Impact of some antiviral drugs on health care utilization for patients with COVID-19: a systematic review and meta-analysis

Wei, Bincai; Zhang, Ruhao; Zeng, Huatang; Wu, Liqun; He, Rongxin; Zheng, Junyao; Xue, Hao; Liu, Jinlin; Liang, Fengchao; Zhu, Bin

doi:10.1080/14787210.2023.2254491

Cited by 2 publications

(2 citation statements)

References 79 publications

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“…However, those needing high-flow oxygen or IMV did not see the same benefit [49]. Remdesivir also seems to speed up recovery, reduce complications, and might slightly decrease the need for ventilation [32,50,51]. Additionally, a 5-day treatment course appears to provide more benefits with fewer side effects and lower costs for non-ventilated patients compared to a 10-day course [50].…”

Section: Discussionmentioning

confidence: 99%

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

Ramón,

Bas,

Herrero

et al. 2024

JCM

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Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir’s clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB’s effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.

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Section: Discussionmentioning

confidence: 99%

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

Ramón,

Bas,

Herrero

et al. 2024

JCM

View full text Add to dashboard Cite

show abstract

“…However, those needing highflow oxygen or IMV didn't see the same benefit [53]. Remdesivir also seems to speed up recovery, reduce complications, and might slightly decrease the need for ventilation [32,[54][55]. Additionally, a 5-day treatment course appears to provide more benefits with fewer side effects and lower costs for non-ventilated patients compared to a 10-day course [54].…”

Section: Discussionmentioning

confidence: 99%

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

Ramón,

Castillo,

Herrero González

et al. 2024

Preprint

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Despite widespread vaccination, early treatments, and improved understanding of the disease, the effects of SARS-CoV-2 infection remain significant worldwide. Many patients still suffer from severe COVID-19, necessitating admission to intensive care units. Remdesivir is a primary treatment option among viral RNA polymerase inhibitors for hospitalized SARS-CoV-2 patients. However, there is a lack of studies examining factors influencing its effectiveness in this context. We conducted a retrospective study throughout 2022, analyzing clinical, laboratory, and sociodemographic data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared and validated to predict factors associated with the loss of clinical benefit from remdesivir in terms of mortality and hospital stay. Data were extracted from electronic health records. The eXtreme Gradient Boost (XGB) method achieved the highest balanced accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes of remdesivir use in terms of mortality included limitation of life support treatment, need for ventilatory support (especially invasive mechanical ventilation) on day 14 after the first dose of remdesivir, lymphopenia, low levels of albumin and hemoglobin, presence of flu and/or coinfection, and cough. Factors associated with worse outcomes of remdesivir use in terms of hospital stay included the number of doses of the COVID-19 vaccine, patchy lung density, bilateral pulmonary radiological status, number of comorbidities, oxygen therapy, troponin and lactate dehydrogenase levels, and asthenia. These findings highlight the effectiveness of XGB as a strong candidate for accurately categorizing COVID-19 patients undergoing remdesivir treatment.

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Impact of some antiviral drugs on health care utilization for patients with COVID-19: a systematic review and meta-analysis

Cited by 2 publications

References 79 publications

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach

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