Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City

  1. Ishan Paranjpe
  2. Adam J Russak
  3. Jessica K De Freitas
  4. Anuradha Lala
  5. Riccardo Miotto
  6. Akhil Vaid
  7. Kipp W Johnson
  8. Matteo Danieletto
  9. Eddye Golden
  10. Dara Meyer
  11. Manbir Singh
  12. Sulaiman Somani
  13. Arjun Kapoor
  14. Ross O'Hagan
  15. Sayan Manna
  16. Udit Nangia
  17. Suraj K Jaladanki
  18. Paul O’Reilly
  19. Laura M Huckins
  20. Patricia Glowe
  21. Arash Kia
  22. Prem Timsina
  23. Robert M Freeman
  24. Matthew A Levin
  25. Jeffrey Jhang
  26. Adolfo Firpo
  27. Patricia Kovatch
  28. Joseph Finkelstein
  29. Judith A Aberg
  30. Emilia Bagiella
  31. Carol R Horowitz
  32. Barbara Murphy
  33. Zahi A Fayad
  34. Jagat Narula
  35. Eric J Nestler
  36. V Fuster
  37. Carlos Cordon-Cardo
  38. Dennis Charney
  39. David L Reich
  40. Allan Just
  41. Erwin P Bottinger
  42. Alexander W Charney
  43. Benjamin S Glicksberg
  44. Girish N Nadkarni

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Abstract

The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive.

Design

Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive.

Setting

All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system.

Participants

Participants over the age of 18 years were included.

Primary outcomes

We investigated in-hospital mortality during the study period.

Results

A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL.

Conclusions

In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

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  1. Version published to 10.1136/bmjopen-2020-040736
  2. ScreenIT

    SciScore for 10.1101/2020.04.19.20062117: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The Mount Sinai Institutional Review Board approved this research under a broad regulatory protocol allowing for analysis of limited patient-level data.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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:
    Our study should be considered in light of several limitations. Since Covid-19 testing is frequently repeated in hospitalized patients and initial testing may result in false negatives, we are unable to determine whether patients developed their infection during or before hospital admission. Furthermore, Covid-19 has a variable incubation period of approximately 8-15 days17, and patients may present to the hospital several days after initial infection or the onset of symptoms. Thus, we are unable to determine patients’ disease duration. Additionally, we separated discharged patients from those who died, but some patients may have expired after discharge. This could affect our listed case mortality rate. Our study is also confined by the inherent limitations (e.g. biases) of EHR data. Although utilizing structured EHR data allows for rapid integration of multiple data streams and real-time analysis, data present only in clinical note text, such as symptoms on presentation are missed. We chose not to perform comprehensive manual chart review to prioritize timely dissemination of our observations. As the Covid-19 pandemic spreads from the current epicenter in New York City to other areas, our report provides meaningful clinical insights that may better inform care for diverse populations. Future work will aim to predict Covid-19 patient outcomes using a variety of approaches, thereby reducing healthcare system burden and permitting improved care delivery.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.04.19.20062117 on medRxiv