Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241127EnglishN2020April7HealthcareClinical Profile and Short - Term Mortality Predictors in Acute Stroke with Emphasis on Stress Hyperglycemia and THRIVE Score : An Observational Study English0109Virendra AtamEnglish Avirup MajumdarEnglish Kamal Kumar SawlaniEnglish D. HimanshuEnglishObjectives: Stress hyperglycemia, the acute and transient hyperglycemia has been studied in many critical illnesses like stroke and myocardial infarction. THRIVE score proved to be an impressive predictor of in-hospital mortality in previous studies but data on Indian population are lacking. This observational study was conducted to assess the different short term mortality predictors in acute stroke. Materials & Methods: A total of 150 acute stroke patients presenting within 24 hours of onset, who had CT scan evidence and/or met minimum WHO criteria for diagnosis of stroke were included. Patients with TIA, recurrent stroke, secondary hyperglycemia, traumatic hematomas were excluded. Blood glucose and THRIVE score values were obtained on admission. Stroke severity assessed by NIHSS score, while mRS score was used for disability assessment. Results: We divided our study cohort into 4 groups. A cut off blood glucose of 140mg/dl was set for defining hyperglycemia. Out of 71 patients in group C(non diabetics with stress hyperglycemia), 54 expired (76.1%). The burden of stress hyperglycemia was more in ischemic stroke(65.1%) as compared to hemorrhagic stroke (57.1%). In a multivariate model, where age, GCS, NIHSS score were kept as predictors of mortality, stress hyperglycemia had been found to be an independent predictor of in-hospital post stroke mortality. THRIVE score of 6 or above predicted mortality in majority of non survivors. Conclusion: Stress hyperglycemia was found to be a significant poor prognostic determinant. While THRIVE score proves to be an impressive prognostic tool, its validation as independent mortality predictor in acute stroke needs further research. EnglishStress hyperglycemia, Stroke, Admission hyperglycemia, Hyperglycemia stress, THRIVE score, Cerebrovascular accidentINTRODUCTION: ‘Stroke’, a leading cause of significant mortality and morbidity, got its name from the Greek word  “ apoplexia” meaning “being struck with a deadly blow”.[1,2] Stroke was the second largest cause of global death (5.5 million) after ischemic heart disease in 2016.[3] Overall mortality was higher in hemorrhagic stroke as compared to ischemic stroke cases. The outcome of stroke is influenced by various factors including severity, type of stroke, predisposing factors and related complications. Stroke has many risk factors- some are modifiable like hypertension, dyslipidemia, smoking, alcohol consumption, sedentary lifestyle and some are non-modifiable factors like age, sex and ethnicity. One of the potentially modifiable risk factors of stroke is stress hyperglycemia or admission hyperglycemia. Various studies showed the adverse effects of admission hyperglycemia on the short term outcome in acute stroke.[4] This acute hyperglycemia in stroke is not always due to type 2 diabetes mellitus but instead may be due to stress response mediated partly by the release of cortisol and norepinephrine.[5] Hyperglycemia can lead to poor outcome in acute stroke by different mechanisms- direct tissue damage mediated by lactate and intracellular acidosis in ischemic brain, increased free fatty acid pool interfering with vasodilation, cerebral vasculopathy induced by hyperglycemia [6]. German stroke study in 2004, predicted that mortality in ischemic stroke increases with i) NIHSS score>25 ii) higher age iii) fever>380c [7]. Weimer et al [8] in 2006 proposed a new prognostic model of hemorrhagic stroke termed Essen ICH score in which he utilized variables like i) Age, ii) NIHSS Score, iii) Level of consciousness. Stress hyperglycemia or transient admission hyperglycemia has been studied in many critical illnesses including myocardial infarction and stroke.[9] The independent prognostic role of stress hyperglycemia in acute stroke is yet to be deciphered. Totaled Health Risks in Vascular Events (THRIVE) score (totaled health risks in vascular events) calculated with age, NIHSS, and the presence of hypertension, diabetes mellitus, and atrial fibrillation proved to be a significant mortality predictor in acute ischemic stroke in previous studies [ 10 ] but sufficient data are lacking. Thus, we planned our study to assess the different prognostic variables including  stress hyperglycemia and THRIVE score and their impact on the outcome in acute stroke patients. MATERIALS AND METHODS: The present study was a prospective observational study conducted in the department of Medicine, King George’s Medical University, Lucknow in the Indian state of Uttar Pradesh. The study protocol was approved by the Institutional Ethical committee, King George’s Medical University. A total of 150 consecutive acute stroke patients who presented to the outpatient department/emergency, within 24 hours of onset of deficit and who had CT scan evidence of stroke and/or who met minimum World Health Organisation criteria for the diagnosis of stroke(i.e. rapidly developing focal neurological deficit lasting 24 hours or more)  were enrolled after taking formal informed written consent from legal guardians. Patients who presented after 24 hours, who had Transient Ischemic Attacks(TIA), recurrent stroke, secondary hyperglycemia, traumatic hematomas, vascular malformations, aneurysms and coagulopathies were excluded from the study. All participants were evaluated by detailed history (including history of diabetes, hypertension and any other comorbidities), clinical examination and severity assessment by NIHSS(National Institute of Health Stroke Scale)score[11] and GCS (Glasgow coma scale) score. Ischemic stroke and hemorrhagic stroke were defined by neurological examination and CT brain (normal CT brain scan or recent infarct or evidence of haemorrhage in the clinically relevant area on scan done within 72 hours of onset). A baseline Electrocardiogram was obtained from all patients. Fresh blood samples at the time of admission were drawn for complete blood counts, blood glucose, Glycosylated haemoglobin(HbA1c), C reactive protein (CRP) and lipid profile(Total cholesterol, LDL-cholesterol, HDL cholesterol).All blood investigations were carried out at the pathology and biochemistry laboratories of King George’s Medical University using standard protocols. Serial random venous blood glucose values were measured on day of admission and at 12 hours,48 hours, 72 hours post admission and on day of discharge. A meticulous drug history was taken (thiazides, betablockers, glucocorticoids, OCPs, cyclosporine) and patients taking such drugs were excluded from the study. Adequate care was taken not to take samples for RBS from sites with intravenous lines. Hyperglycemia in our study was defined as blood glucose > 140 mg/dl or 7.8 mmol/L. We divided our study cohort on the basis of diabetic history or use of medications for diabetes, HbA1c and admission glucose values into four different groups- 1.GROUP A- DIABETICS WITH  HYPERGLYCEMIA ON ADMISSION Patients with a history of diabetes or use of medications or HbA1c>6.5% and admission glucose > 140 mg/dl 2. GROUP B- DIABETICS WITHOUT HYPERGLYCEMIA ON ADMISSION- Patients with a history of diabetes or use of medications or HbA1c>6.5% and admission glucose 140 mg/dl 4. GROUP D- NON DIABETICS WITHOUT HYPERGLYCEMIA ON ADMISSION- Patients without a history of diabetes or use of medications and HbA1c 157mg/dl or 8.71mmol/L with 66.7% sensitivity and 64% specificity.[Fig 1b] The almost same cut off values for both diabetics and non diabetics could be due to the well controlled diabetics being included in our cohort. The in-hospital mortality in our study was 60.7% which is quite higher than previous studies. [30-32]This can be attributed to the greater stroke severity (mean NIHSS score 26.09+ 6.66), inability to perform thrombolysis or surgical interventions and lack of a well equipped stroke unit in our hospital. Group C or the stress hyperglycemia group had the maximum mortality(76.1%) followed by Group A (58.7%).[Table 4] Group C also had a higher mean NIHSS (27.34+ 7.15) which signifies greater stroke severity among them.[Table 5] Admission hyperglycemia is related to greater stroke severity which is evident in previous studies. [33] Most of the available studies related to stress hyperglycemia are on ischemic stroke patients [34-40] with limited data on hemorrhagic stroke. [41,42] Our study cohort consisted of both ischemic and hemorrhagic stroke cases. Stress hyperglycemia proved to be a poor prognostic marker for both types but the effect on the outcome of ischemic stroke is more deleterious than on hemorrhagic type (78% vs 69.6% mortality).[Table 6] Totaled Health Risks in Vascular Events (THRIVE) score (totaled health risks in vascular events) is calculated with age, NIHSS, and the presence of hypertension, diabetes mellitus, and atrial fibrillation and was validated to predicting clinical outcome and hemorrhagic transformation in patients receiving tissue Plasminogen Activator, showed to be a simple score to help clinicians to estimate outcome and death after acute ischemic Stroke. [43]The performance of THRIVE score in predicting short term mortality had been established in a Brazilian study [9]. In our study, a THRIVE score of 6 or above was present in majority of non survivors (69.6%) (p=0.018). A greater proportion of non survivors (88%) with a high THRIVE score (> 6)   had a stress response (stress hyperglycemia) (p=0.002). Our study failed to delineate the impact of stress hyperglycemia on functional recovery after stroke. [Table 9]  A poor functional recovery (mRS> 4) was more evident among diabetics with admission hyperglycemia (Group A) as compared to stress hyperglycemics ( Group C) which is in contrast to the previous studies. [32,34] This discrepancy may be due to a shorter follow up period of patients in our study. The Glycemia in Acute Stroke (GLIAS) study was a large, multicentre cohort study of 476 patients. The study showed that a capillary glucose value >155 mg/dL(8.5 mmol/L) at any time within the first 48 hours, independent of age, stroke severity, or infarct volume, is associated with a higher mortality risk in ischemic stroke patients. [44]  In univariate analysis, in our study, except for age, on admission GCS, NIHSS score, stress hyperglycemia and THRIVE score, none of the other clinical or laboratory parameters showed a significant association with outcome (p Englishhttp://ijcrr.com/abstract.php?article_id=2662http://ijcrr.com/article_html.php?did=2662REFERENCES:  [1]- Pound P, Bury M, Ebrahim S,From Apoplexy to Stroke, Age Ageing 1997;26:331-337 [2]-  Clarke E, Apoplexy in Hippocratic writings. Bull Hist Med 1963;37:301-314 [3]- Gorelick PB. The global burden of stroke: Persistent and disabling. The Lancet Neurology. 2019 May 1;18(5):417-8. [4]- Gentile, NT, Seftchick, MW, Huynh, T. Decreased mortality by normalizing blood glucose after acute ischemic stroke. Acad Emerg Med 2006; 13: 174–180. [5]- Mi D, Wang P, Yang B, Pu Y, Yang Z, Liu L. Correlation of hyperglycemia with mortality after acute ischemic stroke. Therapeutic advances in neurological disorders. 2018 Jan 10;11:1756285617731686 [6]- Mehta KC. Stress hyperglycemia. Med Update. 2005;62:300 [7]-  German Stroke study Collaboration Predicting outcome after acute ischemic stroke: An external validation of prognostic model. Neurology. 2004 Feb;vol 62(4):208–216 [8]- Weimar C, Mieck T, Buchthal J et al. for the German Stroke Study Collaboration. Neurologic worsening during the acute phase of ischemic stroke. Arch Neuroly. 2005;62:393–397 [9]- DiNardo MM, Korytkowski MT, Siminerio LS. The importance of normoglycemia in critically ill patients. Crit Care Nurs Q 2004;27:126-34 [10]- Kuster GW, Dutra LA, Brasil IP, Pacheco EP, Arruda MJ, Volcov C, Domingues RB. Performance of four ischemic stroke prognostic scores in a Brazilian population. Arquivos de neuro-psiquiatria. 2016 Feb;74(2):133-7. [11]- Scale NS. The Internet Stroke Center, cited at http://www.strokecenter.org/professionals/stroke-diagnosis/stroke-assessment-scales/ [12]- Mankovsky BN, Metzger BE, Molitch ME, Biller J. Cerebrovascular disordersin patients with diabetes mellitus. J Diabetes Complicat. (1996) 10:228–42. [13]-  Sacco RL. Identifying patient populations at high risk for stroke. Neurology(1998) 51:S27–30 [14]-  Mankovsky BN, Ziegler D. Stroke in patients with diabetes mellitus. Diabetes Metab Res Rev. (2004) 20:268–87. [15]- Mandava P, Martini SR, Munoz M, Dalmeida W, Sarma AK, Anderson JA, et al. Hyperglycemia worsens outcome after rt-PA primarily in the large-vessel occlusive stroke subtype. Transl Stroke Res. (2014) 5:519–25. [16]- Bruno A, Levine SR, Frankel MR, Brott TG, Lin Y, Tilley BC, et al. Admission glucose level and clinical outcomes in the NINDS rt-PA Stroke Trial. Neurology (2002) 59:669−74. [17]- Uyttenboogaart M, Koch MW, Stewart RE, Vroomen PC, Luijckx GJ, De Keyser J. Moderate hyperglycaemia is associated with favourable outcome in acute lacunar stroke. Brain (2007) [18]- Tapia-Pérez JH, Gehring S, Zilke R, Schneider T. Effect of increased glucose levels on short-term outcome in hypertensive spontaneous intracerebral hemorrhage. Clin Neurol Neurosurg. (2014) 118:37–43. [19]- Stöllberger C, Exner I, Finsterer J, Slany J, Steger C. Stroke in diabetic and nondiabetic patients: course and prognostic value of admission serum glucose. Ann Med. (2005) 37:357–64. [20]- Tan X, He J, Li L, Yang G, Liu H, Tang S, et al. Early hyperglycaemia and the early-term death in patients with spontaneous intracerebral haemorrhage: A meta-analysis. Intern Med J. (2014) 44:254– [21]- Sun S, Pan Y, Zhao X, Liu L, Li H, He Y, et al. Prognostic value of admission blood glucose in diabetic and non-diabetic patients with intracerebral hemorrhage. Sci Rep. (2016) 6:32342 [22]- Namani G, Rampure D M, Murali M. Clinical profile and mortality in patients presenting with intra-cerebral hemorrhage in a tertiary care centre. Schol J Appl Med Sci. (2014) 2:3005–6. [23]- Xi G, Fewel ME, Hua Y, Thompson BG, Hoff JT, Keep RF. Intracerebral hemorrhage: pathophysiology and therapy. Neurocrit Care (2004) 1:5–18. [24]- Capes SE, Hunt D, Malmberg K, Pathak P, Gerstein HC. Stress hyperglycemia and prognosis of stroke in non-diabetic and diabetic patients: A systematic overview. Stroke (2001) 32:2426–32. [25]- Lee SH, Kim BJ, Bae HJ, Lee JS, Lee J, Park BJ, et al. Effects of glucose level on early and long-term mortality after intracerebral haemorrhage: the acute brain bleeding analysis study. Diabetologia (2010) 53:429–34. [26]- Tetri S, Juvela S, Saloheimo P, Pyhtinen J, Hillbom M. Hypertension and diabetes as predictors of early death after spontaneous intracerebral hemorrhage. J Neurosurg. (2009) 110:411–7. [27]- Tan X, He J, Li L, Yang G, Liu H, Tang S, et al. Early hyperglycaemia and the early-term death in patients with spontaneous intracerebral haemorrhage: A meta-analysis. Intern Med J. (2014) 44:254–60 [28]- Guo X, Li H, Zhang Z, Li S, Zhang L, Zhang J, et al. Hyperglycemia and mortality risk in patients with primary intracerebral hemorrhage: A meta-analysis. Mol Neurobiol. (2016) 53:2269–75. [29]- Ribo M, Molina C, Montaner J, Rubiera M, Delgado-Mederos R, Arenillas JF, Quintana M, Alvarez-Sabi?n J. Acute hyperglycemia state is associated with lower tPA-induced recanalization rates in stroke patients. Stroke. 2005 Aug 1;36(8):1705-9. [30]- Kongwad LI, Hegde A, Menon G, Nair R. Influence of admission blood glucose in predicting outcome in patients with Spontaneous intracerebral hemorrhage. Frontiers in neurology. 2018;9:725 [31]- Zhao Y, Yang J, Zhao H, Ding Y, Zhou J, Zhang Y. The association between hyperglycemia and the prognosis of acute spontaneous intracerebral hemorrhage. Neurological research. 2017 Feb 1;39(2):152-7. [32]- Snarska KK, Bachórzewska-Gajewska H, Kapica-Topczewska K, Drozdowski W, Chor??y M, Ku?akowska A, Ma?yszko J. Hyperglycemia and diabetes have different impacts on outcome of ischemic and hemorrhagic stroke. Archives of medical science: AMS. 2017 Feb 1;13(1):100. [33]-  Green SR, Lokesh S, Mathew TK, Jayasingh K, Ragupathy S. A study of stress hyperglycemia and its relationship with the neurological outcome in patients presenting with acute ischemic stroke. International Journal of Advances in Medicine. 2016 Jul;3(3):546. [34]- Gofir A, Mulyono B, Sutarni S. Hyperglycemia as a prognosis predictor of length of stay and functional outcomes in patients with acute ischemic stroke. International Journal of Neuroscience. 2017 Oct 3;127(10):923-9. [35]- Raghavendra Prasad B. N. , Dr. Sandeep Reddy A. , Dr. Prabhakar  et al. Stress hyperglycemia as a prognostic marker in acute ischaemic stroke. EJPMR, 2016,3(3), 247-256. [36]- Levetan CS. Effect of hyperglycemia on stroke outcomes. Endocr Pract 2004;10(Supplement 2):34–9. doi:10.4158/EP.10.S2.34 [37]- . Chang KC, Tseng MC, Weng HH et al, Prediction of length of stay of first-ever ischemic stroke. Stroke 2002;33:2670–4. [38]- Nakao S, Takata S, Uemura H, et al. Relationship between Barthel index scores during the acute phase of rehabilitation and subsequent ADL in stroke patients. J Med Invest 2010;57:81–8. [39]- Williams LS, Rotich J, Qi R, et al. Effects of admission hyperglycemia on mortality and costs in acute ischemic stroke. Neurology 2002;59(1):67–71. [40]- Parsons MW, Barber PA, Desmond PM, et al. Acute hyperglycemia adversely affects stroke outcome: a magnetic resonance imaging and spectroscopy study. Ann Neurol 2002;52(1):20–8. [41]- Kongwad LI, Hegde A, Menon G, Nair R. Influence of admission blood glucose in predicting outcome in patients with Spontaneous intracerebral hemorrhage. Frontiers in neurology. 2018;9:725. [42]- Zhao Y, Yang J, Zhao H, Ding Y, Zhou J, Zhang Y. The association between hyperglycemia and the prognosis of acute spontaneous intracerebral hemorrhage. Neurological research. 2017 Feb 1;39(2):152-7. [43]- Lei C, Wu B, Liu M, Chen Y, Yang H, Wang D et al. Totaled health risks in vascular events score predicts clinical outcomes in patients with cardioembolic and other subtypes of ischemic stroke. Stroke. 2014;45(6):1689-94. [44]- Fuentes B, Castillo J, San José B, Leira R, Serena J, Vivancos J, Dávalos A, Nun?ez AG, Egido J, Di?ez-Tejedor E. The prognostic value of capillary glucose levels in acute stroke: The Glycemia in Acute Stroke (GLIAS) study. Stroke. 2009 Feb 1;40(2):562-8.

Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241127EnglishN2020April7HealthcareAssessment of Left and Right Ventricular Involvement in Patients with Chronic Obstructive Pulmonary Disease with Special Reference to Echocardiography English1013Pijush Kanti BiswasEnglish Arijit SinhaEnglishIntroduction: Chronic obstructive pulmonary disease (COPD) affects > 5% of the population. COPD and congestive heart failure (CHF) frequently coexists and ventricular function has important role in mortality and morbidity among COPD patients. Objectives: Aim of our study was to correlate left and right ventricular function by echocardiography, to correlate it with pulmonary function test (PFT), with age and sex matched control. Materials: Through clinical examination, chest x ray, pulmonary function test, ECG, echocardiography(2D, M mode, Doppler) were done in each cases and controls. Results: Left ventricular, right ventricular, left atrial dimensions were increased compared to control population. Right ventricular end diastolic dimension (RVEDd) and left ventricular end diastolic dimension (LVEDd) ratio is increased in COPD group. E/Ea ratio, PVS, PVD pattern, S/D ratio are altered significantly. RVEF (right ventricular ejection fraction) and TAPSE(tricuspid annular plane systolic excursion) decreased. Conclusion: Echocardiography is an easy and available investigation in clinical practice. To correlate cardiac function abnormality and COPD severity is important for prognostic significance. EnglishCOPD, RV function, LV functionINTRODUCTION COPD is common respiratory problem affecting 5% population with high morbidity and mortality.[1] It is the third common cause of death worldwide.[2] Both ventricles are affected in COPD patients. Congestive heart failure (CHF) and COPD co exists frequently. LV dysfunction worsens survival in COPD patients. COPD patients are presented mostly with preserved ejection fraction(EF) and diastolic/systolic heart failure(HF). [3,4] COPD is a pro-inflammatory state. Oxidative stress in endothelium, related to low FEV1(forced expiratory volume in 1 sec) leads to structural and functional alteration of myocardium.[5] Severe airflow obstruction is related to low LV filling and stroke volume.[8] Important complication of COPD are cardiovascular disease (CVD) and lung cancer. COPD patients has two-fold increase of CVD mortality.[6] Chronic hypoxic injury to endothelium, decreased endothelial NO production, increased vascular endothelial growth factor and serotonin transporter expression leads to pulmonary vascular remodeling and pulmonary arterial hypertension.[7,8] METHODS AND MATERIALS It is a descriptive, cross sectional study among 100 COPD patients and 100 age, sex matched control. COPD patients are included in study from Medicine, Cardiology, Chest Medicine Department as indoor and outdoor cases of R G Kar Medical College, Kolkata. Normal subjects are taken from society without having cardiac or respiratory diseases. The aim of the study was to evaluate both left and right ventricular function by echocardiography. The severity of COPD and Pulmonary Function Test (PFT)  to be correlated with left and right ventricular function. Age and sex matched control are taken for compare. Through clinical examination, Chest X ray PA view, PFT, ECG, Echocardiography were done in COPD and control cases. Individual with acute exacerbation, ischemic heart disease or structural heart disease, bronchial asthma overlap, poor acoustic window are excluded from this study. COPD was diagnosed by GOLD criteria (Global Initiative for Chronic Obstructive Lung Disease). [9] A post bronchodilatation FEV1/FVC ratio =80%, 50-Englishhttp://ijcrr.com/abstract.php?article_id=2663http://ijcrr.com/article_html.php?did=2663 Buist AS, Mc Burine MA, Vollmer WM et al International variation in the presence of COPD ( the BOLD study) : a population based prevalence study, Lancet 2007, 370,741 Minino AM, Murphy SL, Xu J, Kochanek KD, Deaths: final data  for 2008, Nat Vital Stat Rep, 2011, 59;1 Rennard SI, Vestbo J, COPD: the dangerous and underestimate of 15%, Lancet, 2006, 367, 1216 Lainscak M, Cleland JG, Lenzen MJ, Follath F, Komajda M, Swedberg K, International variants in the treatment and comorbidity of left ventricular systolic dysfunction , data from the Euro Heart Failure Survey, Eur J Heart Fail, 2009, 9, 292—299 Barr RG, Mesia  Vele S, Austin JH et al, Impaired flow mediated dilation in associated with low pulmonary function and emphysema in ex-smokers , the Emphysema and Cancer Action Project(EMCAP) study, Am J Respir Crit Care Med, 2007, 176, 1200—1207 Barr RG, Blumke DA, Ahamed FS et al, Percent emphysema, airflow obstruction and impaired left ventricular filling, NEJM, 2010,362, 217--227 Naeije R,  Barbera JA, Pulmonary hypertension associated with COPD,  Crit Care, 2001, 5, 586—589 Peinado VI, Barbera JA, Ramirez J, Gomez FP, Roca J, Jover L, Gimferrer JM, Rodriguez- Roisin R, Endothelial dysfunction , pulmonary arteries of patients with mild COPD, Am J Physiol, 1998,274, 908—913 GOLD, Global strategy for the diagnosis , management and prevention of chronic obstructive pulmonary disease,: GOLD executive summary update,2011, www.goldcopdorg Jardin F, Gueret P, Prost JF, Farcot JC, Ozier Y, Boundaries JP, Tow dimensional echocardiographic assessment of left ventricular function in chronic obstructive pulmonary disease, Am Rev Respir Dis, 1984, 129(1),135—142  Louridas G, Patakas D, Stavropoulos C, Left ventricular function in patients with chronic obstructive pulmonary disease, Cardiology, 1981,67,73—80  Boussuges A, Pinet C, Molenat F, Burnet H, Ambrosi P, Badier M et al, Left atrial and ventricular filling in chronic obstructive pulmonary disease: An echocardiographic and Doppler study, Am J Respir Crit Care Med , 2000, 162, 670—675  Sabapathy K, Thirumelbaba K, Echocardiographic predictors of right ventricular dysfunction in COPD patients, J Evid Based Med Health, 2017, 4(38),2280—2284  Sule TG, Ufuk E, Evaluation of right ventricular function in patients with COPD and its correlation with respiratory functions, J Clin Anal Med, 2019,6147

Radiance Research AcademyInternational Journal of Current Research and Review2231-21960975-5241127EnglishN2020April7HealthcareCorrelation between MPV and Lipid Profile in Eastern India: A Cross Sectional Study English1418Mukherjee BiswaroopEnglish Mallik SreyaEnglish Maitra SomnathEnglishMPV measures the average size of platelets and is an emerging risk factor of atherosclerosis. The functions of platelets include adhesion, shape change and spreading, aggregation, secretion, procoagulant activity and clot retraction. Mean Platelet Volume can be simply analyzed by the ABX pentra automated analyzer using electrical impedance. There are many markers of atherosclerosis. The present study aims to find a correlation of MPV with Total Cholesterol, LDL, HDL,VLDL,Triglyceride. Aims and Objectives: To determine the correlation between MPV and Lipid Profile ( Total cholesterol,LDL,HDL,VLDL,Triglyceride) Materials and methods: 150 patients of Department of General Medicine participated in the study. The patients were selected by simple random sampling. Inclusion and exclusion factors were considered. Patients above 18 years of age were selected. Patients taking medications that reduce platelets, alcoholics and those having hereditary disorders of large platelets were excluded. Results: MPV is inversely correlated with HDL . Thus methods applied to increase HDL in general population will lead to decrease in MPV and lower risk of atherosclerosis. There is a positive correlation of MPV with VLDL and Triglyceride, but the correlations are not statistically significant. There is a negative correlation of MPV with LDL and Total Cholesterol, but the correlations are not statistically significant. EnglishMPV, Lipid profile, HDLINTRODUCTION Platelet size, measured as mean platelet volume (MPV), is a marker of platelet function and is positively associated with indicators of platelet activity, including aggregation and release of thromboxane A2, platelet factor 4, and β-thromboglobulin[1,2]. In normal individuals the platelet count is inversely proportional to MPV; platelet mass (the product of MPV and platelet count) is a near constant. Platelets, or thrombocytes (from Greek θρ?μβος, "clot" and κ?τος, "cell"), are small, irregularly shaped clear cell fragments (i.e. cells that do not have a nucleus), 2–3 µm in diameter,[3] which are derived from fragmentation of precursor megakaryocytes. Megakaryocytes arise from pluripotent stem cell that develops into 2 types of precursors, burst-forming cells and colony-forming cells, both of which express the CD34 antigen [4]. Thrombopoietin (TPO), the primary regulator of thrombopoiesis, is currently the only known cytokine required for megakaryocytes to maintain a constant platelet mass (though TPO is not increased on platelet destruction). TPO is thought to act in conjunction with other factors, including IL-3, IL-6, and IL-11, although these cytokines are not essential for megakaryocyte maturation [4]. When aging, platelets contain decreased levels of sialic acid and they accumulate surface IgG which function in removal of old platelets [5]. Senescent platelets are removed primarily by macrophages in the spleen, although the larger blood flow through the liver allows severely damaged platelets to be removed more quickly by hepatic macrophages [6] Although platelets are incapable of de novo protein synthesis they are very active metabolically and respond rapidly to vascular injury or trauma by undergoing a series of reactions (adhesion, release of granule contents, shape change and aggregation), which ultimately result in the formation of a platelet–fibrin plug.  Platelets bud off megakaryocytes in the marrow. Platelet size and volume (e.g. Mean Platelet Volume) depends on the circumstances of their production in the marrow. MPV is not related to aging of platelets in the circulation. Platelet parameters are very stable in most patients. MPV is increased in conditions with increased platelet production eg. immune thrombocytopenia, disseminated intravascular coagulation, myeloproliferative disorders, pre-eclampsia and recovery from transient hypoplasia (cytotoxic chemotherapy). MPV is decreased in conditions associated with under production of platelets e.g. Bone marrow aplasia.  Mean Platelet Volume (MPV) correlates with the functional status of platelets and is an emerging risk marker for atherothrombosis [7]. There is evidence that platelet function is accentuated in acute ischemic stroke [8]. Increased mean platelet volume (MPV), indicating higher platelet reactivity, is associated with an increased risk of myocardial infraction. MPV appears to be a marker, or even a determinant, of platelet function. Large platelets are more reactive than small platelets in vitro. Large platelets differ from normal or small platelets in the following ways [9]; i. They preferentially and more rapidly aggregate to platelet agonist including ADP, collagen and adrenaline. ii. They produce more prothrombotic and vasoactive factors including arachidonic acid metabolites (e.g. Thromboxane A2), serotonin, β thromboglobulin and ATP. iii. They contain more dense granules. iv. They have higher LDH activity. v. They are associated with a decreased bleeding time (BT; a measure of in vivo haemostatic function) [8]. vi. MPV correlates with platelet aggregation, whether measured in platelet rich plasma or whole blood. vii. Large platelets also express increased levels of adhesion molecules. eg. P- selectin, GPIIb/IIIa although the surface density of these glycoproteins is usually constant independent of platelet volume.[10] MATERIAL AND METHODS  STUDY SETTING: Indoors and Out Patient Department of General Medicine, R. G. Kar Medical College and Hospital, Kolkata which is a tertiary care referral centre DURATION OF STUDY: One year STUDY POPULATION: 150 patients of  Department  of General Medicine  after taking Ethical clearance and signing consent form . INCLUSION CRITERIA 1. Gender: Males/Females 2. Age Range: 18 years and above 3. Socioeconomic group: All EXCLUSION CRITERIA 1. Known cases of hereditary disorders of large platelets. 2. Medications that reduce platelets 3. Alcoholic SAMPLING SIMPLE RANDOM SAMPLING   CONTROLS REQUIRED: No STUDY DESIGN: Descriptive Cross Sectional study PARAMETERS TO BE STUDIED: Sociodemographic parameters: Mean Age, percentage of patients, male/female, of urban/rural area, different religion, and different occupation, sedentary or active life style and type of diet intake. Clinical parameters Blood pressure, BMI, Waist Hip Ratio 3.  Hematological parameters: Mean Hemoglobin (Hb), Total leukocyte count (TLC), Differential leukocyte count (DLC), Platelet Count, and Mean Platelet Volume (MPV). 4. Biochemical parameters: Mean serum urea, creatinine, Bilirubin, Total protein, Albumin, SGOT, SGPT, Glucose (fasting and post prandial), Serum electrolytes, Complete lipid profile Method of MPV Measurement A Blood sample was collected from the antecubital vein using a 5cc syringe and transferred to an EDTA vacutainers. The samples were then taken to the laboratory after storage at room temperature for 2 hours but before 4 hours of collection and analyzed using the ABX pentra automated analyzer using electrical impedance to measure the mean platelet volume. After the analysis the same sample was taken to the central laboratory and a peripheral smear was done to look for platelet aggregates. If platelet aggregates were found then such cases were excluded from the study. The normal range of Mean Platelet Volume measured in EDTA blood is 7.8-11 fl. Values of 11.1 fl and above are considered as abnormally high [11]. DATA ANALYSIS: The Statistical software namely SPSS 20.0, Stata 8.0, MedCalc 9.0.1 and Systat 11.0 were used for the analysis of the data and Microsoft word and Excel have been used to generate graphs, tables etc. STATISTICAL METHODS: Descriptive statistical analysis has been carried out in the present study. Results on continuous measurements are presented on Mean ± SD (Min-Max) and results on categorical measurements are presented in Number (%). Significance is assessed at 5 % level of significance. Student t test (two tailed, independent) has been used to find the significance of study parameters on continuous scale between two groups. Limitation Small sample size RESULTS AND ANALYSIS  The maximum number of patients belonged to age group 56 to 65 years (38%), the second highest number belonged to age group 46 to 55 years (32%) and minimum number were in the under 25 group  In the study population 56% are male and 44% are female.   DISCUSSION When correlating MPV with clinical and laboratory parameters, MPV had statistically insignificant correlations with Hemoglobin (p=0.514), Total Leukocyte Count (p=0.753), VLDL (p=0.226), LDL (p=0.876), Triglyceride(p=0.223) and Total Cholesterol (p=0.973). MPV had a negative correlation with HDL (Pearson coefficient = -0.181) and the correlation is significant at the 0.05 level (2-tailed) (p value=0.026). MPV had a negative correlation with Platelet Count (Pearson coefficient = -0.171) and the correlation is significant at the 0.05 level (2-tailed) (p value=0.036). This finding correlates with findings of previous studies where it is shown that as platelet mass remains constant in a given person, the platelet count decreases as the MPV increases (Platelet Mass = MPV × Platelet Count). CONCLUSION Platelets contribute to atherosclerotic complications and lead to thrombus formation [12].  This study has shown that MPV has inverse correlation with HDL. Thus, interventions targeted towards increasing HDL in population will lead to decreased MPV and thus decreasing  the incidence of atherosclerotic diseases. ACKNOWLEDGEMENT Authors acknowledge the immense help received from the scholars whose articles are cited and included in references of this manuscript. The Authors are also grateful to authors/editors/publishers of all those articles, Journals and books from where the literature of this article has been reviewed and discussed. Source of Funding-No Conflict of Interest-N CONTRIBUTION OF AUTHORS 1.Mukherjee Biswaroop- Concept and Study Design, writing the paper 2.Mallik Sreya- Data Collection and writing the paper 3.Maitra Somnath-Statistical Analysis, writing the paper and being the Corresponding Author Englishhttp://ijcrr.com/abstract.php?article_id=2664http://ijcrr.com/article_html.php?did=2664 Sharp DB, Bath PMW, Martin JF, et al. Cigarette smoking sensitizes and desensitizes impedance-measured ADP-induced platelet aggregation in whole blood. Thromb Haemost. 1995;74:730–735.  D’Erasmo E, Aliberti G, Celi F, et al. Platelet count, mean platelet volume and their relation to prognosis in cerebral infarction. J Intern Med.1990;227:11–14.  Campbell, Neil A.(2008).Biology(8th ed.). London: Pearson Education. p. 912 Patel SR, Hartwig JH, Italiano JE Jr. The biogenesis of platelets from Megakaryocyte proplatelets. The Journal of Clinical Investigation 2005; 115: 3348–3354.   George JN. Platelet IgG: measurement, interpretation, and clinical significance.ProgHemost Thromb.1991; 10:97-126. Buckley MF, James JW, Brown DE, Whyte GS, Dean MG, Chesterman CN, et al. A novel approach to the assessment of variations in the human platelet count. Thromb Haemost 2000; 83: 480-484.  Arch Pathol Lab Med. Vol 133, September 2009;1441-43 Smith N, Pathansali R, Bath P. Platelets and stroke. Vascular medicine 1999; 4:165-172. Bath PMW, Butterworth RJ. Platelet size: measurement, physiology and vascular disease. Blood1996; 7: 157–61. Victor M, Ropper AH, Adams RD. Cerebrovascular diseases. Adam’s and Victor’s Principles of Neurology. Ed. Ropper A H, Samuels MA. 9th ed. United States of America, McGraw Hill 2009. Smith N, Pathansali R, Bath P. Platelets and stroke. Vascular medicine 1999; 4:165-172. Jam Coll Cardiol. 2013;62(18_S2):C116-C116. doi:10.1016/j.jacc.2013.08.354 Greisenegger S, Endler G, Hsieh K, Tentschert S, Mannhalter C, Lalous chek W, Is elevated mean platelet volume associated with a worse outcome in patients with acute ischemic cerebrovascular events? Stroke 2004;35:1688–91.