Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care

Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches improve detection of FH in electronic health records, though clinical impact is under-explored. We assessed performance of an array of machine-learning approaches for enhancing detection of FH, and their clinical utility, within a large primary care population. A retrospective cohort study was done using routine primary care clinical records of 4,027,775 individuals from the United Kingdom with total cholesterol measured from 1 January 1999 to 25 June 2019. Predictive accuracy of five common machine-learning algorithms (logistic regression, random forest, gradient boosting machines, neural networks and ensemble learning) were assessed for detecting FH. Predictive accuracy was assessed by area under the receiver operating curves (AUC) and expected vs observed calibration slope; with clinical utility assessed by expected case-review workload and likelihood ratios. There were 7928 incident diagnoses of FH. In addition to known clinical features of FH (raised total cholesterol or LDL-C and family history of premature coronary heart disease), machine-learning (ML) algorithms identified features such as raised triglycerides which reduced the likelihood of FH. Apart from logistic regression (AUC, 0.81), all four other ML approaches had similarly high predictive accuracy (AUC > 0.89). Calibration slope ranged from 0.997 for gradient boosting machines to 1.857 for logistic regression. Among those screened, high probability cases requiring clinical review varied from 0.73% using ensemble learning to 10.16% using deep learning, but with positive predictive values of 15.5% and 2.8% respectively. Ensemble learning exhibited a dominant positive likelihood ratio (45.5) compared to all other ML models (7.0–14.4). Machine-learning models show similar high accuracy in detecting FH, offering opportunities to increase diagnosis. However, the clinical case-finding workload required for yield of cases will differ substantially between models

Does acupressure help reduce nausea and vomiting in palliative care patients? A double blind randomised controlled trial

Introduction: Nausea and vomiting are common symptoms for patients with advanced cancer. While there is evidence for acupuncture point stimulation for treatment of these symptoms for patients having anticancer treatment, there is little for when they are not related to such treatment.Objective: To determine whether acupressure at the pericardium 6 site can help in the treatment of nausea and vomiting suffered by palliative care patients with advanced cancer.Materials and methods: Double blind randomised controlled trial—active versus placebo acupressure wristbands. In-patients with advanced cancer in two specialist palliative care units who fitted either or both of the following criteria were approached: Nausea that was at least moderate; Vomiting daily on average for the prior 3 days.Results: 57 patients were randomised to have either active or placebo acupressure wristbands. There was no difference in any of the outcome measures between the two groups: change from baseline number of vomits; Visual Analogue Scale for ‘did acupressure wristbands help you to feel better?’; total number of as needed doses of antiemetic medication; need for escalation of antiemetics.Conclusions: In contrast to a previously published feasibility study, active acupressure wristbands were no better than placebo for specialist palliative care in-patients with advanced cancer and nausea and vomiting

The changing clinical pattern of endemic Burkitt lymphoma in Western Africa: Experience from a tertiary center in Ghana

Abstract Background Burkitt lymphoma (BL) is the most common childhood cancer in Ghana, where the endemic variant is the predominant subtype and historically presents as a highly chemo-sensitive jaw tumor. This study aimed to update the current epidemiological characteristics of childhood BL in our institution. Procedure Patient data for all children diagnosed with BL and seen at Korle Bu Teaching Hospital between January 2007 and December 2012 were retrospectively analyzed. Results BL was diagnosed in 173 children

Statin treatment and LDL-cholesterol treatment goal attainment among individuals with familial hypercholesterolaemia in primary care

Objectives: Guidance recommends statin treatment in Familial Hypercholesterolaemia (FH) achieve at least a 50% reduction in low-density lipoprotein cholesterol (LDL-C). We assessed statin prescribing rates and LDL-cholesterol treatment goal attainment among individuals with FH in primary care.Methods: Using primary care electronic health records from the UK Clinical Practice Research Datalink (CPRD), we identified adults with recorded diagnosis of FH, statin treatment, and measures of LDL-cholesterol prior to (baseline) and 12 months after initiating statin treatment. The percentage change in LDL-cholesterol was determined, and then baseline and treatment characteristics were assessed by LDL-cholesterol treatment goal attainment. Results: Of 3,064 adults (mean age 50.8 years) with recorded diagnosis of FH and repeat LDL-C measures, 50% reduction in LDL-cholesterol from baseline, was attained in 895 individuals (29.2%) in 12 months. Compared to those who did not attain this goal, these people were predominantly females, they were older at time of FH diagnosis (53.4 years vs 49.7 years) and first statin treatment (53.2 years vs 49.2 years); and had higher pre-treatment total cholesterol (8.20 (SD 1.38) mmol/l vs 7.57 (SD 1.39) mmol/l) and pre-treatment LDL-cholesterol (5.83 (SD 1.36) mmol vs 5.25 (SD 1.40) mmol/l). A higher proportion of individuals who attained the treatment goal were prescribed high and medium potency statins (24.3% and 71.7% versus 20.2% and 69.3%, respectively).Conclusions: Less than a third of individuals on statin treatment for FH in the community achieve recommended reductions in LDL-cholesterol. Greater awareness and optimisation of treatment for FH using higher potency statins is needed

Long-term body mass index changes in overweight and obese adults and the risk of heart failure, cardiovascular disease and mortality: a cohort study of over 260,000 adults in the UK

Background: Although obesity is a well-recognised risk factor for cardiovascular disease (CVD), the impact of long-term body mass index (BMI) changes in overweight or obese adults, on the risk of heart failure, CVD and mortality has not been quantified.Methods: This population-based cohort study used routine UK primary care electronic health data linked to secondary care and death-registry records. We identified adults who were overweight or obese, free from CVD and who had repeated BMI measures. Using group-based trajectory modelling, we examined the BMI trajectories of these individuals and then determined incidence rates of CVD, heart failure and mortality associated with the different trajectories. Cox-proportional hazards regression determined hazards ratios for incident outcomes. Results: 264,230 individuals (mean age 49.5 years (SD 12.7) and mean BMI 33.8kg/m2 (SD 6.1)) were followed-up for a median duration of 10.9 years. Four BMI trajectories were identified, corresponding at baseline, with World Health Organisation BMI classifications for overweight, class-1, class-2 and class-3 obesity respectively. In all four groups, there was a small, stable upwards trajectory in BMI (mean BMI increase of 1.06kg/m2 (± 3.8)). Compared with overweight individuals, class-3 obese individuals had hazards ratios (HR) of 3.26 (95% CI 2.98-3.57) for heart failure, HR of 2.72 (2.58-2.87) for all-cause mortality and HR of 3.31 (2.84-3.86) for CVD-related mortality, after adjusting for baseline demographic and cardiovascular risk factors. Conclusion: The majority of adults who are overweight or obese retain their degree of overweight or obesity over the long term. Individuals with stable severe obesity experience the worst heart failure, CVD and mortality outcomes. These findings highlight the high cardiovascular toll exacted by continuing failure to tackle obesity

Obesity and long-term outcomes after incident stroke: a prospective population-based cohort study

Background: The association between obesity, major adverse cardiovascular events (MACE), and mortality in patients with incident stroke is not well established. We assessed the relationship between body mass index (BMI) and MACE in patients with incident stroke. Methods: The population-based cohort study identified 30,702 individuals from the Clinical Practice Research Datalink (CPRD GOLD) and Hospital Episode Statistics (HES) databases from the United Kingdom. Individuals were aged 18 years with incident stroke between 1-1-1998 and 31-12-2017, a BMI recorded within 24 months before incident stroke, and no prior history of MACE. BMI was categorised as underweight (≤18.5 kg/m2), normal (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), obesity class I (30.0-34.9 kg/m2), class II (35.0-39.9 kg/m2) and class III (40 kg/m2). MACE was defined as a composite of incident coronary heart disease, recurrent stroke, peripheral vascular disease (PVD), heart failure, and cardiovascular-related mortality. Multivariable Cox regression was used to assess differences in MACE risk between BMI categories. Results: At baseline, 1,217 (4.0%) were underweight, 10,783 (35.1%) had a normal BMI, 10,979 (35.8%) had overweight, 5,206 (17.0%) had obesity class I, 1,749 (5.7%) class II, and 768 (2.5%) class III. In multivariable analysis, higher BMI were associated with lower risk of subsequent MACE [overweight: HR 0.96 95% CI 0.93-0.99)]; PVD [overweight: 0.65 (0.49-0.85); obesity class III: 0.19 (0.50-0.77)]; cardiovascular-related death [overweight: 0.80 (0.74-0.86); obesity class I: 0.79 (0.71-0.88); class II: 0.80 (0.67-0.96)]; and all-cause mortality [overweight: 0.75 (0.71-0.79); obesity class I: 0.75 (0.70-0.81); class II: 0.77 (0.68-0.86)] when compared to those with normal BMI. The results were similar irrespective of sex, diabetes mellitus, smoking or cancer at time of incident stroke.Conclusions: In patients with incident stroke, overweight or obesity were associated with a more favourable prognosis for subsequent MACE, PVD, and mortality, irrespective of sex, diabetes mellitus, smoking or cancer at baseline. As with other cohort studies, our study demonstrates an association. Randomised control trials should be considered to robustly evaluate the impact of weight management recommendations on subsequent cardiovascular outcomes in stroke survivors

Comparing the performance of the novel FAMCAT algorithms and established case-finding criteria for familial hypercholesterolaemia in primary care

Objective Familial hypercholesterolaemia (FH) is a common inherited disorder causing premature coronary heart disease (CHD) and death. We have developed the novel Familial Hypercholesterolaemia Case Ascertainment Tool (FAMCAT 1) case-finding algorithm for application in primary care, to improve detection of FH. The performance of this algorithm was further improved by including personal history of premature CHD (FAMCAT 2 algorithm). This study has evaluated their performance, at 95% specificity, to detect genetically confirmed FH in the general population. We also compared these algorithms to established clinical case-finding criteria.Methods Prospective validation study, in 14 general practices, recruiting participants from the general adult population with cholesterol documented. For 260 participants with available health records, we determined possible FH cases based on FAMCAT thresholds, Dutch Lipid Clinic Network (DLCN) score, Simon-Broome criteria and recommended cholesterol thresholds (total cholesterol >9.0 mmol/L if ≥30 years or >7.5 mmol/L if ≤30 years), using clinical data from electronic and manual extraction of patient records and family history questionnaires. The reference standard was genetic testing. We examined detection rate (DR), sensitivity and specificity for each case-finding criteria.Results At 95% specificity, FAMCAT 1 had a DR of 27.8% (95% CI 12.5% to 50.9%) with sensitivity of 31.2% (95% CI 11.0% to 58.7%); while FAMCAT 2 had a DR of 45.8% (95% CI 27.9% to 64.9%) with sensitivity of 68.8% (95% CI 41.3% to 89.0%). DLCN score ≥6 points yielded a DR of 35.3% (95% CI 17.3% to 58.7%) and sensitivity of 37.5% (95% CI 15.2% to 64.6%). Using recommended cholesterol thresholds resulted in DR of 28.0% (95% CI 14.3% to 47.6%) with sensitivity of 43.8% (95% CI 19.8% to 70.1%). Simon-Broome criteria had lower DR 11.3% (95% CI 6.0% to 20.0%) and specificity 70.9% (95% CI 64.8% to 76.5%) but higher sensitivity of 56.3% (95% CI 29.9% to 80.2%).Conclusions In primary care, in patients with cholesterol documented, FAMCAT 2 performs better than other case-finding criteria for detecting genetically confirmed FH, with no prior clinical review required for case finding

Cost-Effectiveness of Screening Algorithms for Familial Hypercholesterolaemia in Primary Care

Although familial hypercholesterolemia (FH) screening within primary care is considered cost-effective, which screening approach is cost-effective has not been established. This study determines the cost-effectiveness of six case-finding strategies for screening of electronic health records to identify index patients who have genetically confirmed monogenic FH in English primary care. A decision tree was constructed to represent pathways of care for each approach (FH Case Identification Tool (FAMCAT) versions 1 and 2, cholesterol screening, Dutch Lipid Clinic Network (DLCN), Simon Broome criteria, no active screening). Clinical effectiveness was measured as the number of monogenic FH cases identified. Healthcare costs for each algorithm were evaluated from an NHS England perspective over a 12 week time horizon. The primary outcome was the incremental cost per additional monogenic FH case identified (ICER). FAMCAT2 was found to dominate (cheaper and more effective) cholesterol and FAMCAT1 algorithms, and extendedly dominate DLCN. The ICER for FAMCAT2 vs. no active screening was 8111 GBP (95% CI: 4088 to 14,865), and for Simon Broome vs. FAMCAT2 was 74,059 GBP (95% CI: −1,113,172 to 1,697,142). Simon Broome found the largest number of FH cases yet required 102 genetic tests to identify one FH patient. FAMCAT2 identified fewer, but only required 23 genetic tests