Highlights
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Among 1,881,947 patients with HFmrEF/HFpEF, 19,975 (1%) received hsCRP testing.
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HCRU and costs were greater in SI ( n = 3,299) vs without SI ( n = 3,943) after weighting.
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Outpatient visits and hospitalizations were drivers of increased costs in SI.
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SI was associated with a total medical cost difference of $3,329 vs non-SI.
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Patients with HFmrEF/HFpEF with SI are likely to have higher HCRU.
ABSTRACT
Background
Systemic inflammation (SI), detected via high-sensitivity C-reactive protein (hsCRP) testing, has a recognized role in the pathogenesis and long-term outcomes in cardiovascular disease. However, SI in patients with heart failure (HF) with mildly reduced or preserved ejection fraction (HFmrEF/HFpEF) is less characterized. This study aimed to evaluate the impact of SI on real-world healthcare resource utilization (HCRU) and costs.
Methods
This retrospective cohort study included patients within the Komodo Healthcare Map database between January 2016 and April 2024. HFmrEF/HFpEF was classified using a validated claims-based algorithm. Patients with a valid hsCRP test were classified as with SI (hsCRP 2-10 mg/L) or without SI (hsCRP <2 mg/L). Inverse probability of treatment weighting was used to balance patients based on demographic and clinical covariates. During the follow-up period (minimum 12 months following hsCRP test and HF diagnosis), HCRU was summarized descriptively, and per-patient per-year costs (adjusted to April 2024 USD) were estimated using generalized linear models or two-part models.
Results
Among 7,242 propensity-weighted patients with HFmrEF/HFpEF and an eligible hsCRP test, HCRU and costs were greater among those with SI ( n = 3,299) vs without SI ( n = 3,943). SI was associated with higher mean per-patient per-year costs; the total medical cost difference between groups was $3,329 (95% confidence interval [CI]: 1,802-4,857), including all-cause outpatient visits ($1,175 [95% CI: 302-2,049]) and hospitalizations ($1,666 [95% CI: 845-2,488]).
Conclusions
SI was associated with increased economic and resource burden among patients with HFmrEF/HFpEF. SI testing may have a potential role in identifying those likely to have higher HCRU.
Key Points
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Among 1,881,947 patients with HFmrEF/HFpEF, 19,975 (1%) received hsCRP testing.
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•
HCRU and costs were greater in SI (n=3,299) vs without SI (n=3,943) after weighting.
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•
Outpatient visits and hospitalizations were drivers of increased costs in SI.
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•
SI was associated with a total medical cost difference of $3,329 vs non-SI.
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•
Patients with HFmrEF/HFpEF with SI are likely to have higher HCRU.
Background
Nearly 7 million adults are living with heart failure (HF) in the United States, and this population is projected to increase to more than 8 million by 2030. HF contributes to over 1 million hospitalizations annually and approximately one in every eight deaths in the US. , HF is associated with significant economic burden; two recent studies using data from the Medical Expenditure Panel Survey estimated the incremental national healthcare expenditure associated with HF to be between $17.2 billion and $22.3 billion per year.
Patients with HF are classified based on left ventricular ejection fraction (LVEF) and broadly separated into 3 groups: HF with reduced ejection fraction (HFrEF, LVEF ≤40%), HF with mildly reduced ejection fraction (HFmrEF, LVEF 41%-49%), and HF with preserved ejection fraction (HFpEF, LVEF ≥50%). While treatment guidelines are well-established for those with HFrEF, , treatment options for patients with HFmrEF/HFpEF are limited, with only sodium-glucose cotransporter 2 inhibitors and finerenone, a nonsteroidal mineralocorticoid receptor antagonist, demonstrating efficacy in reducing HF hospitalizations and cardiovascular deaths. ,,
Systemic inflammation (SI) has a recognized role in the pathogenesis and long-term outcomes in cardiovascular disease, and it is typically identified by elevated levels of C-reactive protein (CRP) detected via high-sensitivity CRP (hsCRP) testing. , However, less evidence for the role of SI exists for HF, specifically HFmrEF/HFpEF. Among patients with established cardiovascular disease, higher CRP has been associated with an increased risk of incident HF for both HFrEF and HFpEF. , Elevated levels of other non-hsCRP inflammatory markers, particularly interleukin-6, have been associated with progression and poorer outcomes among those with HFmrEF/HFpEF, ,, but data are scant regarding the contribution of SI to disease burden. Among patients with HFmrEF/HFpEF in 3 large cardiovascular outcomes trials, approximately half had hsCRP ≥2 mg/L, and this was associated with higher resting heart rate and systolic blood pressure, poorer kidney function, higher HbA1c, and more severe lipid abnormalities. With the increasing awareness of SI as a contributor to progression in cardiovascular disease, it is important to examine the real-world economic burden associated with SI in those with HFmrEF/HFpEF. Thus, this study sought to assess the association between SI, as identified by elevated CRP detected via hsCRP testing, and healthcare resource utilization (HCRU) and costs among patients with HFmrEF/HFpEF.
Methods
Study design and data source
This retrospective cohort study examined patients with HFmrEF/HFpEF who had an hsCRP test within the Komodo Healthcare Map database. The Komodo Healthcare Map is a large, nationally representative, longitudinal dataset of deidentified claims-based healthcare encounters, including medical and pharmacy claims data and data from healthcare provider visits (eg, electronic health records, laboratory tests, procedures, and imaging) from insured individuals throughout the United States. The study period spanned from January 1, 2016, through April 30, 2024, with a patient identification period from January 1, 2017, through April 30, 2023. To perform a comparison of HCRU and costs between HFmrEF/HFpEF with SI vs without SI, inverse probability of treatment weighting (IPTW) was applied to address potential confounding and ensure balanced comparison between the two groups.
Study population
The study included patients who were at least 18 years old at diagnosis of HF and with at least 12 months of continuous health care plan enrollment prior to and following HF diagnosis. The HF diagnosis date was the date of first inpatient HF diagnosis or the earliest of two outpatient (OP) HF diagnoses occurring within 12 months, based on International Classification of Diseases, tenth revision, clinical modification (ICD-10-CM) codes (I50%) (Supplementary Table SI). Patients were classified as having HFmrEF/HFpEF (LVEF ≥45%) based on the Desai algorithm. As ejection fraction data is frequently unavailable in medical records, this claims-based method was developed using Medicare data to categorize HF subtypes using a combination of variables, including HF-specific diagnosis codes, age, sex, medication use, and comorbidities, and has been validated using both Medicare and commercial claims data. ,
Among patients that were classified using the Desai algorithm as having HFmrEF/HFpEF (LVEF ≥45%), patients with an eligible hsCRP test within 12 months before or after the HF index date were classified as with SI (any eligible hsCRP score ranging from 2 to 10 mg/L) or without SI (hsCRP <2 mg/L). Among these patients, the index date was defined as the later of either the HF diagnosis date or the eligible hsCRP test date, with a requirement for 12 months of continuous health plan enrollment required both before and after the index date. An hsCRP test was considered eligible if it met all of the following criteria: (1) ≤10 mg/L, (2) no evidence of antibiotic, antiviral, or antimycotic medications within 2 weeks prior to or 1 week following the test date, (3) no evidence of corticosteroid use within 1 month prior to test date, (4) test was not taken during an inpatient or emergency room visit or within 30 days following that visit. The rationale for test exclusion based on these criteria was that the test results with these characteristics likely reflect acute inflammation and would not be indicative of chronic SI. For patients with multiple tests, the test closest to HF index was used, and if 2 eligible tests were equidistant pre- and post-HF index, the test prior to HF index was used. If multiple eligible tests were observed on the same day, the test with the highest value was used.
Exclusion criteria included: missing sex or insurance information; evidence within 12 months preindex of other cardiac conditions (eg, pulmonary hypertension, pulmonary embolism, acute myocarditis, cardiomyopathy, or COPD), or LVEF ≤40% from electronic health records; or severe hepatic disease, other infectious diseases, or any major cancer within 12 months before and after index.
Study outcomes
The primary outcomes of this study were to describe HCRU and costs associated with HF hospitalizations and visits among patients with HFmrEF/HFpEF, and the secondary outcomes were to assess the association between SI and healthcare costs in these patients. HCRU and costs (proportion with visit and per-patient per-year [PPPY] visits/dollars) were described for the follow-up period (≥12 months following index date, until termination of health plan enrollment, death, or the end of study period). HCRU and costs outcomes included all-cause hospitalizations, OP visits, and prescriptions, as well as HF-related outcomes defined using claims with a HF diagnosis code in the primary position (HF hospitalizations, urgent HF visits), and/or secondary position (HF-related OP visits) (Supplementary Table SII), and HF-related prescriptions (Supplementary Table SIII).
Statistical analyses
Demographics and clinical characteristics at baseline were described using descriptive statistics, using means and standard deviations for continuous variables and frequency counts and percentages for categorical variables. IPTW weighting was used to balance patients with and without SI based on demographic (age, sex, race, insurance, and geographic region) and clinical (Quan-Charlson Comorbidity Index) covariates, which were chosen based on both clinical relevance and statistical considerations, including inspection for multicollinearity. This approach assigns weights to the patients according to the inverse of their propensity score (or probability) of having SI. Summary statistics for HCRU and cost outcomes were reported pre- and postweighting.
PPPY cost and utilization outcomes were calculated as the total costs (or events) accumulated during observed follow-up, divided by the length of follow-up in years. The association between SI and costs was estimated using a generalized linear model (GLM) with a log link and gamma distribution for the outcomes with limited zero inflation, including total costs, all-cause OP costs, and all-cause and HF-related prescription costs. For outcomes with excessive (≥35%) zeros (costs of all-cause hospitalizations, HF-related hospitalizations, and urgent HF visits), a two-part model was used. The first part modeled the probability of incurring any cost using a probit regression, and the second part utilized a GLM with a log link and gamma distribution to estimate the average costs conditional on having nonzero costs, holding all other variables constant. A log-transformed time offset was used in the second part of the model to account for varying follow-up time. In the two-part model, for patients with any cost/utilization, the natural log of each patient’s follow-up (in years) was included as an offset in the second part (cost among users), annualizing costs/utilization. Incremental effects from the GLM or two-part model were reported as estimates of the impact of SI on costs. Because claims-based costs are highly right-skewed, and a small number of extreme values can disproportionately influence mean estimates and model fit, cost outcomes were winsorized at the 99th percentile. Costs were adjusted for inflation to April 2024 dollars. To estimate variance and construct 95% confidence intervals (CIs) under IPTW weighting and for two-part models, a patient-level nonparametric bootstrap procedure was used. Specifically, 1,000 bootstrap replicates were performed, in each of which patients were resampled with replacement. In each replicate, weighted PPPY estimates were recalculated for with SI and without SI simultaneously, then the difference and ratio within each replicate were computed. The 95% CIs were constructed using the 2.5th and 97.5th percentiles of the estimates from the empirical bootstrap distribution. No further corrections were performed to address multiplicity, as this study was descriptive in nature and did not involve prespecified hypothesis testing. Analyses were performed using R statistical software (version 4.4.1).
Sensitivity analysis
In addition to the primary analysis, which defined HFmrEF/HFpEF using an algorithmic approach that leverages patient characteristics (including demographics, medication use, and comorbidities), a sensitivity analysis was performed among patients with HFmrEF/HFpEF as determined by the presence of an ICD-10-CM code for diastolic HF (Supplementary Table SI). An additional sensitivity analysis compared HF-related prescription costs between SI vs without SI among patients with a test before HF diagnosis, and among patients with a test after HF diagnosis. The interaction term (SI status × timing of hsCRP) was tested in outcome models using GLMs.
Results
Baseline demographics and clinical characteristics
A total of 1,881,947 patients classified as having HFmrEF/HFpEF based on the algorithm were included in the study, with 19,975 (1%) having received hsCRP testing ( Figure 1 ). Patients undergoing hsCRP testing were slightly younger (age 67 vs 68 years old), less likely to be female (51% vs 52%), more likely to be of Hispanic/Latino ethnicity (17% vs 15%), and more likely to have commercial insurance (29% vs 20%) compared to those without testing ( P <.001, for all) (Supplementary Table SIV). Observed PPPY HCRU and costs during follow-up for those with or without an hsCRP test were calculated and provided in Supplementary Table SV.
Cohort selection. *hsCRP tests were excluded if they met any of the following criteria: (1) results >10 mg/L, (2) tests taken within 30 days of any inpatient or emergency room visits/events, (3) tests with evidence of antibiotic, antivirus, and antimycotic medications 2 weeks before or 1 week after the test date, or (4) tests with evidence of corticosteroid during 1 month before the test date. AKI , acute kidney injury; COPD , chronic obstructive pulmonary disease; HCRU , healthcare resource utilization; HF , heart failure; HFmrEF , heart failure with mildly reduced ejection fraction; HFpEF , heart failure with preserved ejection fraction; hsCRP , high-sensitivity C-reactive protein; LVEF , left ventricular ejection fraction; SI , systemic inflammation.
A total of 7,242 patients with an hsCRP test met the necessary criteria for inclusion in the study, including 3,299 (46% of those with hsCRP testing) patients with SI and 3,943 (54%) patients without SI ( Figure 1 ). Before IPTW, patients with SI were slightly younger (67 vs 68 years old) and more likely to be female (52% vs 47%), and more often Black (15% vs 12%), or Hispanic/Latino (19% vs 17%), compared to patients without SI ( P <.001, for all) ( Table 1 ). Test timing relative to HF diagnosis was similar between the two cohorts, with 48.8% of those with SI or without SI receiving an hsCRP test prior to HF diagnosis. For those with SI, the average time between hsCRP test and HF diagnosis was 4 days, and 7 days for those without SI. Baseline use of antihyperlipidemic medications, including statins, was slightly higher among those with SI compared to those without SI (66% vs 64%). Average follow-up time was similar between the two groups: 3.1 years for those with SI or without SI. After weighting, baseline covariates were balanced between the two groups (Supplementary Figures S1 and S2, Supplementary Table SVI).
Table 1
Baseline demographics and clinical characteristics of patients with and without SI
| Preweighting | Postweighting | |||||
|---|---|---|---|---|---|---|
| Characteristic |
With SI
( n = 3,299) |
Without SI
( n = 3,943) |
SMD |
With SI
[Sum of weights = 7,239.8] (ESS = 3,236.2) |
Without SI
[Sum of weights = 7,243.8] (ESS = 3,893.2) |
SMD |
| Age, continuous | ||||||
| Mean (SD) | 66.8 (12.3) | 67.9 (12.2) | 0.09 | 67.4 (12.2) | 67.4 (12.4) | 0.00 |
| Age, categorical | ||||||
| 18-44 | 155 (4.7%) | 153 (3.9%) | 0.09 | 302 (4.2%) | 317 (4.4%) | 0.02 |
| 45-54 | 363 (11.0%) | 380 (9.6%) | 745 (10.3%) | 736 (10.2%) | ||
| 55-64 | 923 (28.0%) | 1,041 (26.4%) | 1,992 (27.5%) | 1,948 (26.9%) | ||
| 65-74 | 809 (24.5%) | 997 (25.3%) | 1,775 (24.5%) | 1,821 (25.1%) | ||
| 75-84 | 936 (28.4%) | 1,220 (30.9%) | 2,159 (29.8%) | 2,159 (29.8%) | ||
| 85+ | 113 (3.4%) | 152 (3.9%) | 267 (3.7%) | 262 (3.6%) | ||
| Sex, n (%) | ||||||
| Female | 1,702 (51.6%) | 1,870 (47.4%) | 0.08 | 3,568 (49.3%) | 3,573 (49.3%) | 0.00 |
| Male | 1,597 (48.4%) | 2,073 (52.6%) | 3,672 (50.7%) | 3,671 (50.7%) | ||
| Race/ethnicity, n (%) | ||||||
| White | 1,706 (51.7%) | 2,117 (53.7%) | 0.17 | 3,824 (52.8%) | 3,823 (52.8%) | 0.00 |
| Hispanic or Latino | 626 (19.0%) | 682 (17.3%) | 1,310 (18.1%) | 1,310 (18.1%) | ||
| Black/African American | 500 (15.2%) | 466 (11.8%) | 969 (13.4%) | 970 (13.4%) | ||
| Asian or Pacific Islander | 108 (3.3%) | 230 (5.8%) | 336 (4.6%) | 337 (4.7%) | ||
| Other | 120 (3.6%) | 190 (4.8%) | 303 (4.2%) | 307 (4.2%) | ||
| Unknown/missing | 239 (7.2%) | 258 (6.5%) | 497 (6.9%) | 497 (6.9%) | ||
| Insurance type, n (%) | ||||||
| Commercial | 1,053 (31.9%) | 1,221 (31.0%) | 0.03 | 2,274 (31.4%) | 2,274 (31.4%) | 0.00 |
| Medicaid | 386 (11.7%) | 440 (11.2%) | 825 (11.4%) | 828 (11.4%) | ||
| Medicare | 1,860 (56.4%) | 2,282 (57.9%) | 4,141 (57.2%) | 4,142 (57.2%) | ||
| Geographic region, n (%) | ||||||
| Northeast | 865 (26.2%) | 1,190 (30.2%) | 0.14 | 2,064 (28.5%) | 2,059 (28.4%) | 0.00 |
| Midwest | 380 (11.5%) | 416 (10.6%) | 798 (11.0%) | 796 (11.0%) | ||
| South | 1,210 (36.7%) | 1,217 (30.9%) | 2,430 (33.6%) | 2,431 (33.6%) | ||
| West | 844 (25.6%) | 1,120 (28.4%) | 1,948 (26.9%) | 1,957 (27.0%) | ||
| Baseline QCI comorbid conditions, n (%) | ||||||
| Myocardial infarction | 451 (13.7%) | 593 (15.0%) | 0.04 | 995 (13.7%) | 1,088 (15.0%) | 0.04 |
| Congestive heart failure | 1,702 (51.6%) | 2,150 (54.5%) | 0.06 | 3,692 (51.0%) | 3,989 (55.1%) | 0.08 |
| Peripheral vascular disease | 1,173 (35.6%) | 1,397 (35.4%) | 0.00 | 2,602 (35.9%) | 2,547 (35.2%) | −0.02 |
| Cerebrovascular disease | 768 (23.3%) | 946 (24.0%) | 0.02 | 1,708 (23.6%) | 1,728 (23.8%) | 0.01 |
| Dementia | 136 (4.1%) | 184 (4.7%) | 0.03 | 302 (4.2%) | 331 (4.6%) | 0.02 |
| Chronic pulmonary disease | 964 (29.2%) | 1,033 (26.2%) | −0.07 | 2,069 (28.6%) | 1,933 (26.7%) | −0.04 |
| Rheumatic disease | 229 (6.9%) | 261 (6.6%) | −0.01 | 489 (6.7%) | 493 (6.8%) | 0.00 |
| Peptic ulcer disease | 82 (2.5%) | 84 (2.1%) | −0.02 | 178 (2.5%) | 155 (2.1%) | −0.02 |
| Mild LIVER DISEASE | 283 (8.6%) | 284 (7.2%) | −0.05 | 598 (8.3%) | 545 (7.5%) | −0.03 |
| Diabetes without chronic complication | 1,639 (49.7%) | 1,746 (44.3%) | −0.11 | 3,596 (49.7%) | 3,217 (44.4%) | −0.11 |
| Diabetes with chronic complication | 926 (28.1%) | 970 (24.6%) | −0.08 | 2,015 (27.8%) | 1,800 (24.8%) | −0.07 |
| Hemiplegia or paraplegia | 63 (1.9%) | 63 (1.6%) | −0.02 | 136 (1.9%) | 117 (1.6%) | −0.02 |
| Renal disease | 914 (27.7%) | 1,012 (25.7%) | −0.05 | 1,988 (27.5%) | 1,861 (25.7%) | −0.04 |
| Any malignancy | 0 (0.0%) | 0 (0.0%) | 0.00 | 0 (0.0%) | 0 (0.0%) | 0.00 |
| Moderate or severe liver disease | 4 (0.1%) | 7 (0.2%) | 0.01 | 9 (0.1%) | 13 (0.2%) | 0.01 |
| Metastatic solid tumor | 0 (0.0%) | 0 (0.0%) | 0.00 | 0 (0.0%) | 0 (0.0%) | 0.00 |
| HIV/AIDS | 4 (0.1%) | 7 (0.2%) | 0.01 | 10 (0.1%) | 12 (0.2%) | 0.01 |
| Baseline QCI including HF, n (%) | ||||||
| 0 | 655 (19.9%) | 822 (20.8%) | 0.05 | 1,485 (20.5%) | 1,482 (20.5%) | 0.00 |
| 1-2 | 1,292 (39.2%) | 1,588 (40.3%) | 2,868 (39.6%) | 2,874 (39.7%) | ||
| 3-4 | 1,044 (31.6%) | 1,172 (29.7%) | 2,219 (30.6%) | 2,219 (30.6%) | ||
| 5+ | 308 (9.3%) | 361 (9.2%) | 668 (9.2%) | 669 (9.2%) | ||
| Baseline QCI excluding HF, n (%) | ||||||
| 0 | 1,201 (36.4%) | 1,629 (41.3%) | 0.10 | 2,701 (37.3%) | 2,946 (40.7%) | 0.07 |
| 1-2 | 1,617 (49.0%) | 1,781 (45.2%) | 3,510 (48.5%) | 3,299 (45.5%) | ||
| 3-4 | 425 (12.9%) | 470 (11.9%) | 907 (12.5%) | 886 (12.2%) | ||
| 5+ | 56 (1.7%) | 63 (1.6%) | 122 (1.7%) | 113 (1.6%) | ||
| Other comorbidities, n (%) | ||||||
| Atrial fibrillation | 722 (21.9%) | 836 (21.2%) | −0.02 | 1,631 (22.5%) | 1,492 (20.6%) | −0.05 |
| Atrial flutter | 131 (4.0%) | 155 (3.9%) | 0.00 | 295 (4.1%) | 281 (3.9%) | −0.01 |
| Autoimmune diseases | 297 (9.0%) | 331 (8.4%) | −0.02 | 636 (8.8%) | 625 (8.6%) | −0.01 |
| Coronary heart disease | 561 (17.0%) | 715 (18.1%) | 0.03 | 1,233 (17.0%) | 1,323 (18.3%) | 0.03 |
| Chronic kidney disease | 868 (26.3%) | 951 (24.1%) | −0.05 | 1,890 (26.1%) | 1,754 (24.2%) | −0.04 |
| Depression | 676 (20.5%) | 805 (20.4%) | 0.00 | 1,434 (19.8%) | 1,513 (20.9%) | 0.03 |
| Dyslipidemia/hyperlipidemia | 2,708 (82.1%) | 3,290 (83.4%) | 0.04 | 5,964 (82.4%) | 6,027 (83.2%) | 0.02 |
| Ischemic stroke | 333 (10.1%) | 393 (10.0%) | 0.00 | 733 (10.1%) | 717 (9.9%) | −0.01 |
| Hypertension | 2,923 (88.6%) | 3,413 (86.6%) | −0.06 | 6,415 (88.6%) | 6,262 (86.4%) | −0.07 |
| Obesity | 1,588 (48.1%) | 1,358 (34.4%) | −0.28 | 3,406 (47.0%) | 2,556 (35.3%) | −0.24 |
| Overweight | 527 (16.0%) | 748 (19.0%) | 0.08 | 1,165 (16.1%) | 1,380 (19.1%) | 0.08 |
| Peripheral artery disease | 901 (27.3%) | 1,023 (25.9%) | −0.03 | 1,984 (27.4%) | 1,878 (25.9%) | −0.03 |
| Smoking | 869 (26.3%) | 1,040 (26.4%) | 0.00 | 1,887 (26.1%) | 1,923 (26.5%) | 0.01 |
| Substance use | 281 (8.5%) | 316 (8.0%) | −0.02 | 602 (8.3%) | 601 (8.3%) | 0.00 |
| Type 1 diabetes | 173 (5.2%) | 171 (4.3%) | −0.04 | 379 (5.2%) | 318 (4.4%) | −0.04 |
| Type 2 diabetes | 1,702 (51.6%) | 1,812 (46.0%) | −0.11 | 3,732 (51.5%) | 3,342 (46.1%) | −0.11 |
| Follow-up time (y) | ||||||
| Mean (SD) | 3.1 (1.6) | 3.1 (1.6) | 0.04 | 3.1 (1.6) | 3.1 (1.6) | 0.05 |
| Time from hsCRP test to HF diagnosis (d) | ||||||
| Mean (SD) | −3.8 (180.2) | −7.0 (179.5) | −0.02 | −5.5 (180.2) | −5.7 (179.8) | 0.00 |
| hsCRP test timing, n (%) | ||||||
| Before HF diagnosis | 1,611 (48.8%) | 1,924 (48.8%) | 0.00 | 3,578 (49.4%) | 3,497 (48.3%) | 0.02 |
| After HF diagnosis | 1,688 (51.2%) | 2,019 (51.2%) | 3,662 (50.6%) | 3,747 (51.7%) | ||
| Number of hsCRP tests post index, n (%) | ||||||
| 1 | 1,165 (35.3%) | 1,430 (36.3%) | 0.02 | 2,535 (35.0%) | 2,641 (36.5%) | 0.03 |
| 2 | 467 (14.2%) | 547 (13.9%) | 1,030 (14.2%) | 1,014 (14.0%) | ||
| 3+ | 1,667 (50.5%) | 1,966 (49.9%) | 3,674 (50.7%) | 3,588 (49.5%) | ||
| HF medications, n (%) | ||||||
| Angiotensin converting enzyme inhibitors | 1,014 (30.7%) | 1,140 (28.9%) | −0.04 | 2,218 (30.6%) | 2,099 (29.0%) | −0.04 |
| Angiotensin receptor blockers | 1,090 (33.0%) | 1,256 (31.9%) | −0.03 | 2,389 (33.0%) | 2,299 (31.7%) | −0.03 |
| HF beta blockers | 1,585 (48.0%) | 1,777 (45.1%) | −0.06 | 3,490 (48.2%) | 3,249 (44.8%) | −0.07 |
| Other beta blockers | 353 (10.7%) | 442 (11.2%) | 0.02 | 773 (10.7%) | 809 (11.2%) | 0.02 |
| Loop diuretics | 1,063 (32.2%) | 1,055 (26.8%) | −0.12 | 2,345 (32.4%) | 1,942 (26.8%) | −0.12 |
| Thiazide diuretics | 955 (28.9%) | 929 (23.6%) | −0.12 | 2,059 (28.4%) | 1,729 (23.9%) | −0.10 |
| Mineralocorticoid receptor antagonists | 220 (6.7%) | 256 (6.5%) | −0.01 | 472 (6.5%) | 477 (6.6%) | 0.00 |
| Hydralazine/nitrate combination | 398 (12.1%) | 445 (11.3%) | −0.02 | 860 (11.9%) | 824 (11.4%) | −0.02 |
| Digoxin (Lanoxin) | 84 (2.5%) | 75 (1.9%) | −0.04 | 188 (2.6%) | 135 (1.9%) | −0.05 |
| Sacubitril/valsartan (Entresto) | 39 (1.2%) | 49 (1.2%) | 0.01 | 86 (1.2%) | 87 (1.2%) | 0.00 |
| Ivabradine (Corlanor) | 1 (0.0%) | 3 (0.1%) | 0.02 | 2 (0.0%) | 6 (0.1%) | 0.02 |
| Sodium-glucose cotransporter-2 inhibitors | 199 (6.0%) | 186 (4.7%) | −0.06 | 440 (6.1%) | 345 (4.8%) | −0.06 |
| HF medications classes, n (%) | ||||||
| No HF medications | 608 (18.4%) | 841 (21.3%) | 0.07 | 1,337 (18.5%) | 1,560 (21.5%) | 0.08 |
| 1st line HF medications | 2,445 (74.1%) | 2,830 (71.8%) | 5,371 (74.2%) | 5,176 (71.5%) | ||
| Other HF medications | 246 (7.5%) | 272 (6.9%) | 532 (7.3%) | 508 (7.0%) | ||
| Other medications, n (%) | ||||||
| Antihyperlipidemic medications | 2,115 (64.1%) | 2,601 (66.0%) | 0.04 | 4,664 (64.4%) | 4,745 (65.5%) | 0.02 |
| Glucagon-like peptide-1 agonists | 204 (6.2%) | 192 (4.9%) | −0.06 | 437 (6.0%) | 365 (5.0%) | −0.04 |
| Immunosuppressive medications for autoimmune diseases, n (%) | ||||||
| General immune suppressants | 1,500 (45.5%) | 1,705 (43.2%) | −0.04 | 3,254 (44.9%) | 3,166 (43.7%) | −0.02 |
| Innate immunity | 19 (0.6%) | 36 (0.9%) | 0.04 | 41 (0.6%) | 68 (0.9%) | 0.04 |
| Adaptive immunity—B cells | 36 (1.1%) | 63 (1.6%) | 0.04 | 77 (1.1%) | 119 (1.6%) | 0.05 |
| Adaptive immunity—Cytokines | 6 (0.2%) | 14 (0.4%) | 0.03 | 14 (0.2%) | 27 (0.4%) | 0.03 |
| Small molecule targeting medications | 7 (0.2%) | 5 (0.1%) | −0.02 | 15 (0.2%) | 10 (0.1%) | −0.02 |
| Nonsteroidal anti-inflammatory drugs (NSAID) | 1,264 (38.3%) | 1,478 (37.5%) | −0.02 | 2,720 (37.6%) | 2,749 (37.9%) | 0.01 |
| Prior HF hospitalization, n (%) | 355 (10.8%) | 405 (10.3%) | −0.02 | 777 (10.7%) | 747 (10.3%) | −0.01 |
| Baseline laboratory data, n (%) | ||||||
| Creatinine | 2,552 (77.4%) | 3,045 (77.2%) | 0.00 | 5,614 (77.5%) | 5,578 (77.0%) | −0.01 |
| eGFR | 2,304 (69.8%) | 2,106 (53.4%) | −0.34 | 5,058 (69.9%) | 3,874 (53.5%) | −0.34 |
| NT-proBNP | 205 (6.2%) | 243 (6.2%) | 0.00 | 441 (6.1%) | 453 (6.3%) | 0.01 |
| Sodium | 570 (17.3%) | 1,332 (33.8%) | 0.39 | 1,268 (17.5%) | 2,435 (33.6%) | 0.38 |
| Cholesterol profile | 543 (16.5%) | 1,235 (31.3%) | 0.35 | 1,214 (16.8%) | 2,252 (31.1%) | 0.34 |
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