RESUMEN
Background: In this longitudinal study, we aimed to determine factors influencing survival outcomes among patients with stroke over a 12-month period. The investigation sought to uncover influential determinants to enhance the precision of prognostic assessments and inform targeted interventions for individuals affected by strokes. Methods: Employing a longitudinal study design, participants were observed for 12 months from baseline, censoring survivors at the endpoint. The dataset originated from a comprehensive study involving stroke patients treated at three referral hospitals in Zimbabwe: Parirenyatwa, Sally Mugabe, and Chitungwiza Central Hospital. The primary outcome variable, the duration of survival until death, was measured in days from the initiation of stroke treatment. Gompertz parametric regression analysis was utilized for data modeling following Accelerated Failure Time (AFT) model diagnostics. Results: In our study, 188 stroke patients were enrolled at baseline. However, 51 patients were excluded from the analysis due to either missing information or loss to follow-up. Among the remaining 137 patients who were tracked over a 12-month period, 42% were censored, and 58% were deceased. Individuals utilizing 'Free Service (older than 65/pensioners/retirees)' hospital bill payment methods showed a decreased risk of death (HR: 0.4, 95% CI: 0.20, 0.80), suggesting a protective effect compared to cash paying patients. Those with a secondary school level education displayed a significantly lower risk of death (HR: 0.2, 95% CI: 0.04, 0.69) compared to those without formal education. Age was a significant factor, with individuals aged 45-65 and those over 65 years showing higher adjusted hazard ratios (HR: 4.9, 95% CI: 1.80, 13.25; HR: 5.5, 95% CI: 1.92, 15.95, respectively) relative to those below 45 years of age. Housing status revealed a protective effect for those residing with parents/relatives (adjusted HR: 0.4, 95% CI: 0.20, 0.66), while individuals with a 'Very severe' functional outcome showed an increased hazard (adjusted HR: 4.9, 95% CI: 1.12, 21.33). Conclusion: The study findings demonstrate that hospital bill payment methods, housing status, educational attainment, functional outcome, and age significantly affect survival outcomes among stroke patients. This highlights the need to consider socio-demographic and clinical variables in the development of prognostic assessments and targeted interventions for individuals recovering from stroke.
RESUMEN
Purpose: Type 2 diabetes mellitus (T2DM) frequently presents with modified cardiometabolic risk profiles, indicative of an elevated susceptibility to cardiovascular disease (CVD). Cardiometabolic risk factors such as obesity, hyperglycemia, hypertension, insulin resistance and dyslipidemia are known contributors to increased CVD hazard in individuals with T2DM. This study evaluated the glycemic control-based cardiometabolic risk profiles of black Zimbabweans with T2DM. Patients and Methods: A cross-sectional study of 116 T2DM patients recruited from diabetic clinics at Parirenyatwa and Sally Mugabe Hospitals, Harare, Zimbabwe, was conducted. Blood samples were collected for glycated hemoglobin (HbA1c) and lipid profile assessment. The Framingham risk scores (FRS) based on body mass index (BMI) and lipid profile were used to determine CVD risk. Parametric variables were analyzed using one-way analysis of variance (ANOVA) with post hoc Bonferroni correction, while non-parametric variables were compared using the Kruskal-Wallis test with post hoc Dunn test for multiple comparisons. Results: The overall frequency of dyslipidemia was 83.6% (n=97) and hypoalphalipoproteinemia was the most prevalent dyslipidemia (79.3%). Median HDLC levels were significantly lower in participants with poor glycemic control (1.12 mmol/L) compared to those with good glycemic control group (1.37 mmol/L) (p=0.011). Despite lack of significant variations in Framingham Risk Scores, there was a trend towards lower FRS-BMI in the good control group (29.8%) compared to the inadequate control (35.4%) and poor control (32.7%) groups (p=0.078). Conclusion: Duration since DM diagnosis was observed to be an important risk factor for poor glycemic control being significantly shorter in those with good glycemic control compared to those with inadequate and poor control. Overall, there was no significant difference in HbA1c status by age but individuals with poor glycemic control were significantly older than those with good control. The most prevalent dyslipidemia among the study participants was hypoalphalipoproteinemia which is reportedly associated with genetic predisposition, warranting further investigations.