Focused liquid ultrasonography in dropsy protocol for quantitative assessment of subcutaneous edema.

BACKGROUND: Although subcutaneous edema is a common symptom of critically ill patients, it is still underreported due to the lack of a systematic method for evaluating it. The present study aims to describe the occurrence and distribution of subcutaneous edema, as well as the risk factors associated with it, in critically ill patients using the focused liquid ultrasonography in dropsy (FLUID) protocol, and to assess their impact on ICU mortality. METHODS: The FLUID protocol and the pitting test were performed on general ICU patients in China. Cohen's Kappa coefficient and Bland-Altman plots were used to evaluate the agreement between the two methods at each measurement site and between the whole-body subcutaneous edema scores, respectively, while a repeated measures ANOVA was performed to compare the differences between the two methods in whole-body and body-part measurements. A generalized linear model was used to evaluate the risk factors for subcutaneous edema development and the relationship between subcutaneous edema severity and ICU mortality. RESULTS: A total of 145 critically ill patients were evaluated using both approaches, of whom 40 (27.6%) experienced subcutaneous edema. Over 1440 measurements, it was found that ultrasound discovered more subcutaneous edema than the pitting test (ultrasound: 522[36.3%], pitting test: 444[30.8%], χ2 = 9.477, p = 0.002). The FLUID protocol scored edema severity significantly higher than the pitting test in the whole body and specific body parts, including the abdominal wall, thighs, chest wall, and hands. Subcutaneous edema exhibited gravity-dependent distribution patterns, particularly in the abdominal wall. The APACHE II, NT-proBNP, serum creatinine, and sepsis were independent risk factors for subcutaneous edema development. The score of ultrasonic subcutaneous edema was related to ICU mortality. CONCLUSIONS: The FLUID protocol provides a comprehensive strategy for the semi-quantitative assessment of subcutaneous edema in critically ill patients. In detecting the onset and severity of edema, ultrasound was found to outperform the pitting test. Subcutaneous edema showed a gravity-dependent distribution pattern, and its severity was associated with mortality.

[Value of serum procalcitonin and acute physiology and chronic health evaluation II score on predicting the prognosis of sepsis in elderly patients].

OBJECTIVE: To explore the value of serum procalcitonin (PCT) and acute physiology and chronic health evaluation II (APACHE II) score on predicting prognosis of elderly patients with sepsis. METHODS: A retrospective cohort study, patients with sepsis who admitted to the department of emergency and the department of geriatric medicine of Peking University Third Hospital from March 2020 to June 2021 were enrolled. Patients' demographics, routine laboratory examinations, APACHE II score that within 24 hours of admission were obtained from their electronic medical records. The prognosis during the hospitalization and one year after discharge were collected, retrospectively. Univariate and multivariate analysis of prognostic factors were performed. And Kaplan-Meier survival curves were used to examine overall survival. RESULTS: A total of 116 elderly patients met inclusion criteria, 55 were alive and 61 were died. On univariate analysis, clinical variables such as lactic acid [Lac, hazard ratio (HR) = 1.16, 95% confidence interval (95%CI) was 1.07-1.26, P < 0.001], PCT (HR = 1.02, 95%CI was 1.01-1.04, P < 0.001), alanine aminotransferase (ALT, HR = 1.00,95%CI was 1.00-1.00, P = 0.143), aspartate aminotransferase (AST, HR = 1.00, 95%CI was 1.00-1.01, P = 0.014), lactate dehydrogenase (LDH, HR = 1.00, 95%CI was 1.00-1.00, P < 0.001), hydroxybutyrate dehydrogenase (HBDH, HR = 1.00, 95%CI was 1.00-1.00, P = 0.001), creatine kinase (CK, HR = 1.00, 95%CI was 1.00-1.00, P = 0.002), MB isoenzyme of creatine kinase (CK-MB, HR = 1.01, 95%CI was 1.01-1.02, P < 0.001), Na (HR = 1.02, 95%CI was 0.99-1.05, P = 0.183), blood urea nitrogen (BUN, HR = 1.02, 95%CI was 0.99-1.05, P = 0.139), fibrinogen (FIB, HR = 0.85, 95%CI was 0.71-1.02, P = 0.078), neutrophil ratio (NEU%, HR = 0.99, 95%CI was 0.97-1.00, P = 0.114), platelet count (PLT, HR = 1.00, 95%CI was 0.99-1.00, P = 0.108) and total bile acid (TBA, HR = 1.01, 95%CI was 1.00-1.02, P = 0.096) shown to be associated with poor prognosis. On multivariable analysis, level of PCT was an important factor influencing the outcome of sepsis (HR = 1.03, 95%CI was 1.01-1.05, P = 0.002). Kaplan-Meier survival curve showed that there was no significant difference with respect to the overall survival between the two groups, with patients of PCT ≤ 0.25 µg/L and PCT > 0.25 µg/L (P = 0.220). It also showed that the overall survival rate in patients with high APACHE II score (> 27 points) was significantly lower than that in patients with low APACHE II score (≤ 27 points, P = 0.015). CONCLUSIONS: Serum PCT level is valuable prognostic factors of elderly patients with sepsis, and higher APACHE II score (> 27 points) indicates a poor prognosis.

Stenotrophomonas maltophilia pneumonia in critical COVID-19 patients.

Stenotrophomonas maltophilia, an environmental aerobic non-fermentative Gram-negative bacilli, has gained attention in many nosocomial outbreaks. COVID-19 patients in intensive care unit have extended hospital stay and are severely immunosuppressed. This study aimed to determine the prevalence and risk factors of S. maltophilia pneumonia in critical COVID-19 patients. A total of 123 COVID-19 patients in ICU admitted between March 2020 and March 2021 were identified from the authors' institutional database and assessed for nosocomial pneumonia. Demographic data and factors predisposing to S. maltophilia pneumonia were collected and analyzed. The mean age was 66 ± 13 years and 74% were males. Median APACHE and SOFA scores were 13 (IQR = 8-19) and 4 (3-6), respectively. The Median NEWS2 score was 6 (Q1 = 5; Q3 = 8). The Median ICU stay was 12 (Q1 = 7; Q3 = 22) days. S. maltophilia was found in 16.3% of pneumonia patients, leading to a lengthier hospital stay (34 vs. 20 days; p < 0.001). Risk factors for S. maltophilia pneumonia included previous treatment with meropenem (p < 0.01), thrombopenia (p = 0.034), endotracheal intubation (p < 0.001), foley catheter (p = 0.009) and central venous catheter insertion (p = 0.016). S. maltophilia nosocomial pneumonia is frequent in critical COVID-19 patients. Many significant risk factors should be addressed to reduce its prevalence and negative impact on outcomes.

Medication Regimen Complexity Index Score at Admission as a Predictor of Inpatient Outcomes: A Machine Learning Approach.

BACKGROUND: In the intensive care unit, traditional scoring systems use illness severity and/or organ failure to determine prognosis, and this usually rests on the patient's condition at admission. In spite of the importance of medication reconciliation, the usefulness of home medication histories as predictors of clinical outcomes remains unexplored. METHODS: A retrospective cohort study was conducted using the medical records of 322 intensive care unit (ICU) patients. The predictors of interest included the medication regimen complexity index (MRCI) at admission, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Sequential Organ Failure Assessment (SOFA) score, or a combination thereof. Outcomes included mortality, length of stay, and the need for mechanical ventilation. Machine learning algorithms were used for outcome classification after correcting for class imbalances in the general population and across the racial continuum. RESULTS: The home medication model could predict all clinical outcomes accurately 70% of the time. Among Whites, it improved to 80%, whereas among non-Whites it remained at 70%. The addition of SOFA and APACHE II yielded the best models among non-Whites and Whites, respectively. SHapley Additive exPlanations (SHAP) values showed that low MRCI scores were associated with reduced mortality and LOS, yet an increased need for mechanical ventilation. CONCLUSION: Home medication histories represent a viable addition to traditional predictors of health outcomes.

Clinical characteristics and risk factors of organ failure and death in necrotizing pancreatitis.

BACKGROUND: Organ failure (OF) and death are considered the most significant adverse outcomes in necrotizing pancreatitis (NP). However, there are few NP-related studies describing the clinical traits of OF and aggravated outcomes. PURPOSE: An improved insight into the details of OF and death will be helpful to the management of NP. Thus, in our research, we addressed the risk factors of OF and death in NP patients. METHODS: We performed a study of 432 NP patients from May 2017 to December 2021. All patients with NP were followed up for 36 months. The primary end-points were risk factors of OF and death in NP patients. The risk factors were evaluated by logistic regression analysis. RESULTS: NP patients with OF or death patients were generally older, had a higher APACHE II score, longer hospital stay, longer ICU stay, as well as a higher incidence of severe acute pancreatitis (SAP), shock and pancreatic necrosis. Independent risk factors related to OF included BMI, APACHE II score and SAP (P < 0.05). Age, shock and APACHE II score (P < 0.05) were the most significant factors correlated with the risk of death in NP patients. Notably, increased mortality was linked to the number of failed organs. CONCLUSIONS: NP is a potentially fatal disease with a long hospital or ICU stay. Our study indicated that the incidence of OF and death in NP patients was 69.9% and 10.2%, respectively. BMI, SAP, APACHE II score, age and shock are potential risk factors of OF and death in NP patients. Clinicians should focus on these factors for early diagnosis and appropriate therapy.

Hyperlactatemia is a predictor of mortality in patients undergoing continuous renal replacement therapy for acute kidney injury.

BACKGROUND: Hyperlactatemia occurs frequently in critically ill patients, and this pathologic condition leads to worse outcomes in several disease subsets. Herein, we addressed whether hyperlactatemia is associated with the risk of mortality in patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury. METHODS: A total of 1,661 patients who underwent CRRT for severe acute kidney injury were retrospectively reviewed between 2010 and 2020. The patients were categorized according to their serum lactate levels, such as high (≥ 7.6 mmol/l), moderate (2.1-7.5 mmol/l) and low (≤ 2 mmol/l), at the time of CRRT initiation. The hazard ratios (HRs) for the risk of in-hospital mortality were calculated with adjustment of multiple variables. The increase in the area under the receiver operating characteristic curve (AUROC) for the mortality risk was evaluated after adding serum lactate levels to the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE) II score-based models. RESULTS: A total of 802 (48.3%) and 542 (32.6%) patients had moderate and high lactate levels, respectively. The moderate and high lactate groups had a higher risk of mortality than the low lactate group, with HRs of 1.64 (1.22-2.20) and 4.18 (2.99-5.85), respectively. The lactate-enhanced models had higher AUROCs than the models without lactates (0.764 vs. 0.702 for SOFA score; 0.737 vs. 0.678 for APACHE II score). CONCLUSIONS: Hyperlactatemia is associated with mortality outcomes in patients undergoing CRRT for acute kidney injury. Serum lactate levels may need to be monitored in this patient subset.

Improving ICU Risk Predictive Models Through Automation Designed for Resiliency Against Documentation Bias.

OBJECTIVES: Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance. DESIGN: Retrospective, multicenter study of ICU patients randomized in 3:2 fashion into development and validation cohorts. Generalized additive models (GAM) with features designed to mitigate biases introduced from documentation of admission diagnosis, Glasgow Coma Scale (GCS), and extreme vital signs were developed using clinical features representing the first 24 hours of ICU admission. SETTING: eICU Research Institute database derived from ICUs participating in the Philips eICU telecritical care program. PATIENTS: A total of 572,985 adult ICU stays discharged from the hospital between January 1, 2017, and December 31, 2018, were included, yielding 509,586 stays in the final cohort; 305,590 and 203,996 in development and validation cohorts, respectively. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Model discrimination was compared against Acute Physiology and Chronic Health Evaluation (APACHE) IVa/IVb models on the validation cohort using the area under the receiver operating characteristic (AUROC) curve. Calibration assessed by actual/predicted ratios, calibration-in-the-large statistics, and visual analysis. Performance metrics were further stratified by subgroups of admission diagnosis and ICU characteristics. Historic data from two health systems with abrupt changes in Glasgow Coma Scale (GCS) documentation were assessed in the year prior to and after data shift. CCOPM outperformed APACHE IVa/IVb for ICU mortality (AUROC, 0.925 vs 0.88) and hospital mortality (AUROC, 0.90 vs 0.86). Better calibration performance was also attained among subgroups of different admission diagnoses, ICU types, and over unique ICU-years. The CCOPM provided more stable predictions compared with APACHE IVa within an external cohort of greater than 120,000 patients from two health systems with known changes in GCS documentation. CONCLUSIONS: These mortality risk models demonstrated excellent performance compared with APACHE while appearing to mitigate bias introduced through major shifts in GCS documentation at two large health systems. This provides evidence to support using automated capture rather than trained personnel for capture of GCS data used in benchmarking ICUs on mortality performance.

Association between body mass index change and mortality in critically ill patients: A retrospective observational study.

OBJECTIVE: Previous studies have emphasized the association between baseline body mass index (BMI) and mortality in patients during a stay in the intensive care unit (ICU). However, to our knowledge, few studies have focused on BMI change during an ICU stay. The aim of this study was to explore the prognostic value of BMI change during ICU hospitalization. METHODS: This was a multicenter, retrospective cohort study with data extracted from the eICU Collaborative Research Database. Logistic regression models were used to explore the relationship between BMI change and mortality in ICU patients. BMI change was calculated as follows: {[discharge ICU weight (kg) - admission ICU weight (kg)] / height (m)2]}. Interaction and subgroup analyses were conducted for patients grouped with baseline BMI on ICU admission (≥30 versus 25-29.9 versus <25 kg/m2), Acute Physiology and Chronic Health Evaluation (APACHE) IV score (<53 versus ≥53), and ICU length of stay (≥3 versus <3 d). RESULTS: Compared with those with weight loss (n = 17 134), patients with weight gain during ICU hospitalization (n = 17 436) were associated with higher hospital mortality (odds ratio [OR], 1.251; 95% confidence interval [CI], 1.155-1.356; P < 0.001) and ICU mortality (OR, 1.360; 95% CI, 1.227-1.506; P < 0.001) after multivariable adjustment. The associations remained robust in patients with different baseline BMI levels and were especially remarkable among those with higher APACHE IV score and the longer ICU stay. CONCLUSIONS: The present study exposed the potential hazard of increasing BMI for hospital and ICU mortalities during ICU hospitalization and indicating that patients in the ICU may benefit from a more balanced nutritional strategy.

Comparison of Prognostic Accuracy of 3 Delirium Prediction Models.

BACKGROUND: Delirium is a severe complication in critical care patients. Accurate prediction could facilitate determination of which patients are at risk. In the past decade, several delirium prediction models have been developed. OBJECTIVES: To compare the prognostic accuracy of the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, and to investigate the difference in prognostic accuracy of the PRE-DELIRIC model between patients receiving and patients not receiving mechanical ventilation. METHODS: This retrospective study involved adult patients admitted to the intensive care unit during a 2-year period. Delirium was assessed by using the Confusion Assessment Method for the Intensive Care Unit or any administered dose of haloperidol or quetiapine. Model discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC); values were compared using the DeLong test. RESULTS: The study enrolled 1353 patients. The AUC values were calculated as 0.716 (95% CI, 0.688-0.745), 0.681 (95% CI, 0.650-0.712), and 0.660 (95% CI, 0.629-0.691) for the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, respectively. The difference in model discrimination was statistically significant for comparison of the PRE-DELIRIC with the E-PRE-DELIRIC (AUC difference, 0.035; P = .02) and Lanzhou models (AUC difference, 0.056; P < .001). In the PRE-DELIRIC model, the AUC was 0.711 (95% CI, 0.680-0.743) for patients receiving mechanical ventilation and 0.664 (95% CI, 0.586-0.742) for those not receiving it (difference, 0.047; P = .27). CONCLUSION: Statistically significant differences in prognostic accuracy were found between delirium prediction models. The PRE-DELIRIC model was the best-performing model and can be used in patients receiving or not receiving mechanical ventilation.

Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data.

High-dimensional LASSO (Hi-LASSO) is a powerful feature selection tool for high-dimensional data. Our previous study showed that Hi-LASSO outperformed the other state-of-the-art LASSO methods. However, the substantial cost of bootstrapping and the lack of experiments for a parametric statistical test for feature selection have impeded to apply Hi-LASSO for practical applications. In this paper, the Python package and its Spark library are efficiently designed in a parallel manner for practice with real-world problems, as well as providing the capability of the parametric statistical tests for feature selection on high-dimensional data. We demonstrate Hi-LASSO's outperformance with various intensive experiments in a practical manner. Hi-LASSO will be efficiently and easily performed by using the packages for feature selection. Hi-LASSO packages are publicly available at https://github.com/datax-lab/Hi-LASSO under the MIT license. The packages can be easily installed by Python PIP, and additional documentation is available at https://pypi.org/project/hi-lasso and https://pypi.org/project/Hi-LASSO-spark.

The combination of lactate level, lactate clearance and APACHE II score better predicts short-term outcomes in critically Ill patients: a retrospective cohort study.

BACKGROUND: The mortality rate is high in critically ill patients due to the difficulty of diagnosis and treatment. Thus, it is very important to explore the predictive value of different indicators related to prognosis in critically ill patients. METHODS: This was a retrospective cohort study of patients in the intensive care unit (ICU) of the Sixth People's Hospital in Shanghai, China. A total of 1465 ICU patients had lactate values > 2.1 mmol/L at least once within 24 h of ICU admission, and arterial blood gas was monitored more than twice during the ICU stay. RESULTS: The predictive value of lactate clearance at 24 h was not high, and the sensitivity and specificity were lower. The predictive value of the lactate level at baseline and the APACHE II score was higher than that of lactate clearance at 24 h in critically ill patients. The predictive value of the lactate level at baseline combined with the APACHE II score was higher than that of the lactate level at baseline or the APACHE II score alone. In addition, the predictive value of lactate clearance at 24 h combined with the APACHE II score was also significantly higher than that of lactate clearance at 24 h or the APACHE II score alone. In particular, the area under the ROC curve reached 0.900, the predictive value was markedly higher than that of the ROC alone, and the sensitivity and specificity were better when these three indicators were combined. CONCLUSIONS: The combination of lactate level, lactate clearance and APACHE II score better predicts short-term outcomes in critically ill patients.

FCGR2C: An emerging immune gene for predicting sepsis outcome.

Background: Sepsis is a life-threatening disease associated with immunosuppression. Immunosuppression could ultimately increase sepsis mortality. This study aimed to identify the prognostic biomarkers related to immunity in sepsis. Methods: Public datasets of sepsis downloaded from the Gene Expression Omnibus (GEO) database were divided into the discovery cohort and the first validation cohort. We used R software to screen differentially expressed genes (DEGs) and analyzed DEGs' functional enrichment in the discovery dataset. Immune-related genes (IRGs) were filtered from the GeneCards website. A Lasso regression model was used to screen candidate prognostic genes from the intersection of DEGs and IRGs. Then, the candidate prognostic genes with significant differences were identified as prognostic genes in the first validation cohort. We further validated the expression of the prognostic genes in the second validation cohort of 81 septic patients recruited from our hospital. In addition, we used four immune infiltration methods (MCP-counter, ssGSEA, ImmuCellAI, and CIBERSORT) to analyze immune cell composition in sepsis. We also explored the correlation between the prognostic biomarker and immune cells. Results: First, 140 genes were identified as prognostic-related immune genes from the intersection of DEGs and IRGs. We screened 18 candidate prognostic genes in the discovery cohort with the lasso regression model. Second, in the first validation cohort, we identified 4 genes (CFHR2, FCGR2C, GFI1, and TICAM1) as prognostic immune genes. Subsequently, we found that FCGR2C was the only gene differentially expressed between survivors and non-survivors in 81 septic patients. In the discovery and first validation cohorts, the AUC values of FCGR2C were 0.73 and 0.67, respectively. FCGR2C (AUC=0.84) had more value than SOFA (AUC=0.80) and APACHE II (AUC=0.69) in evaluating the prognosis of septic patients in our recruitment cohort. Moreover, FCGR2C may be closely related to many immune cells and functions, such as B cells, NK cells, neutrophils, cytolytic activity, and inflammatory promotion. Finally, enrichment analysis showed that FCGR2C was enriched in the phagosome signaling pathway. Conclusion: FCGR2C could be an immune biomarker associated with prognosis, which may be a new direction of immunotherapy to reduce sepsis mortality.

Correlation between serum levels of PTX-3, SIL-2R, inflammatory markers, and APACHE II scores in patients with severe acute pancreatitis.

To investigate the correlation of serum pentraxin 3 (PTX-3), soluble interleukin-2 receptor (SIL-2R), C-reactive protein (CRP), procalcitonin (PCT) levels, and acute physiology and chronic health evaluation II (APACHE II) scores in patients with severe acute pancreatitis (SAP). A total of 30 patients with SAP from October 2020 to October 2021 were selected as the SAP group, and 42 patients with mild acute pancreatitis (MAP) or moderate-severe acute pancreatitis (MSAP) was selected as the control group. The serum levels of PTX-3, SIL-2R, CRP, PCT, and APACHE II scores were evaluated. The serum levels of PTX-3, SIL-2R, CRP, PCT, and APACHE II scores at admission in the SAP group were significantly higher than those in the control group (all P < .05). Spearman analysis showed that serum PTX-3, SIL-2R, CRP, and PCT levels were positively correlated with APACHE II scores (all P < .05). The mortality rate within 28 days was 26.7% in the SAP group; moreover, the serum PTX-3, SIL-2R, CRP, and PCT levels and APACHE II scores at admission in the death group were significantly higher than those in the survival group (all P < .05). The receiver operating curve showed that the combined prediction value of all indicators (PTX-3 + SIL-2R + CRP + PCT + APACHE II) was superior to the single indicators, and the diagnostic sensitivity and specificity were 90.9% and 84.2%, respectively. Serum PTX-3, SIL-2R, CRP, and PCT levels and APACHE II scores have high guiding significance in early diagnosis and prognostic evaluation of SAP patients.

Prognostic predictors for mortality of patients with COVID-19 in an intensive care unit.

INTRODUCTION: Fatality due to COVID-19 continues to be a challenge. Timely identification of critical COVID-19 patients is crucial for their close clinical follow-up and treatment. We aimed to identify the mortality predictors of critical COVID-19 patients. METHODOLOGY: We analyzed medical records of 232 out of 300 patients with COVID-19 hospitalized in the intensive care unit (ICU) whose medical records were available in the hospital database. Non-survivors and survivors were compared for parameters. Medical records of demographics, comorbidities, radiological signs, respiratory support, and laboratory tests on the first day of ICU admission were included. The durations of ICU stay and hospitalization were also evaluated. RESULTS: The patients with Acute Physiology and Chronic Health Evaluation II (APACHE-II) score above 28.5 and the patients with blood urea nitrogen (BUN) above 45.5 mg/dL were significantly more mortal (95% CI: 0.701, p = 0.0001; 95% CI: 0.599, p = 0.022; respectively). Partial oxygen pressure/fraction of inspired oxygen (P/F) ratio below 110.5 mmHg was a predictor for mortality (95% CI: 0.397, p = 0.018). Older age, smoking, crazy paving pattern on computed tomography (CT), and short duration of hospitalization were also predictors of mortality. The patients requiring invasive mechanical ventilation were significantly more mortal whereas the patients requiring high flow oxygen and non-invasive ventilation were significantly more likely to survive. CONCLUSIONS: We recommend evaluating APACHE-II score, BUN value, P/F ratio, age, smoking status, radiological signs on CT, length of hospitalization and modality of respiratory support upon ICU admission to identify critical patients with poor prognoses.

A simple APACHE IV risk dynamic nomogram that incorporates early admitted lactate for the initial assessment of 28-day mortality in critically ill patients with acute myocardial infarction.

BACKGROUND: Early risk stratification is important for patients with acute myocardial infarction (AMI). We aimed to develop a simple APACHE IV dynamic nomogram, combined with easily available clinical parameters within 24 h of admission, thus improving its predictive power to assess the risk of mortality at 28 days. METHODS: Clinical information on AMI patients was extracted from the eICU database v2.0. A preliminary XGBoost examination of the degree of association between all variables in the database and 28-day mortality was conducted. Univariate and multivariate logistic regression analysis were used to perform screening of variables. Based on the multifactorial analysis, a dynamic nomogram predicting 28-day mortality in these patients was developed. To cope with missing data in records with missing variables, we applied the multiple imputation method. Predictive models are evaluated in three main areas, namely discrimination, calibration, and clinical validity. The discrimination is mainly represented by the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Calibration is represented by the calibration plot. Clinical validity is represented by the decision curve analysis (DCA) curve. RESULTS: A total of 504 people were included in the study. All 504 people were used to build the predictive model, and the internal validation model used a 500-bootstrap method. Multivariate analysis showed that four variables, APACHE IV, the first sample of admission lactate, prior atrial fibrillation (AF), and gender, were included in the nomogram as independent predictors of 28-day mortality in AMI. The prediction model had an AUC of 0.819 (95%CI 0.770-0.868) whereas the internal validation model had an AUC of 0.814 (95%CI 0.765-0.860). Calibration and DCA curves indicated that the dynamic nomogram in this study were reflective of real-world conditions and could be applied clinically. The predictive model composed of these four variables outperformed a single APACHE IV in terms of NRI and IDI. The NRI was 16.4% (95% CI: 6.1-26.8%; p = 0.0019) and the IDI was 16.4% (95% CI: 6.0-26.8%; p = 0.0020). Lactate accounted for nearly half of the total NRI, which showed that lactate was the most important of the other three variables. CONCLUSION: The prediction model constructed by APACHE IV in combination with the first sample of admission lactate, prior AF, and gender outperformed the APACHE IV scoring system alone in predicting 28-day mortality in AMI. The prediction dynamic nomogram model was published via a website app, allowing clinicians to improve the predictive efficacy of the APACHE IV score by 16.4% in less than 1 min.

Nutritional risk factors for all-cause mortality of critically ill patients: a retrospective cohort study.

OBJECTIVES: This study aimed to explore the predictive value of single and multiple risk factors for the clinical outcomes of critically ill patients receiving enteral nutrition and to establish an effective evaluation model. DESIGN: Retrospective cohort study. SETTING: Data from the 2020-2021 period were collected from the electronic records of the First Affiliated Hospital, Nanjing Medical University. PARTICIPANTS: 459 critically ill patients with enteral nutrition in the geriatric intensive care unit were included in the study. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was 28-day mortality. The secondary outcomes were 28-day invasive mechanical ventilation time, intensive care unit stay, Nutrition Risk Screening 2002 (NRS2002) score and Acute Physiology and Chronic Health Evaluation II (APACHE II) score. RESULTS: Independent prognostic factors, including prealbumin/procalcitonin (PCT) ratio and APACHE II score, were identified using a logistic regression model and used in the nomogram. The area under the receiver operating characteristic curve and concordance index indicated that the predictive capacity of the model was 0.753. Moreover, both the prealbumin/PCT ratio and the combination model of PCT, prealbumin and NRS2002 had a higher predictive value for clinical outcomes. Subgroup analysis also identified that a higher inflammatory state (PCT >0.5 ng/mL) and major nutritional risk (NRS2002 >3) led to worse clinical outcomes. In addition, patients on whole protein formulae bore less nutritional risk than those on short peptide formulae. CONCLUSIONS: This nomogram had a good predictive value for 28-day mortality in critically ill patients receiving enteral nutrition. Both the prealbumin/PCT ratio and the combination model (PCT, prealbumin and NRS2002), as composite models of inflammation and nutrition, could better predict the prognosis of critically ill patients.

Transitional Care Programs for Patients with High Nursing Activity Scores Reduce Unplanned Readmissions to Intensive Care Units.

Background and Objectives: The main objective of a transitional care program (TCP) is to detect patients with early deterioration following intensive care unit (ICU) discharge in order to reduce unplanned ICU readmissions. Consensus on the effectiveness of TCPs in preventing unscheduled ICU readmissions remains lacking. In this case study assessing the effectiveness of TCP, we focused on the association of unplanned ICU readmission with high nursing activities scores (NASs), which are considered a risk factor for ICU readmission. Materials and Methods: This retrospective observational study analyzed the data of patients admitted to a single-center ICU between January 2016 and December 2019, with an NAS of >53 points at ICU discharge. The following data were extracted: patient characteristics, ICU treatment, acute physiology and chronic health evaluation II (APACHE II) score at ICU admission, Charlson comorbidity index (CCI), 28-day mortality rate, and ICU readmission rate. The primary outcome was the association between unplanned ICU readmissions and the use of a TCP. The propensity score (PS) was calculated using the following variables: age, sex, APACHE II score, and CCI. Subsequently, logistic regression analysis was performed using the PS to evaluate the outcomes. Results: A total of 143 patients were included in this study, of which 87 (60.8%) participated in a TCP. Respiratory failure was the most common cause of unplanned ICU readmission. The unplanned ICU readmission rate was significantly lower in the TCP group. In the logistic regression model, TCP (odds ratio, 5.15; 95% confidence interval, 1.46-18.2; p = 0.01) was independently associated with unplanned ICU readmission. Conclusions: TCP intervention with a focus on patients with a high NAS (>53 points) may prevent unplanned ICU readmission.

Development of New Equations Predicting the Mortality Risk of Patients on Continuous RRT.

Background: Predicting the risk of death in patients admitted to the critical care unit facilitates appropriate management. In particular, among patients who are critically ill, patients with continuous RRT (CRRT) have high mortality, and predicting the mortality risk of these patients is difficult. The purpose of this study was to develop models for predicting the mortality risk of patients on CRRT and to validate the models externally. Methods: A total of 699 adult patients with CRRT who participated in the VolumE maNagement Under body composition monitoring in critically ill patientS on CRRT (VENUS) trial and 1515 adult patients with CRRT in Seoul National University Hospital were selected as the development and validation cohorts, respectively. Using 11 predictor variables selected by the Cox proportional hazards model and clinical importance, equations predicting mortality within 7, 14, and 28 days were developed with development cohort data. Results: The equation using 11 variables had area under the time-dependent receiver operating characteristic curve (AUROC) values of 0.75, 0.74, and 0.73 for predicting 7-, 14-, and 28-day mortality, respectively. All equations had significantly higher AUROCs than the Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores. The 11-variable equation was superior to the SOFA and APACHE II scores in the integrated discrimination index and net reclassification improvement analyses. Conclusions: The newly developed equations for predicting CRRT patient mortality showed superior performance to the previous scoring systems, and they can help physicians manage patients.

Procalcitonin and High APACHE (Acute Physiological and Chronic Health Evaluation) Level Are Associated with the Course of Acute Kidney Injury in Patients with SARS-CoV-2.

Background: Acute kidney injury (AKI) is associated with poor outcomes in patients infected with SARS-CoV-2. Sepsis, direct injury to kidney cells by the virus, and severe systemic inflammation are mechanisms implicated in its development. We investigated the association between inflammatory markers (C-reactive protein, procalcitonin, D-dimer, lactate dehydrogenase, and ferritin) in patients infected with SARS-CoV-2 and the development of AKI. Methods: A prospective cohort study performed at the Civil Hospital (Dr. Juan I. Menchaca) Guadalajara, Mexico, included patients aged >18 years with a diagnosis of SARS-CoV-2 pneumonia confirmed by RT-PCR and who did or did not present with AKI (KDIGO) while hospitalized. Biomarkers of inflammation were recorded, and kidney function was estimated using the CKD-EPI formula. Results: 291 patients were included (68% males; average age, 57 years). The incidence of AKI was 40.5% (118 patients); 21% developed stage 1 AKI, 6% developed stage 2 AKI, and 14% developed stage 3 AKI. The development of AKI was associated with higher phosphate (p = 0.002) (RR 1.39, CI 95% 1.13-1.72), high procalcitonin levels at hospital admission (p = 0.005) (RR 2.09, CI 95% 1.26-3.50), and high APACHE scores (p = 0.011) (RR 2.0, CI 95% 1.17-3.40). The survival analysis free of AKI according to procalcitonin levels and APACHE scores demonstrated a lower survival in patients with procalcitonin >0.5 ng/ml (p = 0.001) and APACHE >15 points (p = 0.004). Conclusions: Phosphate, high procalcitonin levels, and APACHE levels >15 were predictors of AKI development in patients hospitalized with COVID-19.