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Thrombocytopenia and platelet course on hospital mortality in neurological intensive care unit: a retrospective observational study from large database.

ABSTRACT: BACKGROUND:Thrombocytopenia (TP) has been shown to be an independent predictor of mortality in the intensive care unit (ICU) patients. Studies are lacking in the neurological ICU (NICU) population. The aim was to evaluate the incidence of TP in NICU and the relationship between TP and outcomes. METHODS:We conducted a retrospective multicenter study of prospectively collected data of all patients admitted to the NICU between 2014 and 2015 from a large database (eICU Collaborative Research Database). The main exposure was TP at admission and TP developed during NICU stay. Multivariable logistic regression and Cox proportional hazard models were used to evaluate the relationship of TP at admission and platelet course with hospital mortality. The primary outcome was hospital mortality. RESULTS:7450 patients in NICU from 17 hospitals were included. Hospital mortality was 9%. TP at admission was present in 20% of patients, TP developed during NICU stay was present in 13.2% of patients. TP at admission was not associated with hospital mortality after adjusting for confounders (OR 1.14 [95% CI 0.92-1.41, p?=?0.237]). Hospital mortality of continuous TP during NICU stay was 15% while hospital mortality of recovery from TP at admission was 6% (p?

SUBMITTER: Zhou D

PROVIDER: S-EPMC7260747 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Thrombocytopenia and platelet course on hospital mortality in neurological intensive care unit: a retrospective observational study from large database.

Zhou Dawei D Li Zhimin Z Wu Lei L Shi Guangzhi G Zhou Jianxin J

BMC neurology 20200530 1

<h4>Background</h4>Thrombocytopenia (TP) has been shown to be an independent predictor of mortality in the intensive care unit (ICU) patients. Studies are lacking in the neurological ICU (NICU) population. The aim was to evaluate the incidence of TP in NICU and the relationship between TP and outcomes.<h4>Methods</h4>We conducted a retrospective multicenter study of prospectively collected data of all patients admitted to the NICU between 2014 and 2015 from a large database (eICU Collaborative R ...[more]

PMID: 32473647

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Project description:ObjectiveThe predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.DesignA retrospective cohort study.Setting and participantsData were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Data on 1177 heart failure patients were analysed.MethodsPatients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation (n=825, 70%) and a validation (n=352, 30%) group. Independent risk factors for in-hospital mortality were screened using the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression models in the derivation sample. Multivariate logistic regression analysis was used to build prediction models in derivation group, and then validated in validation cohort. Discrimination, calibration and clinical usefulness of the predicting model were assessed using the C-index, calibration plot and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.ResultsAmong the 1177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression and Get With the Guidelines-Heart Failure (GWTG-HF) risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (p<0.05). The XGBoost model was chosen as our final model for its more concise and wider net beneﬁt threshold probability range and was presented as the nomogram.ConclusionsOur nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

Project description:BACKGROUND:There is little descriptive data on Stenotrophomonas maltophilia hospital-acquired pneumonia (HAP) in critically ill patients. The optimal modalities of antimicrobial therapy remain to be determined. Our objective was to describe the epidemiology and prognostic factors associated with S. maltophilia pneumonia, focusing on antimicrobial therapy. METHODS:This nationwide retrospective study included all patients admitted to 25 French mixed intensive care units between 2012 and 2017 with hospital-acquired S. maltophilia HAP during intensive care unit stay. Primary endpoint was time to in-hospital death. Secondary endpoints included microbiologic effectiveness and antimicrobial therapeutic modalities such as delay to appropriate antimicrobial treatment, mono versus combination therapy, and duration of antimicrobial therapy. RESULTS:Of the 282 patients included, 84% were intubated at S. maltophilia HAP diagnosis for duration of 11 [5-18] days. The Simplified Acute Physiology Score II was 47 [36-63], and the in-hospital mortality was 49.7%. Underlying chronic pulmonary comorbidities were present in 14.1% of cases. Empirical antimicrobial therapy was considered effective on S. maltophilia according to susceptibility patterns in only 30% of cases. Delay to appropriate antimicrobial treatment had, however, no significant impact on the primary endpoint. Survival analysis did not show any benefit from combination antimicrobial therapy (HR = 1.27, 95%CI [0.88; 1.83], p = 0.20) or prolonged antimicrobial therapy for more than 7 days (HR = 1.06, 95%CI [0.6; 1.86], p = 0.84). No differences were noted in in-hospital death irrespective of an appropriate and timely empiric antimicrobial therapy between mono- versus polymicrobial S. maltophilia HAP (p = 0.273). The duration of ventilation prior to S. maltophilia HAP diagnosis and ICU length of stay were shorter in patients with monomicrobial S. maltophilia HAP (p = 0.031 and p = 0.034 respectively). CONCLUSIONS:S. maltophilia HAP occurred in severe, long-stay intensive care patients who mainly required prolonged invasive ventilation. Empirical antimicrobial therapy was barely effective while antimicrobial treatment modalities had no significant impact on hospital survival. TRIAL REGISTRATION:clinicaltrials.gov, NCT03506191.

Project description:Background Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear. Methods The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram. Results Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram. Conclusions The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making. Supplementary Information The online version contains supplementary material available at 10.1186/s12871-023-02138-5.

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Thrombocytopenia and platelet course on hospital mortality in neurological intensive care unit: a retrospective observational study from large database.

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Thrombocytopenia and platelet course on hospital mortality in neurological intensive care unit: a retrospective observational study from large database.

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