The triumph of medicine: how overdiagnosis is turning healthy people into patients

Pituitary adenoma or neuroendocrine tumour: the need for an integrated prognostic classification

In the 2022 fifth edition of the WHO Classification of Endocrine Tumours and of Central Nervous System Tumours, pituitary adenomas are reclassified as neuroendocrine tumours (NETs). This change confers an oncology label to neoplasms that are overwhelmingly benign. A comprehensive clinical classification schema is required to guide prognosis, therapy and outcomes for all patients with pituitary adenomas. Pituitary adenomas and NETs exhibit some morphological and ultrastructural similarities. However, unlike NETs, pituitary adenomas are highly prevalent, yet indolent and rarely become malignant. This Perspective presents the outcomes of an interdisciplinary international workshop that addressed the merit and clinical implications of the classification change of pituitary adenoma to NET. Many non-histological factors provide mechanistic insight and influence the prognosis and treatment of pituitary adenoma. We recommend the development of a comprehensive classification that integrates clinical, genetic, biochemical, radiological, pathological and molecular information for all anterior pituitary neoplasms.

Classifying and communicating risks in prediabetes according to fasting glucose and/or glycated hemoglobin: PREDAPS cohort study

OBJECTIVE

Information about prognostic outcomes can be of great help for people with prediabetes and for physicians in the face of scientific controversy about the cutoff point for defining prediabetes. We aimed to estimate different prognostic outcomes in people with prediabetes.

DESIGN

Prospective cohort of subjects with prediabetes according to American Diabetes Association guidelines.

MAIN OUTCOME MEASURES

The probabilities of diabetes onset versus non-onset, the odds against diabetes onset, and the probability of reverting to normoglycemia according to different prediabetes categories were calculated.

RESULTS

The odds against diabetes onset ranged from 29:1 in individuals with isolated FPG of 100-109 mg/dL to 1:1 in individuals with FPG 110-125 mg/dL plus HbA1c 6.0-6.4%. The probability of reversion to normoglycemia was 31.2% (95% CI 24.0-39.6) in those with isolated FPG 100-109 mg/dL and 6.2% (95% CI 1.4-10.0) in those with FPG 110-125 mg/dL plus HbA1c 6.0-6.4%. Of every 100 participants in the first group, 97 did not develop diabetes and 31 reverted to normoglycemia, while in the second group those figures were 52 and 6.

CONCLUSIONS

Using odds of probabilities and absolute numbers might be useful for people with prediabetes and physicians to share decisions on potential interventions.Key pointsCommunicating knowledge on the course of the disease to make clinical decisions is not always done appropriately.Prediabetes is an example where risk communication is important because the prognosis of subjects with prediabetes is very heterogeneous.Depending on fasting plasma glucose and HbA1c levels, the odds of probabilities against diabetes onset ranged from 29: 1 to 1: 1.Depending on fasting plasma glucose and HbA1c levels, the number of subjects in 100 who revert to normoglycemia ranged from 31 to 6.Using probabilities and number absolutes on the prognosis of prediabetes may be useful for people with prediabetes and physicians to share decisions on potential interventions.

The heterogeneity of reversion to normoglycemia according to prediabetes type is not explained by lifestyle factors

Healthy lifestyle interventions and drug therapies are proven to have a positive preventative influence on normal glucose regulation in prediabetes. However, little is known on the specific role that these factors play on reversion to normal glycemia according to type of prediabetes. We used data from the Observational prospective cohort study, The Cohort study in Primary Health Care on the Evolution of Patients with Prediabetes from 2012 to 2015. A total of 1184 individuals aged 30-74 years old were included and classified based on the ADA in three mutually exclusive groups using either fasting plasma glucose (FPG) levels (from 100 to 125 mg/dl, FPG group), HbA_1c (5.7-6.4%, HbA1c group) or both impaired parameters. Information on lifestyle factors and biochemical parameters were collected at baseline. Reversion to normal glucose regulation was calculated at third year of follow-up. Relationship of lifestyle factor and type of prediabetes with reversion were estimated using odds ratios (ORs) with 95% confidence intervals (95% CIs) adjusting by different groups of confounders. Proportion of reversion rates were 31% for FPG group, 31% for HbA1c group and 7.9% for both altered parameters group, respectively. Optimal life style factors such as BMI < 25 kg/m²[OR (95% CI): 1.90 (1.20-3.01)], high adherence to Mediterranean diet 1.78 (1.21-2.63) and absence of abdominal obesity 1.70 (1.19-2.43) were the strongest predictors for reversion to normal glucose. However, those did not modify the ORs of reversion to normal glucose. Taking as reference those with both impaired parameters, subjects with FPG impairment (FPG group) had an OR of 4.87 (3.10-7.65) and 3.72 (2.39-5.78) for HbA1c group. These estimates remained almost the same after further adjustment for biochemical parameters and lifestyle factors (4.55(2.84-7.28) and 3.09 (1.92-4.97), respectively). Optimal lifestyle factors showed to be a positive predictor for reversion to normal glucose regulation however, the differences of reversion risk according type of prediabetes are not explained by lifestyle factors.

Lifestyle factors do not explain the difference on diabetes progression according to type of prediabetes: Results from a Spanish prospective cohort of prediabetic patients

AIMS

We studied the role of lifestyle factors associated to type 2 diabetes (T2DM) onset according to type of prediabetes.

METHODS

We used data from the observational prospective cohort study in Primary Health Care on the Evolution of Patients with Prediabetes in Spain (PREDAPS). Participants were classified by American Diabetes Association criteria using either fasting plasma glucose levels (100-125 mg/dL) (group 1), HbA1c (5.7%-6.4%) (group 2) or both impaired parameters (group 3). Relationship between lifestyles and diabetes onset according to prediabetes at third year of follow up were estimated by Hazard Ratios (HRs) using three sequential models.

RESULTS

Incidence rate of diabetes was 2.27 cases per 1000 person-years (95% CI: 1.4-3.6) for group 1, 1.18 (95% CI: 0.65-2.13) for group 2 and 6.68 (95% CI: 5.71-8.23) for group 3. The most important risk factors were: abdominal obesity (HR: 2.29 (95% CI: 1.49-3.52)) and hypertension (HR: 2.16 (95% CI: 1.41-3.30)). Using as reference group 2, group 3 had a HR of 5.82 (3.13-10.82) and 1.83 (95% CI: 0.85-3.93) for group 1, estimates remained constant when adjusting by lifestyle and metabolic factors.

CONCLUSIONS

Lifestyle and metabolic do not seem to explain the differences on T2DM onset by type of prediabetes.

Overdiagnosis in primary care: framing the problem and finding solutions

Overdiagnosis, is defined as the diagnosis of a condition that, if unrecognized, would not cause symptoms or harm a patient during his or her lifetime, and it is increasingly acknowledged as a consequence of screening for cancer and other conditions. Because preventive care is a crucial component of primary care, which is delivered to the broad population, overdiagnosis in primary care is an important problem from a public health perspective and has far reaching implications. The scope of overdiagnosis as a result of services delivered in primary care is unclear, though overdiagnosis of indolent breast, prostate, thyroid, and lung cancers is well described and overdiagnosis of chronic kidney disease, depression, and attention-deficit/hyperactivity disorder is also recognized. However, overdiagnosis is a known consequence of all screening and can be assumed to occur in many more clinical contexts. Overdiagnosis can harm patients by leading to overtreatment (with associated potential toxicities), diagnosis related anxiety or depression, and labeling, or through financial burden. Many entrenched factors facilitate overdiagnosis, including the growing use of advanced diagnostic technology, financial incentives, a medical culture that encourages greater use of tests and treatments, limitations in the evidence that obscure the understanding of diagnostic utility, use of non-beneficial screening tests, and the broadening of disease definitions. Efforts to reduce overdiagnosis are hindered by physicians' and patients' lack of awareness of the problem and by confusion about terminology, with overdiagnosis often conflated with related concepts. Clarity of terminology would facilitate physicians' understanding of the problem and the growth in evidence regarding its prevalence and downstream consequences in primary care. It is hoped that international coordination regarding diagnostic standards for disease definitions will also help minimize overdiagnosis in the future.

The intersection of physician wellbeing and clinical application of diabetes guidelines

OBJECTIVES

Prediabetes (preDM) and diabetes are complex conditions that place significant strain on medical providers. This can have a negative impact on providers' wellbeing and could impact clinical decisions. We investigated the interplay of caring for patients with prediabetes, physician mental wellbeing, and clinical care.

METHODS

Using the theory of planned behavior, we conducted a secondary analysis to evaluate physicians' self-reported provision of care to patients with preDM. We evaluated the effect of mental wellbeing and perceived barriers to caring for patients with preDM.

RESULTS

Among 1015 academic physicians, a greater perception of barriers to care and a higher percentage of patients seen with preDM were both significantly associated with a less positive physician state of mind. Physician state of mind was not associated with self-reported clinical behavior. Physician perception of patient barriers has a positive correlation with their likelihood of prescribing metformin for preDM.

CONCLUSIONS

Caring for a larger proportion of patients with preDM is associated with worse mental wellbeing. Physician attitudes and subjective norms may predict adherence to guidelines, while physician attitudes and wellbeing affect self-reported prescribing behavior.

PRACTICE IMPLICATIONS

Future research should evaluate ways to lessen the psychological burden of caring for patients with diabetes and preDM.

Role of Patient and Practice Characteristics in Variance of Treatment Quality in Type 2 Diabetes between General Practices

Background

Accounting for justifiable variance is important for fair comparisons of treatment quality. The variance between general practices in treatment quality of type 2 diabetes (T2DM) patients may be attributed to the underlying patient population and practice characteristics. The objective of this study is to describe the between practice differences in treatment, and identify patient and practice level characteristics that may explain these differences.

Methods

The data of 24,607 T2DM patients from 183 general practices in the Netherlands were used. Treatment variance was assessed in a cross-sectional manner for: glucose-lowering drugs/metformin, lipid-lowering drugs/statins, blood pressure-lowering drugs/ACE-inhibitor or ARB. Patient characteristics tested were age, gender, diabetes duration, comorbidity, comedication. Practice characteristics were number of T2DM patients, practice type, diabetes assistant available. Multilevel logistic regression was used to examine the between practice variance in treatment and the effect of characteristics on this variance.

Results

Treatment rates varied considerably between practices (IQR 9.5–13.9). The variance at practice level was 7.5% for glucose-lowering drugs, 3.6% for metformin, 3.1% for lipid-lowering drugs, 10.3% for statins, 8.6% for blood pressure-lowering drugs, and 3.9% for ACE-inhibitor/ARB. Patient and practice characteristics explained 19.0%, 7.5%, 20%, 6%, 9.9%, and 13.4% of the variance respectively. Age, multiple chronic drugs, and ≥3 glucose-lowering drugs were the most relevant patient characteristics. Number of T2DM patients per practice was the most relevant practice characteristic.

Discussion

Considerable differences exist between practices in treatment rates. Patients’ age was identified as characteristic that may account for justifiable differences in especially lipid-lowering treatment. Other patient or practice characteristics either do not explain or do not justify the differences.

Can precision medicine reduce overdiagnosis?

Precision Medicine promises to get the right patient, the right test, the right diagnosis, the right treatment, and in the right amount. Is this hope or hype?