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USING EHR DATA TO DYNAMICALLY PREDICT INCIDENCE OF HOSPITAL-ACQUIRED PRESSURE ULCERS
  1. William Padula1,
  2. Tony Ursitti2,
  3. Laura Ruth Venable2,
  4. Adam Ginensky2,
  5. Mary Beth Makic3,
  6. Heidi Wald3,
  7. Manish Mishra4,
  8. Robert Valuck5,
  9. Donald Hedeker2,
  10. Robert Gibbons2,
  11. David Meltzer2
  1. 1Johns Hopkins Bloomberg School of Public Health, United States
  2. 2University of Chicago, United States
  3. 3University of Colorado School of Medicine, United States
  4. 4Geisel School of Medicine at Dartmouth, United States
  5. 5University of Colorado Skaggs School of Pharmacy and Pharmaceutical Science, United States

Abstract

Background Hospital-acquired pressure ulcers (HAPUs) are costly to treat and can result in Medicare reimbursement penalties. Statistical models can identify patients at greatest HAPU risk and improve prevention.

Objectives To use electronic health record (EHR) data to predict HAPUs among hospitalized patients.

Methods EHR data were obtained from an academic medical center that included hospitalized patients with at least 1 skin examination between 2011–2014. These data contained encounter-level demographic variables, diagnoses, prescription drugs and provider orders. HAPUs were defined by stages III, IV or unstageable pressure ulcers not present-on-admission as a secondary diagnosis, and excluded diagnosis of paraplegia/quadriplegia. Random forests and k-means clustering were applied to reduce the dimensionality of the large dataset. A 2-level mixed-effects logistic regression of patient-encounters evaluated associations between covariates and HAPU incidence (Equation 1).

Results The approach produced a sample population of 23,054 patients with 1,549 HAPUs. The mixed-effects model predicted HAPUs with exceptional (99%) accuracy for a rare event (table 1). The greatest odds ratio (OR) of HAPU incidence was among patients diagnosed with spinal cord injury (ICD-9 907.2: OR=247.4; P<0.001). Other high ORs included osteomyelitis (ICD-9 730: OR=27.7, P<0.001), bed confinement (ICD-9 V49.84: OR=31.7, P<0.001), and prescribed topical/subcutaneous enzymes (OR=5.7, P<0.001).

Conclusions Early detection of HAPUs is feasible and the results of these statistical predictions can allow providers to better target prevention to specific patients. This model also implicates spinal cord injury as a potential risk-factor for unavoidable HAPUs. Providers may be missing opportunities to co-diagnose spinal cord injury with paraplegia/quadriplegia which could improve hospital performance measures.

Equation 1. Mixed-effects Logistic Regression Model

Level-1: Encounter-level Fixed EffectsFormula

Level-2: Patient/Cluster-level Random EffectFormula

Where···

i: Patient   j: Encounter

Table 1

Results of 2-level Mixed-effects Logistic Regression and population-average estimates for HAPU incidence by patient-encounter.

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