Assessment of risk and economic burden of surgical site infection (SSI) posthysterectomy using a U.S. longitudinal database

Cefazolin as the mainstay for antibiotic prophylaxis in patients with a penicillin allergy in obstetrics and gynecology

Cefazolin is the most common antibiotic used for prophylaxis in obstetrics and gynecology. Among those with a penicillin allergy, alternative antibiotics are often chosen for prophylaxis, given fears of cross-reactivity between penicillin and cefazolin. Alternative antibiotics in this setting are associated with adverse sequelae, including surgical site infection, induction of bacterial resistance, higher costs to the healthcare system, and possible Clostridium difficile infection. Given the difference in R1 side chains between penicillin and cefazolin, cefazolin use is safe and should be recommended for patients with a penicillin allergy, including those who experience Immunoglobulin E-mediated reactions such as anaphylaxis. Cefazolin should only be avoided in those who experience a history of a severe, life-threatening delayed hypersensitivity reaction manifested as severe cutaneous adverse reactions (Steven-Johnson Syndrome), hepatitis, nephritis, serum sickness, and hemolytic anemia in response to penicillin administration. In addition, >90% of those with a documented penicillin allergy do not have true allergies on skin testing. Increased referral for penicillin allergy testing should be incorporated into routine obstetric care and preoperative assessment to reduce suboptimal antibiotic prophylaxis use. More education is needed among providers surrounding penicillin allergy assessment and cross-reactivity among penicillins and cephalosporins to optimize antibiotic prophylaxis in obstetrics and gynecology.

Decreasing post hysterectomy surgical site infections with the implementation of a hysterectomy-specific bundle

BACKGROUND

Surgical site infections (SSI) are a common complication of hysterectomy. Surgical bundles have been shown to reduce SSIs. Here we describe a bundle that was used to combat an abnormal rise in SSI events that resulted in a greater than 75% reduction at our institution.

METHODS

A hysterectomy-specific bundle was developed based on the prior success of SSI prevention bundles. Development involved longitudinal education and training to ensure accuracy and compliance. All inpatient abdominal, laparoscopic, and vaginal hysterectomies performed at a tertiary referral center were included. The preintervention, intervention, and postintervention periods were each one year in length. SSI rates were peer-reviewed monthly and overall trends were tracked, including compliance with bundle guidelines.

RESULTS

Preintervention, an abnormal rise in SSI was identified at 3.76%. During the intervention, 309 inpatient hysterectomies were completed. In this period, 6 posthysterectomy SSI events occurred (3.76% vs 1.94%, P = .21). Four SSIs followed laparotomy and 2 followed laparoscopy. Compliance during the intervention period ranged from 79% to 89% with a mean of 85%. In the postintervention period, there were 6 SSI following 689 hysterectomies (3.76% vs 0.87%, P = .004). The majority of SSI occurred after abdominal hysterectomy.

CONCLUSIONS

Implementation of a hysterectomy-specific surgical bundle allowed for a significant reduction in post hysterectomy SSI during a yearlong intervention period and a sustained, further reduction in the postintervention period.

An explainable long short-term memory network for surgical site infection identification

BACKGROUND

Currently, surgical site infection surveillance relies on labor-intensive manual chart review. Recently suggested solutions involve machine learning to identify surgical site infections directly from the medical record. Deep learning is a form of machine learning that has historically performed better than traditional methods while being harder to interpret. We propose a deep learning model, a long short-term memory network, for the identification of surgical site infection from the medical record with an attention layer for explainability.

METHODS

We retrieved structured data and clinical notes from the University of Utah Health System's electronic health care record for operative events randomly selected for manual chart review from January 2016 to June 2021. Surgical site infection occurring within 30 days of surgery was determined according to the National Surgical Quality Improvement Program definition. We trained the long short-term memory model along with traditional machine learning models for comparison. We calculated several performance metrics from a holdout test set and performed additional analyses to understand the performance of the long short-term memory, including an explainability analysis.

RESULTS

Surgical site infection was present in 4.7% of the total 9,185 operative events. The area under the receiver operating characteristic curve and sensitivity of the long short-term memory was higher (area under the receiver operating characteristic curve: 0.954, sensitivity: 0.920) compared to the top traditional model (area under the receiver operating characteristic curve: 0.937, sensitivity: 0.736). The top 5 features of the long short-term memory included 2 procedure codes and 3 laboratory values.

CONCLUSION

Surgical site infection surveillance is vital for the reduction of surgical site infection rates. Our explainable long short-term memory achieved a comparable area under the receiver operating characteristic curve and greater sensitivity when compared to traditional machine learning methods. With explainable deep learning, automated surgical site infection surveillance could replace burdensome manual chart review processes.

Risk and economic burden of surgical site infection following spinal fusion in adults

BACKGROUND

Spinal fusion surgery (SFS) is one of the most common operations in the United States, >450,000 SFSs are performed annually, incurring annual costs >$10 billion.

OBJECTIVES

We used a nationwide longitudinal database to accurately assess incidence and payments associated with management of postoperative infection following SFS.

METHODS

We conducted a retrospective, observational cohort analysis of 210,019 patients undergoing SFS from 2014 to 2018 using IBM MarketScan commercial and Medicaid-Medicare databases. We assessed rates of superficial/deep incisional SSIs, from 3 to 180 days after surgery using Cox proportional hazard regression models. To evaluate adjusted payments for patients with/without SSIs, adjusted for inflation to 2019 Consumer Price Index, we used generalized linear regression models with log-link and γ distribution.

RESULTS

Overall, 6.6% of patients experienced an SSI, 1.7% superficial SSIs and 4.9% deep-incisional SSIs, with a median of 44 days to presentation for superficial SSIs and 28 days for deep-incisional SSIs. Selective risk factors included surgical approach, admission type, payer, and higher comorbidity score. Postoperative incremental commercial payments for patients with superficial SSI were $20,800 at 6 months, $26,937 at 12 months, and $32,821 at 24 months; incremental payments for patients with deep-incisional SSI were $59,766 at 6 months, $74,875 at 12 months, and $93,741 at 24 months. Corresponding incremental Medicare payments for patients with superficial incisional at 6, 12, 24-months were $11,044, $17,967, and $24,096; while payments for patients with deep-infection were: $48,662, $53,757, and $73,803 at 6, 12, 24-months.

CONCLUSIONS

We identified a 4.9% rate of deep infection following SFS, with substantial payer burden. The findings suggest that the implementation of robust evidence-based surgical-care bundles to mitigate postoperative SFS infection is warranted.