Identifying patients at risk for future exacerbations of asthma: Development of a prediction model

Author(s):  
Rik J.B. Loymans ◽  
Persijn J. Honkoop ◽  
Evelien H. Termeer ◽  
Helen K. Reddel ◽  
Jiska B. Snoeck-Stroband ◽  
...  
2016 ◽  
Vol 3 (suppl_1) ◽  
Author(s):  
Natasha Holmes ◽  
J. Owen Robinson ◽  
Sebastian Van Hal ◽  
Wendy Munckhof ◽  
Eugene Athan ◽  
...  

2021 ◽  
pp. 014556132098604
Author(s):  
Krongthong Tawaranurak ◽  
Sinchai Kamolphiwong ◽  
Suthon Sae-wong ◽  
Sangsuree Vasupongayya ◽  
Thossaporn Kamolphiwong ◽  
...  

Objectives: To develop and validate a new clinical prediction model for screening patients at risk for obstructive sleep apnea–hypopnea syndrome (OSAHS). Methods: This study used 2 data sets to develop and validate the model. To build the model, the first data set comprised 892 patients who had diagnostic polysomnography (PSG); data were assessed by multivariate logistic regression analysis. To validate the new model, the second data set comprised 374 patients who were enrolled to undergo overnight PSG. Receiver operating characteristic analysis and all predictive parameters were validated. Results: In the model development phase, univariate analysis showed 6 parameters were significant for prediction apnea–hypopnea index ≥15 events/hour: male sex, choking or apnea, high blood pressure, neck circumference >16 inches (female) or 17 inches (male), waist circumference ≥80 (female) or 90 cm (male), and body mass index >25 kg/m2. Estimated coefficients showed an area under the curve of 0.753. In the model validation phase, the sensitivity and specificity were approximately 93% and 26%, respectively, for identifying OSAHS. Comparison with the Epworth Sleepiness Scale score of ≥10 and STOP-Bang score ≥3 showed sensitivity of 42.26% and 56.23%, respectively, for detecting patients at risk. Conclusions: This new prediction model gives a better result on identifying patients at risk for OSAHS than Epworth Sleepiness Scale and STOP-Bang in terms of sensitivity. Moreover, this model may play a role in clinical decision-making for a comprehensive sleep evaluation to prioritize patients for PSG.


2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
C V Madsen ◽  
B Leerhoey ◽  
L Joergensen ◽  
C S Meyhoff ◽  
A Sajadieh ◽  
...  

Abstract Introduction Post-operative atrial fibrillation (POAF) is currently considered a phenomenon rather than a definite diagnosis. Nevertheless, POAF is associated with an increased rate of complications, including stroke and mortality. The incidence of POAF in acute abdominal surgery has not been reported and prediction of patients at risk has not previously been attempted. Purpose We aim to report the incidence of POAF after acute abdominal surgery and provide a POAF prediction model based on pre-surgery risk-factors. Methods Designed as a prospective, single-centre, cohort study of unselected adult patients referred for acute, general, abdominal surgery. Consecutive patients (>16 years) were included during a three month period. No exclusion criteria were applied. Follow-up was based on chart reviews, including medical history, vital signs, blood samples and electrocardiograms. Chart reviews were performed prior to surgery, at discharge, and three months after surgery. Atrial fibrillation was diagnosed either by specialists in Cardiology or Anaesthesiology on ECG or cardiac rhythm monitoring (≥30 seconds duration). Multiple logistic regression with backward stepwise selection was used for model development. Receiver operating characteristic curves (ROC) including area under the curve (AUC) was produced. The study was approved by the Regional Ethics committee (H-19033464) and comply with the principles of the Declaration of Helsinki of the World Medical Association. Results In total, 466 patients were included. Mean (±SD) age was 51.2 (20.5), 194 (41.6%) were female, and cardiovascular comorbidity was present in ≈10% of patients. Overall incidence of POAF was 5.8% (27/466) and no cases were observed in patients <60 years. Incidence was 15.7% (27/172) for patients ≥60 years. Prolonged hospitalization and death were observed in 40.7% of patients with POAF vs 8.4% patients without POAF (p<0.001). Significant age-adjusted risk-factors were previous atrial fibrillation odds ratio (OR) 6.84 [2.73; 17.18] (p<0.001), known diabetes mellitus OR 3.49 [1.40; 8.69] (p=0.007), and chronic kidney disease OR 3.03 [1.20; 7.65] (p=0.019). A prediction model, based on age, previous atrial fibrillation, diabetes mellitus and chronic kidney disease was produced (Figure 1), and ROC analysis displayed AUC 88.26% (Figure 2). Conclusions A simple risk-stratification model as the one provided, can aid clinicians in identifying those patients at risk of developing POAF in relation to acute abdominal surgery. This is important, as patients developing POAF are more likely to experience complications, such as prolonged hospitalization and death. Closer monitoring of heart rhythm and vital signs should be considered in at-risk patients older than 60 years. Model validation is warranted. FUNDunding Acknowledgement Type of funding sources: None.


2020 ◽  
Author(s):  
Jan Marcusson ◽  
Magnus Nord ◽  
Huan-Ji Dong ◽  
Johan Lyth

Abstract Background: The healthcare for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future healthcare system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine healthcare data. Methods : We used the healthcare data on 40,728 persons, 75-109 years of age to predict hospital in-ward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC. Results: The prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0·69 [95% confidence interval, CI 0·68–0·70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31% and 29%, respectively. Conclusion : A prediction model based on routine administrative healthcare data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for healthcare.


2019 ◽  
Author(s):  
Jan Marcusson ◽  
Magnus Nord ◽  
Huan-Ji Dong ◽  
Johan Lyth

Abstract Background The health care for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future health-care system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine health-care data.Methods We used the health-care data on 40,728 persons, 75-109 years of age to predict hospital in-ward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC.Results The prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0·69 [95% confidence interval, CI 0·68–0·70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31% and 29%, respectively.Conclusion A prediction model based on routine administrative health-care data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for health care.


PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0215459
Author(s):  
Liesbeth B. E. Bosma ◽  
Nienke van Rein ◽  
Nicole G. M. Hunfeld ◽  
Ewout W. Steyerberg ◽  
Piet H. G. J. Melief ◽  
...  

Thorax ◽  
2016 ◽  
Vol 71 (9) ◽  
pp. 838-846 ◽  
Author(s):  
Rik J B Loymans ◽  
Persijn J Honkoop ◽  
Evelien H Termeer ◽  
Jiska B Snoeck-Stroband ◽  
Willem J J Assendelft ◽  
...  

2020 ◽  
Author(s):  
Jan Marcusson ◽  
Magnus Nord ◽  
Huan-Ji Dong ◽  
Johan Lyth

Abstract Background: The healthcare for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future healthcare system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine healthcare data. Methods : We used the healthcare data on 40,728 persons, 75-109 years of age to predict hospital in-ward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC. Results: The prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0·69 [95% confidence interval, CI 0·68–0·70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31% and 29%, respectively. Conclusion : A prediction model based on routine administrative healthcare data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for healthcare.


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