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Background: In a pre-vaccination era serologic tests may be used to evaluate the seroprevalence and efficacy of containment strategies applied to the community. Subsequently, SARS-CoV-2 vaccination has successfully reduced hospitalization and admission to intensive care. The role of antiviral treatment for COVID-19 remains debated. Objective: We investigated the effect of SARS-CoV-2 IgG Spike (S) antibody responses in hospitalized patients on 30-day mortality. Finally, we assessed whether other predictive factors affected mortality after 30 days. Methods: Observational study on COVID-19 patients admitted from October 1, 2021, to January 30, 2022. Results: 520 patients were studied; 108 died at the 30-day follow-up (21%). A borderline significance for mortality was observed in favour of the high antibody titer group (24% vs 17%, p=0.05). From the univariate Cox regression analysis, a high IgG-S titer was significantly correlated to lower 30-day mortality (p=0.04, HR: 0.7; 95%CI: 0.44-0.98). The administration of remdesivir (p=0.01) and the age <65 years (p=2.3e-05) were found to be protective for the considered outcome (respectively, HR: 0.5, 95%CI: 0.34-0.86, and HR: 0.1, 95%CI: 0.04-0.30). Conclusions: S-antibodies and remdesivir could play a protecting role in increasing the survival of hospitalized COVID-19 patients who are not suffering from a critical disease. Advanced age is a risk factor for poor outcomes among infected people.
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To reduce the overburden in the hospital, during the COVID-19 pandemic, some "COVID Committed Home Medical Teams" (CCHTs) were created in Italy. These units consist of a small pool of general practitioners who aim to evaluate all patients with COVID-19 who require a medical examination directly at home. After the first visit (which can end with patient hospitalisation or home management), CCHTs periodically monitor the patients' clinical conditions and vital signs (usually a revaluation every 24-48 hours, except for a sudden worsening). However, this strategy - which reduces the pressure on hospitals - has never been evaluated for patient safety. Our study aims to determine whether a home-based monitoring and treatment strategy for non-severe COVID-19 patients was safe as direct hospital admission by the emergency department. We conducted a retrospective observational study about 1,182 patients admitted to the hospital for COVID-19 between September 2020 and April 2021, confronting in-hospital and 30-day mortality in both CCHT-referred (n=275) and directly admitted by emergency department (n=907). Patients assessed by the CCHT had lower in-hospital and 30-day mortality (18% vs 28%, p=0.001; and 20% vs 30%, p=0.002); but, in the propensity score matching comparison, there was no characteristic between the two groups turned out significantly different. CCHT did not correlate with in-hospital or 30-day mortality. CCHT is a safe strategy to reduce hospital overburden for COVID-19 during pandemic surges.
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During COVID-19 pandemic, implementing and maintaining an antimicrobial stewardship protocol obtained both low rates of MDR microorganisms and low antimicrobial use in an 800-bed hospital network in northern Italy. Infectious diseases specialist consulting was crucial to maintain this protocol active.
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A continuous demand for assistance and an overcrowded emergency department (ED) require early and safe discharge of low-risk severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients. We developed (n = 128) and validated (n = 330) the acute PNeumonia early assessment (aPNea) score in a tertiary hospital and preliminarily tested the score on an external secondary hospital (n = 97). The score's performance was compared to that of the National Early Warning Score 2 (NEWS2). The composite outcome of either death or oral intubation within 30 days from admission occurred in 101 and 28 patients in the two hospitals, respectively. The area under the receiver operating characteristic (AUROC) curve of the aPNea model was 0.86 (95% confidence interval (CI), 0.78-0.93) and 0.79 (95% CI, 0.73-0.89) for the development and validation cohorts, respectively. The aPNea score discriminated low-risk patients better than NEWS2 at a 10% outcome probability, corresponding to five cut-off points and one cut-off point, respectively. aPNea's cut-off reduced the number of unnecessary hospitalizations without missing outcomes by 27% (95% CI, 9-41) in the validation cohort. NEWS2 was not significant. In the external cohort, aPNea's cut-off had 93% sensitivity (95% CI, 83-102) and a 94% negative predictive value (95% CI, 87-102). In conclusion, the aPNea score appears to be appropriate for discharging low-risk SARS-CoV-2-infected patients from the ED.
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BACKGROUND AND AIM: There is a need to determine which clinical variables predict the severity of COVID-19. We analyzed a series of critically ill COVID-19 patients to see if any of our dataset's clinical variables were associated with patient outcomes. METHODS: We retrospectively analyzed the data of COVID-19 patients admitted to the ICU of the Hospital in Pordenone from March 11, 2020, to April 17, 2020. Patients' characteristics of survivors and deceased groups were compared. The variables with a different distribution between the two groups were implemented in a generalized linear regression model (LM) and in an Artificial Neural Network (NN) model to verify the "robustness" of the association with mortality. RESULTS: In the considered period, we reviewed the data of 22 consecutive patients: 8 died. The causes of death were a severe respiratory failure (3), multi-organ failure (1), septic shock (1), pulmonary thromboembolism (2), severe hemorrhage (1). Lymphocyte and the platelet count were significantly lower in the group of deceased patients (p-value 0.043 and 0.020, respectively; cut-off values: 660/mm3; 280,000/mm3, respectively). Prothrombin time showed a statistically significant trend (p-value= 0.065; cut-off point: 16.8/sec). The LM model (AIC= 19.032), compared to the NN model (Mean Absolute Error, MAE = 0.02), was substantially alike (MSE 0.159 vs. 0.136). CONCLUSIONS: In the context of critically ill COVID-19 patients admitted to ICU, lymphocytopenia, thrombocytopenia, and lengthening of prothrombin time were strictly correlated with higher mortality. Additional clinical data are needed to be able to validate this prognostic score.
Subject(s)
COVID-19 , Humans , Intensive Care Units , Neural Networks, Computer , Prognosis , Retrospective Studies , SARS-CoV-2ABSTRACT
BACKGROUND: Early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients who could develop a severe form of COVID-19 must be considered of great importance to carry out adequate care and optimise the use of limited resources. AIMS: To use several machine learning classification models to analyse a series of non-critically ill COVID-19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome. METHODS: We retrospectively analysed non-critically ill patients with COVID-19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected. RESULTS: In the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64-0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction-inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors. CONCLUSIONS: In non-critically ill COVID-19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID-19, such as age or dementia, influence clinical outcomes.