Citations

Background Delirium is a frequent and serious complication in critically ill patients and associated with adverse outcomes. Automated pupillometry has emerged as a potential diagnostic tool for the detection of delirium. However, pupillometric parameters are also influenced by pain, potentially confounding their relationship to delirium. The aim of this study was to evaluate the association between automated pupillometry parameters and delirium in patients with high risk of pain (surgical patients) admitted to intensive care units (ICU). Methods This prospective multicenter observational study included adult surgical patients admitted to an ICU between April 2023 and August 2025. Delirium was assessed every 6 h after admission using CAM-ICU. Automated pupillometry together with pain intensity scores (VAS, CPOT, BPS) were performed concurrent with CAM-ICU. To account for repeated measurements within individual patients, a generalized linear mixed-effects model (GLMM) with patient as a random effect was used to evaluate the association between pupillometric parameters and diagnosed delirium. Results Forty-nine patients were included, yielding 417 measurements, of which 48 (12%) were done in patients who were simultaneously CAM-ICU positive. Among pupillometric variables, only average pupillary latency (LAT avg) was significantly shorter in patients with delirium ( p = 0.01). In the mixed-effects model adjusted for sedation depth, level of consciousness, and pain intensity, LAT avg. was independently associated with delirium (OR 1.55 per 0.01; 95% CI 1.13–2.13; p = 0.007). Conclusion In surgical ICU patients, shortened pupillary light reflex latency is associated with delirium. Automated pupillometry may serve as a useful tool for detection of delirium in this population. Clinical trial registration https://clinicaltrials.gov/study/ Identifier: NCT05811208.

Introduction Studies have revealed circadian rhythms affect many organ systems and time of injury can impact healing, lung function, and inflammation. We hypothesized operative time may impact post-operative risk. Accordingly, the aim of this study was to compare post-operative complications in patients whose surgery started and finished before 12pm (Group 1); after 12pm (Group 2); and started before 12pm and finished after 12pm (Group 3). Methods Patients from two previous prospective studies were included in this post-hoc analysis. Inclusion criteria were indication for lung resection surgery (confirmed or highly suspicious tumor), age ≥18 and ability to undergo cardiopulmonary exercise testing (CPET). Exclusion criterion was inability to perform surgery (either low predicted postoperative oxygen consumption or inoperability). All patients underwent pre-operative CPET and spirometry. Post-operative pulmonary (PPC) and cardiovascular complications (PCC) were prospectively assessed from the hospital stay up to 30 days. Results Data from 497 patients were analyzed. On comparison of groups, there were differences in BMI (highest in Group 3), surgery duration (lowest in Group 1) and frequency of lobectomies (lowest in Group 1). Incidence of PPC was lowest in Group 1 (n=17; 9%) compared to n=17 (21%) in Group 2 and n=37 (17%) in Group 3 (p=0.01). Incidence of PCC was lowest in Group 1 n=25 (13%) compared to n=19 (23%) in Group 2 and n=46 (21%) in Group 3 (p=0.05). Multivariate logistic regression adjusted for confounders (BMI, surgery duration and lobectomy) showed time of surgery after 12pm was independently associated with PPC with OR = 2.17 (95% CI 1.07-4.41); p=0.03. Conclusion Time of surgery may impact the incidence of PPC in lung resection surgery patients with highest risk in patients undergoing the surgery after 12pm.

Rationale Despite significant advances in patient care and outcomes, criteria for cardiopulmonary exercise testing (CPET) in risk stratification guidelines for lung resection have not been updated in over a decade. We hypothesised that CPET no longer holds additional predictive value for postoperative complications. Methods In this secondary analysis, we included lung resection candidates from two prospective, multicentre studies eligible for CPET and assessed with preoperative pulmonary function tests (PFTs) and arterial blood gas analysis. Postoperative pulmonary (PPCs) and cardiovascular complications (PCCs) were documented during hospitalisation. We trained five types of machine learning models applying nested cross-validation to predict complications and compared predictive performance based on four metrics, including area under the receiver operating characteristic curve (AUC-ROC). Results A total of 497 patients were included. PPCs developed in 71 (14%) patients. Adding CPET parameters to PFTs and baseline clinical data did not improve the ability of models to predict PPCs in unselected patients (AUC-ROC=0.72–0.78; p=0.47), nor in those meeting American College of Chest Physicians (ACCPs) (n=236; AUC-ROC=0.64–0.78; p=0.70) or European Respiratory Society/European Society of Thoracic Surgery (ERS/ESTS) criteria (n=168; AUC-ROC=0.59–0.76; p=0.92). PCCs developed in 90 (18%) patients. CPET parameters likewise did not improve model performance for the prediction of PCCs in unselected patients (AUC-ROC=0.65–0.73; p=0.96), nor in the ACCP (AUC-ROC=0.61–0.73; p=0.82) or ERS/ESTS subgroups (AUC-ROC=0.62–0.69; p=0.87). Conclusions In contemporary surgical practice, CPET did not improve the predictive performance of machine learning models for PPCs or PCCs in patients with an indication based on established guidelines or in those without. The role of CPET in preoperative risk stratification for lung resection should be re-evaluated. Trial registration number NCT03498352 , NCT04826575 .

Background Increasing evidence suggests that insufficient sleep is associated with increased morbidity and mortality. Studies linking short sleep with metabolic derangements such as obesity, insulin resistance and dyslipidemia, are especially persuasive. Nevertheless, the vast majority of these data have been obtained from observational studies. We sought to evaluate the effects of 9 days of partial sleep deprivation compared to normal sleep on lipid profile in healthy subjects undergoing an in‐laboratory, experimental protocol. Methods Eight healthy, nonobese, normolipidemic subjects (4 males, age 25.4±6.8 years, body mass index 25.2±3.9 kg/m 2 ) completed a randomized, controlled, crossover, 16‐day in‐patient study. Each sequence consisted of a 4‐day acclimation period, followed by 9 days of experimental partial sleep deprivation (4 hours of sleep opportunity; 00:30 AM – 4:30 AM); or normal sleep (9 hours of sleep opportunity; 10:00 PM – 7:00 AM), and a subsequent 3‐day recovery segment. Subjects consumed a controlled, weight maintenance diet (15% protein, 55% carbohydrates, and 30% fat) during the study. Fasting blood samples for measures of total cholesterol, high‐density lipoprotein (HDL) cholesterol, low‐density lipoprotein (LDL) cholesterol, and triglycerides were obtained in both study sequences on Days 4, 7, 10, 13, and 16. Results Compared to normal sleep, experimental partial sleep deprivation did not cause significant changes in total cholesterol (partial sleep deprivation vs normal sleep: 160.1±21.8 mg/dL vs 164.9±23.7 mg/dL, P=0.724), HDL cholesterol (54.1±14.7 mg/dL vs 52±12.8 mg/dL, P=0.75), LDL cholesterol (86.5±18.9 mg/dL vs 89.5±19.7 mg/dL, P=0.329), or triglycerides (97.7±26.6 mg/dL vs 96.6±38.3 mg/dL, P=0.633). Analysis of time course of lipid values across study days did not reveal appreciable patterns (all P>0.295). Likewise, body weight was similar during both study sequences (partial sleep deprivation vs normal sleep: 69.6±14.2 kg vs 69.2±13.5 kg, P=0.459). Conclusions Experimental sleep restriction does not alter circulating lipids or weight in healthy subjects. Aberrant metabolic effects of insufficient sleep may be mediated by dietary intake and occur when access to food is unrestricted, or may emerge only after longer exposure to sleep curtailment. Support or Funding Information American Heart Association grant 16SDG27250156; National Institutes of Health R01 grants HL114676 and HL114024; European Union project FNUSA‐ICRC no. CZ.1.05/1.1.00/02.0123. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Background: Decreased testosterone (TTST) and sex hormone binding globin (SHBG) contribute to development of cardiometabolic disorders in obese men. We aimed to determine the effect of weight gain and weight loss on TTST and SHBG in healthy men and to examine their relationship with endothelial function. Method: 25 normal weight healthy men underwent an overfeeding protocol to gain 5% body weight over 8 weeks. Following weight gain, they underwent 8 weeks of weight loss. TTST, SHBG and endothelial function (brachial artery flow‐mediated dilation, FMD) were measured at baseline (T1), after weight gain (T2) and after weight loss (T3). Results: Volunteers gained 4 ± 1.3 kg and subsequently lost 3.2 ± 1.5 kg of body weight. TTST levels did not change (T1: 18.85 ± 6.2 nmol/L, T2: 19.3 ± 6.9 nmol/L, T3: 20.3 ± 7.6 nmol/L). However, SHBG decreased during weight gain (p=0.04) and increased during weight loss (p=0.0005) (T1: 22.8 ± 10.5 nmol/L, T2: 20.8 ± 11.1 nmol/L, T3: 25.58 ± 11.5 nmol/L). Furthermore, SHBG reduction during weight gain tended to correlate with decreases in FMD (p=0.06; R 2 =0.15). During weight loss, increases in SHBG correlate negatively with changes in FMD (p=0.008; R 2 =0.32). Conclusion : In normal weight healthy men, SHBG decreases with modest weight gain and increases with weight loss. TTST does not change. Changes in SHBG partly predict improvements in endothelial function with weight loss. Grant Funding Source : Supported by AHA 11SDG7260046, R21 DK81014, AHA 13POST16420009

Abstract Poor ventilatory efficiency, defined as the increase in minute ventilation relative to carbon dioxide production during exercise (VE/VCO2 slope), may be associated with dynamic hyperinflation and thereby promote the development of prolonged air leak (PAL) after lung resection. Consecutive lung lobectomy candidates (n = 96) were recruited for this prospective two-centre study. All subjects underwent pulmonary function tests and cardiopulmonary exercise testing prior to surgery. PAL was defined as the presence of air leaks from the chest tube on the 5th postoperative day and developed in 28 (29%) subjects. Subjects with PAL were not different in terms of age, sex, American Society of Anesthesiologists class, type of surgery (thoracotomy/video-assisted thoracoscopic surgery) and site of surgery (right/left lung; upper/lower lobes). Subjects with PAL had more frequent pleural adhesions (50% vs 21%; P = 0.006) and steeper VE/VCO2 slope (35 ± 7 vs 30 ± 5; P = 0.001). Stepwise logistic regression showed that only the presence of pleural adhesions [odds ratio (OR) 3.9, 95% confidence interval (CI) 1.4–10.9; P = 0.008] and VE/VCO2 slope (OR 1.1, 95% CI 1.0–1.2; P = 0.003) were independently associated with PAL (AUC 0.74, 95% CI 0.62–0.86). We conclude that a high VE/VCO2 slope during exercise may be helpful in identifying patients at greater risk for the development of PAL after lung lobectomy. Clinical trial registration number: ClinicalTrials.gov identifier: NCT03498352.

SummaryLow leptin concentration has been shown to be associated with central sleep apnea in heart failure patients. We hypothesized that low leptin concentration predicts central sleep apnea. Consecutive ambulatory New York Heart Association (NYHA) classes I–IV heart failure patients were studied prospectively, including measurement of serum leptin, echocardiography and polysomnography. Sleep apnea was defined by type (central/mixed/obstructive) and by apnea–hypopnea index ≥5 by polysomnography. Subjects were divided into four groups by polysomnography: (1) central sleep apnea, (2) mixed apnea, (3) no apnea and (4) obstructive sleep apnea. Fifty‐six subjects were included. Eighteen subjects were diagnosed with central sleep apnea, 15 with mixed apnea, 12 with obstructive apnea and 11 with no sleep apnea. Leptin concentration was significantly lower in central sleep apnea compared to obstructive apnea (8 ± 10.7 ng mL−1 versus 19.7 ± 14.7 ng mL−1, P ˂ 0.01) or no sleep apnea (8 ± 10.7 ng mL−1 versus 17.1 ± 8.4 ng mL−1, P ˂ 0.01). Logistic regression showed leptin to be associated independently with central sleep apnea [odds ratio (OR): 0.19; 95% confidence interval (CI): 0.06–0.62; area under the curve (AUC): 0.80, P < 0.01]. For the detection of central sleep apnea, a cut‐off value for leptin concentration 5 ng mL−1 yielded a sensitivity of 50% and specificity of 89%. In conclusion, a low leptin concentration may have utility for the screening of heart failure patients for central sleep apnea.