Browsing by Author "Parminder Singh"

Now showing 1 - 3 of 3

Diabetes and rhino-orbito-cerebral mucormycosis – A deadly duo
(Springer Science and Business Media Deutschland GmbH, 2021) Parminder Singh; Saurabh Arora; Naveen Mittal; Amroz Singh; Rohit Verma; Sarit Sharma; Neeraj Kumar Agrawal; Saloni Goyal
Background: Rhino-orbito-cerebral mucormycosis(ROCM) is an uncommon yet potentially fatal fungal infection predominantly seen in immunocompromised individuals. However, there is very limited data available from India regarding outcome of patients with ROCM and diabetes mellitus. Objective: To ascertain clinical parameters and factors in the final outcome of patients with diabetes mellitus and ROCM. Materials and Methods: This series included retrospective analysis of medical records of 91 patients with diabetes mellitus who were diagnosed with ROCM from january 2007 to june 2019 at a tertiary care hospital in Punjab. Results: The mean age of patients was 52.6 years (range 18–82 years), with men constituting the majority (71.4 %). Ophthalmoplegia was the most frequent presenting feature seen in 77 % of patients followed by proptosis (71 %). Intracranial involvement was seen in 20 % of the patients and cavernous sinus thrombosis was diagnosed in 9(10 %) patients. Out of 91 patients, 81 patients were subjected to appropriate surgical procedure depending upon site and extent of involvement by mucorales. A total of 53 (58.2 %) patients survived while 38(41.8 %) patients succumbed. Delay in presentation to hospital, intracranial extension and loss of vision at presentation adversely affected the outcome (p < 0.05). Aggressive surgical management in the form of multiple debridements was superior to single debridement (p < 0.05). Diabetic ketoacidosis did not significantly affect the outcome (p = 0.359). Conclusions: ROCM in patients with diabetes mellitus, is a rapidly progressive disease with a high fatality rate and grave outcome unless diagnosed early and managed aggressively. © 2021, Springer Nature Switzerland AG.
Early detection of cutaneous complications of insulin therapy in type 1 and type 2 diabetes mellitus
(Elsevier Ltd, 2021) Saurabh Arora; Neeraj Kumar Agrawal; Dhananjaya Melkunte Shanthaiah; Ashish Verma; Sanjay Singh; Shashikant C.U. Patne; Sanjay Kalra; Parminder Singh; Saloni Goyal
Background: Subcutaneous insulin therapy is associated with important injection site complications, which can influence insulin pharmacokinetics resulting in glycemic fluctuations above and below target levels for blood glucose. Objective: Our objective was to assess the prevalence and risk factors of cutaneous complications including insulin derived amyloidosis in insulin-injecting diabetes patients and to study the role of ultrasonography (in comparison to gel-assisted palpation) in early diagnosis of lipohypertrophy (LH). Methods: This was a cross-sectional study conducted at a tertiary care center in India, wherein 500 patients injecting insulin for ≥2 years were randomly enrolled and evaluated for the presence of cutaneous complications of insulin therapy through clinical examination, ultrasonography and punch biopsy of skin. Results: Clinical examination detected LH in 44.6% of patients. Ultrasonography diagnosed additional 13.4% of patients with LH which were missed on clinical examination. Incorrect rotation of sites (P < 0.001) and insulin syringe reusage for more than five times (P < 0.001) significantly increased the risk of LH. Skin biopsy was performed in 100 cases, out of which two patients showed apple green birefringence and its association with insulin was confirmed by positive staining with anti insulin antibody in these two patients. Conclusion: Improper rotation of sites and reuse of needles were the leading causes of LH in Indian diabetic patients. Ultrasonography is more objective and reliable method of detecting LH. Insulin-derived amyloidosis may be a more common complication of insulin therapy than previously thought. © 2021 Primary Care Diabetes Europe
Fully Automated Agatston Score Calculation From Electrocardiography-Gated Cardiac Computed Tomography Using Deep Learning and Multi-Organ Segmentation: A Validation Study
(SAGE Publications Inc., 2024) Ashish Gautam; Prashant Raghav; Vijay Subramaniam; Sunil Kumar; Sudeep Kumar; Dharmendra Jain; Ashish Verma; Parminder Singh; Manphoul Singhal; Vikash Gupta; Samir Rathore; Srikanth Iyengar; Sudhir Rathore
To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P <.001) and high limits of agreement (±1.96 SD; −2000, 2000) in Bland–Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources. © The Author(s) 2024.