Table 1
Death rates attributable to atrial fibrillation from 2000 to 2020 in Chile.
| YEAR | 00 | 02 | 04 | 06 | 08 | 10 | 12 | 14 | 16 | 18 | 20 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Total* | 2.0 | 2.7 | 3.4 | 4.1 | 4.6 | 5.5 | 6.2 | 6.0 | 5.5 | 4.8 | 5.0 |
| Gender | |||||||||||
| Male** | 1.8 | 2.3 | 2.7 | 3.7 | 4.0 | 4.6 | 5.1 | 4.8 | 4.6 | 4.3 | 4.3 |
| Female*** | 2.1 | 3.1 | 4.1 | 4.6 | 5.4 | 6.3 | 7.2 | 7.1 | 6.5 | 5.4 | 5.6 |
| Age (yrs) | |||||||||||
| 40–59* | 0.3 | 0.5 | 0.5 | 0.5 | 0.6 | 0.5 | 0.6 | 0.5 | 0.5 | 0.4 | 0.5 |
| 60–79** | 9.4 | 11.1 | 13.8 | 16 | 15 | 15.4 | 15.8 | 13.5 | 12.2 | 10.1 | 9.7 |
| ≥80+++ | 60.6 | 90.5 | 118.8 | 148.7 | 150 | 194 | 172.3 | 166.6 | 145.8 | 123.8 | 139 |
[i] Note: 95% CI(X̅): *4.5(3.7–5.3), **3.8(3.2–4.5), ***5.2(4.3–6.2), +0.5(0.4–0.5), ++12.9(11.4–14.4), +++137.3(111.9–162.6).
Figure 1
Kaplan-Meier curves for all-cause mortality in the CCTA and ICA groups.
Figure 1
Speckle tracking echocardiogram measuring global longitudinal strain (a, b, c, e) and post systolic shortening (d).
Table 1
Diagnostic accuracy for GLS for predicting significant CAD.
| VARIABLES | SENSITIVITY (%) | SPECIFICITY (%) | PLR | NLR | PPV (%) | NPV (%) | CUT-OFF | AUC (95% CONFIDENCE INTERVAL) | P VALUE |
|---|---|---|---|---|---|---|---|---|---|
| GLS 17 segment | 73.7 | 61.1 | 1.9 | 0.4 | 80.0 | 52.4 | –16.85 | 0.69(0.53, 0.85) | 0.02* |
[i] GLS- Global longitudinal strain PLR-positive likelihood ratio NLR-negative likelihood ratio PPV- positive predictive value NPV- negative predictive value AUC- Area under curve.
Figure 1
A. ROC curve: Larger Model Algorithms, B. PR curve: Larger Model Algorithms, C. ROC curve: Simpler Model Algorithms, D. PR curve: Simpler Model Algorithms.
Table 1
Cotinine levels, PM 2.5 and PM 10 levels following intervention.
| MEDIAN DIFF FROM BASELINE TO POST 1 | P | DIFF FROM BASELINE TO POST 2 | P | |
|---|---|---|---|---|
| Cotinine among spouse Intervention(158) | 66.79(–12.4, 100) | 75(–19.96, 100) | ||
| Control(120) | 66.00(–30.69, 98.68) | 0.31 | 66.66(–10.52, 98.18) | 0.54 |
| Cotinine among children Intervention(35) | 41.6(–184.9, 92.43) | 0.23 | 100(16.6, 100) | 0.32 |
| Control(13) | 63.49(20.34, 94.66) | 81(65, 100) | ||
| PM2.5 lntervention(n = 311) | 47.21(–14.7, 70) | .000 | 68.5(32.08, 85.89) | .000 |
| Control(n = 319) | –21.14(–87.15, –17.7) | –41.62(–211.42, –41.62) | ||
| PM 10 lntervention(n = 310) | 42.56(–20, 66.4) | .000 | 67.79(20.16, 85.05) | .000 |
| Control(n = 297) | –10.4(–79.87, 19.93) | –13.7(162.94, 49.98) |
Figure 1
Age standardized incidence of myocarditis admissions over time.
Figure 2
Frequency of myocarditis according to possible classifications (patient with identifiable class n = 2,083).
Figure 1
The ROC-curves of copeptin and catestatin levels associated with HFpEF: (a) the levels of copeptin associated with HFpEF; (b) the levels of catestatin associated with HFpEF.
Figure 2
The levels of biomarkers depending on NYHA functional classes: (a) copeptin (p = 0.191); (b) catestatin (p < 0.001).
Figure 3
The ROC-curves of catestatin levels associated with adverse outcomes during 12 month follow-up period.
Table 1
Elemental analyses of Daniellia oliveri methanol leaf extract using Atomic Absorption Spectroscopy.
| MACRO ELEMENTS | CONCENTRATION (ppm) | TRACE ELEMENTS | CONCENTRATION (ppm) |
|---|---|---|---|
| Calcium | 2.891 ± 0.12 | Zinc | 1.311 ± 0.02 |
| Sodium | 2.344 ± 0.03 | Copper | 0.057 ± 0.01 |
| Magnesium | 2.866 ± 0.25 | Chromium | 0.101 ± 0.03 |
| Potassium | 1.389 ± 0.41 | Manganese | 1.203 ± 0.05 |
| – | – | Iron | 0.168 ± 0.00 |
Figure 1
High-Performance Liquid Chromatography of Daniellia oliveri methanol leaf extract.
Figure 2
Molecular docking of Rhamnetin with.
Figure 3
Molecular docking of Metroprolol with beta- adrenergic receptor beta-adrenergic receptor.
Figure 4
Molecular docking of Atenonol with beta-adrenergic receptor.
Figure 5
Molecular docking of Rhamnetin with Yap.
Figure 6
Molecular docking of Verteporfin with Yap.
Figure 1
Comparison of blood pressure of patients receiving radiofrequency renal denervation (RF RDN).
Table 2
Multivariable Weighted Cox Proportional Hazard Models of Socioeconomic Factors and Covariates on Heart Disease Mortality, 2007–2018 NHANES, Unweighted N = 26,025.
| VARIABLE | MODEL 1 INDEPENDENT CONTRIBUTION OF EACH FACTOR ON CARDIOVASCULAR MORTALITY | MODEL 2 ADJUSTMENTS TO MODEL 1 WITH DEMOGRAPHICS, STANDARD MODIFIABLE CARDIOVASCULAR RISK FACTORS*, HEALTH CONDITIONS, BIOMARKERS AND MEDICATION USE** | ||||
|---|---|---|---|---|---|---|
| HR | 95% CI | P-VALUE | HR | 95% CI | P-VALUE | |
| Educational Attainment | ||||||
| College Graduate and | Ref. | Ref. | ||||
| Above | ||||||
| <High School | 1.56 | 1.05, 2.32 | 0.029 | 1.28 | 0.83, 1.95 | 0.257 |
| High School Graduate | 1.56 | 1.13, 2.16 | 0.008 | 1.42 | 0.99, 2.03 | 0.054 |
| Some College | 1.33 | 0.90, 1.94 | 0.148 | 1.22 | 0.83, 1.79 | 0.306 |
| Missing | 1.56 | 0.17, 13.99 | 0.689 | 1.13 | 0.12, 10.99 | 0.913 |
| Income-to-Poverty Ratio | ||||||
| 5.00+ | Ref. | Ref. | ||||
| <1.00 | 2.37 | 1.22, 4.60 | 0.011 | 2.37 | 1.16, 4.85 | 0.018 |
| 1.00–1.99 | 1.43 | 0.81, 2.55 | 0.217 | 1.47 | 0.79, 2.73 | 0.222 |
| 2.00–2.99 | 1.27 | 0.76, 2.13 | 0.348 | 1.22 | 0.71, 2.12 | 0.464 |
| 3.00–3.99 | 0.99 | 0.60, 1.65 | 0.970 | 0.98 | 0.59, 1.64 | 0.942 |
| 4.00–4.99 | 1.52 | 0.88, 2.61 | 0.132 | 1.48 | 0.85, 2.59 | 0.163 |
| Missing | 0.64 | 0.27, 1.48 | 0–290 | 0.64 | 0.26, 1.53 | 0.307 |
| Annual Family Income | ||||||
| $45,000+ | Ref. | Ref. | ||||
| <$45,000 | 1.01 | 0.60, 1.70 | 0.959 | 1.05 | 0.61, 1.81 | 0.865 |
| Missing | 1.06 | 0.37, 3.03 | 0.915 | 1.21 | 0.40, 3.63 | 0.730 |
[i] HR = Hazard Ratio;
CI = Confidence Interval.
Participant age is the time scale in all survival models.
*Diabetes, hypertension, high cholesterol, smoking.
**Anti-hypertensive, statin use.
Table 3
Explained Variance in Cardiovascular Disease Mortality.
| DOMAIN | % VARIANCE EXPLAINED (95% CI) |
|---|---|
| Socioeconomic (SES) Factors | |
| Educational Attainment | 2.27 (0.74, 4.58) |
| Income-to-Poverty Ratio | 1.04 (0.08, 2.63) |
| Low Income Status | 3.48 (-0.10, 1.53) |
| Education, Income-to-Poverty Ratio, Low-Income Status | 2.41 (0.86, 5.09) |
| Covariates Alone | |
| Age Alone | 28.52 (23.46, 33.76) |
| Sex and Race Alone | 5.10 (2.59, 8.63) |
| Standard Modifiable Cardiovascular Risk Factors (SMURFs)Alone* | 7.31 (4.40, 11.72) |
| Health Behaviors Alone** | 4.68 (2.75, 8.26) |
| Health Conditions Alone*** | 5.0 (2.41, 8.95) |
| Biomarkers Alone† | 8.06 (5.07, 12.95) |
| Medication Use‡ | 0.90 (0.41, 2.86) |
| Multivariable Models | |
| SES + Sex/Race | 8.82 (5.83, 13.30) |
| SES + Sex/Race + SMURFs | 13.75 (10.15, 19.27) |
| SES + Sex/Race + SMURFs + Health Behaviors | 16.21 (12.87, 22.89) |
| SES + Sex/Race + SMURFS + Health Behaviors + Health Conditions + Biomarkers | 19.32 (15.94, 27.20) |
| SES + Sex/Race + SMURFS + Health Behaviors + Health Conditions + Biomarkers + Medication Use | 19.51 (15.93, 27.56) |
| SES + Sex/Race + SMURFS + Health Behaviors + Health Conditions + Biomarkers + Medication Use + Age | 49.67 (44.68, 57.91) |
[i] *Hypertension, diabetes, high cholesterol, smoking.
**Hours of sleep, alcohol use, physical activity, diet.
***Depressive symptoms, body mass index.
†Systolic blood pressure, total cholesterol, non-HDL-C cholesterol, eGFR, hemoglobin A1c.
‡Anti-hypertensive, statin use.
Figure 1
40 most common comorbids and comorbidity clusters.
Figure 2
Trend of the top 10 comorbids (January 2011-December 2021).
Table 1
Comorbidity clusters with the highest mortality rates.
| COMORBIDITY CLUSTER | CCI | ADMISSIONS (n) | GENDER MALE (%): FEMALE (%) | MEAN AGE (n ± SD) | MEAN LENGTH OF STAY (n ± SD) | MORTALITY (n (%)) |
|---|---|---|---|---|---|---|
| 3 | 447 | 65.55: 34.45 | 66.65 ± 10.82 | 5.36 ± 5.56 | 68 (15.21) |
| 3 | 523 | 70.17: 29.83 | 68.02 ± 13.65 | 5.32 ± 4.99 | 69 (13.19) |
| 7 | 527 | 58.82: 41.18 | 51.53 ± 13.57 | 4.58 ± 4.36 | 65 (12.33) |
| 2 | 665 | 62.71: 37.29 | 68.49 ± 14.48 | 5.17 ± 4.76 | 68 (10.23) |
| 2 | 695 | 63.31: 36.69 | 69.54 ± 11.92 | 4.66 ± 4.62 | 69 (9.93) |
[i] CCI: Charlson Comorbidity Index.
Table 2
Number of total hospitalizations, number of total deaths and mortality rate (%) due to Chronic Rheumatic Heart Disease, according to Brazilian regions, between 2013 and 2022.
| REGION | HOSPITALIZATIONS | DEATHS | MORTALITY RATE (%) |
|---|---|---|---|
| Northern Region | 418 | 24 | 5.74 |
| Northeast Region | 1,815 | 44 | 2.42 |
| Southeast Region | 1,247 | 36 | 2.89 |
| Southern Region | 321 | 19 | 5.92 |
| Central-West Region | 663 | 19 | 2.87 |
| Total | 4,464 | 142 | 3.18 |
| SNO | AGE & SEX | MITRAL VALVE THICKNESS mm | AORTIC VALVE THICKNESS mm | MS MVOA cm2 | MR | AS | AR | PHT | EF % | OTHERS |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 9 M | 5.3 | 3.33 | MILD, 2.6 | MODERATE ECCENTRIC | NIL | MODERATE | NORMAL | 68 | PROMINENT TRABECULATION IN LV |
| 2 | 13M | 7.5 | 2.3 | MILD 2.12 | NIL | NIL | MILD | MODERATE | 70 | THICKENED TRICUSPID VALVE & SUB CHORDAL STRUCTUTRES, RVH, RVD |
| 3 | 14M | 10.3 | 4 | SEVERE 1.5 | MILD | NIL | MILD | MODERATE | 62 | THICKENED TRICUSPID VALVE, RVH |
| 4 | 18 F | 5.5 | 2.4 | SEVERE 1.4 | MILD | NIL | MODERATE | MODERATE | 57 | CHORDAL FUSION SEEN, THICKENED TRICUSPID VALVE |
| 5 | 19M | 5 | 3 | MODERATE 1.7 | MILD | NIL | MODERATE | NORMAL | 64 | |
| 6 | 14 | 4.8 | 2.6 | MODERATE | MILD | NIL | MILD | NORMAL | 66 | TRICUPSID VALVE THICKENED |
Figure 1
Photos of patients previously diagnosed with mucopolysaccharidosis with and an echocardiographic assessment.