Intra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data

BACKGROUND

Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative.

OBJECTIVES

To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs).

METHODS

Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conducted to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each disease, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm.

RESULTS

Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV. Algorithm for mCRPC identification showed a 97% PPV and 99% NPV.

CONCLUSION

The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.

A French Value Set for the EQ-5D-5L

OBJECTIVE

The objective of this study was to develop a French value set for the EQ-5D-5L, for academic and clinical research, and for regulatory requirements for price-setting of drugs and medical devices.

METHOD

This study used the standardized valuation protocol developed by EuroQol, using computer-assisted personal interview software. A representative sample of 1048 French residents were interviewed by a market research company, under the supervision of the research team. Health states were valued using composite time trade-off and a discrete choice experiment. Modeling was used to create values for the 3125 possible health states. The composite time trade-off data were modeled using a Tobit model with censored observations at -1 and correcting for heteroscedasticity. A conditional logit model was used for the discrete choice results, and both models were combined using a hybrid model. An adjusted hybrid model was tested to correct for imbalance in the sample on age and sex compared with the general population. A comparison with the 3-level (3L) value set was performed.

RESULTS

The adjusted model was preferred to comply with the representativeness of the general population. It provided a value set for which all coefficients were logically consistent. Values ranged from - 0.525 to 1. The distribution of values presented a shift towards higher values versus the 3L value set. Ranking of dimensions changed. Pain and discomfort and mobility were the dimensions with the highest potential for disutility compared with mobility and self-care for the 3L instrument.

CONCLUSIONS

This study provides a value set based on societal preferences of the French population, using an improved descriptive instrument of health-related quality-of-life health states. It will contribute to improve the quality of cost-effectiveness analysis in the French context and help stimulate disease-specific quality-of-life references for academic-, institutional-, and industry-promoted studies.