Background. Cervical cancer, especially in underdeveloped areas, poses a great threat to human health. In view of this, we stratified the age and social demographic index (SDI) based on the epidemiological development trend and attributable risk of cervical cancer in countries and regions around the world. Methods. According to the data statistics of the global burden of disease database (GBD) in the past 30 years, we adopted the annual percentage change (EAPCs) to evaluate the incidence trend of cervical cancer, that is, incidence rate, mortality, and disability adjusted life expectancy (DALY). Meanwhile, we investigated the potential influence of SDI on cervical cancer’s epidemiological trends and relevant risk factors for cervical cancer-related mortality. Results. In terms of incidence rate and mortality, the high SDI areas were significantly lower than those of low SDI areas. The incidence and mortality in women aged 20 to 39 were relatively stable, whereas an upward trend existed in patients aged 40 to 59. The global cervical cancer incidence rate increased from 335642 in 1990 to 565541 in 2019 (an increase of 68.50%, with an average annual growth rate of 2.28%), while the age-standardized incidence rate (ASIR) showed a slight downward trend of 14.91/100000 people (95% uncertainty interval [UI], 13.37-17.55) in 1990 to 13.35/100,000 persons (95% UI, 11.37-15.03) in 2019. The number of annual deaths at a global level increased constantly and there were 184,527 (95% UI, 164,836-218,942) deaths in 1990 and 280,479 (95% UI, 238,864-313,930) deaths in 2019, with an increase of 52.00%(average annual growth rate: 1.73%). The annual age-standardized disability adjusted annual life rate showed a downward trend (decline range: 0.95%, 95% confidence interval [CI], from −1.00% to − 0.89%). In addition, smoking and unsafe sex were the main attributable hazard factors in most GBD regions. Conclusions. In the past three decades, the increase in the global burden of cervical cancer is mainly concentrated in underdeveloped regions (concentrated in low SDI). On the contrary, in countries with high sustainable development index, the burden of cervical cancer tends to be reduced. Alarmingly, ASIR in areas with low SDI is on the rise, which suggests that policy makers should pay attention to the allocation of public health resources and focus on the prevention and treatment of cervical cancer in underdeveloped areas, so as to reduce its incidence rate, mortality, and prognosis.
BACKGROUND Urinary tract infection (UTI) is a common type of postoperative infection following cytoreductive surgery for ovarian cancer, which severely impacts the prognosis and quality of life of patients. AIM To develop a machine learning assistant model for the prevention and control of nosocomial infection. METHODS A total of 674 elderly patients with ovarian cancer who were treated at the Department of Gynaecology at Jingzhou Central Hospital between January 31, 2016 and January 31, 2022 and met the inclusion criteria of the study were selected as the research subjects. A retrospective analysis of the postoperative UTI and related factors was performed by reviewing the medical records. Five machine learning-assisted models were developed using two-step estimation methods from the candidate predictive variables. The robustness and clinical applicability of each model were assessed using the receiver operating characteristic curve, decision curve analysis and clinical impact curve. RESULTS A total of 12 candidate variables were eventually included in the UTI prediction model. Models constructed using the random forest classifier, support vector machine, extreme gradient boosting, and artificial neural network and decision tree had areas under the receiver operating characteristic curve ranging from 0.776 to 0.925. The random forest classifier model, which incorporated factors such as age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia, had the highest predictive accuracy. CONCLUSION These findings demonstrate that the machine learning-based prediction model developed using the random forest classifier can be used to identify elderly patients with ovarian cancer who may have postoperative UTI. This can help with treatment decisions and enhance clinical outcomes.
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