AI Article Synopsis

  • Febrile neutropenia (FN) is a serious condition often seen in chemotherapy patients, with a notable mortality rate of 12.5%, making accurate risk assessment vital for effective treatment.
  • This study, conducted at Cipto Mangunkusumo Hospital from 2015 to 2019, aimed to compare the effectiveness of two scoring systems—CISNE and MASCC—in predicting complications during hospitalization for FN patients.
  • The results indicated that CISNE score outperformed MASCC in both solid and hematologic cancers, with significant differences in predictive accuracy, particularly highlighting CISNE's superior ability to forecast in-hospital complications.

Article Abstract

Background: Febrile neutropenia (FN) is an oncologic emergency which commonly occurrs in patients who undergo chemotherapy, with a mortality rate of 12.5%. Risk stratification in FN plays an important role in increasing the accuracy of therapy. This study aims to compare the performance between CISNE score and MASCC score in predicting complications on post-chemotherapy FN in solid and hematologic malignancy.  Methods: This is a retrospective cohort study on FN patients undergoing inpatient treatment at Cipto Mangunkusumo Hospital between July 2015 and December 2019. Basic demographic and clinical data were collected from medical records. Subjects were grouped based on the CISNE and MASCC score, and complications during hospitalization were recorded. Predictive performance of each score was analyzed and compared using area of under curve.  Results: CISNE score showed a better performance both in solid malignancy with AUC of CISNE score (0.80 CI 95% 0.73-0.88, p = 0.00) compared to AUC of MASCC score (0.68; 95% CI 0.59 - 0,78, p = 0.00) and in hematologic malignancy with AUC of CISNE score (0.85; 95% CI 0.77 - 0.93, p = 0.00) and AUC MASCC score (0.65 ; 95% CI 0.54 - 0.76, p = 0.007).

Conclusion: CISNE score showed a better performance compared to MASCC score in predicting in-hospital complication in both solid and hematologic malignancy with cut-off point of 2.

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