BACKGROUND: Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.
OBJECTIVE: This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.
METHODS: Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model\'s performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score.
RESULTS: Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms.
CONCLUSIONS: The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.

BACKGROUND: Pressure sores are sometimes refractory to treatment, often due to malnutrition. Small intestinal bacterial overgrowth (SIBO) obstructs absorption in the digestive tract and causes malnutrition. However, little is known about the association between pressure sore wound healing and SIBO. Here, we report a case of a patient with a refractory sacral pressure sore and SIBO.
METHODS: A 66-year-old woman who was spinal cord injured 14 years before visiting our hospital presented with the chief complaint of a sacral pressure sore, 10.0 × 6.5 cm in size, which was refractory to treatment. Physical examination showed abdominal distension and emaciation, with a body mass index of 15. Further examination revealed elevated serum alkaline phosphatase (1260 U/L), bilateral tibial fracture, multiple rib fracture, and osteoporosis. We diagnosed the patient with osteomalacia with vitamin D deficiency. Despite oral supplementation, serum levels of calcium, phosphorous, and vitamin D remained low. Also, despite concentrative wound therapy for the sacral pressure sore by plastic surgeons, no wound healing was achieved. Due to a suspicion of disturbances in nutrient absorption, we performed bacterial examination of collected gastric and duodenal fluid, which showed high numbers of bacteria in gastric content (104 E. coli, 105 Streptococcus species, and 105 Neisseria species) and duodenal content (106 E. coli, 104 Candida glabrata). Therefore, we diagnosed the patient with SIBO and started selective decontamination of the digestive tract using polymyxin B sulfate and amphotericin B. After starting treatment for SIBO, the sacral pressure sore began to heal and was nearly healed after 285 days. The patient\'s serum levels of calcium, phosphorous, vitamin D, and other fat-soluble vitamins also gradually increased after starting treatment for SIBO.
CONCLUSIONS: We report a case of a patient with a refractory sacral pressure sore that healed after starting treatment for SIBO. We conclude that SIBO may be an overlooked cause of malnutrition and poor wound healing in patients with chronic pressure sores.