Dataset Information

A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy.

ABSTRACT:

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.

SUBMITTER: Ng T

PROVIDER: S-EPMC3396145 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Publications

A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy.

Ng Terence T Chew Lita L Yap Chun Wei CW

Journal of palliative medicine 20120612 8

<h4>Background</h4>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, t ...[more]

PMID: 22690950

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Project description:BackgroundThe emergency department is a critical juncture in the trajectory of care of patients with serious, life-limiting illness. Implementation of a clinical decision support (CDS) tool automates identification of older adults who may benefit from palliative care instead of relying upon providers to identify such patients, thus improving quality of care by assisting providers with adhering to guidelines. The Primary Palliative Care for Emergency Medicine (PRIM-ER) study aims to optimize the use of the electronic health record by creating a CDS tool to identify high risk patients most likely to benefit from primary palliative care and provide point-of-care clinical recommendations.MethodsA clinical decision support tool entitled Emergency Department Supportive Care Clinical Decision Support (Support-ED) was developed as part of an institutionally-sponsored value based medicine initiative at the Ronald O. Perelman Department of Emergency Medicine at NYU Langone Health. A multidisciplinary approach was used to develop Support-ED including: a scoping review of ED palliative care screening tools; launch of a workgroup to identify patient screening criteria and appropriate referral services; initial design and usability testing via the standard System Usability Scale questionnaire, education of the ED workforce on the Support-ED background, purpose and use, and; creation of a dashboard for monitoring and feedback.ResultsThe scoping review identified the Palliative Care and Rapid Emergency Screening (P-CaRES) survey as a validated instrument in which to adapt and apply for the creation of the CDS tool. The multidisciplinary workshops identified two primary objectives of the CDS: to identify patients with indicators of serious life limiting illness, and to assist with referrals to services such as palliative care or social work. Additionally, the iterative design process yielded three specific patient scenarios that trigger a clinical alert to fire, including: 1) when an advance care planning document was present, 2) when a patient had a previous disposition to hospice, and 3) when historical and/or current clinical data points identify a serious life-limiting illness without an advance care planning document present. Monitoring and feedback indicated a need for several modifications to improve CDS functionality.ConclusionsCDS can be an effective tool in the implementation of primary palliative care quality improvement best practices. Health systems should thoughtfully consider tailoring their CDSs in order to adapt to their unique workflows and environments. The findings of this research can assist health systems in effectively integrating a primary palliative care CDS system seamlessly into their processes of care.Trial registrationClinicalTrials.gov Identifier: NCT03424109. Registered 6 February 2018, Grant Number: AT009844-01.

Project description:Appropriate thromboprophylaxis for patients with atrial fibrillation or atrial flutter (AF) remains a national challenge. The recent availability of direct oral anticoagulants (DOACs) with comparable efficacy and improved safety compared with warfarin alters the balance between risk factors for stroke and benefit of anticoagulation. Our objective was to examine the impact of DOACs as an alternative to warfarin on the net benefit of oral anticoagulant therapy (OAT) in a real-world population of AF patients.This is a retrospective cohort study of patients with paroxysmal or persistent nonvalvular AF. We updated an Atrial Fibrillation Decision Support Tool (AFDST) to include DOACs as treatment options. The tool generates patient-specific recommendations based upon individual patient risk factor profiles for stroke and major bleeding using quality-adjusted life-years (QALYs) calculated for each treatment strategy by a decision analytic model. The setting included inpatient and ambulatory sites in an academic health center in the midwestern United States. The study involved 5,121 adults with nonvalvular AF seen for any ambulatory visit or inpatient hospitalization over the 1-year period (January through December 2016). Outcome measure was net clinical benefit in QALYs.When DOACs are a therapeutic option, the AFDST recommends OAT for 4,134 (81%) patients and no antithrombotic therapy or aspirin for 489 (9%). A strong recommendation for OAT could not be made in 498 (10%) patients. When warfarin is the only option, OAT is recommended for 3,228 (63%) patients and no antithrombotic therapy or aspirin for 973 (19%). A strong recommendation for OAT could not be made in 920 (18%) patients. In total, 1,508 QALYs could be gained if treatment were changed to that recommended by the AFDST.Availability of DOACs increases the proportion of patients for whom oral anticoagulation therapy is recommended in a real-world cohort of AF patients and increased projected QALYs by more than 1,500 when all patients are receiving thromboprophylaxis as recommended by the AFDST compared with current treatment.

Project description:BackgroundApproximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census.MethodsA five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN.ResultsCEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool.ConclusionState-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

Project description:BackgroundThere is increasing recognition of the delicate balance between the modest benefits of palliative chemotherapy and the burden of treatment. Decision aids (DAs) can potentially help patients with advanced cancer with these difficult treatment decisions, but providing detailed information could have an adverse impact on patients' well-being. The objective of this randomised phase II study was to evaluate the safety and efficacy of DAs for patients with advanced cancer considering second-line chemotherapy.MethodsPatients with advanced breast or colorectal cancer considering second-line treatment were randomly assigned to usual care (control group) or usual care plus a DA (intervention group) in a 1:2 ratio. A nurse offered a DA with information on adverse events, tumour response and survival. Outcome measures included patient-reported well-being (primary outcome: anxiety) and quality of the decision-making process and the resulting choice.ResultsOf 128 patients randomised, 45 were assigned to the control group and 83 to the intervention group. Median age was 62 years (range 32-81), 63% were female, and 73% had colorectal cancer. The large majority of patients preferred treatment with chemotherapy (87%) and subsequently commenced treatment with chemotherapy (86%). No adverse impact on patients' well-being was found and nurses reported that consultations in which the DAs were offered went well. Being offered the DA was associated with stronger treatment preferences (3.0 vs. 2.5; p=0.030) and increased subjective knowledge (6.7 vs. 6.3; p=0.022). Objective knowledge, risk perception and perceived involvement were comparable between the groups.ConclusionsDAs containing detailed risk information on second-line palliative treatment could be delivered to patients with advanced cancer without having an adverse impact on patient well-being. Surprisingly, the DAs only marginally improved the quality of the decision-making process. The effectiveness of DAs for palliative treatment decisions needs further exploration.Trial registrationNetherlands Trial Registry (NTR): NTR1113 (registered on 2 November 2007).

Dataset Information

A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy.

Background

Materials and methods

Results

Conclusions

Publications

A clinical decision support tool to predict survival in cancer patients beyond 120 days after palliative chemotherapy.

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