A model to predict short-term death or readmission after intensive care unit discharge

doi:10.1016/j.jcrc.2011.08.003

Journal of Critical Care

Volume 27, Issue 4, August 2012, Pages 422.e1-422.e9

https://doi.org/10.1016/j.jcrc.2011.08.003 Get rights and content

Abstract

Objective

Early unplanned readmission to the intensive care unit (ICU) carries a poor prognosis, and post-ICU mortality may be related, in part, to premature ICU discharge. Our objectives were to identify independent risk factors for early post-ICU readmission or death and to construct a prediction model.

Design

Retrospective analysis of a prospective database was done.

Setting

Four ICUs of the French Outcomerea network participated.

Patients

Patients were consecutive adults with ICU stay longer than 24 hours who were discharged alive to same-hospital wards without treatment-limitation decisions.

Main results

Of 5014 admitted patients, 3462 met our inclusion criteria. Age was 60.6 ± 17.6 years, and admission Simplified Acute Physiology Score II (SAPS II) was 35.1 ± 15.1. The rate of death or ICU readmission within 7 days after ICU discharge was 3.0%. Independent risk factors for this outcome were age, SAPS II at ICU admission, use of a central venous catheter in the ICU, Sepsis-related Organ Failure Assessment and Systemic Inflammatory Response Syndrome scores before ICU discharge, and discharge at night. The predictive model based on these variables showed good calibration. Compared with SAPS II at admission or Stability and Workload Index for Transfer at discharge, discrimination was better with our model (area under receiver operating characteristics curve, 0.74; 95% confidence interval, 0.68-0.79).

Conclusion

Among patients without treatment-limitation decisions and discharged alive from the ICU, 3.0% died or were readmitted within 7 days. Independent risk factors were indicators of patients' severity and discharge at night. Our prediction model should be evaluated in other ICU populations.

Introduction

Many critically ill patients experience clinical deterioration or death shortly after discharge from the intensive care unit (ICU). In earlier studies, 8% to 10% of patients discharged from the ICU died or required ICU readmission during the same hospital stay [1], [2], [3], [4], [5]. Studies have demonstrated that ICU discharge decisions depend on organizational factors such as workload and ICU bed availability [6], [7], [8]. Furthermore, premature ICU discharge was responsible for 22% to 42% of readmissions [9], [10] and has led to rank ICU readmission among the top indicators for ICU quality [11]. Intensive care unit readmission has been associated with worsening of the original disease process, higher hospital costs, and increase in hospital mortality [5], [7], [10], [12]. Therefore, knowledge of the risk factors for ICU readmission may help to identify high-risk patients before determining whether discharge is appropriate [7], [9], [12]. A rating scale based on the subjective prognosis by attending intensivists has been reported to predict hospital mortality after ICU discharge [13]. In several countries, critical care outreach teams are available for assessing patients being considered for ICU admission [14]. A tool based on objective data could help discriminating which patients should not be discharged without risk of bad outcome and/or should undergo a special surveillance after the ICU stay [15]. Our objective was to develop a tool for prediction of early ICU readmission or death during the same hospital stay of patients without treatment-limitation decisions. Using a population-based cohort ICU records, we hypothesized that objective factors could discriminate between patients who were and were not likely to die or be readmitted to the ICU within 7 days of ICU discharge.

Section snippets

Methods

We retrospectively studied a prospective cohort of patients from 4 ICUs (named A, B, C, and D) in tertiary care hospitals filling the Outcomerea database. A is a 10-bed medical surgical ICU; B, an 18-bed medical ICU; C, a 10-bed surgical ICU; and D, a 14-bed medical surgical ICU. A and C are located in the same 460-bed nonprofit private hospital, B is located in a 1500-bed public university-affiliated hospital, and D is located in a 600-bed public hospital. In each center, the study was active

Study population

The patient selection is shown on Fig. 1. The age of the 3462 patients included in the study was 60.6 ± 17.6 years, and their admission SAPS II was 35.1 ± 15.1 points. Before ICU admission, they were either in a ward (51.4%), at the emergency department (47.9%), or at home (0.6%). The most common reasons for ICU admission were acute respiratory failure (21.1%), shock (18.1%), coma (13.3%), and acute renal failure (5.2%).

Of the 3462 patients, 224 (6.5%) were either readmitted or died during the

Discussion

In our cohort of patients without treatment-limitation decisions, the incidence of death or ICU readmission after ICU discharge was 6.6%. The incidence was 3.0% within 7 days of ICU discharge, and independent risk factors for this outcome were the patients' age, indicators of severity during the ICU stay, sepsis, organ dysfunction at ICU discharge, and discharge at night. We developed a probability model that predicted death or ICU readmission within 7 days after ICU discharge with good

Conclusion

In a cohort of ICU patients without treatment-limitation decisions, the rate of death or ICU readmission within 7 days after ICU discharge was 3.0%. The independent risk factors for this outcome reflect disease severity during the ICU stay and discharge at night. They are easy to identify in current databases. Our MIR probability model is well calibrated and better in terms of discrimination than prior published models. Its validity should be evaluated in other ICU populations.

The following are

Contributions of the authors to the manuscript

Conception and design: IO, JFT, and BM.

Acquisition of data, analysis and interpretation of data: All.

Participation in writing the article: IO, CS, AF, MGO, JFT, and BM.

Critical revision for intellectual content: All.

Approval of version to be published: All.

References (29)

A.J. Campbell et al.
Predicting death and readmission after intensive care discharge
Br J Anaesth
(2008)
S.A. Frost et al.
Severity of illness and risk of readmission to intensive care: a meta-analysis
Resuscitation
(2009)
R.F. Alban et al.
Readmission to surgical intensive care increases severity-adjusted patient mortality
J Trauma
(2006)
G.S. Cooper et al.
Are readmissions to the intensive care unit a useful measure of hospital performance?
Med Care
(1999)
K.M. Ho et al.
Bayesian approach to predict hospital mortality of intensive care readmissions during the same hospitalisation
Anaesth Intensive Care
(2008)
P.G. Metnitz et al.
Critically ill patients readmitted to intensive care units—lessons to learn?
Intensive Care Med
(2003)
L.M. Chen et al.
Patients readmitted to the intensive care unit during the same hospitalization: clinical features and outcomes
Crit Care Med
(1998)
C.G. Durbin et al.
A case-control study of patients readmitted to the intensive care unit
Crit Care Med
(1993)
C.J. Wu et al.
Reconsidering the transfer of patients from the intensive care unit to the ward: a case study approach
Nurs Health Sci
(2007)
W. Baigelman et al.
Patient readmission to critical care units during the same hospitalization at a community teaching hospital
Intensive Care Med
(1983)

N. Snow et al.

Readmission of patients to the surgical intensive care unit: patient profiles and possibilities for prevention

Crit Care Med

(1985)

Guidelines for intensive care unit admission, discharge, and triage. Task Force of the American College of Critical Care Medicine, Society of Critical Care Medicine

Crit Care Med

(1999)

C. Franklin et al.

Discharge decision-making in a medical ICU: characteristics of unexpected readmissions

Crit Care Med

(1983)

R. Fernandez et al.

A modified McCabe score for stratification of patients after intensive care unit discharge: the Sabadell score

Crit Care

(2006)

Cited by (80)

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We retrospectively analyzed patients admitted to the surgical intensive care unit (2020–2021) and categorized them as either with or without unplanned readmission.
Of 1,112 patients in the derivation cohort, 76 (6.8%) experienced unplanned surgical intensive care unit readmission, with sepsis being the leading cause of readmission (35.5%). Patients who were readmitted had significantly higher in-hospital mortality rates than those who were not. Multivariate analysis identified congestive heart failure, high Sequential Organ Failure Assessment–Hepatic score, use of carbapenem during surgical intensive care unit stay, as well as factors before surgical intensive care unit discharge such as inadequate glycemic control, positive fluid balance, low partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio, and receipt of total parenteral nutrition as independent predictors for unplanned readmission. The scoring model developed using these predictors exhibited good discrimination between readmitted and non-readmitted patients, with an area under the curve of 0.74. The observed rates of unplanned readmission for scores of <4 points and ≥4 points were 4% and 20.2% (P < .001), respectively. The model also demonstrated good performance in the validation cohort, with an area under the curve of 0.74 and 19% observed unplanned readmission rate for scores ≥4 points.
Besides congestive heart failure, clinicians should meticulously re-evaluate critical variables such as the Sequential Organ Failure Assessment–Hepatic score, partial pressure of oxygen in arterial blood/fraction of inspired oxygen ratio, glycemic control, and fluid status before releasing the patient from the surgical intensive care unit. It is crucial to determine the reasons for using carbapenems during surgical intensive care unit stay and the causes for the inability to discontinue total parenteral nutrition before discharging the patient from the surgical intensive care unit.
Optimal discharge of patients from intensive care via a data-driven policy learning framework
2023, Operations Research for Health Care
Clinical decision support tools rooted in machine learning and optimization can provide significant value to healthcare providers through better management of intensive care units. In particular, it is important that intensive care unit patient discharge decisions account for the nuanced trade-off between decreasing the length of stay and the risk of readmission or death after discharge of a patient. This work introduces a comprehensive framework (i.e., not geared towards any particular disease or condition) for capturing this trade-off and to recommend optimal discharge timing decisions given the electronic health records of a patient. A data-driven approach is used to derive a parsimonious, discrete state space representation to represent the physiological condition of a given patient. Based on this model and a given cost function, an infinite-horizon discounted Markov decision process is formulated and solved numerically to compute an optimal discharge policy, whose performance is assessed using off-policy evaluation strategies. Extensive numerical experiments are performed to validate the proposed framework using real-life intensive care unit patient data.
Developing a reflection and analysis tool (We-ReAlyse) for readmissions to the intensive care unit: A quality improvement project
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Readmissions to the intensive care unit are associated with poorer patient outcomes and health prognoses, alongside increased lengths of stay and mortality risk. To improve quality of care and patients’ safety, it is essential to understand influencing factors relevant to specific patient populations and settings. A standardized tool for systematic retrospective analysis of readmissions would help healthcare professionals understand risks and reasons affecting readmissions; however, no such tool exists.
This study’s purpose was to develop a tool (We-ReAlyse) to analyze readmissions to the intensive care unit from general units by reflecting on affected patients’ pathways from intensive care discharge to readmission. The results will highlight case-specific causes of readmission and potential areas for departmental- and institutional-level improvements.
A root cause analysis approach guided this quality improvement project. The tool’s iterative development process included a literature search, a clinical expert panel, and a testing in January and February 2021.
The We-ReAlyse tool guides healthcare professionals to identify areas for quality improvement by reflecting the patient's pathway from the initial intensive care stay to readmission. Ten readmissions were analyzed by using the We-ReAlyse tool, resulting in key insights about possible root causes like the handover process, patient's care needs, the resources on the general unit and the use of different electronic healthcare record systems.
The We-ReAlyse tool provides a visualization/objectification of issues related to intensive care readmissions, gathering data upon which to base quality improvement interventions. Based on the information on how multi-level risk profiles and knowledge deficits contribute to readmission rates, nurses can target specific quality improvements to reduce those rates.
With the We-ReAlyse tool, we have the opportunity to collect detailed information about ICU readmissions for an in-depth analysis. This will allow health professionals in all involved departments to discuss and either correct or cope with the identified issues. In the long term, this will allow continuous, concerted efforts to reduce and prevent ICU readmissions. To obtain more data for analysis and to further refine and simplify the tool, it may be applied to larger samples of ICU readmissions. Furthermore, to test its generalizability, the tool should be applied to patients from other departments and other hospitals. Adapting it to an electronic version would facilitate the timely and comprehensive collection of necessary information. Finally, the tool’s emphasis comprises reflecting on and analyzing ICU readmissions, allowing clinicians to develop interventions targeting the identified problems. Therefore, future research in this area will require the development and evaluation of potential interventions.
Colombian consensus of criteria for intensive care admission: Task force of the Colombian Association of Critical Medicine and Intensive Care (AMCI®)
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Es importante estandarizar los procesos de ingreso a las UCI (UCI), y las prácticas establecidas por consenso determinan intervenciones de calidad que pueden potenciar contextos específicos. Las UCI son servicios finitos de alto costo que requieren políticas para garantizar el uso racional y poder brindar atención de calidad a los pacientes. En respuesta a la variabilidad de los criterios de ingreso a la UCI en el país, la Asociación Colombiana de Medicina Crítica y Cuidados Intensivos (AMCI) convocó un equipo multidisciplinario de expertos en medicina crítica para establecer un pronunciamiento científico mediante la metodología de consenso formal, principalmente por el método Delphi, acerca de las recomendaciones y prácticas que permitan homogenizar los criterios de ingreso a las UCI en Colombia. Como parte de la misión de la AMCI, se pretende impactar positivamente en los distintos niveles del sistema de salud (prestadores, administradores, aseguradores y gobierno) y que al final se refleje en beneficios para los pacientes críticos o en riesgo. El consenso invita a todas las UCI del país a seleccionar sus propios criterios teniendo en cuenta el listado de las recomendaciones que contiene; se aclara que los contenidos son generados en un contexto científico, académico y no comercial. Cada institución de salud debe ser garante, mediante la coordinación asistencial de las UCI, del uso responsable de estos criterios tanto para la atención segura y de calidad de los pacientes como de utilizarlos para las distintas relaciones comerciales que se establezcan con las administradoras de los regímenes de salud. Este documento tiene un alcance nacional y su contenido está previsto actualizarlo en un tiempo no mayor a 4 años.
It is important to standardize the admission processes to the Intensive Care Units (ICU), and the practices established by consensus determine quality interventions that can enhance specific contexts. ICU are finite, high-cost services that require policies to ensure rational use and to provide quality care to patients. In response to the variability of ICU admission criteria in the country, the Colombian Association of Critical Medicine and Intensive Care (AMCI) convened a multidisciplinary team of experts in critical medicine to establish a scientific statement using the formal consensus methodology, mainly by the Delphi method, about the recommendations and practices that allow to homogenize the criteria for admission to ICU in Colombia. As part of the mission of the AMCI, it is intended to have a positive impact on the different levels of the health system — providers, administrators, insurers and government — and that in the end it will be reflected in benefits for critically ill or at-risk patients. The consensus invites all ICU in the country to select their own criteria taking into account the list of recommendations it contains; it is clarified that the contents are generated in a scientific, academic and non-commercial context. Each health institution must be a guarantor, through the care coordination of the ICUs, of the responsible use of these criteria both for the safe and quality care of patients and to use them for the different commercial relationships established with the administrators of the regimens of health. This document has a national scope and its content is expected to be updated in no more than 4 years.
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Los pacientes críticos, a pesar de la recuperación inicial en la unidad de cuidados intensivos (UCI), pueden precisar reingreso en UCI o incluso fallecer en el mismo episodio hospitalario. Los objetivos son: conocer la incidencia e identificar factores de riesgo de reingreso en UCI, y determinar la mortalidad hospitalaria.
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^☆: Conflicts of interest: The authors have no personal or financial conflicts of interest to declare.

^☆☆: Financial support: Outcomerea is supported by nonexclusive educational grants from Pfizer, Aventis Pharma France, Wyeth France, and Ely Lilly and by public grants from the Centre National de la Recherche Scientifique and Institut National de la Santé et la Recherche Medicale. The Outcomerea data warehouse project was also supported by a grant from the Agence Nationale de Valorisation de la Recherche. These grants had no role in the design or conduct of the study; the collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript.

^★: The results reported in this manuscript were presented, in part, at the 2009 annual meetings of the French Society of Intensive Care Medicine, French Society of Anesthesia and Intensive Care, and European Society of Intensive Care Medicine.

¹: The members of the Outcomerea Study Group are listed in the appendix.

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Prognosis and OutcomesA model to predict short-term death or readmission after intensive care unit discharge☆,☆☆,★

Abstract

Objective

Design

Setting

Patients

Main results

Conclusion

Introduction

Section snippets

Methods

Study population

Discussion

Conclusion

Contributions of the authors to the manuscript

Br J Anaesth

Resuscitation

Readmission to surgical intensive care increases severity-adjusted patient mortality

J Trauma

Are readmissions to the intensive care unit a useful measure of hospital performance?

Med Care

Bayesian approach to predict hospital mortality of intensive care readmissions during the same hospitalisation

Anaesth Intensive Care

Critically ill patients readmitted to intensive care units—lessons to learn?

Intensive Care Med

Patients readmitted to the intensive care unit during the same hospitalization: clinical features and outcomes

Crit Care Med

A case-control study of patients readmitted to the intensive care unit

Crit Care Med

Reconsidering the transfer of patients from the intensive care unit to the ward: a case study approach

Nurs Health Sci

Patient readmission to critical care units during the same hospitalization at a community teaching hospital

Intensive Care Med

Readmission of patients to the surgical intensive care unit: patient profiles and possibilities for prevention

Crit Care Med

Guidelines for intensive care unit admission, discharge, and triage. Task Force of the American College of Critical Care Medicine, Society of Critical Care Medicine

Crit Care Med

Discharge decision-making in a medical ICU: characteristics of unexpected readmissions

Crit Care Med

A modified McCabe score for stratification of patients after intensive care unit discharge: the Sabadell score

Crit Care

Prognosis and Outcomes
A model to predict short-term death or readmission after intensive care unit discharge☆,☆☆,★