scholarly journals PICADAR: a diagnostic predictive tool for primary ciliary dyskinesia

2016 ◽  
Vol 47 (4) ◽  
pp. 1103-1112 ◽  
Author(s):  
Laura Behan ◽  
Borislav D. Dimitrov ◽  
Claudia E. Kuehni ◽  
Claire Hogg ◽  
Mary Carroll ◽  
...  
Keyword(s):  
Primary Ciliary Dyskinesia ◽  
Neonatal Intensive Care ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Clinical Prediction Rule ◽  
Predictive Tool ◽  
Ciliary Dyskinesia ◽  
Diagnostic Outcome

Symptoms of primary ciliary dyskinesia (PCD) are nonspecific and guidance on whom to refer for testing is limited. Diagnostic tests for PCD are highly specialised, requiring expensive equipment and experienced PCD scientists. This study aims to develop a practical clinical diagnostic tool to identify patients requiring testing.Patients consecutively referred for testing were studied. Information readily obtained from patient history was correlated with diagnostic outcome. Using logistic regression, the predictive performance of the best model was tested by receiver operating characteristic curve analyses. The model was simplified into a practical tool (PICADAR) and externally validated in a second diagnostic centre.Of 641 referrals with a definitive diagnostic outcome, 75 (12%) were positive. PICADAR applies to patients with persistent wet cough and has seven predictive parameters: full-term gestation, neonatal chest symptoms, neonatal intensive care admittance, chronic rhinitis, ear symptoms, situs inversus and congenital cardiac defect. Sensitivity and specificity of the tool were 0.90 and 0.75 for a cut-off score of 5 points. Area under the curve for the internally and externally validated tool was 0.91 and 0.87, respectively.PICADAR represents a simple diagnostic clinical prediction rule with good accuracy and validity, ready for testing in respiratory centres referring to PCD centres.

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Individual Risk ◽  
European Project ◽  
Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

scholarly journals Whole-exome sequencing accuracy in the diagnosis of primary ciliary dyskinesia

2020 ◽  
Vol 6 (4) ◽  
pp. 00213-2020
Author(s):  
Alex Gileles-Hillel ◽  
Hagar Mor-Shaked ◽  
David Shoseyov ◽  
Joel Reiter ◽  
Reuven Tsabari ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Primary Ciliary Dyskinesia ◽  
Diagnostic Yield ◽  
Characteristic Curve ◽  
Transmission Electron Microscopy Analysis ◽  
Alternative Diagnosis ◽  
Pathogenic Variants ◽  
Whole Exome ◽  
Ciliary Dyskinesia

The diagnosis of primary ciliary dyskinesia (PCD) relies on clinical features and sophisticated studies. The detection of bi-allelic disease-causing variants confirms the diagnosis. However, a standardised genetic panel is not widely available and new disease-causing genes are continuously identified.To assess the accuracy of untargeted whole-exome sequencing (WES) as a diagnostic tool for PCD, patients with symptoms highly suggestive of PCD were consecutively included. Patients underwent measurement of nasal nitric oxide (nNO) levels, ciliary transmission electron microscopy analysis (TEM) and WES. A confirmed PCD diagnosis in symptomatic patients was defined as a recognised ciliary ultrastructural defect on TEM and/or two pathogenic variants in a known PCD-causing gene.Forty-eight patients (46% male) were enrolled, with a median age of 10.0 years (range 1.0–37 years). In 36 patients (75%) a diagnosis of PCD was confirmed, of which 14 (39%) patients had normal TEM. A standalone untargeted WES had a diagnostic yield of 94%, identifying bi-allelic variants in 11 known PCD-causing genes in 34 subjects. A nNO<77 nL·min was nonspecific when including patients younger than 5 years (area under the receiver operating characteristic curve (AUC) 0.75, 95% CI 0.60–0.90). Consecutive WES considerably improved the diagnostic accuracy of nNO in young children (AUC 0.97, 95% CI 0.93–1). Finally, WES established an alternative diagnosis in four patients.In patients with clinically suspected PCD and low nNO levels, WES is a simple, beneficial and accurate next step to confirm the diagnosis of PCD or suggest an alternative diagnosis, especially in preschool-aged children in whom nNO is less specific.

scholarly journals Extended Interval Tobramycin Pharmacokinetics in a Pediatric Patient With Primary Ciliary Dyskinesia Presenting With an Acute Respiratory Exacerbation

2018 ◽  
Vol 23 (2) ◽  
pp. 159-163
Author(s):  
Kristi L. Higgins ◽  
Cady Noda ◽  
Jeremy S. Stultz
Keyword(s):  
Cystic Fibrosis ◽  
Primary Ciliary Dyskinesia ◽  
Elimination Rate ◽  
Area Under The Curve ◽  
Volume Of Distribution ◽  
Treatment Recommendation ◽  
Pharmacokinetic Parameters ◽  
Maximum Serum ◽  
First Case ◽  
Ciliary Dyskinesia

The pharmacokinetics of tobramycin in patients with ciliary dyskinesia have not been previously reported. A 10-year-old female patient with primary ciliary dyskinesia was admitted to the general pediatrics floor with an acute respiratory exacerbation after several months of worsening lung function that was unresponsive to oral antibiotics. Extrapolating from cystic fibrosis dosing regimens, she was given intravenous tobramycin 320 mg (10.3 mg/kg/day) on admission as a result of concern for a Pseudomonas aeruginosa infection. Two-point pharmacokinetic monitoring revealed a maximum serum concentration (Cmax) of 18.9 mg/L and a 24-hour area under the curve (AUC0–24hr) of 58.8 (mg × hr)/L, as well as a volume of distribution (Vd) of 0.5 L/kg and an elimination rate (Ke) of 0.34 hr−1. After a dosage increase to tobramycin 400 mg (12.8 mg/kg/day), pharmacokinetic parameters on 2 assessments were as follows: Vd 0.37 to 0.39 L/kg, Ke 0.33 to 0.39 hr−1, Cmax 27.8 to 28.7 mg/L, and AUC0–24h 78.4 to 89.4 (mg × hr)/L. This was the first case report of aminoglycoside pharmacokinetics in a patient with ciliary dyskinesia. The administration of larger doses (up to 12.8 mg/kg/day) of extended-interval tobramycin, similar to the treatment recommendation of at least 10 mg/kg/day for cystic fibrosis patients, was necessary in this patient to achieve serum concentrations that were appropriate for treatment.

scholarly journals A Predictive Model for Postoperative Progressive Haemorrhagic Injury in Traumatic Brain Injuries

2021 ◽  
Author(s):  
Tiange Chen ◽  
Siming Chen ◽  
Yilei Chen ◽  
Lei Wang ◽  
Yun Wu ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Predictive Model ◽  
Brain Injuries ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Individual Risk ◽  
Lasso Regression ◽  
Predictive Tool

Abstract Background Progressive haemorrhagic injury after surgery in patients with traumatic brain injury often results in poor patient outcomes. This study aimed to develop and validate a practical predictive tool that can reliably estimate the risk of postoperative progressive haemorrhagic injury (PHI) in patients with traumatic brain injury (TBI). Methods Data from 645 patients who underwent surgery for TBI between March 2018 and December 2020 were collected. The outcome was postoperative intracranial PHI, which was assessed on postoperative computed tomography. The least absolute shrinkage and selection operator (LASSO) regression model, univariate analysis, and Delphi method were applied to select the most relevant prognostic predictors. We combined conventional coagulation test (CCT) data, thromboelastography (TEG) variables, and several predictors to develop a predictive model using binary logistic regression and then presented the results as a nomogram. The predictive performance of the model was assessed with calibration and discrimination. Internal validation was assessed. ResultsThe signature, which consisted of 11 selected features, was significantly associated with intracranial PHI (p < 0.05, for both primary and validation cohorts). Predictors in the prediction nomogram included age, S-pressure, D-pressure, pulse, temperature, reaction time, PLT, prothrombin time, activated partial thromboplastin time, FIB, and kinetics values. The model showed good discrimination, with an area under the curve of 0.8694 (95% CI, 0.8083–0.9304), and good 3 calibration. ConclusionThis model is based on a nomogram incorporating CCT and TEG variables, which can be conveniently derived at hospital admission. It allows determination of this individual risk for postoperative intracranial PHI and will facilitate a timely intervention to improve outcomes.

scholarly journals Development of the Chronic Subdural Hematoma Grading System to Predict Postoperative Recurrence

2021 ◽  
Author(s):  
Shuai Han ◽  
Yan Feng ◽  
Na Chuan Xu ◽  
Zhen Xue Li ◽  
Yun Chun Zhang ◽  
...  
Keyword(s):  
Subdural Hematoma ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Chronic Subdural Hematoma ◽  
Cerebral Atrophy ◽  
Predictive Performance ◽  
Postoperative Recurrence ◽  
Expansion Rate ◽  
Grading System ◽  
Grading Systems

Abstract Objective Assessing the risk of postoperative recurrence of chronic subdural hematoma (CSDH) is a clinical focus. To screen the main factors associated with the perioperative hematoma recurrence. We also propose a new prognostic grading system and compare it with previous grading systems to deliver a quick and effective system.Methods We included 242 unilateral patients with CSDH as the training group for modeling. Factors predicting postoperative recurrence requiring reoperation (RrR) were determined using univariate and multivariate regression analyses. The cut-off value for the brain re-expansion rate was determined through receiver operating characteristic curve analysis. Based on these, we developed a new prognostic scoring system and conducted preliminary verification. A verification group including 119 patients with unilateral CSDH was used to verify the predictive performance of the new and other grading systems.Results The key factors for predicting unilateral CSDH recurrence were cerebral re-expansion rate (≤ 40%) at postoperative days 7 – 9 and the preoperative computed tomography density classification (isodense or hyperdense, or separated or laminar types). Cerebral atrophy played a key role in brain re-expansion. The CSDH prognostic grading system ranged from 0 to 3. An increased score was associated with a more accurate progressive increase in the RrR rate. Our grading system demonstrated the best predictive performance compared with other systems (area under the curve = 0.856).Conclusions Our prognostic grading system could quickly and effectively screen high-risk RrR patients with unilateral CSDH. However, increased attention should be paid to brain re-expansion rate after surgery in patients with CSDH.

scholarly journals A predictive model for postoperative progressive haemorrhagic injury in traumatic brain injuries

BMC Neurology ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tiange Chen ◽  
Siming Chen ◽  
Yun Wu ◽  
Yilei Chen ◽  
Lei Wang ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Predictive Model ◽  
Brain Injuries ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Individual Risk ◽  
Lasso Regression ◽  
Predictive Tool

Abstract Background Progressive haemorrhagic injury after surgery in patients with traumatic brain injury often results in poor patient outcomes. This study aimed to develop and validate a practical predictive tool that can reliably estimate the risk of postoperative progressive haemorrhagic injury (PHI) in patients with traumatic brain injury (TBI). Methods Data from 645 patients who underwent surgery for TBI between March 2018 and December 2020 were collected. The outcome was postoperative intracranial PHI, which was assessed on postoperative computed tomography. The least absolute shrinkage and selection operator (LASSO) regression model, univariate analysis, and Delphi method were applied to select the most relevant prognostic predictors. We combined conventional coagulation test (CCT) data, thromboelastography (TEG) variables, and several predictors to develop a predictive model using binary logistic regression and then presented the results as a nomogram. The predictive performance of the model was assessed with calibration and discrimination. Internal validation was assessed. Results The signature, which consisted of 11 selected features, was significantly associated with intracranial PHI (p < 0.05, for both primary and validation cohorts). Predictors in the prediction nomogram included age, S-pressure, D-pressure, pulse, temperature, reaction time, PLT, prothrombin time, activated partial thromboplastin time, FIB, and kinetics values. The model showed good discrimination, with an area under the curve of 0.8694 (95% CI, 0.8083–0.9304), and good calibration. Conclusion This model is based on a nomogram incorporating CCT and TEG variables, which can be conveniently derived at hospital admission. It allows determination of this individual risk for postoperative intracranial PHI and will facilitate a timely intervention to improve outcomes.

Abstract TP13: External Validation of Stroke Prognostication Scales - A Retrospective Cohort Study

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Nauman Jahangir ◽  
Nicholas Lanzotti ◽  
Kyle Gollon ◽  
Mehwish Farooqi ◽  
Michael Buhnerkempe ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Characteristic Curve ◽  
External Validation ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Predictive Performance ◽  
Discriminatory Ability ◽  
Single Center Study

Introduction: In recent years, many scoring models have been proposed to predict clinical outcomes after acute ischemic stroke. Aim of our study was to perform a comparative analysis of these scoring systems to assess predictive reliability. Method: This retrospective single center study included 166 community-based patients presenting with an acute ischemic stroke between 2015 and 2018 who had undergone mechanical thrombectomy with or without IV r-tPA administration prior to the procedure. Patients with unknown 90 day Modified Ranking Scale (mRS) were excluded from the study. We included SPAN-100, THRIVE, HIAT2, iScore , TPI, DRAGON, ASTRAL and HAT predictive models to our study. To predict MRS at 90 days, we first dichotomize mRS into two groups: scores of 0 and 1 and scores 2 and above. We then used logistic regression to find the association between a stroke score and the probability of having a 90-day mRS of 2 or above. Separate univariate logistic regressions were fit for each stroke score. We assessed the ability of each stroke score to predict 90-day mRS using the area-under-the-curve (AUC) of the receiver operating characteristic curve (ROC - plot of sensitivity against 1-specificity). AUC values range from 0.5 to 1 with values above 0.7 showing good discriminatory ability. Results: SPAN-100, HIAT2, iScore, and ASTRAL scores have similar predictive ability with AUC values over 0.7 (Table 1). The ASTRAL score had the highest predictive ability with a score above 31.5 indicating a high likelihood of a 90-day MRS ≥ 2 (Table 1). The TPI, DRAGON, and HAT scores all had AUCs below 0.65 indicating poor predictive performance in our data. Conclusion: The SPAN-100, HIAT2, iScore, and ASTRAL scores reliably predicts 90-day mRS of 2 or greater in patients with acute ischemic stroke.

scholarly journals Flu-IV score: a predictive tool for assessing the risk of invasive mechanical ventilation in patients with influenza-related pneumonia

2021 ◽  
Author(s):  
Liang Chen ◽  
Xiudi Han ◽  
YanLi Li ◽  
Chunxiao Zhang ◽  
Xiqian Xing
Keyword(s):  
Mechanical Ventilation ◽  
Assessment Tool ◽  
Invasive Mechanical Ventilation ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Neuraminidase Inhibitor ◽  
Teaching Hospitals ◽  
Predictive Tool ◽  
Patients At Risk ◽  
A Prospective Study

Abstract Background The need for invasive mechanical ventilation (IMV) is linked to significant morbidity and mortality in patients with influenza-related pneumonia (Flu-p). We aimed to develop an assessment tool to predict IMV among Flu-p patients within 14 days of admission. Methods In total, 1107 Flu-p patients from five teaching hospitals were retrospectively enrolled from January 2012 - December 2019 and used to develop a predictive model. Results Overall, 10.6% (117/1107) of patients underwent IMV within 14 days of admission. Multivariate regression analyses revealed that the following factors were associated with IMV: early neuraminidase inhibitor use (-3 points), lymphocytes < 0.8×109/L (1 point), multi-lobar infiltrates (1 point), systemic corticosteroid use (1 point), age ≥ 65 years old (2 points), PaO2/FiO2 < 300 mmHg (2 points), respiratory rate ≥ 30 breaths/min (3 points), and arterial PH < 7.35 (4 points). A total score of five points was used to identify patients at risk of IMV. This model had a sensitivity of 85.5%, a specificity of 88.8%, and exhibited better predictive performance than the ROX index (AUROC = 0.909 vs 0.594, p = 0.004), modified ROX index (AUROC = 0.909 vs 0.633, p = 0.012), and HACOR scale (AUROC = 0.909 vs 0.622, p < 0.001) using the validation cohort.Conclusions Flu-IV score is a valuable prediction rule for 14-day IMV rates in Flu-p patients. However, it should be validated in a prospective study before implementation.

scholarly journals Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245281
Author(s):  
Bianca Magro ◽  
Valentina Zuccaro ◽  
Luca Novelli ◽  
Lorenzo Zileri ◽  
Ciro Celsa ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Brier Score ◽  
Clinical Prediction ◽  
Duration Of Symptoms ◽  
Derivation Cohort ◽  
Independent Risk Factors

Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. Methods and findings We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39–2.12), diabetes (HR 1.21, 95%CI 1.02–1.45), coronary heart disease (HR 1.40 95% CI 1.09–1.80), chronic liver disease (HR 1.78, 95%CI 1.16–2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002–1.0005). The AUC was 0.822 (95%CI 0.722–0.922) in the derivation cohort and 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). Conclusions A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.

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