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The role of lactate measurement in out-of-hospital cardiac arrest (OHCA) survivors remains controversial. We assessed the association between early lactate-related variables, OHCA characteristics, and long-term neurological outcome. In OHCA patients who received targeted temperature management, lactate levels were measured at 0, 12, and 24 h after the return of spontaneous circulation. We calculated lactate clearance and time-weighted cumulative lactate (TWCL), which represent the area under the time-lactate curve. The area under the receiver operating characteristic curve (AUC) and the adjusted odds ratios (AORs) of lactate-related variables for predicting 6-month poor outcome (Cerebral Performance Category 3–5) were evaluated. Interactions between lactate variables and characteristics of OHCA were evaluated by a multivariable logistic model with interaction terms and subgroup analysis. A total of 347 OHCA patients were included. After adjustment, higher lactate levels at the three time points were associated with a poor outcome (AOR 1.10 [95% CI, 1.03–1.18], AOR 1.15 [95% CI, 1.02–1.29], and AOR 1.36 [95% CI, 1.15–1.60], respectively), while TWCL was the only lactate kinetics variable associated with a poor outcome (AOR 1.29 [95% CI, 1.12–1.49]). We identified several interactions between lactate-related variables and OHCA characteristics. In particular, the AUC of TWCL was excellent in cases of noncardiac etiology (AUC 0.92 [95% CI, 0.86–0.96] but only moderate in cardiac etiology (AUC 0.69 [95% CI, 0.62–0.75]). Early lactate levels, especially at 24 h, and TWCL were independent predictors of neurologic outcome in these patients, whereas lactate clearance was not. The prognostic ability of lactate-related variables varied depending on the OHCA characteristics.

The coronavirus disease 2019 (COVID-19) pandemic situation is a state that has had a great impact on the medical system and society. To respond to the pandemic situation, various methods, such as a pre-triage system, are being implemented in the emergency medical field. However, there are insufficient studies on the effects of this pandemic situation on patients visiting the emergency department (ED), especially those with cardio/cerebrovascular diseases (CVD)1 classified as time-dependent emergencies. We performed a retrospective analysis of a cohort of patients from April 2020 to December 2020 (April 2020 was when the pre-triage system was established) compared to a parallel comparison patient cohort from 2019. The primary outcome was in-hospital mortality. CVD was defined by the patient's final diagnosis. During the same period, the number of patients who had visited the ED after COVID-19 had decreased to 79.1% of the number of patients who had visited the ED before COVID-19. The overall patient mortality and the mortality in the patients cardiovascular disease had both increased, while the mortality from cerebrovascular disease did not increase. Meanwhile, the ED length of stay had increased in all patients but did not increase in the patients with cardiovascular disease. As with prior studies conducted in other regions, in our study, the total number of ED visits were decreased compared to before COVID-19. The overall mortality had increased, particularly in the patients with cardiovascular disease.

Out-of-hospital cardiac arrest (OHCA) outcomes are unsatisfactory despite postcardiac arrest care. Early prediction of prognoses might help stratify patients and provide tailored therapy. In this study, we derived and validated a novel scoring system to predict hypoxic-ischemic brain injury (HIBI) and in-hospital death (IHD). We retrospectively analyzed Korean Hypothermia Network prospective registry data collected from in Korea between 2015 and 2018. Patients without neuroprognostication data were excluded, and the remaining patients were randomly divided into derivation and validation cohorts. HIBI was defined when at least one prognostication predicted a poor outcome. IHD meant all deaths regardless of cause. In the derivation cohort, stepwise multivariate logistic regression was conducted for the HIBI and IHD scores, and model performance was assessed. We then classified the patients into four categories and analyzed the associations between the categories and cerebral performance categories (CPCs) at hospital discharge. Finally, we validated our models in an internal validation cohort. Among 1373 patients, 240 were excluded, and 1133 were randomized into the derivation (n = 754) and validation cohorts (n = 379). In the derivation cohort, 7 and 8 predictors were selected for HIBI (0–8) and IHD scores (0−11), respectively, and the area under the curves (AUC) were 0.85 (95% CI 0.82–0.87) and 0.80 (95% CI 0.77–0.82), respectively. Applying optimum cutoff values of ≥6 points for HIBI and ≥7 points for IHD, the patients were classified as follows: HIBI (−)/IHD (−), Category 1 (n = 424); HIBI (−)/IHD (+), Category 2 (n = 100); HIBI (+)/IHD (−), Category 3 (n = 21); and HIBI (+)/IHD (+), Category 4 (n = 209). The CPCs at discharge were significantly different in each category (p < 0.001). In the validation cohort, the model showed moderate discrimination (AUC 0.83, 95% CI 0.79–0.87 for HIBI and AUC 0.77, 95% CI 0.72–0.81 for IHD) with good calibration. Each category of the validation cohort showed a significant difference in discharge outcomes (p < 0.001) and a similar trend to the derivation cohort. We presented a novel approach for assessing illness severity after OHCA. Although external prospective studies are warranted, risk stratification for HIBI and IHD could help provide OHCA patients with appropriate treatment.

Appropriate decision of emergency department (ED) disposition is essential for improving the outcome of elderly urinary tract infection (UTI) patients. However, studies on early return visit (ERV) to the ED in elderly UTI patients are limited. Therefore, we aimed to identify factors for ERV and hospitalization after return visit (HRV) in this population. Elderly patients discharged from the ED with International Classification of diseases 10th Revision codes of UTI were selected from the registry for evaluation of ED revisit in 6 urban teaching hospitals. Retrospective data were extracted from the electronic medical records and ERV and hospitalization to scheduled revisit (SRV) were compared. Among a total of 419 patients found in the study period, 45 were ERV patients and 24 were HRV patients. Absence of UTI-specific symptoms (odds ratio [OR] 2.789; 95% confidence interval [CI] 1.368–5.687; P = 0.005), C-reactive protein (CRP) levels >30 mg/L (OR 2.436; 95% CI 1.017–3.9; P = 0.024), and body temperature ≥ 38 °C (OR 1.992; 95% CI 1.017–3.9; P = 0.044) were independent risk factors for ERV, and absence of UTI-specific symptoms (OR 3.832; 95% CI 1.455–10.088; P = 0.007), CRP levels >30 mg/L (OR 3.224; 95% CI 1.235–8.419; P = 0.017), and systolic blood pressure ≤ 100 mmHg (OR 3.795;95% CI 1.156–12.462; P = 0.028) were independent risk factors for HRV. However, there was no significant difference in empirical antibiotic resistance in ERV and HRV patients, compared to SRV patients. The independent risk factors of ERV and HRV should be considered for ED disposition in elderly UTI patients; the resistance to empirical antibiotics was not found to affect ERV or HRV within 3 days.

Early prognostication in cardiac arrest survivors is challenging for physicians. Unlike other prognostic modalities, biomarkers are easily accessible and provide an objective assessment method. We hypothesized that in cardiac arrest patients with targeted temperature management (TTM), early circulating microRNA (miRNA) levels are associated with the 6-month neurological outcome. In the discovery phase, we identified candidate miRNAs associated with cardiac arrest patients who underwent TTM by comparing circulating expression levels in patients and healthy controls. Next, using a larger cohort, we validated the prognostic values of the identified early miRNAs by measuring the serum levels of miRNAs, neuron-specific enolase (NSE), and S100 calcium-binding protein B (S100B) 6 h after cardiac arrest. The validation cohort consisted of 54 patients with TTM. The areas under the curve (AUCs) for poor outcome were 0.85 (95% CI (confidence interval), 0.72–0.93), 0.82 (95% CI, 0.70–0.91), 0.78 (95% CI, 0.64–0.88), and 0.77 (95% CI, 0.63–0.87) for miR-6511b-5p, -125b-1-3p, -122-5p, and -124-3p, respectively. When the cut-off was based on miRNA levels predicting poor outcome with 100% specificity, sensitivities were 67.7% (95% CI, 49.5–82.6), 50.0% (95% CI, 32.4–67.7), 35.3% (95% CI, 19.7–53.5), and 26.5% (95% CI, 12.9–44.4) for the above miRNAs, respectively. The models combining early miRNAs with protein biomarkers demonstrated superior prognostic performance to those of protein biomarkers.

Background and Objectives: Static prognostication in comatose out-of-hospital cardiac arrest (OHCA) survivors may overlook delayed recovery, risking premature withdrawal of life-sustaining therapy (WLST). This study aimed to identify distinct longitudinal phenotypes of consciousness recovery and determine the prevalence and characteristics of the Late Awakener phenotype. Materials and Methods: We applied K-means clustering to serial Glasgow Coma Scale motor scores (0, 24, 48, 72 h, Day 5) in 417 adult OHCA survivors treated with targeted temperature management at Seoul St. Mary’s Hospital (2009–2023). Results: Three distinct phenotypes emerged: Early Awakeners (n = 86, 20.6%), Late Awakeners (n = 54, 12.9%), and Non-Awakeners (n = 277, 66.4%). While Early Awakeners had 96.5% good neurological outcomes at 6 months, 79.6% of Late Awakeners also achieved good outcomes despite being indistinguishable from Non-Awakeners at 48 h (mean GCS motor score ≤ 2). Late Awakeners had significantly higher rates of shockable rhythms (72.2% vs. 21.3%, p < 0.001) compared to Non-Awakeners. Conclusions: The identification of a Late Awakener phenotype—comprising 13% of the cohort and one-third of all survivors with good outcomes—challenges early prognostic pessimism. An extended observation window of at least 5–7 days may be warranted for patients with shockable rhythms to avoid premature WLST, even when early motor responses are absent.

Background: Current guidelines recommend prognostication at 72 h after cardiac arrest, yet a subset of patients (Late Awakeners) recover consciousness after this window. This study investigated diagnostic markers to distinguish Late Awakeners from those with permanent poor outcomes (Non-Awakeners) to prevent premature withdrawal of life-sustaining therapy. Methods: We analyzed adult OHCA patients treated with TTM from 2009 to 2019 who remained comatose (Glasgow Coma Scale Motor score < 6) at 72 h. Patients were categorized as Late Awakeners (obeyed commands > 72 h) or Non-Awakeners. The diagnostic performance of maximal Neuron-Specific Enolase (NSE) levels within 72 h and brainstem reflexes was assessed using receiver operating characteristic (ROC) analysis. Model calibration was evaluated using the Hosmer–Lemeshow test, and internal validation was performed using bootstrap resampling. Results: Of 213 patients comatose at 72 h, 20 (9.4%) were identified as Late Awakeners. The median time to awakening was 4.4 days (IQR 3.4–8.3) from ROSC. Late Awakeners exhibited significantly preserved corneal reflexes (85.0% vs. 20.2%) compared to Non-Awakeners. The optimal NSE cut-off value to predict late awakening was <89.5 ng/mL (Sensitivity 95.0%, Specificity 50.3%, AUC 0.801). A multimodal approach combining NSE < 90 ng/mL and preserved corneal reflexes achieved a high specificity of 93.2% and an AUC of 0.899 (optimism-corrected: 0.896) for predicting late recovery. At six-month follow-up, 74.3% of Late Awakeners achieved good neurological outcome (CPC 1–2). Conclusions: Approximately 9% of patients comatose at 72 h eventually regain consciousness with favorable long-term outcomes. A multimodal diagnostic model combining intermediate NSE thresholds and preserved brainstem reflexes can effectively identify these Late Awakeners, suggesting that observation should be extended for patients fitting this profile.

Undertriage—the misclassification of acutely ill patients into low-acuity triage categories—is a persistent patient safety concern, and prior machine learning approaches restricted to vital signs have yielded modest predictive performance. We hypothesized that this ceiling reflects feature restriction rather than an inherent predictive barrier. In this retrospective cohort study of 10,792 adult patients (age ≥ 18) initially triaged as Korean Triage and Acuity Scale (KTAS) level 4 or 5 across two tertiary academic centers during 2025, the primary outcome was triage reclassification—change from initial KTAS 4/5 to final KTAS 1–3 (n = 941; 8.7%). Five nested feature sets of increasing breadth were compared using logistic regression (LR) and gradient-boosting classifiers (GBC). Calibration (slope, intercept, Brier score), sensitivity/specificity/positive and negative predictive values at operating thresholds of 3%, 5%, and 10%, and decision-curve net benefit were evaluated on a held-out test partition. NEWS alone yielded an AUROC of 0.58, whereas the full triage-time panel (Set E; 43 features) achieved a GBC AUROC of 0.72 (95% CI 0.68–0.76; 5-fold CV 0.73 ± 0.02) and an AUPRC of 0.23, approximately doubling the NEWS baseline (0.12). The model was well calibrated, with a Brier score of 0.075, a calibration slope of 0.85 (95% CI 0.70–1.01), and an intercept of −0.30 (95% CI −0.65 to 0.07); both intervals included the ideal values of 1 and 0, indicating that predicted probabilities can be interpreted as approximate absolute event likelihoods. At a 5% operating threshold, sensitivity was 0.79, capturing 79% of reclassifications while flagging 53% of the cohort. Decision curve analysis demonstrated positive net clinical benefit across thresholds of 3–20%, exceeding both a vital-signs-only model and the treat-all/treat-none baselines. Feature importance analysis identified pain score, onset-to-arrival time, heart rate, systolic blood pressure, and age as the dominant predictors. Contextual variables routinely documented at triage—particularly pain score and onset-to-arrival time—together with heart rate and systolic blood pressure form a discriminative composite that exceeds the performance of vital-signs-only models in the KTAS 4/5 subpopulation. The resulting model is well calibrated and provides positive net clinical benefit across the 3–20% threshold range, supporting its potential role as a secondary screening flag for low-acuity patients warranting clinician re-review. External validation in independent cohorts is needed before clinical deployment.

Background: Emergency department evaluation of suspected urolithiasis increasingly relies on non-contrast CT, yet not all patients require imaging. Existing clinical prediction rules help stratify stone probability, but by converting continuous measurements into fixed binary indicators, they offer little insight into why a particular patient is at risk or how much uncertainty remains after each testing stage—questions that bear directly on individualized diagnostic decisions. Methods: We retrospectively analyzed 1000 ED patients with suspected urolithiasis who underwent non-contrast CT (stone prevalence 85.0%). A gradient boosting classifier was trained on 17 continuous clinical and laboratory features and compared against binary-thresholded counterparts and an established scoring system; the 17-feature model achieved AUC 0.771 (95% CI 0.726–0.813) versus 0.723 (95% CI 0.675–0.771) for the reference score on this cohort (DeLong p = 0.001). Individual predictions were explained using an interventional Shapley value approach, and a Shannon entropy-based framework was applied to quantify the marginal diagnostic contribution of each sequential testing stage. Results: Held-out permutation importance identified red blood cell count on microscopy, age, pain duration, and prior stone history as the most influential predictors. Several features showed non-linear contributions that diverged from conventional binary thresholds: creatinine effect crossed zero near 0.90 mg/dL and pain duration peaked between 2 and 5 h. C-reactive protein, absent from existing scoring systems, emerged as a meaningful negative predictor. Sequential entropy analysis showed that dipstick urinalysis provided the largest marginal information gain among non-history stages (6.1% of prior entropy), while physical examination contributed 2.3%. A prevalence sensitivity analysis projected that the framework’s threshold behavior would differ substantially in lower-prevalence populations, underscoring that the cohort-specific cut-points are not portable decision rules. We therefore position the framework as a reasoning aid that complements clinical judgment and imaging, not as a stand-alone triage tool. Conclusions: Explainable machine learning can address questions that aggregate discrimination metrics cannot: which features drive risk for a given patient, how those effects behave across the continuous measurement range, and how much diagnostic uncertainty each testing stage resolves. The Shapley-based explanations and entropy framework developed here offer a structured approach to individualized diagnostic reasoning in the ED evaluation of suspected urolithiasis, functioning as an interpretive adjunct to, rather than a replacement for, existing clinical tools and CT imaging.

The association of body mass index with outcome in patients treated with targeted temperature management (TTM) after out-of-hospital cardiac arrest (OHCA) is unclear. The purpose of this study was to examine the effect of body mass index (BMI) on neurological outcomes and mortality in resuscitated patients treated with TTM after OHCA. This multicenter, prospective, observational study was performed with data from 22 hospitals included in the Korean Hypothermia Network KORHN-PRO registry. Comatose adult patients treated with TTM after OHCA between October 2015 and December 2018 were enrolled. The BMI of each patient was calculated and classified according to the criteria of the World Health Organization (WHO). Each group was analyzed in terms of demographic characteristics and associations with six-month neurologic outcomes and mortality after cardiac arrest (CA). Of 1,373 patients treated with TTM identified in the registry, 1,315 were included in this study. One hundred two patients were underweight (BMI <18.5 kg/m2), 798 were normal weight (BMI 18.5-24.9 kg/m2), 332 were overweight (BMI 25-29.9 kg/m2), and 73 were obese (BMI ≥ 30 kg/m2). The higher BMI group had younger patients and a greater incidence of diabetes and hypertension. Six-month neurologic outcomes and mortality were not different among the BMI groups (p = 0.111, p = 0.234). Univariate and multivariate analyses showed that BMI classification was not associated with six-month neurologic outcomes or mortality. In the subgroup analysis, the underweight group treated with TTM at 33°C was associated with poor neurologic outcomes six months after CA (OR 2.090, 95% CI 1.010-4.325, p = 0.047), whereas the TTM at 36°C group was not (OR 0.88, 95% CI 0.249-3.112, p = 0.843). BMI was not associated with six-month neurologic outcomes or mortality in patients surviving OHCA. However, in the subgroup analysis, underweight patients were associated with poor neurologic outcomes when treated with TTM at 33°C.