AI COVID-19 Survival Calculator Provides Patient's Risk Score, Expected Time to Death and Survival Probability
By HospiMedica International staff writers Posted on 28 Jul 2021 |
Image: COVID Risk Calculator (Photo courtesy of Deep Longevity Limited)
A new artificial intelligence (AI) tool can estimate the expected time-to-death of hospitalized COVID-19 patients.
Deep Longevity Limited (Hong Kong) has announced the publication of its COVID Risk Calculator. The study features a collection of over 5,000 COVID-positive patients admitted to 11 public New York hospitals. Blood tests obtained during the admission were analyzed by a deep-learning neural network - BloodAge, to quantify the intensity of the aging process. The network takes in a typical blood panel and returns their biological age, which can be higher or lower than their chronological age.
Two survival models (Cox proportional hazards, logistic regression) showed BloodAge predictions to have more impact on a patient's survival than chronological age. In terms of expected time-to-death (TTD), each extra BloodAge year was equivalent to a one-day reduction in TTD. One of the survival models was transformed into a TTD calculator. It requires a physician to input 15 variables, including symptoms and comorbidities, to return a patient's COVID Risk Score, expected TTD, and survival probability curve.
Despite the global effort to fight the pandemic, it is still ongoing. Hospitals all over the world are stretched beyond their capacity with the emergence of new strains and the premature relaxation anti-COVID measures'. In such circumstances, risk-stratification of the admitted patients remains an essential, albeit grim, necessity.
"Age was recognized as the main risk factor affecting patients' survival at the very onset of the pandemic. The elderly have been reported to have the highest mortality rate, as well as suffer from more complications in numerous studies. In the meantime, most such studies ignore that there is no universal pace of aging," said Jamie Gibson, Chief Executive Officer of the Company. "Some people age faster than others. This notion is obvious to medical professionals, who have gained the ability to tell overagers and underagers apart throughout the years of practice. However, the official records lack any information on the true, biological age of COVID patients. The research project by Deep Longevity in collaboration with Lincoln Medical Center highlights the importance of quantifying aging rate for accurate survival analysis."
Related Links:
Deep Longevity Limited
Deep Longevity Limited (Hong Kong) has announced the publication of its COVID Risk Calculator. The study features a collection of over 5,000 COVID-positive patients admitted to 11 public New York hospitals. Blood tests obtained during the admission were analyzed by a deep-learning neural network - BloodAge, to quantify the intensity of the aging process. The network takes in a typical blood panel and returns their biological age, which can be higher or lower than their chronological age.
Two survival models (Cox proportional hazards, logistic regression) showed BloodAge predictions to have more impact on a patient's survival than chronological age. In terms of expected time-to-death (TTD), each extra BloodAge year was equivalent to a one-day reduction in TTD. One of the survival models was transformed into a TTD calculator. It requires a physician to input 15 variables, including symptoms and comorbidities, to return a patient's COVID Risk Score, expected TTD, and survival probability curve.
Despite the global effort to fight the pandemic, it is still ongoing. Hospitals all over the world are stretched beyond their capacity with the emergence of new strains and the premature relaxation anti-COVID measures'. In such circumstances, risk-stratification of the admitted patients remains an essential, albeit grim, necessity.
"Age was recognized as the main risk factor affecting patients' survival at the very onset of the pandemic. The elderly have been reported to have the highest mortality rate, as well as suffer from more complications in numerous studies. In the meantime, most such studies ignore that there is no universal pace of aging," said Jamie Gibson, Chief Executive Officer of the Company. "Some people age faster than others. This notion is obvious to medical professionals, who have gained the ability to tell overagers and underagers apart throughout the years of practice. However, the official records lack any information on the true, biological age of COVID patients. The research project by Deep Longevity in collaboration with Lincoln Medical Center highlights the importance of quantifying aging rate for accurate survival analysis."
Related Links:
Deep Longevity Limited
Latest COVID-19 News
- Low-Cost System Detects SARS-CoV-2 Virus in Hospital Air Using High-Tech Bubbles
- World's First Inhalable COVID-19 Vaccine Approved in China
- COVID-19 Vaccine Patch Fights SARS-CoV-2 Variants Better than Needles
- Blood Viscosity Testing Can Predict Risk of Death in Hospitalized COVID-19 Patients
- ‘Covid Computer’ Uses AI to Detect COVID-19 from Chest CT Scans
- MRI Lung-Imaging Technique Shows Cause of Long-COVID Symptoms
- Chest CT Scans of COVID-19 Patients Could Help Distinguish Between SARS-CoV-2 Variants
- Specialized MRI Detects Lung Abnormalities in Non-Hospitalized Long COVID Patients
- AI Algorithm Identifies Hospitalized Patients at Highest Risk of Dying From COVID-19
- Sweat Sensor Detects Key Biomarkers That Provide Early Warning of COVID-19 and Flu
- Study Assesses Impact of COVID-19 on Ventilation/Perfusion Scintigraphy
- CT Imaging Study Finds Vaccination Reduces Risk of COVID-19 Associated Pulmonary Embolism
- Third Day in Hospital a ‘Tipping Point’ in Severity of COVID-19 Pneumonia
- Longer Interval Between COVID-19 Vaccines Generates Up to Nine Times as Many Antibodies
- AI Model for Monitoring COVID-19 Predicts Mortality Within First 30 Days of Admission
- AI Predicts COVID Prognosis at Near-Expert Level Based Off CT Scans