Khan, Muhammad Ali

Abstract Introduction Persistent symptom burden in classic myeloproliferative neoplasms (MPNs) i.e., polycythemia vera (PV), essential thrombocythemia (ET) and myelofibrosis (MF) is linked to shorter survival, more thromboembolic events and impaired quality of life. Identifying demographic, clinical, laboratory, and treatment-related predictors of symptom improvement can inform risk-adapted care and guide supportive interventions. Methods Patients with PV, ET, and MF were identified through a retrospective chart review. Total symptom scores (TSS) and scores of individual symptoms were recorded at the time of first TSS assessment. Demographic (age, sex, race, ethnicity), seasonal (summer: April–September; winter: October–March), clinical (time since diagnosis, depression, obesity, spleen size), treatment-related (phlebotomy, hydroxyurea, interferon-alpha, anagrelide, JAK inhibitors, antidepressants) and laboratory (complete blood counts, serum chemistry) factors were captured at the baseline. Symptom improvement was defined as a ≥50% TSS reduction from baseline. Cumulative incidence analyses, accounting for death as a competing risk, were performed to estimate the probability and timing of symptom improvement. Multicollinearity was assessed using variance inflation factors (VIFs). Highly correlated (VIF ≥5) features and features with ≥15% missing observations were excluded. Multivariable Fine-Gray hazards regression was performed to identify predictors of symptom improvement. Sub-distribution hazard ratios (sHR) with 95% confidence intervals (CI) were computed with p-values <0.05, indicating a statistically significant association with symptom improvement. Results A total of 532 patients (PV: 195, ET: 180, MF: 157) were identified between 2019 and 2024. The median age was 65 (IQR: 57-74) for PV, 65 (52-75) for ET, and 69 (63-75) for MF. Most patients were White (PV: 178, ET: 164, MF: 145) and female (PV: 99, ET: 134, MF: 84). Fatigue was the most frequently reported symptom (PV: 161, ET: 155, MF: 136) whereas weight loss was the least reported (PV: 9, ET: 12, MF: 26). Baseline TSS was highest in MF (median: 15; IQR: 7-27) followed by PV (13; 6-25) and ET (13; 5-23). Symptom improvement was observed in 89 (46%) PV, 71 (39%) ET, and 44 (28%) MF patients. Among those who improved, the cumulative incidence of symptom improvement reached 100% by 55 months in PV, 53 months in ET, and 58 months in MF. At 6, 12, and 24 months, cumulative symptom improvement was 22%, 34%, and 57% in PV; 15%, 25%, and 43% in ET; and 19%, 28%, and 39% in MF. Median time to improvement was not reached in any group. Death before symptom improvement was observed in 5 (3%) PV, 6 (3%) ET, and 37 (24%) MF patients. Multivariable regression analyses in PV identified higher baseline TSS (sHR: 1.02; 95%CI: 1.01-1.03) as associated with symptom improvement (p = 0.02). In ET, lower white blood cell count (sHR: 0.89; 95%CI: 0.81-0.98) and less time since diagnosis (0.99; 0.98-0.99) were associated with symptom improvement (p = 0.02). In MF, more months since diagnosis (sHR: 1.01 95%CI: 1.01-1.01) was associated with symptom improvement (p = 0.01). Conclusions Symptom trajectories differed across MPN subtypes. In PV, a higher baseline symptom burden; in ET, a lower white blood cell count and more recent diagnosis and in MF, a longer disease duration were associated with symptom improvement. These readily available parameters may help inform supportive strategies, tailor response expectations, and identify patients for symptom-directed interventions or clinical trials.

Abstract Background Accurate diagnosis and risk stratification in myeloproliferative neoplasms (MPNs) rely on expert interpretation of complex clinical records. Outside major academic centers, specialist expertise is scarce, and time-consuming chart reviews extend clinic visits and delay treatment decisions, underscoring the need for automated, point-of-care phenotyping for MPN patients. Methods Patients with the ICD codes of PV, ET and MF were identified from the electronic health record. Cases were defined as patients meeting the 2017 WHO diagnostic criteria for PV, ET or MF or those with a definitive MPN diagnosis determined by a hematologist. Controls were the patients who met neither the diagnostic criteria nor had a hematologist-assigned diagnosis. Two clinicians extracted demographic, clinical (symptoms, thrombotic history), laboratory (complete blood count, serum chemistry), genetic (mutations, karyotype), bone marrow morphology, diagnostic mentions and prognostic score information from unstructured clinical notes and pathology reports. Prognostic scores included the conventional risk model for PV, the International Prognostic Score of Thrombosis (IPSET) for ET and the Dynamic International Prognostic Scoring System (DIPSS), DIPSS-plus or Mutation-enhanced International Prognostic Scoring System (MIPSS-70) for MF. Discrepancies in data extraction were resolved by a consensus. A random sample of 510 patients (PV: 85, ET: 85, MF: 85, controls: 255) was split into prompt development (n=60) and test (n=450) sets for iterative workflow engineering and evaluation. The workflow included: (i) a rule-based algorithm for automated retrieval of demographic details, the first bone marrow biopsy report, the first clinical note after biopsy and the latest laboratory results within 90 days preceding biopsy (ii) a HIPAA-compliant LLM (GPT-4 turbo) for data extraction from the clinical notes and pathology reports (iii) a regular expressions-based algorithm for post-processing extracted data into structured tables, (iv) a clinician-informed source prioritization algorithm to select data from preferred sources, (v) a modified WHO-informed diagnostic characterization tool incorporating hematologist-assigned diagnoses to distinguish cases from controls and; (vi) an NCCN guidelines-based algorithm for prognostic tool selection and risk-stratification based on available data. Structured prompts were used to extract demographic, clinical, laboratory, genetic and pathological data in a zero-shot deterministic setting. The performance of automated data extraction, diagnostic characterization and prognostication was compared against the manually curated data. Accuracy and F1 scores were computed for data extraction and prognostication tasks while sensitivity and specificity were calculated to assess diagnostic performance. Results A total of 450 (PV: 150, ET: 150, MF: 150) pathology reports and 50 (PV: 52, ET: 55; MF: 65) clinical notes were identified for 450 test set patients. The test set data extraction accuracy was 95% (reports: 97%, notes: 92%) for PV, 93% (reports: 92%, notes: 94%) for ET and 98% (reports: 99%, notes 97%) for MF. The test set F1 score was 0.95 (reports: 0.96, notes: 0.93) for PV, 0.95 (reports: 0.95, notes: 0.96) for ET and 0.98 (reports: 0.99, notes: 0.97) for MF. PV patients were diagnosed with 100% sensitivity and 98% specificity whereas ET and MF patients were diagnosed with 100% sensitivity and specificity. The accuracy of risk stratification was 100% for PV and ET and 94% for MF. The F1 score for risk stratification was 1.00 for PV and ET and 0.94 for MF. Conclusions The first-in-class automated framework successfully identified pertinent clinical information, performed accurate data extraction from unstructured records and enabled clinician-guided source prioritization for accurate diagnosis and risk stratification of MPN patients. Prospective validation of the framework is required for widespread clinical implementation.

ABSTRACTBackgroundThe treatment landscape for multiple myeloma continues to evolve. Recently, the addition of anti‐CD38 monoclonal antibodies (mAbs) to the triplet regimen, comprising a proteasome inhibitor, an immunomodulatory agent, and a steroid, for transplant‐eligible newly diagnosed multiple myeloma (TENDMM) has shown promising results.AimsTo evaluate the overall efficacy and safety of quadruplet therapy with an anti‐CD38 mAb compared to a triplet regimen.MethodsA systematic search of Medline, Scopus, and EMBASE databases from inception to July 2024 identified relevant randomized controlled trials (RCTs). Efficacy and safety outcomes were derived using random‐effects meta‐analysis. Summarized outcomes include hazard ratios (HR) for progression‐free survival (PFS) and overall survival (OS), odds ratios (OR) for response rates, measurable residual disease (MRD) negativity rate, and grade 3 or higher adverse events (G ≥ 3 AEs).ResultsFive RCTs involving 2963 patients were included. A statistically significant PFS was observed for quadruplet therapy when compared to the triplet regimen (HR 0.44; 95% Confidence Interval [CI] 0.35–0.56). PFS benefit was consistent for the standard risk (SR) group (HR 0.38; 95% CI 0.27–0.52) and high risk (HiR) group (HR 0.62; 95% CI 0.41–0.92). No statistically significant benefit was observed for OS (HR 0.55; 95% CI 0.28–1.08). A statistically significant benefit was observed for the overall response rate (OR 1.77; 95% CI 1.02–3.06) and MRD negativity rate (OR 2.67; 95% CI 1.79–3.99). No significant differences were observed for G ≥ 3 AE (OR 1.21; 95% CI 0.92–1.58), lymphopenia (OR 1.09; 95% CI 0.62–1.89), and anemia (OR 1.06; 95% CI 0.83–1.37). However, a significantly increased risk was observed for all‐grade thrombocytopenia (OR 1.64; 95% CI 1.37–1.97), neutropenia (OR 2.24; 95% CI 1.67–3.02) and infections (OR 1.88; 95% CI 1.07–3.31).ConclusionQuadruplet therapy demonstrated a favorable efficacy and safety profile, with consistent benefit across subgroups. The findings support its potential as the new standard of care for TENDMM.

Introduction Symptom burden in myeloproliferative neoplasms (MPN) is quantified using total symptom scores (TSS) calculated from the patient-reported scores for clinically meaningful symptoms. The impact of demographic, clinical, and laboratory features on these symptoms remains unclear and warrants further investigation. Methods Patients with polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF) were identified from the retrospective chart review. TSS, scores of individual symptoms (fatigue, early satiety, abdominal discomfort, inactivity, concentration difficulties, fever, night sweats, itching, bone pain, weight loss), demographic characteristics (race, ethnicity, age, and gender), clinical features (time since diagnosis, depression status, obesity status), laboratory results and season at the time of visit when the first TSS assessment was performed were recorded. Multicollinearity and homoscedasticity were assessed using variance inflation factors and visual inspection of the data, respectively. A multivariable regression analysis was performed using a robust variance estimator. A p-value <0.05 indicated a statistically significant association with the symptom scores. Results The chart review identified 557 (PV: 201; ET: 191; MF: 165) patients. The median age of patients with PV, ET, and MF was 65 (range: 27-93), 65 (20-96), and 69 (27-96) years, respectively. Most patients were white (PV: 92%; ET: 90%; MF: 93%) and non-Hispanic or Latino (PV: 93%; ET: 96%; MF: 92%). Most patients with PV and ET were female (PV: 51%; ET: 73%), while most patients with MF were male (55%). Among the included patients 22% (PV: 21%; ET: 27%; MF: 18%) had the diagnosis of depression at the time of the clinical visit. TSS was highest in MF (mean (SD): 18.1±14.6), followed by PV (15.7±14.5) and ET (15.1±14.0) patients. Fatigue was the most reported symptom by the patients (PV: 81%; ET: 82%; MF: 84%), whereas weight loss was the least reported symptom (PV: 4.5%; ET: 6.3%; MF: 16%). With regards to the TSS multivariable regression analysis showed that age (beta coefficient: 0.2; p=0.01), depression (7.2; 0.008), and low hemoglobin (1.2; 0.003) in PV, depression (11.7; 0.001) in ET, and depression (9.5; 0.001), splenomegaly (6.9; 0.01), and WBC count (0.16; 0.048) in MF patients were significantly associated with TSS. In terms of individual symptoms in PV, depression was associated with fatigue (beta coefficient: 1.31; p-value: 0.01) and concentration difficulties (1.7; <0.001), high WBC count was associated with early satiety (0.09; 0.03), and non-white race was associated with fever (1.4; 0.007). In ET, depression was associated with fatigue (1.6; <0.001), abdominal discomfort (1.4; <0.001), inactivity (2.5; <0.001), concentration difficulties (2.4; <0.001) and night sweats (1.4; <0.001), anemia was associated with fatigue (1.2; 0.03), fewer months since diagnosis was associated with inactivity (0.01; 0.02) and young age was associated with bone pain (0.02; 0.03). In MF, depression was associated with fatigue (1.8; 0.001), concentration difficulties (1.3; 0.01), fever (1.1; 0.02), and night sweats (2.2; 0.03), low hemoglobin was associated with fatigue (0.46; <0,001) and early satiety (0.31; 0.003), high WBC count was associated with fatigue (0.03; 0.04) and early satiety (0.04; 0.02), low platelet count was associated with abdominal discomfort (0.002; 0.03), splenomegaly was associated with inactivity (2.27; 0.003) and female gender was associated with itching (0.57; 0.01). Conclusions This study demonstrates that depression in PV and ET and depression, low hemoglobin, and a high WBC count in MF contribute to multiple symptoms seen in these patients. Identifying demographic groups at risk of a higher symptom burden and addressing the comorbidities that drive symptoms, in addition to disease-directed therapy, is crucial to improving the quality of life in these patients.

Introduction Prognostication in myelofibrosis (MF) currently relies on manual assessment of clinical, pathological, and laboratory variables, which is inefficient and error-prone. The potential of large language models (LLMs) and rule-based programming to automate data extraction and prognostication in these patients remains unexplored and merits investigation. Methods Patients with MF were identified from a retrospective chart review. Data regarding clinical (age, constitutional symptoms, transfusion requirements), laboratory (hemoglobin, circulating white blood cell, platelet, and blast counts), and pathological (grade of bone marrow fibrosis, chromosomal analyses, cytogenetics) features was extracted from the latest free-text clinical notes and pathology reports using the generative pretrained transformer-4 (GPT-4) model in a zero-shot setting with the prompting parameters set to generate deterministic responses. The patients were randomly sampled into development (15%) and test (85%) sets, with the documents of patients in the development set used for iterative prompt engineering. The finalized prompts were used for data extraction in the test set. Rule-based programming was used for post-processing of the extracted data and ascertaining prognosis via Dynamic International Prognostic Scoring System (DIPSS), DIPSS-plus (DIPSS+), and Mutation-enhanced International Prognostic Scoring System-70 (MIPSS-70). Automatically extracted data and prognostic categories were compared with the manually generated human annotations. Mean accuracy, precision, and recall with 95% confidence intervals (CI) were computed. Detailed error and sensitivity analyses were conducted to assess performance. Results The study included 100 patients (development: 15; test: 85) with 100 clinical notes (development: 15; test: 85) and 90 pathology reports (development: 13; test: 77). The mean age of these patients was 70 (SD: 10.6); 55 (55%) patients were men, and 93 (93%) patients were White. In the development set, the mean accuracy, precision, and recall for data extraction from clinical notes were 94% (95% CI: 90-98%), 92% (84-99%), and 99% (97-100%). For data extraction from pathology reports, the mean accuracy, precision, and recall were 96% (93-100%), 98% (92-100%), and 83% (63-100%). For prognostication, the mean accuracy, precision, and recall were 87% (79-94%), 80% (73-87%), and 100% using DIPSS (accuracy: 80%; precision: 73%; recall: 100%), DIPSS+ (accuracy: 93%; precision: 83%; recall: 100%) and MIPSS-70 (accuracy: 87%, precision: 83%; recall: 100%). In the test set, the mean accuracy, precision, and recall for data extraction from clinical notes were 96% (95% CI: 93-98%), 91% (87-96%), and 98% (97-100%). For data extraction from pathology reports, the mean accuracy, precision, and recall were 95% (92-98%), 93% (87-99%), and 90% (80-100%). For prognostication the mean accuracy, precision, and recall were 95% (94-96%), 89% (88-90%) and 99% (96-100%) using DIPSS (accuracy: 94%; precision: 89%; recall: 100%), DIPSS+ (accuracy: 95%; precision: 88%; recall: 95%) and MIPSS-70 (accuracy: 94%, precision: 90%; recall: 100%). Error analyses identified incorrect extractions by GPT-4 were related to the circulating blasts and constitutional symptoms variables from the clinical notes. Sensitivity analyses excluding the associated variables improved overall accuracy from 94% to 97% and 96% to 97% in the development and test sets, respectively. Similarly, incorrect extractions from the pathology reports were related to the CALR gene mutation variables. Sensitivity analyses excluding the associated variables improved the overall accuracy from 96% to 98% and 95% to 97% in the development and test sets, respectively. Conclusion LLMs and rule-based programming can accurately extract data from the electronic health records and prognosticate MF patients via DIPSS, DIPSS+, and MIPSS-70. The automated approach could significantly streamline clinical and research workflows, providing an efficient and less resource-intensive alternative to manual prognostication.

Introduction: Telemedicine has been widely utilized in the healthcare marketplace since the beginning of the COVID-19 pandemic. Over the last 3 years, virtual care has been widely employed in the care of patients with polycythemia vera (PV), essential thrombocythemia (ET) and myelofibrosis (MF) at Mayo Clinic. These rare disorders have few experts; therefore, telemedicine becomes an optimal approach for patients receive expert care even when they are not close to the medical center. 1 Additionally, telemedicine reduces financial burden for the patients. We aim to estimate potential cost savings to patients utilizing telemedicine. Methods: We identified patients with MPN seen at Mayo Clinic, Arizona who have had at least 2 visits (1 or more virtual visits) between March 2020 and June 2022. We extracted retrospective data from electronic medical records (EMR) including sociodemographic data, disease related data and number of visits - both virtual and in person - of the patients identified. The address recorded from the EMR was assumed to be the patient's location at the time of virtual visit. Driving distances were determined using zip codes in Google Maps and the patients were stratified into quartiles based on round trip travel distances. Patients in quartiles 1 through 3 were assumed to have driven while patients in quartile 4 were assumed to have traveled by plane. Patients in quartile 1 were assumed to have 1 travel day including the follow-up visit and no accommodation, patients in quartile 2 were assumed to have 2 travel days including the follow up visit and 1 night of accommodation whereas patients in quartiles 3 and 4 were assumed to have 2 travel days, 1 day for the follow up visit and 2 nights of accommodation. Mileage rate costs were based on US Internal Revenue Service (IRS) standard mileage rates for medical or moving purposes for 2021. 2 Owing to extreme variability in flight costs, flying costs were assumed to be equal to the driving costs. Per diem costs for accommodation and meals and incidentals were based on the US General Services Administration (GSA) 2021 rates for Phoenix 3 with the cost being 75% of the full per diem rate on traveling days via GSA formulaic approach. Results: One hundred and eight patients having 282 virtual visits were identified. Patients ranged from 30 - 87 years with the mean and median age of 65 years and 67 years respectively. Majority of the patients were female (62%) and white (98%) with gender distribution varying by distance. Descriptive statistics of the estimated out-of-pocket costs for patients by distance quartiles are summarized in Table 1a. Mean estimated cost savings ranged from $46.8 to $877.3 with the savings of $43.8 - $1372.2 at extremes. The total cost savings by all patients was $90,306.7. The breakdown of costs by category for each quartile is shown in Table 1b which shows that the majority of costs saved by patients in quartile 1 were due to avoiding costs spent on meals and incidentals (~90% of mean costs) whereas costs saved due to meals and incidentals for patients in quartile 4 were only ~16% of the mean costs. Conclusions: Our research highlighted telemedicine as a cost-effective approach for patients with MPN who received virtual care from March 2020 - June 2022. Study limitations included not weighing costs for childcare or accompanying individuals and using driving costs instead of flying costs, however, these considerations potentially underestimate the actual cost savings to the patient. Additionally, since the median age of this population was 65, we didn't include loss of wages to the analysis, which may further underestimate the costs. Despite concerns over billing, reimbursement, and state licensure, virtual care is being established as a critical part of medicine moving forward. 1. Doolittle, G.C. and A.O. Spaulding, Providing Access to Oncology Care for Rural Patients via Telemedicine. Journal of Oncology Practice, 2006. 2(5): p. 228-230. 2. Internal Revenue Service. Standard mileage rates for 2021. Available at: https://www.irs.gov/tax-professionals/standard-mileage-rates 3. US General Services Administration. Per diem rates. Available at: https://www.gsa.gov/travel/plan-book/per-diem-rates/per-diem-rates-results?action=perdiems_report&fiscal_year=2021&state=AZ&city=&zip=85054