Development and Validation of the Acute PNeumonia Early Assessment Score for Safely Discharging Low-Risk SARS-CoV-2-Infected Patients from the Emergency Department

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

Artificial neural network model from a case series of covid-19 patients: A prognostic analysis

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\u2019s 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\u2019 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 \u201crobustness\u201d 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. (www.actabiomedica.it)

Acute kidney injury and single-dose administration of aminoglycoside in the Emergency Department: a comparison through propensity score matching

Purpose: According to the Surviving Sepsis Campaign, aminoglycosides (AG) can be administered together with a \u3b2-lactam in patients with septic shock. Some authors propose administering a single dose of an AG combined with a \u3b2-lactam antibiotic in septic patients to extend the spectrum of antibiotic therapy. The aim of this study has been to investigate whether a single shot of AG when septic patients present at the Emergency Department (ED) is associated with acute kidney injury (AKI). Methods: We retrospectively enrolled patients based on a 3-year internal registry of septic patients visited in the Emergency Department (ED) of Pordenone Hospital. We compared the patients treated with a single dose of gentamicin (in addition to the \u3b2-lactam) and those who had not been treated to verify AKI incidence. Results: 355 patients were enrolled. The median age was 71 years (IQR 60-78). Less than 1% of the patients had a chronic renal disease. The most frequent infection source was the urinary tract (31%), followed by intra-abdominal and lower respiratory tract infections (15% for both). 131 patients received gentamicin. Unmatched data showed a significant difference between the two groups in AKI (79/131, 60.3% versus 102/224, 45.5%; p=0.010) and in infectious disease specialist's consultation (77/131, 59% versus 93/224, 41.5%; p=0.002). However, after propensity score matching, no significant difference was found. Conclusion: Our experience shows that a single-shot administration of gentamicin upon admission to the ED does not determine an increased incidence of AKI in septic patients

Classification and analysis of outcome predictors in non-critically ill COVID-19 patients

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\u20130.90, 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

Risk Factors and Outcomes of Infections by Multidrug-Resistant Gram-Negative Bacteria in Patients Undergoing Hematopoietic Stem Cell Transplantation

The objective of this study was to determine risk factors and outcomes of infections by multidrug-resistant gram-negative (MDR GN) bacteria in 241 recipients of hematopoietic stem cell transplantation (HSCT). The cumulative incidence of infections was 10.5% (95% CI, 12.0% to 25.8%), with 57% of infections occurring during the period of severe neutropenia (neutrophil count < .1 × 106/L). In multivariate analysis, allogeneic transplant and colonization with MDR GN bacteria at admission to the transplant unit were significantly associated with an increased risk of infection. Although we observed neither transplant-related mortality (TRM) nor deaths due to infections by MDR GN bacteria after autologous transplant, in the allogeneic setting a significant difference was reported in terms of overall survival (OS) and TRM between patients who developed infections and those who did not (1-year OS, 39% versus 68%; 1-year TRM, 42% versus 19%). In multivariate analysis, refractory disease and development of grades III to IV graft-versus-host disease (GVHD) were factors that affected both TRM and OS, whereas occurrence of infections by MDR GN pathogens significantly reduced OS. We conclude that eligibility to allogeneic HSCT in MDR GN bacteria carriers should be carefully evaluated together with all other factors that independently influence outcome (disease status, donor, and GVHD risk)

Erratum to: Risk Factors and Outcomes of Infections by Multidrug-Resistant Gram-Negative Bacteria in Patients Undergoing Hematopoietic Stem Cell Transplantation [Biol Blood Marrow Transplant (2017) 23 (333–339)] (S1083879116304839) (10.1016/j.bbmt.2016.11.005)

In the above-mentioned article, the third author's name was printed incorrectly. The correct author name should read Massimo Dozzo

Erratum to: Risk Factors and Outcomes of Infections by Multidrug-Resistant Gram-Negative Bacteria in Patients Undergoing Hematopoietic Stem Cell Transplantation [Biol Blood Marrow Transplant (2017) 23 (333\u2013339)] (S1083879116304839) (10.1016/j.bbmt.2016.11.005)

In the above-mentioned article, the third author's name was printed incorrectly. The correct author name should read Massimo Dozzo