Importance: Informed consent in rhinoplasty is a challenging process due to the technically demanding surgery with a wide array of potential complications. Objective: To underscore the significance of informed consent in rhinoplasty, its complications, and identify gaps in the consent process. Design, Setting, and Participants: Dual search and comparison. First a review of the literature for rhinoplasty articles was carried out between January 2000 and November 2017 with at least 100 patients to identify complications and their incidences. Then a comprehensive online search for rhinoplasty consent forms was carried out to identify consent form topics and their incidences. Main Outcomes and Measures: Consent form topics and complication rates from the literature review were reported and directly compared. Results: A total of 117 articles were included, with 36 different complications reported. Twenty-four consent forms were evaluated, yielding 80 different topics. Common complications within the literature included skin problems such as acne (18.3%), numbness (16.7%), hospital revisit (6.5%), primary source revision rate (5.3%), and dissatisfaction (5.0%). The most commonly covered consent topics included infection (100%), bleeding (95.8%), and scarring (95.8%). Unsatisfactory results and need for revision were only covered in 83.3% and 75.0% of consent forms, respectively, despite both being in the top 5 complications cited in the literature. Conclusions and Relevance: Rhinoplasty consent forms generally cover a wide range of topics, with varying incidences of complications cited within the literature. Certain complications such as dissatisfaction and revision surgery should be included in every consent process. These data help provide a contextual framework as well as valuable information in preoperative counseling for patients and physicians regarding rhinoplasty and its potential complications Level of Evidence: NA

IMPORTANCE Nasal base view is important for rhinoplasty analysis. Although some descriptors of nasal base shape exist, they are largely subjective and qualitative.OBJECTIVE To evaluate a parametric model of nasal base shape and compare it with categorization by surgeons to create an objective classification system for clinical evaluation and communication. DESIGN, SETTING, AND PARTICIPANTSRetrospective cohort review of deidentified photographs of 420 patients evaluated for possible facial plastic surgery at a tertiary care academic medical center between January 2013 and June 2017. The nasal bases were classified into 6 shape categories (equilateral, boxy, cloverleaf, flat, round, and narrow) via visual inspection. The contour of each nasal base was traced using MATLAB software (MathWorks Inc). The software then performed a curve fit to the parametric model with output of values for 5 parameters: projection-to-width ratio, the anterior-posterior positioning of the tip bulk, symmetry, degree of lateral recurvature of the nasal base, and size. The differences among shape categories for each parameter were analyzed using 1-way analysis of variance. Pairwise comparisons were then performed to ascertain how the various shapes differed. Finally, a multinomial logistic regression model was used to predict nasal base shape using parameter values. Data were analyzed between April 2017 and January 2018. MAIN OUTCOMES AND MEASURESAn algorithm that categorized nasal base shapes into 6 categories. RESULTSThe 420 nasal base photographs of patients evaluated for possible plastic surgery were categorized into 1 of 6 categories; 305 photographs were readily classified, and the remaining 115 were termed unclassified and were categorized. For both the classified and unclassified nasal base groups, there were statistically significant differences between projection-to-width ratio (classified, F 5,299 = 21.51; unclassified, F 4,100 = 10.59; P < .001), the anterior-posterior positioning of the tip bulk (classified, F 5,299 = 3.76; P = .003; unclassified, F 4,110 = 4.54; P = .002), and degree of lateral recurvature of the nasal base (classified, F 5,299 = 24.14; unclassified, F 4,100 = 7.21; P < .001). A multinomial logistic regression model categorization was concordant with surgeon categorization in 201 of 305 (65.9%) cases of classified nasal bases and 38 of 115 (33.0%) unclassified nasal bases. CONCLUSIONS AND RELEVANCEThe parametric model may provide an objective and numerical approach to analyzing nasal base shape.LEVEL OF EVIDENCE NA.