Risk Factors for Intensive Care Unit Admission and In-hospital Mortality Among Hospitalized Adults Identified through the US Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET)
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Abstract
Background
Currently, the United States has the largest number of reported coronavirus disease 2019 (COVID-19) cases and deaths globally. Using a geographically diverse surveillance network, we describe risk factors for severe outcomes among adults hospitalized with COVID-19.
Methods
We analyzed data from 2491 adults hospitalized with laboratory-confirmed COVID-19 between 1 March–2 May 2020, as identified through the Coronavirus Disease 2019–Associated Hospitalization Surveillance Network, which comprises 154 acute-care hospitals in 74 counties in 13 states. We used multivariable analyses to assess associations between age, sex, race and ethnicity, and underlying conditions with intensive care unit (ICU) admission and in-hospital mortality.
Results
The data show that 92% of patients had ≥1 underlying condition; 32% required ICU admission; 19% required invasive mechanical ventilation; and 17% died. Independent factors associated with ICU admission included ages 50–64, 65–74, 75–84, and ≥85 years versus 18–39 years (adjusted risk ratios [aRRs], 1.53, 1.65, 1.84, and 1.43, respectively); male sex (aRR, 1.34); obesity (aRR, 1.31); immunosuppression (aRR, 1.29); and diabetes (aRR, 1.13). Independent factors associated with in-hospital mortality included ages 50–64, 65–74, 75–84, and ≥ 85 years versus 18–39 years (aRRs, 3.11, 5.77, 7.67, and 10.98, respectively); male sex (aRR, 1.30); immunosuppression (aRR, 1.39); renal disease (aRR, 1.33); chronic lung disease (aRR 1.31); cardiovascular disease (aRR, 1.28); neurologic disorders (aRR, 1.25); and diabetes (aRR, 1.19).
Conclusions
In-hospital mortality increased markedly with increasing age. Aggressive implementation of prevention strategies, including social distancing and rigorous hand hygiene, may benefit the population as a whole, as well as those at highest risk for COVID-19–related complications.
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SciScore for 10.1101/2020.05.18.20103390: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All analyses were performed using the SAS 9.4 software (SAS Institute Inc. SAS Institutesuggested: (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:There are several limitations to our analysis. First, it is likely …
SciScore for 10.1101/2020.05.18.20103390: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources All analyses were performed using the SAS 9.4 software (SAS Institute Inc. SAS Institutesuggested: (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:There are several limitations to our analysis. First, it is likely that not all COVID-19-associated hospitalizations were captured because of the lack of widespread testing capability during the study period and because identification of COVID-19 patients was largely reliant on clinician-directed testing. Second, clinical practices and availability of specific interventions may differ across hospitals, which might have influenced findings. Third, COVID-NET is an ongoing surveillance system, and only 15% of the 16,318 COVID-19 hospitalized patients were included, representing those who were discharged or died in-hospital during March 1–May 2, 2020 and for whom medical records were available and chart abstractions were completed. These restrictions may have resulted in selection bias. However, there was no difference in the age and sex distribution between cases included and excluded from the analysis. The geographic distribution of cases included versus excluded from this analysis differed, which may have impacted the racial and ethnic distribution of cases included in this analysis as compared to the racial and ethnic distribution of the surveillance catchment population; however, as we do not yet have complete data on race/ethnicity for all identified cases, we were not able to assess this further. Nevertheless, COVID-NET encompasses a large geographic area with multiple hospitals and likely offers a more racially and ethnically diverse patient population compared to other s...
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.
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