New metrics for assessing diagnostic potential of candidate biomarkers

Association between fatty liver index and cardiometabolic multimorbidity: evidence from the cross-sectional national health and nutrition examination survey

Background

Metabolic dysfunction associated steatotic liver disease (MASLD) contributes to the cardiometabolic diseases through multiple mechanisms. Fatty liver index (FLI) has been formulated as a non-invasive, convenient, and cost-effective approach to estimate the degree of MASLD. The current study aims to evaluate the correlation between FLI and the prevalent cardiometabolic multimorbidity (CMM), and to assess the usefulness of FLI to improve the detection of the prevalent CMM in the general population.

Methods

26,269 subjects were enrolled from the National Health and Nutrition Examination Survey 1999-2018. FLI was formulated based on triglycerides, body mass index, γ -glutamyltransferase, and waist circumference. CMM was defined as a history of 2 or more of diabetes mellitus, stroke, myocardial infarction.

Results

The prevalence of CMM was 10.84%. With adjustment of demographic, anthropometric, laboratory, and medical history covariates, each standard deviation of FLI leaded to a 58.8% risk increase for the prevalent CMM. The fourth quartile of FLI had a 2.424 times risk for the prevalent CMM than the first quartile, and a trend towards higher risk was observed. Smooth curve fitting showed that the risk for prevalent CMM increased proportionally along with the elevation of FLI. Subgroup analysis demonstrated that the correlation was robust in several conventional subpopulations. Receiver-operating characteristic curve analysis revealed an incremental value of FLI for detecting prevalent CMM when adding it to conventional cardiometabolic risk factors (Area under the curve: 0.920 vs. 0.983, P < 0.001). Results from reclassification analysis confirmed the improvement from FLI.

Conclusion

Our study demonstrated a positive, linear, and robust correlation between FLI and the prevalent CMM, and our findings implicate the potential usefulness of FLI to improve the detection of prevalent CMM in the general population.

Value of the monocyte-to-high-density lipoprotein cholesterol ratio in refining the detection of prevalent heart failure: Insights from the NHANES 1999-2018

The monocyte-to-high-density lipoprotein cholesterol ratio (MHR) is a novel marker that can help estimate the degree of atherosclerosis by considering inflammation and lipid abnormalities. This study aimed to assess the association between the MHR and prevalent heart failure (HF) and to explore the value of the MHR in detecting prevalent HF in the general US population. Our study included 25,374 participants from the National Health and Nutrition Examination Survey (1999-2018). Among the participants, 749 (2.95%) reported a history of HF, and the HF group had a significantly higher MHR than the non-HF group. Adjusted analyses revealed that each standard deviation increase in the MHR was associated with a 27.8% increase in the risk of HF. The association between the MHR and prevalent HF was linear across the entire MHR range. Adding the MHR to conventional cardiovascular risk factors significantly improved the area under the curve (0.875; p < 0.001), continuous net reclassification index (0.187; p < 0.001), and integrated discrimination index (0.004; p < 0.001). Our study suggests a potential association between the MHR and HF risk, and the findings enhance HF risk stratification and provide novel insights into the interplay between the coronary atherosclerotic burden and HF in clinical settings.

Sex-specific association between monocyte to high-density lipoprotein cholesterol and extensive abdominal aortic calcification in humans

Recent studies have identified monocyte-to-high-density lipoprotein cholesterol ratio (MHR) as a simple marker of atherosclerosis. Abdominal aortic calcification (AAC) is a direct result of vascular atherosclerosis. Our study aims to investigate the association between MHR and the prevalent extensive AAC and assess the value of MHR for identifying prevalent extensive AAC. 2857 subjects (28.07%) from the cross-sectional National Health and Nutrition Examination Survey 2013-2014 were included in our study. AAC was detected through dual-energy x-ray absorptiometry and quantified by Kauppila score. Extensive AAC was identified in 153 (10.44% of 1465) females and 146 (10.49% of 1392) males. With the full adjustment, each SD increase of MHR resulted in an 87.3% additional risk for extensive AAC in females. When dividing into quartiles, the top quartile had a 3.472 times risk of prevalent extensive AAC than the bottom quartile. However, no significant association was observed in males. Furthermore, smooth curve fitting implicated that the significant association was linear in the whole range of MHR among females. Additionally, ROC demonstrated an improvement in the identification of extensive AAC only among females when introducing MHR into established risk factors of atherosclerosis (0.808 vs. 0.864, p < 0.001). Finally, category-free net reclassification index and integrated discrimination index also supported the improvement by MHR in females. Our study revealed a linear association between MHR and prevalent extensive AAC in females. Moreover, our results implicated the potential value of MHR to refine the identification of prevalent extensive AAC in females.

Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan

Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.

Comparison of three creatinine-based equations to predict adverse outcome in a cardiovascular high-risk cohort: an investigation using the SPRINT research materials

Background

Novel creatinine-based equations have recently been proposed but their predictive performance for cardiovascular outcomes in participants at high cardiovascular risk in comparison to the established CKD-EPI 2009 equation is unknown.

Method

In 9361 participants from the United States included in the randomized controlled SPRINT trial, we calculated baseline estimated glomerular filtration rate (eGFR) using the CKD-EPI 2009, CKD-EPI 2021, and EKFC equations and compared their predictive value of cardiovascular events. The statistical metric used is the net reclassification improvement (NRI) presented separately for those with and those without events.

Results

During a mean follow-up of 3.1 ± 0.9 years, the primary endpoint occurred in 559 participants (6.0%). When using the CKD-EPI 2009, the CKD-EPI 2021, and the EKFC equations, the prevalence of CKD (eGFR <60 ml/min/1.73 m2 or >60 ml/min/1.73 m2 with an ACR ≥30 mg/g) was 37% vs. 35.3% (P = 0.02) vs. 46.4% (P < 0.001), respectively. The corresponding mean eGFR was 72.5 ± 20.1 ml/min/1.73 m2 vs. 73.2 ± 19.4 ml/min/1.73 m2 (P < 0.001) vs. 64.6 ± 17.4 ml/min/1.73 m2 (P < 0.001). Neither reclassification according to the CKD-EPI 2021 equation [CKD-EPI 2021 vs. CKD-EPI 2009: NRIevents: -9.5% (95% confidence interval (CI) -13.0% to -5.9%); NRInonevents: 4.8% (95% CI 3.9% to 5.7%)], nor reclassification according to the EKFC equation allowed better prediction of cardiovascular events compared to the CKD-EPI 2009 equation (EKFC vs. CKD-EPI 2009: NRIevents: 31.2% (95% CI 27.5% to 35.0%); NRInonevents: -31.1% (95% CI -32.1% to -30.1%)).

Conclusion

Substituting the CKD-EPI 2009 with the CKD-EPI 2021 or the EKFC equation for calculation of eGFR in participants with high cardiovascular risk without diabetes changed the prevalence of CKD but was not associated with improved risk prediction of cardiovascular events for both those with and without the event.

Value of C-Reactive Protein-Triglyceride Glucose Index in Predicting Cancer Mortality in the General Population: Results from National Health and Nutrition Examination Survey

BACKGROUND

Cancer is one of the leading causes of death. The current work aims to investigate the association between C-reactive protein-triglyceride glucose index (CTI) and the risk of incident cancer mortality and to evaluate the usefulness of CTI to refine the risk stratification of cancer mortality.

METHODS

The study enrolled 19,957 subjects from American National Health and Nutrition Examination Survey. CTI was defined as 0.412*Ln(CRP) + ln[T.G. (mg/dL) × FPG (mg/dL)/2]. Cox regression was performed to investigate the association.

RESULTS

During a follow-up of 215417.52 person-years, 736 subjects died due to malignant tumors, and the incidence of cancer mortality was 3.42 per 1,000 person-years. Kaplan-Meier curve revealed that the fourth quartile group had the lowest cancer mortality-free rate (Log-Rank p < 0.001). After full adjustment, each SD increase of CTI cast a 32.7% additional risk of incident cancer mortality. Furthermore, cancer mortality risk elevated proportionally with the increase of CTI. Finally, ROC and reclassification analyses supported the usefulness of CTI in improving the risk stratification of incident cancer mortality.

CONCLUSION

The study revealed a significant association between CTI and cancer mortality risk, suggesting the value of CTI in improving the risk stratification of incident cancer mortality. KEY MESAGESC-reactive protein-triglyceride glucose index (CTI) is positively associated with cancer mortality risk in the general population.The association was linear in the whole range of CTI.CTI could improve the risk prediction of cancer mortality in the general population.

Significance of fatty liver index to detect prevalent ischemic heart disease: evidence from national health and nutrition examination survey 1999-2016

Background

Non-alcoholic fatty liver disease (NAFLD) contributes to the development of ischemic heart disease via multiple mechanisms. Fatty liver index (FLI) has been proposed as an accurate, convenient, and economic surrogate of the severity of NAFLD. Our present study aims to assess the association between FLI and the prevalent IHD and to evaluate the potential value of FLI to refine the detection of prevalent IHD in the general population.

Methods

Our work recruited 32,938 subjects from the National Health and Nutrition Examination Survey 1999-2016. IHD was diagnosed according to the subjects' self-report. FLI was determined based on triglycerides, BMI, γ-glutamyltransferase, and waist circumference.

Results

2,370 (7.20%) subjects were diagnosed with IHD. After adjustment of age, sex, race, current smoking, current drinking, PIR, BMI, WC, TC, TG, GGT, Scr, FPG, SBP, anti-hypertensive therapy, anti-diabetic therapy, and lipid-lowering therapy, one standard deviation increase of FLI resulted in a 27.0% increment of the risk of prevalent IHD. In the quartile analysis, we observed a 1.684 times risk of prevalent IHD when comparing the fourth quartile with the first quartile, and there was a trend towards higher risk across the quartiles. The smooth curve fitting displayed a linear relationship between FLI and the presence of IHD without any threshold or saturation effect. Subgroup analysis revealed a robust association in conventional cardiovascular subpopulations, and the association could be more prominent in female subjects and diabetes patients. ROC analysis demonstrated an incremental value of FLI for detecting prevalent IHD after introducing it to conventional cardiovascular risk factors (AUC: 0.823 vs. 0.859, P for comparison <0.001). Also, results from reclassification analysis implicated that more IHD patients could be correctly identified by introducing FLI into conventional cardiovascular risk factors (continuous net reclassification index: 0.633, P < 0.001; integrated discrimination index: 0.034, P < 0.001).

Conclusion

The current analysis revealed a positive and linear relationship between FLI and the prevalent IHD. Furthermore, our findings suggest the incremental value of FLI to refine the detection of prevalent IHD in the general population.

Prognostic significance of plasma SDF-1 in acute ischemic stroke patients with diabetes mellitus: the CATIS trial

BACKGROUND AND OBJECTIVES

Evidence on the associations between baseline stromal cell-derived factor (SDF)-1 and clinical outcomes in acute ischemic stroke patients is lacking. The present study aimed to examine the relationship between plasma SDF-1 levels and clinical outcomes based on a large multicenter study of the China Antihypertensive Trial in Acute Ischemic Stroke (CATIS).

METHODS

Secondary analysis was conducted among 3,255 participants from the CATIS trial with a baseline measurement of plasma SDF-1 levels. We evaluated the associations between plasma SDF-1 levels and one-year recurrent stroke, cardiovascular events, and all-cause mortality using Cox regression models. We further investigated the prognostic effect of SDF-1 on clinical outcomes in patients with different characteristics.

RESULTS

Higher plasma SDF-1 levels were not associated with recurrent stroke, cardiovascular events, and all-cause mortality at one-year after ischemic stroke (all P trend ≥ 0.05). There were significant interactions between plasma SDF-1 levels and history of diabetes mellitus on recurrent stroke (P = 0.005), cardiovascular events (P = 0.007) and all-cause mortality (P = 0.04) at one year. In patients with diabetes mellitus, plasma SDF-1 was significantly associated with an increased risk of recurrent stroke and cardiovascular events after adjustment for confounders. For example, 1-SD higher log-SDF-1 was associated with a hazard ratio (95% confidence interval) of 1.65 (1.18-2.32) for recurrent stroke and 1.47 (1.08-1.99) for the cardiovascular events, but not all-cause mortality 1.36 (0.96-1.93) at one year. However, there were no associations between plasma SDF-1 and clinical outcomes in patients without diabetes mellitus (all P > 0.05). The addition of plasma SDF-1 to the conventional risk factors model significantly improved the risk prediction of all outcomes. Similarly, findings between elevated SDF-1 levels and two-year outcomes were found only in patients with diabetes mellitus.

CONCLUSIONS

Elevated plasma SDF-1 was significantly associated with an increased risk of recurrent stroke and cardiovascular events only in ischemic patients with diabetes mellitus.

Preoperative Tricuspid Regurgitation Is Associated With Long-Term Mortality and Is Graded More Severe Than Intraoperative Tricuspid Regurgitation

OBJECTIVES

To determine whether preoperative (preop) tricuspid regurgitation (TR) severity grade was associated with postoperative mortality, to examine the correlation between pre-op and intraoperative (intraop) TR grades, and to understand which TR grade had better prognostic predictability in cardiac surgery patients.

DESIGN

Retrospective.

SETTING

Single institution.

PARTICIPANTS

Patients.

INTERVENTIONS

Preop and intraop echocardiography TR grades of 4,232 patients who had undergone cardiac surgeries between 2004 and 2014 were examined.

MEASUREMENTS AND MAIN RESULTS

Kaplan-Meier curves and Cox proportional hazard models were used to determine the association between TR grades and the primary endpoint of all-cause mortality. The Wilcoxon signed-rank test and Spearman's rank correlation were analyzed to assess the similarity and correlation between preop and intraop-grade pairs. Multivariate logistic regression models of the area under the curve characteristics were compared for prognostic implications. Kaplan-Meier curves demonstrated a strong relationship between preop grades and survival. Multivariate models showed significantly increased mortality starting at mild preop TR (mild TR: hazard ratio [HR] 1.24; 95% CI 1.05-1.46, p = 0.013; moderate TR: HR 1.60; 95% CI 1.05-1.97, p < 0.001; severe TR: HR 2.50; 95% CI 1.74-3.58, p < 0.001). Preop TR grades were mostly higher than intraop grades. Spearman's correlation was 0.55 (p < 0.001). The area under the curves of preop and intraop TR-based models were almost identical (0.704 v 0.702 1-year mortality and 0.704 v 0.700 2-year mortality).

CONCLUSIONS

The authors found that echocardiographically-determined preop TR grade at the time of surgical planning was associated with long-term mortality, starting even at a mild grade. Preop grades were higher than intraop grades, with a moderate correlation. Preop and intraop grades exhibited similar prognostic implications.

MRI-based risk stratification for recurrent ischemic stroke in embolic stroke of undetermined source

OBJECTIVE

Leukoaraiosis and other brain MRI-assessed parameters were shown to be associated with recurrent stroke in this population. We aimed to develop an MRI-based predictive tool for risk stratification of ESUS patients.

METHODS

We retrospectively assessed consecutive patients who were diagnosed with ESUS and underwent brain MRI and performed a multivariable analysis with the outcome of recurrent stroke/TIA. Based on the coefficient of each covariate, we generated an integer-based point scoring system. The discrimination and calibration of the score were assessed using the area under the receiver operator characteristic curve, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis. Also, we compared the new score with a previously published score (ALM score).

RESULTS

Among 176 patients followed for an overall period of 902.3 patient-years (median of 74 months), there were 39 recurrent ischemic stroke/TIAs (4.32 per 100 patient-years). Fazekas score (HR: 1.26, 95% CI: 1.03-1.54), enlarged perivascular space (EPVS) (HR: 2.76, 95% CI: 1.12-6.17), NIHSS at admission (HR: 1.11, 95% CI: 1.02-1.18), and infarct subtypes (HR: 2.88, 95% CI: 1.34-6.17) were associated with recurrent stroke/TIA. Accordingly, a score (FENS score) was developed with AUC-ROC values of 0.863, 0.788, and 0.858 for 1, 3, and 5 years, respectively. These were significantly better than the AUC-ROC of ALM score (0.635, 0.695, and 0.705, respectively). The FENS score exhibited better calibration and discrimination ability than the ALM score (Hosmer-Lemeshow test χ2 : 4.402, p = 0.819).

CONCLUSION

The MRI-based FENS score can provide excellent predictive performance for recurrent stroke/TIA and may assist in risk stratification of ESUS patients.

Incremental predictive value of platelet parameters for preeclampsia: results from a large prospective cohort study

BACKGROUND

Platelet parameters during pregnancy were associated with the risk of preeclampsia (PE), but the predictive value of these parameters for PE remained unclear. Our aim was to clarify the individual and incremental predictive value of platelet parameters, including platelet count (PC), mean platelet volume (MPV), plateletcrit (PCT), and platelet distribution width (PDW) for PE.

METHODS

This study was based on the Born in Guangzhou Cohort Study in China. Data on platelet parameters were extracted from medical records of routine prenatal examinations. Receiver operating characteristic (ROC) curve was performed to analyze the predictive ability of platelet parameters for PE. Maternal characteristic factors proposed by NICE and ACOG were used to develop the base model. Detection rate (DR), integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI) were calculated compared with the base model to assess the incremental predictive value of platelet parameters.

RESULTS

A total of 30,401 pregnancies were included in this study, of which 376 (1.24%) were diagnosed with PE. Higher levels of PC and PCT were observed at 12-19 gestational weeks in women who developed PE later. However, no platelet parameters before 20 weeks of gestation reliably distinguished between PE complicated pregnancy and non-PE complicated pregnancy, with all values of the areas under the ROC curves (AUC) below 0.70. The addition of platelet parameters at 16-19 gestational weeks to the base model increased the DR for preterm PE from 22.9 to 31.4% at a fixed false positive rate of 5%, improved the AUC from 0.775 to 0.849 (p = 0.015), and yielded a NRI of 0.793 (p < 0.001), and an IDI of 0.0069 (p = 0.035). Less but significant improvement in prediction performance was also observed for term PE and total PE when all the four platelet parameters were added to the base model.

CONCLUSIONS

Although no single platelet parameter at the early stage of pregnancy identified PE with high accuracy, the addition of platelet parameters to known independent risk factors could improve the prediction of PE.

Plasma sDPP4 (Soluble Dipeptidyl Peptidase-4) and Cognitive Impairment After Noncardioembolic Acute Ischemic Stroke

BACKGROUND

DPP4 (dipeptidyl peptidase-4) inhibitors have been proven to promote neuronal regeneration, reverse the development of cognitive deficits. However, the association of circulating soluble form (sDPP4 [soluble DPP4]) with poststroke cognitive impairment (PSCI) is unclear. We aimed to investigate the association between plasma sDPP4 levels and PSCI in patients with ischemic stroke.

METHODS

A total of 600 noncardioembolic stroke patients were included based on a preplanned ancillary study from the CATIS (China Antihypertensive Trial in Acute Ischemic Stroke). We used the Montreal Cognitive Assessment to evaluate cognitive function at 3 months follow-up after ischemic stroke. Binary logistic regression analyses were performed to investigate the association of plasma sDPP4 levels with subsequent PSCI. We further calculated integrated discrimination improvement and category-free net reclassification improvement to investigate the incremental prognostic effect of plasma sDPP4 beyond the basic model with conventional risk factors.

RESULTS

Plasma sDPP4 was inversely associated with PSCI after ischemic stroke, and the adjusted odds ratio (95% CI) for the highest versus lowest quartile of sDPP4 was 0.49 (0.29-0.81; P for trend=0.011). Each 1-SD increase of logarithm-transformed plasma sDPP4 concentration was associated with 17% (odds ratio, 0.83 [95% CI, 0.70-0.99]) lower risk of PSCI. Adding plasma sDPP4 to the basic model notably improved risk reclassification for PSCI, as shown by a category-free net reclassification improvement of 19.10% (95% CI, 2.52%-35.68%; P=0.03) and integrated discrimination improvement of 0.79% (95% CI, 0.13%-1.46%; P=0.02).

CONCLUSIONS

Higher plasma sDPP4 levels were associated with decreased risk of cognitive impairment after noncardioembolic ischemic stroke.

Value of estimated glucose disposal rate to detect prevalent left ventricular hypertrophy: implications from a general population

BACKGROUND

Insulin resistance plays a pivotal role in developing left ventricular hypertrophy (LVH). Researchers have identified the estimated glucose disposal rate (eGDR) as a simple and cost-effective surrogate of insulin resistance. Our work aims to investigate the association between eGDR and the prevalent LVH and explore the incremental value of eGDR to detect prevalent LVH.

METHODS

The present work enrolled 3839 subjects from a cross-sectional survey conducted between October 2019 to April 2020 in the rural areas of southeastern China. eGDR was calculated based on waist-to-hip circumference ratio, hypertension, and glycated hemoglobin.

RESULTS

The prevalence of LVH was 17.30%. After adjusting demographic, anthropometric, laboratory, and medical history co-variates, each standard deviation increase of eGDR decreased a 29.6% risk of prevalent LVH. When dividing eGDR into quartiles, the top quartile had a 38.4% risk compared to the bottom quartile. Moreover, smooth curve fitting revealed that the association between eGDR and prevalent LVH was linear in the whole range of eGDR. Additionally, subgroup analysis demonstrated that our main finding was robust to age, sex, BMI, hypertension, and diabetes subgroups. Finally, ROC analysis exhibited a significant improvement by adding eGDR into LVH risk factors (0.780 vs. 0.803, P < 0.001), and category-free net reclassification index (0.702, P < 0.001) and integrated discrimination index (0.027, P < 0.001) also confirmed the improvement from eGDR to detect prevalent LVH.

CONCLUSION

Our analysis revealed a linear, robust association between eGDR and prevalent LVH and demonstrated the incremental value of eGDR to optimize the detection of prevalent LVH.

Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke

Background

Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors.

Methods

We predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features. Outcomes included modified Rankin Scale (mRS), Barthel Index (BI), Mini-Mental State Examination (MMSE), Modified Telephone Interview for Cognitive Status (TICS-M), Center for Epidemiologic Studies Depression Scale (CES-D) and survival. The ML models included a Support Vector Machine with a linear kernel and a radial basis function kernel as well as a Gradient Boosting Classifier based on repeated 5-fold nested cross-validation. The leading prognostic features were identified using Shapley additive explanations.

Results

The ML models achieved significant prediction performance for mRS at patient discharge and after 1 year, BI and MMSE at patient discharge, TICS-M after 1 and 3 years and CES-D after 1 year. Additionally, we showed that National Institutes of Health Stroke Scale (NIHSS) was the top predictor for most functional recovery outcomes as well as education for cognitive function and depression.

Conclusion

Our machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction.

Association between weight-adjusted-waist index and heart failure: Results from National Health and Nutrition Examination Survey 1999-2018

Background

Weight-adjusted waist circumference index (WWI) is a novel index positively associated with excessive fat accumulation. The current study aims to evaluate the association between WWI and the prevalent heart failure (HF), and to assess the value of WWI to improve the detection of HF in the general population.

Methods

A total of 25,509 subjects from National Health and Nutrition Examination Survey 1999-2018 were included into our study. WWI was calculated as WC (cm) divided by the square root of weight (kg). HF was identified according to the subjects' reports.

Results

The prevalence of reported HF was 2.96%. With adjustment of demographic, anthropometric, laboratory, and medical history data, one SD increment of WWI could cast an additional 19.5% risk for prevalent HF. After separating WWI into quartiles, the fourth quartile had a 1.670 times risk of prevalent HF compared to the first quartile. Furthermore, smooth curve fitting suggested that the association was linear in the entire range of WWI. Moreover, the association was robust to subgroups of age, sex, race, obesity, hypertension, and diabetes. Additionally, ROC analysis revealed a significant improvement for the detection of prevalent HF from WWI (0.890 vs. 0.894, P < 0.001); And continuous net reclassification index (0.225, P < 0.001) and integrated discrimination index (0.004, P < 0.001) also supported the improvement from WWI.

Conclusion

Our data demonstrated a significant, linear, and robust association between WWI, a simple surrogate for fat mass accumulation, and the risk for prevalent HF in a representative population. Moreover, our results also suggested the potential value of WWI to refine the detection of prevalent HF in the general population.

Evaluation of the added value of Brain Natriuretic Peptide to a validated mortality risk-prediction model in older people using a standardised international clinical assessment tool

The ability to accurately predict the one-year survival of older adults is challenging for clinicians as they endeavour to provide the most appropriate care. Standardised clinical needs assessments are routine in many countries and some enable application of mortality prediction models. The added value of blood biomarkers to these models is largely unknown. We undertook a proof of concept study to assess if adding biomarkers to needs assessments is of value. Assessment of the incremental value of a blood biomarker, Brain Naturetic Peptide (BNP), to a one year mortality risk prediction model, RiskOP, previously developed from data from the international interRAI-HomeCare (interRAI-HC) needs assessment. Participants were aged ≥65 years and had completed an interRAI-HC assessment between 1 January 2013 and 21 August 2021 in Canterbury, New Zealand. Inclusion criteria was a BNP test within 90 days of the date of interRAI-HC assessment. The primary outcome was one-year mortality. Incremental value was assessed by change in Area Under the Receiver Operating Characteristic Curve (AUC) and Brier Skill, and the calibration of the final model. Of 14,713 individuals with an interRAI-HC assessment 1,537 had a BNP within 90 days preceding the assessment and all data necessary for RiskOP. 553 (36.0%) died within 1-year. The mean age was 82.6 years. Adding BNP improved the overall AUC by 0.015 (95% CI:0.004 to 0.028) and improved predictability by 1.9% (0.26% to 3.4%). In those with no Congestive Heart Failure the improvements were 0.029 (0.004 to 0.057) and 4.0% (0.68% to 7.6%). Adding a biomarker to a risk model based on standardised needs assessment of older people improved prediction of 1-year mortality. BNP added value to a risk prediction model based on the interRAI-HC assessment in those patients without a diagnosis of congestive heart failure.

Triglyceride glucose index and its combination with the Get with the Guidelines-Heart Failure score in predicting the prognosis in patients with heart failure

Background

Heart failure (HF) is associated with generalized insulin resistance (IR). Recent studies demonstrated that triglyceride glucose (TyG) is an effective alternative index of IR. However, the relationship between the TyG index and in-hospital mortality in patients with HF is unclear. In the present study, we aimed to clarify the association between the TyG index and in-hospital mortality in patients with HF.

Methods

A retrospective study consisting of 4,411 patients diagnosed with HF from 2015 to 2018 was conducted. All-cause mortality during hospitalization was the primary endpoint. The association between the TyG index and in-hospital mortality was assessed using the logistic regression analysis.

Results

The risk of in-hospital mortality was significantly associated with increased TyG index (OR: 1.886, 95% CI: 1.421–2.501, p < 0.001) under logistic regression with multivariable adjustment. When divided into three groups based on the TyG index, Tertile 3 demonstrated significantly higher in-hospital mortality than the other two Tertiles (OR: 2.076, 95% CI: 1.284–3.354, p = 0.001). Moreover, the TyG index improved the prediction efficiency of the Get with the Guidelines-Heart Failure (GWTG-HF) score (absolute integrated discrimination improvement = 0.006, p < 0.001; category-free net reclassification improvement = 0.075, p = 0.005). In subgroup analysis, the TyG index exhibited similar predictive performance of in-hospital mortality when groups were stratified based on type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD).

Conclusion

TyG is a potential index for predicting in-hospital mortality in patients with HF, independent of T2DM or CAD status. The TyG index may be combined with the GWTG-HF score to further improve its predictive efficacy.

Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study

Background

Insulin resistance is one of the major mechanisms for cardiovascular events. Estimated glucose disposal rate(eGDR) has been demonstrated as a simple, accurate, and cost-effective estimator of insulin resistance. Our study aims to evaluate the correlation between eGDR and the prevalent IHD and assess the incremental value of eGDR for identifying prevalent IHD in the rural general population.

Methods

Our study enrolled 10,895 participants from a cross-sectional survey of a metabolic management program. The survey was conducted in the rural areas of southeastern China between October 2019 and April 2020. eGDR = 21.158 − (0.09 * waist circumference) − (3.407 * hypertension) − (0.551 * HbA1c).

Results

The prevalence of IHD was 4.20%. After adjusting for demographic, anthropometric, laboratory, and medical history covariates, each SD increase of eGDR brought a 25.9% risk reduction for prevalent IHD. After dividing eGDR into groups, the top group had a 58.9% risk reduction than the bottom group. Furthermore, smooth curve fitting demonstrated that the correlation between eGDR and prevalent IHD was linear in the whole range of eGDR. Additionally, AUC suggested that eGDR could significantly improve the identification of prevalent IHD by adding it to cardiovascular risk factors (0.703 vs. 0.711, P for comparison = 0.041). Finally, the category-free net reclassification index and integrated discrimination index also implicated the improvement from eGDR to identify prevalent IHD.

Conclusion

Our data demonstrated a significant, negative, and linear correlation between eGDR and prevalent IHD. Our findings could suggest the potential usefulness of eGDR to improve the identification of prevalent IHD in the rural general population.

Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging

Background

Mortality research has identified biomarkers predictive of all-cause mortality risk. Most of these markers, such as body mass index, are predictive cross-sectionally, while for others the longitudinal change has been shown to be predictive, for instance greater-than-average muscle and weight loss in older adults. And while sometimes markers are derived from imaging modalities such as DXA, full scans are rarely used. This study builds on that knowledge and tests two hypotheses to improve all-cause mortality prediction. The first hypothesis is that features derived from raw total-body DXA imaging using deep learning are predictive of all-cause mortality with and without clinical risk factors, meanwhile, the second hypothesis states that sequential total-body DXA scans and recurrent neural network models outperform comparable models using only one observation with and without clinical risk factors.

Methods

Multiple deep neural network architectures were designed to test theses hypotheses. The models were trained and evaluated on data from the 16-year-long Health, Aging, and Body Composition Study including over 15,000 scans from over 3000 older, multi-race male and female adults. This study further used explainable AI techniques to interpret the predictions and evaluate the contribution of different inputs.

Results

The results demonstrate that longitudinal total-body DXA scans are predictive of all-cause mortality and improve performance of traditional mortality prediction models. On a held-out test set, the strongest model achieves an area under the receiver operator characteristic curve of 0.79.

Conclusion

This study demonstrates the efficacy of deep learning for the analysis of DXA medical imaging in a cross-sectional and longitudinal setting. By analyzing the trained deep learning models, this work also sheds light on what constitutes healthy aging in a diverse cohort.

Three-dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults

OBJECTIVE

This study examined whether body shape and composition obtained by three-dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics.

METHODS

A diverse ambulatory adult population underwent whole-body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics-adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model.

RESULTS

A total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p < 0.0001) with a net reclassification improvement of 0.214 (p < 0.0001).

CONCLUSIONS

Body shape measures from an accessible 3DO scan, adjusted for demographics, predicted MetS better than demographics and/or BMI alone. Risk classification in this population increased by 29% when using 3DO scanning.

Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals: The ILERVAS Study

Background

Although European guidelines recommend vascular ultrasound for the assessment of cardiovascular risk in low-to-moderate risk individuals, no algorithm properly identifies patients who could benefit from it. The aim of this study is to develop a sex-specific algorithm to identify those patients, especially women who are usually underdiagnosed.

Methods

Clinical, anthropometrical, and biochemical data were combined with a 12-territory vascular ultrasound to predict severe atheromatosis (SA: ≥ 3 territories with plaque). A Personalized Algorithm for Severe Atheromatosis Prediction (PASAP-ILERVAS) was obtained by machine learning. Models were trained in the ILERVAS cohort (n = 8,330; 51% women) and validated in the control subpopulation of the NEFRONA cohort (n = 559; 47% women). Performance was compared to the Systematic COronary Risk Evaluation (SCORE) model.

Results

The PASAP-ILERVAS is a sex-specific, easy-to-interpret predictive model that stratifies individuals according to their risk of SA in low, intermediate, or high risk. New clinical predictors beyond traditional factors were uncovered. In low- and high-risk (L&H-risk) men, the net reclassification index (NRI) was 0.044 (95% CI: 0.020-0.068), and the integrated discrimination index (IDI) was 0.038 (95% CI: 0.029-0.048) compared to the SCORE. In L&H-risk women, PASAP-ILERVAS showed a significant increase in the area under the curve (AUC, 0.074 (95% CI: 0.062-0.087), p-value: < 0.001), an NRI of 0.193 (95% CI: 0.162-0.224), and an IDI of 0.119 (95% CI: 0.109-0.129).

Conclusion

The PASAP-ILERVAS improves SA prediction, especially in women. Thus, it could reduce the number of unnecessary complementary explorations selecting patients for a further imaging study within the intermediate risk group, increasing cost-effectiveness and optimizing health resources.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [NCT03228459].

Preoperative NT-proBNP and LVEF for the prediction of acute kidney injury after noncardiac surgery: a single-centre retrospective study

BACKGROUND

Acute kidney injury (AKI) is one of the most common postoperative complications in noncardiac surgical patients, has an important impact on prognosis and is difficult to predict. Whether preoperative N-terminal pro-brain natriuretic peptide (NT-proBNP) concentrations and left ventricular ejection fraction (LVEF) levels can predict postoperative AKI in noncardiac surgical patients is unclear.

METHODS

We included 3,314 patients who underwent noncardiac surgery and had measurements of preoperative NT-proBNP concentrations and LVEF levels at a tertiary academic hospital in China between 2008 and 2018. Multiple logistic regression analysis was used to construct a postoperative AKI risk prediction model for this cohort. Then, NT-proBNP concentrations and LVEF levels were included in the abovementioned model as independent variables, and the predictive ability of these two models was compared.

RESULTS

Postoperative AKI occurred in 223 (6.72%) patients within 1 week after surgery. Preoperative NT-proBNP concentrations and LVEF levels were independent predictors of AKI after adjustment for clinical variables. The area under the receiver operating characteristic curve (AUROC) of the AKI risk predictive model established with clinical baseline variables was 0.767 (95% CI: 0.732, 0.802). When NT-proBNP concentrations and LVEF levels were added to the base model, the AUROC was 0.811 (95% CI: 0.779, 0.843). The addition of NT-proBNP concentrations and LVEF levels improved reclassification by 22.9% (95% CI 10.5-34.4%) for patients who developed postoperative AKI and by 36.3% (95% CI 29.5-43.9%) for those who did not, resulting in a significant overall improvement in net reclassification (NRI: 0.591, 95% CI 0.437-0.752, P < 0.000). The integral discrimination improvement was 0.100 (95% CI: 0.075, 0.125, P < 0.000).The final postoperative AKI prediction model was constructed, and had a good discriminative ability and fitted to the dataset.

CONCLUSIONS

Preoperative NT-proBNP concentrations and LVEF levels were independently associated with the risk of AKI after noncardiac surgery, and they could improve the predictive ability of logistic regression models based on conventional clinical risk factors.

TRIAL REGISTRATION

The protocol was preregistered in the Chinese Clinical Trial Registry ( ChiCTR1900024056 ).

Prognostic value of the systemic inflammation response index in patients with acute ischemic stroke

OBJECTIVES

Inflammation plays an essential role in acute ischemic stroke (AIS). Recent studies have recognized the systemic inflammation response index (SIRI) as a useful index to indicate inflammation status and predict the prognosis of multiple diseases. However, the relationship between SIRI and AIS prognosis is unclear. Our study is aimed to investigate the association between SIRI and the prognosis of AIS.

METHODS

Our study prospectively recruited 287 consecutive patients with first-ever stroke within 72 h after stroke. Demographic and clinical information was collected at baseline. The functional prognosis was assessed 3 months after AIS using the modified Rankin Scale (mRS). A poor outcome was defined as mRS > 2. SIRI was calculated as neutrophil × monocyte/lymphocyte count. Univariate and multivariate analyses were introduced to identify the association between SIRI and AIS prognosis. Receiver operating characteristic curve and reclassification analyses were used to evaluate the predictive value of SIRI for AIS prognosis.

RESULTS

The patients with poor prognosis account for 27.5% of all participants. After fully adjusting for all covariates, each standard deviation increment of SIRI caused 58.9% additional risk for poor prognosis after AIS. When dividing SIRI into quartiles, the fourth quartile had a 6.152 times risk than the first quartile. Moreover, after adding SIRI into established clinical risk factors, AUC showed a significant improvement (0.829 vs. 0.790, p for comparison = .016). Consistently, category-free net reclassification index (NRI, 0.761, 95% CI: 0.517-1.004, p < .001) and integrated discrimination index (IDI, 0.093, 95% CI: 0.0512-0.134, p < .001) confirmed the improvement by SIRI to predict poor prognosis of AIS, CONCLUSION: SIRI is an independent prognostic indicator for AIS. Elevated SIRI is associated with poor functional outcome of AIS. Our findings suggest the usefulness of SIRI to refine the risk stratification of unfavorable prognosis of AIS.

Value of estimated pulse wave velocity to identify left ventricular hypertrophy prevalence: insights from a general population

Background

Aortic stiffness shares a similar profile of risk factors with left ventricular hypertrophy (LVH) and can also lead to LVH by itself. Published data have demonstrated the correlation between aortic stiffness and LVH. Recent data have revealed estimated pulse wave velocity (ePWV) to be a simple and cost-effective marker of the severity of aortic stiffness. Our analysis aimed to explore the association between ePWV and LVH prevalence, and to investigate the incremental value of ePWV for the identification of LVH prevalence.

Methods

The present analysis based on a cross-sectional survey which included 11,597 participants from rural areas of southeastern China between Sep 2020 and Feb 2021. ePWV was formulated based on mean blood pressure and age according to a published algorithm.

Results

The prevalence of LVH was 14.56%. With the adjustment of age, sex, education, income and physical activity level, current drinking and smoking status, BMI, waist circumference, serum creatinine, total cholesterol, high density cholesterol, mean blood pressure, fasting plasma glucose, anti-hypertensive therapy, anti-diabetic therapy, lipid-lowering therapy, and cardiovascular disease history, every standard deviation increment of ePWV associated with a 2.993 times risk of LVH prevalence. When dividing ePWV into quartiles, the top quartile had a 4.520 times risk of LVH prevalence when compared with the bottom quartile. Furthermore, smooth spline analysis displayed that the association was linear in the whole range of ePWV (p for non-linearity = 0.073). Additionally, subgroup analysis revealed the association was robust to sex, obesity and diabetes, and younger people and hypertensive population were more vulnerable to the increase of ePWV than their corresponding counterparts. Finally, ROC analysis showed a significant advancement when introducing ePWV into established risk factors (0.787 vs. 0.810, p for comparison < 0.001), and reclassification analysis also confirmed significant improvement from ePWV to identify LVH prevalence (category-free net reclassification analysis = 0.421, p < 0.001; integrated discrimination index = 0.023, p < 0.001).

Conclusion

Our analysis demonstrated a linear association between ePWV and LVH prevalence. Furthermore, our results suggest younger people and hypertensive population are more likely to have LVH prevalence with the increase of ePWV. More importantly, our findings implicate the incremental value of ePWV to optimize the identification of LVH prevalence in a general Chinese population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02541-9.

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.

Usefulness of estimated pulse wave velocity for identifying prevalent coronary heart disease: findings from a general Chinese population

Background

Aortic stiffness and coronary heart disease (CHD) share a similar spectrum of risk factors; previous studies have identified the association between aortic stiffness and CHD. Recent studies have demonstrated estimated pulse wave velocity (ePWV) as a simple and easy-acquired indicator of aortic stiffness. Our work aims to evaluate the association between ePWV and the prevalence of CHD and assess the value of ePWV for the identification of prevalent CHD.

Methods

The current cross-sectional work included 7012 subjects from rural areas of southeastern China between September 2020 and February 2021. ePWV was calculated from age and mean blood pressure by specific algorithm.

Results

The prevalence of CHD in our population was 3.58% (251 patients among 7012 subjects). After adjusting for age, sex, education, income and exercise level, current smoking and drinking status, body mass index, waist circumference, fasting plasma glucose, total cholesterol, high density lipoprotein, estimated glomerular filtration rate and cerebrovascular diseases, each standard deviation increment of ePWV would produce an additional 37.8% risk of prevalent CHD. Moreover, after dividing ePWV into quartiles, the 4th quartile of ePWV showed a significant risk of prevalent CHD (OR (95% CI): 3.567 (1.963–6.479)) when compared with the 1^st quartile. Additionally, the subgroup analysis showed the association between ePWV and prevalent CHD was robust to several common risk factors of CHD, including age, sex, body mass index, hypertension, diabetes and reduced estimated glomerular filtration rate. Finally, the area under curve (AUC) displayed an improvement when adding ePWV into common CHD risk factors (0.705 vs. 0.718. P = 0.044). Consistently, net reclassification index (0.436, 95% CI: 0.301–0.571, P < 0.001) and integrated discrimination index (0.004, 95% CI: 0.001–0.006, P = 0.002) demonstrated the value of ePWV to optimize the identification of prevalent CHD in the general population.

Conclusion

The present analysis implicates the robust association between ePWV, a simple, rapid, and practical marker of aortic stiffness, and prevalent CHD in the general Chinese population. More importantly, the results suggest the value of ePWV as a potential marker to improve the identification of prevalent CHD.

External validation of the NeuroImaging Radiological Interpretation System and Helsinki computed tomography score for mortality prediction in patients with traumatic brain injury treated in the intensive care unit: a Finnish intensive care consortium study

BACKGROUND

Admission computed tomography (CT) scoring systems can be used to objectively quantify the severity of traumatic brain injury (TBI) and aid in outcome prediction. We aimed to externally validate the NeuroImaging Radiological Interpretation System (NIRIS) and the Helsinki CT score. In addition, we compared the prognostic performance of the NIRIS and the Helsinki CT score to the Marshall CT classification and to a clinical model.

METHODS

We conducted a retrospective multicenter observational study using the Finnish Intensive Care Consortium database. We included adult TBI patients admitted in four university hospital ICUs during 2003-2013. We analyzed the CT scans using the NIRIS and the Helsinki CT score and compared the results to 6-month mortality as the primary outcome. In addition, we created a clinical model (age, Glasgow Coma Scale score, Simplified Acute Physiology Score II, presence of severe comorbidity) and combined clinical and CT models to see the added predictive impact of radiological data to conventional clinical information. We measured model performance using area under curve (AUC), Nagelkerke's R² statistics, and the integrated discrimination improvement (IDI).

RESULTS

A total of 3031 patients were included in the analysis. The 6-month mortality was 710 patients (23.4%). Of the CT models, the Helsinki CT displayed best discrimination (AUC 0.73 vs. 0.70 for NIRIS) and explanatory variation (Nagelkerke's R² 0.20 vs. 0.15). The clinical model displayed an AUC of 0.86 (95% CI 0.84-0.87). All CT models increased the AUC of the clinical model by + 0.01 to 0.87 (95% CI 0.85-0.88) and the IDI by 0.01-0.03.

CONCLUSION

In patients with TBI treated in the ICU, the Helsinki CT score outperformed the NIRIS for 6-month mortality prediction. In isolation, CT models offered only moderate accuracy for outcome prediction and clinical variables outweighing the CT-based predictors in terms of predictive performance.

Combined Effect of Homocysteine and Uric Acid to Identify Patients With High Risk for Subclinical Atrial Fibrillation

Background

Subclinical atrial fibrillation (SCAF) is often asymptomatic nonetheless harmful. In patients with cardiac implantable electronic devices, we evaluated the combined performance of homocysteine and uric acid (UA) biomarkers to discriminate high‐risk patients for SCAF.

Methods and Results

We enrolled 1224 consecutive patients for evaluation of SCAF in patients with cardiac implantable electronic devices in Dalian, China, between January 2013 and December 2019. Clinical data and blood samples were obtained from patients selected according to the absence or presence of atrial high‐rate episodes >6 minutes. Blood samples were obtained, and homocysteine and UA biomarkers were tested in all patients to distinguish their prognostic performance for SCAF. Homocysteine and UA biomarkers were significantly different in SCAF versus no SCAF. On multivariable Cox regression analysis with potential confounders, elevated homocysteine and UA biomarkers were significantly associated with an increased risk of SCAF. A rise of 1 SD in homocysteine (5.7 μmol/L) was associated with an increased risk of SCAF in men and women regardless of their UA levels. Similarly, a 1‐SD increase in UA (91 μmol/L) was associated with an increased risk of SCAF among the patients with high levels of homocysteine in men (hazard ratio, 1.81; 95% CI, 1.43–2.30) and women (hazard ratio, 2.11; 95% CI, 1.69–2.62). The addition of homocysteine and UA to the atrial fibrillation risk factors recommended by the 2020 European Society of Cardiology Guidelines significantly improved risk discrimination for SCAF.

Conclusions

Homocysteine and UA biomarkers were strongly associated with SCAF. The prediction performance of the European Society of Cardiology model for SCAF was increased by the addition of the selected biomarkers.

Registration

URL: https://www.chictr.org.cn; Unique identifier: Chi‐CTR200003837.

Trajectory of Estimated Glomerular Filtration Rate and Malnourishment Predict Mortality and Kidney Failure in Older Adults With Chronic Kidney Disease

Objective: The trajectory patterns of estimated glomerular filtration rates (eGFR) in chronic kidney disease (CKD) older adults with malnourishment and their association with subsequent patient outcomes have not been elucidated. We aimed to assess the eGFR trajectory patterns for predicting patient survival and kidney failure in the elderly without or with malnourishment.

Materials and Methods: Based on a prospective longitudinal cohort, CKD patients aged 65 years or older were enrolled from 2001 to 2013. Among the 3,948 patients whose eGFR trajectory patterns were analyzed, 1,872 patients were stratified by the absence or presence of malnourishment, and 765 patients were identified and categorized as having malnourishment. Four eGFR trajectory patterns [gradual decline (T0), early non-decline and then persistent decline (T1), persistent increase (T2), and low baseline and then progressive increase (T3)] were classified by utilizing a linear mixed-effect model with a quadratic term in time. The malnourishment was defined as body mass index < 22 kg/m², serum albumin < 3.0 mg/dL, or Geriatric Nutritional Risk Index (GNRI) < 98. This study assessed the effectiveness of eGFR trajectory patterns in a median follow-up of 2.27 years for predicting all-cause mortality and kidney failure.

Results: The mean age was 76.9 ± 6.7 years, and a total of 82 (10.7%) patients with malnourishment and 57 (5.1%) patients without malnourishment died at the end of the study. Compared with the reference trajectory T0, the overall mortality of T1 was markedly reduced [adjusted hazard ratio (aHR) = 0.52, 95% confidence interval (CI) 0.32–0.83]. In patients with trajectory, T3 was associated with a high risk for kidney failure (aHR = 5.68, 95% CI 3.12–10.4) compared with the reference, especially higher risk in the presence of malnourishment. Patients with high GNRI values were significantly associated with a lower risk of death and kidney failure, but patients with malnourishment and concomitant alcohol consumption had a higher risk of kidney failure.

Conclusions: Low baseline eGFR and progressively increasing eGFR trajectory were high risks for kidney failure in CKD patients. These findings may be attributed to multimorbidity, malnourishment, and decompensation of renal function.

Dual-energy three-compartment breast imaging for compositional biomarkers to improve detection of malignant lesions

Background

While breast imaging such as full-field digital mammography and digital breast tomosynthesis have helped to reduced breast cancer mortality, issues with low specificity exist resulting in unnecessary biopsies. The fundamental information used in diagnostic decisions are primarily based in lesion morphology. We explore a dual-energy compositional breast imaging technique known as three-compartment breast (3CB) to show how the addition of compositional information improves malignancy detection.

Methods

Women who presented with Breast Imaging-Reporting and Data System (BI-RADS) diagnostic categories 4 or 5 and who were scheduled for breast biopsies were consecutively recruited for both standard mammography and 3CB imaging. Computer-aided detection (CAD) software was used to assign a morphology-based prediction of malignancy for all biopsied lesions. Compositional signatures for all lesions were calculated using 3CB imaging and a neural network evaluated CAD predictions with composition to predict a new probability of malignancy. CAD and neural network predictions were compared to the biopsy pathology.

Results

The addition of 3CB compositional information to CAD improves malignancy predictions resulting in an area under the receiver operating characteristic curve (AUC) of 0.81 (confidence interval (CI) of 0.74-0.88) on a held-out test set, while CAD software alone achieves an AUC of 0.69 (CI 0.60-0.78). We also identify that invasive breast cancers have a unique compositional signature characterized by reduced lipid content and increased water and protein content when compared to surrounding tissues.

Conclusion

Clinically, 3CB may potentially provide increased accuracy in predicting malignancy and a feasible avenue to explore compositional breast imaging biomarkers.

Impact of monocyte to high-density lipoprotein ratio on the identification of prevalent coronary heart disease: insights from a general population

BACKGROUND

Recent studies have identified monocyte to high-density lipoprotein ratio (MHR) as a simple, practical surrogate of atherosclerosis. Considering atherosclerosis is a major mechanism of coronary heart disease (CHD). The present study aims to evaluate the association between MHR and the prevalence of CHD.

METHODS AND RESULTS

The present cross-sectional work included 6442 participants (mean age: 59.57 years, 60.2% females), all of them were included from rural areas of northern China between October 2019 to April 2020. MHR was acquired as monocytes count divided by high-density lipoprotein concentration. Prevalent CHD researched 3.14%. After adjustment of sex, age, current drinking and smoking, BMI, WC, diabetes, hypertension, LDL-C, TG, eGFR, lipid-lowering therapy and cerebrovascular disease history, each standard deviation increase of MHR cast a 39.5% additional CHD risk. Furthermore, the top quartile of MHR had an additional 89.0% CHD risk than the bottom quartile. Besides, smooth curve fitting revealed a linear pattern of the association. Additionally, the stratified evaluation showed a robust correlation among the subgroups divided by CHD risk factors. Finally, area under the curve demonstrated an advancement when including MHR into common CHD risk factors (0.744 vs 0.761, p < 0.001). Consistently, reclassification analysis indicated the improvement from MHR (all P = 0.003).

CONCLUSION

Our work suggests the robust and linear relationship between MHR and the prevalent CHD in a general population, providing epidemiological evidence for laboratory studies. More importantly, the findings implicate the efficacy of MHR to be a potential indicator to identify the prevalent CHD.

Prognostic Effects of Vasomotor Reactivity during Targeted Temperature Management in Post-Cardiac Arrest Patients: A Retrospective Observational Study

Early and precise neurological prognostication without self-fulfilling prophecy is challenging in post-cardiac arrest syndrome (PCAS), particularly during the targeted temperature management (TTM) period. This study aimed to investigate the feasibility of vasomotor reactivity (VMR) using transcranial Doppler (TCD) to determine whether final outcomes of patients with comatose PCAS are predicted. This study included patients who had out-of-hospital cardiac arrest in a tertiary referral hospital over 4 years. The eligible criteria included age ≥18 years, successful return of spontaneous circulation, TTM application, and bedside TCD examination within 72 h. Baseline demographics and multimodal prognostic parameters, including imaging findings, electrophysiological studies, and TCD-VMR parameters, were assessed. The final outcome parameter was cerebral performance category scale (CPC) at 1 month. Potential determinants were compared between good (CPC 1-2) and poor (CPC 3-5) outcome groups. The good outcome group (n = 41) (vs. poor (n = 117)) showed a higher VMR value (54.4% ± 33.0% vs. 25.1% ± 35.8%, p < 0.001). The addition of VMR to conventional prognostic parameters significantly improved the prediction power of good outcomes. This study suggests that TCD-VMR is a useful tool at the bedside to evaluate outcomes of patients with comatose PCAS during the TTM.

Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers

Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complementing human observers, AI allows an in-depth analysis of digitised histological slides of GI and liver cancer and offers a wide range of clinically relevant applications. First, AI can automatically detect tumour tissue, easing the exponentially increasing workload on pathologists. In addition, and possibly exceeding pathologist's capacities, AI can capture prognostically relevant tissue features and thus predict clinical outcome across GI and liver cancer types. Finally, AI has demonstrated its capacity to infer molecular and genetic alterations of cancer tissues from histological digital slides. These are likely only the first of many AI applications that will have important clinical implications. Thus, pathologists and clinicians alike should be aware of the principles of AI-based pathology and its ability to solve clinically relevant problems, along with its limitations and biases.

Serum Bile Acid Profiles Improve Clinical Prediction of Nonalcoholic Fatty Liver in T2DM patients

Background: The present study aimed to assess the ability of serum bile acid profiles to predict the development of nonalcoholic fatty liver (NAFL) in type 2 diabetes mellitus (T2DM) patients. Methods: Using targeted ultraperformance liquid chromatography (UPLC) coupled with triple quadrupole mass spectrometry (TQ/MS), we compared serum bile acid levels in T2DM patients with NAFL (n = 30) and age- and sex-matched T2DM patients without NAFL (n = 36) at the first time. Second, an independent cohort study of T2DM patients with NAFL (n = 17) and age- and sex-matched T2DM patients without NAFL (n = 20) was used to validate the results. The incremental benefits of serum biomarkers, clinical variables alone, or with biomarkers were then evaluated using receiver operating characteristic (ROC) curves and decision curve analysis. The area under the curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to evaluate the biomarker predictive abilities. Results: The serum bile acid profiles in T2DM patients with NAFL were significantly different from T2DM patients without NAFL, as characterized by the significant elevation of LCA, TLCA, TUDCA, CDCA-24G, and TCDCA, which may be potential biomarkers for the identification of NAFL in T2DM patients. Based on the improvement in AUC, IDI, and NRI, the addition of 5 bile acids to a model with clinical variables statistically improved its predictive value. Similar results were found in the validation cohort. Conclusions: These results highlight that the detected biomarkers may contribute to the progression of NAFL in T2DM patients, and these biomarkers particularly in combination may help in the diagnosis of NAFL and allow earlier intervention in T2DM patients.

Impairment of Cardiac Autonomic Nerve Function in Pre-school Children With Intractable Epilepsy

Objective: Intractable epilepsy and uncontrolled seizures could affect cardiac function and the autonomic nerve system with a negative impact on children's growth. The aim of this study was to investigate the variability and complexity of cardiac autonomic function in pre-school children with pediatric intractable epilepsy (PIE). Methods: Twenty four-hour Holter electrocardiograms (ECGs) from 93 patients and 46 healthy control subjects aged 3-6 years were analyzed by the methods of traditional heart rate variability (HRV), multiscale entropy (MSE), and Kurths-Wessel symbolization entropy (KWSE). Receiver operating characteristic (ROC) curve analysis was used to estimate the overall discrimination ability. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) models were also analyzed. Results: Pre-school children with PIE had significantly lower HRV measurements than healthy controls in time (Mean_RR, SDRR, RMSSD, pNN50) and frequency (VLF, LF, HF, LF/HF, TP) domains. For the MSE analysis, area 1_5 in awake state was lower, and areas 6_15 and 6_20 in sleep state were higher in PIE with a significant statistical difference. KWSE in the PIE group was also inferior to that in healthy controls. In ROC curve analysis, pNN50 had the greatest discriminatory power for PIE. Based on both NRI and IDI models, the combination of MSE indices (wake: area1_5 and sleep: area6_20) and KWSE (m = 2, τ = 1, α = 0.16) with traditional HRV measures had greater discriminatory power than any of the single HRV measures. Significance: Impaired HRV and complexity were found in pre-school children with PIE. HRV, MSE, and KWSE could discriminate patients with PIE from subjects with normal cardiac complexity. These findings suggested that the MSE and KWSE methods may be helpful for assessing and understanding heart rate dynamics in younger children with epilepsy.

Biomarker-Guided Risk Assessment for Acute Kidney Injury: Time for Clinical Implementation?

Acute kidney injury (AKI) is a common and serious complication in hospitalized patients, which continues to pose a clinical challenge for treating physicians. The most recent Kidney Disease Improving Global Outcomes practice guidelines for AKI have restated the importance of earliest possible detection of AKI and adjusting treatment accordingly. Since the emergence of initial studies examining the use of neutrophil gelatinase-associated lipocalin (NGAL) and cycle arrest biomarkers, tissue inhibitor metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein (IGFBP7), for early diagnosis of AKI, a vast number of studies have investigated the accuracy and additional clinical benefits of these biomarkers. As proposed by the Acute Dialysis Quality Initiative, new AKI diagnostic criteria should equally utilize glomerular function and tubular injury markers for AKI diagnosis. In addition to refining our capabilities in kidney risk prediction with kidney injury biomarkers, structural disorder phenotypes referred to as "preclinical-" and "subclinical AKI" have been described and are increasingly recognized. Additionally, positive biomarker test findings were found to provide prognostic information regardless of an acute decline in renal function (positive serum creatinine criteria). We summarize and discuss the recent findings focusing on two of the most promising and clinically available kidney injury biomarkers, NGAL and cell cycle arrest markers, in the context of AKI phenotypes. Finally, we draw conclusions regarding the clinical implications for kidney risk prediction.

Comparison of twenty indices of insulin sensitivity in predicting type 2 diabetes in Japanese Americans: The Japanese American Community Diabetes Study

AIMS

We compared 20 previously reported indices of insulin sensitivity derived from samples during an oral glucose tolerance test (OGTT) to determine which was best in predicting incident type 2 diabetes.

METHODS

We prospectively followed 418 Japanese Americans without diabetes for 10-11 years. We compared ability to predict incident diabetes of 20 insulin sensitivity indices-9 based on fasting samples, 7 based on 2-h and/or fasting samples, and 4 based on multiple samples (0, 30, 60, 120 min) during an OGTT-by integrated discrimination improvement, category free net reclassification improvement, and area under the receiver operator characteristic curve.

RESULTS

There were 95 incident cases of diabetes. The Cederholm and Gutt indices, requiring more than only fasting samples, were the best to predict incident diabetes as judged by integrated discrimination improvement (0.187, 0.184), category free net reclassification improvement (0.962, 1.030), and area under the receiver operator characteristic curve (0.864, 0.863, respectively). Fasting indices were clearly inferior to both the Cederholm and Gutt indices.

CONCLUSIONS

Among the 20 indices, the Cederholm and Gutt indices predicted diabetes best but the Gutt index may be preferable because it requires fewer samples during an OGTT.

Usefulness of atherogenic index of plasma for estimating reduced eGFR risk: insights from the national health and nutrition examination survey

AIMS

Previous studies have identified Atherogenic index of plasma (AIP) as a simple measure of atherosclerosis. Because atherosclerosis plays a role in the development of renal damage, our study aims to evaluate the effect of AIP on the risk of reduced eGFR and assess its usefulness to refine the risk stratification of reduced estimated glomerular filtration rate (eGFR).

METHODS

Our study included 15,836 participants from the National Health and Nutritional Survey (NHANES) 2009-2016. Association was investigated by logistic regression. AIP was calculated as log (triglycerides/high-density lipoprotein cholesterol). Reduced eGFR was determined as eGFR < 60 ml/min per 1.73 m*2.

RESULTS

The prevalence of reduced eGFR was 8.01%. In the full model, each SD increase of AIP leaded to 27.4% additional risk for reduced eGFR. After dividing AIP into quartiles, the fourth quartile had a 1.649 times risk than the first quartile. Moreover, smooth curve fitting suggested that the risk of reduced eGFR elevated linearly with the increase of AIP. Subgroup analysis demonstrated that the association between AIP and reduced eGFR was robust in sex, body mass index, hypertension, and diabetes subpopulation, but the association was significantly stronger in black race and people aged less than 50 years old. Additionally, AUC displayed an advancement when introducing AIP into established risk factors (0.875 cs. 0.897, P < 0.001), category-free net reclassification index (0.249, 95% CI: 0.192-0.306, P < 0.001) and integrated discrimination index (0.007, 95% CI: 0.004-0.009, P < 0.001) also suggested the improvement from AIP.

CONCLUSION

The present work suggested a linear association between AIP and reduced eGFR. Furthermore, the results showed that the association was stronger in black race and people aged less than 50 years old. Most importantly, our work implicated the usefulness of AIP to refine the risk stratification of reduced eGFR.

Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning

Machine learning (ML) has shown its potential to improve patient care over the last decade. In organ transplantation, delayed graft function (DGF) remains a major concern in deceased donor kidney transplantation (DDKT). To this end, we harnessed ML to build personalized prognostic models to predict DGF. Registry data were obtained on adult DDKT recipients for model development (n = 55,044) and validation (n = 6176). Incidence rates of DGF were 25.1% and 26.3% for the development and validation sets, respectively. Twenty-six predictors were identified via recursive feature elimination with random forest. Five widely-used ML algorithms-logistic regression (LR), elastic net, random forest, artificial neural network (ANN), and extreme gradient boosting (XGB) were trained and compared with a baseline LR model fitted with previously identified risk factors. The new ML models, particularly ANN with the area under the receiver operating characteristic curve (ROC-AUC) of 0.732 and XGB with ROC-AUC of 0.735, exhibited superior performance to the baseline model (ROC-AUC = 0.705). This study demonstrates the use of ML as a viable strategy to enable personalized risk quantification for medical applications. If successfully implemented, our models may aid in both risk quantification for DGF prevention clinical trials and personalized clinical decision making.

Estimate of prevalent hyperuricemia by systemic inflammation response index: results from a rural Chinese population

OBJECTIVES

Hyperuricemia is a common metabolic disease that is intimately correlated with inflammation. Our study aimed to investigate the value of systemic inflammation response index as a novel inflammatory marker to estimate hyperuricemia in the rural Chinese population.

METHODS

This cross-sectional study used the data of 8,095 Chinese men and women aged ≥35 years from the 2012-2013 Northeast China Rural Cardiovascular Health Study.

RESULTS

The overall prevalence of hyperuricemia was 12.84%. After fully adjusting for potential confounders, each SD increase of SIRI in men and women caused a 21.4% and 37.0% additional risk, respectively, for hyperuricemia. Moreover, smooth curve fitting and subgroup analyses corroborated the linearity and robustness of this correlation. ROC analysis showed the ability of SIRI to estimate hyperuricemia was significantly improved in females (0.741 vs 0.745, P = 0.043), but not in males (0.710 vs 0.714, P = 0.105). The net reclassification improvement (NRI, 0.120 in men vs 0.166 in women) and integrated discrimination improvement (IDI, 0.002 in men vs 0.006 in women) showed a significant improvement for both genders.

CONCLUSIONS

Our present study suggests a linear and robust relationship between SIRI and prevalent hyperuricemia, which implicates the value of SIRI to optimize the risk stratification and prevention of hyperuricemia.

Neurofilament light as an outcome predictor after cardiac arrest: a post hoc analysis of the COMACARE trial

Purpose

Neurofilament light (NfL) is a biomarker reflecting neurodegeneration and acute neuronal injury, and an increase is found following hypoxic brain damage. We assessed the ability of plasma NfL to predict outcome in comatose patients after out-of-hospital cardiac arrest (OHCA). We also compared plasma NfL concentrations between patients treated with two different targets of arterial carbon dioxide tension (PaCO₂), arterial oxygen tension (PaO₂), and mean arterial pressure (MAP).

Methods

We measured NfL concentrations in plasma obtained at intensive care unit admission and at 24, 48, and 72 h after OHCA. We assessed neurological outcome at 6 months and defined a good outcome as Cerebral Performance Category (CPC) 1–2 and poor outcome as CPC 3–5.

Results

Six-month outcome was good in 73/112 (65%) patients. Forty-eight hours after OHCA, the median NfL concentration was 19 (interquartile range [IQR] 11–31) pg/ml in patients with good outcome and 2343 (587–5829) pg/ml in those with poor outcome, p < 0.001. NfL predicted poor outcome with an area under the receiver operating characteristic curve (AUROC) of 0.98 (95% confidence interval [CI] 0.97–1.00) at 24 h, 0.98 (0.97–1.00) at 48 h, and 0.98 (0.95–1.00) at 72 h. NfL concentrations were lower in the higher MAP (80–100 mmHg) group than in the lower MAP (65–75 mmHg) group at 48 h (median, 23 vs. 43 pg/ml, p = 0.04). PaCO₂ and PaO₂ targets did not associate with NfL levels.

Conclusions

NfL demonstrated excellent prognostic accuracy after OHCA. Higher MAP was associated with lower NfL concentrations.

Electronic supplementary material

The online version of this article (10.1007/s00134-020-06218-9) contains supplementary material, which is available to authorized users.

Methodology of a Novel Risk Stratification Algorithm for Patients with Multiple Myeloma in the Relapsed Setting

INTRODUCTION

Risk stratification tools provide valuable information to inform treatment decisions. Existing algorithms for patients with multiple myeloma (MM) were based on patients with newly diagnosed disease, and these have not been validated in the relapsed setting or in routine clinical practice. We developed a risk stratification algorithm (RSA) for patients with MM at initiation of second-line (2L) treatment, based on data from the Czech Registry of Monoclonal Gammopathies.

METHODS

Predictors of overall survival (OS) at 2L treatment were identified using Cox proportional hazards models and backward selection. Risk scores were obtained by multiplying the hazard ratios for each predictor. The K-adaptive partitioning for survival (KAPS) algorithm defined four groups of stratification based on individual risk scores.

RESULTS

Performance of the RSA was assessed using Nagelkerke's R² test and Harrell's concordance index through Kaplan-Meier analysis of OS data. Prognostic groups were successfully defined based on real-world data. Use of a multiplicative score based on Cox modeling and KAPS to define cut-off values was effective.

CONCLUSION

Through innovative methods of risk assessment and collaboration between physicians and statisticians, the RSA was capable of stratifying patients at 2L treatment by survival expectations. This approach can be used to develop clinical decision-making tools in other disease areas to improve patient management.

FUNDING

Amgen Europe GmbH.

Systemic vascular resistance predicts the development of hypertension: the cardiovascular risk in young Finns study

PURPOSE

To study whether systemic hemodynamics, especially systemic vascular resistance, predicts the development of hypertension and improves the risk prediction of incident hypertension beyond common risk factors in the risk models in young adults.

MATERIALS AND METHODS

Typical risk factors for hypertension in the risk prediction models (systolic and diastolic blood pressure, parental history of hypertension, age, sex, body-mass index, smoking), laboratory values (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, insulin, C-reactive protein), heart rate (HR), stroke index (SI), and systemic vascular resistance index (SVRI) calculated by whole-body impedance cardiography were evaluated in 2007 and blood pressure in 2011 in 1293 Finnish adults (aged 30-45 years; females 56%; n = 1058 normotensive in 2007).

RESULTS

Of hemodynamic variables, SVRI and HR evaluated in 2007 were independently associated with systolic blood pressure (p < 0.001 and p = 0.047, respectively) and SVRI with diastolic blood pressure measured in 2011 (p = 0.014), and SVRI and HR were independent predictors of incident hypertension (p < 0.001 and p = 0.024, respectively). SVRI was the most significant predictor of incident hypertension independently of other risk factors (odds ratio 2.73 per 1 standard deviation increase, 95% confidence interval 1.93-3.94, p < 0.001). The extended prediction model (including SVRI) improved the incident hypertension risk prediction beyond other risk factors, with an area under the receiver operating characteristic curve of 0.846 versus 0.817 (p = 0.042) and a continuous net reclassification improvement of 0.734 (p < 0.001).

CONCLUSIONS

These findings suggest that systemic vascular resistance index predicts the incidence of hypertension in young adults and that the evaluation of systemic hemodynamics could provide an additional tool for hypertension risk prediction.

Urine NGAL as a biomarker for septic AKI: a critical appraisal of clinical utility-data from the observational FINNAKI study

Background

Neutrophil gelatinase-associated lipocalin (NGAL) is released from kidney tubular cells under stress as well as from neutrophils during inflammation. It has been suggested as a biomarker for acute kidney injury (AKI) in critically ill patients with sepsis. To evaluate clinical usefulness of urine NGAL (uNGAL), we post-hoc applied recently introduced statistical methods to a sub-cohort of septic patients from the prospective observational Finnish Acute Kidney Injury (FINNAKI) study. Accordingly, in 484 adult intensive care unit patients with sepsis by Sepsis-3 criteria, we calculated areas under the receiver operating characteristic curves (AUCs) for the first available uNGAL to assess discrimination for four outcomes: AKI defined by Kidney Disease: Improving Global Outcomes (KDIGO) criteria, severe (KDIGO 2–3) AKI, and renal replacement therapy (RRT) during the first 3 days of intensive care, and mortality at day 90. We constructed clinical prediction models for the outcomes and used risk assessment plots and decision curve analysis with predefined threshold probabilities to test whether adding uNGAL to the models improved reclassification or decision making in clinical practice.

Results

Incidences of AKI, severe AKI, RRT, and mortality were 44.8% (217/484), 27.7% (134/484), 9.5% (46/484), and 28.1% (136/484). Corresponding AUCs for uNGAL were 0.690, 0.728, 0.769, and 0.600. Adding uNGAL to the clinical prediction models improved discrimination of AKI, severe AKI, and RRT. However, the net benefits for the new models were only 1.4% (severe AKI and RRT) to 2.5% (AKI), and the number of patients needed to be tested per one extra true-positive varied from 40 (AKI) to 74 (RRT) at the predefined threshold probabilities.

Conclusions

The results of the recommended new statistical methods do not support the use of uNGAL in critically ill septic patients to predict AKI or clinical outcomes.

Early acetaminophen-protein adducts predict hepatotoxicity following overdose (ATOM-5)

BACKGROUND & AIMS

Acetaminophen-protein adducts are specific biomarkers of toxic acetaminophen (paracetamol) metabolite exposure. In patients with hepatotoxicity (alanine aminotransferase [ALT] >1,000 U/L), an adduct concentration ≥1.0 nmol/ml is sensitive and specific for identifying cases secondary to acetaminophen. Our aim was to characterise acetaminophen-protein adduct concentrations in patients following acetaminophen overdose and determine if they predict toxicity.

METHODS

We performed a multicentre prospective observational study, recruiting patients 14 years of age or older with acetaminophen overdose regardless of intent or formulation. Three serum samples were obtained within the first 24 h of presentation and analysed for acetaminophen-protein adducts. Acetaminophen-protein adduct concentrations were compared to ALT and other indicators of toxicity.

RESULTS

Of the 240 patients who participated, 204 (85%) presented following acute ingestions, with a median ingested dose of 20 g (IQR 10-40), and 228 (95%) were treated with intravenous acetylcysteine at a median time of 6 h (IQR 3.5-10.5) post-ingestion. Thirty-six (15%) patients developed hepatotoxicity, of whom 22 had an ALT ≤1,000 U/L at the time of initial acetaminophen-protein adduct measurement. Those who developed hepatotoxicity had a higher initial acetaminophen-protein adduct concentration compared to those who did not, 1.63 nmol/ml (IQR 0.76-2.02, n = 22) vs. 0.26 nmol/ml (IQR 0.15-0.41; n = 204; p <0.0001), respectively. The AUROC for hepatotoxicity was 0.98 (95% CI 0.96-1.00; n = 226; p <0.0001) with acetaminophen-protein adduct concentration and 0.89 (95% CI 0.82-0.96; n = 219; p <0.0001) with ALT. An acetaminophen-protein adduct concentration of 0.58 nmol/ml was 100% sensitive and 91% specific for identifying patients with an initial ALT ≤1,000 U/L who would develop hepatotoxicity. Adding acetaminophen-protein adduct concentrations to risk prediction models improved prediction of hepatotoxicity to a level similar to that obtained by more complex models.

CONCLUSION

Acetaminophen-protein adduct concentration on presentation predicted which patients with acetaminophen overdose subsequently developed hepatotoxicity, regardless of time of ingestion. An adduct threshold of 0.58 nmol/L was required for optimal prediction.

LAY SUMMARY

Acetaminophen poisoning is one of the most common causes of liver injury. This study examined a new biomarker of acetaminophen toxicity, which measures the amount of toxic metabolite exposure called acetaminophen-protein adduct. We found that those who developed liver injury had a higher initial level of acetaminophen-protein adducts than those who did not.

CLINICAL TRIAL REGISTRATION

Australian Toxicology Monitoring (ATOM) Study-Australian Paracetamol Project: ACTRN12612001240831 (ANZCTR) Date of registration: 23/11/2012.

Prognostic implication of global myocardial blood flow in patients with ST-segment elevation myocardial infarction

The prognostic implications of cardiovascular magnetic resonance imaging (CMR)-derived hyperemic myocardial blood flow (MBF) in patients with ST-elevation myocardial infarction (STEMI) are unknown. This study sought to investigate the incremental prognostic value of hyperemic MBF over conventional CMR markers to identify patients with high risk of future incidence of patient-oriented composite outcomes (POCO) and major adverse cardiac events (MACE) after STEMI. A total of 237 patients who presented with STEMI were prospectively enrolled. The CMR protocol included left-ventricular ejection fraction (LVEF), late gadolinium enhancement (LGE) and microvascular obstruction (MVO) measurement, and volumetric MBF assessment. During a median follow-up of 2.6 years, 47 patients experienced POCO (primary outcome) and 21 patients had MACE. In a multivariable model, multivessel disease, LGE, MVO, and hyperemic MBF were independently associated with POCO. Addition of hyperemic MBF to the model consisting of GRACE score, multivessel disease, LVEF, LGE, and MVO significantly improved the predictive efficacy (integrated discrimination improvement 0.020, p = 0.021). Patients with low hyperemic MBF had significantly higher incidence of MACE compared to those with high hyperemic MBF in propensity score matching analysis (p = 0.018). In conclusion, CMR-derived hyperemic MBF could provide independent and incremental prognostic value over LVEF, LGE, and MVO in patients with STEMI.

Value of triglyceride-glucose index for the estimation of ischemic stroke risk: Insights from a general population

BACKGROUND AND AIMS

Recent studies have recognized triglyceride-glucose index (TyG) as a practical surrogate of insulin resistance. Previous studies have demonstrated that insulin resistance contributes to ischemic stroke via multiple mechanisms. Our study aimed to investigate the association between TyG and prevalent ischemic stroke, exploring the value of TyG to optimize the risk stratification of ischemic stroke.

METHODS AND RESULTS

This cross-sectional study included 10,900 subjects (mean age: 59.95 years, 59.8% females) from rural areas of northeast China between September 2017 to May 2018. TyG was calculated as ln[fasting triglyceride (mg/dl) × fasting plasma glucose (mg/dl)/2]. The prevalence of ischemic stroke was 5.49%. After adjusting for all covariates, each SD increment of TyG caused 22.8% additional risk for ischemic stroke. When dividing TyG into quartiles, the top quartile had a 1.776 times risk for ischemic stroke against the bottom category. Furthermore, smoothing curve fitting demonstrated this association was linear in the whole range of TyG. Finally, AUC revealed an improvement when introducing TyG into clinical risk factors (0.746 vs 0.751, p = 0.029). Consistently, category-free net reclassification index (0.195, 95% CI: 0.112-0.277, P < 0.001) and integrated discrimination index (0.003, 95% CI: 0.001-0.004, P < 0.001) confirmed the improvement by TyG to stratify ischemic stroke risk.

CONCLUSION

The prevent ischemic stroke correlated proportionally with the increment of TyG, implicating the linearity of TyG as an indicator of ischemic stroke. Our findings suggest the potential value of TyG to optimize the risk stratification of ischemic stroke in a general population.

Meta-analysis of preoperative high-sensitivity cardiac troponin measurement in non-cardiac surgical patients at risk of cardiovascular complications

Background

Patients undergoing major non-cardiac surgery are at risk of cardiovascular complications. Raised levels of high-sensitivity troponin are frequently detected before operation among these patients. However, the prognostic value of high-sensitivity troponin in predicting postoperative outcomes remains unclear.

Methods

A systematic search of PubMed, Embase and Science Citation Index Expanded was undertaken for observational studies published before March 2018 that reported associations between raised preoperative levels of high-sensitivity troponin and postoperative major adverse cardiac events and/or mortality after non-cardiac surgery. Meta-analyses were performed, where possible, using random-effects models.

Results

Seven cohort studies with a total of 4836 patients were included. A raised preoperative high-sensitivity troponin level was associated with a higher risk of short-term major adverse cardiac events (risk ratio (RR) 2·92, 95 per cent c.i. 1·96 to 4·37; I2 = 82·6 per cent), short-term mortality (RR 5·39, 3·21 to 9·06; I2 = 0 per cent) and long-term mortality (RR 2·90, 1·83 to 4·59, I2 = 74·2 per cent). The addition of preoperative high-sensitivity troponin measurement provided improvements in cardiovascular risk discrimination (increase in C-index ranged from 0·058 to 0·109) and classification (quantified by continuous net reclassification improvement) compared with Lee's Revised Cardiac Risk Index alone. There was substantial heterogeneity and inadequate risk stratification analysis in the included studies.

Conclusion

Raised preoperative levels of high-sensitivity troponin appear to represent a risk for postoperative major adverse cardiac events and mortality. Further study is required before high-sensitivity troponin can be used to predict risk stratification in routine clinical practice.

The role of biomarkers in predicting pre-eclampsia in high-risk women

Background

There are limited data on performance of biomarkers to predict pre-eclampsia (PE) in high-risk women. This study investigated the ability of FABP4, PAPP-A, PlGF, sFlt-1 and sEng to predict PE in a high-risk group.

Methods

Non-fasting samples were analysed at 11 + 0–13 + 6 (V1) and 19 + 0–21 + 6 weeks (V2) ( n = 195). Logistic regression models were determined. Area under (AUC) the receiver operating characteristic (ROC) curve analysis was performed. The added value of biomarkers to clinical characteristics for PE prediction was quantified using integrated discrimination improvement (IDI) and net reclassification improvement (NRI) indices.

Results

Prevalence of PE was 12%. Lower concentrations of sFlt-1:PlGF (V1) and PlGF and PlGF:sEng (V2) were seen in women who developed PE. Controlling for baseline characteristics (V1), a doubling of sFlt-1 (pg/mL) (median 896.0, IQR 725.5–1097.0) and sFlt-1:PlGF (median 21.2, IQR 14.7–32.3) was associated with reduction in odds of PE (OR 0.20, 95% CI 0.06–0.65, P = 0.007 and OR 0.48, 95% CI 0.25–0.92, P = 0.04). Addition of sFlt-1 and sFlt-1:PlGF to baseline characteristics non-significantly improved AUC (0.74) (AUC 0.77, P = 0.40 and 0.76, P = 0.39). NRI and IDI analyses confirmed added clinical utility of sFlt-1 (NRI = 0.539, P = 0.01 and IDI = 0.052, P = 0.03). In V2, doubling of PlGF:sEng (median 71.9, IQR 47.0–102.8) was associated with reduction in the risk of PE (OR 0.56, 95% CI 0.35–0.98, P = 0.04). The addition of PlGF:sEng to baseline characteristics non-significantly improved AUC from 0.78 to 0.82 ( P = 0.25) and improved reclassification of cases (NRI = 0.682, P = 0.002).

Conclusions

Screening tests incorporating first trimester sFlt-1 and second trimester PlGF:sEng have potential to aid PE prediction in high-risk pregnancies.