The laboratory parameters-derived CoLab score as an indicator of the host response in ICU COVID-19 patients decreases over time: a prospective cohort study.

The CoLab score was developed and externally validated to rule out COVID-19 among suspected patients presenting at the emergency department. We hypothesized a within-patient decrease in the CoLab score over time in an intensive care unit (ICU) cohort. Such a decrease would create the opportunity to potentially rule out the need for isolation when the infection is overcome. Using linear mixed-effects models, data from the Maastricht Intensive Care COVID (MaastrICCht) cohort were used to investigate the association between time and the CoLab score. Models were adjusted for sex, APACHE II score, ICU mortality, and daily SOFA score. The CoLab score decreased by 0.30 points per day (95% CI - 0.33 to - 0.27), independent of sex, APACHE II, and Mortality. With increasing SOFA score over time, the CoLab score decreased more strongly (- 0.01 (95% CI - 0.01 to - 0.01) additional decrease per one-point increase in SOFA score.) The CoLab score decreased in ICU patients on mechanical ventilation for COVID-19, with a one-point reduction per three days, independent of sex, APACHE II, and ICU mortality, and somewhat stronger with increasing multi-organ failure over time. This suggests that the CoLab score would decrease below a threshold where COVID-19 can be excluded.

[Construction and analysis of early warning and prediction model for risk factors of sepsis-associated encephalopathy].

OBJECTIVE: To investigate the epidemiological characteristics of sepsis-associated encephalopathy (SAE) in patients with sepsis, analyze its risk factors and build a prediction model, which provides evidence for early clinical identification of SAE patients and improvement of clinical outcomes. METHODS: A retrospective observational study was conducted. Sepsis patients admitted to the critical care medical center of the First Affiliated Hospital of Xinjiang Medical University from February 2022 to February 2023 were enrolled. According to whether SAE occurred, the patients were divided into sepsis group and SAE group. The 24 patients without sepsis in the same period were used as controls (non-sepsis group). Demographic data, relevant scores and laboratory test indicators at admission to intensive care unit (ICU), and prognostic indicators were collected. Univariate and multivariate Logistic regression analysis was used to analyze the risk factors for sepsis and SAE. Receiver operator characteristic curve (ROC curve) was drawn. The predictive value of each risk factor for sepsis and SAE. RESULTS: A total of 130 patients with sepsis were included, of which 52 had SAE, and the incidence of SAE was 40.00%. There were significant differences in the length of ICU stay and total length of stay among all groups, while there were no significant differences in hospitalization cost and mechanical ventilation time. Multivariate Logistic regression analysis showed that pulmonary infection [odds ratio (OR) = 46.817, 95% confidence interval (95%CI) was 5.624-389.757, P = 0.000], acute physiology and chronic health evaluation II (APACHE II: OR = 1.184, 95%CI was 1.032-1.358, P = 0.016), sequential organ failure assessment (SOFA: OR = 9.717, 95%CI was 2.618-36.068, P = 0.001), Charson comorbidity index (CCI: OR = 4.836, 95%CI was 1.860-12.577, P = 0.001), hemoglobin (Hb: OR = 0.893, 95%CI was 0.826-0.966, P = 0.005), glutamyltranspeptidase (OR = 1.026, 95%CI was 1.008-1.045, P = 0.006) were independent risk factors for sepsis in ICU patients. Pulmonary infection (OR = 28.795, 95%CI was 3.296-251.553, P = 0.002), APACHE II score (OR = 1.273, 95%CI was 1.104-1.467, P = 0.001), SOFA score (OR = 8.670, 95%CI was 2.330-32.261, P = 0.001), CCI (OR = 5.141, 95%CI was 1.961-13.475, P = 0.001), Hb (OR = 0.922, 95%CI was 0.857-0.993, P = 0.031), glutamyltranspeptidase (OR = 1.020, 95%CI was 1.002-1.038, P = 0.030) were independent risk factors for SAE in sepsis patients. ROC curve analysis showed that the area under the curve (AUC) of pulmonary infection, APACHE II score, SOFA score, CCI, Hb, and glutamyltranspeptidase for predicting sepsis were 0.792, 0.728, 0.987, 0.933, 0.720, and 0.699, respectively; the AUC of the combined prediction of the above 6 variables for sepsis was 1.000, with a sensitivity of 100% and a specificity of 100%. The AUC predicted by pulmonary infection, APACHE II score, SOFA score, CCI, and Hb for SAE were 0.776, 0.810, 0.907, 0.917, and 0.758, respectively; the AUC of the combined prediction of the above 5 variables for SAE was 0.975, with a sensitivity of 97.3% and a specificity of 93.1%. CONCLUSIONS: Sepsis is more severe when accompanied by encephalopathy. Pulmonary infection, Hb, APACHE II score, SOFA score and CCI were independent risk factors of SAE. The combination of the above five indicators has good predictive value for early screening and prevention of the disease.

[Expression and diagnostic value of growth differentiation factor 15 in patients with septic cardiomyopathy].

OBJECTIVE: To explore the expression of growth differentiation factor 15 (GDF15) in patients with septic cardiomyopathy and its value in the diagnosis of septic cardiomyopathy. METHODS: A observational study was conducted. Fifty patients with septic cardiomyopathy admitted to Shanxi Bethune Hospital from May 2022 to March 2023 were selected as the experimental group. Forty-six patients with acute coronary syndrome (ACS) in the same period were selected as the case control group. Forty-nine healthy adults were selected as the healthy control group, who underwent physical examination in the physical examination center during the same period. The demographic data and clinical indicators of the subjects were recorded, and the serum GDF15 level was detected by double sandwich enzyme-linked immunosorbent assay (ELISA). And the 28-day outcome of patients with septic cardiomyopathy was followed up, and they were divided into survival group and death group. The serum GDF15 level of subjects in each group and its correlation with clinical indicators were analyzed and compared. Binary Logistic regression was used to analyze the risk factors of septic cardiomyopathy. Receiver operator characteristic curve (ROC curve) was used to evaluate the value of GDF15 in the diagnosis of septic cardiomyopathy. RESULTS: The serum GDF15 level of experimental group was significantly higher than that in the case control group and healthy control group [ng/L: 314.14 (221.96, 469.56) vs. 39.08 (26.27, 76.85), 6.39 (3.35, 14.42), both P < 0.01]. Correlation analysis showed that serum GDF15 level in patients with septic cardiomyopathy were correlated with cardiac troponin I (cTnI, r = 0.295, P = 0.038), brain natriuretic peptide (BNP, r = 0.464, P = 0.009), sequential organ failure assessment (SOFA, r = 0.363, P = 0.010) and acute physiology and chronic health evaluation II (APACHE II, r = 0.316, P = 0.025). However, there was no significant correlation with white blood cell count, neutrophil count, lymphocyte count, procalcitonin, C-reactive protein, lactic acid, albumin and other clinical indicators (r values were 0.086, 0.123, -0.051, 0.055, 0.119, 0.199, -0.234, all P > 0.05). Serum GDF15 level, SOFA score and APACHE II score in the death group (30 cases) were significantly higher than those in the survival group [20 cases; GDF15 (ng/L): 382.93±159.61 vs. 289.66±158.46, SOFA: 10.00 (7.00, 12.00) vs. 6.00 (5.00, 9.50), APACHE II: 21.70±6.07 vs. 14.85±7.57, all P < 0.05]. Binary Logistic regression analysis showed that serum GDF15 was an independent risk factor for the onset of septic cardiomyopathy [odds ratio (OR) = 1.062, 95% confidence interval (95%CI) was 1.011-1.115, P = 0.016]. ROC curve showed that the area under the curve (AUC) of GDF15 for predicting septic cardiomyopathy was 0.971, the specificity was 100%, and the sensitivity was 90.3%. CONCLUSIONS: The serum GDF15 level of patients with septic cardiomyopathy is significantly increased, and GDF15 may be used as an effective biomarker for the early diagnosis of septic cardiomyopathy.

Development of prognostic models for predicting 90-day neurological function and mortality after cardiac arrest.

BACKGROUND: The survivors of cardiac arrest experienced vary extent of hypoxic ischemic brain injury causing mortality and long-term neurologic disability. However, there is still a need to develop robust and reliable prognostic models that can accurately predict these outcomes. OBJECTIVES: To establish reliable models for predicting 90-day neurological function and mortality in adult ICU patients recovering from cardiac arrest. METHODS: We enrolled patients who had recovered from cardiac arrest at Binhaiwan Central Hospital of Dongguan, from January 2018 to July 2021. The study's primary outcome was 90-day neurological function, assessed and divided into two categories using the Cerebral Performance Category (CPC) scale: either good (CPC 1-2) or poor (CPC 3-5). The secondary outcome was 90-day mortality. We analyzed the relationships between risk factors and outcomes individually. A total of four models were developed: two multivariable logistic regression models (models 1 and 2) for predicting neurological function, and two Cox regression models (models 3 and 4) for predicting mortality. Models 2 and 4 included new neurological biomarkers as predictor variables, while models 1 and 3 excluded. We evaluated calibration, discrimination, clinical utility, and relative performance to establish superiority between the models. RESULTS: Model 1 incorporates variables such as gender, site of cardiopulmonary resuscitation (CPR), total CPR time, and acute physiology and chronic health evaluation II (APACHE II) score, while model 2 includes gender, site of CPR, APACHE II score, and serum level of ubiquitin carboxy-terminal hydrolase L1 (UCH-L1). Model 2 outperforms model 1, showcasing a superior area under the receiver operating characteristic curve (AUC) of 0.97 compared to 0.83. Additionally, model 2 exhibits improved accuracy, sensitivity, and specificity. The decision curve analysis confirms the net benefit of model 2. Similarly, models 3 and 4 are designed to predict 90-day mortality. Model 3 incorporates the variables such as site of CPR, total CPR time, and APACHE II score, while model 4 includes APACHE II score, total CPR time, and serum level of UCH-L1. Model 4 outperforms model 3, showcasing an AUC of 0.926 and a C-index of 0.830. The clinical decision curve analysis also confirms the net benefit of model 4. CONCLUSIONS: By integrating new neurological biomarkers, we have successfully developed enhanced models that can predict 90-day neurological function and mortality outcomes more accurately.

Tocilizumab treatment in COVID-19 patients: therapy's side effects and effect on mortality.

OBJECTIVE: This study aimed to determine the effect of tocilizumab use on mortality and the potential side effects in COVID-19 patients. PATIENTS AND METHODS: The intensive care patients were divided into the tocilizumab group and the control group. Hemogram, biochemistry, acute phase reactant values, age, gender, comorbidity, and culture results were recorded on the 0th, 3rd, 7th, and 14th days. Factors affecting mortality between and within the groups and side effects were examined. RESULTS: 32.14% of the patients were female, and 67.85% were male. The tocilizumab group had high alanine aminotransferase and potassium on day 3. On day 7, low levels of platelet, glucose, international normalized ratio, prothrombin time, and active partial thromboplastin time levels were observed. Procalcitonin, C-reactive protein, and fibrinogen levels were low on days 3 and 7. The relationship between the tocilizumab treatment and mortality was statistically not significant, although the APACHE score was low. In the tocilizumab group, the presence of additional disease and reproduction in culture significantly increased mortality. CONCLUSIONS: Despite the risks of side effects, tocilizumab was used in COVID-19 treatment since it is an interleukin-6 blocker. Although the first publications stated that the treatment could decrease the mortality rate, later meta-analyses did not support these results. Our study also found that using tocilizumab did not make a difference in long-term mortality. We also observed that the known side effects were seen in short-term use.

Magnesium levels and mortality relationship in patients with Acinetobacter baumannii detected in the Intensive Care Unit.

OBJECTIVE: Acinetobacter baumannii (A. baumannii) causes serious nosocomial infections, especially in Intensive Care Units (ICU). Studies have shown that magnesium (Mg) levels change in sepsis. This study aimed to investigate the effect of Mg levels on mortality in patients with A. baumannii sepsis in the ICU. PATIENTS AND METHODS: 140 patients who were hospitalized in the tertiary ICU between January 2018 and March 2020 and who were found to have A. baumannii sepsis in their culture follow-ups were included in the study. Demographic information of the patients, Mg levels during hospitalization and follow-up, and various data in the ICU were recorded. RESULTS: The factors that predicted one-month mortality were old age, APACHE II score, CCIS, A. baumannii detection in the early stages of ICU admission, and high Mg level on day A. baumannii was detected, and the lowest Mg level after A. baumannii was detected in the early period. According to the multivariate logistic regression analysis, age increase [OR (95% CI): 1.062 (1.009-1.117)], APACHE II increase [OR (95% CI): 1.251 (1.141-1.372)], and early detection of A. baumannii during ICU admission [OR (95% CI): 0.902 (0.845-0.962)] were found to be factors that increase one-month mortality. CONCLUSIONS: Hypermagnesemia in patients with A. baumannii indicates longer-term mortality, while a rapid decrease in Mg levels is a predictor of early mortality. Keeping Mg levels of patients within the reference range with frequent Mg measurement reduces mortality. Knowing colonized patients during admission to the ICU may be useful as an indicator of A. baumannii infection development and mortality risk.

Prognostic value of Pleth Variability Index in patients followed up in the Intensive Care Unit.

OBJECTIVE: The Pleth Variability Index (PVI) can guide the approach to hypovolemia, which is sometimes the cause and sometimes the result of major diseases; further studies are needed on this index. Therefore, in the present study, we aimed to evaluate the prognostic value of PVI and its relationship with 28-day mortality. PATIENTS AND METHODS: A total of 158 patients were included. Patients were divided into two groups according to 28-day mortality. Patients who died within 28 days were assigned to Group M (Mortal), while those who survived were included in Group S (Survive). Patients' demographics, definitive diagnosis, arterial blood pressure, fingertip oxygen saturation, PVI, fingertip blood glucose, fever, pulse, shock index, and serum lactate level were recorded. RESULTS: Regarding demographics, no statistically significant difference was found between the two groups in terms of age, gender, and Body Mass Index (BMI) (p=0.356, p=0.966, and p=0.977, respectively). The rate of intubation, the use of vasopressors, Acute Physiology and Chronic Health Evaluation (APACHE) II score, shock index, and PVI values were statistically significantly higher in Group M compared to Group S (for all, p<0.001). Glasgow Coma Score (GCS), Perfusion Index (PI), and length of stay were statistically significantly lower in Group M than in Group S (p<0.001, p<0.001, and p=0.025, respectively). PVI predicted 28-day mortality with 83.8% sensitivity and 97.9% specificity. CONCLUSIONS: PVI, serum lactate level, PI, APACHE II, GCS, and need for vasopressors were independent risk factors for 28-day mortality in the Intensive Care Unit (ICU). PVI and serum lactate have a prognostic value in predicting mortality.

Complicated anorectal sepsis: Validation of scoring system for predicting anorectal sepsis severity.

Anorectal sepsis is a common and potentially serious medical condition characterized by infection and inflammation of the anal canal and surrounding tissues. However, the lack of standardized and comprehensive scoring systems specifically tailored for predicting the severity of anorectal sepsis poses challenges in clinical practice. This study aimed to develop and validate a scoring system for predicting the severity of anorectal sepsis by incorporating relevant patient factors. A retrospective cohort study was conducted at Mansoura University Hospital, a tertiary care center, over a period of 5 years. The study population consisted of 330 patients diagnosed with anorectal sepsis during the study period. A scoring system was developed using multiple variables, with each variable assigned a specific score based on its clinical significance and weight in predicting disease severity. The developed scoring system's predictive performance was evaluated using receiver operating characteristic (ROC) analysis, calculating the area under the ROC curve to assess discriminative ability. Descriptive statistics were used to summarize the demographic and clinical characteristics of the study population. Chi-square tests or t tests were performed to assess differences between non-severe and severe anal sepsis groups. The scoring system consisted of 12 variables, with a maximum total score of 18. The logistic regression analysis revealed significant associations between localized swelling, presentation within 72 hours, multiple drainage sessions, and severe anorectal sepsis. The ROC analysis showed an area under the curve of 0.85, indicating good discriminative ability of the scoring system. The scoring system was developed and validated in a single center, which may limit its generalizability to other settings. The scoring system demonstrated good predictive performance and can be a valuable tool for clinicians in assessing disease severity, guiding treatment decisions, and identifying high-risk patients.

[Establishing a prognostic prediction model for patients with septic shock based on the completion time of fluid resuscitation and the negative fluid balance volumes].

OBJECTIVE: To explore the relationship between the completion time of fluid resuscitation as well as negative fluid balance volumes and the prognosis of patients with septic shock, and to try to construct a prediction model based on the completion time of fluid resuscitation and negative fluid balance volumes, and to verify the predictive efficacy of the model on the prognosis of patients with septic shock. METHODS: Patients with septic shock admitted to Wuxi People's Hospital from April 2020 to April 2023 were selected. The general data (gender, age, body mass index, infection site), pathological indicators on admission, the difference of acute physiology and chronic health evaluation II (APACHE II) and sequential organ failure assessment (SOFA) between admission and 24 hours after fluid resuscitation, the completion time of fluid resuscitation and negative fluid balance volume were recorded. Multivariate Logistic analysis was used to screen the influencing factors of the prognosis of patients with septic shock, and a nomogram model was established. Bootstrap method was used for internal validation of the model. The consistency index, calibration curve and receiver operator characteristic curve (ROC curve) were used to evaluate the accuracy and prediction efficiency of the model. RESULTS: A total of 96 patients with septic shock were enrolled, 38 patients died and 58 patients survived at 28 days. Compared with the survival group, the difference of APACHE II score, SOFA score, the proportion of fluid resuscitation completed within 1 to 3 hours, and the proportion of negative fluid balance volume -500 to -250 mL per day in the death group were lower, and the differences were statistically significant (all P < 0.05). Multivariate Logistic analysis showed that the completion time of fluid resuscitation was a risk factor for the prognosis of patients with septic shock [odds ratio (OR) = 26.285, 95% confidence interval (95%CI) was 9.984-76.902, P < 0.05]. The difference of APACHE II score (OR = 0.045, 95%CI was 0.015-0.131), SOFA score (OR = 0.056, 95%CI was 0.019-0.165) between admission and 24 hours after fluid resuscitation, and negative fluid balance volume (OR = 0.043, 95%CI was 0.015-0.127) were protective factors for the prognosis of patients with septic shock (all P < 0.05). The model validation results showed that the consistency index was 0.681 (95%CI was 0.596-0.924), indicating good discrimination. The calibration curve showed that the calibration curve fitted well with the ideal curve. The ROC curve showed that the sensitivity of the nomogram model for predicting the death of patients with septic shock was 83.7%, the specificity was 97.2%, and the area under the ROC curve (AUC) was 0.931 (95%CI was 0.846-0.985), indicating that the model had good prediction efficiency. CONCLUSIONS: The completion time of fluid resuscitation and negative fluid balance volumes are related to the prognosis of septic shock patients, and the alignment diagram model improve the identification of the risk of death in septic shock patients.

The Comparison of scoring systems: SOFA, APACHE-II, LODS, MODS, and SAPS-II in critically ill elderly sepsis patients.

INTRODUCTION: The elderly population is unique and the prognostic scoring systems developed for the adult population need to be validated. We evaluated the predictive value of frequently used scoring systems on mortality in critically ill elderly sepsis patients. METHODOLOGY: In this single-center, observational, prospective study, critically ill elderly sepsis patients were evaluated. Sequential organ failure evaluation score (SOFA), acute physiology and chronic health evaluation score-II (APACHE-II), logistic organ dysfunction score (LODS), multiple organ dysfunction score (MODS), and simplified acute physiology score-II (SAPS-II) were calculated. The participants were followed up for 28 days for in-hospital mortality. Prognostic scoring systems, demographic characteristics, comorbid conditions, and baseline laboratory findings were compared between "survivor" and "non-survivor" groups. RESULTS: 202 patients with a mean age of 79 (interquartile range, IQR: 11) years were included, and 51% (n = 103) were female. The overall mortality was 41% (n = 83). SOFA, APACHE-II, LODS, MODS, and SAPS-II scores were significantly higher in the non-survivor group (p < 0.001), and higher scores were correlated with higher mortality. The receiver operator characteristics (ROC) - area under curve (AUC) values were 0.802, 0.784, 0.735, 0.702 and 0.780 for SOFA, APACHE-II, LODS, MODS, and SAPS-II, respectively. All prognostic scoring models had a significant discriminative ability on the prediction of mortality among critically ill elderly sepsis patients (p < 0.001). CONCLUSIONS: This study showed that SOFA, APACHE-II, LODS, MODS, and SAPS-II scores are significantly associated with 28-day mortality in critically ill elderly sepsis patients, and can be successfully used for predicting mortality.

Use of Aprotinin versus Tranexamic Acid in Cardiac Surgery Patients with High-Risk for Excessive Bleeding (APACHE) trial: a multicentre retrospective comparative non-randomized historical study.

OBJECTIVES: Following the reintroduction of aprotinin into the European market, the French Society of Cardiovascular and Thoracic Anaesthesiologists recommended its prophylactic use at half-dose for high-risk cardiac surgery patients. We examined whether the use of aprotinin instead of tranexamic acid could significantly reduce severe perioperative bleeding. METHODS: This multicentre, retrospective, historical study included cardiac surgery patients treated with aprotinin or tranexamic acid between December 2017 and September 2020. The primary efficacy end point was the severe or massive perioperative bleeding (class 3-4 of the universal definition of perioperative bleeding). The safety secondary end points included the occurrence of thromboembolic events and all-cause mortality within 30 days after surgery. RESULTS: Among the 693 patients included in the study, 347 received aprotinin and 346 took tranexamic acid. The percentage of patients with severe or massive bleeding was similar in the 2 groups (42.1% vs 43.6%, Adjusted odds ratio [ORadj] = 0.87, 95% confidence interval: 0.62-1.23, P = 0.44), as was the perioperative need for blood products (81.0% vs 83.2%, ORadj = 0.75, 95% confidence interval: 0.48-1.17, P = 0.20). However, the median (Interquartile range) 12 h postoperative blood loss was significantly lower in the aprotinin group (383 ml [241-625] vs 450 ml [290-730], P < 0.01). Compared to tranexamic acid, the intraoperative use of aprotinin was associated with increased risk for thromboembolic events (adjusted Hazard ratio 2.30 [95% Cl: 1.06-5.30]; P = 0.04). CONCLUSIONS: Given the modest reduction in blood loss at the expense of a significant increase in thromboembolic adverse events, aprotinin use in high-risk cardiac surgery patients should be based on a carefully considered benefit-risk assessment.

Prognostic evaluation of quick sequential organ failure assessment score in ICU patients with sepsis across different income settings.

BACKGROUND: There is conflicting evidence on association between quick sequential organ failure assessment (qSOFA) and sepsis mortality in ICU patients. The primary aim of this study was to determine the association between qSOFA and 28-day mortality in ICU patients admitted for sepsis. Association of qSOFA with early (3-day), medium (28-day), late (90-day) mortality was assessed in low and lower middle income (LLMIC), upper middle income (UMIC) and high income (HIC) countries/regions. METHODS: This was a secondary analysis of the MOSAICS II study, an international prospective observational study on sepsis epidemiology in Asian ICUs. Associations between qSOFA at ICU admission and mortality were separately assessed in LLMIC, UMIC and HIC countries/regions. Modified Poisson regression was used to determine the adjusted relative risk (RR) of qSOFA score on mortality at 28 days with adjustments for confounders identified in the MOSAICS II study. RESULTS: Among the MOSAICS II study cohort of 4980 patients, 4826 patients from 343 ICUs and 22 countries were included in this secondary analysis. Higher qSOFA was associated with increasing 28-day mortality, but this was only observed in LLMIC (p < 0.001) and UMIC (p < 0.001) and not HIC (p = 0.220) countries/regions. Similarly, higher 90-day mortality was associated with increased qSOFA in LLMIC (p < 0.001) and UMIC (p < 0.001) only. In contrast, higher 3-day mortality with increasing qSOFA score was observed across all income countries/regions (p < 0.001). Multivariate analysis showed that qSOFA remained associated with 28-day mortality (adjusted RR 1.09 (1.00-1.18), p = 0.038) even after adjustments for covariates including APACHE II, SOFA, income country/region and administration of antibiotics within 3 h. CONCLUSIONS: qSOFA was independently associated with 28-day mortality in ICU patients admitted for sepsis. In LLMIC and UMIC countries/regions, qSOFA was associated with early to late mortality but only early mortality in HIC countries/regions.

Interoperability Is a Process - The Data Sharing Framework.

Common syntax and data semantics are core components of healthcare interoperability standards. However, interoperable data exchange processes are also needed to enable the integration of existing systems between organizations. While solutions for healthcare delivery processes are available and have been widely adopted, support for processes targeting bio-medical research is limited. Our Data Sharing Framework creates a platform to implement research processes like cohort size estimation, reviews and approvals of research proposals, consent checks, record linkage, pseudonymization and data sharing across organizations. The described framework implements a distributed business process engine for executing BPMN 2.0 processes with synchronization and data exchange using FHIR R4 resources. Our reference implementation has been rolled out to 38 organizations across three research consortia in Germany and is available as open source under the Apache 2.0 license.

Predictive value of soluble CD40L combined with APACHE II score in elderly patients with sepsis in the emergency department.

BACKGROUND: The prognostic performance of soluble CD40L (sCD40L) for illness severity in infectious diseases is rarely reported. We investigated the ability of sCD40L combined with Acute Physiology and Chronic Health Evaluation II (APACHE II) score to evaluate mortality in septic patients in the emergency department(ED). METHODS: We enrolled 222 septic patients in the ED of Beijing Chao-Yang Hospital from October 2020 to April 2021. Their serum sCD40L, PCT, lactate (Lac), Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation II (APACHE II) score were used to predict the prognosis of septic patients in terms of 28-day mortality. Serum sCD40L was detected by Human XL Cytokine Luminex. Logistic regression analysis and receiver operating characteristic (ROC) curves were used to assess the prognostic value of the variables. RESULTS: One hundred ninety-five patients met the inclusion criteria, divided into survival group (55 cases) and non-survival group (140 cases). sCD40L, PCT, Lac, SOFA and APACHE II score were found to independently predict 28-day mortality (P < 0.05). The AUC values of sCD40L, PCT, Lac, SOFA and APACHE II score were 0.662,0.727,0.704, 0.719 and 0.716, respectively. There was no difference in the diagnostic value of sCD40L compared with the PCT, Lac, SOFA score or APACHE II score (Z1 = 1.19, P = 0.234; Z2 = 0.77, P = 0.441; Z3 = 1.05, P = 0.294; Z4 = 0.97, P = 0.332). However, the combined evaluation of sCD40L + APACHE II (AUC:0.772, Z = 2.10, P = 0.036) was much better than sCD40L alone in predicting 28-day mortality. CONCLUSION: The predictive value of sCD40L + APACHE II is better than sCD40L alone for 28-day mortality. sCD40L combined with APACHE II score is valuable for predicting 28-day mortality in elderly patients with sepsis.

Application of machine learning for mortality prediction in patients with candidemia: Feasibility verification and comparison with clinical severity scores.

BACKGROUND: Clinical severity scores, such as acute physiology, age, chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), Pitt Bacteremia Score (PBS), and European Confederation of Medical Mycology Quality (EQUAL) score, may not reliably predict candidemia prognosis owing to their prespecified scorings that can limit their adaptability and applicability. OBJECTIVES: Unlike those fixed and prespecified scorings, we aim to develop and validate a machine learning (ML) approach that is able to learn predictive models adaptively from available patient data to increase adaptability and applicability. METHODS: Different ML algorithms follow different design philosophies and consequently, they carry different learning biases. We have designed an ensemble meta-learner based on stacked generalisation to integrate multiple learners as a team to work at its best in a synergy to improve predictive performances. RESULTS: In the multicenter retrospective study, we analysed 512 patients with candidemia from January 2014 to July 2019 and compared a stacked generalisation model (SGM) with APACHE II, SOFA, PBS and EQUAL score to predict the 14-day mortality. The cross-validation results showed that the SGM significantly outperformed APACHE II, SOFA, PBS, and EQUAL score across several metrics, including F1-score (0.68, p < .005), Matthews correlation coefficient (0.54, p < .05 vs. SOFA, p < .005 vs. the others) and the area under the curve (AUC; 0.87, p < .005). In addition, in an independent external test, the model effectively predicted patients' mortality in the external validation cohort, with an AUC of 0.77. CONCLUSIONS: ML models show potential for improving mortality prediction amongst patients with candidemia compared to clinical severity scores.

Validation of PRE-DELIRIC and E-PRE-DELIRIC in a Danish population of intensive care unit patients-A prospective observational multicenter study.

BACKGROUND: Delirium is a clinical condition characterized by an acute change in brain function and is frequently observed in critically ill patients. The condition has been associated with negative outcomes, making it crucial to identify patients who are at risk. Two recent prediction models have been developed to estimate the risk of delirium in intensive care unit (ICU) patients; the prediction model for delirium (PRE-DELIRIC) and the early prediction model for delirium (E-PRE-DELIRIC). We aimed to perform an external validation of these models in a Danish cohort of critically ill patients. METHODS: We conducted a prospective, observational multicenter study to validate the PRE-DELIRIC and E-PRE-DELIRIC models in a population of patients admitted to four general ICUs in the Zealand Region of Denmark. From January 2022 to January 2023 all adult patients acutely admitted to the participating ICUs were assessed for eligibility. Patients had to be admitted to the ICU for >24 h to be included in the study. Included patients were screened with E-PRE-DELIRIC upon ICU admission and PRE-DELIRIC after 24 h of admission and followed throughout their ICU stay with CAM-ICU delirium assessments. Our primary outcomes were the prognostic accuracy measured by Area Under the Receiver Operating Characteristics (AUROC) and the calibration plot for the E-PRE-DELIRIC and PRE-DELIRIC prediction models. RESULTS: We included 660 patients, of whom 660 were assessed with E-PRE-DELIRIC, and 622 were assessed with PRE-DELIRIC. PRE-DELIRIC showed acceptable discrimination with AUROC of 0.70 (95% CI 0.66 to 0.74) and good calibration. E-PRE-DELIRIC had inadequate discrimination AUROC of 0.63 (95% CI 0.58 to 0.67) and poor calibration. CONCLUSION: In a Danish cohort, we found that the PRE-DELIRIC model demonstrated acceptable performance and E-PRE-DELIRIC demonstrated poor performance. In critically ill adult patients PRE-DELIRIC may be useful in identifying patients at high risk of delirium.

Chronic critical illness in critically ill COVID-19 patients.

OBJECTIVES: To evaluate the presence of chronic critical illness (CCI) in COVID-19 patients and compare clinical characteristics and prognosis of patients with and without CCI admitted to intensive care unit (ICU). METHODS: It was a retrospective, observational study at a university hospital ICU. Patients were accepted as CCI if they had prolonged ICU stay (≥14 days) and got ≥1 score for cardiovascular sequential organ failure assessment (SOFA) score and ≥2 score in other parameters on day 14 of ICU admission which was described as persistent organ dysfunction. RESULTS: 131 of 397 (33%) patients met CCI criteria. CCI patients were older (p = 0.003) and frailer (p < 0.001). Their Acute Physiology and Chronic Health Evaluation (APACHE) II and SOFA scores were higher, PaO2/FiO2 ratio was lower (p < 0.001). Requirement of invasive mechanical ventilation (IMV), steroid use, and septic shock on admission were higher in the CCI group (p < 0.001). CCI patients had higher ICU and hospital mortality than other patients (54.2% vs. 19.9% and 55.7% vs. 22.6%, p < 0.001, respectively). Regression analysis revealed that IMV (OR: 8.40, [5.10-13.83], p < 0.001) and PaO2/FiO2 < 150 on admission (OR: 2.25, [1.36-3.71], p = 0.002) were independent predictors for CCI. DISCUSSION: One-third of the COVID-19 patients admitted to the ICU were considered as CCI with significantly higher ICU and hospital mortality.

Impact of missing values on the ability of the acute physiology and chronic health evaluation III and Japan risk of death models to predict mortality.

PURPOSE: This study assessed model performance of the Acute Physiology and Chronic Health Evaluation (APACHE) III and Japan Risk of Death (JROD) when degraded by the number and category of missing variables. We also examined the impact of missing data on predicted mortality for facilities with missing physiological variables. METHODS: We obtained data from the Japanese Intensive care PAtient Database (JIPAD). We calculated observed and predicted mortality rates using the APACHE III and JROD and the standardized mortality ratio (SMR) by the number and category of missing variables. Smoothed spline curves were calculated for the SMR to the missing proportion of the facility. RESULTS: A total of 61,357 patients from 57 ICUs were included between April 2015 and March 2019. The APACHE III and JROD SMRs increased as the number of missing values increased. The SMR in the APACHE III model was elevated in facilities with a larger proportion of missing in each of the APS categories, arterial blood gas, albumin, glucose, and bilirubin. Facilities with a high proportion of missing albumin data preserved their SMRs in only the JROD model. CONCLUSION: An increased number of missing physiological variables resulted in falsely low predicted mortality rates and high SMRs.

Influence of severity of illnesses on risk of death in intensive care unit patients with severity of pressure injuries as mediating variable.

This study was to verify whether the severity of pressure injuries (PIs) in intensive care unit (ICU) patients plays a mediating role in the relationship between severity of their illnesses and risk of death. I examined adult patients admitted to the ICUs between 1 January 2014 and 31 August 2021. The average follow-up period was 11.34 months. A total of 390 ICU patients suffered from PIs. The influences of the APACHE II score of the ICU patients on the mediating variable 'unstageable & DTPIs vs. Stage 1&2 PIs' and on risk of death were significant. After controlling the influence of APACHE II score on risk of death, the influences of mediating variables 'Stage 3&4 PIs vs. Stage 1&2 PIs' and 'unstageable & DTPIs vs. Stage 1&2 PIs' on risk of death were also significant. The regression coefficient of APACHE II score of the ICU patients declined after the severity of PIs was included. The Sobel test on the indirect effects also reached the level of significance. The severity of illnesses is a factor that is beyond my control, severe PIs should still be prevented to lower the risk of death.