Toward personalized skin cancer care: multiple skin cancer development in five cohorts

Importance

Many patients will develop more than one skin cancer, however most research to date has examined only case status.

Objective

Describe the frequency and timing of the treatment of multiple skin cancers in individual patients over time.

Design

Longitudinal claims and electronic health record-based cohort study.

Setting

Vanderbilt University Medical Center database called the Synthetic Derivative, VA, Medicare, Optum Clinformatics® Data Mart Database, IBM Marketscan.

Participants

All patients with a Current Procedural Terminology code for the surgical management of a skin cancer in each of five cohorts.

Exposures

None.

Main Outcomes and Measures

The number of CPT codes for skin cancer treatment in each individual occurring on the same day as an ICD code for skin cancer over time.

Results

Our cohort included 5,508,374 patients and 13,102,123 total skin cancers treated.

Conclusions and Relevance

Nearly half of patients treated for skin cancer were treated for more than one skin cancer. Patients who have not developed a second skin cancer by 2 years after the first are unlikely to develop multiple skin cancers within the following 5 years. Better data formatting will allow for improved granularity in identifying individuals at high risk for multiple skin cancers and those unlikely to benefit from continued annual surveillance. Resource planning should take into account not just the number of skin cancer cases, but the individual burden of disease.

Key points

Question: How many skin cancer patients are treated for more than one skin cancer and how soon after the first skin cancer do they occur?Findings: 43% of patients were treated for more than one skin cancer, the majority of which occurred within two years after the initial skin cancer. Just 3% of patients were treated for 10 or more skin cancers, but these patients accounted for 22% of all of the skin cancer treatments in the cohort Meaning: Nearly half of all skin cancer patients were treated for multiple skin cancers, while those without a second skin cancer after two years were less likely to be treated for a subsequent skin cancer within the next five years.

Voriconazole Metabolism is Associated with the Number of Skin Cancers Per Patient

Voriconazole exposure is associated with skin cancer, but it is unknown how the full spectrum of its metabolizer phenotypes impacts this association. We conducted a retrospective cohort study to determine how variation in metabolism of voriconazole as measured by metabolizer status of CYP2C19 is associated with the total number of skin cancers a patient develops and the rate of development of the first skin cancer after treatment. There were 1,739 organ transplant recipients with data on CYP2C19 phenotype. Of these, 134 were exposed to voriconazole. There was a significant difference in the number of skin cancers after transplant based on exposure to voriconazole, metabolizer phenotype, and the interaction of these two (p < 0.01 for all three). This increase was driven primarily by number of squamous cell carcinomas among rapid metabolizes with voriconazole exposure (p < 0.01 for both). Patients exposed to voriconazole developed skin cancers more rapidly than those without exposure (Fine-Grey hazard ratio 1.78, 95% confidence interval 1.19-2.66). This association was similarly driven by development of SCC (Fine-Grey hazard ratio 1.83, 95% confidence interval 1.14-2.94). Differences in voriconazoles metabolism are associated with an increase in the number of skin cancers developed after transplant, particularly SCC.

Comparison of late-onset and non-late-onset systemic lupus erythematosus individuals in a real-world electronic health record cohort

Objective: Late-onset systemic lupus erythematosus (LO-SLE) is defined as SLE diagnosed at age 50 years or later. Current studies on LO-SLE are small and have conflicting results.Methods: Using a large, electronic health record (EHR)-based cohort of SLE individuals, we compared demographics, disease characteristics, SLE-specific antibodies, and medication prescribing practices in LO (n = 123) vs. NLO-SLE (n = 402) individuals.Results: The median age (interquartile range) at SLE diagnosis was 60 (56-67) years for LO-SLE and 28 (20-38) years for NLO-SLE. Both groups were predominantly female (85% vs. 91%, p = 0.10). LO-SLE individuals were more likely to be White than NLO-SLE individuals (74% vs. 60%, p = 0.005) and less likely to have positive dsDNA (39% vs. 58%, p = 0.001) and RNP (17% vs. 32%, p = 0.02) with no differences in Smith, SSA, and SSB. Autoantibody positivity declined with increasing age at SLE diagnosis. LO-SLE individuals were less likely to develop SLE nephritis (9% vs. 29%, p < 0.001) and less likely to be prescribed multiple classes of SLE medications including antimalarials (90% vs. 95%, p = 0.04), azathioprine (17% vs. 31%, p = 0.002), mycophenolate mofetil (12% vs. 38%, p < 0.001), and belimumab (2% vs. 8%, p = 0.02).Conclusion: LO-SLE individuals may be less likely to fit an expected course for SLE with less frequent positive autoantibodies at diagnosis and lower rates of nephritis, even after adjusting for race. Understanding how age impacts SLE disease presentation could help reduce diagnostic delays in SLE.

Genetic Inhibition of APOL1 Pore-Forming Function Prevents APOL1-Mediated Kidney Disease

SIGNIFICANCE STATEMENT

African Americans are at increased risk of CKD in part due to high-risk (HR) variants in the apolipoprotein L1 ( APOL1 ) gene, termed G1/G2. A different APOL1 variant, p.N264K , reduced the risk of CKD and ESKD among carriers of APOL1 HR variants to levels comparable with individuals with APOL1 low-risk variants in an analysis of 121,492 participants of African ancestry from the Million Veteran Program (MVP). Functional genetic studies in cell models showed that APOL1 p.N264K blocked APOL1 pore-forming function and ion channel conduction and reduced toxicity of APOL1 HR mutations. Pharmacologic inhibitors that mimic this mutation blocking APOL1 -mediated pore formation may be able to prevent and/or treat APOL1 -associated kidney disease.

BACKGROUND

African Americans are at increased risk for nondiabetic CKD in part due to HR variants in the APOL1 gene.

METHODS

We tested whether a different APOL1 variant, p.N264K , modified the association between APOL1 HR genotypes (two copies of G1/G2) and CKD in a cross-sectional analysis of 121,492 participants of African ancestry from the MVP. We replicated our findings in the Vanderbilt University Biobank ( n =14,386) and National Institutes of Health All of Us ( n =14,704). Primary outcome was CKD and secondary outcome was ESKD among nondiabetic patients. Primary analysis compared APOL1 HR genotypes with and without p.N264K . Secondary analyses included APOL1 low-risk genotypes and tested for interaction. In MVP, we performed sequential logistic regression models adjusting for demographics, comorbidities, medications, and ten principal components of ancestry. Functional genomic studies expressed APOL1 HR variants with and without APOL1 p.N264K in cell models.

RESULTS

In the MVP cohort, 15,604 (12.8%) had two APOL1 HR variants, of which 582 (0.5%) also had APOL1 p.N264K . In MVP, 18,831 (15%) had CKD, 4177 (3%) had ESKD, and 34% had diabetes. MVP APOL1 HR, without p.N264K , was associated with increased odds of CKD (odds ratio [OR], 1.72; 95% confidence interval [CI], 1.60 to 1.85) and ESKD (OR, 3.94; 95% CI, 3.52 to 4.41). In MVP, APOL1 p.N264K mitigated the renal risk of APOL1 HR, in CKD (OR, 0.43; 95% CI, 0.28 to 0.65) and ESKD (OR, 0.19; CI 0.07 to 0.51). In the replication cohorts meta-analysis, APOL1 p.N264K mitigated the renal risk of APOL1 HR in CKD (OR, 0.40; 95% CI, 0.18 to 0.92) and ESKD (OR, 0.19; 95% CI, 0.05 to 0.79). In the mechanistic studies, APOL1 p.N264K blocked APOL1 pore-forming function and ion channel conduction and reduced toxicity of APOL1 HR variants.

CONCLUSIONS

APOL1 p.N264K is associated with reduced risk of CKD and ESKD among carriers of APOL1 HR to levels comparable with individuals with APOL1 low-risk genotypes.

Mendelian Randomization Analysis of Genetic Proxies of Thiazide Diuretics and the Reduction of Kidney Stone Risk

Importance

Clinical trial data have called into question the efficacy of thiazide diuretics for the prevention of kidney stones.

Objective

To identify whether there is an association between genetic proxies of thiazide diuretics and the risk of kidney stones.

Design, Setting, and Participants

This genetic association study undertook a mendelian randomization analysis of derived exposures and outcomes from genome-wide association study summary statistics. Genetic proxies of thiazide diuretics were derived from the International Consortium for Blood Pressure. Kidney stone cases and controls were derived from the Million Veteran Program, UK Biobank, and the FinnGen study. These cross-sectional designs do not report a duration of follow-up. Data analysis was performed in May 2023.

Exposure

Genetic proxies of thiazide diuretics were genetic variants in the thiazide-sensitive sodium chloride cotransporter gene associated with systolic blood pressure. Genetic proxies of β-blockers and systolic blood pressure served as negative controls.

Main Outcomes and Measures

The main outcome was the odds of kidney stones. The secondary outcomes were serum laboratory values relevant to the treatment of kidney stones.

Results

The main analysis included up to 1 079 657 individuals, including 50 832 kidney stone cases and 1 028 825 controls. In a meta-analysis of all cohorts, genetic proxies of thiazide diuretics were associated with a lower odds of kidney stones (OR, 0.85; 95% CI, 0.81-0.89; P < .001). Genetic proxies of β-blockers (OR, 1.02; 95% CI, 0.96-1.07; P = .52) and systolic blood pressure (OR, 1.00; 95% CI, 1.00-1.01; P = .49) were not associated with kidney stones. Genetic proxies of thiazide diuretics were associated with higher serum calcium (β [SE], 0.051 [0.0092]; P < .001) and total cholesterol (β [SE], 0.065 [0.015]; P < .001), but lower serum potassium (β [SE], -0.073 [0.022]; P < .001).

Conclusions and Relevance

In this genetic association study, genetic proxies of thiazide diuretics were associated with reduced kidney stone risk. This finding reflects a drug effect over the course of a lifetime, unconstrained by the limited follow-up period of clinical trials.

Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics

MOTIVATION

Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure.

RESULTS

Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research.

AVAILABILITY AND IMPLEMENTATION

phecodeX is available at https://github.com/PheWAS/phecodeX.

Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record

OBJECTIVE

Systemic lupus erythematosus (SLE) poses diagnostic challenges. We undertook this study to evaluate the utility of a phenotype risk score (PheRS) and a genetic risk score (GRS) to identify SLE individuals in a real-world setting.

METHODS

Using a de-identified electronic health record (EHR) database with an associated DNA biobank, we identified 789 SLE cases and 2,261 controls with available MEGAEX genotyping. A PheRS for SLE was developed using billing codes that captured American College of Rheumatology SLE criteria. We developed a GRS with 58 SLE risk single-nucleotide polymorphisms (SNPs).

RESULTS

SLE cases had a significantly higher PheRS (mean ± SD 7.7 ± 8.0 versus 0.8 ± 2.0 in controls; P < 0.001) and GRS (mean ± SD 12.2 ± 2.3 versus 11.0 ± 2.0 in controls; P < 0.001). Black individuals with SLE had a higher PheRS compared to White individuals (mean ± SD 10.0 ± 10.1 versus 7.1 ± 7.2, respectively; P = 0.002) but a lower GRS (mean ± SD 9.0 ± 1.4 versus 12.3 ± 1.7, respectively; P < 0.001). Models predicting SLE that used only the PheRS had an area under the curve (AUC) of 0.87. Adding the GRS to the PheRS resulted in a minimal difference with an AUC of 0.89. On chart review, controls with the highest PheRS and GRS had undiagnosed SLE.

CONCLUSION

We developed a SLE PheRS to identify established and undiagnosed SLE individuals. A SLE GRS using known risk SNPs did not add value beyond the PheRS and was of limited utility in Black individuals with SLE. More work is needed to understand the genetic risks of SLE in diverse populations.

Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods

PURPOSE OF REVIEW

The purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma.

RECENT FINDINGS

Deep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing. There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.

On the cusp: Considering the impact of artificial intelligence language models in healthcare

Goodman et al. discuss how AI technologies like the natural language processing model Chat-GPT could potentially transform healthcare through knowledge dissemination and personalized patient education. Before these tools can be safely integrated into healthcare, research and development of robust oversight mechanisms are necessary to ensure their accuracy and reliability.

Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model

Background

Natural language processing models such as ChatGPT can generate text-based content and are poised to become a major information source in medicine and beyond. The accuracy and completeness of ChatGPT for medical queries is not known.

Methods

Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes/no) or descriptive answers. The physicians then graded ChatGPT-generated answers to these questions for accuracy (6-point Likert scale; range 1 - completely incorrect to 6 - completely correct) and completeness (3-point Likert scale; range 1 - incomplete to 3 - complete plus additional context). Scores were summarized with descriptive statistics and compared using Mann-Whitney U or Kruskal-Wallis testing.

Results

Across all questions (n=284), median accuracy score was 5.5 (between almost completely and completely correct) with mean score of 4.8 (between mostly and almost completely correct). Median completeness score was 3 (complete and comprehensive) with mean score of 2.5. For questions rated easy, medium, and hard, median accuracy scores were 6, 5.5, and 5 (mean 5.0, 4.7, and 4.6; p=0.05). Accuracy scores for binary and descriptive questions were similar (median 6 vs. 5; mean 4.9 vs. 4.7; p=0.07). Of 36 questions with scores of 1-2, 34 were re-queried/re-graded 8-17 days later with substantial improvement (median 2 vs. 4; p<0.01).

Conclusions

ChatGPT generated largely accurate information to diverse medical queries as judged by academic physician specialists although with important limitations. Further research and model development are needed to correct inaccuracies and for validation.

Genetic Determinants of IL-6 Levels and Risk of ESKD

Genetically predicted IL-6 levels are associated with risk of ESKD. Therapeutic modulation of IL-6 could potentially reduce the risk of ESKD.

Rates of Pneumocystis jirovecii pneumonia and prophylaxis prescribing patterns in a large electronic health record cohort of patients with systemic lupus erythematosus

Objective No guidelines exist for Pneumocystis jirovecii pneumonia (PJP) prophylaxis in patients with systemic lupus erythematosus (SLE). Limited data are available on incidence of PJP infection and use of PJP prophylaxis. Using a real-world, electronic health record (EHR) cohort, we investigated the frequency of PJP infections as well as patient and provider factors that impacted use and type of PJP prophylaxis. Methods  In a large, de-identified EHR, we identified possible SLE patients using a previously validated algorithm. PJP ICD-9 or ICD-10-CM billing codes and PJP keywords were used to identify possible PJP cases within this SLE cohort. We assessed for PJP prophylaxis prescribing in all SLE patients using keywords and reviewing medication lists for prophylactic agents. Chart review was used to confirm cases of SLE, PJP, and PJP prophylaxis and to obtain data on demographics, comorbidities, and immunosuppressants. Results Of 977 SLE patients, there were only four with confirmed PJP infection. Two of these patients had concurrent Acquired Immunodeficiency Syndrome, and none were on prophylaxis. Of 977 SLE patients, 132 (14%) were prescribed PJP prophylaxis. Of 617 SLE patients ever prescribed immunosuppressants, 128 (21%) were prescribed PJP prophylaxis. Sulfonamides were the most common prophylaxis prescribed (69%), and possible adverse events were documented in 22 out of 117 instances of being placed on a sulfonamide. Patients of younger age, Black race, nephritis, and renal transplant, and on chronic glucocorticoids were all more likely to have PJP prophylaxis prescribed. Patients who were on transplant induction medications, calcineurin/mTOR inhibitors, cyclophosphamide, and mycophenolate mofetil all were more likely to be prescribed PJP prophylaxis compared to other immunosuppressants. Conclusion PJP is a rare diagnosis among SLE patients, and prior studies may even overestimate its prevalence. PJP prophylaxis was less common in our cohort than previously described. Adverse events related to sulfonamides used for PJP prophylaxis were relatively rare with lower rates than previously reported. Our study demonstrates real-world PJP prophylaxis prescribing patterns in a large cohort of SLE patients.

Differences in Skin Cancer Rates by Transplanted Organ Type and Patient Age After Organ Transplant in White Patients

Importance

Although it is known that patients with thoracic organ transplants develop skin cancer more frequently than those who receive nonthoracic organ transplants, patterns of risk for subsequent skin cancers are unknown.

Objective

To further characterize organ transplant recipients who develop multiple skin cancers and assess for patterns of development of additional skin cancers beyond the first skin cancer diagnosis by patient age and transplanted organ type.

Design, Setting, and Participants

This cohort study used validated electronic health record-based data from a single tertiary care academic medical center to identify 5129 solid organ transplant recipients who underwent transplant surgery between 1992 and 2017 and were older than 18 years at the time of transplant. The cohort was limited to White patients because they have the highest skin cancer risk based on phenotype. The mean follow-up was 6.6 years. Data were analyzed June 9, 2021, to May 31, 2022.

Main Outcomes and Measures

Differences in rates of skin cancer development for first and subsequent skin cancers were measured using t test or analysis of variance and χ2 tests for continuous and categorical variables. Rates of skin cancer development were compared based on organ type and patient age at transplant using Fine-Gray tests and cumulative incidence plots.

Results

A total of 5129 organ transplant recipients (mean [SD] age, 51.3 [12.9] years; 3287 men [64.1%]) were included. Of these, 695 patients (13.6%) had development of at least 1 skin cancer, with 6842 skin cancers identified in the cohort overall. Compared with liver transplant recipients, heart, lung, or kidney recipients were more likely to develop at least 1 skin cancer (χ2 test, 25.6; df, 4; P < .001). There was no significant difference by transplanted organ type in the rate of developing a second or third skin cancer; however, the age at transplant was associated with the time to developing a second (χ2 test, 20.4; df, 4; P < .001) or third (χ2 test, 10.9; df, 4; P < .02) skin cancer.

Conclusions and Relevance

This cohort study found that there was no difference by organ type for development of subsequent skin cancers in organ transplant recipients, and recipients of all organ types developed additional skin cancers at high rates after the initial skin cancer.

Incidence and Treatment of Actinic Keratosis in Older Adults With Medicare Coverage

This cohort study evaluates 10 years of Medicare claims data for outpatient encounters related to actinic keratosis.

Systemic Lupus Erythematosus Delivery Outcomes Are Unchanged Across Three Decades

Objective

Using a large, de‐identified electronic health record database with over 3.2 million patients, we aimed to identify trends of systemic lupus erythematosus (SLE) medication use during pregnancy and birth outcomes from 1989 to 2020.

Methods

Using a previously validated algorithm for SLE deliveries, we identified 255 pregnancies in patients with SLE and 604 pregnancies in controls with no known autoimmune diseases. We examined demographics, medications, SLE comorbidities, and maternal and fetal outcomes in SLE and control deliveries.

Results

Compared with control deliveries, SLE deliveries were more likely to be complicated by preterm delivery (odds ratio [OR]: 6.71; 95% confidence interval [CI]: 4.31‐10.55; P < 0.001) and preeclampsia (OR: 3.22; 95% CI: 1.83‐5.66; P < 0.001) after adjusting for age at delivery, race, and parity. In a longitudinal analysis, medication use during SLE pregnancies remained relatively stable, with some increased use of hydroxychloroquine over time but no increase in aspirin use. For SLE deliveries, preterm delivery and preeclampsia rates remained stable.

Conclusion

We observed rates of preeclampsia and preterm delivery in SLE that were five times higher than the general population and higher compared with other prospective SLE cohorts. Furthermore, we did not observe improved outcomes over time with preeclampsia and preterm delivery. Despite increasing evidence for universal use of hydroxychloroquine and aspirin, we did not observe substantially higher use of these medications over time, particularly for aspirin. Our results demonstrate the continued need to prioritize educational and implementation efforts to improve adverse pregnancy outcomes in SLE.

Women with Rheumatoid Arthritis have similar rates of postpartum maternal outcomes compared to women without autoimmune disease

OBJECTIVE

Limited data exist on the effect of rheumatoid arthritis (RA) on maternal postpartum outcomes. Using a real-world, electronic health record (EHR) cohort, we assessed maternal postpartum outcomes in RA.

METHODS

In a large, de-identified EHR, we identified possible RA deliveries using ≥1 delivery ICD-9 or ICD-10-CM codes and a validated RA algorithm. RA cases were required to be diagnosed by a rheumatologist on chart review. Maternal postpartum outcomes included rates of blood transfusion, rates of infection up to 6 weeks postpartum defined by a clinician, and length of hospital stay. We also identified deliveries to women without autoimmune diseases.

RESULTS

We identified 202 deliveries occurring after RA diagnosis and 596 deliveries to controls without autoimmune diseases. Postpartum infection rates were similar among RA patients and controls (8% vs. 4%, p = 0.10), as were red blood cell transfusion rates (2% vs. 2%, p = 1.00). RA case status was not significantly associated with postpartum infection (OR = 2.10, 95% CI 0.88 - 4.98, p = 0.09) but was significantly associated with preterm birth (OR = 2.11, 95% CI 1.38 - 3.23, p = 0.001). Corticosteroid use during pregnancy was common at 41%, while tumor necrosis factor inhibitor use was 13%. After adjusting for age at delivery and race, corticosteroid use at delivery was not associated with postpartum maternal infections but was associated with a significantly lower birthweight in RA cases.

CONCLUSION

Women with RA have an increased risk of adverse pregnancy outcomes, particularly preterm birth. Our study highlights, however, that maternal postpartum outcomes such as postpartum infection and blood transfusion are not significantly increased in RA patients.

Clinical and Histopathologic Characteristics of Metastatic and Locally Advanced Cutaneous Basal Cell Carcinomas

ABSTRACT

Locally advanced or metastatic basal cell carcinomas (laBCCs or mBCCs) are rare, with few case series providing information on their epidemiology. We aimed to describe the clinical and histologic features of locally advanced and metastatic basal cell carcinomas. Forty cases of laBCC or mBCC were identified by searching Vanderbilt's database from 1984 to January 2019. A retrospective chart review was performed. Pathology slides were available for 23 cases (13 mBCCs and 10 laBCCs). Twenty-one of 23 cases were Clark level IV or V, with a mean depth of invasion of >7 mm for both types. The mean mitotic rate was 4.4 mitoses/mm2 for laBCCs and 3.3 mitoses/mm2 for mBCCs. Ulceration was identified in 7 laBCC and 8 mBCC cases. Perineural invasion was present in 2 laBCC and 6 mBCC cases, with 3 mBCCs invading nerves >0.1 mm. Of 13 mBCC cases, histologic subtypes included infiltrative (n = 9), nodular (n = 7), morpheaform (n = 4), and superficial (n = 2), with multiple patterns present in some specimens. 10 of 13 patients with mBCC had local recurrence before metastasis. In summary, we identified several potential markers of high-risk BCC, including perineural invasion, deep invasion, elevated mitotic rate, and local recurrence of the primary tumor.

Risks of Multiple Skin Cancers in Organ Transplant Recipients: A Cohort Study in 2 Administrative Data Sets

Importance

There are limited reports on the risks of multiple primary skin cancers in organ transplant recipients (OTRs).

Objective

To determine the risks over time and risk factors for OTRs developing (1) any skin cancer posttransplant, (2) a subsequent skin cancer after the first posttransplant skin cancer in the data sets used in the study, and (3) 10 or more skin cancers.

Design, Setting, and Participants

This retrospective cohort study used data from Optum deidentified electronic health record data set (7.7 million patients) and Truven Health MarketScan insurance claims data set (161 million patients) from 2007 to 2017. Skin cancers were identified using diagnosis plus treatment codes for basal cell carcinoma, squamous cell carcinoma, and melanoma; OTRs were identified using 4 or more diagnosis codes for organ transplant. Data analysis took place from January 1, 2007, to December 31, 2017.

Main Outcomes and Measures

Cumulative risks of (1) any skin cancer treatment posttransplant, (2) a subsequent skin cancer treatment after the first posttransplant skin cancer treatment in our data, and (3) 10 or more skin cancer treatments in OTRs. A Wei-Lin-Weissfeld marginal model was used to evaluate risk factors for any skin cancer.

Results

A total of 7390 OTRs in Optum and 133 651 in MarketScan were identified, 4.5% and 13.3% of which had had at least 1 skin cancer treatment, respectively. At 2 years after the initial posttransplant skin cancer in the data sets, OTRs had a 44.0% to 57.0% risk of a subsequent skin cancer treatment and a 3.7% to 6.6% risk of having 10 or more skin cancer treatments. Statistically significant risk factors for any skin cancer included age, history of skin cancer, and history of actinic keratosis in both data sets, and male sex and thoracic transplant in MarketScan.

Conclusions and Relevance

In this retrospective cohort study, approximately half of the OTRs who developed at least 1 posttransplant skin cancer developed a subsequent skin cancer within 2 years, and approximately 1 in 20 developed 10 or more skin cancers. Identifying OTRs at highest risk for multiple primary skin cancers may help target strategies for prevention and early detection.

Validity of Using Billing Codes From Electronic Health Records to Estimate Skin Cancer Counts

Importance

Patients can develop multiple skin cancers, and their medical data can be spread over multiple health care systems. This fragmented care, combined with the lack of skin cancer registries, has limited our ability both to provide accurate estimates of incidence and to study the pathogenesis of multiple skin cancers.

Objective

To assess whether standard diagnostic and procedural codes present in the electronic health records at a single health care system are a valid proxy for estimating the number of overall skin cancers.

Design, Setting, and Participants

Retrospective cohort study of patients seen at a single-center tertiary care hospital (ie, Vanderbilt University Medical Center) between July 1, 2008, and June 30, 2018. All patients with at least 1 electronic health record-based diagnostic or procedural code for any skin cancer and at least 1 pathology report of a skin cancer.

Exposure

The number of International Classification of Disease (ICD) or Current Procedural Terminology (CPT) codes relating to skin cancer.

Main Outcomes and Measures

Pearson correlation coefficient and R2 were calculated for the total number of ICD or CPT codes for skin cancer and histologically verified skin cancers.

Results

In this cohort study of 35 901 patients, the mean (SD) age was 70.4 (14.4) years, 20 404 (56.8%) were men, and 31 623 (88.1%) were White individuals. Of these patients, 6307 had at least 1 ICD or CPT code or pathology report for a skin cancer, of whom 5688 patients had both a CPT code related to skin malignancy and a histologically verified skin cancer. There was a strong linear correlation between the number of CPT codes and pathology records (r = 0.87). There was a poor correlation between the number of ICD codes and pathology records (r = 0.22).

Conclusions and Relevance

This cohort study found that the use of ICD codes was a poor proxy measure for the number of skin cancers per patient. In contrast, CPT codes accounted for more than 75% of the variability in the number of skin cancers (R2 = 0.76) and were a better proxy measure for the total number of skin cancers per patient.

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

PURPOSE

Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma.

EXPERIMENTAL DESIGN

We used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan-Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Aperio AT2 and Leica SCN400).

RESULTS

The multivariable classifier predicted response with AUC 0.800 on images from the Aperio AT2 and AUC 0.805 on images from the Leica SCN400. The classifier accurately stratified patients into high versus low risk for disease progression. Vanderbilt patients classified as high risk for progression had significantly worse PFS than those classified as low risk (P = 0.02 for the Aperio AT2; P = 0.03 for the Leica SCN400).

CONCLUSIONS

Histology slides and patients' clinicodemographic characteristics are readily available through standard of care and have the potential to predict ICI treatment outcomes. With prospective validation, we believe our approach has potential for integration into clinical practice.

Low prevalence of bone mineral density testing in patients with systemic lupus erythematosus and glucocorticoid exposure

SummaryPatients with systemic lupus erythematosus (SLE) have an increased risk of developing osteoporosis and fractures due to systemic inflammation and glucocorticoids (GCs). Professional organizations recommend bone mineral density (BMD) testing in SLE patients on GCs, especially within 6 months of initiation. Using a validated algorithm, we identified SLE patients in an electronic health record cohort with long-term GC exposure (≥90 days). Our primary outcome was ever BMD testing. We assessed the impact of patient and provider factors on testing. We identified 693 SLE cases with long-term GC exposure, 41% of whom had BMD testing performed. Only 18% of patients had BMD testing within 6 months of GC initiation. In a logistic regression model for BMD testing, male sex (OR = 0.49, 95% CI 0.27 - 0.87, p = 0.01) was associated with being less likely to have BMD testing after adjusting for race and ethnicity. In contrast, older age (OR = 1.04, p < 0.001) and nephritis (OR = 1.83, p = 0.003) were associated with being more likely to have BMD testing after adjusting for race and ethnicity. Bone health in SLE patients remains an area in need of improvement with attention to patients who are younger and male.

Development of Phenotyping Algorithms for the Identification of Organ Transplant Recipients: Cohort Study

Background

Studies involving organ transplant recipients (OTRs) are often limited to the variables collected in the national Scientific Registry of Transplant Recipients database. Electronic health records contain additional variables that can augment this data source if OTRs can be identified accurately.

Objective

The aim of this study was to develop phenotyping algorithms to identify OTRs from electronic health records.

Methods

We used Vanderbilt’s deidentified version of its electronic health record database, which contains nearly 3 million subjects, to develop algorithms to identify OTRs. We identified all 19,817 individuals with at least one International Classification of Diseases (ICD) or Current Procedural Terminology (CPT) code for organ transplantation. We performed a chart review on 1350 randomly selected individuals to determine the transplant status. We constructed machine learning models to calculate positive predictive values and sensitivity for combinations of codes by using classification and regression trees, random forest, and extreme gradient boosting algorithms.

Results

Of the 1350 reviewed patient charts, 827 were organ transplant recipients while 511 had no record of a transplant, and 12 were equivocal. Most patients with only 1 or 2 transplant codes did not have a transplant. The most common reasons for being labeled a nontransplant patient were the lack of data (229/511, 44.8%) or the patient being evaluated for an organ transplant (174/511, 34.1%). All 3 machine learning algorithms identified OTRs with overall >90% positive predictive value and >88% sensitivity.

Conclusions

Electronic health records linked to biobanks are increasingly used to conduct large-scale studies but have not been well-utilized in organ transplantation research. We present rigorously evaluated methods for phenotyping OTRs from electronic health records that will enable the use of the full spectrum of clinical data in transplant research. Using several different machine learning algorithms, we were able to identify transplant cases with high accuracy by using only ICD and CPT codes.

Developing and Validating Methods to Assemble Systemic Lupus Erythematosus Births in the Electronic Health Record

OBJECTIVE

Electronic health records (EHRs) represent powerful tools to study rare diseases. We developed and validated EHR algorithms to identify SLE births across centers.

METHODS

We developed algorithms in a training set using an EHR with over 3 million subjects and validated algorithms at two other centers. Subjects at all 3 centers were selected using ≥ 1 SLE ICD-9 or SLE ICD-10-CM codes and ≥ 1 ICD-9 or ICD-10-CM delivery code. A subject was a case if diagnosed with SLE by a rheumatologist and had a birth documented. We tested algorithms using SLE ICD-9 or ICD-10-CM codes, antimalarial use, a positive antinuclear antibody ≥ 1:160, and ever checked dsDNA or complements using both rule-based and machine learning methods. Positive predictive values (PPVs) and sensitivities were calculated. We assessed the impact of case definition, coding provider, and subject race on algorithm performance.

RESULTS

Algorithms performed similarly across all three centers. Increasing the number of SLE codes, adding clinical data, and having a rheumatologist use the SLE code all increased the likelihood of identifying true SLE patients. All the algorithms had higher PPVs in African American vs. Caucasian SLE births. Using machine learning methods, total number of SLE codes and a SLE code from a rheumatologist were the most important variables in the model for SLE case status.

CONCLUSION

We developed and validated algorithms that use multiple types of data to identify SLE births in the EHR. Algorithms performed better in African American mothers than Caucasian mothers.

Risk of adverse events due to high volumes of local anesthesia during Mohs micrographic surgery

General guidelines for the maximum amounts of locally injected lidocaine exist; however, there is a paucity of data in the Mohs micrographic surgery (MMS) literature. This study aimed to determine the safety and adverse effects seen in patients that receive larger amounts of locally injected lidocaine. A retrospective chart review of 563 patients from 1992 to 2016 who received over 30 mL of locally injected lidocaine was conducted. Patient records were reviewed within seven postoperative days for complications. The average amount of anesthesia received was 40 mL, and the average patient weight was 86.69 kg. 1.4% of patients had a complication on the day of surgery, and 4.4% of patients had a complication within 7 days of the surgery. The most common complications were excessive bleeding/hematoma formation and wound infection. Only two complications could be attributable to local anesthetics. Gender, heart disease, hypertension, diabetes, and smoking were not significant risk factors for the development of complications. MMS is a safe outpatient procedure for patients that require over 30 mL of locally injected anesthesia. The safety of high volumes of lidocaine extends to patients with risk factors such as heart disease, hypertension, diabetes, and smoking.

Early Melanoma Nodal Positivity and Biopsy Rates Before and After Implementation of the 7th Edition of the AJCC Cancer Staging Manual

Importance

There has been a continued increase in the incidence of newly diagnosed melanomas, most of which are T1 melanomas. The associations between changes in tumor staging, implemented with the 7th edition of the AJCC Cancer Staging Manual (AJCC 7), and sentinel lymph node biopsy rates and nodal positivity rates remain to be seen.

Objective

To evaluate the change that the implementation of the AJCC 7 had on staging criteria and the distribution of thin melanomas requiring nodal surgery and nodal positivity rates.

Design, Setting, and Participants

Retrospective cross-sectional study from 2004 through 2013 of all adults (≥18 years) diagnosed with a T1 (Breslow depth ≤1.0 mm) melanoma using The National Cancer Database that captures 70% of all newly diagnosed cancers from accredited Commission on Cancer organizations, including both academic and community settings. Data were analyzed in May 2017.

Exposures

Patients were grouped together based on year of diagnosis, before and after 2009.

Main Outcomes and Measures

To determine the sentinel lymph node biopsy rate before and after the implementation of the AJCC 7.

Results

A total of 141 280 patients met inclusion criteria. Of 86 846 patients diagnosed from 2004 through 2009, 53.7% (49 644) were male and had a mean (SD) age of 57.7 (16.4) years. Of 54 434 patients diagnosed from 2010 through 2013, 54.3% (31 086) were male and had a mean (SD) age of 59.5 (15.9) years. After 2010, there was a 3.8% decrease in the number of nodal surgeries performed (32 485 of 86 846 patients [37.6%] vs 18 379 of 54 434 patients [33.8%]; P < .001). The nodal positivity rate decreased 1.0% from (9.8% [3166 of 86 846] to 8.8% [1618 of 54 434]) (P < .001). An increase in the proportion of T1b melanomas being evaluated, from 48.8% to 62.2%, was seen (P < .001). Of T1b melanomas that underwent nodal evaluation from 2004 through 2009, 74.0% had Clark level IV (invasion of the reticular dermis) or Clark level V (invasion of the deep, subcutaneous tissue) and 9.5% were ulcerated. From 2010 through 2013, of the T1b melanomas undergoing nodal evaluation, 82.6% had an elevated mitotic rate only, 3.7% were ulcerated, and 13.7% had both ulceration and an elevated mitotic rate.

Conclusions and Relevance

It appears that after the institution of AJCC 7, there was an overall decrease in the number of T1 melanomas undergoing nodal biopsy without a clinically relevant change in sentinel lymph node positivity, with an increase in the number of T1b melanomas undergoing nodal evaluation.

Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record

Background

Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We developed and validated EHR-based algorithms that incorporate billing codes and clinical data to identify SSc patients in the EHR.

Methods

We used a de-identified EHR with over 3 million subjects and identified 1899 potential SSc subjects with at least 1 count of the SSc ICD-9 (710.1) or ICD-10-CM (M34*) codes. We randomly selected 200 as a training set for chart review. A subject was a case if diagnosed with SSc by a rheumatologist, dermatologist, or pulmonologist. We selected the following algorithm components based on clinical knowledge and available data: SSc ICD-9 and ICD-10-CM codes, positive antinuclear antibody (ANA) (titer ≥ 1:80), and a keyword of Raynaud’s phenomenon (RP). We performed both rule-based and machine learning techniques for algorithm development. Positive predictive values (PPVs), sensitivities, and F-scores (which account for PPVs and sensitivities) were calculated for the algorithms.

Results

PPVs were low for algorithms using only 1 count of the SSc ICD-9 code. As code counts increased, the PPVs increased. PPVs were higher for algorithms using ICD-10-CM codes versus the ICD-9 code. Adding a positive ANA and RP keyword increased the PPVs of algorithms only using ICD billing codes. Algorithms using ≥ 3 or ≥ 4 counts of the SSc ICD-9 or ICD-10-CM codes and ANA positivity had the highest PPV at 100% but a low sensitivity at 50%. The algorithm with the highest F-score of 91% was ≥ 4 counts of the ICD-9 or ICD-10-CM codes with an internally validated PPV of 90%. A machine learning method using random forests yielded an algorithm with a PPV of 84%, sensitivity of 92%, and F-score of 88%. The most important feature was RP keyword.

Conclusions

Algorithms using only ICD-9 codes did not perform well to identify SSc patients. The highest performing algorithms incorporated clinical data with billing codes. EHR-based algorithms can identify SSc patients across a healthcare system, enabling researchers to examine important outcomes.

Phenome-Wide Association Studies Uncover a Novel Association of Increased Atrial Fibrillation in Male Patients With Systemic Lupus Erythematosus

OBJECTIVE

Phenome-wide association studies (PheWAS) scan across billing codes in the electronic health record (EHR) and re-purpose clinical EHR data for research. In this study, we examined whether PheWAS could function as an EHR-based discovery tool for systemic lupus erythematosus (SLE) and identified novel clinical associations in male versus female patients with SLE.

METHODS

We used a de-identified version of the Vanderbilt University Medical Center EHR, which includes more than 2.8 million subjects. We performed EHR-based PheWAS to compare SLE patients with age-, sex-, and race-matched control subjects and to compare male SLE patients with female SLE patients, controlling for multiple testing using a false discovery rate (FDR) P value of 0.05.

RESULTS

We identified 1,097 patients with SLE and 5,735 matched control subjects. In a comparison of patients with SLE and matched controls, SLE patients were shown to be more likely to have International Classification of Diseases, Ninth Revision codes related to the SLE disease criteria. In the PheWAS of male versus female SLE patients, with adjustment for age and race, male patients were shown to be more likely to have atrial fibrillation (odds ratio 4.50, false discovery rate P = 3.23 × 10^-3 ). Chart review confirmed atrial fibrillation, with the majority of patients developing atrial fibrillation after the SLE diagnosis and having multiple risk factors for atrial fibrillation. After adjustment for age, sex, race, and coronary artery disease, SLE disease status was shown to be significantly associated with atrial fibrillation (P = 0.002).

CONCLUSION

Using PheWAS to compare male and female patients with SLE, we identified a novel association of an increased incidence of atrial fibrillation in male patients. SLE disease status was shown to be independently associated with atrial fibrillation, even after adjustment for age, sex, race, and coronary artery disease. These results demonstrate the utility of PheWAS as an EHR-based discovery tool for SLE.

Real-world electronic health record identifies antimalarial underprescribing in patients with lupus nephritis

Antimalarials (AMs) reduce disease activity and improve survival in patients with systemic lupus erythematosus (SLE), but studies have reported low AM prescribing frequencies. Using a real-world electronic health record cohort, we examined if patient or provider characteristics impacted AM prescribing. We identified 977 SLE cases, 94% of whom were ever prescribed an AM. Older patients and patients with SLE nephritis were less likely to be on AMs. Current age (odds ratio = 0.97, p < 0.01) and nephritis (odds ratio = 0.16, p < 0.01) were both significantly associated with ever AM use after adjustment for sex and race. Of the 244 SLE nephritis cases, only 63% were currently on AMs. SLE nephritis subjects who were currently prescribed AMs were more likely to be followed by a rheumatologist than a nephrologist and less likely to have undergone dialysis or renal transplant (both p < 0.001). Non-current versus current SLE nephritis AM users had higher serum creatinine (p < 0.001), higher urine protein (p = 0.05), and lower hemoglobin levels (p < 0.01). As AMs reduce disease damage and improve survival in patients with SLE, our results demonstrate an opportunity to target future efforts to improve prescribing rates among multi-specialty providers.

Phenome-wide association study identifies dsDNA as a driver of major organ involvement in systemic lupus erythematosus

In systemic lupus erythematosus (SLE), dsDNA antibodies are associated with renal disease. Less is known about comorbidities in patients without dsDNA or other autoantibodies. Using an electronic health record (EHR) SLE cohort, we employed a phenome-wide association study (PheWAS) that scans across billing codes to compare comorbidities in SLE patients with and without autoantibodies. We used our validated algorithm to identify SLE subjects. Autoantibody status was defined as ever positive for dsDNA, RNP, Smith, SSA and SSB. PheWAS was performed in antibody positive vs. negative SLE patients adjusting for age and race and using a false discovery rate of 0.05. We identified 1097 SLE subjects. In the PheWAS of dsDNA positive vs. negative subjects, dsDNA positive subjects were more likely to have nephritis ( p = 2.33 × 10^-9) and renal failure ( p = 1.85 × 10^-5). After adjusting for sex, race, age and other autoantibodies, dsDNA was independently associated with nephritis and chronic kidney disease. Those patients negative for dsDNA, RNP, SSA and SSB negative subjects were all more likely to have billing codes for sleep, pain and mood disorders. PheWAS uncovered a hierarchy within SLE-specific autoantibodies with dsDNA having the greatest impact on major organ involvement.

Phenome-wide association study identifies marked increased in burden of comorbidities in African Americans with systemic lupus erythematosus

Background

African Americans with systemic lupus erythematosus (SLE) have increased renal disease compared to Caucasians, but differences in other comorbidities have not been well-described. We used an electronic health record (EHR) technique to test for differences in comorbidities in African Americans compared to Caucasians with SLE.

Methods

We used a de-identified EHR with 2.8 million subjects to identify SLE cases using a validated algorithm. We performed phenome-wide association studies (PheWAS) comparing African American to Caucasian SLE cases and African American SLE cases to matched non-SLE controls. Controls were age, sex, and race matched to SLE cases. For multiple testing, a false discovery rate (FDR) p value of 0.05 was used.

Results

We identified 270 African Americans and 715 Caucasians with SLE and 1425 matched African American controls. Compared to Caucasians with SLE adjusting for age and sex, African Americans with SLE had more comorbidities in every organ system. The most striking included hypertension odds ratio (OR) = 4.25, FDR p = 5.49 × 10^{− 15}; renal dialysis OR = 10.90, FDR p = 8.75 × 10^{− 14}; and pneumonia OR = 3.57, FDR p = 2.32 × 10^{− 8}. Compared to the African American matched controls without SLE, African Americans with SLE were more likely to have comorbidities in every organ system. The most significant codes were renal and cardiac, and included renal failure (OR = 9.55, FDR p = 2.26 × 10^{− 40}) and hypertensive heart and renal disease (OR = 8.08, FDR p = 1.78 × 10^{− 22}). Adjusting for race, age, and sex in a model including both African American and Caucasian SLE cases and controls, SLE was independently associated with renal, cardiovascular, and infectious diseases (all p < 0.01).

Conclusions

African Americans with SLE have an increased comorbidity burden compared to Caucasians with SLE and matched controls. This increase in comorbidities in African Americans with SLE highlights the need to monitor for cardiovascular and infectious complications.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1561-8) contains supplementary material, which is available to authorized users.

Developing Electronic Health Record Algorithms That Accurately Identify Patients With Systemic Lupus Erythematosus

OBJECTIVE

To study systemic lupus erythematosus (SLE) in the electronic health record (EHR), we must accurately identify patients with SLE. Our objective was to develop and validate novel EHR algorithms that use International Classification of Diseases, Ninth Revision (ICD-9), Clinical Modification codes, laboratory testing, and medications to identify SLE patients.

METHODS

We used Vanderbilt's Synthetic Derivative, a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least 1 SLE ICD-9 code (710.0), yielding 5,959 individuals. To create a training set, 200 subjects were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive antinuclear antibody (ANA), ever use of medications, and a keyword of "lupus" in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5,759 subjects.

RESULTS

The algorithm with the highest PPV at 95% in the training set and 91% in the validation set was 3 or more counts of the SLE ICD-9 code, ANA positive (≥1:40), and ever use of both disease-modifying antirheumatic drugs and steroids, while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes.

CONCLUSION

We developed and validated the first EHR algorithm that incorporates laboratory values and medications with the SLE ICD-9 code to identify patients with SLE accurately.