IMPORTANCEAcne is a common dermatologic condition and significantly affects psychosocial health and quality of life. An international task force recommended routine use of quality-of-life measures for clinic visits associated with acne management, but this has yet to translate into clinical practice.OBJECTIVE To assess mean Skindex-16 scores over time among patients with moderate to severe acne receiving isotretinoin treatment. DESIGN, SETTING, AND PARTICIPANTSA longitudinal, retrospective case series study of Skindex-16 data collected at monthly visits from 57 consecutive patients with acne receiving isotretinoin; data were collected and evaluated between November 23, 2016, and January 22, 2019. Continuous variables were compared using quantile regression. Multivariable linear mixed models evaluated mean (95% CI) score trajectory over time.MAIN OUTCOMES AND MEASURE Skindex-16 scores, including normalized scores for the emotional, symptomatic, and functional aspects of having skin disease as well as an overall score.RESULTS 4 %] males, with median [interquartile range] age of 17.2 [15.9-18.1] years) in this case series study completed the Skindex-16 at baseline and at least once during follow-up. Baseline Skindex-16 scores were similar by sex but worse with increasing age. Emotional impact was more bothersome to patients with acne requiring isotretinoin treatment than either symptoms or functioning. Improvements of greater than 50% in overall and Emotional domain scores were seen by month 2 of receiving isotretinoin treatment (eg, overall scores decreased from 39.4 to 17.5 by month 2; a decrease of 22.0; P < .001). Qualitatively, Skindex-16 scores reached their nadir between months 3 and 5; at month 4, overall Skindex-16 scores showed a 4.4-fold improvement (from 39.4 at baseline to 8.9; P < .001) and Emotional domain scores showed a 4.8-fold improvement (from 57.7 at baseline to 11.9; P < .001). CONCLUSIONS AND RELEVANCEThe findings of this case series suggest that patients receiving isotretinoin treatment achieve greater than a 50% improvement in quality of life by month 2 and can expect approximately 4-fold to 5-fold improvements from baseline with a full course of isotretinoin. This study shows the potential of routine administration of quality of life measures to assess patient care in dermatology.
kin cancers, particularly keratinocyte cancers, are the most common malignant neoplasms among White populations and are often not included in cancer registries. 1,2 When data are available, most studies have focused on identifying factors associated with the development of any skin cancer, but many patients develop numerous skin cancers, often with medical records scattered across different health care systems. The number of keratinocyte cancers (hereafter referred to as counts) are clinically important; not only may each one grow and metastasize, but having multiple skin cancers is associated with an increased risk for some internal malignant neoplasms and may be a clinical marker that determines which transplant recipients will respond to skin cancer prevention treatments. 3,4 Most cancer registries do not track the number of individual cancers a patient develops, which limits our capacity to learn about the incidence, outcomes, and pathogenesis of the development of multiple skin cancers.Administrative databases and electronic health records (EHRs) may help address these limitations. International Classification of Disease (ICD), Current Procedural Terminology (CPT), and Systematized Nomenclature of Medicine (SNOMED) codes can be used to identify individuals with a history of skin cancer. [5][6][7] The most widely cited estimates of keratinocyte cancer incidence used CPT codes as a proxy for the total number of skin cancers per person obtained from Medicare data. However, this measure has not been validated in the EHRs from an individual health care system. 1,2 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.
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.
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