Predictors of negative outcomes in hospitalized patients with SARS­CoV­2 pneumonia: A retrospective study.

The coronavirus disease 2019 (COVID-19) pandemic posed a serious threat to human health worldwide after the first case was identified in December 2019. Specific therapeutic options for COVID-19 are lacking; thus, the treatment of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is complex in clinical practice. Despite the development of treatment options and methods to limit the spread of SARS-CoV-2, certain patients experience critical illness and numerous deaths have occurred. Notably, treatment of this disease is complex due to the evolution of viral mutations and variants with different rates of infection. Moreover, specific patient characteristics may be associated with rapid disease progression and poor outcomes. Thus, the present study aimed to identify the specific characteristics of patients who developed poor outcomes, including clinical manifestations, blood samples (blood cell count and coagulation tests) at hospital admission and comorbidities. The present study included a total of 1,813 patients hospitalized with pneumonia and SARS-CoV-2 infection, and mortality rates associated with each patient characteristic were calculated. The characteristics associated with the highest risk of mortality were as follows: Age >90 years (OR, 105; 95% CI, 17.70-2,023.00); oxygen saturation at the time of hospital admission <89% in room air (OR, 14.3; 95% CI, 7.54-30.7), admission to the Intensive Care Unit (OR, 39.4; 95% CI, 27.7-57.0); and a neutrophil/lymphocyte ratio of 8.76-54.2 (OR, 14; 95% CI, 7.62-29.0). Treatment of patients with SARS-CoV-2 pneumonia represents a challenge for the healthcare system, but there are a number of predictors for poor patient outcomes that could be identified at the time of hospital admission.

Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns.

The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person's health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.

Benefits of a 7-week outpatient pulmonary rehabilitation program in COPD patients.

BACKGROUND: Respiratory rehabilitation programs (RR) are essential tools in the management of COPD. AIM: We present the results of a 7-week outpatient rehabilitation program in terms of dyspnea, exercise tolerance and quality of life. MATERIAL AND METHOD: The following parameters were evaluated before and after RR: dyspnea (mMRC scale), pulmonary function (FEVI, RV- residual volume), exercise tolerance (6MWT- 6 minutes walk test, CPET - cardiopulmonary exercise test), quality of life (SGROQ questionnaire). The RR program was outpatient, hospital based (7 weeks, 3 sessions/ week) and included: exercise training, therapeutic education, and psychological support. RESULTS: 25 patients, COPD stage II-IV GOLD (mean FEVI 44.5 +/-13% predicted), mean age 60.4 +/-12 years, 7 females, average BMI 27.14+/-4 kg/m2, average RV residual volume 221.55+/-86% predicted. Mean 6MWTdistance: 407.48 +/- 84 m and mean maximum power (Pmax) obtained on CPET: 75.67+/-30 Watts. All patients were symptomatic with significant dyspnea (3.06+/-0.7 on mMRC scale) and showed a significant impairment of quality of life: SGRO score 46.23+/- 14. At the end of RR program: dyspnea decreased with 0.67points on mMRC scale (p = 0.000), 6MWT distance increased with 58.5 m (p = 0.0071), Pmax obtained during CPET increased with 11.2 W, without reaching statistical significance (p> 0.05). SGRO score decreased by 5.59 points (p = 0.02). There were no significant improvements in FEV1 and RV values (p> 0.05). CONCLUSION: In our COPD patients, the 7 week outpatient rehabilitation program was effective, leading to improvement ofsymptoms, exercise tolerance and quality of life.