Pakistan Journal of Medical & Health Sciences

Short-Course Versus Long-Course Systemic Glucocorticoid Therapy for Hospitalized Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Prospective Comparative Study

MUHAMMAD AMJAD FAROOQ — 2026-05-30

Background: Systemic glucocorticoids have a central place in the ICS of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Nevertheless, the best duration of corticosteroid therapy is a controversial issue as the long-term treatment is likely to cause more steroid-related adverse events in patients without any obvious improvement in clinical outcome. To measure the effectiveness and safety of short-course (5-day) and long-course (14-day) systemic glucocorticoid in hospitalized patients with AECOPD.

Methods: The study was carried out as a prospective comparative study in the Departments of Pulmonology in Shalamar Hospital and Farooq Hospital, Lahore, Pakistan. A total of 170 hospitalized patients with AECOPD were enrolled and equally allocated into two treatment groups (n=85 each). Group A was given oral prednisolone 40 mg/kg/day over 5 days and Group B given oral prednisolone 40 mg/kg/day over 14 days. The two groups got the same standard therapy, bronchodilators, oxygen when necessary, antibiotics when necessary, and nebulization, and supportive care. Success of treatment at Day 14 was the main outcome. Secondary outcomes were the ability to improve the forced expiratory volume in one second (FEV 1 ), length of stay in the hospital, relapse within 30 days and corticosteroid-associated adverse events. Statistics was done using SPSS version 26, and the statistical significance was p<0.05.

Results: The mean age of the participants was 63.8 ± 9.7 years, and 118 (69.4%) were male. Patients who received short-course therapy were successful in treatment (72/84.7) and those who received long-course were successful in treatment (70/82.4) (p=0.689). Improvement in FEV₁ was comparable between the groups (0.18 ± 0.09 L vs. 0.19 ± 0.10 L; p=0.522). Patients on the 5-day schedule had a much lower readmission (4.8 + 1.6 vs. 5.6 + 1.9 days; p=0.004). Thirty-day relapse rates were similar (16.5% vs. 14.1%; p=0.668). Hyperglycemia caused by corticosteroids was significantly less common in the short-course group compared to the long-course group (15.3% vs. 29.4%; p=0.028).

Conclusion: Clinical efficacy of short course systemic glucocorticoid therapy in patients with AECOPD hospitalized had been equivalent to conventional long-course therapy. The 5-day course on oral prednisolone was much better to hospital stay and corticosteroid-induced hyperglycemia in comparison to treatment success and chance of relapses. These findings validate existing guideline suggestions that advocate short-course systemic glucocorticoid treatment option as a superior and less risky treatment option to most hospital-admitted patients with AECOPD.

Association of Vitamin D Deficiency with Endothelial Dysfunction and Subclinical Atherosclerosis in Obese Adults: A Biomarker-Based Evaluation

FEHMIDA BIBI — 2026-05-30

Objective: To assess the relationship between vitamin D deficiency and endothelial dysfunction and subclinical atherosclerosis in obese adults, using vascular and inflammatory biomarkers.

Methods: A cross-sectional analytical study was conducted between March 2024 and February 2025 in a tertiary care teaching hospital. One hundred obese adults (body mass index [BMI] ≥30 kg/m²) were recruited by consecutive sampling. Serum 25-hydroxyvitamin D [25(OH)D], interleukin-6 (IL-6), and high-sensitivity C-reactive protein (hs-CRP) were assessed. Flow-mediated dilation (FMD) of the brachial artery was measured to assess endothelial function, and carotid intima-media thickness (CIMT) was measured by high-resolution ultrasonography to assess subclinical atherosclerosis.

Results: 64.0% of the participants had vitamin D deficiency. The vitamin D-deficient group had a significantly lower FMD and higher CIMT than the non-deficient group (5.9 ± 1.8% vs 8.1 ± 2.2%, p <0.001 and 0.79 ± 0.11 mm vs 0.64 ± 0.09 mm, p <0.001, respectively). The deficient group also had significantly higher serum IL-6 and hs-CRP. Serum vitamin D was positively associated with FMD and inversely associated with CIMT, IL-6 and hs-CRP.

Conclusion: Vitamin D deficiency is strongly linked to endothelial dysfunction, elevated inflammatory markers and early subclinical atherosclerosis in obese adults. This study suggests that vitamin D status could be a useful biomarker-associated predictor of cardiovascular risk in obesity. Carotid Intima-Media Thickness; Flow-Mediated Dilation

Comparison of Angiographic Findings in Diabetic Versus Non-Diabetic Women Presenting with Acute Coronary Syndrome

MUHAMMAD OSAMA RIAZ — 2026-05-30

Background: Diabetes mellitus is a known risk factor of coronary artery disease and it may include the severity and complexity of the coronary involvement in women with acute coronary syndrome (ACS). Women have still been underrepresented in cardiovascular investigations and there is a paucity of evidence to support specifically angiographic disparities between diabetic and non-diabetic women with ACS. The objective is to make a comparison of angiographic findings in diabetic and non-diabetic women with ACS presentation.

Methodology: This was a comparative cross-sectional study that was carried out with 180 female patients who were admitted with ACS with undergone coronary angiography. Two equal groups of patients diabetic (n=90) and non-diabetic (n=90) were identified. The demographic features, cardiovascular risk factors, clinical presentation, angiographic findings were registered. Coronary angiography results such as number of vessels involved, culprit vessel, Lesion morphology, left main disease and treatment strategy were compared among the two groups. Data analysis was done with the SPSS version 26.

Findings: The mean age of diabetic females was significantly older compared to non-diabetic females (61.8±9.4 vs 57.1±10.2 years, p=0.002). The prevalence of hypertension, dyslipidemia and obesity was significantly higher in diabetic patients. The prevalence of triple-vessel disease was markedly higher in diabetic women (34.4 vs 13.3) as compared to single-vessel disease in non-diabetic women (42.2 vs 22.2) (p<0.001). In non-diabetic patients, normal or non-obstructive coronaries were found to be more prevalent (20.0% vs 6.7%). The left main involvement was also more common in diabetics (7.8% vs 3.3%). Calcium and diffuse lesions were much more prevalent in diabetic women. The median angiographic severity score was much greater in diabetics (48.6±21.3 vs 31.4±18.7, p<0.001). Cardiac bypassing was frequently necessary in diabetics.

Conclusion: Female diabetic vs. non-diabetic patients with ACS exhibited more comprehensive and intricate coronary artery disease manifested by higher levels of multivessel disease, diffuse lesions, calcification, left main involvement, and angiographic severity. The results underscore the importance of early risk stratification and preventive management of diabetic women through aggressive management.

Effectiveness of Percutaneous Nephrolithotomy in Children: A Retrospective Study of 76 Patients

ADEEL AHMED — 2026-05-30

Purpose: To determine the effectiveness and safety of percutaneous nephrolithotomy (PCNL) and its use in kids with renal calculi.

Methods: It was a retrospective observational study involving 76 patients with renal stones undergoing PCNL in a Farooq Hospital between January 2022 and December 2024. There was an analysis of demographic characteristics, stone burden, operative details, stone-free rate, hospital stay, and complications. At week 4, ultrasonography and / or plain radiography were used to determine if the patient was stone-free and non-contrast computed tomography was used to evaluate the pertinent equivocal cases. Stone-free was characterized as complete lack of stones or any non-clinically significant residual stones less than 4 mm.

Results: The mean age was 9.3 ± 3.4 years. There were 44 males (57.9%) and 32 females (42.1%). The mean stone size was 22.4 ± 6.8 mm. Single stones were present in 49 patients (64.5%), while 27 (35.5%) had multiple stones. In 12 patients (15.8%), there were partial staghorn stones and 6 (7.9%), complete staghorn stones. Only 63 patients (82.9%), had only 1 percutaneous tract, and 13 (17.1) patients would not need only 1 percutaneous tract. The average healthcare time was 67.8 before and after +18.6 min and average they lost per hemolytic drop of hemoglobin was 1.2 before and after -0.7 g/dL. The initial stone-free rate and PCNL single session was 86.8 percentage (66/76) rate, after auxiliary procedures, this percentage was 94.7 percentage (72/76). Eighth patients (10.5%) had postoperative fever, 3 (3.9%) patients needed blood transfusion, 2 (2.6) patients required urine leakage, and 1 (1.3) patient had sepsis. No belly injury or death observed. The average length of stay at the hospital was 3.7 days.

Conclusion: PCNL is a safe and effective intervention that shows a high rate of stone-free and reasonable complication profile in the management of pediatric renal stones. It is still an option, particularly when children have numerous, large, and intricate calculi in the kidney.

Efficacy and Safety of Short-Course Versus Long-Course Glucocorticoids in Acute Exacerbation of COPD

MUHAMMAD UMAR ABBAS — 2026-05-30

Objective: To estimate the effectiveness and safety of short-course versus traditional long-course systemic glucocorticoid therapy in hospital patients with an acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

Methods: It was a prospective comparative study that involved 170 patients admitted with AECOPD randomly divided into two treatment groups (n = 85 each). Group A was treated to short-course therapy comprising of oral prednisolone 40 mg, one dose per day taken over 5 days, Group B got conventional long-course therapy, comprising of oral prednisolone 40 mg, given once a day and over 14 days. The two groups were subjected to normal management based on guidelines (including inhaled bronchodilators, controlled oxygen, when necessary, antibiotics in case of possible bacterial infection and supportive care). The main eventual finding was successful treatment on Day 14, which is considered any clinical improvement without use of any further systemic corticosteroids, mechanical ventilator, intensive care unit (ICU) admission, or in-hospital death. Outcomes measured secondary were forced expiratory volume in one second (FEV1), length of hospital stay, relapse, and corticosteroid adverse effects in 30 days.

Results: The study population mean age was 63.8 years old with a standard deviation of 9.7 years with 118 (69.4) of them being male. The short-course patients had 72 (84.7) patients treated successfully and the long-course patients had 70 (82.4) PR successfully, no significant difference being found between the two groups (p = 0.689). The mean improvement in post-treatment FEV₁ was comparable (0.18 ± 0.09 L vs. 0.19 ± 0.10 L; p = 0.522). There was also a significant difference in the mean hospital stay of patients undergoing short-course therapy compared to patients undergoing long-course therapy (4.8 ± 1.6 vs. 5.6 ± 1.9 days; p = 0.004). Relapse rates (30 days) were the same in the two groups (16.5% vs. 14.1%; p = 0.668). Nevertheless, hyperglycemia created by corticosteroids was much rarer in the short-course group as compared to the long-course group (15.3% vs. 29.4; p = 0.028).

Conclusion: Short-course systemic glucocorticoid therapy showed similar clinical efficacy to the conventional long-course therapy in hospitalized patients with AECOPD. Moreover, it was also found that shorter regimen was linked with fewer length of stay in hospitals and a much lower rate of corticosteroid hypoglycemia. The results can be utilized to justify administration of a 5-day oral prednisolone course as a safe and effective treatment measure in majority of COPD hospitalized patients with an acute attack.

The Future of Coronary CT Angiography: Artificial Intelligence as a Catalyst for Precision Cardiovascular Care

MUHAMMAD MUNEEB — 2026-05-30

Coronary artery disease (CAD) is the most common cause of death and disability-adjusted life years worldwide and it causes about seven million deaths in the world every year. Cardiovascular disease in the United States alone causes an estimated economic cost of almost US$219 billion each year in the United States, with CAD being the most common and fatal form of the disease. Though invasive coronary angiography (ICA) has long been accepted as the diagnostic gold standard, it is too invasive, is associated with procedural risks, is expensive, and fails to characterize atherosclerotic plaque composition in all aspects thereby restricting its application in routine screening and longitudinal studies. This has led to a change of clinical practice towards non-invasive imaging investigations and more specifically patients with suspected CAD should have a first-line investigation in the form of coronary computed tomography angiography (CCTA). Nonetheless, the dramatic rise in demand of CCTA has posed significant difficulties such as the lack of skillful cardiovascular radiologists worldwide and the amount of time taken to interpret images manually. Complete interpretation of three-vessel CCTA scan can take up to 90 minutes, which restricts the workflow and clinical scalability. Artificial intelligence (AI) has proven to be a revolutionary product with the capabilities to automate the process of image analysis, increase the accuracy of diagnosis, and create a paradigm shift in an entirely anatomical evaluation to a more individualized, prognosis-focused, cardiovascular treatment approach^1-5.

Technical foundations of AI in cardiovascular imaging

The effective implementation of AI in cardiovascular imaging is based on three key enabling elements, namely big clinical data sets, powerful computing platforms, and sophisticated algorithm design. AI also includes the concept of machine learning (ML) and a more advanced form, deep learning (DL). Machine learning programs can detect detailed patterns in large volumes of data to create prediction models by supervised, unsupervised, or reinforcement methods. Deep learning is a multilayered artificial neural network that is based on the human brain design, which can directly learn with raw imaging data. Convolutional neural networks (CNNs) are among those models that have become the foundation of medical image analysis due to their outstanding capability to recognise, classify, segment and extract pixel-level features. Radiomics supplements these technologies by transforming traditional medical images into quantitative data in high dimensions by extracting imaging biomarkers that to the human eye are typically impossible to observe. These characteristics can be used to generate meaningful information about tissue properties, plaque formation, and underlying histopathological changes and, thus, enhance the diagnostic capabilities of cardiovascular imaging (3-8).

Intelligent playing with AI and computerized image acquisition and reconstruction.

Radiation dose reduction has been one of the first clinical uses of AI in CCTA. Due to the repeated imaging of patients with CAD, the reduction of cumulative radiation exposure is an essential clinical concern. Deep learning-enhanced reconstruction algorithms and generative adversarial networks (GANs) can be effectively employed to eliminate noises in low-dose acquisitions and maintain important detail during reconstruction. Technical limitations related to the CT image acquisition are also tackled with the help of AI. Heart rates or cardiac arrhythmias result in motion artefacts that tend to reduce the image quality and diagnostic confidence. Motion correction algorithms via CNN can estimate and counteract heart motion, significantly enhancing temporal resolution without making any hardware adjustments. More so, AI-based acquisitions protocols democratically adjust tube voltage and tube current to patient-specific anatomical features and body mass index, rendering consistent quality of images across different groups of patients, including obese patients^5-9.

Improving the efficiency of workflow

Conventional CCTA interpretation involves several and very laborious steps, such as coronary artery segmentation, vessel centreline extraction and anatomical labeling. Deep learning algorithms are becoming more popular in automating these repetitive tasks. CNN-models capture the coronary centrelines well without the human intervention, including those with severe coronary stenosis or extremely tortuous vessel. Further automated labeling of the coronary arterial tree, by graphical means allows to report anatomically in a standard way and reproducible clinical interpretation. The effect of AI-based automation on the efficiency of workflow is high. In a prospective randomized trial including 1,801 patients, completely automated AI image processing required a further segmentation and reconstruction only about 121 seconds, compared to over 433 seconds with semi-automated procedures. Under an average scan-to-report turnaround time in hours was reduced by 39, 10.5 hours to 6.4 hours showing that AI can be significant in productivity improvements in time with growing clinical demand^7-11.

Accuracy of diagnosis in measuring coronary stenoses

Despite having a great negative predictive ability to rule out CAD, CCTA traditionally has been low in specificity to identify obstructive coronary lesions, especially in vessels with heavy calcium in which bloomingartefact may produce overestimation of the severity of stenosis. In this context, artificial intelligence has brought about a great success in terms of performance in diagnosis. Deep learning models that are compared with quantitative coronary angiography (QCA) have the diagnostic accuracies of 88.4-94.7% at patient and vessel levels. Interestingly, AI systems are always more specific than skilled human readers, especially in the assessment of calcified plaques, which will minimise false-positive diagnoses and redundant invasive studies^7-10. In addition to anatomical analysis, AI-based fractional flow reserve computed tomography (CT-FFR) allows a non-invasive analysis of the lesion-specific haemodynamic relevance. Concoronary anatomy combined with learned haemodynamic relations is accurate in predicting myocardial ischaemia by deep learning models with an area under the receiver operating characteristic curve (AUC) of about 0.93 with much lower computational times than traditional computational fluid dynamics methods.

Figure 1: AI in Coronary Imaging

Perivascular fat analysis is a non-invasive biomarker of imaging coronary inflammation

Such a bedrock of atherosclerotic development and unstable plaque is inflammation. Until recently, objective imaging biomarkers of coronary inflammation were few. Quantitative analysis of pericoronary adipose tissue (PCAT) represents advancements of AI in this area. Mediators discharged by inflamed coronary arteries modify the structure of surrounding adipose tissue, making it more watery and altering its radiographic attenuation. The changes are measured through fat attenuation index (FAI) which is a non-invasive aspect of the biomarker of vascular inflammation. In more recent developments, radiomic imaging characteristics have been used in conjunction with transcriptomic profiles collected using tissue specimens using radio transcriptomic methods, thus allowing specific definition of inflammatory pathways. These combined biomarkers are able to differentiate between patients with acute myocardial infarction and those with stable CAD and have an independent ability to predict future Cardiovascular mortality independent of the severity of stenosis^11-14.

Reconceptualizing Personalized Cardiovascular Risk Stratification.

The end goal of AI integration is to promote accurate cardiovascular medicine. Conventional clinical risk scores often either under- or over-estimate the actual atherosclerotic burden of any individual. In awareness of these constraints, the 2024 Quantitative Cardiovascular Imaging (QCI) Study Group came up with a paradigm shift of plaque-based personalized risk assessment^13-14.

Challenges and Future Direction Current Challenges and Future Direction

Regardless of extraordinary progress, numerous major obstacles may restrict large-scale clinical use. The black box problem of poor interpretability of deep learning models can be seen as one of the fundamental obstacles. These algorithms may be significantly better than human observers, but their non-transparency in decision making can cause clinician hesitation and make them less readily accepted. The issue of model generalizability is also a major problem as it is in any case typical that algorithms that are trained on a dataset of one particular institution perform less well when they are applied to other populations due to variations in scanner technology, imaging protocols, and patient population demographics^12-16. Moreover, legal and ethical aspects of the privacy of patients, the rules in charge of data, cybersecurity, and the responsibility of algorithms would need a thorough regulatory supervision.