ABSTRACT
Introduction: Due to variability in the host response, a uniform treatment strategy for severe COVID-19 may be inadequate. We applied unsupervised clustering methods to large cohorts of COVID-19 ICU patients to derive and validate clinical phenotypes, and to explore treatment responses in these phenotypes. Method(s): Phenotypes were derived in 13.279 critically ill COVID-19 patients admitted to 82 Dutch ICUs from September 2020 to February 2022. Twenty-one features were selected from clinical characteristics measured within 24 h after ICU admission. Phenotypes were assigned using consensus k means clustering. External validation was performed in 6225 critically ill COVID-19 patients admitted to 55 Spanish ICUs from February 2020 to December 2021. Individual patient data on corticosteroids therapy enabled us to investigate phenotype-specific responses in this cohort. Result(s): Three distinct clinical phenotypes were derived (Fig. 1A). Patients with phenotype 1 (43%) were younger, had lower APACHE IV scores, higher BMI as well as a lower P/F ratio and 90-day in-hospital mortality (18%, Fig. 1A). Phenotype 2 patients (37%) were older and had slightly higher APACHE IV scores compared with phenotype 1, a lower BMI, and higher mortality compared to phenotype 1 (24%, p = 2.95e-07). Phenotype 3 (20%) included the oldest patients with the most comorbidities and highest APACHE IV scores, severe renal and metabolic impairment, and the worst outcome (47% mortality, p = 6.6e-16 and p = 6.6e-16 versus phenotypes 1 and 2, respectively). Phenotype distribution and outcome were very similar in the validation cohort (Fig. 1B). This cohort also revealed that corticosteroid therapy only benefited phenotype 3 (65% vs. 54% mortality, p = 2.5e-03, Fig. 1C). Conclusion(s): COVID-19 ICU phenotypes based on clinical data are related to outcome and treatment responses. This can inform treatment decisions as well as randomized trials employing precision medicine approaches.
ABSTRACT
Introduction: A subgroup of critically ill COVID-19 patients develops pulmonary fibroproliferation (PF), which is associated with worse outcomes. We explored the kinetics of fibrosis markers and ventilatory parameters prior to and following use of steroids to treat suspected PF. Furthermore, we investigated the effects of early dexamethasone (DEXA) treatment, the current standard-of-care for COVID-19, on the incidence and time to development of PF and clinical outcomes. Methods: We included 191 critically ill COVID-19 patients spanning two treatment cohorts: no DEXA treatment (pre-DEXA cohort, n = 67) and dexamethasone treatment as standard-of-care (DEXA cohort, n = 124). Kinetics of circulating fibrosis markers and ventilatory parameters were analyzed in suspected PF patients prior to and following initiation of steroid therapy as well as in patients in whom PF was not suspected. Furthermore, associations between PF and clinical outcomes were explored. Results: Patients with suspected PF exhibited higher circulating fibrosis markers, lower lung compliance and PaO2/FiO2 ratios, and increased dead space ventilation. Incidence of suspected PF was 28% in the pre-DEXA cohort and 25% in the DEXA cohort (p = 0.61), and time to development of suspected PF was also similar between cohorts (16 [12-21] vs. 19 [14-23] days from ICU admission, p = 0.11). Time on ventilator, LOS in ICU and mortality were significantly higher in suspected PF patients than in no suspected PF patients, with no differences between the cohorts (Fig. 1). Conclusions: Increased circulating fibrosis markers reflect development of PF in critically ill COVID-19 patients, which is associated with prolonged ICU length of stay and high mortality rates. Introduction of dexamethasone as standard-of-care is not associated with altered incidence of PF or improved clinical outcomes in patients with PF. (Figure Presented).
ABSTRACT
Background : Critically ill patients with COVID-19 are at high risk of thromboembolic events, despite thromboprophylaxis with lowmolecular weight heparins (LWMH), while increased-intensity thromboprophylaxis in this patient population is associated with bleeding. This raises the question whether pharmacokinetic (PK) effects of LMWHs are predictable in these patients. Aims : To investigate whether a dosing algorithm for dalteparin administration could be designed based on clinical parameters, using PK modeling with anti-Xa levels as readout. Methods : In this explorative, observational study, we prospectively included 15 adult COVID-19 patients admitted to the intensive care unit receiving dalteparin in prophylactic-intensity (5000 IU dalteparin once daily (OD) for those <100 kg, 5000 IU dalteparin bidaily (BD) for those ≥100 kg) and therapeutic-intensity (100 IU/kg BD). A minimum of 4 anti-Xa samples per day were collected on regular timepoints over 1-3 days. PK analysis of dalteparin was performed by nonlinear mixed-effect modeling (NONMEM v7.4). The final model was used to perform Monte Carlo simulations to predict anti-Xa levels with different dalteparin regimens. The study was approved by the local medical ethics committee. Results : The data were well-fitted to a linear one compartment model. Wide interindividual variation in the parameters absorption (78%) and clearance (34%) of dalteparin was observed, not explained by clinical covariates such as creatinine clearance for elimination rate. Simulations show that prophylactic dosing in individuals <100 kg result in anti-Xa levels within generally used prophylactic targets, while increased-prophylactic dosing in those ≥100 kg result in supraprophylactic levels in 40% of patients. With therapeuticintensity dosing in secondary thromboprophylaxis, 22% of patients would be subtherapeutically, and 19% patients supratherapeutically dosed. Conclusions : Anti-Xa levels during dalteparin treatment in the critically ill COVID-19 patient are difficult to predict and often off-target. Until data from randomized clinical trials conclude on the best dosing, this suggests that anti-Xa measurements are needed to guide high-intensity dosing in the individual patient.