Traumatic brain injury (TBI) is a significant health problem with a high mortality rate. Inflammatory markers can predict the prognosis of TBI where neuroinflammation is essential. In this study, the prognostic value of the systemic immune-inflammation index (SII), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR) at admission in patients with critical TBI was investigated. Patients with moderately severe TBI in the intensive care unit (ICU) of a tertiary center between June 2020 and June 2022 were retrospectively reviewed. Patients were classified into survivor and mortality groups. The predictive performance of SII, PLR, and NLR levels calculated from blood results at admission and 28-day mortality and patient outcomes were analyzed. One hundred sixty-one patients were included in this study. The median age of the entire population was 41 (18–90) years, and 80.7% (n = 130) of the patients were male. Falls (42.2%) and traffic accidents (40.4%) were the most common causes of TBI. The most common primary diagnoses in patients with TBI were acute subdural hematoma (30.4%) and subarachnoid hemorrhage (26.1%). The SII and NLR levels were significantly higher in the mortality group, and PLR levels were significantly lower (P = .004, P < .001, P < .001, respectively). In multivariate regression analysis, SII and PLR were independent predictors of mortality (P = .031 and P < .001, respectively). In the receiver operating characteristics (ROC) curve analysis, the cutoff value for SII was ≥ 2951, and the area under the curve (AUC) was 0.662 (95% CI, 0.540–0.784). The cutoff value for NLR was ≥ 9.85, AUC was 0.717 (95% CI, 0.600–0.834), and the cutoff value for PLR was ≤ 130.4, AUC was 0.871 (95% CI, 0.796–0.947). 28-day mortality was 21.1%. Neuroinflammation is essential in patients with critical TBI, and inflammatory markers SII, NLR, and PLR have prognostic importance. SII and PLR are independent predictors of mortality. Early detection of those with a poor prognosis in critically ill TBI patients and planning aggressive treatments may contribute to reducing mortality.

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The incidence of postoperative morbidity and mortality in hip fracture patients is high and is associated with nutritional deficiencies. This study investigated the predictive value of preoperative prognostic nutritional index (PNI) on postoperative intensive care unit (ICU) requirement and mortality in geriatric hip fracture patients. Geriatric (≥65 years old) hip fracture patients who underwent surgery between January 2021 and September 2023 were evaluated retrospectively. Patients were classified according to the unit followed in the postoperative period (service group and ICU group) and 28-day mortality (mortality group and survivor group). The predictive value of PNI for ICU requirement and mortality and the factors affecting ICU requirement and mortality were investigated. The study included two hundred twenty-two patients, and 66.2% (n=147) were women. In the postoperative period, 47.7% (n=106) of the patients were followed in the ICU and 52.3% (n=116) in the inpatient service. The 28-day mortality of the patients was 6.8% (n=15). PNI was found to be significantly lower in patients followed in the ICU (group ICU) than in those followed in the service (group S) and in patients who died (group mortality) compared to those who lived (group survivor) (p<0.001 and p=0.029, respectively). In multivariate regression analysis, high American Society of Anesthesiologists (ASA) status and low PNI were determined to be independent risk factors for ICU requirement. Acute Physiology and Chronic Health Assessment II score was an independent predictor of mortality. In ROC curve analysis, the cut-off value of PNI in predicting mortality was 32.5, and the area under the curve was 0.660 (95% CI, 0.516–0.803). In geriatric hip fracture patients, preoperative PNI value can be used, like ASA status, in determining postoperative ICU requirements. Nutritional deficiencies are associated with adverse postoperative outcomes in this patient group, and low PNI values (<32.5) help predict in-hospital mortality.

This study aimed to evaluate the compliance with guidelines, clinical safety, and applicability of ChatGPT-5 responses in obstetric and gynecological emergency scenarios. With the increasing role of AI-powered large language models (LLMs) in healthcare, there is a need to examine their performance in obstetric emergencies systematically. This study was designed as a prospective, scenario-based, double-blind study. A total of 15 obstetric and gynecologic emergency scenarios were created based on the literature and current international guidelines (ACOG, RCOG, WHO). Five standard questions were posed to ChatGPT-5 for each scenario: (1) Most likely diagnosis, (2) Investigations to confirm the diagnosis, (3) Hemodynamic stability assessment, (4) Initial treatment approach, and (5) Advanced management options. The same scenarios were independently answered by two obstetricians, an emergency medicine specialist, and an anesthesiologist, and were considered the “gold standard.” Responses were scored for guideline compliance, patient safety, and critical information gaps. In addition, quality and understandability were evaluated with modified DISCERN (mDISCERN), Global Quality Score (GQS), and readability indexes [Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), Coleman–Liau Index (CLI)]. A total of 75 responses were reviewed. High agreement (5/5) was observed in 5 scenarios (33.3%), moderate agreement (4/5) in 7 scenarios (46.7%), and low agreement (≤ 3/5) in 3 scenarios (20.0%). High agreement was particularly evident for well-defined guideline algorithms, such as postpartum hemorrhage, eclampsia, HELLP syndrome, shoulder dystocia, and ruptured ectopic pregnancy. Deficiencies in moderate agreement scenarios included insufficient emphasis on mortality risk, omission of scoring systems, incomplete steps in sepsis management, and inadequate specification of fertility-sparing approaches. Low agreement scenarios included severe vaginal hemorrhage, acute bleeding due to malignancy, and traumatic gynecologic emergencies. The mean mDISCERN score of the responses was 4.0 ± 0.7, and the mean GQS was 4.1 ± 0.7. Readability analyses showed that responses contained a moderate amount of technical language (FRES score = 40.5 ± 2.5; FKGL score = 11.6 ± 1.2; SMOG score = 10.9 ± 0.8; and CLI score = 10.9 ± 0.8). The mean lexical density was 0.63. ChatGPT-5 generally produced moderate to good guideline-compliant and confident responses in obstetric and gynecological emergency scenarios. However, its performance was limited in complex cases requiring a multidisciplinary approach. The findings suggest that AI-powered large language models can be a complementary tool in obstetric emergency management, but should not be used alone without expert clinician supervision. Larger, comparative, and multidisciplinary studies will provide more reliable evidence for the clinical integration of these technologies.

Lactate is a product of anaerobic metabolism used to determine prognosis in critically ill trauma patients. This study investigates the mortality-predictive performance of lactate, lactate clearance, and lactate-to-albumin ratio (LAR) on admission in patients with polytrauma in a tertiary center’s intensive care unit (ICU). Polytrauma patients in the ICU between June 2019 and June 2022 were evaluated. The diagnosis of polytrauma was made according to the Berlin criteria, a widely accepted and comprehensive system for classifying the severity of multiple injuries. Patients were classified into survivor and mortality groups. The predictive performance of lactate, lactate clearance (24th hour), and LAR for 28-day mortality was compared. The study included 176 patients. The median age of the entire population was 35 (24–50) years, and 78.4% (n = 138) were male. Motor vehicle accidents were the most common cause of polytrauma in patients (48.9%, n = 86). The most common head injuries were detected in the patients (59.1%, n = 104). In the mortality group, median lactate and lactate (24th hour) levels were significantly higher (P < .001). Median albumin and LAR values were significantly lower (P < .001). Although 24-hour lactate clearance was lower in the mortality group, no significant difference was detected (36.1% vs 42.3%, P = .052). In multivariate regression analysis, LAR was an independent predictor of mortality (P < .001). In receiver operating characteristics curve analysis, the cutoff value of lactate was ≥5.4, the area under the curve (AUC) was 0.75 (95% confidence interval [CI], 0.66–0.84), the cutoff value of lactate clearance was ≤39.2, AUC was 0.60, (95% CI, 0.51–0.69), and the cutoff value of LAR was value ≥1.50, AUC 0.83 (95% CI, 0.75–0.90). In critically ill polytrauma patients, LAR on ICU admission is an independent predictor of mortality and has acceptable prognostic value. LAR is superior to lactate and 24-hour lactate clearance in predicting mortality.

BACKGROUND: Although obstetric morbidity and mortality have decreased recently, rates are still high enough to constitute a significant health problem. With the COVID-19 pandemic, many obstetric patients have required treatment in intensive care units (ICU). OBJECTIVES: Evaluate critical obstetric patients who were treated in an ICU for COVID-19 and followed up for 90 days. DESIGN: Medical record review SETTING: Intensıve care unit PATİENTS AND METHODS: Obstetric patients admitted to the ICU between 15 March 2020 and 15 March 2022 and followed up for at least 90 days were evaluated retrospectively. Patients with and without COVID-19 were compared by gestational week, indications, comorbidities, length of stay in the hospital and ICU, requirement for mechanical ventilation, blood transfusion, renal replacement therapy (RRT), plasmapheresis, ICU scores, and mortality. MAIN OUTCOME MEASURES: Clinical outcomes and mortality. SAMPLE SIZE AND CHARACTERISTICS: 102 patients with a mean (SD) maternal age of 29.1 (6.3) years, and median (IQR) length of gestation of 35.0 (7.8) weeks. RESULTS: About 30% (n=31) of the patients were positive for COVID-19. Most (87.2%) were cesarean deliveries; 4.9% vaginal (8.7% did not deliver). COVID-19, eclampsia/preeclampsia, and postpartum hemorrhage were the most common ICU indications. While the 28-day mortality was 19.3% (n=6) in the COVID-19 group, it was 1.4% (n=1) in the non-COVID-19 group ( P <.001). The gestational period was significantly shorter in the COVID-19 group ( P =.01) while the duration of stay in ICU ( P <.001) and mechanical ventilation ( P =.03), lactate ( P =.002), blood transfusions ( P =.001), plasmapheresis requirements ( P =.02), and 28-day mortality were significantly higher ( P <.001). APACHE-2 scores ( P =.007), duration of stay in ICU ( P <.001) and mechanical ventilation ( P <.001), RRT ( P =.007), and plasmapheresis requirements ( P =.005) were significantly higher in patients who died than in those who were discharged. CONCLUSION: The most common indication for ICU admission was COVID-19. The APACHE-2 scoring was helpful in predicting mortality. We think multicenter studies with larger sample sizes are needed for COVID-19 obstetric patients. In addition to greater mortality and morbidity, the infection may affect newborn outcomes by causing premature birth. LIMITATIONS: Retrospectıve, single-center, small population size. CONFLICT OF INTEREST: None.