Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review.

BACKGROUND: Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)-based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment. OBJECTIVE: The objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images. METHODS: We conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not address SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using tables. Employment of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines was evaluated and PROBAST (Prediction Model Risk of Bias Assessment Tool) was used to assess risk of bias (RoB). RESULTS: In total, 10 of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly color images, and the volume of images used ranged from under 50 to thousands. Further, 10 TRIPOD items were reported in at least 4 studies, though 15 items were reported in fewer than 4 studies. PROBAST assessment led to 9 studies being identified as having an overall high RoB, with 1 study having overall unclear RoB. CONCLUSIONS: Research on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future.

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center.

PURPOSE: This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system. METHODS: Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs. Free-text clinical documentation was processed using a commercial NLP engine. RESULTS: The platform contains 141 data elements of 7,019 patients with newly diagnosed breast cancer who received care at our regional cancer center from January 1, 2014, to June 3, 2022. Daily updating of the database takes an average of 56 minutes. Evaluation of the tuning of NLP jobs found overall high performance, with an F1 of 1.0 for 19 variables, with a further 16 variables with an F1 of > 0.95. CONCLUSION: This study describes how data warehousing combined with NLP can be used to create a prospective data and analytics platform to enable a learning health system. Although upfront time investment required to create the platform was considerable, now that it has been developed, daily data processing is completed automatically in less than an hour.

Association Between Delayed Discharge From Acute Care and Rehabilitation Outcomes and Length of Stay: A Retrospective Cohort Study.

OBJECTIVE: To examine the association between discharge delays from acute to rehabilitation care because of capacity strain in the rehabilitation units, patient length of stay (LOS), and functional outcomes in rehabilitation. DESIGN: Retrospective cohort study using an instrumental variable to remove potential biases because of unobserved patient characteristics. SETTING: Two campuses of a hospital network providing inpatient acute and rehabilitation care. PARTICIPANTS: Patients admitted to and discharged from acute care categories of Medicine and Neurology/Musculoskeletal (Neuro/MSK) and subsequently admitted to and discharged from inpatient rehabilitation between 2013 and 2019 (N=10486). INTERVENTIONS: None. MAIN OUTCOME MEASURES: Rehabilitation LOS, FIM scores at admission and discharge, and rehabilitation efficiency defined as FIM score improvement per day of rehabilitation. RESULTS: The final cohort contained 3690 records for Medicine and 1733 for Neuro/MSK categories. For Medicine, 1 additional day of delayed discharge was associated with an average 5.1% (95% confidence interval [CI], 3%-7.3%) increase in rehabilitation LOS and 0.08 (95% CI, 0.03-0.13) reduction in rehabilitation efficiency. For Neuro/MSK, 1 additional day of delayed discharge was associated with an average 11.6% (95% CI, 2.8%-20.4%) increase in rehabilitation LOS and 0.08 (95% CI, -0.07 to 0.23) reduction in rehabilitation efficiency. CONCLUSIONS: Delayed discharge from acute care to rehabilitation because of capacity strain in rehabilitation had a strong association with prolonged LOS in rehabilitation. An important policy implication of this "cascading" effect of delays is that reducing capacity strain in rehabilitation could be highly effective in reducing discharge delays from acute care and improving rehabilitation efficiency.

A contemporary review of machine learning in otolaryngology-head and neck surgery.

One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a substantial amount of interest in machine-learning analytic methods. There has been a drastic increase in the otolaryngology literature volume describing novel applications of machine learning within the past 5 years. In this timely contemporary review, we provide an overview of popular machine-learning techniques, and review recent machine-learning applications in otolaryngology-head and neck surgery including neurotology, head and neck oncology, laryngology, and rhinology. Investigators have realized significant success in validated models with model sensitivities and specificities approaching 100%. Challenges remain in the implementation of machine-learning algorithms. This may be in part the unfamiliarity of these techniques to clinician leaders on the front lines of patient care. Spreading awareness and confidence in machine learning will follow with further validation and proof-of-value analyses that demonstrate model performance superiority over established methods. We are poised to see a greater influx of machine-learning applications to clinical problems in otolaryngology-head and neck surgery, and it is prudent for providers to understand the potential benefits and limitations of these technologies. Laryngoscope, 130:45-51, 2020.

Liquid-Infused Silicone As a Biofouling-Free Medical Material.

There is a dire need for infection prevention strategies that do not require the use of antibiotics, which exacerbate the rise of multi- and pan-drug resistant infectious organisms. An important target in this area is the bacterial attachment and subsequent biofilm formation on medical devices (e.g., catheters). Here we describe nonfouling, lubricant-infused slippery polymers as proof-of-concept medical materials that are based on oil-infused polydimethylsiloxane (iPDMS). Planar and tubular geometry silicone substrates can be infused with nontoxic silicone oil to create a stable, extremely slippery interface that exhibits exceptionally low bacterial adhesion and prevents biofilm formation. Analysis of a flow culture of Pseudomonas aeruginosa through untreated PDMS and iPDMS tubing shows at least an order of magnitude reduction of biofilm formation on iPDMS, and almost complete absence of biofilm on iPDMS after a gentle water rinse. The iPDMS materials can be applied as a coating on other polymers or prepared by simply immersing silicone tubing in silicone oil, and are compatible with traditional sterilization methods. As a demonstration, we show the preparation of silicone-coated polyurethane catheters and significant reduction of Escherichia coli and Staphylococcus epidermidis biofilm formation on the catheter surface. This work represents an important first step toward a simple and effective means of preventing bacterial adhesion on a wide range of materials used for medical devices.