Inpatient Burden and Mortality of Goodpasture’s Syndrome in the United States: Nationwide Inpatient Sample 2003–2014

Abstract

Background: Goodpasture’s syndrome is a rare, life-threatening, small vessel vasculitis. Given its rarity, data on its inpatient burden and resource utilization are lacking. We conducted this study aiming to assess inpatient prevalence, mortality, and resource utilization of Goodpasture’s syndrome in the United States. Methods: The 2003–2014 National Inpatient Sample was used to identify patients with a principal diagnosis of Goodpasture’s syndrome. The inpatient prevalence, clinical characteristics, in-hospital treatment, end-organ failure, mortality, length of hospital stay, and hospitalization cost were studied. Multivariable logistic regression was performed to identify independent factors associated with in-hospital mortality. Results: A total of 964 patients were admitted in hospital with Goodpasture’s syndrome as the principal diagnosis, accounting for an overall inpatient prevalence of Goodpasture’s syndrome among hospitalized patients in the United States of 10.3 cases per 1,000,000 admissions. The mean age of patients was 54 ± 21 years, and 47% were female; 52% required renal replacement therapy, whereas 39% received plasmapheresis during hospitalization. Furthermore, 78% had end-organ failure, with renal failure and respiratory failure being the two most common end-organ failures. The in-hospital mortality rate was 7.7 per 100 admissions. The factors associated with increased in-hospital mortality were age older than 70 years, sepsis, the development of respiratory failure, circulatory failure, renal failure, and liver failure, whereas the factors associated with decreased in-hospital mortality were more recent year of hospitalization and the use of therapeutic plasmapheresis. The median length of hospital stay was 10 days. The median hospitalization cost was $75,831. Conclusion: The inpatient prevalence of Goodpasture’s syndrome in the United States is 10.3 cases per 1,000,000 admissions. Hospitalization of patients with Goodpasture’s syndrome was associated with high hospital inpatient utilization and costs.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

“…Those needs that are classified as unmet require provision of some ample spaces for the purpose of imagination in relation to leveraging the strength associated with big data, as well as relevant artificial intelligence (AI) to improve the overall status of patients with kidney diseases [25]. In this article, we discuss the big data concepts in nephrology, describe the potential use of AI in nephrology and transplantation, and also encourage researchers and clinicians to submit their invaluable research, including original clinical research studies [26][27][28][29][30], database studies from registries [31][32][33], meta-analyses [34][35][36][37][38][39][40][41][42][43][44], and artificial intelligence research [25,[45][46][47][48] in nephrology and transplantation. Table 1 demonstrates known and commonly used databases that have provided big data in nephrology and transplantation [49][50][51].…”

Circulatory Shock among Hospitalized Patients for Salicylate Intoxication

“…Circulatory shock has been associated with increased mortality in several medication conditions [ 6 , 7 ]. Data has been limited, however, on its impact on hospital outcomes and resource utilization in patients with salicylate intoxication.…”

“…We also assessed independent predictors for in-hospital mortality. Even though circulatory shock is associated with poor outcomes in several diseases [ 6 , 7 ], risk factors and impact of circulatory shock on outcomes and resource use in salicylate intoxication have not been well investigated. Therefore, we conducted this study to evaluate risk factors for circulatory shock and its impact on outcomes in hospitalized patients with salicylate intoxication in the United States.…”

Reporting of sex as a variable in cardiovascular studies using cultured cells: A systematic review

“…44,69,70 Demographic factors such as age, sex, race, socioeconomic status, and zip code influence access to health care, compliance with medications, response to therapy and clinical outcomes in the short-and long-term. 23,25,27,36,40,44,46,47,[51][52][53]58,[71][72][73][74][75][76][77][78][79][80] Therefore, in the translational science spectrum, it is crucial to report sex-specific data in experiments and clinical studies, since it has a direct correlation with disparities in outcomes. In 2014, the National Institutes of Health reemphasized the importance of sex as a biological variable for transparency and reproducibility of scientific data.…”