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May 29, 2026Journal of Clinical Oncology0 citations

Predictors of tumor lysis syndrome in non-Hodgkin lymphoma hospitalizations.

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STSrijani ThannirBrooklyn Hospital CenterAKAmeerdad KhanBrooklyn Hospital CenterDTDavin TurkuBrooklyn Hospital Center

Key Points

  • Identify predictors of tumor lysis syndrome (TLS) in hospitalized non-Hodgkin lymphoma (NHL) patients using a national dataset.
  • Cross-sectional analysis of National Inpatient Sample (2016–2020) for adult NHL hospitalizations using ICD-10 codes.
  • Survey-weighted chi-square tests and multivariable logistic regression were performed to evaluate predictors.
  • Statistical significance was set at p<0.05.
  • Out of 350,400 NHL hospitalizations, 0.79% had TLS, with a notable age difference (65.7 vs 69.5 years, p < 0.001).
  • Bone marrow involvement was identified as the strongest predictor of TLS (OR 13.78, 95% CI 5.45–34.84).
  • Electrolyte derangements such as hyperkalemia, hyperphosphatemia, and hypocalcemia were also significant predictors, each with ORs > 7.00.

Abstract

11178 Background: Tumor lysis syndrome (TLS) is a life-threatening oncologic emergency in non-Hodgkin lymphoma (NHL) caused by rapid tumor cell breakdown and severe metabolic derangements. Although TLS is well recognized, most risk data derive from small cohorts or trials, limiting generalizability. We used a nationally representative inpatient dataset to identify predictors of TLS in hospitalized NHL patients. Methods: We performed a cross-sectional analysis of the National Inpatient Sample (2016–2020). Adult NHL hospitalizations were identified using ICD-10 codes. Survey-weighted chi-square tests and multivariable logistic regression evaluated demographic, clinical, and treatment predictors. Analyses accounted for the complex survey design. Statistical significance was defined as p<0. 05. Results: An estimated 350, 400 NHL hospitalizations were identified, of which 0. 79% had TLS. TLS patients were younger (65. 7 vs 69. 5 years, p < 0. 001) and demonstrated different racial distributions, with lower proportions of White patients (67. 8% vs 76. 9%) and higher proportions of Black (12. 7% vs 9. 2%) and Hispanic patients (11. 6% vs 8. 6%) (p < 0. 001). TLS occurred more frequently in hospitals in the West and Northeast (p = 0. 036). TLS admissions had significantly longer stays (10. 48 vs 5. 91 days) and higher charges (174, 972 vs 76, 884; p < 0. 001). Survey-weighted regression identified several strong predictors of TLS. Bone marrow involvement was one of the strongest independent predictors (OR 13. 78 95% CI 5. 45–34. 84). Electrolyte derangements were also powerful predictors: hyperkalemia (OR 8. 20 95% CI 6. 82–9. 86), hyperphosphatemia (OR 8. 28 95% CI 6. 62–10. 36), and hypocalcemia (OR 7. 02 95% CI 5. 67–8. 69). Additional predictors included sepsis (OR 2. 29 95% CI 1. 89–2. 77), neutropenia (OR 1. 71 95% CI 1. 27–2. 31), HIV infection (OR 1. 73 95% CI 1. 05–2. 84), and receipt of inpatient chemotherapy (OR 1. 38 95% CI 1. 01–1. 87). Prior chemotherapy (OR 0. 44 95% CI 0. 30–0. 63) and radiation therapy (OR 0. 61 95% CI 0. 38–0. 99) were protective, suggesting TLS risk is greatest in chemo-naïve or high–tumor burden presentations. Conclusions: TLS in hospitalized NHL patients is associated with distinct demographic patterns and substantial increases in healthcare utilization. Early risk stratification incorporating clinical, laboratory, and treatment factors may improve prevention strategies and outcomes. Predictors of TLS among hospitalized patients with NHL. Predictor Adjusted OR 95% CI p-value Bone marrow involvement 13. 78 5. 45–34. 84 <0. 001 Hyperkalemia 8. 20 6. 82–9. 86 <0. 001 Hyperphosphatemia 8. 28 6. 62–10. 36 <0. 001 Hypocalcemia 7. 02 5. 67–8. 69 <0. 001 Sepsis 2. 29 1. 89–2. 77 <0. 001 Neutropenia 1. 71 1. 27–2. 31 <0. 001 HIV infection 1. 73 1. 05–2. 84 0. 031 Inpatient chemotherapy 1. 38 1. 01–1. 87 0. 041 Prior chemotherapy 0. 44 0. 30–0. 63 <0. 001 Radiation therapy 0. 61 0. 38–0. 99 0. 046

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Cite This Study

Thannir et al. (2026) studied this question.

synapsesocial.com/papers/6a192f88fab5b468c4418aefhttps://doi.org/10.1200/jco.2026.44.16_suppl.11178
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