scholarly journals Circulating proteins to predict adverse COVID-19 outcomes

2021 ◽  
Author(s):  
Chen-Yang Su ◽  
Sirui Zhou ◽  
Edgar Gonzalez-Kozlova ◽  
Guillaume Butler-Laporte ◽  
Elsa Brunet-Ratnasingham ◽  
...  
Keyword(s):  
Prediction Models ◽  
Clinical Care ◽  
Biological Pathways ◽  
Receiver Operator Curve ◽  
Baseline Model ◽  
Unique Protein ◽  
Protein Measurement ◽  
Separate Cohort ◽  
Age And Sex ◽  
Operator Curve

AbstractPredicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4,701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict adverse COVID-19 outcomes in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4,701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different adverse COVID-19 outcomes were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of adverse COVID-19 outcomes. Further research is needed to understand how to incorporate protein measurement into clinical care.

scholarly journals Factors Associated With Intraoperative Conversion to Total Thyroidectomy in Benign Goiters

2019 ◽  
Vol 2 (1) ◽  
pp. 105-109
Author(s):  
Samuel Olatoke ◽  
Olayide Agodirin ◽  
Ganiyu Rahman ◽  
Benjamin Bolaji ◽  
Habeeb Olufemi
Keyword(s):  
Total Thyroidectomy ◽  
Nodular Goiter ◽  
Degenerative Changes ◽  
P Value ◽  
Receiver Operator Curve ◽  
Factors Associated ◽  
Patient Demographics ◽  
Partial Thyroidectomy ◽  
Intraoperative Conversion ◽  
Operator Curve

Background: Decision to undertake total thyroidectomy when gross inspection of the gland raises suspicion of widespread degenerative changes is often intraoperative. Knowing the factors associated with intraoperative conversion to total thyroidectomy may assist preoperative counselling. This study describes the probability of conversion to total thyroidectomy and factors associated with con-version among patients hitherto planned for partial thyroidectomy. Methods: We reviewed 191 records and extracted data on patient demographics, the pre-operative radiograph findings, the weight of excised gland and the operation performed. Descriptive and inferential statistics were performed. Receiver operator curve was used to assess for cut-off point. P-value was set at 0.05. Results: A total of 191 records was reviewed consisting of 181 females (94.8% 95% CI 90.6-97.5) and 10 males (5.2%, 95%CI 2.5-9.4). Only nodular goiters required conversion to total thyroidectomy. The over-all probability of total thyroidectomy was 11%(95% CI 7.0-16.3). The probability of total thyroidectomy in female was 10.5%(95% CI 6.4-16.9) while in male was 20%(95% CI2.5-55.6). The probability of total thyroidectomy in a female with nodular goiter was 8.1%(95% CI 4.8-13.5), compared to 28.6%(95% CI 3.7-71) in males. The risk of total thyroidectomy was associated with the weight of the excised gland. Conclusion: Only nodular goiters required intraoperative conversion to total thyroidecto-my and the probability of conversion was higher in males.

scholarly journals Comparing the Predictive Power of Preoperative Risk Assessment Tools to Best Predict Major Adverse Cardiac Events in Kidney Transplant Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Colin P. Dunn ◽  
Emmanuel U. Emeasoba ◽  
Ari J. Holtzman ◽  
Michael Hung ◽  
Joshua Kaminetsky ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Assessment Tools ◽  
Major Adverse Cardiac Events ◽  
Receiver Operator Curve ◽  
Cardiac Events ◽  
Preoperative Risk ◽  
Preoperative Risk Assessment ◽  
Transplant Patients ◽  
Adverse Cardiac Events ◽  
Operator Curve

Background. Patients undergoing kidney transplantation have increased risk of adverse cardiovascular events due to histories of hypertension, end-stage renal disease, and dialysis. As such, they are especially in need of accurate preoperative risk assessment. Methods. We compared three different risk assessment models for their ability to predict major adverse cardiac events at 30 days and 1 year after transplant. These were the PORT model, the RCRI model, and the Gupta model. We used a method based on generalized U-statistics to determine statistically significant improvements in the area under the receiver operator curve (AUC), based on a common major adverse cardiac event (MACE) definition. For the top-performing model, we added new covariates into multivariable logistic regression in an attempt to create further improvement in the AUC. Results. The AUCs for MACE at 30 days and 1 year were 0.645 and 0.650 (PORT), 0.633 and 0.661 (RCRI), and finally 0.489 and 0.557 (Gupta), respectively. The PORT model performed significantly better than the Gupta model at 1 year (p=0.039). When the sensitivity was set to 95%, PORT had a significantly higher specificity of 0.227 compared to RCRI’s 0.071 (p=0.009) and Gupta’s 0.08 (p=0.017). Our additional covariates increased the receiver operator curve from 0.664 to 0.703, but this did not reach statistical significance (p=0.278). Conclusions. Of the three calculators, PORT performed best when the sensitivity was set at a clinically relevant level. This is likely due to the unique variables the PORT model uses, which are specific to transplant patients.

scholarly journals Extubation Success Prediction in a Multicentric Cohort of Patients with Severe Brain Injury

2017 ◽  
Vol 127 (2) ◽  
pp. 338-346 ◽  
Author(s):  
Karim Asehnoune ◽  
Philippe Seguin ◽  
Sigismond Lasocki ◽  
Antoine Roquilly ◽  
Adrien Delater ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Brain Injury ◽  
Unit Length ◽  
Extubation Failure ◽  
Receiver Operator Curve ◽  
Severe Brain Injury ◽  
University Hospitals ◽  
Operator Curve ◽  
Successful Extubation

Abstract Background Patients with brain injury are at high risk of extubation failure. Methods We conducted a prospective observational cohort study in four intensive care units of three university hospitals. The aim of the study was to create a score that could predict extubation success in patients with brain injury. Results A total of 437 consecutive patients with brain injury were included, and 338 patients (77.3%) displayed successful extubation. In the multivariate analysis, four features were associated with success the day of extubation: age less than 40 yr, visual pursuit, swallowing attempts, and a Glasgow coma score greater than 10. In the score, each item counted as one. A score of 3 or greater was associated with 90% extubation success. The area under the receiver–operator curve was 0.75 (95% CI, 0.69 to 0.81). After internal validation by bootstrap, the area under the receiver–operator curve was 0.73 (95% CI, 0.68 to 0.79). Extubation success was significantly associated with shorter duration of mechanical ventilation (11 [95% CI, 5 to 17 days] vs. 22 days [95% CI, 13 to 29 days]; P < 0.0001), shorter intensive care unit length of stay (15 [95% CI, 9 to 23 days] vs. 27 days [95% CI, 21 to 36 days]; P < 0.0001), and lower in-intensive care unit mortality (4 [1.2%] vs. 11 [11.1%]; P < 0.0001). Conclusions Our score exploring both airway functions and neurologic status may increase the probability of successful extubation in patients with severe brain injury.

scholarly journals Determining the Patient Acceptable Symptomatic State for Patients Undergoing Arthroscopic Partial Meniscectomy in the Knee

2017 ◽  
Vol 5 (3_suppl3) ◽  
pp. 2325967117S0012
Author(s):  
Thomas Zochowski ◽  
Tim Dwyer ◽  
Darrell Ogilvie-Harris ◽  
John S. Theodoropoulos ◽  
Daniel B. Whelan ◽  
...  
Keyword(s):  
Curve Analysis ◽  
Partial Meniscectomy ◽  
Consecutive Series ◽  
Receiver Operator Curve ◽  
Arthroscopic Partial Meniscectomy ◽  
Pain Subscale ◽  
Patient Reported ◽  
Receiver Operator Curve Analysis ◽  
Meniscal Pathology ◽  
Operator Curve

Objectives: Arthroscopic partial meniscectomy is one of the most commonly performed procedures in orthopaedic surgery. However, information on the threshold at which patients consider themselves to be well for patient reported outcome measures (PROMs) after this surgery remains limited. Our goal was to determine the patient acceptable symptomatic state (PASS) for the Knee Injury and Osteoarthritic Outcome Score (KOOS), the International Knee Documentation Committee (IKDC) Subjective Knee Form, the Western Ontario Meniscal Evaluation Tool (WOMET) and the Marx Activity Scale (MAS) in patients with knee meniscal pathology who treated with partial knee meniscectomy. Methods: A consecutive series of patients with knee meniscal pathology treated with arthroscopic partial meniscectomy plus or minus intra-articular debridement were eligible. Other inclusion criteria were: a Kellegren-Lawrence Grade of 0-2, and ligamentous integrity. The KOOS (0-100, 5 subscales), IKDC (0-100), WOMET (0-100) and MAS (0-16) were administered at baseline and 12 months postoperatively. An external anchor question at 1 year postoperatively was utilized to determine PASS values: “Taking into account all the activities you have during your daily life, your level of pain, and also your functional impairment, do you consider that your current state is satisfactory?” A receiver operator curve analysis was used to determine the PASS value at which patients considered their status to be satisfactory. Results: There were 115 patients (mean ± SD age, 53.8 ± 12.0 years), and 57.3% were male. Based on a receiver operator curve analysis, the PASS values - at which patients considered their status to be satisfactory - at 1 year after surgery were 43 (KOOS-symptoms subscale), 83 (KOOS-pain subscale), 84 (KOOS-functions of daily living subscale), 75 (KOOS-function, sport and recreational activity subscale), 56 (KOOS-quality of life subscale), 56 (IKDC), 61 (WOMET), 7 (MAS). The PASS threshold was not affected by baseline scores across the different instruments and there was no relationship between baseline score and likelihood of achieving the PASS. Age and sex were not significantly related to the odds of achieving the PASS for any of the PROMs. Conclusion: This is the first study to determine PASS in four commonly used knee-related PROMs in patients undergoing arthroscopic partial meniscectomy. The findings can allow researchers and clinicians to determine if partial meniscectomy is meaningful to patients and will be helpful for responder analysis in future trials related to knee arthroscopy and the treatment of meniscal pathology.

scholarly journals Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions

2021 ◽  
pp. 1106-1126
Author(s):  
Dylan J. Peterson ◽  
Nicolai P. Ostberg ◽  
Douglas W. Blayney ◽  
James D. Brooks ◽  
Tina Hernandez-Boussard
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Emergency Department ◽  
Emergency Department Visits ◽  
Test Model ◽  
Receiver Operator Curve ◽  
Patients At Risk ◽  
Electronic Health ◽  
Chemotherapy Patients ◽  
Operator Curve

PURPOSE Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data. METHODS Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve. RESULTS Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients. CONCLUSION Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.

scholarly journals Ordinal Prediction Model of 90-Day Modified Rankin Scale in Ischemic Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Michelle Y. Zhang ◽  
Michael Mlynash ◽  
Kristin L. Sainani ◽  
Gregory W. Albers ◽  
Maarten G. Lansberg
Keyword(s):  
Clinical Trials ◽  
Ischemic Stroke ◽  
Prediction Model ◽  
Functional Outcomes ◽  
Prediction Models ◽  
Clinical Care ◽  
External Validation ◽  
Modified Rankin Scale ◽  
Forward Selection ◽  
Using Data

Background and Purpose: Prediction models for functional outcomes after ischemic stroke are useful for statistical analyses in clinical trials and guiding patient expectations. While there are models predicting dichotomous functional outcomes after ischemic stroke, there are no models that predict ordinal mRS outcomes. We aimed to create a model that predicts, at the time of hospital discharge, a patient's modified Rankin Scale (mRS) score on day 90 after ischemic stroke.Methods: We used data from three multi-center prospective studies: CRISP, DEFUSE 2, and DEFUSE 3 to derive and validate an ordinal logistic regression model that predicts the 90-day mRS score based on variables available during the stroke hospitalization. Forward selection was used to retain independent significant variables in the multivariable model.Results: The prediction model was derived using data on 297 stroke patients from the CRISP and DEFUSE 2 studies. National Institutes of Health Stroke Scale (NIHSS) at discharge and age were retained as significant (p < 0.001) independent predictors of the 90-day mRS score. When applied to the external validation set (DEFUSE 3, n = 160), the model accurately predicted the 90-day mRS score within one point for 78% of the patients in the validation cohort.Conclusions: A simple model using age and NIHSS score at time of discharge can predict 90-day mRS scores in patients with ischemic stroke. This model can be useful for prognostication in routine clinical care and to impute missing data in clinical trials.

scholarly journals Sex-specific survival bias and interaction modeling in coronary artery disease risk prediction

2021 ◽  
Author(s):  
Ida Surakka ◽  
Brooke Wolford ◽  
Scott C Ritchie ◽  
Whitney E Hornsby ◽  
Nadia R Sutton ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Information ◽  
Prediction Models ◽  
Atherosclerotic Cardiovascular Disease ◽  
Polygenic Scores ◽  
Ascvd Risk ◽  
Cad Risk ◽  
Age And Sex

Background The 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk score is the standard approach to predict risk of incident cardiovascular events and recently, addition of CAD polygenic scores (PGSCAD) have been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. Objectives This study performed an in-depth evaluation of age and sex effects in genetic CAD risk prediction. Methods The population-based Norwegian HUNT2 cohort of 51,036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372,410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards and Harrells concordance index, sensitivity, and specificity were compared. Results Inclusion of age and sex interactions of PGSCAD to the prediction models increased C-index and sensitivity likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. The two-step approach identified a total of 82.6% of incident CAD cases (74.1% by ASCVD risk score and an additional 8.5% by the PGSCAD interaction model). Conclusion These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age and sex-interactions terms with polygenic scores to optimize detection of individuals at high-risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.

scholarly journals Peran Duke Treadmill Score sebagai Prediktor Penyakit Jantung Koroner pada Pasien dengan Uji Treadmill Positif The Role of Duke Treadmill Score as a Predictor of Coronary Artery Disease in Patients with Positive Treadmill Test Results

2016 ◽  
Vol 3 (2) ◽  
pp. 81
Author(s):  
Muhammad Ikhsan ◽  
Sally Aman Nasution ◽  
Ika Prasetya Wijaya ◽  
Cleopas Martin Rumende
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Coronary Angiography ◽  
Negative Likelihood Ratio ◽  
Receiver Operator Curve ◽  
Treadmill Test ◽  
Duke Treadmill Score ◽  
Artery Disease ◽  
Operator Curve

Pendahuluan. Coronary Artery Disease (CAD) merupakan penyakit yang masih menjadi penyebab utama morbiditas dan mortalitas di dunia. Uji treadmill merupakan modalitas diagnostik untuk CAD yang tersedia secara luas di Indonesia, namun performa ketepatan diagnostiknya masih perlu ditingkatkan. Penelitian yang dilakukan ini menggunakan Duke Treadmill Score (DTS) sebagai prediktor Coronary Artery Disease.Metode. Penelitian ini menggunakan desain studi potong lintang yang dilakukan pada subjek dengan CAD stabil berusia 18-75 tahun yang menjalani uji treadmill dengan hasil positive ischemic response dan sudah dilakukan korangiografi di Poliklinik Pelayanan Jantung Terpadu Rumah Sakit dr. Cipto Mangunkusumo (RSCM) dalam kurun waktu Januari 2011 hingga Desember 2013.Hasil. Didapatkan 103 subjek dengan 37,9% diagnosis CAD signifikan dari corangiografi. Dari ROC (Receiver Operator Curve) ditentukan titik potong DTS pada nilai -8,85. Didapatkan nilai sensitivitas, spesifisitas, nilai duga positif (NDP) dan nilai duga negatif (NDN) DTS masing-masing sebesar 28% (IK 95%: 17%-44%), 95% (IK 95%: 87%-98%), 79% (IK 95%: 52%-92%) dan 69% (IK 95%: 58%-77%).Simpulan. Disimpulkan DTS dapat memprediksi CAD yang signifikan pada titik potong -8,85 untuk pasien uji treadmill positif dengan nilai duga positif yang cukup baik.Kata Kunci: CAD, DTS, uji treadmillThe Role of Duke Treadmill Score as a Predictor of Coronary Artery Disease in Patients with Positive Treadmill Test ResultsIntroduction. Coronary Artery Disease (CAD) is one of the disease entity that leading cause of morbidity and mortality in worldwide. Treadmill test is part of the diagnostic modality which readily available to assess possibility of narrowing coronary artery and guiding us whether we need for the further investigation. Despite of that, treadmill test has limitation in diagnostic accuracy. Duke Treadmill Score (DTS) was also tested as a diagnostic score, and shown to predict significant CAD better than the ST-segment response alone.Methods. This is a cross-sectional study performed in adult patients with stable CAD that underwent treadmill test and coronary angiography in outward patient clinic of the Integrated Cardiac Service in Cipto Mangunkusumo Hospital between January 2011 and December 2013.Results. A total of 103 patients in this study, thirty nine patients (37,9 %) had significant CAD in coronary angiography. Briefly, mean age was 54,71 years and 55 patients (53,4 %) were females. The most common CAD risk factor was hypertension (51,5 %). A mean of DTS score was -3.53, which mostly categorized as intermediate risk (89,3 %). Based on DTS results, cut-off point was determined by using Receiver Operator Curve (ROC) method, in which value of -8,85 considering as a cut-off point. Sensitivity and specificity value of DTS were 28 % (CI 95 %: 17 % to 44 %), and 95 % (CI 95 %: 87 % to 98 %). Positive and negative predictive value were 79 % (CI 95 %: 52 % to 92 %) and 69 % (CI 95 %: 58 % to 77 %). Positive and negative likelihood ratio were 6.02 and 0.75.Conclusions. DTS has a good performance in predicting significant CAD at cut-off point -8,85 in patients with positive treadmill test.Keywords: CAD, DTS, treadmill test

Age and Sex Influence Thyroid Hormone Effects in Target Tissues with Organ-Specific Responses

2020 ◽  
Vol 128 (06/07) ◽  
pp. 469-472
Author(s):  
Helena Kerp ◽  
Janina Gassen ◽  
Dagmar Führer
Keyword(s):  
Thyroid Hormone ◽  
Clinical Presentation ◽  
Clinical Care ◽  
The Elderly ◽  
Rodent Models ◽  
Sex Influence ◽  
Increased Risk ◽  
Hormone Effects ◽  
Organ Specific ◽  
Age And Sex

AbstractAge and sex impact prevalence and clinical features of thyroid disease. Thyroid dysfunction occurs with a higher frequency in elderly patients and females. Moreover, age alters clinical presentation of hyper- and hypothyroidism and onset of thyroid hormone (TH) related co-morbidities leading to increased risk for underdiagnosis and maltreatment in the elderly. Rodent models allow further insights into mechanisms of age- and sex-dependent TH action in target tissues. In this review, we summarize findings from mouse studies showing distinct effects of age and sex on systemic versus organ-specific TH action and discuss their wider implication for clinical care.

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