Blockchain Papers

Alexandre Tran, David Granton, Eddy Fan, Bram Rochwerg

Clinical practice guidelines (CPGs) are used by critical care clinicians to guide practice and inform best care. According to the GRADE framework, evidence synthesis should preferentially rely on randomized controlled trials (RCTs) because they minimize bias and establish causality.1 Despite challenges, critical care is well-suited to randomized studies given its (1) high incidence of acute conditions, (2) protocolized interventions, (3) standardized outcomes, and (4) strong data infrastructure and trial networks.2,3 Despite the advantages, RCTs are often unavailable for CPGs or leave knowledge gaps, particularly for subgroup effects or patient-important outcomes like long-term quality of life related to heterogeneous populations, urgent interventions, and recruitment constraints.4,5 Physicians may hesitate to apply RCT results because (1) enrolled patients differ from real-world populations, (2) key outcomes may be unmeasured, (3) effect estimates may be imprecise, and (4) subgroup analyses may be lacking.6 When RCT evidence is insufficient, high-quality non-randomized studies of interventions (NRSI) can complement trials by approximating causal inference—estimating exposure effects while separating systematic bias from random error.7 High-quality NRSI ­require large, well-validated datasets with minimal missingness and adequate temporal resolution. Without these, even advanced analytics cannot yield credible estimates. NRSI often emulate target trials, aligning eligibility, time zero, and predefined interventions and outcomes.8,9 Design must reflect strong knowledge of confounders and time-varying biases, addressed through advanced data and statistical methods. When based on explicit and credible assumptions (eg, exchangeability, no residual confounding), NRSI can yield valid and generalizable estimates, though such assumptions cannot be proven and still require caution in interpretation.10 Most NRSI are retrospective and lack safeguards standard in RCTs such as trial registration or prespecified outcomes. In target-trial emulation (Table 1), preregistration before data access is critical to prevent selective reporting and analytic flexibility, mirroring RCT practice. These limitations are especially relevant in critical care, given dynamic physiology, urgent decisions, and substantial clinical heterogeneity. These factors complicate exposure timing, increase time-varying confounding, and challenge stability assumptions in target-trial designs. Rigorous cohort definition and analytic strategy are essential when applying NRSI in this context. As causal-inference methods such as target-trial emulation spread, cautious application with methodological rigor and transparency is essential to avoid poorly executed, misleading, or irreproducible NRSI. High-quality NRSI depend not only on analytical sophistication but also on careful data acquisition, explicit protocolization, and transparency in prespecifying exposures, outcomes, and analytic plans—principles that mirror RCT standards. Target trial (ideal RCT) versus emulation. 1. Treatment with ECMO therapy if PaO2/FiO2 < 80 mmHg 2. Treatment with conventional mechanical ventilation without the use of ECMO therapy Adapted from: National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; Committee on Developing a Protocol to Evaluate the Concomitant Prescribing of Opioids and Benzodiazepine Medications and Veteran Deaths and Suicides. An Approach to Evaluate the Effects of Concomitant Prescribing of Opioids and Benzodiazepines on Veteran Deaths and Suicides. Washington (DC): National Academies Press (U.S.); 2019 Sep 24. 2, Specifying the Target Trial. Available from: https://www.ncbi.nlm.nih.gov/books/NBK547516/. Case example: Venovenous extracorporeal membrane oxygenation in patients with acute covid-19 associated respiratory failure: comparative effectiveness study.22 This commentary examines the evolving role of NRSI in developing critical care CPGs. We outline key challenges in conducting and synthesizing critical care research, then describe how high-quality NRSI can complement randomized evidence by (1) aligning effect estimates with RCTs, (2) informing certainty of evidence (CoE), and (3) guiding clinical practice recommendations. We propose practical strategies for CPG panels and domain experts to maximize the utility of NRSI while maintaining methodological rigor. Our goal is to support CPG panelists, researchers, and clinicians in interpreting recommendations that integrate NRSI. These recommendations align with evolving GRADE guidance, operationalizing its principles for critical care applications. GRADE provides a structured approach for rating CoE, the confidence that an estimated effect is close to the truth.11 When ­developing guidelines, the GRADE Evidence-to-Decision (EtD) framework translates synthesized evidence into recommendations by weighing intervention effects, CoE, patient-valued outcomes, and contextual factors such as resource use, equity, acceptability, and feasibility.12 These contextual judgments ensure that evidence is interpreted through a patient- and system-centered lens, recognizing that even high-certainty data require value-based consideration before adoption into practice. A review of critical care CPGs showed reasonable uptake of GRADE, with recommendation strength generally aligned with CoE.13 However, strong recommendations are still often made from low or very low-certainty evidence, often related to evidence gaps in RCTs. This highlights the need to integrate high-quality NRSI into CPG development to strengthen evidence synthesis and uptake. Critical care CPG panels should consistently apply GRADE principles, incorporating all high-quality evidence, including NRSI to augment situations where RCT data may be limited or absent. RCTs are resource-intensive and difficult to conduct in critical care.1 To maintain feasibility, investigators often overestimate effect sizes, leading to underpowered studies that may miss true effects.14,15 Reviews of critical care RCTs show that predicted treatment effects are often exaggerated—nearly 10-fold higher than observed, and that few trials sufficiently justify their sample-size targets.16 Similar overestimation has been reported in sepsis, stroke, and trauma trials.17–19 Among high-profile publications, fewer than half of trials had reproducible results.20 Moreover, a meta-epidemiologic review of more than 600 critical care trials found that only 1 in 16 was at low risk of bias, with little improvement over 4 decades.21 These findings suggest that RCTs alone may not provide sufficient high-quality evidence to inform strong guideline recommendations. Critical care populations are highly heterogeneous, encompassing subgroups with different baseline risks and treatment ­responses. RCTs often target broad syndromes such as sepsis or acute respiratory distress syndrome (ARDS), which likely contributes to many “negative” trials unable to detect differences in outcome.22 Because these studies estimate average treatment effects (ATEs) across diverse patients, potential subgroup benefits can be obscured when other subgroups experience harm.23 This variability, termed heterogeneity of treatment effect (HTE), reflects non-random differences in benefit or harm linked to patient characteristics.24 Understanding HTE (Table 2) is central to precision medicine: treatments that appear neutral on average may conceal offsetting benefit and harm across biologically or contextually distinct subgroups. Explicit exploration of these differences can refine trial design, improve interpretation, and guide targeted recommendations. Methods for assessing heterogeneity of treatment effect. Case example: Heterogeneous treatment effects of therapeutic-dose heparin in patients hospitalized for COVID-19.19 Causal forest and other machine-learning approaches allow for non-linear and interactive modeling of treatment effect heterogeneity but are more susceptible to overfitting and typically require larger sample sizes and external validation. In contrast, regression-based risk modeling approaches are generally more interpretable but may oversimplify interaction effects. RCTs typically assess HTE using pairwise subgroup analyses, but these are often underpowered, rely on arbitrary subgroup thresholds (eg, age <65 vs ≥65), and cannot capture complex interactions.25 The American Thoracic Society (ATS) and European Respiratory Society (ERS) guideline on non-invasive ventilation illustrates these limitations: subgroup evidence for conditions such as acute hypoxemic respiratory failure or ARDS came mostly from small or secondary analyses, yielding sparse data and very low certainty.26 These challenges highlight the need for improved data science approaches to identify and characterize HTE: a priority emphasized in the recent ATS research agenda for sepsis and ARDS.27 Data-driven subgroups (subphenotypes) can integrate multiple patient characteristics to assess effect modification and estimate individualized treatment effects.28,29 These models require rigorous derivation and validation to avoid overfitting, yet no consensus framework currently guides their validation or clinical use. Critical care trialists should adopt realistic effect size and recruitment targets and predefine strategies to evaluate clinically relevant HTE. When RCT evidence is insufficient, we propose strategies for CPG panels to integrate NRSI within the GRADE framework to complement RCTs and strengthen recommendations. In accordance with GRADE guidance, if the CoE from RCTs is judged to be high then the role for NRSI is minimal for the specific comparison and outcome of interest.7 However, RCTs often do not report certain patient-important outcomes such as adverse events, quality of life, or longer-term morbidity or mortality. Even if a particular question and outcome of interest have RCT evidence, the estimates of treatment effect are often limited by imprecision due to aforementioned recruitment and sample size concerns. Treatment effects are often assessed in highly selected populations; trial participants typically represent a small fraction of those screened and even meta-analyses may yield low certainty due to imprecision or inconsistency.30,31 In these situations, guideline panels should consider high-quality NRSI, defined by adherence to TARGET (Transparent Reporting of Observational Studies Emulating a Target Trial) standards, acceptable risk of bias, and robust sensitivity analyses, to supplement RCT evidence.7 Target-trial emulation exemplifies this approach: investigators first design a hypothetical randomized trial addressing the question of interest, then emulate it using observational data.8,32 For instance, an international study using the COVID-19 Critical Care Consortium dataset estimated the effect of VV-ECMO versus conventional ventilation in patients with severe COVID-19, providing real-world evidence where an RCT was impractical due to complexity and cost.33 Similar emulations have evaluated intubation,34 ventilation,35 and corticosteroid strategies36 in critical care—demonstrating how NRSI can inform practice when trials are unfeasible. Consider the example of drotrecogin alfa (activated protein C, rhAPC). Following the PROWESS RCT,37 which demonstrated benefit of rhAPC in patient with septic shock, the large open-label ENHANCE observational study38 reported a similar reduction in mortality with rhAPC but was the first to raise important concerns about serious bleeding, including intracranial hemorrhage. These observational findings influenced early guideline discussions, tempering enthusiasm for the drug, and subsequent RCTs39,40 confirmed this harm and rhAPC was ultimately withdrawn. This highlights that replication across larger datasets remains essential to confirm findings and ensure generalizability beyond selected RCT populations. This sequence illustrates an iterative process: observational signals can generate early warnings or hypotheses that subsequent RCTs confirm or refute. When results diverge, these contrasts can highlight methodological limitations or context-specific factors that warrant further investigation. The TARGET statement outlines 21 reporting items to standardize eligibility, interventions, outcomes, and analyses, improving transparency and reproducibility of emulated trials.41 Adherence to TARGET helps guideline panels assess NRSI rigor and determine when such evidence can complement or upgrade certainty around RCT findings. Similarly, the RCT-DUPLICATE initiative evaluated whether database-derived emulations can reproduce findings from RCTs across 32 cardiovascular studies, including interventions for anticoagulation, antiplatelet therapy, and chronic disease management. The authors found that effect estimates from well-designed emulations closely mirrored their RCT counterparts in both direction and magnitude, demonstrating that real-world data can yield valid causal inference when study design and analytic methods are rigorous.10 Whether successes from other fields will translate to critical care remains uncertain, given its confounding, physiologic complexity, and HTE. A blinded target-trial emulation in this setting reproduced findings of the PreVent RCT examining bag-mask ventilation and hypoxemia,42,43 providing proof-of-principle that short-term physiologic effects can be predicted from observational data, though its value for longer-term or patient-centered outcomes remains untested. Valid causal inference in NRSI requires adherence to key assumptions: exchangeability (no unmeasured confounding), positivity (each patient could receive any treatment), and consistency (observed outcomes reflect potential outcomes under that treatment).8,9 Meeting these assumptions demands careful cohort design, proper time alignment, and analytic techniques that address confounding, such as target-trial emulation, inverse-probability weighting, or doubly robust estimators.44,45 Studies must also handle time-varying confounding and competing risks (eg, death precluding extubation), which can otherwise bias effect estimates.46 To address these concerns, marginal structural models may be used to estimate the causal effect of a time-varying treatment and address the challenge of estimating treatment effects when confounders are influenced by prior treatment—a situation conventional regression models struggle with. CPG panels should systematically appraise NRSI by verifying TARGET adherence, assessing bias with validated tools such as ROBINS-I, and judging how results affect GRADE domains such as imprecision, inconsistency, and indirectness.41,47 Robust sensitivity analyses, testing alternative models, handling missing data, and probing unmeasured confounding, are essential to confirm result stability and should be clearly reported.48,49 Transparent presentation of assumptions and their plausibility further strengthen credibility. When high-certainty RCT evidence already exists for all relevant target populations, additional NRSI are seldom needed (Figure 1). More often, however, critical care trials involve highly selected populations, making complementary NRSI useful for confirming ­treatment effects in broader or under-represented groups.50,51 When RCT and NRSI results are consistent, guideline panels may consider upgrading certainty and recommendation strength in line with GRADE guidance.7 GRADE also allows rating up observational evidence when large effects, dose-response relationships, or confounding that would only diminish an observed benefit are present.52 Conversely, inconsistent or methodologically weak NRSI such as those with implausible assumptions, poor reporting, or critical bias, should be excluded, with the rationale documented. Expanding use of target-trial emulation is promising but must be paired with training and standards to prevent low-quality proliferation that could erode confidence in observational evidence.48 Framework for incorporating NRSI into critical care CPGs. CPG panels should incorporate well-conducted NRSI to strengthen CoE and adopt structured workflows: (1) verifying TARGET adherence, (2) considering potential risk of bias, and (3) linking NRSI results to GRADE domains to ensure transparent, reproducible use of observational evidence. Critical care RCTs often study heterogeneous syndromes using strict eligibility criteria that limit generalizability and obscure subgroup effects. A multicenter simulation of 15 landmark trials found that over half of real-world ICU patients would have been ineligible,53 and a review of 75 high-impact trials showed that 60% used at least one poorly justified exclusion such as language barriers or lack of insurance—further restricting applicability.54 Most RCTs originate from high-income countries, leaving major evidence gaps for critically ill patients in the Global South.55 For example, a Zambian sepsis RCT found higher mortality with early fluid resuscitation—contradicting prior goal-directed therapy trials.56,57 This discordance may be explained by the fact that these trials enrolled predominantly young, malnourished individuals predisposed to pulmonary edema and respiratory failure in a setting with limited ventilatory support. Beyond generating estimates of effectiveness in underrepresented populations, NRSIs also offer a pathway to address structural inequities in evidence generation and utilization. Conducting RCTs in the Global South is often hindered by logistical, regulatory, and infrastructural challenges—including limited research infrastructure, ethical oversight, or funding mechanisms, which systematically exclude these populations from RCTs.55 Well-designed NRSI can help bridge such gaps by leveraging local data to assess effectiveness, feasibility, and contextual factors in resource-limited settings. They can also identify structural and contextual modifiers such as malnutrition, health-system capacity, and disease epidemiology; thereby supporting more equitable, context-specific guideline recommendations.58 Embedding such evidence from the Global South not only broadens external validity but also enhances the global relevance of CPGs—thereby promoting more equitable and relevant evidence-based decision-making for clinicians practicing in resource-limited settings. NRSI can also inform feasibility, acceptability, and which are key factors in CPG For instance, the ATS guideline on ARDS a recommendation for VV-ECMO based on NRSI substantial in and across and NRSI can RCT findings to real-world which patients benefit or are based on risk or A key is which to assess how RCT results to external populations and to identify contextual effect improving both evidence relevance and trial Causal inference using real-world data can evaluate HTE across broader populations, including and patients typically underrepresented in a systematic review found major in methodological rigor for HTE analyses, particularly in testing and for confounding, the need for standardized methods and In critical care, HTE from secondary analyses of RCT In the modeling showed that patient characteristics predicted benefit from specific oxygenation targets for patients with and higher for those with The subsequent Care Medicine a recommendation higher oxygenation targets based on very low-certainty an of the trial found that even when are machine-learning models can identify clinically subgroups with benefit or the value of HTE modeling in acute respiratory These secondary analyses are and but should be by observational studies to evaluate HTE beyond RCTs. The ARDS cohort illustrates the value of non-randomized showed that patients had mortality with higher while no benefit in the example of HTE using real-world ICU Beyond also a global of guideline adherence, and ARDS outcomes. not its and rigor how observational studies can yield at a RCTs informing international ARDS When developing panels should consider how best to incorporate NRSI in HTE. this requires systematically HTE analyses, particularly for subgroups in the and assessing how these findings complement subgroup no GRADE yet panels should still evaluate whether HTE evidence recommendations or can guide research for or in RCTs. CPG panels should apply well-conducted causal-inference analyses to confirm the generalizability of RCT findings and identify clinically important HTE. RCTs the standard for and but well-designed NRSI can augment both the certainty and of evidence. Critical care CPG panels should integrate observational evidence when while recognizing methodological standardized (1) TARGET for reporting, (2) validated risk of bias and (3) explicit GRADE will ensure use of NRSI across guideline High-quality NRSI can CoE and generalizability beyond selective RCT populations, providing a to evaluate HTE. incorporating such studies into CPG development may improve both the generalizability and of recommendations. such as the dataset highlight how NRSI can HTE not in trials As analytic methods and target-trial NRSI will an important role in addressing evidence gaps in critical care. will rely on close across and to ensure that NRSI are and with the rigor of randomized authors the the authors to the and of the is at American of and Critical Care Medicine the which have been as tools used in this

P Arjun, Daniel Anandha Geethan, Kanniga Devi R, K Sanjay

In today's fast-paced healthcare environment, efficient management of patient records and seamless patient-provider communication is crucial. This study proposes a comprehensive solution integrating blockchain technology (Ethereum), frontend web interfaces, natural language processing (NLP), and adverse drug event detecting functions. Through a user-friendly React.js interface, patients input health data securely, while NLP automates medical information extraction from conversations. ADE detectors identify drug interactions and adverse events, enhancing care and safety. Ethereum integration via Metamask ensures secure record storage, with IPFS storage and CIDs enabling encrypted data sharing. A smart contract on Ethereum automates data management for transparency and accountability.

Li Ang Zhang

Sepsis is a high mortality syndrome characterized by organ dysfunction due to a severe and dysregulated acute inflammatory response to infection. Research into therapies for this syndrome has historically ended in failure, which has largely been attributed to the elevated levels of subject heterogeneity. What may have been previously attributed to variability in sepsis may be due to mechanistic differences between patients. Endotypes are distinct subtypes of disease, where underlying causes such as mechanistic or pathway related differences manifest into phenotypes of disease. The lack of mechanistic understanding of immune mediator dynamics and the responses they trigger necessitates a mathematical modeling approach to analyze its complexities. A transfer function model is proposed to describe and cluster the dynamics of key inflammatory mediators. Five sepsis endotypes were discovered and revealed motifs of overwhelming inflammation, various levels of immunosuppression, sustained inflammation, and immunodeficiency. An accurate clinical tool was proposed to classify subjects into endotypes using six-hour trajectories of clinical data. A physiological ordinary differential equation model of sepsis is proposed that characterizes the interactions of inflammatory signaling molecules, neutrophils, and macrophages across the bone, blood, and tissue compartments of the body. This model used to generate individual subject fits against human sepsis data. Population-level parameter analysis implicated macrophage cell death and cytokine half- dynamics in endotype-level differences. Several proof-of-concept statistical models were introduced to demonstrate that it is possible to estimate the pre-hospital time of sepsis subjects and to quantify their sepsis-induced systemic tissue damage. A nearest-neighbor-based method was verified against animal and human data and revealed that identifying infection time-zero of sepsis patients can be quickly estimated with high accuracy using commonly measured clinical features. A logistic regression ensemble model demonstrated revealed early organ dysfunction were significant contributors to systemic damage and mortality. Knowledge of time-zero and systemic damage levels, in combination with an endotype classifier, provides clinicians with a clear depiction of where a subject is located on their sepsis trajectory. Such a decision support system enables therapy timing, early organ support, and targeted therapies to guide personalized treatment and shift patients towards better outcomes in sepsis.

Roman M. Shapiro, Michelle P. Zeller, Theodore E. Warkentin

A 38-year-old woman was brought in to the emergency room (ER) because of altered level of consciousness. She had a known history of seizure disorder but was not on any prescription medications. She also had a history of intravenous (IV) drug abuse, including cocaine, methamphetamine, and heroin. She recently described herself as being “pill-sick” which according to her partner meant that she felt unwell after a recent administration of an illicit drug. In the ER, she was hypoxemic and was intubated. She was febrile, her blood pressure was 90 systolic, heart rate 120/min, and oxygen saturation undetectable by the digital probe. Admission hemoglobin was 11.6 g/dL (mean corpuscular volume [MCV] 86), white blood count 0.8 × 109/L (absolute neutrophil count [ANC], 0.2), and platelet count 97 × 109/L. Rare nucleated red blood cells were seen, but no myeloblasts or other primitive cells. Coagulation studies showed INR 3.1, activated partial thromboplastin time (aPTT) 81 s, and fibrinogen 1.9 g/L. The serum creatinine was 320 µmol/L (reference range, 50–98); serum lactate measured 13.0 mmol/L (reference range, 0.5–2.2). A chest X-ray showed multiple pulmonary infiltrates and right-sided pleural effusion. A CT head scan was negative for any acute abnormality. CT chest was suspicious for a right-sided empyema, but no vegetations were seen on the heart valves. The most striking finding on her admission blood work was leukopenia with near-absence of neutrophils in the peripheral blood. The clinical picture of hypotension, tachycardia, lactic acidosis, renal failure, pulmonary infiltrates, and pleural effusion in the setting of IV drug abuse, together with thrombocytopenia, coagulopathy, and normoblastemia, suggests septicemia in the setting of pneumonia or right-sided infective endocarditis, most likely complicated by disseminated intravascular coagulation (DIC)–although fibrin D-dimer levels would be helpful in supporting the last diagnosis. Severe sepsis can result in a transient leukopenia/neutropenia,1 although complete absence of circulating neutrophils is unusual and points to a possible de novo neutropenic disorder such as drug-induced agranulocytosis2, 3 (although the patient was not receiving any prescription medications). Current guidelines do not recommend starting this patient on G-CSF on admission due to lack of proven mortality benefit.4, 5 Circulating nucleated red blood cells (normoblastemia) portend a poor prognosis in a critically ill patient.6 She had a central venous catheter inserted into her right internal jugular vein and was transferred to the intensive care unit (ICU) on vasopressin and norepinephrine. Blood and urine cultures were sent and the patient was started on piperacillin-tazobactam and vancomycin. Repeat blood work showed progressive thrombocytopenia, with the platelet count measuring 9 × 109/L 20 h later, and with the leukocytes and ANC remaining profoundly reduced at 0.1 and 0, respectively. Repeat coagulation studies showed INR 3.9, aPTT >150, fibrinogen 2.0, and D-dimer > 20,000 μg/L fibrin equivalent units (reference range, <500), antithrombin activity 0.26 U/mL (reference range, 0.77–1.25), and protein C activity 0.14 U/mL (reference range, 0.70–1.80). Chemistry studies showed lactate dehydrogenase (LDH) 769 U/mL (reference range, 100–220), creatine kinase (CK) 3,800 U/mL (reference range, <168), total bilirubin 182 μmol/L (reference range, <21), conjugated bilirubin 117 μmol/L (reference range, <8.6), alanine aminotransferase (ALT) 67 U/L (reference range, <28), alkaline phosphatase 68 U/mL (reference range, 40–120), and creatinine (following initiation of continuous renal replacement therapy [CRRT]) had declined to 130 μmol/L. She was noted to have cold and dusky extremities and several necrotic ulcers were noted over the dorsum of her left hand. All peripheral pulses were intact, without peripheral cyanosis or other evidence of overt limb ischemia. She was started on IV heparin with dose adjusted by anti-factor Xa levels and given antithrombin concentrates (988 units, administered at 12-h intervals) as well as frozen plasma by infusion. HIV testing was ordered, along with hepatitis B and C serology. The fibrin D-dimer was measured to evaluate the presence of DIC. Although D-dimer levels are elevated in many or most hospital patients, greatly elevated levels as seen in this patient (>20,000) are consistent with a diagnosis of DIC.7 The elevated LDH, CK, bilirubin, and creatinine values likely reflect tissue (muscle) injury and organ (liver, renal) dysfunction.8, 9 The antithrombin and protein C activity levels were measured because of the potential role for acquired severe depletion of protein C and antithrombin activity in predisposing to ischemic limb necrosis/gangrene with pulses in critically ill patients who have DIC and acute ischemic hepatitis (“shock liver”).10 Though this patient had only mildly increased liver enzymes, the possibility of chronic liver disease due to viral hepatitis (frequently observed in IV drug abusers) or another etiology could represent an alternative at-risk scenario for limb ischemia.11 Although treatment for DIC-associated limb ischemia is uncertain, we followed a recent suggestion12 to treat with unfractionated heparin, antithrombin concentrates, and plasma infusion. In this patient, the presence of necrotic ulcers on the dorsum of her left hand along with cool, dusky extremities, and elevated LDH and CK levels (indicating possible tissue ischemia13), prompted her medical team to initiate preemptive heparin therapy to minimize the risk of developing acral limb ischemia/gangrene in the setting of liver dysfunction. Of note, with this therapy, the appearance of the limbs improved, becoming warm and of normal color, and there was no development of overt limb ischemia at any later time. On the fourth hospital day, the patient's blood work yielded a hemoglobin 6.7 g/dL, MCV 85.6, WBC 0.2 × 109/L (with ANC 0), platelet count 8 × 109/L, INR 1.2, aPTT 64, anti-factor Xa level 0.18 U/mL (therapeutic range, 0.35–0.70), fibrinogen 3.3, D-dimer 11,500, total bilirubin 270 μmol/L. Blood cultures and pleural fluid cultures were positive for methicillin-sensitive Staphylococcus aureus (MSSA), while hepatitis B and C serology came back negative. Antinuclear antibody (ANA) was sent as part of an autoimmune screen. Bone marrow aspirate and biopsy were attempted in order to better characterize the cause of her persistent profound neutropenia but the patient was too hemodynamically unstable to have the procedure done. She was started on G-CSF (300 μg/day). At this point, the differential diagnosis for neutropenia included septic shock, chronic liver disease, HIV, autoimmune disease, or drug-induced neutropenia/agranulocytosis. As she was receiving appropriate antibiotics since admission, it seemed less likely that infection was the cause of ongoing neutropenia. Severe sepsis is associated with bone marrow dysfunction, but more often as a result (not a cause) of underlying bone marrow suppression.1, 14, 15 Certain infections are known to cause neutropenia, including tuberculosis, HIV, cytomegalovirus, Epstein-Barr virus, and any of the hepatitis viruses.16-19 Although this patient had risk factors for HIV and viral hepatitis, serological studies ruled out these infections. A common association of infection with profound neutropenia occurs following systemic antineoplastic chemotherapy (“febrile neutropenia”). For patients who develop severe sepsis with persisting neutropenia, the pathophysiology is believed sometimes to be hematopoietic stem cell “exhaustion.”14 However, this tends to occur in infants and the elderly where the bone marrow reserve of granulocytic precursors is smaller than in adults.14, 15 The finding of an ANC of zero on presentation to hospital also argues against sepsis-induced bone marrow exhaustion. As persisting absence of circulating neutrophils strongly indicated the possibility of drug-induced agranulocytosis, a thorough re-review of any medications the patient might have received prior to and following admission was undertaken. Although both vancomycin and piperacillin-tazobactam may cause neutropenia,3 these antibiotics were only started after the patient already had an ANC of zero; and because only 4 days had elapsed following admission, the timeline was too soon for superimposed antibiotic-induced neutrophil-reactive antibodies to be plausible. Accordingly, further consideration for a medication or other drug taken prior to admission was given. Of the illicit IV drugs she used, cocaine has been associated with agranulocytosis due to the presence of levamisole as an adulterant.20, 21 On the sixth day of admission, her bloodwork yielded a hemoglobin 8.1 g/dL, WBC 0.6 × 109/L with ANC 0, platelet count 19 × 109/L, INR 1.2, aPTT 46. Her HIV test as well as ANA came back negative. Repeat blood cultures from the fourth day of hospital admission also came back negative. She continued to be hemodynamically unstable requiring increasing vasopressor and inotropic support. Blood culture obtained on the sixth day of hospitalization from the dialysis catheter site yielded budding yeast cells and pseudohyphae (after 28-h incubation), and the infectious disease consultant diagnosed superimposed Candida albicans infection in this patient with persisting profound neutropenia. Despite adding anidulafungin, she died on the eighth day due to multiorgan failure as a result of septic shock. Her ANC remained at 0 throughout her hospitalization. Figure 1 summarizes the patient's clinical course, including serial platelet count and ANC values (panel A), fibrinogen and INR values (panel B), aPTT and anti-factor Xa levels (panel C), with evidence for initially disturbed “procoagulant-anticoagulant” balance,10, 12 with greatly elevated fibrin D-dimer levels and markedly reduced antithrombin and protein C activity levels (panel D), which improved during treatment with heparin, antithrombin concentrates, and frozen plasma infusion. Summary of the patient's clinical course. (A) Serial platelet count and absolute neutrophil count (ANC) levels; timing and dose of intravenous heparin infusion rate is also shown. (B) Serial INR (international normalized ratio) and fibrinogen levels. (C) Serial partial thromboplastin time (aPTT) and anti-factor Xa levels. (D) Serial D-dimer, antithrombin (AT) and protein C (PC) activity levels; timing and dosing of AT concentrates and of plasma infusion are also shown. The patient died 7 days following admission Persistence of agranulocytosis unresponsive to G-CSF suggested an ongoing profound bone marrow suppression effect. Levamisole-induced agranulocytosis appeared likely given this patient's known habitual use of cocaine: it is estimated that 70%–80% of all cocaine in North America contains levamisole as an adulterant.21 Studies on levamisole identified long-term exposure to the drug as a significant risk factor for development of agranulocytosis.21, 22 Drug clearance is normally rapid with normally functioning kidneys, but this patient was anuric. Furthermore, levamisole is not cleared by dialysis, suggesting that plasma levels would not be lowered by CRRT.23 As this patient was injecting cocaine in the dorsum of her hand and had several necrotic lesions located at prior injection sites, the possibility of a persistent depot of levamisole was also considered to explain sustained agranulocytosis due to prolonged exposure to the drug or a relevant metabolite. Neutropenia is a common phenomenon frequently encountered in patients on antineoplastic chemotherapy, as an infection-related event, in certain autoimmune conditions, and in nutritional deficiency.2, 3 In contrast, agranulocytosis is a rare immune-mediated phenomenon triggered by certain medications3 or IV drugs.20, 21 Several medications have a known association with agranulocytosis, with the more common mechanism presumed to be antibodies to a neoepitope formed by a combination of the medication (or metabolite) and a receptor on the neutrophil membrane.24, 25 However, definitive proof is often lacking because detection of such antibodies is challenging and rarely performed. On some occasions, drug-dependent anti-neutrophil antibodies have been detected in the serum of patients with agranulocytosis.25, 26 A recent review of drug-induced agranulocytosis identified medications with the greatest likelihood of inducing agranulocytosis (Table 1), including clozapine, propylthiouracil, carbamazepine, trimethoprim-sulfamethoxazole, beta-lactams, and levamisole, among others.3 Among these, clozapine has the highest frequency of triggering agranulocytosis, estimated at 1 in 125 patients.24, 25 Beta-lactams Vancomycin Dapsone Trimethoprim-sulfamethoxazole 25 days24 82 days27 39 days24 UNKa 0.42/106 per year28 UNK UNK 5.6/106 per year29 Immune-mediated25 Immune-mediated25 Reactive metabolite induces apoptosis25 Immune-mediated29 Diclofenac Sulfasalazine UNK 42 days24 0.14/106 per year28 1:1600 in IBD29 1:6100 in RA29 Inhibit myelopoiesis25 Direct toxicity, immune-mediated30 Methimazole Propylthiouracil 42 days24 36 days24 0.25/106 per year28 0.2-0.5% 31 Immune-mediated32 Reactive metabolite induces apoptosis25, 33, immune-mediated29 Clozapine Chlorpromazine 56 days24 45 days24 0.8% 29 UNK Reactive metabolite induces apoptosis25, 33, inhibit myelopoiesis25 Inhibit myelopoiesis29 Phenytoin Carbamazepine 14 days33 49 days24 UNK 0.09/106 per year28 Reactive metabolite induces apoptosis25 Inhibit myelopoiesis29 Procainamide Captopril Ticlopidine 47 days24 32 days24 39 days24 UNK UNK 0.39/106 per year28 Reactive metabolite induces apoptosis25 Immune-mediated25 Reactive metabolite Induces apoptosis25 Levamisole Deferiprone Rituximabb 60 days24 UNK 4 cycles24 UNK 0.2-0.4/100 patient years29 UNK Immune-mediated25 UNK Immune-mediated25 Among IV drug users, levamisole is becoming a more recognized etiological agent due to its near-ubiquitous presence in cocaine distributed in North America.21, 22 Its association with agranulocytosis was first recognized in patients who received levamisole for treatment of rheumatoid arthritis during in the 1990s.22 Due to its relatively high risk for causing agranulocytosis, levamisole was withdrawn from the market in 2000, but it continues to find use in the veterinary world as an antihelminthic.21, 34 Its physical resemblance to cocaine and the relative ease of acquisition at a low price explain its common use as an adulterant in the manufacture of cocaine.23 Pharmacokinetic studies of levamisole injected into the muscle and fat of animals yielded a short plasma half-life and rapid elimination via the urinary tract within 24 hours.35, 36 However, repeated exposure to low doses of the drug over a period of months can induce the formation of antibodies implicated in agranulocytosis.26 The clinical presentation of patients who develop agranulocytosis during exposure to levamisole-adulterated cocaine is variable, with many patients either asymptomatic or developing a nonspecific flu-like illness. Around 50% of patients may initially present to the ER with mouth ulcers or odynophagia.21 A vasculitic rash has been associated with levamisole exposure that develops in the setting of an occlusive thrombotic vasculopathy. The latter most frequently involves the cheeks and ears and tends to be ANCA-positive.37 However, as there are cases of levamisole-induced agranulocytosis not associated with positive ANA or ANCA, the absence of these autoimmune markers does not rule out levamisole as the culprit drug38 (note: we did not measure ANCA in our patient case). In addition, case reports of patients who develop an autoimmune marker such as ANA, c-ANCA, p-ANCA, or antiphospholipid antibody in the setting of known levamisole exposure describe the disappearance of this marker within 14 months of discontinuation of drug exposure.21 It is not yet clear why a relatively high proportion (approximately 2%–4%) of patients who habitually use cocaine and who are likely chronically exposed to levamisole develop agranulocytosis.21 In previous studies of rheumatoid arthritis patients, risk factors for levamisole-induced agranulocytosis included female sex, HLA-B27, and frequency of drug exposure.21, 39 The expected time for recovery of drug-induced agranulocytosis after stopping the offending drug, without the addition of growth factor support, is a median of 9 days;40 the addition of G-CSF reduces the time for neutrophil recovery to approximately 4-7 days.40 When severe agranulocytosis persists beyond this period, unusual circumstances have been reported. For example, some patients with persisting agranulocytosis unresponsive to G-CSF have parvovirus B19 infection, and recover after administration of IVIgG (implicating removal of circulating virus by IgG anti-parvovirus antibody, reversing virus-induced inhibition of granulopoiesis).41 The typical course of levamisole-associated agranulocytosis usually involves improvement in the neutrophil count beginning several days after discontinuation of the drug, with recombinant G-CSF hastening recovery.21 Given that levamisole is normally metabolized in the liver and secreted via the renal system, the presence of significant liver and/or kidney dysfunction is likely to significantly prolong its half-life.21, 23 Furthermore, hemodialysis does not clear the drug.23 When injected intramuscularly or into adipose tissue a depot of the drug may develop that results in continuous long-term exposure. Given our had an ANC of zero that persisted for 8 days following admission (until death) without any other clear precipitant or cause identified and despite starting G-CSF, we hypothesize that the combination of an intramuscular or subcutaneous depot of levamisole, together with absent renal clearance and hepatic dysfunction, resulted in ongoing exposure to levamisole (or a metabolite) that potentiated pathological effects of levamisole-dependent anti-neutrophil antibodies. The recommended management of levamisole-induced agranulocytosis is drug withdrawal and supportive care.21 Reported experience, mainly based on cocaine users who were not critically ill, typically show rapid recovery within a few days of drug discontinuation. There is a paucity of data in patients who are critically ill with multiorgan dysfunction; indeed, to our knowledge, presentation as septic shock (due to MSSA septicemia) in the setting of agranulocytosis presumed to be caused by levamisole-adulterated cocaine has not been previously reported. The authors have no conflicts of interest to report.