OBJECTIVES: to create a set of evidence- and expert consensus-based falls prevention and management recommendations applicable to older adults for use by healthcare and other professionals that consider: (i) a person-centred approach that includes the perspectives of older adults with lived experience, caregivers and other stakeholders; (ii) gaps in previous guidelines; (iii) recent developments in e-health and (iv) implementation across locations with limited access to resources such as low- and middle-income countries.
METHODS: a steering committee and a worldwide multidisciplinary group of experts and stakeholders, including older adults, were assembled. Geriatrics and gerontological societies were represented. Using a modified Delphi process, recommendations from 11 topic-specific working groups (WGs), 10 ad-hoc WGs and a WG dealing with the perspectives of older adults were reviewed and refined. The final recommendations were determined by voting.
RECOMMENDATIONS: all older adults should be advised on falls prevention and physical activity. Opportunistic case finding for falls risk is recommended for community-dwelling older adults. Those considered at high risk should be offered a comprehensive multifactorial falls risk assessment with a view to co-design and implement personalised multidomain interventions. Other recommendations cover details of assessment and intervention components and combinations, and recommendations for specific settings and populations.
CONCLUSIONS: the core set of recommendations provided will require flexible implementation strategies that consider both local context and resources.
METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented.
FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01-4·94) deaths and 105 million (95·0-116) DALYs for both sexes combined, representing 44·4% (41·3-48·4) of all cancer deaths and 42·0% (39·1-45·6) of all DALYs. There were 2·88 million (2·60-3·18) risk-attributable cancer deaths in males (50·6% [47·8-54·1] of all male cancer deaths) and 1·58 million (1·36-1·84) risk-attributable cancer deaths in females (36·3% [32·5-41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6-28·4) and DALYs by 16·8% (8·8-25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9-42·8] and 33·3% [25·8-42·0]).
INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden.
FUNDING: Bill & Melinda Gates Foundation.
METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.
RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.
CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.
MATERIALS AND METHODS: The authors conducted literature search in three databases (PubMed, Cochrane, and Clinical Key) on July 15th, 2020. The keywords were ("Head and Neck Mucosal Malignancy" OR "Head and Neck Cancer") AND ("Management" OR "Head and Neck Surgery") AND ("COVID-19" OR "Pandemic"). The inclusion criteria were cancer in adult patients, published from 2020 in English, and with available access to full text. The exclusion criteria were comments, letters, and case reports. The articles were critically appraised using the Centre of Evidence-based Medicine (CEBM), University of Oxford and Duke University. The literature search strategy is illustrated using Preferred Reporting Items for Systematic review and meta-analysis (PRISMA) flow diagram.
RESULTS: A total of 150 articles were identified; 21 articles were gathered from Clinical Key, 33 from Cochrane, and 96 from Pubmed. After screening abstracts and reviewing the full text, the authors determined five articles met the inclusion criteria. There are several key points of head and neck cancer management in the COVID-19 pandemic. Head and neck cancer management is considered a high-risk procedure; the clinician should use proper personal protective equipment. Before operative treatment, all patients should undergo a PCR test 14 days before surgery. In diagnosing head and neck cancer, laryngoscopy should be considered carefully; and cytology should be preferred instead. Medically Necessary, Time-sensitive (MeNTS) score is recommended for risk stratification and surgery prioritization; it has three domains: procedure, disease, and patient. However, it is not specified to head and neck cancer; therefore, it should be combined with other references. Stanford University Head and Neck Surgery Division Department of Otolaryngology made surgery prioritization into three groups, urgent (should be operated immediately), can be postponed for 30 days, and can be postponed for 30- 90 days. Some urgent cases and should be operated on immediately include cancers involving the airways, decreased renal function, and metastases. For chemoradiation decision to delay or continue should refer to the goal of treatment, current oncologic status, and tolerance to radiation. In terms of patient's follow up, telephone consultation should be maximized.
CONCLUSION: MeNTS scoring combined with Guideline from Department of Otolaryngology at Stanford University prioritizing criteria can be helpful in decision making of stratifying Risk and prioritizing surgery in head and neck cancer management.
OBJECTIVE: This study aimed to determine the level of anxiety along with anxiety-provoking factors among clinical dental students.
METHODS: This study included dental undergraduate and postgraduate clinical students from a public university. A modified version of the self-administered Moss and McManus questionnaire, which consisted of 50 items, was utilized to evaluate the levels of anxiety. The results were analyzed using SPSS® version 24. The significance level was set at p
METHOD: Variables included in our model are categorized into four pillars: (i) incidence of cases, (ii) reliability of case data, (iii) vaccination, and (iv) variant surveillance. These measures are combined based on weights that reflect their corresponding importance in risk assessment within the context of the pandemic to calculate the risk score for each country. As a validation step, the outcome of the risk stratification from our model is compared against four countries.
RESULTS: Our model is found to have good agreement with these benchmarked risk designations for 27 out of the top 30 countries with the strongest travel ties to Malaysia (90%). Each factor within this model signifies its importance and can be adapted by governing bodies to address the changing needs of border control policies for the recommencement of international travel.
CONCLUSION: In practice, the proposed model provides a turnkey solution for nations to manage transmission risk by enabling stakeholders to make informed, evidence-based decisions to minimize fluctuations of imported cases and serves as a structure to support the improvement, planning, and activation of public health control measures.
OBJECTIVE: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.
METHODS: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score.
RESULTS: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.
CONCLUSIONS: ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.
METHODS: A cross-sectional study was conducted between November 2020 and January 2021. A self-administered questionnaire was distributed to 350 physicians (GPs, residents, specialists, and consultants). Two trained pharmacists distributed the questionnaires in 5 major tertiary governmental hospitals and more than ten private hospitals. Also, private clinics were targeted so that we get a representative sample of physicians at different workplaces.
RESULTS: A total of 270 physicians filled the questionnaire out of 350 physicians approached, with 14 being excluded due to high missing data, giving a final response rate of 73%. Participants had suboptimal knowledge and practices with a high positive attitude toward atherosclerotic cardiovascular diseases risk assessment. The knowledge and practices were higher among consultants, participants from the cardiology department, those with experience years of more than nine years, and those who reported following a specific guideline for cholesterol management or using a risk calculator in their practice. Notably, the risk assessment and counseling practices were lower among physicians who reported seeing more patients per day.
CONCLUSION: Physicians had overall low knowledge, suboptimal practices, and a high positive attitude toward cardiovascular risk assessment. Therefore, physicians' training and continuing medical education regarding cholesterol management and primary prevention clinical practice guidelines are recommended. Also, the importance of adherence to clinical practice guidelines and their impact on clinical outcomes should be emphasized.
METHODOLOGY: A total of 571 healthcare workers at COVID-19 and non-COVID-19 wards as well as the emergency department and laboratory staff at COVID-19 testing labs were recruited. The presence of novel human coronavirus (SARS-CoV-2) and IgM/IgG antibodies were confirmed in all healthcare workers. The healthcare workers responded to an online Google Forms questionnaire that evaluates demographic information and comorbidities, exposure and adherence to infection prevention and control measures against COVID-19. Descriptive analysis was performed using Statistical Package for the Social Sciences 24.0.
RESULTS: Three healthcare workers (0.5%) tested positive for SARS-CoV-2, while the remaining 568 (99.5%) were negative. All were negative for IgM and IgG antibodies during recruitment (day 1) and follow-up (day 15). More than 90% of the healthcare workers followed infection prevention and control practices recommendations regardless of whether they have been exposed to occupational risk for COVID-19.
CONCLUSIONS: The healthcare workers' high level of adherence to infection prevention practices at this hospital helped reduce and minimize their occupational exposure to COVID-19.