Literature DB >> 25559804

Prediction of serious complications in patients with seemingly stable febrile neutropenia: validation of the Clinical Index of Stable Febrile Neutropenia in a prospective cohort of patients from the FINITE study.

Alberto Carmona-Bayonas1, Paula Jiménez-Fonseca2, Juan Virizuela Echaburu2, Maite Antonio2, Carme Font2, Mercè Biosca2, Avinash Ramchandani2, Jerónimo Martínez2, Jorge Hernando Cubero2, Javier Espinosa2, Eva Martínez de Castro2, Ismael Ghanem2, Carmen Beato2, Ana Blasco2, Marcelo Garrido2, Yaiza Bonilla2, Rebeca Mondéjar2, María Ángeles Arcusa Lanza2, Isabel Aragón Manrique2, Aránzazu Manzano2, Elena Sevillano2, Eduardo Castañón2, Mercé Cardona2, Elena Gallardo Martín2, Quionia Pérez Armillas2, Fernando Sánchez Lasheras2, Francisco Ayala de la Peña2.   

Abstract

PURPOSE: To validate a prognostic score predicting major complications in patients with solid tumors and seemingly stable episodes of febrile neutropenia (FN). The definition of clinical stability implies the absence of organ dysfunction, abnormalities in vital signs, and major infections. PATIENTS AND METHODS: We developed the Clinical Index of Stable Febrile Neutropenia (CISNE), with six explanatory variables associated with serious complications: Eastern Cooperative Oncology Group performance status ≥ 2 (2 points), chronic obstructive pulmonary disease (1 point), chronic cardiovascular disease (1 point), mucositis of grade ≥ 2 (National Cancer Institute Common Toxicity Criteria; 1 point), monocytes < 200 per μL (1 point), and stress-induced hyperglycemia (2 points). We integrated these factors into a score ranging from 0 to 8, which classifies patients into three prognostic classes: low (0 points), intermediate (1 to 2 points), and high risk (≥ 3 points). We present a multicenter validation of CISNE.
RESULTS: We prospectively recruited 1,133 patients with seemingly stable FN from 25 hospitals. Complication rates in the training and validation subsets, respectively, were 1.1% and 1.1% in low-, 6.1% and 6.2% in intermediate-, and 32.5% and 36% in high-risk patients; mortality rates within each class were 0% in low-, 1.6% and 0% in intermediate-, and 4.3% and 3.1% in high-risk patients. Areas under the receiver operating characteristic curves in the validation subset were 0.652 (95% CI, 0.598 to 0.703) for Talcott, 0.721 (95% CI, 0.669 to 0.768) for Multinational Association for Supportive Care in Cancer (MASCC), and 0.868 (95% CI, 0.827 to 0.903) for CISNE (P = .002 for comparison between CISNE and MASCC).
CONCLUSION: CISNE is a valid model for accurately classifying patients with cancer with seemingly stable FN episodes.
© 2015 by American Society of Clinical Oncology.

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Year:  2015        PMID: 25559804     DOI: 10.1200/JCO.2014.57.2347

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  32 in total

1.  Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.

Authors:  Xinsong Du; Jae Min; Chintan P Shah; Rohit Bishnoi; William R Hogan; Dominick J Lemas
Journal:  Int J Med Inform       Date:  2020-04-15       Impact factor: 4.046

2.  The prognostic performance of adding patient-reported outcomes to the MASCC risk index to identify low-risk febrile neutropenia patients with solid tumors and lymphomas.

Authors:  Xiao Jun Wang; Denise Yun Ting Goh; Sreemanee Raaj Dorajoo; Alexandre Chan
Journal:  Support Care Cancer       Date:  2017-04-11       Impact factor: 3.603

3.  Management and Preventive Measures for Febrile Neutropenia.

Authors:  Austin J Lucas; Jacqueline L Olin; Megan D Coleman
Journal:  P T       Date:  2018-04

4.  An ED pilot intervention to facilitate outpatient acute care for cancer patients.

Authors:  Gabriel A Brooks; Eddy J Chen; Mark A Murakami; Marios Giannakis; Christopher W Baugh; Deb Schrag
Journal:  Am J Emerg Med       Date:  2016-06-24       Impact factor: 2.469

5.  Severity and predictive factors of adverse events in pemetrexed-containing chemotherapy for non-small cell lung cancer.

Authors:  Tsuyoshi Miyahara; Naoko Sueoka-Aragane; Kentaro Iwanaga; Norio Ureshino; Kazutoshi Komiya; Tomomi Nakamura; Chiho Nakashima; Tomonori Abe; Hisashi Matsunaga; Shinya Kimura
Journal:  Med Oncol       Date:  2017-11-09       Impact factor: 3.064

Review 6.  Optimizing Symptoms and Management of Febrile Neutropenia among Cancer Patients: Current Status and Future Directions.

Authors:  Xiao Jun Wang; Alexandre Chan
Journal:  Curr Oncol Rep       Date:  2017-03       Impact factor: 5.075

7.  Comparison of the MASCC and CISNE scores for identifying low-risk neutropenic fever patients: analysis of data from three emergency departments of cancer centers in three continents.

Authors:  Shin Ahn; Terry W Rice; Sai-Ching J Yeung; Tim Cooksley
Journal:  Support Care Cancer       Date:  2017-11-22       Impact factor: 3.603

8.  The time has come for new models in febrile neutropenia: a practical demonstration of the inadequacy of the MASCC score.

Authors:  A Carmona-Bayonas; P Jiménez-Fonseca; J Virizuela Echaburu; M Sánchez Cánovas; F Ayala de la Peña
Journal:  Clin Transl Oncol       Date:  2017-03-13       Impact factor: 3.405

Review 9.  Rapid Fire: Infectious Disease Emergencies in Patients with Cancer.

Authors:  Stephanie Charshafian; Stephen Y Liang
Journal:  Emerg Med Clin North Am       Date:  2018-06-11       Impact factor: 2.264

10.  Performance of the clinical index of stable febrile neutropenia (CISNE) in different types of infections and tumors.

Authors:  A Carmona-Bayonas; P Jiménez-Fonseca; J Virizuela; M Antonio; C Font; M Biosca; A Ramchandani; J Martinez-Garcia; J Hernando; J Espinosa; E M de Castro; I Ghanem; C Beato; A Blasco; M Garrido; R Mondéjar; M Á Arcusa; I Aragón; A Manzano; E Sevillano; E Castañón; F Ayala
Journal:  Clin Transl Oncol       Date:  2016-08-15       Impact factor: 3.405

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