The clinical burden of newly diagnosed Heart failure among patients with Reduced, mildly Reduced, and preserved ejection fraction.

Background: Contemporary analyses of the distribution of heart failure (HF) patients by groups of ejection fraction are not available or are limited to hospitalized patients. Our objective was to quantify the per-person and system level clinical burden of a broad population of HF patients. Methods: We studied 16,516 patients with a new HF diagnosis recorded in the electronic medical record of a U.S. integrated delivery system between 2005 and 2017. We used the diagnosis date as the index date and the nearest echocardiogram result to classify patients as HFrEF (n = 2,430), HFmrEF (n = 1,646), HFpEF (n = 12,440) and followed them through 2019 for major clinical outcomes (all-cause mortality, HF hospitalizations [HHF], all-cause hospitalizations, incident chronic kidney disease [CKD], progression of eGFR category, progression of CKD, incident type 2 diabetes [T2D], and progression to insulin use). We compared age and sex adjusted incidence rates and rate ratios of the outcomes between the HF types. Results: Incidence rates for most outcomes were significantly higher among patients with HFrEF compared with HFpEF. HHF was 59 % greater, mortality 31 % greater, and CKD incidence 55 % greater, (p < 0.001 for all comparisons). However, the larger size of the HFpEF group generated 4.7-6.7 times as many total outcomes. Conclusions: Regardless of subtype, the presence of HF was associated with poor clinical outcomes. Incidence rates were higher for HFrEF than HFpEF, but as the latter represented 75% of the study population, HFpEF caused a greater overall burden on the health care system, reflecting the high unmet need of target therapies for HFpEF.

Medical Costs of Chronic Kidney Disease and Type 2 Diabetes Among Newly Diagnosed Heart Failure Patients With Reduced, Mildly Reduced, and Preserved Ejection Fraction.

The economic burden of heart failure (HF) is enormous, but studies of HF costs typically consider the disease to be a single entity. We sought to distinguish the medical costs for patients with HF with reduced ejection fraction (HFrEF), mildly reduced ejection fraction (HFmrEF), and HF with preserved ejection fraction (HFpEF). We identified 16,516 adult patients with an incident HF diagnosis and an echocardiogram from 2005 to 2017 in the electronic medical record of Kaiser Permanente Northwest. Using the echocardiogram nearest to the first diagnosis date, we classified patients with HFrEF (ejection fraction [EF] ≤40%), HFmrEF (EF 41% to 49%), or HFpEF (EF ≥50%). We calculated annualized inpatient, outpatient, emergency, pharmaceutical medical utilization and costs and total costs in $2,020, adjusted for age and gender using generalized linear models, with further analysis of the effects of co-morbid chronic kidney disease (CKD) and type 2 diabetes (T2D). For all HF types, 1 in 5 patients were affected by both CKD and T2D, and costs were significantly higher when both co-morbidities were present. Total per-person costs were significantly higher for HFpEF ($33,740, 95% confidence interval $32,944 to $34,536) than HFrEF ($27,669, $25,649 to $29,689) or HFmrEF ($29,484, $27,166 to $31,800), driven by in- and outpatient visits. Across HF types, visits approximately doubled with the presence of both co-morbidities. Due to greater prevalence, HFpEF accounted for the majority of total and resource-specific treatment costs of HF, regardless of the presence of CKD and/or T2D. In summary, the economic burden was greater per HFpEF patient and was further amplified by co-morbid CKD and T2D. HFpEF accounted for the large majority of total HF costs, underscoring the need to implement effective treatments.

The Bidirectional Association of Chronic Kidney Disease, Type 2 Diabetes, Atherosclerotic Cardiovascular Disease, and Heart Failure: The Cardio-Renal-Metabolic Syndrome.

Background: The cardiometabolic syndrome focuses on the association between type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD), whereas the cardiorenal syndrome focuses on the association between chronic kidney disease (CKD) and heart failure (HF). Consideration of these two syndromes as a single entity has not been well described. Methods: We used the electronic medical records of Kaiser Permanente Northwest to identify 387,985 members aged 18+ years with a serum creatinine measured from 2005 to 2017. If the estimated glomerular filtration rate was <60 mL/min per 1.73 m2, we required a second confirmatory measurement 3-12 months later. Patients were followed through 2019. We calculated the age- and gender-adjusted incidence and progression of CKD per 1000 person-years using generalized estimating equations. We used Cox proportional hazard models to assess the time-dependent effect of each condition on incidence of the other conditions. Results: CKD incidence rates were highest in patients with T2DM, ASCVD, and HF (27.0 per 1000 person-years [95% confidence interval (CI) 24.8-29.4] vs. 5.9 [5.8-6.0] in patients with none of these conditions). Similar results were obtained for CKD progression (309.0, 283.9-336.4 for all three conditions vs. 147.9, 143.3-152.4 for no condition). In time-dependent models, all three conditions were independently associated with CKD incidence, being highest for HF (hazard ratio 2.14, 95% CI 2.07-2.21). All relationships between CKD, T2DM, ASCVD, and HF were significant and bidirectional. Conclusions: The presence of CKD, T2DM, HF, and ASCVD each conveys risk on the others. A cardiometabolic renal syndrome comprising these conditions may be an important disease entity that requires a comprehensive treatment approach.

Sudden Cardiac Death in Patients With Type 1 Versus Type 2 Diabetes.

OBJECTIVE: To investigate the association between type 1 diabetes mellitus (T1D) and type 2 diabetes mellitus (T2D) with risk of sudden cardiac arrest (SCA). METHODS: In a prospective community-based study of SCA from February 1, 2002, through November 30, 2019, we ascertained 2771 cases age 18 years of age or older and matched them to 8313 controls based on geography, age, sex, and race/ethnicity. We used logistic regression to evaluate the independent association between diabetes, T1D, T2D, and SCA. RESULTS: Patients had a mean age of 64.5±15.9 years, were 33.3% female and 23.9% non-White race. Overall, 36.7% (n=1016) of cases and 23.8% (n=1981) of controls had diabetes. Among individuals with diabetes, the proportion of T1D was 6.5% (n=66) among cases and 2.0% among controls (n=40). Diabetes was associated with 1.5-times higher odds of SCA. Compared with those without diabetes, the odds ratio and 95% CI for SCA was 4.36 (95% CI, 2.81 to 6.75; P<.001) in T1D and 1.45 (95% CI, 1.30 to 1.63; P<.001) in T2D after multivariable adjustment. Among those with diabetes, the odds of having SCA were 2.41 times higher in T1D than in T2D (95% CI, 1.53 to 3.80; P<.001). Cases of SCA with T1D were more likely to have an unwitnessed arrest, less likely to receive resuscitation, and less likely to survive compared with those with T2D. CONCLUSION: Type 1 diabetes was more strongly associated with SCA compared with T2D and had less favorable outcomes following resuscitation. Diabetes type could influence the approach to risk stratification and prevention of SCA.

Mortality trends in type 1 diabetes: a multicountry analysis of six population-based cohorts.

AIMS/HYPOTHESIS: Mortality has declined in people with type 1 diabetes in recent decades. We examined how the pattern of decline differs by country, age and sex, and how mortality trends in type 1 diabetes relate to trends in general population mortality. METHODS: We assembled aggregate data on all-cause mortality during the period 2000-2016 in people with type 1 diabetes aged 0-79 years from Australia, Denmark, Latvia, Scotland, Spain (Catalonia) and the USA (Kaiser Permanente Northwest). Data were obtained from administrative sources, health insurance records and registries. All-cause mortality rates in people with type 1 diabetes, and standardised mortality ratios (SMRs) comparing type 1 diabetes with the non-diabetic population, were modelled using Poisson regression, with age and calendar time as quantitative variables, describing the effects using restricted cubic splines with six knots for age and calendar time. Mortality rates were standardised to the age distribution of the aggregate population with type 1 diabetes. RESULTS: All six data sources showed a decline in age- and sex-standardised all-cause mortality rates in people with type 1 diabetes from 2000 to 2016 (or a subset thereof), with annual changes in mortality rates ranging from -2.1% (95% CI -2.8%, -1.3%) to -5.8% (95% CI -6.5%, -5.1%). All-cause mortality was higher for male individuals and for older individuals, but the rate of decline in mortality was generally unaffected by sex or age. SMR was higher in female individuals than male individuals, and appeared to peak at ages 40-70 years. SMR declined over time in Denmark, Scotland and Spain, while remaining stable in the other three data sources. CONCLUSIONS/INTERPRETATION: All-cause mortality in people with type 1 diabetes has declined in recent years in most included populations, but improvements in mortality relative to the non-diabetic population are less consistent.

Trends in all-cause mortality among people with diagnosed diabetes in high-income settings: a multicountry analysis of aggregate data.

BACKGROUND: Population-level trends in mortality among people with diabetes are inadequately described. We aimed to examine the magnitude and trends in excess all-cause mortality in people with diabetes. METHODS: In this retrospective, multicountry analysis, we collected aggregate data from 19 data sources in 16 high-income countries or jurisdictions (in six data sources in Asia, eight in Europe, one from Australia, and four from North America) for the period from Jan 1, 1995, to Dec 31, 2016, (or a subset of this period) on all-cause mortality in people with diagnosed total or type 2 diabetes. We collected data from administrative sources, health insurance records, registries, and a health survey. We estimated excess mortality using the standardised mortality ratio (SMR). FINDINGS: In our dataset, there were approximately 21 million deaths during 0·5 billion person-years of follow-up among people with diagnosed diabetes. 17 of 19 data sources showed decreases in the age-standardised and sex-standardised mortality in people with diabetes, among which the annual percentage change in mortality ranged from -0·5% (95% CI -0·7 to -0·3) in Hungary to -4·2% (-4·3 to -4·1) in Hong Kong. The largest decreases in mortality were observed in east and southeast Asia, with a change of -4·2% (95% CI -4·3 to -4·1) in Hong Kong, -4·0% (-4·8 to -3·2) in South Korea, -3·5% (-4·0 to -3·0) in Taiwan, and -3·6% (-4·2 to -2·9) in Singapore. The annual estimated change in SMR between people with and without diabetes ranged from -3·0% (95% CI -3·0 to -2·9; US Medicare) to 1·6% (1·4 to 1·7; Lombardy, Italy). Among the 17 data sources with decreasing mortality among people with diabetes, we found a significant SMR increase in five data sources, no significant SMR change in four data sources, and a significant SMR decrease in eight data sources. INTERPRETATION: All-cause mortality in diabetes has decreased in most of the high-income countries we assessed. In eight of 19 data sources analysed, mortality decreased more rapidly in people with diabetes than in those without diabetes. Further longevity gains will require continued improvement in prevention and management of diabetes. FUNDING: US Centers for Disease Control and Prevention, Diabetes Australia Research Program, and Victoria State Government Operational Infrastructure Support Program.

Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middle-income settings.

BACKGROUND: Diabetes prevalence is increasing in most places in the world, but prevalence is affected by both risk of developing diabetes and survival of those with diabetes. Diabetes incidence is a better metric to understand the trends in population risk of diabetes. Using a multicountry analysis, we aimed to ascertain whether the incidence of clinically diagnosed diabetes has changed over time. METHODS: In this multicountry data analysis, we assembled aggregated data describing trends in diagnosed total or type 2 diabetes incidence from 24 population-based data sources in 21 countries or jurisdictions. Data were from administrative sources, health insurance records, registries, and a health survey. We modelled incidence rates with Poisson regression, using age and calendar time (1995-2018) as variables, describing the effects with restricted cubic splines with six knots for age and calendar time. FINDINGS: Our data included about 22 million diabetes diagnoses from 5 billion person-years of follow-up. Data were from 19 high-income and two middle-income countries or jurisdictions. 23 data sources had data from 2010 onwards, among which 19 had a downward or stable trend, with an annual estimated change in incidence ranging from -1·1% to -10·8%. Among the four data sources with an increasing trend from 2010 onwards, the annual estimated change ranged from 0·9% to 5·6%. The findings were robust to sensitivity analyses excluding data sources in which the data quality was lower and were consistent in analyses stratified by different diabetes definitions. INTERPRETATION: The incidence of diagnosed diabetes is stabilising or declining in many high-income countries. The reasons for the declines in the incidence of diagnosed diabetes warrant further investigation with appropriate data sources. FUNDING: US Centers for Disease Control and Prevention, Diabetes Australia Research Program, and Victoria State Government Operational Infrastructure Support Program.

Prevalence and incidence of microvascular and macrovascular complications over 15 years among patients with incident type 2 diabetes.

INTRODUCTION: Type 2 diabetes (T2D) is a common condition that, if left untreated or poorly managed, can lead to adverse microvascular and macrovascular complications. We estimated the prevalence and incidence of microvascular and macrovascular complications among patients newly diagnosed with T2D within a US integrated healthcare system. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study among patients newly diagnosed with T2D between 2003 and 2014. We evaluated 13 complications, including chronic kidney disease (CKD), cardiovascular disease (CVD), and all-cause mortality through 2018. Multivariable Cox proportional hazards models were used to study factors associated with complications. RESULTS: We identified 135 199 patients with incident T2D. The mean age was 58 years, and 48% were women. The prevalence of CKD was the highest of the complications at the time of T2D diagnosis (prevalence=12.3%, 95% CI 12.2% to 12.5%), while the prevalence of CVD was among the lowest at 3.3% (95% CI 3.2% to 3.3%). The median time to incidence of a T2D complication ranged from 3.0 to 5.2 years. High incidence rates (95% CI) of T2D complications included peripheral neuropathy (26.9, 95% CI 26.5 to 27.3 per 1000 person-years (PY)), CKD (21.2, 95% CI 20.9 to 21.6 per 1000 PY), and CVD (11.9, 95% CI 11.7 to 12.2 per 1000 PY). The trend of 5-year incidence rates of T2D complications by diagnosis year decreased over time (p value<0.001). Older age, non-Hispanic white race/ethnicity, sex, higher A1C, smoking, and hypertension were associated with increased CKD and CVD incidence. CONCLUSION: Though incidence rates of T2D complications were lower in more recent years (2010-2014), a significant proportion of patients had complications at T2D diagnosis. Earlier preventive therapies as well as managing modifiable factors may help delay the development and progression of T2D complications.

Confirming diagnoses of acute pancreatitis with commonly available electronic data.

BACKGROUND: Recorded diagnoses of acute pancreatitis (AP) are often inaccurate resulting in limited utility for case identification in large data sources, especially where electronic medical records (EMR) are not available. Our objectives were to validate diagnoses of AP and to identify an algorithm using additional data to enhance the identification of AP cases in different data sources. METHODS: We randomly sampled 550 persons with an AP diagnosis from inpatient data or outpatient or emergency department diagnoses immediately preceding a hospitalization and 150 negative controls with a differential diagnosis (cholangitis or cholecystitis). We conducted an EMR review to confirm cases of AP and used logistic regression to develop EMR-based and claims-based algorithms to identify confirmed AP cases with variables typically available in electronic data sources. Algorithm performance was assessed using the C statistic, sensitivity, specificity, and positive and negative predictive value. RESULTS: Of the 550 patients with an AP diagnosis, 467 (84.9%) were confirmed cases. An AP diagnosis alone had high sensitivity (98.9%), modest specificity (63.6%), and a C statistic of 0.813. An EMR-based model using an AP diagnosis, body mass index ≥30 kg/m2 , a serum lipase >3 times upper limit of normal and diabetes attained a C-statistic of 0.914. A claims-based model attained a C-statistic of 0.892 using an AP diagnosis and dichotomous variables for whether a serum lipase test and/or an abdominal ultrasound was performed. CONCLUSIONS: Our simple algorithms increased the accuracy of identification of AP cases providing widespread applicability to epidemiological and drug safety studies.

Sudden cardiac arrest with shockable rhythm in patients with heart failure.

BACKGROUND: Patients with shockable sudden cardiac arrest (SCA; ventricular fibrillation/tachycardia) have significantly better resuscitation outcomes than do those with nonshockable rhythm (pulseless electrical activity/asystole). Heart failure (HF) increases the risk of SCA, but presenting rhythms have not been previously evaluated. OBJECTIVE: We hypothesized that based on unique characteristics, HFpEF (HF with preserved ejection fraction; left ventricular ejection fraction [LVEF] ≥50%), bHFpEF (HF with borderline preserved ejection fraction; LVEF >40% and <50%), and HFrEF (HF with reduced ejection fraction; LVEF ≤40%) manifest differences in presenting rhythm during SCA. METHODS: Consecutive cases of SCA with HF (age ≥18 years) were ascertained in the Oregon Sudden Unexpected Death Study (2002-2019). LVEF was obtained from echocardiograms performed before and unrelated to the SCA event. Presenting rhythms were identified from first responder reports. Logistic regression was used to evaluate the independent association of presenting rhythm with HF subtype. RESULTS: Of 648 subjects with HF and SCA (median age 72 years; interquartile range 62-81 years), 274 had HFrEF (23.4% female), 92 had bHFpEF (35.9% female), and 282 had HFpEF (42.5% female). The rates of shockable rhythms were 44.5% (n = 122), 48.9% (n = 45), and 27.0% (n = 76) for HFrEF, bHFpEF, and HFpEF, respectively (P < .001). Compared with HFpEF, the adjusted odds ratios for shockable rhythm were 1.86 (95% confidence interval 1.27-2.74; P = .002) in HFrEF and 2.26 (95% CI 1.35-3.77; P = .002) in bHFpEF. The rates of survival to hospital discharge were 10.6% (n = 29) in HFrEF, 22.8% (n = 21) in bHFpEF, and 9.9% (n = 28) in HFpEF (P = .003). CONCLUSION: The rates of shockable rhythm during SCA depend on the HF clinical subtype. Patients with bHFpEF had the highest likelihood of shockable rhythm, correlating with the highest rates of survival.

Health Care Costs by Type of Expenditure across eGFR Stages among Patients with and without Diabetes, Cardiovascular Disease, and Heart Failure.

BACKGROUND: CKD is associated with higher health care costs that increase with disease progression. However, research is lacking on the type of health care costs associated with CKD across all stages in a general population with a substantial comorbidity burden. METHODS: Using electronic medical records of an integrated delivery system, we evaluated health care costs by expenditure type in general and in patients with CKD by eGFR and presence of comorbidities. We categorized 146,132 patients with eGFR data in 2016 or 2017 and examined nonmutually exclusive groups according to presence of diabetes mellitus, cardiovascular disease, or heart failure. We used 1 year of follow-up data to calculate outpatient, inpatient, emergency, pharmaceutical, dialysis, and total health care costs by eGFR (Kidney Disease Improving Global Outcomes-defined eGFR categories), adjusted for age, sex, and nonwhite race. RESULTS: Mean total health care costs among patients with CKD without comorbidities were 31% higher than among patients without CKD ($7374 versus $5631, respectively). Hospitalizations accounted for 35% of total costs among those with CKD and no comorbidities but up to 55% among patients with CKD and heart failure. The proportion of costs attributable to hospitalizations accelerated with declining kidney function, reaching as high as 66%. CONCLUSIONS: Poorer kidney function and the presence of diabetes mellitus, cardiovascular disease, or heart failure drive substantial health care costs and increase the proportion of costs attributable to inpatient care. The large contribution of inpatient costs begins in earlier stages of CKD and escalates as kidney function declines. Additional therapies to reduce CKD incidence, slow CKD progression, and lower hospitalization risk are needed to benefit patients and reduce CKD's economic burden.

Time in suboptimal glycemic control over 10 years for patients newly diagnosed with type 2 diabetes.

OBJECTIVE: To estimate time in suboptimal glycemic control among patients with incident type 2 diabetes (T2D) over 10 years. METHODS: We calculated percent of time in suboptimal glycemic control using three A1C thresholds (8%, 7.5%, 7%) following T2D diagnosis. Stratified analyses were conducted based on age and A1C levels at T2D diagnosis. RESULTS: We identified 28,315 patients with incident T2D. Percent of time in suboptimal glycemic control increased with T2D duration. Mean percent time in suboptimal A1C control in the first 2 years following diagnosis was 30%, 34% and 40% for the 8%, 7.5%, and 7% thresholds, respectively. In the 6-10 years following T2D diagnosis, the percent time in suboptimal A1C control increased to 39%, 48% and 61%, for the 8%, 7.5%, and 7% thresholds, respectively. Time in suboptimal glycemic control was longer among younger patients aged 20-44 versus ≥65 years and those with higher A1C (>8%) versus lower A1C (<7%) at diagnosis. CONCLUSIONS: Over 10 years following diagnosis, T2D patients spent one-third to over one-half of their time in suboptimal glycemic control. Reducing time spent above desired A1C targets could lower risk of microvascular and macrovascular complications.

Kidney disease progression and all-cause mortality across estimated glomerular filtration rate and albuminuria categories among patients with vs. without type 2 diabetes.

BACKGROUND: Studies of progression of kidney dysfunction typically focus on renal replacement therapy or percentage decline in estimated glomerular filtration rate (eGFR) as outcomes. Our aim was to compare real-world patients with and without T2D to estimate progression from and to clinically defined categories of kidney disease and all-cause mortality. METHODS: This was an observational cohort study of 31,931 patients with and 33,201 age/sex matched patients without type 2 diabetes (T2D) who had a serum creatinine and urine albumin-to-creatinine ratio (UACR) or dipstick proteinuria (DP) values. We used the first available serum creatinine value between 2006 and 2012 to calculate baseline eGFR and categorized them and the corresponding UACR/DP values using the Kidney Disease Improving Global Outcomes (KDIGO) categories. To assess our primary outcomes, we extracted probabilities of eGFR progression or mortality from life-table analyses and conducted multivariable Cox regression analyses of relative risk adjusted for age, sex, race/ethnicity, smoking, ischemic heart disease, heart failure, and use of renal-angiotensin-aldosterone system inhibitors. RESULTS: Patterns of eGFR decline were comparable among patients with vs. without T2D with larger percentage declines at higher albuminuria levels across all eGFR categories. eGFR decline was generally larger among T2D patients, particularly in those with severely increased albuminuria. Across all CKD categories, risk of progression to the next higher category of eGFR was substantially increased with increasing albuminuria. For example, the risk was 23.5, 36.2, and 65.1% among T2D patients with eGFR 30-59 ml/min/1.73m2 and UACR < 30, 30-299, and > 300 mg/dL, respectively (p < 0.001). Other comparisons were similarly significant. Among patients with low eGFR and normal to mildly increased albuminuria, the relative risk was up to 8-fold greater for all-cause mortality compared with the non-CKD subgroup (eGFR> 60 ml/min/1.73m2 with normal to mildly increased albuminuria). CONCLUSIONS: Presence of albuminuria was associated with accelerated eGFR decline independent of T2D. Risk for adverse outcomes was remarkably high among patients with CKD and normal to mildly increased albuminuria levels. Independent of T2D or albuminuria, a substantial risk for adverse outcomes exists for CKD patients in a routine care setting.

Correction to: Kidney disease progression and all-cause mortality across estimated glomerular filtration rate and albuminuria categories among patients with vs. without type 2 diabetes.

An amendment to this paper has been published and can be accessed via the original article.

Predictors of Hyperkalemia and Hypokalemia in Individuals with Diabetes: a Classification and Regression Tree Analysis.

BACKGROUND: Both hyperkalemia and hypokalemia can lead to cardiac arrhythmias and are associated with increased mortality. Information on the predictors of potassium in individuals with diabetes in routine clinical practice is lacking. OBJECTIVE: To identify predictors of hyperkalemia and hypokalemia in adults with diabetes. DESIGN: Retrospective cohort study, with classification and regression tree (CART) analysis. PARTICIPANTS: 321,856 individuals with diabetes enrolled in four large integrated health care systems from 2012 to 2013. MAIN MEASURES: We used a single serum potassium result collected in 2012 or 2013. Hyperkalemia was defined as a serum potassium ≥ 5.5 mEq/L and hypokalemia as < 3.5 mEq/L. Predictors included demographic factors, laboratory measurements, comorbidities, medication use, and health care utilization. KEY RESULTS: There were 2556 hypokalemia events (0.8%) and 1517 hyperkalemia events (0.5%). In univariate analyses, we identified concordant predictors (associated with increased probability of both hyperkalemia and hypokalemia), discordant predictors, and predictors of only hyperkalemia or hypokalemia. In CART models, the hyperkalemia "tree" had 5 nodes and a c-statistic of 0.76. The nodes were defined by prior potassium results and eGFRs, and the 5 terminal "leaves" had hyperkalemia probabilities of 0.2 to 7.2%. The hypokalemia tree had 4 nodes and a c-statistic of 0.76. The hypokalemia tree included nodes defined by prior potassium results, and the 4 terminal leaves had hypokalemia probabilities of 0.3 to 17.6%. Individuals with a recent potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no hypokalemia in the previous year had a < 1% rate of either hypokalemia or hyperkalemia. CONCLUSIONS: The yield of routine serum potassium testing may be low in individuals with a recent serum potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no recent history of hypokalemia. We did not examine the effect of recent changes in clinical condition or medications on acute potassium changes.

The association between heart failure hospitalization and self-reported domains of health.

BACKGROUND: We compared self-reported domains of health between patients who with vs. without a recent heart failure (HF) hospitalization. METHODS: We fielded a 59-item questionnaire that included the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12) to age/sex-matched groups of 2000 HF patients who had and had not had a recent HF hospitalization. We entered questionnaire responses and electronic medical record data into multivariable logistic regression models to identify independent associations with a HF hospitalization. RESULTS: After two mailings, we received 468 completed questionnaires for response rate of 23.4%. Patients with a recent HF hospitalization had significantly lower scores on the KCCQ-12 Quality of Life (52.6 vs. 59.6, p = 0.016) and Social Limitations (48.4 vs. 55.5, p = 0.009) scales as well as the Clinical Summary Scale (50.8 vs. 55.3, p = 0.048) and Total KCCQ-12 score (49.6 vs. 56.8, p = 0.003). In sequential logistic regression models designed to achieve parsimony, Total KCCQ was a strong predictor of being in the recent hospitalization group. When using the KCCQ-12 sub-scales, the Social Limitations scale was a strong predictor of being in the recent hospitalization group. CONCLUSIONS: After accounting for comorbidities and other risk factors, a HF hospitalization appears to profoundly limit social activities which can increase the risk of poor outcomes.

Risk of cardiovascular events in patients with hypertriglyceridaemia: A review of real-world evidence.

AIMS: To describe the real-world prevalence and consequences of hypertriglyceridaemia. MATERIALS AND METHODS: We searched two large patient databases, the National Health and Nutrition Examination Survey (NHANES) database (2007-2014) and the Optum Research Database, as well as electronic medical records from two Kaiser Permanente regions. RESULTS: The NHANES data showed that ~26% of US adults, including nearly one-third of statin users, had at least borderline hypertriglyceridaemia (triglycerides [TGs] ≥1.69 mmol/L), and ~40% of adults with diabetes had levels of ≥150 mg/dL despite statin use. The Optum analyses demonstrated that those with TG levels ≥1.69 mmol/L who were on statins had a significantly increased risk of composite initial major cardiovascular (CV) events (hazard ratio [HR] 1.26, 95% confidence interval [CI] 1.19-1.34; P < 0.001 vs. patients with TGs <150 mg/dL). This was accompanied by increased healthcare utilization and direct healthcare costs (HR 1.12, 95% CI 1.08-1.16; P < 0.001). In the analyses of the Kaiser Permanente records, patients with diabetes and TG levels 2.26-5.64 mmol/L had significantly higher adjusted incidence rates of non-fatal myocardial infarction (rate ratio 1.30, 95% CI 1.08-1.58; P = 0.006), non-fatal stroke (rate ratio 1.23; 95% CI 1.01-1.49; P = 0.037) and coronary revascularization (rate ratio 1.21; 95% CI 1.02-1.43; P = 0.027), but not unstable angina (rate ratio 1.33; 95% CI 0.87-2.03; P = 0.185) compared with patients with TG levels <1.69 mmol/L. CONCLUSIONS: Real-world analyses suggest that elevated TGs are prevalent and commonly associated with increased CV risk. CV outcomes trials in patients with established hypertriglyceridaemia will clarify whether strategies to reduce TG levels can ameliorate residual CV risk in patients taking statins.

An association between right ventricular dysfunction and sudden cardiac death.

BACKGROUND: The effectiveness of severely reduced left ventricular ejection fraction (LVEF <35%) as a predictor of sudden cardiac death (SCD) has diminished, and improvements in risk stratification await discovery of novel markers. Right ventricular (RV) abnormalities can be observed in conditions such as chronic obstructive pulmonary disease and sleep apnea, which have been linked to SCD. OBJECTIVE: The purpose of this study was to evaluate whether RV abnormalities were associated with SCD after accounting for LVEF and other patient characteristics. METHODS: In a large, prospective ongoing community-based study of SCD in the Portland, Oregon, metropolitan area, SCD cases (age ≥18 years; 2002-2014) were compared to controls with coronary artery disease but no SCD. Using a novel archive of digital echocardiograms, a standardized approach was used to evaluate RV basal diameter, RV end-diastolic area, and right ventricular fractional area change (RVFAC). RESULTS: A total of 350 subjects were studied, including 81 SCD cases (age 68.7 ± 13.6 years; 73% male) and 269 controls (age 66.5 ± 10.2 years; 69% male). In multivariate analysis, RVFAC was significantly associated with SCD (odds ratio 1.14 for each 5% decrease; 95% confidence interval 1.03-1.25; P = .01). When modeled with LVEF ≤35%, RVFAC ≤35% was significantly associated with increased risk of SCD. Individuals with both left ventricular and RV dysfunction had a 3× higher odds of SCD than those with neither (odds ratio 3.19; 95% confidence interval 1.33-7.68; P = .01). CONCLUSION: RV dysfunction was associated with a significantly increased risk of SCD independent of LVEF and, when combined with LVEF, had additive effects on SCD risk.

Diabetes Care Editors' Expert Forum 2018: Managing Big Data for Diabetes Research and Care.

Technological progress in the past half century has greatly increased our ability to collect, store, and transmit vast quantities of information, giving rise to the term "big data." This term refers to very large data sets that can be analyzed to identify patterns, trends, and associations. In medicine-including diabetes care and research-big data come from three main sources: electronic medical records (EMRs), surveys and registries, and randomized controlled trials (RCTs). These systems have evolved in different ways, each with strengths and limitations. EMRs continuously accumulate information about patients and make it readily accessible but are limited by missing data or data that are not quality assured. Because EMRs vary in structure and management, comparisons of data between health systems may be difficult. Registries and surveys provide data that are consistently collected and representative of broad populations but are limited in scope and may be updated only intermittently. RCT databases excel in the specificity, completeness, and accuracy of their data, but rarely include a fully representative sample of the general population. Also, they are costly to build and seldom maintained after a trial's end. To consider these issues, and the challenges and opportunities they present, the editors of Diabetes Care convened a group of experts in management of diabetes-related data on 21 June 2018, in conjunction with the American Diabetes Association's 78th Scientific Sessions in Orlando, FL. This article summarizes the discussion and conclusions of that forum, offering a vision of benefits that might be realized from prospectively designed and unified data-management systems to support the collective needs of clinical, surveillance, and research activities related to diabetes.