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A nomogram to predict the risk of unfavourable outcome in COVID-19: a retrospective cohort of 279 hospitalized patients in Paris area. | LitMetric

AI Article Synopsis

  • The study aimed to identify predictive factors for unfavorable outcomes in COVID-19 patients hospitalized in Paris, focusing on factors leading to the need for artificial ventilation or death.
  • Researchers analyzed data from 279 COVID-19 patients, finding 31.5% experienced unfavorable outcomes, with key risk factors including age, weight, respiratory rate, fever, and specific blood markers.
  • A nomogram was developed to predict the probability of adverse outcomes within 14 days, providing a useful tool for early detection and improving clinical care for at-risk patients.

Article Abstract

Objective: To identify predictive factors of unfavourable outcome among patients hospitalized for COVID-19.

Methods: We conducted a monocentric retrospective cohort study of COVID-19 patients hospitalized in Paris area. An unfavourable outcome was defined as the need for artificial ventilation and/or death. Characteristics at admission were analysed to identify factors predictive of unfavourable outcome using multivariable Cox proportional hazard models. Based on the results, a nomogram to predict 14-day probability of poor outcome was proposed.

Results: Between March 15th and April 14th, 2020, 279 COVID-19 patients were hospitalized after a median of 7 days after the first symptoms. Among them, 88 (31.5%) patients had an unfavourable outcome: 48 were admitted to the ICU for artificial ventilation, and 40 patients died without being admitted to ICU. Multivariable analyses retained age, overweight, polypnoea, fever, high C-reactive protein, elevated us troponin-I, and lymphopenia as risk factors of an unfavourable outcome. A nomogram was established with sufficient discriminatory power (C-index 0.75), and proper consistence between the prediction and the observation.

Conclusion: We identified seven easily available prognostic factors and proposed a simple nomogram for early detection of patients at risk of aggravation, in order to optimize clinical care and initiate specific therapies. KEY MESSAGES Since novel coronavirus disease 2019 pandemic, a minority of patients develops severe respiratory distress syndrome, leading to death despite intensive care. Tools to identify patients at risk in European populations are lacking. In our series, age, respiratory rate, overweight, temperature, C-reactive protein, troponin and lymphocyte counts were risk factors of an unfavourable outcome in hospitalized adult patients. We propose an easy-to-use nomogram to predict unfavourable outcome for hospitalized adult patients to optimize clinical care and initiate specific therapies.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7877983PMC
http://dx.doi.org/10.1080/07853890.2020.1803499DOI Listing

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