Persistent symptoms up to four months after community and hospital‐managed SARS‐CoV‐2 infection

Darley, D.R.; Dore, Gregory J.; Cysique, Lucette A; Wilhelm, Kay; Andresen, David; Tonga, Katrina O.; Byrne, Anthony; Plit, M.; Masters, Jeffrey; Tang, Helen; Brew, Bruce J.; Cunningham, Philip; Kelleher, Anthony D.; Matthews, Gail V.

doi:10.5694/mja2.50963

Cited by 46 publications

(61 citation statements)

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“…The most frequently reported psychiatric deficits were depression and/or anxiety. In total, this was examined in 47 studies ( Al-Aly et al, 2021 , Alemanno et al, 2021 , Boari et al, 2021 , Chen et al, 2020 , Chevinsky et al, 2021 , Daher et al, 2020 , Darley et al, 2021 , Daugherty et al, 2021 , De Lorenzo et al, 2020 , Frontera et al, 2021 , Gennaro et al, 2021 , González et al, 2021 , Halpin et al, 2021 , Huang et al, 2021a , Iqbal et al, 2021 , Lorenzo et al, 2021 , Matalon et al, 2021 , Mattioli et al, 2021 , Mazza et al, 2020 , Mazza et al, 2021 , Méndez et al, 2021 , Monti et al, 2021 , Morin et al, 2021 , Negrini et al, 2021 , Noviello et al, 2021 , Ortelli et al, 2021 , Poyraz et al, 2021 , Romero-Duarte et al, 2021 , Sykes et al, 2021 , Tanriverdi et al, 2021 , Taquet et al, 2021a , Taquet et al, 2021b , Tomasoni et al, 2021 , Townsend et al, 2021 , van den Borst et al, 2020 , Venturelli et al, 2021 , Wang et al, 2020b , Wang et al, 2020c , Whiteside et al, 2021 , Wong et al, 2020 , Wu et al, 2020 , Xiong et al, 2021 , Yuan et al, 2020 , Zhou et al, 2020 , D'Cruz et al, 2021 , de Graaf et al, 2021 , Weerahandi et al, 2021 ). The results range from no indication of depression or anxiety ( Daher et al, 2020 , Zhou et al, 2020 ) to >30% at follow-up ( De Lorenzo et al, 2020 , Frontera et al, 2021 , Iqbal et al, 2...…”

Section: Resultsmentioning

confidence: 99%

“…Disease severity was in several studies suggested to be a risk factor. Al-Aly et al, 2021 , Darley et al, 2021 , Huang et al, 2021a , Taquet et al, 2021a and ( Halpin et al, 2021 ) but not ( de Graaf et al, 2021 ) reported that anxiety and/or depression was highest in patients recovering from severe COVID-19, Gennaro et al (2021) showed that duration of hospitalization correlated with depressive symptoms, Alemanno et al (2021) reported a correlation between depressive symptoms and severity of the initial disease and de Graaf et al (2021) showed that worse post-COVID functional status was associated with depression. Alemanno et al (2021) also reported that the depressive symptomatology did not improve compared to depressive status at admission.…”

Section: Resultsmentioning

confidence: 99%

“…Patients suffered from reduced QoL. No indicators for depression or anxiety Darley et al, 2021 (Australia) To characterise the effects of COVID-19 during the first year after diagnosis Prospective cohort study CogState Cognitive Test Battery; DMI-10 78 51 (65%) 47 69 days Adult hospitalised or non-hospitalised COVID-19 survivors. Not reported 22% suffered from fatigue, 10% displayed mild/moderate cognitive impairment and 21% had depression at follow up.…”

Section: Methodsmentioning

confidence: 99%

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Psychiatric and neuropsychiatric sequelae of COVID-19 – A systematic review

Schou

Joca

Wegener

et al. 2021

Brain, Behavior, and Immunity

318

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It has become evident that coronavirus disease 2019 (COVID-19) has a multi-organ pathology that includes the brain and nervous system. Several studies have also reported acute psychiatric symptoms in COVID-19 patients. An increasing number of studies are suggesting that psychiatric deficits may persist after recovery from the primary infection. In the current systematic review, we provide an overview of the available evidence and supply information on potential risk factors and underlying biological mechanisms behind such psychiatric sequelae. We performed a systematic search for psychiatric sequelae in COVID-19 patients using the databases PubMed and Embase. Included primary studies all contained information on the follow-up period and provided quantitative measures of mental health. The search was performed on June 4th 2021. 1725 unique studies were identified. Of these, 66 met the inclusion criteria and were included. Time to follow-up ranged from immediately after hospital discharge up to 7 months after discharge, and the number of participants spanned 3 to 266,586 participants. Forty studies reported anxiety and/or depression, 20 studies reported symptoms- or diagnoses of post-traumatic stress disorder (PTSD), 27 studies reported cognitive deficits, 32 articles found fatigue at follow-up, and sleep disturbances were found in 23 studies. Highlighted risk factors were disease severity, duration of symptoms, and female sex. One study showed brain abnormalities correlating with cognitive deficits, and several studies reported inflammatory markers to correlate with symptoms. Overall, the results from this review suggest that survivors of COVID-19 are at risk of psychiatric sequelae but that symptoms generally improve over time.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Psychiatric and neuropsychiatric sequelae of COVID-19 – A systematic review

Schou

Joca

Wegener

et al. 2021

Brain, Behavior, and Immunity

318

278

View full text Add to dashboard Cite

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“…This study investigated 2 cohorts of RT-PCR-confirmed convalescent individuals recruited from February to October 2020 in Australia (Table 1, Fig 1A). The Adapting to Pandemic Threats (ADAPT) cohort included 83 patients diagnosed at a community-based fever clinic whose sera was collected at 2 time points post-PCR positivity during the first wave (March to August, n = 166 samples) [46]. The second wave included sera from 17 patients recruited between July and October.…”

Section: Pau : Pleasenotethatasperplosstyle; Thewordsubjectsshouldnotbeusedforhumanpatie Articipantsmentioning

confidence: 99%

SARS-CoV-2 neutralizing antibodies: Longevity, breadth, and evasion by emerging viral variants

et al. 2021

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The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antibody neutralization response and its evasion by emerging viral variants and variant of concern (VOC) are unknown, but critical to understand reinfection risk and breakthrough infection following vaccination. Antibody immunoreactivity against SARS-CoV-2 antigens and Spike variants, inhibition of Spike-driven virus–cell fusion, and infectious SARS-CoV-2 neutralization were characterized in 807 serial samples from 233 reverse transcription polymerase chain reaction (RT-PCR)–confirmed Coronavirus Disease 2019 (COVID-19) individuals with detailed demographics and followed up to 7 months. A broad and sustained polyantigenic immunoreactivity against SARS-CoV-2 Spike, Membrane, and Nucleocapsid proteins, along with high viral neutralization, was associated with COVID-19 severity. A subgroup of “high responders” maintained high neutralizing responses over time, representing ideal convalescent plasma donors. Antibodies generated against SARS-CoV-2 during the first COVID-19 wave had reduced immunoreactivity and neutralization potency to emerging Spike variants and VOC. Accurate monitoring of SARS-CoV-2 antibody responses would be essential for selection of optimal responders and vaccine monitoring and design.

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“…Further, patients at risk may benefit from early therapeutic agents for COVID-19 [ 14 , 16 ]. In addition, due to the high prevalence of long-term COVID-19 symptoms and the association of severity of COVID-19 and severity of long-term COVID-19 symptoms [ 18 – 20 ], the need for medical interventions avoiding COVID-19 disease progression in patients at risk is further emphasized.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

et al. 2021

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Purpose While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. Methods We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). Results The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. Conclusion We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

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Persistent symptoms up to four months after community and hospital‐managed SARS‐CoV‐2 infection

Cited by 46 publications

References 9 publications

Psychiatric and neuropsychiatric sequelae of COVID-19 – A systematic review

Psychiatric and neuropsychiatric sequelae of COVID-19 – A systematic review

SARS-CoV-2 neutralizing antibodies: Longevity, breadth, and evasion by emerging viral variants

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

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