Background Heart failure (HF) continues to be the major cause of hospitalizations. Despite numerous significant therapeutic progress, the mortality rate of HF is still high. This longitudianl cohort study aimed to investigate the associations between hematologic inflammatory indices neutrophil percentage-to-albumin ratio (NPAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and all-cause mortality in community-dwelling adults with HF. Methods Adults aged 20 and older with HF in the US National Health and Nutrition Examination Survey (NHANES) database 2005–2016 were included and were followed through the end of 2019. Univariate and multivariable Cox regression analyses were performed to determine the associations between the three biomarkers and all-cause mortality. The receiver operating characteristics (ROC) curve analysis was conducted to evaluate their predictive performance on mortality. Results A total of 1,207 subjects with HF were included, representing a population of 4,606,246 adults in the US. The median follow-up duration was 66.0 months. After adjustment, the highest quartile of NPAR (aHR = 1.81, 95%CI: 1.35, 2.43) and NLR (aHR = 1.59, 95%CI: 1.18, 2.15) were significantly associated with increased mortality risk compared to the lowest quartile during a median follow-up duration of 66.0 months. Elevated PLR was not associated with mortality risk. The area under the ROC curve (AUC) of NPAR, NLR, and PLR in predicting deaths were 0.61 (95%CI: 0.58, 0.65), 0.64 (95%CI: 0.6, 0.67), and 0.58 (95%CI:0.55, 0.61), respectively. Conclusions In conclusion, elevated NPAR and NLR but not PLR are independently associated with increased all-cause mortality among community-dwelling individuals with HF. However, the predictive performance of NPAR and NLR alone on mortality was low.

The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patients with breast tumors (n = 331) before NST. Pyradiomics was used for feature extraction, and 107 features of seven classes were extracted. Feature selection was performed on the basis of the intraclass correlation coefficient (ICC), and six ICC thresholds (0.7–0.95) were examined to identify the feature set resulting in optimal model performance. Clinical factors, such as age, clinical stage, cancer cell type, and cell surface receptors, were used for prediction. We tried six machine learning algorithms, and clinical, radiomics, and clinical–radiomics models were trained for each algorithm. Radiomics and clinical–radiomics models with gray level co-occurrence matrix (GLCM) features only were also built for comparison. The linear support vector machine (SVM) regression model trained with radiomics features of ICC ≥0.85 in combination with clinical factors performed the best (AUC = 0.87). The performance of the clinical and radiomics linear SVM models showed statistically significant difference after correction for multiple comparisons (AUC = 0.69 vs. 0.78; p < 0.001). The AUC of the radiomics model trained with GLCM features was significantly lower than that of the radiomics model trained with all seven classes of radiomics features (AUC = 0.85 vs. 0.87; p = 0.011). Integration of clinical and CT-based radiomics features was helpful in the pretreatment prediction of pCR to NST in breast cancer.

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