The Evolution of Commercial Negative Pressure Wound Therapy Systems over the Past Three Decades.

SIGNIFICANCE: Since the introduction of the first commercial negative pressure wound therapy (NPWT) system nearly three decades ago, several key technological innovations have led to wide adoption of the therapy. This is a review of the history and innovation of commercial NPWT systems for adjunctive management of open wounds. RECENT ADVANCES: Technical modifications have broadened NPWT options to include innovative dressing interfaces, tubing configurations, power sources, capability of topical wound solution instillation or irrigation, canister versus canister-free configurations, smart technology, and disposable versus larger reusable therapy units. While these options complicate product selection, they have greatly expanded the potential to manage a wide variety of wounds in patients who previously may not have been candidates for NPWT. CRITICAL ISSUES: Basic yet mandatory requirements of NPWT include delivering an accurate level of negative pressure to the wound bed, maintaining a seal, removing wound surface exudate through the dressing interface, and patient adherence to prescribed therapy. Meeting these requirements is challenging in the face of variable wound types, wound locations, exudate levels and exudate viscosity. While there are a growing number of marketed NPWT systems, each may have different characteristics and performance. Evaluating the functionality of each system and relevant accessories is complicated, especially as additional manufacturers enter the market. Understanding key innovations and specific challenges they are intended to solve may aid healthcare providers in selecting appropriate NPWT technologies for patients. FUTURE DIRECTIONS: Evolving technology including artificial intelligence will likely play a major role in re-defining NPWT safety, simplicity, and reliability.

Development and validation of a point-of-care clinical risk score to predict surgical site complication-associated readmissions following open spine surgery.

Background: Surgical site complications (SSCs) contribute to increased healthcare costs. Predictive analytics can aid in identifying high-risk patients and implementing optimization strategies. This study aimed to develop and validate a risk-assessment score for SSC-associated readmissions (SSC-ARs) in patients undergoing open spine surgery. Methods: The Premier Healthcare Database (PHD) of adult patients (n=157,664; 3,182 SSC-ARs) between January 2019 and September 2020 was used for retrospective data analysis to create an SSC risk score using mixed effects logistic regression modeling. Full and reduced models were developed using patient-, facility-, or procedure-related predictors. The full model used 37 predictors and the reduced used 19. Results: The reduced model exhibited fair discriminatory capability (C-statistic =74.12%) and demonstrated better model fit [Pearson chi-square/degrees of freedom (DF) =0.93] compared to the full model (C-statistic =74.56%; Pearson chi-square/DF =0.92). The risk scoring system, based on the reduced model, comprised the following factors: female (1 point), blood disorder [2], congestive heart failure [2], dementia [3], chronic pulmonary disease [2], rheumatic disease [3], hypertension [2], obesity [2], severe comorbidity [2], nicotine dependence [1], liver disease [2], paraplegia and hemiplegia [3], peripheral vascular disease [2], renal disease [2], cancer [1], diabetes [2], revision surgery [2], operative hours ≥5 [4], emergency/urgent surgery [2]. A final risk score (sum of the points for each surgery; range, 0-40) was validated using a 1,000-surgery random hold-out sample (C-statistic =85.16%). Conclusions: The resulting SSC-AR risk score, composed of readily obtainable clinical information, could serve as a robust predictive tool for unplanned readmissions related to wound complications in the preoperative setting of open spine surgery.

Development and validation of a point-of-care clinical risk score to predict surgical site infection following open spinal fusion.

Background: Surgical site infection (SSI) after open spine surgery increases healthcare costs and patient morbidity. Predictive analytics using large databases can be used to develop prediction tools to aid surgeons in identifying high-risk patients and strategies for optimization. The purpose of this study was to develop and validate an SSI risk-assessment score for patients undergoing open spine surgery. Methods: The Premier Healthcare Database of adult open spine surgery patients (n = 157,664; 2,650 SSIs) was used to create an SSI risk scoring system using mixed effects logistic regression modeling. Full and reduced multilevel logistic regression models were developed using patient, surgery or facility predictors. The full model used 38 predictors and the reduced used 16 predictors. The resulting risk score was the sum of points assigned to 16 predictors. Results: The reduced model showed good discriminatory capability (C-statistic = 0.75) and good fit of the model ([Pearson Chi-square/DF] = 0.90, CAIC=25,517) compared to the full model (C-statistic = 0.75, [Pearson Chi-square/DF] =0.90, CAIC=25,578). The risk scoring system, based on the reduced model, included the following: female (5 points), hypertension (4), blood disorder (8), peripheral vascular disease (9), chronic pulmonary disease (6), rheumatic disease (16), obesity (12), nicotine dependence (5), Charlson Comorbidity Index (2 per point), revision surgery (14), number of ICD-10 procedures (1 per procedure), operative time (1 per hour), and emergency/urgent surgery (12). A final risk score as the sum of the points for each surgery was validated using a 1,000-surgery random hold-out (independent from the study cohort) sample (C-statistic = 0.77). Conclusions: The resulting SSI risk score composed of readily obtainable clinical information could serve as a strong prediction tool for SSI in preoperative settings when open spine surgery is considered.