<p>Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model</p>

Abstract

Purpose: To characterise patients with chronic obstructive pulmonary disease (COPD) who are rehospitalised for an acute exacerbation, to estimate the cost of these hospitalisations, to characterise high risk patient sub groups and to identify factors potentially associated with the risk of rehospitalisation. Patients and Methods: This was a retrospective study using the French National Hospital Discharge Database. All patients aged ≥40 years hospitalised for an acute exacerbation of COPD between 2015 and 2016 were identified and followed for six months. Patients with at least one rehospitalisation for acute exacerbation of COPD constituted the rehospitalisation analysis population. A machine learning model was built to study the factors associated with the risk of rehospitalisation using decision tree analysis. A direct cost analysis was performed from the perspective of national health insurance. Results: A total of 143,006 eligible patients were hospitalised for an acute exacerbation of COPD (AECOPD) in 2015-2016 (mean age: 74 years; 62.1% men). 25,090 (18.8%) were rehospitalised for another exacerbation within six months. In this study, 8.5% of patients died during or immediately following the index hospitalisation and 10.5% died during or immediately after rehospitalisation (p <0.001). The specific cost of these rehospitalisations was € 5304. The overall total cost per patient of all AECOPD-related stays was € 9623, being significantly higher in patients who were rehospitalised (€ 16,275) compared to those who were not (€ 8208). In decision tree analysis, the most important driver of rehospitalisation was hospitalisation in the previous two years (contributing 85% of the information). Conclusion: Rehospitalisations for acute exacerbations of COPD carry a high epidemiological and economic burden. Since hospitalisation for an acute exacerbation is the most important determinant of future rehospitalisations, management of COPD needs to focus on interventions aimed at decreasing the rehospitalisation risk of in order to lower the burden of disease.

The Association with COPD Readmission Rate and Access to Medical Institutions in Elderly Patients

“…In our study, we set the continuous category criterion for the length of stay at 7 days, making it almost consistent with the aforementioned results. Other studies have shown that older patients are more fragile and more likely to be hospitalized, 20 , 21 , 23 but our study showed no significant association of re-admission with age. One potential explanation may be the age distribution in our study.…”

“…Male sex and length of stay were associated with increased risk of COPD re-admission in retrospective studies. 6 , 20 , 21 , 27 , 28 It can be inferred that the COPD re-admission rate will be higher in men, as men have a higher prevalence of COPD, because a larger proportion of men are smokers and their compliance with medical recommendations is less than that of women. 29 Additionally, although the mechanism remains incompletely understood, factors associated with this include the susceptibility to toxin inhalation, airway structures, and female sex hormones.…”

“…18 MUAs are defined using the time relevance Index (TRI), a proven index of accessibility, to identify places with low accessibility and poor quality of care. 19 Previously, several studies have investigated risk factors for COPD re-admission, 5,6,[20][21][22][23] but no studies on MUAs and COPD re-admission. Therefore, we investigated the factors associated with the risk of COPD re-admission in Korea and used TRIs to compare COPD re-admission in general populations with areas defined as MUAs.…”

<p>Remote Patient Monitoring for the Detection of COPD Exacerbations</p>

“…Previous studies have associated changes in breathing frequency, oxygen saturation, end-of-effort heart rate, and walking distance with exacerbation events. 19,[29][30][31] Other studies have used machine learning to associate imagebased measures of airway volume and resistance 20 or hospitalization history 21 with exacerbation risk. Despite these different approaches, several systematic reviews have cast doubt on the benefits of telemonitoring interventions for COPD on mortality, quality of life, exercise capacity, and exacerbation-related outcomes.…”

“…19 More recently, machine learning methods have been used to associate airway volume from functional imaging 20 and history of previous hospital admission with COPD exacerbations. 21 Despite newer approaches and improved technologies for remote patient monitoring, the expectation of being able to impact healthcare utilization seems elusive. One reason is that instead of being able to detect exacerbations reliably, RPM can generate frequent alerts, or false positive signals, that actually result in unnecessary interventions.…”