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Impact of Missing Physiologic Data on Performance of the Simplified Acute Physiology Score 3 Risk-Prediction Model*

Engerström, Lars (author)
Linköpings universitet,Institutionen för medicin och hälsa,Medicinska fakulteten,Region Östergötland, Anestesi- och intensivvårdskliniken VIN,Region Östergötland, Thorax-kärlkliniken i Östergötland
Nolin, Thomas (author)
Central Hospital Kristianstad, Sweden
Mårdh, Caroline (author)
Landstinget Värmland, Sweden
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Sjöberg, Folke (author)
Linköpings universitet,Avdelningen för Kirurgi, Ortopedi och Onkologi,Medicinska fakulteten,Region Östergötland, Hand- och plastikkirurgiska kliniken US
Karlström, Göran (author)
Landstinget Varmland, Sweden
Fredrikson, Mats (author)
Linköpings universitet,Avdelningen för neuro- och inflammationsvetenskap,Forum Östergötland
Walther, Sten (author)
Linköpings universitet,Avdelningen för kardiovaskulär medicin,Medicinska fakulteten,Region Östergötland, Thorax-kärlkliniken i Östergötland
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 (creator_code:org_t)
Lippincott Williams & Wilkins, 2017
2017
English.
In: Critical Care Medicine. - : Lippincott Williams & Wilkins. - 0090-3493 .- 1530-0293. ; 45:12, s. 2006-2013
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

Keyword

intensive care unit; intensive care unit mortality; health status indicator; risk adjustment; severity of illness

Publication and Content Type

ref (subject category)
art (subject category)

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