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Comparing Time-Fixed Mortality Prediction Models and Their Effect on ICU Performance Metrics Using the Simplified Acute Physiology Score 3.
Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Thoracic and Vascular Surgery. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Anaesthesiology and Intensive Care in Norrköping.
Prescient Healthcare Consulting, Charlottesville, VA.
The Swedish Intensive Care Registry, Karlstad, Sweden.
Linköping University, Department of Clinical and Experimental Medicine, Division of Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
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2016 (English)In: Critical Care Medicine, ISSN 0090-3493, E-ISSN 1530-0293, Vol. 44, no 11Article in journal (Refereed) Published
Abstract [en]

OBJECTIVES: To examine ICU performance based on the Simplified Acute Physiology Score 3 using 30-day, 90-day, or 180-day mortality as outcome measures and compare results with 30-day mortality as reference.

DESIGN: Retrospective cohort study of ICU admissions from 2010 to 2014.

SETTING: Sixty-three Swedish ICUs that submitted data to the Swedish Intensive Care Registry.

PATIENTS: The development cohort was first admissions to ICU during 2011-2012 (n = 53,546), and the validation cohort was first admissions to ICU during 2013-2014 (n = 57,729).

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Logistic regression was used to develop predictive models based on a first level recalibration of the original Simplified Acute Physiology Score 3 model but with 30-day, 90-day, or 180-day mortality as measures of outcome. Discrimination and calibration were excellent for the development dataset. Validation in the more recent 2013-2014 database showed good discrimination (C-statistic: 0.85, 0.84, and 0.83 for the 30-, 90-, and 180-d models, respectively), and good calibration (standardized mortality ratio: 0.99, 0.99, and 1.00; Hosmer-Lemeshow goodness of fit H-statistic: 66.4, 63.7, and 81.4 for the 30-, 90-, and 180-d models, respectively). There were modest changes in an ICU's standardized mortality ratio grouping (< 1.00, not significant, > 1.00) when follow-up was extended from 30 to 90 days and 180 days, respectively; about 11-13% of all ICUs.

CONCLUSIONS: The recalibrated Simplified Acute Physiology Score 3 hospital outcome prediction model performed well on long-term outcomes. Evaluation of ICU performance using standardized mortality ratio was only modestly sensitive to the follow-up time. Our results suggest that 30-day mortality may be a good benchmark of ICU performance. However, the duration of follow-up must balance between what is most relevant for patients, most affected by ICU care, least affected by administrative policies and practically feasible for caregivers.

Place, publisher, year, edition, pages
Lippincott Williams & Wilkins, 2016. Vol. 44, no 11
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:liu:diva-133991DOI: 10.1097/CCM.0000000000001877ISI: 000400824800003PubMedID: 27513546OAI: oai:DiVA.org:liu-133991DiVA, id: diva2:1066097
Note

Funding agencies: Swedish Intensive Care Registry, SIR

Available from: 2017-01-17 Created: 2017-01-17 Last updated: 2018-09-17
In thesis
1. The significance of risk adjustment for the assessment of results in intensive care.: An analysis of risk adjustment models used in Swedish intensive care.
Open this publication in new window or tab >>The significance of risk adjustment for the assessment of results in intensive care.: An analysis of risk adjustment models used in Swedish intensive care.
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

To study the development of mortality in intensive care over time or compare different departments, you need some kind of risk adjustment to make analysis meaningful since patient survival varies with severity of the disease. With the aid of a risk adjustment model, expected mortality can be calculated. The actual mortality rate observed can then be compared to the expected mortality rate, giving a risk-adjusted mortality.

In-hospital mortality is commonly used when calculating riskadjusted mortality following intensive care, but in-hospital mortality is affected by the duration of care and transfer between units. Time-fixed measurements such as 30-day mortality are less affected by this and are a more objective measure, but the intensive care models that are available are not adapted for this measure. Furthermore, how length of follow-up affects risk adjusted mortality has not been studied. The degree and pattern of loss of physiological data that exists and how this affects performance of the model has not been properly studied. General intensive care models perform poorly for cardiothoracic intensive care where admission is often planned, where cardiovascular physiology is more affected by extra corporeal circulation and where the reasons for admission are usually not the same.

The model used in Sweden for adult general intensive care patients is the Simplified Acute Physiology Score 3 (SAPS3). SAPS3 recalibrations were made for in-hospital mortality and 30-, 90- and 180-day mortality. Missing data were simulated, and the resulting performance compared to performance in datasets with originally missing data.

We conclude that SAPS3 works equally well using 30-day mortality as in-hospital mortality.

The performance with both 90- and 180-day mortality as outcome was also good. It was found that the model was stable when validated in other patients than it was recalibrated with.

We conclude that the amount of data missing in the SIR has a limited effect on model performance, probably because of active data selection based on the patient's status and reason for admission.

A model for cardiothoracic intensive care based on variables available on arrival at Swedish cardiothoracic intensive care units was developed and found to perform well.  

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 88
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1637
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:liu:diva-151308 (URN)10.3384/diss.diva-151308 (DOI)9789176852286 (ISBN)
Public defence
2018-10-12, Fornborgen, Vrinnevisjukhuset, Norrköping, 09:00 (English)
Opponent
Supervisors
Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2019-09-30Bibliographically approved

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Sjöberg, FolkeFredrikson, MatsWalther, Sten M
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Department of Medical and Health SciencesFaculty of Medicine and Health SciencesDepartment of Thoracic and Vascular SurgeryDepartment of Anaesthesiology and Intensive Care in NorrköpingDivision of Clinical SciencesDepartment of Hand and Plastic SurgeryDivision of Neuro and Inflammation ScienceDivision of Cardiovascular Medicine
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