Model Receiver Operating Characteristic Research Articles

Development and validation of a nomogram model for individualized prediction of hypertension risk in patients with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) with hypertension (DH) is the most common diabetic comorbidity. Patients with DH have significantly higher rates of cardiovascular disease morbidity and mortality. The objective of this study was to develop and validate a nomogram model for the prediction of an individual's risk of developing DH. A total of 706 T2DM patients who met the criteria were selected and divided into a training set (n = 521) and a validation set (n = 185) according to the discharge time of patients. By using multivariate logistic regression analysis and stepwise regression, the DH nomogram prediction model was created. Calibration curves were used to evaluate the model's accuracy, while decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to evaluate the model's clinical applicability and discriminatory power. Age, body mass index (BMI), diabetic nephropathy (DN), and diabetic retinopathy (DR) were all independent risk factors for DH (P < 0.05). Based on independent risk factors identified by multivariate logistic regression, the nomogram model was created. The model produces accurate predictions. If the total nomogram score is greater than 120, there is a 90% or higher chance of developing DH. In the training and validation sets, the model's ROC curves are 0.762 (95% CI 0.720–0.803) and 0.700 (95% CI 0.623–0.777), respectively. The calibration curve demonstrates that there is good agreement between the model’s predictions and the actual outcomes. The decision curve analysis findings demonstrated that the nomogram model was clinically helpful throughout a broad threshold probability range. The DH risk prediction nomogram model constructed in this study can help clinicians identify individuals at high risk for DH at an early stage, which is a guideline for personalized prevention and treatments.

Objective Acoustic Quantification of Perceived Voice Tremor Severity

Purpose This study compared auditory-perceptual measures of presence/absence and severity of vocal tremor to acoustic markers of vocal tremor. The validity (both concurrent and diagnostic) of various acoustic markers of vocal tremor was also assessed. Method Fifty-six midvowel sustained [a:] recordings were selected to yield a representative convenience sample of vocal tremor. After training with 10 synthesized samples, 4 female audiologists rated these samples on "voice tremor severity" on a continuous 10-cm scale. Afterward, 15 randomly selected recordings were presented a 2nd time for intrarater reliability assessment. Customized audio signal processing in Praat yielded 12 acoustic measures of rate, extent and perturbation of fundamental frequency (f 0), and intensity level (IL) modulation. Enter-type multiple linear regression analysis was applied to weight and combine these acoustic variables into an acoustic model of vocal tremor severity. Results After removing the vocal tremor severity ratings of 1 of the audiologists because of insufficient intra- and interrater reliability, mean single-measures consistency-type intraclass correlation coefficients equaled .83 within raters and .72 between raters. Correlation between mean ratings and the 12 acoustic markers ranged from .76 for median extent of f 0 modulation to .11 for rate of IL modulation. Correlation between mean ratings and the acoustic model was .89. Analysis of this model's receiver operating characteristics yielded an area under receiver operating characteristic curve of .93, denoting sensitivity of .87 and specificity of .91. Conclusions This study demonstrated that auditory-perceptual ratings of vocal tremor severity are guided primarily by f 0 and IL modulation extent, less by modulation perturbation, and least by modulation rate. The acoustic model covering all these modulation properties yielded acceptable results in terms of both concurrent and diagnostic validity. However, external cross-validation of this model is warranted before applying it in clinical voice/speech assessment.

Static and Dynamic Patient Characteristics as Predictors of Criminal Recidivism: A Prospective Study in a Dutch Forensic Psychiatric Sample.

If clinicians in forensic psychiatry want to reduce risk of reoffending in their patients, they require insight into dynamic risk factors, and evidence that these add predictive power to static risk indicators. Predictors need to be evaluated under clinically realistic circumstances. This study aimed to validate dynamic and static variables as predictors of reconviction in a naturalistic outcome study. Data on static and dynamic risk factors were collected for 151 patients discharged from Dutch forensic psychiatric hospitals. Community follow-up was prospective, with a 5.5 year minimum. A prediction model was developed using Cox regression analysis. The magnitude of the predictive power of this model was estimated using receiver operating characteristic (ROC) analysis. The final prediction model contained four static and no dynamic predictors. The model's ROC area under the curve was .79 (95% CI .69-.89). Clinical risk ratings were non-predictive. Post hoc analyses exploring the influence of subgroups of patients did not yield better models. It is concluded that a small set of static predictors yielded a good estimate of future reconvictions; inclusion of dynamic predictors did not add predictive power.