Diabetes Care: Diabetes and heart failure hospitalization risk score based on biomarkers

 More than 400 million people worldwide suffer from type 2 diabetes (T2DM), and the burden of diabetes-related cardiovascular complications is increasing
.

In addition to having significant adverse effects on the quality of life of patients and the utilization of medical care, heart failure (HF) is a particularly important prognostic complication of T2DM, because it has a higher mortality rate compared with patients without heart failure4 To 10 times

Heart failure (HF) is a particularly important prognostic complication of T2DM because its mortality rate is 4 to 10 times higher than that of patients without heart failure

Biomarker scores are based on integers, including high-sensitivity cardiac troponin T≥6 ng/l, N-terminal b-type natriuretic peptide ≥125 pg/ml, high-sensitivity C-reactive protein ≥3 mg/l, and left ventricular hypertrophy on ECG If there is an abnormality in each item, the score is 1 point
.

The Pearson chi-square test of categorical variables and the Kruskal-Wallis test of continuous variables were used to evaluate the differences in baseline characteristics between biomarker layers

Results: The preliminary analysis included 6799 patients with blood glucose disorders (diabetes: 33.
2%; prediabetes: 66.
8%)
.

In the pre-diabetes and diabetes cohorts, the biomarker scores showed good discrimination and correction ability in predicting the risk of heart failure at 5 and 10 years

Figure 1 The relative strength of each variable in the multivariate risk model of heart failure hospitalization; the relative contribution of each variable is evaluated according to some Waldχ ² values
.

ACR, albumin/creatinine ratio; CAD, coronary artery disease; GFR: glomerular filtration rate; hsTnT, high-sensitivity troponin T; NT-proBNP, N-terminal pro-b-type natriuretic peptide

Figure 1 The relative strength of each variable in the multivariate risk model of heart failure hospitalization; the relative contribution of each variable is evaluated according to some Waldχ ² values

Figure 2 Multivariate spline method shows the relationship between (A) high-sensitivity troponin T (HsTnT) and (B) N-terminal B-type natriuretic peptide (NT-proBNP) baseline concentration and heart failure (HHF) hospitalization risk Continuous relationship
.

Figure 2 Multivariate spline method shows the relationship between (A) high-sensitivity troponin T (HsTnT) and (B) N-terminal B-type natriuretic peptide (NT-proBNP) baseline concentration and heart failure (HHF) hospitalization risk Continuous relationship
.

Figure 3 The incidence of heart failure cardiovascular death or hospitalization by risk category in the derivation and validation cohort.
In the derivation and validation cohort, the biomarker-based risk scores determined the significant risk gradient for the composite endpoint of cardiovascular death or heart failure hospitalization , The incidence of each cohort is comparable
.

The score has a good resolution for the composite endpoint, and the C index in the derivative and validation cohorts are 0.

Figure 3 The incidence of heart failure cardiovascular death or hospitalization by risk category in the derivation and validation cohort.

Figure 4 In the derivation and verification cohort, the incidence of heart failure inpatients with no previous heart failure classified by risk category is divided into low-risk (0-3 points), medium-risk (4-6 points), and high-risk (7-8).
Points), extremely high risk (9 points)
.

Note that the highest score for patients without a history of heart failure is only 9 points

Figure 4 In the derivation and verification cohort, the incidence of heart failure inpatients with no previous heart failure classified by risk category is divided into low-risk (0-3 points), medium-risk (4-6 points), and high-risk (7-8).
Points), extremely high risk (9 points)
.
Note that the highest score for patients without a history of heart failure is only 9 points
.

Figure 5 A calibration chart of the observed heart failure hospitalization rate and the predicted hospitalization rate in the external validation cohort
.
This score has a nearly perfect calibration in the external verification cohort, and the Greenwood-nam-D'Agostino statistic is p=0.
93 (insignificant p-values ​​indicate proper calibration)
.
HHF, heart failure hospitalization; KM, Kaplan-Meier
.

Figure 5 A calibration chart of the observed heart failure hospitalization rate and the predicted hospitalization rate in the external validation cohort
.
This score has a nearly perfect calibration in the external verification cohort, and the Greenwood-nam-D'Agostino statistic is p=0.
93 (insignificant p-values ​​indicate proper calibration)
.
HHF, heart failure hospitalization; KM, Kaplan-Meier
.

Figure 6 According to the risk category predicted by the risk score based on biomarkers, the therapeutic effect of dapagliflozin in patients without a history of heart failure
.
Absolute risk reduction (ARR) is obtained by subtracting the Kaplan-Meier event rate of HHF in patients receiving placebo treatment from the Kaplan-Meier event rate of HHF within 4 years in patients receiving placebo treatment.
-Meier event rate is calculated
.
Note that the highest score for patients without a history of heart failure is only 9 points; therefore, the high-risk (7-8) and extremely high-risk (9-11) groups collapsed in this number
.
ARR, absolute risk reduction; HR, hazard ratio
.

Figure 6 According to the risk category predicted by the risk score based on biomarkers, the therapeutic effect of dapagliflozin in patients without a history of heart failure
.
Absolute risk reduction (ARR) is obtained by subtracting the Kaplan-Meier event rate of HHF in patients receiving placebo treatment from the Kaplan-Meier event rate of HHF within 4 years in patients receiving placebo treatment.
-Meier event rate is calculated
.
Note that the highest score for patients without a history of heart failure is only 9 points; therefore, the high-risk (7-8) and extremely high-risk (9-11) groups collapsed in this number
.
ARR, absolute risk reduction; HR, hazard ratio
.

Figure 7 Evaluation of the therapeutic effect of saxagliptin based on the risk category predicted by the risk score based on biomarkers
.
Previous studies have shown that saxagliptin treatment is associated with an increased risk of hospitalization for heart failure
.
The study explored the difference in relative and absolute risk of Kaplan-Meier (KM) events in HHF patients two years after the use of saxagliptin and placebo based on the risk categories predicted by the risk score of biomarkers
.
The hazard ratio of HHF shows that saxagliptin and placebo, the absolute risk difference (ARDS) is calculated based on the difference between HHF (saxagliptin-placebo) for 2 years
.
The incidence of hemorrhagic heart failure in the low-risk group was very low, and there was no difference between patients taking saxagliptin and patients taking placebo
.
Among patients in the medium-risk, high-risk, and very high-risk categories, patients taking saxagliptin had a higher risk of heart failure compared with placebo (HR1.
26, 95%CI1.
03-1.
55)
.

Figure 7 Evaluation of the therapeutic effect of saxagliptin based on the risk category predicted by the risk score based on biomarkers
.
Previous studies have shown that saxagliptin treatment is associated with an increased risk of hospitalization for heart failure
.
The study explored the difference in relative and absolute risk of Kaplan-Meier (KM) events in HHF patients two years after the use of saxagliptin and placebo based on the risk categories predicted by the risk score of biomarkers
.
The hazard ratio of HHF shows that saxagliptin and placebo, the absolute risk difference (ARDS) is calculated based on the difference between HHF (saxagliptin-placebo) for 2 years
.
The incidence of hemorrhagic heart failure in the low-risk group was very low, and there was no difference between patients taking saxagliptin and patients taking placebo
.
Among patients in the medium-risk, high-risk, and very high-risk categories, patients taking saxagliptin had a higher risk of heart failure compared with placebo (HR1.
26, 95%CI1.
03-1.
55)
.

Conclusion: In adults with diabetes and pre-diabetes, biomarker scores can stratify the risk of heart failure and match the corresponding treatment plan for heart failure
.

In adults with diabetes and pre-diabetes, biomarker scores can stratify the risk of heart failure and match the corresponding treatment plan for heart failure
.

Original source:

Original source:

Berg DD, Wiviott SD, Scirica BM,et al.
A Biomarker-Based Score for Risk of Hospitalization for Heart Failure in Patients With Diabetes.
Diabetes Care 2021 Sep 17

A Biomarker-Based Score for Risk of Hospitalization for Heart Failure in Patients With Diabetes.
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