Samples collected into lithium heparin PST™ tubes show pre-analytical glucose loss at 1 h that is independent of baseline glucose and cellular count. Furthermore, immediate plasma separation using these tubes attenuates glucose loss across a wide range of glucose concentrations.
Aims: Liver cirrhosis increases the risk of developing dysglycaemia (pre-diabetes and diabetes), thus people with cirrhosis should undergo regular screening for dysglycaemia. The utility of screening using the laboratory glycated haemoglobin (HbA 1c ) test has been questioned in this setting. This study examines the relationship between different potential screening modalities: 75 g oral glucose tolerance test (OGTT) and HbA 1c , using continuous glucose monitoring (CGM) as a comparator.Methods: Participants ≥18 years with no known diabetes, were recruited from a gastroenterology cirrhosis surveillance register. Study measurements included a 75 g OGTT, laboratory HbA 1c and two weeks of 'blinded' CGM (Freestyle Libre Pro). The possibility of intravascular haemolysis affecting HbA 1c interpretation was also assessed.Results: All 20 participants had compensated cirrhosis. OGTT tended to diagnose more dysglycaemia (N = 7) than did HbA 1c (N = 4). Bland-Altman analysis showed laboratory and CGM-estimated HbA 1c were broadly comparable, with a difference of 4mmol/mol (95% CI −3 to 12), or 0.4% (95% CI −0.3 to 1.1). Laboratory HbA 1c tended to be higher than the CGM-estimated HbA 1c , perhaps reflecting positive lifestyle changes in participants during their two weeks of wearing 'blinded' CGM (Hawthorne effect). In the population studied, there was no evidence that haemolysis affected interpretation of HbA 1c results. Conclusions:In the setting of compensated cirrhosis, the OGTT and HbA 1c remain standard screening test for diabetes, but multiple studies show the OGTT diagnoses more people with dysglycaemia than does the HbA 1c . Blinded CGM in an ambulatory, real world setting provides additional insights into glycaemic excursions but cannot be used to diagnose dysglycaemia.
SummaryAn adolescent with type 1 diabetes and a history of self-harm, which included intentional overdoses and insulin omission, presented with an insulin degludec overdose. She had been commenced on the ultra-long-acting insulin, degludec, with the aim of reducing ketoacidosis episodes in response to intermittent refusal to take insulin. Insulin degludec was administered under supervision as an outpatient. Because it was anticipated that she would attempt a degludec overdose at some stage, the attending clinicians implemented a proactive management plan for this (and related) scenarios. This included long-term monitoring of interstitial glucose using the Abbott Freestyle Libre flash glucose monitor. The patient took a witnessed overdose of 242 units of degludec (usual daily dose, 32 units). She was hospitalised an hour later. Inpatient treatment was guided primarily by interstitial glucose results, with capillary and venous glucose tests used as secondary measures to assess the accuracy of interstitial glucose values. Four days of inpatient treatment was required. The patient was managed with high glycaemic loads of food and also intermittent intravenous dextrose. No hypoglycaemia was documented during the admission. In summary, while a degludec overdose may require several days of inpatient management, in situations where proactive management is an option and the dose administered is relatively modest, it may be possible to avoid significant hypoglycaemia. In addition, this case demonstrates that inpatient interstitial glucose monitoring may have a role in managing insulin overdose, especially in situations where the effect of the insulin overdose on glucose levels is likely to be prolonged.Learning points:Degludec overdoses have a prolonged effect on blood glucose levels, but if the clinical situation allows for early detection and management, treatment may prove easier than that which is typically needed following overdoses of a similar dose of shorter acting insulins.Inpatient real-time interstitial monitoring helped guide management, which in this context included the prescription of high dietary carbohydrate intake (patient led) and intravenous 10% dextrose (nurse led).Use of inpatient interstitial glucose monitoring to guide therapy might be considered ‘off label’ use, thus, both staff and also patients should be aware of the limitations, as well as the benefits, of interstitial monitoring systems.The Libre flash glucose monitor provided nurses with low cost, easy-to-use interstitial glucose results, but it is nevertheless advisable to check these results against conventional glucose tests, for example, capillary ‘finger-stick’ or venous glucose tests.
Background Primary care providers, dermatology specialists, and health care access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial intelligence (AI) offers the promise of diagnostic support for nonspecialists, but real-world clinical validation of AI in primary care is lacking. Objective We aimed to (1) assess the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions and (2) evaluate the quality of images obtained in primary care using the study camera (3Gen DermLite Cam v4 or similar). Methods This was a single-center, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardized camera was processed by the AI algorithm. We evaluated the image quality and compared two teledermatologists’ diagnoses by consensus (the “gold standard”) with AI and histology where applicable. Results Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain, or malignant. Uncertain lesions were not included in the sensitivity and specificity analyses. Uncertain lesions included lesions that had either diagnostic or management uncertainties. For the remaining 242 lesions, the sensitivity was 97.26% (95% CI 93.13%-99.25%) and the specificity was 97.92% (95% CI 92.68%-99.75%). The AI algorithm was compared with the histological diagnoses for 123 lesions. The sensitivity was 100% (95% CI 95.85%-100%) and the specificity was 72.22% (95% CI 54.81%-85.80%). Conclusions The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment health care and improve access to dermatology. Further real-life studies need to be conducted on a larger scale to assess the reliability, usability, and cost-effectiveness of the algorithm in primary care. Acknowledgments MoleMap NZ, who developed the AI algorithm, provided some funding for this study. HT's salary was partially sponsored by MoleMap NZ, who developed the AI algorithm. AB is a shareholder and consultant to Molemap Ltd provider of the AI algorithm. Conflicts of Interest None declared.
BACKGROUND Primary care providers, dermatology specialists, and health care access are key components of primary prevention, early diagnosis, and treatment of skin cancer. Artificial intelligence (AI) offers the promise of diagnostic support for nonspecialists, but real-world clinical validation of AI in primary care is lacking. OBJECTIVE We aimed to (1) assess the reliability of an AI-based clinical triage algorithm in classifying benign and malignant skin lesions and (2) evaluate the quality of images obtained in primary care using the study camera (3Gen DermLite Cam v4 or similar). METHODS This was a single-center, prospective, double-blinded observational study with a predetermined study design. We recruited participants with suspected skin cancer in 20 primary care practices who were referred for assessment via teledermatology. A second set of photographs taken using a standardized camera was processed by the AI algorithm. We evaluated the image quality and compared two teledermatologists’ diagnoses by consensus (the “gold standard”) with AI and histology where applicable. RESULTS Our primary outcome assessment stratified 391 skin lesions by management as benign, uncertain, or malignant. Uncertain lesions were not included in the sensitivity and specificity analyses. Uncertain lesions included lesions that had either diagnostic or management uncertainties. For the remaining 242 lesions, the sensitivity was 97.26% (95% CI 93.13%-99.25%) and the specificity was 97.92% (95% CI 92.68%-99.75%). The AI algorithm was compared with the histological diagnoses for 123 lesions. The sensitivity was 100% (95% CI 95.85%-100%) and the specificity was 72.22% (95% CI 54.81%-85.80%). CONCLUSIONS The AI algorithm demonstrates encouraging results, with high sensitivity and specificity, concordant with previous AI studies. It shows potential as a triage tool in conjunction with teledermatology to augment health care and improve access to dermatology. Further real-life studies need to be conducted on a larger scale to assess the reliability, usability, and cost-effectiveness of the algorithm in primary care.
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