scholarly journals Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Meron Mehari Kifle ◽  
Tsega Tekeste Teklemariam ◽  
Adam Mengesteab Teweldeberhan ◽  
Eyasu Habte Tesfamariam ◽  
Amanuel Kidane Andegiorgish ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Prediction Models ◽  
Malaria Incidence ◽  
Malaria Risk ◽  
Information Criteria ◽  
Low Risk ◽  
Rainfall Data ◽  
Moderate Risk ◽  
Acceptable Accuracy

Background. Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics. Objectives. To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data. Methods. Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. Cluster validity was examined by the maximum absolute coordinate change and analysis of variance (ANOVA) with a conservative post hoc test (Bonferroni) as the multiple comparison test. Autocorrelation and cross-correlation analyses were performed to detect the autocorrelation of malaria incidence and the lagged effect of rainfall on malaria incidence. The effect of rainfall on malaria incidence was assessed using seasonal autoregressive integrated moving average (SARIMA) models. Ljung–Box statistics for model diagnosis and stationary R-squared and Normalized Bayesian Information Criteria for model fit were used. Model validity was assessed by analyzing the observed and predicted incidences using the spearman correlation coefficient and paired samples t-test. Results. A four cluster map (high risk, moderate risk, low risk, and very low risk) was the most valid stratification system for the reported malaria incidence in Eritrea. Monthly incidences were influenced by incidence rates in the previous months. Monthly incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. The constructed models had acceptable accuracy as 73.1%, 46.3%, 53.4%, and 50.7% of the variance in malaria transmission were explained by rainfall in the high-risk, moderate-risk, low-risk, and very low-risk clusters, respectively. Conclusion. Change in rainfall patterns affect malaria incidence in Eritrea. Using routine malaria case reports and rainfall data, malaria incidences can be forecasted with acceptable accuracy. Further research should consider a village or health facility level modeling of malaria incidence by including other climatic factors like temperature and relative humidity.

scholarly journals Study of in-hospital outcome in acute myocardial infarction in correlation with thrombolysis in myocardial infarction risk score

2019 ◽  
Vol 7 (6) ◽  
pp. 2377
Author(s):  
Pravin Shingade ◽  
Vinay Meshram ◽  
Umesh Madavi
Keyword(s):  
Myocardial Infarction ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Moderate Risk ◽  
Hospital Outcome ◽  
Timi Risk Score

Background: The Thrombolysis in Myocardial Infarction (TIMI) risk score is purportedly an integral score for mortality risk prediction in fibrinolysis-eligible patients with STEMI. Attempt was made to evaluate the same by correlating risk stratification by TIMI score with hospital outcome of such patients.Methods: There were 145 cases of STEMI were studied and TIMI risk scores were calculated and analysed vis-à-vis various relevant parameters. The patients were divided into three risk groups: ‘low-risk’, ‘moderate-risk’ and ‘high-risk’ based on their TIMI scores. All patients received routine anti-ischemic therapy and were thrombolysed subsequently, monitored in ICCU and followed during hospital stay for occurrence of post-MI complications.Results: There were 79 patients (54.5%) belonged to low-risk group, 48 (33.1%) to moderate-risk group and 18 (12.4%) to high-risk group according to TIMI risk score. The mortality (total 17 deaths) was observed to be highest in the high-risk group (55.6%), followed by moderate-risk (12.2%) and low-risk group (1.28%) respectively. Out of the 7 potentially suspect variables studied, Killips classification grade 2-4 had the highest relative risk (RR-15.85), followed by systolic BP <100mmHg (RR- 10.48), diabetes mellitus (RR- 2.79) and age >65 years (RR- 2.59).Conclusions: The TIMI risk scoring system seems to be one simple, valid and practical bed side tool in quantitative risk stratification and short-term prognosis prediction in patients with STEMI.

scholarly journals Proposal for the use of echocardiography in bloodstream infections due to different streptococcal species

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sandra Chamat-Hedemand ◽  
Niels Eske Bruun ◽  
Lauge Østergaard ◽  
Magnus Arpi ◽  
Emil Fosbøl ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
High Risk ◽  
Blood Culture ◽  
Positive Blood Culture ◽  
Bloodstream Infections ◽  
Low Risk ◽  
Moderate Risk ◽  
Streptococcal Species ◽  
Very High

Abstract Background Infective endocarditis (IE) is diagnosed in 7–8% of streptococcal bloodstream infections (BSIs), yet it is unclear when to perform transthoracic (TTE) and transoesophageal echocardiography (TOE) according to different streptococcal species. The aim of this sub-study was to propose a flowchart for the use of echocardiography in streptococcal BSIs. Methods In a population-based setup, we investigated all patients admitted with streptococcal BSIs and crosslinked data with nationwide registries to identify comorbidities and concomitant hospitalization with IE. Streptococcal species were divided in four groups based on the crude risk of being diagnosed with IE (low-risk < 3%, moderate-risk 3–10%, high-risk 10–30% and very high-risk > 30%). Based on number of positive blood culture (BC) bottles and IE risk factors (prosthetic valve, previous IE, native valve disease, and cardiac device), we further stratified cases according to probability of concomitant IE diagnosis to create a flowchart suggesting TTE plus TOE (IE > 10%), TTE (IE 3–10%), or “wait & see” (IE < 3%). Results We included 6393 cases with streptococcal BSIs (mean age 68.1 years [SD 16.2], 52.8% men). BSIs with low-risk streptococci (S. pneumoniae, S. pyogenes, S. intermedius) are not initially recommended echocardiography, unless they have ≥3 positive BC bottles and an IE risk factor. Moderate-risk streptococci (S. agalactiae, S. anginosus, S. constellatus, S. dysgalactiae, S. salivarius, S. thermophilus) are guided to “wait & see” strategy if they neither have a risk factor nor ≥3 positive BC bottles, while a TTE is recommended if they have either ≥3 positive BC bottles or a risk factor. Further, a TTE and TOE are recommended if they present with both. High-risk streptococci (S. mitis/oralis, S. parasanguinis, G. adiacens) are directed to a TTE if they neither have a risk factor nor ≥3 positive BC bottles, but to TTE and TOE if they have either ≥3 positive BC bottles or a risk factor. Very high-risk streptococci (S. gordonii, S. gallolyticus, S. mutans, S. sanguinis) are guided directly to TTE and TOE due to a high baseline IE prevalence. Conclusion In addition to the clinical picture, this flowchart based on streptococcal species, number of positive blood culture bottles, and risk factors, can help guide the use of echocardiography in streptococcal bloodstream infections. Since echocardiography results are not available the findings should be confirmed prospectively with the use of systematic echocardiography.

scholarly journals Infiltration and sealing for managing non-cavitated proximal lesions: a systematic review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanyuan Chen ◽  
Dongru Chen ◽  
Huancai Lin
Keyword(s):  
Systematic Review ◽  
High Risk ◽  
Meta Analysis ◽  
Low Risk ◽  
Caries Risk ◽  
Moderate Risk ◽  
Risk Levels ◽  
Non Invasive ◽  
Randomised Controlled ◽  
Caries Lesions

Abstract Background Infiltration and sealing are micro-invasive treatments for arresting proximal non-cavitated caries lesions; however, their efficacies under different conditions remain unknown. This systematic review and meta-analysis aimed to evaluate the caries-arresting effectiveness of infiltration and sealing and to further analyse their efficacies across different dentition types and caries risk levels. Methods Six electronic databases were searched for published literature, and references were manually searched. Split-mouth randomised controlled trials (RCTs) to compare the effectiveness between infiltration/sealing and non-invasive treatments in proximal lesions were included. The primary outcome was obtained from radiographical readings. Results In total, 1033 citations were identified, and 17 RCTs (22 articles) were included. Infiltration and sealing reduced the odds of lesion progression (infiltration vs. non-invasive: OR = 0.21, 95% CI 0.15–0.30; sealing vs. placebo: OR = 0.27, 95% CI 0.18–0.42). For both the primary and permanent dentitions, infiltration and sealing were more effective than non-invasive treatments (primary dentition: OR = 0.30, 95% CI 0.20–0.45; permanent dentition: OR = 0.20, 95% CI 0.14–0.28). The overall effects of infiltration and sealing were significantly different from the control effects based on different caries risk levels (OR = 0.20, 95% CI 0.14–0.28). Except for caries risk at moderate levels (moderate risk: OR = 0.32, 95% CI 0.01–8.27), there were significant differences between micro-invasive and non-invasive treatments (low risk: OR = 0.24, 95% CI 0.08–0.72; low to moderate risk: OR = 0.38, 95% CI 0.18–0.81; moderate to high risk: OR = 0.17, 95% CI 0.10–0.29; and high risk: OR = 0.14, 95% CI 0.07–0.28). Except for caries risk at moderate levels (moderate risk: OR = 0.32, 95% CI 0.01–8.27), infiltration was superior (low risk: OR = 0.24, 95% CI 0.08–0.72; low to moderate risk: OR = 0.38, 95% CI 0.18–0.81; moderate to high risk: OR = 0.20, 95% CI 0.10–0.39; and high risk: OR = 0.14, 95% CI 0.05–0.37). Conclusion Infiltration and sealing were more efficacious than non-invasive treatments for halting non-cavitated proximal lesions.

Identification of Molecular Biomarkers and Pathways for Risk Stratification in Human Papilloma Virus-Associated Cancers

2021 ◽  
Author(s):  
Eun Jung Kwon ◽  
Hye Ran Lee ◽  
Ju Ho Lee ◽  
Mihyang Ha ◽  
Yun Hak Kim ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Expression Patterns ◽  
Risk Groups ◽  
Molecular Biomarkers ◽  
Low Risk ◽  
Receptor Interaction ◽  
Prognostic Impact ◽  
Tumor Microenvironments ◽  
Cell Immunity

Abstract Background: Human papillomavirus (HPV) is the major cause of cervical cancer (CC) etiology; its contribution to head and neck cancer (HNC) incidence is steadily increasing. As individual patients’ response to the treatment of HPV-associated cancer is variable, there is a pressing need for the identification of biomarkers for risk stratification that can help determine the intensity of treatment. Methods: We have previously reported a novel prognostic and predictive indicator (HPPI) scoring system in HPV-associated cancers regardless of the anatomical locations by analyzing the TCGA and GEO databases. In this study, we comprehensively investigated the association of group-specific expression patterns of common differentially expressed genes (DEGs) between high-risk and low-risk groups in HPV-associated CC and HNC, identifying a molecular biomarkers and pathways for the risk stratification. Results: Among the identified 174 DEGs, expression of the genes associated with extracellular matrix (ECM)-receptor interaction pathway (ITGA5, ITGB1, LAMB1, LAMC1) were increased in high-risk groups in both HPV-associated CC and HNC while expression of the genes associated with the T-cell immunity (CD3D, CD3E, CD8B, LCK, and ZAP70) were decreased vise versa. The individual genes showed statistically significant prognostic impact on HPV-associated cancers but not on HPV-negative cancers. The expression levels of identified genes were similar between HPV-negative and HPV-associated high-risk groups with distinct expression patterns only in HPV-associated low-risk groups. Each group of genes showed negative correlations, and distinct patterns of immune cell infiltration in tumor microenvironments. Conclusion: These results identify molecular biomarkers and pathways for risk stratification in HPV-associated cancers regardless of anatomical locations. The identified targets are selectively working in only HPV-associated cancers, but not in HPV-negative cancers indicating possibility of the selective targets governing HPV-infective tumor microenvironments.

scholarly journals Pre-hospital risk assessment in suspected non-ST-elevation acute coronary syndrome: A prospective observational study

2018 ◽  
Vol 9 (1_suppl) ◽  
pp. 5-12 ◽  
Author(s):  
Dominique N van Dongen ◽  
Rudolf T Tolsma ◽  
Marion J Fokkert ◽  
Erik A Badings ◽  
Aize van der Sluis ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Heart Score ◽  
Coronary Syndrome ◽  
Risk Patients ◽  
St Elevation

Background: Pre-hospital risk stratification of non-ST-elevation acute coronary syndrome (NSTE-ACS) by the complete HEART score has not yet been assessed. We investigated whether pre-hospital risk stratification of patients with suspected NSTE-ACS using the HEART score is accurate in predicting major adverse cardiac events (MACE). Methods: This is a prospective observational study, including 700 patients with suspected NSTE-ACS. Risk stratification was performed by ambulance paramedics, using the HEART score; low risk was defined as HEART score ⩽ 3. Primary endpoint was occurrence of MACE within 45 days after inclusion. Secondary endpoint was myocardial infarction or death. Results: A total of 172 patients (24.6%) were stratified as low risk and 528 patients (75.4%) as intermediate to high risk. Mean age was 53.9 years in the low risk group and 66.7 years in the intermediate to high risk group ( p<0.001), 50% were male in the low risk group versus 60% in the intermediate to high risk group ( p=0.026). MACE occurred in five patients in the low risk group (2.9%) and in 111 (21.0%) patients at intermediate or high risk ( p<0.001). There were no deaths in the low risk group and the occurrence of acute myocardial infarction in this group was 1.2%. In the high risk group six patients died (1.1%) and 76 patients had myocardial infarction (14.4%). Conclusions: In suspected NSTE-ACS, pre-hospital risk stratification by ambulance paramedics, including troponin measurement, is accurate in differentiating between low and intermediate to high risk. Future studies should investigate whether transportation of low risk patients to a hospital can be avoided, and whether high risk patients benefit from immediate transfer to a hospital with early coronary angiography possibilities.

scholarly journals Establishment of an Ultrasound Malignancy Risk Stratification Model for Thyroid Nodules Larger Than 4 cm

2021 ◽  
Vol 11 ◽  
Author(s):  
Xuehua Xi ◽  
Ying Wang ◽  
Luying Gao ◽  
Yuxin Jiang ◽  
Zhiyong Liang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Low Risk ◽  
Validation Dataset ◽  
Intermediate Risk ◽  
Solid Mass ◽  
Incidence And Mortality ◽  
Risk Stratification Model

BackgroundThe incidence and mortality of thyroid cancer, including thyroid nodules &gt; 4 cm, have been increasing in recent years. The current evaluation methods are based mostly on studies of patients with thyroid nodules &lt; 4 cm. The aim of the current study was to establish a risk stratification model to predict risk of malignancy in thyroid nodules &gt; 4 cm.MethodsA total of 279 thyroid nodules &gt; 4 cm in 267 patients were retrospectively analyzed. Nodules were randomly assigned to a training dataset (n = 140) and a validation dataset (n = 139). Multivariable logistic regression analysis was applied to establish a nomogram. The risk stratification of thyroid nodules &gt; 4 cm was established according to the nomogram. The diagnostic performance of the model was evaluated and compared with the American College Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), Kwak TI-RADS and 2015 ATA guidelines using the area under the receiver operating characteristic curve (AUC).ResultsThe analysis included 279 nodules (267 patients, 50.6 ± 13.2 years): 229 were benign and 50 were malignant. Multivariate regression revealed microcalcification, solid mass, ill-defined border and hypoechogenicity as independent risk factors. Based on the four factors, a risk stratified clinical model was developed for evaluating nodules &gt; 4 cm, which includes three categories: high risk (risk value = 0.8-0.9, with more than 3 factors), intermediate risk (risk value = 0.3-0.7, with 2 factors or microcalcification) and low risk (risk value = 0.1-0.2, with 1 factor except microcalcification). In the validation dataset, the malignancy rate of thyroid nodules &gt; 4 cm that were classified as high risk was 88.9%; as intermediate risk, 35.7%; and as low risk, 6.9%. The new model showed greater AUC than ACR TI-RADS (0.897 vs. 0.855, p = 0.040), but similar sensitivity (61.9% vs. 57.1%, p = 0.480) and specificity (91.5% vs. 93.2%, p = 0.680).ConclusionMicrocalcification, solid mass, ill-defined border and hypoechogenicity on ultrasound may be signs of malignancy in thyroid nodules &gt; 4 cm. A risk stratification model for nodules &gt; 4 cm may show better diagnostic performance than ACR TI-RADS, which may lead to better preoperative decision-making.

scholarly journals Assessment of risk of developing diabetes using Indian diabetes risk score in the urban field practice area of Rajarajeswari Medical College and Hospital, Bangalore

2019 ◽  
Vol 7 (1) ◽  
pp. 170
Author(s):  
Nazia N. Shaik ◽  
Swapna M. Jaswanth ◽  
Shashikala Manjunatha
Keyword(s):  
At Risk ◽  
High Risk ◽  
Risk Score ◽  
Diabetes Risk ◽  
Low Risk ◽  
Risk Category ◽  
Moderate Risk ◽  
Medical College ◽  
Field Practice ◽  
Diabetes Risk Score

Background: Diabetes is one of the largest global health emergencies of the 21st century. As per International Federation of Diabetes some 425 million people worldwide are estimated to have diabetes. The prevalence is higher in urban versus rural (10.2% vs 6.9%). India had 72.9 million people living with diabetes of which, 57.9% remained undiagnosed as per the 2017 data. The objectives of the present study were to identify subjects who at risk of developing Diabetes by using Indian diabetes risk score (IDRS) in the Urban field practice area of Rajarajeswari Medical College and Hospital (RRMCH).Methods: A cross sectional study was conducted using a Standard questionnaire of IDRS on 150 individuals aged ≥20 years residing in the Urban field practice area of RRMCH. The subjects with score <30, 30-50, >or =60 were categorized as having low risk, moderate risk and high risk for developing diabetes type-2 respectively.Results: Out of total 150 participants, 36 (24%) were in high-risk category (IDRS≥60), the majority of participants 61 (41%) were in the moderate-risk category (IDRS 30–50) and 53 (35%) participants were found to be at low-risk (<30) for diabetes. Statistical significant asssociation was found between IDRS and gender, literacy status, body mass index (p<0.0000l).Conclusions: It is essential to implement IDRS which is a simple tool for identifying subjects who are at risk for developing diabetes so that proper intervention can be carried out at the earliest to reduce the burden of diabetes.

Algorithms for Individual Prediction of COVID-19 infection and Hospitalization among Symptomatic patients based on health data (Preprint)

2021 ◽  
Author(s):  
Rossella Murtas ◽  
Nuccia Morici ◽  
Chiara Cogliati ◽  
Massimo Puoti ◽  
Barbara Omazzi ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Health Data ◽  
Low Risk ◽  
Risk Prediction Models ◽  
Predictive Algorithms ◽  
Medium Risk ◽  
Risk Patients ◽  
At Home

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has generated a huge strain on the health care system worldwide. The metropolitan area of Milan, Italy was one of the most hit area in the world. OBJECTIVE Robust risk prediction models are needed to stratify individual patient risk for public health purposes METHODS Two predictive algorithms were implemented in order to foresee the probability of being a COVID-19 patient and the risk of being hospitalized. The predictive model for COVID-19 positivity was developed in 61.956 symptomatic patients, whereas the model for COVID-19 hospitalization was developed in 36.834 COVID-19 positive patients. Exposures considered were age, gender, comorbidities and symptoms associated with COVID-19 (vomiting, cough, fever, diarrhoea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnoea). RESULTS The predictive models showed a good fit for predicting COVID-19 disease [AUC 72.6% (95% CI 71.6%-73.5%)] and hospitalization [AUC 79.8% (95% CI 78.6%-81%)]. Using these results, 118,804 patients with COVID-19 from October 25 to December 11, 2020 were stratified into low, medium and high risk for COVID-19 severity. Among the overall population, 67.030 (56%) were classified as low-risk, 43.886 (37%) medium-risk, and 7.888 (7%) high-risk, with 89% of the overall population being assisted at home, 9% hospitalized, and 2% dead. Among those assisted at home, most people (60%) were classified as low risk, whereas only 4% were classified at high risk. According to ordinal logistic regression, the OR of being hospitalised or dead was 5.0 (95% CI 4.6-5.4) in high-risk patients and 2.7 (95% CI 2.6-2.9) in medium-risk patients, as compared to low-risk patients. CONCLUSIONS A simple monitoring system, based on primary care datasets with linkage to COVID-19 testing results, hospital admissions data and death records may assist in proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

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