Aim This study aims to investigate the prognostic value of admission lactate level in predicting 28-day mortality among geriatric patients admitted to the intensive care unit (ICU) and to compare its performance with established severity scores, including APACHE II and SOFA. Materials and methods In this retrospective cohort study, data of patients aged ≥ 65 years who were admitted to a tertiary ICU between December 2024 and June 2025 were analyzed. Patients with ICU stays < 24 h or incomplete records were excluded. Demographic characteristics, comorbidities, admission lactate, APACHE II, and SOFA scores were recorded. The primary outcome was the association between admission lactate and 28-day mortality. Secondary outcomes included ICU length of stay, mechanical ventilation duration, and comparative prognostic accuracy of lactate versus APACHE II and SOFA. Results A total of 241 patients were included. The 28-day mortality rate was 32.8%. Non-survivors had significantly higher admission lactate levels compared to survivors (6.29 ± 5.42 vs. 2.44 ± 1.80 mmol/L, p < 0.001). In multivariable logistic regression, admission lactate remained an independent predictor of mortality after adjustment for age, sex, comorbidity, and APACHE II (OR 1.29, 95% CI 1.09–1.52, p = 0.003). ROC analysis showed that lactate predicted 28-day mortality with an AUC of 0.77, compared to 0.92 for APACHE II and 0.88 for SOFA. Mortality increased across lactate categories: 14.2% (< 2 mmol/L), 27.8% (2–4 mmol/L), and 62.5% (> 4 mmol/L) (p < 0.001). Conclusion Admission lactate is a simple and readily obtainable biomarker that independently predicts short-term mortality in geriatric ICU patients. While not outperforming established severity scores, lactate provides immediate risk stratification at the bedside and may aid clinical decision-making in this vulnerable population.

Background and Objective: Multidrug and carbapenem resistant gram-negative bacilli bloodstream infections cause high mortality in intensive care units (ICUs). Predicting mortality can improve treatment and support end-of-life decisions. This study aimed to develop a machine learning model to predict mortality in ICU patients with these infections. Methods: This retrospective cohort study was conducted at a tertiary care medical center between 2017 and 2023. Adult ICU patients with bloodstream infections caused by multidrug and carbapenem resistant Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii were included. Demographic, clinical, and laboratory data were collected. Mann-Whitney U and Chi-square tests were used to compare the groups. Multivariable analysis with binary logistic regression was used to identify mortality risk factors. Ten machine learning classifiers were evaluated using stratified 5-fold cross-validation, and model predictions were interpreted with SHapley Additive exPlanations (SHAP). Results: 197 patients were included, with a 15-day mortality rate of 48%. The Light Gradient Boosting Machine (LightGBM) classifier showed the best performance, with an AUROC of 0.94, AUPRC of 0.952, accuracy of 0.868, precision of 0.906, recall of 0.822, F1 score of 0.855, Matthews Correlation Coefficient (MCC) of 0.744, and Brier score of 0.131. SHAP analysis revealed coagulopathy, rapid access to antibiotics, septic shock, SOFA score, platelet count, C-reactive protein (CRP) level, and time-related parameters as the most important predictive features. Conclusion: The LightGBM model showed promising results in predicting mortality in ICU patients. This model may support early intervention and assist in complex end-of-life decisions. This study was registered at ClinicalTrials.gov( https://clinicaltrials.gov/ct2/show/NCT06167083 )

Ağrının Tarihçesi Ahmet DÜZGÜN Ağrı ve Genetik Ali İhsan YÜREKLİ Yoğun Bakım Hastalarında Ağrı Biyokimyası Cemal NAS Yoğun Bakımda Ağrı Fizyolojisi Bedih BALKAN Ağrı Mekanizmaları ve Ağrıya Yaklaşım Mehmet ERTARGIN Yoğun Bakımda Ağrı Değerlendirmesi Ali Kendal OĞUZ Yoğun Bakım Ünitesinde Kullanılan Ağrı Değerlendirme Ölçekleri Maşallah ÇAKIRER Yoğun Bakım Hastalarında Ağrı ve Etik Abdulkadir YEKTAŞ Yoğun Bakımda Akut Ağrı Tedavisi ve Analjezikler Kaan KARA Kronik Ağrı Sendromlu Hastanın Yoğun Bakım Ünitesinde Takibi Burhan Sami KALIN Yoğun Bakım’da Ağrı Tedavi Yöntemleri Erhan GÖKÇEK Yoğun Bakımda Kullanılan Girişimsel Ağrı Yöntemleri Seher YANATMA Yoğun Bakımda Ağrılı Hastaya Psikiyatrik Yaklaşım Ahmet DEMİRDAĞ Kevser ERDOĞAN Yoğun Bakımda Yatan Kanser Hastaları ve Palyatif Bakım Hastalarında Ağrı Tedavisi Abid KARAGÖZ

Objective: To evaluate the prognostic value of arterial pH at ICU admission for short-term survival after cardiac arrest. Materials and Methods: This single-center observational study included 60 adults (≥18 years) admitted to the ICU between May 2024 and June 2025 after successful resuscitation with return of spontaneous circulation (ROSC). Clinical, demographic, and laboratory data were collected. Survivors and non-survivors at day 7 were compared using logistic regression and ROC analyses to determine independent predictors of mortality. Results: The mean age was 61 ± 17 years, and 43% of patients were women. Seven-day mortality was 51.7%. Non-survivors were older and had higher APACHE II scores, lactate and procalcitonin levels, longer CPR-to-ROSC intervals, lower arterial pH values, and shorter ICU stays (all p < 0.05). In multivariable logistic regression analysis, age, higher APACHE II score, elevated procalcitonin levels, and lower arterial pH were independently associated with 7-day mortality. Lower arterial pH remained significantly associated with mortality (OR: 1.53; 95% CI: 0.93–2.86; p = 0.044). ROC analysis yielded an AUC of 0.668, with an optimal pH cut-off value of 7.13 (sensitivity 61.3%, specificity 75.9%). Conclusion: Lower arterial pH at ICU admission independently predicts 7-day mortality after cardiac arrest, emphasizing the importance of early recognition and correction of acidosis in post-resuscitation care

Objective: Comorbidity burden is an important determinant of prognosis in critically ill patients. Although the Charlson Comorbidity Index (CCI) is widely used for mortality prediction, simpler multimorbidity-based approaches may provide similar prognostic information with greater practicality. This study compared the prognostic performance of the age-adjusted CCI and a simple multimorbidity count for predicting 28-day mortality in critically ill adults. Methods: This retrospective observational cohort study included 198 adult patients admitted to a general medical-surgical intensive care unit between June and December 2025. Comorbidity burden was assessed using the age-adjusted CCI and a simple unweighted multimorbidity count based on the same 19 chronic conditions. Receiver operating characteristic (ROC) curve analysis was used to evaluate discriminative performance. Independent associations with 28-day mortality were assessed using multivariable logistic regression. Results: The overall 28-day mortality rate was 23.2%. Non-survivors had significantly higher APACHE II scores and greater comorbidity burden according to both measures. The simple multimorbidity count showed numerically better discrimination than the CCI for predicting 28-day mortality (AUC, 0.67 vs 0.62), although the difference was not statistically significant (P = .11). In multivariable analysis, both the multimorbidity count (adjusted OR, 1.59; 95% CI, 1.26-2.00; P < .001) and the CCI (adjusted OR, 1.18; 95% CI, 1.01-1.37; P = .03) were independently associated with mortality. Conclusions: A simple multimorbidity count demonstrated prognostic performance comparable to that of the age-adjusted CCI. Its simplicity and ease of use may make it a practical tool for early risk stratification in heterogeneous ICU populations.

Background/Objectives: Trauma remains a leading global cause of mortality and morbidity, particularly in low- and middle-income countries. Multitrauma patients often require intensive care and exhibit high mortality rates. Prognostic scoring systems such as the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Revised Trauma Score (RTS) are widely used to predict outcomes; however, their performance in middle-income settings like Turkey has not been well established. This study aimed to evaluate the prognostic accuracy of APACHE II and RTS in predicting intensive care unit (ICU) mortality among multitrauma patients. Methods: This retrospective observational study was conducted in the General ICU of Diyarbakır Gazi Yaşargil Training and Research Hospital between January 2022 and December 2024. Adult patients (≥18 years) with multitrauma involving at least two anatomical regions were included. Demographic data, clinical scores (APACHE II, RTS, Sequential Organ Failure Assessment [SOFA], Injury Severity Score [ISS], Trauma and Injury Severity Score [TRISS]), laboratory parameters, complications, and outcomes were recorded. Univariate and multivariate logistic regression analyses identified independent predictors of ICU mortality. Receiver operating characteristic (ROC) analysis assessed the discriminatory ability of prognostic scores. Results: A total of 145 patients were analyzed, with an ICU mortality rate of 13.1%. Non-survivors had significantly higher APACHE II (27 ± 4 vs. 13 ± 6, p < 0.001) and SOFA scores (7 [4–13] vs. 2 [1–6], p < 0.001) and lower RTS (4.2 ± 1.7 vs. 7.2 ± 1.1, p < 0.001). Acute kidney injury, sepsis, septic shock, and mechanical ventilation were more frequent among non-survivors (p < 0.01 for all). In multivariate analysis, APACHE II (odds ratio [OR]= 1.54, 95% confidence interval [CI] 1.16–2.04, p = 0.003) and RTS (OR = 0.36, 95% CI 0.16–0.81, p = 0.013) were independent predictors of ICU mortality. ROC analysis showed excellent prognostic performance for APACHE II (area under the curve [AUC] = 0.959) and RTS (AUC = 0.888). Conclusions: In this retrospective single-center cohort, APACHE II and RTS were independently associated with ICU mortality and demonstrated good discriminatory performance for early risk stratification in critically ill multitrauma patients. The lower mortality observed in our cohort should be interpreted cautiously, as it may also reflect differences in case-mix, injury severity, ICU admission practices, and referral patterns. These findings require external validation in larger multicenter cohorts before broader generalization. Future studies should also validate the identified cut-off values and further investigate the prognostic role of biomarkers such as procalcitonin.