Classical Regression and Predictive Modeling

Derivation and Internal Validation of a Disease-Specific Cardiovascular Risk Prediction Model for Patients With Psoriatic Arthritis and Psoriasis

OBJECTIVE

To address suboptimal cardiovascular risk prediction in patients with psoriatic disease (PsD), we developed and internally validated a five-year disease-specific cardiovascular risk prediction model.

METHODS

We analyzed data from a prospective cohort of participants with PsD without a history of cardiovascular events. Traditional cardiovascular risk factors and PsD-related measures of disease activity were considered as potential predictors. The study outcome included nonfatal and fatal cardiovascular events. A base prediction model included 10 traditional cardiovascular risk factors. Eight PsD-related factors were assessed by adding them to the base model to create expanded models, which were controlled for PsD therapies. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression with 10-fold cross-validation. Model performance was assessed using measures of discrimination and calibration and measures of sensitivity and specificity.

RESULTS

Between 1992 and 2020, 85 of 1,336 participants developed cardiovascular events. Discrimination of the base model (with traditional cardiovascular risk factors alone) was excellent, with an area under the receiver operator characteristic curve (AUC) of 85.5 (95% confidence interval [CI] 81.9-89.1). Optimal models did not select any of the tested disease-specific factors. In a sensitivity analysis, which excluded lipid lowering and antihypertensive treatments, the number of damaged joints was selected in the expanded model. However, this model did not improve risk discrimination compared to the base model (AUC 85.5, 95% CI 82.0-89.1).

CONCLUSION

Traditional cardiovascular risk factors alone are effective in predicting cardiovascular risk in patients with PsD. A risk score based on these factors performed well, indicating excellent discrimination and calibration.

The effects of dynamic motion instability system training on motor function and balance after stroke: A randomized trial

BACKGROUND

The balance and postural control of humans is related to the coordination of dynamic perception and movement. Multiple senses, such as vision, vestibular sense, proprioception, and/or a single sensory disorder, would lead to its integration disorder and induce imbalance and abnormal gait.

OBJECTIVE

The present study aimed to determine the effects of dynamic motion instability system training (DMIST) on the balance and motor function of hemiplegic patients after stroke.

METHODS

In this assessor-blinded, randomized controlled trial, the participants allocated to the intervention group (n = 20) received 30 minutes of conventional treatment and 20 minutes of DMIST training. Participants randomized to the control group (n = 20) received the same dose of conventional therapy and 20 minutes of general balance training. Rehabilitation was performed 5 times per week for 8 weeks. The primary outcome was the Fugl-Meyer assessment for the lower extremity (FMA-LE), and the secondary outcomes were the Berg balance scale (BBS) and gait function. Data were collected at baseline and immediately after the intervention.

RESULTS

After 8 weeks (t1), both groups showed significant post-intervention improvements in BBS, FMA-LE, gait speed and stride length (P < 0.05); there were significant positive correlations between the increase in FMA-LE and gait speed and stride length. Compared with the control group, the DMIST group showed significant post-intervention improvements in FMA-LE, gait speed and stride length (P < 0.05). However, no significant differences between the groups were found over time with respect to BBS (P > 0.05). The experiences of patients with DMIST were positive, and no serious adverse events were related to the interventions.

CONCLUSION

Supervised DMIST could be highly effective in treating lower-limb motor function in patients with stroke. Frequent (weekly) and medium-term (8 weeks) dynamic motion instability-guided interventions might be highly effective in enhancing motor function, and subsequently improving gait in stroke patients.

Evolution of Making Clinical Predictions in Neurosurgery

Prediction of clinical outcomes is an essential task for every physician. Physicians may base their clinical prediction of an individual patient on their intuition and on scientific material such as studies presenting population risks and studies reporting on risk factors (prognostic factors). A relatively new and more informative approach for making clinical predictions relies on the use of statistical models that simultaneously consider multiple predictors that provide an estimate of the patient's absolute risk of an outcome. There is a growing body of literature in the neurosurgical field reporting on clinical prediction models. These tools have high potential in supporting (not replacing) neurosurgeons with their prediction of a patient's outcome. If used sensibly, these tools pave the way for more informed decision-making with or for individual patients. Patients and their significant others want to know their risk of the anticipated outcome, how it is derived, and the uncertainty associated with it. Learning from these prediction models and communicating the output to others has become an increasingly important skill neurosurgeons have to master. This article describes the evolution of making clinical predictions in neurosurgery, synopsizes key phases for the generation of a useful clinical prediction model, and addresses some considerations when deploying and communicating the results of a prediction model. The paper is illustrated with multiple examples from the neurosurgical literature, including predicting arachnoid cyst rupture, predicting rebleeding in patients suffering from aneurysmal subarachnoid hemorrhage, and predicting survival in glioblastoma patients.