Trends in outcomes and hospitalization of patients with multiple myeloma and hip fractures: An analysis of the national inpatient sample (2016 - 2021)

Abstract Background: Multiple myeloma (MM) accounts for approximately 10% of hematologic malignancies in the United States, with incidence rates rising both nationally and globally in recent years. Despite this growing burden, there is limited evidence regarding disparities in outcomes and trends of patients with MM – particularly in relation to comorbid conditions such as head and neck of femur (hip) fractures. Hip fractures can significantly affect morbidity and mortality, yet their impact on survival among patients with MM remains poorly understood. In this study, we analyzed national data to evaluate the difference in outcomes, hospitalizations, and risk factors for MM patients with hip fractures, stratified based on demographic characteristics. Our aim is to better understand disparities and inform targeted public health interventions to care for vulnerable patients. Methods: Using data collected from the National Inpatient Sample (NIS) database, we identified patients over the age of 18 with a primary diagnosis of MM who sustained a hip fracture and were hospitalized from 2016 to 2021. Demographic characteristics, in-hospital mortality, insurance coverage and total hospital charges, and length of stay were assessed. Univariate comparisons were performed using chi-square and t-tests, and multivariate logistic regression was used to adjust for confounding variables. Results: Of the 591,455 patients admitted with a primary diagnosis of MM, 1.2% (N=6940) had a concomitant hip fracture. The average age of patients admitted for MM was 69.5 years old, and most patients who were admitted were White (63.4%), followed by Black (22.5%), Hispanic (8.5%), Asian/Pacific islander (2.2%), Native American (0.4%), and all other races (3.0%). Female patients were more likely to sustain a hip fracture (OR: 1.60, P <0.001). Native Americans were more likely to have a hip fracture (OR: 2.89, p<0.001), however there was not statistically significant increased risk among the other racial groups. Median household income quartile also did not correlate with increased risk of hip fracture. Mean difference of length of stay and total hospital charge both were significantly increased for MM patients with hip fractures (p<0.001). Black and patients of all other races had statistically significant longer hospital stays after adjusting for age, gender, and Charlson category, and all other races apart from Native Americans incurred higher hospitalization costs (p<0.05). Mortality among MM patients with and without hip fractures was significantly increased in Hispanic (OR: 1.35, p < 0.05), Native American (OR: 1.63, p <0.05), and all other races (OR: 1.23, p<0.05). When comparing all MM patients to those with hip fractures, concomitant hypercalcemia (OR: 1.75, p<0.001) and DVT (OR: 1.57, P=0.002) both had significantly increased risk. Decreased risk of hip fracture was observed in patients with acute respiratory failure (OR: 0.45, p<0.001), anemia (OR: 0.48, p<0.001), neutropenia (OR 0.48, p<0.001), acute kidney injury (OR 0.64, p<0.001), sepsis (OR: 0.38, p<0.001), and stroke (OR: 0.41, p<0.05). There was no significant association between MM patients with hip fractures and co-occurring cardiac arrest or dysrhythmias, pulmonary embolism, pneumonia, thrombocytopenia, or delirium. Conclusions: The analysis presented in this study reveals substantial racial disparities in outcomes and financial burden for patients with MM and hip fractures. Length of stay and total hospital charge are both significantly increased in patients with hip fractures, and this burden disproportionately affects patients of racial minorities. Higher mortality was also observed in Hispanic, Native American, and patients of all other races. Our findings underscore the need to bridge gaps in care for underserved populations and reduce disparities in outcomes and financial burden for patients with MM. Future studies should further examine the effect of disease stage and socioeconomic data to better understand these disparities.