External validation of the BE-ALIVE score for predicting 30-day mortality in patients presenting with acute coronary syndromes.

INTRODUCTION: The BE-ALIVE score is an additive scoring system for estimating 30-day mortality in patients presenting with an acute coronary syndrome (ACS) [1]. However, it had only previously been tested on an internal validation cohort. The aim was to assess the scoring system on an external validation cohort. METHODS: The scoring system comprises six domains: (1) Base Excess (1 point for < -2 mmols/L), (2) Age (<65 years: 0 points, 65-74: 1 point, 75-84: 2 points, ≥ 85: 3 points), (3) Lactate (<2 mmols/L: 0 points, 2-4.9: 1 point, 5-9.9: 3 points, ≥ 10: 6 points), (4) Intubated & Ventilated (2 points), (5) Left Ventricular function (normal or mildly impaired: -1 point, moderately impaired: 1 point, severely impaired: 3 points) and (6) External / out of hospital cardiac arrest (1 point). We applied the BE-ALIVE score was applied to 205 consecutive patients at a different institution. RESULTS: Calibration was strong, with an observed to expected ratio of 1.01, a calibration slope of 1.26 and calibration in the large of -0.03. The Spiegelhalter's Z-statistic was -0.95 (p = 0.34). The AUC was 0.95 (0.92-0.98) in the external validation cohort versus 0.90 (0.85-0.95) during internal validation. Overall performance was excellent with a Brier score of 0.07 versus 0.06 during internal validation. The negative predictive value for 30-day mortality of a BE-ALIVE score < 4 was 98 %, with a positive predicted value of a score ≥ 10 of 95 %. CONCLUSIONS: The BE-ALIVE score remains a robust predictor of 30-day mortality in an external validation cohort.

Risk and resilience models in child development.

This chapter provides the most comprehensive review of risk and resilience models for child development thus far, synthesizing these interdisciplinary frameworks for ease of use in research and practice. This review specifically focuses on process models with broader conceptualizations of risk and resilience that have effects across multiple developmental domains. Risk and resilience models alike agree that alleviating risk factors is beneficial for children's development, including risks ranging from proximal issues with households (e.g., instability) and caregivers (e.g., insecure attachment, abuse) to relatively distal influences like structural racism and socioeconomic status. Resilience models further add that children who experience risks are not inherently doomed to poorer outcomes, but can draw upon positive factors in development to combat negative effects from risk, which cannot always be avoided. Major positive factors include loving relationships, educational resources, and cultural assets. Risk and resilience are highly multidisciplinary fields that have contributed much to our understanding of human development, with ample room for continued growth. Understanding of risk and resilience processes, especially during sensitive developmental periods like early childhood, provides valuable insight for prevention and intervention research and practices. Risk and resilience models share an interest in deciphering the developmental processes that hinder and help children across domains so that kids can live their best lives, resulting in a better off society for all.

Machine learning-based model for worsening heart failure risk in Chinese chronic heart failure patients.

AIMS: This study aims to develop and validate an optimal model for predicting worsening heart failure (WHF). Multiple machine learning (ML) algorithms were compared, and the results were interpreted using SHapley Additive exPlanations (SHAP). A clinical risk calculation tool was subsequently developed based on these findings. METHODS AND RESULTS: This nested case-control study included 200 patients with chronic heart failure (CHF) from the China-Japan Friendship Hospital (September 2019 to December 2022). Sixty-five variables were collected, including basic information, physical and chemical examinations, and quality of life assessments. WHF occurrence within a 3-month follow-up was the outcome event. Variables were screened using LASSO regression, univariate analysis, and comparison of key variables in multiple ML models. Eighty per cent of the data was used for training and 20% for testing. The best models were identified by integrating nine ML algorithms and interpreted using SHAP, and to develop a final risk calculation tool. Among participants, 68 (34.0%) were female, with a mean age (standard deviation, SD) of 68.57 (12.80) years. During the follow-up, 60 participants (30%) developed WHF. N-terminal pro-brain natriuretic peptide (NT-proBNP), creatinine (Cr), uric acid (UA), haemoglobin (Hb), and emotional area score on the Minnesota Heart Failure Quality of Life Questionnaire were critical predictors of WHF occurrence. The random forest (RF) model was the best model to predict WHF with an area under the curve (AUC) (95% confidence interval, CI) of 0.842 (0.675-1.000), accuracy of 0.775, sensitivity of 0.900, specificity of 0.833, negative predictive value of 0.800, and positive predictive value of 0.600 for the test set. SHAP analysis highlighted NT-proBNP, UA, and Cr as significant predictors. An online risk predictor based on the RF model was developed for personalized WHF risk assessment. CONCLUSIONS: This study identifies NT-proBNP, Cr, UA, Hb, and emotional area scores as crucial predictors of WHF in CHF patients. Among the nine ML algorithms assessed, the RF model showed the highest predictive accuracy. SHAP analysis further emphasized NT-proBNP, UA, and Cr as the most significant predictors. An online risk prediction tool based on the RF model was subsequently developed to enhance early and personalized WHF risk assessment in clinical settings.

American Association for Thoracic Surgery Quality Gateway: A Surgeon Case Study of Its Application in Adult Cardiac Surgery for Quality Assurance.

OBJECTIVE: To demonstrate applying American Association for Thoracic Surgery Quality Gateway (AQG) outcomes models to a Surgeon Case Study of quality assurance in adult cardiac surgery. METHODS: The case study includes 6,989 cardiac and thoracic aorta operations performed in adults at Cleveland Clinic by one surgeon from 2001 to 2023. AQG models were used to predict expected probabilities for operative mortality and major morbidity, and to compare hospital outcomes, surgery type, risk profile, and individual risk-factor levels using virtual (digital) twin causal inference. These models were based on postoperative procedural outcomes after 52,792 cardiac operations performed in 19 hospitals of 3 high-performing hospital systems with overall hospital mortality of 2.0%, analyzed by advanced machine learning for rare events. RESULTS: For individual surgeons, their patients, hospitals, and hospital systems, the Surgeon Case Study demonstrated that AQG provides expected outcomes across the entire spectrum of cardiac surgery, from single-component primary operations to complex multi-component reoperations. Actionable opportunities for quality improvement based on virtual twins is illustrated for patients, surgeons, hospitals, risk profile groups, operations, and risk factors vis-à-vis other hospitals. CONCLUSIONS: Using minimal data collection and models developed using advanced machine learning, this case study shows that probabilities can be generated for operative mortality and major morbidity after virtually all adult cardiac operations. It demonstrates the utility of 21st century causal inference (virtual [digital] twin) tools for assessing quality for surgeons asking "How am I doing?" their patients asking "What are my chances?" and the profession asking "How can we get better?"

Child welfare predictive risk models and legal decision making.

BACKGROUND: Child welfare agencies around the world have experimented with algorithmic predictive modeling as a method to assist in decision making regarding foster child risk, removal and placement. OBJECTIVE: Thus far, all of the predictive risk models have been confined to the employees of the various child welfare agencies at the early removal stages and none have been used by attorneys in legal arguments or by judges in making child welfare legal decisions. This study will show the effects of a predictive model on legal decision making within a child welfare context. PARTICIPANTS AND SETTING: Lawyers, judges and law students with experience in child welfare or juvenile law were recruited to take an online randomized vignette survey. METHODS: The survey consisted of two vignettes describing complex foster child removal and placement legal decisions where participants were exposed to one of three randomized predictive risk model scores. They were then asked follow up questions regarding their decisions to see if the risk models changed their answers. RESULTS: Using structural equation modeling, high predictive model risk scores showed consistent ability to change legal decisions about removal and placement across both vignettes. Medium and low scores, though less consistent, also significantly influenced legal decision making. CONCLUSIONS: Child welfare legal decision making can be affected by the use of a predictive risk model, which has implications for the development and use of these models as well as legal education for attorneys and judges in the field.

Patients' and Clinicians' Perceptions of the Clinical Utility of Predictive Risk Models for Chemotherapy-Related Symptom Management: Qualitative Exploration Using Focus Groups and Interviews.

BACKGROUND: Interest in the application of predictive risk models (PRMs) in health care to identify people most likely to experience disease and treatment-related complications is increasing. In cancer care, these techniques are focused primarily on the prediction of survival or life-threatening toxicities (eg, febrile neutropenia). Fewer studies focus on the use of PRMs for symptoms or supportive care needs. The application of PRMs to chemotherapy-related symptoms (CRS) would enable earlier identification and initiation of prompt, personalized, and tailored interventions. While some PRMs exist for CRS, few were translated into clinical practice, and human factors associated with their use were not reported. OBJECTIVE: We aim to explore patients' and clinicians' perspectives of the utility and real-world application of PRMs to improve the management of CRS. METHODS: Focus groups (N=10) and interviews (N=5) were conducted with patients (N=28) and clinicians (N=26) across 5 European countries. Interactions were audio-recorded, transcribed verbatim, and analyzed thematically. RESULTS: Both clinicians and patients recognized the value of having individualized risk predictions for CRS and appreciated how this type of information would facilitate the provision of tailored preventative treatments or supportive care interactions. However, cautious and skeptical attitudes toward the use of PRMs in clinical care were noted by both groups, particularly in relationship to the uncertainty regarding how the information would be generated. Visualization and presentation of PRM information in a usable and useful format for both patients and clinicians was identified as a challenge to their successful implementation in clinical care. CONCLUSIONS: Findings from this study provide information on clinicians' and patients' perspectives on the clinical use of PRMs for the management of CRS. These international perspectives are important because they provide insight into the risks and benefits of using PRMs to evaluate CRS. In addition, they highlight the need to find ways to more effectively present and use this information in clinical practice. Further research that explores the best ways to incorporate this type of information while maintaining the human side of care is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT02356081; https://clinicaltrials.gov/study/NCT02356081.

Implications of Five Different Risk Models In Primary Prevention Guidelines.

BACKGROUND: A lack of consensus exists across guidelines as to which risk model should be used for the primary prevention of cardiovascular disease (CVD). Our objective was to determine potential improvements in the number needed to treat (NNT) and number of events prevented (NEP) using different risk models in patients eligible for risk stratification. METHODS: A retrospective observational cohort was assembled from primary care patients in Ontario, Canada between January 1st, 2010, to December 31st, 2014 and followed for up to 5 years. Risk estimation was undertaken in patients 40-75 years of age, without CVD, diabetes, or chronic kidney disease using the Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs), a recalibrated FRS (R-FRS), Systematic Coronary Risk Evaluation 2 (SCORE2), and the low-risk region recalibrated SCORE2 (LR-SCORE2). RESULTS: The cohort consisted of 47,399 patients (59% women, mean age 54). The NNT with statins was lowest for SCORE2 at 40, followed by LR-SCORE2 at 41, R-FRS at 43, PCEs at 55, and FRS at 65. Models that selected for individuals with a lower NNT recommended statins to fewer, but higher risk patients. For instance, SCORE2 recommended statins to 7.9% of patients (5-year CVD incidence 5.92%). The FRS, however, recommended statins to 34.6% of patients (5-year CVD incidence 4.01%). Accordingly, the NEP was highest for the FRS at 406 and lowest for SCORE2 at 156. CONCLUSIONS: Newer models such as SCORE2 may improve statin allocation to higher risk groups with a lower NNT but prevent fewer events at the population level.

Predicting clinically significant prostate cancer following suspicious mpMRI: analyses from a high-volume center.

PURPOSE: mpMRI is routinely used to stratify the risk of clinically significant prostate cancer (csPCa) in men with elevated PSA values before biopsy. This study aimed to calculate a multivariable risk model incorporating standard risk factors and mpMRI findings for predicting csPCa on subsequent prostate biopsy. METHODS: Data from 677 patients undergoing mpMRI ultrasound fusion biopsy of the prostate at the TUM University Hospital tertiary urological center between 2019 and 2023 were analyzed. Patient age at biopsy (67 (median); 33-88 (range) (years)), PSA (7.2; 0.3-439 (ng/ml)), prostate volume (45; 10-300 (ml)), PSA density (0.15; 0.01-8.4), PI-RADS (V.2.0 protocol) score of index lesion (92.2% ≥3), prior negative biopsy (12.9%), suspicious digital rectal examination (31.2%), biopsy cores taken (12; 2-22), and pathological biopsy outcome were analyzed with multivariable logistic regression for independent associations with the detection of csPCa defined as ISUP ≥ 3 (n = 212 (35.2%)) and ISUP ≥ 2 (n = 459 (67.8%) performed on 603 patients with complete information. RESULTS: Older age (OR: 1.64 for a 10-year increase; p < 0.001), higher PSA density (OR: 1.60 for a doubling; p < 0.001), higher PI-RADS score of the index lesion (OR: 2.35 for an increase of 1; p < 0.001), and a prior negative biopsy (OR: 0.43; p = 0.01) were associated with csPCa. CONCLUSION: mpMRI findings are the dominant predictor for csPCa on follow-up prostate biopsy. However, PSA density, age, and prior negative biopsy history are independent predictors. They must be considered when discussing the individual risk for csPCa following suspicious mpMRI and may help facilitate the further diagnostical approach.

Perfusion quality odds (PEQUOD) trial: validation of the multifactorial dynamic perfusion index as a predictor of cardiac surgery-associated acute kidney injury.

OBJECTIVES: The multifactorial dynamic perfusion index was recently introduced as a predictor of cardiac surgery-associated acute kidney injury. The multifactorial dynamic perfusion index was developed based on retrospective data retrieved from the patient files. The present study aims to prospectively validate this index in an external series of patients, through an on-line measure of its various components. METHODS: Inclusion criteria were adult patients undergoing cardiac surgery with cardiopulmonary bypass. Data collection included preoperative factors and cardiopulmonary bypass-related factors. These were collected on-line using a dedicated monitor. Factors composing the multifactorial dynamic perfusion index are the nadir haematocrit, the nadir oxygen delivery, the time of exposure to a low oxygen delivery, the nadir mean arterial pressure, cardiopulmonary bypass duration, the use of red blood cell transfusions and the peak arterial lactates. RESULTS: Two hundred adult patients were investigated. The multifactorial dynamic perfusion index had a good (c-statistics 0.81) discrimination for cardiac surgery-associated acute kidney injury (any stage) and an excellent (c-statistics 0.93) discrimination for severe patterns (stage 2-3). Calibration was modest for cardiac surgery-associated acute kidney injury (any stage) and good for stage 2-3. The use of vasoconstrictors was an additional factor associated with cardiac surgery-associated acute kidney injury. CONCLUSIONS: The multifactorial dynamic perfusion index is validated for discrimination of cardiac surgery-associated acute kidney injury risk. It incorporates modifiable risk factors, and may help in reducing the occurrence of cardiac surgery-associated acute kidney injury.

Contemporary Risk Models for In-Hospital and 30-Day Mortality After Percutaneous Coronary Intervention.

PURPOSE OF REVIEW: Risk models for mortality after percutaneous coronary intervention (PCI) are underutilized in clinical practice though they may be useful during informed consent, risk mitigation planning, and risk adjustment of hospital and operator outcomes. This review analyzed contemporary risk models for in-hospital and 30-day mortality after PCI. RECENT FINDINGS: We reviewed eight contemporary risk models. Age, sex, hemodynamic status, acute coronary syndrome type, heart failure, and kidney disease were consistently found to be independent risk factors for mortality. These models provided good discrimination (C-statistic 0.85-0.95) for both pre-catheterization and comprehensive risk models that included anatomic variables. There are several excellent models for PCI mortality risk prediction. Choice of the model will depend on the use case and population, though the CathPCI model should be the default for in-hospital mortality risk prediction in the United States. Future interventions should focus on the integration of risk prediction into clinical care.

Predicting absolute risk for a person with missing risk factors.

We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a perfectly calibrated model gives unbiased absolute risk estimates in a new target population, even if the predictor distribution differs from the training data. However, if predictors are missing in target population members, a reference dataset with complete data is needed to impute them and to estimate absolute risk, conditional only on the observed predictors. If the predictor distributions of the reference data and the target population differ, this approach yields biased estimates. We compared the bias and mean squared error of absolute risk predictions for seven methods that assume predictors are missing at random (MAR). Some methods imputed individual missing predictors, others imputed linear predictor combinations (risk scores). Simulations were based on real breast cancer predictor distributions and outcome data. We also analyzed a real breast cancer dataset. The largest bias for all methods resulted from different predictor distributions of the reference and target populations. No method was unbiased in this situation. Surprisingly, violating the MAR assumption did not induce severe biases. Most multiple imputation methods performed similarly and were less biased (but more variable) than a method that used a single expected risk score. Our work shows the importance of selecting predictor reference datasets similar to the target population to reduce bias of absolute risk predictions with missing risk factors.

Personalized survivorship care: Routine breast cancer risk assessment in the gynecologic oncology clinic.

INTRODUCTION: Gynecologic and breast cancers share several risk factors. Breast cancer risk assessment tools can identify those at elevated risk and allow for enhanced breast surveillance and chemoprevention, however such tools are underutilized. We aim to evaluate the use of routine breast cancer risk assessment in a gynecologic oncology clinic. METHODS: A patient-facing web-based tool was used to collect personal and family history and run four validated breast cancer risk assessment models (Tyrer-Cuzick (TC), Gail, BRCAPRO, and Claus) in a gynecologic oncology clinic. We evaluated completion of the tools and identification of patients at elevated risk for breast cancer using the four validated models. RESULTS: A total of 99 patients were included in this analysis. The BRCAPRO model had the highest completion rate (84.8%), followed by the TC model (74.7%), Gail model (74.7%), and the Claus model (52.1%). The TC model identified 21.6% of patients completing the model as having ≥20% lifetime risk of breast cancer, compared to 6.8% by the Gail model, and 0% for both the BRCAPRO and Claus models. The Gail model identified 52.5% of patients as having ≥1.67% 5-year risk of breast cancer. Among patients identified as high-risk for breast cancer and eligible for screening, 9/9 (100%) were referred to a high-risk breast clinic. CONCLUSION: Among patients that completed the TC breast cancer risk assessment in a gynecologic oncology clinic, approximately 1 in 5 were identified to be at significantly elevated lifetime risk for breast cancer. The gynecologic oncologist's office might offer a convenient and feasible setting to incorporate this risk assessment into routine patient care, as gynecologic oncologists often have long-term patient relationships and participate in survivorship care.

SUMF1 overexpression promotes tumorous cell growth and migration and is correlated with the immune status of patients with glioma.

BACKGROUND: Glioma is a prevalent type of malignant tumor. To date, there is a lack of literature reports that have examined the association between sulfatase modifying factor 1 (SUMF1) and glioma. METHODS: The levels of SUMF1 were examined, and their relationships with the diagnosis, prognosis, and immune microenvironment of patients with glioma were investigated. Cox and Lasso regression analysis were employed to construct nomograms and risk models associated with SUMF1. The functions and mechanisms of SUMF1 were explored and verified using gene ontology, cell counting kit-8, wound healing, western blotting, and transwell experiments. RESULTS: SUMF1 expression tended to increase in glioma tissues. SUMF1 overexpression was linked to the diagnosis of cancer, survival events, isocitrate dehydrogenase status, age, and histological subtype and was positively correlated with poor prognosis in patients with glioma. SUMF1 overexpression was an independent risk factor for poor prognosis. SUMF1-related nomograms and high-risk scores could predict the outcome of patients with glioma. SUMF1 co-expressed genes were involved in cytokine, T-cell activation, and lymphocyte proliferation. Inhibiting the expression of SUMF1 could deter the proliferation, migration, and invasion of glioma cells through epithelial mesenchymal transition. SUMF1 overexpression was significantly associated with the stromal score, immune cells (such as macrophages, neutrophils, activated dendritic cells), estimate score, immune score, and the expression of the programmed cell death 1, cytotoxic T-lymphocyte associated protein 4, CD79A and other immune cell marker. CONCLUSION: SUMF1 overexpression was found to be correlated with adverse prognosis, cancer detection, and immune status in patients with glioma. Inhibiting the expression of SUMF1 was observed to deter the proliferation, migration, and invasion of cancer cells. The nomograms and risk models associated with SUMF1 could predict the prognosis of patients with glioma.

Risk Prediction Models for Oral Cancer: A Systematic Review.

In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (n = 20), alcohol consumption (n = 18) and smoking (n = 17). Six of the included models incorporated genetic information and three used biomarkers as predictors. Including information on human papillomavirus status was shown to improve model performance; however, this was only included in a small number of models. Most of the identified models (n = 13) showed good or excellent discrimination (AUROC > 0.7). Only fourteen models had been validated and only two of these validations were carried out in populations distinct from the model development population (external validation). Conclusions: Several risk prediction models have been identified that could be used to identify individuals at the highest risk of oral cancer within the context of screening programmes. However, external validation of these models in the target population is required, and, subsequently, an assessment of the feasibility of implementation with a risk-stratified screening programme for oral cancer.

Models for predicting venous thromboembolism in ambulatory patients with lung cancer: A systematic review and meta-analysis.

AIMS: The incidence of venous thromboembolism (VTE) in patients with lung cancer is relatively high, and risk stratification models are vital for the targeted application of thromboprophylaxis. We aimed to review VTE risk prediction models that have been developed in patients with lung cancer and evaluated their performance. METHODS AND RESULTS: Twenty-four eligible studies involving 123,493 patients were included. The pooled incidence of VTE within 12 months was 11 % (95 % CI 8 %-14 %). With the identified four VTE risk assessment tools, meta-analyses did not show a significant discriminatory capability of stratifying VTE risk for Khorana, PROTECHT and CONKO scores. The pooled sensitivity and specificity of the Khorana score were 24 % (95 % CI 11 %-44 %) and 84 % (95 % CI 73 %-91 %) at the 3-point cut-off, and 43 % (95 % CI 35 %-52 %) and 61 % (95 % CI 52 %-69 %) at the 2-point cut-off. However, a COMPASS-CAT score of ≥ 7 points indicated a significantly high VTE risk, with a RR of 4.68 (95 % CI 1.05-20.80). CONCLUSIONS: The Khorana score lacked discriminatory capability in identifying patients with lung cancer at high VTE risk, regardless of the cut-off value. The COMPASS-CAT score had better performance, but further validation is needed. The results indicate the need for robust VTE risk assessment tools specifically designed and validated for lung cancer patients. Future research should include relevant biomarkers as important predictors and consider the combined use of risk tools. PROSPERO registration number: CRD42021245907.

Comparison of six hepatocellular carcinoma prediction models in Japanese patients after sustained virologic response undergoing rigorous surveillance for hepatocellular carcinoma.

BACKGROUND AND AIM: While several predictive models for the development of hepatocellular carcinoma (HCC) have been proposed, including those for patients with chronic hepatitis C virus (HCV) infection who have achieved sustained virologic response (SVR), the best model may differ between regions. We compared the ability of six reported models to stratify the risk of post-SVR HCC in Japan, where rigorous surveillance and early detection of HCC is common. METHODS: A total of 6048 patients with no history of HCC who achieved SVR by oral direct-acting antiviral drugs were enrolled in this nationwide study. Patients continued HCC surveillance every 6 months after SVR. The incidence of post-SVR HCC was compared between risk groups using the aMAP score, FIB-4 index, Tahata model, GAF4 criteria, GES score, and ADRES score. RESULTS: During the observation period with a median duration of 4.0 years after SVR, post-SVR HCC developed in 332 patients (5.5%). All six models performed significantly at stratifying the incidence of HCC. However, Harrell's C-index was below 0.8 for all models (range, 0.660-0.748), indicating insufficient stratification ability. CONCLUSION: Although all six proposed models demonstrated a good ability to predict the development of post-SVR HCC, their ability to stratify the risk of post-SVRHCC was unsatisfactory. Further studies are necessary to identify the best model for assessing the risk of post-SVR HCC in regions where early detection of HCC is common.

Association of stress hyperglycemia ratio with in-hospital new-onset atrial fibrillation and long-term outcomes in patients with acute myocardial infarction.

AIMS: To investigate the predictive value and prognostic impact of stress hyperglycemia ratio (SHR) for new-onset atrial fibrillation (NOAF) complicating acute myocardial infarction (AMI). MATERIALS AND METHODS: This retrospective study included 2145 AMI patients without AF history between February 2014 and March 2018. SHR was calculated using fasting blood glucose (mmol/L)/[1.59*HbA1c (%)-2.59]. The association between SHR and post-MI NOAF was assessed with multivariable logistic regression analyses. The primary outcome was a composite of cardiac death, heart failure hospitalisation, recurrent MI, and ischaemic stroke (MACE). Cox regression-adjusted hazard ratios with 95% confidence intervals (CI) were estimated for MACE. RESULTS: A total of 245 (11.4%) patients developed NOAF. In the multivariable logistic regression analyses, SHR (each 10% increase) was significantly associated with increased risks of NOAF in the whole population (OR: 1.05, 95% CI: 1.01-1.10), particularly in non-diabetic individuals (OR:1.08, 95% CI: 1.01-1.17). During a median follow-up of 2.7 years, 370 (18.5%) MACEs were recorded. The optimal cut-off value of SHR for MACE prediction was 1.119. Patients with both high SHR (≥1.119) and NOAF possessed the highest risk of MACE compared to those with neither high SHR nor NOAF after multivariable adjustment (HR: 2.18, 95% CI: 1.39-3.42), especially for diabetics (HR: 2.63, 95% CI: 1.41-4.91). Similar findings were observed using competing-risk models. CONCLUSIONS: SHR is an independent predictor of post-MI NOAF in non-diabetic individuals. Diabetic patients with both high SHR and NOAF had the highest risk of MACE, suggesting that therapies targeting SHR may be considered in these patients. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03533543.

Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform.

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.

Heart failure risk scores in advanced heart failure patients: insights from the LEVO-D registry.

AIMS: The prevalence of advanced heart failure (HF) is increasing due to the growing number of patients with HF and their better treatment and survival. There is a scarcity of data on the accuracy of HF web-based risk scores in this selected population. This study aimed to assess mortality prediction performance of the Meta-Analysis Global Group in Chronic HF (MAGGIC-HF) risk score and the model of the Barcelona Bio-HF Risk Calculator (BCN-Bio-HF) containing N terminal pro brain natriuretic peptide in HF patients receiving intermittent inotropic support with levosimendan as destination therapy. METHODS AND RESULTS: Four hundred and three advanced HF patients from 23 tertiary hospitals in Spain receiving intermittent inotropic support with levosimendan as destination therapy were included. Discrimination for all-cause mortality was compared by area under the curve (AUC) and Harrell's C-statistic at 1 year. Calibration was assessed by calibration plots comparing observed versus expected events based on estimated risk by each calculator. The included patients were predominantly men, aged 71.5 [interquartile range 64-78] years, with reduced left ventricular ejection fraction (27.5 ± 9.4%); ischaemic heart disease was the most prevalent aetiology (52.5%). Death rate at 1 year was 26.8%, while the predicted 1-year mortality by BCN-Bio-HF and MAGGIC-HF was 17.0% and 22.1%, respectively. BCN-Bio-HF AUC was 0.66 (Harrell's C-statistic 0.64), and MAGGIC-HF AUC was 0.62 (Harrell's C-statistic 0.61). CONCLUSIONS: The two evaluated risk scores showed suboptimal discrimination and calibration with an underestimation of risk in advanced HF patients receiving levosimendan as destination therapy. There is a need for specific scores for advanced HF.