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Background <p>Leukemic transformation represents a pivotal event in the natural history of chronic myelomonocytic leukemia (CMML), yet conventional survival analysis may yield biased estimates by treating non-transformation deaths as censored observations. We hypothesised that competing risk methodology would provide more accurate transformation estimates and could alter the interpretation of treatment-associated effects compared with traditional approaches.</p> Methods <p>We retrospectively analysed 102 patients with CMML diagnosed and treated at Ganzhou People’s Hospital between January 2014 and November 2025. Cumulative incidence functions (CIF) were estimated using the Aalen–Johansen method with death without transformation as the competing event. Fine–Gray subdistribution hazard models identified independent predictors, with parallel Cox regression for methodological comparison. Treatments were classified by primary modality at baseline (HMA, chemotherapy, best supportive care (BSC), allo-HSCT) and entered as a stratification covariate rather than as a randomised exposure.</p> Results <p>During follow-up, 54 patients (52.9%) developed acute myeloid leukemia and 7 (6.9%) died without transformation. The 12-, 24-, and 36-month CIF were 46.9%, 63.4%, and 73.8%, respectively, whereas 1 − Kaplan–Meier estimates were 49.2%, 67.2%, and 78.4% (4.9–6.2% relative overestimation). Baseline treatment strategy was associated with transformation risk: compared with HMA, BSC showed a higher subdistribution hazard (SHR 4.77, 95% CI 1.86–12.22, <i>P</i> = 0.001) and chemotherapy SHR 2.16 (1.04–4.48, <i>P</i> = 0.039), with allo-HSCT not significantly associated (SHR 0.52, <i>P</i> = 0.261); these associations should be interpreted as exploratory given non-random allocation and small subgroup sizes (allo-HSCT <i>n</i> = 13, BSC <i>n</i> = 22). Intermediate and high-risk karyotypes independently predicted transformation (SHR 3.09 and 2.98, both <i>P</i> &lt; 0.05). Within the NGS-tested subgroup, <i>SETBP1</i> (SHR 3.09, <i>P</i> = 0.009) and <i>TP53</i> (SHR 3.66, <i>P</i> = 0.009) mutations marked small subsets at very high transformation risk. For allo-HSCT, Cox regression yielded a significant protective effect (HR 0.31, <i>P</i> = 0.026) that became non-significant in the Fine–Gray model, illustrating how cause-specific analysis can overstate the isolated anti-transformation effect of a treatment that simultaneously reduces mortality.</p> Conclusions <p>This study shows how competing-risk analysis complements standard Kaplan–Meier estimates of overall and progression-free survival when the cumulative incidence of a specific event, leukaemic transformation, is the quantity of interest. Although the modest, single-centre sample and non-randomised treatment allocation preclude definitive treatment recommendations, cytogenetic risk was the strongest disease-related prognostic factor, and molecular profiling provided additional risk discrimination within the NGS-tested subgroup. Cumulative incidence functions should therefore be reported routinely alongside Kaplan–Meier estimates in CMML and other myeloid neoplasms with non-negligible competing mortality.</p>

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Cumulative incidence and prognostic factors for leukemic transformation in chronic myelomonocytic leukemia: a competing risk analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Jungao Huang,
  • Chaoqiang Zheng,
  • Yulan Liu,
  • Changhao Li,
  • Xiaoqin Xin

摘要

Background

Leukemic transformation represents a pivotal event in the natural history of chronic myelomonocytic leukemia (CMML), yet conventional survival analysis may yield biased estimates by treating non-transformation deaths as censored observations. We hypothesised that competing risk methodology would provide more accurate transformation estimates and could alter the interpretation of treatment-associated effects compared with traditional approaches.

Methods

We retrospectively analysed 102 patients with CMML diagnosed and treated at Ganzhou People’s Hospital between January 2014 and November 2025. Cumulative incidence functions (CIF) were estimated using the Aalen–Johansen method with death without transformation as the competing event. Fine–Gray subdistribution hazard models identified independent predictors, with parallel Cox regression for methodological comparison. Treatments were classified by primary modality at baseline (HMA, chemotherapy, best supportive care (BSC), allo-HSCT) and entered as a stratification covariate rather than as a randomised exposure.

Results

During follow-up, 54 patients (52.9%) developed acute myeloid leukemia and 7 (6.9%) died without transformation. The 12-, 24-, and 36-month CIF were 46.9%, 63.4%, and 73.8%, respectively, whereas 1 − Kaplan–Meier estimates were 49.2%, 67.2%, and 78.4% (4.9–6.2% relative overestimation). Baseline treatment strategy was associated with transformation risk: compared with HMA, BSC showed a higher subdistribution hazard (SHR 4.77, 95% CI 1.86–12.22, P = 0.001) and chemotherapy SHR 2.16 (1.04–4.48, P = 0.039), with allo-HSCT not significantly associated (SHR 0.52, P = 0.261); these associations should be interpreted as exploratory given non-random allocation and small subgroup sizes (allo-HSCT n = 13, BSC n = 22). Intermediate and high-risk karyotypes independently predicted transformation (SHR 3.09 and 2.98, both P < 0.05). Within the NGS-tested subgroup, SETBP1 (SHR 3.09, P = 0.009) and TP53 (SHR 3.66, P = 0.009) mutations marked small subsets at very high transformation risk. For allo-HSCT, Cox regression yielded a significant protective effect (HR 0.31, P = 0.026) that became non-significant in the Fine–Gray model, illustrating how cause-specific analysis can overstate the isolated anti-transformation effect of a treatment that simultaneously reduces mortality.

Conclusions

This study shows how competing-risk analysis complements standard Kaplan–Meier estimates of overall and progression-free survival when the cumulative incidence of a specific event, leukaemic transformation, is the quantity of interest. Although the modest, single-centre sample and non-randomised treatment allocation preclude definitive treatment recommendations, cytogenetic risk was the strongest disease-related prognostic factor, and molecular profiling provided additional risk discrimination within the NGS-tested subgroup. Cumulative incidence functions should therefore be reported routinely alongside Kaplan–Meier estimates in CMML and other myeloid neoplasms with non-negligible competing mortality.