Association Between ICU-Acquired Hypernatremia and In-Hospital Mortality: Data From the Medical Information Mart for Intensive Care III and the Electronic ICU Collaborative Research Database

Objectives: Arterial hyperoxia is reportedly a risk factor for poor outcomes in patients with hemorrhagic brain injury (HBI). However, most previous studies have only evaluated the effects of hyperoxia using static oxygen partial pressure (PaO2) values. This study aimed to investigate the association between overall dynamic oxygenation status and HBI outcomes, using longitudinal PaO2 data.Methods: Data were extracted from the Medical Information Mart for Intensive Care III database. Longitudinal PaO2 data obtained within 72 h of admission to an intensive care unit were analyzed, using a group-based trajectory approach. In-hospital mortality was used as the primary outcomes. Multivariable logistic models were used to explore the association between PaO2 trajectory and outcomes.Results: Data of 2,028 patients with HBI were analyzed. Three PaO2 trajectory types were identified: Traj-1 (mild hyperoxia), Traj-2 (transient severe hyperoxia), and Traj-3 (persistent severe hyperoxia). The initial and maximum PaO2 of patients with Traj-2 and Traj-3 were similar and significantly higher than those of patients with Traj-1. However, PaO2 in patients with Traj-2 decreased more rapidly than in patients with Traj-3. The crude in-hospital mortality was the lowest for patients with Traj-1 and highest for patients with Traj-3 (365/1,303, 209/640, and 43/85 for Traj-1, Traj-2, and Traj-3, respectively; p < 0.001), and the mean Glasgow Coma Scale score at discharge (GCSdis) was highest for patients with Traj-1 and lowest in patients with Traj-3 (13 [7–15], 11 [6–15], and 7 [3–14] for Traj-1, Traj-2, and Traj-3, respectively; p < 0.001). The multivariable model revealed that the risk of death was higher in patients with Traj-3 than in patients with Traj-1 (odds ratio [OR]: 3.3, 95% confidence interval [CI]: 1.9–5.8) but similar for patients with Traj-1 and Traj-2. Similarly, the logistic analysis indicated the worst neurological outcomes in patients with Traj-3 (OR: 3.6, 95% CI: 2.0–6.4, relative to Traj-1), but similar neurological outcomes for patients in Traj-1 and Traj-2.Conclusion: Persistent, but not transient severe arterial hyperoxia, was associated with poor outcome in patients with HBI.

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Association between High Cumulative Numbers of Elevated Heart Rate and Mortality in Neurological ICU patients: Retrospective Analysis of eICU Collaborative Research Database

10.21203/rs.2.9183/v1 ◽

2019 ◽

Author(s):

Dawei Zhou ◽

Zhimin Li ◽

Shaolan Zhang ◽

Jianxin Zhou ◽

Guangzhi Shi

Keyword(s):

Heart Rate ◽

Logistic Regression ◽

Hospital Mortality ◽

Collaborative Research ◽

Research Database ◽

Neurological Intensive Care Unit ◽

Increased Risk ◽

The Mean ◽

Apache Iv ◽

Elevated Heart Rate

Abstract Background Heart rate is routinely measured in Neurological intensive care unit(NICU), but its prognostic value remains debated. We sought to evaluate the association of high cumulative numbers of elevated Heart Rate (HcneHR) with mortality in NICU patients. Methods We used a large observational eICU Collaborative Research Database (eICU-CRD), where continous heart rate monitoring every 5 minute was available. We collected periodic heart rate, disease severity (APACHE IV score), NICU and hospital mortality and other information in 8347 patient admissions from the eICU-CRD. The cumulative numbers of Heart Rate (cneHR) were defined as >100 beats/min in first admittion 24 hours, and if cneHR ≥10,then was defined as higt cneHR(HcneHR). The primary outcome was NICU mortality. The other outcomes were hospital mortality, length of NICU stay and APACHE IV score. Multivariable logistic regression was used to assess for association for HcneHR and other covariance with NICU and hospiltal discharge status. Results The mean age of patients were 63 years, and the most frequent disease categories of NICU in eICU-CRD were postoperation (25%), stroke(19%), traumatic brain injury(14%). The mean APACHE IV score was 50. Overall NICU mortality of the cohort at discharge was 4%, and hospital mortality was 8%. The NICU mortality of HcneHR patients was 7%. Adjusted logistic regression for HcneHR showed a significantly increased risk of NICU death with odds ratio 1.61(confidence interval, 1.26-2.06; P <0 .001). Conclusions In adult neurocritically ill patients, we found a significant association for HcneHR with elevated mortality and several others important patient-centered outcomes.

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Modelling hospital outcome: problems with endogeneity

BMC Medical Research Methodology ◽

10.1186/s12874-021-01251-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

John L. Moran ◽

John D. Santamaria ◽

Graeme J. Duke ◽

Keyword(s):

Logistic Regression ◽

Intensive Care ◽

Hospital Mortality ◽

Information Criteria ◽

Mortality Data ◽

Treatment Assignment ◽

Apache Iii ◽

Mortality Modelling ◽

Error Terms ◽

Linear Probability

Abstract Background Mortality modelling in the critical care paradigm traditionally uses logistic regression, despite the availability of estimators commonly used in alternate disciplines. Little attention has been paid to covariate endogeneity and the status of non-randomized treatment assignment. Using a large registry database, various binary outcome modelling strategies and methods to account for covariate endogeneity were explored. Methods Patient mortality data was sourced from the Australian & New Zealand Intensive Society Adult Patient Database for 2016. Hospital mortality was modelled using logistic, probit and linear probability (LPM) models with intensive care (ICU) providers as fixed (FE) and random (RE) effects. Model comparison entailed indices of discrimination and calibration, information criteria (AIC and BIC) and binned residual analysis. Suspect covariate and ventilation treatment assignment endogeneity was identified by correlation between predictor variable and hospital mortality error terms, using the Stata™ “eprobit” estimator. Marginal effects were used to demonstrate effect estimate differences between probit and “eprobit” models. Results The cohort comprised 92,693 patients from 124 intensive care units (ICU) in calendar year 2016. Patients mean age was 61.8 (SD 17.5) years, 41.6% were female and APACHE III severity of illness score 54.5(25.6); 43.7% were ventilated. Of the models considered in predicting hospital mortality, logistic regression (with or without ICU FE) and RE logistic regression dominated, more so the latter using information criteria indices. The LPM suffered from many predictions outside the unit [0,1] interval and both poor discrimination and calibration. Error terms of hospital length of stay, an independent risk of death score and ventilation status were correlated with the mortality error term. Marked differences in the ventilation mortality marginal effect was demonstrated between the probit and the "eprobit" models which were scenario dependent. Endogeneity was not demonstrated for the APACHE III score. Conclusions Logistic regression accounting for provider effects was the preferred estimator for hospital mortality modelling. Endogeneity of covariates and treatment variables may be identified using appropriate modelling, but failure to do so yields problematic effect estimates.

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Admission Oxygen Saturation and Mortality in Acute Pulmonary Embolism Patients: Observational Data From Large ICU Databases

10.21203/rs.3.rs-580015/v1 ◽

2021 ◽

Author(s):

Xiaolin Xu ◽

Anping Peng ◽

Jing Tian ◽

Runnan Shen ◽

Guochang You ◽

...

Keyword(s):

Pulmonary Embolism ◽

Logistic Regression ◽

Intensive Care ◽

Hospital Mortality ◽

Oxygen Saturation ◽

Acute Pulmonary Embolism ◽

Research Database ◽

Optimal Range ◽

Subgroup Analyses ◽

The Relationship

Abstract Background The relationship between blood oxygenation and clinical outcomes of acute pulmonary embolism (APE) patients in intensive care unit (ICU) is unclear, which could be nonlinear. The study aimed to determine the association between admission pulse oximetry-derived oxygen saturation (SpO2) levels and mortality, and to determine the optimal range with real-world data. Methods Patients diagnosed with APE on admission and staying in ICU for at least 24 hours in the Medical Information Mart for Intensive Care III (MIMIC-III) database and the eICU Collaborative Research Database (eICU-CRD) were included. Logistic regression and restricted cubic spline (RCS) models were applied to determine the nonlinear relationship between mean SpO2 levels within the first 24 hours after ICU admission and in-hospital mortality, from which we derived an optimal range of SpO2. Subgroup analyses were based on demographics, treatment information, scoring system and comorbidities. Results We included 1109 patients who fulfilled inclusion criteria, among whom 129 (12%) died during hospitalization and 80 (7.2%) died in ICU. The RCS showed that the relationship between admission SpO2 levels and in-hospital mortality of APE patients was nonlinear and U-shaped. The optimal range of SpO2 with the lowest mortality was 95–98%. Multivariate stepwise logistic regression analysis with backward elimination confirmed that the admission SpO2 levels of 95%-98% was associated with decreased hospital mortality compared to the group with SpO2 < 95% (Odds ratio [OR] = 2.321; 95% confidence interval [CI]: 1.405–3.786; P < 0.001) and 100% (OR = 2.853; 95% CI: 1.294–5.936; P = 0.007), but there was no significant difference compared with 99% SpO2 (OR = 0.670, 95% CI: 0.326–1.287; P > 0.05). This association was consistent across subgroup analyses. Conclusions The relationship between admission SpO2 levels and in-hospital mortality followed a U-shaped curve among patients with APE. The optimal range of SpO2 for APE patients was 95–98%.

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A nomogram to predict in-hospital mortality of neonates admitted to the intensive care unit

International Health ◽

10.1093/inthealth/ihab012 ◽

2021 ◽

Author(s):

Xihua Huang ◽

Zhenyu Liang ◽

Tang Li ◽

Yu Lingna ◽

Wei Zhu ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Hospital Mortality ◽

Predictive Model ◽

Sofa Score ◽

Medical Information ◽

Hospital Death ◽

Operating Characteristics ◽

Distribution Width ◽

Mimic Iii

Abstract Background To explore the influencing factors for in-hospital mortality in the neonatal intensive care unit (NICU) and to establish a predictive nomogram. Methods Neonatal data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Both univariate and multivariate logit binomial general linear models were used to analyse the factors influencing neonatal death. The area under the receiver operating characteristics (ROC) curve was used to assess the predictive model, which was visualized by a nomogram. Results A total of 1258 neonates from the NICU in the MIMIC-III database were eligible for the study, including 1194 surviving patients and 64 deaths. Multivariate analysis showed that red cell distribution width (RDW) (odds ratio [OR] 0.813, p=0.003) and total bilirubin (TBIL; OR 0.644, p<0.001) had protective effects on neonatal in-hospital death, while lymphocytes (OR 1.205, p=0.025), arterial partial pressure of carbon dioxide (PaCO2; OR 1.294, p=0.016) and sequential organ failure assessment (SOFA) score (OR 1.483, p<0.001) were its independent risk factors. Based on this, the area under the curve of this predictive model was up to 0.865 (95% confidence interval 0.813 to 0.917), which was also confirmed by a nomogram. Conclusions The nomogram constructed suggests that RDW, TBIL, lymphocytes, PaCO2 and SOFA score are all significant predictors for in-hospital mortality in the NICU.

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The knowledge of Cormack–Lehane intubation grade and intensive care unit outcome

Journal of the Intensive Care Society ◽

10.1177/1751143719832178 ◽

2019 ◽

Vol 21 (1) ◽

pp. 48-56

Author(s):

Vinodh B Nanjayya ◽

Christopher J Hebel ◽

Patrick J Kelly ◽

Jason McClure ◽

David Pilcher

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Hospital Mortality ◽

Invasive Mechanical Ventilation ◽

Severity Of Illness ◽

Secondary Outcome ◽

Mortality Data ◽

Icu Admission ◽

Icu Outcome ◽

Multivariable Analyses

Background For patients on invasive mechanical ventilation (MV), it is unclear if knowledge of intubation grade influences intensive care unit (ICU) outcome. We aimed to determine if there was an independent relationship between knowledge of intubation grade during ICU admission and in-hospital mortality. Methods We performed a retrospective cohort study of all patients receiving invasive MV at the Alfred ICU between December 2011 and February 2015. Demographics, details of admission, the severity of illness, chronic health status, airway detail (unknown or known Cormack–Lehane (CL) grade), MV duration and in-hospital mortality data were collected. Univariable and multivariable analyses were conducted to assess the relationship. The primary outcome was in-hospital mortality, and the secondary outcome was the duration of MV. Results Amongst 3556 patients studied, 611 (17.2%) had an unknown CL grade. Unadjusted mortality was higher in patients with unknown CL grade compared to known CL grade patients (21.6% vs. 9.9%). After adjusting for age, sex, severity of illness, type of ICU admission, cardiac arrest, limitations to treatment and diagnosis, having an unknown CL grade during invasive MV was independently associated with an increase in mortality (adjusted OR 1.5, 95% CI 1.14–1.98, p < 0.01). Conclusion Amongst ICU patients receiving MV, not knowing CL grade appears to be independently associated with increased mortality. This information should be communicated and documented in all patients receiving MV in ICU.

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Reliability of In-hospital Mortality as a Quality Indicator in Clinical Quality Registries

Methods of Information in Medicine ◽

10.3414/me12-02-0070 ◽

2013 ◽

Vol 52 (05) ◽

pp. 432-440 ◽

Cited By ~ 10

Author(s):

N. Peek ◽

E. de Jonge ◽

D. Dongelmans ◽

G. van Berkel ◽

N. de Keizer ◽

...

Keyword(s):

Intensive Care ◽

Quality Of Care ◽

Hospital Mortality ◽

Quality Indicator ◽

Outcome Data ◽

Mortality Data ◽

Quality Of Data ◽

Local Data ◽

Data Errors

SummaryObjectives: Errors in the registration or extraction of patient outcome data, such as in-hospital mortality, may lower the reliability of the quality indicator that uses this (partly) incorrect data. Our aim was to measure the reliability of in-hospital mortality registration in the Dutch National Intensive Care Evaluation (NICE) registry.Methods: We linked data of the NICE registry with an insurance claims database, resulting in a list of discrepancies in in-hospital mortality. Eleven Intensive Care Units (ICUs) were visited where local data sources were investigated to find the true in-hospital mortality status of the discrepancies and to identify the causes of the data errors in the NICE registry. Original and corrected Stand -ardized Mortality Ratios (SMRs) were calculated to determine if conclusions about quality of care changed compared to the national benchmark.Results: In eleven ICUs, 23,855 records with 460 discrepancies were identified of which 255 discrepancies (1.1% of all linked records) were due to incorrect in-hospital mortality registration in the NICE registry. Two programming errors in computer software of six ICUs caused 78% of errors, the remainder was caused by manual transcription errors and failure to record patient outcomes. For one ICU the performance became concordant with the national benchmark after correction, instead of being better.Conclusions: The reliability of in-hospital mortality registration in the NICE registry was good. This was reflected by the low number of data errors and by the fact that conclusions about the quality of care were only affected for one ICU due to systematic data errors. We recommend that registries frequently verify the software used in the registration process, and compare mortality data with an external source to assure consistent quality of data.

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Optimal Oxygen Saturation Targets in Patients with Sepsis- Associated Encephalopathy: A Cohort Study from the MIMIC-IV Database

10.21203/rs.3.rs-478223/v1 ◽

2021 ◽

Author(s):

Lina Zhao ◽

Yunying Wang ◽

Zengzheng Ge ◽

Huadong Zhu ◽

Yi Li

Keyword(s):

Cohort Study ◽

Intensive Care ◽

Hospital Mortality ◽

Oxygen Saturation ◽

Medical Information ◽

Generalized Additive Models ◽

Additive Models ◽

Optimal Range ◽

Oxygenation Status ◽

The Impact

Abstract Objectives: Patients with sepsis-associated encephalopathy (SAE) in the intensive care unit (ICU) are treated with supplemental oxygen. However, few studies have investigated the impact of oxygenation status on the patient with SAE, and the optimal oxygenation status target remains unclear. We aimed to investigate the relationship between optimal oxygenation status and patients with SAE.Methods: This study is a retrospective cohort study. Patients were diagnosed with sepsis3.0 at the first ICU admission between 2008 and 2019 from Medical Information Mart for Intensive Care IV (MIMIC IV). We use generalized additive models to estimate the optimal oxygen saturation targets in patients with SAE. Multivariate logistic analysis to further confirm it. Measurements and Main Results: A total of 6714 patients with SAE were included. The incidence of patients with SAE was 66.8%, and hospital mortality was 7.9%. SpO2≤92% was the independent risk factor of incidence in patients with SAE. The optimal range of SpO2 was 93%–97%, which can reduce the incidence of patients with SAE. The optimal range of SpO2 was 92%–96%, reducing the hospital mortality of patients with SAE.Conclusions: The optimal range of SpO2 was 93%–96% reduce the hospital mortality and incidence of patients with SAE. SAE patients need conservative oxygen therapy

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Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding

BMJ Health & Care Informatics ◽

10.1136/bmjhci-2020-100245 ◽

2021 ◽

Vol 28 (1) ◽

pp. e100245

Author(s):

Riccardo Levi ◽

Francesco Carli ◽

Aldo Robles Arévalo ◽

Yuksel Altinel ◽

Daniel J Stein ◽

...

Keyword(s):

Artificial Intelligence ◽

Intensive Care Unit ◽

Intensive Care ◽

Clinical Decision Making ◽

Medical Information ◽

Learning Algorithm ◽

Observation Time ◽

Research Database ◽

Gi Bleeding ◽

Healthcare Applications

ObjectiveGastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU.MethodsA machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates.ResultsThe optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all.ConclusionsThe potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.

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