Threshold analysis in the presence of both the diagnostic and the therapeutic risk

Determinants of referral for suspected coronary artery disease: a qualitative study based on decision thresholds

BACKGROUND

Chest pain is a frequent consultation issue in primary care, with coronary artery disease (CAD) being a serious potential cause. Primary care physicians (PCPs) assess the probability for CAD and refer patients to secondary care if necessary. Our aim was to explore PCPs' referral decisions, and to investigate determinants which influenced those decisions.

METHODS

PCPs working in Hesse, Germany, were interviewed in a qualitative study. We used 'stimulated recall' with participants to discuss patients with suspected CAD. With a sample size of 26 cases from nine practices we reached inductive thematic saturation. Interviews were audio-recorded, transcribed verbatim and analyzed by inductive-deductive thematic content analysis. For the final interpretation of the material, we used the concept of decision thresholds proposed by Pauker and Kassirer.

RESULTS

PCPs reflected on their decisions for or against a referral. Aside from patient characteristics determining disease probability, we identified general factors which can be understood as influencing the referral threshold. These factors relate to the practice environment, to PCPs themselves and to non-diagnostic patient characteristics. Proximity of specialist practice, relationship with specialist colleagues, and trust played a role. PCPs sometimes felt that invasive procedures were performed too easily. They tried to steer their patients through the system with the intent to avoid over-treatment. Most PCPs were unaware of guidelines but relied on informal local consensus, largely influenced by specialists. As a result, PCPs gatekeeping role was limited.

CONCLUSIONS

We could identify a large number of factors that impact referral for suspected CAD. Several of these factors offer possibilities to improve care at the clinical and system level. The threshold model proposed by Pauker and Kassirer was a useful framework for this kind of data analysis.

Evaluation of the Indications for Sentinel Node Biopsy in Early-Stage Melanoma with the Advent of Adjuvant Systemic Therapy: An International, Multicenter Study

BACKGROUND

Patients presenting with early-stage melanoma (AJCC pT1b-pT2a) reportedly have a relatively low risk of a positive SNB (~5-10%). Those patients are usually found to have low-volume metastatic disease after SNB, typically reclassified to AJCC stage IIIA, with an excellent prognosis of ~90% 5-year survival. Currently, adjuvant systemic therapy is not routinely recommended for most patients with AJCC stage IIIA melanoma. The purpose was to assess the SN-positivity rate in early-stage melanoma and to identify primary tumor characteristics associated with high-risk nodal disease eligible for adjuvant systemic therapy METHODS: An international, multicenter retrospective cohort study from 7 large-volume cancer centers identified 3,610 patients with early primary cutaneous melanomas 0.8-2.0 mm in Breslow thickness (pT1b-pT2a; AJCC 8th edition). Patient demographics, primary tumor characteristics, and SNB status/details were analyzed.

RESULTS

The overall SNB-positivity rate was 11.4% (412/3610). Virtually all SNB-positive patients (409/412; 99.3%) were reclassified to AJCC stage IIIA. Multivariate analysis identified age, T-stage, mitotic rate, primary site and subtype, and lymphovascular invasion as independent predictors of sentinel node status. A mitotic rate of >1/mm2 was associated with a significantly increased SN-positivity rate and was the only significant independent predictor of high-risk SNB metastases (>1 mm maximum diameter).

CONCLUSIONS

The new treatment paradigm brings into question the role of SNB for patients with early-stage melanoma. The results of this large international cohort study suggest that a reevaluation of the indications for SNB for some patients with early-stage melanoma is required.

On the effect of uncertainty on personal vaccination decisions

This study investigates the effect of ambiguity on personal vaccination decisions. We first characterize the vaccination decision in the absence of ambiguity. We then show that uncertainty about the probability of side effects and the efficacy of the vaccine always reduces take-up under ambiguity aversion. However, uncertainty about the underlying disease, being the probability of sickness or the probability of a severe course of disease, may either encourage or discourage vaccination. Our results are relevant for policy because reducing uncertainty associated with the vaccine always has the desired effect whereas reducing uncertainty associated with the disease may have unintended consequences.

The treatment decision under uncertainty: The effects of health, wealth and the probability of death

Medical treatment reduces diagnostic risk, increases therapeutic risk and lowers the probability of death. This paper analyzes the effects of initial health, wealth and the probability of death on the propensity to treat under diagnostic and therapeutic risk. It shows that treatment propensity increases with the probability of death, but can decrease with the severity of illness. The effect of wealth depends on the cross-derivative of the utility function with respect to health and wealth. These results have implications for treatment decisions at the end of life.

The threshold model revisited

BACKGROUND

The threshold model represents one of the most significant advances in the field of medical decision-making, yet it often does not apply to the most common class of clinical problems, which include health outcomes as a part of definition of disease. In addition, the original threshold model did not take a decision-maker's values and preferences explicitly into account.

METHODS

We reformulated the threshold model by (1) applying it to those clinical scenarios, which define disease according to outcomes that treatment is designed to affect, (2) taking into account a decision-maker's values.

RESULTS

We showed that when outcomes (eg, morbidity) are integral part of definition of disease, the classic threshold model does not apply (as this leads to double counting of outcomes in the probabilities and utilities branches of the model). To avoid double counting, the model can be appropriately analysed by assuming diagnosis is certain (P = 1). This results in deriving a different threshold-the threshold for outcome of disease (M_t ) instead of threshold for probability of disease (P_t ) above which benefits of treatment outweigh its harms. We found that M_t  ≤ P_t , which may explain differences between normative models and actual behaviour in practice. When a decision-maker values outcomes related to benefit and harms differently, the new threshold model generates decision thresholds that could be descriptively more accurate.

CONCLUSIONS

Calculation of the threshold depends on careful disease versus utility definitions and a decision-maker's values and preferences.