A Clinical Decision Support Tool To Predict Survival in Cancer Patients beyond 120 Days after Palliative Chemotherapy.
Background: Palliative chemotherapy is often administered to terminally ill cancer patients to relieve symptoms. Yet, unnecessary use of chemotherapy can worsen patients' quality of life due to treatment-related toxicities. Thus, accurate prediction of survival in terminally ill patients can help cl...
| Publicado en: | Journal of Palliative Medicine Vol. 15; no. 8; pp. 863 - 870 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Mary Ann Liebert, Inc.
Aug2012
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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=104485371&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104485371 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10966218 C1U jtl: Journal of Palliative Medicine issn: 10966218 maglogo: N pubinfo: dt: Aug2012 vid: 15 iid: 8 pid: 1365 pub: Mary Ann Liebert, Inc. place: New Rochelle, New York artinfo: ui: 104485371 78191778 10.1089/jpm.2011.0417 NLM22690950 104485371 ppf: 863 ppct: 7 formats: tig: atl: A Clinical Decision Support Tool To Predict Survival in Cancer Patients beyond 120 Days after Palliative Chemotherapy. aug: au: Ng, Terence Chew, Lita Yap, Chun Wei affil: Department of Pharmacy, National University of Singapore, Singapore. sug: subj: Decision Support Systems, Clinical Cancer Patients Palliative Care Survival Chemotherapy, Cancer Prognosis Quality of Life Human Singapore Algorithms Classification Sensitivity and Specificity Data Mining Random Sample Retrospective Design Descriptive Statistics Neural Networks (Computer) Data Analysis Software Serum Albumin Funding Source ab: Background: Palliative chemotherapy is often administered to terminally ill cancer patients to relieve symptoms. Yet, unnecessary use of chemotherapy can worsen patients' quality of life due to treatment-related toxicities. Thus, accurate prediction of survival in terminally ill patients can help clinicians decide on the most appropriate palliative care for these patients. However, studies have shown that clinicians often make imprecise predictions of survival in cancer patients. Hence, the purpose of this study was to create a clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy. Materials and Methods: Data were obtained from a retrospective study of 400 randomly selected terminally ill cancer patients in the National Cancer Centre Singapore (NCCS) from 2008 to 2009. After removing patients with missing data, there were 325 patients remaining for model development. Three classification algorithms, naive Bayes (NB), neural network (NN), and support vector machine (SVM) were used to create the models. A final model with the best prediction performance was then selected to develop the tool. Results: The NN model had the best prediction performance. The accuracy, specificity, sensitivity, and area under the curve (AUC) of this model were 78%, 82%, 74%, and 0.857, respectively. Five patient attributes (albumin level, alanine transaminase level (ATL), absolute neutrophil count, Eastern Cooperative Oncology Group (ECOG) status, and number of metastatic sites) were included in the model. Conclusions: A decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy was created. With further validation, this tool coupled with the professional judgment of clinicians can help improve patient care. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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