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Malkeet Gupta

Other affiliations: Antelope Valley Hospital
Bio: Malkeet Gupta is an academic researcher from Ronald Reagan UCLA Medical Center. The author has contributed to research in topics: Head injury & Warfarin. The author has an hindex of 4, co-authored 4 publications receiving 37 citations. Previous affiliations of Malkeet Gupta include Antelope Valley Hospital.

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Journal ArticleDOI
TL;DR: Clinicians should have a low threshold for neuroimaging when evaluating patients receiving warfarin or a combination of aspirin and clopidogrel in combination for significant intracranial injury, but not those receiving aspirin alone.

22 citations

Journal ArticleDOI
TL;DR: The instrument’s performance in assigning “high-risk” status to patients requiring neurosurgical intervention among a cohort of 11,770 blunt head injury patients is examined and confirmation of the hypothesis that the lower 95% confidence limit for its sensitivity in detecting serious injury exceeds 99.0 is needed is confirmed.
Abstract: Clinicians, afraid of missing intracranial injuries, liberally obtain computed tomographic (CT) head imaging in blunt trauma patients. Prior work suggests that clinical criteria (National Emergency X-Radiography Utilization Study [NEXUS] Head CT decision instrument [DI]) can reliably identify patients with important injuries, while excluding injury, and the need for imaging in many patients. Validating this DI requires confirmation of the hypothesis that the lower 95% confidence limit for its sensitivity in detecting serious injury exceeds 99.0%. A secondary goal of the study was to complete an independent validation and comparison of the Canadian and NEXUS Head CT rules among the subgroup of patients meeting the inclusion and exclusion criteria.We conducted a prospective observational study of the NEXUS Head CT DI in 4 hospital emergency departments between April 2006 and December 2015. Implementation of the rule requires that patients satisfy 8 criteria to achieve "low-risk" classification. Patients are excluded from "low-risk" classification and assigned "high-risk" status if they fail to meet 1 or more criteria. We examined the instrument's performance in assigning "high-risk" status to patients requiring neurosurgical intervention among a cohort of 11,770 blunt head injury patients. The NEXUS Head CT DI assigned high-risk status to 420 of 420 patients requiring neurosurgical intervention (sensitivity, 100.0% [95% confidence interval [CI]: 99.1%-100.0%]). The instrument assigned low-risk status to 2,823 of 11,350 patients who did not require neurosurgical intervention (specificity, 24.9% [95% CI: 24.1%-25.7%]). None of the 2,823 low-risk patients required neurosurgical intervention (negative predictive value [NPV], 100.0% [95% CI: 99.9%-100.0%]). The DI assigned high-risk status to 759 of 767 patients with significant intracranial injuries (sensitivity, 99.0% [95% CI: 98.0%-99.6%]). The instrument assigned low-risk status to 2,815 of 11,003 patients who did not have significant injuries (specificity, 25.6% [95% CI: 24.8%-26.4%]). Significant injuries were absent in 2,815 of the 2,823 patients assigned low-risk status (NPV, 99.7% [95% CI: 99.4%-99.9%]). Of our patients, 7,759 (65.9%) met the inclusion and exclusion criteria of the Canadian Head CT rule, including 111 patients (1.43%) who required neurosurgical intervention and 306 (3.94%) who had significant intracranial injuries. In our study, the Canadian criteria for neurosurgical intervention identified 108 of 111 patients requiring neurosurgical intervention to yield a sensitivity of 97.3% (95% CI: 92.3%-99.4%) and exhibited a specificity of 58.8% (95% CI: 57.7%-59.9%). The NEXUS rule, when applied to this same cohort, identified all 111 patients requiring neurosurgical intervention, yielding a sensitivity of 100% (95% CI: 96.7%-100.0%) with a specificity of 32.6% (95% CI: 31.5%-33.6%). Our study found that the Canadian medium-risk factors identified 301 of 306 patients with significant injuries (sensitivity = 98.4%; 95% CI: 96.2%-99.5%), while the NEXUS rule identified 299 of these patients (sensitivity = 97.7%; 95% CI: 95.3%-99.1%). In our study, the Canadian medium-risk rule exhibited a specificity of 12.3% (95% CI: 11.6%-13.1%), while the NEXUS rule exhibited a specificity of 33.3% (95% CI: 32.3%-34.4%). Limitations of the study may arise from application of the rule by different clinicians in different environments. Clinicians may vary in their interpretation and application of the instrument's criteria and risk assignment and may also vary in deciding which patients require intervention. The instrument's specificity is also subject to spectrum bias and may change with variations in the proportion of "low-risk" patients seen in other centers.The NEXUS Head CT DI reliably identifies blunt trauma patients who require head CT imaging and could significantly reduce the use of CT imaging.

22 citations

Journal ArticleDOI
TL;DR: A limited number of simple clinical characteristics appear to be able to identify skin and soft tissue infection patients who require high‐level inpatient services, and whether patients who do not exhibit these criteria can be safely discharged from the ED is needed.

11 citations

Journal ArticleDOI
TL;DR: The Pediatric NEXUS Head CT DI reliably identifies blunt trauma patients who require head CT imaging and could significantly reduce the use of CT imaging.
Abstract: Background Data suggest that clinicians, when evaluating pediatric patients with blunt head trauma, may be overordering head computed tomography (CT). Prior decision instruments (DIs) aimed at aiding clinicians in safely forgoing CTs may be paradoxically increasing CT utilization. This study evaluated a novel DI that aims for high sensitivity while also improving specificity over prior instruments. Methods We conducted a planned secondary analysis of the NEXUS Head CT DI among patients less than 18 years old. The rule required patients satisfy seven criteria to achieve "low-risk" classification. Patients were assigned "high-risk" status if they fail to meet one or more criteria. Our primary outcome was the ability of the rule to identify all patients requiring neurosurgical intervention. Results The study enrolled 1,018 blunt head injury pediatric patients. The DI assigned high-risk status to 27 of 27 patients requiring neurosurgical intervention (sensitivity = 100.0%, 95% confidence interval [CI] = 87.2%-100%]). The instrument assigned low-risk status to 330 of 991 patients who did not require neurosurgical intervention (specificity = 33.3%, 95% CI = 30.3%-36.3%). None of the 991 low-risk patients required neurosurgical intervention (negative predictive value [NPV] = 100%, 95% CI = 99.6%-100%). The DI correctly assigned high-risk status to 48 of the 49 patients with significant intracranial injuries, yielding a sensitivity of 98.0% (95% CI = 89.1%-99.9%). The instrument assigned low-risk status to 329 of 969 patients who did not have significant injuries to yield a specificity of 34.0% (95% CI = 31.0%-37.0%). Significant injuries were absent in 329 of the 330 patients assigned low-risk status to yield a NPV of 99.7% (95% CI = 98.3%-100%). Conclusions The Pediatric NEXUS Head CT DI reliably identifies blunt trauma patients who require head CT imaging and could significantly reduce the use of CT imaging.

10 citations


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Journal ArticleDOI
TL;DR: The aim was to provide pragmatic, clear, and easy-to-follow clinical guidance for coagulation management in adult patients with TBI and potential or known intake of platelet inhibitors, vitamin K antagonists, or non-vitamin K antagonist oral anticoagulants.
Abstract: There is a high degree of uncertainty regarding optimum care of patients with potential or known intake of oral anticoagulants and traumatic brain injury (TBI). Anticoagulation therapy aggravates the risk of intracerebral hemorrhage but, on the other hand, patients take anticoagulants because of an underlying prothrombotic risk, and this could be increased following trauma. Treatment decisions must be taken with due consideration of both these risks. An interdisciplinary group of Austrian experts was convened to develop recommendations for best clinical practice. The aim was to provide pragmatic, clear, and easy-to-follow clinical guidance for coagulation management in adult patients with TBI and potential or known intake of platelet inhibitors, vitamin K antagonists, or non-vitamin K antagonist oral anticoagulants. Diagnosis, coagulation testing, and reversal of anticoagulation were considered as key steps upon presentation. Post-trauma management (prophylaxis for thromboembolism and resumption of long-term anticoagulation therapy) was also explored. The lack of robust evidence on which to base treatment recommendations highlights the need for randomized controlled trials in this setting.

40 citations

Journal ArticleDOI
TL;DR: The risk of adverse outcome following mild head injury in patients taking direct oral anticoagulants appears low, and these findings would support shared patient-clinician decision making, rather than routine imaging, following minor head injury while taking DOACs.
Abstract: Background Patients taking direct oral anticoagulants (DOACs) commonly undergo CT head imaging after minor head injury, regardless of symptoms or signs. However, the risk of intracranial haemorrhage (ICH) in such patients is unclear, and further research has been recommended by the UK National Institute for Health and Care Excellence head injury guideline group. Methods An observational cohort study was performed in the UK South Yorkshire major trauma centre between 26 June and 3 September 2018. Adult patients taking DOACs with minor head injury were prospectively identified, with case ascertainment supplemented by screening of radiology and ED information technology systems. Clinical and outcome data were subsequently collated from patient records. The primary endpoint was adverse outcome within 30 days, comprising: neurosurgery, ICH or death due to head injury. A previously published meta-analysis was updated with the current results and the findings of other recent studies. Results 148 patients with minor head injury were included (GCS 15, n=107, 72%; GCS 14, n=41, 28%). Patients were elderly (median 82 years) and most frequently injured from ground level falls (n=142, 96%). Overall risk of adverse outcome was 3.4% (5/148, 95% CI 1.4% to 8.0%). Five patients had ICH, of whom one died within 30 days. One patient was treated with prothrombin complex concentrate but no patient received critical care management or underwent neurosurgical intervention. Updated random effects meta-analysis, including the current results and two further recent studies, showed a weighted overall risk of adverse outcome of 3.2% (n=29/787, 95% CI 2.0% to 4.4%). Conclusions The risk of adverse outcome following mild head injury in patients taking DOACs appears low. These findings would support shared patient-clinician decision making, rather than routine imaging, following minor head injury while taking DOACs.

23 citations

Journal ArticleDOI
TL;DR: Clinicians should have a low threshold for neuroimaging when evaluating patients receiving warfarin or a combination of aspirin and clopidogrel in combination for significant intracranial injury, but not those receiving aspirin alone.

22 citations

Journal ArticleDOI
TL;DR: In this article, the diagnostic accuracy of an artificial intelligence decision support system (DSS) in diagnosing intracranial hemorrhage (ICH) on non-contrast head CT scans was evaluated by both a certified neuroradiologist and Aidoc.
Abstract: Objective To determine the institutional diagnostic accuracy of an artificial intelligence (AI) decision support systems (DSS), Aidoc, in diagnosing intracranial hemorrhage (ICH) on noncontrast head CTs and to assess the potential generalizability of an AI DSS. Methods This retrospective study included 3,605 consecutive, emergent, adult noncontrast head CT scans performed between July 1, 2019, and December 30, 2019, at our institution (51% female subjects, mean age of 61 ± 21 years). Each scan was evaluated for ICH by both a certificate of added qualification certified neuroradiologist and Aidoc. We determined the diagnostic accuracy of the AI model and performed a failure mode analysis with quantitative CT radiomic image characterization. Results Of the 3,605 scans, 349 cases of ICH (9.7% of studies) were identified. The neuroradiologist and Aidoc interpretations were concordant in 96.9% of cases and the overall sensitivity, specificity, positive predictive value, and negative predictive value were 92.3%, 97.7%, 81.3%, and 99.2%, respectively, with positive predictive values unexpectedly lower than in previously reported studies. Prior neurosurgery, type of ICH, and number of ICHs were significantly associated with decreased model performance. Quantitative image characterization with CT radiomics failed to reveal significant differences between concordant and discordant studies. Discussion This study revealed decreased diagnostic accuracy of an AI DSS at our institution. Despite extensive evaluation, we were unable to identify the source of this discrepancy, raising concerns about the generalizability of these tools with indeterminate failure modes. These results further highlight the need for standardized study design to allow for rigorous and reproducible site-to-site comparison of emerging deep learning technologies.

21 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluated the performance of an artificial intelligence decision support system, Aidoc, for the detection of cervical spinal fractures on non-contrast cervical spine CT scans and to conduct a failure mode analysis to identify areas of poor performance.
Abstract: BACKGROUND AND PURPOSE: Artificial intelligence decision support systems are a rapidly growing class of tools to help manage ever-increasing imaging volumes. The aim of this study was to evaluate the performance of an artificial intelligence decision support system, Aidoc, for the detection of cervical spinal fractures on noncontrast cervical spine CT scans and to conduct a failure mode analysis to identify areas of poor performance. MATERIALS AND METHODS: This retrospective study included 1904 emergent noncontrast cervical spine CT scans of adult patients (60 [SD, 22] years, 50.3% men). The presence of cervical spinal fracture was determined by Aidoc and an attending neuroradiologist; discrepancies were independently adjudicated. Algorithm performance was assessed by calculation of the diagnostic accuracy, and a failure mode analysis was performed. RESULTS: Aidoc and the neuroradiologist’s interpretation were concordant in 91.5% of cases. Aidoc correctly identified 67 of 122 fractures (54.9%) with 106 false-positive flagged studies. Diagnostic performance was calculated as the following: sensitivity, 54.9% (95% CI, 45.7%–63.9%); specificity, 94.1% (95% CI, 92.9%–95.1%); positive predictive value, 38.7% (95% CI, 33.1%–44.7%); and negative predictive value, 96.8% (95% CI, 96.2%–97.4%). Worsened performance was observed in the detection of chronic fractures; differences in diagnostic performance were not altered by study indication or patient characteristics. CONCLUSIONS: We observed poor diagnostic accuracy of an artificial intelligence decision support system for the detection of cervical spine fractures. Many similar algorithms have also received little or no external validation, and this study raises concerns about their generalizability, utility, and rapid pace of deployment. Further rigorous evaluations are needed to understand the weaknesses of these tools before widespread implementation.

17 citations