Antarctic deployment screening exposed major gaps between cardiovascular risk calculators
Antarctic medicine leaves little margin for error. Once personnel are deployed, access to advanced diagnostics, inpatient care, and rapid evacuation is constrained by distance, weather, and logistics, so pre-deployment cardiovascular triage has to be unusually conservative. In a retrospective analysis of 154 people referred for enhanced cardiac evaluation before assignment to the U.S. Antarctic Program, contemporary risk tools did not behave as interchangeable instruments. Their disagreement was not subtle, and in a setting where a single missed event can strand a patient far from definitive care, that discrepancy carries immediate operational weight.
The cohort was predominantly male, with a mean age of 44.6 years, and abnormal ECG findings drove most referrals. Those referral patterns fit the practical reality of occupational screening: the first signal is often not a symptom but an incidental electrical abnormality, a family history concern, or a risk-factor burden that raises doubt about safe deployment. The investigators estimated 10-year risk using the Framingham Risk Score, the ACC/AHA Pooled Cohort Equations, and the newer AHA PREVENT model. Median predicted risk remained low by PREVENT and Framingham, while PCE placed the same group into a clearly higher range.
PREVENT and Framingham tracked closely while PCE consistently ran higher
Among the full cohort, median risk was 2.7% by PREVENT, 2.3% by Framingham, and 6.1% by PCE. In 72 participants with all three scores available, 51% were classified discordantly. PREVENT and Framingham showed close agreement, with a kappa of 0.77, while agreement between PREVENT and PCE was poor, with kappas of 0.24 to 0.35. The direction of the mismatch mattered: PCE repeatedly estimated higher risk than the other two tools.
That pattern is not just a statistical nuisance. Occupational programs depend on thresholds, and a model that systematically pushes workers into a higher risk stratum can alter clearance decisions, prompt more imaging, or trigger referrals that may never be clinically necessary. By contrast, a model that aligns with Framingham while extending into more contemporary domains may reduce friction during transition without abandoning familiar risk language. PREVENT has additional features that make it attractive in this setting: it is race-agnostic, incorporates renal function, extends to heart failure, and applies from age 30 onward.
PCE also proved less usable. It was calculable in 52.6% of participants, compared with 70.8% for PREVENT. In a screening population where incomplete data are common and time to decision is short, feasibility is not a minor technical issue. A calculator that cannot be applied to nearly half the cohort weakens its value as a gatekeeping tool, especially when the setting demands clear decisions rather than theoretical precision.
Clearance decisions, evacuations, and the cost of missing disease in remote medicine
Most participants were cleared for deployment: 132 of 154, or 85.7%. Eight individuals, 5.2%, were not physically qualified, and 6 of those NPQ decisions were cardiovascular in origin. Those outcomes show why pre-deployment triage exists at all. Antarctic service is not a routine employment venue; it is a medically isolated environment where unstable coronary disease, severe arrhythmia risk, or decompensated structural disease can become an evacuation problem instead of an outpatient problem.
A particularly striking operational finding came from the evacuation data. During the study period, there were 22 cardiac medical evacuations, and 21 occurred in people who had not undergone prior advanced cardiovascular screening. One screened individual was evacuated for symptoms that ultimately had no cardiac cause. The imbalance suggests that advanced screening may be catching some higher-risk candidates before deployment, while also reminding clinicians that symptoms in this environment can be alarming without proving a cardiac mechanism.
For a remote expedition program, the practical consequence is straightforward: the wrong person on the ice can force a costly evacuation, while the wrong person left unscreened can create an avoidable emergency far from help. The investigators’ data do not show that advanced screening eliminates all downstream events, but they do show that most evacuations happened outside the enhanced screening pathway. In a region where environmental exposure and logistical isolation magnify every medical decision, that distribution is hard to ignore.
Why the newer model may fit this cohort better than older equations
The reason PREVENT may be a better fit here is partly methodological and partly biological. Contemporary risk prediction increasingly benefits from variables that reflect broader cardiometabolic and renal burden, because vascular risk is not defined only by age, cholesterol, and blood pressure. Renal dysfunction, for example, tracks with endothelial injury, arterial stiffness, and systemic inflammatory stress, all of which matter when judging whether a person can tolerate weeks or months in a remote station with limited escalation options. A model that includes those dimensions may better mirror the real physiology of an older, screening-selected workforce.
Framingham’s close agreement with PREVENT in this analysis may also ease implementation. If an occupational system has long relied on older risk estimates, a newer calculator that lands near the same territory but adds modern variables may be easier to adopt than a model that inflates risk across the board. PCE, by contrast, consistently estimated higher risk in this cohort, which could reflect differences in derivation, the characteristics of a fitness-selected population, or the way the equation performs in people being screened for expedition duty rather than general primary prevention.
Targeted imaging may still have a role, especially for intermediate-risk candidates. Coronary artery calcium scoring is the clearest example raised by the investigators, and the logic is clinically coherent: in a person whose calculator-based risk is uncertain, anatomic evidence of coronary plaque burden can refine the decision more directly than a score derived from population averages. In a deployment system, where the decision is not simply treatment but whether a person should enter a medically austere environment, that extra layer of stratification can be useful.
The study remains a preprint and was conducted retrospectively, so the findings should be treated as hypothesis-generating rather than definitive policy. Even so, the message is concrete: in Antarctic deployment screening, risk calculators are not interchangeable, and PREVENT appears to align more closely with Framingham while remaining more usable than PCE. The remaining question is how well any calculator, even the best of the three, performs when the consequence of error is not just a clinic visit but a medical event thousands of miles from routine care.