Volv Global Poster: Early prediction of ARDS in community-acquired pneumonia patients using machine learning
Presenting new research on whether a machine learning model can flag which patients with CAP are most likely to progr...
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The floppy-infant sign present in almost all untreated Type 1 cases from birth or early infancy.
Near universal in untreated Type 1 disease, reflecting loss of anterior horn cells.
Fine tremor of outstretched hands, a characteristic finding in around 70% of Type 2 patients.
Affects more than 60% of Type 2 patients by adolescence as trunk muscles weaken.
Gowers sign and waddling gait mark ambulatory Type 3 onset after 18 months.
The earliest pipeline decisions carry lasting consequences: the wrong indication chosen, indications pursued in the wrong order, or capital committed where expected returns already trail its cost. Mistakes made this early are expensive to correct later.
When patients cannot be found, recruitment stalls and the burn rate climbs. High screen-failure rates add further cost and delay, and a mixed cohort makes the results harder to interpret.
Finds the undiagnosed and mis-coded patients conventional feasibility misses, widening the recruitable pool.
Surfaces diagnosed-but-hidden, trial-eligible patients already in the data, so sites enrol faster.
Predicts who will progress or respond, enriching the cohort so the signal is not diluted.
Without strong, timely evidence, payers can restrict access or cut the price. An under-characterised population weakens the budget case, and HTA approval demands more evidence than regulatory approval alone.
Generates the real-world evidence and outcomes payers ask for, ahead of the submission deadline.
Finds the undiagnosed patients missing from the count, so the eligible population holds up under scrutiny.
Surfaces the diagnosed-but-eligible patients who expand the eligible population count.
Healthcare data is fragmented across markets, so evidence has to be rebuilt market by market, slowing decisions. Time to an accurate diagnosis varies by who the patient is and where they are treated.
Works across fragmented, differently-coded data by design, so insight holds up country to country without a rebuild.
Detects patients ahead of typical diagnosis, closing the gap in awareness and pathway that drives unequal delay.
A launch planned without a view of findable patients is poorly resourced from day one. Targeting too broad a population slows early uptake, and real-world evidence gathered too late leaves little time to build a strong access dossier.
Finds the treatable patients, so the launch is planned against real demand.
Adds the eligible, reachable patients that launch planning is missing.
Builds the access evidence early enough to matter.
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Presenting new research on whether a machine learning model can flag which patients with CAP are most likely to progr...
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