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

Objective Acute 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. Methods This 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. Results In 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. Conclusion A Boolean-logic text search is an efficient alternative to manual database entry for mapping of ICU admission diagnosis to structured APACHE IV concepts.

[1]  W. Knaus,et al.  The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. , 1991, Chest.

[2]  K. Polderman,et al.  Using risk adjustment systems in the ICU: avoid scoring an “own goal” , 2005, Intensive Care Medicine.

[3]  K. Polderman,et al.  Inter-observer variability in APACHE II scoring: effect of strict guidelines and training , 2001, Intensive Care Medicine.

[4]  V. Herasevich,et al.  Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. , 2010, Mayo Clinic proceedings.

[5]  S. Lemeshow,et al.  Modeling the severity of illness of ICU patients. A systems update. , 1994, JAMA.

[6]  J. L. Gall,et al.  APACHE II--a severity of disease classification system. , 1986, Critical care medicine.

[7]  K. Polderman,et al.  Intra-observer variability in APACHE II scoring , 2001, Intensive Care Medicine.

[8]  Nicolette de Keizer,et al.  Cross-Mapping APACHE IV "Reasons for Intensive Care Admission" Classification to SNOMED CT , 2008, MIE.

[9]  Rainu Kaushal,et al.  Bmc Medical Informatics and Decision Making Assessing the Level of Healthcare Information Technology Adoption in the United States: a Snapshot , 2005 .

[10]  W J Sibbald,et al.  Interobserver variability in data collection of the APACHE II score in teaching and community hospitals. , 1999, Critical care medicine.

[11]  Rainer Röhrig,et al.  Automatic calculation of a modified APACHE II score using a patient data management system (PDMS) , 2002, Int. J. Medical Informatics.

[12]  J. Zimmerman,et al.  Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients* , 2006, Critical care medicine.

[13]  D. E. Lawrence,et al.  APACHE—acute physiology and chronic health evaluation: a physiologically based classification system , 1981, Critical care medicine.

[14]  Mitzi L. Dean,et al.  Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders. , 2008, Chest.

[15]  W. Knaus,et al.  Statistical validation of a severity of illness measure. , 1983, American journal of public health.

[16]  K. Lenz,et al.  Patient data management systems in intensive care — the situation in Europe , 1995, Intensive Care Medicine.

[17]  A Junger,et al.  Outcome Prediction in a Surgical ICU Using Automatically Calculated SAPS II Scores , 2003, Anaesthesia and intensive care.

[18]  Limits of ICD-9-CM code usefulness in epidemiological studies of contact and other types of dermatitis. , 1998, American journal of contact dermatitis : official journal of the American Contact Dermatitis Society.

[19]  M M Shabot,et al.  Automatic extraction of intensity-intervention scores from a computerized surgical intensive care unit flowsheet. , 1987, American journal of surgery.