Case Study: Surgical Site Infection: Methods of Prevention & Tools for Predicting Risk

  • Surgical Site Infection: A Case Study

Case Study: Surgical Site Infection: Methods of Prevention & Tools for Predicting Risk

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Medical History

A 55-year-old white male has degenerative arthritis of the knees. 10 months ago, the patient underwent total knee replacement on the left knee without complication. The surgical incisions had closed without signs of surgical site infection. At the time, he made a good recovery. He is fully functional and fully independent. Patient is a chain smoker with a wife and two children. He works a desk job at the local newspaper company and has relatively moderate stress levels.

Prior to current presentation, the patient underwent arthroscopy on the right knee as an outpatient. Patient had suffered from non-inflammatory degenerative arthritis for years, and his physician noticed frayed and irregular cartilage in his knees about two months before his right knee arthroscopy as noted from an MRI. Several years ago, the patient had also suffered an ACL and meniscus tear as a result of playing pick-up basketball at the gym. The torn ligament and cartilage were repaired shortly after injury with no significant problems following the reconstructive surgery. A screening test via nasal swab for methicillin resistant Staphylococcus aureus (MRSA) performed at that time was negative.

Presentation and Examination

On a Friday morning, ten days after the right knee arthroscopy procedure, the patient noted swelling in the right knee with erythema. The patient also felt febrile with a general malaise. He went the Emergency Department that evening because of worsening symptoms and the onset of pain. His oral temperature was 101.8°F. The patient had tachycardia and an elevated white blood cell count. With a working diagnosis of septic arthritis, the knee was aspirated. The fluid was sent to the laboratory for Gram stain and culture. The patient was started on broad-spectrum antibiotics and admitted to the general hospital ward under the primary care physician’s service.

The following morning, Saturday, the family physician forgot to see the patient during morning rounds. The orthopedic surgeon was notified later that morning of the patient’s admission while performing an emergency operation. As the morning progressed, the patient complained of increasing knee pain. The wound site was erythematous with significant edema, and the patient’s fever had still not subsided. His wife noted that the patient was becoming confused. The day shift nurse tried several times to call the family physician but could not get a hold of him.

In the early afternoon the nurse evaluated the patient using an early warning score (EWS) (Modified Early Warning Score for a Hospital Inpatient) and called in the immediate response team. A clinical diagnosis of sepsis was made and the patient was transferred to the ICU. The laboratory reported Gram-positive cocci in clusters on the Gram stain performed on the knee fluid collected in the ED. The presumptive diagnosis was infection with Staphylococcus aureus, possibly methicillin resistant. The patient was started on intravenous vancomycin.

Once stable, the right knee was debrided and irrigated. There was no evidence of involvement of the left knee prosthesis. The patient was discharged on the seventh hospital day with antibiotic infusions thru a PICC line to continue as an outpatient.

Two weeks later the patient presented with fever, cramping, abdominal pain, and diarrhea. A rapid test for Clostridium difficile was positive for toxin A. The stool culture was negative for other pathogens. Patient was relatively stable and responded to therapy without complications and without recurrence.

Diagnoses

Surgical site infection with MRSA, sepsis, Clostridium difficile

Analytics: Easily Implemented Methods of Prevention & Tools for Predicting Risk

Medical algorithms can save hundreds of thousands of lives, and prevent the adverse outcomes that were showcased here. The Medical Algorithms Company, or “TMAC,” is a platform with analytics which physicians can use to address many of the complications that arose in this case.

Arthroscopy is a minimally invasive surgical procedure on a joint in which an examination and sometimes treatment of damage is performed with an endoscope that is inserted into the joint through a small incision. There are several algorithms that can predict risk of SSI and MRSA after this type of procedure: Risk factors for SSI after Arthroscopy and MRSA Risk Prediction Score. The former analytic identifies the following as risk factors: (1) perioperative intra-articular corticosteroid injections; (2) prolonged tourniquet application time; (3) age of the patient; (4) complex procedure; (5) history of previous procedures on the joint; (6) failure to re-prepare the surgical site if there is a conversation to arthrotomy; (7) use of contaminated instruments or sterile supplies; (8) other breaks in infection control measures; (9) active infection elsewhere; (10) placement of a foreign body or ACL graft.

For admission ten days later with a swollen knee and fever, an analytic to detect suspicion of septic arthritis (Septic Arthritis After Knee Arthroplasty Perioperative Risk Factors) can be used to evaluate a patient for clinical signs and symptoms suggestive of the condition.

The Patient-at-Risk Early Warning Score could have been used to aid in determining the patient’s condition upon admission, while the Sepsis Score could have been used to evaluate multi-organ failure in ICU patients. While the sepsis score does not predict outcome, it does calculate and describe the sequence of complications in a critically ill patient.

Onset of C. difficile disease two weeks after surgery could have been assessed as “severe” or “very severe” with the Severity Index for Clostridium difficile Infection (CDI). This analytic can assist in identification of a patient who may require more aggressive management.

Take Home Points

  • Sepsis and surgical site infection are both preventable with the implementation of analytics in physician practice
  • Medical algorithms, if used strategically and appropriately, can be instrumental in diagnosis, assessment and disease management

The medical algorithms highlighted in this case study are available at The Medical Algorithms Company and also on the Apervita health analytics platform.

About Apervita

Apervita is the leading health analytics community and marketplace, where prominent health professionals and enterprises from around the globe are transforming the world’s health knowledge into thousands of health analytics.

At Apervita, we believe that health researchers and practitioners have already created the greatest wealth of health knowledge that has ever existed and it is just waiting to be unleashed to improve health. Today, the majority of this knowledge is paper-based or locked into proprietary systems. The Apervita community is already unlocking them, turning them into computable and shareable analytics and putting them to work to improve health. They are addressing some of the biggest health challenges, such as the 100,000s of patients that die prematurely every year in the United States from chronic disease, complications and preventable adverse events.

Apervita is a secure, self-service platform, that enables health professionals and enterprises to author, publish and subscribe to a market of evidence-based algorithms, quality and safety measures, pathways, and protocols, easily connecting them to data and workflow. Available to every health professional and powerful enough for the entire health enterprise, Apervita provides health analytics at a tenth of today’s cost, in a hundredth of the time.

About the Authors

Farva Jafri is a manager at Apervita, Inc. on the Content & Authoring and Legal teams. She has a Juris Doctorate and MBA from University of Illinois at Urbana-Champaign, and a Masters of Public Health from the University of Southern California Keck School of Medicine. Farva has an extensive background in consulting, marketing, research and legal fields. She works closely with TMAC to bring many of their high-quality algorithms onto Apervita’s platform.

Dr. Chad Rudnick, MD, FAAP is a board-certified pediatrician in Boca Raton, FL. A proponent of incorporating medical technology into his practice, Dr. Rudnick uses telemedicine and medical algorithms from The Medical Algorithms Company in his daily practice to better serve his patients and their families. An accomplished medical writer, he maintains a popular pediatric blog, All Things Pediatric, and has written for numerous online and print publications including KevinMD.com.

John Svirbely, MD is a founder and Chief Medical Officer of The Medical Algorithms Company and the primary author of its medical algorithms. John is a co-founder of the Medical Algorithms Project and has developed its medical content for nearly 20 years. He has a BA degree from the Johns Hopkins University and his MD from the University of Maryland. He is a board-certified pathologist with a fellowship in medical microbiology and biomedical computing at Ohio State University. Currently he is in private practice in Cincinnati, Ohio. He has authored multiple books and articles on medical algorithms.

Note: This case study is fictional and based on actual events.


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2016-12-15T10:06:15+00:00