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BACKGROUND: The triple burden of disease, i.e. communicable diseases, non-communicable diseases and injuries, has significantly affected the healthcare system of Pakistan during the last three decades. Therefore, this study aims to determine and analyse the 30-year disease burden trends through prevalence, death rates and percentages. METHODS: The data for the last three decades, i.e. 1990 to 2019, was extracted from the Global Burden of Disease for Pakistan. Percentage change in prevalence and deaths over 30 years was calculated. Poisson regression analysis was performed to evaluate the triple disease burden trends and the incidence rate ratio. RESULTS: A relative decrease of 23.4% was noted in the prevalence rate of communicable diseases except for human immunodeficiency virus and dengue fever. A relative increase of 1.4% was noted in the prevalence rate of non-communicable diseases. A relative increase of 56.1% was recorded in the prevalence rate of injuries. The prevalence rate ratios of communicable diseases significantly decreased to 0.9796 [95% CI: 0.9887-0.9905], but the prevalence rate of injury increased to 1.0094 [95% CI: 1.0073-1.01145], respectively. CONCLUSION: Pakistan must take the next steps and develop strategies to decrease this burden and mortality rates in the population to create better outcomes and therefore help the healthcare system overall.
RESUMO
BACKGROUND: Prediction of serious outcomes among patients with physiological instability is crucial in airway management. In this study, we aim to develop a score to predict serious outcomes following intubation in critically ill adults with physiological instability by using clinical and laboratory parameters collected prior to intubation. METHOD: This single-center analytical cross-sectional study was conducted in the Emergency Department from 2016 to 2020. The airway score was derived using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) methodology. To gauge model's performance, the train-test split technique was utilized. The discrete random number generation approach was used to divide the dataset into two groups: development (training) and validation (testing). The validation dataset's instances were used to calculate the final score, and its validity was measured using ROC analysis and area under the curve (AUC). By computing the Youden's J statistic using the metrics sensitivity, specificity, positive predictive value, and negative predictive value, the discriminating factor of the additive score was determined. RESULTS: The mean age of the 1021 patients who needed endotracheal intubations was 52.2 years (± 17.5), and 632 (62%) of them were male. In the development dataset, there were 527 (64.9%) physiologically difficult airways, 298 (36.7%) post-intubation hypotension, 124 (12%) cardiac arrest, 347 (42.7%) shock index > 0.9, and 456 [56.2%] instances of pH < 7.3. On the contrary, in the validation dataset, there were 143 (68.4%) physiologically difficult airways, 33 (15.8%) post-intubation hypotension, 41 (19.6%) cardiac arrest, 87 (41.6%) shock index > 0.9, and 121 (57.9%) had pH < 7.3, respectively. There were 12 variables in the difficult airway physiological score (DAPS), and a DAPS of 9 had an area under the curve of 0.857. The accuracy of DAPS was 77%, the sensitivity was 74%, the specificity was 83.3%, and the positive predictive value was 91%. CONCLUSION: DAPS demonstrated strong discriminating ability for anticipating physiologically challenging airways. The proposed model may be helpful in the clinical setting for screening patients who are at high risk of deterioration.