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Who Dies after ICU Discharge? Retrospective Analysis of Prognostic Factors for In-Hospital Mortality of ICU Survivors.

ABSTRACT: We investigated the causes of inpatient death after intensive care unit (ICU) discharge and determined predictors of in-hospital mortality in Korea. Using medical ICU registry data of Seoul National University Hospital, we performed a retrospective cohort study involving patients who were discharged alive from their first ICU admission with at least 24 hours of ICU length of stay (LOS). From January 2011 to August 2013, 723 patients were admitted to ICU and 383 patients were included. The estimated in-hospital mortality rate was 11.7% (45/383). The most common cause of death was respiratory failure (n = 25, 56%) followed by sepsis and cancer progression; the causes of hospital death and ICU admission were the same in 64% of all deaths; sudden unexpected deaths comprised about one-fifth of all deaths. In order to predict in-hospital mortality among ICU survivors, multivariate analysis identified presence of solid tumor (odds ratio [OR], 4.06; 95% confidence interval [CI], 2.01-8.2; P < 0.001), hematologic disease (OR, 4.75; 95% CI, 1.51-14.96; P = 0.013), Sequential Organ Failure Assessment (SOFA) score upon ICU admission (OR, 1.08; 95% CI, 0.99-1.17; P = 0.075), and hemoglobin (Hb) level (OR, 0.67; 95% CI, 0.52-0.86; P = 0.001) and platelet count (Plt) (OR, 0.99; 95% CI, 0.99-1.00; P = 0.033) upon ICU discharge as significant factors. In conclusion, a significant proportion of in-hospital mortality is predictable and those who die in hospital after ICU discharge tend to be severely-ill, with comorbidities of hematologic disease and solid tumor, and anemic and thrombocytopenic upon ICU discharge.

SUBMITTER: Lee J

PROVIDER: S-EPMC5290115 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Who Dies after ICU Discharge? Retrospective Analysis of Prognostic Factors for In-Hospital Mortality of ICU Survivors.

Lee Jungsil J Cho Young Jae YJ Kim Se Joong SJ Kim Se Joong SJ Yoon Ho Il HI Park Jong Sun JS Lee Choon Taek CT Lee Jae Ho JH Lee Yeon Joo YJ

Journal of Korean medical science 20170301 3

We investigated the causes of inpatient death after intensive care unit (ICU) discharge and determined predictors of in-hospital mortality in Korea. Using medical ICU registry data of Seoul National University Hospital, we performed a retrospective cohort study involving patients who were discharged alive from their first ICU admission with at least 24 hours of ICU length of stay (LOS). From January 2011 to August 2013, 723 patients were admitted to ICU and 383 patients were included. The estima ...[more]

PMID: 28145659

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Project description:BackgroundVariation in intensive care unit (ICU) readmissions and in-hospital mortality after ICU discharge may indicate potential for improvement and could be explained by ICU discharge practices. Our objective was threefold: (1) describe variation in rates of ICU readmissions within 48 h and post-ICU in-hospital mortality, (2) describe ICU discharge practices in Dutch hospitals, and (3) study the association between rates of ICU readmissions within 48 h and post-ICU in-hospital mortality and ICU discharge practices.MethodsWe analysed data on 42,040 admissions to 82 (91.1%) Dutch ICUs in 2011 from the Dutch National Intensive Care Evaluation (NICE) registry to describe variation in standardized ICU readmission and post-ICU mortality rates using funnel-plots. We send a questionnaire to all Dutch ICUs. 75 ICUs responded and their questionnaire data could be linked to 38,498 admissions in the NICE registry. Generalized estimation equations analyses were used to study the association between ICU readmissions and post-ICU mortality rates and the identified discharge practices, i.e. (1) ICU discharge criteria; (2) bed managers; (3) early discharge planning; (4) step-down facilities; (5) medication reconciliation; (6) verbal and written handover; (7) monitoring of post-ICU patients; and (8) consulting ICU nurses. In all analyses, the outcomes were corrected for patient-related confounding factors.ResultsThe standardized rate of ICU readmissions varied between 0.14 and 2.67 and 20.8% of the hospitals fell outside the 95% control limits and 3.6% outside the 99.8% control limits. The standardized rate of post-ICU mortality varied between 0.07 and 2.07 and 17.1% of the hospitals fell outside the 95% control limits and 4.9% outside the 99.8% control limits. We could not demonstrate an association between the eight ICU discharge practices and rates of ICU readmissions or post-ICU in-hospital mortality. Implementing a higher number of ICU discharge practices was also not associated with better patient outcomes.ConclusionsWe found both variation in patient outcomes and variation in ICU discharge practices between ICUs. However, we found no association between discharge practices and rates of ICU readmissions or post-ICU mortality. Further research is necessary to find factors, which may influence these patient outcomes, in order to improve quality of care.

Project description:BACKGROUND:Serum potassium concentrations are commonly between 3.5 and 5.0 mmol/l. Standardised protocols for potassium range and supplementation in the ICU are lacking. The purpose of this retrospective analysis of ICU patients was to investigate potassium concentrations, variability and supplementation, and their association with in-hospital mortality. METHODS:ICU patients ??18 years, with ??2 serum potassium values, treated at the Charité - Universitätsmedizin Berlin between 2006 and 2018 were eligible for inclusion. We categorised into groups of mean potassium concentrations:?<?3.0, 3.0-3.5, >?3.5-4.0, >?4.0-4.5, >?4.5-5.0, >?5.0-5.5, >?5.5 mmol/l and potassium variability: 1st, 2nd and ??3rd standard deviation (SD). We analysed the association between the particular groups and in-hospital mortality and performed binary logistic regression analysis. Survival curves were performed according to Kaplan-Meier and tested by Log-Rank. In a subanalysis, the association between potassium supplementation and in-hospital mortality was investigated. RESULTS:In 53,248 ICU patients with 1,337,742 potassium values, the lowest mortality (3.7%) was observed in patients with mean potassium concentrations between?>?3.5 and 4.0 mmol/l and a low potassium variability within the 1st SD. Binary logistic regression confirmed these results. In a subanalysis of 22,406 ICU patients (ICU admission: 2013-2018), 12,892 (57.5%) received oral and/or intravenous potassium supplementation. Potassium supplementation was associated with an increase in in-hospital mortality in potassium categories from?>?3.5 to 4.5 mmol/l and in the 1st, 2nd and ??3rd SD (p?<?0.001 each). CONCLUSIONS:ICU patients may benefit from a target range between 3.5 and 4.0 mmol/l and a minimal potassium variability. Clear potassium target ranges have to be determined. Criteria for widely applied potassium supplementation should be critically discussed. Trial registration German Clinical Trials Register, DRKS00016411. Retrospectively registered 11 January 2019, http://www.drks.de/DRKS00016411.

Project description:BackgroundSepsis is a global healthcare challenge and reliable tools are needed to identify patients and stratify their risk. Here we compare the prognostic accuracy of the sepsis-related organ failure assessment (SOFA), quick SOFA (qSOFA), systemic inflammatory response syndrome (SIRS), and national early warning system (NEWS) scores for hospital mortality and other outcomes amongst patients with suspected infection at an academic public hospital.Measurements and main results10,981 adult patients with suspected infection hospitalized at a U.S. academic public hospital between 2011-2017 were retrospectively identified. Primary exposures were the maximum SIRS, qSOFA, SOFA, and NEWS scores upon inclusion. Comparative prognostic accuracy for the primary outcome of hospital mortality was assessed using the area under the receiver operating characteristic curve (AUROC). Secondary outcomes included mortality in ICU versus non-ICU settings, ICU transfer, ICU length of stay (LOS) >3 days, and hospital LOS >7 days. Adjusted analyses were performed using a model of baseline risk for hospital mortality. 774 patients (7.1%) died in hospital. Discrimination for hospital mortality was highest for SOFA (AUROC 0.90 [95% CI, 0.89-0.91]), followed by NEWS (AUROC 0.85 [95% CI, 0.84-0.86]), qSOFA (AUROC 0.84 [95% CI, 0.83-0.85]), and SIRS (AUROC 0.79 [95% CI, 0.78-0.81]; p<0.001 for all comparisons). NEWS (AUROC 0.94 [95% CI, 0.93-0.95]) outperformed other scores in predicting ICU transfer (qSOFA AUROC 0.89 [95% CI, 0.87-0.91]; SOFA AUROC, 0.84 [95% CI, 0.82-0.87]; SIRS AUROC 0.81 [95% CI, 0.79-0.83]; p<0.001 for all comparisons). NEWS (AUROC 0.86 [95% CI, 0.85-0.86]) was also superior to other scores in predicting ICU LOS >3 days (SOFA AUROC 0.84 [95% CI, 0.83-0.85; qSOFA AUROC, 0.83 [95% CI, 0.83-0.84]; SIRS AUROC, 0.75 [95% CI, 0.74-0.76]; p<0.002 for all comparisons).ConclusionsMultivariate prediction scores, such as SOFA and NEWS, had greater prognostic accuracy than qSOFA or SIRS for hospital mortality, ICU transfer, and ICU length of stay. Complex sepsis scores may offer enhanced prognostic performance as compared to simple sepsis scores in inpatient hospital settings where more complex scores can be readily calculated.

Dataset Information

Who Dies after ICU Discharge? Retrospective Analysis of Prognostic Factors for In-Hospital Mortality of ICU Survivors.

Publications

Who Dies after ICU Discharge? Retrospective Analysis of Prognostic Factors for In-Hospital Mortality of ICU Survivors.

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