This study aims to develop cancer-associated venous thromboembolism (CA-VTE) risk prediction models using survival machine learning (ML) algorithms.

This study employed a double-cohort study design (retrospective and prospective). The retrospective cohort (n ​= ​1036) was used as training set (70.0%, n ​= ​725) and internal validation set (30.0%, n ​= ​311); while the prospective cohort (n ​= ​321) was used as external validation set. Seven survival ML algorithms, including COX regression, classification, regression and survival tree, random survival forest, gradient boosting survival machine tree, extreme gradient boosting survival tree, survival support vector analysis, and survival artificial neural network, were applied to train CA-VTE models.

Univariate analysis and LASSO-COX regression both selected five predictors: age, previous VTE history, ICU/CCU, CCI, and D-dimer. The seven survival ML models (C-index: 0.709-0.760; Brier Score: 0.212-0.243) all outperformed Khorana Score (C-index: 0.632; Brier Score: 0.260) in external validation set. Among all models, the COX_DD model (COX regression ​+ ​D-dimer) performed best. However, ML models and Khorana Score predicted CA-VTE risk on ≥ 7 days of hospitalization with an increase in Brier Score ≥ 0.25, showing poor calibration.

In this study, the CA-VTE risk prediction models developed in seven survival ML algorithms outperformed Khorana Score. Combining with D-dimer can improve model performance. Applying the nomogram based on the optimal COX_DD model allows oncology nurse to reassess CA-VTE risk once a week. The prediction models developed using survival ML algorithms in this study may contribute to the dynamic and accurate risk assessment of CA-VTE for cancer survivors.

Previous studies have reported that renal function is associated brain structure and cognitive dysfunction. However, the association between renal function and memory-related disease was not well characterized.

Altogether, 5,282 individuals were included in this study based on China Longitudinal Study of Health and Retirement. Four estimated glomerular filtration rate indicators (eGFR), including CG, CKD-EPIscr, CKD-EPIscr-cys, and CKD-EPIcys were used to evaluate the association between renal function and memory-related disease.

The multivariable-adjusted HRs (95% CIs) of the memory-related disease in the low eGFR group (eGFR < 90 mL/min/1.73m2) were 1.56 (1.13-2.16) for CG, 1.56 (1.19-2.06) for CKD-EPIscr, 1.45 (1.06-1.99) for CKD-EPIscr-cys and 1.27 (0.91-1.77) for CKD-EPIcys, respectively. Similarly, each SD increase of eGFR was associated with reduced risk of memory-related disease on continuous analyses. Subgroup analyses further confirmed these associations. Moreover, the addition of eGFR to conventional risk factors improved the predictive power for memory-related disease (net reclassification improvement: 13.90% for CG, 19.83% for CKD-EPIscr and 30.65% for CKD-EPIscr-cys).

In conclusion, impaired renal function was associated with the increasing risk of memory-related disease, indicating that renal function may be a potential indicator for memory-related disease. Further studies from other races and populations are needed to replicate our findings and to clarify the potential mechanisms.

Predicting medium-term survival after admission is necessary for identifying end-of-life patients who may benefit from goals of care (GOC) discussions. Considering that several patients have multiple hospital admissions, this study leverages patients' longitudinal data and information collected routinely at admission to predict the Hospital One-year Mortality Risk.

We propose the Ensemble Longitudinal Network (ELN) to predict one-year mortality using patients' longitudinal records. The model was evaluated: (i) with only predictors reported upon admission (AdmDemo); and (ii) also with diagnoses available later during patients' stay (AdmDemoDx). Using records of 123,646 patients with 250,812 hospitalizations from 2011 to 2021, our dataset was split into a learning set (2011-2017) to compare models with and without longitudinal information using nested cross-validation, and a holdout set (2017-2021) to assess clinical utility towards GOC discussions.

The ELN achieved a significant increase in predictive performance using longitudinal information (p-value < 0.05) for both the AdmDemo and AdmDemoDx predictors. For randomly selected hospitalizations in the holdout set, the ELN showed: (i) AUROCs of 0.83 (AdmDemo) and 0.87 (AdmDemoDx); and (ii) superior decision-making properties, notably with an increase in precision from 0.25 for the standard process to 0.28 (AdmDemo) and 0.36 (AdmDemoDx). Feature importance analysis confirmed that the utility of the longitudinal information increases with the number of patient hospitalizations.

Integrating patients' longitudinal data provides better insights into the severity of illness and the overall patient condition, in particular when limited information is available during their stay.

Hemogram inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), red-cell distribution width (RDW), and mean platelet volume (MPV) have been associated with type 2 diabetes mellitus (T2DM) and its complications, namely diabetic kidney diseases (DKD). We aimed to develop and validate logistic regression (LR) and CatBoost diagnostic models and study the role of adding these markers to the models.

All individuals who were managed in our secondary care center from March 2020 to December 2023 were identified. After excluding the ineligible patients, train-test splitting, and data preprocessing, two baseline LR and CatBoost-based models were developed using demographic, clinical, and laboratory features. The AUC-ROC of the models with biomarkers (NLR, PLR, RDW, and MPV) was compared to the baseline models. We calculated net reclassification improvement (NRI) and integrated discrimination index (IDI).

One thousand and eleven T2DM patients were eligible. The AUC-ROC of both LR (0.738) and CatBoost (0.715) models was comparable. Adding target inflammatory markers did not significantly change the AUC-ROC in both LR and CatBoost models. Adding RDW to the baseline LR model reclassified 41.7% of patients without DKD, in the cost of misclassification of 38.4% of DKD cases. This change was absent in CatBoost models, and other markers did not achieve improved NRI or IDI.

The basic models with demographical and clinical features had acceptable performance. Adding RDW to the basic LR model improved the reclassification of the non-DKD participants. However, adding other hematological indices did not significantly improve the LR and CatBoost models' performance.

The online version contains supplementary material available at 10.1007/s40200-024-01523-2.

A novel marker left atrioventricular coupling index (LACI) has been proved to be associated with cardiovascular events in patients without history of cardiovascular disease. However, the studies on cardiac magnetic resonance-derived LACI in hypertrophic cardiomyopathy (HCM) patients are limited, and the prognostic value of LACI has still not been studied thoroughly, so we aimed to explore the association between LACI and adverse clinical outcomes in HCM patients.

A total of 206 HCM patients underwent cardiac magnetic resonance examination were retrospectively enrolled. LACI is defined by the ratio between the left atrial (LA) volume and the left ventricular (LV) volume in LV end-diastolic phase. The composite endpoint was categorized into death-related, heart failure-related, and arrhythmia-related events, reflecting mortality risk, heart failure progression, and arrhythmia burden, respectively. Receiver operating characteristics curve analysis was used to determine the optimal cut-off value for LACI to distinguish HCM patients at high risk of adverse clinical outcome. Multivariable Cox regression models were built including significant clinical variables, LA ejection fraction (LAEF), LA volume index (LAVI), late gadolinium enhancement (LGE) extent and LACI. The improvement of discrimination by adding LACI to a clinical model was assessed using C-statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Thirty-four HCM patients reached the endpoint during a median follow-up time of 60 [interquartile range (50-68)] months. In the multivariate Cox regression analysis, LACI [hazard ratio 1.054, 95% confidence interval (CI): 1.037, 1.071; P < 0.001] was an independent predictor of the composite events after adjustment for age and atrial fibrillation. Then 40.09% was identified as an optimal cut-off for LACI in the risk stratification. Integrating LACI to the clinical model yielded higher C-statistic 0.892 with 95% CI (0.861, 0.922) compared with LA diameter, LAEF, LAVI and LGE extent, providing an improvement in prediction of high-risk patients (NRI = 0.627, 95% CI: 0.112-0.934; IDI = 0.295, 95% CI: 0.016-0.709).

LACI is an independent risk factor for clinical adverse outcome and is superior to conventional LA parameters and LGE extent for the identification of high-risk HCM patients.

With different infarct morphological characteristics included, the relationship between single subcortical infarction type and neurological deterioration (ND) remains unclear. Similarly, the diagnostic value of the known risk factors is also uncertain.

We conducted a prospective observational study at a tertiary teaching hospital affiliated with Fudan University, enrolling patients with anterior circulation single subcortical infarction within 24 hours of symptom onset from 2017 to 2018. Clinical data, magnetic resonance imaging infarct characteristics, and echocardiographic indices were analyzed using a multivariable logistic regression model to identify independent ND predictors. The receiver operating characteristic curve with multiple testing corrections was performed to assess the discriminatory abilities of different models and the calibration curve for the accuracy of the optimal model.

The study included 298 patients, with 80 (26.85%) experiencing ND. Multivariate analysis identified admission National Institutes of Health Stroke Scale score (odds ratio [OR], 1.197 [95% CI, 1.067-1.343], P=0.002), proximal single subcortical infarction (OR, 3.311 [95% CI, 1.608-6.817], P=0.001), maximal diameter on axial DWI (OR, 1.651 [95% CI, 1.042-2.617], P=0.033), and left ventricular fractional shortening (OR, 0.001 [95% CI, 0.000-0.282], P=0.021) as independent predictors of ND. The optimal model, including the independent predictors and parent artery disease, demonstrated improved discrimination (area under the curve=0.762) and good calibration (Hosmer-Lemeshow P=0.51). left ventricular fractional shortening contributed positively to this model's performance (net reclassification improvement: 24.8%, P=0.051; integrated discrimination index: 2.1%, P=0.018).

When considering different infarct morphological characteristics simultaneously, SSI type remains an independent predictor of ND in patients with anterior circulation SSI. Furthermore, our research indicated left ventricular fractional shortening as a novel predictor, which can improve the discriminative ability of the prediction model.

The association of disturbance in gut microbiota with hypertension (HTN) defined on three separate occasions among children and adolescents remains unclear. In this study, we aimed to compare the differences in gut microbiota composition and diversity between children with HTN and those with normal blood pressure (BP).

Data and stool samples were collected from the second follow-up of a childhood cardiovascular health cohort study in 2021. 16 S ribosomal RNA gene sequencing was conducted to determine the relative abundance of microbial taxa in 51 children aged 10-14 years with HTN and 51 children with normal BP.

Compared with children with normal BP, those with HTN had decreased gut microbiome diversity. At the genus level, after adjusting for the false discovery rate (FDR), the proportions of several gut microbiota such as Blautia (PFDR=0.042), Coprococcus (PFDR=0.042), Eubacterium_ventriosum_group (PFDR=0.027), Christensenellaceae_R-7_group (PFDR=0.027), and norank_f__Lachnospiraceae (PFDR=0.015) significantly decreased in children with HTN compared to those with normal BP. Receiver operating characteristic analysis, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were performed and showed that the genera norank_f__Lachnospiraceae and Dorea significantly enhanced the ability of body mass index to differentiate between children with HTN and those with normal BP (area under the receiver operating characteristic curve: 0.95, 95% confidence interval 0.91-0.99; NRI > 0; IDI = 0.12, P < 0.05). Phylogenetic Investigation of Communities by Reconstruction of Unobserved States showed that the mean proportions of cofactors and vitamins metabolism pathway and the glycan anabolism pathway were higher in children with HTN.

Disturbances in the abundance and diversity of gut microbiota may contribute to the development of HTN in children. Gut microbiota biomarkers may be of significant importance in the early identification and diagnosis of childhood HTN.

Not applicable.

Peripheral artery disease (PAD) and dyslipidemia are both independent predictors of cardiovascular disease, but the association between individual lipid species and subclinical PAD, assessed by ankle-brachial index (ABI), is lacking in large-scale longitudinal studies.

We used liquid chromatography-mass spectrometry to repeatedly measure 1,542 lipid species from 1,886 American Indian adults attending 2 clinical examinations (mean ~5 years apart) in the Strong Heart Family Study. We used generalized estimating equation models to identify baseline lipid species associated with change in ABI and the Cox frailty regression to examine whether lipids associated with change in ABI were also associated with incident coronary heart disease (CHD). We also examined the longitudinal association between change in lipid species and change in ABI and the cross-sectional association of individual lipids with ABI. All models were adjusted for age, sex, body mass index, smoking, alcohol use, hypertension, estimated glomerular filtration rate, diabetes, and lipid-lowering medication.

Baseline levels of 120 lipid species, including glycerophospholipids, glycerolipids, fatty acids, and sphingomyelins, were associated with change in ABI. Among these, higher baseline levels of 3 known lipids (phosphatidylinositol[16:0/20:4], triacylglycerol[48:2], triacylglycerol[55:1]) were associated with a lower risk of CHD (hazard ratios [95% CIs] ranged from 0.67 [0.46-0.99] to 0.76 [0.58-0.99]), while cholesterol was associated with a higher risk of CHD (hazard ratio [95% CI] = 1.37 [1.00-1.87]). Longitudinal changes in 32 lipids were significantly associated with change in ABI during 5-year follow-up. Plasma levels of glycerophospholipids, triacylglycerols, and glycosylceramides were significantly associated with ABI in the cross-sectional analysis.

Altered plasma lipidome is significantly associated with subclinical PAD in American Indians beyond traditional risk factors. If validated, the identified lipid species may serve as novel biomarkers for PAD in this high-risk but understudied population.

Quick Sequential Organ Failure Assessment (qSOFA) is a simple and easy tool for identifying patients with suspected infection, who are at a high risk of poor outcome. However, its predictive performance is still insufficient. The Eastern Cooperative Oncology Group Performance Status (ECOG-PS) score, a tool to evaluate physical function, has been recently reported to be useful in predicting the prognosis of patients with pneumonia. We aimed to evaluate the added value of ECOG-PS to qSOFA in predicting 30-day mortality in older patients admitted with suspected infections.

Between 2018 and 2019, we prospectively collected data from adults aged 65 years or older, admitted with suspected infection at two acute care hospitals. Predictive performance was compared between two logistic regression models: one using qSOFA score alone (qSOFA model) and the other in which ECOG-PS was added to qSOFA (extended model).

Of the 1536 enrolled patients, 135 (8.8%) died within 30 days. The area under the curve of the extended model was significantly higher than that of the qSOFA model (0.67 vs. 0.64, p = 0.008). When the risk groups were categorized as follows: low (<5%), intermediate (5%-10%), and high (≥10%), 5.0% of those who died and 2.1% of those who survived were correctly reclassified by the extended model with an overall categorized net reclassification improvement of 0.03 (95% confidence interval: -0.06 to 0.30).

Adding the ECOG-PS score could improve the performance of qSOFA in predicting mortality in older patients admitted with suspected infection.

Our study aimed at evaluating the association between plasma human cartilage glycoprotein-39 (YKL-40) and depressive symptoms at 3 months among acute ischemic stroke patients.

Plasma YKL-40 levels were measured in 619 patients with ischemic stroke who participated in the China Antihypertensive Trial in Acute Ischemic Stroke (CATIS). The patients' depressive symptoms at 3 months after stroke were assessed using the Hamilton Rating Scale for Depression (HRSD-24).

During the 3-month follow-up period, 242 (39.1 %) participants were classified as experiencing depressive symptoms. Patients in the highest quartile of YKL-40 had a 1.98-fold (95 %CI: 1.19-3.30, P for trend = 0.02) risk of depressive symptoms compared with those in the lowest quartile. Per 1-SD increase of logarithm-transformed YKL-40 was associated with a 32 % (95 % CI: 10 %-58 %) increased risk for the depressive symptoms. The multiple-adjusted spline regression model confirmed dose-response relationships between YKL-40 levels and depressive symptoms (P for linearity = 0.02). Adding YKL-40 to a model containing conventional risk factors significantly improved the discriminatory power (area under the receiver operating characteristic curve improved by 0.02, P = 0.04) and reclassification power for depressive symptoms (net reclassification improvement = 18.77 %, P = 0.02; integrated discrimination improvement = 1.30 %, P = 0.005).

Elevated YKL-40 levels might be a potential risk marker of depressive symptoms at 3 months among acute ischemic stroke patients.

URL: https://www.

gov; Unique identifier: NCT01840072.

To determine local OCT structural correlates of deep visual sensitivity defects (threshold of ≤10 decibels on microperimetry) in early atrophic age-related macular degeneration (AMD).

Prospective observational study.

Forty eyes from 40 participants, with at least incomplete retinal pigment epithelium (RPE) and outer retinal atrophy, or more advanced atrophic lesion(s).

Participants underwent ≥2 targeted, high-density microperimetry tests of atrophic lesions of interest in 1 eye, and high-density 3×3-mm volume scans of that region on a swept-source OCT angiography device, all at a single visit. Seven OCT-defined features of atrophy were manually annotated: hypertransmission, RPE attenuation/disruption, complete RPE loss, ellipsoid zone disruption, external limiting membrane (ELM) disruption, subsidence of the outer plexiform layer and inner nuclear layer, and/or hyporeflective wedge-shaped band, and outer nuclear layer (ONL) thickness.

Association between OCT-defined features of atrophy and presence of a deep visual sensitivity defect at a local, pointwise level.

All OCT-defined features of atrophy were individually associated with the presence of a deep visual sensitivity defect at a pointwise level in univariable mixed-effects logistic regression analyses (P < 0.001 for all). However, only hypertransmission, complete RPE loss, ELM disruption, and ONL thickness remained significantly and independently associated with deep visual sensitivity defects in a multivariable analysis (P ≤ 0.011). A prediction model incorporating these 4 OCT features (partial area under the curve [pAUC] at ≥90% specificity = 0.80) outperformed models using any single feature alone in predicting the presence of deep visual sensitivity defects (pAUC = 0.65 to 0.78, respectively; P ≥ 0.040).

The study identified hypertransmission, complete RPE loss, ELM disruption, and ONL thickness as key OCT-defined features of atrophy independently associated with deep visual sensitivity defects. These findings are important when considering anatomical outcome measures for evaluating interventions for early atrophic AMD that are most likely to capture beneficial treatment effects that will be accompanied by evidence of functional preservation if measured directly.

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Current treatment paradigms assume aortic regurgitation (AR) patients to be a homogenous population, but varied courses of disease progression and outcomes are observed clinically.

The aim of this study was to first use unsupervised machine learning to identify unique patient phenoclusters in AR, and subsequently evaluate their prognostic relevance.

Clinical and cardiac magnetic resonance (CMR) characterization of moderate or severe AR patients was performed across 4 U.S.

Data from 2 centers were used for derivation of phenoclusters and validation was performed in the other 2. The outcome was all-cause death. An unsupervised clustering pipeline, Partition Around Medoids, used 23 clinical and CMR variables to derive patient clusters independent of outcomes.

Included were 972 patients with mean age 62 ± 23.2 years, 754 (78%) male, 680 (70%) trileaflet valve, and 330 (34%) underwent valve surgery. Over a median follow-up of 2.58 years (Q1-Q3: 1.03-5.50 years), the overall mortality rate was 12%. Four clusters were derived: 1) a younger predominantly male phenotype with majority of bicuspid aortic valve and high extent of left ventricular (LV) remodeling (1% mortality); 2) older male patients with predominantly tricuspid valves and intermediate outcomes (10% mortality); 3) older predominantly male patients with the highest burden of comorbidities, LV scarring, and dysfunction (22% mortality); and 4) a phenotype of predominantly female patients with high mortality and relatively higher symptoms burden, relatively lower extent of LV remodeling, and rate of aortic valve replacement (20% mortality). The clustering algorithm was independently associated with survival after adjustment for time-dependent aortic valve replacement and traditional risk markers of prognosis in patients with AR (C statistic 0.77 vs 0.75; P = 0.009 in the validation cohort).

Unique patient phenoclusters of AR are described using a machine learning approach leveraging comprehensive CMR and clinical characterization. This approach may be an opportunity for a precision medicine approach to enhance risk stratification of patients with AR. Female patients with AR pose a unique phenotype with high mortality, which deserves greater attention.

Recent changes in the regulatory assessment of in vitro medical tests reflect a growing recognition of the need for more stringent clinical evidence requirements to protect patient safety and health. Under current regulations in the United States and Europe, when needed for regulatory approval, clinical performance reports must provide clinical evidence tailored to the intended purpose of the test and allow assessment of whether the test will achieve the intended clinical benefit. The quality of evidence must be proportionate to the risk for the patient and/or public health. These requirements now cover both commercial and laboratory developed tests (LDT) and demand a sound understanding of the fundamentals of clinical performance measures and study design to develop and appraise the study plan and interpret the study results. However, there is a lack of harmonized guidance for the laboratory profession, industry, regulatory agencies and notified bodies on how the clinical performance of tests should be measured. The Working Group on Test Evaluation (WG-TE) of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) is a multidisciplinary group of laboratory professionals, clinical epidemiologists, health technology assessment experts, and representatives of the in vitro diagnostic (IVD) industry. This guidance paper aims to promote a shared understanding of the principles of clinical performance measures and study design. Measures of classification performance, also referred to as discrimination, such as sensitivity and specificity are firmly established as the primary measures for evaluating the clinical performance for screening and diagnostic tests. We explain these measures are just as relevant for other purposes of testing. We outline the importance of defining the most clinically meaningful classification of disease so the clinical benefits of testing can be explicitly inferred for those correctly classified, and harm for those incorrectly classified. We introduce the key principles and a checklist for formulating the research objective and study design to estimate clinical performance: (1) the purpose of a test e.g. diagnosis, screening, risk stratification, prognosis, prediction of treatment benefit, and corresponding research objective for assessing clinical performance; (2) the target condition for clinically meaningful classification; (3) clinical performance measures to assess whether the test is fit-for-purpose; and (4) study design types. Laboratory professionals, industry, and researchers can use this checklist to help identify relevant published studies and primary datasets, and to liaise with clinicians and methodologists when developing a study plan for evaluating clinical performance, where needed, to apply for regulatory approval.

To assess whether muscle power (force times velocity) outperforms strength as a risk indicator and predictor of mortality.

Anthropometric, clinical and vital status, muscle power, and strength data were assessed in 3889 individuals aged 46 to 75 years (2636 [67.8%] men) who were participants in the CLINIMEX Exercise prospective cohort between February 13, 2001, and October 31, 2022. Study participants were stratified by sex and categorized into 4 groups according to the distribution of the results of relative muscle power and strength (adjusted for body weight) measured, respectively, by handgrip and upper row movement tests.

Death rates were 14.2% (373 of 2636) and 8.9% (111 of 1253) for men and women, respectively, during a median (IQR) follow-up of 10.8 years (6.7 to 15.5 years). In multivariable Cox proportional hazards regression analyses, the hazard ratios (95% CIs) for mortality comparing the lowest vs highest categories of relative muscle power were 5.88 (2.28 to 15.17; P<.001) and 6.90 (1.61 to 29.58; P=.009) for men and women, respectively. The corresponding hazard ratios (95% CIs) for relative strength were 1.62 (0.89 to 2.96; P=.11) and 1.71 (0.61 to 4.80; P=.31), respectively. Sex-specific results of risk prediction analyses revealed that improvements in C index provided by relative power over relative strength were 0.0110 (95% CI, 0.0039 to 0.0182) in men and 0.0112 (95% CI, -0.0040 to 0.0265) in women.

In this large prospective study, relative muscle power was a stronger predictor of mortality than relative strength in middle-aged and older men and women. Evaluating and training muscle power could be of clinical and practical relevance.

The role of inflammation in shaping death risk in diabetes is still unclear.

To study whether inflammation is associated with and helps predict mortality risk in patients with type 2 diabetes. To explore the intertwined link between inflammation and tryptophan metabolism on death risk.

There were 2 prospective cohorts: the aggregate Gargano Mortality Study (1731 individuals; 872 all-cause deaths) as the discovery sample, and the Foggia Mortality Study (490 individuals; 256 deaths) as validation sample. Twenty-seven inflammatory markers were measured. Causal mediation analysis and in vitro studies were carried out to explore the link between inflammatory markers and the kynurenine to tryptophan ratio (KTR) in shaping mortality risk.

Using multivariable stepwise Cox regression analysis, interleukin (IL)-4, IL-6, IL-8, IL-13, RANTES, and interferon gamma-induced protein-10 (IP-10) were independently associated with death. An inflammation score (I score) comprising these 6 molecules is strongly associated with death in both the discovery and the validation cohorts HR (95% CI) 2.13 (1.91-2.37) and 2.20 (1.79-2.72), respectively. The I score improved discrimination and reclassification measures (all P < .01) of 2 mortality prediction models based on clinical variables. The causal mediation analysis showed that 28% of the KTR effect on mortality was mediated by IP-10. Studies in cultured endothelial cells showed that 5-methoxy-tryptophan, an anti-inflammatory metabolite derived from tryptophan, reduces the expression of IP-10, thus providing a functional basis for the observed causal mediation.

Adding the I score to clinical prediction models may help identify individuals who are at greater risk of death. Deeply addressing the intertwined relationship between low-grade inflammation and imbalanced tryptophan metabolism in shaping mortality risk may help discover new therapies targeting patients characterized by these abnormalities.

Plasma volume status (PVS) is recognized as a marker of systemic congestion, but its clinical utility in patients with mitral regurgitation (MR) undergoing transcatheter edge-to-edge mitral valve repair (M-TEER) has not been well established. This study aimed to evaluate the prognostic significance of PVS in these patients.

Data from 3763 patients who underwent M-TEER were analysed from a Japanese multicentre registry. Patients were classified into functional MR (FMR) and degenerative MR (DMR) according to MR aetiology, and the median PVS values for each were calculated (FMR 12.7, DMR 14.4). The median value was used as the cut-off, stratifying the cohort into a high PVS group (n = 1882) and a low PVS group (n = 1881). All-cause mortality, cardiovascular death, and heart failure (HF) hospitalization between these two groups were compared up to 3 years in the overall, FMR, and DMR populations. The cumulative incidence rates of all-cause mortality, cardiovascular death, and HF hospitalization were higher in the high PVS group than in the low PVS group (47.0% vs. 22.2%, P < 0.001, 31.6% vs. 13.6%, P < 0.001, and 35.9% vs. 24.7%, P < 0.001, respectively). Similar trends in terms of all-cause mortality, cardiovascular death, and HF hospitalization were observed in the FMR and DMR cohorts (all P < 0.05). In the multivariate Cox regression analysis, the high PVS compared with the low PVS group was independently associated with the increased risk of all-cause death (hazard ratio [HR], 1.02; 95% confidence interval [CI], 1.01-1.03; P < 0.001), cardiovascular death (HR, 1.02; 95% CI, 1.01-1.03, P < 0.001) and HF hospitalization (HR, 1.02; 95% CI, 1.01-1.02, P < 0.001). An independent association between a high PVS and all-cause death, cardiovascular death, and HF hospitalization was also found in FMR and DMR sub-groups (all P < 0.05) while reducing MR severity to moderate or less after M-TEER was associated with improved outcomes in both the high and low PVS groups.

Preoperative PVS is a strong independent prognostic marker in patients undergoing M-TEER, correlating with increased risk of mortality and HF hospitalization. PVS may provide valuable clinical insights for patient stratification and management strategies in M-TEER patients.

The application of artificial intelligence (AI) in laboratory medicine will revolutionize predictive modeling using clinical laboratory information. Machine learning (ML), a sub-discipline of AI, involves fitting algorithms to datasets and is broadly used for data-driven predictive modeling in various disciplines. The majority of ML studies reported in systematic reviews lack key aspects of quality assurance. In clinical laboratory medicine, it is important to consider how differences in analytical methodologies, assay calibration, harmonization, pre-analytical errors, interferences, and physiological factors affecting measured analyte concentrations may also affect the downstream robustness and reliability of ML models. In this article, we address the need for quality improvement and proper validation of ML classification models, with the goal of bringing attention to key concepts pertinent to researchers, manuscript reviewers, and journal editors within the field of pathology and laboratory medicine. Several existing predictive modeling guidelines and recommendations can be readily adapted to the development of ML models in laboratory medicine. We summarize a basic overview of ML and key points from current guidelines including advantages and pitfalls of applied ML. In addition, we draw a parallel between validation of clinical assays and ML models in the context of current regulatory frameworks. The importance of classification performance metrics, model explainability, and data quality along with recommendations for strengthening journal submission requirements are also discussed. Although the focus of this article is on the application of ML in laboratory medicine, many of these concepts extend into other areas of medicine and biomedical science as well.

Strategies to estimate risk of cancer therapy-related cardiac dysfunction (CTRCD) before initiating cardiotoxic cancer treatment are needed. We hypothesized that baseline ECG markers could identify patients at risk for CTRCD.

In this retrospective cohort study, 1278 female patients with stage I-III HER2 (human epidermal growth factor receptor 2)-positive breast cancer meeting the following inclusion criteria were included: baseline ECG with QRS <120 milliseconds, baseline echocardiogram, and ≥1 follow-up echocardiogram. Quantitative measurements of ECG waveform parameters were performed using MUSE (GE Healthcare). The primary outcome of interest was CTRCD at 1 year, defined by left ventricular ejection fraction decline (≥10% to <53% or ≥16% from baseline), or clinical heart failure (New York Heart Association class III/IV). Mean age was 51.7±11.1 years, 990 (77%) received anthracyclines, and all received HER2-targeted therapy. CTRCD occurred in 160 (13%) patients. In a multivariable Cox proportional hazards model adjusting for our previously published CTRCD risk score (composed of patient and treatment-specific factors), 4 ECG markers remained independently associated with CTRCD risk: QRS axis, R-wave duration (lead II), ST segment deviation (lead II), and Sokolow-Lyon voltage (all P<0.05). Compared with a model using only clinical CTRCD risk variables, addition of ECG parameters provided incremental value for predicting CTRCD risk (P<0.001, likelihood ratio test) with continuous net reclassification improvement of 34.9% and integrated discrimination improvement of 3.4%.

Baseline ECG variables are predictive of subsequent CTRCD and provide incremental value to established clinical risk factors for CTRCD risk classification.

Over the past decade, coronary computed tomographic angiography (CCTA) has been the most robust non-invasive method for evaluating significant coronary stenosis. Thanks to new technologies, it is now possible to determine the fractional flow reserve (FFR) non-invasively using computed tomographic (CT) images. The aim of this work was to evaluate the incremental diagnostic value of CT-derived FFR for ischemia detection. In this retrospective monocentric study, we investigated 421 patients who underwent CCTA and subsequent ischemia testing between 04/2009 and 06/2020. Endpoint was ischemia on a coronary vessel level assessed by CMR (n = 20), SPECT (n = 225), invasive angiography (stenosis ≥ 90%; n = 80) or invasive FFR (positive if ≤ 0.8; n = 96). CT-FFR was derived from CCTA images by a machine learning (ML) based software prototype. Patients averaged 66.5 [58.2-73.6] years of age and 72.7% (n = 306) were male. Overall, 52.5% (n = 221) had hypertension and 67.9% (n = 286) had hypercholesteremia. Logistic regression analysis on a per vessel base showed that the diagnostic model with CT-FFR plus CCTA had significantly better-fit criteria than the diagnostic model with CCTA alone (log-likelihood χ2 230.21 vs. 192.17; p for difference < 0.001). In particular, the area under curve (AUC) by receiver operating characteristics curve (ROC) analysis for CT-FFR plus CCTA (0.87) demonstrated greater discrimination of hemodynamic ischemia compared to CCTA alone (0.83; p for difference < 0.0001). Combined CCTA and CT-FFR have improved diagnostic accuracy compared to CCTA alone in detecting ischemia on the coronary vessel level and thus could reduce the use of invasive coronary angiography in the future.

Kidney stone disease is a multifactorial disease with increasing incidence worldwide. Trans-ancestry GWAS has become a popular strategy to dissect genetic structure of complex traits. Here, we conduct a large trans-ancestry GWAS meta-analysis on kidney stone disease with 31,715 cases and 943,655 controls in European and East Asian populations. We identify 59 kidney stone disease susceptibility loci, including 13 novel loci and show similar effects across populations. Using fine-mapping, we detect 1612 variants at these loci, and pinpoint 25 causal signals with a posterior inclusion probability >0.5 among them. At a novel locus, we pinpoint TRIOBP gene and discuss its potential link to kidney stone disease. We show that a cross-population polygenic risk score, PRS-CSxEAS&EUR, exhibits superior predictive performance for kidney stone disease than other polygenic risk scores constructed in our study. Relative to individuals in the third quintile of PRS-CSxEAS&EUR, those in the lowest and highest quintiles exhibit distinct kidney stone disease risks with odds ratios of 0.57 (0.51-0.63) and 1.83 (1.68-1.98), respectively. Our results suggest that kidney stone disease patients with higher polygenic risk scores are younger at onset. In summary, our study advances the understanding of kidney stone disease genetic architecture and improves its genetic predictability.

Coronary radial wall strain (RWS) represents a novel approach enabling discrimination of vulnerable plaques with prognostic significance.

This study sought to evaluate the prognostic impact of RWS in diabetic patients with non-flow-limiting coronary stenosis when compared with optical coherence tomography-detected vulnerability features (OCT-VFs).

This was a post hoc analysis of the COMBINE OCT-FFR dataset. The primary endpoint was lesion-oriented composite endpoint (LOCE), a composite of cardiac death, target vessel-related myocardial infarction, and clinically driven target lesion revascularization.

RWS was assessed in 435 eligible non-flow-limiting lesions from 366 patients. Lesion-level maximal RWS was predictive of lesions with any OCT-VFs (area under the curve: 0.630 [95% CI: 0.571-0.688]; P < 0.001). The median follow-up was 3.2 years (Q1-Q3: 2.2-4.1 years). With a prespecified cutoff of ≥13.0%, the incidence of LOCE was 17.0% (15/88; 95% CI: 9.0%-25.1%) in RWS-positive vs 6.8% (19/278; 95% CI: 3.8%-9.8%) in RWS-negative patients (HR: 2.70; 95% CI: 1.37-5.32; P = 0.004). Positive RWS predicted LOCE independently from any OCT-VFs (direct effect β = 0.099 [95% CI: 0.029-0.168]; P = 0.006; indirect effect β = 0.004 [95% CI: -0.008 to 0.015]; P = 0.555; mediation proportion 3.9% [95% CI: -5.0% to 20.3%]). Adding RWS to any OCT-VFs mainly improved the reclassification for LOCE in the lower-risk strata (positive continuous net reclassification improvement [cNRI] -0.060 [95% CI: -0.420 to 0.318]; P = 0.749; negative cNRI 0.583 [95% CI: 0.474-0.681]; P < 0.001; integrated discrimination improvement 0.066 [95% CI: 0.013-0.182]; P = 0.010).

In diabetic patients with non-flow-limiting stenosis, RWS can help to localize stenoses with OCT-VFs. RWS predicts increased risk for LOCE, both independently from-and incrementally beyond-OCT-VFs. (Combined Optical Coherence Tomography Morphologic and Fractional Flow Reserve Hemodynamic Assessment of Non-Culprit Lesions to Better Predict Adverse Event Outcomes in Diabetes Mellitus Patients [COMBINE OCT-FFR]; NCT02989740).

The value of carotid ultrasound in real-world practice remains controversial. We investigated the outcomes of people with vascular risk factors according to an easy carotid-plaque burden scale (CPB-scale). Predictive yield of the addition CPB-scale to ESC-SCORE2 (CPB-SCORE2 table) was assessed.

A cohort of participants without preexisting cardiovascular disease (CVD) was evaluated for clinical outcomes according to the number of plaques by segment. The usefulness of the CPB-SCORE2 table was investigated.

A total of 1004 patients were followed for a mean of 12.5 years for major adverse cardiovascular events (MACEs) and death. The CPB-scale was independently associated with MACEs; compared to those in the low-risk group, the corresponding adjusted hazard ratios (95% confidence intervals) for MACEs among the intermediate and high-risk groups were 13.1 (4.87-35.5) and 19.4 (7.27-51.9), respectively. Similarly, the risk of death was greater for participants stratified as high-risk than for those in the low-risk group (adjusted HR 3.36 [1.58-7.15]). According to our CPB-SCORE2 table, 149 of 178 (84%) CV events were detected in the high-risk group and exhibited greater sensitivity than did the SCORE2 Table, 84%; vs. 62%; but slightly less specificity, 62%; vs. 68%. Our table shows the improved performance of SCORE2; c-statistics: 0.74 vs. 0.68; p<0.001 for net reclassification index and integrated discrimination index.

A simple prognostic CPB-scale was strongly associated with the long-term risk of developing a first MACE and all-cause death. Adding the CPB-scale to the SCORE2 may improve risk prediction with easy applicability in clinical practice.

Real-life binary classification problems often involve imbalanced datasets, where the majority class outnumbers the minority class. We previously developed the U-smile method, which comprises the U-smile plot and the BA, RB and I coefficients, to assess the usefulness of a new variable added to a reference prediction model and validated it under class balance. In this study, we evaluated the U-smile method under class imbalance, proposed a three-level approach of the U-smile method, and used the I coefficients as a weighting factor for point size in the U-smile plots of the BA and RB coefficients. Using real data from the Heart Disease dataset and generated random variables, we built logistic regression models to assess four new variables added to the reference model (nested setting). These models were evaluated at seven pre-defined imbalance levels of 1%, 10%, 30%, 50%, 70%, 90% and 99% of the event class. The results of the U-smile method were compared to those of certain traditional measures: Brier skill score, net reclassification index, difference in F1-score, difference in Matthews correlation coefficient, difference in the area under the receiver operating characteristic curve of the new and reference models, and the likelihood-ratio test. The reference model overfitted to the majority class at higher imbalance levels. The BA-RB-I coefficients of the U-smile method identified informative variables across the entire imbalance range. At higher imbalance levels, the U-smile method indicated both prediction improvement in the minority class (positive BA and I coefficients) and reduction in overfitting to the majority class (negative RB coefficients). The U-smile method outperformed traditional evaluation measures across most of the imbalance range. It proved highly effective in variable selection for imbalanced binary classification, making it a useful tool for real-life problems, where imbalanced datasets are prevalent.

Sepsis-associated acute kidney injury (SA-AKI) significantly impacts global health. Early identification of SA-AKI patients at inflammatory and immune risk, followed by timely interventions, is critical for improving outcomes. The pan-immune-inflammation value (PIV) reflects systemic inflammation and immune status. However, its prognostic value in SA-AKI remains unexplored.

This retrospective cohort study analyzed SA-AKI patients in the MIMIC-IV database. Cox regression assessed the association between PIV and mortality, while restricted cubic spline (RCS) regression explored the relationship between PIV and 30-day and 365-day mortality.

A total of 2,473 SA-AKI patients in our study were categorized into PIV quartiles: T1 (≤ 214), T2 (214-679), T3 (679-2,039), and T4 (> 2,039). PIV showed a nonlinear association with mortality. Higher PIV quartiles were linked to increased mortality, with 30-day rates of 26%, 22%, 35%, and 41% (P < 0.001) and 365-day mortality rates of 34%, 31%, 46%, and 54% (P < 0.001). Adjusted hazard ratios (HR) for 30-day mortality across quartiles were 1.00 (reference), 1.04(0.82, 1.31), 1.54 (1.25, 1.9), and 1.62 (1.32, 1.98), respectively. For 365-day mortality, the HR and 95% CI were 1.00 (reference), 1.06 (0.87, 1.30), 1.58 (1.32, 1.90), and 1.70 (1.42, 2.03). After adding PIV to SOFA score, the integrated discrimination improvement (IDI) for 30-day mortality was 0.005, and the net reclassification improvement (NRI) was 0.103. For 365-day mortality, the IDI was 0.009, and the NRI was 0.124. Regarding the APACHE II score, the IDI for 30-day mortality was 0.003, and the NRI was 0.081. For 365-day mortality, the IDI was 0.006, and the NRI was 0.107.

Elevated PIV independently predicts both short- and long-term adverse outcomes in SA-AKI patients. Incorporating PIV into established critical illness prediction models, such as SOFA and APACHE II, enhances their prognostic accuracy.

Background/Objectives: Proper confidence interval estimation of the area under the receiver operating characteristic curve (AUC), the net reclassification index (NRI), and the integrated discrimination improvement (IDI) is an area of ongoing research. The most common confidence interval estimation methods employ asymptotic theory. However, developments demonstrate that degeneration of the normal distribution assumption under the null hypothesis exists for measures such as the change in AUC (ΔAUC) and IDI, and confidence intervals estimated under the normal distribution assumption may be invalid. We aim to study the performance of confidence intervals derived assuming asymptotic theory and those derived with non-parametric bootstrapping methods. Methods: We examine the performance of ΔAUC, NRI, and IDI in both the logistic and survival regression context. We explore empirical distributions and compare coverage probabilities of asymptotic confidence intervals with those produced from bootstrapping methods through simulation. Results: The primary finding in both the logistic framework and the survival analysis framework is that the percentile CIs performed well regarding coverage, without compromise to their width; this finding was robust in most scenarios. Conclusions: Our results suggest that the asymptotic intervals are only appropriate when a strong effect size of the added parameter exists, and that the percentile bootstrap interval exhibits at least a reasonable coverage while maintaining the shortest width in nearly all simulated scenarios, making this interval the most reliable choice. The intent is that these recommendations improve the accuracy in the estimation and the overall assessment of discrimination improvement.

The Node Reporting and Data System (Node-RADS) offers a reliable framework for lymph node assessment, but its prognostic significance remains unexplored. This study aims to investigate the added prognostic value of Node-RADS in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant chemotherapy (NAC) followed by gastrectomy.

This single-center retrospective study included 118 patients with LAGC underwent NAC and gastrectomy. The maximum Node-RADS score and the number of metastatic lymph node stations (defined as LNM-Station) were evaluated on pretreatment CT. The pretreatment Node-RADS-CT and Node-RADS-integrated models were developed using Cox regression to predict overall survival (OS) and disease-free survival (DFS). The pretreatment cN-CT models, cN-integrated models, as well as post-NAC pathological models were also developed in comparison. The performance of the models was assessed in terms of discrimination, calibration and clinical applicability.

The LNM-Station was significantly associated with OS and DFS (all p < 0.05). The Node-RADS-CT model showed higher Harrell's consistency index (C-index) than cN-CT model (0.755 vs. 0.693 for OS, p = 0.017; 0.759 vs. 0.706 for DFS, p = 0.018). The Node-RADS-integrated model also achieved higher C-index than cN-integrated model (0.771 vs. 0.731 for OS, p = 0.091; 0.773 vs. 0.733 for DFS, p = 0.053). The net reclassification improvement (NRI) of the Node-RADS-integrated model at 5 years was 0.379 for OS and 0.364 for DFS (all p < 0.05). The integrated discrimination improvement (IDI) of the Node-RADS-integrated model was 0.103 for OS and 0.107 for DFS (all p < 0.05). The C-indices (OS: 0.745; DFS: 0.746) of pathological models were slightly lower than those of Node-RADS-based models (all p > 0.05).

The baseline Node-RADS score and LNM-Station were effective prognostic indicators for LAGC. The pretreatment CT Node-RADS-based models can offer added prognostic value for LAGC, compared with clinical N stage.

The occurrence of deep venous thrombosis (DVT) following total hip arthroplasty (THA) poses a substantial risk of morbidity and mortality, highlighting the need for preoperative risk stratification and prophylaxis initiatives. However, there exists a paucity of big-data-driven predictive models for DVT risk following elective hip arthroplasty. Therefore, this study aimed to develop and assess machine learning (ML) models in predicting DVT risk following THA using a national patient cohort.

We hypothesized that machine learning models would accurately predict patient-specific DVT risk in patients undergoing elective total hip arthroplasty.

The ACS-NSQIP national database was queried to identify 70,733 THA patients from 2013 to 2020, including 317 patients (0.45%) with DVT. Artificial neural network, random forest, histogram-based gradient boosting, k-nearest neighbor, and support vector machine algorithms were trained and utilized to predict the risk of DVT following THA. Model performance was assessed using discrimination, calibration, and potential clinical utility.

Histogram-based gradient boosting demonstrated the best prediction performance with an area under the receiver operating curve of 0.93 (discrimination), a slope of 0.92 (closely aligned with actual outcomes), an intercept of 0.18 (minimal prediction bias), and a Brier score of 0.010 (high accuracy). The model also demonstrated clinical utility with greater net benefit than alternative decision criteria in the decision curve analysis. Length of stay, international normalized ratio, age, and partial thromboplastin time were the strongest predictors of DVT after primary THA.

Machine learning models demonstrated excellent predictive performance in terms of discrimination, calibration, and decision curve analysis. Further research is warranted in terms of external validation to realize the potential of these algorithms as a valuable adjunct tool for risk stratification in patients undergoing THA.

III; Retrospective study.

Polyamines have been suggested to implicated in inflammation, ischemic stroke, and mental disorders, but the associations of polyamines with post-stroke depression (PSD) remain unclear. We aimed to prospectively investigate the associations of plasma putrescine, spermidine and spermine with PSD among ischemic stroke patients in a multicenter cohort study.

We measured plasma putrescine, spermidine and spermine levels at baseline among 635 ischemic stroke patients from a preplanned ancillary study of the CATIS (China Antihypertensive Trial in Acute Ischemic Stroke). The study outcome was depression (Hamilton Depression Rating Scale score ≥8) at 3-month follow-up after ischemic stroke.

Plasma putrescine and spermidine were positively associated with the risk of PSD. The adjusted odds ratios of PSD for the highest versus lowest tertile of putrescine and spermidine were 1.77 (95 % CI, 1.13-2.78; ptrend = 0.014) and 1.77 (95 % CI, 1.11-2.82; ptrend = 0.013), respectively. Multivariable-adjusted spline regression analyses showed linear associations of plasma putrescine (p = 0.002 for linearity) and spermidine (p = 0.008 for linearity) with PSD. In addition, plasma putrescine (continuous net reclassification improvement [NRI]: 26.33 %, p = 0.002; integrated discrimination improvement [IDI]: 1.06 %, p = 0.009) and spermidine (continuous NRI: 20.72 %, p = 0.013; IDI: 1.04 %, p = 0.010) could significantly improve the risk reclassification of PSD beyond the established risk factors.

High plasma putrescine and spermidine levels were associated with increased risk of PSD among ischemic stroke patients. Our findings suggest that plasma polyamines should be implicated in the pathophysiologic processes of PSD and may be the potential intervention targets for PSD.

To construct a computed tomography (CT)-based clinical-radiomics nomogram for estimating overall survival (OS) in advanced hepatocellular carcinoma (HCC) patients receiving transcatheter arterial chemoembolization (TACE) in combination with camrelizumab and apatinib.

A retrospective recruitment of 150 patients with clinically or pathologically confirmed HCC was conducted, followed by their division into training cohort (n = 105) and test cohort (n = 45). To generate the radiomics score (Rad-score), a series of analyses were performed, including Pearson correlation analysis, univariate Cox analysis, and least absolute shrinkage and selection operator Cox regression analysis. Subsequently, a clinical-radiomics nomogram was constructed using the Rad-score combined with independent clinical prognostic factors, followed by assessments of its calibration, discrimination, reclassification, and clinical utility.

Five CT radiomics features were selected. The Rad-score showed a significant correlation with OS (P < 0.001). The clinical-radiomics nomogram demonstrated superior performance in estimating OS, with a concordance index (C-index) of 0.840, compared to the radiomics nomogram (C-index: 0.817) and the clinical nomogram (C-index: 0.661). It also exhibited high 1-year and 2-year area under the curves of 0.936 and 0.946, respectively. Additionally, the clinical-radiomics nomogram markedly enhanced classification accuracy for OS outcomes, as evidenced by net reclassification improvement and integrated discrimination improvement. Decision curve analysis confirmed its clinical utility.

A CT-based clinical-radiomics nomogram exhibits strong potential for predicting OS in advanced HCC patients undergoing TACE combined with camrelizumab and apatinib.

Few studies have systematically compared the overlap and complementarity of family history (FH) and polygenic risk score (PRS) in terms of disease risk. We here investigated the impacts of FH and PRS on the risk of incident diseases or age at disease onset, as well as their clinical value in risk prediction. We analyzed 12 diseases in the prospective cohort study of UK Biobank (N = 461,220). First, restricted mean survival time analysis was performed to evaluate the influences of FH and PRS on age at onset. Then, Cox proportional hazards model was employed to estimate the effects of FH and PRS on the incident risk. Finally, prediction models were constructed to examine the clinical value of FH and PRS in the incident disease risk. Compared to negative FH, positive FH led to an earlier onset, with an average of 2.29 years earlier between the top and bottom 2.5% PRSs and high blood pressure showing the greatest difference of 6.01 years earlier. Both FH and PRS were related to higher incident risk; but they only exhibited weak interactions on high blood pressure and Alzheimer's disease/dementia, and provided relatively independent and partially complementary information on disease susceptibility, with PRS explaining 7.0% of the FH effect but FH accounting for only 1.1% of the PRS effect for incident cases. Further, FH and PRS showed additional predictive value in risk evaluation, with breast cancer showing the greatest improvement (31.3%). FH and PRS significantly affect a variety of diseases, and they are not interchangeable measures of genetic susceptibility, but instead offer largely independent and partially complementary information. Incorporating FH, PRS, and clinical risk factors simultaneously leads to the greatest predictive value for disease risk assessment.

Transcatheter edge-to-edge repair (TEER) is used to treat patients with mitral regurgitation (MR). The Geriatric Nutritional Risk Index (GNRI) is a well-known nutritional marker that predicts mortality risk.

The objectives of this study were to elucidate the clinical association between the degree of GNRI and different etiologies of MR and to clarify the patient samples for whom GNRI is more relevant to clinical outcomes following TEER.

Data from 3,554 patients with MR who underwent TEER were analyzed using a Japanese multicenter registry. The patients were classified into 4 groups: GNRI <82, GNRI 82 to 92, GNRI 92 to 98, and GNRI >98. Procedural and clinical outcomes were compared between GNRI groups. Short- and long-term all-cause mortality were explored using Cox regression analysis.

Among the 3,554 patients, the median GNRI was 92.3. The mean follow-up period was 586.8 ± 436.5 days; 806 patients died during the follow-up period. Thirty-day mortality occurred in 51 patients (1.4%), and the GNRI <82 group had the highest 30-day mortality rate. Kaplan-Meier curves showed significantly better prognoses for the entire cohort, functional MR, and degenerative MR across the 4 groups (P < 0.001). GNRI values, even after adjustment for multiple confounders, showed a stepwise increase in risk of death in the GNRI 92 to 98, GNRI 82 to 92, and GNRI <82 groups compared to GNRI >98 as the reference.

Regardless of MR etiology, GNRI is a useful predictor of short- and long-term mortality in patients undergoing TEER. Although TEER is effective for MR patients in malnourished states, further studies focused on the value of identifying and addressing malnutrition in this population are needed.

Central lymph node metastasis (CLNM) is associated with high recurrence rate and low survival in patients with papillary thyroid carcinoma (PTC). However, there is no satisfactory model to predict CLNM in PTC. This study aimed to integrate PTC deep learning feature based on ultrasound (US) images, fat radiomics features based on computed tomography (CT) images and clinical characteristics to construct a multimodal and multi-region nomogram (MMRN) for predicting the CLNM in PTC.

We enrolled 661 patients diagnosed with PTC by thyroidectomy from two independent centers. Patients were divided into the primary cohort, internal test cohort (ITC), and external test cohort (ETC), and collected their US images and CT images. Resnet50 was employed to predict the CLNM status of PTC based on US images. Using radiomics feature extraction methods to extract fat radiomics features from CT images. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression. The predictive performance of the MMRN was evaluated using five-fold cross-validation. We comprehensively evaluated the DLRCN and compared it with five radiologists.

In the ITC and ETC, the area under the curves (AUCs) of MMRN were 0.829 (95 % CI: 0.822, 0.835) and 0.818 (95 % CI: 0.808, 0.828). The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (P > 0.05). Decision curve analysis showed that the MMRN was clinically useful. Under equal specificity or sensitivity, the performance of MMRN increased by 6.5 % or 2.9 % compared to radiologist assessments. The incorporation of fat radiomics features led to significant net reclassification improvement (NRI) and integrated discrimination improvement (IDI) (NRI=0.174, P < 0.05, IDI=0.035, P < 0.05).

The MMRN demonstrated good performance in predicting the CLNM status of PTC, which was comparable to radiologist assessments. The fat radiomics features exhibited supplementary value for predicting CLNM in PTC.

Despite apparently curative treatment, many patients with colorectal cancer develop subsequent metastatic disease. Current prognostic models are criticised because they are based on standard staging and omit novel biomarkers. Improved prognostication is an unmet need.

To improve prognostication for colorectal cancer by developing a baseline multivariable model of standard clinicopathological predictors, and to then improve prediction via addition of promising novel imaging, genetic and immunohistochemical biomarkers.

Prospective multicentre cohort.

Thirteen National Health Service hospitals.

Consecutive adult patients with colorectal cancer.

Collection of prespecified standard clinicopathological variables and more novel imaging, genetic and immunohistochemical biomarkers, followed by 3-year follow-up to identify postoperative metastasis.

Best multivariable prognostic model including perfusion computed tomography compared with tumour/node staging. Secondary outcomes: Additive benefit of perfusion computed tomography and other biomarkers to best baseline model comprising standard clinicopathological predictors; measurement variability between local and central review; biological relationships between perfusion computed tomography and pathology variables.

Between 2011 and 2016, 448 participants were recruited; 122 (27%) were withdrawn, leaving 326 (226 male, 100 female; mean ± standard deviation 66 ± 10.7 years); 183 (56%) had rectal cancer. Most cancers were locally advanced [≥ T3 stage, 227 (70%)]; 151 (46%) were node-positive (≥ N1 stage); 306 (94%) had surgery; 79 (24%) had neoadjuvant therapy. The resection margin was positive in 15 (5%); 93 (28%) had venous invasion; 125 (38%) had postoperative adjuvant chemotherapy; 81 (25%, 57 male) developed recurrent disease. Prediction of recurrent disease by the baseline clinicopathological time-to-event Weibull multivariable model (age, sex, tumour/node stage, tumour size and location, treatment, venous invasion) was superior to tumour/node staging: sensitivity: 0.57 (95% confidence interval 0.45 to 0.68), specificity 0.74 (95% confidence interval 0.68 to 0.79) versus sensitivity 0.56 (95% confidence interval 0.44 to 0.67), specificity 0.58 (95% confidence interval 0.51 to 0.64), respectively. Addition of perfusion computed tomography variables did not improve prediction significantly: c-statistic: 0.77 (95% confidence interval 0.71 to 0.83) versus 0.76 (95% confidence interval 0.70 to 0.82). Perfusion computed tomography parameters did not differ significantly between patients with and without recurrence (e.g. mean ± standard deviation blood flow of 60.3 ± 24.2 vs. 61.7 ± 34.2 ml/minute/100 ml). Furthermore, baseline model prediction was not improved significantly by the addition of any novel genetic or immunohistochemical biomarkers. We observed variation between local and central computed tomography measurements but neither improved model prediction significantly. We found no clear association between perfusion computed tomography variables and any immunohistochemical measurement or genetic expression.

The number of patients developing metastasis was lower than expected from historical data. Our findings should not be overinterpreted. While the baseline model was superior to tumour/node staging, any clinical utility needs definition in daily practice.

A prognostic model of standard clinicopathological variables outperformed tumour/node staging, but novel biomarkers did not improve prediction significantly. Biomarkers that appear promising in small single-centre studies may contribute nothing substantial to prognostication when evaluated rigorously.

It would be desirable for other researchers to externally evaluate the baseline model.

This trial is registered as ISRCTN95037515.

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 09/22/49) and is published in full in Health Technology Assessment; Vol. 29, No. 8. See the NIHR Funding and Awards website for further award information.