USING EHR DATA TO DYNAMICALLY PREDICT INCIDENCE OF HOSPITAL-ACQUIRED PRESSURE ULCERS
Authors: Padula William , Ursitti Tony , Venable Laura Ruth , Ginensky Adam , Makic Mary Beth , Wald Heidi , Mishra Manish , Valuck Robert , Hedeker Donald , Gibbons Robert , Meltzer David 
Published: October 2015

BackgroundHospital-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.ObjectivesTo use electronic health record (EHR) data to predict HAPUs among hospitalized patients.MethodsEHR 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).ResultsThe 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).ConclusionsEarly 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 ModelLevel-1: Encounter-level Fixed EffectsLevel-2:Patient/Cluster-level Random EffectWhere···i: Patient   j: EncounterTable 1Results of 2-level Mixed-effects Logistic Regression and population-average estimates for HAPU incidence by patient-encounter.2-Level Mixed-effects Logitistic RegressionPopulation-average Estimates (GEE)Variable  EstimateOdds RatioStandard ErrorP-valueEstimateOdds RatioStandard ErrorP-valueIntercept−9.469  0.0000.331<0.001−7.0160.0010.175<0.001Age0.0491.0500.003<0.0010.0401.0410.002<0.001Bronchoscopy0.5131.6710.2480.0380.4081.5040.1900.032Culture, Stool0.5141.6720.1580.0010.4141.5120.1230.001Urinalysis Chemistry Screen0.8412.3190.089<0.0010.6932.0000.073<0.001Pre-albumin0.9832.6740.091<0.0010.7612.1410.067<0.001Vancomycin Random Assay0.9592.6090.094<0.0010.7282.0710.069<0.001Culture & Stain, Wound Drainage0.7902.2030.135<0.0010.5821.7900.098<0.001Topical Sulfonamides1.7325.6530.115<0.0011.2973.6600.078<0.001Topical/Mucous Membrane/Subcutaneous Enzymes3.38929.6220.276<0.0012.1098.2360.167<0.001Phosphate Replacement0.2601.2970.0850.0020.2041.2270.0680.003Antifu ngal Agent0.4721.6030.094<0.0010.3851.4700.075<0.001Erythropoiesis-stimulating Agent0.9952.7050.134<0.0010.7682.1560.098<0.001ICD-9 V49.84: Bed Confinement3.45731.7300.539<0.0012.1989.0030.334<0.001ICD-9 907.2: Spinal Cord Injury5.511247.3640.500<0.0013.81445.3110.318<0.001ICD-9 730.15: Chronic Osteomyelitis – Pelvic a nd Thigh Regions3.32327.7460.697<0.0012.31210.0940.474<0.001ICD-9 730.18: Chronic Osteomyelitis – Other3.00120.0990.806<0.0011.9326.9040.516<0.001Variance(lntercept)4.3550.486Log-likelihood−4737.36