Jagtiani, Pemla
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Biography
Pemla Jagtiani is a Medical Student in the Class of 2026 at SUNY Downstate Medical Center, Brooklyn, NY. She graduated from Stony Brook University in 2022 (BS, Biology with Neuroscience specialization; Minor: Mathematics). During her undergraduate years, her focus primarily revolved around basic science investigations, particularly in elucidating the complexities of the Zona Incerta and exploring various neuronal subtypes within this critical brain region.Upon transitioning to medical school, Pemla's research interests evolved to encompass a more clinical-oriented approach, leveraging her foundational knowledge in neuroscience to address pressing issues in patient care. With aspirations of pursuing a Neurosurgical residency, Pemla's latest pursuits center around the development and application of prognostic models in guiding treatment decisions for complex neurological conditions, particularly in the realms of neuro-oncology and spinal pathologies.
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Publication Open Access Enhancing Surgical Task Adherence Through a Rewards-Driven Mobile Application: A Single-Arm Intervention Feasibility Study(Springer Science and Business Media LLC, 2024-05-23) Jagtiani, Pemla; Mastrokostas, Paul G; Inzerillo, Sean; Betchen, Simone AIntroduction: Ensuring patients follow preoperative and postoperative instructions is vital for maximizing surgical success. This pilot study investigates the feasibility of using monetary incentives through a nudge engine application-based model of omnichannel communication to prompt adherence to preoperative and postoperative instructions. Methods: Over a six-month period, we conducted a longitudinal study employing the TheraPay® Rewards app at Maimonides Medical Center in Brooklyn, United States. Our recruitment efforts targeted English and Spanish-speaking patients with smartphones through in-person visits and phone calls. Participants received a $15 credit on a gift card for each completed task. The tasks included preoperative validations such as obtaining primary care physician clearance, completing preoperative assessments, undergoing preoperative scans with accompanying compact disks (CDs), and discontinuing specific medications. Postoperative validations included attending postoperative visits, proper incision care, discontinuation of narcotics at three weeks, and initiation of the first physical therapy session. Results: We enrolled 16 patients with a mean age of 59.5 years (SD 11.68), the majority being male (n = 10, 62.5%). Preoperatively, task completion rates ranged from 83% to 100%. Postoperatively, rates varied from 20% to 100%. Preoperative task adherence averaged at 98.7% (SD 2.2%), while postoperative adherence averaged 60% (SD 21%). Conclusion: Our study indicates that financial incentives delivered through a gamified approach effectively encourage patients to complete essential preoperative tasks, suggesting a promise for enhancing adherence. Nonetheless, the decrease in postoperative task adherence highlights the necessity for careful implementation. Future investigations should compare cancellation rates and outcome measures to gain deeper insights into the effectiveness of app-based incentives in improving surgical outcomes and patient adherence.Publication Open Access Prevalence of Acute Alcohol Use in Traumatic Brain Injury Patients During the COVID-19 Pandemic: A Retrospective Analysis From Queens, New York(Springer Science and Business Media LLC, 2024-04-24) Jagtiani, Pemla; Young, Tirone; Ahmed, Wasil; Devarajan, Alex; Hickman, Zachary L; Jones, SalazarBackground This study investigates the impact of New York's relaxed alcohol consumption policies during the coronavirus disease (COVID-19) pandemic on alcohol-related traumatic brain injuries (TBIs) among patients admitted to a Level 1 trauma center in Queens. Given the limited research available, this study critically explores the link between public health policies and trauma care. It aims to address a significant gap in the literature and highlight the implications of alcohol regulations during global health emergencies. Methodology A retrospective analysis was conducted among trauma patients from 2019 to 2021. The study period was divided into the following three periods: pre-lockdown (March 7, 2019, to July 31, 2019), lockdown (March 7, 2020, to July 31, 2020), and post-lockdown (March 7, 2021, to July 31, 2021). Data on demographics, injury severity, comorbidities, and outcomes were collected. The study focused on assessing the correlation between New York's alcohol policies and alcohol-related TBI admissions during these periods. Results A total of 1,074 admissions were analyzed. The study found no significant changes in alcohol-positive patients over the full calendar years of 2019, 2020, and 2021 (42.65%, 38.91%, and 31.16% respectively; p = 0.08711). Specifically, during the lockdown period, rates of alcohol-positive TBI patients remained unchanged, despite the relaxed alcohol policies. There was a decrease in alcohol-related TBI admissions in 2021 compared to 2020 during the lockdown period. Conclusions Our study concludes that New York's specific alcohol policies during the COVID-19 pandemic were not correlated with an increase in alcohol-related TBI admissions. Despite the relaxation of alcohol consumption laws, there was no increase in alcohol positivity among TBI patients. The findings suggest a complex relationship between public policies, alcohol use, and trauma during pandemic conditions, indicating that factors other than policy relaxation might influence alcohol-related trauma incidences.Publication Open Access The MOST (Mortality Score for TBI): A novel prediction model beyond CRASH-Basic and IMPACT-Core for isolated traumatic brain injury(Elsevier BV, 2024-10) Karabacak, Mert; Jagtiani, Pemla; Dams-O'Connor, Kristen; Legome, Eric; Hickman, Zachary L.; Margetis, KonstantinosBackground: Due to significant injury heterogeneity, outcome prediction following traumatic brain injury (TBI) is challenging. This study aimed to develop a simple model for high-accuracy mortality risk prediction after TBI. Study design: Data from the American College of Surgeons (ACS) Trauma Quality Program (TQP) from 2019 to 2021 was used to develop a summary score based on age, the Glasgow Coma Scale (GCS) component subscores, and pupillary reactivity data. We then compared the predictive accuracy to that of the Corticosteroid Randomisation After Significant Head Injury Trial (CRASH)-Basic and International Mission for Prognosis and Analysis of Clinical Trial in TBI (IMPACT)-Core models. Two separate series of sensitivity analyses were conducted to further assess our model's generalizability. We evaluated predictive performance of the models with discrimination [the area under the receiver-operating characteristic curves (AUC), sensitivity, specificity] and calibration (Brier score). Discriminative ability was compared with DeLong tests. Results: 259,404 patients were included in the present study (mean age, 60 years; 93,495 (36 %) female). The mortality score after TBI (MOST) model (AUC = 0.875) had better discrimination (DeLong test p values < 0.00001) than CRASH-Basic (AUC = 0.837) and IMPACT-Core (AUC = 0.821) models, and superior calibration (MOST = 0.02729, CRASH-Basic = 0.02962, IMPACT-Core = 0.02962) in predicting in-hospital mortality. The MOST model similarly outperformed in predicting 3-, 7-, 14-, and 30-day mortality. Conclusion: The MOST model can be rapidly calculated and outperforms two widely used models for predicting mortality in TBI patients. It utilizes a larger, contemporaneous dataset that reflects modern trauma care.Publication Open Access Exploring Pediatric Vertebral, Sacral, and Pelvic Osteosarcomas through the NCDB: Demographics, Treatment Utilization, and Survival Outcomes(MDPI AG, 2024-08-21) Jagtiani, Pemla; Karabacak, Mert; Carr, Matthew T.; Bahadir, Zeynep; Morgenstern, Peter F.; Margetis, KonstantinosBackground and objectives: Retrieve data from the National Cancer Database (NCDB) to examine information on the epidemiological prevalence, treatment strategies, and survival outcomes of pediatric vertebral, sacral and pelvic osteosarcomas. Methods: We reviewed NCDB data from 2008 to 2018, concentrating on vertebral, sacral, and pelvic osteosarcomas in children 0 to 21 years. Our analysis involved logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. Results: The study population included 207 patients. For vertebral osteosarcomas, 62.5% of patients were female, and 78.1% were white. Regional lymph node involvement predicted 80 times higher mortality hazard (p = 0.021). Distant metastasis predicted 25 times higher mortality hazard (p = 0.027). For sacral and pelvic osteosarcomas, 58.3% of patients were male, and 72% were white. Patients with residual tumor were 4 times more likely to have prolonged LOS (p = 0.031). No residual tumor (HR = 0.53, p = 0.03) and radiotherapy receipt (HR = 0.46, p = 0.034) were associated with lower mortality hazards. Distant metastasis predicted 3 times higher mortality hazard (p < 0.001). Hispanic ethnicity was linked to lower resection odds (OR = 0.342, p = 0.043), possibly due to language barriers affecting patient understanding and care decisions. Conclusions: In conclusion, our examination of NCDB offers a thorough exploration of demographics, treatment patterns, and results, highlighting the importance of personalized approaches to enhance patient outcomes.Publication Open Access Technical Optimization of Decompressive Craniectomy for Possible Conversion to Hinge Craniotomy in Traumatic Brain Injury(Springer Science and Business Media LLC, 2023-05-31) Ahmed, Abdul-Kareem; Jagtiani, Pemla; Jones, SalazarHinge craniotomy for the management of elevated intracranial pressure (ICP) in traumatic brain injury remains a technique not widely adopted. The hinged bone flap decreases the allowable intracranial volume expansion, which can lead to persistent post-operative elevated ICP and the need for salvage craniectomy. Herein, we describe the technical nuances in performing a decompressive craniectomy that, when optimized, allows for stronger consideration for hinge craniotomy as a definitive technique. To conclude, hinge craniotomy is a reasonable option in the setting of traumatic brain injury. Trauma neurosurgeons can consider the technical steps to optimize a decompressive craniectomy and perform hinge craniotomy when allowable.Publication Open Access Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma(Oxford University Press (OUP), 2024-06-11) Karabacak, Mert; Jagtiani, Pemla; Di, Long; Shah, Ashish H; Komotar, Ricardo J; Margetis, KonstantinosBackground: Glioblastoma (GBM) remains associated with a dismal prognoses despite standard therapies. While population-level survival statistics are established, generating individualized prognosis remains challenging. We aim to develop machine learning (ML) models that generate personalized survival predictions for GBM patients to enhance prognostication. Methods: Adult patients with histologically confirmed IDH-wildtype GBM from the National Cancer Database (NCDB) were analyzed. ML models were developed with TabPFN, TabNet, XGBoost, LightGBM, and Random Forest algorithms to predict mortality at 6, 12, 18, and 24 months postdiagnosis. SHapley Additive exPlanations (SHAP) were employed to enhance the interpretability of the models. Models were primarily evaluated using the area under the receiver operating characteristic (AUROC) values, and the top-performing models indicated by the highest AUROCs for each outcome were deployed in a web application that was created for individualized predictions. Results: A total of 7537 patients were retrieved from the NCDB. Performance evaluation revealed the top-performing models for each outcome were built using the TabPFN algorithm. The TabPFN models yielded mean AUROCs of 0.836, 0.78, 0.732, and 0.724 in predicting 6, 12, 18, and 24 month mortality, respectively. Conclusions: This study establishes ML models tailored to individual patients to enhance GBM prognostication. Future work should focus on external validation and dynamic updating as new data emerge.Publication Open Access Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application(Springer Science and Business Media LLC, 2023-10-26) Karabacak, Mert; Jagtiani, Pemla; Carrasquilla, Alejandro; Germano, Isabelle M.; Margetis, KonstantinosWHO grade II and III gliomas demonstrate diverse biological behaviors resulting in variable survival outcomes. In the context of glioma prognosis, machine learning (ML) approaches could facilitate the navigation through the maze of factors influencing survival, aiding clinicians in generating more precise and personalized survival predictions. Here we report the utilization of ML models in predicting survival at 12, 24, 36, and 60 months following grade II and III glioma diagnosis. From the National Cancer Database, we analyze 10,001 WHO grade II and 11,456 grade III cranial gliomas. Using the area under the receiver operating characteristic (AUROC) values, we deploy the top-performing models in a web application for individualized predictions. SHapley Additive exPlanations (SHAP) enhance the interpretability of the models. Top-performing predictive models are the ones built with LightGBM and Random Forest algorithms. For grade II gliomas, the models yield AUROC values ranging from 0.813 to 0.896 for predicting mortality across different timeframes, and for grade III gliomas, the models yield AUROCs ranging from 0.855 to 0.878. ML models provide individualized survival forecasts for grade II and III glioma patients across multiple clinically relevant time points. The user-friendly web application represents a pioneering digital tool to potentially integrate predictive analytics into neuro-oncology clinical practice, to empower prognostication and personalize clinical decision-making.Publication Open Access Mapping the Degenerative Cervical Myelopathy Research Landscape: Topic Modeling of the Literature(SAGE Publications, 2024-05-17) Karabacak, Mert; Jagtiani, Pemla; Zipser, Carl Moritz; Tetreault, Lindsay; Davies, Benjamin; Margetis, KonstantinosStudy design: Topic modeling of literature. Objectives: Our study has 2 goals: (i) to clarify key themes in degenerative cervical myelopathy (DCM) research, and (ii) to evaluate the current trends in the popularity or decline of these topics. Additionally, we aim to highlight the potential of natural language processing (NLP) in facilitating research syntheses. Methods: Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic, an NLP-based topic modeling method. We specified a minimum topic size of 25 documents and 50 words per topic. After the models were trained, they generated a list of topics and corresponding representative documents. We utilized linear regression models to examine trends within the identified topics. In this context, topics exhibiting increasing linear slopes were categorized as "hot topics," while those with decreasing slopes were categorized as "cold topics". Results: Our analysis retrieved 3510 documents that were classified into 21 different topics. The 3 most frequently occurring topics were "OPLL" (ossification of the posterior longitudinal ligament), "Anterior Fusion," and "Surgical Outcomes." Trend analysis revealed the hottest topics of the decade to be "Animal Models," "DCM in the Elderly," and "Posterior Decompression" while "Morphometric Analyses," "Questionnaires," and "MEP and SSEP" were identified as being the coldest topics. Conclusions: Our NLP methodology conducted a thorough and detailed analysis of DCM research, uncovering valuable insights into research trends that were otherwise difficult to discern using traditional techniques. The results provide valuable guidance for future research directions, policy considerations, and identification of emerging trends.Publication Open Access Exploiting Natural Language Processing to Unveil Topics and Trends of Traumatic Brain Injury Research(Mary Ann Liebert Inc, 2024-03-01) Karabacak, Mert; Jain, Ankita; Jagtiani, Pemla; Hickman, Zachary L.; Dams-O'Connor, Kristen; Margetis, KonstantinosTraumatic brain injury (TBI) has evolved from a topic of relative obscurity to one of widespread scientific and lay interest. The scope and focus of TBI research have shifted, and research trends have changed in response to public and scientific interest. This study has two primary goals: first, to identify the predominant themes in TBI research; and second, to delineate "hot" and "cold" areas of interest by evaluating the current popularity or decline of these topics. Hot topics may be dwarfed in absolute numbers by other, larger TBI research areas but are rapidly gaining interest. Likewise, cold topics may present opportunities for researchers to revisit unanswered questions. We utilized BERTopic, an advanced natural language processing (NLP)-based technique, to analyze TBI research articles published since 1990. This approach facilitated the identification of key topics by extracting sets of distinctive keywords representative of each article's core themes. Using these topics' probabilities, we trained linear regression models to detect trends over time, recognizing topics that were gaining (hot) or losing (cold) relevance. Additionally, we conducted a specific analysis focusing on the trends observed in TBI research in the current decade (the 2020s). Our topic modeling analysis categorized 42,422 articles into 27 distinct topics. The 10 most frequently occurring topics were: "Rehabilitation," "Molecular Mechanisms of TBI," "Concussion," "Repetitive Head Impacts," "Surgical Interventions," "Biomarkers," "Intracranial Pressure," "Posttraumatic Neurodegeneration," "Chronic Traumatic Encephalopathy," and "Blast Induced TBI," while our trend analysis indicated that the hottest topics of the current decade were "Genomics," "Sex Hormones," and "Diffusion Tensor Imaging," while the cooling topics were "Posttraumatic Sleep," "Sensory Functions," and "Hyperosmolar Therapies." This study highlights the dynamic nature of TBI research and underscores the shifting emphasis within the field. The findings from our analysis can aid in the identification of emerging topics of interest and areas where there is little new research reported. By utilizing NLP to effectively synthesize and analyze an extensive collection of TBI-related scholarly literature, we demonstrate the potential of machine learning techniques in understanding and guiding future research prospects. This approach sets the stage for similar analyses in other medical disciplines, offering profound insights and opportunities for further exploration.Publication Open Access Optimising early detection of degenerative cervical myelopathy: a systematic review of quantitative screening tools for primary care(BMJ, 2025-01-11) Inzerillo, Sean; Jagtiani, Pemla; Jones, SalazarBackground: Early diagnosis of degenerative cervical myelopathy (DCM) is often challenging due to subtle, non-specific symptoms, limited disease awareness and a lack of definitive diagnostic criteria. As primary care physicians are typically the first to encounter patients with early DCM, equipping them with effective screening tools is crucial for reducing diagnostic delays and improving patient outcomes. This systematic review evaluates the efficacy of quantitative screening methods for DCM that can be implemented in primary care settings. Methods: A systematic search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted across PubMed, Embase and Cochrane Library up to July 2024 using keywords relevant to DCM screening. Studies were included if they evaluated the sensitivity and specificity of DCM screening tools applicable to primary care settings. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results: The search identified 14 studies evaluating 18 screening methods for DCM. Questionnaires consistently showed high diagnostic accuracy, with Youden indices exceeding 0.60, while only three out of nine conventional physical performance tests met the same threshold. Sensor-assisted tests, particularly those using advanced technology like finger-wearable gyro sensors, exhibited the highest diagnostic accuracy but present challenges related to accessibility and learning curves. Conclusion: This review highlights the potential of quantitative screening methods for early DCM detection in primary care. While questionnaires and conventional tests are effective and accessible, sensor-assisted tests offer greater accuracy but face implementation challenges. A tailored, multifaceted approach is crucial for improving outcomes. Future research should focus on validating these tools in diverse populations and standardising diagnostic criteria.