The use of teledermatology for the diagnosis of skin cancer in adults

Background: Early accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and squamous cell carcinoma (SCC) are high risk skin cancers which have the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions. Teledermatology provides a way for generalist clinicians to access the opinion of a specialist dermatologist for skin lesions that they consider to be suspicious without referring the patients concerned through the normal referral pathway. Teledermatology consultations can be ‘store-and-forward’ with electronic digital images of a lesion sent to a dermatologist for review at a later time, or can be live and interactive consultations using video conferencing to connect the patient, referrer and dermatologist in real time. Objectives: To determine the diagnostic accuracy of teledermatology for the detection of any skin cancer (melanoma, BCC or cSCC) in adults, and to compare its accuracy with that of in-person diagnosis. Search methods: We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials; MEDLINE; EMBASE; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles. Selection criteria: Studies evaluating skin cancer diagnosis for teledermatology alone, or in comparison with face-to-face diagnosis by a specialist clinician, compared with a reference standard of histological confirmation or clinical follow-up and expert opinion. Studies evaluating the referral accuracy of teledermatology compared with a reference standard of face-to-face diagnosis by a specialist clinician were also included. Data collection and analysis: Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). We contacted authors of included studies where information related to the target condition of any skin cancer was missing. Data permitting, we estimated summary sensitivities and specificities using the bivariate hierarchical model. Due to scarcity of data, no covariate investigations were undertaken for this review. For illustrative purposes, estimates of sensitivity and specificity were plotted on coupled forest plots for diagnostic threshold and target condition under consideration. Main results: Twenty-two studies were included reporting diagnostic accuracy data for 4057 lesions and 879 malignant cases (16 studies) and referral accuracy data for reported data for 1449 lesions and 270 ‘positive’ cases as determined by the reference standard face-to-face decision (six studies). Methodological quality was variable with poor reporting hindering assessment. The overall risk of bias was rated as high or unclear for participant selection, reference standard and participant flow and timing in at least half of all studies; the majority were considered at low risk of bias for the index test. The applicability of study findings were of high or unclear concern for the majority of studies in all domains assessed due to the recruitment of study participants from secondary care settings or specialist clinics rather than from the primary or community-based settings in which teledermatology is more likely to be used and due to the acquisition of lesion images by dermatologists or in specialist imaging units rather than by primary care clinicians. Seven studies provided data for the primary target condition of any skin cancer (1588 lesions and 638 malignancies). For the correct diagnosis of lesions as malignant using photographic images, summary sensitivity was 94.9% (95% CI 90.1 to 97.4%) and summary specificity 84.3% (95% CI 48.5 to 96.8%) (from four studies). Individual study estimates using dermoscopic images or a combination of photographic and dermoscopic images generally suggested similarly high sensitivities with highly variable specificities. Limited comparative data suggested similar diagnostic accuracy between teledermatology assessment and in-person diagnosis by a dermatologist; however, data were too scarce to draw firm conclusions. For the detection of invasive melanoma or atypical intraepidermal melanocytic variants both sensitivities and specificities were more variable. Sensitivities ranged from 59% (95% CI 42% to 74%) to 100% (95% CI 54% to 100%) and specificities from 30% (95% CI 22% to 40%) to 100% (95% CI 93% to 100%), with reported diagnostic thresholds including the correct diagnosis of melanoma, classification of lesions as ‘atypical’ or ‘typical as well as the decision to refer or to excise a lesion. Referral accuracy data comparing teledermatology against a face-to-face reference standard suggested good agreement for lesions considered to require some positive action by face to face assessment (sensitivities of over 90%). For lesions considered of less concern when assessed face-to-face (e.g. for those not recommended for excision or referral), agreement was more variable with teledermatology specificities ranging from 57% (95% CI 39 to 73%) to 100% (95% CI 86% to 100%), suggesting that remote assessment is more likely recommend excision, referral or follow-up compared to in-person decisions. Authors' conclusions: Studies were generally small and heterogeneous and methodological quality was difficult to judge due to poor reporting. Bearing in mind concerns regarding the applicability of study participants and of lesion image acquisition in specialist settings, our results suggest that teledermatology can correctly identify the majority of malignant lesions. Using a more widely defined threshold to identify ‘possibly’ malignant cases or lesions that should be considered for excision is likely to appropriately triage those lesions requiring face-to-face assessment by a specialist. Despite the increasing use of teledermatology on an international level, the evidence base to support its ability to accurately diagnose lesions and to triage lesions from primary to secondary care is lacking and further prospective and pragmatic evaluation is needed

Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma

Background: Melanoma accounts for a small proportion of all skin cancer cases but is responsible for the majority of skin cancer-related deaths. Early detection and treatment can improve survival. Smartphone applications are readily accessible and potentially offer an instant risk assessment of the likelihood of malignancy, so that the right people seek further medical attention from a clinician for more detailed assessment of the lesion. There is, however, a risk that melanomas will be missed and treatment delayed if the application reassures the user that their lesion is low risk. Objectives: To determine the diagnostic accuracy of smartphone applications to rule out cutaneous invasive melanoma and intraepidermal melanocytic variants in adults with concerns about suspicious skin lesions. Search methods: We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials; MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles. Selection criteria: Studies of any design evaluating smartphone applications intended for use by individuals in a community setting who have lesions that might be suspicious for melanoma or intraepidermal melanocytic variants compared with a reference standard of histological confirmation or clinical follow-up and expert opinion. Data collection and analysis: Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). Due to scarcity of data and poor quality of studies, no meta-analysis was undertaken for this review. For illustrative purposes, estimates of sensitivity and specificity were plotted on coupled forest plots for each application under consideration. Main results: This review reports on two cohorts of lesions published in two studies. Both studies were at high risk of bias from selective participant recruitment, and high rates of non-evaluable images. Concerns about applicability of findings were high due to inclusion only of lesions already selected for excision in a dermatology clinic setting, and image acquisition by clinicians rather than by smartphone app users. Data for five mobile phone applications were reported for 332 suspicious skin lesions with 86 melanomas across the two studies. Across the four artificial intelligence-based applications which classified lesion images (photographs) as melanomas (one application) or as high risk or ‘problematic’ lesions (three applications) using a pre-programmed algorithm, sensitivities ranged from 7% (95% CI: 2%, 16%) to 73% (95% CI: 52%, 88%) and specificities from 37% (95% CI: 29% to 46%) to 94% (95% CI: 87%, 97%). The single application using store-and-forward review of lesion images by a dermatologist had a sensitivity of 98% (95% CI: 90%, 100%) and specificity 30% (95% CI: 22%, 40%). The number of test failures (lesion images analysed by the applications but classed as ‘not evaluable’ and excluded by the study authors) ranged from 3 to 31 (or 2% to 18% of lesions analysed). The store-and-forward application had one of the highest rates of test failure (15%). At least one melanoma was classed as ‘not evaluable’ in three of the four application evaluations. Authors' conclusions: Smartphone applications using artificial intelligence-based analysis have not yet demonstrated sufficient promise in terms of accuracy, and are associated with a high likelihood of missing melanomas. Applications based on store-and-forward images could have a potential role in the timely presentation of people with potentially malignant lesions by facilitating active self-management health practices and early engagement of those with suspicious skin lesions; however, they may incur a significant increase in resource and workload. Given the paucity of evidence and low methodological quality, no implications for practice can be drawn. Nevertheless, this is a rapidly advancing field and new and better applications with robust reporting of studies could change these conclusions substantially

The Comprehensive Post-Acute Stroke Services (COMPASS) study: design and methods for a cluster-randomized pragmatic trial

Background: Patients discharged home after stroke face significant challenges managing residual neurological deficits, secondary prevention, and pre-existing chronic conditions. Post-discharge care is often fragmented leading to increased healthcare costs, readmissions, and sub-optimal utilization of rehabilitation and community services. The COMprehensive Post-Acute Stroke Services (COMPASS) Study is an ongoing cluster-randomized pragmatic trial to assess the effectiveness of a comprehensive, evidence-based, post-acute care model on patient-centered outcomes. Methods: Forty-one hospitals in North Carolina were randomized (as 40 units) to either implement the COMPASS care model or continue their usual care. The recruitment goal is 6000 patients (3000 per arm). Hospital staff ascertain and enroll patients discharged home with a clinical diagnosis of stroke or transient ischemic attack. Patients discharged from intervention hospitals receive 2-day telephone follow-up; a comprehensive clinic visit within 2 weeks that includes a neurological evaluation, assessments of social and functional determinants of health, and an individualized COMPASS Care PlanTM integrated with a community-specific resource database; and additional follow-up calls at 30 and 60 days post-stroke discharge. This model is consistent with the Centers for Medicare and Medicaid Services transitional care management services provided by physicians or advanced practice providers with support from a nurse to conduct patient assessments and coordinate follow-up services. Patients discharged from usual care hospitals represent the control group and receive the standard of care in place at that hospital. Patient-centered outcomes are collected from telephone surveys administered at 90 days. The primary endpoint is patient-reported functional status as measured by the Stroke Impact Scale 16. Secondary outcomes are: caregiver strain, all-cause readmissions, mortality, healthcare utilization, and medication adherence. The study engages patients, caregivers, and other stakeholders (including policymakers, advocacy groups, payers, and local community coalitions) to advise and support the design, implementation, and sustainability of the COMPASS care model. Discussion: Given the high societal and economic burden of stroke, identifying a care model to improve recovery, independence, and quality of life is critical for stroke survivors and their caregivers. The pragmatic trial design provides a real-world assessment of the COMPASS care model effectiveness and will facilitate rapid implementation into clinical practice if successful

Development and Validation of a Risk Score for Chronic Kidney Disease in HIV Infection Using Prospective Cohort Data from the D:A:D Study

Ristola M. on työryhmien DAD Study Grp ; Royal Free Hosp Clin Cohort ; INSIGHT Study Grp ; SMART Study Grp ; ESPRIT Study Grp jäsen.Background Chronic kidney disease (CKD) is a major health issue for HIV-positive individuals, associated with increased morbidity and mortality. Development and implementation of a risk score model for CKD would allow comparison of the risks and benefits of adding potentially nephrotoxic antiretrovirals to a treatment regimen and would identify those at greatest risk of CKD. The aims of this study were to develop a simple, externally validated, and widely applicable long-term risk score model for CKD in HIV-positive individuals that can guide decision making in clinical practice. Methods and Findings A total of 17,954 HIV-positive individuals from the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study with >= 3 estimated glomerular filtration rate (eGFR) values after 1 January 2004 were included. Baseline was defined as the first eGFR > 60 ml/min/1.73 m2 after 1 January 2004; individuals with exposure to tenofovir, atazanavir, atazanavir/ritonavir, lopinavir/ritonavir, other boosted protease inhibitors before baseline were excluded. CKD was defined as confirmed (>3 mo apart) eGFR In the D:A:D study, 641 individuals developed CKD during 103,185 person-years of follow-up (PYFU; incidence 6.2/1,000 PYFU, 95% CI 5.7-6.7; median follow-up 6.1 y, range 0.3-9.1 y). Older age, intravenous drug use, hepatitis C coinfection, lower baseline eGFR, female gender, lower CD4 count nadir, hypertension, diabetes, and cardiovascular disease (CVD) predicted CKD. The adjusted incidence rate ratios of these nine categorical variables were scaled and summed to create the risk score. The median risk score at baseline was -2 (interquartile range -4 to 2). There was a 1: 393 chance of developing CKD in the next 5 y in the low risk group (risk score = 5, 505 events), respectively. Number needed to harm (NNTH) at 5 y when starting unboosted atazanavir or lopinavir/ritonavir among those with a low risk score was 1,702 (95% CI 1,166-3,367); NNTH was 202 (95% CI 159-278) and 21 (95% CI 19-23), respectively, for those with a medium and high risk score. NNTH was 739 (95% CI 506-1462), 88 (95% CI 69-121), and 9 (95% CI 8-10) for those with a low, medium, and high risk score, respectively, starting tenofovir, atazanavir/ritonavir, or another boosted protease inhibitor. The Royal Free Hospital Clinic Cohort included 2,548 individuals, of whom 94 individuals developed CKD (3.7%) during 18,376 PYFU (median follow-up 7.4 y, range 0.3-12.7 y). Of 2,013 individuals included from the SMART/ESPRIT control arms, 32 individuals developed CKD (1.6%) during 8,452 PYFU (median follow-up 4.1 y, range 0.6-8.1 y). External validation showed that the risk score predicted well in these cohorts. Limitations of this study included limited data on race and no information on proteinuria. Conclusions Both traditional and HIV-related risk factors were predictive of CKD. These factors were used to develop a risk score for CKD in HIV infection, externally validated, that has direct clinical relevance for patients and clinicians to weigh the benefits of certain antiretrovirals against the risk of CKD and to identify those at greatest risk of CKD.Peer reviewe

Procédé de production de lipase

publication date: 2004-11-10; filing date: 2004-04-2

Non-random geographic distribution of patients with cutaneous T-cell lymphoma in the Greater Pittsburgh Area

Background: Environmental hazards may play a role in the etiology of cutaneous T-cell lymphoma (CTCL). Some studies have found an increased incidence of CTCL among workers in chemical science, transportation, and manufacturing industries, but other studies have not. This discrepancy may be attributable to population migration, complicating accurate assessment of lifetime exposures. The Pittsburgh population has very low migration rates and most CTCL patients seen at the University of Pittsburgh Medical Center (UPMC) Cutaneous Lymphoma Center are life-long local residents. The Greater Pittsburgh Area used to be an industrial hub. There are residential communities positioned within close proximity to inactive industrial sites that continue to contain pollutants.Objective: To determine whether CTCL patients’ residences cluster within specific Pittsburgh regions, in particular, those with high levels of environmental pollutants.Methods: Our study included patients diagnosed with CTCL at the UPMC Cutaneous Lymphoma Center between 2000 and 2012. We mapped the longitudinal and latitudinal coordinates of patients’ residences at diagnosis, superfund sites, toxic release inventory sites, particular matter levels, and dermatologists’ offices using ArcMap 10.1. We then performed a SaTScan analysis using zip codes to assess for geographic clustering of patients’ residences in the Pittsburgh metropolitan statistical area. We assessed for a correlation between case distribution and both environmental hazards sites and dermatologist density in the area.Results: We identified 274 patients with CTCL in the Greater Pittsburgh area. We identified a statistically significant geographic cluster (p<.001) in zip code 15213, which is the most densely populated neighborhood in Pittsburgh and the site of the region’s only CTCL clinic.  We observed no relationship between the locations of superfund sites, toxic release inventory sites, or particular matter levels and CTCL case distribution.Conclusion: Our findings do not support an association between exposure to environmental toxins and CTCL. CTCL cases clustered in areas with the highest population density, which also happen to include a regional CTCL center. To evaluate a possibility of urban pollutants playing a role in etiology of CTCL, dermatologist density and access to care need to be addressed as potential confounders in the future studies

Non-random geographic distribution of patients with cutaneous T-cell lymphoma in the Greater Pittsburgh Area

Background: Environmental hazards may play a role in the etiology of cutaneous T-cell lymphoma (CTCL). Some studies have found an increased incidence of CTCL among workers in chemical science, transportation, and manufacturing industries, but other studies have not. This discrepancy may be attributable to population migration, complicating accurate assessment of lifetime exposures. The Pittsburgh population has very low migration rates and most CTCL patients seen at the University of Pittsburgh Medical Center (UPMC) Cutaneous Lymphoma Center are life-long local residents. The Greater Pittsburgh Area used to be an industrial hub. There are residential communities positioned within close proximity to inactive industrial sites that continue to contain pollutants.Objective: To determine whether CTCL patients’ residences cluster within specific Pittsburgh regions, in particular, those with high levels of environmental pollutants.Methods: Our study included patients diagnosed with CTCL at the UPMC Cutaneous Lymphoma Center between 2000 and 2012. We mapped the longitudinal and latitudinal coordinates of patients’ residences at diagnosis, superfund sites, toxic release inventory sites, particular matter levels, and dermatologists’ offices using ArcMap 10.1. We then performed a SaTScan analysis using zip codes to assess for geographic clustering of patients’ residences in the Pittsburgh metropolitan statistical area. We assessed for a correlation between case distribution and both environmental hazards sites and dermatologist density in the area.Results: We identified 274 patients with CTCL in the Greater Pittsburgh area. We identified a statistically significant geographic cluster (p<.001) in zip code 15213, which is the most densely populated neighborhood in Pittsburgh and the site of the region’s only CTCL clinic.  We observed no relationship between the locations of superfund sites, toxic release inventory sites, or particular matter levels and CTCL case distribution.Conclusion: Our findings do not support an association between exposure to environmental toxins and CTCL. CTCL cases clustered in areas with the highest population density, which also happen to include a regional CTCL center. To evaluate a possibility of urban pollutants playing a role in etiology of CTCL, dermatologist density and access to care need to be addressed as potential confounders in the future studies