The MB score: a new risk stratification index to predict the need for advanced tools in lead extraction procedures

EP Europace ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 613-621
Author(s):  
Luca Bontempi ◽  
Antonio Curnis ◽  
Paolo Della Bella ◽  
Manuel Cerini ◽  
Andrea Radinovic ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Value ◽  
Scoring System ◽  
Event Rate ◽  
Lead Extraction ◽  
A Posteriori ◽  
Derivation Cohort ◽  
Study Population ◽  
Stepwise Procedure ◽  
Passive Fixation

Abstract Aims A validated risk stratification schema for transvenous lead extraction (TLE) could improve the management of these procedures. We aimed to derive and validate a scoring system to efficiently predict the need for advanced tools to achieve TLE success. Methods and results Between November 2013 and March 2018, 1960 leads were extracted in 973 consecutive TLE procedures in two national referral sites using a stepwise approach. A procedure was defined as advanced extraction if required the use of powered sheaths and/or snares. The study population was a posteriori 1:1 randomized in derivation and validation cohorts. In the derivation cohort, presence of more than two targeted leads (odds ratio [OR] 1.76, P = 0.049), 3-year-old (OR 3.04, P = 0.001), 5-year-old (OR 3.48, P < 0.001), 10-year-old (OR 3.58, P = 0.008) oldest lead, implantable cardioverter-defibrillator (OR 3.84, P < 0.001), and passive fixation lead (OR 1.91, P = 0.032) were selected by a stepwise procedure and constituted the MB score showing a C-statistics of 0.82. In the validation group, the MB score was significantly associated with the risk of advanced extraction (OR 2.40, 95% confidence interval 2.02-2.86, P < 0.001) and showed an increase in event rate with increasing score. A low value (threshold = 1) ensured 100% sensibility and 100% negative predictive value, while a high value (threshold = 5) allowed a specificity of 92.8% and a positive predictive value of 91.9%. Conclusion In this study, we developed and tested a simple point-based scoring system able to efficiently identify patients at low and high risk of needing advanced tools during TLE procedures.

scholarly journals 447 Evaluation of JPI System as Prognostic Tool in Cases of Perforation Peritonitis

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
N Singh ◽  
B J Singh ◽  
A Sharma ◽  
M S Khalsa ◽  
H Singh
Keyword(s):  
Predictive Value ◽  
Scoring System ◽  
Scoring Systems ◽  
Government Medical College ◽  
Surgical Practice ◽  
Medical College ◽  
Perforation Peritonitis ◽  
Study Population ◽  
Adequate Management ◽  
Significant Factors

Abstract Aim Perforation peritonitis is a commonly encountered condition in surgical practice in any hospital. One of the reasons for high mortality in these conditions is lack of risk stratification resulting in delay in providing adequate management, although a number of scoring systems are available to stratify these patients according to severity, but most of these cannot be used in developing countries like India due to their dependency on sophisticated investigation which are usually lacking in most parts of these countries. This study aims to evaluate a simple scoring system for peritonitis. Method Fifty cases with diagnosis of peritonitis coming to Government Medical College, Amritsar were stratified according to Jabalpur peritonitis Index and their outcome was examined. Results Mortality steadily increases with increase in JPI score. Patients with JPI score of < 9 and >9 had mortality rate of 2.5% and 60% respectively)(p < 0.01) and similarly morbidity of 30% and 80% (p < 0.0001).Duration of pain >24 hours, age >50 years, mean SBP <100 mmHg, serum creatinine >1.5 mg/dl, respiratory rate >24/min, heart rate >110/min and feculent exudate intra operatively were found to be independently significant factors in predicting the mortality among the study population. For a score of 9, the sensitivity was 85.7%, specificity was 90.7% and positive predictive value for mortality is 40% and negative predictive value of 97.5%. Conclusions This study proves that JPI scoring system is a simple and effective tool for assessing the morbidity and mortality in patients with peritonitis.

High Predictive Value of the Revised International Prognostic Scoring System (IPSS-R): An External Analysis of 646 Patients From a Multiregional Italian MDS Registry

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1702-1702 ◽  
Author(s):  
Emanuela Messa ◽  
Daniela Gioia ◽  
Andrea Evangelista ◽  
Bernardino Allione ◽  
Emanuele Angelucci ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Value ◽  
Scoring System ◽  
Prognostic Score ◽  
Real Life ◽  
Progression Free Survival ◽  
International Prognostic Scoring System ◽  
Transfusion Dependency ◽  
Prognostic Power

Abstract Abstract 1702 Background: Prognostic assessment has a crucial role in clinical evaluation of patients (pts) affected by myelodysplastic syndrome (MDS). Recently a Revised International Prognostic Scoring System (IPSS-R) has been developed (Greenberg et al, 2012) to improve the standard IPSS (Greenberg et al, 1997): it identifies five different prognostic categories mainly based on stratification of cytogenetic risk. Another prognostic score proposed in clinical practice is WPSS, based on transfusion dependency and WHO morphologic classification (Malcovati et al, 2005) subsequently modified (rWPSS) introducing level of hemoglobin in lieu of the previous not well defined variable of transfusion dependency (Malcovati et al, 2011). Aims: Aim of our study was to evaluate in a cohort of MDS pts enrolled in the Multiregional Italian MDS Registry the prognostic value of IPSS-R respect to IPSS and compare it with both WPSS and rWPSS. Materials and methods: Among the 1918 MDS pts enrolled in the Multiregional Italian MDS Registry from 1999 to 2012 we excluded all the cases already included in the IWG-PM database that generated the IPSS-R. We thus obtained a cohort of 646 pts with complete follow up. We evaluated the prognostic power of IPSS-R respect to IPSS, WPSS and rWPSS respectively by Harrell's C statistics, analyzing as endpoints overall survival (OS), leukemic evolution (LE) and progression free survival (PFS). For LE we considered leukemic evolution as an event, while all the other causes of death were competing events. For PFS we consider either leukemic evolution or death for any causes as an event. Results: Median age of MDS patients was 75 years (interquartile range: 69–80 years). 378 (59%) out of 646 pts were males. WHO classification was as follows: 33% RCMD, 10% RAEB-1, 9% RAEB-2, 6% CMML, 2% MDS-U, the remaining 40% were RARS, RA, isolated 5q deletion. Median follow up of censored pts was 17 months. According to IPSS score, 47% of pts were low risk, 39% Int-1, 10% Int-2 and 4% high risk. WPSS stratification was as follows: 31% were very low (VL) risk, 37% low (L), 19% intermediate (I), 11% high (H) and 2% very high (VH). By applying rWPSS stratification we obtained 30% VL, 35% L, 17% I, 15% H and 3% VH risk pts. IPSS-R risk stratification was as follows: 20% VL, 46% L, 20% I, 9% H and 5% VH risk pts. OS was analyzed according to the different scores by Kaplan-Meyer method. All prognostic systems allowed the identification of survival curves with significant differences among the different categories of risk stratification. IPSS-R application defined OS curves which better defined patients prognostic categories as shown in fig 1. In fact Harrel's C statistics demonstrated a better predictive value of the IPSS-R respect to IPSS, but also respect to WPSS and rWPSS (C=0,73; 0,63; 0,65; 0,64 respectively). Similar results have been obtained also considering time to LE (fig 2). Harrel's C statistics for LE was 0,84; 0,76; 0,78; 0,77 respectively in IPSS-R, IPSS, WPSS, rWPSS risk stratification groups. Moreover, we analyzed PFS outcomes (fig 3). Also in this case, IPSS-R showed the greatest prognostic power: Harrel's C statistics was 0,76; 0,67; 0,66; 0,69 respectively in IPSS-R, IPSS, WPSS, rWPSS risk stratification groups. Conclusions: In our hands, IPSS-R score demonstrated a better prognostic power respect to previously published prognostic systems (IPSS, WPSS, rWPSS). The cohort of MDS patients we employed to validate the new prognostic scoring system has a short follow up (17 months), due to the exclusion of cases already used to establish the IPSS-R system, and the majority of these are lower risk ones. We can conclude that a careful classification based on cytogenetic examination improve the prognostic power of the score. Thus, IPSS-R is confirmed to be a refined tool, easily applicable in real life and empowered respect to the currently used scores to define MDS patient prognosis. Disclosures: Saglio: Bristol-Myers Squibb: Consultancy, Speakers Bureau.

scholarly journals Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score

2020 ◽  
Author(s):  
Stephen R Knight ◽  
Antonia Ho ◽  
Riinu Pius ◽  
Iain Buchan ◽  
Gail Carson ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Predictive Value ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
Clinical Decision ◽  
Validation Dataset ◽  
Peripheral Oxygen Saturation ◽  
Oxygen Saturation Level ◽  
Derivation Cohort ◽  
Risk Stratification Score

Objectives To develop and validate a pragmatic risk score to predict mortality for patients admitted to hospital with covid-19. Design Prospective observational cohort study: ISARIC WHO CCP-UK study (ISARIC Coronavirus Clinical Characterisation Consortium [4C]). Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited between 21 May and 29 June 2020. Setting 260 hospitals across England, Scotland, and Wales. Participants Adult patients (≥18 years) admitted to hospital with covid-19 admitted at least four weeks before final data extraction. Main outcome measures In-hospital mortality. Results There were 34 692 patients included in the derivation dataset (mortality rate 31.7%) and 22 454 in the validation dataset (mortality 31.5%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea, and C-reactive protein (score range 0-21 points). The 4C risk stratification score demonstrated high discrimination for mortality (derivation cohort: AUROC 0.79; 95% CI 0.78 - 0.79; validation cohort 0.78, 0.77-0.79) with excellent calibration (slope = 1.0). Patients with a score ≥15 (n = 2310, 17.4%) had a 67% mortality (i.e., positive predictive value 67%) compared with 1.0% mortality for those with a score ≤3 (n = 918, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (AUROC range 0.60-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). Conclusions We have developed and validated an easy-to-use risk stratification score based on commonly available parameters at hospital presentation. This outperformed existing scores, demonstrated utility to directly inform clinical decision making, and can be used to stratify inpatients with covid-19 into different management groups. The 4C Mortality Score may help clinicians identify patients with covid-19 at high risk of dying during current and subsequent waves of the pandemic. Study registration ISRCTN66726260

scholarly journals Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Verena Schöning ◽  
Evangelia Liakoni ◽  
Christine Baumgartner ◽  
Aristomenis K. Exadaktylos ◽  
Wolf E. Hautz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Clinical Outcomes ◽  
Predictive Value ◽  
Validation Cohort ◽  
Tertiary Hospital ◽  
Learning Models ◽  
Clinical Risk ◽  
Machine Learning Models ◽  
Risk Stratification Score

Abstract Background Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. Methods In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58). Conclusion With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.

Echocardiography for the 10-year prediction of cardiomyopathy in long-term survivors of childhood cancer

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J.M Leerink ◽  
H.J.H Van Der Pal ◽  
E.A.M Feijen ◽  
P.G Meregalli ◽  
M.S Pourier ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Median Time ◽  
Cancer Diagnosis ◽  
Predictive Value ◽  
Validation Cohort ◽  
Funding Source ◽  
Derivation Cohort ◽  
After Cancer ◽  
Radiotherapy Dose

Abstract Background Childhood cancer survivors (CCS) treated with anthracyclines and/or chest-directed radiotherapy receive life-long echocardiographic surveillance to detect cardiomyopathy early. Current risk stratification and surveillance frequency recommendations are based on anthracycline- and chest-directed radiotherapy dose. We assessed the added prognostic value of an initial left ventricular ejection fraction (EF) measurement at >5 years after cancer diagnosis. Patients and methods Echocardiographic follow-up was performed in asymptomatic CCS from the Emma Children's Hospital (derivation; n=299; median time after diagnosis, 16.7 years [inter quartile range (IQR) 11.8–23.15]) and from the Radboud University Medical Center (validation; n=218, median time after diagnosis, 17.0 years [IQR 13.0–21.7]) in the Netherlands. CCS with cardiomyopathy at baseline were excluded (n=16). The endpoint was cardiomyopathy, defined as a clinically significant decreased EF (EF<40%). The predictive value of the initial EF at >5 years after cancer diagnosis was analyzed with multivariable Cox regression models in the derivation cohort and the model was validated in the validation cohort. Results The median follow-up after the initial EF was 10.9 years and 8.9 years in the derivation and validation cohort, respectively, with cardiomyopathy developing in 11/299 (3.7%) and 7/218 (3.2%), respectively. Addition of the initial EF on top of anthracycline and chest radiotherapy dose increased the C-index from 0.75 to 0.85 in the derivation cohort and from 0.71 to 0.92 in the validation cohort (p<0.01). The model was well calibrated at 10-year predicted probabilities up to 5%. An initial EF between 40–49% was associated with a hazard ratio of 6.8 (95% CI 1.8–25) for development of cardiomyopathy during follow-up. For those with a predicted 10-year cardiomyopathy probability <3% (76.9% of the derivation cohort and 74.3% of validation cohort) the negative predictive value was >99% in both cohorts. Conclusion The addition of the initial EF >5 years after cancer diagnosis to anthracycline- and chest-directed radiotherapy dose improves the 10-year cardiomyopathy prediction in CCS. Our validated prediction model identifies low-risk survivors in whom the surveillance frequency may be reduced to every 10 years. Calibration in both cohorts Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Dutch Heart Foundation

scholarly journals Clinical risk scores for the early prediction of severe outcomes in patients hospitalized for COVID-19

2021 ◽  
Author(s):  
Walter Ageno ◽  
◽  
Chiara Cogliati ◽  
Martina Perego ◽  
Domenico Girelli ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Predictive Value ◽  
Clinical Laboratory ◽  
Area Under The Curve ◽  
Orotracheal Intubation ◽  
Risk Scores ◽  
Algorithm Selection ◽  
Derivation Cohort ◽  
Neutrophil Lymphocyte Ratio ◽  
Predictive Risk

AbstractCoronavirus disease of 2019 (COVID-19) is associated with severe acute respiratory failure. Early identification of high-risk COVID-19 patients is crucial. We aimed to derive and validate a simple score for the prediction of severe outcomes. A retrospective cohort study of patients hospitalized for COVID-19 was carried out by the Italian Society of Internal Medicine. Epidemiological, clinical, laboratory, and treatment variables were collected at hospital admission at five hospitals. Three algorithm selection models were used to construct a predictive risk score: backward Selection, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. Severe outcome was defined as the composite of need for non-invasive ventilation, need for orotracheal intubation, or death. A total of 610 patients were included in the analysis, 313 had a severe outcome. The subset for the derivation analysis included 335 patients, the subset for the validation analysis 275 patients. The LASSO selection identified 6 variables (age, history of coronary heart disease, CRP, AST, D-dimer, and neutrophil/lymphocyte ratio) and resulted in the best performing score with an area under the curve of 0.79 in the derivation cohort and 0.80 in the validation cohort. Using a cut-off of 7 out of 13 points, sensitivity was 0.93, specificity 0.34, positive predictive value 0.59, and negative predictive value 0.82. The proposed score can identify patients at low risk for severe outcome who can be safely managed in a low-intensity setting after hospital admission for COVID-19.

scholarly journals 3124 Early Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG

2019 ◽  
Vol 3 (s1) ◽  
pp. 38-38
Author(s):  
Safa Kaleem ◽  
Christa B. Swisher
Keyword(s):  
Positive Predictive Value ◽  
Real Time ◽  
Predictive Value ◽  
Quantitative Eeg ◽  
University Hospital ◽  
Duke University ◽  
Study Population ◽  
Time To Detection ◽  
Sensitivity Specificity ◽  
Improve Patient

OBJECTIVES/SPECIFIC AIMS: 1. Determine positive predictive value, negative predictive value, sensitivity, and specificity of Neuro ICU nurse interpretation of real-time bedside qEEG. 2. Determine difference in time to detection of first seizure between Neuro ICU nurse qEEG interpretation and EEG fellow reads of cEEG. 3. Determine what qualities of seizures make detection by neuro ICU nurses more or less likely – e.g. duration of seizures, type of seizures, spatial extent of seizures. METHODS/STUDY POPULATION: Recruit neuro ICU nurses taking care of 150 patients admitted to the Neuro ICU at Duke University Hospital who are initiated on cEEG monitoring. Nurses will be consented for their participation in the study. Neuro ICU nurses will evaluate the qEE RESULTS/ANTICIPATED RESULTS: From literature estimates of a 20% seizure prevalence in critical care settings, we hope to have 30 patients with seizures and 120 without. Based on prior study in the Duke Neuro ICU, we hypothesize that Neuro ICU nurses will have sensitivity and DISCUSSION/SIGNIFICANCE OF IMPACT: This is the first prospective study of neuro ICU nurse interpretation of real-time bedside qEEG in patients with unknown NCSE/NCS presence. If nurse sensitivity, specificity, and positive predictive value are clinically useful, which we deem would be so at a sensitivity of 70% or greater, with acceptable false alarm rate, nurse readings of qEEG could significantly decrease the time to treatment of seizures in the Neuro ICU patient population, and perhaps could improve patient outcomes.

scholarly journals Bronchopulmonary dysplasia: a predictive scoring system for very low birth weight infants. A diagnostic accuracy study with prospective data collection

2021 ◽  
Author(s):  
Ikbel El Faleh ◽  
◽  
Mohamed Faouzi ◽  
Mark Adams ◽  
Roland Gerull ◽  
...  
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Bronchopulmonary Dysplasia ◽  
Scoring System ◽  
Very Low Birth Weight ◽  
Validation Cohort ◽  
Supplementary Oxygen ◽  
Derivation Cohort ◽  
National Cohort ◽  
Predictive Risk

AbstractOur aim was to develop and validate a predictive risk score for bronchopulmonary dysplasia (BPD), according to two clinically used definitions: 1. Need for supplementary oxygen during ≥ 28 cumulative days, BPD28, 2. Need for supplementary oxygen at 36 weeks postmenstrual age (PMA), BPD36. Logistic regression was performed in a national cohort (infants born in Switzerland with a birth weight < 1501 g and/or between 23 0/7 and 31 6/7 weeks PMA in 2009 and 2010), to identify predictors of BPD. We built the score as the sum of predicting factors, weighted according to their ORs, and analysed its discriminative properties by calculating the area under the ROC (receiver operating characteristic) curves (AUCs). This score was then applied to the Swiss national cohort from the years 2014–2015 to perform external validation. The incidence of BPD28 was 21.6% in the derivation cohort (n = 1488) and 25.2% in the validation cohort (n = 2006). The corresponding numbers for BPD36 were 11.3% and 11.1%, respectively. We identified gestational age, birth weight, antenatal corticosteroids, surfactant administration, proven infection, patent ductus arteriosus and duration of mechanical ventilation as independent predictors of BPD28. The AUCs of the BPD risk scores in the derivation cohort were 0.90 and 0.89 for the BPD28 and BPD36 definitions, respectively. The corresponding AUCs in the validation cohort were 0.92 and 0.88, respectively.Conclusion: This score allows for predicting the risk of a very low birth weight infant to develop BPD early in life and may be a useful tool in clinical practice and neonatal research. What is Known:• Many studies have proposed scoring systems to predict bronchopulmonary dysplasia (BPD).• Such a risk prediction may be important to identify high-risk patients for counselling parents, research purposes and to identify candidates for specific treatment. What is New:• A predictive risk score for BPD was developed and validated in a large national multicentre cohort and its performance assessed by two indices of accuracy.• The developed scoring system allows to predict the risk of BPD development early but also at any day of life with high validity.

“A COMPARATIVE STUDY OF RIPASA SCORE AND ALVARADO SCORE IN THE DIAGNOSIS OF ACUTE APPENDICITIS”

2021 ◽  
pp. 25-28
Author(s):  
M. Vijaya Kumar ◽  
Manasa Manasa
Keyword(s):  
Acute Appendicitis ◽  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Scoring System ◽  
Local Population ◽  
Scoring Systems ◽  
Signs And Symptoms ◽  
Alvarado Score ◽  
Predictive Values ◽  
Ripasa Score

Acute appendicitis is the most common condition encountered in the Emergency department .Alvarado and Modied Alvarado scores are the most commonly used scoring system used for diagnosing acute appendicitis.,but its performance has been found to be poor in certain population . Hence our aim was to compare the diagnostic accuracy of RIPASA and ALVARADO Scoring system and study and compare sensitivity, specicity and predictive values of these scoring systems. The study was conducted in Government district hospital Nandyal . We enrolled 176 patients who presented with RIF pain . Both RIPASA and ALVARADO were applied to them. Final diagnosis was conrmed either by CT scan, intra operative nding or post operative HPE report. Sensitivity,specicity, positive predictive value, negative predictive value, diagnostic accuracy was calculated both for RIPASA and ALVARADO. It was found that sensitivity and specicity of the RIPASA score in our study are 98.7% and 83.3%, respectively. PPV and NPV were 98.1% and 88.2% and sensitivity and specicity of the Alvardo score in our study are 94.3% and 83.3%, respectively. PPV and NPV were 98% and 62.5%.Diagnostic accuracy of RIPASA score and Alvarado score are 97% and 93% respectively. RIPASA is a more specic and accurate scoring system in our local population when compared to ALVARADO . It reduces the number of missed appendicitis cases and also convincingly lters out the group of patients that would need a CT scan for diagnosis (score 5-7.5 ) BACKGROUND: Acute appendicitis is one of the most commonly dealt surgical emergencies, with a lifetime prevalence rate of approximately 1 one in seven. The incidence is 1.5–1.9 per 1,000 in the male and female population, and is approximately 1.4 times greater in men than in women. Despite being a common problem, it remains a difcult diagnosis to establish, particularly among the young, the elderly and females of reproductive age, where a host of other genitourinary and gynaecological inammatory conditions can present with signs and symptoms that are 2 similar to those of acute appendicitis. A delay in performing an appendectomy in order to improve its diagnostic accuracy increases the risk of appendicular perforation and peritonitis, which in turn increases morbidity and mortality. A variable combination of clinical signs and symptoms has been used together with laboratory ndings in several scoring systems proposed for suggesting the probability of Acute Appendicitis and the possible subsequent management pathway. The Raja Isteri Pengiran Anak Saleha Appendicitis (RIPASA) and ALVARADO score are new diagnostic scoring systems developed for the diagnosis of Acute Appendicitis and has been shown to have signicantly higher sensitivity, specicity and diagnostic accuracy. AIMS AND OBJECTIVES PRIMARY OBJECT 1. To compare RIPASA Scoring system and ALVARADO Scoring system in terms of diagnostic accuracy in Acute Appendicitis. 2. To study and compare sensitivity, specicity and predictive values of above scoring systems. SECONDARY OBJECT 1. To study the rate of negative appendicectomy based on above scoring systems. CONCLUSION: The RIPASA score is a simple scoring system with high sensitivity and specicity for the diagnosis of acute appendicitis. The 14 clinical parameters are all present in a good clinical history and examination and can be easily and quickly applied. Therefore, a decision on the management can be made early. Although the RIPASA score was developed for the local population of Brunei, we believe that it should be applicable to other regions. The RIPASA score presents greater Diagnostic accuracy and Sensitivity and equal specicity as a diagnostic test compared to the Alvarado score and is helpful in making appropriate therapeutic decisions. In hospitals like ours, the diagnosis of AA relies greatly on the clinical evaluation performed by surgeons. An adequate clinical scoring system would avoid diagnostic errors, maintaining a satisfactory low rate of negative appendectomies by adequate patient stratication, while limiting patient exposure to ionizing radiation, since 21 there is an increased risk of developing cancer with computed tomography, particularly for the paediatric age group.

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