Prediction of outcome in cancer patients with febrile neutropenia: a prospective validation of the Multinational Association for Supportive Care in Cancer risk index in a Chinese population and comparison with the Talcott model and artificial neural network.
Purpose: We aimed to validate the Multinational Association for Supportive Care in Cancer (MASCC) risk index, and compare it with the Talcott model and artificial neural network (ANN) in predicting the outcome of febrile neutropenia in a Chinese population.Methods: We prospectively enrolled adult ca...
| Publicado en: | Supportive Care in Cancer Vol. 19; no. 10; pp. 1625 - 1636 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104579465&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104579465 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09414355 O1J jtl: Supportive Care in Cancer issn: 09414355 maglogo: N pubinfo: dt: Oct2011 vid: 19 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104579465 65183078 NLM20820815 2011261197 10.1007/s00520-010-0993-8 NLM20820815 104579465 ppf: 1625 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Prediction of outcome in cancer patients with febrile neutropenia: a prospective validation of the Multinational Association for Supportive Care in Cancer risk index in a Chinese population and comparison with the Talcott model and artificial neural network. aug: au: Hui EP Leung LK Poon TC Mo F Chan VT Ma AT Poon A Hui EK Mak SS Lai M Lei KI Ma BB Mok TS Yeo W Zee BC Chan AT Hui, Edwin Pun Leung, Linda K S Poon, Terence C W Mo, Frankie affil: Department of Clinical Oncology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong, SAR, China sug: subj: Antineoplastic Agents Adverse Effects Models, Statistical Neural Networks (Computer) Neutropenia Chemically Induced Febrile Neutropenia Adult Antineoplastic Agents Therapeutic Use China Prospective Studies Female Fever Chemically Induced Human Male Middle Age Neoplasms Drug Therapy Neutropenia Ethnology Predictive Value of Tests ROC Curve Sensitivity and Specificity Treatment Outcomes Adult: 19-44 years Middle Aged: 45-64 years Female Male ab: Purpose: We aimed to validate the Multinational Association for Supportive Care in Cancer (MASCC) risk index, and compare it with the Talcott model and artificial neural network (ANN) in predicting the outcome of febrile neutropenia in a Chinese population.Methods: We prospectively enrolled adult cancer patients who developed febrile neutropenia after chemotherapy and risk classified them according to MASCC score and Talcott model. ANN models were constructed and temporally validated in prospectively collected cohorts.Results: From October 2005 to February 2008, 227 consecutive patients were enrolled. Serious medical complications occurred in 22% of patients and 4% died. The positive predictive value of low risk prediction was 86% (95% CI = 81-90%) for MASCC score ≥ 21, 84% (79-89%) for Talcott model, and 85% (78-93%) for the best ANN model. The sensitivity, specificity, negative predictive value, and misclassification rate were 81%, 60%, 52%, and 24%, respectively, for MASCC score ≥ 21; and 50%, 72%, 33%, and 44%, respectively, for Talcott model; and 84%, 60%, 58%, and 22%, respectively, for ANN model. The area under the receiver-operating characteristic curve was 0.808 (95% CI = 0.717-0.899) for MASCC, 0.573 (0.455-0.691) for Talcott, and 0.737 (0.633-0.841) for ANN model. In the low risk group identified by MASCC score ≥ 21 (70% of all patients), 12.5% developed complications and 1.9% died, compared with 43.3%, and 9.0%, respectively, in the high risk group (p < 0.0001).Conclusions: The MASCC risk index is prospectively validated in a Chinese population. It demonstrates a better overall performance than the Talcott model and is equivalent to ANN model. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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