The obesity paradox in critically ill patients: a causal learning approach to a casual finding

Subcutaneous adipose tissue radiodensity: An emerging risk factor for severe COVID-19

BACKGROUND

Adipose tissue radiodensity and metabolic activity may influence COVID-19 outcomes. This study evaluated the association between adipose tissue characteristics and clinical outcomes in COVID-19 patients.

METHODS

Two retrospective cohorts of hospitalized COVID-19 patients were analyzed. Subcutaneous adipose tissue radiodensity (SATR) and visceral adipose tissue radiodensity were assessed by computed tomography. Fluorine-18-labelled fluorodeoxyglucose PET/computed tomography measured adipose tissue metabolic activity. Associations with mortality, length of stay, ventilation requirement, and complications were examined using regression analyses.

RESULTS

High SATR was independently associated with increased mortality risk (OR: 2.70; P = 0.033), longer hospitalization (P < 0.001), higher rates of mechanical ventilation (P = 0.007), and complications: acute kidney injury (P = 0.001), secondary infection (P = 0.007), shock (P = 0.010), and pulmonary embolism (P = 0.011). SATR positively correlated with SAT glucose uptake (ρ = 0.52) and negatively with leptin levels (ρ = -0.48).

CONCLUSIONS

Elevated SATR at COVID-19 diagnosis predicts disease severity and worse outcomes. SATR is a potential prognostic biomarker for acute and chronic inflammatory conditions.

Specific and Non-specific Aspects and Future Challenges of ICU Care Among COVID-19 Patients with Obesity: A Narrative Review

PURPOSE OF REVIEW

Since the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic has infected nearly 800 million people and caused almost seven million deaths. Obesity was quickly identified as a risk factor for severe COVID-19, ICU admission, acute respiratory distress syndrome, organ support including mechanical ventilation and prolonged length of stay. The relationship among obesity; COVID-19; and respiratory, thrombotic, and renal complications upon admission to the ICU is unclear.

RECENT FINDINGS

The predominant effect of a hyperinflammatory status or a cytokine storm has been suggested in patients with obesity, but more recent studies have challenged this hypothesis. Numerous studies have also shown increased mortality among critically ill patients with obesity and COVID-19, casting doubt on the obesity paradox, with survival advantages with overweight and mild obesity being reported in other ICU syndromes. Finally, it is now clear that the increase in the global prevalence of overweight and obesity is a major public health issue that must be accompanied by a transformation of our ICUs, both in terms of equipment and human resources. Research must also focus more on these patients to improve their care. In this review, we focused on the central role of obesity in critically ill patients during this pandemic, highlighting its specificities during their stay in the ICU, identifying the lessons we have learned, and identifying areas for future research as well as the future challenges for ICU activity.

Obesity is associated with improved early survival but increased late mortality in surgical patients with Sepsis: A propensity matched analysis

BACKGROUND

While obesity is a risk factor for postoperative complications, its impact following sepsis is unclear. The primary objective of this study was to evaluate the association between obesity and mortality following admission to the surgical intensive care unit (SICU) with sepsis.

METHODS

We conducted a single center retrospective review of SICU patients grouped into obese (n = 766, body mass index ≥30 kg/m 2 ) and nonobese (n = 574; body mass index, 18-29.9 kg/m 2 ) cohorts. Applying 1:1 propensity matching for age, sex, comorbidities, sequential organ failure assessment, and transfer status, demographic data, comorbidities, and sepsis presentation were compared between groups. Primary outcomes included in-hospital and 90-day mortality, ICU length of stay, need for mechanical ventilation (IMV) and renal replacement therapy (RRT). p < 0.05 was considered significant.

RESULTS

Obesity associates with higher median ICU length of stay (8.2 vs. 5.6, p < 0.001), need for IMV (76% vs. 67%, p = 0.001), ventilator days (5 vs. 4, p < 0.004), and RRT (23% vs. 12%, p < 0.001). In-hospital (29% vs. 18%, p < 0.0001) and 90-day mortality (34% vs. 24%, p = 0.0006) was higher for obese compared with nonobese groups. Obesity independently predicted need for IMV (odds ratio [OR], 1.6; 95% confidence interval [CI], 1.2-2.1), RRT (OR, 2.2; 95% CI, 1.5-3.1), in-hospital (OR, 2.1; 95% CI, 1.5-2.8), and 90-day mortality (HR, 1.4; 95% CI, 1.1-1.8), after adjusting for sequential organ failure assessment, age, sex, and comorbidities. Comparative survival analyses demonstrate a paradoxical early survival benefit for obese patients followed by a rapid decline after 7 days (logrank p = 0.0009).

CONCLUSION

Obesity is an independent risk factor for 90-day mortality for surgical patients with sepsis, but its impact appeared later in hospitalization. Understanding differences in systemic responses between these cohorts may be important for optimizing critical care management.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

Joint effect of atrial fibrillation and obesity on mortality in critically ill patients

BACKGROUND

The interplay between atrial fibrillation (AF) and obesity on mortality in critically ill patients warrants detailed exploration, given their individual impacts on patient prognosis. This study aimed to assess the associations between AF, obesity, and 1-year mortality in a critically ill population.

METHODS

Utilizing data from the Medical Information Mart for Intensive Care (MIMIC)-IV database, we conducted a retrospective analysis of adult patients admitted to the intensive care unit. The primary endpoint was 1-year mortality, analyzed through Cox regression with hazard ratio (HR) and Kaplan-Meier survival methods.

RESULTS

The study included 25,654 patients (median age 67.0 years, 40.6% female), with 39.0% having AF and 36.1% being obese. Multivariate COX regression analysis revealed that AF was associated with a 14.7% increase in the risk of 1-year mortality (p < 0.001), while obesity was linked to a 13.9% reduction in mortality risk (p < 0.001). The protective effect of obesity on mortality was similar in patients with (HR = 0.85) and without AF (HR = 0.86). AF led to a slightly higher risk of mortality in patients without obesity (HR = 1.16) compared to those with obesity (HR = 1.13). Kaplan-Meier survival curves highlighted that non-obese patients with AF had the lowest survival rate, whereas the highest survival was observed in obese patients without AF.

CONCLUSIONS

AF significantly increased 1-year mortality risk in critically ill patients, whereas obesity was associated with a decreased mortality risk. The most adverse survival outcomes were identified in non-obese patients with AF.

Associations between body mass index, biological age and frailty in the critically ill

BACKGROUND

The relationship between body mass index (BMI) and outcomes in the acute care setting is controversial, with evidence suggesting that obesity is either protective - which is also called obesity paradox - or associated with worse outcomes. The purpose of this study was to assess whether BMI was related to frailty and biological age, and whether BMI remained predictive of mortality after adjusting for frailty and biological age.

SUBJECTS

Of the 2950 patients who had a biological age estimated on admission to the intensive care unit, 877 (30 %) also had BMI and frailty data available for further analysis in this retrospective cohort study.

METHODS

Biological age of each patient was estimated using the Levine PhenoAge model based on results of nine blood tests that were reflective of DNA methylation. Biological age in excess of chronological age was then indexed to the local study context by a linear regression to generate the residuals. The associations between BMI, clinical frailty scale, and the residuals were first analyzed using univariable analyses. Their associations with mortality were then assessed by multivariable analysis, including the use of a 3-knot restricted cubic spline function to allow non-linearity.

RESULTS

Both frailty (p = 0.003) and the residuals of the biological age (p = 0.001) were related to BMI in a U-shaped fashion. BMI was not related to hospital mortality, but both frailty (p = 0.015) and the residuals of biological age (OR per decade older than chronological age 1.50, 95 % confidence interval [CI] 1.04-2.18; p = 0.031) were predictive of mortality after adjusting for chronological age, diabetes mellitus and severity of acute illness.

CONCLUSIONS

BMI was significantly associated with both frailty and biological age in a U-shaped fashion but only the latter two were related to mortality. These results may, in part, explain why obesity paradox could be observed in some studies.

Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records

Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of "doubly robust" nonparametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHRs). In this article, we investigate causal modeling of an RCT-established causal association: the effect of classes of antihypertensive on incident cancer risk. We develop a transformer-based model, targeted bidirectional EHR transformer (T-BEHRT) coupled with doubly robust estimation to estimate average risk ratio (RR). We compare our model to benchmark statistical and deep learning models for causal inference in multiple experiments on semi-synthetic derivations of our dataset with various types and intensities of confounding. In order to further test the reliability of our approach, we test our model on situations of limited data. We find that our model provides more accurate estimates of relative risk [least sum absolute error (SAE) from ground truth] compared with benchmark estimations. Finally, our model provides an estimate of class-wise antihypertensive effect on cancer risk that is consistent with results derived from RCTs.

Obesity and Extracorporeal Membrane Oxygenation (ECMO): Analysis of Outcomes

Traditionally, patients with obesity have been deemed ineligible for extracorporeal life support (ELS) therapies such as extracorporeal membrane oxygenation (ECMO), given the association of obesity with chronic health conditions that contribute to increased morbidity and mortality. Nevertheless, a growing body of literature suggests the feasibility, efficacy, and safety of ECMO in the obese population. This review provides an in-depth analysis of the current literature assessing the effects of obesity on outcomes among patients supported with ECMO (venovenous [VV] ECMO in noncoronavirus disease 2019 and coronavirus disease 2019 acute respiratory distress syndrome, venoarterial [VA] ECMO, and combined VV and VA ECMO), offer a possible explanation of the current findings on the basis of the obesity paradox phenomenon, provides a framework for future studies addressing the use of ELS therapies in the obese patient population, and provides guidance from the literature for many of the challenges related to initiating, maintaining, and weaning ELS therapy in patients with obesity.

The impact of body mass index on long-term survival after ICU admission due to COVID-19: A retrospective multicentre study

Objective

The impact of obesity on long-term survival after intensive care unit (ICU) admission with severe coronavirus disease 2019 (COVID-19) is unclear. We aimed to quantify the impact of obesity on time to death up to two years in patients admitted to Australian and New Zealand ICUs.

Design

Retrospective multicentre study.

Setting

92 ICUs between 1st January 2020 through to 31st December 2020 in New Zealand and 31st March 2022 in Australia with COVID-19, reported in the Australian and New Zealand Intensive Care Society adult patient database.

Participants

All patients with documented height and weight to estimate the body mass index (BMI) were included. Obesity was classified patients according to the World Health Organization recommendations.

Interventions and main outcome measures

The primary outcome was survival time up to two years after ICU admission. The effect of obesity on time to death was assessed using a Cox proportional hazards model. Confounders were acute illness severity, sex, frailty, hospital type and jurisdiction for all patients.

Results

We examined 2,931 patients; the median BMI was 30.2 (IQR 25.6-36.0) kg/m2. Patients with a BMI ≥30 kg/m2 were younger (median [IQR] age 57.7 [46.2-69.0] vs. 63.0 [50.0-73.6]; p < 0.001) than those with a BMI <30 kg/m2. Most patients (76.6%; 2,244/2,931) were discharged alive after ICU admission. The mortality at two years was highest for BMI categories <18.5 kg/m2 (35.4%) and 18.5-24.9 kg/m2 (31.1%), while lowest for BMI ≥40 kg/m2 (14.5%). After adjusting for confounders and with BMI 18.5-24.9 kg/m2 category as a reference, only the BMI ≥40 kg/m2 category patients had improved survival up to 2 years (hazard ratio = 0.51; 95%CI: 0.34-0.76).

Conclusions

The obesity paradox appears to exist beyond hospital discharge in critically ill patients with COVID-19 admitted in Australian and New Zealand ICUs. A BMI ≥40 kg/m2 was associated with a higher survival time of up to two years.

Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

PURPOSE

The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments.

METHODS

We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results.

RESULTS

Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE.

CONCLUSIONS

There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.

Associations between body mass index and all-cause mortality: A systematic review and meta-analysis

Fasting insulin and c-reactive protein confound the association between mortality and body mass index. An increase in fat mass may mediate the associations between hyperinsulinemia, hyperinflammation, and mortality. The objective of this study was to describe the "average" associations between body mass index and the risk of mortality and to explore how adjusting for fasting insulin and markers of inflammation might modify the association of BMI with mortality. MEDLINE and EMBASE were searched for studies published in 2020. Studies with adult participants where BMI and vital status was assessed were included. BMI was required to be categorized into groups or parametrized as non-first order polynomials or splines. All-cause mortality was regressed against mean BMI squared within seven broad clinical populations. Study was modeled as a random intercept. β coefficients and 95% confidence intervals are reported along with estimates of mortality risk by BMIs of 20, 30, and 40 kg/m2 . Bubble plots with regression lines are drawn, showing the associations between mortality and BMI. Splines results were summarized. There were 154 included studies with 6,685,979 participants. Only five (3.2%) studies adjusted for a marker of inflammation, and no studies adjusted for fasting insulin. There were significant associations between higher BMIs and lower mortality risk in cardiovascular (unadjusted β -0.829 [95% CI -1.313, -0.345] and adjusted β -0.746 [95% CI -1.471, -0.021]), Covid-19 (unadjusted β -0.333 [95% CI -0.650, -0.015]), critically ill (adjusted β -0.550 [95% CI -1.091, -0.010]), and surgical (unadjusted β -0.415 [95% CI -0.824, -0.006]) populations. The associations for general, cancer, and non-communicable disease populations were not significant. Heterogeneity was very large (I2  ≥ 97%). The role of obesity as a driver of excess mortality should be critically re-examined, in parallel with increased efforts to determine the harms of hyperinsulinemia and chronic inflammation.

Body mass index does not impact survival in COVID-19 patients requiring veno-venous extracorporeal membrane oxygenation

INTRODUCTION

With the increased demand for veno-venous extracorporeal membrane oxygenation (VV ECMO) during the COVID-19 pandemic, guidelines for patient candidacy have often limited this modality for patients with a body mass index (BMI) less than 40 kg/m2. We hypothesize that COVID-19 VV ECMO patients with at least class III obesity (BMI ≥ 40) have decreased in-hospital mortality when compared to non-COVID-19 and non-class III obese COVID-19 VV ECMO populations.

METHODS

This is a single-center retrospective study of COVID-19 VV ECMO patients from January 1, 2014, to November 30, 2021. Our institution used BMI ≥ 40 as part of a multi-disciplinary VV ECMO candidate screening process in COVID-19 patients. BMI criteria were not considered for exclusion criteria in non-COVID-19 patients. Univariate and multivariable analyses were performed to assess in-hospital mortality differences.

RESULTS

A total of 380 patients were included in our analysis: The COVID-19 group had a lower survival rate that was not statistically significant (65.7% vs.74.9%, p = .07). The median BMI between BMI ≥ 40 COVID-19 and non-COVID-19 patients was not different (44.5 vs 45.5, p = .2). There was no difference in survival between the groups (73.3% vs. 78.5%, p = .58), nor was there a difference in survival between the COVID-19 BMI ≥ 40 and BMI < 40 patients (73.3, 62.7, p= .29). Multivariable logistic regression with the outcome of in-hospital mortality was performed and BMI was not found to be significant (OR 0.99, 95% CI 0.89, 1.01; p = .92).

CONCLUSION

BMI ≥ 40 was not an independent risk factor for decreased in-hospital survival in this cohort of VV ECMO patients at a high-volume center. BMI should not be the sole factor when deciding VV ECMO candidacy in patients with COVID-19.

Visceral obesity measured using computed tomography scans: No significant association with mortality in critically ill patients

INTRODUCTION

The association between obesity and outcome in critical illness is unclear. Since the amount of visceral adipose tissue(VAT) rather than BMI mediates the health effects of obesity we aimed to investigate the association between visceral obesity, BMI and 90-day mortality in critically ill patients.

METHOD

In 555 critically ill patients (68% male), the VAT Index(VATI) was measured using Computed Tomography scans on the level of vertebra L3. The association between visceral obesity, BMI and 90-day mortality was investigated using univariable and multivariable analyses, correcting for age, sex, APACHE II score, sarcopenia and muscle quality.

RESULTS

Visceral obesity was present in 48.1% of the patients and its prevalence was similar in males and females. Mortality was similar amongst patients with and without visceral obesity (27.7% vs 24.0%, p = 0.31). The corrected odds ratio of 90-day mortality for visceral obesity was 0.667 (95%CI 0.424-1.049, p = 0.080). Using normal BMI as reference, the corrected odds ratio for overweight was 0.721 (95%CI 0.447-1.164 p = 0.181) and for obesity 0.462 (95%CI 0.208-1.027, p = 0.058).

CONCLUSION

No significant association of visceral obesity and BMI with 90-day mortality was observed in critically ill patients, although obesity and visceral obesity tended to be associated with improved 90-day mortality.

Age Moderates the Effect of Obesity on Mortality Risk in Critically Ill Patients With COVID-19: A Nationwide Observational Cohort Study

OBJECTIVES

A high body mass index (BMI) is associated with an unfavorable disease course in COVID-19, but not among those who require admission to the ICU. This has not been examined across different age groups. We examined whether age modifies the association between BMI and mortality among critically ill COVID-19 patients.

DESIGN

An observational cohort study.

SETTING

A nationwide registry analysis of critically ill patients with COVID-19 registered in the National Intensive Care Evaluation registry.

PATIENTS

We included 15,701 critically ill patients with COVID-19 (10,768 males [68.6%] with median [interquartile range] age 64 yr [55-71 yr]), of whom 1,402 (8.9%) patients were less than 45 years.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

In the total sample and after adjustment for age, gender, Acute Physiology and Chronic Health Evaluation IV, mechanical ventilation, and use of vasoactive drugs, we found that a BMI greater than or equal to 30 kg/m 2 does not affect hospital mortality (adjusted odds ratio [OR adj ] = 0.98; 95% CI, 0.90-1.06; p = 0.62). For patients less than 45 years old, but not for those greater than or equal to 45 years old, a BMI greater than or equal to 30 kg/m 2 was associated with a lower hospital mortality (OR adj = 0.59; 95% CI, 0.36-0.96; p = 0.03).

CONCLUSIONS

A higher BMI may be favorably associated with a lower mortality among those less than 45 years old. This is in line with the so-called "obesity paradox" that was established for other groups of critically ill patients in broad age ranges. Further research is needed to understand this favorable association in young critically ill patients with COVID-19.

Evaluating the association between obesity and discharge functional status after pediatric injury

BACKGROUND

Children with obesity frequently have functional impairment after critical illness. Although obesity increases morbidity risk after trauma, the association with functional outcomes in children is unknown.

OBJECTIVE

To evaluate the association of weight with functional impairment at hospital discharge in children with serious injuries.

METHODS

This secondary analysis of a multicenter prospective study included children <15 years old with a serious injury. Four weight groups, underweight, healthy weight, overweight, and obesity/severe obesity were defined by body mass index z-scores. The functional status scale (FSS) measured impairment across six functional domains before injury and at hospital discharge. New domain morbidity was defined as a change ≥2 points. The association between weight and functional impairment was determined using logistic regression adjusting for demographics, physiological measures, injury details, presence of a severe head injury, and physical abuse.

RESULTS

Although most patients discharged with good/unchanged functional status, new domain morbidity occurred in 74 patients (17%). New FSS domain morbidity occurred in 13% of underweight, 14% of healthy weight, 15% of overweight, and 26% of obese/severe obese patients. Compared to healthy weight patients, those with obesity had more frequent new domain morbidity (p = 0.01), while the other weight groups had similar morbidity. However, after adjustment for confounders, weight was not associated with new functional morbidity at discharge.

CONCLUSION

Patients with obesity have greater frequency of new domain morbidity after a serious injury; however, after accounting for injury characteristics, weight group is not independently associated with new functional morbidity at hospital discharge after injury in children.

LEVEL OF EVIDENCE

III.

Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences

Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.

Obesity, leptin and host defence of Streptococcus pneumoniae: the case for more human research

Pneumococcal pneumonia is the leading cause of community-acquired pneumonia. Obesity is a risk factor for pneumonia. Host factors play a critical role in susceptibility to pulmonary pathogens and outcome from pulmonary infections. Obesity impairs innate and adaptive immune responses, important in the host defence against pneumococcal disease. One area of emerging interest in understanding the complex relationship between obesity and pulmonary infections is the role of the hormone leptin. There is a substantive evidence base supporting the associations between obesity, leptin, pulmonary infections and host defence mechanisms. Despite this, there is a paucity of research that specifically focuses on Streptococcus pneumoniae (pneumococcal) infections, which are the leading cause of community-acquired pneumonia hospitalisations and mortality worldwide. Much of the evidence examining the role of leptin in relation to S. pneumoniae infections has used genetically mutated mice. The purpose of this mini review is to explore the role leptin plays in the host defence of S. pneumoniae in subjects with obesity and posit an argument for the need for more human research.

Obesity and critical care nutrition: current practice gaps and directions for future research

Background

This review has been developed following a panel discussion with an international group of experts in the care of patients with obesity in the critical care setting and focuses on current best practices in malnutrition screening and assessment, estimation of energy needs for patients with obesity, the risks and management of sarcopenic obesity, the value of tailored nutrition recommendations, and the emerging role of immunonutrition. Patients admitted to the intensive care unit (ICU) increasingly present with overweight and obesity that require individualized nutrition considerations due to underlying comorbidities, immunological factors such as inflammation, and changes in energy expenditure and other aspects of metabolism. While research continues to accumulate, important knowledge gaps persist in recognizing and managing the complex nutritional needs in ICU patients with obesity. Available malnutrition screening and assessment tools are limited in patients with obesity due to a lack of validation and heterogeneous factors impacting nutrition status in this population. Estimations of energy and protein demands are also complex in patients with obesity and may include estimations based upon ideal, actual, or adjusted body weight. Evidence is still sparse on the role of immunonutrition in patients with obesity, but the presence of inflammation that impacts immune function may suggest a role for these nutrients in hemodynamically stable ICU patients. Educational efforts are needed for all clinicians who care for complex cases of critically ill patients with obesity, with a focus on strategies for optimal nutrition and the consideration of issues such as weight stigma and bias impacting the delivery of care.

Conclusions

Current nutritional strategies for these patients should be undertaken with a focus on individualized care that considers the whole person, including the possibility of preexisting comorbidities, altered metabolism, and chronic stigma, which may impact the provision of nutritional care. Additional research should focus on the applicability of current guidelines and evidence for nutrition therapy in populations with obesity, especially in the setting of critical illness.

[Assessment and technical monitoring of nutritional status of patients in intensive and intermediate care units : Position paper of the Section Metabolism and Nutrition of the German Interdisciplinary Association for Intensive and Emergency Medicine (DIVI)]

At the time of admission to an intensive or intermediate care unit, assessment of the patients' nutritional status may have both prognostic and therapeutic relevance with regard to the planning of individualized medical nutrition therapy (MNT). MNT has definitely no priority in the initial treatment of a critically ill patient, but is often also neglected during the course of the disease. Especially with prolonged length of stay, there is an increasing risk of malnutrition with considerable prognostic macro- and/or micronutrient deficit. So far, there are no structured, evidence-based recommendations for assessing nutritional status in intensive or intermediate care patients. This position paper of the Section Metabolism and Nutrition of the German Interdisciplinary Association for Intensive and Emergency Medicine (DIVI) presents consensus-based recommendations for the assessment and technical monitoring of nutritional status of patients in intensive and intermediate care units. These recommendations supplement the current S2k guideline "Clinical Nutrition in Intensive Care Medicine" of the German Society for Nutritional Medicine (DGEM) and the DIVI.

Evaluation of the Relationship between Nutritional Status of COVID-19 Patients Admitted to the ICU and Patients' Prognosis: A Cohort Study

Background

Malnutrition in COVID-19 critically ill patients can lead to poor prognosis. This study aimed to evaluate the association between nutritional status (or risk) and the prognosis of critically ill COVID-19 patients. In this study, prognosis is the primary outcome of "hospital mortality" patients. The second outcome is defined as "need for mechanical ventilation."

Methods and Materials

In this single-center prospective cohort study, 110 patients admitted to the Intensive Care Unit of Imam Khomeini Hospital Complex (Tehran, Iran) between April and September 2021 were enrolled. Participants formed a consecutive sample. MNA-SF, NRS-2002, mNUTRIC, and PNI scores were used to evaluate nutritional assessment. Patients' lab results and pulse oximetric saturation SpO₂/FiO₂ (SF) ratio at the time of intensive care unit (ICU) admission were collected. Patients were screened for nutritional status and categorized into two groups, patients at nutritional risk and nonrisk.

Results

Sixty-five (59.1%) of all patients were men. The overall range of age was 52 ± 15. Thirty-six (32.7%) of patients were obese (BMI ≥ 30). The hospital mortality rate was 59.1% (n = 65). According to the different criteria, malnutrition rate was 67.3% (n = 74) (NRS), 28.2% (n = 31) (MNA), 34.5% (n = 38) (mNUTRIC), and 58.2% (n = 64) (PNI). There was a statistically significant association between chronic kidney disease (CKD) and mNUTRIC risk (OR = 13.5, 95% CI (1.89-16.05), P=0.002), diabetes mellitus (DM) and MNA risk (OR = 2.82, 95% CI (1.01-7.83), P=0.041), hypertension (HTN) and MNA risk (OR = 5.63, 95% CI (2.26-14.05), P < 0.001), and malignancy and mNUTRIC risk (P=0.048). The nutritional risk (all tools) significantly increased the odds of in-hospital death and need for mechanical ventilation. The length of stay was not significantly different in malnourished patients.

Conclusion

In the critical care setting of COVID-19 patients, malnutrition is prevalent. Malnutrition (nutritional risk) is associated with an increased risk of need for mechanical ventilation and in-hospital mortality. Patients with a history of HTN, CKD, DM, and cancer are more likely to be at nutritional risk at the time of ICU admission.

Comparison of Clinical Profiles and Mortality Outcomes Between Influenza and COVID-19 Patients Invasively Ventilated in the ICU: A Retrospective Study From All Paris Public Hospitals From 2016 to 2021

Studies comparing outcomes of ICU patients admitted for either COVID-19 or seasonal influenza are limited. Our objective was to describe baseline clinical profiles, care procedures, and mortality outcomes by infection status (influenza vs COVID-19) of patients who received invasive mechanical ventilation in the ICU.

Characterization of Necrotizing Soft Tissue Infections in Obese and Non-Obese Patients

Background: The impact of obesity on the pathogenesis and prognosis of necrotizing soft tissue infections (NSTIs) is unclear. The goal of this study was to characterize differences in NSTI presentation and outcomes by obesity status. Patients and Methods: A retrospective analysis of institutional data for patients diagnosed with NSTIs were identified (n = 619; 2011-2020). Patients were divided based on obesity (body mass index [BMI] ≥ 30 kg/m²) and non-obese (BMI <30 kg/m²). Primary outcomes included NSTI location, micro-organisms, and index hospitalization data. Multiple logistic regression was used to model predictors of in-hospital and 90-day mortality. Results: The obese cohort (n = 390; 63%) had higher rates of congestive heart failure and type 2 diabetes mellitus. There were no differences in length of stay, mortality, or discharge disposition between groups. A higher rate of respiratory failure was observed in the obese versus non-obese group (36.7% vs. 20.9%; p < 0.0005). The obese cohort was associated with perineal (40.8% vs. 27.0%) and torso NSTIs (20.9% vs. 15.8%; p < 0.005) but reduced staphylococcal (19.2% vs. 27.4%; p = 0.02) and group A streptococcal (2.6% vs. 6.5%; p = 0.03) infections, and increased polymicrobial infections. Class 2 obesity was a negative predictor for in-hospital mortality (odds ratio [OR], 0.1; 95% confidence interval [CI], 0.03-0.5) and 90-day mortality (OR, 0.3; 95% CI, 0.1-0.8), when adjusting for demographic data, type of infection, and baseline comorbidities. Conclusions: Necrotizing soft tissue infections in obesity may present with unique distributions and microbial characteristics. Class 2 obesity may exhibit a survival benefit compared with non-obese patients, suggestive of an obesity paradox.

Mortality risk in patients with underweight or obesity with peripheral artery disease: a meta-analysis including 5,735,578 individuals

OBJECTIVES

The "obesity paradox" - in which patients with obesity exhibit superior survival than normal-weight counterparts - has been reported for several diseases. However, obesity is a well-known risk factor for cardiovascular disease, and whether the obesity paradox is present in peripheral artery disease (PAD) is unknown.

METHODS

A comprehensive search for studies that reported mortality in patients with PAD grouped by BMI identified 12 studies. We compared the survival of underweight patients with those who were not underweight, and patients with obesity against those without. Underweight was defined by a BMI value of <18.5 kg/m² in most studies and obesity by BMI ≥ 30 kg/m². Subgroup analyses were performed according to length of follow-up, presentation of PAD, and mode of revascularization. Meta-regression analyses were conducted, with covariates including age, sex, presence of coronary artery disease (CAD) and diabetes mellitus (DM).

RESULTS

The mortality risk of underweight patients with PAD was significantly higher compared to those who are not underweight (HR 1.72, 95% CI 1.38-2.14; I² = 84.2%). In contrast, the mortality risk of patients with obesity with PAD was significantly lower than those without (HR 0.78, 95% CI 0.62-0.97; I² = 89.8%). These findings remained consistent regardless of the presentation of PAD, revascularization, age, sex, or presence of CAD. The risk of death in the short-term of underweight patients (HR 1.50, 95% CI 0.47-4.72) and patients with obesity (HR 0.86, 95% CI 0.66-1.13) were not significantly different from their counterparts. The meta-regression showed that of the association between obesity and better survival was more pronounced in studies with a greater proportion of patients with concomitant CAD (regression coefficient -0.029, 95% CI -0.054 to -0.004).

CONCLUSIONS

In patients with PAD, mortality is higher among underweight patients and lower among patients with obesity. The mechanisms underlying the obesity paradox in patients with PAD remain to be elucidated, and further evidence is required to guide optimal weight control strategies in these patients.

The impact of obesity on outcomes of patients admitted to intensive care after cardiac arrest

PURPOSE

Studies examining the association between obesity and mortality in cardiac arrest patients have been conflicting which might either be due to residual confounding, or a reliance on estimating the conditional effects rather than the marginal (causal) effects of obesity. We estimated the conditional and causal effects of obesity on mortality in cardiac arrest patients using the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database (APD).

MATERIALS AND METHODS

This retrospective registry-based cohort study from ICUs of Australia and New Zealand included all ICU patients admitted with cardiac arrest between 2010 and 2020 with height and weight data recorded. The conditional and marginal effects of obesity on mortality was estimated using multivariate binary logistic regression and Targeted Maximum Likelihood Estimation (TMLE) respectively. The primary outcome was in-hospital mortality.

RESULTS

A total 13,970 patients had complete data and were available for analysis. In multivariate binary logistic regression, there was no difference in the odds of in-hospital mortality for the obese versus non-obese groups; adjusted OR = 0.95, 95% CI = 0.87-1.03; p 0.25. Results were similar using TMLE (Marginal OR= 0.97; 95% CI = 0.91-1.02, p = 0.62).

CONCLUSION

After adjustment, there was no association between obesity and outcomes in cardiac arrest patients admitted to ICU.

A Dose Response Association Between Body Mass Index and Mortality in Patients with Peripheral Artery Disease: A Meta-analysis Including 5 729 272 Individuals

OBJECTIVE

Obesity is a significant risk factor for atherosclerotic cardiovascular disease; however, the "obesity paradox", in which obese patients enjoy superior survival, has been observed in various cardiovascular conditions. Whether this phenomenon exists for peripheral artery disease (PAD) remains uncertain. The goal of this study was to evaluate the relationship between body mass index (BMI) and mortality in patients with PAD.

METHODS

A comprehensive literature search identified seven eligible cohort studies that reported the association between BMI and all cause mortality in patients with PAD. A dose response meta-analysis was done for all cause mortality, short term (30 day or in hospital) mortality and long term mortality. The dose response association between BMI and mortality was also assessed in patients who received endovascular therapy (EVT).

RESULTS

The non-linear dose response analysis showed that higher BMI values were associated with a lower mortality risk from the range between 15 kg/m² to approximately 33 - 34 kg/m². The risk of mortality increased slightly thereafter. This relationship was consistent with that of long term mortality but was not apparent in short term mortality. A U shaped relationship was also observed between BMI and mortality in patients who received EVT with the lowest mortality observed at around 30 kg/m².

CONCLUSION

The obesity paradox was evident in the analysis of long term survival among patients with PAD, with the lowest mortality rates observed in obese patients. However, this association was not observed for short term or in hospital mortality.

Reconciling the obesity paradox: Obese patients suffer the highest critical illness associated mortality rates

The obesity paradox refers to the observation that obese patients admitted to intensive care units (ICU) have lower case fatality as compared to healthy weight patients. However, selection bias could explain the apparent paradox. Our objective was to assess whether obese people have a different overall burden of critical illness associated mortality. A retrospective population-based cohort study was conducted in North Brisbane ICUs during 2017-2019. Patients were classified as underweight, healthy weight, overweight, and obese according to BMIs <18.5, 18.5-24.9, 25-29.9, and ≥ 30 kg/m², respectively. ICU admission incidence rates were 245.6, 138.2, 178.9, and 421.9 per 100,000 population; 90-day all cause case fatalities were 24.0%, 17.0%, 18.1%, and 16.0%; and critical illness associated mortality rates were 58.8, 23.4, 32.4, and 67.7 per 100,000 population among underweight, healthy weight, overweight, and obese patients, respectively. As compared to patients of healthy weight, those who were underweight (relative risk; RR 2.51; 95% CI, 1.79-3.44), overweight (RR 1.38; 95% CI, 1.16-1.65), and obese (RR 2.89; 2.43-3.43) were each at significantly higher risk for critical illness associated mortality. While obese patients have lower case fatality they are at much higher risk for ICU admission and as result suffer the highest burden of critical illness associated mortality in our region.

The Impact of Obesity in Critical Illness

The prevalence of obesity is rising worldwide. Adipose tissue exerts anatomic and physiological effects with significant implications for critical illness. Changes in respiratory mechanics cause expiratory flow limitation, atelectasis, and V̇/Q̇ mismatch with resultant hypoxemia. Altered work of breathing and obesity hypoventilation syndrome may cause hypercapnia. Challenging mask ventilation and peri-intubation hypoxemia may complicate intubation. Patients with obesity are at increased risk of ARDS and should receive lung-protective ventilation based on predicted body weight. Increased positive end expiratory pressure (PEEP), coupled with appropriate patient positioning, may overcome the alveolar decruitment and intrinsic PEEP caused by elevated baseline pleural pressure; however, evidence is insufficient regarding the impact of high PEEP strategies on outcomes. Venovenous extracorporeal membrane oxygenation may be safely performed in patients with obesity. Fluid management should account for increased prevalence of chronic heart and kidney disease, expanded blood volume, and elevated acute kidney injury risk. Medication pharmacodynamics and pharmacokinetics may be altered by hydrophobic drug distribution to adipose depots and comorbid liver or kidney disease. Obesity is associated with increased risk of VTE and infection; appropriate dosing of prophylactic anticoagulation and early removal of indwelling catheters may decrease these risks. Obesity is associated with improved critical illness survival in some studies. It is unclear whether this reflects a protective effect or limitations inherent to observational research. Obesity is associated with increased risk of intubation and death in SARS-CoV-2 infection. Ongoing molecular studies of adipose tissue may deepen our understanding of how obesity impacts critical illness pathophysiology.

Costs associated with depression and obesity among cardiovascular patients: medical expenditure panel survey analysis

Background

There is a lack of information on the cost of depression associated with metabolic syndrome and cardiovascular diseases in the literature.

Methods

We evaluated the synergistic effects of depression and obesity on total expenditures for cardiovascular conditions using data from the Medical Expenditure Panel Survey (MEPS) database. We analyzed MEPS data from 1996 to 2017 comprising adult cardiovascular subjects. We categorized individuals following a combination of International Classification of Diseases ICD-9-CM and ICD-10 codes, and depression symptoms as evaluated using the Patient Health Questionnaire-2 (PHQ-2) depression screening tool. Our sample comprised cardiovascular patients aged 18 years and older, with a body mass index (BMI) between 18.5 and 60. Our study comprised unweighted sample of 96,697 (weighted sample of 938,835,031) adults, a US-nationwide representative sample of cardiovascular disease patients. The four response categories were: no depression; unrecognized depression; asymptomatic depression; and symptomatic depression. Our evaluated outcomes were total annual healthcare expenditures, including dental, emergency room, hospital outpatient, hospital inpatient, office-based, prescription, and home health care expenses.

Results

Asymptomatic and symptomatic depression was more frequent among obese individuals than in individuals with a normal BMI (p <  0.001). Total expenditure was highest among symptomatic depression individuals (17,536) and obese (9871) with cardiovascular disease. All the expenditure outcomes were significantly higher among symptomatic depression individuals than those without depression (p <  0.001), except for dental costs. All healthcare expenditures associated with obesity were higher compared to individuals with normal BMI with p <  0.001, except for emergency and home healthcare costs. Most importantly, among obese individuals, all healthcare expenditures were significantly higher (p <  0.001) in those with symptomatic depression than those without depression, except for dental costs, where the difference was not significant (0.899). Therefore, obesity and depression entail increased expenses in patients with cardiovascular disease.

Conclusions

We found incremental expenditures among unrecognized, asymptomatic, and symptomatic depressed individuals with obesity compared to non-depressed, non-obese subjects. However, these are preliminary results that should be further validated using different methodologies.

Relationship Between Obesity and Ventilator-Associated Pneumonia: A Post Hoc Analysis of the NUTRIREA2 Trial

BACKGROUND

Patients with obesity are at higher risk for community-acquired and nosocomial infections. However, no study has specifically evaluated the relationship between obesity and ventilator-associated pneumonia (VAP).

RESEARCH QUESTION

Is obesity associated with an increased incidence of VAP?

STUDY DESIGN AND METHODS

This study was a post hoc analysis of the Impact of Early Enteral vs Parenteral Nutrition on Mortality in Patients Requiring Mechanical Ventilation and Catecholamines (NUTRIREA2) open-label, randomized controlled trial performed in 44 French ICUs. Adults receiving invasive mechanical ventilation and vasopressor support for shock and parenteral nutrition or enteral nutrition were included. Obesity was defined as BMI ≥ 30 kg/m² at ICU admission. VAP diagnosis was adjudicated by an independent blinded committee, based on all available clinical, radiologic, and microbiologic data. Only first VAP episodes were taken into account. Incidence of VAP was analyzed by using the Fine and Gray model, with extubation and death as competing risks.

RESULTS

A total of 699 (30%) of the 2,325 included patients had obesity; 224 first VAP episodes were diagnosed (60 and 164 in obese and nonobese groups, respectively). The incidence of VAP at day 28 was 8.6% vs 10.1% in the two groups (hazard ratio, 0.85; 95% CI 0.63-1.14; P = .26). After adjustment on sex, McCabe score, age, antiulcer treatment, and Sequential Organ Failure Assessment at randomization, the incidence of VAP remained nonsignificant between obese and nonobese patients (hazard ratio, 0.893; 95% CI, 0.66-1.2; P = .46). Although no significant difference was found in duration of mechanical ventilation and ICU length of stay, 90-day mortality was significantly lower in obese than in nonobese patients (272 of 692 [39.3%] patients vs 718 of 1,605 [44.7%]; P = .02). In a subgroup of patients (n = 123) with available pepsin and alpha-amylase measurements, no significant difference was found in rate of abundant microaspiration of gastric contents, or oropharyngeal secretions between obese and nonobese patients.

INTERPRETATION

Our results suggest that obesity has no significant impact on the incidence of VAP.

How to ventilate obese patients in the ICU

Obesity is an important risk factor for major complications, morbidity and mortality related to intubation procedures and ventilation in the intensive care unit (ICU). The fall in functional residual capacity promotes airway closure and atelectasis formation. This narrative review presents the impact of obesity on the respiratory system and the key points to optimize airway management, noninvasive and invasive mechanical ventilation in ICU patients with obesity. Non-invasive strategies should first optimize body position with reverse Trendelenburg position or sitting position. Noninvasive ventilation (NIV) is considered as the first-line therapy in patients with obesity having a postoperative acute respiratory failure. Positive pressure pre-oxygenation before the intubation procedure is the method of reference. The use of videolaryngoscopy has to be considered by adequately trained intensivists, especially in patients with several risk factors. Regarding mechanical ventilation in patients with and without acute respiratory distress syndrome (ARDS), low tidal volume (6 ml/kg of predicted body weight) and moderate to high positive end-expiratory pressure (PEEP), with careful recruitment maneuver in selected patients, are advised. Prone positioning is a therapeutic choice in severe ARDS patients with obesity. Prophylactic NIV should be considered after extubation to prevent re-intubation. If obesity increases mortality and risk of ICU admission in the overall population, the impact of obesity on ICU mortality is less clear and several confounding factors have to be taken into account regarding the “obesity ICU paradox”.