Predicting Hospital Readmissions in Patients Receiving Novel-Dose Sacubitril/Valsartan Therapy: A Competing-Risk, Causal Mediation Analysis.
Changchun HouXinxin HaoNing SunXiaolin LuoZhichun GaoLing ChenXi LiuZhexue QinPublished in: Journal of cardiovascular pharmacology and therapeutics (2023)
Backgrounds: Our study aimed to identify and predict patients with heart failure (HF) taking novel-dose Sacubitril/Valsartan (S/V) at risk for all-cause readmission, as well as investigate the possible role of left ventricular reverse remodeling (LVRR). Methods and results: There were 464 patients recruited from December 2017 to September 2021 in our hospital with a median follow-up of 660 days (range, 17-1494). Competing risk analysis with Gray's Test showed statistically significant differences in all-cause readmission ( p -value< .001) across the three different dose groups. Models 1 and 2 were developed based on the results of univariable competing risk analysis, least absolute shrinkage and selection operator approach, backward stepwise regression, and multivariable competing risk analysis. The internal verification (data-splitting method) indicated that Model 1 had better discrimination, calibration, and clinical utility. The corresponding nomogram showed that patients aged 75 years and above, or taking the lowest-dose S/V (≤50 mg twice a day), or diagnosed with ventricular tachycardia, or valvular heart disease, or chronic obstructive pulmonary disease, or diabetes mellitus were at the highest risk of all-cause readmission. In the causal mediation analysis, LVRR was considered as a critical mediator that negatively affected the difference of novel-dose S/V in readmission. Conclusions: A significant association was detected between novel-dose S/V and all-cause readmission in HF patients, in part negatively mediated by LVRR. The web-based nomogram could provide individual prediction of all-cause readmission in HF patients receiving novel-dose S/V. The effects of different novel-dose S/V are still needed to be explored further in the future.
Keyphrases
- ejection fraction
- end stage renal disease
- chronic kidney disease
- newly diagnosed
- chronic obstructive pulmonary disease
- left ventricular
- healthcare
- peritoneal dialysis
- prognostic factors
- type diabetes
- stem cells
- emergency department
- squamous cell carcinoma
- machine learning
- acute coronary syndrome
- depressive symptoms
- patient reported outcomes
- pulmonary hypertension
- social support
- left atrial
- patient reported
- mitral valve
- big data
- lymph node metastasis
- cell therapy
- adverse drug