Internal and emergency medicine
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Machine learning (ML) techniques may improve readmission prediction performance in heart failure (HF) patients. This study aimed to assess the ability of ML algorithms to predict unplanned all-cause 30-day readmissions in HF elderly patients, and to compare them with conventional LACE (Length of hospitalization, Acuity, Comorbidities, Emergency department visits) index. All patients aged ≥ 65 years discharged alive between 2010 and 2019 after a hospitalization for acute HF were included in this retrospective cohort study. ⋯ Among elderly patients, the rate of all-cause unplanned 30-day readmissions after hospitalization due to an acute HF was high. ML models performed better than the conventional LACE index for predicting readmissions. ML models can be proposed as promising tools for the identification of subjects at high risk of hospitalization in this clinical setting, enabling care teams to target interventions for improving overall clinical outcomes.
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To determine the predictive factors of mortality after hospitalization for acute heart failure (AHF) in an internal medicine department. Retrospective observational analysis conducted on 164 patients hospitalized for AHF in 2016-2017. Demographic, clinical and biological characteristics were assessed during hospitalization. ⋯ In elderly multimorbid patients, AHF prognosis appears to be influenced by nutritional criteria, including lower BMI, hypoalbuminemia, and hyperuricemia (independently of renal function). These results underline the importance of nutritional status, especially as therapeutic options are available. This consideration paves the way for further research in this field.
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The CHA2DS2-VASc score incorporates several comorbidities which have prognostic implications in COVID-19. We assessed whether a modified score (M-R2CHA2DS2-VASc), which includes pre-admission kidney function and male sex, could be used to classify mortality risk among people hospitalized with COVID-19. This retrospective study included adults admitted for COVID-19 between March and December 2020. ⋯ Higher category was also associated with increased need for mechanical ventilation and renal replacement therapy. All-cause 90-day mortality remained significantly associated with M-R2CHA2DS2-VASc. The M-R2CHA2DS2-VASc score is associated with 30-day mortality rates among patients hospitalized with COVID-19, and adds predictive value when combined with initial COVID-19 severity.
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Observational Study
CovHos score for predicting severe respiratory failure in COVID-19 patients presenting at the emergency department.
Hospitalization of COVID-19 patients in low-intensity wards may put patients at risk in case of clinical deterioration. We tested CovHos score in predicting severe respiratory failure (SFR) at emergency department (ED) admission. This is a monocentric observational prospective study enrolling adult COVID-19 patients admitted to the ED of IRCCS AOU di Bologna Policlinico S. ⋯ In patients with symptoms onset up to 8 days, a CovHos cut-off of 22 was able to predict SRF with a sensitivity of 91.7% and a specificity of 78.6% (AUROC 0.901; CI 95% 0.861-0.941). Negative predictive value (NPV) was 97.1%. A CovHos score lower than 22, in patients with COVID-19 symptoms onset dated 8 or less days prior to the ED admittance, had a NPV of 97.1% for the development of SRF, meaning that almost none of those patients will evolve into SRF and could be therefore suitable for a lower intensity of care.