The Association Between the Baseline and the Change in Neutrophil-to-Lymphocyte Ratio and Short-Term Mortality in Patients With Acute Respiratory Distress Syndrome

Association between neutrophil to lymphocyte ratio and all-cause mortality in critical patients with coronary artery disease - a study based on the MIMIC-IV database

Background

Neutrophil-to-lymphocyte ratio (NLR) has been presented as a possible indicator associated with the outcomes of growing patients and an available predictor of inflammation. Nevertheless, just a handful of researches shed light on the association between NLR and the consequences of critical patients with coronary artery disease (CAD). The study aimed to investigate the correlation between NLR and all-cause mortality of short-term and long-term in patients with CAD.

Methods

We obtained objective data from the Medical Information Mart for Intensive Care (MIMIC)-IV version 2.2, a comprehensive and large-scale single-center database. NLR was calculated separately. Patients were categorized by quartiles of NLR: Q1 group (NLR < 3.56), Q2 (NLR 3.56-5.54), Q3 group (NLR 5.54-9.05), Q4 group (NLR > 9.05). Kaplan-Meier survival curves based on NLR quartiles were created to compare all-cause mortality rates, and the log-rank test evaluated the differences between groups. The hazard ratio (HR) of NLR as a risk factor for outcome events was assessed using the Cox proportional risk model with the Q1 group serving as the reference group and restricted cubic spline (RCS) with the infection points of 5.54.

Results

A total of 3,692 patients were included in this study. The 30-day mortality rate among the patients was 8.85%, while the 365-day mortality rate was 16.98%. High NLR (NLR > 5.54) was significantly associated with 30-day mortality [HR, 3.99,95% confident interval (CI), (3.03-5.24); P < 0.001] and 365-day mortality [HR, 5.72, 95% CI (3.83-8.54); P < 0.001] in patients with critical CAD in the completely adjusted Cox proportional risk model. RCS analysis revealed a U-shaped relationship between NLR and outcome events.

Conclusion

In patients diagnosed with critical CAD, a significant correlation was observed between NLR and all-cause mortality, particularly among individuals exhibiting elevated NLR levels. These findings suggest that NLR may serve as a valuable prognostic marker for evaluating both short-term and long-term mortality risk in this patient population.

Neutrophil-to-Lymphocyte Ratio Predicts Dialysis Timing & Prognosis in Critically Ill Patients

BACKGROUND AND AIMS

The neutrophil-to-lymphocyte ratio (NLR) is a cost-effective indicator of inflammation, which may impact decisions regarding therapy for patients undergoing continuous renal replacement therapy (CRRT), even with ongoing clinical arguments. This study aimed to examine the correlation between NLR and the prognosis of critically ill patients undergoing CRRT, specifically about mortality and morbidity. Additionally, the study sought to assess NLR's potential as a prognostic indicator for CRRT initiation.

METHODS

Data were retrospectively analyzed from 175 critically ill patients who received CRRT. Clinical factors and biochemical markers were compared between survivors and non-survivors at admission, before CRRT, and at 24 and 72 h post-CRRT initiation.

RESULTS

Elevated NLR levels were significantly associated with increased in-hospital mortality. Neutrophil counts showed statistical significance across all measurement points, while NLR and lymphocyte counts were significant only on the third day of CRRT (p 0.001 and 0.011, respectively). Non-survivors had higher NLR values than survivors and experienced shorter hospital stays (median 22 vs. 44 days for survivors, p < 0.001). Patients with higher baseline NLR values also had more complications.

CONCLUSIONS

The NLR shows potential as a prognostic predictor for mortality in CRRT patients. Its integration into clinical practice could enhance patient care and treatment timing, and further studies should validate its clinical utility.

Single-center nomogram model for sepsis complicated by acute lung injury

OBJECTIVE

To construct and validate a nomogram model for predicting sepsis complicated by acute lung injury (ALI).

METHODS

The healthcare records of 193 sepsis patients hospitalized at The Affiliated Tai'an City Central Hospital of Qingdao University from January 2022 to December 2023 were retrospectively reviewed. Among these patients, 69 were in the ALI group and 124 in the non-ALI group. A nomogram prediction model was constructed using logistic regression analysis. Its predictive performance was evaluated through various measures, including the area under the curve (AUC), calibration curve, decision curve, sensitivity, specificity, accuracy, recall rate, and precision rate.

RESULTS

The predictive factors included the neutrophil/lymphocyte ratio (NLR), oxygenation index (PaO2/FiO2), tumor necrosis factor-α (TNF-α), and acute physiology and chronic health evaluation II (APACHE II). The nomogram training set achieved an AUC of 0.959 (95% CI: 0.924-0.995), an accuracy of 92.59%, a recall of 96.70%, and a precision of 92.63%. In the validation set, the AUC was 0.938 (95% CI: 0.880-0.996), with an accuracy of 89.66%, a recall of 93.94%, and a precision of 88.57%. The calibration curve demonstrated that the prediction results were consistent with the actual findings. The decision curve indicated that the model has clinical applicability.

CONCLUSION

NLR, PaO2/FiO2, TNF-α, and APACHE II are closely associated with ALI in sepsis patients. A nomogram model based on these four variables shows strong predictive performance and may be used as a clinical decision-support tool to help physicians better identify high-risk groups.

Tree-based ensemble machine learning models in the prediction of acute respiratory distress syndrome following cardiac surgery: a multicenter cohort study

BACKGROUND

Acute respiratory distress syndrome (ARDS) after cardiac surgery is a severe respiratory complication with high mortality and morbidity. Traditional clinical approaches may lead to under recognition of this heterogeneous syndrome, potentially resulting in diagnosis delay. This study aims to develop and external validate seven machine learning (ML) models, trained on electronic health records data, for predicting ARDS after cardiac surgery.

METHODS

This multicenter, observational cohort study included patients who underwent cardiac surgery in the training and testing cohorts (data from Nanjing First Hospital), as well as those patients who had cardiac surgery in a validation cohort (data from Shanghai General Hospital). The number of important features was determined using the sliding windows sequential forward feature selection method (SWSFS). We developed a set of tree-based ML models, including Decision Tree, GBDT, AdaBoost, XGBoost, LightGBM, Random Forest, and Deep Forest. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and Brier score. The SHapley Additive exPlanation (SHAP) techinque was employed to interpret the ML model. Furthermore, a comparison was made between the ML models and traditional scoring systems. ARDS is defined according to the Berlin definition.

RESULTS

A total of 1996 patients who had cardiac surgery were included in the study. The top five important features identified by the SWSFS were chronic obstructive pulmonary disease, preoperative albumin, central venous pressure_T4, cardiopulmonary bypass time, and left ventricular ejection fraction. Among the seven ML models, Deep Forest demonstrated the best performance, with an AUC of 0.882 and a Brier score of 0.809 in the validation cohort. Notably, the SHAP values effectively illustrated the contribution of the 13 features attributed to the model output and the individual feature's effect on model prediction. In addition, the ensemble ML models demonstrated better performance than the other six traditional scoring systems.

CONCLUSIONS

Our study identified 13 important features and provided multiple ML models to enhance the risk stratification for ARDS after cardiac surgery. Using these predictors and ML models might provide a basis for early diagnostic and preventive strategies in the perioperative management of ARDS patients.

Association between glucocorticoid administration and outcomes in patients with ARDS based on the MIMIC-III database

This study aimed to investigate the association between glucocorticoid administration and outcomes in critically ill patients with ARDS using the Medical Information Mart for Intensive Care (MIMIC)-III database. Data were collected from the MIMIC-III database, which consists of critically ill participants between 2001 and 2012 in the USA. A total of 1831 adult critically ill patients with ARDS were enrolled from the MIMIC-III database. The 60-day and in-hospital mortality, were the primary endpoints. Secondary outcomes included length of stay (LOS) in the hospital and intensive care unit (ICU), 28-day ventilator-free days, ICU mortality, and 28-day mortality. A total of 1831 patients were included in the data analysis. After propensity score (PS) matching, 464 patients diagnosed with ARDS were matched between the glucocorticoid treatment and control groups. Glucocorticoids were associated with increased in-hospital mortality [hazard ratio (HR) 1.32; 95% CI 1.01-1.71; P = .039], longer ICU stay [HR 2.25; 95% CI 0.84-3.65; P = .002], and shorter ventilation-free days at 28 days in all ARDS patients [HR -2.70; 95% CI -4.28--1.13; P = .001]. The 60-day mortality was higher in the glucocorticoid group (44.83% vs 35.34%; P = .154; HR 1.24; 95% CI 0.93-1.66). Excluding the impact of the glucocorticoid initiation time, from day 15 to day 60, mortality was significantly higher in the glucocorticoid group compared to the non-glucocorticoid group (27.16% vs 12.70%; P < .001; HR 1.75; 95% CI 1.32-2.32). Glucocorticoid administration was associated with worse 60-day and in-hospital survival, longer ICU stay, and shorter ventilator-free days on day 28 in patients with ARDS. Our findings suggest careful consideration of glucocorticoids for ARDS.

Association of the Neutrophil-to-Lymphocyte Ratio with 90-Day Functional Outcomes in Patients with Acute Ischemic Stroke

The neutrophil-to-lymphocyte ratio (NLR), an inflammatory marker, plays an important role in the inflammatory mechanisms of the pathophysiology and progression of acute ischemic stroke (AIS). The aim of this study was to identify the potential factors associated with functional prognosis in AIS. A total of 303 AIS patients were enrolled in this study; baseline information of each participant, including demographic characteristics, medical history, laboratory data, and 90-day functional outcome, was collected. Multivariate logistic regression analysis revealed that NLR, systolic blood pressure (SBP) and National Institutes of Health Stroke Scale (NIHSS) score were found to be independent factors for poor functional outcomes. Receiver operating characteristic (ROC) curve analysis was performed to estimate the predictive value of the NLR for 90-day functional outcome, with the best predictive cutoff value being 3.06. In the multivariate logistic regression analysis, three models were constructed: Model 1, adjusted for age, sex, SBP, and TOAST classification (AUC = 0.694); Model 2, further adjusted for the NIHSS score at admission (AUC = 0.826); and Model 3, additionally adjusted for the NLR (AUC = 0.829). The NLR at admission was an independent predictor of 90-day prognosis in patients with AIS. The risk factors related to poor 90-day functional outcomes were higher SBP, higher NLR, and a greater NIHSS score.

Neutrophil to lymphocyte ratio in pediatric patients with asthmatic exacerbation and community-acquired pneumonia

BACKGROUND

Compared with a lower neutrophil to lymphocyte ratio(NLR), a higher one denotes severe asthma exacerbation in hospitalized asthmatic children. In addition, NLR is significantly higher in pediatric patients with community-acquired pneumonia (CAP) than those without. Nevertheless, its role in pediatric patients with concomitant asthmatic exacerbation and CAP remains unknown.

METHODS

In this retrospective study including 1032 pediatric patients aged 5 to 14 years old, the diagnostic and prognostic value of NLR in children with concomitant asthmatic exacerbation and non-severe CAP were investigated.

RESULTS

The sensitivity and specificity of NLR for a diagnosis of CAP in patients with asthmatic exacerbation were 56.9% and 90.1%, respectively. The cutoff value of NLR for a diagnosis of CAP in patients with asthmatic exacerbation was 4.15 (P < 0.001). The cumulative asthmatic exacerbation during 3-month followup of patients with high NLR were 23 (21.3%) and 58 (42.0%) in the asthma and asthmatic CAP groups, respectively (P < 0.001). The patients with high NLR who had unimproved CAP were 15 (8.3%) and 23 (12.2%) in the CAP and asthmatic CAP groups, respectively (P = 0.006). Multivariate analyses showed that along with the increase of NLR by 1.0 point, the HR for the occurrence of asthmatic exacerbation and unimproved CAP were 2.91 [1.83-3.96] (P = 0.001) and 3.38 [1.66-5.10] (P < 0.001), respectively.

CONCLUSIONS

NLR had high and moderate diagnostic value for the exclusion and indication of CAP, respectively, in pediatric patients with asthmatic exacerbation. It also had prognostic value for the outcomes of pediatric patients with concomitant asthmatic exacerbation and CAP.

The impact of aspirin exposure prior to intensive care unit admission on the outcomes for patients with sepsis-associated acute respiratory failure

Objectives: This present study aimed to infer the association between aspirin exposure prior to ICU admission and the clinical outcomes of patients with Sepsis-associated acute respiratory failure (S-ARF). Methods: We obtained data from the Medical Information Mart for Intensive Care IV 2.0. Patients were divided into pre-ICU aspirin exposure group and Non-aspirin exposure group based on whether they took aspirin before ICU admission. The primary outcome is 28-day mortality. Augmented inverse propensity weighted was used to explore the average treatment effect (ATE) of the pre-ICU aspirin exposure. A generalized additive mixed model was used to analyze the longitudinal data of neutrophil to lymphocyte ratio (NLR), red cell distribution width (RDW), oxygenation index (P/F), dynamic lung compliance (Cdyn), mechanical power (MP), and mechanical power normalized to predicted body weight (WMP) in the two groups. A multiple mediation model was constructed to explore the possible mediators between pre-ICU aspirin exposure and outcomes of patients with S-ARF. Results: A total of 2090 S-ARF patients were included in this study. Pre-ICU aspirin exposure decreased 28-day mortality (ATE, -0.1945, 95% confidence interval [CI], -0.2786 to -0.1103, p < 0.001), 60-day mortality (ATE, -0.1781, 95% Cl, -0.2647 to -0.0915, p < 0.001), and hospital mortality (ATE, -0.1502, 95%CI, -0.2340 to -0.0664, p < 0.001). In subgroup analysis, the ATE for 28-day mortality, 60-day mortality, and hospital mortality were not statistically significant in the coronary care unit group, high-dose group (over 100 mg/d), and no invasive mechanical ventilation (IMV) group. After excluding these non-beneficiaries, Cdyn and P/F ratio of the pre-ICU aspirin exposure group increased by 0.31mL/cmH₂O (SE, 0.21, p = 0.016), and 0.43 mmHg (SE, 0.24, p = 0.041) every hour compared to that of non-aspirin exposure group after initialing IMV. The time-weighted average of NLR, Cdyn, WMP played a mediating role of 8.6%, 24.7%, and 13% of the total effects of pre-ICU aspirin exposure and 28-day mortality, respectively. Conclusion: Pre-ICU aspirin exposure was associated with decreased 28-day mortality, 60-day mortality, and hospital mortality in S-ARF patients except those admitted to CCU, and those took a high-dose aspirin or did not receive IMV. The protective effect of aspirin may be mediated by a low dynamic level of NLR and a high dynamic level of Cdyn and WMP. The findings should be interpreted cautiously, given the sample size and potential for residual confounding.

Correlation analysis between the static and the changed neutrophil-to-lymphocyte ratio and in-hospital mortality in critical patients with acute heart failure

OBJECTIVE

Association between neutrophil-to-lymphocyte ratio (NLR) on admission and poor prognosis in patients with acute heart failure (AHF) has been well established. However, the relationship between dynamic changes in NLR and in-hospital mortality in AHF patients has not been studied. Our purpose was to determine if an early change in NLR within the first week after AHF patients was admitted to intensive care unit (ICU) was associated with in-hospital mortality.

METHODS

Data from the medical information mart for intensive care IV (the MIMIC-IV) database was analyzed. The effect of baseline NLR on in-hospital mortality in critical patients with AHF was evaluated utilizing smooth curve fitting and multivariable logistic regression analysis. Moreover, comparison of the dynamic change in NLR among survivors and non-survivors was performed using the generalized additive mixed model (GAMM).

RESULTS

There were 1169 participants who took part in the present study, 986 of whom were in-hospital survivors and 183 of whom were in-hospital non-survivors. The smooth curve fitting revealed a positive relationship between baseline NLR and in-hospital mortality, and multivariable logistic regression analysis indicated that baseline NLR was an independent risk factor for in-hospital mortality (OR 1.04, 95% CI 1.02,1.07, P-value = 0.001). After adjusting for confounders, GAMM showed that the difference in NLR between survivors and non-survivors grew gradually during the first week after ICU admission, and the difference grew by an average of 0.51 per day (β = 0.51, 95% CI 0.45-0.56, P-value <0.001).

CONCLUSIONS

Baseline NLR was associated with poor prognosis in critical patients with AHF. Early rises in NLR were linked to higher in-hospital mortality, which suggests that keeping track of how NLR early changes might help identify short-term prognosis of critical patients with AHF.

The serial changes of Neutrophile-Lymphocyte Ratio and correlation to weight loss after Laparoscopic Sleeve Gastrectomy

Purpose

This study aims to identify the pre- and postoperative changes in the neutrophil-lymphocyte ratio (NLR) and its correlations to clinical characteristics in obese patients who underwent laparoscopic sleeve gastrectomy (LSG).

Method

Retrospectively, we included patients who has undergone LSG in our institution between January 2019 and April 2021. A total of 100 patients whose body mass index over 32.5 and received primary laparoscopic sleeve gastrectomy without infectious condition were included.

Results

There was a significant decline in NLR (T0 vs. POM3 2.21 vs. 1.78, p = 0.005), neutrophil (T0 vs. POM3 5369 vs. 4050, p < 0.001) and lymphocyte count (T0 vs. POM3 2440: 2100, p < 0.001, respectively) at postoperative 3 months (POM3) compared to preoperative (T0) levels, but similar between POM3 and POM6. The declined counts (Neutrophile vs. Lymphocyte 1445.5/µl vs. 323.5/µl, p < 0.001) and percentage (Neutrophile vs. Lymphocyte 25.11% vs. 13.07%, p < 0.001) of neutrophile are higher than lymphocyte from T0 to POM3, but similar in POM3 and POM6. Preoperative NLR has a significant correlation with the preoperative body weight, preoperative insulin level, and excessive body weight loss (EBWL) at POM3. Preoperative NLR <2.36 had a sensitivity of 67.6% and a specificity of 62.5% in predicting successful weight loss (EBWL > 37.7%) at POM3 (AUC = 0.635, p = 0.032).

Conclusion

There was a significant decline in NLR, neutrophil, and lymphocyte count from T0 to POM3, but similar between POM3 and POM6. The declined counts and percentage of neutrophile are higher than lymphocyte. Preoperative NLR shows the potential to be used as a prognostic biomarker for predicting successful weight loss at POM3 after LSG. Further studies could be designed to evaluate the value of prediction in successful outcome after LSG and figure out the relationship between the changes of neutrophil function and oncogenesis.

The Neutrophil-to-Lymphocyte Ratio is Associated with the Requirement and the Duration of Invasive Mechanical Ventilation in Acute Respiratory Distress Syndrome Patients: A Retrospective Study

Background

Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality and most ARDS patients require ventilatory support. Applying appropriate ventilation strategies based on patients' individual situations has a direct impact upon patients' outcome. The neutrophil-to-lymphocyte ratio (NLR) has been shown to predict the early requirement of invasive mechanical ventilation (IMV) in patients with coronavirus disease 2019 (COVID-19). Our study aimed to investigate the relationship between baseline NLR and IMV in ARDS.

Methods

A retrospective study was performed on patients who were diagnosed with ARDS using the Berlin definition and admitted to the First Affiliated Hospital of Soochow University from 2017 to 2022. Clinical data within 24 h after the ARDS diagnosis were collected from the medical record system. Based on the ventilation strategies during hospitalization, patients were divided into three groups and their clinical characteristics were compared. Furthermore, logistic regression analysis was used to screen the independent risk factors for IMV. STROBE checklist was used for this manuscript.

Results

520 ARDS patients were included and the median NLR value in IMV group was significantly higher than that of other groups (P < 0.001). NLR was significantly associated with the requirement of IMV in ARDS patients (OR, 1.042; 95% CI, 1.025–1.060; P < 0.001), other independent risk factors included PaO₂/FiO₂, Hb, lactate, and use of vasoactive drugs (all P < 0.05). Moreover, we found that the duration of IMV was longer in patients with high NLR (8[IQR, 3–13], 10[IQR, 6–16], respectively, P=0.025).

Conclusions

Our results revealed that high baseline NLR level was significantly correlated with an increased risk of IMV in patients with ARDS. Furthermore, higher NLR was associated with prolonged duration of IMV in patients with ARDS.

Development and validation of a clinical risk model to predict the hospital mortality in ventilated patients with acute respiratory distress syndrome: a population-based study

Background

Large variability in mortality exists in patients of acute respiratory distress syndrome (ARDS), especially those with invasive ventilation. The aim of this study was to develop a model to predict risk of in-hospital death in ventilated ARDS patients.

Methods

Ventilated patients with ARDS from two public databases (MIMIC-III and eICU-CRD) were randomly divided as training cohort and internal validation cohort. Least absolute shrinkage and selection operator (LASSO) and then Logistic regression was used to construct a predictive model with demographic, clinical, laboratory, comorbidities and ventilation variables ascertained at first 24 h of ICU admission and invasive ventilation. Our model was externally validated using data from another database (MIMIC-IV).

Results

A total of 1075 adult patients from MIMIC-III and eICU were randomly divided into training cohort (70%, n = 752) and internal validation cohort (30%, n = 323). 521 patients were included from MIMIC-IV. From 176 potential predictors, 9 independent predictive factors were included in the final model. Five variables were ascertained within the first 24 h of ICU admission, including age (OR, 1.02; 95% CI: 1.01–1.03), mean of respiratory rate (OR, 1.04; 95% CI: 1.01–1.08), the maximum of INR (OR, 1.14; 95% CI: 1.03–1.31) and alveolo-arterial oxygen difference (OR, 1.002; 95% CI: 1.001–1.003) and the minimum of RDW (OR, 1.17; 95% CI: 1.09–1.27). And four variables were collected within the first 24 h of invasive ventilation: mean of temperature (OR, 0.70; 95% CI: 0.57–0.86), the maximum of lactate (OR, 1.15; 95% CI: 1.09–1.22), the minimum of blood urea nitrogen (OR, 1.02; 95% CI: 1.01–1.03) and white blood cell counts (OR, 1.03; 95% CI: 1.01–1.06). Our model achieved good discrimination (AUC: 0.77, 95% CI: 0.73–0.80) in training cohort but the performance declined in internal (AUC: 0.75, 95% CI: 0.69–0.80) and external validation cohort (0.70, 95% CI: 0.65–0.74) and showed modest calibration.

Conclusions

A risk score based on routinely collected variables at the start of admission to ICU and invasive ventilation can predict mortality of ventilated ARDS patients, with a moderate performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02057-0.

A propensity score-matching analysis of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker exposure on in-hospital mortality in patients with acute respiratory failure

STUDY OBJECTIVE

To explore the impact of pre-hospital ACEI and ARB exposure on the prognosis of ARF patients.

DESIGN

A single-center retrospective cohort study.

SETTING

Medical Information Mart for Intensive Care-III (MIMIC-III) database.

PATIENTS

The patients meeting ICD-9 code of acute respiratory failure were enrolled.

INTERVENTION

The primary exposure was the pre-hospital exposure of ACEI and ARB.

MEASUREMENT AND MAIN RESULTS

The primary outcome was in-hospital mortality. Multiple logistic regression analysis was conducted to determine the independent effect of ACEI/ARB exposure on mortality. Propensity score matching (PSM) method was adopted to reduce bias of the confounders. Subgroup analysis and sensitivity analysis were used to test the stability of the conclusion. 5335 adult ARF patients were enrolled. Mortality was significantly decreased in patients with ACEI/ARB exposure before and after PSM, and the adjusted odds ratio (OR) of ACEI/ARB exposure was 0.56 (95% CI 0.43-0.72). In the subgroup analysis, ACEI/ARB lost its protective effect in young subgroup, but no significant interaction was found between ACEI/ARB exposure and age (p = 0.082). The point estimation and lower 95% limit of E-value was 2.97 and 2.12. In sensitivity analysis, ACEI/ARB exposure showed similar effect in ARDS cohort, but no significantly difference was found in the MIMIC-IV database, which may be explained by small sample size of the ACEI/ARB group.

CONCLUSIONS

Among patients with acute respiratory failure, pre-hospital ACEI/ARB exposure was associated with better outcomes and acted as an independent factor. The relationship between ACEI/ARB and prognosis of ARF is worth investigating further.

Association between body mass index and short-term mortality in patients with intra-abdominal infections: a retrospective, single-centre cohort study using the Medical Information Mart for Intensive Care database

OBJECTIVES

This study aimed to determine the relationship between the body mass index (BMI) and short-term mortality of patients with intra-abdominal infection (IAI) using the Medical Information Mart for Intensive Care (MIMIC-III) database.

DESIGN

Retrospective cohort study.

SETTING

Adult intensive care units (ICUs) at a tertiary hospital in the USA .

PARTICIPANTS

Adult IAI ICU patients from 2001 to 2012 in the MIMIC-III database.

INTERVENTIONS

In univariate analysis, we compared the differences in the characteristics of patients in each BMI group. Cox regression models were used to evaluate the relationships between BMI and short-term prognosis.

PRIMARY AND SECONDARY OUTCOME MEASURES

90-day survival.

RESULTS

In total, 1161 patients with IAI were included. There were 399 (34.4%) patients with a normal BMI (<25 kg/m²), 357 (30.8%) overweight patients (25-30 kg/m²) and 405 (34.9%) obese patients (>30 kg/m²) who tended to be younger (p<0.001) and had higher Sequential Organ Failure Assessment scores (p<0.05). The mortality of obese patients at 90 days was lower than that of patients with a normal BMI (20.74% vs 23.25%, p<0.05), but their length of stay in the ICU was higher (4.9 days vs 3.6 days, p<0.001); however, their rate of mechanical ventilation utilisation was higher (61.48% vs 56.86%, p<0.05). In the Cox regression model, we also confirmed that BMI was a protective factor in patients with IAIs, and the adjusted mortality rate of patients with a higher BMI was 0.97 times lower than that of patients with a lower BMI (p<0.001, HR=0.97, 95% CI 0.96 to 0.99).

CONCLUSIONS

IAI patients with an overweight or obese status might have lower 90-day mortality than patients with a normal BMI.

Development and Validation of Nomogram to Predict Long-Term Prognosis of Critically Ill Patients with Acute Myocardial Infarction

Purpose

Acute myocardial infarction (AMI) is a common cardiovascular disease with a poor prognosis. The aim of this study was to construct a nomogram for predicting the long-term survival of critically ill patients with AMI. This nomogram will help in assessing disease severity, guiding treatment, and improving prognosis.

Patients and Methods

The clinical data of patients with AMI were extracted from the MIMIC-III v1.4 database. Cox proportional hazards models were adopted to identify independent prognostic factors. A nomogram for predicting the long-term survival of these patients was developed on the basis of the results of multifactor analysis. The discriminative ability and accuracy of the multifactor analysis were evaluated according to concordance index (C-index) and calibration curves.

Results

A total of 1202 patients were included in the analysis. The patients were randomly divided into a training set (n = 841) and a validation set (n = 361). Multivariate analysis revealed that age, blood urea nitrogen, respiratory rate, hemoglobin, pneumonia, cardiogenic shock, dialysis, and mechanical ventilation, all of which were incorporated into the nomogram, were independent predictive factors of AMI. Moreover, the nomogram exhibited favorable performance in predicting the 4-year survival of patients with AMI. The training set and the validation set had a C-index of 0.789 (95% confidence interval [CI]: 0.765–0.813) and 0.762 (95% CI: 0.725–0.799), respectively.

Conclusion

The nomogram constructed herein can accurately predict the long-term survival of critically ill patients with AMI.