Definicións de competitividade

In this study, a total 226 consecutive in-hospital stroke cases at HUSM were analysed. Table 3-1 shows that the mean age of the whole cohort was 60.8 years (SD = 14.0). Stroke patients who died when in hospital were older (mean age = 62.2 years, SD = 15.0) compared to those who survived (mean age = 60.4 years, SD = 13.6) but the difference was not statistically significant at p = 0.05. The mean GCS score for the whole cohort was 12.4 but patients who were alive at discharge had a significantly higher mean GCS score than those who died (mean GCS score = 13.7 vs. 8.4; p < 0.001). A higher GCS score indicates a higher level of consciousness. The difference in the mean SBP and DBP between patients who were alive and dead at discharge was small (SBP, 163.1 mmHg vs. 163.3 mmHg, p = 0.971; DBP, 91.6 vs. 92.8, p = 0.705).

Table 3-1 also shows that patients who were alive at discharge had a significantly higher mean Hg level than those who were dead at discharge (13.3 g/dl vs. 12.4 g/dl, p = 0.010), but there was no difference in mean platelet levels between the two groups (238.5 ×

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109_{/l vs. 229.5 × 10}9_{/l, p = 0.474). The mean TWC count was higher in patients who died during}

admission (12.5 × 109_{/l vs. 9.8 × 10}9_{/l, p < 0.001). The mean serum potassium, serum sodium,}

blood urea and capillary blood sugar levels were not significantly different between patients who were dead and alive at discharge (potassium = 3.9 mmol/l vs. 3.8 mmol/l, p = 0.251; sodium = 137.5 mmol/l vs. 138.6 mmol/l, p = 0.178; urea = 7.6 mmol/l vs. 8.7 mmol/l, p = 0.201; capillary blood sugar = 9.6 mmol/l vs. 10.5 mmol/l, p = 0.274). The mean length of stay (LOS, in days) between patients who were alive or dead at discharge were not statistically different (p=0.210).

Table 3-2 shows that 93.3% (211/226) of patients in our study cohort were Malay, which is consistent with the racial demographics of the state of Kelantan. A nationwide population survey in 2010 showed that 92.7% of the Kelantan population was Malay. Married patients on admission represented 90.7% (205/226) of the cohort, and 57.0% (129/226) of patients were female. Of the total subjects, 38.5% (87/226) had been referred from either tertiary hospitals or district hospitals. Based on self-reporting, 32.7% (74/226) of the cohort had a history of diabetes, 65.5% (148/226) had a history of high blood pressure and 11.9% (27/226) had an abnormal lipid profile.

Of all the categorical variables, types of referral were the most significant variable (p- value < 0.001) associated with the survival status at discharge, followed by abnormal lipid profile (p-value = 0.010) and sex (p-value = 0.014). More referred patients, female patients and patients with normal lipid profile were dead on discharge.

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Table 3-1 On-admission numerical variables for all patients (regardless of survival status at discharge) and for patients based on survival status at discharge, n=226

Variable All patients

Mean(SD) n=226 Alive at discharge, Mean(SD) n=173 Dead at discharge, Mean(SD) n=53 p-value a Age (years) 60.8(14.0) 60.4(13.6) 62.2(15.0) 0.412

Glasgow Coma Scale 12.4(3.8) 13.7(2.7) 8.4(4.0) <0.001

Systolic BP (mmHg) 163.2(34.4) 163.1(32.0) 163.3(41.7) 0.971 Diastolic BP (mmHg) 91.9(20.0) 91.6(18.2) 92.8(25.2) 0.705 Heart rate (per min) 82.7(20.9) 81.9(20.3) 85.4(22.6) 0.293

Haemoglobin (g/dl) 13.1(2.2) 13.3(2.1) 12.4(2.4) 0.010

Platelet count (per mm3₎

236.3(74.6) 238.5(72.7) 229.5(80.5) 0.474 Total white cell count

(per microlitre)

10.5(3.9) 9.8(3.5) 12.5(4.2) <0.001

Natrium (mmol/l) 137.8(5.2) 137.5(3.6) 138.6(8.5) 0.178

Potassium (mmol/l) 3.9(0.6) 3.9(0.5) 3.8(0.7) 0.251

Urea (mmol/l) 7.9(5.5) 7.6(4.8) 8.7(7.2) 0.201

Capillary Blood Sugar (mmol/l)

9.8(4.9) 9.6(5.1) 10.5(3.9) 0.278

LOSb _{6.5(7.8) 6.1(7.5) 7.8(8.7) 0.210}

a _{Independent t-test} b _{Length of stay (days)}

For the risk of dying in hospital from stroke, age and GCS score were the only significant prognostic factors in simple Cox regression analysis, as shown in Table 3-3. The unadjusted risk for in-hospital stroke fatality decreased by 17% with a 1-point increase in the GCS score. A 1-year increase in age increased the unadjusted risk for in-hospital fatality by 2.4% (95% confidence interval [CI]: 1.002, 1.047); a 10-year increase in age increased the crude risk for stroke fatality by 26.7%.

When we examined the relationship between categorical variables and stroke fatality using Cox proportional hazard regression, we found that stroke subtype (haemorrhagic/non- haemorrhagic stroke) was the only significant variable in the simple Cox proportional hazard regression. Others, such as sex, types of referral and abnormal lipid profile that had been shown to be statistically significant (based on Pearson chi-square analyses) in Table 3-2 were

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no longer significant. The likely reason for these results to differ is because the Pearson chi- square examines the independence of distribution, i.e. how well the observed distribution of data fits with the distribution expected were the variables to be independent, while the Cox proportional hazard regression method models the incidence or hazard rate per population at-risk per unit time.

Table 3-5 shows the two models that predict in-hospital stroke fatality at HUSM: model 1 (n=225) contains age and GCS score and model 2 (n=226) contains age and stroke subtype. We propose that the best model is model 1 (age and GCS score) based on subjective assessment. We felt that model 1 is more practical in daily clinical practice. Even though model 1 seems to have has a better fit (log-likelihood = -205.60, LR chi-square = 40.1, degree of freedom [dof] =2) than model 2, the quantitative comparison could not be done especially using the likelihood ratio test because of different sample sizes (however, the sample sizes are only different by 1; n=225 vs n=226). Being practical means that the GCS score can be assessed very quickly even by non-clinicians at almost no extra cost. With model 2, computed tomography (CT) scan images to assist clinicians are needed. It is also a poorer fit than model 1 (log-likelihood = -223.33, LR = 12.9, dof = 2). The need for CT scans translates into extra cost, extra time and more training for clinicians. When the model has stroke subtypes, GCS and age together as the covariates, the adjusted hazard ratios become 1.52 (p-value = 0.181), 0.83 (p- value < 0.001) and 1.03 (p-value = 0.006), respectively. Based on the level of significance at 10% for the Wald statistic, the covariate stroke subtype can be dropped from the model. In addition to that, the confounding effect between stroke subtypes and GCS is very likely, hence only one of them should be the covariate at one time.

Based on the ‘global test’ and the ‘detail test’ in Stata, our model does not violate the proportional hazard assumption in Cox regression. The p-value for the global test was 0.855 and for the specific test, the p-value for GCS score and age was 0.986 and 0.576, respectively.

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Based on the non-significant p-values, we could not reject the null hypothesis that both the hazard for age and GCS score were proportional (slopes of residuals against time were zero).