Identifying Persistent Postconcussion Symptom Risk in a Pediatric Sports Medicine Clinic

Background: Although most children report symptom resolution within a month of a concussion, some patients experience persistent postconcussion symptoms (PPCS) that continues for more than 1 month. Identifying patients at risk for PPCS soon after an injury can provide useful clinical information. Purpose: To determine if the Predicting Persistent Post-concussive Problems in Pediatrics (5P) clinical risk score, an emergency department (ED)–derived and validated tool, is associated with developing PPCS when obtained in a primary care sports concussion setting. Study Design: Cohort study; Level of evidence, 3. Methods: We conducted a study of patients seen at a pediatric sports medicine concussion clinic between May 1, 2013, and October 1, 2017, who were <19 years of age and evaluated within 10 days of a concussion. The main outcome was PPCS, defined as symptoms lasting >28 days. Nine variables were used to calculate the 5P clinical risk score, and we assessed the association between the 5P clinical risk score and PPCS occurrence. The secondary outcome was total symptom duration. Results: We examined data from 230 children (mean age, 14.8 ± 2.5 years; 50% female; mean time from injury to clinical assessment, 5.6 ± 2.7 days). In univariable analyses, a greater proportion of those who developed PPCS reported feeling slowed down (72% vs 44%, respectively; P < .001), headache (94% vs 72%, respectively; P < .001), sensitivity to noise (71% vs 43%, respectively; P < .001), and fatigue (82% vs 51%, respectively; P < .001) and committed ≥4 errors in tandem stance (33% vs 7%, respectively; P < .001) than those who did not. Higher 5P clinical risk scores were associated with increased odds of developing PPCS (adjusted odds ratio [OR], 1.62 [95% CI, 1.30-2.02]) and longer symptom resolution times (β = 8.40 [95% CI, 3.25-13.50]). Among the individual participants who received a high 5P clinical risk score (9-12), the majority (82%) went on to experience PPCS. The area under the curve for the 5P clinical risk score was 0.75 (95% CI, 0.66-0.84). After adjusting for the effect of covariates, fatigue (adjusted OR, 2.93) and ≥4 errors in tandem stance (adjusted OR, 7.40) were independently associated with PPCS. Conclusion: Our findings extend the potential use for an ED-derived clinical risk score for predicting the PPCS risk into the sports concussion clinic setting. While not all 9 predictor variables of the 5P clinical risk score were independently associated with the PPCS risk in univariable or multivariable analyses, the combination of factors used to calculate the 5P clinical risk score was significantly associated with the odds of developing PPCS. Thus, obtaining clinically pragmatic risk scores soon after a concussion may be useful for early treatments or interventions to mitigate the PPCS risk.

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Abstract 14206: Assessing a Clinical Risk Score and the Impact of Race in Breast Cancer Patients at Risk of Cardiotoxicity

Circulation ◽

10.1161/circ.142.suppl_3.14206 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Zachary Brumberger ◽

Mary Branch ◽

Joseph Rigdon ◽

Suji Vasu

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Risk Score ◽

Significant Risk ◽

Statistical Characteristics ◽

Risk Scores ◽

Clinical Risk Score ◽

Breast Cancer Patients ◽

Clinical Risk ◽

The Impact

Introduction: Cardiotoxicity is a well-known risk in breast cancer patients treated with anthracyclines and trastuzumab. Ezaz et al. developed a clinical risk score (CRS) to risk stratify these patients. Despite evidence that African American (AA) race is a significant risk factor for cardiotoxicity, no study has assessed the impact of AA race on this CRS. Here we assess the discrimination ability of the Ezaz et al. CRS with the addition of AA race. Methods: This is a retrospective cohort utilizing a registry of 118 patients with stage I-IV breast cancer treated with anthracyclines and/or trastuzumab. Patients without baseline echocardiography data or with baseline LVEF < 50% were excluded. The CRS from Ezaz et al. consisting of age, adjuvant chemotherapy, coronary artery disease, atrial fibrillation or flutter, diabetes mellitus, hypertension, and renal failure was calculated with the addition of AA race. Cardiotoxicity was defined by an LVEF decline of ≥ 10% to LVEF < 53% from baseline. Results: In our 118 patient cohort, the mean age was 59 years, 23 (20%) AA patients, 65 (55%) patients considered low risk (scores of 0-3) and 53 (45%) considered moderate to high risk (scores ≥4). After a follow up of 3 months to 5 years, 14 (12%) patients developed cardiotoxicity. Table 1 lists the CRS changes in statistical characteristics and predictability with the addition of AA race. In comparing the models, the AUC c-statistic increased from 0.609 to 0.642 (95% CI 0.47-0.75, 95% CI 0.49-0.79 respectively; P value = 0.56) with the addition of AA race ( Figure 1 ). Conclusions: In this study, the Ezaz et al. CRS demonstrated improved discrimination and sensitivity with the addition of AA race. This study suggests AA race improves the predictive ability of the Ezaz et al. CRS. Given the limited size of our study, we promote that this should be hypothesis-driving and encourage further investigation on the path to develop an important risk stratification tool.

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Proposal of COVID-19 Clinical Risk Score for the management of suspected COVID-19 cases: a case control study

10.21203/rs.3.rs-42603/v1 ◽

2020 ◽

Author(s):

Sho Nakakubo ◽

Masaru Suzuki ◽

Keisuke Kamada ◽

Yu Yamashita ◽

Junichi Nakamura ◽

...

Keyword(s):

Risk Score ◽

Diagnostic Testing ◽

Risk Scores ◽

Clinical Risk Score ◽

Clinical Risk ◽

Ct Findings ◽

Clinical Scoring ◽

History Of ◽

Taste And Smell

Abstract BackgroundNo clinical scoring system has yet been established to estimate the likelihood of COVID-19 and to determine the suitability of diagnostic testing in suspected COVID-19 patients.MethodsThis was a single-center, retrospective, observational study of patients with suspected COVID-19 and confirmed COVID-19. Patient background, clinical course, laboratory and CT findings, and the presence of alternative diagnoses were evaluated. Clinical risk scores were developed based on clinical differences between patients with and those without COVID-19.ResultsAmong 110 patients suspected of COVID-19, 60.9% underwent PCR testing based on the judgment of physicians. Two patients were found to have COVID-19. The clinical characteristics of 108 non-COVID-19 patients were compared with those of 23 confirmed COVID-19 patients. Patients with COVID-19 were more likely to have a history of high-risk exposures and abnormal sense of taste and smell. Significantly higher rates of subnormal white blood cell count, lower eosinophil count, and lower procalcitonin level were observed in the COVID-19 group than in the non-COVID-19 group. When blood tests, CT findings, and the presence of alternative diagnoses were scored on an 11-point scale, i.e., “COVID-19 Clinical Risk Score”, the COVID-19 group scored significantly higher than the non-COVID-19 group, with more than four points in the COVID-19 group. All non-COVID cases that did not undergo PCR had a score of 4 or less.ConclusionsThe COVID-19 Clinical Risk Score enables risk classification of patients suspected of having COVID-19 and can help in decision-making in clinical practice, including appropriateness of diagnostic testing.

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Do Rockwood frailty score and Charlson comorbidity index help to risk stratify outpatient versus inpatient management of acute decompensated heart failure?

European Heart Journal ◽

10.1093/ehjci/ehaa946.1190 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

K.A.I Neoh ◽

L Sevdynidis ◽

J Hatherley ◽

J Tay ◽

H Douglas ◽

...

Keyword(s):

Heart Failure ◽

Risk Score ◽

Acute Decompensated Heart Failure ◽

Decompensated Heart Failure ◽

Risk Scores ◽

Clinical Risk Score ◽

Exact Test ◽

Clinical Risk ◽

Co Morbidity ◽

Frailty Score

Abstract Introduction Acute decompensated heart failure (ADHF) is associated with frailty and co-morbidities which influence prognosis. The Rockwood Frailty Score (RFS) and age-adjusted Charlson co-morbidity index (CCI) have been used to predict outcomes in hospitalised ADHF patients. Purpose To describe the relationship of CCI, RFS and clinical risk score -Get With The Guidelines Score (GWTG) with mortality in ADHF treated as outpatients (OP) versus hospitalised inpatients (IP). Methods This retrospective analysis compared 2 cohorts of consecutive ADHF patients - hospitalised in-patients (IP) versus outpatients (OP) who were treated with bolus intravenous diuretics in a specialist heart failure nurse delivered OP HF unit (Ambulatory HF Unit -AHFU) with input from various specialties (renal, palliative, ascitic, pleural teams) from Nov 16 to Dec 17. Mean follow-up duration was similar for both groups (IP=19.5±4.1 months; OP=19.3±3.9 months, p=0.6). Mortality was compared at 1, 3 and 12 months based on RFS (no frailty <5, mild to moderate frailty 5/6, severe frailty - 7 to 9). Results were expressed as mean±SD and analysed using One-Way ANOVA and Chi-squared with Fisher's exact test test. Results 410 consecutive patients (482 admissions) were hospitalised (inpatients -IP) and 231 OP (289 OP visits) were treated in the AHFU. IP group had significantly higher mean CCI (IP=6.55±2; OP=6.10±1.9; p=0.006) and mean RFS (IP 5.2±1.2; OP 4.9±1.1; p=0.002). Mean Clinical Risk Score GWTG was similar (IP=38.9±7.2; OP=38.4±6.6; p=0.44). Mean survival was significantly lower in IP (IP=378±270 days; OP=437±228; P=0.003). As shown in the table higher RFS predicts increased mortality risk (1 month, 3 month and 12 month). Conclusions Rockwood Frailty Score predicts mortality in ADHF and assessment of RFS can play an important role in risk stratifying and decision-making in addition to clinical risk-scores, with regards to suitability for outpatient treatment of ADHF. Funding Acknowledgement Type of funding source: None

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Proposal of COVID-19 Clinical Risk Score for the management of suspected COVID-19 cases: a case control study

BMC Infectious Diseases ◽

10.1186/s12879-020-05604-4 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Sho Nakakubo ◽

Masaru Suzuki ◽

Keisuke Kamada ◽

Yu Yamashita ◽

Junichi Nakamura ◽

...

Keyword(s):

Risk Score ◽

Diagnostic Testing ◽

Risk Scores ◽

Clinical Risk Score ◽

Clinical Risk ◽

Cell Counts ◽

Ct Findings ◽

Clinical Scoring ◽

History Of ◽

White Blood Cell Counts

Abstract Background No clinical scoring system has yet been established to estimate the likelihood of coronavirus disease (COVID-19) and determine the suitability of diagnostic testing in suspected COVID-19 patients. Methods This was a single-center, retrospective, observational study of patients with suspected COVID-19 and confirmed COVID-19. Patient background, clinical course, laboratory and computed tomography (CT) findings, and the presence of alternative diagnoses were evaluated. Clinical risk scores were developed based on clinical differences between patients with and without COVID-19. Results Among 110 patients suspected of having COVID-19, 60.9% underwent polymerase chain reaction (PCR) testing based on the judgment of physicians. Two patients were found to have COVID-19. The clinical characteristics of 108 non-COVID-19 patients were compared with those of 23 confirmed COVID-19 patients. Patients with COVID-19 were more likely to have a history of high-risk exposures and an abnormal sense of taste and smell. The COVID-19 group had significantly higher rates of subnormal white blood cell counts, lower eosinophil counts, and lower procalcitonin levels than the non-COVID-19 group. When blood test results, CT findings, and the presence of alternative diagnoses were scored on an 11-point scale (i.e., “COVID-19 Clinical Risk Score”), the COVID-19 group scored significantly higher than the non-COVID-19 group, more than four points in the COVID-19 group. All non-COVID patients who did not undergo PCR had a score of 4 or less. Conclusions The COVID-19 Clinical Risk Score may enable the risk classification of patients suspected of having COVID-19 and can help in decision-making in clinical practice, including appropriateness of diagnostic testing. Further studies and prospective validation with an increased sample size are required.

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