Individual-level precision diagnosis for coronavirus disease 2019 related severe outcome: an early study in New York

  1. Chaorui C. Huang
  2. Hong Xu

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Abstract

Because of inadequate information provided by the on-going population level risk analyses for Coronavirus disease 2019 (COVID-19), this study aimed to evaluate the risk factors and develop an individual-level precision diagnostic method for COVID-19 related severe outcome in New York State (NYS) to facilitate early intervention and predict resource needs for patients with COVID-19. We analyzed COVID-19 related hospital encounter and hospitalization in NYS using Statewide Planning and Research Cooperative System hospital discharge dataset. Logistic regression was performed to evaluate the risk factors for COVID-19 related mortality. We proposed an individual-level precision diagnostic method by taking into consideration of the different weights and interactions of multiple risk factors. Age was the greatest risk factor for COVID-19 related fatal outcome. By adding other demographic variables, dyspnea or hypoxemia and multiple chronic co-morbid conditions, the model predictive accuracy was improved to 0.85 (95% CI 0.84–0.85). We selected cut-off points for predictors and provided a general recommendation to categorize the levels of risk for COVID-19 related fatal outcome, which can facilitate the individual-level diagnosis and treatment, as well as medical resource prediction. We further provided a use case of our method to evaluate the feasibility of public health policy for monoclonal antibody therapy.

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  1. Version published to 10.1038/s41598-023-35966-z
  2. ScreenIT

    SciScore for 10.1101/2021.11.30.21267086: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    5 All statistical analyses were performed using SAS, version 9.4, SAS Institute.
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)
    SAS Institute
    suggested: (Statistical Analysis System, RRID:SCR_008567)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite this limitation, our study primarily aimed to address three considerations to improve the existing research and knowledge, as well as to explain the scientific thinking and methodology, so that the scientific community can replicate the study in much larger scale datasets with more complete medical information to modify and refine the approach and methodology, and eventually make it useful in clinical practice. First, the window for early intervention can be short. We need a measurement or scale, which can be applied to rapidly screen the at-risk patients for severe outcome, so that early and timely intervention is possible. In our study, we used patients’ demographic information (age, sex, race/ethnicity), symptom of dyspnea, and medical history of chronic co-morbid conditions, which can be conveniently obtained by inquiry, and hypoxemia, which can be rapidly assessed in out-patient clinic settings. Secondly, among all the risk factors we discovered in this study for COVID-19 related severe outcome, age may possibly be the most important one, which was consistent with a previous study.6 We showed in the study that age by itself can achieve 0.77-0.80 diagnostic accuracy, represented by the area under the ROC curve, while none of the single co-morbid conditions can reach such high diagnostic accuracy in predicting the fatal outcome. Instead, all the multiple co-morbid conditions together only improved around 4% of diagnostic accuracy on top of age. In addition, the odd...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.11.30.21267086 on medRxiv