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Development Of A Hospital-Acquired Pressure Injury Predictive Model Using The Maryland Health Services Cost Review Commission Database (2012-2017)
Pragna N. Shetty, MPH, Franca Kraenzlin, MD, MHS, Pooja S. Yesantharao, MS, Justin M. Sacks, MD MBA FACS.
The Johns Hopkins School of Medicine the Department of Plastic and Reconstructive Surgery, Baltimore, MD, USA.

Purpose: Each year, 60,000 patients die of complications from a hospital-acquired pressure injury (HAPI) alone; HAPIs also incur over $10 billion in health care spending annually. Despite guidelines placing increased pressure on early detection and prevention, HAPIs still occur at high rates in intensive care unit (ICU) patients who are higher risk due to the severity of their illness and decreased mobility. Studies of HAPIs in the nursing home and spinal cord injury populations have shown that risk factors range from patientsí medical history to payer status. In this study, we sought to identify pertinent risk factors and develop a prediction model estimating an ICU patientsí risk of developing a stage 3 or stage 4 HAPI.
Methods: This study was approved by the institutional review board. We used the Maryland Health Services Cost Review Commission database from 2012 to 2017. A total of 289,898 adult ICU patients were identified after applying the exclusion criteria; 4,204 stage 3 and 4 HAPIs identified. We conducted a bivariate logistic regression risk factor analysis to determine which variables would be included in the final regression model. A 10-fold cross-validation and area under the curve test were conducted to observe the validation and accuracy of the final model.
Results: The final predictive model showed that females were 0.92 times as likely to develop a HAPI as compared to male patients. Black patients were 2.01 times as likely to develop a HAPI as their white counterparts. Patients with diagnoses of spinal cord injury, diabetes mellitus, or stroke were also more likely to develop a HAPI than patients without these diagnoses. Both Medicare and Medicaid patients were at greater odds of developing a HAPI compared to the commercially insured cohort with ORs of 2.40 and 1.81, respectively. Those with body mass indexes classified as underweight had an OR of 2.39 of developing a HAPI compared to those who were normal or overweight while those with morbid obesity were 28% less likely to develop a HAPI. The area under the curve for this model was 0.77, indicating good predictive accuracy in this patient cohort (Figure 1).
Conclusion: This proposed prediction model showed good accuracy at predicting which patients are more likely to develop a HAPI, incorporating patient factors from race/ethnicity and payer status to medical history and ICU unit. While the tool should be externally validated on a different cohort of patients, it is a starting point to help focus interventions and quality improvement for the prevention of this disease. The use of this predictive model can be used to more prudently allocate scarce resources in the delivery of health care.


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