Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Batlle, Jordi de; Torres, Gerard; González, Jessica; Gonzalo‐Calvo, David de; Targa, Adriano; Gort‐Paniello, Clara; Moncusí‐Moix, Anna; Ceccato, Adrián; Fernández-Barat, Laia; Ferrer, Ricard; Garcia-Gasulla, Darío; Menéndez, Rosário; Motos, Anna; Peñuelas, Óscar; Riera, Jordi; Bermejo-Martín, Jesús F.; Peñasco, Yhivian; Ricart, Pilar; Martín-Delgado, María Cruz; Aguilera, Luciano; Rodríguez, Alejandro García; Varela, Maria Victoria Boado; Suárez-Sipmann, Fernando; Laderas, Juan Carlos Pozo; Solé‐Violán, Jordi; Nieto, Maite; Novo, Mariana Andrea; Barberán, José; Villar, Rosario Amaya; Garnacho-Montero, José; García-Garmendia, Jose Luis; Gómez, José Manuel Naranjo; Lorente, José A.; Ortíz, Aaron Blandino; Lomas, Luis Tamayo; López-Ramos, Esther; Úbeda, Alejandro; Catalán-González, Mercedes; Sánchez-Miralles, Ángel; Varela, Ignacio Martínez; García, Ruth Noemí Jorge; Franco, Nieves; Gumucio-Sanguino, Víctor D.; García, Arturo Huerta; Bustamante-Munguira, Elena; Valdivia, Luis Jorge; Caballero, Jesús; Gallego, Elena; Gándara, Amalia Martínez de la; Castellanos-Ortega, Álvaro; Trenado, Josep; Marín‐Corral, Judith; Albaiceta, Guillermo M.; Torre, María del Carmen de la; Loza-Vázquez, Ana; Vidal, Pablo; Messa, Juan López; Añón, J.M.; Pérez, Cristina Carbajales; Sagredo, Víctor; Bofill, Neus; Carbonell, Nieves; Socías, Lorenzo; Barberà, Carme; Estella, Ángel; Mendez, Manuel Valledor; Dı́az, Emili; Lago, Ana López; Torres, Antoni; Barbé, Ferrán

doi:10.1016/j.lanepe.2022.100422

Cited by 7 publications

(6 citation statements)

References 42 publications

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“…The same is true for the observed shift of inflammatory classical monocytes to "immunoparalysis" in severe COVID-19 6 . Multiple models may thus be required to simulate different aspects of severe human COVID-19, especially given the numerous risk factors influencing disease progression 50,51 . Hence, systematic approaches as proposed here might be a helpful tool also in other diseases to identify which processes in specific animal-cell types reflect the human states.…”

Section: Discussionmentioning

confidence: 99%

Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease

Friedrich,

Pennitz,

Wyler

et al. 2024

Preprint

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Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of COVID-19 patient data with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and severe COVID-19 patients, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamster's response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species.

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Section: Discussionmentioning

confidence: 99%

Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease

Friedrich,

Pennitz,

Wyler

et al. 2024

Preprint

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“…Compared with patients without AKI, mortality was higher in patients with AKI (32.5% versus 10.4%), and 39.2% of patients with AKI did not recover from urinary organ performance by the end of the follow up period or after ninety days [42]. On the other hand, other studies have not shown AKI to be a risk factor for COVID-19 deaths [30,43].…”

Section: Discussionmentioning

confidence: 99%

“…In another study from Saudi Arabia, the mean age was 66 years, 56.7% of participants were male, and the most common comorbidities were DM2 (70.2%), HTN (73.9%), heart failure (21.4%), CHF (16.8%), and a mean BMI of 29.7 [24] also, without significant differences between both groups with and without AKI. In a multicenter ICU study involving 5,866 COVID-19 patients from 55 hospitals in Spain, the main age was 63 years, most of them were men (70.4%), and the most common concomitant diseases were HTN (50.4%), obesity (35.5%), and DM2 (24.9%) [43]. In a systematic review of patients with COVID-19 and AKI, the most common comorbidities were HTN 61.4%, hyperlipidemia 57.1%, DM2 40%, and CKD 22.2 % [44].…”

Section: Discussionmentioning

confidence: 99%

Acute kidney injury in Coronavirus disease-19 related pneumonia in the intensive care unit: a retrospective multicenter study, Saudi Arabia

Eldaboosy

Awad²,

Farouk³

et al. 2023

Multidis Res Med

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Background: Acute kidney injury (AKI) poses a significant morbidity and mortality risk to critically ill COVID-19 patients. The aim of this study was to investigate the incidence, predictors, and outcomes of AKI in patients admitted to the intensive care unit with critically ill COVID-19 pneumonia.Methods: A multicenter retrospective study in Saudi Arabia of adult patients aged at least 18 years diagnosed with COVID-19 pneumonia and admitted to the intensive care unit between May 2020 and May 2021. The occurrence of acute kidney injury and associated risk factors, the need for continuous renal replacement therapy, and the outcome were reported.Results: The study included 340 patients admitted to the ICU with COVID-19. Their mean age was 66.7±13.4 years, ranging from 49 to 84 years, and most of them were men (63.8%). The most common concomitant diseases were hypertension (71.5%), diabetes (62.4%), IHD (37.6%), CKD (20%), heart failure (19.4%), and 81.2% suffered from ARDS. AKI occurred in 60.3% of patients, 38% were stage 1, 16.6% were stage 2, and 45.4% were stage 3. Approximately 39% of patients required continuous renal replacement therapy (CRRT), of which 76.2% were stage 3, which was significantly higher than the other stages (p<0.001). AKI patients suffered significantly from asthma and had lower levels of C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), and blood urea nitrogen (BUN) and higher creatinine levels than patients without AKI (p<0.05 all). The overall mortality rate was 39.4%, and the mortality rate was significantly higher in patients with AKI than in patients without AKI (48.3% versus 25.9%; p<0.001).Conclusion: AKI is common in adults admitted to the intensive care unit with COVID-19 and is associated with an increased risk of death. Early detection of AKI and appropriate treatment can positively impact COVID-19 the outcome. CRRT is the preferred dialysis method in critically ill ICU patients with AKI.

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“…Conditions often cluster together and share pathological mechanisms [ 51 ]. Clusters of pre-existing conditions have been identified, through machine learning techniques, which are associated with poor outcomes in mixed [ 52 ] and COVID-19 [ 53 ] ICU populations. These include condition clusters around renal failure and cardiovascular disease with high associated mortality [ 32 , 52 , 53 ].…”

Section: Why Is Multimorbidity Associated With Worse Outcomes After C...mentioning

confidence: 99%

Do critical illness survivors with multimorbidity need a different model of care?

Stewart,

Bradley,

Smith

et al. 2023

Crit Care

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There is currently a lack of evidence on the optimal strategy to support patient recovery after critical illness. Previous research has largely focussed on rehabilitation interventions which aimed to address physical, psychological, and cognitive functional sequelae, the majority of which have failed to demonstrate benefit for the selected outcomes in clinical trials. It is increasingly recognised that a person’s existing health status, and in particular multimorbidity (usually defined as two or more medical conditions) and frailty, are strongly associated with their long-term outcomes after critical illness. Recent evidence indicates the existence of a distinct subgroup of critical illness survivors with multimorbidity and high healthcare utilisation, whose prior health trajectory is a better predictor of long-term outcomes than the severity of their acute illness. This review examines the complex relationships between multimorbidity and patient outcomes after critical illness, which are likely mediated by a range of factors including the number, severity, and modifiability of a person’s medical conditions, as well as related factors including treatment burden, functional status, healthcare delivery, and social support. We explore potential strategies to optimise patient recovery after critical illness in the presence of multimorbidity. A comprehensive and individualized approach is likely necessary including close coordination among healthcare providers, medication reconciliation and management, and addressing the physical, psychological, and social aspects of recovery. Providing patient-centred care that proactively identifies critical illness survivors with multimorbidity and accounts for their unique challenges and needs is likely crucial to facilitate recovery and improve outcomes.

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Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Cited by 7 publications

References 42 publications

Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease

Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease

Acute kidney injury in Coronavirus disease-19 related pneumonia in the intensive care unit: a retrospective multicenter study, Saudi Arabia

Do critical illness survivors with multimorbidity need a different model of care?

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