Development and internal validation of a clinical prediction model for non-recovery in older adults with back pain

Aims To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score. Methods In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism. Results Of the 377 patients used for model derivation, 58 (15%) had an acute AVF postoperatively. The following preoperative measures on multivariable analysis were summarized in the five-point AVA score: intravertebral instability (≥ 5 mm), focal kyphosis (≥ 10°), duration of symptoms (≥ 30 days), intravertebral cleft, and previous history of vertebral fracture. Internal validation showed a mean optimism of 0.019 with a corrected AUC of 0.77. A cut-off of ≤ one point was chosen to classify a low risk of AVF, for which only four of 137 patients (3%) had AVF with 92.5% sensitivity and 45.6% specificity. A cut-off of ≥ four points was chosen to classify a high risk of AVF, for which 22 of 38 (58%) had AVF with 41.5% sensitivity and 94.5% specificity. Conclusion In this study, the AVA score was found to be a simple preoperative method for the identification of patients at low and high risk of postoperative acute AVF. This model could be applied to individual patients and could aid in the decision-making before vertebral augmentation. Cite this article: Bone Joint J 2022;104-B(1):97–102.

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Predicting pain recovery in patients with acute low back pain: a study protocol for a broad validation of a prognosis prediction model

BMJ Open ◽

10.1136/bmjopen-2020-040785 ◽

2020 ◽

Vol 10 (10) ◽

pp. e040785

Author(s):

Fernanda Gonçalves Silva ◽

Tatiane Mota da Silva ◽

Gabriele Alves Palomo ◽

Mark Jonathan Hancock ◽

Lucíola da Cunha Menezes Costa ◽

...

Keyword(s):

Low Back Pain ◽

Back Pain ◽

Prediction Model ◽

Sao Paulo ◽

Validation Dataset ◽

Low Back ◽

São Paulo ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Acute Low Back Pain

BackgroundThe clinical course of acute low back pain (LBP) is generally favourable; however, there is significant variability in the prognosis of these patients. A clinical prediction model to predict the likelihood of pain recovery at three time points for patients with acute LBP has recently been developed. The aim of this study is to conduct a broad validation test of this clinical prediction model, by testing its performance in a new sample of patients and a different setting.MethodsThe validation study with a prospective cohort design will recruit 420 patients with recent onset non-specific acute LBP, with moderate pain intensity, seeking care in the emergency departments of hospitals in São Paulo, Brazil. The primary outcome measure will be days to recovery from pain. The predicted probability of pain recovery for each individual will be computed based on predictions of the development model and this will be used to test the performance (calibration and discrimination) in the validation dataset.DiscussionThe findings of this study will better inform about the performance of the clinical prediction model, helping both clinicians and patients. If the model’s performance is acceptable, then future research should evaluate the impact of the prediction model, assessing whether it produces a change in clinicians’ behaviour and/or an improvement in patient outcomes.Ethics and disseminationEthics were granted by the Research Ethics Committee of the Universidade Cidade de São Paulo, #20310419.4.0000.0064. Study findings will be disseminated widely through peer-reviewed publications and conference presentations.

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Development and Internal Validation of a Multivariable Prediction Model for Adrenocortical-Carcinoma-Specific Mortality

Cancers ◽

10.3390/cancers12092720 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2720

Author(s):

Madeleine H. T. Ettaieb ◽

Sander M. J. van Kuijk ◽

Annelies de Wit-Pastoors ◽

Richard A. Feelders ◽

Eleonora P. M. Corssmit ◽

...

Keyword(s):

Prediction Model ◽

Adrenocortical Carcinoma ◽

Cox Proportional Hazards ◽

Adrenal Tumors ◽

Mortality Data ◽

Clinical Prediction ◽

Discriminative Ability ◽

Internal Validation ◽

Clinical Prediction Model ◽

Specific Mortality

Adrenocortical carcinoma (ACC) has an incidence of about 1.0 per million per year. In general, survival of patients with ACC is limited. Predicting survival outcome at time of diagnosis is a clinical challenge. The aim of this study was to develop and internally validate a clinical prediction model for ACC-specific mortality. Data for this retrospective cohort study were obtained from the nine centers of the Dutch Adrenal Network (DAN). Patients who presented with ACC between 1 January 2004 and 31 October 2013 were included. We used multivariable Cox proportional hazards regression to compute the coefficients for the prediction model. Backward stepwise elimination was performed to derive a more parsimonious model. The performance of the initial prediction model was quantified by measures of model fit, discriminative ability, and calibration. We undertook an internal validation step to counteract the possible overfitting of our model. A total of 160 patients were included in the cohort. The median survival time was 35 months, and interquartile range (IQR) 50.7 months. The multivariable modeling yielded a prediction model that included age, modified European Network for the Study of Adrenal Tumors (mENSAT) stage, and radical resection. The c-statistic was 0.77 (95% Confidence Interval: 0.72, 0.81), indicating good predictive performance. We developed a clinical prediction model for ACC-specific mortality. ACC mortality can be estimated using a relatively simple clinical prediction model with good discriminative ability and calibration.

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The creation, internal validation and external validation of a novel clinical prediction model for the early diagnosis of prostate cancer

International Journal of Surgery ◽

10.1016/j.ijsu.2015.07.573 ◽

2015 ◽

Vol 23 ◽

pp. S120-S121

Author(s):

R. Foley ◽

K. Murphy ◽

D. Lundon ◽

R. Power ◽

A. Perry ◽

...

Keyword(s):

Prostate Cancer ◽

Early Diagnosis ◽

Prediction Model ◽

External Validation ◽

Clinical Prediction ◽

Internal Validation ◽

Clinical Prediction Model ◽

The Creation

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A Prediction Model of Sufficient Filter Lifespan in Anticoagulation-free CRRT Patients

10.21203/rs.3.rs-73156/v1 ◽

2020 ◽

Author(s):

Wei Zhang ◽

Ming Bai ◽

Ling Zhang ◽

Yan Yu ◽

Yangping Li ◽

...

Keyword(s):

Prediction Model ◽

Predictive Value ◽

Primary Outcome ◽

Area Under The Curve ◽

Bleeding Risk ◽

Multivariable Logistic Regression Analysis ◽

Clinical Prediction ◽

Internal Validation ◽

Clinical Prediction Model ◽

Final Model

Abstract Background: Anticoagulation-free continuous renal replacement therapy (CRRT) was recommended by the current clinical guideline for patients with increased bleeding risk and contraindications of citrate and resulted in heterogeneous filter lifespan. There was no prediction model to identify the patients would have sufficient filter lifespan when they have to accept CRRT without the use of any anticoagulation. The purpose of our present study is to develop a clinical prediction model of sufficient filter lifespan in anticoagulation-free CRRT patients.Method: Patients who underwent anticoagulation-free CRRT in our center between June 2013 and June 2019 were retrospectively included. The primary outcome was sufficient filter lifespan (≥ 24 hours). The final model was established by using multivariable logistic regression analysis. And, the prediction model was validated in an external cohort. Results: A total of 170 patients were included in the development cohort. Sufficient filter lifespan were observed in 80 patients. The probability of sufficient filter lifespan could be calculated using the following regression formula: P (%) = exp (Z)/1 + exp (Z), where Z = 0.49896-(0.08552*BMI)+(0.44107*T)+(0.03373*MAP)-(0.03389*WBC)+(1.51579*[vasopressor=1])-(0.01132*PLT)+(0.00422*ALP)-(2.66910*pH)-(0.00214*UA)+(0.05992*BUN)+(0.00400*Db)–(0.00014*D-dimer)+(0.02818*APTT). The area under the curve (AUC) of the stepwise model and internal validation model was 0.82 (95%CI [0.76-0.88]) and 0.8 (95%CI [0.74-0.87]), respectively. At the optimal cut-off value of -0.1052, the positive predictive value and the negative predictive value of the stepwise model was 0.77 and 0.79, respectively. The AUC of the external model was 0.82 (95%CI [0.69-0.96]). Conclusion: The use of a prediction model instead of an assessment based only on coagulation parameters could facilitate the identification of the patients with filter lifespan of ≥ 24 hours when they accepted anticoagulation-free CRRT.

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Faculty Opinions recommendation of Predicting recovery in patients with acute low back pain: A Clinical Prediction Model.

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

10.3410/f.727224631.793528334 ◽

2017 ◽

Author(s):

Massimo Allegri ◽

Susanna Gerboni

Keyword(s):

Low Back Pain ◽

Back Pain ◽

Prediction Model ◽

Low Back ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Acute Low Back Pain

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