Harris, B.

Body positioning exerts profound physiological effects in critically ill patients, influencing respiratory mechanics, ventilation-perfusion matching, hemodynamics, and overall clinical outcomes. Transitioning from an upright to a recumbent position alters thoracoabdominal pressure relationships, reduces ribcage contribution to tidal ventilation, and shifts ventilatory load to the diaphragm. These changes decrease functional residual capacity and promote airway closure in dependent lung regions, predisposing patients to atelectasis—an effect further amplified by intra-abdominal pathology or diaphragmatic weakness. Positioning also reshapes pulmonary perfusion and ventilation, which impact cardiopulmonary hemodynamics. These physiologic principles translate to important therapeutic applications across disease states. In ARDS, prone positioning improves oxygenation via a number of mechanisms, reduces ventilator-induced lung injury, and confers substantial mortality benefit. Prone position may also improve air flow distribution in COPD and enhance venous return in select cardiac physiologies. Conversely, conditions such as ascites, morbid obesity, and hepatopulmonary syndrome exemplify pathologic interactions between abdominal pressure, lung volumes, and posture, often requiring tailored positioning strategies to optimize respiratory performance. Overall, body positioning represents a potent, underutilized intervention in critical care. Understanding the mechanistic foundations of position-dependent changes in ventilation, perfusion, and pleural pressure can help clinicians strategically leverage positioning to improve patient outcomes across a spectrum of pulmonary and systemic illnesses.

Purpose Survivors of critical illness often acquire significant physical, cognitive, and psychological burdens following their intensive care unit (ICU) stay, termed “post-intensive care syndrome” (PICS). Screening for PICS is generally not performed outside of research settings largely due to lack of a simple assessment tool and time constraints. Methods This was a prospective cohort study at a single academic medical center in the USA. We included adults (18 years or older) who spent greater than 48 h in any ICU within the medical center. Following transfer out of the ICU and at 3 months, psychological, cognitive, and physical functioning were assessed using the Hospital Anxiety and Depression Scale, Impact of Event Scale-Revised, Montreal Cognitive Assessment-blind, and Barthel Index. We compared these validated questionnaires to a brief, 15-item PICS screening tool (5 questions/domain) that was developed by our multidisciplinary study team and administered to study participants after ICU discharge to general care. The primary objectives of this study were to assess the ability of validated screening tools administered at ICU discharge to predict PICS at 3 months and develop a novel screening tool for identifying patients at risk of PICS. Major Findings 191 participants completed the initial questionnaire, and 109 (56%) completed the 3-month follow-up. PICS tool scores were compared to 3-month follow-up data from validated questionnaires to evaluate predictive performance. Overall, the screening tool weakly-to-moderately correlated with continuous outcomes of the validated measures at 3-month follow-up. When evaluating sensitivity and specificity for individual domains, the screening tool had an area under curve of >0.7 for all three domains, indicating moderate-to-good sensitivity and specificity. Conclusions We developed a brief PICS screening tool that is feasible to use without prior training that modestly predicted the development of PICS following ICU stay.

Abstract Introduction: Chronic obstructive pulmonary disease (COPD) is a top 5 cause of death and bears significant morbidity and mortality. The role of multimorbidity in patients with COPD has been shown to affect quality of life, increase risk of disease exacerbation, and mortality. COPD-targeted treatments have shown limited effects on mortality, but this patient population may benefit from practitioners more aggressively managing other comorbid conditions. Understanding which diseases have the largest impact on mortality in COPD patients may help providers understand which comorbidities to manage aggressively. Methods: We used the National Vital Statistics System (NVSS; 2019 data release), which collects all death certificate data for US citizens along with basic demographic information. We only included those who died with COPD as a diagnosis on their death certificate, defined as those who had international classification of disease, version 10 (ICD-10) codes J40-J44. We then dichotomized the causes of death by COPD vs non-COPD causes, based on the underlying cause of death (UCOD) on the death certificate. UCOD is defined by the NVSS as “the disease or injury which initiated the train of events leading directly to death.” Demographic information was collected, race was self-reported, and level of education was dichotomized as less than HS diploma vs higher. We used chi-square or t-test to determine statistical significance (defined as p <0.05) between groups for demographic information. Results: 308,856 US adults had an ICD-10 code associated with COPD written on their death certificate, of these 156,499 (50.6%) had a UCOD other than COPD. Overall, mean age 77.1 years (SD 2.6 years), 49% were female. The 10 most common diagnoses of those who died of non-COPD causes are presented in Figure 1, with the three most common causes being lung cancer (23689, 15.1%), atherosclerotic heart disease (22,848, 14.6%), and acute myocardial infarction (6422, 4.1%). Six of the top 10 are cardiovascular diseases. Conclusion: Patients with COPD are at high risk of mortality from comorbid conditions, with around 50% dying from non-COPD etiologies according to death certificate data. Most of these etiologies have evidence-based and mortality-reducing interventions such as lung cancer screening and management of cardiovascular risk factors including cholesterol-lowering medications and antihypertensives. While we don't have more granular data in terms of medications and therapies, these findings suggest that guideline-oriented approaches, both in terms of lung cancer screening and cardiovascular-risk reduction, may have a strong impact on mortality reduction in this patient population.

Socioeconomic status (SES) is a multi‐faceted theoretical construct associated with stroke risk and outcomes. Knowing which SES measures best correlate with population stroke metrics would improve its accounting in observational research and inform interventions. Using the Centers for Disease Control and Prevention's (CDC) Population Level Analysis and Community Estimates (PLACES) and other publicly available databases, we conducted an ecological study comparing correlations of different United States county‐level SES, health care access and clinical risk factor measures with age‐adjusted stroke prevalence. The prevalence of adults living below 150% of the federal poverty level most strongly correlated with stroke prevalence compared to other SES and non‐SES measures (correlation coefficient = 0.908, R2 = 0.825; adjusted partial correlation coefficient: 0.589, R2 = 0.347). ANN NEUROL 2024;96:739–744