Adequacy of Nutritional Intakes during the Year after Critical Illness: An Observational Study in a Post-ICU Follow-Up Clinic.

peer reviewedWhether nutritional intakes in critically ill survivors after hospital discharge are adequate is unknown. The aims of this observational study were to describe the energy and protein intakes in ICU survivors attending a follow-up clinic compared to empirical targets and to explore differences in outcomes according to intake adequacy. All adult survivors who attended the follow-up clinic at 1, 3 and 12 months (M1, M3, M12) after a stay in our intensive care unit (ICU) ≥ 7 days were recruited. Average energy and protein intakes over the 7 days before the face-to-face consultation were quantified by a dietician using food anamnesis. Self-reported intakes were compared empirically to targets for healthy people (FAO/WHO/UNU equations), for critically ill patients (25 kcal/kg/day and 1.3 g protein/kg/day). They were also compared to targets that are supposed to fit post-ICU patients (35 kcal/kg/day and 1.5 g protein/kg/day). Blood prealbumin level and handgrip strength were also measured at each timepoint. A total of 206 patients were analyzed (49, 97 and 60 at the M1, M3 and M12, respectively). At M1, M3 and M12, energy intakes were 73.2 [63.3-86.3]%, 79.3 [69.3-89.3]% and 82.7 [70.6-93.7]% of healthy targets (p = 0.074), respectively. Protein intakes were below 0.8 g/kg/day in 18/49 (36.7%), 25/97 (25.8%) and 8/60 (13.3%) of the patients at M1, M3 and M12, respectively (p = 0.018), and the protein intakes were 67.9 [46.5-95.8]%, 68.5 [48.8-99.3]% and 71.7 [44.9-95.1]% of the post-ICU targets (p = 0.138), respectively. Prealbumin concentrations and handgrip strength were similar in patients with either inadequate energy intakes or inadequate protein intakes, respectively. In our post-ICU cohort, up to one year after discharge, energy and protein intakes were below the targets that are supposed to fit ICU survivors in recovery phase

Exercise Limitation after Critical Versus Mild COVID-19 Infection: A Metabolic Perspective

peer reviewedExercise limitation in COVID-19 survivors is poorly explained. In this retrospective study, cardiopulmonary exercise testing (CPET) was coupled with an oxidative stress assessment in COVID-19 critically ill survivors (ICU group). Thirty-one patients were included in this group. At rest, their oxygen uptake (VO2) was elevated (8 [5.6–9.7] mL/min/kg). The maximum effort was reached at low values of workload and VO2 (66 [40.9–79.2]% and 74.5 [62.6–102.8]% of the respective predicted values). The ventilatory equivalent for carbon dioxide remained within normal ranges. Their metabolic efficiency was low: 15.2 [12.9–17.8]%. The 50% decrease in VO2 after maximum effort was delayed, at 130 [120–170] s, with a still-high respiratory exchange ratio (1.13 [1–1.2]). The blood myeloperoxidase was elevated (92 [75.5–106.5] ng/mL), and the OSS was altered. The CPET profile of the ICU group was compared with long COVID patients after mid-disease (MLC group) and obese patients (OB group). The MLC patients (n = 23) reached peak workload and predicted VO2 values, but their resting VO2, metabolic efficiency, and recovery profiles were similar to the ICU group to a lesser extent. In the OB group (n = 15), no hypermetabolism at rest was observed. In conclusion, the exercise limitation after a critical COVID-19 bout resulted from an altered metabolic profile in the context of persistent inflammation and oxidative stress. Altered exercise and metabolic profiles were also observed in the MLC group. The contribution of obesity on the physiopathology of exercise limitation after a critical bout of COVID-19 did not seem relevant

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Measured Energy Expenditure Using Indirect Calorimetry in Post-Intensive Care Unit Hospitalized Survivors: A Comparison with Predictive Equations

peer reviewedActual energy needs after a stay in intensive care units (ICUs) are unknown. The aims of this observational study were to measure the energy expenditure (mEE) of ICU survivors during their post-ICU hospitalization period, and to compare this to the estimations of predictive equations (eEE). Survivors of an ICU stay ≥ 7 days were enrolled in the general ward during the first 7 days after ICU discharge. EE was measured using the Q-NRG calorimeter in canopy mode. This measure was compared to the estimated EE using the Harris–Benedict (HB) equation multiplied by a 1.3 stress factor, the Penn–State (PS) equation or the 30 kcal weight-based (WB) equation. A total of 55 adults were included (67.3% male, age 60 (52–67) y, body mass index 26.1 (22.2–29.7) kg/m2). Indirect calorimetry was performed 4 (3–6) d after an ICU stay of 12 (7–16) d. The mEE was 1682 (1328–1975) kcal/d, corresponding to 22.9 (19.1–24.2) kcal/kg/day. The eEE values derived using HB and WB equations were significantly higher than mEE: 3048 (1805–3332) and 2220 (1890–2640) kcal/d, respectively (both p < 0.001). There was no significant difference between mEE and eEE using the PS equation: 1589 (1443–1809) kcal/d (p = 0.145). The PS equation tended to underestimate mEE with a bias of −61.88 kcal and a wide 95% limit of agreement (−717.8 to 594 kcal). Using the PS equation, agreement within 15% of the mEE was found in 32/55 (58.2%) of the patients. In the present cohort of patients who survived a prolonged ICU stay, mEE was around 22–23 kcal/kg/day. In this post-ICU hospitalization context, none of the tested equations were accurate in predicting the EE measured by indirect calorimetry

Characteristics of Mid-Term Post-Intensive Care Syndrome in Patients Attending a Follow-Up Clinic: A Prospective Comparison Between COVID-19 and Non-COVID-19 Survivors.

peer reviewed[en] UNLABELLED: At present, it is not clear if critically ill COVID-19 survivors have different needs in terms of follow-up compared with other critically ill survivors, and thus if duplicated post-ICU trajectories are mandatory. OBJECTIVES: To compare the post-intensive care syndrome (PICS) of COVID-19 acute respiratory distress syndrome and non-COVID-19 (NC) survivors referred to a follow-up clinic at 3 months (M3) after ICU discharge. DESIGN SETTING AND PARTICIPANTS: Adults who survived an ICU stay greater than or equal to 7 days and attended the M3 consultation were included in this observational study performed in a post-ICU follow-up clinic of a single tertiary hospital. MAIN OUTCOMES AND MEASURES: Patients underwent a standardized assessment, addressing health-related quality of life (3-level version of EQ-5D), sleep disorders (Pittsburgh Sleep Quality Index [PSQI]), physical status (Barthel index, handgrip and quadriceps strengths), mental health disorders (Hospital Anxiety and Depression Scale and Impact of Event Scale-Revised [IES-R]), and cognitive impairment (Montreal Cognitive Assessment [MoCA]). RESULTS: A total of 143 survivors (86 COVID and 57 NC) attended the M3 consultation. Their median age and severity scores were similar. NC patients had a shorter ICU stay (10 d [8-17.2 d]) compared with COVID group (18 d [10.8-30 d]) (p = 0.001). M3 outcomes were similar in the two groups, except for a higher PSQI (p = 0.038) in the COVID group (6 [3-9.5]) versus NC group (4 [2-7]), and a slightly lower Barthel index in the NC group (100 [100-100]) than in the COVID group (100 [85-100]) (p = 0.026). However, the proportion of patients with abnormal values at each score was similar in the two groups. Health-related quality of life was similar in the two groups. The three MoCA (≥ 26), IES-R (<33), and Barthel (=100) were normal in 58 of 143 patients (40.6%). In contrast, 68.5% (98/143) had not returned to their baseline level of daily activities. CONCLUSIONS AND RELEVANCE: In our follow-up clinic at 3 months after discharge, the proportion of patients presenting alterations in the main PICS domains was similar whether they survived a COVID-19 or another critical illness, despite longer ICU stay in COVID group. Cognition and sleep were the two most affected PICS domains

Mid-term acylcarnitine profile evolution in survivors of a prolonged ICU stay

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Impact d’une consultation post-soins intensifs sur le devenir des patients 6 mois après un séjour prolongé en soins intensifs : étude de cohorte rétrospective.

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