[Development and validation of a nomogram for predicting 3-month mortality risk in patients with sepsis-associated acute kidney injury].

OBJECTIVE: To develop and evaluate a nomogram prediction model for the 3-month mortality risk of patients with sepsis-associated acute kidney injury (S-AKI). METHODS: Based on the American Medical Information Mart for Intensive Care- IV (MIMIC- IV), clinical data of S-AKI patients from 2008 to 2021 were collected. Initially, 58 relevant predictive factors were included, with all-cause mortality within 3 months as the outcome event. The data were divided into training and testing sets at a 7 : 3 ratio. In the training set, univariate Logistic regression analysis was used for preliminary variable screening. Multicollinearity analysis, Lasso regression, and random forest algorithm were employed for variable selection, combined with the clinical application value of variables, to establish a multivariable Logistic regression model, visualized using a nomogram. In the testing set, the predictive value of the model was evaluated through internal validation. The receiver operator characteristic curve (ROC curve) was drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of nomogram model and Oxford acute severity of illness score (OASIS), sequential organ failure assessment (SOFA), and systemic inflammatory response syndrome score (SIRS). The calibration curve was used to evaluate the calibration, and decision curve analysis (DCA) was performed to assess the net benefit at different probability thresholds. RESULTS: Based on the survival status at 3 months after diagnosis, patients were divided into 7 768 (68.54%) survivors and 3 566 (31.46%) death. In the training set, after multiple screenings, 7 variables were finally included in the nomogram model: Logistic organ dysfunction system (LODS), Charlson comorbidity index, urine output, international normalized ratio (INR), respiratory support mode, blood urea nitrogen, and age. Internal validation in the testing set showed that the AUC of nomogram model was 0.81 [95% confidence interval (95%CI) was 0.80-0.82], higher than the OASIS score's 0.70 (95%CI was 0.69-0.71) and significantly higher than the SOFA score's 0.57 (95%CI was 0.56-0.58) and SIRS score's 0.56 (95%CI was 0.55-0.57), indicating good discrimination. The calibration curve demonstrated that the nomogram model's calibration was better than the OASIS, SOFA, and SIRS scores. The DCA curve suggested that the nomogram model's clinical net benefit was better than the OASIS, SOFA, and SIRS scores at different probability thresholds. CONCLUSIONS: A nomogram prediction model for the 3-month mortality risk of S-AKI patients, based on clinical big data from MIMIC- IV and including seven variables, demonstrates good discriminative ability and calibration, providing an effective new tool for assessing the prognosis of S-AKI patients.

Clinical complexity of an Italian cardiovascular intensive care unit: the role of mortality and severity risk scores.

AIMS: The identification of patients at greater mortality risk of death at admission into an intensive cardiovascular care unit (ICCU) has relevant consequences for clinical decision-making. We described patient characteristics at admission into an ICCU by predicted mortality risk assessed with noncardiac intensive care unit (ICU) and evaluated their performance in predicting patient outcomes. METHODS: A total of 202 consecutive patients (130 men, 75 ±â€Š12 years) were admitted into our tertiary-care ICCU in a 20-week period. We evaluated, on the first 24 h data, in-hospital mortality risk according to Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score 3 (SAPS 3); Sepsis related Organ Failure Assessment (SOFA) Score and the Mayo Cardiac intensive care unit Admission Risk Score (M-CARS) were also calculated. RESULTS: Predicted mortality was significantly lower than observed (5% during ICCU and 7% at discharge) for APACHE II and SAPS 3 (17% for both scores). Mortality risk was associated with older age, more frequent comorbidities, severe clinical presentation and complications. The APACHE II, SAPS 3, SOFA and M-CARS had good discriminative ability in distinguishing deaths and survivors with poor calibration of risk scores predicting mortality. CONCLUSION: In a recent contemporary cohort of patients admitted into the ICCU for a variety of acute and critical cardiovascular conditions, scoring systems used in general ICU had good discrimination for patients' clinical severity and mortality. Available scores preserve powerful discrimination but the overestimation of mortality suggests the importance of specific tailored scores to improve risk assessment of patients admitted into ICCUs.

Development and validation of risk prediction model for bacterial infections in acute liver failure patients.

Infections significantly increase mortality in acute liver failure (ALF) patients, and there are no risk prediction models for early diagnosis and treatment of infections in ALF patients. This study aims to develop a risk prediction model for bacterial infections in ALF patients to guide rational antibiotic therapy. The data of ALF patients admitted to the Second Hospital of Hebei Medical University in China from January 2017 to January 2022 were retrospectively analyzed for training and internal validation. Patients were selected according to the updated 2011 American Association for the Study of Liver Diseases position paper on ALF. Serological indicators and model scores were collected within 24 h of admission. New models were developed using the multivariate logistic regression analysis. An optimal model was selected by receiver operating characteristic (ROC) analysis, Hosmer-Lemeshow test, the calibration curve, the Brier score, the bootstrap resampling, and the decision curve analysis. A nomogram was plotted to visualize the results. A total of 125 ALF patients were evaluated and 79 were included in the training set. The neutrophil-to-lymphocyte ratio and sequential organ failure assessment (SOFA) were integrated into the new model as independent predictive factors. The new SOFA-based model outperformed other models with an area under the ROC curve of 0.799 [95% confidence interval (CI): 0.652-0.926], the superior calibration and predictive performance in internal validation. High-risk individuals with a nomogram score ≥26 are recommended for antibiotic therapy. The new SOFA-based model demonstrates high accuracy and clinical utility in guiding antibiotic therapy in ALF patients.

Automated Calculator for the Pediatric Sequential Organ Failure Assessment Score: Development and External Validation in a Single-Center 7-Year Cohort, 2015-2021.

OBJECTIVES: The pediatric Sequential Organ Failure Assessment (pSOFA) score summarizes severity of organ dysfunction and can be used to predict in-hospital mortality. Manual calculation of the pSOFA score is time-consuming and prone to human error. An automated method that is open-source, flexible, and scalable for calculating the pSOFA score directly from electronic health record data is desirable. DESIGN: Single-center, retrospective cohort study. SETTING: Quaternary 40-bed PICU. PATIENTS: All patients admitted to the PICU between 2015 and 2021 with ICU stay of at least 24 hours. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We used 77 records to evaluate the automated score. The automated algorithm had an overall accuracy of 97%. The algorithm calculated the respiratory component of two cases incorrectly. An expert human annotator had an initial accuracy of 75% at the patient level and 95% at the component level. An untrained human annotator with general clinical research experience had an overall accuracy of 16% and component-wise accuracy of 67%. Weighted kappa for agreement between the automated method and the expert annotator's initial score was 0.92 (95% CI, 0.88-0.95), and between the untrained human annotator and the automated score was 0.50 (95% CI, 0.36-0.61). Data from 9146 patients (in-hospital mortality 3.6%) were included to validate externally the discriminability of the automated pSOFA score. The admission-day pSOFA score had an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77-0.82). CONCLUSIONS: The developed automated algorithm calculates pSOFA score with high accuracy and is more accurate than a trained expert rater and nontrained data abstracter. pSOFA's performance for predicting in-hospital mortality was lower in our cohort than it was for the originally derived score.

Dynamic Prediction of Mortality Using Longitudinally Measured Pediatric Sequential Organ Failure Assessment Scores: A Joint Modeling Approach.

OBJECTIVES: The pediatric Sequential Organ Failure Assessment (pSOFA) score was designed to track illness severity and predict mortality in critically ill children. Most commonly, pSOFA at a point in time is used to assess a static patient condition. However, this approach has a significant drawback because it fails to consider any changes in a patients' condition during their PICU stay and, especially, their response to initial critical care treatment. We aimed to evaluate the performance of longitudinal pSOFA scores for predicting mortality. DESIGN: Single-center, retrospective cohort study. SETTING: Quaternary 40-bed PICU. PATIENTS: All patients admitted to the PICU between 2015 and 2021 with at least 24 hours of ICU stay. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We calculated daily pSOFA scores up to 30 days, or until death or discharge from the PICU, if earlier. We used the joint longitudinal and time-to-event data model for the dynamic prediction of 30-day in-hospital mortality. The dataset, which included 9146 patients with a 30-day in-hospital mortality of 2.6%, was divided randomly into training (75%) and validation (25%) subsets, and subjected to 40 repeated stratified cross-validations. We used dynamic area under the curve (AUC) to evaluate the discriminative performance of the model. Compared with the admission-day pSOFA score, AUC for predicting mortality between days 5 and 30 was improved on average by 6.4% (95% CI, 6.3-6.6%) using longitudinal pSOFA scores from the first 3 days and 9.2% (95% CI, 9.0-9.5%) using scores from the first 5 days. CONCLUSIONS: Compared with admission-day pSOFA score, longitudinal pSOFA scores improved the accuracy of mortality prediction in PICU patients at a single center. The pSOFA score has the potential to be used dynamically for the evaluation of patient conditions.

Diastolic/systolic blood pressure ratio for predicting febrile children with sepsis and progress to septic shock in the emergency department.

OBJECTIVE: Given the scarcity of studies analyzing the clinical predictors of pediatric septic cases that would progress to septic shock, this study aimed to determine strong predictors for pediatric emergency department (PED) patients with sepsis at risk for septic shock and mortality. METHODS: We conducted chart reviews of patients with ≥ 2 age-adjusted quick Sequential Organ Failure Assessment score (qSOFA) criteria to recognize patients with an infectious disease in two tertiary PEDs between January 1, 2021, and April 30, 2022. The age range of included patients was 1 month to 18 years. The primary outcome was development of septic shock within 48 h of PED attendance. The secondary outcome was sepsis-related 28-day mortality. Initial important variables in the PED and hemodynamics with the highest and lowest values during the first 24 h of admission were also analyzed. RESULTS: Overall, 417 patients were admitted because of sepsis and met the eligibility criteria for the study. Forty-nine cases progressed to septic shock within 48 h after admission and 368 were discharged without progression. General demographics, laboratory data, and hemodynamics were analyzed by multivariate analysis. Only the minimum diastolic blood pressure/systolic blood pressure ratio (D/S ratio) during the first 24 h after admission remained as an independent predictor of progression to septic shock and 28-day mortality. The best cutoff values of the D/S ratio for predicting septic shock and 28-day mortality were 0.52 and 0.47, respectively. CONCLUSIONS: The D/S ratio is a practical bedside scoring system in the PED and had good discriminative ability in predicting the progression of septic shock and in-hospital mortality in PED patients. Further validation is essential in other settings.

Patterns of Multiple Organ Dysfunction and Renal Recovery in Critically Ill Children and Young Adults Receiving Continuous Renal Replacement Therapy.

OBJECTIVES: Acute kidney injury requiring dialysis (AKI-D) commonly occurs in the setting of multiple organ dysfunction syndrome (MODS). Continuous renal replacement therapy (CRRT) is the modality of choice for AKI-D. Mid-term outcomes of pediatric AKI-D supported with CRRT are unknown. We aimed to describe the pattern and impact of organ dysfunction on renal outcomes in critically ill children and young adults with AKI-D. DESIGN: Retrospective cohort. SETTING: Two large quarternary care pediatric hospitals. PATIENTS: Patients 26 y old or younger who received CRRT from 2014 to 2020, excluding patients with chronic kidney disease. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Organ dysfunction was assessed using the Pediatric Logistic Organ Dysfunction-2 (PELOD-2) score. MODS was defined as greater than or equal to two organ dysfunctions. The primary outcome was major adverse kidney events at 30 days (MAKE30) (decrease in estimated glomerular filtration rate greater than or equal to 25% from baseline, need for renal replacement therapy, and death). Three hundred seventy-three patients, 50% female, with a median age of 84 mo (interquartile range [IQR] 16-172) were analyzed. PELOD-2 increased from 6 (IQR 3-9) to 9 (IQR 7-12) between ICU admission and CRRT initiation. Ninety-seven percent of patients developed MODS at CRRT start and 266 patients (71%) had MAKE30. Acute kidney injury (adjusted odds ratio [aOR] 3.55 [IQR 2.13-5.90]), neurologic (aOR 2.07 [IQR 1.15-3.74]), hematologic/oncologic dysfunction (aOR 2.27 [IQR 1.32-3.91]) at CRRT start, and progressive MODS (aOR 1.11 [IQR 1.03-1.19]) were independently associated with MAKE30. CONCLUSIONS: Ninety percent of critically ill children and young adults with AKI-D develop MODS by the start of CRRT. Lack of renal recovery is associated with specific extrarenal organ dysfunction and progressive multiple organ dysfunction. Currently available extrarenal organ support strategies, such as therapeutic plasma exchange lung-protective ventilation, and other modifiable risk factors, should be incorporated into clinical trial design when investigating renal recovery.

Usefulness of the qSOFA score for predicting hospital mortality in cancer patients.

BACKGROUND: The quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) is a score that has been proposed to quickly identify patients at higher risk of death. OBJECTIVE: To describe the usefulness of the qSOFA score to predict in-hospital mortality in cancer patients. MATERIAL AND METHODS: Cross-sectional study carried out between January 2021 and December 2022. Hospital mortality was the dependent variable. The area under the ROC curve (AUC) was calculated to determine the discriminative ability of qSOFA to predict in-hospital mortality. RESULTS: A total of 587 cancer patients were included. A qSOFA score higher than 1 obtained a sensitivity of 57.2%, specificity of 78.5%, a positive predictive value of 55.4% and negative predictive value of 79.7%. The AUC of qSOFA for predicting in-hospital mortality was 0.70. In-hospital mortality of patients with qSOFA scores of 2 and 3 points was 52.7 and 64.4%, respectively. In-hospital mortality was 31.9% (187/587). CONCLUSION: qSOFA showed acceptable discriminative ability for predicting in-hospital mortality in cancer patients.

ANTECEDENTES: El quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) es una puntuación propuesta para identificar de forma rápida a pacientes con mayor probabilidad de morir. OBJETIVO: Describir la utilidad de la puntuación qSOFA para predecir mortalidad hospitalaria en pacientes con cáncer. MATERIAL Y MÉTODOS: Estudio transversal realizado entre enero de 2021 y diciembre de 2022. La mortalidad hospitalaria fue la variable dependiente. Se calculó el área bajo la curva ROC (ABC) para determinar la capacidad discriminativa de qSOFA para predecir mortalidad hospitalaria. RESULTADOS: Se incluyeron 587 pacientes con cáncer. La puntuación qSOFA < 1 obtuvo una sensibilidad de 57.2 %, una especificidad de 78.5 %, un valor predictivo positivo de 55.4 % y un valor predictivo negativo de 79.7 %. El ABC de qSOFA para predecir mortalidad hospitalaria fue de 0.70. La mortalidad hospitalaria de los pacientes con qSOFA de 2 y 3 puntos fue de 52.7 y 64.4 %, respectivamente. La mortalidad hospitalaria fue de 31.9 % (187/587). CONCLUSIÓN: qSOFA mostró capacidad discriminativa aceptable para predecir mortalidad hospitalaria en pacientes con cáncer.

SOFA score performs worse than age for predicting mortality in patients with COVID-19.

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA score in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. We used age and the maximum SOFA score as predictor variables in separate univariate logistic regression models for in-hospital mortality and calculated area under the receiver operator characteristic curves (AU-ROCs) and area under precision-recall curves (AU-PRCs) for each predictor in both cohorts. Among the COVID-19 cohort, age (AU-ROC 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care.

Hepatic function markers as prognostic factors in patients with acute kidney injury undergoing continuous renal replacement therapy.

Acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT), secondary to cardiovascular disease and sepsis, is associated with high in-hospital mortality. Although studies have examined cardiovascular disease and sepsis in AKI, the association between AKI and hepatic functional impairment remains unclear. We hypothesized that hepatic function markers would predict mortality in patients undergoing CRRT. We included 1,899 CRRT patients from a multi-centre database. In Phase 1, participants were classified according to the total bilirubin (T-Bil) levels on the day of, and 3 days after, CRRT initiation: T-Bil < 1.2, 1.2 ≤ T-Bil < 2, and T-Bil ≥ 2 mg/dL. In Phase 2, propensity score matching (PSM) was performed to examine the effect of a T-Bil cutoff of 1.2 mg/dL (supported by the Sequential Organ Failure Assessment score); creating two groups based on a T-Bil cutoff of 1.2 mg/dL 3 days after CRRT initiation. The primary endpoint was total mortality 90 days after CRRT initiation, which was 34.7% (n = 571). In Phase 1, the T-Bil, aspartate transaminase (AST), alanine transaminase (ALT), and AST/ALT (De Ritis ratio) levels at CRRT initiation were not associated with the prognosis, while T-Bil, AST, and the De Ritis ratio 3 days after CRRT initiation were independent factors. In Phase 2, T-Bil ≥1.2 mg/dL on day 3 was a significant independent prognostic factor, even after PSM [hazard ratio: 2.41 (95% CI; 1.84-3.17), p < 0.001]. T-Bil ≥1.2 mg/dL 3 days after CRRT initiation predicted 90-day mortality. Changes in hepatic function markers in acute renal failure may enable stratification of high-risk patients.

Restrictive calcium replacement in septic shock: a multicenter before-after intervention study. / Reposición restrictiva de calcio en pacientes con shock séptico: estudio de intervención antes-después.

OBJECTIVES: To study the impact of a restrictive calcium replacement protocol in comparison with a liberal one in patients with septic shock. MATERIAL AND METHODS: Multicenter retrospective before-after study that estimated the impact of implementing a restrictive calcium replacement protocol in patients with septic shock. Patients admitted to an intensive care unit between May 2019 and April 2021 were assigned to liberal calcium replacement, and those admitted between May 2021 and April 2022 were assigned to a restrictive protocol. The primary outcome measure was 28-day mortality. Patients were matched with propensity scores. RESULTS: A total of 644 patients were included; liberal replacement was used in 453 patients and the restrictive replacement in 191. We paired 553 patients according to propensity scores, 386 in the liberal group and 167 in the restrictive group. Mortality did not differ significantly between the groups at 28 days (35.3% vs 32.3%, respectively; hazard ratio, 0.97; 95% CI, 0.72-1.29) or after resolution of septic shock (81.5% vs 83.8%; hazard ratio, 0.89; 95% CI, 0.73-1.09). Nor did scores on the Sepsis-related Organ Failure Assessment scale differ (2.1 vs 2.6; P = 0.20). CONCLUSION: The implementation of a restrictive calcium replacement protocol in patients with septic shock was not associated with a decrease in 28-day mortality in comparison with use of a liberal protocol. However, we were able to reduce calcium replacement without adverse effects.

OBJETIVO: Investigar el efecto de un protocolo de reposición restrictiva de calcio frente a una estrategia liberal en pacientes con shock séptico. METODO: Estudio multicéntrico, antes-después y retrospectivo que evaluó el efecto de la implementación de un protocolo de reposición restrictiva de calcio en pacientes con shock séptico. Los pacientes que ingresaron en unidades de cuidados intensivos (UCI) entre mayo de 2019 y abril de 2021 se asignaron al grupo con administración liberal, y los que se presentaron entre mayo de 2021 y abril de 2022 ­tras la implementación del protocolo­ al grupo con administración restrictiva. La variable de resultado principal fue la mortalidad a 28 días. Se realizó un emparejamiento por puntuación de propensión. RESULTADOS: Se incluyeron 644 pacientes, 453 en el grupo liberal y 191 en el grupo restrictivo. De los que 553 se emparejaron (386 en el grupo liberal, y 167 en el grupo restrictivo). No hubo diferencias entre los dos grupos en la mortalidad a los 28 días (35,3% vs 32,3%; HR: 0,97; IC 95%: 0,72-1,29), en la finalización del shock (81,5% vs a 83,8%; HR: 0,89; IC 95%: 0,73-1,09) ni en la puntuación de la escala SOFA (2,1 vs 2,6; p = 0,20). CONCLUSIONES: La implementación de un protocolo de administración restrictiva de calcio, en pacientes con shock séptico, no se asoció a una disminución de la mortalidad a los 28 días en comparación con una administración liberal. No obstante, la reposición de calcio podría reducirse sin efectos adversos.

SOFA in sepsis: with or without GCS.

PURPOSE: Sepsis is a global public health burden. The sequential organ failure assessment (SOFA) is the most commonly used scoring system for diagnosing sepsis and assessing severity. Due to the widespread use of endotracheal intubation and sedative medications in sepsis, the accuracy of the Glasgow Coma Score (GCS) is the lowest in SOFA. We designed this multicenter, cross-sectional study to investigate the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis. METHODS: First, 3048 patients with sepsis admitted to Peking Union Medical College Hospital (PUMCH) were enrolled in this survey. The data were collected from June 8, 2013 to October 12, 2022. Second, 18,108 patients with sepsis in the eICU database were enrolled. Third, 2397 septic patients with respiratory system ≥ 3 points in SOFA in the eICU database were included. We investigated the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis in various ICUs of PUMCH, and then we validated the results in the eICU database. MAIN RESULTS: In data of ICUs in PUMCH, the predictive efficiency of SOFA without GCS (AUROC [95% CI], 24 h, 0.724 [0.688, 0.760], 48 h, 0.734 [0.699, 0.769], 72 h, 0.748 [0.713, 0.783], 168 h, 0.781 [0.747, 0.815]) was higher than that of SOFA with GCS (AUROC [95% CI], 24 h, 0.708 [0.672, 0.744], 48 h, 0.721 [0.685, 0.757], 72 h, 0.735 [0.700, 0.757], 168 h, 0.770 [0.736, 0.804]) on ICU mortality in patients with sepsis, and the difference was statistically significant (P value, 24 h, 0.001, 48 h, 0.003, 72 h, 0.004, 168 h, 0.005). In septic patients with respiratory system ≥ 3 points in SOFA in the eICU database, although the difference was not statistically significant (P value, 24 h, 0.148, 48 h, 0.178, 72 h, 0.132, 168 h, 0.790), SOFA without GCS (AUROC [95% CI], 24 h, 0.601 [0.576, 0.626], 48 h, 0.625 [0.601, 0.649], 72 h, 0.639 [0.615, 0.663], 168 h, 0.653 [0.629, 0.677]) had a higher predictive efficiency on ICU mortality than SOFA with GCS (AUROC [95% CI], 24 h, 0.591 [0.566, 0.616], 48 h, 0.616 [0.592, 0.640], 72 h, 0.628 [0.604, 0.652], 168 h, 0.651 [0.627, 0.675]). CONCLUSIONS: In severe sepsis, it is realistic and feasible to discontinue the routine GCS for SOFA in patients with a respiratory system ≥ 3 points, and even better predict ICU mortality.

Construction and evaluation of short-term and long-term mortality risk prediction model for patients with sepsis based on MIMIC-IV database. / 基于MIMIC-IVæž„å»ºåŠè¯„ä¼°è„“æ¯’ç—‡æ‚£è€ è¿‘æœŸå’Œè¿œæœŸæ­»äº¡é£Žé™©é¢„æµ‹æ¨¡åž‹.

OBJECTIVES: Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk. METHODS: Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model. RESULTS: A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value. CONCLUSIONS: The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.

[Early diagnostic value of Presepsin in sepsis: a prospective study on a population with suspected sepsis in fever clinics].

OBJECTIVE: To analyze the early diagnostic value of plasma soluble cluster of differentiation 14 subtype (sCD14-ST, Presepsin) in sepsis in a population with suspected sepsis in fever clinic. METHODS: A prospective observational study was conducted. The patients admitted to the fever clinic of Beijing Chaoyang Hospital from April to December 2022 were enrolled as the study objects. According to sequential organ failure assessment (SOFA) score, the patients were divided into low SOFA score group (SOFA score ≤3) and high SOFA score group (SOFA score > 3). Venous blood was collected at the time of admission. The level of plasma Presepsin was detected by chemiluminescence enzyme-linked immunoassay. The level of plasma procalcitonin (PCT) was detected by enzyme-linked immunofluorescence method. The level of C-reactive protein (CRP) was detected by scattering turbidimetry. White blood cell count (WBC) and neutrophil count (NEUT) were measured by automatic blood cell analyzer. For patients with fear of cold or chills, venous blood of upper limbs was taken for blood culture at the time of admission. The differences in inflammatory biomarkers were compared between the two groups. Binary multivariate Logistic regression analysis was used to screen the early risk factors of sepsis in fever outpatients with suspected sepsis. Receiver operator characteristic curve (ROC curve) was drawn to investigate the early diagnostic value of Presepsin and other inflammatory markers in sepsis, and to analyze the optimal cut-off value. RESULTS: A total of 149 fever outpatients with suspected sepsis were enrolled, including 92 patients with low SOFA score and 57 patients with high SOFA score. Plasma PCT and Presepsin levels in the high SOFA score group were significantly higher than those in the low SOFA score group [PCT (µg/L): 0.77 (0.18, 2.02) vs. 0.22 (0.09, 0.71), Presepsin (ng/L): 1 129.00 (785.50, 1 766.50) vs. 563.00 (460.50, 772.25), both P < 0.01]. There was no significant difference in WBC, NEUT, CRP or positive rate of blood culture between the high and low SOFA score groups [WBC (×109/L): 11.32±5.47 vs. 11.14±5.29, NEUT (×109/L): 9.88±4.89 vs. 9.60±5.10, CRP (mg/L): 54.05 (15.95, 128.90) vs. 46.11 (19.60, 104.60), blood culture positivity rate: 42.3% (11/26) vs. 29.4% (10/34), all P > 0.05]. Multivariate Logistic regression analysis showed that Presepsin was an early risk factor for sepsis in suspected sepsis patients in fever clinics [odds ratio (OR) = 16.96, 95% confidence interval (95%CI) was 6.35-45.29, P = 0.000]. ROC curve analysis showed that the early diagnostic value of Presepsin in sepsis was significantly better than WBC, NEUT, CRP, PCT, and blood culture [the area under the ROC curve (AUC) and 95%CI: 0.832 (0.771-0.899) vs. 0.522 (0.424-0.619), 0.532 (0.435-0.629), 0.533 (0.435-0.632), 0.664 (0.574-0.753), 0.554 (0.458-0.650)]. When the optimal cut-off value of Presepsin was 646.50 ng/L, its sensitivity and positive predictive value were higher than those of WBC, NEUT, CRP, and PCT (sensitivity: 89.5% vs. 38.6%, 68.4%, 38.6%, 57.9%; positive predictive value: 64.6% vs. 44.9%, 44.3%, 47.8%, 55.9%). CONCLUSIONS: Plasma PCT and Presepsin have early diagnostic value for sepsis in suspected sepsis patients in fever clinics, and Presepsin is more sensitive than PCT and can be used as an early marker of sepsis.

Value of modified qSOFA, glucose and lactate in predicting prognosis in children with sepsis in the PICU.

The purpose was to investigate how well age-adjusted modified quick Sequential Organ Failure Assessment (qSOFA) scores paired with blood glucose and lactate levels predict the outcomes of septicemic children in the pediatric intensive care unit (PICU). One hundred children who were diagnosed with sepsis and septic shock in the PICU of Henan Children's Hospital were eligible, and other 20 patients in the same hospital at different times were selected as a validation set. Respiratory rate (RR), heart rate (HR), capillary refill time (CRT), and Alert, Voice, Pain, Unresponsive (AVPU) scale were included in the age-adjusted modified qSOFA scoring criteria for scoring. The primary outcome was 28-day all-cause mortality. The predictive values were evaluated by the ROC curve. In the sepsis group, 50 patients were male, and 50 patients were female. The 28-day all-cause mortality rate was 52%. Fifty-one patients with age-adjusted modified qSOFA scores >1. The serum lactate level was 2.4 mmol/L, and the blood glucose level was 9.3 mmol/L. The AUCs for the age-adjusted modified qSOFA score, serum lactate and blood glucose levels for the prediction of 28-day all-cause mortality in children with sepsis were 0.719, 0.719 and 0.737, respectively. The cut-off values were one point, 3.8 mmol/L and 10 mmol/L, respectively. The AUC of the age-adjusted modified qSOFA score for the validation set of was 0.925. When the three indices were combined, the AUC was 0.817, the Hosmer-Lemeshow goodness-of-fit test showed χ2 = 2.428 and p = .965. When children with sepsis are admitted to the ICU, we recommend performing rapid scoring and rapid bedside lactate and glucose testing to determine the early prognosis.

Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures.

Using clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs has proven challenging, as vital signs are typically sampled sporadically. We proposed a novel, deep temporal interpolation and clustering network to simultaneously extract latent representations from irregularly sampled vital signs and derive phenotypes. Four distinct clusters were identified. Phenotype A (18%) had the greatest prevalence of comorbid disease with increased prevalence of prolonged respiratory insufficiency, acute kidney injury, sepsis, and long-term (3-year) mortality. Phenotypes B (33%) and C (31%) had a diffuse pattern of mild organ dysfunction. Phenotype B's favorable short-term clinical outcomes were tempered by the second highest rate of long-term mortality. Phenotype C had favorable clinical outcomes. Phenotype D (17%) exhibited early and persistent hypotension, high incidence of early surgery, and substantial biomarker incidence of inflammation. Despite early and severe illness, phenotype D had the second lowest long-term mortality. After comparing the sequential organ failure assessment scores, the clustering results did not simply provide a recapitulation of previous acuity assessments. This tool may impact triage decisions and have significant implications for clinical decision-support under time constraints and uncertainty.

Sepsis death risk factor score based on systemic inflammatory response syndrome, quick sequential organ failure assessment, and comorbidities.

OBJECTIVE: In this study, we aimed to evaluate the death risk factors of patients included in the sepsis protocol bundle, using clinical data from qSOFA, SIRS, and comorbidities, as well as development of a mortality risk score. DESIGN: This retrospective cohort study was conducted between 2016 and 2021. SETTING: Two university hospitals in Brazil. PARTICIPANTS: Patients with sepsis. INTERVENTIONS: Several clinical and laboratory data were collected focused on SIRS, qSOFA, and comorbidities. MAIN VARIABLE OF INTEREST: In-hospital mortality was the primary outcome variable. A mortality risk score was developed after logistic regression analysis. RESULTS: A total of 1,808 patients were included with a death rate of 36%. Ten variables remained independent factors related to death in multivariate analysis: temperature ≥38 °C (odds ratio [OR] = 0.65), previous sepsis (OR = 1.42), qSOFA ≥ 2 (OR = 1.43), leukocytes >12,000 or <4,000 cells/mm3 (OR = 1.61), encephalic vascular accident (OR = 1.88), age >60 years (OR = 1.93), cancer (OR = 2.2), length of hospital stay before sepsis >7 days (OR = 2.22,), dialysis (OR = 2.51), and cirrhosis (OR = 3.97). Considering the equation of the binary regression logistic analysis, the score presented an area under curve of 0.668, is not a potential model for death prediction. CONCLUSIONS: Several risk factors are independently associated with mortality, allowing the development of a prediction score based on qSOFA, SIRS, and comorbidities data, however, the performance of this score is low.

A comparison of Simplified Acute Physiology Score II and Sepsis-related Organ Failure Assessment Score for prediction of mortality after Intensive Care Unit cardiac arrest.

BACKGROUND: This study investigates the predictive value and suitable cutoff values of the Sepsis-related Organ Failure Assessment Score (SOFA) and Simplified Acute Physiology Score II (SAPS-II) to predict mortality during or after Intensive Care Unit Cardiac Arrest (ICU-CA). METHODS: In this secondary analysis the ICU database of a German university hospital with five ICU was screened for all ICU-CA between 2016-2019. SOFA and SAPS-II were used for prediction of mortality during ICU-CA, hospital-stay and one-year-mortality. Receiver operating characteristic curves (ROC), area under the ROC (AUROC) and its confidence intervals were calculated. If the AUROC was significant and considered "acceptable," cutoff values were determined for SOFA and SAPS-II by Youden Index. Odds ratios and sensitivity, specificity, positive and negative predictive values were calculated for the cutoff values. RESULTS: A total of 114 (78 male; mean age: 72.8±12.5 years) ICU-CA were observed out of 14,264 ICU-admissions (incidence: 0.8%; 95% CI: 0.7-1.0%). 29.8% (N.=34; 95% CI: 21.6-39.1%) died during ICU-CA. SOFA and SAPS-II were not predictive for mortality during ICU-CA (P>0.05). Hospital-mortality was 78.1% (N.=89; 95% CI: 69.3-85.3%). SAPS-II (recorded within 24 hours before and after ICU-CA) indicated a better discrimination between survival and death during hospital stay than SOFA (AUROC: 0.81 [95% CI: 0.70-0.92] vs. 0.70 [95% CI: 0.58-0.83]). A SAPS-II-cutoff-value of 43.5 seems to be suitable for prognosis of hospital mortality after ICU-CA (specificity: 87.5%, sensitivity: 65.6%; SAPS-II>43.5: 87.5% died in hospital; SAPS-II<43.5: 65.6% survived; odds ratio:13.4 [95% CI: 3.25-54.9]). Also for 1-year-mortality (89.5%; 95% CI: 82.3-94.4) SAPS-II showed a better discrimination between survival and death than SOFA: AUROC: 0.78 (95% CI: 0.65-0.91) vs. 0.69 (95% CI: 0.52-0.87) with a cutoff value of the SAPS-II of 40.5 (specificity: 91.7%, sensitivity: 64.3%; SAPS-II>40.5: 96.4% died; SAPS-II<40.5: 42.3% survived; odd ratio: 19.8 [95% CI: 2.3-168.7]). CONCLUSIONS: Compared to SOFA, SAPS-II seems to be more suitable for prediction of hospital and 1-year-mortality after ICU-CA.