The novel coil procedure was led by David Santiago-Dieppa, MD, a cerebrovascular and endovascular neurosurgeon at UC San Diego Health and associate professor of neurological surgery at the University of California San Diego School of Medicine. According to Santiago-Dieppa, chronic subdural hematoma is an increasingly common condition in neurosurgical practice, and patients stand to benefit from minimally invasive approaches that can reduce the need for repeat operations. The first patient treated with the new coil technology at UC San Diego Health is recovering well, an encouraging early signal as clinicians continue to evaluate the device’s potential. He emphasized that advances in both coil-based and liquid embolization technologies are giving physicians more options to tailor treatment to each patient’s anatomy and clinical needs, with the ultimate goal of more consistently treating the source of these brain bleeds and improving outcomes.

Chronic subdural hematoma develops when blood collects between the brain and its outermost covering, the dura mater, often following a minor head injury that may have seemed trivial at the time. Because the bleeding accumulates slowly over days or weeks, symptoms such as headaches, confusion, memory problems, gait difficulties, and drowsiness can emerge gradually and are sometimes mistaken for normal aging or dementia. The condition is becoming steadily more common for two converging reasons: the population is growing older, and a rising number of people take blood-thinning medications such as anticoagulants and antiplatelet drugs, which impair the body’s ability to stop even small bleeds. That demographic and pharmacological trajectory is what drives the projection that chronic subdural hematoma may top the list of adult cranial neurosurgical conditions within the decade.

For much of modern neurosurgical history, the standard treatment was surgical drainage, in which a surgeon drills one or more small holes in the skull to evacuate the accumulated blood. While often effective, surgery carries risks for elderly patients and, crucially, does not address the underlying vascular source that can allow fluid and blood to reaccumulate. Recurrence rates after drainage alone have historically been a persistent clinical challenge, forcing a subset of patients back to the operating room for repeat procedures that compound their risk and prolong recovery.

In recent years, a minimally invasive alternative called middle meningeal artery embolization has transformed the treatment landscape. The middle meningeal artery, or MMA, is a branch of the external carotid artery that supplies the dura mater and is intimately involved in the formation and persistence of chronic subdural hematomas. During embolization, physicians navigate a tiny microcatheter through the vasculature, typically from an access point in the groin or wrist, until it reaches the MMA. They then deploy an embolic material to block the artery, cutting off the blood supply that feeds the bleed and helping prevent it from returning. Rather than replacing surgery in every case, embolization is often combined with drainage or used in select patients as a stand-alone strategy, and studies have shown it can reduce the likelihood that patients will need additional procedures while potentially improving long-term outcomes.

As MMA embolization has gained widespread adoption, device developers have raced to improve how physicians seal the target artery. Two broad technological philosophies have emerged. Coil-based approaches use specialized metallic coils to mechanically obstruct blood flow within the vessel, while liquid embolic materials are designed to flow with the bloodstream and penetrate smaller vessel branches farther from the treatment site, occluding a more diffuse vascular network. Each strategy has trade-offs related to vessel anatomy, procedural control, and the completeness of closure. The newly introduced coil technology adopted at UC San Diego Health is engineered to help physicians shut down the artery more completely and efficiently than previous generations of embolization coils. Its flexible design allows delivery through small and tortuous blood vessels, and it can treat a longer section of the artery than earlier coils, potentially reducing the number of devices and steps required in a given procedure.

Santiago-Dieppa’s role in the technology extended beyond the operating room. Drawing on extensive experience performing MMA embolization procedures, he provided clinical insights during the device’s development phase, helping shape a tool intended to improve procedural efficiency and the quality of artery closure. That clinician-inventor feedback loop, in which frontline operators inform device design before and after regulatory approval, has become an increasingly important pathway for translating neurovascular innovations into routine practice. It also reflects a broader trend in which academic medical centers serve simultaneously as early adopters, evaluators, and co-developers of emerging technologies.

UC San Diego Health’s leadership in this field is not limited to device adoption. Researchers in the Department of Neurological Surgery at UC San Diego School of Medicine developed the Patency After Coil Embolization, or PACE, scoring system, a standardized method for measuring how completely blood flow through the middle meningeal artery has been eliminated after treatment. The system gives clinicians and researchers a common language for grading technical success, and early research suggests that more complete closure of the artery may be linked to better resolution of the brain bleed over time. By quantifying the degree of occlusion achieved with different devices and techniques, tools like the PACE score help determine which patients benefit most from which embolization strategy, moving the field toward evidence-based personalization.

The institution has also been deeply involved in the clinical trial infrastructure supporting liquid embolic agents. J. Scott Pannell, MD, director of neurointerventional surgery, endovascular neurosurgeon, and neurointerventional radiologist at UC San Diego Health, and professor of neurological surgery and radiology at UC San Diego School of Medicine, served as a site investigator for the EMBOLISE clinical trial, which evaluated the addition of MMA embolization to standard treatment for chronic subdural hematoma. Evidence from that national trial and others supported the recent FDA approval of a liquid embolic for this indication, and Pannell’s involvement helped bring new embolization techniques and clinical research opportunities to patients in San Diego. He noted that the growing body of evidence supporting MMA embolization has fundamentally changed how physicians approach the condition, and that liquid embolic technologies offer a primary option that may allow more complete treatment in select patients. In his view, participation in trials such as EMBOLISE has been critical to understanding how these therapies can improve patient outcomes.

The convergence of these developments, from next-generation coils to approved liquid embolics and standardized occlusion scoring, points toward a future in which chronic subdural hematoma treatment becomes more predictable, effective, and accessible for the growing number of patients diagnosed each year. Physicians can now select among mechanical and liquid occlusion strategies based on vascular anatomy, the extent of dural supply, and patient-specific risk factors, rather than relying on a one-size-fits-all surgical approach. UC San Diego Health, nationally recognized for its treatment of complex brain, spine, and cerebrovascular disorders and ranked among the nation’s best in neurology and neurosurgery in the 2026-2027 U.S. News Best Hospitals rankings, continues to advance this evolution through research, clinical trials, and multidisciplinary care for conditions ranging from stroke and aneurysms to traumatic brain injury. As the population ages and anticoagulant use rises, the ability to treat the vascular source of chronic brain bleeds through a catheter rather than a craniotomy may prove to be one of the most consequential shifts in neurosurgery this decade.

Subject of Research: Minimally invasive middle meningeal artery embolization technologies for chronic subdural hematoma

Article Title: First health system on West Coast to use new brain bleed technologies

Article References: First health system on West Coast to use new brain bleed technologies. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: chronic subdural hematoma, middle meningeal artery embolization, neurosurgery, embolization coils, liquid embolic, UC San Diego Health, EMBOLISE trial, PACE score, minimally invasive procedures, brain bleed, neurointerventional surgery, aging population

The tool in question is the FUNC score, short for the Functional Outcome in Patients with Primary Intracerebral Hemorrhage score, developed by neurologists at Massachusetts General Hospital and first published in the journal Stroke in 2008. The score distills a handful of variables available at hospital admission — the volume and location of the bleed on initial imaging, the patient’s age, the Glasgow Coma Scale score on arrival, and whether the patient was already taking an anticoagulant medication such as warfarin — into a single number ranging from zero to eleven. In its original validation, the score predicted whether patients would achieve functional independence, defined as a Glasgow Outcome Scale score of four or higher, ninety days after the hemorrhage. That three-month window, however, has increasingly been recognized as an arbitrary snapshot of what is often a much longer recovery.

Recovery after brain injury is not a fixed event but a moving target. Emerging evidence, including a growing body of work on recovery trajectories after intracerebral hemorrhage, shows that many patients continue to improve between three and twelve months after the bleed. In some cohorts, a meaningful proportion of survivors who were dependent at three months walk, feed themselves, and manage their own affairs a year later. If clinicians base early prognostic conversations only on tools validated at three months, they risk anchoring families to an overly pessimistic view — or, conversely, they may lack the statistical justification to make confident statements about long-term independence at all. Recognizing this gap, a team led by Joel Neves Briard, a postdoctoral researcher in Columbia’s Department of Neurology, and senior author Jan Claassen, professor of neurology and critical care specialist, set out to ask whether the FUNC score could be stretched, without distortion, to a twelve-month horizon.

The team conducted a single-center prospective cohort study, enrolling adult patients admitted with primary intracerebral hemorrhage between February 2009 and January 2018. Prospective enrollment matters in this kind of research: rather than reconstructing cases from medical records after the fact, investigators collected structured data on patients as they arrived and followed them forward, reducing the bias and missingness that plague retrospective analyses. All patients included in the analysis had a primary hemorrhage — meaning the bleed was not secondary to trauma, aneurysm rupture, or vascular malformation — and each patient’s FUNC score was calculated at admission using the same variables that have anchored the score since its debut. Twelve months later, surviving patients’ functional status was assessed using the Glasgow Outcome Scale, a five-point measure that ranges from death to good recovery and that classifies patients scoring four or five as broadly independent.

The cohort ultimately comprised 535 patients, a substantial sample for this disease. The patients were, on average, seriously ill: their median age was 68 years, 44 percent were women, and the median National Institutes of Health Stroke Scale score at presentation was 16, a level indicating moderate to severe neurological impairment. Median FUNC score in the cohort was 8 on the eleven-point scale. Among the 445 patients with a known Glasgow Outcome Scale assessment at twelve months, 99 — roughly 22 percent — had achieved functional independence. That figure alone carries a message for families: even in a cohort skewing severely ill, more than one in five patients was living independently a year after a brain bleed, a reminder that early appearances can mislead.

The statistical heart of the study lay in testing whether the FUNC score’s predictions still held at one year. The researchers used logistic regression to calculate the area under the receiver operating characteristic curve, or AUC, the standard gauge of a prediction model’s ability to separate those who will experience an outcome from those who will not. An AUC of 0.5 indicates performance no better than a coin flip, while 1.0 represents perfect discrimination. For twelve-month functional independence, the FUNC score achieved an AUC of 0.79, with a 95 percent confidence interval of 0.75 to 0.84 — a level of discrimination broadly comparable to the score’s originally validated three-month performance. In practical terms, the score’s simple admission-time arithmetic remained nearly as informative about the one-year future as about the three-month one.

Discrimination, however, is only half the story. A model can rank patients correctly yet still misjudge how likely the outcome actually is, which is why the researchers also evaluated calibration — the agreement between predicted probabilities and observed frequencies — using a calibration plot, alongside the Brier score, a composite measure of both discrimination and calibration that penalizes confident wrong answers. The calibration plot showed reasonable agreement between predicted and observed outcomes across the probability range, with modest evidence that the score slightly overestimated the likelihood of independence at the low end of predicted probabilities. The Brier score came in at 0.15, a respectable figure for a binary clinical outcome. To address the patients lost to follow-up, the team handled missing twelve-month outcomes using multiple imputation by chained equations, a modern statistical technique that fills gaps by modeling each incomplete variable conditional on all others, preserving uncertainty rather than pretending missing data do not exist.

Finally, the investigators asked a question that has become central to the modern evaluation of clinical prediction tools: does the score actually help make decisions? Using decision curve analysis, a method developed by Vickers and Elkin that quantifies the net clinical benefit of acting on a model’s predictions across a range of risk thresholds, the team found that the FUNC score delivered measurable net benefit across threshold probabilities from 5 to 50 percent. In other words, within the range of probabilities most relevant to real clinical conversations, basing decisions on the score beat both the strategy of assuming every patient will be independent and the strategy of assuming none will be — the two extremes any prognostic tool must outperform to be worth the bedside clinician’s attention.

The implications reach beyond the family meeting. The authors point to two principal applications. The first is counseling: armed with a validated twelve-month prediction, clinicians can offer families a more honest account of long-term prospects, tempering the nihilism that has historically shadowed intracerebral hemorrhage care. This matters because premature withdrawal of life-sustaining therapy, driven in part by excessively pessimistic early prognostication, remains a documented concern in hemorrhagic stroke, and formal guidelines for neuroprognostication in critically ill adults with intracerebral hemorrhage explicitly caution against self-fulfilling prophecies. The second application is methodological. The FUNC score can support sliding dichotomy, a trial-design strategy in which the outcome threshold for success is adjusted to each patient’s predicted baseline risk — an approach pioneered in traumatic brain injury research that increases statistical power by recognizing that a modest recovery for the sickest patients may be as meaningful as a full recovery for the mildest ones.

The researchers are careful about limits. This was a single-center study at a major academic medical center, and prediction models notoriously lose calibration when transplanted to different populations and care environments; external validation across centers is needed before the twelve-month FUNC score becomes standard practice. The field’s own reporting standards, codified in the TRIPOD+AI statement for clinical prediction models, demand exactly this kind of transparency about performance, calibration, and intended use. Still, the study’s message is striking in its simplicity. A tool invented nearly two decades ago, computed from five variables a clinician can gather before the patient has even reached the intensive care unit, appears to hold its predictive power across the full year in which the brain does its slow, stubborn work of healing. For the families sitting in hospital corridors, that is not just a statistical achievement — it is a reason to keep asking the question, and to keep hoping the answer changes.

Subject of Research: Prediction of 12-month functional independence after primary intracerebral hemorrhage using the FUNC score

Article Title: Extension of the FUNC Score for Prediction of 12-Month Functional Independence after Primary Intracerebral Hemorrhage

Article References: Extension of the FUNC Score for Prediction of 12-Month Functional Independence after Primary Intracerebral Hemorrhage. (n.d.). https://doi.org/10.1007/s12028-026-02661-6

Image Credits: AI Generated

DOI: 10.1007/s12028-026-02661-6

Keywords: intracerebral hemorrhage, stroke, FUNC score, prognostication, functional outcome, Glasgow Outcome Scale, neurocritical care, prediction model, recovery, brain bleed, Extension, FUNC

D-dimer is far from a new player in stroke medicine. The molecule appears whenever the body forms a clot and then dissolves it, so elevated concentrations signal active coagulation and fibrinolysis. Previous studies had already linked high D-dimer levels after aneurysmal subarachnoid hemorrhage to poor outcomes, but the mechanism driving that early surge remained contested. Was the marker simply reflecting the size of the hemorrhage itself, or was it tracking deeper disturbances of hemostasis, systemic inflammation, or secondary complications such as delayed cerebral ischemia? Disentangling these possibilities requires detailed imaging data and sophisticated statistical modeling, both of which the Nanchang team brought to bear. Their study drew on the PROSAH-MPC cohort, a single-center database of 473 patients treated at the Second Affiliated Hospital of Nanchang University, each of whom had complete clinical, radiological, and laboratory records from the moment of admission.

The centerpiece of the analysis was a quantitative variable that few centers routinely measure: total bleeding volume. Using deep learning-based segmentation of non-contrast CT scans, the researchers computed the absolute volume of subarachnoid blood for every patient, an approach they had validated in earlier work. Rather than grading hemorrhage on the coarse modified Fisher scale, which lumps patients into broad categories, total bleeding volume offers a continuous, patient-specific measure of the initial insult. When the team stratified patients by admission D-dimer quartiles, a clear gradient emerged. Patients in the highest quartile were substantially more likely to suffer an unfavorable outcome, defined as a modified Rankin scale score of 3 to 6 at twelve months, meaning moderate to severe disability or death. Across the entire cohort, 125 patients, or 26.4 percent, experienced such unfavorable outcomes.

The statistical backbone of the study was multivariable logistic regression, adjusted for the standard confounders that muddy any observational analysis. Even after accounting for age, clinical severity as measured by the Hunt–Hess grade, radiological severity on the modified Fisher score, and other clinical variables, elevated admission D-dimer remained independently associated with poor functional outcome, with an adjusted odds ratio of 1.08 per unit increase and a 95 percent confidence interval of 1.02 to 1.16. That effect size may look modest, but in a condition where every percentage point of prognostic accuracy matters, the signal is clinically meaningful. More striking was the discovery of a statistically significant interaction between D-dimer and total bleeding volume, with a P value for interaction of 0.032. In plain terms, the prognostic impact of the blood marker depended on how much blood was actually present in the subarachnoid spaces, and vice versa.

This interaction finding is where the study moves beyond simple biomarker association and begins to touch on mechanism. One leading hypothesis, advanced by the authors in light of prior literature, is that large volumes of subarachnoid blood elevate intracranial pressure and physically stress the delicate neurovascular environment, while simultaneously releasing procoagulant and fibrinolytic products that drive D-dimer upward. The blood clot burden and the fibrin turnover it provokes may thus represent two faces of the same pathological process. An alternative interpretation is that acute microthrombosis within cortical vessels, a phenomenon increasingly documented after subarachnoid hemorrhage, generates both the biochemical signature and much of the delayed ischemic damage that destroys neurons in the days after the bleed. The new data cannot definitively adjudicate between these mechanisms, but they establish that the two variables are not redundant: each captures prognostic information the other misses.

To formalize these relationships into a practical prediction tool, the team turned to machine learning. Using the Boruta algorithm, an established wrapper method for feature selection that compares each candidate variable against randomized shadow features, they identified five top-ranked predictors: admission D-dimer, total bleeding volume, age, Hunt–Hess grade, and the modified Fisher score. Seven different machine learning architectures were then trained to predict twelve-month functional outcome. A composite of D-dimer, total bleeding volume, and Hunt–Hess grade achieved an area under the receiver operating characteristic curve of 0.867, outperforming any single variable alone, with D-dimer alone reaching 0.735, total bleeding volume 0.783, and the Hunt–Hess grade 0.833. Among the full algorithmic models, XGBoost, a gradient-boosted decision tree method celebrated for its performance on tabular clinical data, achieved the highest discriminative power with an AUC of 0.904 on the held-out internal test set.

What elevates this work above many machine learning studies in medicine is its commitment to interpretability. Black-box predictions are notoriously hard to trust in the intensive care unit, where clinicians must justify every decision. The researchers therefore applied SHapley Additive exPlanations, or SHAP, a technique rooted in cooperative game theory that assigns each feature a quantified contribution to every individual prediction. The SHAP analysis confirmed that D-dimer and total bleeding volume were among the dominant contributors to the model’s output, alongside the established severity scales. Crucially, SHAP interaction plots revealed a synergistic adverse effect: at higher D-dimer concentrations, increasing total bleeding volume pushed predictions steeply toward poor outcome, visually mirroring the statistical interaction term from the regression analysis. For individual patients, force plots showed exactly which variables drove a given forecast, offering clinicians a transparent rationale rather than an inscrutable score.

The clinical implications are tantalizing but must be tempered by the study’s design. As a retrospective, single-center cohort, the findings require external validation before admission D-dimer can be woven into formal prognostic scores or triage protocols. The study also complied with the STROBE reporting guidelines and used structured approaches such as the CHARMS checklist for prediction model appraisal, along with E-value sensitivity analyses to probe unmeasured confounding, all of which strengthen confidence in the internal validity. Nonetheless, the appeal of the approach lies in its practicality: D-dimer is already measured in virtually every emergency department in the world, costs pennies, and returns results within minutes. If combined with automated CT segmentation pipelines that are rapidly maturing, a bedside risk estimate could plausibly be generated within the first hour of hospital arrival, precisely the window when decisions about transfer, aneurysm securing, and blood pressure management are made.

For patients and families, the difference between a 70 percent and a 90 percent accurate prognosis on day one can shape everything from the intensity of neurocritical care to the timing of family conversations. For researchers, the study adds to a growing body of evidence that hemostatic activation is not an epiphenomenon of aneurysmal subarachnoid hemorrhage but a central axis of injury, one that intersects with bleeding volume, intracranial pressure, and delayed cerebral ischemia. Whether interventions targeting coagulation or fibrinolysis could one day improve outcomes remains an open question, and the authors are careful not to overclaim therapeutic implications. What their work does establish, with unusual statistical rigor and computational transparency, is that the humble fibrin fragment measured on admission encodes genuine, quantifiable information about a patient’s twelve-month trajectory. In a disease where early brain injury unfolds within hours, that information, delivered through interpretable machine learning models, may prove to be exactly the early warning system neurointensivists have been seeking.

Subject of Research: Prognostic value of admission D-dimer and total bleeding volume in aneurysmal subarachnoid hemorrhage using interpretable machine learning.

Article Title: Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling

Article References: Wu, Y., Hu, P., Yang, X., Liao, Q., Chen, Z., Zhang, S., Xiao, B., Lv, S., Wu, M., Yan, T., Zhu, X., Ye, M., & Tu, W. (2026). Prognostic Value of Admission D-dimer Levels and Total Bleeding Volume in Aneurysmal Subarachnoid Hemorrhage: A Retrospective Cohort Study with Machine Learning-Based Modeling. Neurocritical Care. https://doi.org/10.1007/s12028-026-02558-4

Image Credits: AI Generated

DOI: 10.1007/s12028-026-02558-4

Keywords: aneurysmal subarachnoid hemorrhage, D-dimer, total bleeding volume, machine learning, XGBoost, SHAP, prognosis, biomarkers, neurocritical care, stroke, CT imaging, functional outcomes