Search Results (47)

Introduction/objectives: Peritonitis resulting from Candida spp. is common among critically ill patients and has been associated with adverse clinical outcomes. This study aimed to determine the effects of isolates of Candida species in patients with peritonitis on in-hospital mortality, general hospital stay, and intensive care unit (ICU) stays. Methods: This retrospective cohort study was conducted in two highly complex hospitals in Bogotá, Colombia, specifically by reference to patients who were hospitalized in the ICU between 2016 and 2022 with a clinical and microbiological diagnosis of peritonitis. For the analysis conducted for this research, two groups were established: patients with isolates of Candida spp. in the peritoneum and patients who had at least one bacterial microorganism in the culture. Multivariate logistic regression models and counting models featuring different mortality outcomes, different lengths of stay in the ICU, and different lengths of stay in the hospital were generated to evaluate the effect of the presence of Candida spp. and to account for potentially confounding variables. Results: A total of 373 patients, including 83 with Candida spp. and 290 with a bacterial etiology, were identified. Among the former group of patients, the most frequently identified species were C. albicans (50, 60.2%), C. tropicalis (18, 21.7%), and C. glabrata (7, 8.4%), whereas among the latter group, E. coli (186, 48.5%), K. pneumoniae (110, 29.8%), and E. faecalis (63, 16.9%) were most frequent. The 30-day mortality rate among patients with peritonitis and Candida isolates was 36.1%, and the corresponding rate among patients in the bacterial peritonitis group was 31.4% (p = 0.071). After adjustments were made to account for covariates, no significant differences were observed in mortality at 30 days (OR 0.75, 95% CI 0.20–1.18), length of hospital stay (iRR 1.11, 95% CI 0.90–1.40), or length of stay in the ICU (iRR 1.11, 95% CI 0.39) with respect to patients with peritonitis without fungal isolates. The Simplified Acute Physiology Score (SAPS2) (OR 1.04, 95% CI 1.03–1.06), World Society of Emergency Surgery (WSES) score (OR 1.11, (1.03–1.19), previous use of antifungals (OR 2.33, 1.21–4.52), and connective tissue disease (OR 3.71, 95% CI 1.30–10.99) were associated with 30-day mortality. Conclusions: The isolation of Candida species in peritoneal fluid from critically ill patients with peritonitis was not significantly associated with in-hospital mortality, length of hospital stay, or length of ICU stay after adjustments were made to account for other variables. Full article

Objective: To evaluate the prognostic utility of the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score 3 (SAPS 3) in COVID-19 patients and assess whether incorporating C-reactive protein (CRP), procalcitonin, lactate, and lactate dehydrogenase (LDH) enhances their predictive accuracy. Methods: Single-center, observational, cohort study. We analyzed a database of adult ICU patients with severe or critical COVID-19 treated at a large academic center. We used binary logistic regression for all analyses. We assessed the predictive performance of SAPS 3 and SOFA scores within 24 h of admission, individually and in combination with serum lactate, LDH, CRP, and procalcitonin. We examined the independent association of these biomarkers with hospital mortality. We evaluated discrimination using the C-statistic and determined clinical utility with decision curve analysis. Results: We included 1395 patients, 66% of whom required mechanical ventilation, and 59.7% needed vasopressor support. Patients who died (39.7%) were significantly older (61.1 ± 15.9 years vs. 50.1 ± 14.5 years, p < 0.001) and had more comorbidities than survivors. Among the biomarkers, only procalcitonin was independently associated with higher mortality in the multivariable analysis, in a non-linear pattern. The AUROC for predicting hospital mortality was 0.771 (95% CI: 0.746–0.797) for SAPS 3 and 0.781 (95% CI: 0.756–0.805) for the SOFA score. A model incorporating the SOFA score, age, and procalcitonin demonstrated high AUROC of 0.837 (95% CI: 0.816–0.859). These associations with the SOFA score showed greater clinical utility. Conclusions: The SOFA score may aid clinical decision-making, and incorporating procalcitonin and age could further enhance its prognostic utility. Full article

Background and Objectives: Immunodeficiency associated with aging comorbidities increases the vulnerability of geriatric patients to sepsis. Early recognition and management of sepsis are essential in this population. This study evaluated the relationships between RDW, MPV, and MPV/PLT ratios and mortality in geriatric sepsis patients. Materials and Methods: This retrospective study was conducted between 2020 and 2024 in the Intensive Care Unit of the Department of Anesthesiology and Reanimation at a university hospital. Patients aged ≥ 65 years with a SOFA score of ≥2 were included. Demographic data (sex, age, height, weight, and BMI), hemogram parameters (RDW, MPV, and PLT), blood gas, and biochemical values were analyzed. Furthermore, their comorbidities; site of infection; ICU length of stay; vital signs; and SOFA, APACHE II, and SAPS II scores, recorded within the first 24 h following ICU admission, were evaluated. Statistical analysis was performed using the chi-square test, Student’s t-test, the Mann–Whitney U test, the Monte Carlo exact test, and ROC analysis. A p-value of <0.05 was considered statistically significant. Results: A total of 247 patients were included, with 46.2% (n = 114) classified as non-survivors during the 28-day follow-up period. Among them, 64.9% (n = 74) were male, with a mean age of 78.22 ± 8.53 years. Significant differences were also found in SOFA, APACHE-II, and SAPS-II scores between non-survivors and survivors (SOFA: 7.64 ± 3.16 vs. 6.78 ± 2.78, p = 0.023; APACHE-II: 21.31 ± 6.36 vs. 19.27 ± 5.88, p = 0.009; SAPS-II: 53.15 ± 16.04 vs. 46.93 ± 14.64, p = 0.002). On days 1, 3, and 5, the MPV/PLT ratio demonstrated a statistically significant predictive value for 28-day mortality. The optimal cut-off values were >0.03 on day 1 (AUC: 0.580, 95% CI: 0.516–0.642, sensitivity: 72.81%, specificity: 65.91%, p = 0.027), >0.04 on day 3 (AUC: 0.602, 95% CI: 0.538–0.663, sensitivity: 60.53%, specificity: 60.61%, p = 0.005), and >0.04 on day 5 (AUC: 0.618, 95% CI: 0.554–0.790, sensitivity: 66.14%, specificity: 62.88%, p = 0.001). Conclusions: The MPV and MPV/PLT ratios demonstrated statistically significant but limited predictive value for 28-day mortality in geriatric patients with sepsis. In contrast, the limited prognostic value of RDW may be related to variability in the inflammatory response and other underlying conditions. The correlations found between SOFA, APACHE II, and SAPS II scores highlight their importance in mortality risk prediction. Full article

Background: Heart failure (HF) ranks among the foremost causes of mortality globally, exhibiting particularly high prevalence and significant impact within intensive care units (ICUs). This study sought to develop, validate, and deploy a time-dependent machine learning model aimed at predicting the one-year all-cause mortality risk in ICU patients diagnosed with HF, thereby facilitating precise prognostic evaluation and risk stratification. Methods: This study encompassed a cohort of 8960 ICU patients with HF sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (version 3.1). This latest version of the database added data from 2020 to 2022 on the basis of version 2.2 (covering data from 2008 to 2019); therefore, data spanning 2008 to 2019 (n = 5748) were designated for the training set, while data from 2020 to 2022 (n = 3212) were reserved for the test set. The primary endpoint of interest was one-year all-cause mortality. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to select predictive features from an initial pool of 64 candidate variables (including demographic characteristics, vital signs, comorbidities and complications, therapeutic interventions, routine laboratory data, and disease severity scores). Four predictive models were developed and compared: Cox proportional hazards, random survival forest (RSF), Cox proportional hazards deep neural network (DeepSurv), and eXtreme Gradient Boosting (XGBoost). Model performance was assessed using the concordance index (C-index) and Brier score, with model interpretability addressed through SHapley Additive exPlanations (SHAP) and time-dependent Survival SHapley Additive exPlanations (SurvSHAP(t)). Results: This study revealed a one-year mortality rate of 46.1% within the population under investigation. In the training set, LASSO effectively identified 24 features in the model. In the test set, the XGBoost model exhibited superior predictive performance, as evidenced by a C-index of 0.772 and a Brier score of 0.161, outperforming the Cox model (C-index: 0.740, Brier score: 0.175), the RSF model (C-index: 0.747, Brier score: 0.178), and the DeepSur model (C-index: 0.723, Brier score: 0.183). Decision curve analysis validated the clinical utility of the XGBoost model across a broad spectrum of risk thresholds. Feature importance analysis identified the red cell distribution width-to-albumin ratio (RAR), Charlson Comorbidity Index, Simplified Acute Physiology Score II (SAPS II), Acute Physiology Score III (APS III), and the age–bilirubin–INR–creatinine (ABIC) score as the top five predictive factors. Consequently, an online risk prediction tool based on this model has been developed and is publicly accessible. Conclusions: The time-dependent XGBoost model demonstrated robust predictive capability in evaluating the one-year all-cause mortality risk in critically ill HF patients. This model offered a useful tool for early risk identification and supported timely interventions. Full article

Background: Current severity scoring systems in intensive care units (ICUs) are complex and time-consuming, limiting their utility for rapid clinical decision-making. This study aimed to develop and validate simplified prediction models using readily available biomarkers for assessing in-hospital mortality risk. Methods: We analyzed 19,720 adult ICU patients in this retrospective study. Three prediction models were developed: a basic model using lactate-to-albumin ratio (LAR) and neutrophil percent-to-albumin ratio (NPAR) and two enhanced models incorporating mechanical ventilation and continuous renal replacement therapy. Model performance was evaluated against Sequential Organ Failure Assessment (SOFA) score and Acute Physiology and Chronic Health Evaluation (APACHE) II score using machine learning approaches and validated through comprehensive subgroup analyses. Results: Among individual biomarkers, SOFA score showed the highest discriminatory power (area under these curves [AUC] 0.931), followed by LAR (AUC 0.830), CAR (AUC 0.749), and NPAR (AUC 0.748). Our enhanced Model 3 demonstrated exceptional predictive performance (AUC 0.929), statistically comparable to SOFA (p = 0.052), and showed a trend toward superiority over APACHE II (AUC 0.900, p = 0.079). Model 2 performed comparably to APACHE II (AUC 0.913, p = 0.430), while Model 1, using only LAR and NPAR, achieved robust performance (AUC 0.898) despite its simplicity. Subgroup analyses across different ICU types demonstrated consistent performance of all three models, supporting their broad clinical applicability. Conclusions: This study introduces novel, simplified prediction models that rival traditional scoring systems in accuracy while offering significantly faster implementation. These findings represent a crucial step toward more efficient and practical risk assessment in critical care, potentially enabling earlier clinical interventions and improved patient outcomes. Full article

Background: A machine learning prognostic mortality scoring system was developed to address challenges in patient selection for clinical trials within the Intensive Care Unit (ICU) environment. The algorithm incorporates Red blood cell Distribution Width (RDW) data and other demographic characteristics to predict ICU mortality alongside existing ICU mortality scoring systems like Simplified Acute Physiology Score (SAPS). Methods: The developed algorithm, defined as a Mixed-effects logistic Random Forest for binary data (MixRFb), integrates a Random Forest (RF) classification with a mixed-effects model for binary outcomes, accounting for repeated measurement data. Performance comparisons were conducted with RF and the proposed MixRFb algorithms based solely on SAPS scoring, with additional evaluation using a descriptive receiver operating characteristic curve incorporating RDW’s predictive mortality ability. Results: MixRFb, incorporating RDW and other covariates, outperforms the SAPS-based variant, achieving an area under the curve of 0.882 compared to 0.814. Age and RDW were identified as the most significant predictors of ICU mortality, as reported by the variable importance plot analysis. Conclusions: The MixRFb algorithm demonstrates superior efficacy in predicting in-hospital mortality and identifies age and RDW as primary predictors. Implementation of this algorithm could facilitate patient selection for clinical trials, thereby improving trial outcomes and strengthening ethical standards. Future research should focus on enriching algorithm robustness, expanding its applicability across diverse clinical settings and patient demographics, and integrating additional predictive markers to improve patient selection capabilities. Full article

Background/Objectives: Cardiogenic shock (CS) remains a critical condition with high mortality rates despite advances in treatment. This study aimed to evaluate the prognostic significance of urinary output at various time intervals during CS and its effectiveness as a predictor of 30-day mortality, particularly in comparison to the Simplified Acute Physiology Score 3 (SAPS 3). Methods: We conducted a retrospective analysis of 96 patients diagnosed with CS, assessing urinary output at different intervals (0–6 h, 6–12 h, 12–24 h, and 0–24 h) as potential predictors of 30-day mortality. SAPS 3 was calculated for all patients, and its predictive value was compared to that of urinary output using both univariate and multivariate analyses. Additional analyses included ROC curve assessment and Kaplan–Meier survival analysis. Results: Urinary output at 6–12 h was significantly associated with 30-day mortality in univariate analysis. Area under the receiver operating characteristic curves (AUROCs) for urinary output at 0–6 h, 6–12 h, and 12–24 h was 0.61 (p = 0.07), 0.63 (p = 0.04), and 0.61 (p = 0.08), respectively. These AUROCs did not differ significantly between the three urinary output parameters. Regarding the cumulative urinary output of 0–24 h, the most pronounced impact was observed in patients producing less than 0.5 mL/kg/h. In multivariate analysis, when combined with SAPS 3, the predictive power of urinary output diminished. SAPS 3 alone demonstrated significant predictive value with an AUROC of 0.77 (p < 0.001). Conclusions: While early urinary output is a valuable predictor of 30-day mortality in patients with CS, its prognostic strength is limited when considered alongside comprehensive risk assessments like SAPS 3. These findings suggest that a multifaceted approach, incorporating both early and comprehensive indicators, is essential for accurately predicting outcomes in CS patients. Full article

Background: Numerous prognostic scores have been developed and used in intensive care; however, the applicability and effectiveness of these scores in critically ill patients with acute kidney injury may vary due to the characteristics of this population. Objective: To assess the predictive capacity of the Simplified Acute Physiology Score III (SAPS III), Sequential Sepsis-related Organ Failure Assessment (SOFA) and Nursing Activities Score (NAS) prognostic scoring systems for acute kidney injury in critically ill patients. Methods: Cohort, prospective and quantitative study with follow-up of 141 critical patients in intensive care. A questionnaire was used to collect information about the capacity of prognostic scoring systems to predict AKI. Mann–Whitney, Kruskal–Wallis and Bonferroni-corrected Mann–Whitney tests were used and the statistical significance was considered to be at two-sided p < 0.05. Results: It was revealed that 41.85% of patients developed acute kidney injury during their stay in the Intensive Care Unit and indicated greater severity assessed by the medians of prognostic scoring systems—SAPS III [55 (42–65 vs. 38 (32–52), p < 0.001], SOFA [3.3 (2.26–5.00) vs. 0.66 (0.06–2.29), p < 0.001] and NAS [90 (75–95) vs. 97 (91–103), p < 0.001]—when compared to patients without kidney damage. Conclusions: The SAPS III, SOFA and NAS prognostic scoring systems showed good predictive capacity for acute kidney injury in critically ill patients. This study was not registered. Full article

The isolation of Candida may be related to comorbidity, prolonged mechanical ventilation, and survival during intensive care unit (ICU) stay, especially with non-albicans Candida (NAC). To examine the frequency of Candida isolation, associated comorbidities and outcomes in the surgical ICU in Osijek University Hospital, Croatia, the data from the electronic database from May 2016 to 30 June 2023 were analyzed. In a cross-sectional study examining 15,790 microbiological samples, different strains of Candida were observed in 581 samples from 236 patients. The control group (N = 261) was 130 consecutive patients from March to May 2019 and 131 in the same months in 2020 (pre- and post-COVID-19). Comorbidities, duration of mechanical ventilation, and survival were compared. Patients with isolated Candida were more often non-elective and had significantly more heart, kidney, and liver diseases and sepsis than the control group (p < 0.001). The duration of mechanical ventilation was 9.2 [2.2–9.24], 96 [24–146], 160 [19.5–343], and 224 [73.5–510] hours in the controls, in patients with Candida albicans, in patients with NAC, and in patients with ≥2 Candida species isolated, respectively. The mortality was significantly higher (42%) in patients with isolated Candida than in the control group (19%, p < 0.001). In a multivariate analysis adjusted for patients’ age, the Simplified Acute Physiology Score II, days of ICU, and type of admission, only sepsis on admission was an independent predictor of mortality (odds ratio = 2.27). Full article

Background: A subarachnoid hemorrhage due to an aneurysmal rupture (aSAH) is a serious condition with severe neurological consequences. The World Federation of Neurosurgical Societies (WFNS) classification is a reliable predictor of death and long-term disability in patients with aSAH. Poor-grade neurological conditions on admission in aSAH (PG-aSAH) are often linked to high mortality rates and unfavorable outcomes. However, more than one-third of patients with PG-aSAH may recover and have good functional outcomes if aggressive treatment is provided. We developed a risk model called Identifying POor GRade Outcomes (IPOGRO) to predict 6-month mRS outcomes in PG-aSAH patients as a secondary analysis of a previously published study. Methods: All consecutive patients in poor-grade neurological conditions (WFNS IV-V) admitted to our institute from 2010 to 2020 due to aSAH were considered. Clinical and neuroradiological parameters were employed in the univariable analysis to evaluate the relationship with a 6-month modified Rankin Scale (mRS). Then, a multivariable multinomial regression model was performed to predict 6-month outcomes. Results: 149 patients with PG-aSAH were included. Most patients were surgically treated, with only 33.6% being endovascularly treated. The 6-month mRS score was significantly associated with clinical parameters on admission, such as lowered Glasgow Coma Scale (GCS), leukocytosis, hyperglycemia, raised Systolic Blood Pressure (SBP), greater Simplified Acute Physiology Score (SAPS II score), increased initial serum Lactic Acid (LA) levels, and the need for Norepinephrine (NE) administration. Neuroradiological parameters on the initial CT scan showed a significant association with a worsening 6-month mRS. The IPOGRO risk model analysis showed an association between a WFNS V on admission and a poor outcome (mRS 4-5), while raised SBP was associated with mortality. Conclusions: Our IPOGRO risk model indicates that PG-aSAH patients with higher SBP at admission had an increased risk of death at 6-month follow-up, whereas patients with WFNS grade V at admission had an increased risk of poor outcome but not mortality. Full article

COVID-19 has generated a global impact due to its contagiousness and high lethality rates, with a large number of deaths occurring in intensive care units (ICUs). This study aimed to verify the occurrence of and understand the factors related to mortality in adult patients with COVID-19 admitted to the ICU in a tertiary hospital. This is a retrospective cohort study, which included COVID-19 patients admitted between March 2020 and December 2021. A total of 588 patients were included, of whom the majority (55.27%) did not survive. Invasive mechanical ventilation was the strongest predictor of the risk of death in the ICU with OR = 97.85 (95% CI = 39.10–244.86; p < 0.001), along with age and Simplified Acute Physiology Score 3 (SAPS3). The length of the ICU stay was protective. Evaluating patients on invasive mechanical ventilation in isolation, using an adjusted model, we found the following risk factors: use of vasopressin, renal replacement therapy, red cell distribution width > 15, use of hydrocortisone, and age in years. Protective factors included the days of mechanical ventilation use, being admitted from another service, and being of female sex. Identifying early predictors of mortality in patients with COVID-19 who require hospitalization is essential in the search for actions to prevent and manage complications, which can increase the survival of these patients and reduce the impact on health services. Full article

Background and Objectives: Sepsis represents a global health challenge and requires advanced diagnostic and prognostic approaches due to its elevated rate of morbidity and fatality. Our study aimed to assess the value of a novel set of six biomarkers combined with severity scores in predicting 28 day mortality among patients presenting with sepsis in the Emergency Department (ED). Materials and Methods: This single-center, observational, prospective cohort included sixty-seven consecutive patients with septic shock and sepsis enrolled from November 2020 to December 2022, categorized into survival and non-survival groups based on outcomes. The following were assessed: procalcitonin (PCT), soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1), the soluble form of the urokinase plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and azurocidin 1 (AZU1), alongside clinical scores such as the Quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II and III (SAPS II/III), the National Early Warning Score (NEWS), Mortality in Emergency Department Sepsis (MEDS), the Charlson Comorbidity Index (CCI), and the Glasgow Coma Scale (GCS). The ability of each biomarker and clinical score and their combinations to predict 28 day mortality were evaluated. Results: The overall mortality was 49.25%. Mechanical ventilation was associated with a higher mortality rate. The levels of IL-6 were significantly higher in the non-survival group and had higher AUC values compared to the other biomarkers. The GCS, SOFA, APACHEII, and SAPS II/III showed superior predictive ability. Combining IL-6 with suPAR, AZU1, and clinical scores SOFA, APACHE II, and SAPS II enhanced prediction accuracy compared with individual biomarkers. Conclusion: In our study, IL-6 and SAPS II/III were the most accurate predictors of 28 day mortality for sepsis patients in the ED. Full article

Background/Objectives: Septic shock is a severe condition with high mortality necessitating precise prognostic tools for improved patient outcomes. This study aimed to evaluate the collective predictive value of the Simplified Acute Physiology Score 3 (SAPS-3) and lactate measurements (initial, peak, last, and clearance rates within the first 24 h) in patients with septic shock. Specifically, it sought to determine how these markers enhance predictive accuracy for 28-day mortality beyond SAPS-3 alone. Methods: This retrospective cohort study analyzed data from 66 septic shock patients at two ICUs of Vienna General Hospital (2017–2019). SAPS-3 and lactate levels (initial, peak, last measurement within 24 h, and 24 h clearance) were obtained from electronic health records. Logistic regression models were constructed to identify predictors of 28-day mortality, and receiver operating characteristic (ROC) curves assessed predictive accuracy. Results: Among 66 patients, 36 (55%) died within 28 days. SAPS-3 scores significantly differed between survivors and non-survivors (76 vs. 85 points; p = 0.016). First, last, and peak lactate were significantly higher in non-survivors compared to survivors (all p < 0.001). The combination of SAPS-3 and first lactate produced the highest predictive accuracy (AUC = 80.6%). However, 24 h lactate clearance was not predictive of mortality. Conclusions: Integrating SAPS-3 with lactate measurements, particularly first lactate, improves predictive accuracy for 28-day mortality in septic shock patients. First lactate serves as an early, robust prognostic marker, providing crucial information for clinical decision-making and care prioritization. Further large-scale studies are needed to refine these predictive tools and validate their efficacy in guiding treatment strategies. Full article

Background/Objectives: This study sought to evaluate the efficacy of various lactate measurements within the first 24 h post-intensive care unit (ICU) admission for predicting 30-day mortality in cardiogenic shock patients. It compared initial lactate levels, 24 h levels, peak levels, and 24 h clearance, alongside the Simplified Acute Physiology Score 3 (SAPS3) score, to enhance early treatment decision-making. Methods: A retrospective analysis of 64 patients assessed the prognostic performance of lactate levels and SAPS3 scores using logistic regression and AUROC calculations. Results: Of the baseline parameters, only the SAPS3 score predicted survival independently. The lactate level after 24 h (LL) was the most accurate predictor of mortality, outperforming initial levels, peak levels, and 24 h-clearance, and showing a significant AUROC. LL greater than 3.1 mmol/L accurately predicted mortality with high specificity and moderate sensitivity. Conclusions: Among lactate measurements for predicting 30-day mortality in cardiogenic shock, the 24 h lactate level was the most effective one, suggesting its superiority for early prognostication over initial or peak levels and lactate clearance. Full article

We investigated the prognostic performance of scoring systems by the intensive care unit (ICU) type. This was a retrospective observational study using data from the Marketplace for Medical Information in the Intensive Care IV database. The primary outcome was in-hospital mortality. We obtained Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation (APACHE) III, and Simplified Acute Physiology Score (SAPS) II scores in each ICU type. Prognostic performance was evaluated with the area under the receiver operating characteristic curve (AUROC) and was compared among ICU types. A total of 29,618 patients were analyzed, and the in-hospital mortality was 12.4%. The overall prognostic performance of APACHE III was significantly higher than those of SOFA and SAPS II (0.807, [95% confidence interval, 0.799–0.814], 0.785 [0.773–0.797], and 0.795 [0.787–0.811], respectively). The prognostic performance of SOFA, APACHE III, and SAPS II scores was significantly different between ICU types. The AUROC ranges of SOFA, APACHE III, and SAPS II were 0.723–0.826, 0.728–0.860, and 0.759–0.819, respectively. The neurosurgical and surgical ICUs had lower prognostic performance than other ICU types. The prognostic performance of scoring systems in patients with suspected infection is significantly different according to ICU type. APACHE III systems have the highest prediction performance. ICU type may be a significant factor in the prognostication. Full article