Table 2.
Logistic regression analysis of chloroquine AE reports.
| Coefficients | Estimate | Std. Error | Adjusted OR | 95% CI | p value |
|---|---|---|---|---|---|
| Model 1a: Chloroquine usage and disease state as predictors of cardiac adverse reactions | |||||
| n = 641,270 AIC = 288,032 | |||||
| Chloroquine | 0.914 | 0.0803 | 2.49 | (2.12–2.91) | < 2 × 10–16* |
| SLE | 0.378 | 0.0302 | 1.46 | (1.37–1.55) | < 2 × 10–16* |
| Model 1b: Chloroquine, NSAID, and aspirin usage and disease state as predictors of cardiac adverse reactions | |||||
| n = 641,270 AIC = 281,114 | |||||
| Chloroquine | 0.805 | 0.0815 | 2.24 | (1.90–2.62) | < 2 × 10–16* |
| SLE | 0.426 | 0.0306 | 1.53 | (1.44–1.62) | < 2 × 10–16* |
| Number of Concurrent NSAIDs | 0.631 | 0.0104 | 1.88 | (1.84–1.92) | < 2 × 10–16* |
| Aspirin | 1.123 | 0.0190 | 3.08 | (2.96–3.19) | < 2 × 10–16* |
| Model 2a: Chloroquine usage, age, sex, and disease state as predictors of cardiac adverse reactions | |||||
| n = 464,528 AIC = 209,758 | |||||
| Chloroquine | 1.005 | 0.0930 | 2.73 | (2.27–3.27) | < 2 × 10–16* |
| SLE | 0.928 | 0.0361 | 2.53 | (2.36–2.71) | < 2 × 10–16* |
| Male | 0.451 | 0.0139 | 1.57 | (1.53–1.61) | < 2 × 10–16* |
| Age (odds per year) | 0.026 | 0.0005 | 1.03 | (1.03–1.03) | < 2 × 10–16* |
| Model 2b: Chloroquine, NSAID, and aspirin usage, age, sex, and disease state as predictors of cardiac adverse reactions | |||||
| n = 464,528 AIC = 204,824 | |||||
| Chloroquine | 0.901 | 0.0942 | 2.46 | (2.04–2.95) | < 2 × 10–16* |
| SLE | 0.939 | 0.0366 | 2.56 | (2.38–2.74) | < 2 × 10–16* |
| Male | 0.414 | 0.0141 | 1.51 | (1.47–1.55) | < 2 × 10–16* |
| Age (odds per year) | 0.024 | 0.0005 | 1.02 | (1.02–1.03) | < 2 × 10–16* |
| Number of Concurrent NSAIDs | 0.661 | 0.0118 | 1.94 | (1.89–1.98) | < 2 × 10–16* |
| Aspirin | 0.944 | 0.0217 | 2.57 | (2.46–2.68) | < 2 × 10–16* |
* = coefficients with significant p values.