The effect of COVID-19 epidemic on vital signs in hospitalized patients: a pre-post heat-map study from a large teaching hospital.
Atypical pneumonia
COVID-19
Data science
Data visualization
Heat maps
Vital signs
Journal
Journal of clinical monitoring and computing
ISSN: 1573-2614
Titre abrégé: J Clin Monit Comput
Pays: Netherlands
ID NLM: 9806357
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
received:
15
09
2020
accepted:
27
04
2021
pubmed:
11
5
2021
medline:
7
6
2022
entrez:
10
5
2021
Statut:
ppublish
Résumé
The Lombardy SARS-CoV-2 outbreak in February 2020 represented the beginning of COVID-19 epidemic in Italy. Hospitals were flooded by thousands of patients with bilateral pneumonia and severe respiratory, and vital sign derangements compared to the standard hospital population. We propose a new visual analysis technique using heat maps to describe the impact of COVID-19 epidemic on vital sign anomalies in hospitalized patients. We conducted an electronic health record study, including all confirmed COVID-19 patients hospitalized from February 21st, 2020 to April 21st, 2020 as cases, and all non-COVID-19 patients hospitalized in the same wards from January 1st, 2018 to December 31st, 2018. All data on temperature, peripheral oxygen saturation, respiratory rate, arterial blood pressure, and heart rate were retrieved. Derangement of vital signs was defined according to predefined thresholds. 470 COVID-19 patients and 9241 controls were included. Cases were older than controls, with a median age of 79 vs 76 years in non survivors (p = < 0.002). Gender was not associated with mortality. Overall mortality in COVID-19 hospitalized patients was 18%, ranging from 1.4% in patients below 65 years to about 30% in patients over 65 years. Heat maps analysis demonstrated that COVID-19 patients had an increased frequency in episodes of compromised respiratory rate, acute desaturation, and fever. COVID-19 epidemic profoundly affected the incidence of severe derangements in vital signs in a large academic hospital. We validated heat maps as a method to analyze the clinical stability of hospitalized patients. This method may help to improve resource allocation according to patient characteristics.
Identifiants
pubmed: 33970387
doi: 10.1007/s10877-021-00715-y
pii: 10.1007/s10877-021-00715-y
pmc: PMC8108436
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
829-837Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature B.V.
Références
Huang C, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506.
pubmed: 31986264
pmcid: 7159299
WHO announces COVID-19 outbreak a pandemic. 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 .
Richardson S, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA. 2020;323:2052–9.
doi: 10.1001/jama.2020.6775
Yang X, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020;8:475–81.
doi: 10.1016/S2213-2600(20)30079-5
John Hopkins. https://coronavirus.jhu.edu/map.html . https://www.worldometers.info/coronavirus .
Grasselli G, Pesenti A, Cecconi M. Critical care utilization for the COVID-19 Outbreak in Lombardy, Italy: early experience and forecast during an emergency response. JAMA—J Am Med Assoc. 2020;323:1545.
doi: 10.1001/jama.2020.4031
COVID-19 ITALIA—desktop. https://opendatadpc.maps.arcgis.com/apps/dashboards/b0c68bce2cce478eaac82fe38d4138b1 .
ISTAT. Annuario statitistico italiano, Annu. Istat. 2019. https://www.istat.it/it/archivio/236772 .
Grasselli G, et al. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy. JAMA. 2020;323:1574–81.
doi: 10.1001/jama.2020.5394
Zhou F, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–62.
doi: 10.1016/S0140-6736(20)30566-3
Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020. https://doi.org/10.1016/S0140-6736(20)30211-7 .
doi: 10.1016/S0140-6736(20)30211-7
pubmed: 34338215
pmcid: 7683950
Wang D, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA—J Am Med Assoc. 2020;323:1061–9.
doi: 10.1001/jama.2020.1585
Jayasundera R, Neilly M, Smith T, Myint P. Are early warning scores useful predictors for mortality and morbidity in hospitalised acutely unwell older patients? A systematic review. J Clin Med. 2018;7(10):309.
doi: 10.3390/jcm7100309
Goldhill DR, McNarry AF, Hadjianastassiou VG, Tekkis PP. The longer patients are in hospital before intensive care admission the higher their mortality. Intensive Care Med. 2004;30(10):1908–13.
doi: 10.1007/s00134-004-2386-2
Von Mettenheim HJ, Breitner MH. Decision analytics with heatmap visualization for multi-step ensemble data. Bus Inf Syst Eng. 2014;6(3):131–40.
doi: 10.1007/s12599-014-0326-4
Peluso S, et al. A Bayesian spatiotemporal statistical analysis of out-of-hospital cardiac arrests. Biom J. 2020;62:1105–19.
doi: 10.1002/bimj.201900166
Samartín-Ucha M, et al. Devising of a risk map on the management of high risk alert medication in a high level university hospital. Farm Hosp. 2019;43(3):110–5.
pubmed: 31072289
Johnson AEW, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016. https://doi.org/10.1038/sdata.2016.35 .
doi: 10.1038/sdata.2016.35
pubmed: 27727238
pmcid: 5058338
Moore N, Middleton PM, Ren S. Lost capacity in emergency departments and its economic implications: a simulation study and economic analysis. Emerg Med Australas. 2020;32:974–9.
doi: 10.1111/1742-6723.13526
Sánchez López JD, Cambil Martín J, Villegas Calvo M, Toledo Páez MA, Cariati P, Moreno Martín ML. Development of a risk map in an oral and maxillofacial surgical unit. J Healthc Qual Res. 2019;34(4):209–16.
doi: 10.1016/j.jhqr.2019.05.003
Mojica E, Izarzugaza E, Gonzalez M, Astobiza E, Benito J, Mintegi S. Elaboration of a risk map in a paediatric emergency department of a teaching hospital. Emerg Med J. 2016;33(10):684–9.
doi: 10.1136/emermed-2015-205336
Bellani G, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315(8):788–800.
doi: 10.1001/jama.2016.0291
Bhatraju PK, et al. Covid-19 in critically ill patients in the Seattle region—case series. N Engl J Med. 2020. https://doi.org/10.1056/NEJMoa2004500 .
doi: 10.1056/NEJMoa2004500
pubmed: 32227758
pmcid: 7143164
Guan WJ, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708–20.
doi: 10.1056/NEJMoa2002032
Liljehult J, Christensen T. Early warning score predicts acute mortality in stroke patients. Acta Neurol Scand. 2016;133(4):261–7.
doi: 10.1111/ane.12452
Cei M, Bartolomei C, Mumoli N. In-hospital mortality and morbidity of elderly medical patients can be predicted at admission by the modified early warning score: a prospective study. Int J Clin Pract. 2009;63(4):591–5.
doi: 10.1111/j.1742-1241.2008.01986.x
Goldfrad C, Rowan K. Consequences of discharges from intensive care at night. Lancet. 2000;355(9210):1138–42.
doi: 10.1016/S0140-6736(00)02062-6
Carenzo L, et al. Hospital surge capacity in a tertiary emergency referral centre during the COVID-19 outbreak in Italy. Anaesthesia. 2020. https://doi.org/10.1111/anae.15072 .
doi: 10.1111/anae.15072
pubmed: 32246838