Epidemiology, Outcomes, and Healthcare Utilization in Acute Myeloid Leukemia: A Study Using the National Inpatient Sample - 2021
Authors: Rajarajan Srinishant , Babu Kalaivani , Helfand Alexander , Roy Arya Mariam , Chattaraj Asmi , Ramanan Sruthi P , Ramesh Nithya , Ayanambakkam Attanathi Adanma Anji , Correa Amit 
Published: December 2024

Background: Acute Myeloid Leukemia (AML) is a malignant hematologic condition characterized by rapid clonal proliferation of immature blast cells. Understanding its epidemiology, outcomes, and healthcare utilization is critical for improving patient management and resource allocation. This study aims to define the epidemiology, demographic characteristics, and outcomes of patients diagnosed with AML using data from the National Inpatient Sample (NIS) - 2021. Specific outcomes include mortality, length of hospital stay, and hospitalization charges.

Methods: Data from the NIS - 2021 was analyzed using Stata software. AML cases were identified using the appropriate ICD-10 codes. Primary outcome was mortality of patients with AML and secondary outcomes included hospital length of stay (LOS) and total hospitalization charges (TOTCHG). Key demographic variables (age, sex, race), socioeconomic status (ZIP income quartile), Charlson Comorbidity Index, admission on a weekend, hospital region, teaching status, and bed size were considered in the analysis.

Results: A total of 73,190 cases of AML were identified, with a mean age of 59.83 years (95% CI: 59.09 - 60.57) and a female proportion of 45.94%.

Mortality: The overall mortality rate was 8.41% (95% CI: 7.94 - 8.91). Significant factors associated with higher mortality included older age (OR: 1.022, 95% CI: 1.018 - 1.027, p &lt; 0.001), Black race (OR: 1.429, 95% CI: 1.181 - 1.729, p &lt; 0.001), Other race (OR: 2.861, 95% CI: 1.386 - 5.907, p = 0.004), higher Charlson Comorbidity Index (OR: 1.138, 95% CI: 1.100 - 1.178, p &lt; 0.001), and weekend admissions (OR: 1.355, 95% CI: 1.174 - 1.563, p &lt; 0.001). Factors such as sex, Hispanic race, Native American race, Asian/Pacific Islander race, some ZIP income quartiles, and hospital teaching status were not statistically significant predictors of mortality.

Length of Hospital Stay: The mean LOS was 12.48 days (95% CI: 12.01 - 12.95). Factors associated with longer stays included hospital teaching status (Coefficient: 3.976, 95% CI: 3.040 - 4.912, p &lt; 0.001), large hospital bed size (Coefficient: 3.355, 95% CI: 2.170 - 4.539, p &lt; 0.001), and Asian/Pacific Islander race (Coefficient: 2.392, 95% CI: 0.900 - 3.884, p = 0.002), while older age was associated with shorter stays (Coefficient: -0.135, 95% CI: -0.159 - -0.110, p &lt; 0.001). Hospitals in the Midwest region (Coefficient: -1.229, 95% CI: -2.437 - -0.021, p = 0.046) and South region (Coefficient: -1.584, 95% CI: -2.664 - -0.504, p = 0.004) were also associated with shorter stays. Factors such as sex, ZIP income quartiles, race categories (White, Hispanic, Native American, and Other), Charlson Comorbidity Index, weekend admissions, and hospitals in the Northeast and West were not statistically significant for LOS.

Total hospitalization charges: The mean TOTCHG were $186,733.40 (95% CI: $171,255.70 - $202,211.10). Significant factors influencing higher charges included hospital teaching status (Coefficient: $71,457, p &lt; 0.001), large hospital bed size (Coefficient: $60,006, p &lt; 0.001), Hispanic race (Coefficient: $49,138, 95%, p = 0.001), and Asian/Pacific Islander race (Coefficient: $54,696, p = 0.001). Conversely, older age was associated with lower charges (Coefficient: -$2,421, p &lt; 0.001). Additionally, the Charlson Comorbidity Index (Coefficient: $3,845, 95%, p = 0.014) and hospitals in the Midwest (Coefficient: -$60,752, p &lt; 0.001) and South (Coefficient: -$43,288, 95%, p = 0.019) were significant. Factors such as sex and income quartiles were not significant predictors of outcomes.

Conclusion: This study provides a comprehensive overview of the epidemiology, demographic characteristics, and outcomes of AML patients in the inpatient setting. Significant predictors of mortality, LOS, and TOTCHG were identified, highlighting the importance of patient demographics, comorbidities, and hospital characteristics in influencing AML outcomes. Factors such as race, Charlson Comorbidity Index, and hospital teaching status were significant across multiple outcomes. Conversely, sex and some ZIP income quartiles were not significant predictors of outcomes. These findings can inform healthcare policy and resource allocation to improve AML patient care.