Although Mycosis Fungoides (MF) and Sézary Syndrome (SS) are the most common forms of cutaneous T-cell lymphoma, little has been reported on non-cancer Cause of Death (COD) in such patients. Assess COD for U.S. patients diagnosed with MF or SS. Determine nationwide standardized mortality ratios for non-cancer COD in patients diagnosed with MF or SS. The Surveillance, Epidemiology, and End Results (SEER) database (2000–2020) was utilized to extract data (including COD) from records of patients with an MF or SS diagnosis. Frequency and standardized mortality ratios (SMRs) for non-cancer COD were calculated. Of 7,344 patients diagnosed with MF or SS, 1,782 (24.3

10570 Background: Primary prevention of breast cancer with oral drugs causes systemic effects which are unacceptable to 85% of high-risk women. Transdermal delivery through breast skin can minimize systemic exposure, but individual variation of permeation, the effects of breast radiation, or increased dose have not been studied. We report a prospective single-arm trial addressing these aspects, using 4-hydroxytamoxifen (4-OHT) an active metabolite of oral tamoxifen. Methods: Women with unilateral breast cancer treated with breast conservation and radiotherapy applied 4-OHT gel to both breasts for 4±1 weeks. In Cohort 1 (2 mg/breast/day) we evaluated inter-individual variation of 4-OHT skin permeation in the non-radiated breast based on demographic and skin characteristics. In Cohort 2 (4 mg/breast/day) we evaluated the effect of increased dose. All participants underwent bilateral post-intervention dermal punch and core needle breast biopsy. Drug concentration in tissue and plasma was measured using liquid chromatography-mass spectroscopy. Multivariable linear regression was used to examine the predictors of drug concentration in tissue and plasma. Stepwise model selection was conducted using AIC criteria, with multicollinearity assessed via variance inflation factor (VIF). Results: Of 156 consented women, 120 completed intervention and were evaluable for the primary endpoint (breast tissue drug concentration). On multivariable analysis, breast tissue 4-OHT concentration increased with age and White race. Drug concentration was equivalent in the radiated and the non-radiated breasts and was two-fold higher in Cohort 2 (4 mg/breast/day) compared to Cohort 1 (2 mg/breast/day), (p = 0.001). Plasma drug concentrations were low in both cohorts; 0.27ng/mL in Cohort 1 and 0.15ng/mL in Cohort 2, (p = < 0.001). Conclusions: Transdermal delivery of breast cancer prevention drugs is a viable strategy, with higher dose achieving proportional increase in tissue concentration, without increased leakage into the circulation. Further work should pursue formulations of highly active drug metabolites with excellent permeation. Clinical trial information: NCT04009044 .

INTRODUCTION:The aim of this this systematic review and meta-analysis was to assess the prevalence of skin cancer in post-liver transplant recipients (LTRs). EVIDENCE ACQUISITION:Five databases were systematically searched until 20th April 2023. Search terms included ("liver transplantation" OR "liver transplant") AND ("skin cancer" OR "melanoma" OR "squamous cell carcinoma" OR "non-melanoma skin cancer" OR "post-transplant cancer" OR "post transplant cancer"). Random effect model were used to overcome the significant heterogeneity observed. EVIDENCE SYNTHESIS:A total of 34 studies were included. The overall skin cancer prevalence in LTRs was 4.6% (95% CI: 3.4-6.1). Subgroup analysis based upon the follow-up duration of each study indicated that skin cancer prevalence increased with longer follow-up durations: 0-4 years, 0.8% (95% CI: 0.4-1.9), 4-8 years, 4.3% (95% CI: 3.1-5.8), and 8-12 years, 5.3% (95% CI: 1.9-14). Furthermore, subgroup analysis based upon continental distribution of skin cancer indicated that Australia 21% (95% CI: 13-31) followed by South America 9.4% (95% CI: 5.8-15) had the highest prevalence of skin cancer. SCC was the most common type of skin cancer with a prevalence of 2.6% (95% CI: 1.5-4.5), subsequently followed by BCC 2.5% (95% CI: 1.5-4.2). CONCLUSIONS:Skin cancer following liver transplantation is not a rare condition. Substantial dermatological surveillance programs are recommended in post-liver transplant recipients to improve the quality of life as well as the associated mortality; especially with the increased prevalence after long follow-up durations.

Journal of the European Academy of Dermatology and VenereologyEarly View LETTER TO THE EDITOR Rosacea of the scalp: Results from a retrospective and prospective randomized controlled study Federica Dall'Oglio, Federica Dall'Oglio orcid.org/0009-0008-0006-8060 Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorMaria Rita Nasca, Maria Rita Nasca orcid.org/0000-0002-7107-9487 Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorFrancesco Lacarrubba, Francesco Lacarrubba orcid.org/0000-0002-0860-2060 Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorPasquale Vitale, Pasquale Vitale orcid.org/0009-0004-5525-817X Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorHelga Platania, Helga Platania Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorBeatrice Nardone, Beatrice Nardone orcid.org/0000-0003-1509-3791 Department of Dermatology, Northwestern University, Chicago, Illinois, USA Dermatology Unit, Kore University, Enna, ItalySearch for more papers by this authorGiuseppe Micali, Corresponding Author Giuseppe Micali [email protected] orcid.org/0000-0002-5157-3939 Dermatology Clinic, University of Catania, Catania, Italy Correspondence Giuseppe Micali, Dermatology Clinic, University of Catania, via S. Sofia 78 - 95123 Catania, Italy. Email: [email protected]Search for more papers by this author Federica Dall'Oglio, Federica Dall'Oglio orcid.org/0009-0008-0006-8060 Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorMaria Rita Nasca, Maria Rita Nasca orcid.org/0000-0002-7107-9487 Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorFrancesco Lacarrubba, Francesco Lacarrubba orcid.org/0000-0002-0860-2060 Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorPasquale Vitale, Pasquale Vitale orcid.org/0009-0004-5525-817X Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorHelga Platania, Helga Platania Dermatology Clinic, University of Catania, Catania, ItalySearch for more papers by this authorBeatrice Nardone, Beatrice Nardone orcid.org/0000-0003-1509-3791 Department of Dermatology, Northwestern University, Chicago, Illinois, USA Dermatology Unit, Kore University, Enna, ItalySearch for more papers by this authorGiuseppe Micali, Corresponding Author Giuseppe Micali [email protected] orcid.org/0000-0002-5157-3939 Dermatology Clinic, University of Catania, Catania, Italy Correspondence Giuseppe Micali, Dermatology Clinic, University of Catania, via S. Sofia 78 - 95123 Catania, Italy. Email: [email protected]Search for more papers by this author First published: 10 February 2024 https://doi.org/10.1111/jdv.19852Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Open Research DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. REFERENCES 1Dall'Oglio F, Nasca MR, Micali G. Emerging topical drugs for the treatment of rosacea. Expert Opin Emerg Drugs. 2021; 26(1): 27–38. 10.1080/14728214.2021.1887138 PubMedWeb of Science®Google Scholar 2Dall'Oglio F, Nasca MR, Gerbino C, Micali G. Advances in pharmacotherapy for rosacea: what is the current state of the art? Expert Opin Pharmacother. 2022; 23(16): 1845–1854. 10.1080/14656566.2022.2142907 PubMedWeb of Science®Google Scholar 3Fortuna MC, Garelli V, Pranteda G, Romaniello F, Cardone M, Carlesimo M, et al. A case of scalp rosacea treated with low dose doxycycline and probiotic therapy and literature review on therapeutic options. Dermatol Ther. 2016; 29(4): 249–251. 10.1111/dth.12355 CASPubMedWeb of Science®Google Scholar 4Miguel-Gomez L, Fonda-Pascual P, Vano-Galvan S, Carrillo-Gijon R, Muñoz-Zato E. Extrafacial rosacea with predominant scalp involvement. Indian J Dermatol Venereol Leprol. 2015; 81(5): 511–513. 10.4103/0378-6323.162340 PubMedWeb of Science®Google Scholar 5Bostanci O, Borelli C, Schaller M. Treatment of extrafacial rosacea with low-dose isotretinoin. Acta Derm Venereol. 2010; 90(4): 409–410. 10.2340/00015555-0888 PubMedWeb of Science®Google Scholar 6Pereira TM, Vieira AP, Basto AS. Rosacea with extensive extrafacial lesions. Int J Dermatol. 2008; 47(1): 52–55. 10.1111/j.1365-4632.2007.03360.x PubMedWeb of Science®Google Scholar 7Dupont C. How common is extrafacial rosacea? J Am Acad Dermatol. 1986; 14(5 Pt 1):839. 10.1016/S0190-9622(86)80532-1 CASPubMedWeb of Science®Google Scholar 8Micali G, Verzì AE, Lacarrubba F. Alternative uses of dermoscopy in daily clinical practice: an update. J Am Acad Dermatol. 2018; 79(6): 1117–1132.e1. 10.1016/j.jaad.2018.06.021 PubMedWeb of Science®Google Scholar 9Wong CS, Kirby B. Demodicidosis in scalp rosacea? Clin Exp Dermatol. 2004; 29(3): 318–319. 10.1111/j.1365-2230.2004.01534.x CASPubMedWeb of Science®Google Scholar 10Gajewska M. Rosacea of common male baldness. Br J Dermatol. 1975; 93(1): 63–66. 10.1111/j.1365-2133.1975.tb06477.x CASPubMedWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

Background: Although MF and SS are the most common forms of cutaneous T-cell lymphoma little has been reported on non-cancer COD in such patients. In a nationwide database, COD subsequent to MF or SS was assessed.

Background: Safety concerns for venous thromboembolism (VTE) risk have emerged in the post-marketing studies for JAK inhibitors (JAKi) in rheumatoid arthritis (RA), prompting the FDA to require a Boxed Warning for the drug class in 20191. Although, the FDA approved upadacitinib (UPA) and abrocitinib (ABR) for atopic dermatitis (AD) and baricitinib (BAR) for alopecia areata (AA) in 2022, the risk for VTE with JAKi in AD and AA has not been fully explored. The aim of this study was to determine from FAERS data if a safety signal exists for JAKi exposure and VTE in patients with AD and AA.

BackgroundReferral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.ObjectiveTo assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.DesignRetrospective, cohort study.ParticipantsData from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19.Main measuresOne-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically.Key resultsCompared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum.ConclusionsThese findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.

BACKGROUND:Cutaneous melanoma is a cancer arising in melanocyte skin cells and is the deadliest form of skin cancer worldwide. Although some risk factors are known, accurate prediction of disease progression and probability for metastasis are difficult to ascertain, given the complexity of the disease and the absence of reliable predictive markers. Since early detection and treatment are essential to enhance survival, this study utilizing machine learning (ML) aims to further delineate additional risk factors associated with cutaneous melanoma.METHODS:A Bayesian Gaussian Mixture ML model was created with data from 2056 patients diagnosed with cutaneous melanoma and then used to group the patients into six Clusters based on a Silhouette Score analysis. A t-distributed stochastic neighbor embedding (t-SNE) model was used to visualize the six Clusters.RESULTS:Statistical analysis revealed that Cluster 4 showed a significantly higher rate of metastatic disease, as well as higher Breslow depth at diagnosis, compared to the other five Clusters. Compared to the other five Clusters, patients represented in Cluster 4 also had lower healthcare utilization, fewer dermatology clinic visits, fewer primary care providers, and less frequent colonoscopies and mammograms, and were more likely to smoke and less likely to have a prior diagnosis of basal cell carcinoma.CONCLUSIONS:This study uncovers gaps in healthcare utilization of services among patient groups with cutaneous melanoma as well as possible implications for management of disease progression. Data-driven analyses emphasize the importance of routine clinic visits to dermatologists and/or primary care physicians (PCPs) for early detection and management of cutaneous melanoma. The findings from this study demonstrate that unsupervised ML methodology may serve to define the best candidate patients to benefit from enhanced dermatology/primary care which, in turn, is expected to improve outcomes for cutaneous melanoma.

Recent reports have been shown an increased risk of de-novo malignancies in liver transplant recipients (LTRs) which is reported to be a leading cause of mortality in these patients. The aim of this systematic review and meta-analysis was to assess the prevalence of skin cancer in LTRs. We systematically searched in five databases till 20th April 2023 through the search term ("liver transplantation" OR "liver transplant") AND ("skin cancer" OR "melanoma" OR "squamous cell carcinoma" OR "non-melanoma skin cancer" OR "post-transplant cancer" OR "post transplant cancer"). We used random effect model to overcome the significant heterogeneity we observed. A total of 34 studies, with 147154 LTRs were included. The pooled prevalence of skin cancer (all types) was 4.8% (95%confidence interval (CI): 3.6-6.5). Subgroup analysis based upon the follow up duration of each study, indicated that skin cancer prevalence increased with long duration of follow up: 0-4 years, 2.4% (95%CI: 0.5-9.9), 4-8 years, 4.2% (95%CI 2.9-6.2), and >8 years, 7.6% (95%CI: 4.7-12). Australia followed by South America had the highest prevalence of skin cancer, 20.6% (95%CI: 12.9-31.3), and 9.4% (95%CI: 5.8-14.8), in order; while Asia had the lowest prevalence 0.4% (95%CI: 0-3.8). The most common type of skin cancers was non-melanoma skin cancer reported as a combined type with a prevalence of 2.9% (95%CI: 1.1-6.9), followed by squamous cell carcinoma, basal cell carcinoma and Bowen’s cancer, 2.5% (95%CI: 1.4-4.4), 2.5% (95%CI: 1.5-4.1) and 0.8% (95%CI: 0.2-3.2), in order. Consistent with what it has been reported for other organ transplants, the results from this study showed that skin cancer following liver transplantation is not rare. LTRs in Australia should receive annual dermatologic examination due to their high prevalence of skin cancers. Moreover, non-melanoma skin cancers may be the prevalent type if skin cancer is suspected in LTRs. Substantial dermatological surveillance programs are recommended in LTRs to improve quality of life as well as the associated mortality; especially with the increased prevalence after long follow up durations.

Background: Malignant melanoma is a life-threatening condition, but prognostic risk factors for developing advanced stage melanoma do not include other clinical features such as demographic or medical comorbidities. The aim of this study was to apply a machine learning approach to detect clinical features associated with advanced melanoma.

Reflectance confocal microscopy (RCM) is a noninvasive technique that allows real-time, high-resolution imaging of the skin. It provides en face tissue sections of the epidermis and upper dermis at a cellular-level resolution close to conventional histopathology. Several studies have reported RCM findings in plaque psoriasis, along with their histopathologic correlation (González et al., 1999; Guida et al., 2022; Lacarrubba et al., 2018; Verzì et al., 2019). One of the main RCM features seen at the dermo‒epidermal junction is the presence of enlarged dermal papillae (DP) containing dark, dilated canalicular structures, which histopathologically correspond to multiple capillary loops within the papillae (Verzì et al., 2019) and dermoscopically to uniformly distributed red dots (Lacarrubba et al., 2016, 2015; Wang et al., 2022).

Introduction: Nationwide, U.S. population-based data for MF in young persons is lacking. The aim of this study was to assess epidemiology for MF in the children and YA populations.

Acral melanoma (AM) has the worst prognosis of all cutaneous malignant melanomas (CMM). Differences between palmar and plantar tumors have not been well characterized at the population level. The objective of this study was to investigate the differences in demographics, incidence, and survival between palmar and plantar AM. The 2004–2016 National Cancer Database (NCDB) and 2000–2018 Surveillance, Epidemiology, and Results (SEER) databases were used to evaluate differences between palmar and plantar AM. Data were analyzed using Chi-square test, Fisher’s exact, T-test, or likelihood ratio test. A total of 5002 participants were included in the study. A greater percentage of tumors occurred on the plantar surface (82.0%) than the palmar surface (18.0%). The incidence of plantar tumors is four times greater than palmar tumors (1.7 vs 0.4 cases per 1,000,000 people per year). Palmar melanomas were more likely to occur in Whites (84.6% vs 76.8%, p < 0.001) and be treated with amputation (28.1% vs 12.9%, p < 0.001) compared to plantar melanomas. Disease-specific five-year survival was similar for all palmar (80.8%) and plantar tumors (78.2%). While subtle differences do exist between palmar and plantar tumors, they behave similarly overall and should be treated as one entity.

Objectives: The purpose of this study is to define differences in the demographics, incidence, and survival between palmar and plantar acral melanoma (AM) using two large national datasets. Methods: Data from the 2004-2016 National Cancer Database (NCDB) and 2000-2018 Surveillance, Epidemiology, and End Results (SEER) database were analyzed as they assessed different outcome measures. For NCDB data, inclusion criteria were a known diagnosis of an acral melanoma and a known Breslow depth. The final sample size was 5002 cases. Differences in demographics were assessed by the chi-square test, Fisher's exact, or T-test. Results: Among all individuals, a greater percentage of acral tumors occurred on the plantar surface (82.0%) than the palmar surface (18.0%). Compared to the plantar surface, palmar melanoma were more likely to occur in whites (84.6% vs 76.8%) and were more likely to be treated with amputation (28.1% vs 12.9%, p<0.001). Hispanics had an earlier age of onset of palmar melanoma than whites (56.6 vs 64.4, p<0.001). Asians had a greater Breslow depth than whites for palmar (3.3mm vs 2.1mm, p=0.008) and plantar (2.9mm vs 2.3mm, p=0.006) tumors. The estimated rate of plantar tumors for all races is four times more common than palmar tumors (1.7 vs 0.4 casers per 1,000,000 people per year). Disease-specific five-year survival was similar for all palmar and plantar tumors (80.8% and 78.2%). Hispanics (75.8%) and Blacks (70.3%) had the lowest five-year survival rates for palmar and plantar tumors, respectively. Conclusions: The majority of AMs occur on the lower limb. Between palmar and plantar tumors, differences were observed in age of onset, location, and treatment type by race/ethnicity. Disease specific five-year survival was similar for palmar and plantar tumors.

Wound dehiscence is a common postsurgical complication following surgical skin closure. Research on the causes of dehiscence is limited.1Walming S. Angenete E. Block M. Bock D. Gessler B. Haglind E. Retrospective review of risk factors for surgical wound dehiscence and incisional hernia.BMC Surg. 2017; 17: 19Crossref PubMed Scopus (79) Google Scholar,2Aksamija G. Mulabdic A. Rasic I. Aksamija L. Evaluation of risk factors of surgical wound dehiscence in adults after laparotomy.Med Arch. 2016; 70: 369-372Crossref PubMed Scopus (22) Google Scholar This study sought to use the electronic data warehouse of an academic medical center to determine demographic factors, comorbidities, and medications associated with an elevated risk of dehiscence irrespective of the surgical specialty of the treating physician, procedure type, method of anesthesia, operative setting, anatomic location, and patient access to care. This case-control study received Northwestern University Institutional Review Board approval (STU00203300). The Northwestern Enterprise Data Warehouse (EDW), a registry containing data from patient electronic health records, was used to identify dehiscence cases. The keyword "wound dehiscence" was searched in the EDW from October 2014 to September 2015. Four reviewers independently verified that dehiscence had been correctly classified. Included cases contained documentation of "skin dehiscence," "wound dehiscence" or "superficial dehiscence" within 45 days of a surgical procedure, with "wound dehiscence" listed as the International Classification of Diseases, Ninth Revision code or reason for the encounter. For each case, 4 contemporaneous controls were matched based on Current Procedural Terminology code. Demographic factors and comorbidities were identified by searching PubMed with the key words ("dehiscence" OR "wound disruption") AND "comorbidities." Of these, age, gender, race, body mass index, and Charlson Comorbidity Index (CCI) were predefined in the EDW. Bivariate analysis using simple logistic regression or chi-square was performed, followed by multiple logistic regression using P value of <.05. Four-hundred forty-seven dehiscence cases were identified and compared with 1625 controls. Descriptive statistics are shown in Tables I and II. Multivariable analysis (Table I; Table II) confirmed significantly higher dehiscence risk of Hispanic patients; obese patients; those receiving anticoagulant medications or steroids; and those with keloids, diabetes, or anemia. CCI of ≥4 was associated with lower odds of dehiscence than CCI of ≤ 3.Table IBivariate and multivariable analysis of demographic factors and medications associated with dehiscencePatient characteristicsDehiscence (n = 447)No. (%)No dehiscence (n = 1625)No. (%)Unadjusted odds ratio (95% CI)P valueAdjusted odds ratio (95% CI)P valueAge, y 0-39102 (22.8%)307 (18.9%)1.34 (1.00-1.78).05 40-59150 (33.6%)604 (37.2%)[Reference] 60-79167 (37.4%)596 (36.7%)1.13 (0.88-1.45).34 80+28 (6.3%)118 (7.3%)0.96 (0.61-1.50).84Sex Male202 (45.2%)735 (45.2%)[Reference] Female245 (54.8%)890 (54.8%)1.00 (0.81-1.23).99Race∗Variable that was statistically significant in the bivariate analysis.†Variable that was statistically significant in the multivariable analysis. Caucasian269 (60.2%)1154 (71.0%)[Reference][Reference] African American58 (13.0%)168 (10.3%)1.48 (1.07-2.05).021.12 (0.76-1.64).58 Asian10 (2.2%)47 (2.9%)0.91 (0.46-1.83).7971.06 (0.49-2.32).88 Hispanic74 (16.6%)165 (10.2%)1.92 (1.42-2.61)<.012.23 (1.54-3.24)<.01 Other36 (8.1%)91 (5.6%)BMI, kg/m2∗Variable that was statistically significant in the bivariate analysis.†Variable that was statistically significant in the multivariable analysis. <18.59 (2.0%)54 (3.3%)0.75 (0.36-1.56).440.61 (0.26-1.44).26 18.51-25.0115 (25.7%)516 (31.8%)[Reference][Reference] 25.01-30124 (27.7%)517 (31.8%)1.08 (0.81-1.43).611.01 (0.72-1.42).95 >30.1177 (39.6%)494 (30.4%)1.61 (1.23-2.10)<.011.46 (1.05-2.01).02 Unknown22 (4.9%)44 (2.7%)Anticoagulant∗Variable that was statistically significant in the bivariate analysis.†Variable that was statistically significant in the multivariable analysis. None134 (30.0%)873 (53.7%)[Reference][Reference] Heparin108 (24.2%)179 (11.0%)3.93 (2.91-5.31)<.014.44 (3.07-6.44)<.01 LMWH72 (16.1%)116 (7.1%)4.04 (2.86-5.71)<.013.66 (2.42-5.53)<.01 Other‡Anticoagulant other category includes director factor II, direct factor X, thrombolytics, and warfarin.9 (2.0%)31 (1.9%)1.89 (0.88-4.06).101.92 (0.79-4.71).16 Multiple anticoagulants124 (27.7%)426 (26.2%)1.90 (1.45-2.49)<.011.49 (1.03-2.15).04Steroid∗Variable that was statistically significant in the bivariate analysis.†Variable that was statistically significant in the multivariable analysis. None398 (89.0%)1542 (94.9%)[Reference][Reference] Yes49 (11.0%)83 (5.1%)2.29 (1.58-3.31)<.012.65 (1.73-4.07)<.01Tobacco use Yes32 (7.2%)129 (7.9%)0.95 (0.63-1.42).79 No342 (76.5%)1304 (80.2%)[Reference] Unknown73 (16.3%)192 (11.8%)BMI, Body mass index; LMWH, low-molecular-weight heparin.∗ Variable that was statistically significant in the bivariate analysis.† Variable that was statistically significant in the multivariable analysis.‡ Anticoagulant other category includes director factor II, direct factor X, thrombolytics, and warfarin. Open table in a new tab Table IIBivariate and multivariable analysis of comorbidities associated with dehiscenceComorbiditiesDehiscence (n = 447)No./%No dehiscence (n = 1625)No./%Unadjusted odds ratio (95% CI)P valueAdjusted odds ratio (95% CI)P valueScleroderma Yes2 (0.4%)2 (0.1%)3.65 (0.51-26.0).20 No445 (99.6%)1623 (99.9%)[Reference]Keloid∗Variable that was statistically significant in the multivariable analysis. Yes8 (1.8%)13 (0.8%)2.26 (0.93-5.49).722.92 (1.06-8.02).04 No439 (98.2%)1612 (99.2%)[Reference][Reference]Jaundice Yes5 (1.1%)12 (0.7%)1.56 (0.55-4.46).40 No430 (96.2%)1613 (99.3%)[Reference] Unknown12 (2.7%)0 (0%)Chronic lung disease Yes74 (16.6%)318 (19.6%)0.84 (0.64-1.11).23 No361(80.8%)1307 (80.4%)[Reference] Unknown12 (2.7%)0 (0%)Diabetes∗Variable that was statistically significant in the multivariable analysis.†Variable that was statistically significant in the bivariate analysis. Yes104 (23.3%)288 (17.7%)1.46 (1.13-1.88)<.011.49 (1.07-2.07).02 No331 (74.0%)1337 (82.3%)[Reference][Reference] Unknown12 (2.7%)0 (0%)Hypoalbuminemia Yes7 (1.6%)15 (0.9%)1.76 (0.71-4.33).22 No428 (95.7%)1610 (99.1%)[Reference] Unknown12 (2.7%)0 (0%)Chronic kidney disease Yes53 (11.9%)162 (10.0%)1.25 (0.90-1.74).18 No382 (85.5%)1463 (90.0%)[Reference] Unknown12 (2.7%)0 (0%)ESRD Yes21 (4.7%)57 (3.5%)1.40 (0.84-2.32).20 No414 (92.6%)1568 (96.5%)[Reference] Unknown12 (2.7%)0 (0%)Anemia∗Variable that was statistically significant in the multivariable analysis.†Variable that was statistically significant in the bivariate analysis. Yes173 (38.7%)460 (28.3%)1.67 (1.34-2.08)<.011.60 (1.19-2.15)<.01 No262 (58.6%)1165 (71.7%)[Reference][Reference]CCI∗Variable that was statistically significant in the multivariable analysis. ≤3297 (66.4%)1,013 (62.3%)[Reference][Reference] ≥4150 (33.6%)612 (37.7%)0.84 (0.67-1.04).110.47 (0.35-0.64)<.01CCI, Charlson Comorbidity Index; ESRD, end-stage renal disease.∗ Variable that was statistically significant in the multivariable analysis.† Variable that was statistically significant in the bivariate analysis. Open table in a new tab BMI, Body mass index; LMWH, low-molecular-weight heparin. CCI, Charlson Comorbidity Index; ESRD, end-stage renal disease. Overall, this study confirms some risk factors, such as obesity and anticoagulation, previously suspected to be associated with increased risk of dehiscence. In contradistinction to other published reports, the current analysis did not detect a heightened risk of dehiscence in men, the elderly, or patients with certain risk factors, including chronic renal disease, liver or lung disease, scleroderma, hypoalbuminemia, or tobacco use. Our study is limited in that certain procedure-specific factors were not assessed. Although the circumstances of particular surgical procedures may adjust risk, we were most interested in risk factors applicable to closed skin wounds in general, irrespective of the details of the operative circumstances. As such, we believe the associations we documented will be helpful not only to dermatologists but the field of medicine in general. An additional limitation was the use of a single-center database. Finally, data quality was reliant on the correct initial input into the electronic health record and correct EDW coding of variables. By assessing a recent large cohort of patients undergoing diverse medical procedures, this study provides generalizable information about dehiscence risk. The results may help physicians counsel patients and be prepared for unavoidable complications. In addition, incipient dehiscence in selected groups may be managed by increased postoperative surveillance, including by telecommunication. None disclosed.