A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours.

BACKGROUND: A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement. METHODS: The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria. RESULTS: Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < 0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P < 0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI. CONCLUSION: U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI.

AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study.

BACKGROUND: Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days. METHODS: The prospective cohort study enrolled 929 patients altogether who were admitted in ICU including 680 patients in training set (Jiefang Campus) and 249 patients in external testing set (Binjiang Campus). After performing strict inclusion and exclusion criteria, 421 patients were selected in training set for constructing predictive model and 167 patients were selected in external testing for evaluating the predictive performance of resulting model. Urine and blood samples were collected for kidney injury associated biomarkers detection. Baseline clinical information and laboratory data of the study participants were collected. We determined the average prediction efficiency of six machine learning models through 10-fold cross validation. RESULTS: In total, 78 variables were collected when admission in ICU and 43 variables were statistically significant between AKI and non-AKI cohort. Then, 35 variables were selected as independent features for AKI by univariate logistic regression. Spearman correlation analysis was used to remove two highly correlated variables. Three ranking methods were used to explore the influence of 33 variables for further determining the best combination of variables. The gini importance ranking method was found to be applicable for variables filtering. The predictive performance of AKIMLpred which constructed by the XGBoost algorithm was the best among six machine learning models. When the AKIMLpred included the nine features (NGAL, IGFBP7, sCysC, CAF22, KIM-1, NT-proBNP, IL-6, IL-18 and L-FABP) with the highest influence ranking, its model had the best prediction performance, with an AUC of 0.881 and an accuracy of 0.815 in training set, similarly, with an AUC of 0.889 and an accuracy of 0.846 in validation set. Moreover, the performace was slightly outperformed in testing set with an AUC of 0.902 and an accuracy of 0.846. The SHAP algorithm was used to interpret the prediction results of AKIMLpred. The web-calculator of AKIMLpred was shown for predicting AKI with more convenient(https://www.xsmartanalysis.com/model/list/predict/model/html?mid=8065&symbol=11gk693982SU6AE1ms21). AKIMLpred was better than the optimal model built with only routine tests for predicting AKI in critically ill patients within 7 days. CONCLUSION: The model AKIMLpred constructed by the XGBoost algorithm with selecting the nine most influential biomarkers in the gini importance ranking method had the best performance in predicting AKI in critically ill patients within 7 days. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.

Clinical characteristics of and risk factors for secondary bloodstream infection after pneumonia among patients infected with methicillin-resistant Staphylococcus aureus.

Purpose: To investigate the clinical features and risk factors for methicillin-resistant Staphylococcus aureus (MRSA) pneumonia (MP) with secondary MRSA bloodstream infections (MRSA-BSI) (termed MP-BSI) compared with MP alone and to study the incidence of MP-BSI among patients with MP. Methods: This was a retrospective, single-center study with clinical data derived from previous medical records. The cases were divided into groups: MP alone and MP-BSI. The determination of independent risk factors for MP-BSI relied on logistic regression analysis. Additionally, the crude outcomes were compared. Results: A total of 435 patients with MP were recruited, with 18.9% (82/435) having MP-BSI. The median age was 62 (interquartile range, 51,72) years, and 74.5% of the patients were male. Multivariate analysis revealed that immunosuppression, community-acquired MP (CA-MP), time from initial to targeted antibiotic use, high Sequential Organ Failure Assessment (SOFA) score, increased respiratory rate, and elevated γ-GT level (all p < 0.05) were independent risk factors for MP-BSI, while targeted treatment with linezolid was a protective factor. Patients with MP-BSI had a longer duration of hospitalization (median days, 27.5 vs. 19, p = 0.001), a higher 28-day mortality rate (24.4% vs. 11.0%, p = 0.001), and a higher in-hospital mortality rate (26.8% vs. 14.7%, p = 0.009) than those with MP alone. Conclusion: Secondary MRSA-BSI among patients with MP is not rare. Immunosuppression, CA-MP, time from initial to targeted antibiotic use, high SOFA score, increased respiratory rate and elevated γ-GT level are all independent risk factors for MP-BSI; however, linezolid, as a targeted antibiotic, is a protective factor. Moreover, patients with MP may have worse clinical outcomes when they develop MRSA-BSI.

Comprehensive analysis of fifteen hub genes to identify a promising diagnostic model, regulated networks, and immune cell infiltration in acute kidney injury.

BACKGROUND: Acute kidney injury is a common clinical problem with no sensitive and specific diagnostic biomarkers and definitive treatments. The underlying molecular mechanisms of acute kidney injury are unclear. Therefore, it is pivotal to explore the underlying mechanisms and screen for novel diagnostic biomarkers, and therapeutic targets. METHODS: The present study identified 15 hub genes by WGCNA analysis. LASSO-based logistic regression analysis was used to select key features and construct a diagnostic model of AKI. In addition, GO and KEGG analyses were performed and TF-mRNA and miRNA-mRNA network analysis and immune infiltration analysis of hub genes were performed to reveal the underlying mechanisms of AKI. RESULTS: A diagnostic model was constructed by LASSO-based logistic regression analysis and was validated by RT-qPCR based on 15 hub genes. GO and KEGG analyses revealed DEGs were enriched in oxidation-reduction process, cell adhesion, proliferation, migration, and metabolic process. The enriched TFs were BRD2, EP300, ETS1, MYC, SPI1, and ZNF263. The enriched miRNAs were miR-181c-5p, miR-218-5p, miR-485-5p, miR-532-5p and miR-6884-5p. The immune infiltration analysis showed that Macrophages M2 was decreasing significantly revealing a protective factor for further AKI treatment. CONCLUSIONS: The present study identified 15 hub genes based on WGCNA. Development and validation of a potentially diagnostic model based on 15 hub genes. In addition, exploring the interaction between transcriptional factors and 15 hub genes, and miRNA-mRNA relationship pairs. Furthermore, immune infiltration analysis was performed by analyzing gene expression profiles of AKI. Our study provides some basis for further experimental studies.

The effect of renin-angiotensin-aldosterone system inhibitors in patients with hypertension and COVID-19: A meta-analysis of randomized controlled trials and propensity score-matched studies.

Background: High-quality evidence for whether the use of renin-angiotensin-aldosterone system (RAAS) inhibitors worsens clinical outcomes for patients with coronavirus disease 2019 (COVID-19) is lacking. The present study aimed to evaluate the effect of RAAS inhibitors on disease severity and mortality in patients with hypertension and COVID-19 using randomized controlled trials (RCTs) and propensity score-matched (PSM) studies. Methods: A literature search was conducted with PubMed, Embase, and Scopus databases from 31 December 2019 to 10 January 2022. We included RCTs and PSM studies comparing the risk of severe illness or mortality in patients with hypertension and COVID-19 treated or not treated with RAAS inhibitors. Individual trial data were combined to estimate the pooled odds ratio (OR) with a random-effects model. Results: A total of 17 studies (4 RCTs and 13 PSM studies) were included in the meta-analysis. The use of RAAS inhibitors was not associated with an increased risk of severe illness (OR=1.00, 95% confidence interval [CI]: 0.88-1.14, I2=28%) or mortality (OR=0.96, 95% CI: 0.83-1.11, I2=16%) for patients with hypertension and COVID-19. Furthermore, there was no significant difference in the severity of COVID-19 when patients continued or discontinued treatment with RAAS inhibitors (OR=1.01, 95% CI: 0.78-1.29, I2=0%). Conclusions: This study suggests that there was no association between treatment with RAAS inhibitors and worsened COVID-19 disease outcomes. Our findings support the current guidelines that RAAS inhibitors should be continued in the setting of the COVID-19 pandemic. However, the benefit of RAAS inhibitor medications for COVID-19 patients should be further validated with more RCTs.

Rapid and sensitive detection of L-FABP for prediction and diagnosis of acute kidney injury in critically ill patients by chemiluminescent immunoassay.

BACKGROUND: Acute kidney injury (AKI) was a common clinical complication among critically ill patients in Intensive Care Unit with high morbidity and mortality. Human liver fatty acid-binding protein (L-FABP) as a renal tubular injury biomarker was considered a predictor of AKI; however, high-throughput and sensitive detection methods were still urgently needed. We constructed a sensitive and rapid detection method for detecting L-FABP and for exploring the clinical application of L-FABP as a predictor for AKI. METHODS: We developed an automated detection method of chemiluminescent immunoassay to measure L-FABP and evaluated the analytical performance of the new methodology including analytical selectivity, analytical sensitivity, linear range, the minimum limit of detection (LOD), repeatability, and accuracy. One hundred patients were enrolled in this study to explore the predictive and diagnostic ability for AKI. RESULTS: The chemiluminescent immune-based L-FABP assay had outstanding analytical sensitivity including the detection limit of 0.88 ng/ml, and a wide linear range of 2 ng/ml to 160 ng/ml. It also exhibited excellent repeatability with intra-analysis CVs of 8.73%, 4.72%, and 3.79%, respectively, and the inter-analysis CVs of 13.47%, 7.28%, and 5.94%, respectively. The recovery rate assay exhibited a good accuracy with three L-FABP concentration of 99.76%, 102.27%, and 96.92%, respectively. The reference interval of L-FABP was between 0.88 ng/ml and 5.98 ng/ml. The evaluation of predictive and diagnostic performance showed that higher concentration of L-FABP indicated higher risk of AKI occurrence and disease progression. CONCLUSIONS: The clinical application of rapid and sensitive detection method of L-FABP based on the newly developed chemiluminescent immunoassay could offer benefits for patients. L-FABP was a potentially predictive and diagnostic biomarker for AKI.

National Early Warning Score Does Not Accurately Predict Mortality for Patients With Infection Outside the Intensive Care Unit: A Systematic Review and Meta-Analysis.

Background: The prognostic value of the national early warning score (NEWS) in patients with infections remains controversial. We aimed to evaluate the prognostic accuracy of NEWS for prediction of in-hospital mortality in patients with infections outside the intensive care unit (ICU). Methods: We searched PubMed, Embase, and Scopus for related articles from January 2012 to April 2021. Sensitivity, specificity, and likelihood ratios were pooled by using the bivariate random-effects model. Overall prognostic performance was summarized by using the area under the curve (AUC). We performed subgroup analyses to assess the prognostic accuracy of NEWS in selected populations. Results: A total of 21 studies with 107,008 participants were included. The pooled sensitivity and specificity of NEWS were 0.71 and 0.60. The pooled AUC of NEWS was 0.70, which was similar to quick sequential organ failure assessment (qSOFA, AUC: 0.70) and better than systemic inflammatory response syndrome (SIRS, AUC: 0.60). However, the sensitivity (0.55) and AUC (0.63) of NEWS were poor in elder patients. The NEWS of 5 was more sensitive, which was a better threshold for activating urgent assessment and treatment. Conclusions: The NEWS had good diagnostic accuracy for early prediction of mortality in patients with infections outside the ICU, and the sensitivity and specificity were more moderate when compared with qSOFA and SIRS. Insufficient sensitivity and poor performance in the elder population may have limitations as an early warning score for adverse outcomes. NEWS should be used for continuous monitoring rather than a single time point predictive tool.

The Prognostic Accuracy of National Early Warning Score 2 on Predicting Clinical Deterioration for Patients With COVID-19: A Systematic Review and Meta-Analysis.

Background: During the coronavirus disease 2019 (COVID-19) pandemic, the National Early Warning Score 2 (NEWS2) is recommended for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. Therefore, our purpose is to assess the prognostic accuracy of NEWS2 on predicting clinical deterioration for patients with COVID-19. Methods: We searched PubMed, Embase, Scopus, and the Cochrane Library from December 2019 to March 2021. Clinical deterioration was defined as the need for intensive respiratory support, admission to the intensive care unit, or in-hospital death. Sensitivity, specificity, and likelihood ratios were pooled by using the bivariate random-effects model. Overall prognostic performance was summarized by using the area under the curve (AUC). We performed subgroup analyses to assess the prognostic accuracy of NEWS2 in different conditions. Results: Eighteen studies with 6,922 participants were included. The NEWS2 of five or more was commonly used for predicting clinical deterioration. The pooled sensitivity, specificity, and AUC were 0.82, 0.67, and 0.82, respectively. Benefitting from adding a new SpO2 scoring scale for patients with hypercapnic respiratory failure, the NEWS2 showed better sensitivity (0.82 vs. 0.75) and discrimination (0.82 vs. 0.76) than the original NEWS. In addition, the NEWS2 was a sensitive method (sensitivity: 0.88) for predicting short-term deterioration within 72 h. Conclusions: The NEWS2 had moderate sensitivity and specificity in predicting the deterioration of patients with COVID-19. Our results support the use of NEWS2 monitoring as a sensitive method to initially assess COVID-19 patients at hospital admission, although it has a relatively high false-trigger rate. Our findings indicated that the development of enhanced or modified NEWS may be necessary.

Clinical characteristics and risk factors of polymicrobial Staphylococcus aureus bloodstream infections.

BACKGROUND: Although Staphylococcus aureus bloodstream infections (SA-BSI) are a common and important infection, polymicrobial SA-BSI are infrequently reported. The aim of this study was to investigate the clinical characteristics and risk factors of polymicrobial SA-BSI in comparison with monomicrobial SA-BSI. METHODS: A single-center retrospective observational study was performed between Jan 1, 2013, and Dec 31, 2018 at a tertiary hospital. All patients with SA-BSI were enrolled, and their clinical data were gathered by reviewing electronic medical records. RESULTS: A total of 349 patients with SA-BSI were enrolled including 54 cases (15.5%) with polymicrobial SA-BSI. In multivariable analysis, burn injury (adjusted odds ratio [OR], 7.04; 95% confidence interval [CI], 1.71-28.94), need of blood transfusion (aOR, 2.72; 95% CI, 1.14-6.50), use of mechanical ventilation (aOR, 3.11; 95% CI, 1.16-8.30), the length of prior hospital stay (aOR, 1.02; 95% CI, 1.00-1.03), and pneumonia as primary site of infection (aOR, 4.22; 95% CI, 1.69-10.51) were independent factors of polymicrobial SA-BSI. In comparison with monomicrobial SA-BSI, patients with polymicrobial SA-BSI had longer length of ICU stay [median days, 23(6.25,49.25) vs. 0(0,12), p < 0.01] and hospital stay [median days, 50(21.75,85.75) vs. 28(15,49), p < 0.01], and showed a higher 28-day mortality (29.6% vs. 15.3%, p = 0.01). CONCLUSIONS: Burn injury, blood transfusion, mechanical ventilation, the length of prior hospital stay, and pneumonia as a primary site of infection are independent risk factors for polymicrobial SA-BSI. In addition, patients with polymicrobial SA-BSI might have worse outcomes compared with monomicrobial SA-BSI.

Should FEV1/FEV6 replace FEV1/FVC ratio to detect airway obstruction? A metaanalysis.

BACKGROUND: The conventional FEV(1)/FVC test is the "gold standard" to quantitate airway obstruction, but elderly subjects or patients with severe respiratory diseases quite frequently cannot make such an effort. Many studies have investigated the usefulness of FEV(1)/forced expired volume in 6 s (FEV(6)) measurements as an alternative for FEV(1)/FVC for diagnosis of airway obstruction. We conducted a meta-analysis to determine the FEV(1)/FEV(6) substitute for FEV(1)/FVC in the diagnosis of airway obstruction. METHODS: After a systematic review of all-language studies, sensitivity, specificity, and other measures of accuracy of FEV(1)/FEV(6) in the diagnosis of airway obstruction were pooled using random-effects models. Summary receiver operating characteristic curves were used to summarize overall test performance. RESULTS: Eleven studies met our inclusion criteria. The summary estimates for FEV(1)/FEV(6) in the diagnosis of airway obstruction in the studies included were as follows: sensitivity, 0.89 (95% confidence interval [CI], 0.83 to 0.93); specificity, 0.98 (95% CI, 0.95 to 0.99); positive likelihood ratio, 45.46 (95% CI, 18.26 to 113.21); negative likelihood ratio, 0.11 (95% CI, 0.08 to 0.17); diagnostic odds ratio, 396.02 (95% CI, 167.32 to 937.31); and diagnostic score, 5.98 (95% CI, 5.12 to 6.84). CONCLUSIONS: FEV(1)/FEV(6) is a sensitive and specific test for the diagnosis of airway obstruction. FEV(1)/FEV(6) can be used as a valid alternative for FEV(1)/FVC in the diagnosis of airway obstruction.

Effect on quality of life of continuous positive airway pressure in patients with obstructive sleep apnea syndrome: a meta-analysis.

Continuous positive airway pressure (CPAP) is the standard treatment for obstructive sleep apnea syndrome (OSAS). However, the impact of CPAP on quality of life (QOL) is controversial. The aim of this study was to systematically review and determine whether CPAP improves QOL in patients with OSAS. We performed a comprehensive literature search to identify studies published between 1966 and 2007 comparing values of CPAP with control. Weighted mean difference (WMD) was used to analyze the data. The pooled WMD was calculated by using a fixed or random-effect model. The outcomes for 1,256 patients from 16 studies, of whom 656 patients underwent CPAP and 600 were controls, were included. CPAP led to significant improvements in the Nottingham health profile part 2 (WMD=1.657; 95% CI=3.005, -0.308; p=0.016), but there was no difference in other general QOL scores. Patients undergoing CPAP scored better in physical function (WMD=3.457; 95% CI=0.144, 6.771; p=0.041), body pain (WMD=4.017; 95% CI= -0.008, 8.042; p=0.05), energy vitality (WMD=6.984; 95% CI = 0.557, 13.411; p=0.033) and physical component summary (PCS) (WMD=2.040; 95% CI=0.045, 4.035; p=0.045) using the SF-36 tool. This meta-analysis shows that CPAP does not improve general QOL scores but does improve physical domains and vitality. Study design and QOL questionnaire tools are important to capture and evaluate information efficiently. However, generic QOL instruments may not be adequate in detecting important changes in quality of life in patients with OSAS.