Increased pace of aging in COVID-related mortality

Deep Longevity releases an AI-based predictor of COVID time-to-death.

Deep Longevity, which is spearheading the provision of deep biomarkers of aging, has announced the publication of a COVID risk calculator that can estimate the expected time-to-death (TTD) of hospitalised COVID-19 patients. The calculator is based on a peer-reviewed academic publication titled Increased pace of aging in COVID-related mortality, published in the peer-reviewed journal Life.

Longevity.Technology: Despite the global effort to fight the pandemic, the battle is not yet won. Hospitals all over the world are stretched beyond capacity with the emergence of new strains and the premature relaxation of anti-COVID measures’. In such circumstances, risk-stratification of the admitted patients remains an essential, albeit grim, necessity. This calculator will be useful for healthcare provision planning, and as the unequal impact and the lifespan and healthspan implications of long COVID become better understood, might prove a useful resource in mitigating or reversing its effects.

Jamie Gibson, Chief Executive Officer of Deep Longevity’s parent company Regent Pacific, said: “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. 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 [1].”

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 analysed 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 [2].

One of the survival models was transformed into a TTD calculator, which is available online at 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. The authors emphasise the limitations of this calculator and urge anyone to read the original paper.

BloodAge is available for consumer use at and the Young.AI app (available in the Apple App Store), which allows longitudinal tracking of age predictions, and for use by academics at, available for one-time testing.


Image credits: Mufid Majnun / Unsplash and YoungAI