Prognosis predictive value of the Oxford Acute Severity of Illness Score for sepsis: a retrospective cohort study

Nucleated red blood cell distribution in critically ill patients with acute pancreatitis: a retrospective cohort study

OBJECTIVES

This study examined the potential association between nucleated red blood cell (NRBC) levels and mortality in critically ill patients with acute pancreatitis (AP) in the intensive care unit, due to limited existing research on this correlation.

METHODS

This retrospective cohort study utilized data from the MIMIC-IV v2.0 and MIMIC-III v1.4 databases to investigate the potential relationship between NRBC levels and patient outcomes. The study employed restricted cubic splines (RCS) regression analysis to explore non-linear associations. The impact of NRBC on prognosis was assessed using a generalized linear model (GLM) with a logit link, adjusted for potential confounders. Furthermore, four machine learning models, including Gradient Boosting Classifier (GBC), Random Forest, Gaussian Naive Bayes, and Decision Tree Classifier model, were constructed using NRBC data to generate risk scores and evaluate the potential of NRBC in predicting patient prognosis.

RESULTS

A total of 354 patients were enrolled in the study, with 162 (45.8%) individuals aged 60 years or older and 204 (57.6%) males. RCS regression analysis demonstrated a non-linear relationship between NRBC levels and 90-day mortality. Receiver Operating Characteristic (ROC) analysis identified a 1.7% NRBC cutoff to distinguish survivor from non-survivor patients for 90-day mortality, yielding an Area Under the Curve (AUC) of 0.599, with a sensitivity of 0.475 and specificity of 0.711. Elevated NRBC levels were associated with increased risks of 90-day mortality in both unadjusted and adjusted models (all Odds Ratios > 1, P < 0.05). Assessment of various machine learning models with nine variables, including NRBC, Sex, Age, Simplified Acute Physiology Score II, Acute Physiology Score III, Congestive Heart Failure, Vasopressin, Norepinephrine, and Mean Arterial Pressure, indicated that the GBC model displayed the highest predictive accuracy for 90-day mortality, with an AUC of 0.982 (95% CI 0.970-0.994). Post hoc power analysis showed a statistical power of 0.880 in the study.

CONCLUSIONS

Elevated levels of NRBC are linked to an increased mortality risk in critically ill patients with AP, suggesting its potential for predicting mortality.

Comparison of different intensive care scoring systems and Glasgow Aneurysm score for aortic aneurysm in predicting 28-day mortality: a retrospective cohort study from MIMIC-IV database

OBJECTIVE

This study aims to assess the performance of various scoring systems in predicting the 28-day mortality of patients with aortic aneurysms (AA) admitted to the intensive care unit (ICU).

METHODS

We utilized data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) to perform a comparative analysis of various predictive systems, including the Glasgow Aneurysm Score (GAS), Simplified Acute Physiology Score (SAPS) III, SAPS II, Logical Organ Dysfunction System (LODS), Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and The Oxford Acute Illness Severity Score (OASIS). The discrimination abilities of these systems were compared using the area under the receiver operating characteristic curve (AUROC). Additionally, a 4-knotted restricted cubic spline regression was employed to evaluate the association between the different scoring systems and the risk of 28-day mortality. Finally, we conducted a subgroup analysis focusing on patients with abdominal aortic aneurysms (AAA).

RESULTS

This study enrolled 586 patients with AA (68.39% male). Among them, 26 patients (4.4%) died within 28 days. Comparative analysis revealed higher SAPS II, SAPS III, SOFA, LODS, OASIS, and SIRS scores in the deceased group, while no statistically significant difference was observed in GAS scores between the survivor and deceased groups (P = 0.148). The SAPS III system exhibited superior predictive value for the 28-day mortality rate (AUROC 0.805) compared to the LODS system (AUROC 0.771), SOFA (AUROC 0.757), SAPS II (AUROC 0.759), OASIS (AUROC 0.742), SIRS (AUROC 0.638), and GAS (AUROC 0.586) systems. The results of the univariate and multivariate logistic analyses showed that SAPS III was statistically significant for both 28-day and 1-year mortality. Subgroup analyses yielded results consistent with the overall findings. No nonlinear relationship was identified between these scoring systems and 28-day all-cause mortality (P for nonlinear > 0.05).

CONCLUSION

The SAPS III system demonstrated superior discriminatory ability for both 28-day and 1-year mortality compared to the GAS, SAPS II SIRS, SOFA, and OASIS systems among patients with AA.

Predictive Value of Multiple Scoring Systems in the Prognosis of Septic Patients with Different Infection Sites: Analysis of the Medical Information Mart for the Intensive Care IV Database

The heterogeneity nature of sepsis is significantly impacted by the site of infection. This study aims to explore the predictive value of multiple scoring systems in assessing the prognosis of septic patients across different infection sites. Data for this retrospective cohort study were extracted from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) (v2.2). Adult patients meeting the criteria for sepsis 3.0 and admitted to the intensive care unit (ICU) were enrolled. Infection sites included were pneumonia, urinary tract infection (UTI), cellulitis, abdominal infection, and bacteremia. The primary outcome assessed was 28-day mortality. The sequential Organ Failure Assessment (SOFA) score, Oxford Acute Severity of Illness Score (OASIS), and Logistic Organ Dysfunction System (LODS) score were compared. Binomial logistic regression analysis was conducted to evaluate the association between these variables and mortality. Additionally, differences in the area under the curve (AUC) of receiver operating characteristic (ROC) among the scoring systems were analyzed. A total of 4721 patients were included in the analysis. The average 28-day mortality rate was 9.4%. Significant differences were observed in LODS, OASIS, and SOFA scores between the 28-day survival and non-survival groups across different infection sites (p < 0.01). In the pneumonia group and abdominal infection group, both the LODS and OASIS scoring systems emerged as independent risk factors for mortality in septic patients (odds ratio [OR]: 1.165, 95% confidence interval [CI]: 1.109-1.224, p < 0.001; OR: 1.047, 95% CI: 1.028-1.065, p < 0.001) (OR: 1.200, 95% CI: 1.091-1.319, p < 0.001; OR: 1.060, 95% CI: 1.025-1.095, p < 0.001). For patients with UTI, the LODS, OASIS, and SOFA scoring systems were identified as independent risk factors for mortality (OR: 1.142, 95% CI: 1.068-1.220, p < 0.001; OR: 1.062, 95% CI: 1.037-1.087, p < 0.001; OR: 1.146, 95% CI: 1.046-1.255, p = 0.004), with the AUC of LODS score and OASIS significantly higher than that of the SOFA score (p = 0.006). Among patients with cellulitis, the OASIS and SOFA scoring systems were identified as independent risk factors for mortality (OR: 1.055, 95% CI: 1.007-1.106, p = 0.025; OR: 1.187, 95% CI: 1.005-1.403, p = 0.044), with no significant difference in prognosis prediction observed (p = 0.243). In the bacteremia group, the LODS scoring system was identified as an independent risk factor for mortality (OR: 1.165, 95% CI: 1.109-1.224, p < 0.001). The findings suggest that LODS scores offer better prognostic accuracy for predicting the mortality risk in septic patients with pneumonia, abdominal infections, bacteremia, and UTI compared to SOFA scores.

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medical Information Mart for Intensive Care (MIMIC-IV, v2.2, MIMIC-III, v1.4) were used in this study. The study cohort from MIMIC-IV was randomized to the training set (0.7) and the internal validation set (0.3), with MIMIC-III (2001 to 2008) as external validation. The features with more than 20% missing values were deleted and the remaining features were multiple interpolated. Lasso-CV that lasso linear model with iterative fitting along a regularization path in which the best model is selected by cross-validation was used to select important features for model development. Eight machine learning models including Random Forest (RF), Logistic Regression, Decision Tree, Extreme Gradient Boost (XGBoost), K Nearest Neighbor, Support Vector Machine, Generalized Linear Models in which the best model is selected by cross-validation (CV_glmnet), and Linear Discriminant Analysis (LDA) were developed. Shapley additive interpretation (SHAP) was used to improve the interpretability of the optimal model. At last, a total of 1043 patients were included, of whom 710 were from MIMIC-IV and 333 from MIMIC-III. Twenty-four clinically relevant parameters were selected for model construction. For the prediction of 28-day mortality of SALI in the internal validation set, the area under the curve (AUC (95% CI)) of RF was 0.79 (95% CI: 0.73-0.86), and which performed the best. Compared with the traditional disease severity scores including Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction Score (LODS), Systemic Inflammatory Response Syndrome (SIRS), and Acute Physiology Score III (APS III), RF also had the best performance. SHAP analysis found that Urine output, Charlson Comorbidity Index (CCI), minimal Glasgow Coma Scale (GCS_min), blood urea nitrogen (BUN) and admission_age were the five most important features affecting RF model. Therefore, RF has good predictive ability for 28-day mortality prediction in SALI. Urine output, CCI, GCS_min, BUN and age at admission(admission_age) within 24 h after intensive care unit(ICU) admission contribute significantly to model prediction.

The value of five scoring systems in predicting the prognosis of patients with sepsis-associated acute respiratory failure

Our study aimed to identify the optimal scoring system for predicting the prognosis of patients with sepsis-associated acute respiratory failure (SA-ARF). All data were taken from the fourth version of the Markets in Intensive Care Medicine (MIMIC-IV) database. Independent risk factors for death in hospitals were confirmed by regression analysis. The predictive value of the five scoring systems was evaluated by receiving operating characteristic (ROC) curves. Kaplan‒Meier curves showed the impact of acute physiology score III (APSIII) on survival and prognosis in patients with SA-ARF. Decision curve analysis (DCA) identified a scoring system with the highest net clinical benefit. ROC curve analysis showed that APS III (AUC: 0.755, 95% Cl 0.714-0.768) and Logical Organ Dysfunction System (LODS) (AUC: 0.731, 95% Cl 0.717-0.7745) were better than Simplified Acute Physiology Score II (SAPS II) (AUC: 0.727, 95% CI 0.713-0.741), Oxford Acute Severity of Illness Score (OASIS) (AUC: 0.706, 95% CI 0.691-0.720) and Sequential Organ Failure Assessment (SOFA) (AUC: 0.606, 95% CI 0.590-0.621) in assessing in-hospital mortality. Kaplan‒Meier survival analysis patients in the high-APS III score group had a considerably poorer median survival time. The DCA curve showed that APS III may provide better clinical benefits for patients. We demonstrated that the APS III score is an excellent predictor of in-hospital mortality.

Exploring the utility of a latent variable as comprehensive inflammatory prognostic index in critically ill patients with cerebral infarction

Objective

We introduce the comprehensive inflammatory prognostic index (CIPI), a novel prognostic tool for critically ill cerebral infarction patients, designed to meet the urgent need for timely and convenient clinical decision-making in this high-risk patient group.

Methods

Using exploratory factor analysis on selected indices-neutrophil to lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SIII)-we derived CIPI, a latent variable capturing their combined predictive power. Data from 1,022 patients in the Medical Information Mart for Intensive Care (MIMIC)-IV database were used to develop CIPI-based survival models, with the robustness and area under the receiver operating characteristic curve (AUC) performance of CIPI validated against an independent dataset of 326 patients from the MIMIC-III CareVue subset. The CIPI's predictive power for in-hospital and intensive care unit (ICU) mortality was assessed through Kaplan-Meier analysis, univariate and multivariate Cox regression models, and time-dependent AUC analysis. Linearity, subgroup sensitivity analyses and interaction effects with CIPI were also evaluated.

Results

CIPI was an independent prognostic factor, demonstrating a statistically significant association with in-hospital and ICU mortality, when assessed as a continuous and a categorical variable. It showed a linear relationship with mortality rates and demonstrated stability across most subgroups, with no significant interactions observed. Its predictive capabilities for in-hospital and ICU mortality among critically ill cerebral infarction patients matched those of established prognostic indices in the MIMIC database.

Conclusion

Our study indicates that CIPI is a reliable and effective prognostic tool for critically ill cerebral infarction patients in predicting in-hospital and ICU mortality. Its straightforward calculation, rooted in routine blood tests, enhances its practicality, promising significant utility in clinical settings.

Predictive value of the Oxford Acute Severity of Illness Score in acute stroke patients with stroke-associated pneumonia

Background

Stroke-associated pneumonia (SAP) is associated with a poor prognosis and a high mortality rate in stroke patients. However, the accuracy of early prediction of SAP is insufficient, and there is a lack of effective prognostic evaluation methods. Therefore, in this study, we investigated the predictive value of the Oxford Acute Severity of Illness Score (OASIS) in SAP to provide a potential reference index for the incidence and prognosis of SAP.

Methods

We recruited a total of 280 patients with acute ischemic stroke who had been diagnosed and treated in the Zhumadian Central Hospital between January 2021 and January 2023. These patients were divided into an SAP group (86 cases) and a non-SAP group (194 cases) according to SAP diagnostic criteria by expert consensus on the diagnosis and treatment of SAP. We collated general and clinical data from all patients, including the survival of SAP patients during the follow-up period. Multivariate logistic regression was used to analyze the risk factors for SAP. Kaplan-Meier and multivariate COX regression analyses were used to investigate the relationship between OASIS and the prognosis of SAP, and a receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of OASIS for SAP.

Results

Our analyses identified body temperature, C-reactive protein, procalcitonin, OASIS, and a prolonged length of intensive care unit (ICU) stay as the main risk factors for SAP (all Ps < 0.05). Advanced age and an elevated OASIS were identified as the main risk factors for death in SAP patients (all Ps < 0.05). The risk of death in patients with OASIS of 31-42 points was significantly higher than that in patients with OASIS of 12-20 points (HR = 5.588, 95% CI = 1.531-20.401, P = 0.009). ROC curve analysis further showed that OASIS had a high predictive value for morbidity and the incidence of death in SAP patients.

Conclusion

OASIS can effectively predict the onset and death of SAP patients and provides a potential reference index for early diagnosis and the prediction of prognosis in patients with SAP. Our findings should be considered in clinical practice.

Prognostic impact of blood urea nitrogen to albumin ratio on patients with sepsis: a retrospective cohort study

To investigate the ability of the ratio of blood urea nitrogen (BUN) to serum albumin ratio (BAR) in patients with sepsis in intensive care units (ICUs) to predict the prognosis of short-and long-term death. Data are from the Marketplace for Intensive Care Medical Information IV (MIMIC-IV v2.0) database for patients with sepsis as defined by SEPSIS-3. The primary outcome was 30-day mortality and the secondary outcome was 360-day mortality. Kaplan-Meier (KM) survival curves were plotted to describe differences in BAR mortality in different subgroups and area under the curve (AUC) analysis was performed to compare the predictive value of sequential organ failure assessment (SOFA), BAR, blood urea nitrogen (BUN) and albumin. Multivariate Cox regression models and subgroup analysis were used to determine the correlation between BAR and 30-day mortality and 360-day mortality. A total of 7656 eligible patients were enrolled in the study with a median BAR of 8.0 mg/g, including 3837 in the ≤ 8.0 group and 3819 in the BAR > 8.0 group, with 30-day mortality rates of 19.1% and 38.2% (P < 0.001) and 360-day mortality rates of 31.1% and 55.6% (P < 0.001). Multivariate Cox regression models showed an increased risk of death for 30-day mortality (HR = 1.219, 95% CI 1.095-1.357; P < 0.001) and 360-day mortality (HR = 1.263, 95% CI 1.159-1.376; P < 0.001) in the high BAR group compared to the low BAR group. For the 30-day outcome, the area under the curve (AUC) was 0.661 for BAR and 0.668 for 360-day BAR. In the subgroup analysis, BAR remained an isolated risk factor for patient death. As a clinically inexpensive and readily available parameter, BAR can be a valuable forecaster of prognosis in patients with sepsis in the intensive care unit.

Urine output for predicting in-hospital mortality of intensive care patients with cardiogenic shock

Background

The role of urine output (UO) in the first 24 h of admission in the clinical management of cardiogenic shock (CS) patients has not been elucidated.

Methods

This study retrospectively analyzed intensive care CS patients in the MIMIC-IV database. Binomial logistic regression analysis was conducted to evaluate whether UO was an independent risk factor for in-hospital mortality in CS patients. The performance of UO in predicting mortality was evaluated by the receiver operating characteristic (ROC) curve and compared with the Oxford Acute Severity of Illness Score (OASIS). The clinical net benefit of UO in predicting mortality was determined using the decision curve analysis (DCA). Survival analysis was performed with Kaplan-Meier curves.

Results

After adjusting for confounding factors including diuretic use and acute kidney injury (AKI), UO remained an independent risk factor for in-hospital mortality in CS patients. The areas under the ROC curves (AUCs) of UO for predicting in-hospital mortality were 0.712 (UO, ml/day) and 0.701 (UO, ml/kg/h), which were comparable to OASIS (AUC = 0.695). In terms of clinical net benefit, UO was comparable to OASIS, with different degrees of benefit at different threshold probabilities. Survival analysis showed that the risk of in-hospital death in the low-UO (≤857 ml/day) group was 3.0143 times that of the high-UO (>857 ml/day) group.

Conclusions

UO in the first 24 h of admission is an independent risk factor for in-hospital mortality in intensive care CS patients and has moderate predictive value in predicting in-hospital mortality.

Simplified acute physiology score III is excellent for predicting in-hospital mortality in coronary care unit patients with acute myocardial infarction: A retrospective study

Background

Coronary care unit (CCU) patients with acute myocardial infarction (AMI) lack effective predictors of in-hospital mortality. This study aimed to investigate the performance of four scoring systems in predicting in-hospital mortality in CCU patients with AMI.

Methods

The baseline data, the logistic organ dysfunction system (LODS), the Oxford acute severity of illness score (OASIS), the simplified acute physiology score II (SAPS II), and the simplified acute physiology score III (SAPS III) scores of the patients were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC-IV) database. Independent risk factors for in-hospital mortality were identified by regression analysis. We performed receiver operating characteristic (ROC) curves and compared the area under the curve (AUC) to clarify the predictive value of the four scoring systems. Meanwhile, Kaplan-Meier curves and decision curve analysis (DCA) were performed to determine the optimal scoring system for predicting in-hospital mortality.

Results

A total of 1,098 patients were included. The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI before and after the propensity score matching (PSM) analysis. The discrimination of in-hospital mortality by SAPS III was superior to that of LODS, OASIS, and SAPS II. The AUC of the SAPS III scoring system was the highest among the four scoring systems, at 0.901 (before PSM) and 0.736 (after PSM). Survival analysis showed that significantly more in-hospital mortality occurred in the high-score SAPS III group compared to the low-score SAPS III group before PSM (HR 7.636, P < 0.001) and after PSM (HR 2.077, P = 0.005). The DCA curve of SAPS III had the greatest benefit score across the largest threshold range compared to the other three scoring systems.

Conclusion

The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI. The predictive value for in-hospital mortality with SAPS III is superior to that of LODS, OASIS, and SAPS II. The results of the DCA analysis suggest that SAPS III may provide a better clinical benefit for patients. We demonstrated that SAPS III is an excellent scoring system for predicting in-hospital mortality for CCU patients with AMI.

Predicting medical events and ICU requirements using a multimodal multiobjective transformer network

Effective utilization of premium hospital resources such as intensive care unit (ICU), operating theater (OT), mechanical ventilator, endotracheal tube, and so on plays a significant role in providing high-quality care to critically ill patients within reasonable costs. Non-availability of specialized resources can lead to dire consequences for such patients, and in the worst case, may even turn out to be fatal. However, these resources cannot be kept idle, as they are expensive to maintain. Therefore, one of the core functions of hospital management is targeted at planning and managing these critical resources in order to provide efficient and effective health-care services to the end-users. Predictive technologies play a big role in this. In this article, we present methods for predicting the length of stay in ICU as well as the need for critical interventions for a patient based on the vital signs, laboratory measurements, and the nursing notes of the patient prepared within the first 24 h of ICU stay. The model has been built and cross-validated on the publicly available Medical Information Mart for Intensive Care (MIMIC-III v1.4) data set. We show that the proposed model performs way better than most of the earlier models in the prediction of ICU stay, which had used patient vitals primarily. Experimental results also demonstrate the advantage of using a multiobjective model over independent models for the prediction of ICU stay and critical interventions. The proposed model uses Local Interpretable Model-agnostic Explanations (LIME) that help in identifying the features responsible for predictive decisions. This is very useful in building trust and confidence in the prediction model among clinical practitioners.

Association between red blood cell distribution width to albumin ratio and prognosis of patients with sepsis: A retrospective cohort study

Background

Red blood cell distribution width (RDW) to albumin ratio (RAR) is associated with poor prognosis in diabetic comorbidities and cancer. However, the association between RAR and prognosis in patients with sepsis remains unclear, which was investigated in this study.

Methods

We conducted a retrospective cohort study based on the Medical Information Mart for Intensive Care (MIMIC) IV version 2.0 database. The primary outcome of this study was 28-day mortality. Secondary outcomes included 90-day mortality, in-hospital mortality, length of hospital stay, and length of intensive care unit (ICU) stay. Multivariate regression analysis and subgroup analysis were performed to investigate the association between RAR and prognosis in patients with sepsis.

Results

A total of 14,639 participants were included in this study. The mean age of the participants was 65.2 ± 16.3 years and the mean RAR was 5.5 ± 1.9 % /g/dl. For 28-day mortality, after adjusting for covariates, HRs [95% confidence intervals (CIs)] for tertiles 2 (4.4–5.8) and 3 (RAR > 5.8) were 1.33 (1.20, 1.46) and 1.98 (1.79, 2.19), respectively. Similar results were observed for 90-day mortality and in-hospital mortality. According to Kaplan-Meier curve analysis, the higher RAR group had higher 28-day mortality and 90-day mortality.

Conclusion

Our study shows that RAR is significantly associated with poor clinical prognosis in sepsis. The higher the RAR, the higher the 28-day, 90-day, and in-hospital mortality.

Clinical characteristics and risk factors associated with ICU-acquired infections in sepsis: A retrospective cohort study

Intensive care unit (ICU)-acquired infection is a common cause of poor prognosis of sepsis in the ICU. However, sepsis-associated ICU-acquired infections have not been fully characterized. The study aims to assess the risk factors and develop a model that predicts the risk of ICU-acquired infections in patients with sepsis.

No sex differences in the incidence, risk factors and clinical impact of acute kidney injury in critically ill patients with sepsis

Background

Sex-stratified medicine is an important aspect of precision medicine. We aimed to compare the incidence and risk factors of acute kidney injury (AKI) for critically ill men and women with sepsis. Furthermore, the short-term mortality was compared between men and women with sepsis associated acute kidney injury (SA-AKI).

Method

This was a retrospective study based on the Medical Information Mart for Intensive Care IV database. We used the multivariable logistic regression analysis to evaluate the independent effect of sex on the incidence of SA-AKI. We further applied three machine learning methods (decision tree, random forest and extreme gradient boosting) to screen for the risk factors associated with SA-AKI in the total, men and women groups. We finally compared the intensive care unit (ICU) and hospital mortality between men and women with SA-AKI using propensity score matching.

Results

A total of 6463 patients were included in our study, including 3673 men and 2790 women. The incidence of SA-AKI was 83.8% for men and 82.1% for women. After adjustment for confounders, no significant association was observed between sex and the incidence of SA-AKI (odds ratio (OR), 1.137; 95% confidence interval (CI), 0.949-1.361; p=0.163). The machine learning results revealed that body mass index, Oxford Acute Severity of Illness Score, diuretic, Acute Physiology Score III and age were the most important risk factors of SA-AKI, irrespective of sex. After propensity score matching, men had similar ICU and hospital mortality to women.

Conclusions

The incidence and associated risk factors of SA-AKI are similar between men and women, and men and women with SA-AKI experience comparable rates of ICU and hospital mortality. Therefore, sex-related effects may play a minor role in developing SA-AKI. Our study helps to contribute to the knowledge gap between sex and SA-AKI.

A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database

Objective

Bone and bone marrow are the third most frequent sites of metastases from many cancers and are associated with low survival and high morbidity rates. Currently, there are no effective bedside tools to predict the morbidity risk of these patients in general intensive care units (ICUs). The main objective of this study was to establish and validate a nomogram to predict the morbidity risk of patients with bone and bone marrow metastases.

Methods

Data on patients with bone and bone marrow metastases were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The patients were divided into training and validation cohorts. The data were analyzed using univariate and multivariate Cox regression methods. Factors significantly and independently prognostic of survival were used to construct a nomogram predicting 30-day morbidity. The nomogram was validated by various methods, including Harrell’s concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA).

Results

The study included 610 patients in the training cohort and 262 in the validation cohort. Multivariate Cox regression analysis showed that temperature, SpO₂, Sequential Organ Failure Assessment (SOFA) score, Oxford Acute Severity of Illness Score (OASIS), comorbidities with coagulopathy, white blood cell count, heart rate, and respiratory rate were independent predictors of patient survival. The resulting nomogram had good discriminative ability, as shown by high AUCs, and was well calibrated, as demonstrated by calibration curves. Improvements in NRI and IDI values suggested that the nomogram was superior to the SOFA scoring system. DCA curves revealed that the nomogram showed good value in clinical applications.

Conclusion

This prognostic nomogram, based on demographic and laboratory parameters, was predictive of the 30-day morbidity rate in patients with secondary malignant neoplasms of the bone and bone marrow, suggesting its applicability in clinical practice.

The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury

Objective

To compare the performance of the Oxford Acute Severity of Illness Score (OASIS), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Simplified Acute Physiology Score II (SAPS II), and the Sequential Organ Failure Assessment (SOFA) score in predicting 28-day mortality in acute kidney injury (AKI) patients.

Methods

Data were extracted from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 2954 patients with complete clinical data were included in this study. Receiver operating characteristic (ROC) curves were used to analyze and evaluate the predictive effects of the four scoring systems on the 28-day mortality risk of AKI patients and each subgroup. The best cutoff value was identified by the highest combined sensitivity and specificity using Youden’s index.

Results

Among the four scoring systems, the area under the curve (AUC) of OASIS was the highest. The comparison of AUC values of different scoring systems showed that there were no significant differences among OASIS, APACHE II, and SAPS II, which were better than SOFA. Moreover, logistic analysis revealed that OASIS was an independent risk factor for 28-day mortality in AKI patients. OASIS also had good predictive ability for the 28-day mortality of each subgroup of AKI patients.

Conclusion

OASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of AKI patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute.

Trial Registration

This study was registered at www.chictr.org.cn (registration number Chi CTR-ONC-11001875). Registered on 14 December 2011.

SAPS III is superior to SOFA for predicting 28-day mortality in sepsis patients based on Sepsis 3.0 criteria

INTRODUCTION

The discrimination and calibration accuracy of prediction models tends to become poor over time. The performance of predictive models should be reevaluated periodically. The aim of this study was to reassess the discrimination of the six commonly used models for predicting 28-day mortality in patients with sepsis based on the Sepsis 3.0 criteria.

METHODS

Patient data were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC IV) database. The systemic inflammatory response syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS), Logistic Organ Dysfunction System (LODS), and Simplified Acute Physiology Score II (SAPS II) and III (SAPS III) scores were calculated and collected. The area under the receiver operating characteristic curve (AUROC) was used to compare the discrimination abilities of the models using non-parametric Wilcoxon statistics. The Delong method was used to perform pairwise comparisons of the AUROCs of the models. Multiple subgroup analyses for age, body mass index, and sex were performed with regard to the 28-day mortality prediction of the models.

RESULTS

A total of 12 691 patients were included. The mean age of the patients was 65.97 ± 15.77 years; 7673 patients (60.50%) were male. The mean SIRS, SOFA, OASIS, SAPS II, LODS, and SAPS III scores were higher in the non-survivor group than in the survivor group. The discrimination for 28-day mortality with the SAPS III (AUROC 0.812, 95% confidence interval (CI) 0.802-0.822) and LODS (AUROC 0.804, 95% CI 0.743-0.765) models was superior to that of the SIRS (AUROC 0.575, 95% CI 0.562-0.589), SOFA (AUROC 0.612, 95% CI 0.598-0.626), OASIS (AUROC 0.753, 95% CI 0.742-0.764), and SAPS II (AUROC 0.754, 95% CI 0.743-0.765) models. The Youden index of the SAPS III model was 0.484, which was the highest among the models. Subgroup analyses showed similar results to the overall results.

CONCLUSIONS

The discrimination for 28-day mortality with the SAPS III and LODS models was superior to that of the SIRS, SOFA, OASIS, and SAPS II models. The SAPS III model showed the best discrimination capacity for 28-day mortality compared with the other models.

Preoperative Glucose-to-Lymphocyte Ratio is an Independent Predictor for Acute Kidney Injury After Cardiac Surgery in Patients in Intensive Care Unit

Background

We aimed to investigate the association between preoperative glucose-to-lymphocyte ratio (GLR) and cardiac surgery associated with acute kidney injury (CSA-AKI) in patients in the intensive care unit (ICU).

Methods

The Medical Information Mart for Intensive Care IV (MIMIC-IV version 1.0) database was used to identify adults' patients who performed cardiac surgery during ICU stay. The primary outcome was the development of AKI based on the KDIGO criteria. Multivariable logistic regression was applied to investigate the association between GLR and clinical outcomes, and propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were also used to validate our findings.

Results

The optimal cut-off value for GLR was 1.28. Among the 7181 patients who conducted cardiac surgery, 2072 high-GLR group (≥1.28) patients and 2072 low-GLR group (<1.28) patients, had similar propensity scores were included in this study. After matching, the high-GLR group had a significantly higher incidence of AKI (odds ratio, OR, 3.28, 95% confidence index, 95% CI, 2.81-3.84, P <0.001) even after adjustment for confounding factors. Moreover, the performance of GLR was superior to that of SOFA scores and GLR plus clinical model could add more net benefit for CSA-AKI than clinical model alone.

Conclusion

Preoperative GLR could serve as a good predictor for CSA-AKI in patients in ICU.

Association between lactate/albumin ratio and all-cause mortality in patients with acute respiratory failure: A retrospective analysis

Previous studies have shown that lactate/albumin ratio (LAR) can be used as a prognostic biomarker to independently predict the mortality of sepsis and severe heart failure. However, the role of LAR as an independent prognostic factor in all-cause mortality in patients with acute respiratory failure (ARF) remains to be clarified. Therefore, we retrospectively analyzed 2170 patients with ARF in Medical Information Mart for Intensive Care Database III from 2001 to 2012. By drawing the receiver operating characteristic curve, LAR shows a better predictive value in predicting the 30-day mortality of ARF patients (AUC: 0.646), which is higher than that of albumin (AUC: 0.631) or lactate (AUC: 0.616) alone, and even higher than SOFA score(AUC: 0.642). COX regression analysis and Kaplan-Meier curve objectively and intuitively show that high LAR is a risk factor for patients with ARF, which is positively correlated with all-cause mortality. As an easy-to-obtain and objective biomarker, LAR deserves further verification by multi-center prospective studies.

Risk factor identification and predictive models for central line requirements for patients on vasopressors

Vasopressors are ubiquitous in intensive care units. While central venous catheters are the preferred route of infusion, recent evidence suggests peripheral administration may be safe for short, single-agent courses. Here, we identify risk factors and develop a predictive model for patient central venous catheter requirement using the Medical Information Mart for Intensive Care, a single-centre dataset of patients admitted to an intensive care unit between 2008 and 2019. Using prior literature, a composite endpoint of prolonged single-agent courses (>24 hours) or multi-agent courses of any duration was used to identify likely central venous catheter requirement. From a cohort of 69,619 intensive care unit stays, there were 17,053 vasopressor courses involving one or more vasopressors that met study inclusion criteria. In total, 3807 (22.3%) vasopressor courses involved a single vasopressor for less than six hours, 7952 (46.6%) courses for less than 24 hours and 5757 (33.8%) involved multiple vasopressors of any duration. Of these, 3047 (80.0%) less than six-hour and 6423 (80.8%) less than 24-hour single vasopressor courses used a central venous catheter. Logistic regression models identified associations between the composite endpoint and intubation (odds ratio (OR) 2.36, 95% confidence intervals (CI) 2.16 to 2.58), cardiac diagnosis (OR 0.72, CI 0.65 to 0.80), renal impairment (OR 1.61, CI 1.50 to 1.74), older age (OR 1.002, Cl 1.000 to 1.005) and vital signs in the hour before initiation (heart rate, OR 1.006, CI 1.003 to 1.009; oxygen saturation, OR 0.996, CI 0.993 to 0.999). A logistic regression model predicting the composite endpoint had an area under the receiver operating characteristic curve (standard deviation) of 0.747 (0.013) and an accuracy of 0.691 (0.012). This retrospective study reveals a high prevalence of short vasopressor courses in intensive care unit settings, a majority of which were administered using central venous catheters. We identify several important risk factors that may help guide clinicians deciding between peripheral and central venous catheter administration, and present a predictive model that may inform future prospective trials.

The Relationship Between Prognostic Nutritional Index and All-Cause Mortality in Critically Ill Patients: A Retrospective Study

Purpose

The effectiveness and prognostic value of the prognostic nutritional index (PNI) in critically ill patients are unknown. Hence, this study aimed to analyze the relationship between the PNI and all-cause mortality in critically ill patients.

Patients and Methods

Patient data were obtained from the Multiparameter Intelligent Monitoring in Intensive Care III database. The relationship between the PNI and in-hospital mortality was analyzed using receiver operating characteristic curve analysis and a logistic regression model. Propensity score matching (PSM) was used to eliminate the bias caused by confounding factors. The Kaplan-Meier curve and Cox regression model were used to test the effect of the PNI on 30-, 90-, 180-, and 365-day mortality.

Results

A low PNI score is an independent risk factor for in-hospital mortality in critically ill patients. A total of 3644 cases were successfully matched using PSM. The PSM group with balanced covariates obtained similar results in the three models, which were statistically significant. The Kaplan-Meier curve and Cox regression model showed that the PNI was negatively correlated with 30-, 90-, 180-, and 365-day all-cause mortality.

Conclusion

The PNI score is an independent risk factor for all-cause mortality in critically ill patients, where a low PNI score is associated with increased mortality.

A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit

BACKGROUND

Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We hypothesized machine learning could be applied to critically ill patients and would outperform currently used mortality scores.

METHODS

The current Deep-FLAIM model evaluates the statistically significant risk factors and then supply these risk factors to deep neural network to predict mortality in trauma patients admitted to the intensive care unit (ICU). We analyzed adult patients (≥18 years) admitted to the trauma ICU in the publicly available database Medical Information Mart for Intensive Care III version 1.4. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we applied deep neural network and other traditional machine learning models like Linear Discriminant Analysis, Gaussian Naïve Bayes, Decision Tree Model, and k-nearest neighbor models.

RESULTS

We identified a total of 3,041 trauma patients admitted to the trauma surgery ICU. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being serum anion gap (hazard ratio [HR], 2.46; 95% confidence interval [CI], 1.94-3.11), sodium (HR, 2.11; 95% CI, 1.61-2.77), and chloride (HR, 2.11; 95% CI, 1.69-2.64) abnormalities on laboratories, while clinical variables included the diagnosis of sepsis (HR, 2.03; 95% CI, 1.23-3.37), Quick Sequential Organ Failure Assessment score (HR, 1.52; 95% CI, 1.32-3.76). And Systemic Inflammatory Response Syndrome criteria (HR. 1.41; 95% CI, 1.24-1.26). After we used these clinically significant variables and applied various machine learning models to the data, we found out that our proposed DNN outperformed all the other methods with test set accuracy of 92.25%, sensitivity of 79.13%, and specificity of 94.16%; positive predictive value, 66.42%; negative predictive value, 96.87%; and area under the curve of the receiver-operator curve of 0.91 (1.45-1.29).

CONCLUSION

Our novel Deep-FLAIM model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

LEVEL OF EVIDENCE

Prognostic study, level II.

Prognostic role of red blood cell distribution width in patients with sepsis: a systematic review and meta-analysis

Background

Outcome prediction for patients with sepsis may be conductive to early aggressive interventions. Numerous biomarkers and multiple scoring systems have been utilized in predicting outcomes, however, these tools were either expensive or inconvenient.

We performed a meta-analysis to evaluate the prognostic role of red blood cell distribution width (RDW) in patients with sepsis.

Methods

The online databases of Embase, Web of science, Pubmed, Corchrane library, Chinese Wanfang database, CNKI database were systematically searched from the inception dates to June, 24th, 2020, using the keywords red cell distribution width and sepsis. The odds ratio (OR) or Hazards ratio (HR) with corresponding 95% confidence intervals (95%CI) were pooled to evaluate the association between baseline RDW and sepsis. A random-effects model was used to pool the data, and statistical heterogeneity between studies was evaluated using the I² statistic. Sensitivity and subgroup analyses were performed to detect the publication bias and origin of heterogeneity.

Results

Eleven studies with 17,961 patients with sepsis were included in the meta-analysis. The pooled analyses indicated that increased baseline RDW was associated with mortality (HR = 1.14, 95%CI 1.09–1.20, Z = 5.78, P < 0.001) with significant heterogeneity (I² = 80%, P_{heterogeneity} < 0.001). Similar results were found in the subgroup analysis stratified by site of infection, comorbidity, Newcastle-Ottawa Scale (NOS) score, study design, patients’ country. The predefined subgroup analysis showed that NOS score may be the origin of heterogeneity.

Conclusions

For patients with sepsis, baseline RDW may be a useful predictor of mortality, patients with increased RDW are more likely to have higher mortality.

[Comparison of four scoring systems for predicting ICU mortality in patients with sepsis]

OBJECTIVE

To evaluate the value of Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score Ⅱ (SAPS-Ⅱ), Oxford Acute Severity of Illness Score (OASIS) and Logistic Organ Dysfunction System (LODS) scoring systems for predicting ICU mortality in patients with sepsis.

METHODS

We collected the data of a total of 2470 cases of sepsis recorded in the MIMIC-III database from 2001 to 2012 and retrieved the scores of SOFA, SAPS-Ⅱ, OASIS and LODS of the patients within the first day of ICU admission. We compared with the score between the survivors and the non-survivors and analyzed the differences in the area under the ROC curve (AUC) of the 4 scoring systems. Binomial logistic regression was performed to compare the predictive value of the 4 scoring systems for ICU mortality of the patients.

RESULTS

In the 2470 patients with sepsis, 1966 (79.6%) survived and 504 (20.4%) died in the ICU. Compared with the survivors, the non-survivors had a significantly older mean age, higher proportion of patients receiving mechanical ventilation, and higher initial lactate level, creatinine, urea nitrogen, SOFA score, SAPS-Ⅱ score, OASIS score and LODS score (P < 0.05) but with significantly lower body weight and platelet counts (P < 0.05). The AUCs of the SOFA score, SAPS-Ⅱ score, OASIS score, and LODS score were 0.729 (P < 0.001), 0.768 (P < 0.001), 0.757 (P < 0.001), and 0.739 (P < 0.001), respectively. The AUC of SAPS-Ⅱ score was significantly higher than those of SOFA score (Z=3.679, P < 0.001) and LODS score (Z=3.698, P < 0.001) but was comparable with that of OASIS score (Z=1.102, P=0.271); the AUC of OASIS score was significantly higher than that of LODS score (Z=2.172, P=0.030) and comparable with that of SOFA score (Z=1.709, P=0.088). For predicting ICU mortality in patients without septic shock, the AUC of SAPS-Ⅱ score was 0.769 (0.743-0.793), the highest among the 4 scoring systems; in patients with septic shock, the AUCs SAPS-Ⅱ score and OASIS score, 0.768 (0.745-0.791) and 0.762 (0.738-0.785), respectively, were significantly higher than those of the other two scoring systems. Binomial logistic regression showed the corrected SOFA, SAPS-Ⅱ, and OASIS scores, but not LODS scores, were significantly correlated with ICU mortality in patients with sepsis, and their ORs were 1.08 (95% CI: 1.03-1.14, P=0.001), 1.04 (95% CI: 1.02-1.05, P < 0.001), 1.04 (95% CI: 1.01-1.06, P=0.001), 0.96 (95% CI: 0.89-1.04, P=0.350), respectively.

CONCLUSIONS

The scores of SOFA, SAPS-Ⅱ, OASIS, and LODS can predict ICU mortality in patients with sepsis, but SAPS-Ⅱ and OASIS scores have better predictive value than SOFA and LODS scores.

Association Between Comorbid Chronic Obstructive Pulmonary Disease and Prognosis of Patients Admitted to the Intensive Care Unit for Non-COPD Reasons: A Retrospective Cohort Study

Background and Aim

Chronic obstructive pulmonary disease (COPD) is a rather common comorbid condition among patients admitted to the intensive care unit (ICU), while evidence of how this comorbidity affects prognosis is limited. This study aimed to investigate the associations between COPD comorbidity and prognoses of patients who were admitted to the ICU for non-COPD reasons, and to examine whether the associations varied between different types of ICU.

Methods

A retrospective cohort study was performed using data extracted from a freely accessible critical care database (MIMIC-III). Adult (≥18 years) patients of first ICU admission in the database were enrolled as study participants but those with a primary diagnosis of COPD were excluded. The primary endpoint was 28-day mortality after ICU admission and multivariable Cox regression analyses were employed to assess the associations between COPD comorbidity and the study endpoints. Different adjusting models including a propensity score were used to adjust potential confounders.

Results

A total of 29,499 patients were enrolled finally, among which 3,332 patients (11.30%) were comorbid with COPD. A higher 28-day mortality was observed among patients with COPD than those without COPD (13.90% versus 8.07%, P<0.001), but there was no statistically significant difference in the proportion of patients who needed mechanical ventilation on the first day after ICU admission between the two groups. Multivariable Cox regression analyses found a significant association between COPD comorbidity and 28-day mortality (adjusted hazard ratio=1.32, 95% confidence interval=1.19-1.47, P<0.0001). The associations were broadly consistent among patients admitted to different types of ICU, but a much higher estimate was observed in patients admitted to cardiac surgery recovery unit (adjusted hazard ratio=2.03, 95% confidence interval=1.44-2.86, P<0.0001).

Conclusion

Comorbid COPD increased the risk of 28-day mortality among patients admitted to the ICU for non-COPD reasons, especially for those admitted to the cardiac surgery recovery unit.