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

Supportive Care in Cancer

Volumn 19, Issue 10, 2011, Pages 1625-1635

  Hui, Edwin Pun ^a,b   Leung, Linda K S ^a   Poon, Terence C W ^b   Mo, Frankie ^a   Chan, Vicky T C ^a   Ma, Ada T W ^a   Poon, Annette ^a   Hui, Eugenie K ^a   Mak, So Shan ^a   Lai, Maria ^b   Lei, Kenny I K ^a,b   Ma, Brigette B Y ^a,b   Mok, Tony S K ^a,b   Yeo, Winnie ^a,b   Zee, Benny C Y ^b   Chan, Anthony T C ^a,b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Artificial neural networks; Discriminatory accuracy; Febrile neutropenia; Receiver operating characteristics curve; Risk prediction model

Indexed keywords

ANTINEOPLASTIC AGENT;

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER PATIENT; CHINESE; COHORT ANALYSIS; COMPARATIVE STUDY; COMPLICATION; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; DIAGNOSTIC ERROR; DIAGNOSTIC TEST ACCURACY STUDY; FALSE POSITIVE RESULT; FEBRILE NEUTROPENIA; FEMALE; HIGH RISK PATIENT; HUMAN; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; MORTALITY; MULTINATIONAL ASSOCIATION FOR SUPPORT CARE IN CANCER RISK INDEX; NAMED INVENTORIES, QUESTIONNAIRES AND RATING SCALES; PREDICTIVE VALUE; PRIORITY JOURNAL; PROSPECTIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; TALCOTT MODEL; TREATMENT OUTCOME; VALIDATION STUDY;

ADULT; ANTINEOPLASTIC AGENTS; CHINA; COHORT STUDIES; FEMALE; FEVER; HUMANS; MALE; MIDDLE AGED; MODELS, STATISTICAL; NEOPLASMS; NEURAL NETWORKS (COMPUTER); NEUTROPENIA; PREDICTIVE VALUE OF TESTS; PROSPECTIVE STUDIES; ROC CURVE; SENSITIVITY AND SPECIFICITY; TREATMENT OUTCOME;

EID: 80052448364     PISSN: 09414355     EISSN: 14337339     Source Type: Journal    
DOI: 10.1007/s00520-010-0993-8     Document Type: Article

References (34)

1. EJ Anaissie S Vadhan-Raj 1995 Is it time to redefine the management of febrile neutropenia in cancer patients? Am J Med 98 221 223 7872336 10.1016/S0002-9343(99)80366-0 1:STN:280:DyaK2M7nslWrtg%3D%3D
2. ND Baskaran GG Gan K Adeeba 2008 Applying the Multinational Association for Supportive Care in Cancer risk scoring in predicting outcome of febrile neutropenia patients in a cohort of patients Ann Hematol 87 563 569 18437382 10.1007/s00277-008-0487-7
3. WG Baxt 1995 Application of artificial neural networks to clinical medicine Lancet 346 1135 1138 7475607 10.1016/S0140-6736(95)91804-3 1:STN:280:DyaK28%2Fjs1Cgsg%3D%3D
4. L Bottaci PJ Drew JE Hartley MB Hadfield R Farouk PW Lee IM Macintyre GS Duthie JR Monson 1997 Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions Lancet 350 469 472 9274582 10.1016/S0140-6736(96)11196-X 1:STN:280:DyaK2svitVSktA%3D%3D (Pubitemid 27343301)
5. TJ Bryce MW Dewhirst CE Floyd Jr V Hars DM Brizel 1998 Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck Int J Radiat Oncol Biol Phys 41 339 345 9607349 10.1016/S0360-3016(98)00016-9 1:STN:280:DyaK1c3mvVeltw%3D%3D (Pubitemid 28243545)
6. H Cherif E Johansson M Bjorkholm M Kalin 2006 The feasibility of early hospital discharge with oral antimicrobial therapy in low risk patients with febrile neutropenia following chemotherapy for hematologic malignancies Haematologica 91 215 222 16461306
7. Cheung M, Online calculator: calculate risk in febrile neutropenia (MASCC). QxMD Hematology http://www.qxmd.com/hematology/MASCC-Febrile- Neutropenia-Risk.php
8. SS Cross RF Harrison RL Kennedy 1995 Introduction to neural networks Lancet 346 1075 1079 7564791 10.1016/S0140-6736(95)91746-2 1:STN:280: DyaK28%2FisF2huw%3D%3D
9. A Das T Ben-Menachem GS Cooper A Chak MV Sivak Jr JA Gonet RC Wong 2003 Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model Lancet 362 1261 1266 14575969 10.1016/S0140-6736(03)14568-0 (Pubitemid 37324252)
10. A Das T Ben-Menachem FT Farooq GS Cooper A Chak MV Sivak Jr RC Wong 2008 Artificial neural network as a predictive instrument in patients with acute nonvariceal upper gastrointestinal hemorrhage Gastroenterology 134 65 74 18061180 10.1053/j.gastro.2007.10.037 (Pubitemid 350309326)
11. ES de Bont E Vellenga JC Swaanenburg V Fidler PJ Visser-van Brummen WA Kamps 1999 Plasma IL-8 and IL-6 levels can be used to define a group with low risk of septicaemia among cancer patients with fever and neutropenia Br J Haematol 107 375 380 10583227 10.1046/j.1365-2141.1999.01707.x (Pubitemid 29530226)
12. L de Souza Viana JC Serufo MO da Costa Rocha RN Costa RC Duarte 2008 Performance of a modified MASCC index score for identifying low-risk febrile neutropenic cancer patients Support Care Cancer 16 841 846 17960431 10.1007/s00520-007-0347-3 (Pubitemid 351809802)
13. LS Elting C Lu CP Escalante SH Giordano JC Trent C Cooksley EB Avritscher YC Shih J Ensor BN Bekele RJ Gralla JA Talcott K Rolston 2008 Outcomes and cost of outpatient or inpatient management of 712 patients with febrile neutropenia J Clin Oncol 26 606 611 18235119 10.1200/JCO.2007.13.8222 (Pubitemid 351264355)
14. RW Finberg JA Talcott 1999 Fever and neutropenia-how to use a new treatment strategy N Engl J Med 341 362 363 10423472 10.1056/NEJM199907293410509 1:STN:280:DyaK1Mzjs12qtw%3D%3D (Pubitemid 29350308)
15. EJ Giamarellos-Bourboulis P Grecka G Poulakou K Anargyrou N Katsilambros H Giamarellou 2001 Assessment of procalcitonin as a diagnostic marker of underlying infection in patients with febrile neutropenia Clin Infect Dis 32 1718 1725 11360214 10.1086/320744 1:STN:280:DC%2BD3M3ns1SmsA%3D%3D (Pubitemid 32532002)
16. A Jimeno A Garcia-Velasco O del Val E Gonzalez-Billalabeitia S Hernando R Hernandez A Sanchez-Munoz A Lopez-Martin I Duran L Robles H Cortes-Funes L Paz-Ares 2004 Assessment of procalcitonin as a diagnostic and prognostic marker in patients with solid tumors and febrile neutropenia Cancer 100 2462 2469 15160353 10.1002/cncr.20275 1:CAS:528:DC%2BD2cXltFajsrc%3D (Pubitemid 38657897)
17. WV Kern 2006 Risk assessment and treatment of low-risk patients with febrile neutropenia Clin Infect Dis 42 533 540 16421798 10.1086/499352 (Pubitemid 43202527)
18. J Klastersky M Paesmans A Georgala F Muanza B Plehiers L Dubreucq Y Lalami M Aoun M Barette 2006 Outpatient oral antibiotics for febrile neutropenic cancer patients using a score predictive for complications J Clin Oncol 24 4129 4134 16943529 10.1200/JCO.2005.03.9909 1:CAS:528:DC%2BD28XhtVantbrP (Pubitemid 46622289)
19. J Klastersky M Paesmans EB Rubenstein M Boyer L Elting R Feld J Gallagher J Herrstedt B Rapoport K Rolston J Talcott 2000 The Multinational Association for Supportive Care in Cancer risk index: a multinational scoring system for identifying low-risk febrile neutropenic cancer patients J Clin Oncol 18 3038 3051 10944139 1:STN:280:DC%2BD3cvivFCrsg%3D%3D (Pubitemid 30644129)
20. B Ma W Yeo P Hui WM Ho PJ Johnson 2002 Acute toxicity of adjuvant doxorubicin and cyclophosphamide for early breast cancer-a retrospective review of Chinese patients and comparison with an historic Western series Radiother Oncol 62 185 189 11937245 10.1016/S0167-8140(02)00003-8 1:CAS:528: DC%2BD38XisV2lsL8%3D (Pubitemid 34286451)
21. CS Oude Nijhuis SM Daenen E Vellenga WT van der Graaf JA Gietema HJ Groen WA Kamps ES de Bont 2002 Fever and neutropenia in cancer patients: the diagnostic role of cytokines in risk assessment strategies Crit Rev Oncol Hematol 44 163 174 12413633 10.1016/S1040-8428(01)00220-7 1:STN:280: DC%2BD38nlsVWgsw%3D%3D (Pubitemid 36159044)
22. M Paesmans B Rapoport J Maertens C Slabber A Ferrant J Wingard M Aoun L Dubreucq B Plehiers J Klastersky 2003 Multicentric prospective validation of the mascc risk-index score for identification of febrile neutropenic cancer patients at low-risk for serious medical complications Proc Am Soc Clin Oncol 22 556 abstr 2235
23. TC Poon AT Chan B Zee SK Ho TS Mok TW Leung PJ Johnson 2001 Application of classification tree and neural network algorithms to the identification of serological liver marker profiles for the diagnosis of hepatocellular carcinoma Oncology 61 275 283 11721174 10.1159/000055334 1:CAS:528:DC%2BD3MXos1Wns7s%3D (Pubitemid 33135836)
24. TC Poon TT Yip AT Chan C Yip V Yip TS Mok CC Lee TW Leung SK Ho PJ Johnson 2003 Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes Clin Chem 49 752 760 12709366 10.1373/49.5.752 1:CAS:528:DC%2BD3sXjsVahs7Y%3D (Pubitemid 36513254)
25. O Ramilo W Allman W Chung A Mejias M Ardura C Glaser KM Wittkowski B Piqueras J Banchereau AK Palucka D Chaussabel 2007 Gene expression patterns in blood leukocytes discriminate patients with acute infections Blood 109 2066 2077 17105821 10.1182/blood-2006-02-002477 1:CAS:528:DC%2BD2sXjtFequr0%3D (Pubitemid 46348207)
26. DJ Sargent 2001 Comparison of artificial neural networks with other statistical approaches: results from medical data sets Cancer 91 1636 1642 11309761 10.1002/1097-0142(20010415)91:8+<1636::AID-CNCR1176>3.0.CO;2-D 1:STN:280:DC%2BD3M3hvFWgtg%3D%3D (Pubitemid 32319240)
27. G Schwarzer W Vach M Schumacher 2000 On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology Stat Med 19 541 561 10694735 10.1002/(SICI)1097-0258(20000229)19:4<541::AID-SIM355>3. 0.CO;2-V 1:STN:280:DC%2BD3c7mtV2ksA%3D%3D (Pubitemid 30126883)
28. JA Talcott R Finberg RJ Mayer L Goldman 1988 The medical course of cancer patients with fever and neutropenia. Clinical identification of a low-risk subgroup at presentation Arch Intern Med 148 2561 2568 3196123 10.1001/archinte.148.12.2561 1:STN:280:DyaL1M%2FmtFamuw%3D%3D (Pubitemid 19048173)
29. JA Talcott RD Siegel R Finberg L Goldman 1992 Risk assessment in cancer patients with fever and neutropenia: a prospective, two-center validation of a prediction rule J Clin Oncol 10 316 322 1732432 1:STN:280:DyaK387itF2ntw%3D%3D
30. JA Talcott A Whalen J Clark PP Rieker R Finberg 1994 Home antibiotic therapy for low-risk cancer patients with fever and neutropenia: a pilot study of 30 patients based on a validated prediction rule J Clin Oncol 12 107 114 8270967 1:STN:280:DyaK2c%2Fps1Sntg%3D%3D (Pubitemid 24020628)
31. A Uys BL Rapoport R Anderson 2004 Febrile neutropenia: a prospective study to validate the Multinational Association of Supportive Care of Cancer (MASCC) risk-index score Support Care Cancer 12 555 560 15197637 10.1007/s00520-004-0614-5 (Pubitemid 39062546)
32. A Uys BL Rapoport H Fickl PW Meyer R Anderson 2007 Prediction of outcome in cancer patients with febrile neutropenia: comparison of the Multinational Association of Supportive Care in Cancer risk-index score with procalcitonin, C-reactive protein, serum amyloid A, and interleukins-1beta, -6, -8 and -10 Eur J Cancer Care (Engl) 16 475 483 10.1111/j.1365-2354.2007.00780.x 1:STN:280:DC%2BD2snitVKmtg%3D%3D
33. O Uzun EJ Anaissie 1999 Outpatient therapy for febrile neutropenia: who, when, and how? J Antimicrob Chemother 43 317 320 10223585 10.1093/jac/43.3.317 1:CAS:528:DyaK1MXitFGhsL0%3D (Pubitemid 29121869)
34. J Wyatt 1995 Nervous about artificial neural networks? Lancet 346 1175 1177 7475653 10.1016/S0140-6736(95)92893-6 1:STN:280:DyaK28%2FlsVygsQ%3D%3D