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 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.


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.
Department of Pharmacy, National University of Singapore, Singapore.
Terence
T
Ng
Lita
L
Chew
Chun Wei
CW
Yap
D000970:Antineoplastic Agents / Q000008:administration & dosage* / Q000009:adverse effects / Q000592:standards; D001499:Bayes Theorem; D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical / Q000458:organization & administration* / Q000592:standards; D018450:Disease Progression; D006801:Humans; D008017:Life Expectancy; D009369:Neoplasms / Q000188:drug therapy* / Q000473:pathology / Q000503:physiopathology; D016571:Neural Networks, Computer; D049451:Nomograms; D010166:Palliative Care / Q000379:methods* / Q000592:standards; D011379:Prognosis; D011788:Quality of Life; D012189:Retrospective Studies; D018434:Sickness Impact Profile; D012846:Singapore
D000970:Antineoplastic Agents