scholarly journals Diagnostic accuracy of subjective dyspnoea in detecting hypoxaemia among outpatients with COVID-19: a retrospective cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e046282
Author(s):  
Linor Berezin ◽  
Alice Zhabokritsky ◽  
Nisha Andany ◽  
Adrienne K Chan ◽  
Jose Estrada-Codecido ◽  
...  

ObjectivesThe majority of patients with mild-to-moderate COVID-19 can be managed using virtual care. Dyspnoea is challenging to assess remotely, and the accuracy of subjective dyspnoea measures in capturing hypoxaemia have not been formally evaluated for COVID-19. We explored the accuracy of subjective dyspnoea in diagnosing hypoxaemia in COVID-19 patients.MethodsThis is a retrospective cohort study of consecutive outpatients with COVID-19 who met criteria for home oxygen saturation monitoring at a university-affiliated acute care hospital in Toronto, Canada from 3 April 2020 to 13 September 2020. Dyspnoea measures were treated as diagnostic tests, and we determined their sensitivity (SN), specificity (SP), negative/positive predictive value (NPV/PPV) and positive/negative likelihood ratios (+LR/−LR) for detecting hypoxaemia. In the primary analysis, hypoxaemia was defined by oxygen saturation <95%; the diagnostic accuracy of subjective dyspnoea was also assessed across a range of oxygen saturation cutoffs from 92% to 97%.ResultsDuring the study period, 89/501 (17.8%) of patients met criteria for home oxygen saturation monitoring, and of these 17/89 (19.1%) were diagnosed with hypoxaemia. The presence/absence of dyspnoea had limited accuracy for diagnosing hypoxaemia, with SN 47% (95% CI 24% to 72%), SP 80% (95% CI 68% to 88%), NPV 86% (95% CI 75% to 93%), PPV 36% (95% CI 18% to 59%), +LR 2.4 (95% CI 1.2 to 4.7) and −LR 0.7 (95% CI 0.4 to 1.1). The SN of dyspnoea was 50% (95% CI 19% to 81%) when a cut-off of <92% was used to define hypoxaemia. A modified Medical Research Council dyspnoea score >1 (SP 98%, 95% CI 88% to 100%), Roth maximal count <12 (SP 100%, 95% CI 75% to 100%) and Roth counting time <8 s (SP 93%, 95% CI 66% to 100%) had high SP that could be used to rule in hypoxaemia, but displayed low SN (≤50%).ConclusionsSubjective dyspnoea measures have inadequate accuracy for ruling out hypoxaemia in high-risk patients with COVID-19. Safe home management of patients with COVID-19 should incorporate home oxygenation saturation monitoring.

CMAJ Open ◽  
2017 ◽  
Vol 5 (1) ◽  
pp. E222-E228 ◽  
Author(s):  
Daniel Kobewka ◽  
Paul Ronksley ◽  
Dan McIsaac ◽  
Sunita Mulpuru ◽  
Alan Forster

2021 ◽  
Author(s):  
Chihiro Saito ◽  
Eiji Nakatani ◽  
Yoko Sato ◽  
Naoko Katuki ◽  
Masaki Tago ◽  
...  

Abstract Background In several current fall prediction models, the reported predictors vary from one model to another. We developed and validated a new fall prediction model for patients admitted to an acute care hospital by identifying predictors of falls considering a combination of background factors and one crucial stratum. Methods We conducted a retrospective cohort study of patients admitted to Shizuoka General Hospital from April 2019 to September 2020, aged 20 years or older. We developed and validated a new fall prediction model by identifying predictors of falls stratified by essential activities of daily living (ADL) indicators and integrating these models. Results A total of 22,988 individuals were included in the analysis, with 653 (2.8 %) experiencing all falls and 400 (1.7 %) experiencing falls with medical resources during the study period. Multivariate analysis was performed after one stratification level, using bedridden rank (ability to move around in daily life) as a stratifying variable, a clinically important variable and highly correlated with 17 other variables. The results of multivariate analysis showed that the risk factors for falls (high risk) were age (high), sex (men), and ambulance transport (yes) for rank J (independence/autonomy); age (high),) and sex (men) for rank A (house-bound); There were no predictors for rank B (chair-bound); and there was ophthalmologic disease (no) for rank C (bed-bound). The c-index indicating the prediction model’s performance for falls within 28 days of hospitalisation was 0.705 (95 % CI, 0.664–0.746). Hosmer-Lemeshow goodness-of-fit statistics were significant (χ2 = 192.06; 8 degrees of freedom; p < 0.001). The c-index for the entire unstratified sample was 0.703 (95 % CI, 0.661–0.746), indicating that the predictive model stratified by bedriddenness rank was accurate (p < 0.001). Conclusion We identified predictors of falls using important ADLs (bedriddenness rank) and developed a more accurate prediction model in acute care hospital settings. This predictive model is an essential tool for fall prevention.


2017 ◽  
Vol 40 (25) ◽  
pp. 3050-3053 ◽  
Author(s):  
Tomoko Nakazora ◽  
Konosuke Iwamoto ◽  
Tetsuhito Kiyozuka ◽  
Hirohiko Arimoto ◽  
Toshiki Shirotani ◽  
...  

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Lorenzo Sommella ◽  
Chiara de Waure ◽  
Anna Maria Ferriero ◽  
Amalia Biasco ◽  
Maria Teresa Mainelli ◽  
...  

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