Infection control teams and clinical laboratory managers may want to look at this new product designed to improve the diagnosis and treatment of sepsis
Accurate and fast diagnosis of sepsis for patients arriving in emergency departments is the goal of a new product that was just cleared by the federal Food and Drug Administration (FDA). It is also the newest example of how artificial intelligence (AI) continues to find its way into pathology and clinical laboratory medicine.
Sepsis is one of the deadliest killers in US hospitals. That is why there is interest in the recent action by the FDA to grant marketing authorization for an AI-powered sepsis detection software through the agency’s De Novo Classification Request. The DNCR “provides a marketing pathway to classify novel medical devices for which general controls alone, or general and special controls, provide reasonable assurance of safety and effectiveness for the intended use, but for which there is no legally marketed predicate device,” the FDA’s website states.
Unlike a single analyte assay that is run in a clinical laboratory, Prenosis’ AI/ML software uses 22 diagnostic and predictive parameters, along with ML algorithms, to analyze data and produce a clinically actionable answer on sepsis.
It is important for clinical laboratory managers and pathologists to recognize that this diagnostic approach to sepsis brings together a number of data points commonly found in a patient’s electronic health record (EHR), some of which the lab generated and others the lab did not generate.
“Sepsis is a serious and sometimes deadly complication. Technologies developed to help prevent this condition have the potential to provide a significant benefit to patients,” said Jeff Shuren, MD, JD, Director of the FDA’s Center for Devices and Radiological Health, in a statement. “The FDA’s authorization of the Prenosis Sepsis ImmunoScore software establishes specific premarket and post-market requirements for this device type.” Clinical laboratory EHRs contain some of the data points Prenosis’ diagnostic software uses. (Photo copyright: US Food and Drug Administration.)
How it Works
To assist doctors diagnose sepsis, the ImmunoScore software is first integrated into the patient’s hospital EHR. From there, it leverages 22 parameters including:
White blood cell count to produce a score that informs caregivers of the patient’s risk for sepsis within 24 hours, MedTech Dive reported.
Instead of requiring a doctor or nurse to look at each parameter separately, the SaMD tool uses AI “to evaluate all those markers at once”, CNBC noted. It then produces a risk score and four discrete risk stratification categories (low, medium, high, and very high) which correlate to “a patient’s risk of deterioration” represented by:
By sharing these details—a number from one to 100 for each of the 22 diagnostic and predictive parameters—Sepsis ImmunoScore helps doctors determine which will likely contribute most to the patient’s risk for developing sepsis, MedTech Dive reported.
“A lot of clinicians don’t trust AI products for multiple reasons. We are trying very hard to counter that skepticism by making a tool that was validated by the FDA first, and then the second piece is we’re not trying to replace the clinician,” Bobby Reddy Jr., PhD, Prenosis co-founder and CEO, told MedTech Dive.
Big Biobank and Blood Sample Data
Prenosis, which says its goal is the “enabling [of] precision medicine in acute care” developed Sepsis ImmunoScore using the company’s own biobank and a dataset of more than 100,000 blood samples from more than 25,000 patients.
AI algorithms drew on this biological/clinical dataset—the largest in the world for acute care patients suspected of having serious infections, according to Prenosis—to “elucidate patterns in rapid immune response.”
“It does not work without data, and the data started at Carle,” said critical care specialist Karen White, MD, PhD, Carle Foundation Hospital, St. Louis, MO, in the news release. “The project involved a large number of physicians, research staff, and internal medicine residents at Carle who helped recruit patients, collect data, and samples,” she said.
Opportunity for Clinical Laboratories
Sepsis is a life-threatening condition based on an “extreme response to an infection” that affects nearly 1.7 million adults in the US each year and is responsible for 350,000 deaths, according to US Centers for Disease Control and Prevention (CDC) data.
A non-invasive diagnostic tool like Sepsis ImmunoScore will be a boon to emergency physicians and the patients they treat. Now that the FDA has authorized the SaMD diagnostic tool to go to market, it may not be long before physicians can use the information it produces to save lives.
Clinical laboratory managers inspired by the development of Sepsis ImmunoScore may want to look for similar ways they can take certain lab test results and combine them with other data in an EHR to create intelligence that physicians can use to better treat their patients. The way forward in laboratory medicine will be combining lab test results with other relevant sets of data to create clinically actionable intelligence for physicians, patients, and payers.
Radiological method using AI algorithms to detect, locate, and identify cancer could negate the need for invasive, painful clinical laboratory testing of tissue biopsies
This will be of interest to histopathologists and radiologist technologists who are working to develop AI deep learning algorithms to read computed tomography scans (CT scans) to speed diagnosis and treatment of cancer patients.
“Researchers used the CT scans of 170 patients treated at The Royal Marsden with the two most common forms of retroperitoneal sarcoma (RPS)—leiomyosarcoma and liposarcoma—to create an AI algorithm, which was then tested on nearly 90 patients from centers across Europe and the US,” the news release notes.
The researchers then “used a technique called radiomics to analyze the CT scan data, which can extract information about the patient’s disease from medical images, including data which can’t be distinguished by the human eye,” the new release states.
The research team sought to make improvements with this type of cancer because these tumors have “a poor prognosis, upfront characterization of the tumor is difficult, and under-grading is common,” they wrote. The fact that AI reading of CT scans is a non-invasive procedure is major benefit, they added.
“This is the largest and most robust study to date that has successfully developed and tested an AI model aimed at improving the diagnosis and grading of retroperitoneal sarcoma using data from CT scans,” said the study’s lead oncology radiologist Christina Messiou, MD, (above), Consultant Radiologist at The Royal Marsden NHS Foundation Trust and Professor in Imaging for Personalized Oncology at The Institute of Cancer Research, London, in a news release. Invasive medical laboratory testing of cancer biopsies may eventually become a thing of the past if this research becomes clinically available for oncology diagnosis. (Photo copyright: The Royal Marsden.)
Study Details
RPS is a relatively difficult cancer to spot, let alone diagnose. It is a rare form of soft-tissue cancer “with approximately 8,600 new cases diagnosed annually in the United States—less than 1% of all newly diagnosed malignancies,” according to Brigham and Women’s Hospital.
In their published study, the UK researchers noted that, “Although more than 50 soft tissue sarcoma radiomics studies have been completed, few include retroperitoneal sarcomas, and the majority use single-center datasets without independent validation. The limited interpretation of the quantitative radiological phenotype in retroperitoneal sarcomas and its association with tumor biology is a missed opportunity.”
According to the ICR news release, “The [AI] model accurately graded the risk—or how aggressive a tumor is likely to be—[in] 82% of the tumors analyzed, while only 44% were correctly graded using a biopsy.”
Additionally, “The [AI] model also accurately predicted the disease type [in] 84% of the sarcomas tested—meaning it can effectively differentiate between leiomyosarcoma and liposarcoma—compared with radiologists who were not able to diagnose 35% of the cases,” the news release states.
“There is an urgent need to improve the diagnosis and treatment of patients with retroperitoneal sarcoma, who currently have poor outcomes,” said the study’s first author Amani Arthur, PhD, Clinical Research Fellow at The Institute of Cancer Research, London, and Registrar at The Royal Marsden NHS Foundation Trust, in the ICR news release.
“The disease is very rare—clinicians may only see one or two cases in their career—which means diagnosis can be slow. This type of sarcoma is also difficult to treat as it can grow to large sizes and, due to the tumor’s location in the abdomen, involve complex surgery,” she continued. “Through this early research, we’ve developed an innovative AI tool using imaging data that could help us more accurately and quickly identify the type and grade of retroperitoneal sarcomas than current methods. This could improve patient outcomes by helping to speed up diagnosis of the disease, and better tailor treatment by reliably identifying the risk of each patient’s disease.
“In the next phase of the study, we will test this model in clinic on patients with potential retroperitoneal sarcomas to see if it can accurately characterize their disease and measure the performance of the technology over time,” Arthur added.
Importance of Study Findings
Speed of detection is key to successful cancer diagnoses, noted Richard Davidson, Chief Executive of Sarcoma UK, a bone and soft tissue cancer charity.
“People are more likely to survive sarcoma if their cancer is diagnosed early—when treatments can be effective and before the sarcoma has spread to other parts of the body. One in six people with sarcoma cancer wait more than a year to receive an accurate diagnosis, so any research that helps patients receive better treatment, care, information and support is welcome,” he told The Guardian.
According to the World Health Organization, cancer kills about 10 million people worldwide every year. Acquisition and medical laboratory testing of tissue biopsies is both painful to patients and time consuming. Thus, a non-invasive method of diagnosing deadly cancers quickly, accurately, and early would be a boon to oncology practices worldwide and could save thousands of lives each year.
Clinical laboratories and point-of-care settings may have a new diagnostic test if this novel handheld device and related technology is validated by clinical trials
Efforts to develop breath analyzers that accurately identify viral infections, such as SARS-CoV-2 and Influenza, have been ongoing for years. The latest example is ViraWarn from Opteev Technologies in Baltimore, Maryland, and its success could lead to more follow-up PCR tests performed at clinical laboratories.
“Breath is one of the most appealing non-invasive sample types for diagnosis of infectious and non-infectious disease,” said Opteev in its FDA Pre-EUA application. “Exhaled breath is very easy to provide and is less prone to user errors. Breath contains a number of biomarkers associated with different ailments that include volatile organic compounds (VOCs), viruses, bacteria, antigens, and nucleic acid.”
Further clinical trials and the FDA Pre-EUA are needed before ViraWarn can be made available to consumers. In the meantime, Opteev announced that the CES (Consumer Electronic Show) had named ViraWarn as a 2023 Innovation Award Honoree in the digital health category.
“ViraWarn is designed to allow users an ultra-fast and convenient way to know if they are spreading a dangerous respiratory virus. With a continued increase in COVID-19 and a new surge in RSV and influenza cases, we’re eager to bring ViraWarn to market so consumers can easily blow into a personal device and find out if they are positive or negative,” said Conrad Bessemer (above), Opteev President and Co-Founder, in a news release.
Opteev is a subsidiary of Novatec, a supplier of machinery and sensor technology, and a sister company to Prophecy Sensorlytics, a wearable sensors company.
The ViraWarn breath analyzer uses a silk-based sensor that “traces the electric discharge of respiratory viruses coupled with an artificial intelligence (AI) processor to filter out any potential inaccuracies,” according to the news release.
Here is how the breath analyzer (mouthpiece, attached biosensor chamber, and attached printed circuit board chamber) is deployed by a user, according to the Opteev website:
The user turns on the device and an LED light indicates readiness.
The user blows twice into the mouthpiece.
A carbon filter stops bacteria and VOCs and allows virus particles to pass through.
As “charge carriers,” virus particles have a “cumulative charge.”
Electrical data are forwarded to the AI processor.
The AI processer delivers a result.
Within 60 seconds, a red signal indicates a positive presence of a virus and a green signal indicates negative one.
“The interaction of the virus with a specially designed liquid semiconductive medium, or a solid polymer semiconductor, generates changes in the conductivity of the electrical biosensor, which can then be picked up by electrodes. Such electrical data can be analyzed using algorithms and make a positive or negative call,” explains an Opteev white paper on the viral screening process.
While the ViraWarn breath analyzer can identify the presence of a virus, it cannot distinguish between specific viruses, the company noted. Therefore, a clinical laboratory PCR test is needed to confirm results.
Other Breath Tests
Opteev is not the only company developing diagnostic tests using breath samples.
For clinical laboratory managers and pathologists, Opteev’s ViraWarn is notable in breath diagnostics development because it is a personal hand-held tool. It empowers people to do self-tests and other disease screenings, all of which would need to be confirmed with medical laboratory testing in the case of positive results.
Further, it is important to understand that consumers are the primary target for this novel diagnostic device. This is consistent with investor-funding companies wanting to develop testing solutions that can be used by consumers. At the same time, a device like ViraWarn could be used by clinical laboratories in their patient service centers to provide rapid test results.
