Olson, Lyle

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.

Abstract Aims Monitoring pulmonary congestion in chronic heart failure (HF) reduces decompensation and hospitalization, but conventional methods such as weight and symptom tracking are often unreliable. As fluid accumulation affects the lungs and vocal tract, subtle voice alterations may serve as a non-invasive signal for early detection of worsening HF. Methods and results The Voice Analysis for Monitoring Patients with HF trial (VAMP-HF, NCT06566911) prospectively enrolled 104 patients hospitalized with acute decompensated HF (ADHF) across two academic centres in the USA and Germany. Daily voice recordings were collected from admission to discharge, with breathing features extracted from speech and acoustic features from sustained vowels. A machine-learning model was trained to classify recordings as admission-phase vs. discharge-phase using leave-one-patient-out. Patients with clinical deterioration, insufficient audio quality, or short length of stay were excluded. Seventy-nine patients were included in the final dataset. The model classified admission and discharge with an F1-score of 0.83 (95% CI: 0.77–0.90; AUC = 0.90). In patients with higher audio volume (N = 54), performance reached 0.89 (95% CI: 0.82–0.94; AUC = 0.91). When applied to intermediate hospitalization days, model-predicted scores showed progressive increases from admission towards discharge. Performance remained robust irrespective of significant weight loss during hospitalization. Conclusion In this pilot study, structured voice and breathing analysis discriminated hospitalization phase from admission through discharge in patients with ADHF. This non-invasive approach captured progressive changes during the hospital course and warrants further investigation with concurrent objective congestion markers to establish physiological specificity.

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 .