Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy

Abstract

PURPOSE Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients. MATERIALS AND METHODS CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared. RESULTS CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82). CONCLUSION Machine learning–based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

“…The Machine Learning models described incorporated a variety of data inputs from patients in multiple different settings, leveraged several distinct algorithm types, and varied in the composition of stakeholder involvement. AI System Development of the predictive models included data from EHRs and/or administrative databases [22][23][24][25][26][27][28][29][30][31] as well as prospectively collected data such as physiological data from wearables 32 and imaging data 24 . Vital signs, laboratory values, diagnoses, and clinical notes were common sources of input data from the EHR.…”

“…Other types of input data included activities of daily living collected from nursing flowsheets 30 and nursing assessments 25 . Most models leveraged data from hospitalized adult patients; however, several models included data from outpatient settings 23,24 and even from the community 31 .…”

“…Algorithms comprised a variety of contemporary machine learning algorithms including, but not limited to, decision tree-based methods, neural networks, and ensemble methods. While five studies did not report the expertise of personnel involved in model development, other papers reported clinicians, (e.g., nurses, physicians, occupational therapists, case managers) 23,24,27,32 data scientists 26 , statisticians 24 , informaticians 25 , policymakers 26 , and un-specified hospital staff 26,32 as being involved in model development in some capacity. The nursing-relevant outcomes targeted by the data science systems fell within three clinical domains: mortality/deterioration 22,24,25,32 , health care utilization/resource allocation 23,26,27,29,30 , and hospital acquired infections/COVID-19 28,31 .…”

“…Murphree 29 reported that 43% of patients identified as high risk were accepted to the palliative care service using the model. Han 24 reported that physicians changed their prediction in 4 of 37 cases after being presented with the model prediction and compared the CDSS versus physician accuracy, specificity, positive predictive value (PPV) and negative predictive value (NPV), and sensitivity (CDSS outperformed physicians on all but the latter measure). Altieri Dunn et al 22 reported on monthly compliance rates of their system, amount of missing data, and the time it takes for nurse users to enter data features into the system to generate a prediction.…”

“…Project team composition varied among studies. Of those that reported team membership, roles reported included physicians 23,24,27 , nurses 26,27 , Informatics/IT 25,26,29 , data scientists and policy makers 26 . Some studies also used more general terms to describe team membership such as hospital stakeholders, hospital staff, or subject matter experts so the roles of some team members could not be specifically identified 23,26,30 .…”