Response to “Early prediction of noninvasive ventilation failure in COPD patients: derivation, internal validation, and external validation of a simple risk score”

Abstract Background Early identification of noninvasive ventilation (NIV) failure is a promising strategy for reducing mortality in chronic obstructive pulmonary disease (COPD) patients. However, a risk-scoring system is lacking. Methods To develop a scale to predict NIV failure, 500 COPD patients were enrolled in a derivation cohort. Heart rate, acidosis (assessed by pH), consciousness (assessed by Glasgow coma score), oxygenation, and respiratory rate (HACOR) were entered into the scoring system. Another two groups of 323 and 395 patients were enrolled to internally and externally validate the scale, respectively. NIV failure was defined as intubation or death during NIV. Results Using HACOR score collected at 1–2 h of NIV to predict NIV failure, the area under the receiver operating characteristic curves (AUC) was 0.90, 0.89, and 0.71 for the derivation, internal-validation, and external-validation cohorts, respectively. For the prediction of early NIV failure in these three cohorts, the AUC was 0.91, 0.96, and 0.83, respectively. In all patients with HACOR score > 5, the NIV failure rate was 50.2%. In these patients, early intubation (< 48 h) was associated with decreased hospital mortality (unadjusted odds ratio = 0.15, 95% confidence interval 0.05–0.39, p < 0.01). Conclusions HACOR scores exhibited good predictive power for NIV failure in COPD patients, particularly for the prediction of early NIV failure (< 48 h). In high-risk patients, early intubation was associated with decreased hospital mortality.

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Development of an Early Prediction Model for Postoperative Delirium in Neurosurgical Patients Admitted to the ICU after Elective Craniotomy (E-PREPOD-NS): A Secondary Analysis of A Prospective Cohort Study

10.21203/rs.3.rs-271659/v1 ◽

2021 ◽

Author(s):

Hua-Wei Huang ◽

Guo-Bin Zhang ◽

Hao-Yi Li ◽

Chun-Mei Wang ◽

Yu-Mei Wang ◽

...

Keyword(s):

Prediction Model ◽

Risk Score ◽

Postoperative Delirium ◽

Risk Model ◽

External Validation ◽

Multivariate Logistic Regression Analysis ◽

Early Prediction ◽

Intracranial Surgery ◽

Neurosurgical Patients ◽

History Of

Abstract Background: Postoperative delirium (POD) is a significant clinical problem in neurosurgical patients after intracranial surgery. Identification of high-risk patients may optimise individual perioperative management, but an adequate and simple risk model for use at super early phase after operation has not been developed.Methods: Adult patients were admitted to the ICU after elective intracranial surgery under general anaesthesia. The POD was diagnosed as Confusion Assessment Method for the ICU positive on postoperative day 1 to 3. Multivariate logistic regression analysis was used to develop the early prediction model (E-PREPOD-NS) and the final model was validated with 200 bootstrap samples.Results: Among 800 patients included in the study, POD occurred in 157 cases (19.6%). We identified nine variables independently associated with POD in the final E-PREPOD-NS model: age > 65 years [odds ratio (OR) = 3.336, 95% confidence interval (CI) = 1.765-6.305, 1 risk score point], education level < 9 years (OR = 2.528, 95% CI = 1.446-4.419, 1 point), history of smoking (OR = 2.582, 95% CI = 1.611-4.140, 1 point), history of diabetes (OR = 2.541, 95% CI = 1.201-5.377, 1 point), supra-tentorial lesions (OR = 3.424, 95% CI = 2.021-5.802, 1 point), anesthesia duration > 360 min (OR = 1.686, 95% CI = 1.062-2.674, 0.5 point), GCS <9 at ICU admission (OR = 6.059, 95% CI = 3.789-9.690, 1.5 points), metabolic acidosis (OR = 13.903, 95% CI = 6.248-30.938, 2.5 points), and positioning of neurosurgical drainage tube (OR = 1.924, 95% CI = 1.132-3.269, 0.5 point). The area under the receiver operator curve (AUROC) of the risk score for prediction of POD was 0.865 (95% CI = 0.835-0.895). After internal validation by bootstrap, the AUROC was 0.851 (95% CI = 0.791-0.912). The model showed good calibration (Hosmer-Lemeshow P = 0.593).Conclusions: The E-PREPOD-NS model based on nine perioperative risk factors can predict POD in patients admitted to the ICU after elective intracranial surgery with fairly good accuracy. External validation is needed before use in clinical practice.

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False-negative RT-PCR for COVID-19 and a diagnostic risk score: a retrospective cohort study among patients admitted to hospital

BMJ Open ◽

10.1136/bmjopen-2020-047110 ◽

2021 ◽

Vol 11 (2) ◽

pp. e047110 ◽

Cited By ~ 2

Author(s):

Ankur Gupta-Wright ◽

Colin Kenneth Macleod ◽

Jessica Barrett ◽

Sarah Ann Filson ◽

Tumena Corrah ◽

...

Keyword(s):

Cohort Study ◽

Risk Score ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

External Validation ◽

False Negative ◽

Early Warning Score ◽

Risk Scores ◽

Rt Pcr ◽

Internal Validation

ObjectiveTo describe the characteristics and outcomes of patients with a clinical diagnosis of COVID-19 and false-negative SARS-CoV-2 reverse transcription-PCR (RT-PCR), and develop and internally validate a diagnostic risk score to predict risk of COVID-19 (including RT-PCR-negative COVID-19) among medical admissions.DesignRetrospective cohort study.SettingTwo hospitals within an acute NHS Trust in London, UK.ParticipantsAll patients admitted to medical wards between 2 March and 3 May 2020.OutcomesMain outcomes were diagnosis of COVID-19, SARS-CoV-2 RT-PCR results, sensitivity of SARS-CoV-2 RT-PCR and mortality during hospital admission. For the diagnostic risk score, we report discrimination, calibration and diagnostic accuracy of the model and simplified risk score and internal validation.Results4008 patients were admitted between 2 March and 3 May 2020. 1792 patients (44.8%) were diagnosed with COVID-19, of whom 1391 were SARS-CoV-2 RT-PCR positive and 283 had only negative RT-PCRs. Compared with a clinical reference standard, sensitivity of RT-PCR in hospital patients was 83.1% (95% CI 81.2%–84.8%). Broadly, patients with false-negative RT-PCR COVID-19 and those confirmed by positive PCR had similar demographic and clinical characteristics but lower risk of intensive care unit admission and lower in-hospital mortality (adjusted OR 0.41, 95% CI 0.27–0.61). A simple diagnostic risk score comprising of age, sex, ethnicity, cough, fever or shortness of breath, National Early Warning Score 2, C reactive protein and chest radiograph appearance had moderate discrimination (area under the receiver–operator curve 0.83, 95% CI 0.82 to 0.85), good calibration and was internally validated.ConclusionRT-PCR-negative COVID-19 is common and is associated with lower mortality despite similar presentation. Diagnostic risk scores could potentially help triage patients requiring admission but need external validation.

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Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

10.1101/2021.08.17.21262156 ◽

2021 ◽

Author(s):

Rong Hua ◽

Jianhao Xiong ◽

Gail Li ◽

Yidan Zhu ◽

Zongyuan Ge ◽

...

Keyword(s):

Deep Learning ◽

Risk Score ◽

Learning Algorithm ◽

External Validation ◽

Population Based ◽

Validation Dataset ◽

Internal Validation ◽

Deep Learning Algorithm ◽

Dementia Risk ◽

Fundus Photographs

AbstractImportanceThe Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognized tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, effective, convenient and noninvasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed.ObjectiveTo develop and validate a deep learning algorithm using retinal fundus photographs for estimating the CAIDE dementia risk score and identifying individuals with high dementia risk.DesignA deep learning algorithm trained via fundus photographs was developed, validated internally and externally with cross-sectional design.SettingPopulation-based.ParticipantsA health check-up population with 271,864 adults were randomized into a development dataset (95%) and an internal validation dataset (5%). The external validation used data from the Beijing Research on Ageing and Vessel (BRAVE) with 1,512 individuals.ExposuresThe estimated CAIDE dementia risk score generated from the algorithm.Main Outcome and MeasureThe algorithm’s performance for identifying individuals with high dementia risk was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI).ResultsThe study involved 258,305 participants (mean aged 42.1 ± 13.4 years, men: 52.7%) in development, 13,559 (mean aged 41.2 ± 13.3 years, men: 52.5%) in internal validation, and 1,512 (mean aged 59.8 ± 7.3 years, men: 37.1%) in external validation. The adjusted coefficient of determination (R2) between the estimated and actual CAIDE dementia risk score was 0.822 in the internal and 0.300 in the external validations, respectively. The algorithm achieved an AUC of 0.931 (95%CI, 0.922–0.939) in the internal validation group and 0.782 (95%CI, 0.749–0.815) in the external group. Besides, the estimated CAIDE dementia risk score was significantly associated with both comprehensive cognitive function and specific cognitive domains.Conclusions and RelevanceThe present study demonstrated that the deep learning algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population-based setting. Our findings suggest that fundus photography may be utilized as a noninvasive and more expedient method for dementia risk stratification.Key PointsQuestionCan a deep learning algorithm based on fundus images estimate the CAIDE dementia risk score and identify individuals with high dementia risk?FindingsThe algorithm developed by fundus photographs from 258,305 check-up participants could well identify individuals with high dementia risk, with area under the receiver operating characteristic curve of 0.931 in internal validation and 0.782 in external validation dataset, respectively. Besides, the estimated CAIDE dementia risk score generated from the algorithm exhibited significant association with cognitive function.MeaningThe deep learning algorithm based on fundus photographs has potential to screen individuals with high dementia risk in population-based settings.

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Abstract # OR-20: External Validation of the Leicester Self-Assessment Diabetes Risk Score in a Population with Intellectual Disability

Endocrine Practice ◽

10.1016/s1530-891x(20)43810-8 ◽

2016 ◽

Vol 22 ◽

pp. 12

Author(s):

Laura Gray ◽

Yogini Chudasama ◽

Alison Dunkley ◽

Freya Tyrer ◽

Rebecca Spong ◽

...

Keyword(s):

Intellectual Disability ◽

Risk Score ◽

External Validation ◽

Diabetes Risk ◽

Self Assessment ◽

Diabetes Risk Score

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Faculty Opinions recommendation of Predictors of noninvasive ventilation failure in patients with hematologic malignancy and acute respiratory failure.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1124168.581307 ◽

2008 ◽

Author(s):

Antoni Torres ◽

Jacobo Sellares

Keyword(s):

Respiratory Failure ◽

Acute Respiratory Failure ◽

Noninvasive Ventilation ◽

Hematologic Malignancy ◽

Ventilation Failure

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QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207321666180213092352 ◽

2018 ◽

Vol 21 (3) ◽

pp. 204-214 ◽

Cited By ~ 3

Author(s):

Vesna Rastija ◽

Maja Molnar ◽

Tena Siladi ◽

Vijay Hariram Masand

Keyword(s):

Antioxidant Activity ◽

Antioxidant Activities ◽

Dipicolinic Acid ◽

External Validation ◽

Qsar Model ◽

Internal Validation ◽

Qsar Analysis ◽

Dragon Descriptors ◽

Qsar Models ◽

New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

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External Validation of the VOCAL‐Penn Cirrhosis Surgical Risk Score in Two Large, Independent Health Systems

Liver Transplantation ◽

10.1002/lt.26060 ◽

2021 ◽

Author(s):

Nadim Mahmud ◽

Zachary Fricker ◽

Sarjukumar Panchal ◽

James D. Lewis ◽

David S. Goldberg ◽

...

Keyword(s):

Health Systems ◽

Risk Score ◽

External Validation ◽

Surgical Risk

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Risk of Conversion to Arthroplasty After Hip Arthroscopy: Validation of a Published Risk Score Using an Independent, Prospectively Collected Database

The American Journal of Sports Medicine ◽

10.1177/0363546521993829 ◽

2021 ◽

pp. 036354652199382

Author(s):

Mario Hevesi ◽

Devin P. Leland ◽

Philip J. Rosinsky ◽

Ajay C. Lall ◽

Benjamin G. Domb ◽

...

Keyword(s):

Risk Score ◽

Roc Curve ◽

Hip Arthroscopy ◽

External Validation ◽

Area Under The Curve ◽

Harris Hip Score ◽

Brier Score ◽

Level Of Evidence ◽

Center Edge ◽

Modified Harris Hip Score

Background: Hip arthroscopy is rapidly advancing and increasingly commonly performed. The most common surgery after arthroscopy is total hip arthroplasty (THA), which unfortunately occurs within 2 years of arthroscopy in up to 10% of patients. Predictive models for conversion to THA, such as that proposed by Redmond et al, have potentially substantial value in perioperative counseling and decreasing early arthroscopy failures; however, these models need to be externally validated to demonstrate broad applicability. Purpose: To utilize an independent, prospectively collected database to externally validate a previously published risk calculator by determining its accuracy in predicting conversion of hip arthroscopy to THA at a minimum 2-year follow-up. Study Design: Cohort study (diagnosis); Level of evidence, 1. Methods: Hip arthroscopies performed at a single center between November 2015 and March 2017 were reviewed. Patients were assessed pre- and intraoperatively for components of the THA risk score studied—namely, age, modified Harris Hip Score, lateral center-edge angle, revision procedure, femoral version, and femoral and acetabular Outerbridge scores—and followed for a minimum of 2 years. Conversion to THA was determined along with the risk score’s receiver operating characteristic (ROC) curve and Brier score calibration characteristics. Results: A total of 187 patients (43 men, 144 women, mean age, 36.0 ± 12.4 years) underwent hip arthroscopy and were followed for a mean of 2.9 ± 0.85 years (range, 2.0-5.5 years), with 13 patients (7%) converting to THA at a mean of 1.6 ± 0.9 years. Patients who converted to THA had a mean predicted arthroplasty risk of 22.6% ± 12.0%, compared with patients who remained arthroplasty-free with a predicted risk of 4.6% ± 5.3% ( P < .01). The Brier score for the calculator was 0.04 ( P = .53), which was not statistically different from ideal calibration, and the calculator demonstrated a satisfactory area under the curve of 0.894 ( P < .001). Conclusion: This external validation study supported our hypothesis in that the THA risk score described by Redmond et al was found to accurately predict which patients undergoing hip arthroscopy were at risk for converting to subsequent arthroplasty, with satisfactory discriminatory, ROC curve, and Brier score calibration characteristics. These findings are important in that they provide surgeons with validated tools to identify the patients at greatest risk for failure after hip arthroscopy and assist in perioperative counseling and decision making.

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