Predictive Model Offers COVID-19 Guidelines for Healthcare Workers

Researchers from Stanford University’s department of surgery have developed a predictive model that provides best practice guidelines for operating room team members during the COVID-19 pandemic and helps conserve the personal protective equipment (PPE) they wear.

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The guidelines are published as an article in press in the Journal of the American College of Surgeons ahead of print.

Hospital and medical school leaders at Stanford convened a PPE taskforce to develop a common predictive algorithm for PPE use. The taskforce consisted of members from the operating room, interventional suites, and endoscopy, as well as quality improvement and infectious disease experts.

The taskforce reviewed current data about COVID-19 transmission in hospital and non-hospital settings, and information on operating room risks during past outbreaks, like SARS and Ebola.

The group then developed a predictive algorithm based on the urgency of the procedure, potential for aerosolization and release of virus droplets at the surgical site, and evidence that a patient has been infected.

"We developed institutional guidelines based on how soon the surgical cases needed to be performed, the patient's condition, the risk that a surgeon would access an area of body where the amount of virus could be high, and the risk that a patient could be infected with COVID-19," said Joseph Forrester, MD, MSc, an assistant professor in general surgery and lead author of the algorithm article.

The model initially triaged patients by severity of illness into urgent and emergency procedures. Researchers stratified urgent cases into high- and low-risk procedures depending on the anticipated viral burden at the surgical site, and procedures categorized as aerosol-generating (AGP) were classified as high-risk. These procedures include those involving the aerodigestive tract, endoscopy, and open or laparoscopic surgery on the bowel with gross contamination.

The guidelines assume that every patient is potentially infected with COVID-19 until proven otherwise by a negative RT-PCR test. When operating on COVID-19 positive patients or performing an AGP, the guidelines require operating room members to wear an N-95 respirator mask, as well as a gown, gloves, and eye protection.

Only when a RT-PCR test is negative for COVID-19 can surgical team members wear standard surgical clothing.

If a patient was exhibiting viral symptoms, such as a fever, cough, or sore throat, a surgeon may consider delaying an urgent or emergency procedure. If a delay compromises the well-being of the patient, the surgeon orders in-house RT-PCR COVID-19 testing with a 24-hour turnaround. If the patient’s status does not allow for a 24-hour wait, the case is considered to be an emergency and the patient is presumed to be COVID-19 positive.

The guidelines make special considerations for the use of PPE during and after bag mask ventilation and endotracheal intubation, which both pose a high risk for viral transmission. All healthcare providers who are not directly involved with intubation are asked to leave the operating room beforehand.

Anesthesiologists should also wear N-95 face masks and droplet-protective PPE, and cleaning staff should take droplet precautions when cleaning any operating room.

"You never know what's going to happen, and it's better to be prepared and use the right PPE at the right time and be careful not to waste PPE. Guidelines like ours show that healthcare providers on the frontline are trying to take care of every person with a serious surgical condition and make sure they have enough equipment to carry that mission through safely,” said Forrester.

The researchers noted that other organizations using these guidelines may need to adjust them according to local testing availability, PPE availability, anticipated PPE utilization rates, and supply-chain distributions.

With this predictive model and these new guidelines, Stanford researchers hope to protect healthcare workers and conserve PPE for optimal patient care.

“The current COVID-19 pandemic has necessitated rapid development of novel hospital guidelines across a spectrum of clinical environments,” researchers concluded.

“Despite COVID-19 being a new threat, we have shown that by developing an easy-to-follow decision tree algorithm for the interventional platform teams, we can ensure optimal healthcare worker safety.”