Prognostic assessment plays a crucial role in guiding therapeutic decision-making for patients with sepsis, particularly in intensive care settings. This study aimed to develop a multivariable model to predict 28-day mortality among intensive care unit (ICU) patients with sepsis by integrating serum lactate levels, the National Early Warning Score (NEWS), and the Predisposition, Infection, Response, and Organ Dysfunction (PIRO) score. Demographic information, clinical characteristics, and laboratory findings routinely collected at ICU admission were used to calculate the NEWS and PIRO scores for each patient. Patients were categorized as survivors or non-survivors based on their outcome. Both logistic regression and Cox proportional hazards models were applied for mortality prediction analysis. The final analysis included 205 patients diagnosed with sepsis (mean age: 73.6 ± 13.2 years; 53.2% male), of whom 109 died during hospitalization. Logistic regression analysis revealed that lactate, NEWS, and PIRO scores were independently associated with 28-day mortality. Combining lactate levels with NEWS and PIRO significantly enhanced mortality prediction, with the greatest accuracy observed when all three parameters were integrated. Pairwise analyses demonstrated that adding lactate to the base model significantly improved predictive accuracy (DBA: −0.103, p = 0.003), and incorporating lactate into a model already including NEWS further enhanced its predictive value (DBA: −0.042, p = 0.037). In conclusion, serum lactate measured at initial ICU admission provides valuable prognostic information for predicting 28-day mortality in sepsis patients. Furthermore, combining lactate levels with NEWS and PIRO scores substantially enhances the accuracy of mortality prediction in these patients.

Introduction: The appraisal of disease severity and prediction of adverse outcomes using risk stratification tools at early disease stages is crucial to diminish mortality from coronavirus disease 2019 (COVID-19). While lung ultrasound (LUS) as an imaging technique for the diagnosis of lung diseases has recently gained a leading position, data demonstrating that it can predict adverse outcomes related to COVID-19 is scarce. The main aim of this study is therefore to assess the clinical significance of bedside LUS in COVID-19 patients who presented to the emergency department (ED). Methods: Patients with a confirmed diagnosis of SARS-CoV-2 pneumonia admitted to the ED of our hospital between March 2021 and May 2021 and who underwent a 12-zone LUS and a lung computed tomography scan were included prospectively. Logistic regression and Cox proportional hazard models were used to predict adverse events, which was our primary outcome. The secondary outcome was to discover the association of LUS score and computed tomography severity score (CT-SS) with the composite endpoints. Results: We assessed 234 patients [median age 59.0 (46.8-68.0) years; 59.4% M), including 38 (16.2%) in-hospital deaths for any cause related to COVID-19. Higher LUS score and CT-SS was found to be associated with ICU admission, intubation, and mortality. The LUS score predicted mortality risk within each stratum of NEWS. Pairwise analysis demonstrated that after adjusting a base prediction model with LUS score, significantly higher accuracy was observed in predicting both ICU admission (DBA −0.067, P = .011) and in-hospital mortality (DBA −0.086, P = .017). Conclusion: Lung ultrasound can be a practical prediction tool during the course of COVID-19 and can quantify pulmonary involvement in ED settings. It is a powerful predictor of ICU admission, intubation, and mortality and can be used as an alternative for chest computed tomography while monitoring COVID-19-related adverse outcomes.

Community-acquired pneumonia (CAP) is a leading cause of ICU admissions, with significant morbidity and mortality. Traditional risk stratification tools, such as CURB-65, the pneumonia severity index (PSI), and computed tomography severity scores (CT-SS) are widely used for prognosis but could be improved by incorporating novel biomarkers. This retrospective study evaluated the fibrinogen-to-albumin ratio (FAR) as an additional predictor of 30-day mortality in ICU patients with CAP. A total of 158 CAP patients admitted to a tertiary care ICU were included. Baseline data encompassed demographic, clinical, laboratory, and radiological parameters, including FAR, CURB-65, PSI, and CT-SS. Logistic regression and receiver operating characteristic curve (ROC) analyses were conducted to assess mortality predictors. The 30-day mortality rate was 70.88% (112/158). Higher FAR, PSI, CURB-65, CT-SS, and lactate levels were independently associated with increased mortality (P < 0.05). FAR demonstrated strong discriminatory power (area under the receiver operating characteristic [AUROC]: 0.704) and significantly improved the predictive accuracy of established models. Adding FAR to PSI increased the AUROC from 0.705 to 0.791 (P = 0.009), while combining FAR, CT-SS, and PSI yielded the highest predictive accuracy (AUROC: 0.844, P = 0.032). These findings suggest that FAR, which reflects both inflammation and nutritional status, complements traditional risk assessment tools by providing a dynamic perspective. Integrating FAR into existing models enhances the identification of high-risk patients, enabling timely interventions and more efficient resource allocation in the ICU.

Prognostic prediction and estimation of severity at early stages of acute pancreatitis (AP) are crucial to reduce the complication rates and mortality. The objective of the present study is to evaluate the predicting ability of different clinical and radiological scores in AP. We retrospectively collected demographic and clinical data from 159 patients diagnosed with AP admitted to Canakkale Onsekiz Mart University Hospital between January 2017 and December 2019. Bedside index for severity AP (BISAP), and acute phys-iology and chronic health evaluation II (APACHE II) score at admission, Ranson and modified Glasgow Prognostic Score (mGPS) score at 48 h after admission were calculated. Modified computed tomography severity index (CTSI) was also calculated for each patient. Area under the curve (AUC) was calculated for each scoring system for predicting severe AP, pancreatic necrosis, length of hospital stay, and mortality by determining optimal cutoff points from the (ROC) curves. mGPS and APACHE II had the highest AUC (0.929 and 0.823, respectively) to predict severe AP on admission with the best specificity and sensitivity. In predicting mortality BISAP (with a sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of 75.0%, 70.9%, 98.2%, and 12.0%, respectively, [AUC: 0.793]) and APACHE II (with a sensitivity, specificity, NPV and PPV of 87.5%, 86.1%, 99.2%, and 25.0%, respectively, [AUC: 0.840]). mGPS can be a valuable tool in predicting the patients more likely to develop severe AP and maybe somewhat better than BISAP score, APACHE II Ranson score, and mCTSI.

Journal of Emergency Medicine Case Reports

Evaluating disease severity and predicting adverse outcomes using various risk prediction tools in early disease stages is essential to reduce sepsis-related mortality. Unfortunately, there is still no clear consensus on the best score. The present study aimed to develop and validate a multivariable risk prediction model for 30-days mortality by combining the lactate-to-albumin (L/A) ratio, Modified Early Warning Score (MEWS), and quick Sequential Organ Failure Assessment (qSOFA) in sepsis patients admitted to the intensive care unit (ICU). This retrospective study included ICU patients with suspected sepsis. We computed L/A ratio, MEWS, and qSOFA within 24 hours of ICU admission. Patients were followed until either death/hospital discharge or 30 days, whichever came first. The predictive performance of each scoring system and their combinations was assessed using logistic regression and receiver operating characteristic curve analyses. A total of 130 patients with sepsis admitted to the ICU were included in the study. The mortality rate was 63.07% (82/130). A higher L/A ratio, MEWS, and qSOFA were found to be associated with mortality in ICU sepsis patients. A statistically significant difference in terms of predicting mortality was demonstrated in the pairwise comparison after combining the L/A ratio with both the qSOFA and MEWS (difference between areas: −0.098, P = .011 and difference between areas: −0.098, P = .013 respectively). Mortality models combining L/A ratio with selected clinical variables have improved mortality prediction performance compared with models that use MEWS and qSOFA alone. The L/A ratio at ICU admission provide valuable prognostic information for predicting 30-days mortality in sepsis patients. Combining these ratio with MEWS and qSOFA improves the accuracy of predicting mortality in patients with sepsis.