Applying an Evidence-Based Assessment Model to Identify Students at Risk for Perceived Academic Problems following Concussion

2016 ◽  
Vol 22 (10) ◽  
pp. 1038-1049 ◽  
Author(s):  
Danielle M. Ransom ◽  
Alison R. Burns ◽  
Eric A. Youngstrom ◽  
Christopher G. Vaughan ◽  
Maegan D. Sady ◽  
...  
Keyword(s):  
At Risk ◽  
Receiver Operating Characteristic ◽  
Symptom Severity ◽  
Operating Characteristic ◽  
Executive Dysfunction ◽  
Evidence Based ◽  
Academic Problems ◽  
Post Injury ◽  
Students At Risk ◽  
Receiver Operating

AbstractObjectives: The aim of this study was to demonstrate the utility of an evidence-based assessment (EBA) model to establish a multimodal set of tools for identifying students at risk for perceived post-injury academic problems. Methods: Participants included 142 students diagnosed with concussion (age: M=14.95; SD=1.80; 59% male), evaluated within 4 weeks of injury (median=16 days). Demographics, pre-injury history, self- and parent-report measures assessing symptom severity and executive functions, and cognitive test performance were examined as predictors of self-reported post-injury academic problems. Results: Latent class analysis categorized participants into “high” (44%) and “low” (56%) levels of self-reported academic problems. Receiver operating characteristic analyses revealed significant discriminative validity for self- and parent-reported symptom severity and executive dysfunction and self-reported exertional response for identifying students reporting low versus high academic problems. Parent-reported symptom ratings [area under the receiver operating characteristic curve (AUC)=.79] and executive dysfunction (AUC=.74), and self-reported ratings of executive dysfunction (AUC=.84), symptoms (AUC=.80), and exertional response (AUC=.70) each classified students significantly better than chance (ps<.001). Hierarchical logistic regression indicated that, of the above, self-reported symptoms and executive dysfunction accounted for the most variance in the prediction of self-reported academic problems. Conclusions: Post-concussion symptom severity and executive dysfunction significantly predict perceived post-injury academic problems. EBA modeling identified the strongest set of predictors of academic challenges, offering an important perspective in the management of concussion by applying traditional strengths of neuropsychological assessment to clinical decision making. (JINS, 2016, 22, 1038–1049)

scholarly journals Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients

2021 ◽  
Author(s):  
Victor Alfonso Rodriguez ◽  
Shreyas Bhave ◽  
Ruijun Chen ◽  
Chao Pang ◽  
George Hripcsak ◽  
...  
Keyword(s):  
At Risk ◽  
Mechanical Ventilation ◽  
Renal Replacement Therapy ◽  
Receiver Operating Characteristic ◽  
Replacement Therapy ◽  
Operating Characteristic ◽  
Prediction Models ◽  
External Validation ◽  
Electronic Health Record Data ◽  
Receiver Operating

Abstract Objective Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. Materials and Methods For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output. Results The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. Discussion Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. Conclusions We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

A Predictive Model of Intrinsic Factors Associated with Long-Stay Nursing Home Care After Hospitalization

2020 ◽  
pp. 105477382098527
Author(s):  
Jane Flanagan ◽  
Marie Boltz ◽  
Ming Ji
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Predictive Model ◽  
Operating Characteristic ◽  
Resource Planning ◽  
Term Care ◽  
Intrinsic Factors ◽  
Patients At Risk ◽  
Receiver Operating

We aimed to build a predictive model with intrinsic factors measured upon admission to skilled nursing facilities (SNFs) post-acute care (PAC) to identify older adults transferred from SNFs to long-term care (LTC) instead of home. We analyzed data from Massachusetts in 23,662 persons admitted to SNFs from PAC in 2013. Explanatory logistic regression analysis identified single “intrinsic predictors” related to LTC placement. To assess overfitting, the logistic regression predictive model was cross-validated and evaluated by its receiver operating characteristic (ROC) curve. A 12-variable predictive model with “intrinsic predictors” demonstrated both high in-sample and out-of-sample predictive accuracy in the receiver operating characteristic ROC and area under the ROC among patients at risk of LTC placement. This predictive model may be used for early identification of patients at risk for LTC after hospitalization in order to support targeted rehabilitative approaches and resource planning.

Evidence-Based Assessment of ADHD in Youth Using a Receiver Operating Characteristic Approach

2016 ◽  
Vol 47 (5) ◽  
pp. 808-820 ◽  
Author(s):  
Matthew A. Jarrett ◽  
Anna Van Meter ◽  
Eric A. Youngstrom ◽  
Dane C. Hilton ◽  
Thomas H. Ollendick
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Evidence Based ◽  
Receiver Operating

scholarly journals Predicting Risk of Postoperative Lung Injury in High-risk Surgical Patients

2014 ◽  
Vol 120 (5) ◽  
pp. 1168-1181 ◽  
Author(s):  
Daryl J. Kor ◽  
Ravi K. Lingineni ◽  
Ognjen Gajic ◽  
Pauline K. Park ◽  
James M. Blum ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Surgical Patients ◽  
Patients At Risk ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract Background: Acute respiratory distress syndrome (ARDS) remains a serious postoperative complication. Although ARDS prevention is a priority, the inability to identify patients at risk for ARDS remains a barrier to progress. The authors tested and refined the previously reported surgical lung injury prediction (SLIP) model in a multicenter cohort of at-risk surgical patients. Methods: This is a secondary analysis of a multicenter, prospective cohort investigation evaluating high-risk patients undergoing surgery. Preoperative ARDS risk factors and risk modifiers were evaluated for inclusion in a parsimonious risk-prediction model. Multiple imputation and domain analysis were used to facilitate development of a refined model, designated SLIP-2. Area under the receiver operating characteristic curve and the Hosmer–Lemeshow goodness-of-fit test were used to assess model performance. Results: Among 1,562 at-risk patients, ARDS developed in 117 (7.5%). Nine independent predictors of ARDS were identified: sepsis, high-risk aortic vascular surgery, high-risk cardiac surgery, emergency surgery, cirrhosis, admission location other than home, increased respiratory rate (20 to 29 and ≥30 breaths/min), Fio2 greater than 35%, and Spo2 less than 95%. The original SLIP score performed poorly in this heterogeneous cohort with baseline risk factors for ARDS (area under the receiver operating characteristic curve [95% CI], 0.56 [0.50 to 0.62]). In contrast, SLIP-2 score performed well (area under the receiver operating characteristic curve [95% CI], 0.84 [0.81 to 0.88]). Internal validation indicated similar discrimination, with an area under the receiver operating characteristic curve of 0.84. Conclusions: In this multicenter cohort of patients at risk for ARDS, the SLIP-2 score outperformed the original SLIP score. If validated in an independent sample, this tool may help identify surgical patients at high risk for ARDS.

Übereinstimmung von klinischer Diagnose, strukturierten Interviews und Selbstbeurteilungsfragebögen bei Depression im Kindes- und Jugendalter

2012 ◽  
Vol 40 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Kathrin Dolle ◽  
Gerd Schulte-Körne ◽  
Nikolaus von Hofacker ◽  
Yonca Izat ◽  
Antje-Kathrin Allgaier
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Kinder Und Jugendliche ◽  
Prädiktive Werte ◽  
Receiver Operating

Fragestellung: Die vorliegende Studie untersucht die Übereinstimmung von strukturierten Kind- und Elterninterviews sowie dem klinischen Urteil bei der Diagnostik depressiver Episoden im Kindes- und Jugendalter. Zudem prüft sie, ob sich die Treffsicherheit und die optimalen Cut-off-Werte von Selbstbeurteilungsfragebögen in Referenz zu diesen verschiedenen Beurteilerperspektiven unterscheiden. Methodik: Mit 81 Kindern (9–12 Jahre) und 88 Jugendlichen (13–16 Jahre), die sich in kinder- und jugendpsychiatrischen Kliniken oder Praxen vorstellten, und ihren Eltern wurden strukturierte Kinder-DIPS-Interviews durchgeführt. Die Kinder füllten das Depressions-Inventar für Kinder und Jugendliche (DIKJ) aus, die Jugendlichen die Allgemeine Depressions-Skala in der Kurzform (ADS-K). Übereinstimmungen wurden mittels Kappa-Koeffizienten ermittelt. Optimale Cut-off-Werte, Sensitivität, Spezifität sowie positive und negative prädiktive Werte wurden anhand von Receiver operating characteristic (ROC) Kurven bestimmt. Ergebnisse: Die Interviews stimmten untereinander sowie mit dem klinischen Urteil niedrig bis mäßig überein. Depressive Episoden wurden häufiger nach klinischem Urteil als in den Interviews festgestellt. Cut-off-Werte und Validitätsmaße der Selbstbeurteilungsfragebögen variierten je nach Referenzstandard mit den schlechtesten Ergebnissen für das klinische Urteil. Schlussfolgerungen: Klinische Beurteiler könnten durch den Einsatz von strukturierten Interviews profitieren. Strategien für den Umgang mit diskrepanten Kind- und Elternangaben sollten empirisch geprüft und detailliert beschrieben werden.

Use of Receiver Operating Characteristic (ROC) Curves to Evaluate Computer Confidence Threshold and Clinical Performance in the Diagnosis of Appendicitis

1978 ◽  
Vol 17 (03) ◽  
pp. 157-161 ◽  
Author(s):  
F. T. De Dombal ◽  
Jane C. Horrocks
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Clinical Performance ◽  
Roc Curves ◽  
Final Diagnosis ◽  
Confidence Threshold ◽  
Computer Aided ◽  
Computer Confidence ◽  
Overall Performance ◽  
Receiver Operating

This paper uses simple receiver operating characteristic (ROC) curves (i) to study the effect of varying computer confidence of threshold levels and (ii) to evaluate clinical performance in the diagnosis of acute appendicitis. Over 1300 patients presenting to five centres with abdominal pain of short duration were studied in varying detail. Clinical and computer-aided diagnostic predictions were compared with the »final« diagnosis. From these studies it is concluded the simplistic setting of a 50/50 confidence threshold for the computer program is as »good« as any other. The proximity of a computer-aided system changed clinical behaviour patterns; a higher overall performance level was achieved and clinicians performance levels became associated with the »mildly conservative« end of the computers ROC curve. Prior forecasts of over-confidence or ultra-caution amongst clinicians using the computer-aided system have not been fulfilled.

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