Association between hematocrit and the 30-day mortality of patients with sepsis: A retrospective analysis based on the large-scale clinical database MIMIC-IV

A U shaped association between the HCT-ALB and hospital mortality in patients with sepsis

The difference between hematocrit and serum albumin (HCT-ALB) demonstrates diagnostic significance in infectious diseases, yet the nonlinear relationship between HCT-ALB and hospital mortality in ICU patients with sepsis remains unexplored. This retrospective multicenter cohort study analyzed 7,546 ICU sepsis patients (mean age 66 ± 16 years) to elucidate the HCT-ALB-mortality relationship. Using Cox proportional hazards models with smooth curve fitting, we identified a U-shaped association: Threshold analysis revealed an inflection point at 6.1. Below this threshold, each unit HCT-ALB increase corresponded to reduced mortality risk (adjusted HR 0.986, 95%CI 0.972-0.999; P = 0.036). Conversely, values ≥ 6.1 predicted escalating risk (adjusted HR 1.048 per unit increase, 95%CI 1.037-1.060; P < 0.0001). Significant age interaction was observed (P for interaction < 0.05), with heightened mortality risk in elderly patients (≥ 65 years: HR 1.022, 95%CI 1.014-1.031). These findings establish HCT-ALB as a non-linear predictor of sepsis outcomes, emphasizing its critical threshold dynamics and age-dependent prognostic implications.

Admission hematocrit and fluctuating blood urea nitrogen levels predict the efficacy of blood purification treatment in severe acute pancreatitis patients

This study aimed to evaluate the prognostic significance of the levels of admission hematocrit (HCT) and the changes in the initial blood urea nitrogen (BUN) levels in predicting the efficacy of blood purification (BP) therapy in ameliorating severe acute pancreatitis (SAP) patients at admission. A retrospective study was conducted on 139 SAP patients from the People's Hospital of Guangxi Zhuang Autonomous Region from 2013 to 2022 and the data retrieved from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for 346 SAP patients. The patients were stratified based on their HCT0 levels at admission; HCT < 44% (n = 93) and HCT ≥ 44% (n = 46) and ΔBUN levels within the first 24 h post-admission; ΔBUN ≤ 0 (n = 78) and ΔBUN > 0 (n = 61). Propensity score matching (PSM) was performed on factors such as age and gender to control for differences among the strata. The clinical outcomes of the patients receiving or not receiving BP therapy were compared based on the mentioned criteria. Patients with HCT0 ≥ 44%, who were treated with BP showed no significant difference in the 28-day mortality. However, a significant increase in hospital expenses and prolonged ICU stays was observed (P < 0.05). Conversely, patients with ΔBUN ≤ 0 who received BP therapy demonstrated relatively high 28-day mortality rates, prolonged ICU stays, increased hospital expenses, and low SOFA scores (P < 0.05). The analyses of MIMIC-IV database data corroborated these findings. The predictive efficacy of BP therapy in SAP patients was significantly influenced by the changes in BUN levels at 24 h post-admission compared to the initial levels of HCT on admission. Selecting SAP patients suitable for BP treatment should be based on the changes in BUN levels to enhance effective therapeutic outcomes.

Nonlinear association between hematocrit levels and short-term all-cause mortality in ICU patients with acute pancreatitis: insights from a retrospective cohort study

OBJECTIVES

The purpose of this study was to investigate the relationship between hematocrit levels and the mortality of patients with acute pancreatitis (AP), since limited research has examined this association in intensive care unit (ICU).

METHODS

In this study, clinical data were retrieved from Medical Information Mart for Intensive Care database for patients diagnosed with AP. Nonlinear relationships between hematocrit and prognosis were examined through Locally Estimated Scatterplot Smoothing (LOESS) regression, restricted cubic splines (RCS), and U-test analyses. The impact of hematocrit on prognosis was further explored using with a binomial generalized linear model with a logit link, while adjusting for potential confounding factors.

RESULTS

The study encompassed 1,914 patients with AP, revealing a significant difference in hematocrit levels between survivors and non-survivors (33.6 (29.5, 38.1) vs. 32.1 (28.1, 37.4), P < 0.001). Hematocrit emerged as an independent prognostic indicator for mortality in both univariate and multivariate logistic regression analyses (all P < 0.05). Findings from LOESS regression, RCS regression, and the U-test indicated a U-shaped correlation between hematocrit levels and 28-day mortality, with both elevated and decreased hematocrit levels leading to increased mortality risk (P for overall < 0.001). Tertile grouping revealed that lower hematocrit levels (< 30.8%) were associated with heightened 28-day mortality risk (Crude model: Odds ratio (OR) (95%Confidence Interval (CI)) = 1.665 (1.198-2.314); fully adjusted model: adjusted OR = 1.474 (1.005-2.161), all P < 0.05). Survival analyses further supported the adverse prognosis associated with low hematocrit levels.

CONCLUSIONS

The findings of this study indicate that in AP patients in the intensive care unit, only low HCT levels were identified as a risk factor for 28-day mortality, despite the presence of a U-shaped correlation between HCT levels and 28-day all-cause mortality.

The foundational capabilities of large language models in predicting postoperative risks using clinical notes

Clinical notes recorded during a patient's perioperative journey holds immense informational value. Advances in large language models (LLMs) offer opportunities for bridging this gap. Using 84,875 preoperative notes and its associated surgical cases from 2018 to 2021, we examine the performance of LLMs in predicting six postoperative risks using various fine-tuning strategies. Pretrained LLMs outperformed traditional word embeddings by an absolute AUROC of 38.3% and AUPRC of 33.2%. Self-supervised fine-tuning further improved performance by 3.2% and 1.5%. Incorporating labels into training further increased AUROC by 1.8% and AUPRC by 2%. The highest performance was achieved with a unified foundation model, with improvements of 3.6% for AUROC and 2.6% for AUPRC compared to self-supervision, highlighting the foundational capabilities of LLMs in predicting postoperative risks, which could be potentially beneficial when deployed for perioperative care.

Inflammatory markers predict efficacy of immunotherapy in advanced non-small cell lung cancer: a preliminary exploratory study

OBJECTIVE

The purpose of this study is to analyze the predictive value of neutrophil to lymphocyte ratio (NLR), lymphocyte count to monocyte count ratio (LMR), platelet to lymphocyte ratio (PLR), platelet count multiplied by neutrophil count to lymphocyte count ratio (SII), red blood cell distribution width (RDW), packed cell volume (PCV), and plateletcrit (PCT) levels in advanced non-small cell lung cancer (NSCLC) patients treated with PD-1/PD-L1 inhibitors.

MATERIALS AND METHODS

From March 2019 to August 2023, we screened 104 of 153 patients with stage III unresectable local advanced NSCLC and IV NSCLC who received PD-1/PD-L1 inhibitor therapy at our hospital and met the inclusion and exclusion criteria for analysis. All patients were collected for clinical information, including baseline blood indicator (NLR, PLR, LMR, SII, CRP, RDW, PCV and PCT) levels before PD-1/PD-L1 inhibitor therapy and blood indicator levels and imaging evaluation results every two cycles after PD-1/PD-L1 inhibitor therapy. We analyzed the predicted impact of baseline blood indicators on PD-1/PD-L1 inhibitor treatment response, the discriminatory power of blood indicators on treatment response after efficacy evaluation, and the dynamic changes in blood indicators during PD-1/PD-L1 inhibitor treatment.

RESULTS

In our study data, baseline levels of NLR, PLR, LMR, SII, CRP, RDW, PCV, and PCT did not provide good predictive identification of PD-1/PD-L1 inhibitor primary resistance and effective treatment response populations. These indicators showed no significant distribution differences in Mann Whitney Wilcoxon analysis, univariate and multivariate logistic regression analysis between the primary resistance group and the effective treatment response group. We validated the NLR threshold of 5 from multiple previous studies in the data of this study, and patients with NLR > 5 also did not show a significant tendency towards the primary resistance group. The levels of NLR, PLR, LMR, SII, CRP, RDW, PCV, and PCT after efficacy evaluation also cannot effectively distinguish primary drug resistance and effective treatment response populations. However, in the longitudinal data analysis before and after PD-1/PD-L1 inhibitor treatment, we found that the NLR, SII, and CRP levels of patients who responded effectively were significantly reduced compared to baseline status. But this phenomenon was not observed in PD patients.

CONCLUSIONS

PD-1/PD-L1 inhibitors treatment significantly altered the levels of NLR, SII, and CRP in patients with advanced NSCLC. Dynamic monitoring of NLR, SII, and CRP levels may have potential application value in monitoring the therapeutic efficacy of ICIs. In our study, the baseline status of blood indicator levels did not achieve good primary drug resistant patient identification. The potential value of blood indicators in predicting primary resistance to ICI should be further explored in larger research cohorts.

Risk Factors for Bloodstream Infection in Patients Receiving Peripheral Parenteral Nutrition

Objective Intravenous fluid therapy, including peripheral parenteral nutrition (PPN), administered via a peripheral intravenous catheter (PVC) can occasionally lead to bloodstream infections (BSIs). PPN may thus be a risk factor for PVC-related BSI (PVC-BSI). However, the risk factors and incidence of PVC-BSI have not been previously reported, and evidence for these conditions remains unclear. Methods We retrospectively collected data from 391 patients who underwent PPN therapy with PVC at the Fukujuji Hospital from August 2022 to November 2023. We compared 20 patients who developed BSI during PPN therapy (BSI group) with 371 who did not develop BSI during PPN therapy (no-infection group). Results The incidence rate of PVC-BSI during PPN therapy was 5.1%. The BSI group had a significantly longer average daily infusion time of PPNs [median 24.0 (range 6.0-24.0) h vs. 6.0 (2.0-24.0) h, p<0.001] and of all intravenous fluids [median 24.0 (range 8.8-24.0) h vs. 10.3 (2.0-24.0) h, p<0.001] than the no infection group. An average daily infusion time of PPNs ≥12.0 h and an average daily infusion time of intravenous fluids ≥18.0 h were identified as predictive risk factors for BSI. When both risk factors were present, the sensitivity, specificity, and odds ratio for the development of BSI were 85.0%, 83.2%, and 27.9, respectively. Conclusion This study identified the incidence of and risk factors for developing BSI, such as a longer average daily infusion time of PPNs and all intravenous fluids, in patients receiving PPN therapy.

Development of a Prediction Model for the Risk of Infection in Patients with Aplastic Anemia: Survival Analysis in Recurrent Events

BACKGROUND

In patients with aplastic anemia (AA), infection-related complications are the leading cause of mortality. However, limited knowledge about the predictive factors for infection in these patients exists. Thus, this study aimed to evaluate risk factors for infection and develop a risk prediction model for the occurrence of infection in patients with AA.

MATERIALS AND METHODS

Between January 2004 and December 2020, 206 patients with AA ≥15 years of age were included in this study. Survival analysis using recurrent event methodologies was conducted to identify predictive factors associated with infection, including the Anderson and Gill model; Prentice, Williams, and Peterson (PWP) Total Time model; PWP Gap Time model; marginal model; and frailty models. The best model was determined using backward stepwise regression, and internal validation was performed using the bootstrapping method with 500 re-samplings.

RESULTS

With a median follow-up of 2.95 years, the incidence rate of infection among patients with AA was 32.8 events per 100 person-years. The PWP Total Time model revealed that cirrhosis comorbidity, lymphocytes ≥80%, and previous infection increased the risk of infection, while bone marrow cellularity ≥20% offered protection. The bone marrow cellularity, lymphocyte percentage, previous infection, cirrhosis, and hematocrit (BLICH) model was generated to predict the risk of infection. The internal validation showed a good calibration of this model.

CONCLUSION

Cirrhosis, lymphocytes ≥80%, previous infection, and bone marrow cellularity <20% are risk factors for infection in patients with AA. The BLICH model can predict the risk of infection in these patients.

Red Blood Cell Distribution Width/Hematocrit Ratio: A New Predictor of 28 Days All-Cause Mortality of AECOPD Patients in ICU

Purpose

Elevated red blood cell distribution width (RDW) and decreased hematocrit (HCT) levels are associated with poor prognosis in chronic obstructive pulmonary disease (COPD) patients, but their significance in intensive care unit (ICU) patients with acute exacerbation of COPD (AECOPD) remains uncertain. The RDW/HCT ratio may offer a more comprehensive assessment compared to individual markers, potentially enhancing prognostic accuracy. Furthermore, the utility of RDW/HCT in improving traditional ICU scoring systems remains unexplored.

Patients and Methods

The optimal RDW/HCT ratio cutoff was identified via ROC curve analysis, guiding classification into high and low ratio groups. Univariate and multivariate logistic regression analyses, Kaplan-Meier survival curves, and propensity score matching (PSM) were performed to evaluate the association between RDW/HCT ratio and 28-day all-cause mortality. The predictive value of RDW/HCT ratio compared to traditional ICU scoring systems was assessed using the area under the curve (AUC). Additionally, the eICU database was utilized to validate the robustness of the association between RDW/HCT and mortality in patients with AECOPD.

Results

624 patients were included, with 361 in the low RDW/HCT ratio group and 263 in the high ratio group. PSM yielded 145 matched pairs of patients with balanced baseline characteristics. Multivariate logistic regression analysis revealed that patients with RDW/HCT ratio ≥ 0.473 had significantly higher 28-day all-cause mortality compared to those with RDW/HCT ratio < 0.473 (p < 0.001). Combining RDW/HCT ratio with SOFA score improved the diagnostic accuracy significantly (p=0.029).

Conclusion

The RDW/HCT ratio is an independent predictor of 28-day all-cause mortality in AECOPD patients in the ICU. It can be used for a preliminary assessment before a systematic evaluation of the patient, indicating its potential value in early assessment of disease severity. In a comprehensive evaluation, combining the RDW/HCT ratio with the SOFA score can further enhance predictive accuracy.

Risk Factors of In-Hospital Venous Thromboembolism and Prognosis After Emergent Ventral Hernia Repair

Background: The risk factors and association of venous thromboembolism (VTE) following emergent ventral hernia repair (EVHR) remains uncertain. This aim of the study aims was to establish the predictors of VTE after EVHR and its influence on the long-term outcomes. Methods: A total of 2093 patients from the MIMIC-IV database who underwent EVHR were recruited. Multivariate logistic regression and nomogram models were developed to predict in-hospital VTE and mortality. Calibration and receiver operating characteristic (ROC) curves were utilized to assess the model's effectiveness and reliability. Decision curve analysis (DCA) was performed to evaluate the net clinical benefits of the model. Results: The rate of in-hospital VTE was 1.6% (33/2093) after EVHR. Four independent potential factors were established after multivariate analysis, and the abovementioned risk factors fit into the nomogram. The prediction model presented good performance metrics (C-index: 0.857), the calibration and ROC curves demonstrated the accurate prediction power, and DCA indicated the superior net benefit of the established model. In-hospital and 1-year mortality rates were 0.8% (17/2093) and 4.1% (86/2076) after EVHR. The potential factors were included in the mortality prediction nomogram. The prediction model presented good performance metrics (C-index of 0.957 and 0.828, respectively), the calibration and ROC curves were consistent with the actual results, and DCA indicated the superior net benefit of the established model. Conclusion: The nomogram, derived from the logistic regression model, demonstrated excellent predictive performance for VTE occurrence and prognosis in patients following EVHR. This model could serve as a valuable reference for clinical decision-making regarding VTE prevention and for enhancing post-EVHR prognosis.

Trajectory clustering of immune cells and its association with clinical outcomes after aneurysmal subarachnoid hemorrhage

Background and purpose

The immune response following aneurysmal subarachnoid hemorrhage (aSAH) can exacerbate secondary brain injury and impact clinical outcomes. As the immune response after aSAH is a dynamic process, we aim to track and characterize immune cell trajectories over time to identify patterns associated with various clinical outcomes.

Methods

In this retrospective single-center study of patients with aSAH, we analyzed immune cell count trajectories, including neutrophil, monocyte, and lymphocyte counts, collected from day 1 to day 14. These trajectories were classified into four distinct clusters utilizing the k-means longitudinal clustering method. A comprehensive multivariable analysis was performed to explore the associations of these immune cell clusters with various clinical outcomes. These outcomes included a Modified Rankin Scale score (mRS) of 3 to 6, indicative of poor functional outcomes, along with complications including shunt dependency, vasospasm, and secondary cerebral infarction.

Results

In this study, 304 patients with aSAH were analyzed. The trajectories of immune cell counts, including neutrophils, monocytes, and lymphocytes, were successfully categorized into four distinct clusters for each immune cell type. Within neutrophil clusters, both persistent neutrophilia and progressive neutrophilia were associated with poor functional outcomes, shunt dependency, and vasospasm, with resolving neutrophilia showing a lesser degree of these associations. Within monocyte clusters, early monocytosis was associated with vasospasm, whereas delayed monocytosis was associated with shunt dependency. Within lymphocyte clusters, both early transient lymphopenia and early prolonged lymphopenia were associated with poor functional outcomes.

Conclusion

Our study demonstrates that distinct immune cell trajectories post-aSAH, identified through unsupervised clustering, are significantly associated with specific clinical outcomes. Understanding these dynamic immune responses may provide key insights with potential for future therapeutic strategies.

Association between lactate to hematocrit ratio and 30-day all-cause mortality in patients with sepsis: a retrospective analysis of the Medical Information Mart for Intensive Care IV database

Background

The lactate to hematocrit ratio (LHR) has not been assessed for predicting all-cause death in sepsis patients. This study aims to evaluate the relationship between LHR and 30-day all-cause mortality in sepsis patients.

Methods

This retrospective study used the data from Medical information mart for intensive care IV (MIMIC-IV, version 2.0). Our study focused on adult sepsis patients who were initially hospitalized in the Intensive care unit (ICU). The prognostic significance of admission LHR for 30-day all-cause mortality was evaluated using a multivariate Cox regression model, ROC curve analysis, Kaplan-Meier curves, and subgroup analyses.

Results

A total of 3,829 sepsis patients participated in this study. Among the cohort, 8.5% of individuals died within of 30 days (p < 0.001). The area under the curve (AUC) for LHR was 74.50% (95% CI: 71.6-77.50%), higher than arterial blood lactate (AUC = 71.30%), hematocrit (AUC = 64.80%), and shows no significant disadvantage compared to qSOFA, SOFA, and SAPS II. We further evaluated combining LHR with qSOFA score to predict mortality in sepsis patients, which shows more clinical significance. ROC curve analysis showed that 6.538 was the optimal cutoff value for survival and non-survival groups. With LHR ≥6.538 vs. LHR <6.538 (p < 0.001). Subgroup analysis showed significant interactions between LHR, age, sex, and simultaneous acute respiratory failure (p = 0.001-0.005).

Conclusion

LHR is an independent predictor of all-cause mortality in sepsis patients after admission, with superior predictive ability compared to blood lactate or hematocrit alone.

Risk and mediation analyses of hemoglobin glycation index and survival prognosis in patients with sepsis

An increasing number of studies have reported the close relation of the hemoglobin glycation index (HGI) with metabolism, inflammation, and disease prognosis. However, the prognostic relationship between the HGI and patients with sepsis remains unclear. Thus, this study aimed to analyze the association between the HGI and all-cause mortality in patients with sepsis using data from the MIMIC-IV database. In this study, 2605 patients with sepsis were retrospectively analyzed. The linear regression equation was established by incorporating glycated hemoglobin (HbA1c) and fasting plasma glucose levels. Subsequently, the HGI was calculated based on the difference between the predicted and observed HbA1c levels. Furthermore, the HGI was divided into the following three groups using X-tile software: Q1 (HGI ≤  - 0.50%), Q2 (- 0.49% ≤ HGI ≤ 1.18%), and Q3 (HGI ≥ 1.19%). Kaplan-Meier survival curves were further plotted to analyze the differences in 28-day and 365-day mortality among patients with sepsis patients in these HGI groups. Multivariate corrected Cox proportional risk model and restricted cubic spline (RCS) were used. Lastly, mediation analysis was performed to assess the factors through which HGI affects sepsis prognosis. This study included 2605 patients with sepsis, and the 28-day and 365-day mortality rates were 19.7% and 38.9%, respectively. The Q3 group had the highest mortality risk at 28 days (HR = 2.55, 95% CI: 1.89-3.44, p < 0.001) and 365 days (HR = 1.59, 95% CI: 1.29-1.97, p < 0.001). In the fully adjusted multivariate Cox proportional hazards model, patients in the Q3 group still displayed the highest mortality rates at 28 days (HR = 2.02, 95% CI: 1.45-2.80, p < 0.001) and 365 days (HR = 1.28, 95% CI: 1.08-1.56, p < 0.001). The RCS analysis revealed that HGI was positively associated with adverse clinical outcomes. Finally, the mediation effect analysis demonstrated that the HGI might influence patient survival prognosis via multiple indicators related to the SOFA and SAPS II scores. There was a significant association between HGI and all-cause mortality in patients with sepsis, and patients with higher HGI values had a higher risk of death. Therefore, HGI can be used as a potential indicator to assess the prognostic risk of death in patients with sepsis.

Nonlinear relationship of red blood cell indices (MCH, MCHC, and MCV) with all-cause and cardiovascular mortality: A cohort study in U.S. adults

BACKGROUND

In recent years, increasing attention has been focused on the impact of red blood cell indices (RCIs) on disease prognosis. We aimed to investigate the association of mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular volume (MCV) with mortality.

METHODS

The study used cohort data from U.S. adults who participated in the 1999-2008 National Health and Nutrition Examination Survey. All-cause mortality was the primary outcome during follow-up, with secondary cardiovascular mortality outcomes. COX regression was applied to analyze the connection between RCIs and mortality. We adopted three models to minimize potential bias. Smooth-fit curves and threshold effect analyses were utilized to observe the dose-response relationship between RCIs and all-cause and cardiovascular mortality. In addition, we performed sensitivity analyses.

RESULTS

21,203 individuals were enrolled in our research. During an average 166.2 ± 54.4 months follow-up, 24.4% of the population died. Curve fitting indicated a U-shaped relationship between MCV and MCH with all-cause mortality, and the relationship of MCHC to all-cause mortality is L-shaped. We identified inflection points in the relationship between MCV, MCH, and MCHC and all-cause mortality as 88.56732 fl, 30.22054 pg, 34.34624 g/dl (MCV <88.56732 fl, adjusted HR 0.99, 95 CI% 0.97-1.00; MCV >88.56732 fl, adjusted HR 1.05, 95 CI% 1.04-1.06. MCH <30.22054 pg, adjusted HR 0.95, 95 CI% 0.92-0.98; MCH >30.22054 pg, adjusted HR 1.08, 95 CI% 1.04-1.12. MCHC <34.34624 g/dl, adjusted HR 0.88, 95 CI% 0.83-0.93). Besides, the MCV curve was U-shaped in cardiovascular mortality (MCV <88.56732 fl, adjusted HR 0.97, 95 CI% 0.94-1.00; MCV >88.56732 fl, adjusted HR 1.04, 95 CI% 1.01-1.06).

CONCLUSION

This cohort study demonstrated that RCIs (MCH, MCHC, and MCV) were correlated with mortality in the general population. Three RCIs were nonlinearly correlated with all-cause mortality. In addition, there were nonlinear relationships between MCH and MCV and cardiovascular mortality.

Acute blood loss anemia in hospitalized patients is associated with adverse outcomes: An analysis of the Nationwide Inpatient Sample

BACKGROUND

Acute blood loss anemia is the most common form of anemia and often results from traumatic injuries or gastrointestinal bleeding. There are limited studies analyzing outcomes associated with acute blood loss anemia in hospitalized patients.

METHODS

The Nationwide Inpatient Sample (NIS) was analyzed from 2010 to 2014 (n = 133,809). The impact of acute blood loss anemia on in-hospital mortality, length of stay (LOS), healthcare cost, and disposition was determined using regression modeling adjusted for age, gender, race, and comorbidities.

RESULTS

Hospitalized patients with acute blood loss anemia had significantly higher healthcare cost (adj OR 1.04; 95% CI: 1.04-1.05), greater lengths of stay (adj OR 1.18; 95% CI: 1.17-1.18), and were less likely to be discharged home compared to the general medical population (adj OR 0.27; 95% CI: 0.26-0.28). Acute blood loss anemia was associated with increased risk for mortality in unadjusted models (unadj 1.16; 95% CI: 1.12-1.20) but not in adjusted models (adj OR 0.91; 95% CI: 0.88-0.94). When analyzing comorbidities, a "muscle loss phenotype" had the strongest association with mortality in patients with acute blood loss anemia (adj OR 4.48; 95% CI: 4.35-4.61). The top five primary diagnostic codes associated with acute blood loss anemia were long bone fractures, GI bleeds, cardiac repair, sepsis, and OB/Gyn related causes. Sepsis had the highest association with mortality (18%, adj OR 2.59; 95% CI: 2.34-2.86) in those with acute blood loss anemia.

CONCLUSIONS

Acute blood loss anemia is associated with adverse outcomes in hospitalized patients.

Postoperative SIRS after thermal ablation of HCC: Risk factors and short-term prognosis

Background

We aimed to explore the potential risk factors and short-term prognosis for SIRS after thermal ablation of hepatocellular carcinoma (HCC).

Methods

Data from patients with HCC who underwent thermal ablation in the Third Affiliated Hospital of Sun Yat-sen University between January 2015 and August 2021 were retrieved from the perioperative database. Pre-, intra- and postoperative data between SIRS group and non-SIRS group were compared and multivariate logistic regression analysis was performed to identify the risk factors for SIRS after thermal ablation.

Results

A total of 1491 patients were enrolled and 234 (15.7 %) patients developed SIRS after thermal ablation. Compared with those without SIRS, patients with SIRS had a longer hospital stay, higher hospitalization costs and higher risk of more severe postoperative complications. In the multivariate logistic regression analysis, current smoking (OR 1.58, 95 %CI 1.09-2.29), decreased HCT (OR 1.51,95 %CI 1.11-2.04), NEUT < 1.5 × 109/L(OR 1.74, 95 %CI 1.14-2.65), NEUT% < 0.5 or > 0.7 (OR 1.36, 95 %CI 1.01-1.83) and PT > 16.3s (OR 2.42, 95 %CI 1.57-3.74) were significantly associated with postoperative SIRS.

Conclusions

Current smoking, decreased HCT, neutropenia, abnormal percentage of neutrophils and prolonged PT are the independent risk factors for SIRS after thermal ablation of HCC, which worsens outcomes of patients. This study can help identify high-risk population and guide appropriate care so as to reduce the incidence of postoperative SIRS.

LASSO-Based Identification of Risk Factors and Development of a Prediction Model for Sepsis Patients

OBJECTIVE

The objective of this study was to utilize LASSO regression (Least Absolute Shrinkage and Selection Operator Regression) to identify key variables in septic patients and develop a predictive model for intensive care unit (ICU) mortality.

METHODS

We conducted a cohort consisting of septic patients admitted to the ICU between December 2016 and July 2019. The disease severity and laboratory index were analyzed using LASSO regression. The selected variables were then used to develop a model for predicting ICU mortality. AUCs of ROCs were applied to assess the prediction model, and the accuracy, sensitivity and specificity were calculated. Calibration were also used to assess the actual and predicted values of the predictive model.

RESULTS

A total of 1733 septic patients were included, among of whom 382 (22%) died during ICU stay. Ten variables, namely mechanical ventilation (MV) requirement, hemofiltration (HF) requirement, norepinephrine (NE) requirement, septicemia, multiple drug-resistance infection (MDR), thrombocytopenia, hematocrit, red-cell deviation width coefficient of variation (RDW-CV), C-reactive protein (CRP), and antithrombin (AT) III, showed the strongest association with sepsis-related mortality according to LASSO regression. When these variables were combined into a predictive model, the area under the curve (AUC) was found to be 0.801. The AUC of the validation group was 0.791. The specificity of the model was as high as 0.953. Within the probability range of 0.25 to 0.90, the predictive performance of the model surpassed that of individual predictors within the cohort.

CONCLUSION

Our findings suggest that a predictive model incorporating the variables of MV requirement, HF requirement, NE requirement, septicemia, MDR, thrombocytopenia, HCT, RDW-CV, CRP, and AT III exhibiting an 80% likelihood of predicting ICU mortality in sepsis and demonstrates high accuracy.

Applying artificial neural network in predicting sepsis mortality in the emergency department based on clinical features and complete blood count parameters

A complete blood count (CBC) is routinely ordered for emergency department (ED) patients with infections. Certain parameters, such as the neutrophil-to-lymphocyte ratio (NLR), might have prognostic value. We aimed to evaluate the prognostic value of the presenting CBC parameters combined with clinical variables in predicting 30-day mortality in adult ED patients with infections using an artificial neural network (ANN). We conducted a retrospective study of ED patients with infections between 17 December 2021 and 16 February 2022. Clinical variables and CBC parameters were collected from patient records, with NLR, monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) calculated. We determined the discriminatory performance using the area under the receiver operating characteristic curve (AUROC) and performed a 70/30 random data split and supervised ANN machine learning. We analyzed 558 patients, of whom 144 (25.8%) had sepsis and 60 (10.8%) died at 30 days. The AUROCs of NLR, MLR, PLR, and their sum were 0.644 (95% CI 0.573-0.716), 0.555 (95% CI 0.482-0.628), 0.606 (95% CI 0.529-0.682), and 0.610 (95% CI 0.534-0.686), respectively. The ANN model based on twelve variables including clinical variables, hemoglobin, red cell distribution width, NLR, and PLR achieved an AUROC of 0.811 in the testing dataset.

Construction and validation of a risk assessment model for acute kidney injury in patients with acute pancreatitis in the intensive care unit

BACKGROUND

To construct and validate a risk assessment model for acute kidney injury (AKI) in patients with acute pancreatitis (AP) in the intensive care unit (ICU).

METHODS

A total of 963 patients diagnosed with acute pancreatitis (AP) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database was included. These patients were randomly divided into training set (N = 674) and validation set (N = 289) at a ratio of 7:3. Clinical characteristics were utilized to establish a nomogram for the prediction of AKI during ICU stay. These variables were selected by the least absolute shrinkage and selection operation (LASSO) regression and included in multivariate logistic regression analysis. Variables with P-values less than 0.05 were included in the final model. A nomogram was constructed based on the final model. The predicted accuracy of the nomogram was assessed by calculating the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Moreover, calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) were performed to evaluate model performance. Decision curve analysis (DCA) evaluated the clinical net benefit of the model.

RESULTS

A multivariable model that included 6 variables: weight, SOFA score, white blood cell count, albumin, chronic heart failure, and sepsis. The C-index of the nomogram was 0.82, and the area under the receiver operating characteristic curve (AUC) of the training set and validation set were 0.82 (95% confidence interval:0.79-0.86) and 0.76 (95% confidence interval: 0.70-0.82), respectively. Calibration plots showed good consistency between predicted and observed outcomes in both the training and validation sets. DCA confirmed the clinical value of the model and its good impact on actual decision-making.

CONCLUSION

We identified risk factors associated with the development of AKI in patients with AP. A risk prediction model for AKI in ICU patients with AP was constructed, and improving the treatment strategy of relevant factors in the model can reduce the risk of AKI in AP patients.

Association between ferritin to albumin ratio and 28-day mortality in patients with sepsis: a retrospective cohort study

OBJECTIVES

The ratio of ferritin to albumin (FAR) has been proposed as a novel prognostic indicator for COVID-19. However, the role of FAR in predicting the all-cause mortality rate in patients with sepsis has not been evaluated. Therefore, the aim of this study is to elucidate the correlation between FAR and the 28-day all-cause mortality rate in patients with sepsis.

METHODS

This study used data from the Medical Information Mart for Intensive Care IV database (v2.0) for a retrospective cohort analysis. The study focused on adult patients with sepsis who were admitted to the intensive care unit. The primary objective was to assess the predictive capability of FAR in determining the 28-day all-cause mortality rate among patients with sepsis.

RESULTS

The study involved 1553 sepsis patients in total. Based on the survival status of sepsis patients within 28 days, they were divided into two groups: a survival group consisting of 973 patients, and a death group consisting of 580 patients. The results revealed a 28-day mortality rate of 37.35% among sepsis patients. The multivariable Cox regression analysis revealed that FAR was an independent predictor of the 28-day all-cause mortality rate in patients with sepsis (hazard ratio [HR]: 1.17-1.19; 95% confidence interval 1.11-1.26; P < 0.001). The FAR demonstrated a higher area under the curve (AUC) of 61.01% (95% confidence interval 58.07-63.96%), compared to serum ferritin (60.48%), serum albumin (55.56%), and SOFA score (56.97%). Receiver operating characteristic curve (ROC) analysis determined the optimal cutoff value for FAR as 364.2215. Kaplan-Meier analysis revealed a significant difference in the 28-day all-cause mortality rate between patients with FAR ≥ 364.2215 and those with FAR < 364.2215 (P < 0.001). Furthermore, subgroup analysis showed no significant interaction between FAR and each subgroup.

CONCLUSIONS

This study revealed a significant correlation between FAR and the 28-day mortality rate in patients with sepsis. Higher FAR values were strongly associated with increased mortality rates within 28 days.

The predictive values of admission characteristics for 28-day all-cause mortality in septic patients with diabetes mellitus: a study from the MIMIC database

Background

Septic patients with diabetes mellitus (DM) are more venerable to subsequent complications and the resultant increase in associated mortality. Therefore, it is important to make tailored clinical decisions for this subpopulation at admission.

Method

Data from large-scale real-world databases named the Medical Information Mart for Intensive Care Database (MIMIC) were reviewed. The least absolute selection and shrinkage operator (LASSO) was performed with 10 times cross-validation methods to select the optimal prognostic factors. Multivariate COX regression analysis was conducted to identify the independent prognostic factors and nomogram construction. The nomogram was internally validated via the bootstrapping method and externally validated by the MIMIC III database with receiver operating characteristic (ROC), calibration curves, decision curve analysis (DCA), and Kaplan-Meier curves for robustness check.

Results

A total of 3,291 septic patients with DM were included in this study, 2,227 in the MIMIC IV database and 1,064 in the MIMIC III database, respectively. In the training cohort, the 28-day all-cause mortality rate is 23.9% septic patients with DM. The multivariate Cox regression analysis reveals age (hazard ratio (HR)=1.023, 95%CI: 1.016-1.031, p<0.001), respiratory failure (HR=1.872, 95%CI: 1.554-2.254, p<0.001), Sequential Organ Failure Assessment score (HR=1.056, 95%CI: 1.018-1.094, p=0.004); base excess (HR=0.980, 95%CI: 0.967-0.992, p=0.002), anion gap (HR=1.100, 95%CI: 1.080-1.120, p<0.001), albumin (HR=0.679, 95%CI: 0.574-0.802, p<0.001), international normalized ratio (HR=1.087, 95%CI: 1.027-1.150, p=0.004), red cell distribution width (HR=1.056, 95%CI: 1.021-1.092, p=0.001), temperature (HR=0.857, 95%CI: 0.789-0.932, p<0.001), and glycosylated hemoglobin (HR=1.358, 95%CI: 1.320-1.401, p<0.001) at admission are independent prognostic factors for 28-day all-cause mortality of septic patients with DM. The established nomogram shows satisfied accuracy and clinical utility with AUCs of 0.870 in the internal validation and 0.830 in the external validation cohort as well as 0.820 in the septic shock subpopulation, which is superior to the predictive value of the single SOFA score.

Conclusion

Our results suggest that admission characteristics show an optimal prediction value for short-term mortality in septic patients with DM. The established model can support intensive care unit physicians in making better initial clinical decisions for this subpopulation.

Blood microsampling technologies: Innovations and applications in 2022

With the development of highly sensitive bioanalytical techniques, the volume of samples necessary for accurate analysis has reduced. Microsampling, the process of obtaining small amounts of blood, has thus gained popularity as it offers minimal-invasiveness, reduced logistical costs and biohazard risks while simultaneously showing increased sample stability and a potential for the decentralization of the approach and at-home self-sampling. Although the benefits of microsampling have been recognised, its adoption in clinical practice has been slow. Several microsampling technologies and devices are currently available and employed in research studies for various biomedical applications. This review provides an overview of the state-of-the-art in microsampling technology with a focus on the latest developments and advancements in the field of microsampling. Research published in the year 2022, including studies (i) developing strategies for the quantitation of analytes in microsamples and (ii) bridging and comparing the interchangeability between matrices and choice of technology for a given application, is reviewed to assess the advantages, challenges and limitations of the current state of microsampling. Successful implementation of microsampling in routine clinical care requires continued efforts for standardization and harmonization. Microsampling has been shown to facilitate data-rich studies and a patient-centric approach to healthcare and is foreseen to play a central role in the future digital revolution of healthcare through continuous monitoring to improve the quality of life.

The correlation of hemoglobin and 28-day mortality in septic patients: secondary data mining using the MIMIC-IV database

BACKGROUND

Previous studies found minimal evidence and raised controversy about the link between hemoglobin and 28-day mortality in sepsis patients. As a result, the purpose of this study was to examine the association between hemoglobin and 28-day death in sepsis patients by analyzing the Medical Intensive Care IV (MIMIC-IV) database from 2008 to 2019 at an advanced medical center in Boston, Massachusetts.

METHODS

We extracted 34,916 sepsis patients from the MIMIC-IV retrospective cohort database, using hemoglobin as the exposure variable and 28-day death as the outcome variable, and after adjusting for confounders (demographic indicators, Charlson co-morbidity index, SOFA score, vital signs, medication use status (glucocorticoids, vasoactive drugs, antibiotics, and immunoglobulins, etc.)), we investigated the independent effects of hemoglobin and 28-day risk of death by binary logistic regression as well as two-piecewise linear model, respectively.

RESULTS

Hemoglobin levels and 28-day mortality were shown to be non-linearly related.The inflection points were 104 g/L and 128 g/L, respectively. When HGB levels were between 41 and 104 g/L, there was a 10% decrease in the risk of 28-day mortality (OR: 0.90; 95% CI: 0.87 to 0.94, p-value = 0.0001). However, in the range of 104-128 g/L, we did not observe a significant association between hemoglobin and 28-day mortality (OR: 1.17; 95% CI: 1.00 to 1.35, P value = 0.0586). When HGB was in the range of 128-207 g/L, there was a 7% increase in the risk of 28-day mortality for every 1 unit increase in HGB (OR: 1.07; 95% CI: 1.01 to 1.15, P value = 0.0424).

CONCLUSION

In patients with sepsis, baseline hemoglobin was related to a U-shaped risk of 28-day death. When HGB was in the range of 12.8-20.7 g/dL, there was a 7% increase in the risk of 28-day mortality for every 1 unit increase in HGB.

The PaO2/FiO2 is independently associated with 28-day mortality in patients with sepsis: a retrospective analysis from MIMIC-IV database

BACKGROUND

To clarify the relationship between the PaO₂/FiO₂ and 28-day mortality in patients with sepsis.

METHODS

This was a retrospective cohort study regarding MIMIC-IV database. Nineteen thousand two hundred thirty-three patients with sepsis were included in the final analysis. PaO₂/FiO₂ was exposure variable, 28-day mortality was outcome variable. PaO₂/FiO₂ was log-transformed as LnPaO₂/FiO₂. Binary logistic regression was used to explore the independent effects of LnPaO₂/FiO₂ on 28-day mortality using non-adjusted and multivariate-adjusted models. A generalized additive model (GAM) and smoothed curve fitting was used to investigate the non-linear relationship between LnPaO₂/FiO₂ and 28-day mortality. A two-piecewise linear model was used to calculate the OR and 95% CI on either side of the inflection point.

RESULTS

The relationship between LnPaO₂/FiO₂ and risk of 28-day death in sepsis patients was U-shape. The inflection point of LnPaO₂/FiO₂ was 5.30 (95%CI: 5.21-5.39), which indicated the inflection point of PaO₂/FiO₂ was 200.33 mmHg (95%CI: 183.09 mmHg-219.20 mmHg). On the left of inflection point, LnPaO₂/FiO₂ was negatively correlated with 28-day mortality (OR: 0.37, 95%CI: 0.32-0.43, p < 0.0001). On the right of inflection point, LnPaO₂/FiO₂ was positively correlated with 28-day mortality in patients with sepsis (OR: 1.53, 95%CI: 1.31-1.80, p < 0.0001).

CONCLUSIONS

In patients with sepsis, either a high or low PaO₂/FiO₂ was associated with an increased risk of 28-day mortality. In the range of 183.09 mmHg to 219.20 mmHg, PaO₂/FiO₂ was associated with a lower risk of 28-day death in patients with sepsis.

The correlation between the hemoglobin-to-red cell distribution width ratio and all-cause mortality in patients with malignant tumors and sepsis: A retrospective cohort study using the MIMIC-IV database

Objective

The aim of the study was to investigate the correlation between the hemoglobin-to-red cell distribution width ratio (HRR) and all-cause mortality in patients with malignant tumors and sepsis.

Methods

All patients who met the inclusion criteria of the Medical Information Mart for Intensive Care (MIMIC)-IV were selected and divided into four groups according to the quartile range of HRR distribution. Kaplan-Meier (K-M) analysis was used to plot the 28-day survival curve, and the log-rank test was used to compare the prognosis in each HRR group. A Cox proportional hazards regression model was used to evaluate the prognosis of HRR as both a continuous and categorical variable, and a restricted cubic spline was used to study the effect of HRR, as a continuous variable, on the mortality in patients with malignant tumors and sepsis. Interaction and subgroup analyses were performed to evaluate the consistency of correlations.

Results

A total of 3926 patients were included in the study, including 934 patients in the HRR ≤ 4.97 group, 988 patients in the 4.97 < HRR ≤ 6.26 group, 1005 patients in the 6.26 < HRR ≤ 7.84 group, and 999 patients in the HRR ≥ 7.84 group. According to the K-M analysis, the 28-day survival rate was the lowest in the HRR ≤ 4.97 group (59.53%), and there were significant differences in survival rates among different HRR levels (P < 0.001). The Cox proportional hazards regression model found that after adjusting for various potential confounding factors, HRR was negatively correlated with 28-day and 365-day mortality, and the risk of death in the HRR ≥ 7.84 group was significantly lower than that in the HRR ≤ 4.97 group (P = 0.030 and P = 0.008, respectively). The restricted cubic spline plot revealed a linear and negative relationship between the HRR and the 28-day and 365-day mortality rates. Subgroup analysis revealed an interaction between HRR, blood urea nitrogen, and SAPS II scores (P = 0.010 and P = 0.048, respectively).

Conclusion

Low HRR is an independent risk factor for all-cause mortality in patients with malignant tumors and sepsis and could be used as a prognostic indicator for these patients.

Identification and control for the effects of bioinformatic globin depletion on human RNA-seq differential expression analysis

When profiling blood samples by RNA-sequencing (RNA-seq), RNA from haemoglobin (Hgb) can account for up to 70% of the transcriptome. Due to considerations of sequencing depth and power to detect biological variation, Hgb RNA is typically depleted prior to sequencing by hybridisation-based methods; an alternative approach is to deplete reads arising from Hgb RNA bioinformatically. In the present study, we compared the impact of these two approaches on the outcome of differential gene expression analysis performed using RNA-seq data from 58 human tuberculosis (TB) patient or contact whole blood samples-29 globin kit-depleted and 29 matched non-depleted-a subset of which were taken at TB diagnosis and at six months post-TB treatment from the same patient. Bioinformatic depletion of Hgb genes from the non-depleted samples (bioinformatic-depleted) substantially reduced library sizes (median = 57.24%) and fewer long non-coding, micro, small nuclear and small nucleolar RNAs were captured in these libraries. Profiling published TB gene signatures across all samples revealed inferior correlation between kit-depleted and bioinformatic-depleted pairs when the proportion of reads mapping to Hgb genes was higher in the non-depleted sample, particularly at the TB diagnosis time point. A set of putative "globin-fingerprint" genes were identified by directly comparing kit-depleted and bioinformatic-depleted samples at each timepoint. Two TB treatment response signatures were also shown to have decreased differential performance when comparing samples at TB diagnosis to six months post-TB treatment when profiled on the bioinformatic-depleted samples compared with their kit-depleted counterparts. These results demonstrate that failure to deplete Hgb RNA prior to sequencing has a negative impact on the sensitivity to detect disease-relevant gene expression changes even when bioinformatic removal is performed.

Predicting length of stay in ICU and mortality with temporal dilated separable convolution and context-aware feature fusion

In healthcare, Intensive Care Unit (ICU) bed management is a necessary task because of the limited budget and resources. Predicting the remaining Length of Stay (LoS) in ICU and mortality can assist clinicians in managing ICU beds efficiently. This study proposes a deep learning method based on several successive Temporal Dilated Separable Convolution with Context-Aware Feature Fusion (TDSC-CAFF) modules, and a multi-view and multi-scale feature fusion for predicting the remaining LoS and mortality risk for ICU patients. In each TDSC-CAFF module, temporal dilated separable convolution is used to encode each feature separately, and context-aware feature fusion is proposed to capture comprehensive and context-aware feature representations from the input time-series features, static demographics, and the output of the last TDSC-CAFF module. The CAFF outputs of each module are accumulated to achieve multi-scale representations with different receptive fields. The outputs of TDSC and CAFF are concatenated with skip connection from the output of the last module and the original time-series input. The concatenated features are processed by the proposed Point-Wise convolution-based Attention (PWAtt) that captures the inter-feature context to generate the final temporal features. Finally, the final temporal features, the accumulated multi-scale features, the encoded diagnosis, and static demographic features are fused and then processed by fully connected layers to obtain prediction results. We evaluate our proposed method on two publicly available datasets: eICU and MIMIC-IV v1.0 for LoS and mortality prediction tasks. Experimental results demonstrate that our proposed method achieves a mean squared log error of 0.07 and 0.08 for LoS prediction, and an Area Under the Receiver Operating Characteristic Curve of 0.909 and 0.926 for mortality prediction, on eICU and MIMIC-IV v1.0 datasets, respectively, which outperforms several state-of-the-art methods.

A non-linear positive relationship between serum phosphate and clinical outcomes in sepsis

OBJECTIVE

This study aimed to evaluate the possible relationship between serum phosphate and short-term outcomes in sepsis.

METHODS

This was a retrospective study. Sepsis patients in MIMIC-IV database were included. Based on the quartiles of serum phosphate, all sepsis patients were divided into four groups. Univariable and multivariable regression analyses were constructed for discussing the relationship between different parameters and 30-day mortality in sepsis. A generalized additive model was performed for exploring the association of serum phosphate with 30-day mortality.

RESULTS

6251 sepsis patients including 4368 survivors and 1883 non-survivors were included. A significant relationship between serum phosphate and 30-day mortality was found after adjusting for all potential confounders (OR = 1.19, 95%CI:1.13-1.26, P < 0.0001). The relationship was non-linear with an inflection point of 6.8 mg/dl. On the left side of the inflection point (≤6.8 mg/dl, n = 5911 (94.56%)), the OR was 1.24 (95%CI: 1.17-1.31, P < 0.0001). On the right side of the inflection point (>6.8 mg/dl, n = 340 (5.44%)), the OR was 0.94 (95%CI:0.78-1.13, P = 0.5038).

CONCLUSION

A non-linear positive relationship was found between serum phosphate and 30-day mortality in sepsis. Serum phosphate was associated with mortality in sepsis. Our results could be used for screening out those sepsis patients with higher risk of worse outcomes.

Factors associated with severe childhood community-acquired pneumonia: a retrospective study from two hospitals

Background

Community-acquired pneumonia (CAP) is the leading cause of death in children globally. Indonesia is ranked 1st in South East Asia with the highest burden of pneumonia. Identification of risk factors is necessary for early intervention and better management. This study intended to describe CAP’s clinical signs and laboratory findings and explore the risk factors of severe CAP among children in Indonesia.

Methods

This was a retrospective study of childhood hospitalizations in Siloam General Hospitals and Siloam Hospitals Lippo Village from December 2015 to December 2019. Demographic data, clinical signs, and laboratory findings were collected and processed using IBM SPSS 26.0.

Results

This study included 217 participants with 66 (30.4%) severe pneumonia cases. Multivariate analysis shows that fever that lasts more than 7 days (ORadj = 4.95; 95%CI 1.61–15.21, Padj = 0.005) and increase in respiratory rate (ORadj = 1.05, 95%CI 1.01–1.08, Padj = 0.009) are two predictors of severe pneumonia. Meanwhile, a normal hematocrit level (ORadj = 0.9; 95%CI 0.83–0.98, Padj = 0.011) and children with normal BMI (ORadj = 0.7; 95%CI 0.57–0.84, Padj < 0.001) are significant independent predictors of severe pneumonia. The Hosmer-Lemeshow test shows that this model is a good fit with a P-value of 0.281. The AUC for this model is 0.819 (95%CI = 0.746–0.891, P-value < 0.001) which shows that this model has good discrimination.

Conclusion

Pediatric CAP hospitalizations with fever lasting > 7 days and tachypnea were at higher risk for progressing to severe pneumonia. A normal hematocrit level and a normal BMI are protective factors for severe pneumonia.