Prescription Medications and Overall Survival in Metastatic Hormone Sensitive Prostate Cancer.

BACKGROUND/AIM: With new therapies for metastatic prostate cancer, patients are living longer, increasing the need for better understanding of the impact of comorbid disease. Prescription medications may risk-stratify patients independent of established methods, such as the Charlson Comorbidity Index (CCI) and guide treatment selection. PATIENTS AND METHODS: In a nationwide retrospective study of US Veterans, we used multivariable logistic regression and Cox proportional hazard modeling to evaluate the association between number and class of prescription medications and overall survival (OS) with age, race, body-mass index, prostate specific antigen (PSA), and Charlson comorbidities as covariates in veterans treated for de novo metastatic hormone sensitive prostate cancer (mHSPC) between 2010-2021. RESULTS: Among 8,434 Veterans, a median of nine medications and five medication classes were filled in the year prior to initial treatment with abiraterone or enzalutamide for mHSPC. Veterans on 1-4 medications had an average survival of 38 months compared to 5-9 medicines (33 months), 10-14 medicines (27 months), and 15+ medicines (22 months) (p<0.001). After adjusting for age, race, body mass index (BMI), PSA, CCI, and year of diagnosis, both the number of medications and medication classes were associated with increased mortality. The adjusted hazard ratio (aHR) [95% confidence interval (CI)] was 1.03 (1.02-1.03) for the number of medications and 1.05 (1.04-1.07) for medication classes. Medications within ATC B (blood/blood forming organs), ATC C (cardiovascular), and ATC N (nervous) were associated with worse OS, with aHRs of 1.14 (1.07, 1.21), 1.14 (1.06, 1.22), and 1.12 (1.06, 1.19), respectively. CONCLUSION: The number and class of medications were independently associated with overall survival in patients undergoing treatment for mHSPC. With new therapies for advanced prostate cancer, patients are living longer, highlighting the need for a better understanding of the impact of comorbid diseases. Simple methods to assess disease burden and prognosticate survival have the potential to guide treatment decisions.

Inhaled medications for chronic obstructive pulmonary disease predict surgical complications and survival in stage I non-small cell lung cancer.

Background: Lung function is routinely assessed prior to surgical resection for non-small cell lung cancer (NSCLC). Further assessment of chronic obstructive pulmonary disease (COPD) using inhaled COPD medications to determine disease severity, a readily available metric of disease burden, may predict postoperative outcomes and overall survival (OS) in lung cancer patients undergoing surgery. Methods: We retrospectively evaluated clinical stage I NSCLC patients receiving surgical treatment within the Veterans Health Administration from 2006-2016 to determine the relationship between number and type of inhaled COPD medications (short- and long-acting beta2-agonists, muscarinic antagonists, or corticosteroids prescribed within 1 year before surgery) and postoperative outcomes including OS using multivariable models. We also assessed the relationship between inhaled COPD medications, disease severity [measured by forced expiratory volume in 1 second (FEV1)], and diagnosis of COPD. Results: Among 9,741 veterans undergoing surgery for clinical stage I NSCLC, patients with COPD were more likely to be prescribed inhaled medications than those without COPD [odds ratio (OR) =5.367, 95% confidence interval (CI): 4.886-5.896]. Increased severity of COPD was associated with increased number of prescribed inhaled COPD medications (P<0.0001). The number of inhaled COPD medications was associated with prolonged hospital stay [adjusted OR (aOR) =1.119, 95% CI: 1.076-1.165), more major complications (aOR =1.117, 95% CI: 1.074-1.163), increased 90-day mortality (aOR =1.088, 95% CI: 1.013-1.170), and decreased OS [adjusted hazard ratio (aHR) =1.061, 95% CI: 1.042-1.080]. In patients with FEV1 ≥80% predicted, greater number of prescribed inhaled COPD medications was associated with increased 30-day mortality (aOR =1.265, 95% CI: 1.062-1.505), prolonged hospital stay (aOR =1.130, 95% CI: 1.051-1.216), more major complications (aOR =1.147, 95% CI: 1.064-1.235), and decreased OS (aHR =1.058, 95% CI: 1.022-1.095). When adjusting for other drug classes and covariables, short-acting beta2-agonists were associated with increased 90-day mortality (aOR =1.527, 95% CI: 1.120-2.083) and decreased OS (aHR =1.087, 95% CI: 1.005-1.177). Conclusions: In patients with early-stage NSCLC, inhaled COPD medications prescribed prior to surgery were associated with both short- and long-term outcomes, including in patients with FEV1 ≥80% predicted. Routine assessment of COPD medications may be a simple method to quantify operative risk in early-stage NSCLC patients.

Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach.

BACKGROUND: The Centers for Medicare and Medicaid Services projects that health care costs will continue to grow over the next few years. Rising readmission costs contribute significantly to increasing health care costs. Multiple areas of health care, including readmissions, have benefited from the application of various machine learning algorithms in several ways. OBJECTIVE: We aimed to identify suitable models for predicting readmission charges billed by hospitals. Our literature review revealed that this application of machine learning is underexplored. We used various predictive methods, ranging from glass-box models (such as regularization techniques) to black-box models (such as deep learning-based models). METHODS: We defined readmissions as readmission with the same major diagnostic category (RSDC) and all-cause readmission category (RADC). For these readmission categories, 576,701 and 1,091,580 individuals, respectively, were identified from the Nationwide Readmission Database of the Healthcare Cost and Utilization Project by the Agency for Healthcare Research and Quality for 2013. Linear regression, lasso regression, elastic net, ridge regression, eXtreme gradient boosting (XGBoost), and a deep learning model based on multilayer perceptron (MLP) were the 6 machine learning algorithms we tested for RSDC and RADC through 10-fold cross-validation. RESULTS: Our preliminary analysis using a data-driven approach revealed that within RADC, the subsequent readmission charge billed per patient was higher than the previous charge for 541,090 individuals, and this number was 319,233 for RSDC. The top 3 major diagnostic categories (MDCs) for such instances were the same for RADC and RSDC. The average readmission charge billed was higher than the previous charge for 21 of the MDCs in the case of RSDC, whereas it was only for 13 of the MDCs in RADC. We recommend XGBoost and the deep learning model based on MLP for predicting readmission charges. The following performance metrics were obtained for XGBoost: (1) RADC (mean absolute percentage error [MAPE]=3.121%; root mean squared error [RMSE]=0.414; mean absolute error [MAE]=0.317; root relative squared error [RRSE]=0.410; relative absolute error [RAE]=0.399; normalized RMSE [NRMSE]=0.040; mean absolute deviation [MAD]=0.031) and (2) RSDC (MAPE=3.171%; RMSE=0.421; MAE=0.321; RRSE=0.407; RAE=0.393; NRMSE=0.041; MAD=0.031). The performance obtained for MLP-based deep neural networks are as follows: (1) RADC (MAPE=3.103%; RMSE=0.413; MAE=0.316; RRSE=0.410; RAE=0.397; NRMSE=0.040; MAD=0.031) and (2) RSDC (MAPE=3.202%; RMSE=0.427; MAE=0.326; RRSE=0.413; RAE=0.399; NRMSE=0.041; MAD=0.032). Repeated measures ANOVA revealed that the mean RMSE differed significantly across models with P<.001. Post hoc tests using the Bonferroni correction method indicated that the mean RMSE of the deep learning/XGBoost models was statistically significantly (P<.001) lower than that of all other models, namely linear regression/elastic net/lasso/ridge regression. CONCLUSIONS: Models built using XGBoost and MLP are suitable for predicting readmission charges billed by hospitals. The MDCs allow models to accurately predict hospital readmission charges.

Big Data Application in Biomedical Research and Health Care: A Literature Review.

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care.