ABSTRACT
Background: Integrated palliative home care (IHPC) is delivered to patients with progressive end-stage diseases. During the COVID-19 pandemic, IHPC needed to provide high-quality home care services for patients who were treated at home, with the goal of avoiding unnecessary care, hospital admissions, and emergency department (ED) visits. This study aimed to compare the ED visits of IHPC recipients in a large Italian region before and during the first two waves of the COVID-19 pandemic and to find sociodemographic or clinical characteristics associated with changes in ED visits during the first two waves of COVID-19 pandemic, compared with the period before. Methods: Administrative databases were used to identify sociodemographic and clinical variables of IHPC recipients admitted before and during the pandemic. The obtained data were balanced by applying a propensity score. The average number of ED visits before and during the pandemic was calculated by using the Welch's t test and stratified by all the variables. Results: Before and during the pandemic, 5155 and 3177 recipients were admitted to IHPC, respectively. These individuals were primarily affected by neoplasms. ED visits of IHPC recipients reduced from 1346 to 467 before and during the pandemic, respectively. A reduced mortality among IHCP patients who had at least one ED visit during the pandemic (8% during the pandemic versus 15% before the pandemic) was found. The average number of ED visits decreased during the pandemic [0.143, confidence interval (CI) = (0.128-0.158) versus 0.264, CI = (0.242-0.286) before the pandemic; p < 0.001] for all ages and IHPC duration classes. The presence of a formal caregiver led to a significant decrease in ED use. Medium and high emergency ED admissions showed no difference, whereas a decrease in low-level emergency ED admissions during the pandemic [1.27, CI = (1.194-1.345) versus 1.439, CI = (1.3-1.579) before the pandemic; p = 0.036] was found. Conclusion: ED visits among IHPC recipients were significantly decreased during the first two waves of the COVID-19 pandemic, especially in those individuals characterized by a low level of emergency. This did not result in an increase in mortality among IHPC recipients. These findings could inform the reorganization of home care services after the pandemic.
ABSTRACT
BACKGROUND AND OBJECTIVES: Multiple Sclerosis (MS) is a neurological disease associated with various and heterogeneous clinical characteristics. Given its complex nature and its unpredictable evolution over time, there isn't an established and exhaustive clinical protocol (or tool) for its diagnosis nor for monitoring its progression. Instead, different clinical exams and physical/psychological evaluations need to be taken into account. The Expanded Disability Status Scale (EDSS) is the most used clinical scale, but it suffers from several limitations. Developing computational solutions for the identification of bio-markers of disease progression that overcome the downsides of currently used scales is crucial and is gaining interest in current literature and research. METHODS: This Review focuses on the importance of approaching MS diagnosis and monitoring by investigating correlations between cognitive impairment and clinical data that refer to different MS domains. We review papers that integrate heterogeneous data and analyse them with statistical methods to understand their applicability into more advanced computational tools. Particular attention is paid to the impact that computational approaches can have on personalized-medicine. RESULTS: Personalized medicine for neuro-degenerative diseases is an unmet clinical need which can be addressed using computational approaches able to efficiently integrate heterogeneous clinical data extracted from both private and publicly available electronic health databases. CONCLUSIONS: Reliable and explainable Artificial Intelligence are computational approaches required to understand the complex and demonstrated interactions between MS manifestations as well as to provide reliable predictions on the disease evolution, representing a promising research field.
