The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning

An efficient interpretable stacking ensemble model for lung cancer prognosis

Lung cancer significantly contributes to global cancer mortality, posing challenges in clinical management. Early detection and accurate prognosis are crucial for improving patient outcomes. This study develops an interpretable stacking ensemble model (SEM) for lung cancer prognosis prediction and identifies key risk factors. Using a Kaggle dataset of 1000 patients with 22 variables, the model classifies prognosis into Low, Medium, and High-risk categories. The bootstrap method was employed for evaluation metrics, while SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) assessed model interpretability. Results showed SEM's superior interpretability over traditional models, such as Random Forest, Logistic Regression, Decision Tree, Gradient Boosting Machine, Extreme Gradient Boosting Machine, and Light Gradient Boosting Machine. SEM achieved an accuracy of 98.90 %, precision of 98.70 %, F1 score of 98.85 %, sensitivity of 98.77 %, specificity of 95.45 %, Cohen's kappa value of 94.56 %, and an AUC of 98.10 %. The SEM demonstrated robust performance in lung cancer prognosis, revealing chronic lung cancer and genetic risk as major factors.

The role of quality of life data as an endpoint for collecting real-world evidence within geroscience clinical trials

With geroscience research evolving at a fast pace, the need arises for human randomized controlled trials to assess the efficacy of geroprotective interventions to prevent age-related adverse outcomes, disease, and mortality in normative aging cohorts. However, to confirm efficacy requires a long-term and costly approach as time to the event of morbidity and mortality can be decades. While this could be circumvented using sensitive biomarkers of aging, current molecular, physiological, and digital endpoints require further validation. In this review, we discuss how collecting real-world evidence (RWE) by obtaining health data that is amenable for collection from large heterogeneous populations in a real-world setting can help speed up validation of geroprotective interventions. Further, we propose inclusion of quality of life (QoL) data as a biomarker of aging and candidate endpoint for geroscience clinical trials to aid in distinguishing healthy from unhealthy aging. We highlight how QoL assays can aid in accelerating data collection in studies gathering RWE on the geroprotective effects of repurposed drugs to support utilization within healthy longevity medicine. Finally, we summarize key metrics to consider when implementing QoL assays in studies, and present the short-form 36 (SF-36) as the most well-suited candidate endpoint.

Exploring the potential of machine learning in gynecological care: a review

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.

Using Machine Learning to Predict Unplanned Hospital Utilization and Chemotherapy Management From Patient-Reported Outcome Measures

PURPOSE

Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to assess if patient-reported outcome measures (PROMs) improve performance of machine learning (ML) models predicting hospital admissions, triage events (contacting helpline or attending hospital), and changes to chemotherapy.

MATERIALS AND METHODS

Clinical trial data were used and contained responses to three PROMs (European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire [QLQ-C30], EuroQol Five-Dimensional Visual Analogue Scale [EQ-5D], and Functional Assessment of Cancer Therapy-General [FACT-G]) and clinical information on 508 participants undergoing chemotherapy. Six feature sets (with following variables: [1] all available; [2] clinical; [3] PROMs; [4] clinical and QLQ-C30; [5] clinical and EQ-5D; [6] clinical and FACT-G) were applied in six ML models (logistic regression [LR], decision tree, adaptive boosting, random forest [RF], support vector machines [SVMs], and neural network) to predict admissions, triage events, and chemotherapy changes.

RESULTS

The comprehensive analysis of predictive performances of the six ML models for each feature set in three different methods for handling class imbalance indicated that PROMs improved predictions of all outcomes. RF and SVMs had the highest performance for predicting admissions and changes to chemotherapy in balanced data sets, and LR in imbalanced data set. Balancing data led to the best performance compared with imbalanced data set or data set with balanced train set only.

CONCLUSION

These results endorsed the view that ML can be applied on PROM data to predict hospital utilization and chemotherapy management. If further explored, this study may contribute to health care planning and treatment personalization. Rigorous comparison of model performance affected by different imbalanced data handling methods shows best practice in ML research.

Application of the patient-reported outcome-based postoperative symptom management model in lung cancer: a multicenter randomized controlled trial protocol

INTRODUCTION

Lung cancer is the most common cancer in China, with the highest mortality rate. Surgery is the primary treatment for early lung cancer. However, patients with lung cancer have a heavy burden of symptoms within 3 months after surgery, which seriously affects their quality of life (QOL). The symptom management model based on the patient-reported outcome (PRO) is considered the best caregiving model. The clinical evidence about the symptom management of lung cancer within 3 months after the operation is very limited. Herein, we propose a randomized controlled trial to evaluate the PRO score-based monitoring and alert system for follow-up on psychological and physiological symptoms of lung cancer patients within 3 months after surgery and further investigate the effect of intervention measures based on this PRO score-based system.

METHODS AND ANALYSIS

This multicenter, open-label, randomized, parallel superiority trial will be conducted at four hospitals in China. A total of 440 lung cancer patients will be recruited in this study, who will be randomly assigned to the intervention group or the control group in a ratio of 1:1. Any of the target symptoms reaches the preset threshold (score ≥ 4), the patients will accept the symptom management advices based on the PRO. The patients in the control group will follow the current standard procedure of symptom management. The symptom management system is an electronic management system based on WeChat mini programs. All patients will be evaluated for symptoms through the lung cancer module of the MDASI lung cancer-specific scale on the day before surgery, days 1, 3, 5, and 7 after surgery, and once a week during the 12-week post-discharge period. Simultaneously, the EORTC QLQ-C30 scale will be used to evaluate patients' quality of life at baseline and the fourth and twelfth week after the surgery. The mean number of symptom threshold events of the intervention and the control groups were compared by t-test, and the changes of PRO were compared by a mixed effect model. The primary endpoint has been set as the 12-week post-discharge period.

DISCUSSION

This study will test the feasibility of the symptom management system based on the mobile social media applet in postoperative caregiving and the efficacy of psychiatrist-assisted treatment and provide evidence in managing the symptoms of patients in the medium and long term.

TRIALS REGISTRATION

Trials registration number: ChiCTR 2200058876, Registered 18 April 2022.

The impact of comorbidities, neutrophil-to-lymphocyte ratio, and drug toxicities on quality of life in lung cancer patients receiving EGFR-TKI therapy

BACKGROUND

Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are used as the standard first-line treatment for patients with advanced EGFR-mutated non-small cell lung cancer (NSCLC). However, the impact of comorbidities and treatment toxicities on quality of life (QoL) was seldom investigated.

OBJECTIVE

We aimed to investigate the association of comorbidities, adverse events (AEs), and QoL in treatment-naïve advanced NSCLC patients receiving EGFR-TKI treatments.

METHODS

This multi-center prospective observational study was conducted to evaluate QoL and AEs at baseline, the 2nd, 4th, 12th, and 24th week. Clinical characteristics, comorbidities, and pre-treatment laboratory data were recorded. QoL was assessed by using the summary score of the EORTC QLQ-C30 and the dermatology life quality index. The impact of comorbidities, neutrophil-to-lymphocyte ratio (NLR), and AEs on QoL was analyzed by generalized estimating equations.

RESULTS

A total of 121 patients were enrolled. Diarrhea (p = 0.033), anorexia (p < 0.001), and NLR ≥4 (p = 0.017) were significantly associated with a QoL impairment. Among skin toxicities, acneiform rash (p = 0.002), pruritus (p = 0.002), visual analogue scale for pruritus (≥3 and < 7, p = 0.006; ≥7, p = 0.001) and pain (1-3, p = 0.041) were associated with a QoL impairment. No significant association was found between comorbidities and QoL changes.

CONCLUSION

Diarrhea, anorexia, skin pain, and pruritus may cause a deterioration in QoL in patients receiving EGFR-TKI therapy. NLR may be a potential predictive factor for QoL impairment. Aggressive management and close monitoring for these clinical factors are crucial to improve QoL.

A weighted distance-based dynamic ensemble regression framework for gastric cancer survival time prediction

Accurate prediction of gastric cancer patient survival time is essential for clinical decision-making. However, unified static models lack specificity and flexibility in predictions owing to the varying survival outcomes among gastric cancer patients. We address these problems by using an ensemble learning approach and adaptively assigning greater weights to similar patients to make more targeted predictions when predicting an individual's survival time. We treat these problems as regression problems and introduce a weighted dynamic ensemble regression framework. To better identify similar patients, we devise a method to measure patient similarity, considering the diverse impacts of features. Subsequently, we use this measure to design both a weighted K-means clustering method and a fuzzy K-means sampling technique to group patients and train corresponding base regressors. To achieve more targeted predictions, we calculate the weight of each base regressor based on the similarity between the patient to be predicted and the patient clusters, culminating in the integration of the results. The model is validated on a dataset of 7791 patients, outperforming other models in terms of three evaluation metrics, namely, the root mean square error, mean absolute error, and the coefficient of determination. The weighted dynamic ensemble regression strategy can improve the baseline model by 1.75%, 2.12%, and 13.45% in terms of the three respective metrics while also mitigating the imbalanced survival time distribution issue. This enhanced performance has been statistically validated, even when tested on six public datasets with different sizes. By considering feature variations, patients with distinct survival profiles can be effectively differentiated, and the model predictive performance can be enhanced. The results generated by our proposed model can be invaluable in guiding decisions related to treatment plans and resource allocation. Furthermore, the model has the potential for broader applications in prognosis for other types of cancers or similar regression problems in various domains.

Optimizing lung cancer classification through hyperparameter tuning

Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.

Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review

Knowledge discovery in databases (KDD) is crucial in analyzing data to extract valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in diseases with high incidence, mortality, and costs, like cancer. ML techniques can develop more accurate predictive models for cancer patients' clinical outcomes, aiding informed healthcare decision-making. However, cancer prediction modeling faces challenges because of the unbalanced nature of the datasets, where there is a small minority category of patients with a cancer diagnosis compared to a majority category of cancer-free patients. Imbalanced datasets pose statistical hurdles like bias and overfitting when developing accurate prediction models. This systematic review focuses on breast cancer prediction articles published from 2008 to 2023. The objective is to examine ML methods used in three critical steps of KDD: preprocessing, data mining, and interpretation which address the imbalanced data problem in breast cancer prediction. This work synthesizes prior research in ML methods for breast cancer prediction. The findings help identify effective preprocessing strategies, including balancing and feature selection methods, robust predictive models, and evaluation metrics of those models. The study aims to inform healthcare providers and researchers about effective techniques for accurate breast cancer prediction.

Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?

BACKGROUND

The worldwide society is currently facing an epidemiological shift due to the significant improvement in life expectancy and increase in the elderly population. This shift requires the public and scientific community to highlight successful aging (SA), as an indicator representing the quality of elderly people's health. SA is a subjective, complex, and multidimensional concept; thus, its meaning or measuring is a difficult task. This study seeks to identify the most affecting factors on SA and fed them as input variables for constructing predictive models using machine learning (ML) algorithms.

METHODS

Data from 1465 adults aged ≥ 60 years who were referred to health centers in Abadan city (Iran) between 2021 and 2022 were collected by interview. First, binary logistic regression (BLR) was used to identify the main factors influencing SA. Second, eight ML algorithms, including adaptive boosting (AdaBoost), bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XG-Boost), random forest (RF), J-48, multilayered perceptron (MLP), Naïve Bayes (NB), and support vector machine (SVM), were trained to predict SA. Finally, their performance was evaluated using metrics derived from the confusion matrix to determine the best model.

RESULTS

The experimental results showed that 44 factors had a meaningful relationship with SA as the output class. In total, the RF algorithm with sensitivity = 0.95 ± 0.01, specificity = 0.94 ± 0.01, accuracy = 0.94 ± 0.005, and F-score = 0.94 ± 0.003 yielded the best performance for predicting SA.

CONCLUSIONS

Compared to other selected ML methods, the effectiveness of the RF as a bagging algorithm in predicting SA was significantly better. Our developed prediction models can provide, gerontologists, geriatric nursing, healthcare administrators, and policymakers with a reliable and responsive tool to improve elderly outcomes.

Optimizing an existing prediction model for quality of life one-year post-intensive care unit: An exploratory analysis

BACKGROUND

This study aimed to improve the PREPARE model, an existing linear regression prediction model for long-term quality of life (QoL) of intensive care unit (ICU) survivors by incorporating additional ICU data from patients' electronic health record (EHR) and bedside monitors.

METHODS

The 1308 adult ICU patients, aged ≥16, admitted between July 2016 and January 2019 were included. Several regression-based machine learning models were fitted on a combination of patient-reported data and expert-selected EHR variables and bedside monitor data to predict change in QoL 1 year after ICU admission. Predictive performance was compared to a five-feature linear regression prediction model using only 24-hour data (R²  = 0.54, mean square error (MSE) = 0.031, mean absolute error (MAE) = 0.128).

RESULTS

The 67.9% of the included ICU survivors was male and the median age was 65.0 [IQR: 57.0-71.0]. Median length of stay (LOS) was 1 day [IQR 1.0-2.0]. The incorporation of the additional data pertaining to the entire ICU stay did not improve the predictive performance of the original linear regression model. The best performing machine learning model used seven features (R²  = 0.52, MSE = 0.032, MAE = 0.125). Pre-ICU QoL, the presence of a cerebro vascular accident (CVA) upon admission and the highest temperature measured during the ICU stay were the most important contributors to predictive performance. Pre-ICU QoL's contribution to predictive performance far exceeded that of the other predictors.

CONCLUSION

Pre-ICU QoL was by far the most important predictor for change in QoL 1 year after ICU admission. The incorporation of the numerous additional features pertaining to the entire ICU stay did not improve predictive performance although the patients' LOS was relatively short.

Prognostic value of patient-reported outcome measures (PROMs) in adults with non-small cell Lung Cancer: a scoping review

Background

There is growing interest in the collection and use of patient-reported outcome measures (PROMs) to support clinical decision making in patients with non-small cell lung cancer (NSCLC). However, an overview of research into the prognostic value of PROMs is currently lacking.

Aim

To explore to what extent, how, and how robustly the value of PROMs for prognostic prediction has been investigated in adults diagnosed with NSCLC.

Methods

We systematically searched Medline, Embase, CINAHL Plus and Scopus for English-language articles published from 2011 to 2021 that report prognostic factor study, prognostic model development or validation study. Example data charting forms from the Cochrane Prognosis Methods Group guided our data charting on study characteristics, PROMs as predictors, predicted outcomes, and statistical methods. Two reviewers independently charted the data and critically appraised studies using the QUality In Prognosis Studies (QUIPS) tool for prognostic factor studies, and the risk of bias assessment section of the Prediction model Risk Of Bias ASsessment Tool (PROBAST) for prognostic model studies.

Results

Our search yielded 2,769 unique titles of which we included 31 studies, reporting the results of 33 unique analyses and models. Out of the 17 PROMs used for prediction, the EORTC QLQ-C30 was most frequently used (16/33); 12/33 analyses used PROM subdomain scores instead of the overall scores. PROMs data was mostly collected at baseline (24/33) and predominantly used to predict survival (32/33) but seldom other clinical outcomes (1/33). Almost all prognostic factor studies (26/27) had moderate to high risk of bias and all four prognostic model development studies had high risk of bias.

Conclusion

There is an emerging body of research into the value of PROMs as a prognostic factor for survival in people with NSCLC but the methodological quality of this research is poor with significant bias. This warrants more robust studies into the prognostic value of PROMs, in particular for predicting outcomes other than survival. This will enable further development of PROM-based prediction models to support clinical decision making in NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10151-z.

Artificial intelligence empowered Digital Health Technologies in Cancer Survivorship Care: a scoping review

Objective

The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process.

Methods

Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review.

Results

43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing.

Conclusions

While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.

Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke

Health related quality of life (HRQOL) reflects individuals perceived of wellness in health domains and is often deteriorated after stroke. Precise prediction of HRQOL changes after rehabilitation interventions is critical for optimizing stroke rehabilitation efficiency and efficacy. Machine learning (ML) has become a promising outcome prediction approach because of its high accuracy and easiness to use. Incorporating ML models into rehabilitation practice may facilitate efficient and accurate clinical decision making. Therefore, this study aimed to determine if ML algorithms could accurately predict clinically significant HRQOL improvements after stroke sensorimotor rehabilitation interventions and identify important predictors. Five ML algorithms including the random forest (RF), k-nearest neighbors (KNN), artificial neural network, support vector machine and logistic regression were used. Datasets from 132 people with chronic stroke were included. The Stroke Impact Scale was used for assessing multi-dimensional and global self-perceived HRQOL. Potential predictors included personal characteristics and baseline cognitive/motor/sensory/functional/HRQOL attributes. Data were divided into training and test sets. Tenfold cross-validation procedure with the training data set was used for developing models. The test set was used for determining model performance. Results revealed that RF was effective at predicting multidimensional HRQOL (accuracy: 85%; area under the receiver operating characteristic curve, AUC-ROC: 0.86) and global perceived recovery (accuracy: 80%; AUC-ROC: 0.75), and KNN was effective at predicting global perceived recovery (accuracy: 82.5%; AUC-ROC: 0.76). Age/gender, baseline HRQOL, wrist/hand muscle function, arm movement efficiency and sensory function were identified as crucial predictors. Our study indicated that RF and KNN outperformed the other three models on predicting HRQOL recovery after sensorimotor rehabilitation in stroke patients and could be considered for future clinical application.

Mechanisms and Methods to Understand Depressive Symptoms

Depressive symptoms, feelings of sadness, anger, and loss that interfere with a person's daily life, are prevalent health concerns across populations that significantly result in adverse health outcomes with direct and indirect economic burdens at a national and global level. This article aims to synthesize known mechanisms of depressive symptoms and the established and emerging methodologies used to understand depressive symptoms; implications and directions for future nursing research are discussed. A comprehensive search was performed by Cumulative Index to Nursing and Allied Health Literature, MEDLINE, and PUBMED databases between 2000-2021 to examine contributing factors of depressive symptoms. Many environmental, psychological, and physiological factors are associated with the development or increased severity of depressive symptoms (anhedonia, fatigue, sleep and appetite disturbances to depressed mood). This paper discusses biological and psychological theories that guide our understanding of depressive symptoms, as well as known biomarkers (gut microbiome, specific genes, multi-cytokine, and hormones) and established and emerging methods. Disruptions within the nervous system, hormonal and neurotransmitters levels, brain structure, gut-brain axis, leaky-gut syndrome, immune and inflammatory process, and genetic variations are significant mediating mechanisms in depressive symptomology. Nursing research and practice are at the forefront of furthering depressive symptoms' mechanisms and methods. Utilizing advanced technology and measurement tools (big data, machine learning/artificial intelligence, and multi-omic approaches) can provide insight into the psychological and biological mechanisms leading to effective intervention development. Thus, understanding depressive symptomology provides a pathway to improve patients' health outcomes, leading to reduced morbidity and mortality and the overall nation-wide economic burden.Supplemental data for this article is available online at https://doi.org/10.1080/01612840.2021.1998261 .

Systematic review of the effect of aquatic therapeutic exercise in breast cancer survivors

BACKGROUND

Aquatic therapeutic exercise can be equally effective or even superior to land-based exercise in improving several clinical variables. However, there is still a lack of knowledge on the effects compared to land-based interventions particularly in breast cancer (BC) patients.

OBJECTIVE

The objective of this study is to examine the effects of aquatic therapeutic exercise on pain, shoulder mobility, lymphedema, cardiorespiratory fitness, muscle strength, body composition, pulmonary function, cancer-related fatigue (CRF) and health-related quality of life (HRQoL) and which parameters are effective compared to similar land-based interventions.

METHODS

The databases used were PubMed, Scopus, Web of Science, Cochrane Library and CINAHL, retrieving 145 articles.

RESULTS

Eleven studies were included. Aquatic therapeutic exercise is feasible, safe, well tolerated and achieved high percentages of adherence. As for the assessed outcomes, moderate to large improvements were found compared to usual care or to land-based physical exercise interventions in pain, shoulder range of motion, pulmonary function, HRQoL, cardiorespiratory fitness and muscle strength. Inconclusive results were found for lymphedema, body composition and CRF.

CONCLUSIONS

Aquatic therapeutic exercise interventions using a combination of endurance, strength, mobility, stretching and breathing exercises resulted in improvements in common side effects of BC and its treatments. More studies on CRF, body composition and lymphedema need to be done to further evaluate the impact of the intervention on these outcomes.

A Principal Component of Quality-of-Life Measures Is Associated with Survival: Validation in a Prospective Cohort of Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

Simple Summary

There is a paucity of literature on the association between health-related quality-of-life (HRQOL) measures and survival outcomes among patients with early-stage non-small-cell lung cancer following stereotactic body radiation therapy (SBRT). To address this knowledge gap, we performed a secondary analysis of a prospective randomized clinical trial using principal component analysis (PCA). A total of 70 patients were enrolled and completed HRQOL questionnaires prior to and 3 months after SBRT. Using PCA, one of the eigenvectors, PC1, incorporated changes in global health status, functional HRQOL performance, and symptom burden, and it was associated with progression-free survival and overall survival outcomes. Changes in HRQOL measures based on PCA may help identify a subgroup of high-risk patients, and further studies would be warranted to tailor potential additional interventions in this subgroup to improve their outcomes.

Abstract

The association between HRQOL metrics and survival has not been studied in early stage non-small-cell lung cancer (NSCLC) patients undergoing SBRT. The cohort was derived via a post-hoc analysis of a prospective randomized clinical trial examining definitive SBRT for peripheral, early-stage NSCLC with a single or multi-fraction regimen. Patients completed HRQOL questionnaires prior to and 3 months after treatment. Using principal component analysis (PCA), changes in each HRQOL scale following treatment were reduced to two eigenvectors, PC1 and PC2. Cox regression was employed to analyze associations with survival-based endpoints. A total of 70 patients (median age 75.6 years; median follow-up 41.1 months) were studied. HRQOL and symptom comparisons at baseline and 3 months were vastly unchanged except for improved coughing (p = 0.02) and pain in the chest at 3 months (p = 0.033). PC1 and PC2 explained 21% and 9% of variance, respectively. When adjusting for covariates, PC1 was significantly correlated with progression-free (PFS) (HR = 0.78, 95% CI 0.67–0.92, p = 0.003) and overall survival (OS) (HR = 0.76, 95% CI 0.46, p = 0.041). Changes in global health status, functional HRQOL performance, and/or symptom burden as described by PC1 values are significantly associated with PFS and OS. The PC1 quartile may facilitate the identification of at-risk patients for additional interventions.

Machine learning prediction of dropping out of outpatients with alcohol use disorders

BACKGROUND

Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probability of these patients to continue outpatient treatment, predicting and managing patients who might discontinue treatment becomes necessary. Accordingly, we developed a machine learning (ML) algorithm to predict which the risk of patients dropping out of outpatient treatment schemes.

METHODS

A total of 839 patients were selected out of 2,206 patients admitted for AUD in three hospitals under the Catholic Central Medical Center in Korea. We implemented six ML models-logistic regression, support vector machine, k-nearest neighbor, random forest, neural network, and AdaBoost-and compared the prediction performances thereof.

RESULTS

Among the six models, AdaBoost was selected as the final model for recommended use owing to its area under the receiver operating characteristic curve (AUROC) of 0.72. The four variables affecting the prediction based on feature importance were the length of hospitalization, age, residential area, and diabetes.

CONCLUSION

An ML algorithm was developed herein to predict the risk of patients with AUD in Korea discontinuing outpatient treatment. By testing and validating various machine learning models, we determined the best performing model, AdaBoost, as the final model for recommended use. Using this model, clinicians can manage patients with high risks of discontinuing treatment and establish patient-specific treatment strategies. Therefore, our model can potentially enable patients with AUD to successfully complete their treatments by identifying them before they can drop out.