scholarly journals Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248438
Author(s):  
Jeffrey A. Kline ◽  
Carlos A. Camargo ◽  
D. Mark Courtney ◽  
Christopher Kabrhel ◽  
Kristen E. Nordenholz ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Emergency Care ◽  
Point Of Care ◽  
Unmet Need ◽  
Prediction Rule ◽  
Negative Likelihood Ratio ◽  
Prediction Score ◽  
Measured Temperature ◽  
Clinical Prediction ◽  
Patient Reported

Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79–0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8–96.3%), specificity of 20.0% (19.0–21.0%), negative likelihood ratio of 0.22 (0.19–0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.

scholarly journals Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments

2021 ◽  
Author(s):  
Jeffrey A. Kline ◽  
Carlos A. Camargo ◽  
D. Mark Courtney ◽  
Christopher Kabrhel ◽  
Kristen E. Nordenholz ◽  
...  
Keyword(s):  
Emergency Care ◽  
Point Of Care ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
The Novel ◽  
Validation Sample ◽  
Clinical Prediction ◽  
Clinical Criteria ◽  
Novel Coronavirus ◽  
Receiver Operating

AbstractObjectivesAccurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction rule for SARS-CoV-2 infection that uses simple criteria widely available at the point of care.MethodsData came from the Registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical predictors and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction rule was derived from a 50% random sample (n=9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables.ResultsMultivariable regression yielded a 13-variable score, which was simplified to 13-point rule: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n=9,975), the score produced an area under the receiver operating character curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified rule, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), likelihood ratio negative of 0.22 (0.19-0.26). Increasing points on the simplified rule predicted higher probability of infection (e.g., >75% probability with +5 or more points).ConclusionCriteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decision about isolation and testing at high throughput checkpoints.Key pointsQuestionCan clinical criteria, derived solely from interview and vital signs accurately estimate the probability of infection from the novel coronavirus (SARS-CoV-2) that causes COVID-19?FindingsFrom derivation sample (n=9,925), we derived a set of 13 clinical criteria that produced an area under the receiver operating characteristic curve of 0.80 (0.79-0.81) in a validation sample (n=9,925). At a score of zero, the simplified version of the criteria produced sensitivity of 95.6% (94.8 to 96.3%), and specificity of 20.0% (19.0 to 21.0%).MeaningClinical criteria can estimate the probability of SARS-CoV-2 infection.

scholarly journals Elevated triglyceride-to-HDL cholesterol ratio is an indicator for insulin resistance in middle-aged and elderly Taiwanese population: a cross-sectional study

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Wei-Chung Yeh ◽  
Yu-Chung Tsao ◽  
Wen-Cheng Li ◽  
I-Shiang Tzeng ◽  
Liang-Sien Chen ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Logistic Regression ◽  
Correlation Coefficient ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Multiple Logistic Regression ◽  
Clinical Prediction ◽  
Middle Aged ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation

Abstract Background Previous studies have reported that the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio could be a simple clinical indicator of insulin resistance (IR), but the results indicated that there were heterogeneities between different ethnicities. We aimed to investigate the association between TG/HDL-C and IR (as measured by homeostasis model assessment of IR [HOMA-IR]), and establish a clinical prediction rule for IR in middle-aged and elderly Taiwanese. Methods A total of 398 subjects were recruited, and each subject completed a questionnaire that included personal and medical history data, and underwent anthropometric measurement and blood sampling. IR was defined as HOMA-IR index value ≥2.0. Chi-squared test, independent two-sample t-test, Pearson’s correlation coefficient, and multiple logistic regression were used to evaluate the association between IR and TG/HDL-C ratio. A receiver operating characteristic (ROC) analysis was conducted to evaluate the ability of the developed clinical prediction rule to correctly discriminate between subjects of IR positive and IR negative groups. Results A significant association between IR and TG/HDL-C ratio was identified with a Pearson’s correlation coefficient of 0.35 (p-value< 0.001). In multiple logistic regression, high BMI (OR = 1.23; 95% C.I. = 1.13–1.33), hypertension (OR = 1.90; 95% C.I. = 1.12–3.21), diabetes mellitus (OR = 5.44; 95% C.I. = 2.93–10.08) and high TG/HDL ratio (OR = 1.45; 95% C.I. = 1.23–1.72) were significantly associated with the risk of elevated HOMA-IR. The area under ROC curves for TG/HDL-C ratio was 0.729 and the optimal threshold value was 2.197 where the corresponding of sensitivity and specificity were 72.4 and 65.1%. Conclusions Our findings showed that the elevated TG/HDL-C ratio was significantly associated with IR and could be used as an indicator of IR among the middle-aged and elderly population in Taiwan. It is clinically available, thus eliminating any additional costs. Future research is warranted to investigate the use of TG/HDL-C ratio combined with other risk factors for predicting IR under diverse ethnic backgrounds.

scholarly journals Examining patient distress and unmet need for support across UK renal units with varying models of psychosocial care delivery: a cross-sectional survey study

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e036931
Author(s):  
Maaike Seekles ◽  
Paula Ormandy ◽  
Daiga Kamerāde
Keyword(s):  
Logistic Regression ◽  
Service Provision ◽  
Care Delivery ◽  
Unmet Need ◽  
Survey Study ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Psychosocial Service ◽  
Patient Reported ◽  
Need For Support

ObjectiveTo examine in-centre haemodialysis patients’ emotional distress and need for support across UK renal units with varying models of psychosocial service provision.DesignThe study used a cross-sectional survey design. Logistic regression analysis was used to examine patient distress, as captured by the Distress Thermometer, and need for support, across different renal units.SettingSeven renal units across England, Wales and Scotland. The units were purposively selected so that varying workforce models of renal psychosocial services were represented.ParticipantsIn total, 752 patients were on dialysis in the participating centres on the days of data collection. All adult patients, who could understand English, and with capacity (as determined by the nurse in charge), were eligible to participate in the study. The questionnaire was completed by 509 patients, resulting in an overall response rate of 67.7%.Outcome measuresThe prevalence of distress and patient-reported need for support.ResultsThe results showed that 48.9% (95% CI 44.5 to 53.4) of respondents experienced distress. A significant association between distress and models of renal psychosocial service provision was found (χ2(6)=15.05, p=0.019). Multivariable logistic regression showed that patients in units with higher total psychosocial staffing ratios (OR 0.65 (95% CI 0.47 to 0.89); p=0.008) and specifically higher social work ratios (OR 0.49 (95% CI 0.33 to 0.74); p=0.001) were less likely to experience distress, even after controlling for demographic variables. In addition, a higher patient-reported unmet need for support was found in units where psychosocial staffing numbers are low or non-existent (χ2(6)=37.80, p<0.001).ConclusionsThe novel findings emphasise a need for increased incorporation of dedicated renal psychosocial staff into the renal care pathway. Importantly, these members of staff should be able to offer support for psychological as well as practical and social care-related issues.

scholarly journals Peran Metode Noninvasif dalam Mendeteksi Varises Esofagus Signifikan pada Anak dengan Hipertensi Portal

Sari Pediatri ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 182
Author(s):  
William Jayadi Iskandar ◽  
Hanifah Oswari
Keyword(s):  
Risk Score ◽  
Full Text ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Prediction Score ◽  
Cochrane Library ◽  
Clinical Prediction

Latar belakang. Esofagogastroduodenoskopi (EGD) penting dilakukan pada anak dengan hipertensi portal untuk mendeteksi varises esofagus signifikan (derajat II, III, atau stigmata perdarahan), tetapi prosedur ini invasif dan traumatik.Tujuan. Mengetahui kemampuan metode noninvasif dibandingkan EGD dalam menentukan varises esofagus signifikan pada anak dengan hipertensi portal.Metode. Penelusuran literatur melalui Pubmed, Scopus, dan Cochrane Library dilakukan pada tanggal 25 Juni 2019. Kriteria inklusi adalah subyek anak hingga berusia 18 tahun, dipublikasi dalam 5 tahun terakhir, berbahasa Inggris, dan tersedia full text. Kriteria eksklusi adalah subyek pascaoperasi atau tidak membahas metode noninvasif. Artikel terpilih kemudian dinilai secara kritis.Hasil. Tiga buah artikel penelitian ditemukan, terdiri atas sebuah telaah sistematik dan dua buah penelitian observasional. Metode noninvasif yang memiliki sensitivitas tinggi adalah clinical prediction rule (80%), varices prediction rule (80%), dan risk score (85,7%). Metode yang memiliki spesifisitas tinggi adalah King’s variceal prediction score (72,7%).Kesimpulan. Metode noninvasif dapat digunakan untuk memilih prioritas pasien anak dengan hipertensi portal yang perlu dilakukan EGD untuk menentukan varises esofagus signifikan.

scholarly journals Predictive Factors and Clinical Prediction Score for Serious Intracranial Causes in Acute Nontraumatic Headache at an Emergency Department

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Siriwimon Tantarattanapong ◽  
Lalita Chalongkulasak
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Predictive Factors ◽  
Neurological Deficit ◽  
Abrupt Onset ◽  
Prediction Score ◽  
Clinical Prediction ◽  
Multivariate Logistic Regression ◽  
Alteration Of Consciousness ◽  
Significant Factors

Purpose. The objectives of this study were to investigate the predictive factors and develop a clinical prediction score to identify serious intracranial causes in acute nontraumatic headache (NTH). Methods. A retrospective chart review study was conducted from 2013 to 2018 in acute NTH patients who visited the emergency department. The patients were divided into serious intracranial headache and nonserious intracranial headache groups. The two groups were compared in regard to the baseline characteristics, clinical presentation, physical examination, investigation, and diagnosis. The significant factors to predict a serious intracranial cause were examined using a multivariate logistic regression model. The coefficients from the multivariate logistic regression were used to plot the receiver operating characteristic curve to develop a clinical prediction score. Results. From 2,372 patients, 454 met the inclusion criteria. Of the 454 patients with acute NTH, 88 (19.4%) patients were serious intracranial cause. The seven significant factors that predicted serious intracranial cause were abrupt onset (odds ratio (OR) 7.96, 95% confidence interval (CI) 2.77‒22.91), awakening pain (OR 3.14, 95% CI 4.15–6.82), duration of headache >1 week (OR 10.59, 95% CI 2.9–38.7), fever (OR 6.01, 95% CI 2.07–17.46), worst headache ever (OR 12.95, 95% CI 5.69–29.45), alteration of consciousness (OR 13.55, 95% CI 2.07‒88.88), and localizing neurological deficit (OR 5.28, 95% CI 1.6‒17.46). A score ≥3 out of 10 points of the clinical prediction score was likely to identify a serious intracranial cause of acute NTH with a sensitivity and specificity of 87.50% (95% CI 78.73–93.59%) and 87.70% (95% CI 83.90–90.89%), respectively. The area under the curve was 0.933. Conclusion. Abrupt onset, awakening pain, duration of headache >1 week, fever, worst headache ever, alteration of consciousness, and localizing neurological deficit were the significant predictive factors for serious intracranial cause of acute NTH.

Development of a clinical prediction score for congestive heart failure diagnosis in the emergency care setting: The Brest score

2016 ◽  
Vol 34 (12) ◽  
pp. 2277-2283 ◽  
Author(s):  
Adrien Basset ◽  
Emmanuel Nowak ◽  
Philippe Castellant ◽  
Christophe Gut-Gobert ◽  
Grégoire Le Gal ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Emergency Care ◽  
Care Setting ◽  
Prediction Score ◽  
Failure Diagnosis ◽  
Clinical Prediction ◽  
Heart Failure Diagnosis

Derivation and Validation of a Clinical Prediction Score for Isolation of Inpatients With Suspected Pulmonary Tuberculosis

2008 ◽  
Vol 29 (10) ◽  
pp. 927-932 ◽  
Author(s):  
Kara S. Rakoczy ◽  
Stuart H. Cohen ◽  
Hien H. Nguyen
Keyword(s):  
Validation Cohort ◽  
Multivariable Analysis ◽  
Prediction Rule ◽  
Prediction Score ◽  
Risk Scores ◽  
Clinical Prediction ◽  
Foreign Born ◽  
Factors Associated ◽  
Suspected Tuberculosis ◽  
Immunocompromised State

Background.The use of a clinical prediction score to improve the practice of instituting airborne-transmission precautions in patients with suspected tuberculosis holds promise for increasing appropriate isolation and decreasing unnecessary isolation. The objective of this study was to derive and validate a clinical prediction score for patients with suspected tuberculosis.Methods.We used a case—control study design to evaluate differences between patients with a diagnosis of tuberculosis and those placed under airborne precautions who had negative culture results. We developed risk scores based on a multivariable analysis of independently significant factors associated with tuberculosis. Subsequently, we evaluated the sensitivity and specificity of the score in a separate (validation) cohort of patients.Results.Within our population, we found 4 clinical factors associated with tuberculosis: chronic symptoms (odds ratio [OR], 10.2 [95% confidence interval {CI}, 2.95-35.4]), upper lobe disease on chest radiograph (OR, 5.27 [95% CI, 1.6-17.23]), foreign-born status (OR, 7.01 [95% CI, 2.1-23.8]), and immunocompromised state other than human immunodeficiency virus infection (OR, 8.14 [95% CI, 2.08-31.8]). Shortness of breath (OR, 0.13 [95% CI, 0.04-0.45]) was found to be associated with non-tuberculosis diagnoses and considered a negative predictor in the model. Using a cut-off point to maximize sensitivity, we applied the prediction rule to the validation cohort, resulting in a sensitivity of 97% and a specificity of 42%.Conclusion.The tuberculosis prediction rule derived from our patient population could improve utilization of airborne precautions. Clinical prediction rules continue to show their utility for improvement in isolation practices in different demographic areas.

scholarly journals Early Detection of Diabetic Peripheral Neuropathy: A Focus on Small Nerve Fibres

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 165
Author(s):  
Jamie Burgess ◽  
Bernhard Frank ◽  
Andrew Marshall ◽  
Rashaad S. Khalil ◽  
Georgios Ponirakis ◽  
...  
Keyword(s):  
Peripheral Neuropathy ◽  
Nerve Fibre ◽  
Diabetic Peripheral Neuropathy ◽  
Point Of Care ◽  
Unmet Need ◽  
Screening Tools ◽  
Nerve Fibres ◽  
Diagnostic Laboratory ◽  
Corneal Confocal Microscopy ◽  
Small Nerve

Diabetic peripheral neuropathy (DPN) is the most common complication of both type 1 and 2 diabetes. As a result, neuropathic pain, diabetic foot ulcers and lower-limb amputations impact drastically on quality of life, contributing to the individual, societal, financial and healthcare burden of diabetes. DPN is diagnosed at a late, often pre-ulcerative stage due to a lack of early systematic screening and the endorsement of monofilament testing which identifies advanced neuropathy only. Compared to the success of the diabetic eye and kidney screening programmes there is clearly an unmet need for an objective reliable biomarker for the detection of early DPN. This article critically appraises research and clinical methods for the diagnosis or screening of early DPN. In brief, functional measures are subjective and are difficult to implement due to technical complexity. Moreover, skin biopsy is invasive, expensive and lacks diagnostic laboratory capacity. Indeed, point-of-care nerve conduction tests are convenient and easy to implement however questions are raised regarding their suitability for use in screening due to the lack of small nerve fibre evaluation. Corneal confocal microscopy (CCM) is a rapid, non-invasive, and reproducible technique to quantify small nerve fibre damage and repair which can be conducted alongside retinopathy screening. CCM identifies early sub-clinical DPN, predicts the development and allows staging of DPN severity. Automated quantification of CCM with AI has enabled enhanced unbiased quantification of small nerve fibres and potentially early diagnosis of DPN. Improved screening tools will prevent and reduce the burden of foot ulceration and amputations with the primary aim of reducing the prevalence of this common microvascular complication.

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