Background. Although the neutrophil percentage‐to‐albumin ratio (NPAR) has proven to be a robust systemic inflammation‐based predictor of mortality in a wide range of diseases, the prognostic value of the NPAR in critically ill patients with cardiogenic shock (CS) remains unknown. This study aimed at investigating the association between the admission NPAR and clinical outcomes in CS patients using real‐world data. Methods. Critically ill patients diagnosed with CS in the Medical Information Mart for Intensive Care‐III (MIMIC‐III) database were included in our study. The study endpoints included all‐cause in‐hospital, 30‐day, and 365‐day mortality in CS patients. First, the NPAR was analyzed as a continuous variable using restricted cubic spline Cox regression models. Second, X‐tile analysis was used to calculate the optimal cut‐off values for the NPAR and divide the cohort into three NPAR groups. Moreover, multivariable Cox regression analyses were used to assess the association of the NPAR groups with mortality. Results. A total of 891 patients hospitalized with CS were enrolled in this study. A nonlinear relationship between the NPAR and in‐hospital and 30‐day mortality was observed (all P values for nonlinear trend<0.001). According to the optimal cut‐off values by X‐tile, NPARs were divided into three groups: group I (NPAR < 25.3), group II (25.3 ≤ NPAR < 34.8), and group III (34.8 ≤ NPAR). Multivariable Cox analysis showed that higher NPAR was independently associated with increased risk of in‐hospital mortality (group III vs. group I: hazard ratio [HR] 2.60, 95% confidence interval [CI] 1.72‐3.92, P < 0.001), 30‐day mortality (group III vs. group I: HR 2.42, 95% CI 1.65‐3.54, P < 0.001), and 365‐day mortality (group III vs. group I: HR 6.80, 95% CI 4.10‐11.26, P < 0.001) in patients with CS. Conclusions. Admission NPAR was independently associated with in‐hospital, 30‐day, and 365‐day mortality in critically ill patients with CS.

Background. Although serum calcium has been proven to be a predictor of mortality in a wide range of diseases, its prognostic value in critically ill patients with cardiogenic shock (CS) remains unknown. This retrospective observational study is aimed at investigating the association of admission calcium with mortality among CS patients. Methods. Critically ill patients diagnosed with CS in the Medical Information Mart for Intensive Care‐III (MIMIC‐III) database were included in our study. The study endpoints included 30‐day, 90‐day, and 365‐day all‐cause mortalities. First, admission serum ionized calcium (iCa) and total calcium (tCa) levels were analyzed as continuous variables using restricted cubic spline Cox regression models to evaluate the possible nonlinear relationship between serum calcium and mortality. Second, patients with CS were assigned to four groups according to the quartiles (Q1‐Q4) of serum iCa and tCa levels, respectively. In addition, multivariable Cox regression analyses were used to assess the independent association of the quartiles of iCa and tCa with clinical outcomes. Results. A total of 921 patients hospitalized with CS were enrolled in this study. A nonlinear relationship between serum calcium levels and 30‐day mortality was observed (all P values for nonlinear trend < 0.001). Furthermore, multivariable Cox analysis showed that compared with the reference quartile (Q3: 1.11 ≤ iCa < 1.17 mmol/L), the lowest serum iCa level quartile (Q1: iCa < 1.04 mmol/L) was independently associated with an increased risk of 30‐day mortality (Q1 vs. Q3: HR 1.35, 95% CI 1.00‐1.83, P = 0.049), 90‐day mortality (Q1 vs. Q3: HR 1.36, 95% CI 1.03‐1.80, P = 0.030), and 365‐day mortality (Q1 vs. Q3: HR 1.28, 95% CI 1.01‐1.67, P = 0.046) in patients with CS. Conclusions. Lower serum iCa levels on admission were potential predictors of an increased risk of mortality in critically ill patients with CS.

Background: Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach. Methods: The current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguously clustered pairs (PAC) analysis. We calculated the standardized mean difference (SMD) of each variable and used the cutoff of ± 0.3 to identify each cluster’s key features. We examined the relationship between the phenotypes and several clinical endpoints utilizing logistic regression (LR) analysis. Results: The consensus cluster analysis identified two clusters (Cluster 1: n = 9,848; Cluster 2: n = 12,077). The key features of patients in Cluster 1, compared with Cluster 2, included: lower blood pressure, lower eGFR (estimated glomerular filtration rate), higher BUN (blood urea nitrogen), higher creatinine, lower albumin, higher potassium, lower bicarbonate, lower red blood cell (RBC), higher red blood cell distribution width (RDW), higher SOFA score, higher APS III score, and higher APACHE IV score on admission. The results of LR analysis showed that the Cluster 2 was associated with lower in-hospital mortality (odds ratio [OR]: 0.374; 95% confidence interval [CI]: 0.347–0.402; P < 0.001), ICU mortality (OR: 0.349; 95% CI: 0.318–0.382; P < 0.001), and the incidence of acute kidney injury (AKI) after admission (OR: 0.478; 95% CI: 0.452–0.505; P < 0.001). Conclusions: ML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes.

Background: The clinical efficiency of routine oxygen therapy is uncertain in patients with acute heart failure (AHF) who do not have hypoxemia. The aim of this study was to investigate the association between oxygen therapy and clinical outcomes in normoxemic patients hospitalized with AHF using real-world data. Methods: Normoxemic patients diagnosed with AHF on ICU admission from the electronic ICU (eICU) Collaborative Research Database were included in the current study, in which the study population was divided into the oxygen therapy group and the ambient-air group. Propensity score matching (PSM) was applied to create a balanced covariate distribution between patients receiving supplemental oxygen and those exposed to ambient air. Linear regression and logistic regression models were performed to assess the associations between oxygen therapy and length of stay (LOS), and all-cause in-hospital as well as ICU mortality rates, respectively. A series of sensitivity and subgroup analyses were conducted to further validate the robustness of our findings. Results: A total of 2922 normoxemic patients with AHF were finally included in the analysis. Overall, 42.1% (1230/2922) patients were exposed to oxygen therapy, and 57.9% (1692/2922) patients did not receive oxygen therapy (defined as the ambient-air group). After PSM analysis, 1122 pairs of patients were matched: each patient receiving oxygen therapy was matched with a patient without receiving supplemental oxygen. The multivariable logistic model showed that there was no significant interaction between the ambient air and oxygen group for all-cause in-hospital mortality [odds ratio ( OR ) 1.30; 95% confidence interval (CI) 0.92–1.82; P = 0.138] or ICU mortality ( OR 1.39; 95% CI 0.83–2.32; P = 0.206) in the post-PSM cohorts. In addition, linear regression analysis revealed that oxygen therapy was associated with prolonged ICU LOS ( OR 1.11; 95% CI 1.06–1.15; P < 0.001) and hospital LOS ( OR 1.06; 95% CI 1.01–1.10; P = 0.009) after PSM. Furthermore, the absence of an effect of supplemental oxygen on mortality was consistent in all subgroups. Conclusion: Routine use of supplemental oxygen in AHF patients without hypoxemia was not found to reduce all-cause in-hospital mortality or ICU mortality.

Background: This research aimed to investigate the predictive performance of log odds of positive lymph nodes (LODDS) for the long-term prognosis of patients with node-positive lung neuroendocrine tumors (LNETs).Methods: We collected 506 eligible patients with resected N1/N2 classification LNETs from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The study cohort was split into derivation cohort (n=300) and external validation cohort (n=206) based on different geographic regions. Nomograms were constructed based on the derivation cohort and validated using the external validation cohort to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS) of patients with LNETs. The accuracy and clinical practicability of nomograms were tested by Harrell’s concordance index (C-index), integrated discrimination improvement (IDI), net reclassification improvement (NRI), calibration plots, and decision curve analyses.Results: The Cox proportional-hazards model showed the high LODDS group (-0.79≤LODDS) had significantly higher mortality compared to those in the low LODDS group (LODDS<-0.79) for both CSS and OS. In addition, age at diagnosis, sex, histotype, type of surgery, radiotherapy, and chemotherapy were also chosen as predictors in Cox regression analyses using stepwise Akaike information criterion method and included in the nomograms. The values of C-index, NRI, and IDI proved that the established nomograms were better than the conventional eighth edition of the TNM staging system. The calibration plots for predictions of the 1-, 3-, and 5-year CSS/OS were in excellent agreement. Decision curve analyses showed that the nomograms had value in terms of clinical application.Conclusions: We created visualized nomograms for CSS and OS of LNET patients, facilitating clinicians to bring individually tailored risk assessment and therapy.

Many studies have shown that myocardial infarction (MI) is significantly associated with atrial fibrillation (AF), but the causal relationship between MI and AF has not been established. Therefore, we performed this Mendelian randomization (MR) study to investigate the relationship between MI and AF. We used a publicly available summary statistical dataset for MI based on genome-wide analysis studies (GWAS; ebi-a-GCST011364; 14,825 cases and 2680 controls) and a summary statistical dataset for AF based on an European GWAS (finn-b-I9_AF_REIMB; 10,516 cases and 116,926 controls). The 2‐sample bidirectional MR analysis was performed using the inverse-variance weighted (IVW), MR-Egger, and weighted median methods. The causal effect of MI on AF was analyzed using 30 MI-specific single nucleotide polymorphisms (SNPs) that were characterized as instrumental variables (IVs) based on the GWAS data. The causal effect of MI on AF was confirmed by the IVW (odds ratio [OR] 1.42; 95% confidence interval [CI] 1.27–1.58; P < .001), MR-Egger (OR: 1.49; 95% CI: 1.15–1.93; P = .005), and weighted median (OR: 1.42; 95% CI: 1.24–1.63; P < .001) analyses. Furthermore, in the reverse MR analyses, the causal effect of AF on MI was analyzed using 20 AF-specific SNPs that were screened as IVs. The causal effect of AF on MI was significant based on the results from the IVW method (OR: 1.05; 95% CI: 1.00–1.09; P = .033). In conclusion, the bidirectional MR analyses demonstrated a clear bidirectional causal association between MI and AF.