Wave comparisons of clinical characteristics and outcomes of COVID-19 admissions - Exploring the impact of treatment and strain dynamics

Freeman, Anna; Watson, Alastair; O’Regan, Paul; Wysocki, Oskar; Burke, Hannah; Freitas, André; Livingstone, Rodney; Dushianthan, Ahilanandan; Celinski, Michael; Batchelor, James; Phan, Hang; Borca, Florina; Fitzpatrick, Paul; Landers, Dónal

doi:10.1016/j.jcv.2021.105031

Cited by 16 publications

(25 citation statements)

References 26 publications

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“…In contrast, remdesivir was prescribed early on (it was introduced in guidelines in late May 2020), but its use decreased soon after, being replaced by tocilizumab from January 2021 onwards. Observed changes in treatment pattern over time are, however, in line with evolving knowledge and, consequently, changes in treatment guidelines9 and have also been reported in other studies: an English study, for example, reported a decrease in the use of remdesivir, and an increase in the use of tocilizumab, during their study period 35. Interestingly, the number of patients receiving more than one of the studied drugs—most prominently, dexamethasone and tocilizumab—increased over time; anecdotally, the addition of dexamethasone to both tocilizumab and remdesivir treatment has been described in other observational studies, mostly from the USA 15 33.…”

Section: Discussionsupporting

confidence: 80%

“…Observed changes in treatment pattern over time are, however, in line with evolving knowledge and, consequently, changes in treatment guidelines 9 and have also been reported in other studies: an English study, for example, reported a decrease in the use of remdesivir, and an increase in the use of tocilizumab, during their study period. 35 Interestingly, the number of patients receiving more than one of the studied drugs—most prominently, dexamethasone and tocilizumab—increased over time; anecdotally, the addition of dexamethasone to both tocilizumab and remdesivir treatment has been described in other observational studies, mostly from the USA. 15 33 Since there was wide variety in both the combination and sequencing of drugs and treatment decisions might have been influenced by a number of factors, including disease severity and other, unknown, patient-related aspects, interpretation of these findings is very difficult without further information and would, at this point, be merely speculative.…”

Section: Discussionmentioning

confidence: 99%

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Assessing medication use patterns in patients hospitalised with COVID-19: a retrospective study

et al. 2022

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ObjectiveTo describe patterns of medication use—that is, dexamethasone; remdesivir; and tocilizumab—in the management of patients hospitalised with COVID-19.Design and settingRetrospective observational study, using routinely collected, linked electronic data from clinical practice in Scotland. Data on drug exposure in secondary care has been obtained from the Hospital Electronic Prescribing and Medicines Administration System.ParticipantsPatients being treated with the drugs of interest and hospitalised for COVID-19 between 1 March 2020 and 10 November 2021.OutcomesIdentification of patients subject to the treatments of interest; summary of patients’ baseline characteristics; description of medication use patterns and treatment episodes. Analyses were descriptive in nature.ResultsOverall, 4063 patients matching the inclusion criteria were identified in Scotland, with a median (IQR) age of 64 years (52–76). Among all patients, 81.4% (n=3307) and 17.8% (n=725) were treated with one or two medicines, respectively; dexamethasone monotherapy accounted for the majority (n=3094, 76.2%) followed by dexamethasone in combination with tocilizumab (n=530, 13.0%). Treatment patterns were variable over time but roughly followed the waves of COVID-19 infections; however, the different drugs were used to varying degrees during the study period.The median (IQR) treatment duration differed by medicine: dexamethasone 5 days (2–9); remdesivir 5 days (2–5); and tocilizumab 1 day (1–1). The overall median (IQR) length of hospital stay among all patients included in the study cohort was 9 days (5–17); 24.7% of patients died in hospital.ConclusionThe use of adjuvant medicines in patients hospitalised with COVID-19 appears in line with evolving evidence and changing treatment guidelines. In-hospital electronic prescribing systems are a valuable source of information, providing detailed patient-level data on in-hospital drug use.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Assessing medication use patterns in patients hospitalised with COVID-19: a retrospective study

et al. 2022

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“…As part of the Research Evaluation Alongside Clinical Treatment in COVID-19 (REACT) observational and biobanking study of 16COVID-19,17 data were collected for COVID-19 positive patients who were admitted to University Hospital Southampton between 7 March and 4 September 2020.…”

Section: Methodsmentioning

confidence: 99%

“…As part of the Research Evaluation Alongside Clinical Treatment in COVID-19 (REACT) observational and biobanking study of 16…”

Section: Study Design and Settingmentioning

confidence: 99%

Biomarker identification using dynamic time warping analysis: a longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital

et al. 2022

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ObjectivesCOVID-19 is a heterogeneous disease, and many reports have described variations in demographic, biochemical and clinical features at presentation influencing overall hospital mortality. However, there is little information regarding longitudinal changes in laboratory prognostic variables in relation to disease progression in hospitalised patients with COVID-19.Design and settingThis retrospective observational report describes disease progression from symptom onset, to admission to hospital, clinical response and discharge/death among patients with COVID-19 at a tertiary centre in South East England.ParticipantsSix hundred and fifty-one patients treated for SARS-CoV-2 between March and September 2020 were included in this analysis. Ethical approval was obtained from the HRA Specific Review Board (REC 20/HRA/2986) for waiver of informed consent.ResultsThe majority of patients presented within 1 week of symptom onset. The lowest risk patients had low mortality (1/45, 2%), and most were discharged within 1 week after admission (30/45, 67%). The highest risk patients, as determined by the 4C mortality score predictor, had high mortality (27/29, 93%), with most dying within 1 week after admission (22/29, 76%). Consistent with previous reports, most patients presented with high levels of C reactive protein (CRP) (67% of patients >50 mg/L), D-dimer (98%>upper limit of normal (ULN)), ferritin (65%>ULN), lactate dehydrogenase (90%>ULN) and low lymphocyte counts (81%<lower limit of normal (LLN)). Increases in platelet counts and decreases in CRP, neutrophil:lymphocyte ratio (p<0.001), lactate dehydrogenase, neutrophil counts, urea and white cell counts (all p<0.01) were each associated with discharge.ConclusionsSerial measurement of routine blood tests may be a useful prognostic tool for monitoring treatment response in hospitalised patients with COVID-19. Changes in other biochemical parameters often included in a ‘COVID-19 bundle’ did not show significant association with outcome, suggesting there may be limited clinical benefit of serial sampling. This may have direct clinical utility in the context of escalating healthcare costs of the pandemic.

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“…The COVID-19 pandemic manifested itself as multiple geographically and temporally distinct waves that exhibited different characteristics such as different heterogeneous patient populations and different standard treatment methods 28 . Moreover, virus variants, changing vaccination rates, waning of vaccine protection, and emergence of novel COVID-19 treatments also contributed to a highly dynamic environment for patient characteristics and outcomes [29][30][31][32] . A commonly reported change was related to the significant decrease of case mortality rate of hospitalized patients in certain areas [33][34][35] , as well as the number of intubated patients 35 and overall hospitalized patients between waves.…”

mentioning

confidence: 99%

Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

et al. 2022

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Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.

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Wave comparisons of clinical characteristics and outcomes of COVID-19 admissions - Exploring the impact of treatment and strain dynamics

Cited by 16 publications

References 26 publications

Assessing medication use patterns in patients hospitalised with COVID-19: a retrospective study

Assessing medication use patterns in patients hospitalised with COVID-19: a retrospective study

Biomarker identification using dynamic time warping analysis: a longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital

Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

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