Krewulak KD, Poulin TG, Rana B
… +3 more, Kupsch S, Stelfox HT, Fiest KM
J Crit Care
· 2026 Jun · PMID 41723933
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INTRODUCTION: Research coordinators play a central role in approaching ICU patients and families for research, yet little is known about how factors such as attire influence first impressions and study perceptions. OBJEC...INTRODUCTION: Research coordinators play a central role in approaching ICU patients and families for research, yet little is known about how factors such as attire influence first impressions and study perceptions. OBJECTIVE: We examined patients' and families' perceptions of coordinator attire, its importance, and associations with participant characteristics. METHODS: We conducted a cross-sectional, survey-based study of consecutively admitted ICU patients and family members across four ICUs. The survey included open-and closed-ended questions on attire preferences and factors influencing first impressions. Open-ended responses were analyzed using content analysis to complement quantitative findings. RESULTS: A total of 329 participants completed the survey, most of whom were family members (213/329, 65%) and women (199/329, 60%). Just over half (173/329, 53%) reported that attire influenced their perception of the study, whereas 145/329 (44%) said it did not, and 11/329 (3%) preferred not to say. Smart casual (129/329, 39%) and business casual (98/329, 30%) were the most preferred styles. Family members were more likely than patients to report that neat grooming (OR 2.28, 95% CI 1.25-4.16), good hygiene (OR 3.75, 1.70-8.27), wearing a name tag (OR 2.67, 1.33-5.37), and a study button or lanyard (OR 4.78, 2.22-10.28) positively influenced their impressions. CONCLUSIONS: Research coordinator attire is important to many ICU patients and families, who prefer business or smart casual styles that convey professionalism and approachability. Beyond attire, hygiene, demeanor, communication, and visible identification (e.g., name tags or study badges) strongly shape first impressions.
Lin TL, Chang PH, Lai WH
… +8 more, Chen YJ, Lin YC, Lin YH, Li WF, Liu YW, Wang CC, Chen IL, Chen KF
J Crit Care
· 2026 Jun · PMID 41719611
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BACKGROUND: Patients readmitted to the surgical intensive care unit (SICU) face a high risk of mortality and increased hospital costs. Identifying patients at risk of SICU readmission is crucial. This study aims to devel...BACKGROUND: Patients readmitted to the surgical intensive care unit (SICU) face a high risk of mortality and increased hospital costs. Identifying patients at risk of SICU readmission is crucial. This study aims to develop a machine-learning (ML) model to predict SICU readmission. METHODS: This is a retrospective study based on data collected from the electronic healthcare records of Chang Gung Memorial Hospital. The model development cohort included adult patients admitted to the SICU from July 2020 to December 2022 at the Kaohsiung branch, while the external validation cohort consisted of patients admitted to the SICU from January 2023 to August 2023 at the Linkou branch. Various ML models, including Logistic Regression (LR), Random Forest, Gradient Boosting (GB), Artificial Neural Networks, and Support Vector Machines, were compared to determine the best model. RESULTS: Of the 982 patients in the development cohorts, 68 (6.9%) experienced SICU readmission. The GB model outperformed other methods, achieving an AUROC of 0.82 (95% CI: 0.70-0.93) in the internal validation cohort. Eleven features significantly influence SICU readmission, with the central venous catheter usage days, the pre-ICU stay duration, blood urea nitrogen, and carbapenem usage days ranking as the top four important factors. The GB model also surpasses three previously published traditional logistic regression methods in the external validation cohort, with AUROCs of 0.80 (95% CI: 0.73-0.86), 0.73 (95% CI: 0.63-0.83), 0.70 (95% CI: 0.60-0.79), and 0.65 (95% CI: 0.50-0.73), respectively. CONCLUSION: Machine learning models offer greater accuracy and reliability compared to traditional regression methods when predicting SICU readmission.
Rousseau AF, Cavalier E, Lambermont B
… +6 more, Colson C, Minguet P, Neis-Gilson S, Kuksi E, Esser N, Preiser JC
J Crit Care
· 2026 Jun · PMID 41712972
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BACKGROUND: Dyslipidemia is frequent during critical illness, but its post-ICU evolution remains poorly characterized. This study evaluated changes in total cholesterol and triglyceride levels three months (M3) after ICU...BACKGROUND: Dyslipidemia is frequent during critical illness, but its post-ICU evolution remains poorly characterized. This study evaluated changes in total cholesterol and triglyceride levels three months (M3) after ICU discharge. METHOD: We retrospectively analyzed a prospective cohort of ICU survivors who attended the M3 post-ICU consultation, with available measurements of total cholesterol, triglycerides, and C-reactive protein (CRP) blood levels. M3 lipid levels were compared to those obtained during the first ICU week and, when available, within three months before ICU admission. Hypertriglyceridemia (HTG) and hypocholesterolemia were defined as triglycerides >210 mg/dL (non-fasting) and total cholesterol ≤120 mg/dL, respectively. RESULTS: Among 188 patients, HTG was present in 42 (22.3%) at M3, with median triglycerides of 250 [223-329] mg/dL. In 69% of these patients, HTG occurred de novo. HTG was unrelated to diabetes, BMI, or ICU exposures, and correlated weakly with CRP (rs = 0.15, p = 0.025). Hypocholesterolemia was observed in 23 (12.2%) patients, mostly under lipid-lowering therapy. No significant associations were found between lipid abnormalities and muscle strength or readmissions. In a subgroup of 49 patients with pre-admission blood measurements, total cholesterol was higher at M3 than during or before ICU (p < 0.0001), while triglycerides were higher at M3 than before ICU admission (p = 0.019). CONCLUSION: Three months after ICU discharge, lipid recovery in survivors appears incomplete. While total cholesterol levels tend to normalize, HTG emerges in approximately 20% of the patients as an underrecognized metabolic sequela of critical illness.
J Crit Care
· 2026 Jun · PMID 41655405
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Cardiogenic shock (CS) remains a leading cause of death in intensive cardiac care. Outcomes are limited by delayed recognition of hypoperfusion, heterogeneous phenotypes, and late escalation of therapies. Diagnosis and r...Cardiogenic shock (CS) remains a leading cause of death in intensive cardiac care. Outcomes are limited by delayed recognition of hypoperfusion, heterogeneous phenotypes, and late escalation of therapies. Diagnosis and risk stratification have progressed with the introduction of the SCAI staging system, which provides a common language for clinical severity and guides escalation of care. Echocardiography and invasive hemodynamics remain central for defining ventricular phenotype, detecting mechanical complications, and tailoring therapy. Early activation of multidisciplinary shock teams is increasingly adopted to coordinate rapid assessment and structured management. Treatment focuses on restoring perfusion, correcting the underlying cause, and preventing further organ injury. Norepinephrine is generally preferred as first-line vasopressor, while inotropes, including dobutamine and milrinone, are selected according to physiologic profile rather than theoretical advantages. Mechanical circulatory support (MCS) should be considered early in refractory hypoperfusion, using integrated clinical, metabolic, echocardiographic, and PAC-derived triggers when feasible. Multiorgan support (ventilation, renal replacement therapy, and ECMO-related strategies such as LV unloading/venting) should be aligned with shock trajectory and goals of care. CS management should shift from a "one-size-fits-all" model to an early, phenotype-driven strategy with explicit perfusion targets and timely MCS escalation, supported by shock teams and networks. Emerging biomarkers and machine-learning tools may further improve risk stratification and treatment timing.
Lertussavavivat T, Sriswasdi S, Faisatjatham S
… +13 more, Sukmark T, Peerapornratana S, Lumlertgul N, Mahamitra N, Panaput T, Trongtrakul K, Bhurayanontachai R, Khositrangsikun K, Surasit K, Jonny J, Sengthavisouk N, Tungsanga K, Srisawat N
J Crit Care
· 2026 Jun · PMID 41655404
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BACKGROUND: Machine learning models for predicting acute kidney injury (AKI) prognosis have primarily been developed in resource-rich settings, with limited validation in resource-limited environments. This study applied...BACKGROUND: Machine learning models for predicting acute kidney injury (AKI) prognosis have primarily been developed in resource-rich settings, with limited validation in resource-limited environments. This study applied machine learning techniques to predict in-hospital mortality and major adverse kidney events within 28 days (MAKE-28) among critically ill patients with AKI in Southeast Asia. METHOD: Data were derived from the Southeast Asia AKI cohort, a prospective multicenter study of critically ill patients. Demographic, clinical, and laboratory variables collected at ICU admission were analyzed. Logistic regression, random forest, and extreme gradient boosting (XGBoost) were used to develop prediction models, with recursive feature elimination applied for feature selection. RESULTS: Of 6993 ICU patients, 1650 individuals with AKI were included for analysis. Of these, 778 (47.1%) died during hospitalization and 1204 (73.9%) experienced MAKE-28. The three models demonstrated comparable performance in predicting MAKE-28 and hospital mortality (AUC 0.73-0.76 for MAKE-28 outcome and AUC 0.71-0.75 for hospital mortality). Discrimination ability was moderate, and all machine learning approaches outperformed conventional clinical scores. No difference in performance was observed between logistic regression and more complex machine learning models. CONCLUSION: Machine learning models using routinely available clinical variables may offer useful prognostic information for AKI outcomes in resource-limited settings and outperform traditional scoring systems. External validation is required to confirm generalizability and support clinical implementation.
Kitisin N, Raykateeraroj N, Hikasa Y
… +4 more, Caroli A, Nübel J, Eastwood G, Neto AS
J Crit Care
· 2026 Jun · PMID 41650703
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BACKGROUND: Acute kidney injury (AKI) is common in the intensive care unit (ICU) but is often detected only after creatinine rises or oliguria develops. Although novel biomarkers allow earlier detection, their cost limit...BACKGROUND: Acute kidney injury (AKI) is common in the intensive care unit (ICU) but is often detected only after creatinine rises or oliguria develops. Although novel biomarkers allow earlier detection, their cost limits use. The urine albumin-to-creatinine ratio (uACR) is inexpensive, yet its role in AKI risk stratification remains uncertain. METHODS: In a prospective single-centre cohort of mixed ICU patients, adults with existing AKI or at high risk (modified AKI Risk Based on Creatinine [ARBOC] score ≥ 3) were enrolled. uACR was measured at enrollment (uACR at Time 0) and 24 h (uACR at Time 1). Outcomes included the prevalence and prognostic value of elevated uACR (≥ 3.4 mg/mmol) for incident, progressive, or persistent AKI (> 48 h), and for ≥30% decline in estimated glomerular filtration rate (eGFR) at discharge. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), change in AUC, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision-curve analysis. RESULTS: Of 1010 patients screened, 203 were analysed (89 with and 114 without AKI at enrollm). Elevated uACR was frequent (72% with AKI; 52% without). In AKI patients, high uACR correlated with longer and more persistent AKI. Discrimination for incident AKI was modest (AUC 0.61 and 0.66 for uACR at Time 0 and uACR at Time 1) but higher for persistent AKI (both AUC 0.68). Adding uACR at Time 0 to ARBOC improved reclassification (NRI 0.48; IDI 0.04) and clinical net benefit. CONCLUSIONS: uACR modestly identified incident AKI and more strongly predicted persistent AKI. As a simple biomarker, uACR may serve as a low-cost adjunct to existing ICU risk stratification tools, but requires further validation before consideration for routine clinical use.
Al-Sagban A, Algodi M, Saab O
… +6 more, Al-Obaidi H, Graham H, Abuelazm MT, Krishnan A, Tuhy T, Lammi M
J Crit Care
· 2026 Jun · PMID 41643462
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BACKGROUND: Pulmonary congestion is a prognostic marker for heart failure (HF) morbidity and mortality; however, the current congestion evaluation depends on traditional physical examination, which lacks adequate sensiti...BACKGROUND: Pulmonary congestion is a prognostic marker for heart failure (HF) morbidity and mortality; however, the current congestion evaluation depends on traditional physical examination, which lacks adequate sensitivity. Lung ultrasound (LUS) has been investigated as a more sensitive method to guide decongestion in decompensated HF. METHODS: A systematic review and meta-analysis synthesizing evidence from randomized controlled trials (RCTs) obtained from PubMed, CENTRAL, Scopus, and Web of Science until March 2025. Using Stata MP v. 17, we used the fixed-effects model to report dichotomous outcomes using the risk ratio (RR) and continuous outcomes using the standardized mean difference with a 95% confidence interval (CI). PROSPERO ID: CRD42024620337. RESULTS: Nine RCTs with 1095 patients were included. LUS-guided management significantly decreased the risk of HF hospitalization/all-cause mortality (RR: 0.72, [95% CI 0.56, 0.93], p = 0.01), HF hospitalization (RR: 0.65, [95% CI 0.48, 0.88], p = 0.01), and HF urgent visits (RR: 0.38, [95% CI 0.22, 0.66], p < 0.0001). There was no significant difference between LUS-guided management and standard of care regarding the incidence of hypotension (RR: 1.87, [95% CI 0.56, 6.20], p = 0.31), hypokalemia (RR: 0.93, [95% CI 0.48, 1.82], p = 0.83), hyperkalemia (RR: 0.98, [95% CI 0.62, 1.53], p = 0.91), and acute kidney injury/impaired renal function (RR: 1.08, [95% CI 0.66, 1.77], p = 0.75). CONCLUSION: LUS-guided decongestion was associated with a significant decrease in the risk of HF re-hospitalization and HF urgent visits, with a tolerable safety profile, compared to standard care, with similar rates of hypotension, hypokalemia, hyperkalemia, and AKI.