OBJECTIVE: To construct a predictive model to foretell congenital muscular torticollis (CMT) on the basis of clinical correlates. DESIGN: Correlation study. SETTING: Regional hospital. PARTICIPANTS: A consecutive series of 1021 newborn infants. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Participants underwent portable ultrasonography to diagnose CMT. Significant clinical correlates were identified to construct a predictive model using the logistic regression model. RESULTS: Forty of 1021 infants were diagnosed with CMT using ultrasonography, yielding an overall incidence of 3.92%. Birth body length (odds ratio [OR]=1.38; 95% confidence interval [CI], 1.49-2.38), facial asymmetry (OR=21.75; 95% CI, 6.6-71.7), plagiocephaly (OR=22.3; 95% CI, 7.01-70.95), perineal trauma during delivery (OR=4.26; 95% CI, 1.25-14.52), and primiparity (OR=6.32; 95% CI, 2.34-17.04) were significant correlates. A predictive logistic regression model with the incorporation of these 4 correlates was developed. We used cross-validation with a receiver operating characteristic curve to validate the predictive model. CONCLUSIONS: Our study successfully developed a quantitative predictive model for estimating the risk of CMT on the basis of clinical correlates only. This model has good discriminative ability for classifying CMT and non-CMT by yielding acceptable values of false-negative and false-positive cases.
OBJECTIVE: To construct a predictive model to foretell congenital muscular torticollis (CMT) on the basis of clinical correlates. DESIGN: Correlation study. SETTING: Regional hospital. PARTICIPANTS: A consecutive series of 1021 newborn infants. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Participants underwent portable ultrasonography to diagnose CMT. Significant clinical correlates were identified to construct a predictive model using the logistic regression model. RESULTS: Forty of 1021 infants were diagnosed with CMT using ultrasonography, yielding an overall incidence of 3.92%. Birth body length (odds ratio [OR]=1.38; 95% confidence interval [CI], 1.49-2.38), facial asymmetry (OR=21.75; 95% CI, 6.6-71.7), plagiocephaly (OR=22.3; 95% CI, 7.01-70.95), perineal trauma during delivery (OR=4.26; 95% CI, 1.25-14.52), and primiparity (OR=6.32; 95% CI, 2.34-17.04) were significant correlates. A predictive logistic regression model with the incorporation of these 4 correlates was developed. We used cross-validation with a receiver operating characteristic curve to validate the predictive model. CONCLUSIONS: Our study successfully developed a quantitative predictive model for estimating the risk of CMT on the basis of clinical correlates only. This model has good discriminative ability for classifying CMT and non-CMT by yielding acceptable values of false-negative and false-positive cases.