Stratification of telehealthcare for patients with chronic obstructive pulmonary disease using a predictive algorithm as decision support:a pilot study

Introduction The number of patients needing care who suffer from chronic obstructive pulmonary disease (COPD) is expected to increase in the future. The consequences thereof will increase the socio-economic burden for both patients and society. Telehealthcare technologies have shown potential in reducing hospitalisation-related costs and in improving health-related quality of life (HRQOL) for some COPD patients, but not all. The aim of this study was to investigate the potential of predictive algorithms for helping the general practitioner to stratify telehealthcare for COPD patients in a way that maximises HRQOL and minimises COPD-related costs. Methods Data from 553 COPD patients based in the North Denmark Region were analysed and used as predictors for four multiple linear regression models. The models were trained and evaluated for their abilities to predict individual patient’s future health- and cost-related developments, with and without telehealthcare. Results The average root-mean-square error (RMSE) of the health and cost models was 5.265 HRQOL scores and US dollars (US$)5430.49, respectively. The accuracy regarding the polarity of the predicted changes ranged from 61–65% for the health models and 74–75% for the cost models. While differences in the magnitude of predictions with and without telehealthcare were statistically significant ( p &lt; 0.01), the polarity of predictions was similar across models in 82.05% of all cases. Discussion Our results indicate that it may be possible to predict the magnitude and polarity of a COPD patient’s future health- and cost-related developments with and without telehealthcare. Predictive algorithms may provide a useful decision support tool in stratifying telehealthcare for COPD patients. </jats:sec

Using random forest machine learning on data from a large, representative cohort of the general population improves clinical spirometry references

Abstract Introduction Spirometry is associated with several diagnostic difficulties, and as a result, misdiagnosis of chronic obstructive pulmonary disease (COPD) occurs. This study aims to investigate how random forest (RF) can be used to improve the existing clinical FVC and FEV1 reference values in a large and representative cohort of the general population of the US without known lung disease. Materials and methods FVC, FEV1, body measures, and demographic data from 23 433 people were extracted from NHANES. RF was used to develop different prediction models. The accuracy of RF was compared with the existing Danish clinical references, an improved multiple linear regression (MLR) model, and a model from the literature. Results The correlation between actual and predicted FVC and FEV1 and the 95% confidence interval for RF were found to be FVC = 0.85 (0.85; 0.86) (p < 0.001), FEV1 = 0.92 (0.92; 0.93) (p < 0.001), and existing clinical references were FVC = 0.66 (0.64; 0.68) (p < 0.001) and FEV1 = 0.69 (0.67; 0.70) (p < 0.001). Slope and intercept for the RF models predicting FVC and FEV1 were FVC 1.06 and −238.04 (mL), FEV1: 0.86 and 455.36 (mL), and for the MLR models, slope and intercept were FVC: 0.99 and 38.56 39 (mL), and FEV1: 1.01 and −56.57‐57 (mL). Conclusions The results point toward machine learning models such as RF have the potential to improve the prediction of estimated lung function for individual patients. These predictions are used as reference values and are an important part of assessing spirometry measurements in clinical practice. Further work is necessary in order to reduce the size of the intercepts obtained through these results