scholarly journals Development and validation of a clinical risk score to predict SARS-CoV-2 infection in emergency department patients: The CCEDRRN COVID-19 Infection Score (CCIS)

2021 ◽  
Author(s):  
Andrew D. McRae ◽  
Corinne M. Hohl ◽  
Rhonda J. Rosychuk ◽  
Shabnam Vatanpour ◽  
Gelareh Ghaderi ◽  
...  
Keyword(s):  
Emergency Department ◽  
Risk Score ◽  
Health Research ◽  
Disease Incidence ◽  
Empiric Therapy ◽  
Health Science ◽  
Superior Performance ◽  
Manuscript Preparation ◽  
Continuous Variables ◽  
Derivation Cohort

Background Clinicians face decisions around the need for severe acute respiratory coronavirus 2 (SARS-CoV-2) testing, patient isolation, and empiric therapy when patients arrive in acute care hospitals. Our objective was to develop a risk score that can accurately quantify a patient's probability of SARS-CoV-2 infection. Methods This observational study enrolled consecutive adults who presented to the emergency departments of 32 hospitals participating in Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) and were tested for SARS-CoV-2. We divided our study population by randomly assigning study sites into derivation (75%) and validation (25%) cohorts. We pre-specified predictors and used multiple imputation for variables with incomplete data. In the derivation cohort, we fit models using logistic regression, with spline functions for continuous variables, to predict the primary outcome of a positive SARS-CoV-2 nucleic acid test. We used a fast step-down procedure to select a concise model. The final reduced model had points allocated to each variable based on their predictive strength. We then validated the model in the geographically distinct validation cohort. Findings We derived a ten-item CCEDRRN COVID-19 Infection Score using data from 21,743 patients. This score included variables from history and physical examination, and an indicator of local disease incidence. The score had a C-statistic of 0.838 with excellent calibration. We externally validated the rule in 5,295 patients. The score maintained excellent discrimination and calibration, and had superior performance compared to another previously published risk score. Interpretation The CCEDRRN COVID-19 Infection Score uses clinical characteristics and publicly available indicators of disease incidence to quantify a patient's probability of SARS-CoV-2 infection. The score can identify patients at sufficiently high risk of SARS-CoV-2 infection to warrant isolation and empiric therapy prior to test confirmation, while also identifying patients at sufficiently low risk of infection that they may not need testing. Funding The network is funded by the Canadian Institutes of Health Research (447679), BC Academic Health Science Network Society, BioTalent Canada, Genome BC (COV024; VAC007), Ontario Ministry of Colleges and Universities (C-655-2129), the Saskatchewan Health Research Foundation (5357) and the Fondation CHU de Quebec (Octroi #4007). These organizations are not-for-profit, and had no role in study conduct, analysis, or manuscript preparation.

scholarly journals CCEDRRN COVID-19 Infection Score (CCIS): development and validation in a Canadian cohort of a clinical risk score to predict SARS-CoV-2 infection in patients presenting to the emergency department with suspected COVID-19

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e055832
Author(s):  
Andrew D McRae ◽  
Corinne M Hohl ◽  
Rhonda Rosychuk ◽  
Shabnam Vatanpour ◽  
Gelareh Ghaderi ◽  
...  
Keyword(s):  
Emergency Department ◽  
Nucleic Acid ◽  
Risk Score ◽  
Emergency Departments ◽  
Disease Incidence ◽  
Empirical Therapy ◽  
Superior Performance ◽  
Clinical Risk Score ◽  
Clinical Risk ◽  
Test Result

ObjectivesTo develop and validate a clinical risk score that can accurately quantify the probability of SARS-CoV-2 infection in patients presenting to an emergency department without the need for laboratory testing.DesignCohort study of participants in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry. Regression models were fitted to predict a positive SARS-CoV-2 test result using clinical and demographic predictors, as well as an indicator of local SARS-CoV-2 incidence.Setting32 emergency departments in eight Canadian provinces.Participants27 665 consecutively enrolled patients who were tested for SARS-CoV-2 in participating emergency departments between 1 March and 30 October 2020.Main outcome measuresPositive SARS-CoV-2 nucleic acid test result within 14 days of an index emergency department encounter for suspected COVID-19 disease.ResultsWe derived a 10-item CCEDRRN COVID-19 Infection Score using data from 21 743 patients. This score included variables from history and physical examination and an indicator of local disease incidence. The score had a c-statistic of 0.838 with excellent calibration. We externally validated the rule in 5295 patients. The score maintained excellent discrimination and calibration and had superior performance compared with another previously published risk score. Score cut-offs were identified that can rule-in or rule-out SARS-CoV-2 infection without the need for nucleic acid testing with 97.4% sensitivity (95% CI 96.4 to 98.3) and 95.9% specificity (95% CI 95.5 to 96.0).ConclusionsThe CCEDRRN COVID-19 Infection Score uses clinical characteristics and publicly available indicators of disease incidence to quantify a patient’s probability of SARS-CoV-2 infection. The score can identify patients at sufficiently high risk of SARS-CoV-2 infection to warrant isolation and empirical therapy prior to test confirmation while also identifying patients at sufficiently low risk of infection that they may not need testing.Trial registration numberNCT04702945.

scholarly journals A Peri-Implant Disease Risk Score for Patients with Dental Implants: Validation and the Influence of the Interval between Maintenance Appointments

2019 ◽  
Vol 8 (2) ◽  
pp. 252 ◽  
Author(s):  
Miguel de Araújo Nobre ◽  
Francisco Salvado ◽  
Paulo Nogueira ◽  
Evangelista Rocha ◽  
Peter Ilg ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Disease Risk ◽  
Disease Incidence ◽  
Risk Groups ◽  
Risk Profile ◽  
Moderate Risk ◽  
C Statistic ◽  
Disease Risk Score ◽  
Very High

Background: There is a need for tools that provide prediction of peri-implant disease. The purpose of this study was to validate a risk score for peri-implant disease and to assess the influence of the recall regimen in disease incidence based on a five-year retrospective cohort. Methods: Three hundred and fifty-three patients with 1238 implants were observed. A risk score was calculated from eight predictors and risk groups were established. Relative risk (RR) was estimated using logistic regression, and the c-statistic was calculated. The effect/impact of the recall regimen (≤ six months; > six months) on the incidence of peri-implant disease was evaluated for a subset of cases and matched controls. The RR and the proportional attributable risk (PAR) were estimated. Results: At baseline, patients fell into the following risk profiles: low-risk (n = 102, 28.9%), moderate-risk (n = 68, 19.3%), high-risk (n = 77, 21.8%), and very high-risk (n = 106, 30%). The incidence of peri-implant disease over five years was 24.1% (n = 85 patients). The RR for the risk groups was 5.52 (c-statistic = 0.858). The RR for a longer recall regimen was 1.06, corresponding to a PAR of 5.87%. Conclusions: The risk score for estimating peri-implant disease was validated and showed very good performance. Maintenance appointments of < six months or > six months did not influence the incidence of peri-implant disease when considering the matching of cases and controls by risk profile.

scholarly journals Mortality risk prediction in lupus patients complicated with invasive infection in the emergency department: LUPHAS score

2019 ◽  
Vol 11 ◽  
pp. 1759720X1988555 ◽  
Author(s):  
Wanlong Wu ◽  
Jun Ma ◽  
Yuhong Zhou ◽  
Chao Tang ◽  
Feng Zhao ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Risk Score ◽  
Roc Curve ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Invasive Infection ◽  
Roc Curve Analysis ◽  
All Cause Mortality ◽  
Qsofa Score

Background: Infection remains a major cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). This study aimed to establish a clinical prediction model for the 3-month all-cause mortality of invasive infection events in patients with SLE in the emergency department. Methods: SLE patients complicated with invasive infection admitted into the emergency department were included in this study. Patient’s demographic, clinical, and laboratory characteristics on admission were retrospectively collected as baseline data and compared between the deceased and the survivors. Independent predictors were identified by multivariable logistic regression analysis. A prediction model for all-cause mortality was established and evaluated by receiver operating characteristic (ROC) curve analysis. Results: A total of 130 eligible patients were collected with a cumulative 38.5% 3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure Assessment (qSOFA) score, and age at baseline were independent predictors for all-cause mortality (LUPHAS). In contrast, a history of hydroxychloroquine use was protective. In a combined, odds ratio-weighted LUPHAS scoring system (score 3–22), patients were categorized to three groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk (score 16–22), with mortalities of 4.9% (2/41), 45.9% (28/61), and 78.3% (18/23) respectively. ROC curve analysis indicated that a LUPHAS score could effectively predict all-cause mortality [area under the curve (AUC) = 0.86, CI 95% 0.79–0.92]. In addition, LUPHAS score performed better than the qSOFA score alone (AUC = 0.69, CI 95% 0.59–0.78), or CURB-65 score (AUC = 0.69, CI 95% 0.59–0.80) in the subgroup of lung infections ( n = 108). Conclusions: Based on a large emergency cohort of lupus patients complicated with invasive infection, the LUPHAS score was established to predict the short-term all-cause mortality, which could be a promising applicable tool for risk stratification in clinical practice.

scholarly journals The REDS score: a new scoring system to risk-stratify emergency department suspected sepsis: a derivation and validation study

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e030922 ◽  
Author(s):  
Narani Sivayoham ◽  
Lesley A Blake ◽  
Shafi E Tharimoopantavida ◽  
Saad Chughtai ◽  
Adil N Hussain ◽  
...  
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Risk Category ◽  
Suspected Sepsis ◽  
Suspected Infection ◽  
Derivation Cohort ◽  
High Risk Category ◽  
Component Scores

ObjectiveTo derive and validate a new clinical prediction rule to risk-stratify emergency department (ED) patients admitted with suspected sepsis.DesignRetrospective prognostic study of prospectively collected data.SettingED.ParticipantsPatients aged ≥18 years who met two Systemic Inflammatory Response Syndrome criteria or one Red Flag sepsis criteria on arrival, received intravenous antibiotics for a suspected infection and admitted.Primary outcome measureIn-hospital all-cause mortality.MethodThe data were divided into derivation and validation cohorts. The simplified-Mortality in Severe Sepsis in the ED score and quick-SOFA scores, refractory hypotension and lactate were collectively termed ‘component scores’ and cumulatively termed the ‘Risk-stratification of ED suspected Sepsis (REDS) score’. Each patient in the derivation cohort received a score (0–3) for each component score. The REDS score ranged from 0 to 12. The component scores were subject to univariate and multivariate logistic regression analyses. The receiver operator characteristic (ROC) curves for the REDS and the components scores were constructed and their cut-off points identified. Scores above the cut-off points were deemed high-risk. The area under the ROC (AUROC) curves and sensitivity for mortality of the high-risk category of the REDS score and component scores were compared. The REDS score was internally validated.Results2115 patients of whom 282 (13.3%) died in hospital. Derivation cohort: 1078 patients with 140 deaths (13%). The AUROC curve with 95% CI, cut-off point and sensitivity for mortality (95% CI) of the high-risk category of the REDS score were: derivation: 0.78 (0.75 to 0.80); ≥3; 85.0 (78 to 90.5). Validation: 0.74 (0.71 to 0.76); ≥3; 84.5 (77.5 to 90.0). The AUROC curve and the sensitivity for mortality of the REDS score was better than that of the component scores. Specificity and mortality rates for REDS scores of ≥3, ≥5 and ≥7 were 54.8%, 88.8% and 96.9% and 21.8%, 36.0% and 49.1%, respectively.ConclusionThe REDS score is a simple and objective score to risk-stratify ED patients with suspected sepsis.

scholarly journals Skor prediksi sindrom disfungsi multi-organ pada pasien multitrauma

2016 ◽  
Vol 8 (2) ◽  
Author(s):  
Leo Rendy ◽  
Heber B. Sapan ◽  
Laurens T. B. Kalesaran ◽  
Julius H. Lolombulan
Keyword(s):  
At Risk ◽  
Emergency Department ◽  
Injury Severity Score ◽  
Sequential Organ Failure Assessment ◽  
Organ Failure ◽  
Risk Score ◽  
Severity Score ◽  
Injury Severity ◽  
Failure Assessment ◽  
The Mean

Abstract: Multiple organ dysfunction syndrome (MODS) in patients with major trauma remains to be frequent and devastating complication during clinical course in emergency department and intensive care unit (ICU). The ability to easily and accurately identify patients at risk for MODS postinjury especially in multitrauma cases would be very valuable. This study aimed to construct an instrument for prediction of the development of MODS in adult multitrauma patients using clinical and laboratory data available in the first day at prahospital and emergency department (hospital) setting. This was a prospective study. Samples were adult multitrauma patients with Injury Severity Score (ISS) ≥16, aged 16-65 years old, admitted to 4 academic Level-I trauma center from September 2014 to September 2015. Sequential organ failure assessment (SOFA) score was used to determine MODS during hospitalization. A risk score created from the final regression model consisted of significant variables as MODS predictor. The results showed that there were 98 multitrauma patients as samples. The mean age was 35.2 years old; mostly male (85.71%); the mean of ISS was 23.6; mostly (76.53%) were caused by blunt injury mechanism. MODS was encountered in 43 patients (43.87%). The prediction risk score consists of Revised Trauma Score (RTS) (<7.25) and serum lactate level ≥2 mmol/L. This study also verified several independent risk factors for post multitrauma MODS, such as ISS >25, presence of SIRS, shock grade 2 or more, and white blood cell count >12,000/mm3. Conclusion: We derived a novel, simple, and applicable instrument to predict MODS in adult following multitrauma. The use of this scoring system may allow early identification of multitrauma patients who are at risk for MODS and result in more aggressive targeted resuscitation and better referral allocation based on regional trauma system.Keywords: MODS, multitrauma, emergency department, MODS prediction scoreAbstrak: Sindrom disfungsi multi-organ (MODS) merupakan komplikasi buruk yang sering terjadi sepanjang perjalanan klinis pasien trauma mayor di Unit Gawat Darurat (UGD) maupun di ruang perawatan intensif. Suatu nilai patokan yang dapat memprediksi MODS pascatrauma secara akurat sejak dini tentunya sangat berharga bagi tatalaksana pasien terutama pada kasus multitrauma. Penelitian ini bertujuan untuk membuat suatu instrumen yang dapat memrediksi perkembangan MODS pada pasien dewasa multitrauma dengan menggunakan data klinis dan laboratorium yang tersedia pada 24 jam pertama pasca trauma pada seting fase prahospital maupun di fase hospital sejak di UGD. Jenis penelitian ini prospektif, mengumpulkan pasien multitrauma dengan Injury Severity Score (ISS) ≥16, rentang usia 16-65 tahun, di 4 pusat trauma level-1 rumah sakit pendidikan selama 1 tahun (September 2014-2015). Dilakukan pencatatan data klinis dan laboratorium sesuai perkembangan pasien. Skor sequential organ failure assessment (SOFA) digunakan untuk menentukan adanya MODS selama perawatan. Skor prediksi dibuat dengan membangun model regresi logistik yang signifikan untuk memrediksi terjadinya MODS pasca multitrauma. Hasil penelitian mendapatkan 98 sampel multitrauma yang memenuhi kriteria inklusi dengan rerata usia 35,2 tahun, sebagian besar laki-laki (85,71%) dengan rerata ISS 23,6, dan disebabkan oleh trauma tumpul (76,53%). MODS terjadi pada 43 pasien (43,87%). Skor prediksi terdiri dari RTS dengan (cut off point 7,25) dan kadar laktat serum (cut off point 3,44 mmol/mL). Penelitian ini juga memverifikasi beberapa faktor risiko individual terjadinya MODS pasca multitrauma yaitu ISS>25, adanya SIRS, syok derajat 2 atau lebih, dan leukositosis >12.000. Simpulan: Kami melaporkan instrumen baru yang praktis untuk memrediksi MODS pada pasien multitrauma dewasa. Skor ini memungkinkan identifikasi dini pasien trauma yang berisiko akan mengalami MODS sehingga dapat menjadi tanda alarm dilakukannya resusitasi yang lebih agresif dan tepat serta alokasi rujukan pasien yang lebih efisien berdasarkan sistem trauma regional.Kata kunci: MODS, multitrauma, UGD, skor prediksi MODS

scholarly journals The SMART Framework: Integration of Citizen Science, Community-Based Participatory Research, and Systems Science for Population Health Science in the Digital Age (Preprint)

2019 ◽  
Author(s):  
Tarun Reddy Katapally
Keyword(s):  
Big Data ◽  
Population Health ◽  
Citizen Science ◽  
Participatory Research ◽  
Health Research ◽  
Community Based Participatory Research ◽  
Health Science ◽  
Systems Science ◽  
Community Based ◽  
Science Community

UNSTRUCTURED Citizen science enables citizens to actively contribute to all aspects of the research process, from conceptualization and data collection, to knowledge translation and evaluation. Citizen science is gradually emerging as a pertinent approach in population health research. Given that citizen science has intrinsic links with community-based research, where participatory action drives the research agenda, these two approaches could be integrated to address complex population health issues. Community-based participatory research has a strong record of application across multiple disciplines and sectors to address health inequities. Citizen science can use the structure of community-based participatory research to take local approaches of problem solving to a global scale, because citizen science emerged through individual environmental activism that is not limited by geography. This synergy has significant implications for population health research if combined with systems science, which can offer theoretical and methodological strength to citizen science and community-based participatory research. Systems science applies a holistic perspective to understand the complex mechanisms underlying causal relationships within and between systems, as it goes beyond linear relationships by utilizing big data–driven advanced computational models. However, to truly integrate citizen science, community-based participatory research, and systems science, it is time to realize the power of ubiquitous digital tools, such as smartphones, for connecting us all and providing big data. Smartphones have the potential to not only create equity by providing a voice to disenfranchised citizens but smartphone-based apps also have the reach and power to source big data to inform policies. An imminent challenge in legitimizing citizen science is minimizing bias, which can be achieved by standardizing methods and enhancing data quality—a rigorous process that requires researchers to collaborate with citizen scientists utilizing the principles of community-based participatory research action. This study advances SMART, an evidence-based framework that integrates citizen science, community-based participatory research, and systems science through ubiquitous tools by addressing core challenges such as citizen engagement, data management, and internet inequity to legitimize this integration.

Abstract 17048: Dederivation and Validation of a Simple Heart Risk Score in 608,544 Thai Adults: HCUR Study

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Parinya Chamnan ◽  
Weera Mahawanakul ◽  
Prasert Boongird ◽  
Wannee Nitiyanant ◽  
Wichai Aekplakorn ◽  
...  
Keyword(s):  
General Population ◽  
Risk Score ◽  
Retrospective Cohort ◽  
Validation Cohort ◽  
Information Criteria ◽  
Global Model ◽  
Derivation Cohort ◽  
Risk Equation ◽  
Risk Algorithm ◽  
Incident Cases

Introduction: Most heart risk prediction equations were developed in Western populations. These risk scores are likely to perform less well in Asian populations, who have different background risk. Hypothesis: This study aimed to develop and validate a new risk algorithm for estimating 5-year risk of developing coronary heart disease (CHD) in a large retrospective cohort of Thai general population. Methods: This retrospective cohort was derived from the linkage of 2006 health checks data with diagnostic information from electronic health records of 608,544 men and women aged 20 years and above residing in Ubon Ratchathani. It was randomly and evenly divided into the derivation and validation cohorts. An outcome of interest was first recorded diagnosis of CHD over a period of 6 years between January 2006 and December 2012. A Cox proportional hazards model was used to estimate effects of risk factors on CHD risk and to derive a risk equation in the derivation cohort. Measures of discrimination, global model fits and calibration were calculated in the validation cohort. Results: The derivation cohort comprised of 304,272 individuals, who contributed 1,757,369 person-years of follow-up and 1,272 incident cases of CHD, while the validation cohort comprised of 304,272 individuals (1,757,312 person-years), with 1,290 incident cases of stroke. The risk equation was 0.0580 x Age (years) + 0.5739 x Sex (Male=1) + 0.3850 x Hypertension (present=1) + 0.7080 x Diabetes (present=1) + 0.0386 x Body mass index (kg/m 2 ) + 0.2117 x Central obesity (present=1) - 0.1389 (if exercise 1-2 days/week) or -0.3975 (if exercise 3-5 days/week) or - 0.5598 (if exercise >5 days/week). The stroke risk equation had a reasonably good discriminatory ability in the validation cohort with the area under the receiver operating characteristic curve of 0.790 (95%CI 0.779-0.801). The risk equation had good global model fit as measured by Bayesian information criteria. The Gronnesby and Borgan test showed good calibration, with chi-square statistic of 809.45 (p<0.001). Conclusions: This simple heart risk score is the first risk algorithm to estimate the 5-year risk of CHD in a Thai general population. The risk score does not need laboratory tests and can therefore be used in clinical settings and by the public.

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