Dataset Information

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

ABSTRACT:

Objectives

To develop a scoring system model that predicts mortality within 30?days of admission of patients older than 80?years admitted to intensive care units (ICUs).

Design

Prospective cohort study.

Setting

A total of 306 ICUs from 24 European countries.

Participants

Older adults admitted to European ICUs (N?=?3730; median age?=?84?years [interquartile range?=?81-87 y]; 51.8% male).

Measurements

Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves.

Results

The 30-day-mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30-day mortality in 91.1% of all patients who die.

Conclusion

A predictive model of cumulative events predicts 30-day mortality in patients older than 80?years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision-making capacity.

SUBMITTER: de Lange DW

PROVIDER: S-EPMC6850576 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

de Lange Dylan W DW Brinkman Sylvia S Flaatten Hans H Boumendil Ariane A Morandi Alessandro A Andersen Finn H FH Artigas Antonio A Bertolini Guido G Cecconi Maurizio M Christensen Steffen S Faraldi Loredana L Fjølner Jesper J Jung Christian C Marsh Brian B Moreno Rui R Oeyen Sandra S Öhman Christina Agvald CA Bollen Pinto Bernardo B de Smet Anne Marie G A AMGA Soliman Ivo W IW Szczeklik Wojciech W Valentin Andreas A Watson Ximena X Zafeiridis Tilemachos T Guidet Bertrand B

Journal of the American Geriatrics Society 20190412 6

<h4>Objectives</h4>To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs).<h4>Design</h4>Prospective cohort study.<h4>Setting</h4>A total of 306 ICUs from 24 European countries.<h4>Participants</h4>Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81-87 y]; 51.8% male).<h4>Measurements</h4>Overall, 24 variables available during ICU admission were inc ...[more]

PMID: 30977911

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Project description:BackgroundPremorbid conditions influence the outcome of acutely ill adult patients aged 80 years and over who are admitted to the ICU. The aim of this study was to determine the influence of such premorbid conditions on 6 month survival.MethodsProspective cohort study in 242 ICUs from 22 countries including patients 80 years or above, admitted over a 6 months period to an ICU between May 2018 and May 2019. Only emergency (acute) ICU admissions in adult patients ≥ 80 years of age were eligible. Patients who were admitted after planned/elective surgery were excluded. We measured the Clinical Frailty Scale (CFS), the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), disability with the Katz activities of daily living (ADL) score, comorbidities and a Polypharmacy Score (CPS).ResultsOverall, the VIP2 study included 3920 patients. During ICU stay 1191 patients died (30.9%), and another 436 patients (11.1%) died after ICU discharge but within the first 30 days of admission, and an additional 895 patients died hereafter but within the first 6 months after admission (22.8%). The 6 months mortality was 64%. The median CFS was 4 (IQR 3-6). Frailty (CFS ≥ 5) was present in 26.6%. Cognitive decline (IQCODE above 3.5) was found in 30.2%. The median IQCODE was 3.19. A Katz ADL of 4 or less was present in 27.7%. Patients who surviving > 6 months were slightly younger (median age survivors 84 with IQR 81-86) than patients dying within the first 6 months (median age 84, IQR 82-87, p = 0.013), were less frequently frail (CFS > 5 in 19% versus 34%, p < 0.01) and were less dependent based on their Katz activities of daily living measurement (median Katz score 6, IQR 5-6 versus 6 points, IQR 3-6, p < 0.01).ConclusionsWe found that Clinical Frailty Scale, age, and SOFA at admission were independent prognostic factors for 6 month mortality after ICU admission in patients age 80 and above. Adding other geriatric syndromes and scores did not improve the model. This information can be used in shared-decision making.Clinicaltrialsgov: NCT03370692.

Project description:Aims/hypothesisHospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia.MethodsWe used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score ('base' model). In the second model, we added to the 'base' model the 20 most common medical conditions and applied a stepwise backward selection of variables ('disease' model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics.ResultsIn derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models.Conclusions/interpretationThis simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia.

Dataset Information

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

Objectives

Design

Setting

Participants

Measurements

Results

Conclusion

Publications

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

Similar Datasets

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