Impact of COVID-19 on Quality of Life After Hospital Discharge in Patients Treated With Noninvasive Ventilation/Continuous Positive Airway Pressure: An Observational, Prospective Multicenter Study.

BACKGROUND: COVID-19 has caused a worldwide pandemic that has significantly affected people's health and daily lives. Patients hospitalized with COVID-19 have impaired physical and psychological conditions. Despite this, few studies have examined the physical and psychological health of COVID-19 patients after discharge from the hospital. AIMS: To assess the impact of COVID-19 pneumonia on quality of life and residual disability, anxiety, and insomnia at 4 and 8 months after hospital discharge in patients treated with continuous positive airway pressure and noninvasive ventilation. METHODS: This is a prospective, multicenter, longitudinal observational study. The STROBE checklist was observed. We administered the Post-COVID-19 Functional Status scale to identify and objectively quantify the degree of residual disability and the impact on the patient's functional status. Simultaneously, we used the EuroQOL-5D scale to measure the quality of life, the 7-item Generalized Anxiety Disorder Scale to monitor anxiety, and the Insomnia Severity Index to monitor insomnia. RESULTS: The study enrolled 199 patients 4 months after discharge; 145 completed the 8-month follow-up. The quality of life, insomnia, pain, and movement ability seem to worsen over time in our cohort of patients. The quality of life deteriorated significantly between 4 and 8 months after discharge especially in terms of mobility (27.6% of the patients interviewed) and pain (31% of the patients interviewed). There was a significant correlation between COVID-19 symptoms and comorbidities present at admission and inadequate quality-of-life perception. DISCUSSION: The recognition of early signs and symptoms in patients who have already experienced COVID-19 disease is essential for early detection and prevention of chronicity. Health care professionals need to pay more attention to the physical and psychological rehabilitation of COVID-19 patients after hospital discharge. To determine the long-term impacts of COVID-19, a long-term follow-up of COVID-19 patients after discharge is required.

Quality of life in patients with PICC diagnosed with mesothelioma: Results of a multicenter epidemiological survey (LifePICC).

BACKGROUND: Pleural mesothelioma (PM) is a rare and aggressive cancer. PICC devices are widely used in cancer patients. The aim of the study is to evaluate the quality of life of patients with PICC diagnosed with PM treated at the Hospital of Casale Monferrato and Alessandria (Italy), an area with a high incidence of asbestos-related diseases. STUDY DESIGN AND METHODS: Longitudinal prospective observational study with data collection at PICC insertion (T0), after 3 months (T1), 6 months (T2), and 9 months (T3). Participants were aged >18 years, diagnosed with PM, eligible for PICC insertion. Questionnaires used: EORTC QLQ-C30, EORTC QLQ-LC13, and HADS rating scale. RESULTS: Twenty-eight patients were enrolled. The mean age was 68.93 years (SD 9.13), mostly male (57.1%). The most frequent cancer stage at diagnosis was III (39.3%), then I (32.1%), and IV (21.4%). 85.7% were treated with chemotherapy, 14.3% also with immunotherapy. 96.4% of patients reported no complications during PICC implantation. The perception of health status and quality of life, measured on a scale of 1-7, was in line with an average score of 5 during the evaluation period. The total anxiety and depression score remained normal for most patients (0-7). CONCLUSIONS: The PICC management involved a multidisciplinary team with different skills: study findings revealed the key role that dedicated nurses play in PICC placement and ensuring patient problems are promptly addressed. From our study results, PICC placement does not seem to negatively impact the patient's quality of life.

A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2.

BACKGROUND: This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used to improve the medical management of patients suffering from complex diseases, and in particular to predict the death risk in hospitalized patients with SARS-Cov-2 based on admission data. METHODS: This work is based on an observational ambispective study that comprised patients older than 18 years with a positive SARS-Cov-2 diagnosis that were admitted to the hospital Azienda Ospedaliera "SS Antonio e Biagio e Cesare Arrigo", Alessandria, Italy from February, 24 2020 to May, 31 2021, and that completed the disease treatment inside this structure. The patients'medical history, demographic, epidemiologic and clinical data were collected from the electronic medical records system and paper based medical records, entered and managed by the Clinical Study Coordinators using the REDCap electronic data capture tool patient chart. The dataset was used to train and to evaluate predictive ML models. RESULTS: We overall trained, analysed and evaluated 19 predictive models (both supervised and unsupervised) on data from 824 patients described by 43 features. We focused our attention on models that provide an explanation that is understandable and directly usable by domain experts, and compared the results against other classical machine learning approaches. Among the former, JRIP showed the best performance in 10-fold cross validation, and the best average performance in a further validation test using a different patient dataset from the beginning of the third COVID-19 wave. Moreover, JRIP showed comparable performances with other approaches that do not provide a clear and/or understandable explanation. CONCLUSIONS: The ML supervised models showed to correctly discern between low-risk and high-risk patients, even when the medical disease context is complex and the list of features is limited to information available at admission time. Furthermore, the models demonstrated to reasonably perform on a dataset from the third COVID-19 wave that was not used in the training phase. Overall, these results are remarkable: (i) from a medical point of view, these models evaluate good predictions despite the possible differences entitled with different care protocols and the possible influence of other viral variants (i.e. delta variant); (ii) from the organizational point of view, they could be used to optimize the management of health-care path at the admission time.

Baseline clinical characteristics and prognostic factors in hospitalized COVID-19 patients aged ≤ 65 years: A retrospective observational study.

BACKGROUND: Individual differences in susceptibility to SARS-CoV-2 infection, symptomatology and clinical manifestation of COVID-19 have thus far been observed but little is known about the prognostic factors of young patients. METHODS: A retrospective observational study was conducted on 171 patients aged ≤ 65 years hospitalized in Alessandria's Hospital from 1st March to 30th April 2020 with laboratory confirmed COVID-19. Epidemiological data, symptoms at onset, clinical manifestations, Charlson Comorbidity Index, laboratory parameters, radiological findings and complications were considered. Patients were divided into two groups on the basis of COVID-19 severity. Multivariable logistic regression analysis was used to establish factors associated with the development of a moderate or severe disease. FINDINGS: A total of 171 patients (89 with mild/moderate disease, 82 with severe/critical disease), of which 61% males and a mean age (± SD) of 53.6 (± 9.7) were included. The multivariable logistic model identified age (50-65 vs 18-49; OR = 3.23 CI95% 1.42-7.37), platelet count (per 100 units of increase OR = 0.61 CI95% 0.42-0.89), c-reactive protein (CPR) (per unit of increase OR = 1.12 CI95% 1.06-1.20) as risk factors for severe or critical disease. The multivariable logistic model showed a good discriminating capacity with a C-index value of 0.76. INTERPRETATION: Patients aged ≥ 50 years with low platelet count and high CRP are more likely to develop severe or critical illness. These findings might contribute to improved clinical management.

A feasibility exploratory study of a novel modality of using patient-reported outcomes (PROsEXPLOR) in the real world.

INTRODUCTION: Patient-reported outcomes (PROs) can help clinicians better evaluate chemotherapy and immunotherapy toxicity based on patient perspectives. In this exploratory study, we tested a simplified PRO questionnaire (sPQ) in routine clinical practice and patient satisfaction with this tool. METHODS: We included 16 items related to the main toxicities of chemotherapy and immunotherapy to be filled in by patients. A baseline sPQ was completed by patients before starting treatment and then in the interval between courses for a total of 4 sPQs. Patients communicated the results to a data manager, who alerted the referral oncologist in case of replies differing from the basal or previous sPQ. According to the severity of symptoms, the patient was then referred to the team nurse, the general practitioner, or another specialist. A satisfaction survey was also completed. RESULTS: In a 3-month interval, 27 patients were enrolled. Fatigue and nausea were the most frequent symptoms reported as worsening during treatment. The oncologist was involved in the management of adverse events in 4 cases, home therapy variations were recommended by the dedicated nurse in 14 cases, additional visits were performed in 6 patients, and 1 patient was admitted to the oncology ward. None of the patients had unplanned visits to the emergency department or to the hospital. The sPQ was judged to be simple, useful, and satisfactory. CONCLUSIONS: Using sPQs in routine clinical practice was feasible and well-accepted by patients. PROs allowed us to recognize and promptly manage adverse events, reducing unplanned emergency department or hospital visits to zero.