Automatic calculation of a modified APACHE II score using a patient data management system (PDMS)

Jünger, Alexander; Böttger, Sebastian; Engel, J.; Benson, Matthew A.; Michel, A.; Röhrig, Rainer; Jost, Andreas; Hempelmann, G.

doi:10.1016/s1386-5056(02)00014-x

Cited by 23 publications

(15 citation statements)

References 19 publications

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“…These and several other factors are included in the APACHE-2 score. 8 However, when this score was added to the regression model, this did not alter the findings of the current analysis, which suggests that the hyperglycemia per se is associated with impaired outcome. [9][10][11] In patients with acute myocardial infarction, hyperglycemia has been associated with higher free fatty acid concentrations, 12 insulin resistance and impaired myocardial glucose metabolism, which may result in an increased oxygen consumption and, consequently, a more severe ischemic state and larger infarct size.…”

Section: Discussionmentioning

confidence: 56%

Hyperglycemia at admission and during hospital stay are independent risk factors for mortality in high risk cardiac patients admitted to an intensive cardiac care unit

Lipton

Barendse

Domburg

et al. 2013

European Heart Journal: Acute Cardiovascular Care

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Aims: Hyperglycemia is associated with increased mortality in cardiac patients. However, the predictive value of admission-and average glucose levels in patients admitted to an intensive cardiac care unit (ICCU) has not been described. Methods: Observational study of patients admitted to the ICCU of a tertiary medical center in whom glucose levels were measured at and during admission. Over a 19-month period, 1713 patients were included. Mean age was 63±14 years, 1228 (72%) were male, 228 (17%) had known diabetes. Median (interquartile) glucose levels at admission were 7.9 (6.5-10.1) mmol/l; median glucose levels during ICCU admission (873 patients with three or more measurements) were 7.3 (6.7-8.3) mmol/l. Cox regression analysis was performed including the variables age, gender, admission diagnosis, length of stay, prior (cardio)vascular disease and diabetes. Results: A 1 mmol/l increase in admission glucose level (above 9 mmol/l) was associated with a 10% (95% confidence interval (CI): 7 -13%) increased risk for all-cause mortality. A 1 mmol/l higher average glucose level (above 8 mmol/l) was an additional independent predictor of mortality (HR 1.11, 95% CI: 1.03 -1.20). At 30 days, 16.8% (97/579) of the patients with an admission glucose level in the highest tertile (>9.8 mmol/L) had died vs 5.2% (59/1134) of those with a lower admission glucose level. Conclusion: In a high risk ICCU population, both high admission glucose levels as well as high average glucose levels during hospitalization were independently associated with increased mortality, even when accounting for other risk factors and parameters of disease severity.

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Section: Discussionmentioning

confidence: 56%

Hyperglycemia at admission and during hospital stay are independent risk factors for mortality in high risk cardiac patients admitted to an intensive cardiac care unit

Lipton

Barendse

Domburg

et al. 2013

European Heart Journal: Acute Cardiovascular Care

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“…Several other clinical scores have been automated for clinical practice—examples include APACHE II [32,33], APACHE IV [34], CHA 2 DS 2 -VASc [35], Charlson comorbidity score [36], and early warning systems [37]. These studies primarily focused on algorithm validation rather than information delivery.…”

Section: Discussionmentioning

confidence: 99%

Iterative User Interface Design for Automated Sequential Organ Failure Assessment Score Calculator in Sepsis Detection

Aakre¹,

Kitson²,

Li³

et al. 2017

JMIR Hum Factors

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BackgroundThe new sepsis definition has increased the need for frequent sequential organ failure assessment (SOFA) score recalculation and the clerical burden of information retrieval makes this score ideal for automated calculation.ObjectiveThe aim of this study was to (1) estimate the clerical workload of manual SOFA score calculation through a time-motion analysis and (2) describe a user-centered design process for an electronic medical record (EMR) integrated, automated SOFA score calculator with subsequent usability evaluation study.MethodsFirst, we performed a time-motion analysis by recording time-to-task-completion for the manual calculation of 35 baseline and 35 current SOFA scores by 14 internal medicine residents over a 2-month period. Next, we used an agile development process to create a user interface for a previously developed automated SOFA score calculator. The final user interface usability was evaluated by clinician end users with the Computer Systems Usability Questionnaire.ResultsThe overall mean (standard deviation, SD) time-to-complete manual SOFA score calculation time was 61.6 s (33). Among the 24% (12/50) usability survey respondents, our user-centered user interface design process resulted in >75% favorability of survey items in the domains of system usability, information quality, and interface quality.ConclusionsEarly stakeholder engagement in our agile design process resulted in a user interface for an automated SOFA score calculator that reduced clinician workload and met clinicians’ needs at the point of care. Emerging interoperable platforms may facilitate dissemination of similarly useful clinical score calculators and decision support algorithms as “apps.” A user-centered design process and usability evaluation should be considered during creation of these tools.

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“…Automated calculation of the APACHE II score from EMRs in the ICU has been attempted previously. Junger and colleagues at University Hospital Giessen, Germany,20 used SQL scripts on a dataset of 524 patients. In their retrospective study, physiological parameters and age were extracted directly from the EMR database, and International Classification of Diseases, Version 9 (ICD-9) was used to map chronic diseases.…”

Section: Discussionmentioning

confidence: 99%

“…In their retrospective study, physiological parameters and age were extracted directly from the EMR database, and International Classification of Diseases, Version 9 (ICD-9) was used to map chronic diseases. The AUC for the automatically calculated modified APACHE II score was 0.790 (95% CI 0.712 to 0.825) and a goodness-of-fit test showed good calibration 20. They found all the acute physiological parameters easy to collect, but chronic health conditions, which are entered manually as free text by the medical personnel, were difficult to map.…”

Section: Discussionmentioning

confidence: 99%

Mapping physicians' admission diagnoses to structured concepts towards fully automatic calculation of acute physiology and chronic health evaluation score

et al. 2011

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ObjectiveAcute Physiology and Chronic Health Evaluation (APACHE) is most widely used as a mortality prediction score in US intensive care units (ICUs), but its calculation is onerous. The authors aimed to develop and validate automatic mapping of physicians' admission diagnoses to structured concepts for automated APACHE IV calculation.MethodsThis retrospective study was conducted in medical ICUs of a tertiary healthcare and academic centre. Boolean-logic text searches were used to map admission diagnoses, and these were compared with conventional APACHE database entry by bedside nurses and a gold-standard physician chart review. The primary outcome was APACHE IV predicted hospital mortality. The tool was developed in a larger cohort of ICU patients.ResultsIn a derivation cohort of 192 consecutive critically ill patients, the diagnosis coefficient coded by three different methods had a positive correlation, highest between manual and gold standard (r2=0.95; mean square error (MSE)=0.040) and least between manual and automatic tool (r2=0.88; MSE=0.066). The automatic tool had an area under the curve (95% CI) value of 0.82 (0.74 to 0.90) which was similar to the physician gold standard, 0.83 (0.75 to 0.91) and standard manual entry, 0.81 (0.73 to 0.89). The Hosmer–Lemeshow goodness-of-fit test demonstrated good calibration of automatically calculated APACHE IV score (χ2=6.46; p=0.6). The automatic tool demonstrated excellent discrimination with an area under the curve value of 0.87 (95% CI 0.83 to 0.92) and good calibration (p=0.58) in the validation cohort of 593 patients.ConclusionA Boolean-logic text search is an efficient alternative to manual database entry for mapping of ICU admission diagnosis to structured APACHE IV concepts.

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Automatic calculation of a modified APACHE II score using a patient data management system (PDMS)

Cited by 23 publications

References 19 publications

Hyperglycemia at admission and during hospital stay are independent risk factors for mortality in high risk cardiac patients admitted to an intensive cardiac care unit

Hyperglycemia at admission and during hospital stay are independent risk factors for mortality in high risk cardiac patients admitted to an intensive cardiac care unit

Iterative User Interface Design for Automated Sequential Organ Failure Assessment Score Calculator in Sepsis Detection

Mapping physicians' admission diagnoses to structured concepts towards fully automatic calculation of acute physiology and chronic health evaluation score

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