The role of continuous renal replacement therapy (Crrt) in Coronavirus disease 2019 (Covid-19) patients

Paramitha, Maharani Pradnya; Suyanto, Joshua Christian; Puspitasari, S.

doi:10.1016/j.tacc.2021.06.003

Cited by 4 publications

(7 citation statements)

References 53 publications

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“…The mortality and morbidity associated with COVID-19 infection are primarily attributed to ARDS and resultant sepsis [6,[21][22][23][24][25]. The risk of end-organ failure is high in critically ill COVID-19 patients and is primarily attributed to multi-organ inflammation [25,26]. In this regard, Morrow et al elucidated the importance of cardio-renal homeostasis in ensuring hemodynamic stability and treatment outcomes in COVID-19 patients [27].…”

Section: Discussionmentioning

confidence: 99%

Impact of Mechanical Ventilation and Renal Replacement Therapy on Clinical Outcomes among Critically Ill COVID-19 Patients

Sindi

2023

Reports

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Background: Poor clinical outcomes in patients with severe COVID-19 occur due to many factors that require elucidation. The objective of this study was to describe the outcomes of critically ill patients with COVID-19 and identify the risk factors for mortality. Materials and Methods: The study was a single-centered cross-sectional, observational study involving COVID-19 patients admitted to the intensive care units (ICUs) of a tertiary care hospital in the Middle East and North Africa (MENA) region. The patients were admitted between 1 March and 31 December 2020. Logistic regression analysis was used to identify risk factors for mortality. Results: Of 107 patients admitted, 98 (91.6%) were ≥40 years old and 84 (78.5%) were males. The mean oxygen saturation at admission was 79.6 ± 12.6%, the duration of ICU stay was 13.0 ± 11.6 days, and 65 (60.7%) of the patients received mechanical ventilation. Major comorbidities included hypertension (57%), diabetes (56%), and chronic kidney disease (CKD) (15.5%). The overall mortality rate was 51.4%; this was higher in patients who received mechanical ventilation (60 vs. 38.1%; p = 0.03) and those with co-morbid hypertension (60.7 vs. 39.1%; p = 0.03). Risk factors for mortality were: need for mechanical ventilation agent of record adjusted Odds ratio (aOR) 4.4 (1.6–12.6), co-morbid hypertension aOR 5.8 (1.6–21.1), having CKD aOR 5.4 (1.2–25.6) and receiving renal replacement therapy aOR 4.3 (1.4–13.0). Conclusions: The use of mechanical ventilation or renal replacement therapy among critically ill COVID-19 patients could potentially predict worse outcomes. Patients with existing hypertension or CKD may carry a higher mortality risk.

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Section: Discussionmentioning

confidence: 99%

Impact of Mechanical Ventilation and Renal Replacement Therapy on Clinical Outcomes among Critically Ill COVID-19 Patients

Sindi

2023

Reports

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“…The key aim of our study was to develop the foundations of an EHR data-screening tool that may assist clinicians in the early identification of patients among a population of highly heterogeneous hospitalized patients with COVID-19 likely to deteriorate to hyperinflammation and require ICU-level care that may include respiratory support across a broad spectrum of modalities, such as HFNC, Nasal intermittent positive pressure ventilation, intubation or mechanical ventilation, and extracorporeal membrane oxygenation. Patients with hyperinflammation may also develop life-threatening comorbidities such as septic shock [ 64 ] and AKI [ 65 ], driving the need for specialized care, such as vasopressors [ 66 ], intermittent dialysis, and CRRT [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records

Velez,

Wang,

Garibaldi

et al. 2023

JMIR Form Res

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Background There is significant heterogeneity in disease progression among hospitalized patients with COVID-19. The pathogenesis of SARS-CoV-2 infection is attributed to a complex interplay between virus and host immune response that in some patients unpredictably and rapidly leads to “hyperinflammation” associated with increased risk of mortality. The early identification of patients at risk of progression to hyperinflammation may help inform timely therapeutic decisions and lead to improved outcomes. Objective The primary objective of this study was to use machine learning to reproducibly identify specific risk-stratifying clinical phenotypes across hospitalized patients with COVID-19 and compare treatment response characteristics and outcomes. A secondary objective was to derive a predictive phenotype classification model using routinely available early encounter data that may be useful in informing optimal COVID-19 bedside clinical management. Methods This was a retrospective analysis of electronic health record data of adult patients (N=4379) who were admitted to a Johns Hopkins Health System hospital for COVID-19 treatment from 2020 to 2021. Phenotypes were identified by clustering 38 routine clinical observations recorded during inpatient care. To examine the reproducibility and validity of the derived phenotypes, patient data were randomly divided into 2 cohorts, and clustering analysis was performed independently for each cohort. A predictive phenotype classifier using the gradient-boosting machine method was derived using routine clinical observations recorded during the first 6 hours following admission. Results A total of 2 phenotypes (designated as phenotype 1 and phenotype 2) were identified in patients admitted for COVID-19 in both the training and validation cohorts with similar distributions of features, correlations with biomarkers, treatments, comorbidities, and outcomes. In both the training and validation cohorts, phenotype-2 patients were older; had elevated markers of inflammation; and were at an increased risk of requiring intensive care unit–level care, developing sepsis, and mortality compared with phenotype-1 patients. The gradient-boosting machine phenotype prediction model yielded an area under the curve of 0.89 and a positive predictive value of 0.83. Conclusions Using machine learning clustering, we identified and internally validated 2 clinical COVID-19 phenotypes with distinct treatment or response characteristics consistent with similar 2-phenotype models derived from other hospitalized populations with COVID-19, supporting the reliability and generalizability of these findings. COVID-19 phenotypes can be accurately identified using machine learning models based on readily available early encounter clinical data. A phenotype prediction model based on early encounter data may be clinically useful for timely bedside risk stratification and treatment personalization.

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“…The incidence in ICU is estimated at 20–40 %. Among COVID-19 patients who develop AKI, about 1.5–9 % of them required CRRT [79] . In ICU, severe AKI occurs usually in the context of Multi Organ Failure.…”

Section: Oxiris and Polymethyl Methacrylate (Pmma) Membranesmentioning

confidence: 99%

“…Like AKI in general population, in Covid-19 patients the management requires general and supportive measures and initiation of renal replacement therapy (RRT) when considered necessary based on the KIDGO (Kidney Disease Improving Global Outcomes) guidelines, for example severe electrolyte imbalance and metabolic acidosis, uremia, uremic pericarditis, and hypervolemia. In particular, mechanically ventilated patients due to ARDS, may benefit from early initiation of RRT because volume overload in these patients could reduce the efficacy of ventilatory support [79] . In critically ill patients is generally preferred continuous RRT (CRRT) due to hemodynamically instability.…”

Section: Oxiris and Polymethyl Methacrylate (Pmma) Membranesmentioning

confidence: 99%

“…The CRRT dosing follows the recommendation of the KDIGO guidelines as in non Covid-19 patients with AKI with a minimum dose of 20–25 mL/kg/h. In severe sepsis a high volume hemoﬁltration with clearance dose > 35 mL/kg/h has been used in order to help remove inﬂammatory mediators, even though there are still conflicting views regarding the optimum dose [79] . The use of high cut-off (HCO) filters possessing pores with size > 60 kDa have a great permeability and could remove molecules with high molecular weight such as cytokines and other inflammatory parameters.…”

Section: Oxiris and Polymethyl Methacrylate (Pmma) Membranesmentioning

confidence: 99%

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Apheresis and COVID-19 in intensive care unit (ICU)

Xanthi¹,

Vasiliki²,

Aloizos³

2022

Transfusion and Apheresis Science

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The role of continuous renal replacement therapy (Crrt) in Coronavirus disease 2019 (Covid-19) patients

Cited by 4 publications

References 53 publications

Impact of Mechanical Ventilation and Renal Replacement Therapy on Clinical Outcomes among Critically Ill COVID-19 Patients

Impact of Mechanical Ventilation and Renal Replacement Therapy on Clinical Outcomes among Critically Ill COVID-19 Patients

Identification and Prediction of Clinical Phenotypes in Hospitalized Patients With COVID-19: Machine Learning From Medical Records

Apheresis and COVID-19 in intensive care unit (ICU)

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