scholarly journals Cardiovascular risk prediction in healthy older people

GeroScience ◽  
2021 ◽  
Author(s):  
Johannes T. Neumann ◽  
Le T. P. Thao ◽  
Emily Callander ◽  
Enayet Chowdhury ◽  
Jeff D. Williamson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cox Regression ◽  
External Validation ◽  
Randomised Trial ◽  
Antihypertensive Agents ◽  
Major Adverse Cardiovascular Events ◽  
Final Model ◽  
Elderly Individuals ◽  
Healthy Elderly ◽  
Increased Risk

AbstractIdentification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations.

Resistance training, insulin sensitivity and muscle function in the elderly

2006 ◽  
Vol 42 ◽  
pp. 75-88 ◽  
Author(s):  
Flemming Dela ◽  
Michael Kjaer
Keyword(s):  
Physical Activity ◽  
Muscle Strength ◽  
The Elderly ◽  
Activity Level ◽  
Elderly Individuals ◽  
Healthy Elderly ◽  
Age Related ◽  
Increased Risk ◽  
Improved Function ◽  
Improve Muscle Strength

Ageing is associated with a loss in both muscle mass and in the metabolic quality of skeletal muscle. This leads to sarcopenia and reduced daily function, as well as to an increased risk for development of insulin resistance and type 2 diabetes. A major part, but not all, of these changes are associated with an age-related decrease in the physical activity level and can be counteracted by increased physical activity of a resistive nature. Strength training has been shown to improve insulin-stimulated glucose uptake in both healthy elderly individuals and patients with manifest diabetes, and likewise to improve muscle strength in both elderly healthy individuals and in elderly individuals with chronic disease. The increased strength is coupled to improved function and a decreased risk for fall injuries and fractures. Elderly individuals have preserved the capacity to improve muscle strength and mass with training, but seem to display a reduced sensitivity towards stimulating protein synthesis from nutritional intake, rather than by any reduced response in protein turnover to exercise.

External validation of the Glasgow prognostic score (mGPS) for renal cancer.

2012 ◽  
Vol 30 (5_suppl) ◽  
pp. 378-378
Author(s):  
Viraj A. Master ◽  
Timothy V. Johnson ◽  
Omer Kucuk ◽  
Daniel Canter ◽  
John Pattaras ◽  
...  
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Inflammatory Responses ◽  
Prognostic Score ◽  
External Validation ◽  
Glasgow Prognostic Score ◽  
Low Risk ◽  
High Risk Patients ◽  
Increased Risk ◽  
Risk Patients

378 Background: Inflammation has been termed the 7th hallmark of cancer (Hanahan and Weinberg Cell 2011). Measurement of systemic inflammatory responses in malignancy is possible using a selective combination of two commonly available, cost-effective serum assays. The combination of these two serum markers, C-reactive protein (CRP) and albumin, is termed the modified Glasgow prognostic score (mGPS), and is strongly correlated with outcome in a variety of cancers, including mRCC. Recently, mGPS has been shown to be predictive of outcome in localized RCC (ASCO GU 2010 #390). We sought to externally validate these results. Methods: Nephrectomized patients with clinically localized (T1-T4N0M0) clear cell RCC with negative surgical margins were followed for a mean of 25 months (range: 1-81 months). Relapse and survival was identified through routine follow-up. Patients were categorized by mGPS score as Low Risk (mGPS = 0 points), Intermediate Risk (mGPS = 1 point), and High Risk (mGPS = 2 points). One point was assigned to patients for an elevated CRP (>10 mg/L) and hypoalbuminemia (<3.5 mg/dL). Patients with normal CRP and hypoalbuminemia were assigned 0 points. Kaplan-Meier and multivariate Cox regression analyses examined relapse-free survival (RFS) and overall survival (OS) across patient and disease characteristics. Results: Of 248 patients, 17.9% relapsed and 18.6% died. Of Low, Intermediate, and High Risk patients, 7.2%, 7.7%, and 45.5%, respectively relapsed and 5.2%, 15.4%, and 39.4%, respectively died during the study. In multivariate analysis including stage and grade, mGPS was significantly associated with RFS and OS. Compared to Low-Risk patients, High-Risk patients experienced a 3-fold (OR: 2.906, 95% CI: 1.055-8.001) increased risk of relapse and 4-fold (HR: 3.722, 95% CI: 1.046-13.245) increased risk of mortality. AUC is 0.813, which compares very favorably to existing prognostic algorithms. Conclusions: In this external validation cohort of US patients, mGPS continues to be a predictor of relapse and overall mortality following nephrectomy for localized RCC. Clinicians may consider using mGPS as an adjunct to identify high-risk patients for possible enrollment into clinical trials, or for patient counseling.

A machine learning tool to predict mortality risk among patients with metastatic cancer in outpatient oncology care.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1560-1560
Author(s):  
Brandon Butler ◽  
Nadaa Tayiab ◽  
Serra Phu ◽  
Susan Nga Hoang ◽  
Brian Turnwald ◽  
...  
Keyword(s):  
Machine Learning ◽  
End Of Life ◽  
Metastatic Cancer ◽  
External Validation ◽  
Selection Criterion ◽  
Study Cohort ◽  
Multiple Time ◽  
Care Model ◽  
Final Model ◽  
Oncology Care

1560 Background: End-of-life management is a well-known challenging aspect of cancer care. In particular, timely hospice enrollment is a leading quality metric in the Oncology Care Model that has substantial room for improvement. An automated algorithmic tool that can incorporate the wealth of available EHR data and rapidly identify patients with a high risk of imminent mortality could be a valuable asset to supplement important clinical decisions and improve timely hospice care. Methods: A retrospective study cohort was formed using patients with metastatic cancer from US Oncology Network (USON) practices participating in the Oncology Care Model (OCM) between January 1, 2017 and June 30, 2019. Patients were required to have at least one record for lab values and vital signs in the EHR database. Patients were excluded from the study cohort if they were not enrolled in the OCM program or did not have a diagnosis for metastatic cancer. The patients satisfying the selection criterion were used to train and optimize the model. The training dataset was also used for internal validation and hyperparameter tuning until the final model was produced. As external validation, the final model was independently tested on 3 separate holdout datasets including OCM patients between July 1, 2019 and March 31, 2020. To avoid bias, all holdout datasets used for validation were excluded from the model. Results: A multivariable model to predict 90-day mortality was developed using a retrospective dataset derived from EHR data and Medicare claims data. A logistic regression algorithm using L1 (lasso) regularization yielded the best performance compared to other model candidates. The performance on the training cohort was given by a cross-validated AUC score of 0.85 (95% CI, 0.84 to 0.86). Further, external validation conducted using 3 independent holdout datasets demonstrated impressive generalizability marked by stable performance scores across multiple time periods (AUC between 0.84 and 0.85). Conclusions: This study builds upon previous work and further establishes the utility of machine learning to predict risk of imminent mortality for advanced cancer patients using available EHR data. A data-driven tool that estimates the probability of 90-day mortality could be leveraged as a powerful supplementary aid to clinicians managing end-of-life care at oncology practices.

scholarly journals Prognostic value of ST2 for MACEs and all-cause mortality in patients with coronary artery disease during a long-term follow up

2020 ◽  
Author(s):  
Man Li ◽  
Lei Duan ◽  
Yulun Cai ◽  
Benchuan Hao ◽  
Jianqiao Chen ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Events ◽  
Prognostic Value ◽  
Cox Regression ◽  
Major Adverse Cardiovascular Events ◽  
Kaplan Meier ◽  
Increased Risk ◽  
Artery Disease

Abstract Background: Suppression of tumorigenesis-2 is implicated in the myocardial overload and it was long been recognized as an inflammation marker related to heart failure and acute coronary syndromes, but the data on prognostic value of suppression of tumorigenesis-2 on patients with coronary artery disease remains limited. The study ought to investigate the prognostic value of suppression of tumorigenesis-2 in patients with established coronary artery disease.Methods: In this prospective cohort study, a total of 3641 consecutive patients were included. The primary end point was major adverse cardiovascular events. Kaplan-Meier survival estimates indicated that the patients with higher levels of ST2 (ST2> 19 ng/ml) had a significantly increased risk of MACEs (log-rank p＜0.001) and all-cause death (log-rank p＜0.001). The secondary end point was all-cause death. The association between suppression of tumorigenesis-2 and outcomes was investigated using multivariable COX regression.Results: During a median follow up of 6.4 years, there were 775 patients had the occurrence of major adverse cardiovascular events and 275 patients died. Kaplan-Meier survival estimates indicated that the patients with higher levels of ST2 (ST2> 19 ng/ml) had a significantly increased risk of MACEs (log-rank p＜0.001) and all-cause death (log-rank p＜0.001). Multiple COX regression models showed that higher level of suppression of tumorigenesis-2 was an independent predictor in developing major adverse cardiovascular events (HR=1.36, 95% CI 1.17-1.56, p＜0.001) and all-cause death (HR=2.01, 95%CI 1.56-2.59, p＜0.001). The addition of suppression of tumorigenesis-2 to established risk factors significantly improved risk prediction of the composite outcome of major adverse cardiovascular events and all-cause death (c-statistic, net reclassification index, and integrated discrimination improvement, all p＜0.05).Conclusions: Higher level of suppression of tumorigenesis-2 is significantly associated with long-term all-cause death and major adverse cardiovascular events. Suppression of tumorigenesis-2 may provide incremental prognostic value beyond traditional risk factors.

scholarly journals Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

2021 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Jaehyeong Cho ◽  
Jimyung Park ◽  
Eugene Jeong ◽  
Jihye Shin ◽  
Sangjeong Ahn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Postoperative Mortality ◽  
Random Forest Model ◽  
Forest Model ◽  
Laboratory Values ◽  
Increased Risk

Background: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. Methods: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy. Results: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model. Conclusions: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.

scholarly journals Detectable High-Sensitivity Cardiac Troponin within the Population Reference Interval Conveys High 5-Year Cardiovascular Risk: An Observational Study

2018 ◽  
Vol 64 (7) ◽  
pp. 1044-1053 ◽  
Author(s):  
Martin P Than ◽  
Sally J Aldous ◽  
Richard W Troughton ◽  
Christopher J Pemberton ◽  
A Mark Richards ◽  
...  
Keyword(s):  
Cardiac Troponin ◽  
Troponin I ◽  
Cox Regression ◽  
Limit Of Detection ◽  
High Sensitivity ◽  
Major Adverse Cardiovascular Events ◽  
Reference Limit ◽  
Coronary Syndrome ◽  
Increased Risk

Abstract BACKGROUND Increased cardiac troponin I or T detected by high-sensitivity assays (hs-cTnI or hs-cTnT) confers an increased risk of adverse prognosis. We determined whether patients presenting with putatively normal, detectable cTn concentrations [&gt; limit of detection and &lt; upper reference limit (URL)] have increased risk of major adverse cardiovascular events (MACE) or all-cause mortality. METHODS A prospective 5-year follow-up of patients recruited in the emergency department with possible acute coronary syndrome (ACS) and cTn concentrations measured with hs-cTnI (Abbott) and hs-cTnT (Roche) assays. Cox regression models were generated with adjustment for covariates in those without MACE on presentation. Hazard ratios (HRs) for hs-cTn were calculated relative to the HRs at the median concentration. RESULTS Of 1113 patients, 836 were without presentation MACE. Of these, 138 incurred a MACE and 169 died during a median 5.8-year follow-up. HRs for MACE at the URLs were 2.3 (95% CI, 1.7–3.2) for hs-cTnI and 1.8 (95% CI, 1.3–2.4) for hs-cTnT. Corresponding HRs for mortality were 1.7 (95% CI, 1.2–2.2) for hs-cTnI and 2.3 (95 % CI, 1.7–3.1) for hs-cTnT. The HR for MACE increased with increasing hs-cTn concentration similarly for both assays, but the HR for mortality increased at approximately twice the rate for hs-cTnT than hs-cTnI. Patients with hs-cTnI ≥10 ng/L or hs-cTnT ≥16 ng/L had the same percentage of MACE at 5-year follow-up (33%) as patients with presentation MACE. CONCLUSIONS Many patients with ACS ruled out and putatively normal but detectable hs-cTnI concentrations are at similar long-term risk as those with MACE. hs-cTnT concentrations are more strongly associated with 5-year mortality than hs-cTnI.

Association of Baseline HbA1c With Cardiovascular and Renal Outcomes: Analyses From DECLARE-TIMI 58

2022 ◽  
Author(s):  
Avivit Cahn ◽  
Stephen D. Wiviott ◽  
Ofri Mosenzon ◽  
Sabina A. Murphy ◽  
Erica L. Goodrich ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Events ◽  
Cox Regression ◽  
Major Adverse Cardiovascular Events ◽  
Atherosclerotic Cardiovascular Disease ◽  
Baseline Hba1c ◽  
Renal Outcomes ◽  
Increased Risk ◽  
Hba1c Levels

<b>Objective:</b> Current guidelines recommend prescribing SGLT-2 inhibitors to patients with type 2 diabetes and established or at high risk for atherosclerotic cardiovascular disease (ASCVD), irrespective of HbA1c levels. We studied the association of HbA1c with cardiovascular and renal outcomes and whether the benefit of dapagliflozin varies by baseline HbA1c. <p><b>Methods:</b> In the Dapagliflozin Effect on Cardiovascular Events (DECLARE)-TIMI 58 trial 17,160 patients with type 2 diabetes were randomized to dapagliflozin or placebo for a median follow up of 4.2 years. Cardiovascular and renal outcomes by baseline HbA1c in the overall population, and with dapagliflozin vs. placebo in HbA1c subgroups were studied by Cox regression models.</p> <p><b>Results:</b> In the overall population, increasing HbA1c was associated with higher risk of cardiovascular death or hospitalization for heart failure (CVD/HHF), major adverse cardiovascular events (MACE; CVD, myocardial infarction, ischemic stroke) and of the cardiorenal outcome (adjusted HR [95% CI] 1.12 [1.06-1.19], 1.08 [1.04-1.13] and 1.17 [1.11-1.24] per 1% increase respectively). Elevated HbA1c was associated with an increased risk for MACE and for the cardiorenal outcome significantly more in patients with multiple risk factors (MRF), vs. patients with established ASCVD (P-interaction 0.0064 and 0.0093 respectively). Dapagliflozin led to a decrease in the risk of CVD/HHF, HHF and the cardiorenal outcome vs. placebo with no heterogeneity by baseline HbA1c (P-interaction >0.05).</p> <p><b>Conclusions</b>: High HbA1c levels were associated with greater cardiovascular and renal risk, particularly in the MRF population, yet the benefits of dapagliflozin were observed in all subgroups irrespective of baseline HbA1c, including patients with HbA1c<7%.</p>

scholarly journals Atrial high‑rate episodes and risk of major adverse cardiovascular events in patients with dual chamber permanent pacemakers: a retrospective study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei-Da Lu ◽  
Ju-Yi Chen
Keyword(s):  
Cardiovascular Events ◽  
Cox Regression ◽  
Pacemaker Implantation ◽  
Composite Endpoint ◽  
High Rate ◽  
Major Adverse Cardiovascular Events ◽  
Dual Chamber ◽  
Cox Regression Analysis ◽  
Increased Risk ◽  
History Of

AbstractPatients with atrial high-rate episodes (AHRE) are at higher risk of major adverse cardiovascular events (MACE). The cutoff threshold for AHRE duration for MACE, with/without history of atrial fibrillation (AF) or myocardial infarction (MI), is unknown. A total of 481 consecutive patients with/without history of AF or MI receiving dual-chamber pacemaker implantation were included. The primary outcome was a composite endpoint of MACE after AHRE ≥ 5 min, ≥ 6 h, and ≥ 24 h. AHRE was defined as > 175 bpm (MEDTRONIC) or > 200 bpm (BIOTRONIK) lasting ≥ 5 min. Cox regression analysis with time-dependent covariates was conducted. Patients’ mean age was 75.3 ± 10.7 years and 188 (39.1%) developed AHRE ≥ 5 min, 115 (23.9%) ≥ 6 h, and 83 (17.3%) ≥ 24 h. During follow-up (median 39.9 ± 29.8 months), 92 MACE occurred (IR 5.749%/year, 95% CI 3.88–5.85). AHRE ≥ 5 min (HR 5.252, 95% CI 2.575–10.715, P < 0.001) and ≥ 6 h (HR 2.548, 95% CI 1.284–5.058, P = 0.007) was independently associated with MACE, but not AHRE ≥ 24 h. Patients with history of MI (IR 17.80%/year) had higher MACE incidence than those without (IR 3.77%/year, p = 0.001). Significant differences were found between MACE patients with/without history of AF in AHRE ≥ 5 min but not AHRE ≥ 6 h or ≥ 24 h. Patients with dual-chamber pacemakers who develop AHRE have increased risk of MACE, particularly after history of AF or MI.

scholarly journals Novel risk stratification algorithm for estimating the risk of death in patients with relapsed multiple myeloma: external validation in a retrospective chart review

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e034209 ◽  
Author(s):  
Roman Hájek ◽  
Sebastian Gonzalez-McQuire ◽  
Zsolt Szabo ◽  
Michel Delforge ◽  
Lucy DeCosta ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Risk Stratification ◽  
Real World ◽  
Goodness Of Fit ◽  
Cox Regression ◽  
External Validation ◽  
Risk Groups ◽  
Real World Data ◽  
Risk Of Death ◽  
Increased Risk

Objectives and designA novel risk stratification algorithm estimating risk of death in patients with relapsed multiple myeloma starting second-line treatment was recently developed using multivariable Cox regression of data from a Czech registry. It uses 16 parameters routinely collected in medical practice to stratify patients into four distinct risk groups in terms of survival expectation. To provide insight into generalisability of the risk stratification algorithm, the study aimed to validate the risk stratification algorithm using real-world data from specifically designed retrospective chart audits from three European countries.Participants and settingPhysicians collected data from 998 patients (France, 386; Germany, 344; UK, 268) and applied the risk stratification algorithm.MethodsThe performance of the Cox regression model for predicting risk of death was assessed by Nagelkerke’s R2, goodness of fit and the C-index. The risk stratification algorithm’s ability to discriminate overall survival across four risk groups was evaluated using Kaplan-Meier curves and HRs.ResultsConsistent with the Czech registry, the stratification performance of the risk stratification algorithm demonstrated clear differentiation in risk of death between the four groups. As risk groups increased, risk of death doubled. The C-index was 0.715 (95% CI 0.690 to 0.734).ConclusionsValidation of the novel risk stratification algorithm in an independent ‘real-world’ dataset demonstrated that it stratifies patients in four subgroups according to survival expectation.

scholarly journals Hyperuricemia as a prognostic marker for long-term outcomes in patients with myocardial infarction with nonobstructive coronary arteries

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Wenjian Ma ◽  
Side Gao ◽  
Sizhuang Huang ◽  
Jiansong Yuan ◽  
Mengyue Yu
Keyword(s):  
Myocardial Infarction ◽  
Coronary Arteries ◽  
Cox Regression ◽  
Serum Uric Acid Level ◽  
Area Under The Curve ◽  
Study Endpoint ◽  
Major Adverse Cardiovascular Events ◽  
Primary Study ◽  
Propensity Score Matching Analysis ◽  
Increased Risk

Abstract Background Hyperuricemia (HUA) has been proved as a predictor of worse outcomes in patients with coronary artery disease. Here, we investigated the prognostic value of HUA in a distinct population with myocardial infarction with nonobstructive coronary arteries (MINOCA). Methods A total of 1179 MINOCA patients were enrolled and divided into HUA and non-HUA groups. HUA was defined as a serum uric acid level ≥ 420 μmol/L in men or ≥ 357 μmol/L in women. The primary study endpoint was a composite of major adverse cardiovascular events (MACE), including all-cause death, nonfatal MI, nonfatal stroke, revascularization, and hospitalization for unstable angina or heart failure. Kaplan–Meier, Cox regression, and receiver-operating characteristic analyses were performed. Results Patients with HUA (prevalence of 23.5%) had a significantly higher incidence of MACE (18.7% vs. 12.8%; p = 0.015) than patients without during the median follow-up of 41.7 months. HUA was closely associated with an increased risk of MACE even after multivariable adjustment (hazard ratio 1.498, 95% confidence interval: 1.080 to 2.077; p = 0.016). HUA remained a robust risk factor of MACE after propensity score matching analysis. Moreover, HUA showed an area under the curve (AUC) of 0.59 for predicting MACE. Incorporation of HUA to the thrombolysis in myocardial infarction (TIMI) score yielded a significant improvement in discrimination for MACE. Conclusions HUA was independently associated with poor prognosis after MINOCA. Routine assessment of HUA may facilitate risk stratification in this specific population.

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