Drug interactions and creatinine levels are associated with QTc prolongation in intensive care units: a prospective, observational study

Development and Validation of Nomogram for the Prediction of Malignant Ventricular Arrhythmia Including Circulating Inflammatory Cells in Patients with Acute ST-Segment Elevation Myocardial Infarction

Background

Malignant ventricular arrhythmia (MVA) can seriously affect the hemodynamic changes of the body. In this study, we developed and validated a nomogram to predict the in-hospital MVA risk in patients with STEMI after emergency PCI.

Methods

The multivariable logistic regression analysis included variables with a P<0.05 in the univariate logistic regression analysis and investigated the independent predictors affecting in-hospital MVA after PCI in patients with STEMI in the training cohort. The construction of a nomogram model used independent predictors to predict the risk of in-hospital MVA, and C-index, Hosmer-Lemeshow (HL) test, calibration curves, decision curve analysis (DCA), and receiver operating characteristic (ROC) were used to validate the nomogram.

Results

Killip class [OR=5.034 (95% CI: 1.596-15.809), P=0.005], CK-MB [OR=1.002 (95% CI: 1.001-1.004), P=0.022], serum potassium [OR=0.618 (95% CI: 0.406-0.918), P=0.020], NLR [OR=1.073 (95% CI: 1.034-1.115), P<0.001], and monocyte [OR=1.974 (95% CI: 1.376-2.925), P<0.001] were the independent predictors of in-hospital MVA after PCI in patients with STEMI. A nomogram including the 5 independent predictors was developed to predict the risk of in-hospital MVA. The C-index, equivalent to the area under the ROC curve (AUC), was 0.803 (95% confidence interval [CI]: 0.738-0.868) in the training cohort, and 0.801 (95% CI:0.692-0.911) in the validation cohort, showing that the nomogram had a good discrimination. The HL test (χ²=8.439, P=0.392 in the training cohort; χ²=9.730, P=0.285 in the validation cohort) revealed a good calibration. The DCA suggested an obvious clinical net benefit.

Conclusion

Killip class, CK-MB, serum potassium, NLR, and monocyte were independent factors for in-hospital MVA after PCI in patients with STEMI. The nomogram model constructed based on the above factors to predict the risk of in-hospital MVA had satisfactory discrimination, calibration, and clinical effectiveness, and was an excellent tool for early prediction of the risk of in-hospital MVA after PCI in patients with STEMI.

Clinically important drug-drug interactions in patients admitted to hospital with COVID-19: drug pairs, risk factors, and management

OBJECTIVES

Coronavirus disease 2019 (COVID-19) is an emerging viral infection without any approved treatment. Investigational therapies for COVID-19 may cause clinically important drug-drug interactions (DDIs). We aimed to study potential DDIs (pDDIs) and their risk factors in COVID-19 patients admitted to the hospital.

METHODS

We conducted a cross-sectional study in a tertiary respiratory hospital dedicated to COVID-19 patients. The Lexi-Interact database was used to investigate clinically important pDDIs. The database output including interacting drug pairs, risk rating, reliability rating, mechanism, and management was evaluated. Associations between the occurrence of pDDIs and probable risk factors were assessed by logistic regression analysis.

RESULTS

Medical charts of 227 patients were reviewed. About 38% of the patients had at least one clinically important pDDI. More than half of the interactions were between protease inhibitors (lopinavir/ritonavir) and regularly prescribed medications for the management of comorbidities or COVID-19 symptoms (e.g., atorvastatin, alprazolam, salmeterol, and tamsulosin). Ischemic heart disease, chronic respiratory diseases, and ICU admission were significantly associated with the occurrence of pDDIs.

CONCLUSIONS

We recommend considering the risk factors for the emergence of clinically important DDIs in the pharmacotherapy of COVID-19 patients. Using an alternative medication or dose adjustments may be required in high-risk patients.