that her graph stays connected. By their results it follows that for this connected version of the game, the threshold bias on $K_n$ and the threshold probability on $G\sim G_{n,p}$ for winning the game drastically differ from the corresponding values for the usual Maker-Breaker version, assuming Maker's bias to be 1. However, they observed that the threshold biases of both versions played on $K_n$ are still of the same order if instead Maker is allowed to claim two edges in every round. Naturally, this made London and Pluhár ask whether a similar phenomenon can be observed when a $(2:2)$ game is played on $G_{n,p}$. We prove that this is not the case, and determine the threshold probability for winning this game to be of size $n^{-2/3+o(1)}$.