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Dynamic changes in heparin-binding protein as a prognostic biomarker for 30-day mortality in sepsis patients in the intensive care unit.

ABSTRACT: Heparin-binding protein (HBP) has been shown to be a robust predictor of the progression to organ dysfunction from sepsis, and we hypothesized that dynamic changes in HBP may reflect the severity of sepsis. We therefore aim to investigate the predictive value of baseline HBP, 24-h, and 48-h HBP change for prediction of 30-day mortality in adult patients with sepsis. This is a prospective observational study in an intensive care unit of a tertiary center. Patients aged 20 years or older who met SEPSIS-3 criteria were prospectively enrolled from August 2019 to January 2020. Plasma levels of HBP were measured at admission, 24 h, and 48 h and dynamic changes in HBP were calculated. The Primary endpoint was 30-day mortality. We tested whether the biomarkers could enhance the predictive accuracy of a multivariable predictive model. A total of 206 patients were included in the final analysis. 48-h HBP change (HBPc-48 h) had greater predictive accuracy of area under the curve (AUC: 0.82), followed by baseline HBP (0.79), PCT (0.72), lactate (0.71), and CRP (0.65), and HBPc-24 h (0.62). Incorporation of HBPc-48 h into a clinical prediction model significantly improved the AUC from 0.85 to 0.93. HBPc-48 h may assist clinicians with clinical outcome prediction in critically ill patients with sepsis and can improve the performance of a prediction model including age, SOFA score and Charlson comorbidity index.

SUBMITTER: Dou QL

PROVIDER: S-EPMC9232494 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Dynamic changes in heparin-binding protein as a prognostic biomarker for 30-day mortality in sepsis patients in the intensive care unit.

Dou Qing-Li QL Liu Jiangping J Zhang Wenwu W Wang Ching-Wei CW Gu Yanan Y Li Na N Hu Rui R Hsu Wan-Ting WT Huang Amy Huaishiuan AH Tong Hoi Sin HS Hsu Tzu-Chun TC Hsu Cheng-An CA Xu Jun J Lee Chien-Chang CC

Scientific reports 20220624 1

Heparin-binding protein (HBP) has been shown to be a robust predictor of the progression to organ dysfunction from sepsis, and we hypothesized that dynamic changes in HBP may reflect the severity of sepsis. We therefore aim to investigate the predictive value of baseline HBP, 24-h, and 48-h HBP change for prediction of 30-day mortality in adult patients with sepsis. This is a prospective observational study in an intensive care unit of a tertiary center. Patients aged 20 years or older who met S ...[more]

PMID: 35750778

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Project description:BackgroundStroke patients admitted to an intensive care unit (ICU) follow a particular survival pattern with a high short-term mortality, but if they survive the first 30 days, a relatively favourable subsequent survival is observed.ObjectivesThe development and validation of two prognostic models predicting 30-day mortality for ICU patients with ischaemic stroke and for ICU patients with intracerebral haemorrhage (ICH), analysed separately, based on parameters readily available within 24 h after ICU admission, and with comparison with the existing Acute Physiology and Chronic Health Evaluation IV (APACHE-IV) model.DesignObservational cohort study.SettingAll 85 ICUs participating in the Dutch National Intensive Care Evaluation database.PatientsAll adult patients with ischaemic stroke or ICH admitted to these ICUs between 2010 and 2019.Main outcome measuresModels were developed using logistic regressions and compared with the existing APACHE-IV model. Predictive performance was assessed using ROC curves, calibration plots and Brier scores.ResultsWe enrolled 14 303 patients with stroke admitted to ICU: 8422 with ischaemic stroke and 5881 with ICH. Thirty-day mortality was 27% in patients with ischaemic stroke and 41% in patients with ICH. Important factors predicting 30-day mortality in both ischaemic stroke and ICH were age, lowest Glasgow Coma Scale (GCS) score in the first 24 h, acute physiological disturbance (measured using the Acute Physiology Score) and the application of mechanical ventilation. Both prognostic models showed high discrimination with an AUC 0.85 [95% confidence interval (CI), 0.84 to 0.87] for patients with ischaemic stroke and 0.85 (0.83 to 0.86) in ICH. Calibration plots and Brier scores indicated an overall good fit and good predictive performance. The APACHE-IV model predicting 30-day mortality showed similar performance with an AUC of 0.86 (95% CI, 0.85 to 0.87) in ischaemic stroke and 0.87 (0.86 to 0.89) in ICH.ConclusionWe developed and validated two prognostic models for patients with ischaemic stroke and ICH separately with a high discrimination and good calibration to predict 30-day mortality within 24 h after ICU admission.Trial registrationTrial registration: Dutch Trial Registry ( https://www.trialregister.nl/ ); identifier: NTR7438.

Project description:BackgroundThe hemoglobin glycation index (HGI) represents the difference between the observed and predicted values of haemoglobin A1c (HbA1c). However, the association between HGI and prognosis of heart failure (HF) is not completely clarified yet and requires more investigation. This study aimed to explore the connection between HGI and mortality in HF patients.MethodsThe data for the study were derived from the MIMIC-IV database from 2008 to 2019, a publicly available clinical database in intensive care. A linear regression equation between HbA1c and fasting blood glucose (FBG) was established to calculate predicted HbA1c. The endpoints were 30-day and 365-day all-cause mortality. Kaplan-Meier analysis was utilized to compare survival rates across groups differentiated by their HGI levels. The Cox regression models and restricted cubic spline (RCS) analysis were utilized to analyze the association between HGI and mortality.ResultsThe study collected a total of 2846 patients with HF (40.1% male), of whom 305 patients (10.7%) died within 30 days and 954 patients (33.5%) died within 365 days. Kaplan-Meier curves revealed patients with higher HGI had significantly higher mortality risks (log-rank P < 0.001). A high HGI was significantly associated with 30-day mortality (adjusted HR [aHR]: 2.36, 95% CI: 1.74-3.20, P < 0.001) and 365-day mortality (aHR: 1.40, 95% CI: 1.16-1.68, P < 0.001) after adjustment for potential confounders. Likewise, each unit increase in the HGI correlated with a 1.42-fold higher risk of 30-day mortality (aHR: 1.42, 95% CI: 1.28-1.57, P < 0.001) and 1.19-fold higher risk of 365-day mortality (aHR: 1.19, 95% CI: 1.11-1.68, P < 0.001). RCS analysis suggested an L-shaped nonlinear association between HGI and clinical endpoints (P for nonlinearity < 0.001), with an inflection point value of - 1.295. Subgroup analysis and sensitivity analysis revealed that the correlation between HGI and 30-day and 365-day all-cause mortality remained consistent.ConclusionsIn ICU-admitted HF patients, HGI was independently associated with increased risks of 30-day and 365-day mortality and the identification of high HGI (> 0.709) provided a valuable tool for clinicians to detect high-risk populations. Integrating HGI into routine clinical practice might strengthen the prognosis-based decision making improve HF patient outcomes.

Project description:BackgroundGiven the increased attention to sepsis at the population level there is a need to assess hospital performance in the care of sepsis patients using widely-available administrative data. The goal of this study was to develop an administrative risk-adjustment model suitable for profiling hospitals on their 30-day mortality rates for patients with sepsis.MethodsWe conducted a retrospective cohort study using hospital discharge data from general acute care hospitals in Pennsylvania in 2012 and 2013. We identified adult patients with sepsis as determined by validated diagnosis and procedure codes. We developed an administrative risk-adjustment model in 2012 data. We then validated this model in two ways: by examining the stability of performance assessments over time between 2012 and 2013, and by examining the stability of performance assessments in 2012 after the addition of laboratory variables measured on day one of hospital admission.ResultsIn 2012 there were 115,213 sepsis encounters in 152 hospitals. The overall unadjusted mortality rate was 18.5%. The final risk-adjustment model had good discrimination (C-statistic = 0.78) and calibration (slope and intercept of the calibration curve = 0.960 and 0.007, respectively). Based on this model, hospital-specific risk-standardized mortality rates ranged from 12.2 to 24.5%. Comparing performance assessments between years, correlation in risk-adjusted mortality rates was good (Pearson's correlation = 0.53) and only 19.7% of hospitals changed by more than one quintile in performance rankings. Comparing performance assessments after the addition of laboratory variables, correlation in risk-adjusted mortality rates was excellent (Pearson's correlation = 0.93) and only 2.6% of hospitals changed by more than one quintile in performance rankings.ConclusionsA novel claims-based risk-adjustment model demonstrated wide variation in risk-standardized 30-day sepsis mortality rates across hospitals. Individual hospitals' performance rankings were stable across years and after the addition of laboratory data. This model provides a robust way to rank hospitals on sepsis mortality while adjusting for patient risk.

Dataset Information

Dynamic changes in heparin-binding protein as a prognostic biomarker for 30-day mortality in sepsis patients in the intensive care unit.

Publications

Dynamic changes in heparin-binding protein as a prognostic biomarker for 30-day mortality in sepsis patients in the intensive care unit.

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