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OBJECTIVE To investigate general practitioners' course of action after detection of elevated thyroid stimulating hormone (TSH) levels regarding repeat testing, direct levothyroxine replacement, or neither. METHODS We conducted a retrospective study of adults without prior evidence of thyroid disease and with a first detection of elevated TSH levels from January 1, 2015, to December 31, 2020, using data from electronic medical records of a Swiss primary care database. We determined the occurrence of either repeat TSH testing or direct levothyroxine initiation in primary care during 12-month follow-up and determined associations with demographic and clinical factors. RESULTS Of the 1 591 patients included (median age 65 years, 64.4% female, median TSH 5.7 mIU/L), 34.3% received repeat TSH testing and 12.4% received direct levothyroxine replacement in primary care during follow-up. Repeat TSH testing showed the strongest association with overt hypothyroidism and was more common among patients with high primary care utilization and among patients aged 40-64 years compared to patients aged <40 years. Direct levothyroxine initiation was more likely for TSH levels >7 mIU/L, overt hypothyroidism, female patients, and nonurban practices. CONCLUSIONS While the degree of thyroid dysfunction was the main driver of follow-up, we identified important gaps in the primary care-based monitoring of elevated TSH levels in young patients and in patients with infrequent consultations. We also observed potential overtreatment of women and patients in nonurban areas. Our findings highlight the need for standardization and dissemination of guidelines for the management of elevated TSH levels among general practitioners.

In the United States, a public-health crisis of opioid overuse has been observed, and in Europe, prescriptions of opioids are strongly increasing over time. The objective was to develop and validate a multivariable prognostic model to be used at the beginning of an opioid prescription episode, aiming to identify individual patients at high risk for long-term opioid use based on routinely collected data. Predictors including demographics, comorbid diseases, comedication, morphine dose at episode initiation, and prescription practice were collected. The primary outcome was long-term opioid use, defined as opioid use of either >90 days duration and ≥10 claims or >120 days, independent of the number of claims. Traditional generalized linear statistical regression models and machine learning approaches were applied. The area under the curve, calibration plots, and the scaled Brier score assessed model performance. More than four hundred thousand opioid episodes were included. The final risk prediction model had an area under the curve of 0.927 (95% confidence interval 0.924-0.931) in the validation set, and this model had a scaled Brier score of 48.5%. Using a threshold of 10% predicted probability to identify patients at high risk, the overall accuracy of this risk prediction model was 81.6% (95% confidence interval 81.2% to 82.0%). Our study demonstrated that long-term opioid use can be predicted at the initiation of an opioid prescription episode, with satisfactory accuracy using data routinely collected at a large health insurance company. Traditional statistical methods resulted in higher discriminative ability and similarly good calibration as compared with machine learning approaches.