The Epidemiology of Smoking in Older Adults: A National Cohort Study.

BACKGROUND: Older smokers account for the greatest tobacco-related morbidity and mortality in the USA, while quitting smoking remains the single most effective preventive health intervention for reducing the risk of smoking-related illness. Yet, knowledge about patterns of smoking and smoking cessation in older adults is lacking. OBJECTIVE: Assess trends in prevalence of cigarette smoking between 1998 and 2018 and identify patterns and predictors of smoking cessation in US older adults. DESIGN: Retrospective cohort study PARTICIPANTS: Individuals aged 55+ enrolled in the nationally representative Health and Retirement Study, 1998-2018 MAIN MEASURES: Current smoking was assessed with the question: "Do you smoke cigarettes now?" Quitting smoking was defined as having at least two consecutive waves (between 2 and 4 years) in which participants who were current smokers in 1998 reported they were not currently smoking in subsequent waves. KEY RESULTS: Age-adjusted smoking prevalence decreased from 15.9% in 1998 (95% confidence interval (CI) 15.2, 16.7) to 11.2% in 2018 (95% CI 10.4, 12.1). Among 2187 current smokers in 1998 (mean age 64, 56% female), 56% of those living to age 90 had a sustained period of smoking cessation. Smoking less than 10 cigarettes/day was strongly associated with an increased likelihood of quitting smoking (subdistribution hazard ratio 2.3; 95% CI 1.9, 2.8), compared to those who smoked more than 20 cigarettes/day. CONCLUSIONS: Smoking prevalence among older persons has declined and substantial numbers of older smokers succeed in quitting smoking for a sustained period. These findings highlight the need for continued aggressive efforts at tobacco cessation among older persons.

Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data.

BACKGROUND: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods. OBJECTIVE: The objective of this study was to compare machine learning and traditional regression models for 10-year mortality prediction using EHR data. DESIGN: This was a cohort study. SETTING: Veterans Affairs (VA) EHR data. PARTICIPANTS: Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each). MEASUREMENTS AND ANALYTIC METHODS: The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination (c-statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models. RESULTS: Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c-statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c-statistic [0.837, 95% confidence interval (CI): 0.835-0.839]. The full (unselected) logistic regression model had the highest c-statistic of regression models (0.833, 95% CI: 0.830-0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830-0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics. LIMITATION: Our results should be confirmed in non-VA EHRs. CONCLUSION: The differences in c-statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.

Predicting Life Expectancy to Target Cancer Screening Using Electronic Health Record Clinical Data.

BACKGROUND: Guidelines recommend breast and colorectal cancer screening for older adults with a life expectancy >10 years. Most mortality indexes require clinician data entry, presenting a barrier for routine use in care. Electronic health records (EHR) are a rich clinical data source that could be used to create individualized life expectancy predictions to identify patients for cancer screening without data entry. OBJECTIVE: To develop and internally validate a life expectancy calculator from structured EHR data. DESIGN: Retrospective cohort study using national Veteran's Affairs (VA) EHR databases. PATIENTS: Veterans aged 50+ with a primary care visit during 2005. MAIN MEASURES: We assessed demographics, diseases, medications, laboratory results, healthcare utilization, and vital signs 1 year prior to the index visit. Mortality follow-up was complete through 2017. Using the development cohort (80% sample), we used LASSO Cox regression to select ~100 predictors from 913 EHR data elements. In the validation cohort (remaining 20% sample), we calculated the integrated area under the curve (iAUC) and evaluated calibration. KEY RESULTS: In 3,705,122 patients, the mean age was 68 years and the majority were male (97%) and white (85%); nearly half (49%) died. The life expectancy calculator included 93 predictors; age and gender most strongly contributed to discrimination; diseases also contributed significantly while vital signs were negligible. The iAUC was 0.816 (95% confidence interval, 0.815, 0.817) with good calibration. CONCLUSIONS: We developed a life expectancy calculator using VA EHR data with excellent discrimination and calibration. Automated life expectancy prediction using EHR data may improve guideline-concordant breast and colorectal cancer screening by identifying patients with a life expectancy >10 years.

Do functional status and Medicare claims data improve the predictive accuracy of an electronic health record mortality index? Findings from a national Veterans Affairs cohort.

BACKGROUND: Electronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index. METHODS: This was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots. RESULTS: In the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration. CONCLUSIONS: Incorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.

Biomarkers of Kidney Tubule Health, CKD Progression, and Acute Kidney Injury in SPRINT (Systolic Blood Pressure Intervention Trial) Participants.

RATIONALE & OBJECTIVE: The Systolic Blood Pressure Intervention Trial (SPRINT) compared the effect of intensive versus standard systolic blood pressure targets on cardiovascular morbidity and mortality. In this ancillary study, we evaluated the use of exploratory factor analysis (EFA) to combine biomarkers of kidney tubule health in urine and plasma and then study their role in longitudinal estimated glomerular filtration rate (eGFR) change and risk of acute kidney injury (AKI). STUDY DESIGN: Observational cohort nested in a clinical trial. SETTING & PARTICIPANTS: 2,351 SPRINT participants with eGFR < 60 mL/min/1.73 m2 at baseline. EXPOSURE: Levels of neutrophil gelatinase-associated lipocalin (NGAL), interleukin 18 (IL-18), chitinase-3-like protein (YKL-40), kidney injury molecule 1 (KIM-1), monocyte chemoattractant protein 1 (MCP-1), α1-microglobulin (A1M) and ß2-microglobulin (B2M), uromodulin (UMOD), fibroblast growth factor 23 (FGF-23), and intact parathyroid hormone (PTH). OUTCOME: Longitudinal changes in eGFR and risk of AKI. ANALYTICAL APPROACH: We performed EFA to capture different tubule pathophysiologic processes. We used linear mixed effects models to evaluate the association of each factor with longitudinal changes in eGFR. We evaluated the association of the tubular factors scores with AKI using Cox proportional hazards regression. RESULTS: From 10 biomarkers, EFA generated 4 factors reflecting tubule injury/repair (NGAL, IL-18, and YKL-40), tubule injury/fibrosis (KIM-1 and MCP-1), tubule reabsorption (A1M and B2M), and tubule reserve/mineral metabolism (UMOD, FGF-23, and PTH). Each 1-SD higher tubule reserve/mineral metabolism factor score was associated with a 0.58% (95% CI, 0.39%-0.67%) faster eGFR decline independent of baseline eGFR and albuminuria. Both the tubule injury/repair and tubule injury/fibrosis factors were independently associated with future risk of AKI (per 1 SD higher, HRs of 1.18 [95% CI, 1.10-1.37] and 1.23 [95% CI, 1.02-1.48], respectively). LIMITATIONS: The factors require validation in other settings. CONCLUSIONS: EFA allows parsimonious subgrouping of biomarkers into factors that are differentially associated with progressive eGFR decline and AKI. These subgroups may provide insights into the pathological processes driving adverse kidney outcomes.

Association of Urine Biomarkers of Kidney Tubule Injury and Dysfunction With Frailty Index and Cognitive Function in Persons With CKD in SPRINT.

RATIONALE & OBJECTIVE: The associations of the glomerular markers of kidney disease, estimated glomerular filtration rate (eGFR) and albuminuria, with frailty and cognition are well established. However, the relationship of kidney tubule injury and dysfunction with frailty and cognition is unknown. STUDY DESIGN: Observational cross-sectional study. SETTING & PARTICIPANTS: 2,253 participants with eGFR<60mL/min/1.73m2 in the Systolic Blood Pressure Intervention Trial (SPRINT). EXPOSURE: Eight urine biomarkers: interleukin 18 (IL-18), kidney injury molecule 1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), chitinase-3-like protein 1 (YKL-40), monocyte chemoattractant protein 1 (MCP-1), α1-microglobulin (A1M), ß2-microglobulin (B2M), and uromodulin (Umod). OUTCOME: Frailty was measured using a previously validated frailty index (FI), categorized as fit (FI≤0.10), less fit (0.100.21). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). ANALYTICAL APPROACH: Associations between kidney tubule biomarkers with categorical FI were evaluated using multinomial logistic regression with the fit group as the reference. Cognitive function was evaluated using linear regression. Models were adjusted for demographic, behavioral, and clinical variables including eGFR and urine albumin. RESULTS: Three of the 8 urine biomarkers of tubule injury and dysfunction were independently associated with FI. Each 2-fold higher level of urine KIM-1, a marker of tubule injury, was associated with a 1.22 (95% CI, 1.01-1.49) greater odds of being in the frail group. MCP-1, a marker of tubulointerstitial fibrosis, was associated with a 1.30 (95% CI, 1.04-1.64) greater odds of being in the frail group, and A1M, a marker of tubule reabsorptive capacity, was associated with a 1.48 (95% CI, 1.11-1.96) greater odds of being in the frail group. These associations were independent of confounders including eGFR and urine albumin, and were stronger than those of urine albumin with FI (1.15 [95% CI, 0.99-1.34]). Higher urine B2M, another marker of tubule reabsorptive capacity, was associated with worse cognitive scores at baseline (ß: -0.09 [95% CI, -0.17 to-0.01]). Urine albumin was not associated with cognitive function. LIMITATIONS: Cross-sectional design, and FI may not be generalizable in other populations. CONCLUSIONS: Urine biomarkers of tubule injury, fibrosis, and proximal tubule reabsorptive capacity are variably associated with FI and worse cognition, independent of glomerular markers of kidney health. Future studies are needed to validate these results among other patient populations.

Markers of kidney tubule function and risk of cardiovascular disease events and mortality in the SPRINT trial.

AIMS: Biomarkers of kidney tubule injury, inflammation and fibrosis have been studied extensively and established as risk markers of adverse kidney and cardiovascular disease (CVD) outcomes. However, associations of markers of kidney tubular function with adverse clinical events have not been well studied, especially in persons with chronic kidney disease (CKD). METHODS AND RESULTS: Using a sample of 2377 persons with CKD at the baseline Systolic Blood Pressure Intervention Trial (SPRINT) visit, we evaluated the association of three urine tubular function markers, alpha-1 microglobulin (α1m), beta-2 microglobulin (ß2m), and uromodulin, with a composite CVD endpoint (myocardial infarction, acute coronary syndrome, stroke, acute decompensated heart failure, or death from cardiovascular causes) and mortality using Cox proportional hazards regression, adjusted for baseline estimated glomerular filtration rate (eGFR), albuminuria, and CVD risk factors. In unadjusted analysis, over a median follow-up of 3.8 years, α1m and ß2m had positive associations with composite CVD events and mortality, whereas uromodulin had an inverse association with risk for both outcomes. In multivariable analysis including eGFR and albuminuria, a two-fold higher baseline concentration of α1m was associated with higher risk of CVD [hazard ratio (HR) 1.25; 95% confidence interval (CI): 1.10-1.45] and mortality (HR 1.25; 95% CI: 1.10-1.46), whereas ß2m had no association with either outcome. A two-fold higher uromodulin concentration was associated with lower CVD risk (HR 0.79; 95% CI: 0.68-0.90) but not mortality (HR 0.86; 95% CI: 0.73-1.01) after adjusting for similar confounders. CONCLUSION: Among non-diabetic persons with CKD, biomarkers of tubular function are associated with CVD events and mortality independent of glomerular function and albuminuria.

Cancer Survivorship and Subclinical Myocardial Damage.

Cancer survivors might have an excess risk of cardiovascular disease (CVD) resulting from toxicities of cancer therapies and a high burden of CVD risk factors. We sought to evaluate the association of cancer survivorship with subclinical myocardial damage, as assessed by elevated high-sensitivity cardiac troponin T (hs-cTnT) test results. We included 3,512 participants of the Atherosclerosis Risk in Communities Study who attended visit 5 (2011-2013) and were free of CVD (coronary heart disease, heart failure, or stroke). We used multivariate logistic regression to evaluate the cross-sectional associations of survivorship from any, non-sex-related, and sex-related cancers (e.g., breast, prostate) with elevated hs-cTnT (≥14 ng/L). Of 3,512 participants (mean age, 76 years; 62% women; 21% black), 19% were cancer survivors. Cancer survivors had significantly higher odds of elevated hs-cTnT (OR = 1.26, 95% CI: 1.03, 1.53). Results were similar for survivors of non-sex-related and colorectal cancers, but there was no association between survivorship from breast and prostate cancers and elevated hs-cTnT. Results were similar after additional adjustments for CVD risk factors. Survivors of some cancers might be more likely to have elevated hs-cTnT than persons without prior cancer. The excess burden of subclinical myocardial damage in this population might not be fully explained by traditional CVD risk factors.

The SPRINT trial suggests that markers of tubule cell function in the urine associate with risk of subsequent acute kidney injury while injury markers elevate after the injury.

Urine markers can quantify tubular function including reabsorption (α-1 microglobulin [α1m]) and ß-2-microglobulin [ß2m]) and protein synthesis (uromodulin). Individuals with tubular dysfunction may be less able to compensate to insults than those without, despite similar estimated glomerular filtration rate (eGFR) and albuminuria. Among Systolic Blood Pressure Intervention Trial (SPRINT) participants with an eGFR under 60 ml/min/1.73m2, we measured urine markers of tubular function and injury (neutrophil gelatinase-associated lipocalin [NGAL], kidney injury molecule-1 [KIM-1], interleukin-18 [IL-18], monocyte chemoattractant protein-1, and chitinase-3-like protein [YKL-40]) at baseline. Cox models evaluated associations with subsequent acute kidney injury (AKI) risk, adjusting for clinical risk factors, baseline eGFR and albuminuria, and the tubular function and injury markers. In a random subset, we remeasured biomarkers after four years, and compared changes in biomarkers in those with and without intervening AKI. Among 2351 participants, 184 experienced AKI during 3.8 years mean follow-up. Lower uromodulin (hazard ratio per two-fold higher (0.68, 95% confidence interval [0.56, 0.83]) and higher α1m (1.20; [1.01, 1.44]) were associated with subsequent AKI, independent of eGFR and albuminuria. None of the five injury markers were associated with eventual AKI. In the random subset of 947 patients with repeated measurements, the 59 patients with intervening AKI versus without had longitudinal increases in urine NGAL, IL-19, and YKL-40 and only 1 marker of tubule function (α1m). Thus, joint evaluation of tubule function and injury provided novel insights to factors predisposing to AKI, and responses to kidney injury.

Urinary Biomarkers of Tubular Damage Are Associated with Mortality but Not Cardiovascular Risk among Systolic Blood Pressure Intervention Trial Participants with Chronic Kidney Disease.

BACKGROUND: Kidney tubulointerstitial fibrosis on biopsy is a strong predictor of chronic kidney disease (CKD) progression, and CKD is associated with elevated risk of cardiovascular disease (CVD). Tubular health is poorly quantified by traditional kidney function measures, including estimated glomerular filtration rate (eGFR) and albuminuria. We hypothesized that urinary biomarkers of tubular injury, inflammation, and repair would be associated with higher risk of CVD and mortality in persons with CKD. METHODS: We measured urinary concentrations of interleukin-18 (IL-18), kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, monocyte chemoattractant protein-1, and chitinase-3-like protein-1 (YKL-40) at baseline among 2,377 participants of the Systolic Blood Pressure Intervention Trial who had an eGFR < 60 mL/min/1.73 m2. We used Cox proportional hazards models to evaluate biomarker associations with CVD events and all-cause mortality. RESULTS: At baseline, the mean age of participants was 72 ± 9 years, and eGFR was 48 ± 11 mL/min/1.73 m2. Over a median follow-up of 3.8 years, 305 CVD events (3.6% per year) and 233 all-cause deaths (2.6% per year) occurred. After multivariable adjustment including eGFR, albuminuria, and urinary creatinine, none of the biomarkers showed statistically significant associations with CVD risk. Urinary IL-18 (hazard ratio [HR] per 2-fold higher value, 1.14; 95% CI 1.01-1.29) and YKL-40 (HR per 2-fold higher value, 1.08; 95% CI 1.02-1.14) concentrations were each incrementally associated with higher mortality risk. Associations were similar when stratified by randomized blood pressure arm. CONCLUSIONS: Among hypertensive trial participants with CKD, higher urinary IL-18 and YKL-40 were associated with higher risk of mortality, but not CVD.