The Journal of the Association of Physicians of India最新文献

Accurate prediction of patient outcomes in intensive care units (ICUs) is crucial for enhancing clinical decision-making, patient care, and resource allocation. Traditional scoring systems like Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), and Sequential Organ Failure Assessment (SOFA), while valuable, fall short of fully capturing the complexities of critically ill patients. Advances in machine learning (ML) enable the analysis of high-dimensional data, including electronic health records (EHRs), physiological parameters, and genomic information, providing a more comprehensive approach to outcome prediction. This review aims to assess the impact of ML techniques, including deep learning (DL), ensemble machine learning (EML), and reinforcement learning (RL), in improving ICU outcome predictions, particularly in identifying high-risk patients and enabling proactive interventions. Machine learning models have shown superiority over traditional systems, enabling more accurate identification of critical patients. However, implementing ML in ICU settings comes with challenges, including data quality, model interpretability, ethical concerns, and workflow integration. Collaborative efforts between clinicians, data scientists, and multidisciplinary teams, supported by shared databases like Medical Information Mart for Intensive Care (MIMIC), are essential for developing generalizable ML models that work across diverse healthcare environments. Future research should focus on improving real-time prediction using wearable technology and personalized risk assessments to further individualize ICU care. Ethical considerations, particularly data privacy and model transparency, must be addressed as ML becomes more integrated into critical care.

Background: Sarcopenia is a frequent and prognostically significant complication of liver cirrhosis. Hand grip strength (HGS) has emerged as a simple, noninvasive tool for assessing muscle function, yet limited data exist on its utility in Indian cirrhotic populations.

Aim: To evaluate the association of HGS with established prognostic scores and biochemical parameters in Indian patients with cirrhosis.

Materials and methods: In this cross-sectional observational study, 100 adult cirrhotic patients were assessed between August 2022 and December 2023. HGS was measured using a validated hand-held dynamometer. Correlations between HGS and clinical scores of severity of cirrhosis [Child-Turcotte-Pugh (CTP), Model for End-Stage Liver Disease (MELD)] and biochemical markers were analyzed using appropriate statistical methods.

Results: Mean patient age was 59.2 ± 8.46 years; 85% were male. The most common etiologies were alcohol (46%) and viral hepatitis (26%). HGS declined significantly with increasing liver disease severity: CTP A (34.0 ± 1.48 kg), B (21.63 ± 1.07 kg), and C (13.5 ± 2.87 kg) (p < 0.0001). HGS was inversely correlated with MELD score (r = -0.820) and showed strong positive correlations with serum albumin (r = +0.872) and hemoglobin (r = +0.59). Age, international normalized ratio (INR), and bilirubin were negatively correlated with HGS.

Conclusion: HGS is strongly associated with liver disease severity and key biochemical indicators. As a bedside, radiation-free tool, it offers a practical method for assessing sarcopenia in cirrhosis, especially in resource-limited settings.

Background and aim: Abdominal tuberculosis (ATB) poses significant diagnostic challenges due to its varied clinical manifestations and its ability to mimic other diseases. Histopathology is a promising diagnostic tool to diagnose ATB. This narrative review aims to synthesize evidence on the evolving role of histopathology in diagnosing ATB, highlighting its integration with molecular and microbiological diagnostics, and discussing its limitations and emerging technologies.

Methodology: A structured search of databases including PubMed, Scopus, Web of Science, and Google Scholar was performed, focusing on literature published from January 2002. The review includes peer-reviewed original articles on the diagnosis of ATB using histopathology and integrated diagnostic modalities.

Results: Histopathology remains crucial for diagnosing ATB, especially in resource-limited settings, due to its ability to visualize granulomatous inflammation and other cellular features. The integration of histopathology with molecular diagnostics like GeneXpert Mycobacterium tuberculosis/rifampicin (MTB/RIF) and tuberculosis polymerase chain reaction (TB-PCR) has improved diagnostic accuracy. However, limitations include diagnostic overlap with other conditions and the impact of prior treatment on tissue samples. Emerging technologies such as digital pathology and artificial intelligence (AI)-driven image analysis are poised to enhance diagnostic precision.

Conclusion: The review underscores the importance of a multimodal diagnostic approach, combining histopathology with other techniques to improve sensitivity and specificity. As ATB continues to be a global health concern, advancements in histopathological techniques and interdisciplinary collaboration are essential for timely and accurate diagnosis.

Background: Traditional lipid parameters like low-density lipoprotein (LDL), high-density lipoprotein (HDL), and total cholesterol (TC) are commonly used in evaluating cardiovascular risk. Recently, emerging biomarkers such as apolipoprotein B (ApoB) and apolipoprotein A1 (ApoA1) are proposed to provide improved accuracy in assessing atherosclerotic risk. This study examined the association between conventional and novel lipid parameters and plaque burden in statin-naïve acute coronary syndrome (ACS) patients.

Methodology: We enrolled 81 statin-naïve patients with ACS. Each underwent both standard and extended lipid profiling. Coronary angiograms were evaluated using the Gensini score to quantify plaque burden. All participants were followed for 28 days to monitor for major adverse cardiac events (MACE).

Results: The average age was 51 years, with males comprising 77%. The ST-segment elevation myocardial infarction (STEMI) was observed in 58% of cases, non-ST-segment elevation myocardial infarction (NSTEMI) in 31%, and unstable angina in 11%. There was a significant correlation between the Gensini score and TC/HDL ratio (r = 0.35), LDL/HDL ratio (r = 0.31), and ApoB levels (r = 0.24). LDL and the ApoB/ApoA1 ratio did not exhibit significant associations with plaque burden. STEMI patients had higher LDL/HDL and TC/HDL ratios compared to those with NSTEMI or unstable angina. MACE occurred in 16% of participants, with no significant difference across ACS subtypes.

Conclusion: The ratios of TC/HDL, LDL/HDL, and ApoB levels were positively associated with coronary plaque burden. While conventional lipid parameters continue to serve well in cardiovascular risk assessment (CRA), ApoB presents a promising standalone marker for identifying atherogenic risk and may serve as a practical alternative in clinical practice.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease (NAFLD), has emerged as a significant public health concern, affecting approximately 25% of the global population with its prevalence rising from 22% in 1991 to 37% in 2019.¹ While the hepatic consequences of MASLD, such as steatohepatitis, fibrosis, and cirrhosis, are well documented, its systemic implications are increasingly coming to light. While traditionally viewed as a hepatic disorder, growing evidence highlights MASLD as a multisystem disease with profound implications on cardiovascular health. Atherosclerotic cardiovascular disease (ASCVD) has now been recognized as the leading cause of mortality in patients with MASLD, surpassing liver-related complications. MASLD is present in up to 75% of patients with type 2 diabetes mellitus (T2DM). Notably, MASLD is linked to a higher risk of cardiovascular diseases (CVD), including arrhythmia, atherosclerotic heart disease, heart failure, and CVD-related mortality.² The association between MASLD and ASCVD is particularly alarming, positioning MASLD as a critical gateway for cardiovascular morbidity and mortality.

Background: The geriatric population is highly vulnerable to both clinical and psychological comorbidities; the psychological health of geriatric patients remains underassessed, particularly in government outpatient department (OPD) settings where multiple chronic illnesses, low socioeconomic status, and hospitalization disrupt emotional well-being.

Aims and objectives: To evaluate the prevalence and association of chronic diseases with depression, anxiety, cognitive impairment, and sleep quality in elderly patients.

Materials and methods: A cross-sectional study was conducted on 100 patients aged ≥60 years attending the medicine OPD. Geriatric depression scale (GDS-15), generalized anxiety disorder scale (GAD-7), mini-mental state examination (MMSE), and Pittsburgh sleep quality index (PSQI) scales were used to assess psychological status. Clinical parameters and recent hospitalization history were also recorded.

Results: Hypertension (45%), diabetes (35%), and osteoarthritis (25%) were the most prevalent chronic illnesses. Mild depression (56%), mild anxiety (45%), cognitive impairment (45%), and poor sleep quality (78%) were common. Coronary artery disease (CAD) was significantly correlated with depression (p = 0.008), diabetes with cognitive decline (p = 0.002), and recent hospitalization with cognitive scores (p = 0.006).

Conclusion: The study underscores the need for integrated psychological assessment and intervention in geriatric medicine, even in resource-constrained OPD environments. Chronic illnesses such as CAD and diabetes significantly affect psychological well-being in elderly patients. Routine psychological screening is essential in OPDs.

Introduction: Cardiac dysfunction is one of the major causes of mortality in patients with sepsis. The acute physiology and chronic health evaluation II (APACHE II), quick sequential organ failure assessment (qSOFA), and other prognostic scores for sepsis have established data regarding their accuracy in predicting mortality. We have assessed the prognostic role of cardiac biomarkers [creatine phosphokinase-myocardial band (CPK-MB), troponin I (Trop I), and probrain natriuretic peptide (proBNP)] in patients with sepsis and compared it with the APACHE II score.

Materials and methods: A total of 126 patients (63 in each group) participated in this case-control study in a large tertiary care teaching hospital. Patients with sepsis who required hospitalization were enrolled in the case group and compared with another group of nonsepticemic patients. They were taken for detailed evaluation and investigation on day 1 and day 3. Our study included proBNP, CPK-MB, Trop I, and APACHE II score.

Results: Both the case and control groups comprised 63 patients each. It was observed that the cardiac biomarkers (proBNP, Trop I, CPK-MB) were markedly higher among cases than in controls. Similarly, these markers were also found markedly higher in fatal cases than survivors in the case group. Out of all three biomarkers, proBNP was correlated well with mortality as much as the APACHE II score. It was also observed that increasing trends in the levels of biomarkers depict prognosis more effectively than a single value.

Conclusion: We conclude that cardiac biomarkers can be routinely used as dynamic markers for the prediction of mortality and prognosis in patients with sepsis. ProBNP may be useful in predicting mortality in patients with sepsis.