**1. Introduction**

Goodpasture's syndrome (GS) is a rare, life-threatening, small vessel vasculitis that is mediated by circulating anti-glomerular basement membrane (anti-GBM) autoantibodies against the NC1 domain of the alpha 3 chain of type IV collagen, targeting capillaries of the kidneys and lungs [1–4]. It is considered as one of the organ-specific autoimmune diseases that typically presents as a rapidly progressive glomerulonephritis (RPGN), accompanied by alveolar hemorrhage with pathology characterized by crescentic glomerulonephritis with classic linear polyclonal immunoglobulin (Ig) G deposits on immunofluorescence staining of the GBM on analysis of kidney biopsy samples [1,2,5–7]. Without prompt diagnosis and treatment, patients with GS can develop organ failure, resulting in significant morbidities and mortality [1–3].

Among European and Asian populations, the incidence of GS is estimated to have a frequency of 0.5 to 1.8 cases per million population per year [1–3,8,9]. A recent study from Ireland reported a nationwide disease incidence of GS of 1.64 per million population per year [10]. While it is well known that patients with GS can have both pulmonary and renal involvement requiring a mechanical ventilator and renal replacement therapy [3,4], data on its inpatient burden and resource utilization are lacking.

Thus, we conducted this study using the 2003–2014 National Inpatient Sample (NIS) database to assess inpatient prevalence, mortality, and resource utilization of GS in the United States.

#### **2. Materials and Methods**

#### *2.1. Data Source*

The 2003–2014 NIS database was used to conduct this cohort study. The NIS is the publicly available, inpatient, all-payer database in the United States. This database was developed and maintained by the Healthcare Cost and Utilization Project (HCUP) under the sponsorship of the Agency for Healthcare Research and Quality (AHRQ). The dataset contains more than 7 million hospitalizations annually, which were obtained from a 20% stratified sample of over 4000 non-federal acute care hospitals in more than 40 states of the United States. A survey procedure using discharge weights provided by the HCUP-NIS database was used to generate national estimates for 95% of hospitalizations nationwide [11]. This dataset includes codes for principal and secondary diagnosis as well as codes for procedures performed during the hospitalization.

#### *2.2. Study Population*

All patients with a principal diagnosis of GS, based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9 CM) diagnosis code of 446.21 for the hospitalization were included.

#### *2.3. Variables and Outcome of Interest*

Patient characteristics included age, sex, race, year of hospitalization, smoking, hemoptysis, and the presence of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, which consisted of granulomatosis polyangiitis, microscopic polyangiitis, and sepsis. Treatments included respiratory support consisting of invasive mechanical ventilation and non-invasive ventilation, renal replacement therapy, therapeutic plasmapheresis, and blood transfusion. Patient outcomes included organ failure or dysfunction, which consisted of respiratory failure, circulatory failure, renal failure, liver failure, hematologic failure, metabolic failure, and neurologic failure, as well as in-hospital mortality. Resource utilization included length of hospital stay and hospitalization cost.

## *2.4. Statistical Analysis*

Discharge-level weights published by the HCUP were used to estimate the total number of GS patients. Continuous variables were summarized as mean±standard deviation for normally-distributed data, and median with interquartile range for skewed data. Categorical variables were summarized as count with percentage. The annual inpatient prevalence of GS in hospitalized patients in the United States from 2003 to 2014 was calculated. Independent factors associated with in-hospital mortality were identified using multivariable logistic regression with the forward stepwise selection method. A two-tailed *p*-value of less than 0.05 was considered statistically significant. All analyses were performed using JMP statistical software (version 10, SAS Institute, Cary, NC).
