JMIR Publications

ABSTRACT

Title: Identifying Hemophagocytic Lymphohistiocytosis using Electronic Health Records and Describing the Impact of Treatment on Outcomes Authors: Suheyla Ocak MD1, Martin Yi MDA2, Agata Wolochacz BSc2, Ida Mehrdadi BSc2, Ahmed Naqvi MD1, Sumit Gupta MD PhD1-2, Lillian Sung MD PhD1-3, Adam Yan MD MBI1-3 1Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada, M5G 1X8 2Child Health Evaluative Sciences, The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada, M5G 1X8 3Information Management Technology, The Hospital for Sick Children, 555 University Ave, Toronto, ON, Canada, M5G 1X8 ADDRESS FOR CORRESPONDENCE: Adam Yan MD, MBI Division of Haematology/Oncology The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G1X8, Canada Telephone: 416-813-5287 Fax: 416-813-5979 Email: adam.yan@sickkids.ca RUNNING HEAD: HLH in the electronic health record KEY WORDS: HLH, electronic health record, pediatrics WORD COUNT: Abstract 256; Text 2537; Tables 4; Figures 1; Appendices 1 ABSTRACT

Objective:

To compare different approaches to using the electronic health record (EHR) to build a cohort of Hemophagocytic Lymphohistiocytosis (HLH) patients, and to evaluate characteristics and outcomes of patients meeting the HLH-2004 diagnostic criteria who received HLH-directed therapies to those who did not.

Methods:

Three approaches to cohort development in the EHR were taken by identifying patients with: (1) an HLH-specific ICD-10 code, (2) an HLH-specific treatment plan, and (3) meeting the HLH-2004 clinical criteria for diagnosis of HLH. Among patients who met the HLH-2004 criteria, we evaluated the characteristics and outcomes of patients who received HLH-directed therapies to those who did not. HLH treatment was defined as either any chemotherapy, or HLH-specific therapy (dexamethasone, methylprednisolone, anakira, ruxolitinib, cyclosporine, etoposide or emapalumab).

Results:

We identified 388 patients with possible HLH across the three cohorts. An HLH ICD-10 diagnosis (n=220) and meeting five or more clinical criteria (n=245) were much more common than a HLH treatment plan (n=42). Among the patients meeting HLH-2004 clinical criteria, 193 (79%) received HLH-directed therapy. There was no difference in any specific HLH criteria between those who did and did not receive HLH-directed therapy. In-hospital mortality was very high among both groups and was 15.0% among those who received HLH-directed therapy and 13.5% among those who did not receive HLH-directed therapy. Among 1325 patients with an elevated ferritin and fever, only 252 (19%) met >5 clinical criteria.

Conclusions:

Constructing HLH cohorts from EHR data is challenging, with diagnosis codes, treatment plans, and clinical criteria each capturing distinct but overlapping populations. INTRODUCTION Hemophagocytic lymphohistiocytosis (HLH) is a syndrome represented by excessive inflammation resulting in organ dysfunction.1 Traditionally, HLH has been classified as primary, where there is a documented or presumed genetic etiology,2,3 or secondary, where excessive inflammation can be attributed to a trigger such as infection, malignancy or a rheumatoloical condition in the absence of a genetic etiology.4 Although there are an increasing number of genes identified responsible for primary HLH,5 there are patients with presumed primary HLH without a known mutation. Further, patients with both primary and secondary HLH may require HLH-directed therapy. Consequently, diagnostic criteria have been developed for HLH (Appendix 1), which often guide decision making about treatment initiation. Criteria were initially proposed in 20042,3, and recently updated in 2024.2 In general, diagnosis of HLH can be made based upon molecular or functional cellular findings consistent with HLH in combination with meeting at least 5 clinical criteria (Table 1). While these criteria have been widely used for diagnosis and treatment decision making, there have been questions raised about the specificity of these criteria, particularly as they relate to secondary HLH.4 Most studies of HLH have focused on small cohorts of participants who have received a clinical diagnosis of HLH. To evaluate the utility of HLH criteria, it may be informative to determine the association of each individual HLH criterion against an overall clinical HLH diagnosis, whether HLH-specific treatments received, or HLH outcome such as mortality. Most studies of HLH patients use small highly curated datasets. Few studies have leveraged electronic health record (EHR) data which may represent broader populations and contain a wider range of clinical information. Leveraging EHR data to facilitate clinical research of HLH requires construction of an accurate patient cohort. Given that EHR data is not entered with the goal of facilitating future downstream research, manually entered data such as diagnosis codes can often be incorrect or missing. Computable phenotypes are machine-evaluable definitions for a given condition developed using EHR data. To our knowledge, no attempt has been made to utilize diverse EHR data to develop a machine-evaluable approach to HLH identification.6–8 The primary objective of this study was to compare various approaches to using EHR data to build a cohort of HLH patients- specifically we compared three approaches: (1) HLH-specific diagnosis code usage, (2) HLH-specific treatment plan application and (3) HLH clinical criteria, among all patients at a pediatric hospital. Our hypothesis was that the use of diagnosis codes and treatment plans would under-capture patients, and the use of clinical criteria would over capture patients. Among patients who met at least five HLH clinical criteria, the secondary objective was to compare characteristics and outcomes among those who received HLH-directed therapy vs those who did not. METHODS This observational study was approved by the Research Ethics Board at The Hospital for Sick Children (SickKids). The requirement for informed consent and assent were waived given the retrospective nature of the study. Data Source The data source was the SickKids Enterprise-wide Data in Azure Repository (SEDAR).9 SEDAR is a curated and validated version of the Epic Clarity database organized by clinically relevant units such as patients, encounters, laboratory tests and medication administrations as examples. This project focused on the following SEDAR tables: patient, diagnoses, cancer treatment plans, hospital encounter, non-hospital encounter, medication administration, prescriptions, laboratory tests, pathology results, flowsheets and notes. Operationalizing HLH Criteria We used the HLH-2004 criteria as these would have been the criteria in place for most of the patient cohort (Table 1). The time window to evaluate clinical criteria were centered on an episode, which was usually an inpatient admission spanning admission to discharge. A criteria was considered met if it occurred at any point during the episode. Five of the criteria were based on the laboratory results table (ferritin, cytopenia, hypertriglyceridemia or hypofibrinogenemia, sCD25 and low NK cell activity). Fever was defined as an oral temperature at least 38.3° C once or 38.0°to 38.2° C for at least one hour.10 Two of the criteria required searching of text. Hemophagocytosis was identified by searching for “h(a)emophagocytosis” or “h(a)emophagocytic” in all pathology results. These reports were manually reviewed to identify true hemophagocytosis. Splenomegaly was identified by searching all notes for any of the following terms “splenomegaly”, “big spleen”, “organomegaly”, and “enlarged spleen”. Negative terms were excluded using the following terms within three words previous to the splenomegaly term: “no”, “none”, “absence”, “without” and “negative”. The number of mentions were too high to manually review each note. Thus, a random sample of 20 notes underwent chart review to validate the approach. We found that 19/20 were correct. One of 20 was incorrect. We considered this satisfactory to proceed. Eligibility Criteria We established three cohorts of HLH “diagnosis” based upon encounters that occurred between June 2, 2018 and May 31, 2025. First, patients with a coded diagnosis of HLH were those with an ICD10 code of D76.1 (hemophagocytic lymphohistiocytosis) or D76.2 (hemophagocytic syndrome, infection-associated). Second, cancer treatment plans are electronic care plans that are a component of Epic’s Beacon oncology module. All chemotherapy at our institution must be ordered within a treatment plan, however treatment plan use is restricted to use by oncologists. To that end, an oncologist would therefore order a drug such as emapalumab within a treatment plan, while a rheumatologist using the same drug would not. We manually identified either treatment plan or protocol display names that included “HLH” or “h(a)emophagocytosis”. Use of a treatment plan was defined as having a HLH specific treatment plan applied in Epic. The third approach consisted of identifying the number of HLH 2004 clinical criteria within an encounter. The HLH-specific encounter was the encounter with the maximum number of criteria. If there were more than one encounter with this number of criteria, the first encounter was selected. For establishment of HLH based on clinical criteria, we identified encounters with at least five criteria within that encounter regardless of encounter length. Procedure We were interested in two measures of whether patients received HLH treatment; these might be administered within or outside of an HLH treatment plan. First, we considered any chemotherapy. Second, we considered HLH-directed therapy which we defined as receipt of any dosage of dexamethasone, methylprednisolone, anakira, ruxolitinib, cyclosporine, etoposide or emapalumab.11–15 We compared demographic features, HLH-related variables and clinical outcomes among those with at least five HLH clinical criteria by those who received HLH-directed therapy (yes vs. no) during the encounter where the criteria were met. Demographic features were sex, and age group (0-<1, 1-4, 5-14 and 15+). HLH-related variables were whether there was an ICD-10 HLH diagnosis associated with that encounter, timing of ICD-10 HLH diagnosis relative to the encounter, and specific clinical criteria met. Clinical outcomes were services involved, length of stay, intensive care unit admission, in-hospital mortality, 30-day mortality, and whether the patient underwent stem cell transplantation following the encounter. We also evaluated the five most common admission diagnoses among the cohort with at least five clinical criteria.. As an exploratory objective, we hypothesized that high ferritin and fever are common conditions in pediatric patients. Thus, we identified all encounters that met ferritin and fever criteria and described the distribution of the other criteria in this cohort and stratified by whether there were at least five clinical criteria present during an HLH-specific encounter. Analysis To compare patients who received HLH-directed therapy yes vs. no among patients who met at least five clinical criteria, we used Wilcoxon rank sum test for continuous variables and Chi square or Fisher’s exact test for categorical variables. Analyses were performed with R4.2.2 (Vienna, Austria). RESULTS Figure 1 shows the intersection of the 388 patients identified as having HLH using our three approaches to HLH-cohort creation (diagnosis code, treatment plan or HLH criteria). All patients with an HLH-specific treatment plan also had either an ICD-10 HLH diagnosis or met clinical HLH criteria. Only 36 patients were identified by all three methods as having HLH. Table 2 shows the number of patients with an HLH ICD-10 diagnosis code, an HLH treatment plan or who met at least five HLH clinical criteria. An HLH ICD-10 diagnosis (n=220) and meeting five or more clinical criteria (n=245) were much more common than an HLH treatment plan (n=42). Among the 220 patients with an HLH ICD-10 code, only 35 patients received chemotherapy during the HLH-specific encounter. However, 85 patients received HLH-directed therapy during the HLH-specific encounter. Conversely, among the 245 patients who met at least five clinical criteria, 78 received chemotherapy and 193 received HLH-directed therapy during the HLH-specific encounter. Table 3 shows among demographic features, those with sepsis were less likely to receive HLH-directed therapy (13.5% vs. 4.7%, P=0.050). Those receiving HLH-directed therapy were more likely to have an ICD-10 HLH diagnosis code during that encounter (P=0.001). Notably, there was no difference in any specific HLH criteria between those who did and did not receive HLH-directed therapy although high ferritin was almost universal in both groups. In-hospital mortality during the hospital where the patient met the HLH criteria was very high among both groups and was 15.0% among those who received HLH-directed therapy and 13.5% among those who did not received HLH-directed therapy. There was no significant difference in either in-hospital mortality or 30-day mortality by HLH-directed treatment. Table 4 shows that among the 1,325 encounters with high ferritin and fever, cytopenia, hypertriglyeridemia/hypofibrinogenmia and splenomegaly were very common. Of the 1,325 encounters, 19% had >5 HLH criteria. Even among those with less than five clinical criteria, cytopenia was present in 44% while hypertriglycermidemia/hypofibrinogenemia and splenomegaly were present in 24% and 27% respectively. Appendix 1 shows the distribution of clinical criteria among those with an HLH ICD-10 code and among those with at least five clinical criteria. Some patients with their first HLH ICD-10 diagnosis code had no clinical criteria. DISCUSSION In this study, we compared three approaches to constructing an electronic health record (EHR)-derived cohort of pediatric patients with hemophagocytic lymphohistiocytosis (HLH): (1) ICD-10 diagnosis codes, (2) HLH-specific treatment plans, and (3) fulfillment of HLH-2004 clinical criteria. Our findings demonstrate substantial variation in the populations identified by each approach, with diagnosis codes and clinical criteria identifying a far greater number of encounters than HLH-specific treatment plans. We also identified that among patients meeting at least five HLH-2004 clinical criteria, a large proportion received HLH-directed therapy, and that amongst a cohort of patients with fever and an elevated ferritin, a substantial portion have other findings of HLH such as cytopenia, hypertriglyeridemia/hypofibrinogenmia or splenomegaly. Comparing our three approaches to EHR-based cohort construction yields numerous interesting insights. First, we observed that HLH ICD-10 codes were relatively common in our dataset. A large proportion of patients with an HLH ICD-10 code did not receive HLH-directed therapy, suggesting that coded diagnoses may be used for suspected but unconfirmed HLH. Conversely, while HLH treatment plans were highly specific, they were rarely used. This is likely because many pediatric HLH patients do not require chemotherapy and receive HLH-directed medications that are ordered outside of a chemotherapy plan. Thus, the use of treatment plans likely underestimates the true HLH population. Clinical criteria, while non-specific, identified the broadest cohort, consistent with the recognition that HLH represents a clinical syndrome overlapping with other hyperinflammatory states. The exclusive use of these criteria can also be problematic in an EHR cohort, where certain laboratory tests may be done at a referring institution such as for a patient with HLH being referred for hematopoietic stem cell transplant. This is supported by the fact that we had some patients who met zero HLH clinical criteria, but had an HLH ICD10 code applied. Future research leveraging EHR data to assemble an HLH cohort needs to consider the advantages and disadvantages of different techniques for cohort assembly to select the best approach for a given project. For example a researcher interested in understanding the diagnostic odyssey of HLH patients prior to treatment to improve time to diagnosis might use diagnostic criteria as this would more broadly capture patients compared to using a more narrow approach like diagnostic codes. Future work should also explore usage of combination of approaches such as having both HLH-criteria met and an HLH diagnosis code and using additional filters in the treatment approach such as dosages or duration for medications that might be used for indications other than HLH management such as dexamethasone. It will also be important to validate these approaches at other institutions to understand generalizability. Using our newly developed EHR based cohort of children’s with HLH, we were able to generate new insights the management and outcomes of patients with HLH. Of the 245 patients who met the HLH-2004 criteria for HLH, 27% received no HLH-directed therapy. No differences were identified in the occurrence of HLH-specific criteria such as ferritin, or fever between the two cohorts. From this work it remains unclear as to what drove the difference in clinical decision making to start HLH-directed therapy for patients meeting 5 criteria. We did identify that patients who did not receive HLH-directed therapy were more likely to have sepsis than those who did.16 This is not surprising given that we know that sepsis and HLH have an overlapping presentation. Patients who received HLH-directed therapy also had significantly longer length of stay (33.4 vs. 20.8 days). It is important to note, that regardless of whether or not a patient who met 5 criteria received therapy, their 30-day mortality risk was approximately 1 in 6. This is similar to other contemporary studies of patients treated for HLH where mortality rates have ranged from 23 to 55%13,17–19. Given the high mortality rate in those who did not receive therapy, it raises the question whether or not we are failing to treat a subset of patients with HLH with appropriate HLH-directed therapies. We also evaluated the distribution of HLH criteria within an EHR-derived cohort of patients with high ferritin and fever. An elevated ferritin and fever are often used as the entrance criteria into a “rule-out” HLH algorithm at many institutions that results in the ordering of additional HLH-specific labs or consultation of an HLH-specific team such as hematology/oncology and/or rheumatology.15,20 In this cohort that mirrors a potential cohort of patients where an HLH work-up and diagnosis may be considered, we identified that a significant proportion of patients will meet additional HLH-criteria. However, only 20% of the patients in this cohort met >5 HLH-criteria, suggesting that if fever and ferritin are used as entrance criteria into an HLH-algorithm, 5 patients would need to be evaluated to identify 1 patient meeting the HLH-clinical criteria. The strengths of this study are access to all data in a curated and validated EHR data set, which allows inclusion of patients without an HLH diagnosis. Unlike prior studies restricted to curated clinical cohorts, our approach provides insight into the challenges of applying HLH diagnostic frameworks in real-world, unselected populations. However, limitations of this study include that it was done at a single pediatric institution so results may not generalize to other institutions. Specifically, in the Canadian context we may underutilize ICD codes compared to American peers given different implications on billing and renumeration.21 Second, we were limited in validating the performance of our cohorts against a gold-standard cohort, as no such curated dataset exists that includes both primary and secondary HLH patients. Third, EHR-derived data are prone to misclassification. Diagnosis codes may not reflect confirmed diagnoses, laboratory results may be incomplete as we are a large referral center so some labs may have been done at a referring institution and not included in our dataset, and text-mining strategies (e.g., for splenomegaly) have imperfect specificity. Finally, treatment receipt was defined by medication administration records, which may not fully capture intent of medication prescribing. Constructing HLH cohorts from EHR data is challenging, with diagnosis codes, treatment plans, and clinical criteria each capturing distinct but overlapping populations. While clinical criteria identify the broadest group, they lack specificity, underscoring the limitations of applying rigid diagnostic frameworks to real-world EHR data. Developing accurate, machine-evaluable HLH phenotypes will be critical to advancing both clinical care and research in this rare but important condition. Conflicts of Interest: The authors have no conflicts of interest to report REFERENCES 1. Janka GE, Lehmberg K. Hemophagocytic lymphohistiocytosis: pathogenesis and treatment. Hematology / the Education Program of the American Society of Hematology American Society of Hematology Education Program. 2013;2013. doi:10.1182/asheducation-2013.1.605 2. Henter J-I, Sieni E, Eriksson J, et al. Diagnostic guidelines for familial hemophagocytic lymphohistiocytosis revisited. Blood. 2024;144(22):2308–2318. . 3. Henter JI, Horne AC, Aricó M, et al. HLH-2004: Diagnostic and therapeutic guidelines for hemophagocytic lymphohistiocytosis. Pediatr Blood Cancer. 2007;48(2). doi:10.1002/pbc.21039 4. Naymagon L, Roehrs P, Hermiston M, et al. Perspectives on the current diagnostic and treatment paradigms in secondary hemophagocytic lymphohistiocytosis (HLH). Orphanet journal of rare diseases. 2025;20(1):200. . 5. Sepulveda FE, de Saint Basile G. Hemophagocytic syndrome: primary forms and predisposing conditions. Curr Opin Immunol. 2017;49. doi:10.1016/j.coi.2017.08.004 6. Richesson RL, Smerek MM, Blake Cameron C. A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications. EGEMS (Wash DC). 2016 Jul 5;4(3):1232. doi: 10.13063/2327-9214.1232. PMID: 27563686; PMCID: PMC4975566. 7. Phillips CA, Razzaghi H, Aglio T, et al. Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data. Pediatr Blood Cancer. 2019;66(9). doi:10.1002/pbc.27876 8. Hochheiser H, Castine M, Harris D, Savova G, Jacobson RS. An information model for computable cancer phenotypes. BMC Med Inform Decis Mak. 2016;16(1). doi:10.1186/s12911-016-0358-4 9. Guo LL, Calligan M, Vettese E, et al. Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR). Heliyon. Nov 2023;9(11):e21586. doi:10.1016/j.heliyon.2023.e21586. 10. Freifeld AG, Bow EJ, Sepkowitz KA, et al. Clinical practice guideline for the use of antimicrobial agents in neutropenic patients with cancer: 2010 Update by the Infectious Diseases Society of America. Clinical Infectious Diseases. 2011;52(4). doi:10.1093/cid/cir073 11. Zhang Q, Zhao YZ, Ma HH, et al. A study of ruxolitinib response–based stratified treatment for pediatric hemophagocytic lymphohistiocytosis. Blood. 2022;139(24). doi:10.1182/blood.2021014860 12. Locatelli F, Jordan MB, Allen C, et al. Emapalumab in Children with Primary Hemophagocytic Lymphohistiocytosis. New England Journal of Medicine. 2020;382(19). doi:10.1056/nejmoa1911326 13. Eloseily EM, Weiser P, Crayne CB, et al. Benefit of Anakinra in Treating Pediatric Secondary Hemophagocytic Lymphohistiocytosis. Arthritis and Rheumatology. 2020;72(2). doi:10.1002/art.41103 14. Shakoory B, Carcillo JA, Chatham WW, et al. Interleukin-1 Receptor Blockade Is Associated with Reduced Mortality in Sepsis Patients with Features of Macrophage Activation Syndrome: Reanalysis of a Prior Phase III Trial∗. Crit Care Med. 2016;44(2). doi:10.1097/CCM.0000000000001402 15. Halyabar O, Chang MH, Schoettler ML, et al. Calm in the midst of cytokine storm: A collaborative approach to the diagnosis and treatment of hemophagocytic lymphohistiocytosis and macrophage activation syndrome. Pediatric Rheumatology. 2019;17(1). doi:10.1186/s12969-019-0309-6 16. Machowicz R, Janka G, Wiktor-Jedrzejczak W. Similar but not the same: Differential diagnosis of HLH and sepsis. Crit Rev Oncol Hematol. 2017;114. doi:10.1016/j.critrevonc.2017.03.023 17. Gregory J, Greenberg J, Basu S. Outcomes analysis of children diagnosed with hemophagocytic lymphohistiocytosis in the PICU. Pediatric Critical Care Medicine. 2019;20(4). doi:10.1097/PCC.0000000000001827 18. Dao D, Xoay TD, Galeano BK, Phuc PH, Ouellette Y. Etiologies and Clinical Outcomes of Patients with Secondary Hemophagocytic Lymphohistiocytosis at a Tertiary PICU. Pediatric Critical Care Medicine. 2019;20(7). doi:10.1097/PCC.0000000000001980 19. Henter JI, Samuelsson-Horne AC, Aricò M, et al. Treatment of hemophagocytic lymphohistiocytosis with HLH-94 immunochemotherapy and bone marrow transplantation. Blood. 2002;100(7). doi:10.1182/blood-2002-01-0172 20. Taylor ML, Hoyt KJ, Han J, et al. An Evidence-Based Guideline Improves Outcomes for Patients With Hemophagocytic Lymphohistiocytosis and Macrophage Activation Syndrome. Journal of Rheumatology. 2022;49(9). doi:10.3899/jrheum.211219 21. Guo LL, Morse KE, Aftandilian C, et al. Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare. BMC Med Inform Decis Mak. 2024;24(1). doi:10.1186/s12911-024-02449-8 Table 1: HLH 2004 and 2024 Diagnostic Criteria HLH 20043 HLH 20242 Criterion 1 or 2 is fulfilled. 1, 2, or 3 below is fulfilled. 1. A molecular diagnosis consistent with HLH 1. A molecular diagnosis consistent with FHL in a patient with signs/symptoms suggestive of HLH 2. Functional cellular findings consistent with FHL in a patient with signs/symptoms suggestive of HLH 2. Diagnostic criteria for HLH fulfilled (5 of the 8 criteria below): 3. Clinical diagnostic criteria for FHL with at least 5 of the 7 criteria below fulfilled: Fever Fever ≥38.5°C Splenomegaly Splenomegaly (≥2 cm below the costal margin) Cytopenias (affecting ≥2 of 3 lineages in the peripheral blood) Cytopenias (affecting ≥2 of 3 lineages in the peripheral blood) Hemoglobin <90 g/L (hemoglobin <100 g/L in infants <4 wk) Hemoglobin <90 g/L (hemoglobin <100 g/L in infants <4 wk) Platelets <100 x 109/L Platelets <100 x 109/L Neutrophils <1.0 x 109/L Neutrophils <1.0 x 109/L Hypertriglyceridemia and/or hypofibrinogenemia Hypertriglyceridemia and/or hypofibrinogenemia Fasting triglycerides ≥3.0 mmol/L (i.e., ≥265 mg/dl) Fasting triglycerides ≥3.0 mmol/L Fibrinogen ≤1.5 g/L Fibrinogen ≤1.5 g/L Hemophagocytosis in bone marrow or spleen or lymph nodes. No evidence of malignancy. Hemophagocytosis Low or no NK cell activity (according to local laboratory reference) Ferritin ≥500 µg/L Ferritin ≥500 µg/L sCD25 (i.e., soluble IL-2 receptor) ≥2,400 U/ml sCD25 (i.e., soluble IL-2 receptor) ≥2,400 U/ml Table 2: Number of Patients with Possible Hemophagocytic Lymphohistiocytosis by Different Approaches HLH ICD10 Diagnosis Code HLH Treatment Plan Five or More HLH Criteria Total Number of Patients 220 42 245 Number of Patients with Chemotherapy During HLH-specific Encounter 35 37 78 Number of Patients with Chemotherapy During Any Encounter 73 38 99 Number Patients with Chemotherapy or HLH-Directed Therapy during HLH-specific Encounter 85 41 198 Number Patients with Chemotherapy or HLH-Directed Therapy during Any Encounter 143 42 213 Number Patients with HLH-Directed Therapy during HLH-specific Encounter 85 41 193 Number Patients with HLH-Directed Therapy during Any Encounter 142 42 212 Abbreviations: HLH – hemophagocytic lymphohistiocytosis; ICD – International Statistical Classification of Diseases and Related Health Problems * HLH-specific encounter is associated with first HLH diagnosis, first HLH treatment plan or first encounter with the maximum number of criteria met ** HLH-direct therapy includes: dexamethasone, methylprednisolone, anakira, ruxolitinib, cyclosporine, etoposide and emapalumab Table 3: Number of Patients with at least Five HLH Criteria Stratified by Receipt of HLH-Directed Therapy HLH-Directed Therapy (n=193) No HLH-Directed Therapy (n=52) P Value Demographic Features Male Sex, n (%) 96 (49.7) 27 (51.9) 0.902 Age Group in Years, n (%) 0.344 0-< 1 43 (22.3) 13 (25) 1-4 44 (22.8) 12 (23.1) 5-14 65 (33.7) 14 (26.9) 15+ 41 (21.2) 12 (23.1) Primary Admission Diagnosis*, n (%) Fever 37 (19.2) 14 (26.9) 0.303 Sepsis 9 (4.7) 7 (13.5) 0.050 Leukemia 12 (6.2) 2 (3.8) 0.751 ALL 8 (4.1) 4 (7.7) 0.490 Neutropenia 6 (3.1) 3 (5.8) 0.624 HLH-Related Variables ICD-10 HLH Diagnosis, n (%) 77 (31.4) 5 (9.6) <0.001 ICD-10 HLH Diagnosis Relative to Encounter, n (%) 0.001 Before 8 (4.1) 0 (0) During 55 (28.5) 3 (5.8) After 9 (4.7) 2 (3.8) Never had HLH diagnosis 121 (62.7) 47 (90.4) HLH Criteria Present, n (%) High ferritin 190 (98.4) 52 (100) 0.846 Fever 177 (91.7) 47 (90.4) 0.981 Cytopenia of at least 2 lineages 176 (91.2) 48 (92.3) 1.000 Hypertriglyceridemia or hypofibrinogenemia 183 (94.8) 47 (90.4) 0.391 Splenomegaly 175 (90.7) 46 (88.5) 0.831 sCD25** 91 (47.2) 26 (50) 0.835 Hemophagocytosis 33 (17.1) 3 (5.8) 0.068 Low NK cell activity** 13 (6.7) 1 (1.9) 0.322 Clinical Outcomes Services Involved, n (% Oncology 109 (56.5) 22 (42.3) 0.097 Haematology 103 (53.4) 33 (63.5) 0.253 Rheumatology 89 (46.1) 16 (30.8) 0.068 Infectious Diseases 159 (82.4) 45 (86.5) 0.615 Immunology 46 (23.8) 13 (25) 1.000 Neurology 69 (35.8) 17 (32.7) 0.805 Median Length of Stay (IQR) 33.4 (81.1, 14.3) 20.8 (33, 8.5) 0.001 Intensive Care Unit, n (%) 111 (57.5) 22 (42.3) 0.072 In-hospital Mortality, n (%) 29 (15.0) 7 (13.5) 0.950 Mortality within 30 days, n (%) 32 (16.6) 8 (15.4) 1.000 Stem Cell Transplant Following Encounter, n (%) 13 (6.7) 1 (1.9) 0.322 * Five most common admitting diagnoses for patients with at least 5/8 HLH criteria (n=245) ** Number of encounters where measured for HLH-directed therapy yes vs. no: sCD25: 135 vs 36 NK cell activity: 53 vs 7 Table 4: Distribution of Criteria among Encounters with High Ferritin and Fever* All Encounters (n= 1325 encounters) 5 or More Criteria (n=252 encounters) < 5 Criteria (n=1073 encounters) Cytopenia of at least 2 lineages 706 (53%) 231 (92%) 475 (44%) Hypertriglyceridemia or hypofibrinogenemia 485 (37%) 230 (91%) 255 (24%) Splenomegaly 519 (39%) 229 (91%) 290 (27%) sCD25** 159 (12%) 114 (45%) 45 (4%) Hemophagocytosis 27 (2%) 27 (11%) 0 (0%) Low NK cell activity** 12 (1%) 11 (4%) 1 (0.1%) *Table represents all encounters where ferritin and fever met threshold for HLH diagnosis without considering if patient was diagnosed with HLH by any approach. There may be multiple episodes per patient ** Number of encounters where measured for 5+ vs < 5 criteria were: sCD25: 172 vs 165 NK cell activity: 55 vs 24 Figure 1 Intersection of HLH Cohorts Derived Using Three Different Approaches to Cohort Creation (n=388 patients)