The Impact of Digital Health Solutions on Bridging the Health Care Gap in Rural Areas: A Scoping Review.

Digital health tools can improve health care access and outcomes for individuals with limited access to health care, particularly those residing in rural areas. This scoping review examines the existing literature on using digital tools in patients with limited access to health care in rural areas. It assesses their effectiveness in improving health outcomes. The review adopts a comprehensive search strategy to identify relevant studies from electronic databases, and the selected studies are analyzed descriptively. The findings highlight the advantages and barriers of digital health interventions in rural populations. The advantages include increased access to health care practitioners through teleconsultations, improved health care outcomes through remote monitoring, better disease management through mobile health applications and wearable devices, and enhanced access to specialized care and preventive programs. However, limited internet connectivity and a lack of familiarity with digital tools are barriers that must be addressed to ensure equitable access to digital health interventions in rural areas. Overall, digital tools improve health outcomes for individuals with limited health care access in rural areas.

Lymphedema Rates Following Axillary Lymph Node Dissection With and Without Immediate Lymphatic Reconstruction: A Prospective Trial.

BACKGROUND: Immediate lymphatic reconstruction (ILR) has been proposed to decrease lymphedema rates. The primary aim of our study was to determine whether ILR decreased the incidence of lymphedema in patients undergoing axillary lymph node dissection (ALND). METHODS: We conducted a two-site pragmatic study of ALND with or without ILR, employing surgeon-level cohort assignment, based on breast surgeons' preferred standard practice. Lymphedema was assessed by limb volume measurements, patient self-reporting, provider documentation, and International Classification of Diseases, Tenth Revision (ICD-10) codes. RESULTS: Overall, 230 patients with breast cancer were enrolled; on an intention-to-treat basis, 99 underwent ALND and 131 underwent ALND with ILR. Of the 131 patients preoperatively planned for ILR, 115 (87.8%) underwent ILR; 72 (62.6%) were performed by one breast surgical oncologist and 43 (37.4%) by fellowship-trained microvascular plastic surgeons. ILR was associated with an increased risk of lymphedema when defined as ≥10% limb volume change on univariable analysis, but not on multivariable analysis, after propensity score adjustment. We did not find a statistically significant difference in limb volume measurements between the two cohorts when including subclinical lymphedema (≥5% inter-limb volume change), nor did we see a difference in grade between the two cohorts on an intent-to-treat or treatment received basis. For all patients, considering ascertainment strategies of patient self-reporting, provider documentation, and ICD-10 codes, as a single binary outcome measure, there was no significant difference in lymphedema rates between those undergoing ILR or not. CONCLUSION: We found no significant difference in lymphedema rates between patients undergoing ALND with or without ILR.

Identification of Potential Factors Associated with Cellulitis Following Lymphovenous Bypass Surgery in Breast Cancer Survivors.

BACKGROUND: Breast cancer is one of the most common types of cancer, with around 2.3 million cases diagnosed in 2020. One in five cancer patients develops chronic lymphedema caused by multifactorial triggers and treatment-related factors. This can lead to swelling, skin infections, and limb dysfunction, negatively affecting the patient's quality of life. This retrospective cohort study aimed to determine the associations between demographic and breast cancer characteristics and postoperative cellulitis in breast cancer survivors who underwent lymphovenous bypass surgery (LVB) at Mayo Clinic, Florida. METHODS: We performed a retrospective chart review. Data were collected retrospectively from 2016 to 2022. Sixty adult breast cancer survivors who underwent LVB were included in the final analysis based on specific inclusion and exclusion criteria. Patients were excluded if they did not meet the inclusion criteria or had incomplete follow-up data. Demographic and surgical data were extracted, including body mass index (BMI), type of anastomosis, number of anastomoses, and preoperative cellulitis status. Lymphedema measurements were performed using tape measurements. Fisher's exact test was used to determine statistically significant associations between variables and postoperative cellulitis. RESULTS: Postoperative cellulitis was more common in patients aged 60 to 69 years (43.2%), whites (75.0%), overweight or obese (90.9%), with one to four anastomoses (81.8%), and nonsmokers (79.5%). The mean International Society of Lymphology (ISL) criteria for both postoperative cellulitis and no postoperative cellulitis was 1.93. Statistically significant associations with postoperative cellulitis were found for the number of anastomoses (p = 0.021), smoking status (p = 0.049), preoperative cellulitis (p = 0.04), and the length of years with lymphedema diagnosis variable (p = 0.004). CONCLUSION: Our results suggest that a greater number of anastomoses, smoking, preoperative cellulitis, and years with lymphedema are significantly associated with an increased risk of postoperative cellulitis. Awareness of these risk factors is crucial for monitoring and early treatment of infections following surgery.

Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems.

Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4's 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making.

Comparative Analysis of Large Language Models in Emergency Plastic Surgery Decision-Making: The Role of Physical Exam Data.

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI's ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model's performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise.

Clinical and Surgical Applications of Large Language Models: A Systematic Review.

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions: LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.

Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review.

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.

Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care.

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA's responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.

AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries.

Background: OpenAI's ChatGPT (San Francisco, CA, USA) and Google's Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods: Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t-test, and sensitivity testing. Results: Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p-value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions: Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice.

The usefulness of artificial intelligence in breast reconstruction: a systematic review.

BACKGROUND: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction. METHODS: A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction. RESULTS: A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification. CONCLUSIONS: In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.

Age as a risk factor for breast cancer-related lymphedema: a systematic review.

PURPOSE: Breast cancer-related lymphedema (BCRL) has been widely reported in the medical literature. Various patient characteristics, including age, have been investigated as possible risk factors for this disease entity, but the existence and direction of the cause-and-effect relationship are still unclear. In this review, we aimed to evaluate the effect of age on development of BRCL. METHODS: PubMed, Scopus, and Ovid MEDLINE were searched for relevant articles, which were found to be published between 1974 and 2020. RESULTS: Twenty-six studies involving 19,396 patients were selected. The average age of patients was 54.9. 26 studies were included in the final analysis, and 13 articles reported no association between age and BCRL development. CONCLUSIONS: Though studies presented different findings, the majority did not identify age as a risk factor for development of lymphedema. However, the level of evidence of individual studies was low. In this article, we call attention to the need for uniform design of lymphedema studies and diagnosis. IMPLICATIONS FOR CANCER SURVIVORS: All patients should be informed and screened regularly for lymphedema during and after the treatment independent of their age.

Measuring Biomechanical Properties Using a Noninvasive Myoton Device for Lymphedema Detection and Tracking: A Pilot Study.

Background: Improved techniques for lymphedema detection and monitoring of disease progression are needed. This study aims to use the noninvasive MyotonPRO Device to detect differences in biomechanical skin characteristics in patients with breast cancer-related lymphedema (BCRL). Methods: The handheld Myoton device was used to measure skin parameters including dynamic skin stiffness, oscillation frequency (tone), mechanical stress relaxation time, and creep in 11 women diagnosed with BCRL. Seven anatomical sites were measured bilaterally for each participant. The average values in the affected arms were compared with those in the contralateral unaffected arms. Results: Among the 11 female participants with unilateral BCRL Stages 0 to II, the combined averages for dynamic skin stiffness and frequency measurements were decreased in the affected arms when compared with those for the contralateral control arms (ratio < 1). The median ratio of stiffness (affected to unaffected control arm) was 0.91 (interquartile range [IQR] 0.78-1.03) while frequency was 0.94 (IQR 0.89-1.0). Skin relaxation time and creep averages were increased in the affected arms. The relaxation time median ratio (affected to unaffected control arm) was 1.07 (IQR 1.02-1.14) and the median ratio of creep was 1.06 (IQR 1.03-1.16). Conclusions: This study suggests the Myoton can detect differences in skin biomechanical parameters of the affected and unaffected arms in patients with BCRL. Larger studies are needed to draw strong conclusions.

Effects of diet-induced obesity in the development of lymphedema in the animal model: A literature review.

BACKGROUND: Obesity poses deleterious consequences on every organ system, especially the lymphatic network. However, the underlying cellular mechanisms through which obesity causes lymphatic dysfunction remains unclear. We aimed to summarize experimental studies that evaluated the effect of obesity on the lymphatic system on animal models. METHODS: We used the following terms to search the Ovid EMBASE, Ovid MEDLINE(R), Cochrane, and Scopus databases: "lymphedema", "lymphatic diseases", "lymphatic system/complications* ", "lymphatic system/injuries* ", "lymphatic system/abnormalities* ", AND "obesity/complications* ", "diet/high-fat", "adipogenesis" and "lipid metabolism disorder". From a total of 166 articles identified in the initial search, 13 met our eligibility criteria. RESULTS: Long-term exposure to high-fat diet in mice demonstrated significant amount of adipose tissue deposition which sets off an inflammatory cascade resulting in disruption of the chemokine gradient, inhibition of lymphangiogenesis, and changes in gene expression of lymphatic endothelial cells, that alter vessel permeability and induce cell death. Reduced contractile properties of lymphatic collectors, dilated capillaries, increased tissue pressure, and reduced hydraulic conductivity collectively contribute to reduced impaired lymphatic drainage. Aerobic exercise has shown reversal of lymphatic dysfunction in the obese and pharmacological interventions targeting T-cells, iNOS and VEGFR-3 signaling have the potential to combat acquired lymphedema. CONCLUSION: Scientists should focus their future experiments on developing therapies that regulate expression of T-cell derived cytokines and VEGFR-3 expression whereas clinicians are urged to counsel their patients to reduce weight through aerobic exercise.

Influence of Nonsyndromic Bicoronal Synostosis and Syndromic Influences on Orbit and Periorbital Malformation.

BACKGROUND: Oculoorbital disproportion in patients with craniosynostosis has similarities and dissimilarities between syndromic and nonsyndromic cases. The authors hypothesized that these two conditions have specific individual influences as they relate to development of the orbital and periorbital skeletons. METHODS: A total of 133 preoperative computed tomography scans (nonsyndromic bicoronal synostosis, n = 38; Apert syndrome bicoronal synostosis subtype, n = 33; Crouzon syndrome bicoronal synostosis subtype, n = 10; controls, n = 52) were included. Craniometric and volumetric analyses related to the orbit and periorbital anatomy were performed. RESULTS: Orbital cavity volume was mildly restricted in nonsyndromic bicoronal synostosis (7 percent, p = 0.147), but more so in Apert and Crouzon syndromes [17 percent (p = 0.002) and 21 percent (p = 0.005), respectively]. The sphenoid side angle in Apert syndrome was wider than when compared to Crouzon syndrome (p = 0.043). The ethmoid side angle in Apert patients, however, was narrower (p = 0.066) than that in Crouzon patients. Maxilla anteroposterior length was more restricted in Apert syndrome than Crouzon syndrome (21 percent, p = 0.003) and nonsyndromic cases (26 percent, p < 0.001). The posterior nasal spine position was retruded in Crouzon syndrome (39 percent, p < 0.001), yet the anterior nasal spine position was similar in Apert and Crouzon syndromes. CONCLUSIONS: Orbit and periorbital malformation in syndromic craniosynostosis is likely the combined influence of syndromic influences and premature suture fusion. Apert syndrome expanded the anteriorly contoured lateral orbital wall associated with bicoronal synostosis, whereas Crouzon syndrome had more infraorbital rim retrusion, resulting in more severe exorbitism. Apert syndrome developed maxillary hypoplasia, in addition to the maxillary retrusion, observed in Crouzon syndrome and nonsyndromic bicoronal synostosis patients. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, II.

Orbital and Periorbital Dysmorphology in Untreated Pfeiffer Syndrome.

BACKGROUND: Visual impairment secondary to orbital and periorbital dysmorphology is frequent in Pfeiffer syndrome patients. The etiopathogenesis of this aberrancy, however, remains unclear. METHODS: Untreated Pfeiffer syndrome patients (n = 31) and normal control subjects (n = 43) were compared. Craniometric and volumetric analyses related to the orbital and periorbital anatomy were performed using Materialise (Leuven, Belgium) software. RESULTS: Overall, orbital cavity volume of Pfeiffer patients is reduced by 28 percent (p < 0.001), compared to normal, starting before 3 months of age (p = 0.004). Globe volume was diminished by 10 percent (p = 0.041) before 3 months of age, yet tended to catch up thereafter. However, the retrobulbar soft-tissue volume remained smaller beyond 1 year of age (17 percent, p = 0.003). Globe volume projection beyond the bony orbit increased in all observed ages (82 percent, p < 0.001). The volumes of sphenoid bone, maxilla, and mandible proportionately were restricted by 24 to 25 percent (p = 0.003 to 0.035) before 3 months of age. The volume of maxilla and mandible gradually approximate normal; however, the sphenoid bone volume in Pfeiffer patients remains less than normal (p = 0.002) into childhood. The anteroposterior length of both the zygoma and the maxilla was reduced by 14 percent (p < 0.001). Anterior positioning of the zygoma is less by 23 percent (p < 0.001) in Pfeiffer patients overall, with anterior positioning of maxilla reduced similarly by 23 percent (p < 0.001). CONCLUSIONS: Pfeiffer syndrome patients develop decreased retrobulbar soft-tissue and globe volume, along with a restricted orbital cavity volume in infancy. Significant hypoplasia of the sphenoid bone is associated with more severe central facial (maxilla) retrusion, compared to lateral facial structures (zygoma). CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, II.

Orbitofacial morphology changes with different suture synostoses in Crouzon syndrome.

This study aims to investigate the influence of different cranial vault suture synostoses on orbital and periorbital morphological development in Crouzon syndrome. Computed tomography (CT) scans of Crouzon syndrome patients who had not undergone operation were subgrouped as follows: type I: bicoronal synostosis; type II: sagittal synostosis; type III: pansynostosis; type IV: perpendicular combinations of suture synostoses; and type V: bilateral squamosal synostosis. CT scans were measured using Materialise software. CT scans of 80 Crouzon syndrome patients and 72 normal controls were included. Orbital bony cavity volume was reduced in all subgroups (16-24%), including type V bilateral squamosal synostosis (16%, p = 0.003), although the reduction in type II sagittal synostosis Crouzon patients failed to reach statistical significance (p = 0.071). Globe volume was reduced only in type I bicoronal synostosis (9%, p = 0.018), while the retrobulbar soft tissue volume decreased in type III pansynostosis group by 11% (p = 0.005). Globe volume projection beyond the orbital rim was increased in all groups (p < 0.001), with the greatest increase in type IV perpendicular combination of sutures synostoses, by 100% (p < 0.001). The anteroposterior length of maxilla was significantly shortened in type I (10%, p = 0.028) and type III (9%, p = 0.022) but developed normally in other groups, although the maxilla was posteriorly displaced in all groups (all p ≤ 0.026). The influence of squamosal synostosis on craniofacial malformation is not equivalent to that of a major vault suture. Therefore, subtype suture fusion individualized surgical interventions, after initial occipital expansion, likely provide additional benefits in mitigating oculo-orbital disproportion.

Using Facial Recognition Tools for Health Assessment.

The number of applications for facial recognition technology is increasing due to the improvement in image quality, artificial intelligence, and computer processing power that has occurred during the last decades. Algorithms can be used to convert facial anthropometric landmarks into a computer representation, which can be used to help identify nonverbal information about an individual's health status. This article discusses the potential ways a facial recognition tool can perform a health assessment. Because facial attributes may be considered biometric data, clinicians should be informed about the clinical, ethical, and legal issues associated with its use.

Facial Dysmorphology in Saethre-Chotzen Syndrome.

PURPOSE: Classic features of Saethre-Chotzen syndrome (SCS) described in the literature include a prominent nasal bridge, eyelid ptosis, telorbitism, maxillary hypoplasia, and mandibular prognathism. The purpose of this study was to evaluate objectively the bony features of SCS. METHODS: Preoperative computer tomography scans of 15 SCS patients, 23 normal controls, 13 bicoronal nonsyndromic, and 7 unicoronal nonsyndromic craniosynostosis patients were included for analysis. Unaffected controls and nonsyndromic patients were age- and sex-matched to SCS patients. Morphometric cephalometrics were analyzed using three-dimensional computer tomography reconstructions. Mann-Whitney U were used to compare facial measurements between SCS and normal and nonsyndromic craniosynostosis controls. RESULTS: Telorbitism was present in bicoronal SCS patients only (P = 0.04) but absent in the unicoronal and bicoronal/metopic cohorts. The angle of the nasal bone relative to the sella was not different between SCS and controls (P = 0.536), although the angle of the nasal bone relative to the forehead was decreased in SCS by 15.5° (P < 0.001). Saethre-Chotzen syndrome had a 2.6° maxillary retrusion relative to controls (P = 0.03). In addition, SCS patients aged 4 to 7 months had a wider (39.34 versus 35.04, P = 0.017) and anteroposteriorly foreshortened (32.12 versus 35.06, P = 0.039) maxilla. There was no difference in mandibular prognathism among SCS patients as measured by the sella-nasion-B point angle compared to controls (P = 0.705). CONCLUSIONS: Despite classic descriptions, on morphometric analysis SCS patients did not demonstrate consistency across all suture subtypes in terms of telorbitism, a broad nasal bridge, or mandibular prognathism. Rather, SCS subtypes of SCS based on suture pathology more closely resemble nonsyndromic patients.