Pengategorian Provinsi Berdasarkan Kelompok Sukses dan Gagal dalam Penanganan Pandemik Covid-19 di Indonesia Menurut Analisis Diskriminan

An assessment of categorizing the handling of Covid-19 by the Regional Government is needed, this categorization includes the handling performance (KP) and the death rate (TK) of Covid-19. This was done to see how serious the local government is in handling Covid-19. The approach in this study uses a descriptive approach, this approach aims to describe or describe the categorization of provinces based on groups that have succeeded and failed in handling the Covid-19 pandemic in Indonesia. In this research, the method used is quantitative method. The quantitative approach used is discriminant analysis. The conclusion of this study is that the discriminant function formed in this study is Z = 0.893 KP + 0.451 TK. The results of eigenvalues in this study indicate that the magnitude of Canonical Correlation is 0.797 or the amount of Square Canonical Correlation (CR2) = (0.797) 2 or equal to 0.635. So it can be concluded that 63.5% of the variation between groups of successful and failed provinces can be explained by the discriminant variables of the KP and TK ratios. The view of the matrix structure in this study shows that the amount of loading for KP is 0.842 and the amount of loading for TK is 0.332. The two variables of the Covid 19 handling ratio are high enough so that the discriminant score can be interpreted as a measure of the success of the handling of Covid 19 at the Provincial Government. Meanwhile, the results of the classification matrix show that 32 observations have been classified correctly and only two observations are classified incorrectly, namely the observations number 18 and 19, so the classification accuracy is (32/34) or 94.1%.

S-reflex score: a predictor score of vaso-vagal reflex syncope

Funding Acknowledgements Type of funding sources: None. Introduction Reflex syncope is one of the most common causes of syncope, usually associated with unspecified triggers and prodromes. The probability of occurrence is higher when concomitant factors coexist whether inherent to individual or related to environment, and changes in conventional tests may prove useful in their diagnosis. Objective Identify predictive factors in the initial investigation in order to establish a predictor score of vasovagal reflex syncope (VVS). Methods Observational and retrospective study, with descriptive analysis and correlation of patients followed in syncope appointment at a Cardiology Center from 1 January 2015 to 31 Novembe 2019. Descriptive analysis on patient characteristics and complementary exams were carried out. The correlation test used between categorical variables was Chi-square and among continuous variables the T-Student test with a significance level of 95%. Independent predictors of VVS were identified through binary logistic regression considering a p = 0.05, with subsequent application of a discriminatory function using the lambda Wilks test to determine the discriminant score of variables under analysis. SPSS 24.0 was used for statistical analysis. Results Identified N694 patients, 52% male, mean age of 63 years. 15.7% of patients with suspected VVS in a first impression. At the end, 22.9% diagnosed with VVS and of these 66% had syncope recurrence. 42% had long prodromes (p = 0.013), 17% with heat prodromes (p = 0.012), in 11.3% the trigger was the meal (p = 0.031), 12.2% suffered trauma (p = 0.07) and 59.7% had ECG with pathological q wave (p = 0.00), thus showing to be independet predictors of VVS. A predictor score of VVS was determined using the formula = -0.761 + (0.529.Long_Prodromes) + (0.721.Heat_Prodromes) + (0.313.Trigger_Meal) + (2,431.ECG_q) - (0.542.Trauma), with a cutoff value of 0.258, specificity of 90.5% with discriminative power of 87%. Conclusion The final diagnosis of VVS was higher than suspicions during initial clinical investigation and 66% of these patients had recurrence. The independent predictors factors of VVS are long prodromes, heat prodromes, meal as a trigger, ECG with q waves and trauma. The S-Reflex score was determined with a good discriminative power with high specificity. Considering clinical variables and conventional exams, this score could be useful to guide the strategy for syncope patients after the first evaluation to a more cost-effective strategy.

Recurrent syncope, which patient should we follow more closely

Introduction The importance of education is well recognized in patients presenting with syncope, in order to reduce the recurrence rate. Purpose To determine a predictive score of recurrent syncopal episodes after the first medical assessment. Methods We conducted a retrospective study enrolling patients followed in our Syncope Consultation from January 2015 to November 2019. Clinical and episodes characteristics, as well as diagnostic studies were analysed. Correlation between variables was performed by the Chi-square and T-Student tests, with a significance level of 95%. Independent predictors of recurrent syncope were identified through a binary logistic regression analysis, considering p=0.05. Then, a discriminatory function was applied using the Wilks lambda test to determine the discriminant score of the analysed groups. SPSS 24.0 was used for statistical analysis. Results A total of 694 patients were included, and 420 (60.5%) had recurrent syncope at the first evaluation. After educational approach, 97 (14%) maintained recurrent episodes. In this subgroup, the mean age was 63.7±22.8 years-old and 88.7% already had previous recurrent syncope (vs 56.1%; p<0.001). The prodrome of malaise was common (40.2% vs 26.8%; p=0.008), but 32% of these patients had syncope without prodromes (vs 21.8%; p=0.032). They also had frequently first-degree atrioventricular (AV) block (22.5% vs 6.8%; p<0.001) and 51.7% had a final diagnosis of reflex syncope. No previous medication with calcium channel blockers (CCB) (p<0.001), malaise (p=0.011), not having Q-waves in the electrocardiogram (p=0.022) and the presence of first-degree AV block (p<0.001) were independent predictors of recurrent syncope. A predictive score of recurrence was determined using the formula: 0.108 − 1.556 x (medication with CCB) + 0.989 x (malaise) − 1.031 x (Q-waves) + 2.406 x (first degree AV block). Variables should be replaced by 1 or 0, depending on whether the condition is present or not. A cut-off of 0.283 was obtained with a specificity of 96.1% and a discriminative power of 81.2%. Conclusion In our patients presenting with syncope, recurrence rate reduced from 60,5% to 14% just with educational measures. To help identify patients who maintain recurrence, we determined a predictive score using clinical data from the first visit, with a good discriminative power and excellent specificity. It could be used to strengthen education, to direct diagnostic studies and to shorten follow-up visits, but it still needs validation to be used in clinical practice. Funding Acknowledgement Type of funding source: None

Integrated selection criteria in sugarcane breeding programs using discriminant function analysis

Background Selection indices help the plant breeders to discriminate desirable genotypes on the basis of phenotypic performance. Therefore, the present study was conducted to evaluate thirty sugarcane genotypes (clones) along with two check cultivars in two cropping seasons at Mattana Agricultural Research Station. Results The results showed the studied traits observed in all genotypes were significantly different. The results could significantly discriminate between low and high sugar yield genotypes by describing eleven traits including sugar yield (ton/fed), cane yield (ton/fed), number of stalk/m2, stalk weight (kg), stalk height (cm), stalk diameter (cm), number of internodes, Brix %, sucrose %, purity %, and sugar recovery %. High sugar yield genotypes were selected by discriminant analysis. The discriminant score (DS) could explain 79.2% of sugar yield variations and had a significant canonical correlation (0.89**). Results of discriminant function analysis (DFA) indicated that the most important traits, in order of appearance, are stalk weight, stalk height, purity %, Brix%, and cane yields. Conclusions Genotypes, G.2017-43, G.2017-42, G.2017-29, G.2017-33, and G.2017-44, showed the highest values of the discriminant score and were recognized as the highest yielder sugarcane genotypes. While the genotypes named Vis, G.2017-30, G.2017-10, G.2017-27, G.2017-25, G.2017-70, G.2017-41, G.2017-40, G.2017-35, and G.2017-58, recognized as the lowest yielder sugarcane genotypes which represent the lowest values of the discriminant score.

Multimarker Approach to Identify Patients with Coronary Artery Disease at High Risk for Subsequent Cardiac Adverse Events: The Multi-Biomarker Study

In our prospective non-randomized, single-center cohort study (n = 161), we have evaluated a multimarker approach including S100 calcium binding protein A12 (S100A1), interleukin 1 like-receptor-4 (IL1R4), adrenomedullin, copeptin, neutrophil gelatinase-associated lipocalin (NGAL), soluble urokinase plasminogen activator receptor (suPAR), and ischemia modified albumin (IMA) in prediction of subsequent cardiac adverse events (AE) during 1-year follow-up in patients with coronary artery disease. The primary endpoint was to assess the combined discriminatory predictive value of the selected 7 biomarkers in prediction of AE (myocardial infarction, coronary revascularization, death, stroke, and hospitalization) by canonical discriminant function analysis. The main secondary endpoints were the levels of the 7 biomarkers in the groups with/without AE; comparison of the calculated discriminant score of the biomarkers with traditional logistic regression and C-statistics. The canonical correlation coefficient was 0.642, with a Wilk’s lambda value of 0.78 and p < 0.001. By using the calculated discriminant equation with the weighted mean discriminant score (centroid), the sensitivity and specificity of our model were 79.4% and 74.3% in prediction of AE. These values were higher than that of the calculated C-statistics if traditional risk factors with/without biomarkers were used for AE prediction. In conclusion, canonical discriminant analysis of the multimarker approach is able to define the risk threshold at the individual patient level for personalized medicine.

Analisis Diskriminan Fisher untuk Hubungan Incidence Rate dengan Indeks Entomologi berdasarkan Klasifikasi Ekosistem

Discriminant analysis is one of the statistical techniques that may use to provide the most appropriate estimation for classifying individuals into one group based on the independent variable score (discriminant score). There are 2 main assumptions in discriminant analysis such as fulfilled data normality and similarity of variant-covariants. This study aims to determine whether there is a relationship between DHF Incidence Rate (IR) and entomology index if a region is classified as a coast-not a coast and rural-urban. This research conducted in 78 districts in Indonesia carried out in Disease Reservoir and Vector Specific Research from 2016 to 2017. The geographical area of ​​Indonesia which has a tropical climate with three months of rainy season in December, January, February and three months of the dry season in June, July, August can be a hyperendemic area of ​​DHF. This condition is exacerbated by the development of increasingly complex urban areas and the development of rural areas into cities that reduce environmental quality and have an impact on the expansion of the habitat of Aedes aegypti as vector of DHF. The data to be analyzed are the entomology index in the form of numbers of HI, BI, CI and ABJ against IR. The results of the analysis provide information that the very low value of Canonical Correlation is 0.076 classified as coast and not coast so that there is no relationship between the independent variable and the dependent variable. While the Canonical Correlation value is quite high, which is 0.219 classified as rural and urban showed that there is a relationship between the independent variable and the dependent variable. Based on the results, densely populated ecosystems in urban or rural areas have a great chance of cases of dengue hemorrhagic fever, so people need to monitor mosquito larvae to control DHF. Abstrak Analisis diskriminan adalah salah satu teknik statistik yang dapat digunakan untuk memberikan pendugaan yang paling tepat untuk mengklasifikasikan individu ke dalam salah satu kelompok berdasarkan skor variabel bebas (skor diskriminan). Terdapat 2 asumsi utama dalam melakukan analisis diskriminan, yaitu normalitas data harus terpenuhi dan kesamaan varian-kovarian. Penelitian ini bertujuan untuk mengetahui apakah terdapat hubungan antara Incidence Rate (IR) DBD dengan indeks entomologi jika suatu wilayah diklasifikasi menjadi pantai-bukan pantai dan perdesaan-perkotaan. Penelitian telah dilakukan di 78 kabupaten di Indonesia pada Riset Khusus Vektor dan Reservoir Penyakit tahun 2016 hingga 2017. Wilayah geografis Indonesia yang beriklim tropis dengan tiga bulan musim hujan pada Desember, Januari, Februari dan tiga bulan musim kemarau pada Juni, Juli, Agustus dapat menjadi wilayah hiperendemik DBD. Kondisi tersebut diperparah oleh perkembangan wilayah perkotaan yang semakin kompleks dan perkembangan wilayah pedesaan menjadi kota yang menurunkan kualitas lingkungan hidup dan berdampak pada perluasan habitat nyamuk Aedes aegypti vektor penyakit DBD. Data yang akan dianalisis adalah data indeks entomologi berupa angka HI, BI, CI dan ABJ terhadap IR. Hasil analisis memberikan informasi bahwa nilai Canonical Correlation yang sangat rendah yaitu 0,076, jika diklasifikasi menjadi pantai dan bukan pantai menunjukkan tidak terdapat hubungan antara variabel bebas dengan variabel terikat. Nilai Canonical Correlation yang cukup tinggi yaitu 0,219, jika diklasifikasi menjadi perdesaan dan perkotaan menunjukkan terdapat hubungan antara variabel bebas dengan variabel terikat nya. Berdasarkan hasil penelitian ini, ekosistem padat penduduk di perkotaan atau perdesaan memiliki peluang besar terhadap adanya kasus demam berdarah dengue, sehingga masyarakat perlu melakukan monitoring terhadap jentik nyamuk untuk pengendalian DBD.

P4579Prediction of CABG indication in patients admitted for NSTEMI with the new CABG DAPE2S score

Introduction A significant number of patients admitted for Non-ST Elevation Myocardial Infarction (NSTEMI) have multivessel complex coronary artery disease (CAD) and benefit from Coronary Artery Bypass Graft surgery (CABG). These patients frequently present high-risk surgical profiles, constituting a challenging group when it comes to balancing ischemic and haemorrhagic risk. Objective To develop a simple predictive risk model of referral to CABG in patients admitted for NSTEMI. Methods The authors present a retrospective, descriptive and correlational study including all patients admitted for NSTEMI in a Cardiology department between the 1st of October 2010 and the 1st of October 2018. Demographic profile, clinical characteristics, risk factors and hospitalization data of NSTEMI patients referred to CABG were studied, and a correlational analysis was performed with Chi-square test for categorical variables and t-Student test for continuous variables (confidence level of 95%). Independent predictors of CABG in patients with NSTEMI were identified through Binary logistic regression analysis, using a significance level of 0,05. A discriminatory function was subsequently applied, and the Wilks lambda test was used to determine the discriminant score for the studied groups. The authors used SPSS 24,0 for statistical analysis. Results A total of 2476 patients were included, 668 (27%) of which were female, with a mean age of 68,5±13,4 years. In the studied sample, 273 patients (11%) were proposed to CABG. The authors found a significant association between CABG and multiple clinical, laboratorial and therapeutical variables, but after multivariate analysis only male sex, previous Diabetes Mellitus, previous angina, previous Percutaneous coronary intervention, absence of a normal EKG, ST segment depression at admission, sinus rythm and brain natriuretic peptide (BNP) >100pg/mL proved to be independent predictors of referral. Using these variables, the authors developed a risk model to predict CABG referral in NSTEMI patients: −0,614 − (0,756 x female sex) + (0,305 x diabetes) + (0,631 x angina) − (1,513 x previous PCI) + (1,216 x sinus rythm) + (0,672 x ST depression) − (0,806 x normal EKG) + (0,562 x BNP>100). In this function, variables should be substituted by 1 or 0, depending on wheter the condition they specify is present or absent. The optimal discrimination cutoff was 0,23, with a 64% sensibility and 59% specificity, and a discriminant power of 60%. Conclusion Being able to predict referral to surgical revascularization in NSTEMI may help physicians to optimize a specific approach in each patient, in particular with regard to anti-thrombotic strategies. The authors developed a risk predicting model for CABG in NSTEMI patients based on simple clinical and laboratory variables, which will require validation in a larger cohort, before it can be applied in a clinical context.

P844NAS2H score, a novel predictive score of 1-year all cause mortality in Acute Coronary Syndromes

Introduction In patients admitted for Acute Coronary Syndromes (ACS), mortality is influenced by several clinical and therapeutical factors, and management of these patients should be guided by an estimate of individual risk. Objective To develop a simple predictive model of 1-year mortality in patients admitted for ACS. Methods The authors present a retrospective, descriptive and correlational study including all patients admitted for ACS in a Cardiology department between the 1st of October 2010 and the 1st of October 2017. A 1-year (1y) follow-up was made through registry consultation and phone call by a Cardiologist. Patients with 1y mortality (1yM) events were studied regarding baseline demographic and clinical characteristics, risk factors and hospitalization data, and a correlational analysis with Chi-square test for categorical variables and t-Student test for continuous variables (confidence level of 95%) was performed. Independent predictors of 1yM were identified through binary logistic regression analysis, using a significance level of 0,05. A discriminatory function was applied, and the Wilks lambda test was used to determine the discriminant score for the studied groups. The authors used SPSS 24,0 for statistical analysis. Results A total of 3251 patients were included, 826 (25,4%) of which were female, with a mean age of 65,5±13,4 years. In the studied sample, 268 patients (8,2%) died in the year following hospital discharge; this group had a mean age of 65,6±13,2 years, and 80 (29,9%) were female patients. There was a significant association between 1yM and multiple clinical, therapeutical and laboratorial variables, but after multivariate analysis only age greater than 65 years old (yo) [p=0,001], previous stroke [p=0,005], haemoglobin (Hb) <10mg/dL [p<0,001], brain natriuretic peptide (BNP) >100pg/mL [p=0,001], and left ventricular ejection fraction (LVEF) <50% [p <0,001] proved to be independent predictors of the studied outcome. Using these variables, the authors developed a scoring model to predict 1yM in patients admitted for ACS with the following formula = 0,002 + (0,736 x Age >65yo) + (0,91 x previous stroke) + (2,562 x Hb <10) + (0,63 x BNP >100) - (1,207 x FEVE >50%). In this function, variables should be substituted by 1 or 0, depending on wheter they are present or not. The discrimination cutoff was 0,57, with a 70,6% sensibility and 75,9% specificity, and a discriminant power of 75,4%. Conclusion Defining the mortality risk of ACS patients after discharge represents a real challenge and demands a careful evaluation of multiple factors in an attempt to achieve an accurate estimation of risk. The authors developed a predicting model for 1yM in ACS patients, with a good discriminant power, based on simple variables. The present score will require validation in a larger cohort of ACS patients before it can be applied in a clinical context.