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Impact of Missing Physiologic Data on Performance of the Simplified Acute Physiology Score 3 Risk-Prediction Model*
Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Anaesthesiology and Intensive Care in Norrköping. Region Östergötland, Heart and Medicine Center, Department of Thoracic and Vascular Surgery.
Central Hospital Kristianstad, Sweden.
Landstinget Värmland, Sweden.
Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. 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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2017 (English)In: Critical Care Medicine, ISSN 0090-3493, E-ISSN 1530-0293, Vol. 45, no 12, p. 2006-2013Article in journal (Refereed) Published
Abstract [en]

Objectives: The Simplified Acute Physiology 3 outcome prediction model has a narrow time window for recording physiologic measurements. Our objective was to examine the prevalence and impact of missing physiologic data on the Simplified Acute Physiology 3 models performance. Design: Retrospective analysis of prospectively collected data. Setting: Sixty-three ICUs in the Swedish Intensive Care Registry. Patients: Patients admitted during 2011-2014 (n = 107,310). Interventions: None. Measurements and Main Results: Model performance was analyzed using the area under the receiver operating curve, scaled Briers score, and standardized mortality rate. We used a recalibrated Simplified Acute Physiology 3 model and examined model performance in the original dataset and in a dataset of complete records where missing data were generated (simulated dataset). One or more data were missing in 40.9% of the admissions, more common in survivors and low-risk admissions than in nonsurvivors and high-risk admissions. Discrimination did not decrease with one to two missing variables, but accuracy was highest with no missing data. Calibration was best in the original dataset with a mix of full records and records with some missing values (area under the receiver operating curve was 0.85, scaled Brier 27%, and standardized mortality rate 0.99). With zero, one, and two data missing, the scaled Brier was 31%, 26%, and 21%; area under the receiver operating curve was 0.84, 0.87, and 0.89; and standardized mortality rate was 0.92, 1.05 and 1.10, respectively. Datasets where the missing data were simulated for oxygenation or oxygenation and hydrogen ion concentration together performed worse than datasets with these data originally missing. Conclusions: There is a coupling between missing physiologic data, admission type, low risk, and survival. Increased loss of physiologic data reduced model performance and will deflate mortality risk, resulting in falsely high standardized mortality rates.

Place, publisher, year, edition, pages
Lippincott Williams & Wilkins, 2017. Vol. 45, no 12, p. 2006-2013
Keywords [en]
intensive care unit; intensive care unit mortality; health status indicator; risk adjustment; severity of illness
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:liu:diva-143619DOI: 10.1097/CCM.0000000000002706ISI: 000416235200040PubMedID: 28906285OAI: oai:DiVA.org:liu-143619DiVA, id: diva2:1165614
Note

Funding Agencies|Swedish Intensive Care Registry; Department of Cardiothoracic Anaesthesia and Intensive Care, University Hospital, Linkoping, Sweden

Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2018-09-17Bibliographically approved
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)
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Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2018-09-17Bibliographically approved

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Engerström, LarsSjöberg, FolkeFredrikson, MatsWalther, Sten
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Department of Medical and Health SciencesFaculty of Medicine and Health SciencesDepartment of Anaesthesiology and Intensive Care in NorrköpingDepartment of Thoracic and Vascular SurgeryDivision of Surgery, Orthopedics and OncologyDepartment of Hand and Plastic SurgeryDivision of Neuro and Inflammation ScienceForum ÖstergötlandDivision of Cardiovascular Medicine
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