Critical Care Explorations

Background. Capillary refill time is a resuscitation target in septic shock,1-4 but bedside measurement is examiner-dependent. An ICU monitor co-records a photoplethysmogram on the pulse oximeter and intermittent noninvasive blood pressure cuff cycles; if the probe and the cuff share a limb, each cycle is an unplanned vascular occlusion test on the distal microvascular bed. Standard practice places the two on opposite limbs. Objective. To measure how often, in MIMIC-IV-WDB v0.1.0, charted cuff cycles show the photoplethysmographic morphology expected of a same-limb cuff and probe, and to characterize the candidate capillary refill-like signal when that morphology is present. Methods. MIMIC-IV-WDB v0.1.05 was linked to the MIMIC-IV clinical database.6 A pre-registered rule-based detector identified candidate occlusion-reperfusion signatures on the 1-Hz perfusion-index envelope around each charted cuff timestamp. The primary endpoint was the proportion of cuff cycles suitable for analysis that were detector-positive at a 15-second reperfusion threshold, with 95% confidence intervals estimated by resampling patients at a fixed seed. A secondary analysis used a locally hosted multimodal language model (a Gemma-3 derivative on a non-device server) to adjudicate the same signature on perfusion-index plots; no MIMIC-IV-WDB content left the workstation. Results. Of 9,224 charted cuff cycles, 8,909 had a usable pulse-oximeter waveform, and 268 cycles in 15 patients (4.30% of the 6,236 cuff cycles suitable for analysis, 95% CI 2.60 to 6.03) met the primary 15-second threshold. The language model adjudicated the same cycles and called 1,367 of the 8,909 cycles with a usable waveform (15.34%) signature-present, roughly five times the detectors count. Because no laterality ground truth exists, agreement with a single blinded reader served as the comparator rather than accuracy. The two methods were about equally concordant with the reader: precision was 0.25 (95% CI 0.14 to 0.39) for the detector and 0.24 (95% CI 0.10 to 0.35) for the language model, although reweighting to the full population of cycles with a usable waveform lowered the language model to 0.030 (95% CI 0.009 to 0.053). These estimates are reference-limited: a blinded re-read of a 150-card subsample showed only moderate intra-rater reliability (Cohen {kappa} 0.46 to 0.59) with systematic undercalling on the first pass, and rescoring against the corrected re-read roughly doubled precision for both methods. Conclusions. Opportunistic extraction of capillary refill-like signals from archived ICU pulse oximetry is limited in two distinct ways. First, sensor geometry limits how often the signal is recordable: cuff cycles rarely show the morphology expected of a same-limb cuff and probe pair, consistent with opposite-limb placement, so the bottleneck is geometry rather than signal processing. Second, the modest reliability of morphology adjudication limits how well any single flagged cycle can be confirmed: against a blinded reader the detector is a usable screen but a noisy confirmer, the reference is itself only moderately reliable, and the language model is no more concordant despite flagging many more cycles. The minority of cycles in which the morphology appears contain a candidate signal that may merit prospective study under controlled placement with laterality recorded.

Background: Accurate early identification of sepsis remains a major clinical challenge due to its heterogeneous presentation and overlap of clinical signs with the non-infectious systemic inflammatory response syndrome (SIRS). Timely differentiation is crucial for improving patient outcomes, meeting sepsis bundle requirements and reducing inappropriate antimicrobial use. We hypothesized that clinical and laboratory data available within the first 3 hours of patient presentation could be used to identify patients with sepsis to an actionable level of accuracy, in lieu of traditional microbiology results which would not become available until at least 12-24 hours. Data from two independent studies were used to quantify the diagnostic value of demographic, vital, clinical-laboratory, and microbiological data available at three time points for distinguishing retrospectively diagnosed critically ill patients with either sepsis or non-infectious SIRS. A particular focus of this work was an assessment of the utility of SeptiCyte RAPID (Immunexpress Inc., Seattle, Washington, USA) as an aid to sepsis diagnosis, producing actionable data within 1 hour. Methods: Data from two independent study cohorts were analysed. The 510k cohort consisted of 419 adult patients in intensive care (ICU) (MARS, VENUS, and NEPTUNE trials). The Andalusian cohort consisted of 353 ICU patients from the PANGEA study. Logistic regression models, selected by a greedy search algorithm and validated by repeated cross-validation, were used to determine the contributions of different variables to diagnostic accuracy. Diagnostic performance was quantified by area under the receiver operating characteristic curve (AUC). Results: For the 510k cohort, a baseline AUC of 0.69-0.73 was observed using 5-7 vital and demographic variables assessed immediately upon presentation (time T1). The addition of clinical-laboratory variables, in particular SeptiCyte RAPID, within 1-3 hours post-presentation (time T2) increased the AUC to 0.83-0.85). Finally, the addition of microbiological data 12-24 hours post-presentation (time T3) further improved the AUC to 0.90-0.91. Similar results were obtained for the Andalusian cohort. AUC values at the three time points were as follows: At time T1, AUC = 0.67 based solely on vital signs and demographics; at time T2, AUC = 0.87 based on vitals + demographics + SeptiCyte RAPID or other clinical laboratory data; at time T3, AUC = 0.93 based on vitals + demographics + SeptiCyte RAPID or other clinical laboratory data + microbiology results). For both cohorts, the most significant variables included temperature, mean arterial pressure, respiratory rate, suspected infection site; SeptiCyte RAPID, procalcitonin, confirmed bacterial infection and positive blood culture confirmation. Conclusions: Accuracy of identification of sepsis increases markedly as demographics and vital signs are supplemented with clinical-laboratory information, and ultimately with microbiological culture results. The fastest improvement occurs within the first 3 hours when laboratory data, and in particular SeptiCyte RAPID results, become available. Integrating rapid host-response testing with SeptiCyte RAPID into time-based diagnostic frameworks may enhance early sepsis recognition, improve antimicrobial stewardship, and support guideline-driven clinical decisions.

Overview: SeptiCyte RAPID is an FDA-cleared gene expression test that quantifies host immune response to aid in the diagnosis of sepsis. The test yields a score (the SeptiScore) ranging from 0-15, distributed across four bands (1-4) based on increased likelihood of sepsis. Each band can be characterized by average positive and negative likelihood ratios (LR+, LR- respectively) for the discrimination of sepsis versus the non-infectious systemic inflammatory response syndrome (SIRS). Methods: A retrospective analysis of prospectively collected data from a combined cohort of critically ill patients suspected of sepsis (N=889), recruited across 19 hospitals in the USA and Europe. The analysis quantified the LR+ and LR- parameters as a function of SeptiScore, for discrimination of sepsis vs. SIRS in patients admitted to ICU. Hypotheses: (1) The likelihood ratio (LR) framework provides a clinically useful interpretive approach that complements the previously used SeptiScore banding scheme; (2) Low Band 1 SeptiScores are associated with sufficiently small LR- to support the use of SeptiCyte RAPID as a rule-out test for sepsis; (3) High Band 4 SeptiScores are associated with sufficiently large LR+ to support the use of SeptiCyte RAPID as a rule-in test for sepsis; and (4) SeptiScore-derived LR+ and LR- values can be combined with estimates of pre-test probability (derived from patient characteristics and/or other diagnostic tests) to generate individualized, patient-specific post-test probabilities of sepsis. Results: The SeptiCyte RAPID test demonstrates strong diagnostic performance in distinguishing sepsis from SIRS. The likelihood ratios across different score bands provide clear clinical utility: the median LR+ was 3.26 (range 2.57-4.24) for Band 3, and 6.97 (range 4.35-15.57) for Band 4 providing evidence toward ruling in sepsis at high SeptiScores. Conversely, the median LR- was 0.16 (range 0.14-0.20) for Band 2 and 0.085 (range 0.014-0.16) for Band 1, providing evidence toward ruling out sepsis at low SeptiScores. A higher-resolution analysis of SeptiCyte RAPID performance confirmed these trends by evaluating LR+ and LR- at specific values within each band. The sepsis group was further stratified according to whether patients were classified as blood-culture positive (BC+) or blood culture negative (BC-), and the detailed LR+ and LR- analyses were repeated. A monotonic increase in likelihood ratio with increasing SeptiScore was consistently observed, independent of whether sepsis patients were culture-positive, culture-negative, or unstratified with respect to blood culture status. Conclusion: High SeptiScores have correspondingly high LR+ values, and low SeptiScores have correspondingly low LR- values, both of which may have clinical utility. High likelihood ratios for band 4 SeptiScores, which precede traditional microbiology results, may provide clinicians with early confidence of a sepsis diagnosis and microbiology diagnostic stewardship. Low likelihood ratios for band 1 SeptiScores may prompt clinicians to consider an alternate diagnosis to sepsis. Such results, obtained early in the diagnostic workup process, may lead to fewer missed diagnoses and more efficient use of hospital resources.

ABSTRACT Background: Corticosteroids reduce mortality in severe COVID-19 requiring oxygen or invasive mechanical ventilation, yet emerging data suggest that SARS-CoV-2-associated acute lung injury is biologically heterogeneous and that treatment response may vary across molecularly defined disease states. Lung-derived molecular endotypes of severe COVID-19-associated acute lung injury have been described, but direct molecular profiling is not routinely available at the bedside. We evaluated whether a clinical predictor of previously defined lung molecular endotype identifies heterogeneity in corticosteroid treatment effect among mechanically ventilated patients with COVID-19. Methods: We utilized a single-center cohort of 5,000 patients with COVID-19 treated at the University of North Carolina Hospital between January 1, 2020, and December 31, 2022, to emulate a target trial assessing the effect of corticosteroid receipt on mortality, length of stay, and incident organ support. Confounding was addressed through inverse probability of treatment weighting (IPTW). Outcomes for severely ill patients requiring mechanical ventilation were compared to the RECOVERY trial results, with subsequent moderation analysis and stratified analysis by clinically predicted lung molecular endotype and vaccination status. The primary outcome was 28-day mortality. Secondary Outcomes were time to discharge alive and progression to additional organ support. Results: This emulated target trial showed a directionally favorable but non-statistically significant association between corticosteroid treatment and reduced 28-day mortality in patients requiring mechanical ventilation for SARS-CoV-2 infection. A clinical predictor of lung molecular endotype moderated the effect of corticosteroids on 28-day mortality (p-value for interaction 0.038) and identified distinct predicted endotype-specific treatment effect. Corticosteroid treatment was associated with lower 28-day mortality in the predicted Hyper-Inflammatory endotype (OR 0.62, 95% CI 0.39, 0.99) but not in the predicted Metabolic Dysregulation endotype (OR 1.15, 95% CI 0.82, 1.61). We did not detect significant effect modification by vaccination status (p-value for interaction 0.65), although inference was limited by the small, vaccinated subgroup (28-mortality OR 0.78, 95% CI 0.37, 1.65 in vaccinated vs 0.94, 95% CI 0.70, 1.26 in unvaccinated). Conclusions: In this target trial emulation of mechanically ventilated patients with severe COVID-19, corticosteroid treatment showed a directionally favorable but non-statistically significant association with reduced 28-day mortality in the overall cohort. However, a clinical predictor of lung molecular endotype identified significant heterogeneity in treatment effect, with benefit concentrated in the predicted Hyper-Inflammatory endotype and no apparent benefit in the predicted Metabolic Dysregulation endotype. These findings support prospective validation of clinically deployable endotype-guided corticosteroid treatment strategies in acute lung injury and ARDS.

Purpose: Polypharmacy is highly prevalent among critically ill patients, yet it's independent impact on intensive care unit (ICU) outcomes in sepsis remains critically unexplored. We aimed to evaluate whether pre-admission polypharmacy independently predicts ICU mortality and provides incremental prognostic value using the medication reconciliation module of the MIMIC-IV-ED linked database. Materials and Methods: We conducted a retrospective cohort study of 3,347 adults admitted to the ICU who met Sepsis-3 criteria. Pre-admission polypharmacy was categorized as none (0-4), standard (5-9), or high (>=10 medications). Multivariable logistic regression, propensity score matching, and reclassification analyses (NRI/IDI) were performed. The primary outcome was in-hospital ICU mortality. Results: High polypharmacy was present in 58.9% of patients. Crude ICU mortality increased sequentially: 18.5% (none), 26.0% (standard), and 27.5% (high; p < 0.001). After multivariable adjustment, high polypharmacy independently predicted in-hospital ICU mortality (aOR 1.45, 95% CI (1.10-1.91)), and 28-day mortality (aOR 1.47). Drug-class analysis identified statins as significantly protective (aOR 0.56), whereas RAS blockers combined with diuretics increased acute kidney injury risk (aOR 1.49). Propensity matching confirmed the primary mortality association (matched aOR 1.28). Conclusions: By utilizing the ED medication reconciliation table, this study proves high polypharmacy represents a distinct 'pharmacologic frailty', independent of acute severity. Available instantly at triage, this zero-latency metric provides significant early prognostic value (SOFA NRI = 0.24) and identifies actionable high-risk interactions (e.g., RAS blockers plus diuretics) for immediate, targeted pharmacist-led intervention upon ICU admission.

Objective: Readmissions to the PICU are associated with increased morbidity and mortality. A prediction model that can identify children at risk of readmission at the time of transfer can allow providers to intervene and potentially improve patient outcomes. The objective of this study was to derive and validate machine learning models to predict PICU readmission at the time of transfer. Design: Retrospective observational cohort study Setting: Three quaternary care PICUs in the city of Chicago Patients: All children admitted to the PICU between 2012 and 2019. Measurements: The primary outcome was unplanned readmission to the PICU within 48 hours of transfer to the inpatient ward. Predictor variables included vital signs, patient characteristics, and laboratory results. We developed and externally validated four models to predict PICU readmission: logistic regression, elastic net, random forest, and XGBoost. Main Results: This study included 35,601 patients, with readmission rates ranging from 2.2-3.7% by site. The performance of models during internal validation was consistent at the three sites, with the area under the receiver operating characteristic (AUC) values between 0.70 and 0.73 and no difference across the four models. Model performance decreased significantly during external validation (AUCs of 0.60-0.69). The variables most important to the prediction differed at each site. Conclusion: Machine learning models for predicting readmissions to the PICU have limited generalizability. Locally derived models demonstrated modest performance in our study and could potentially inform provider decision-making if prospectively validated. Externally developed models are unlikely to perform well at predicting PICU readmissions.

Background. Capillary refill time, an examiner-dependent bedside test of distal microvascular perfusion, has become a resuscitation target in septic shock,1,2,3,4 motivating a continuous surrogate computed from the photoplethysmogram (PPG, the optical waveform the pulse oximeter on every ICU patient already records).5,6,7,8 Objective. We attempted three PPG-derived candidate measures on the MIMIC-IV Waveform Database (MIMIC-IV-WDB v0.1.0) and asked, by inspecting randomly drawn examples, whether each captured its intended physiology before any downstream modeling. Methods. MIMIC-IV-WDB v0.1.09 was linked to MIMIC-IV.10 The signals were a cuff-anchored perfusion-index recovery (reactive hyperemia when the cuff shares an arm with the probe), a slow Mayer-wave-band power ratio of the perfusion index (sympathetic vasomotor tone), and a per-beat diastolic exponential decay time constant (a refill-like recovery time). For each signal we drew 10 random examples at a fixed seed and checked them against a checklist fixed in advance. Each was read by the author and, separately, by MedGemma 1.5, a multimodal medical language model run locally. A synthetic test with a known time constant checked the third signal. Results. The cuff-anchored signal showed the expected occlusion-reperfusion shape on 268 of 6,236 evaluable cuff cycles (4.30%) in 15 of 19 patients, consistent with opposite-limb placement of the probe and cuff. The slow-band ratio returned a stable cohort value, but a clear, stationary peak appeared in only4 of 10 random windows. The per-beat fit met its goodness-of-fit threshold in 10 of 10 beats, yet a cardiac-frequency heuristic flagged a possible fit on the heart-rate oscillation in 7 of 10, and in 5 of 17 patients the time constant lay where an exponential is indistinguishable from a straight line. A 0.5Hz high-pass pre-filter implanted its own approximately 318 ms time constant regardless of truth. The language model tracked the human on clear positives but reported the pattern present on every call it returned, never absent. Conclusions. Two of the three candidate signals did not reflect their intended physiology in most examples, and the third was constrained by sensor placement. Inspecting a few random raw inputs against a checklist written in advance is an inexpensive upstream check before downstream inference on PPG-derived microvascular signals.

Background: The importance of lactate trajectory during the first day of cardiogenic shock is increasingly recognized. We aimed to assess the association between admission-day lactate trajectory and in-hospital mortality, and to identify same-day interventions predictive of lactate clearance. Methods: We analyzed adult patients admitted with cardiogenic shock between October 2015 and June 2023, using the Vizient(R) Clinical Data Base. Early lactate clearance was defined as lactate <2.5 mmol/L by the end of the admission day. We used multivariable logistic regression to assess the association between lactate change and in-hospital mortality, and to identify interventions associated with lactate clearance. Results: Among 40,434 patients with cardiogenic shock, 30.1% achieved same-day lactate normalization, which was associated with lower in-hospital mortality (aOR 0.51; 95% CI 0.48-0.54). Lactate change showed the greatest prognostic importance, with observed mortality exceeding 80% among those with lactate increase >5 mmol/L regardless of baseline values. After adjustment, lactate change showed a positive exponential relationship with mortality, with aORs ranging from 0.25 (95% CI 0.23-0.27) for a -10 mmol/L change to 3.99 (95% CI 3.58-4.40) for a +10 mmol/L change. The intervention most strongly associated with early lactate clearance was pulmonary artery catheter (PAC; aOR 1.28 [95% CI 1.19-1.37]). Conclusions: Nearly 1 in 3 patients with cardiogenic shock achieved early lactate clearance, which was associated with lower mortality. The magnitude of lactate change had profound prognostic implications regardless of the initial value. Among day 1 interventions, PAC use had the strongest association with lactate clearance.

Background Electronic health record documentation patterns may reflect workflow complexity, monitoring intensity, and operational strain in intensive care settings. However, documentation-derived features can be sensitive to local documentation culture, data capture systems, and outcome definitions. Retrospective validation across multiple datasets is therefore needed before these signals are used in workflow intelligence or clinical AI governance tools. Objective To evaluate whether documentation-density and documentation-timing features show reproducible retrospective signal for ICU workflow complexity and long-stay proxy outcomes across de-identified critical care datasets, while distinguishing workflow and long-stay associations from unsupported claims about mortality prediction, burden reduction, or deployment readiness. Methods We synthesized retrospective validation results from de-identified ICU and workflow datasets generated through a prespecified documentation-density validation program. Feature families included Documentation Burden Score style features, Shift-End Documentation Rate style features, documentation reliability style metadata, and all-documentation feature sets where available. Outcomes included long ICU length of stay proxies, mortality where available, and workflow proxy endpoints. Models compared baseline feature sets with enhanced models containing documentation-density or workflow features. Performance was summarized using area under the receiver operating characteristic curve, Brier score where reported, delta AUROC, bootstrap confidence intervals where reported, and label-shuffle controls where available. Results The strongest external long-stay proxy evidence came from the NWICU chartevents analysis, which included 28,612 ICU stays, 20,267 stays with chart events, and 9,619,759 chart events. For ICU length of stay greater than the median, baseline AUROC was 0.5252. Enhanced AUROC was 0.9512 for Documentation Burden Score features, 0.9214 for Shift-End Documentation Rate features, 0.8470 for documentation reliability style features, and 0.9517 for all documentation features. Corresponding label-shuffle enhanced AUROCs were near random, ranging from 0.4897 to 0.5064. For ICU length of stay greater than the 75th percentile, baseline AUROC was 0.5155. Enhanced AUROC was 0.9433 for Documentation Burden Score features, 0.9194 for Shift-End Documentation Rate features, 0.8118 for documentation reliability style features, and 0.9427 for all documentation features, with label-shuffle enhanced AUROCs from 0.4836 to 0.4999. Additional retrospective support was observed in eICU workflow analyses, HiRID first-24-hour documentation-density analyses, MIMIC-IV HF ICU internal analyses, MIMIC-IV-Note metadata extensions, and nursing-chart or lab density proxy analyses. However, cross-institution discrimination transfer was weak without recalibration, and several analyses remained proxy validations rather than final clinical validations. Conclusions Documentation-density and documentation-timing features show promising retrospective signal for ICU workflow complexity and long-stay proxy outcomes, especially in NWICU chartevents and selected internal dataset-specific analyses. These findings support further preregistered, prospective, silent-mode validation of documentation-derived workflow intelligence. They do not establish prospective clinical performance, mortality reduction, clinician burden reduction, autonomous deterioration prediction, or deployment readiness.

BACKGROUND: Critical care pharmacists (CCPs) reduce adverse drug events (ADEs) and mortality in the intensive care unit (ICU). Board certification is the established professional standard for CCPs but its impact on ICU patient outcomes, including its relationship between CCP characteristics and workload, remain unclear. The purpose of this study was to evaluate the association between pharmacist board certification, CCP workload characteristics, and patient outcomes. METHODS: This was a pre-planned analysis of the multicenter, observational Optimizing Pharmacist Team Integration for ICU Patient Management (OPTIM) study, including adult ICU patients cared for by CCPs. Patients cared for exclusively by board certified pharmacists on every ICU day were categorized as the BCP group; those with at least one day of care from a non board certified pharmacist comprised the non BCP group. The primary outcome was hospital mortality; secondary outcomes included the hazard of discharge alive (HDA) from the ICU and hospital. Multivariable logistic regression was used to evaluate the association between BCP and mortality; Fine-Gray competing risk models were used to assess the relationship between BCP and ICU and hospital HDA. RESULTS: A total of 201 pharmacists (184 BCPs; 17 non BCPs) from 63 institutions caring for 20,537 ICU patients were included. Care provided exclusively by a BCP (vs. >/= 1 day by a non-BCP) was associated with lower mortality (OR 0.80, 95% CI 0.69 to 0.92, p=0.002) and both a higher ICU HDA (HR 1.08, 95% CI 1.03 to 1.13, p<0.001) and hospital HDA (HR 1.19, 95% CI 1.13 to 1.26, p<0.001). CONCLUSION: Daily ICU care delivered by pharmacists with board certification was independently associated with reduced mortality and improved hazard of discharge alive from the ICU. Board-certified pharmacists may enhance the quality and/or efficiency of critical care pharmacy services. These findings support the role of board certification as a modifiable factor to improve patient outcomes and optimize workload in the ICU.

Objective: To characterize genome-wide DNA methylation patterns associated with sepsis using the Infinium Methylation EPIC v2.0 platform and to evaluate the feasibility of pooled methylation profiling in a pilot critical care cohort. Design: Single-center pilot epigenome-wide association study using pooled whole-blood genomic DNA and pool-level bioinformatic analysis. Setting: Academic medical center. Patients: Fifty critically ill adults enrolled within 48 hours of illness onset and 20 healthy controls. Interventions: None. Measurements and Main Results: Critically ill patients required mechanical ventilation and/or vasopressor support. Sepsis was defined according to Sepsis-3 criteria. Seventy individual samples were organized into 14 intended pools of 5 individuals each: 7 sepsis pools, 3 critically ill non-septic pools, and 4 healthy-control pools. One critically ill non-septic pool was excluded because of poor DNA quality, yielding 13 analyzable pools. For the primary pooled comparison, 7 sepsis pools were compared with 6 non-sepsis comparator pools comprising 2 critically ill non-septic and 4 healthy-control pools. After quality control and preprocessing with SeSAMe, 876,094 CpG sites were retained. The initial pool-level screen identified 170,897 candidate differentially methylated regions. Application of stringent secondary filters (false discovery rate <= 1%, absolute delta-beta >= 7.5%, and >= 5 CpGs per region) yielded a high-confidence subset with marked directional skewing, including 155 hypomethylated and 32 hypermethylated regions in sepsis. Differentially methylated region-associated genes were enriched in myeloid leukocyte activation, myeloid leukocyte-mediated immunity, defense response to bacterium, neutrophil granule biology, and hematopoietic cell lineage pathways. Additional signals involved microRNA-associated targets, ribosome biogenesis, RNA processing, long noncoding RNAs, and previously uncharacterized loci. Conclusions: In this pilot pooled EPIC v2.0 study, sepsis was associated with a biologically coherent, predominantly hypomethylated methylation signature enriched in myeloid and host-defense pathways. These findings support the feasibility of pooled methylation profiling for discovery-oriented sepsis biobank studies but should be interpreted as hypothesis-generating given the pool-level design, limited effective sample size, heterogeneous comparator group, and lack of direct validation against individual-level methylation profiles.

Background: Post-operative hypotension and vasoplegia are well recognised following cardiac surgery but remain poorly characterised after major non-cardiac surgery, despite associations with acute kidney injury (AKI), cardiovascular complications, and increased mortality. Dysregulation of the renin angiotensin aldosterone system (RAAS) may underpin haemodynamic instability in this setting, yet data in abdominal surgery are limited. Objectives: The POLECAT (Perioperative delta Renin) study aims to determine whether changes in circulating renin concentration (delta renin) from pre-operative baseline to the early post-operative period are associated with post-operative vasoplegia in patients undergoing major abdominal surgery requiring intensive care admission. Methods: POLECAT is a single-centre, prospective observational study conducted at a UK tertiary referral hospital. Adult patients undergoing planned or emergency abdominopelvic surgery with anticipated intensive care admission are enrolled. Blood samples are obtained pre-operatively, within four hours post-operatively, and on post-operative day one to measure renin and a panel of endothelial, renal, and immune biomarkers. The primary outcome is post-operative vasoplegia, defined as the requirement for a vasopressor infusion at 08:00 on post-operative day one. Secondary outcomes include alternative vasoplegia definitions, AKI (KDIGO criteria), vasopressor burden, organ dysfunction, cardiovascular complications, length of stay, and mortality. Multivariable regression, receiver operating characteristic analyses, and predefined subgroup analyses will be performed, with sensitivity analyses addressing missing data. Conclusions: This study will clarify the relationship between peri-operative RAAS dysfunction and vasoplegia following major abdominal surgery. Findings may support biomarker-guided risk stratification and inform future interventional trials targeting haemodynamic instability in this high-risk population.

Background World Symposium on Pulmonary Hypertension (WSPH) Group 2 pulmonary hypertension (PH) is a clinically integrated phenotype attributed to left heart disease, whereas pre- versus post-capillary classification is operationalized primarily by pulmonary capillary wedge pressure (PCWP). Although current recommendations emphasize contextual interpretation and provocative testing for intermediate PCWP values, the relationship between PCWP-based classification and underlying phenotype has not been systematically evaluated. We aim to quantify phenotype-hemodynamic discordance across the PCWP spectrum and evaluate a staged physiology-guided framework incorporating inhaled nitric oxide (iNO), ventricular geometry, and provocative testing. Methods We studied 1,032 participants from the NHLBI-sponsored PVDOMICS cohort with multidisciplinary adjudicated phenotypes integrating clinical, imaging, physiologic, and hemodynamic data. Stage-specific PCWP thresholds classified pre- versus post-capillary physiology at rest, during iNO, and during provocation (fluid challenge or invasive cardiopulmonary exercise testing [iCPET]). Echocardiographic right ventricular-to-left ventricular (RV/LV) ratio was evaluated as a marker of ventricular interdependence. Restricted cubic spline and staged concordance analyses defined certainty-based PCWP ranges and incremental diagnostic yield. Results Adjudicated Group 2 phenotype was present in 37.0% of participants. Resting PCWP demonstrated good discrimination (AUC 0.86), but substantial bidirectional phenotype-hemodynamic discordance persisted across intermediate PCWP ranges. At a resting PCWP of 12 mmHg, 25% of participants classified as pre-capillary had adjudicated Group 2 PH, whereas at 18 mmHg, 35% classified as post-capillary remained discordant non-Group 2. Concordance did not approach 90% until PCWP values were <9 mmHg or >24 mmHg. Dynamic testing incrementally improved concordance within these overlap zones. Nearly half of adjudicated Group 2 PH participants (46.5%) were not identified by resting PCWP alone; incorporation of iNO and provocative testing increased cumulative Group 2 identification by 63.4% and improved sensitivity from 79.9% to 83.7%. Model discrimination improved from an AUC of 0.863 to 0.908 (likelihood-ratio P<0.001). iNO increased PCWP in discordant Pre/G2 participants, unmasking latent left-sided limitation, while lowering PCWP in discordant Post/NonG2 participants, consistent with ventricular interdependence. RV/LV ratio [&ge;]0.94 reduced discordant Post/NonG2 classification by 70.5%, and incorporation of PCWP/cardiac output slope improved physiologic specificity during exercise. Conclusions Group 2 PH is a dynamic, load-dependent phenotype inadequately characterized by resting PCWP alone. Intermediate PCWP values represent continuous probabilities of bidirectional discordance rather than discrete diagnostic states. A staged physiology-guided approach integrating iNO, ventricular geometry, and provocative testing improves concordance between hemodynamic classification and clinically integrated phenotype assignment.

Objective: To develop, calibrate, and interpret machine learning models for predicting in-hospital mortality among intensive care unit (ICU) patients using clinical data collected during the first 24 hours of admission. Methods: We analyzed 53,866 adult ICU admissions from the MIMIC-IV (v2.2) database, including 5,787 in-hospital deaths (10.7%). An enhanced feature-engineering pipeline generated 88 laboratory-based features that captured distributional characteristics, temporal trends, and measurement frequency. Five machine learning classifiers were evaluated: L2-regularized logistic regression, random forest, XGBoost, LightGBM, and a calibrated soft-voting ensemble. Models were developed using a stratified 64:8:8:20 split for training, validation and hyperparameter tuning, calibration, and testing. Performance was assessed on a held-out test set (n = 10,774) using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), Brier score, calibration analysis, decision curve analysis (DCA), and SHAP-based model interpretation. Results: The calibrated ensemble achieved the best overall performance, with an AUROC of 0.856 (95% CI: 0.846-0.867), an AUPRC of 0.449 (95% CI: 0.418-0.480), and a Brier score of 0.078. XGBoost (AUROC 0.856; AUPRC 0.435) and LightGBM (AUROC 0.854; AUPRC 0.436) demonstrated performance comparable to the ensemble and significantly outperformed logistic regression (AUROC 0.823; AUPRC 0.376), yielding absolute AUROC improvements of approximately 0.031-0.033 (p < 0.001). Calibration substantially improved probabilistic predictions, reducing Brier scores by 42% for XGBoost (0.134 to 0.078) and 50% for LightGBM (0.151 to 0.076). Decision curve analysis demonstrated consistent net clinical benefit across the 5%-20% risk-threshold range. Key predictors included age, blood urea nitrogen, ICU subtype, measurement frequency, and lactate-related features. Model performance remained robust across ICU subtypes, with AUROC values exceeding 0.79. Conclusion: A calibrated and interpretable machine learning framework based on early ICU clinical data provides accurate and clinically actionable mortality risk estimates. By integrating trajectory-aware feature engineering, probabilistic calibration, and decision-analytic evaluation, this approach advances ICU mortality prediction toward more reliable and trustworthy clinical decision support systems.

Background: The Functional Outcome in Patients with Primary Intracerebral Hemorrhage (FUNC) score was initially validated for prediction of functional independence on the Glasgow Outcome Scale (GOS) 90 days after intracerebral hemorrhage (ICH), but recovery often extends beyond three months. Aims: Our objective was to extend the FUNC score for prediction of 12-month functional independence to strengthen its utility for family counseling and research methodology. Methods: We conducted a single-center prospective cohort study enrolling adult patients with primary ICH between February 2009 and January 2018. We calculated FUNC scores at admission and assessed GOS 12 months after ICH. The primary outcome was 12-month functional independence, defined as a GOS score [&ge;]4. We calculated the area under the receiver operating characteristic curve (AUC) of the FUNC score using logistic regression, handling missing GOS with multiple imputation by chained equations. We evaluated score calibration using a calibration curve and the Brier score, and we assessed clinical utility using decision curve analysis. We explored the statistical efficiency gains of using FUNC-based sliding dichotomy thresholds for favorable outcome definitions by running simulations of a clinical trial with 1:1 randomization. We ran 5000 simulations for each sample size (100 to 1000, in increments of 10) and treatment effect (odds ratio of 1.5, 2.0 and 2.5) combination and calculated efficiency gains for each respective treatment effect as the percentage reduction in sample size required to have 80% power using sliding versus fixed dichotomy thresholds. Results: A total of 535 patients were included (median [IQR] age 68 [54-79], 237 [44%] female, median [IQR] NIHSS 16 [6-25], median [IQR] FUNC 8 [6-9]). Overall, 99 of 445 (22%) patients with known 12-month GOS achieved functional independence. The FUNC score had an AUC of 0.79 (95%-CI: 0.75-0.84) for 12-month functional independence. The calibration plot was reasonable, with modest evidence of overestimation at low predicted probabilities, and the Brier score was 0.15. A net benefit was observed across 5-50% threshold probabilities. Sliding dichotomy had an efficiency gain of 27% for a treatment effect of OR=2.0, and a gain of 22% for a treatment effect of OR=2.5. The efficiency gain for a treatment effect of OR=1.5 could not be calculated because the fixed dichotomy did not reach 80% power despite a sample size of 1000 patients. Conclusions: The FUNC score's predictive performance for 12-month functional independence was comparable to its originally validated 3-month discrimination. Following external validation across centers, the FUNC score may be leveraged to counsel families on global measures of long-term functional independence and to implement sliding dichotomy methodology in ICH research.

Rationale: Conventional measures of obstructive sleep apnea severity, particularly the apnea-hypopnea index, do not adequately capture event-level neurophysiologic responses to respiratory events. Whether post-apnea/hypopnea arousal dynamics provide prognostic information beyond established metrics remains unknown. Objectives: To determine whether post-apnea/hypopnea arousal dynamics are associated with all-cause and cardiovascular mortality. Methods: We conducted a retrospective analysis of in-home polysomnography data from 8,053 adults across four community-based cohorts. Peak time (PT; latency to maximal arousal probability), peak height (PH; maximal arousal probability), and area under the curve (AUC; cumulative arousal probability) were derived from peri-stimulus time histograms aligned to event termination. Associations with mortality were examined using multivariable Cox models and random-effects meta-analysis. Measurements and Main Results: PT, but not PH or AUC, was associated with mortality. In pooled analyses, each 1-second delay in PT was associated with higher all-cause mortality in males (hazard ratio [HR], 1.04; 95% confidence interval [CI], 1.02-1.06) and females (HR, 1.03; 95% CI, 1.00-1.06). For cardiovascular mortality, each 1-second delay in PT was associated with higher risk in males (HR, 1.05; 95% CI, 1.02-1.08) but not females (HR, 1.04; 95% CI, 0.99-1.10). Associations were driven primarily by non-rapid eye movement sleep and remained materially unchanged after additional adjustment for apnea-hypopnea index, arousal index, and hypoxic burden. Conclusions: Delayed arousal timing after apnea/hypopnea termination was associated with increased mortality risk independent of conventional measures of obstructive sleep apnea severity. Event-level arousal timing may provide prognostic information beyond count-based and hypoxemia-based metrics.

High-frequency physiological monitoring in ICUs can identify impending deterioration hours before clinical recognition yet extracting reliable early-warning signals from noisy vital-sign streams remains challenging. We present SIgnose, an interpretable prediction framework for early detection of abnormal shock index (SI), built from routinely monitored vital signs using physiologic variability and nonlinear time-series features. SIgnose was developed on the eICU Collaborative Research Database and externally validated on the MIMIC-III adult database and a pediatric SafeICU cohort (AIIMS New Delhi), with additional prospective validation in the pediatric ICU. We benchmarked three representation strategies: (i) engineered physiologic variability and nonlinear time-series features, (ii) deep learning, and (iii) Llama-3.1-8B embeddings with low-rank adaptation. Physiologic variability features consistently demonstrated superior cross-cohort generalization. The final model used 3,970 features from five vital signs to predict abnormal SI up to 8 hours ahead, achieving AUROC 0.861 (95% CI 0.859-0.863) and AUPRC 0.927 (95% CI 0.925-0.929) on eICU. External validation yielded AUROC 0.870 (95% CI 0.863-0.876) and AUPRC 0.935 (95% CI 0.930-0.940) on MIMIC-III, and AUROC 0.875 (95% CI 0.863-0.888) and AUPRC 0.915 (95% CI 0.898-0.930) on SafeICU; prospective pediatric validation (n = 88) achieved AUROC 0.885 (95% CI 0.868-0.902) and AUPRC 0.911 (95% CI 0.882-0.936). SHAP interpretability analysis identified heart rate variability, respiratory trend dynamics, and multi-scale blood pressure variability as key early-warning signatures. These findings establish SIgnose as a reproducible, low-compute, early-warning framework and demonstrate that physiologic variability features provide robust, generalizable representations for early deterioration detection across adult and pediatric critical care.

Background: Apnoea of prematurity is common and may cause desaturation and/or bradycardia. There is marked variability in infants cardiorespiratory responses to apnoea, despite standardised clinical thresholds. Factors influencing apnoea-related cardiorespiratory instability and whether instability can be predicted warrant investigation. Methods: 181,511 apnoeas >5 seconds were identified from continuous physiological recordings from 146 preterm infants <37 weeks postmenstrual age. Cardiorespiratory instability was defined as bradycardia (>30% heart rate reduction) and/or oxygen desaturation (<85%). Mixed-effects models assessed clinical, demographic and dynamic modulators of the relationship between apnoea duration and cardiorespiratory instability. Machine learning (XGBoost) was used to train models to predict apnoea-related cardiorespiratory instability. Results: Longer duration apnoeas were associated with increased instability, although variability was substantial and 3.6% of apnoeas <10 seconds were associated with cardiorespiratory instability, while 61.2% of apnoeas [&ge;]20 seconds were not. Multiple clinical/demographic (postmenstrual and gestational age, sex, weight z-score, and ventilation mode) and dynamic (baseline heart rate, oxygen saturation, and recent apnoea clustering) factors were associated with increased instability risk. Apnoea-related cardiorespiratory instability could be predicted with a balanced test accuracy of 75.8% when incorporating all features, while a model using only clinical/demographic features achieved 66.0%. Conclusions: Multiple factors influence cardiorespiratory responses to apnoea. Predictive modelling may enable personalised apnoea definitions, improving individualised care.

Intensive Care Unit (ICU) readmissions are associated with adverse clinical outcomes and increased healthcare costs. Although existing models for predicting 30-day ICU readmission show high predictive performance, they fail to account for model uncertainty, potentially resulting in overconfident and unreliable decision-making. We propose a novel Ensemble Bayesian Model Averaging (EBMA)-based framework which balances predictive discrimination with uncertainty by penalizing models that are confident but incorrect. It achieved excellent calibration (Brier score = 0.051), while maintaining discriminatory performance comparable to or exceeding that of the best individual models (AUROC > 0.716). These findings suggest that our EBMA-based framework provides a more robust and clinically reliable approach for ICU readmission prediction and decision support.

Importance: Severe stroke is a leading cause of death and disability worldwide. Survivors and their families face long-term unmet needs, including care that does not reflect patients' values, fragmented care, and high rates of psychological distress among caregivers. Objective: To describe the conceptual framework of the longitudinal transdisciplinary neuropalliative care support (LOTUS) intervention and assess its fidelity in a pilot feasibility study. Design: Pilot feasibility randomized study; fidelity was assessed using weekly checklists completed by the LOTUS nurse and qualitative analysis of weekly LOTUS team meeting transcripts. Setting: Single comprehensive stroke center in Western New York. Participants: Patients hospitalized with severe stroke and their caregivers. Dyads were randomized to usual care or intervention. Intervention: The LOTUS intervention is implemented in a stepped-care fashion using 5 strategies: Awareness, Assistance, Adjustment, Acceptance and Alignment (5As). Led by a specially trained nurse with a chaplain, social worker, psychologist, and neuropalliative care physician, the LOTUS team follows dyads from early in the hospital course through 6 months. Main Outcomes and Measures: Fidelity, the degree to which the intervention was delivered as intended, assessed via (1) utilization of 5A activities from weekly LOTUS checklists; (2) thematic analysis of weekly LOTUS team meeting transcripts. Results: Of 26 patients in the trial, 13 were randomized to intervention. The LOTUS nurse completed 108 checklists, with an average of 619 minutes of direct contact per participant over 6 months. Each component of the 5A's was utilized. Awareness and Assistance predominated early after enrollment and revolved around personhood, support, and self-efficacy. Adjustment was especially relevant during care transitions and was typically supported by the LOTUS social worker. Acceptance and Alignment were more prevalent during later meetings, with the LOTUS psychologist supporting identification and modeling of coping skills and the LOTUS physician guiding prognosis and goals-of-care conversations. The LOTUS nurse served as primary point of contact, providing continuity and a trusting relationship, while other team members functioned in a predominantly advisory role. Conclusions: The LOTUS intervention was delivered with fidelity to the 5A-framework, supporting a future randomized clinical trial to evaluate its efficacy in patients with severe stroke and their caregivers.