Systemic sclerosis is associated with increased in-patient mortality in patients hospitalized for heart failure.

AIMS: We aimed to analyse the characteristics and in-hospital outcomes of patients hospitalized for heart failure (HF) with co-morbid systemic sclerosis (SSc) and compare them to those without SSc, using data from the National Inpatient Sample from years 2016 to 2019. METHODS AND RESULTS: International Classification of Diseases, Tenth Revision diagnosis codes were used to identify hospitalized patients with a primary diagnosis of HF and secondary diagnoses of SSc from the National Inpatient Sample database from 2016 to 2019. Patients were divided into two groups: those with and without a secondary diagnosis of SSc. Baseline characteristics including demographics and co-morbidities, outcomes of mortality, length of stay (LOS), and costs were compared between the two groups. Multivariable logistic regression analysis was performed to adjust for confounders and assess the impact of SSc on in-hospital mortality, cost, and LOS. A total of 4 709 724 hospitalizations for HF were identified, with 8150 (0.17%) having a secondary diagnosis of SSc. These patients were predominantly female (82.3% vs. 47.8%; P = 0.01), younger (mean age of 67.4 vs. 71.4; P < 0.01), and had significantly lower rates of traditional cardiovascular risk factors such as coronary artery disease (35.8% vs. 50.6%; P < 0.01), hyperlipidaemia (39.1% vs. 52.9%; P < 0.01), diabetes (22.5% vs. 49.1%; P < 0.01), obesity (13.2% vs. 25.0%; P < 0.01), and hypertension (20.2% vs. 23.8%; P < 0.01). Higher rates of co-morbid pulmonary disease in the form of interstitial lung disease (23.1% vs. 2.0%; P < 0.01) and pulmonary hypertension (36.6% vs. 12.7%; P < 0.01) were noted in the SSc cohort. Unadjusted in-hospital mortality was significantly higher in the HF with SSc group [5.1% vs. 2.6%; odds ratio: 1.99; 95% confidence interval (CI): 1.60-2.48; P < 0.001]. Unadjusted mortality was also higher among female (86.7% vs. 47.0%; P < 0.01), Black (15.7% vs. 13.0%; P < 0.01), and Hispanic (13.3% vs. 6.9%; P < 0.01) patients in the SSc cohort. After adjusting for potential confounders, SSc remained independently associated with higher in-hospital mortality (adjusted odds ratio: 1.81; 95% CI: 1.44-2.28; P < 0.001). Patients with HF and SSc also had longer LOS (6.4 vs. 5.4; adjusted mean difference [AMD]: 0.37, 95% CI: 0.05-0.68; P = 0.02) and higher hospitalization costs ($67 363 vs. $57 128; AMD: 198.9; 95% CI: -4780 to 5178; P = 0.93). CONCLUSIONS: In patients hospitalized for HF, those with SSc were noted to have higher odds of in-hospital mortality than those without SSc. Patients with HF and SSc were more likely to be younger, female, and have higher rates of co-morbid interstitial lung disease and pulmonary hypertension at baseline with fewer traditional cardiovascular risk factors.

Artificial intelligence in rheumatoid arthritis: potential applications and future implications.

The widespread adoption of digital health records, coupled with the rise of advanced diagnostic testing, has resulted in an explosion of patient data, comparable in scope to genomic datasets. This vast information repository offers significant potential for improving patient outcomes and decision-making, provided one can extract meaningful insights from it. This is where artificial intelligence (AI) tools like machine learning (ML) and deep learning come into play, helping us leverage these enormous datasets to predict outcomes and make informed decisions. AI models can be trained to analyze and interpret patient data, including physician notes, laboratory testing, and imaging, to aid in the management of patients with rheumatic diseases. As one of the most common autoimmune diseases, rheumatoid arthritis (RA) has attracted considerable attention, particularly concerning the evolution of diagnostic techniques and therapeutic interventions. Our aim is to underscore those areas where AI, according to recent research, demonstrates promising potential to enhance the management of patients with RA.