ABSTRACT
Background Since January 2020, coronavirus disease 19 (COVID-19) has rapidly spread all over the world. An early assessment of illness severity is crucial for the stratification of patients in order to address them to the right intensity path of care. We performed an analysis on a large cohort of COVID-19 patients (n=581) hospitalized between March 2020 and May 2021 in our intensive care unit (ICU) at Policlinico Riuniti di Foggia hospital. Through an integration of the scores, demographic data, clinical history, laboratory findings, respiratory parameters, a correlation analysis, and the use of machine learning our study aimed to develop a model to predict the main outcome. Methods We deemed eligible for analysis all adult patients (age >18 years old) admitted to our department. We excluded all the patients with an ICU length of stay inferior to 24 h and the ones that declined to participate in our data collection. We collected demographic data, medical history, D-dimers, NEWS2, and MEWS scores on ICU admission and on ED admission, PaO2/FiO2 ratio on ICU admission, and the respiratory support modalities before the orotracheal intubation and the intubation timing (early vs late with a 48-h hospital length of stay cutoff). We further collected the ICU and hospital lengths of stay expressed in days of hospitalization, hospital location (high dependency unit, HDU, ED), and length of stay before and after ICU admission;the in-hospital mortality;and the in-ICU mortality. We performed univariate, bivariate, and multivariate statistical analyses. Results SARS-CoV-2 mortality was positively correlated to age, length of stay in HDU, MEWS, and NEWS2 on ICU admission, D-dimer value on ICU admission, early orotracheal intubation, and late orotracheal intubation. We found a negative correlation between the PaO2/FiO2 ratio on ICU admission and NIV. No significant correlations with sex, obesity, arterial hypertension, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, diabetes mellitus, dyslipidemia, and neither MEWS nor NEWS on ED admission were observed. Considering all the pre-ICU variables, none of the machine learning algorithms performed well in developing a prediction model accurate enough to predict the outcome although a secondary multivariate analysis focused on the ventilation modalities and the main outcome confirmed how the choice of the right ventilatory support with the right timing is crucial. Conclusion In our cohort of COVID patients, the choice of the right ventilatory support at the right time has been crucial, severity scores, and clinical judgment gave support in identifying patients at risk of developing a severe disease, comorbidities showed a lower weight than expected considering the main outcome, and machine learning method integration could be a fundamental statistical tool in the comprehensive evaluation of such complex diseases.
ABSTRACT
The aim of this study is to describe the features, the outcomes, and the clinical issues related to Remdesivir administration of a cohort of 220 patients (pts) with COVID-19 hospitalized throughout the last two pandemic waves in Italy. One hundred and nine pts were enrolled from 1 September 2020, to 28 February 2021 (Group A) and 111 from 1 March to 30 September 2021 (Group B). Notably, no differences were reported between the two groups neither in the timing of hospitalization. nor in the timing of Remdesivir administration from symptoms onset. Remarkably, a higher proportion of pts with severe COVID-19 was observed in Group B (25% vs. 10%, p < 0.001). At univariate and multivariate analysis, rather than the timing of Remdesivir administration, age, presence of coexisting conditions, D-dimers, and O2 flow at admission correlated positively to progression to non-invasive ventilation, especially for patients in Group B. However, the rate of admission in the Intensive Care Unit and/or death was comparable in the two groups (7% vs. 4%). Negligible variations in serum GOT, GPT, GGT, and eGFR levels were detected. A mean reduction in heart rate was noticed within the first three days of antiviral treatment (p < 0.001). Low rate of ICU admission, high rate of clinical recovery, and good drug safety were observed in COVID-19 patients treated with Remdesivir during two diverse pandemic waves.
ABSTRACT
OBJECTIVES: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has heavily impacted Italy. The government's restriction measures have attenuated the burden on hospitals. The association of high viral replication with disease severity suggests the potential for lower viral load in milder clinical presentations. METHODS: The reverse-transcription-polymerase-chain-reaction (RT-PCR) profile of 944 consecutive, non-replicate, positive retropharyngeal swabs was collected from 3 March to 8 June 2020 to investigate the temporal profile of SARS-CoV-2 viral load in the region of Capitanata, Apulia. Cycle threshold (Ct) values of 3 targets (N [nucleocapsid protein], E [envelope protein] and RdRP [RNA-dependent RNA-polymerase]) were analysed. RESULTS: The median Ct values of the 3 targets increased considerably over the study period, showing a progressive and constant weekly change. The negative detection rate of E and RdRP increased over time. These data suggest that SARS-CoV-2 viral load progressively decreased along the outbreak course. During the first epidemic peak (March and April) the viral load among patients >80-years was significantly higher than for younger subjects. However, in May this age-dependent difference disappeared, underlying viral load reduction in the elderly. CONCLUSIONS: An attenuation of viral transmission or pathogenicity during the epidemic course is suggested, likely due to restriction measures, although viral factors might also be considered.