Validity of administrative database code algorithms to identify vascular access placement, surgical revisions, and secondary patency

Simple compared to covariate-constrained randomization methods in balancing baseline characteristics: a case study of randomly allocating 72 hemodialysis centers in a cluster trial

Background and aim

Some parallel-group cluster-randomized trials use covariate-constrained rather than simple randomization. This is done to increase the chance of balancing the groups on cluster- and patient-level baseline characteristics. This study assessed how well two covariate-constrained randomization methods balanced baseline characteristics compared with simple randomization.

Methods

We conducted a mock 3-year cluster-randomized trial, with no active intervention, that started April 1, 2014, and ended March 31, 2017. We included a total of 11,832 patients from 72 hemodialysis centers (clusters) in Ontario, Canada. We randomly allocated the 72 clusters into two groups in a 1:1 ratio on a single date using individual- and cluster-level data available until April 1, 2013. Initially, we generated 1000 allocation schemes using simple randomization. Then, as an alternative, we performed covariate-constrained randomization based on historical data from these centers. In one analysis, we restricted on a set of 11 individual-level prognostic variables; in the other, we restricted on principal components generated using 29 baseline historical variables.

We created 300,000 different allocations for the covariate-constrained randomizations, and we restricted our analysis to the 30,000 best allocations based on the smallest sum of the penalized standardized differences. We then randomly sampled 1000 schemes from the 30,000 best allocations. We summarized our results with each randomization approach as the median (25th and 75th percentile) number of balanced baseline characteristics. There were 156 baseline characteristics, and a variable was balanced when the between-group standardized difference was ≤ 10%.

Results

The three randomization techniques had at least 125 of 156 balanced baseline characteristics in 90% of sampled allocations. The median number of balanced baseline characteristics using simple randomization was 147 (142, 150). The corresponding value for covariate-constrained randomization using 11 prognostic characteristics was 149 (146, 151), while for principal components, the value was 150 (147, 151).

Conclusion

In this setting with 72 clusters, constraining the randomization using historical information achieved better balance on baseline characteristics compared with simple randomization; however, the magnitude of benefit was modest.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05590-1.

Validating vascular access data in the Swedish Renal Registry SRR

BACKGROUND

All Swedish dialysis units register data on vascular access in the Swedish Renal Registry (SRR). This study assessed external and internal validity of vascular access data in the SRR and its use as a tool in clinical practice.

METHODS

For external validation, all procedures for placed fistulas, open and endovascular reinterventions registered in the SRR in 2011 to 2017 were cross-matched with data from the Swedish National Patient Registry. A two-stage sampling selected 12/60 dialysis units for internal validation. Data on current vascular access for 10 randomly selected patients at each unit were compared with medical record data. SRR data on placed fistulas from 2017 were cross-checked with data from local surgical units. Registrations of central venous catheters (CVCs) as temporary or permanent were used as a proxy for clinical utilization of the registry and analyzed separately.

RESULTS

External validity increased from 74% to 83% during the observation period. In all, 1037 datapoints were used in internal validation, with a 95% match between SRR registrations and medical records. Registrations of CVCs, fistulas, and interventions were reliable, with few missing data or mismatches. Vascular access type initiating hemodialysis was missing or incorrect in either the SRR or medical records for 14/120 patients. Registrations of placed fistulas in 2017 matched in all but four (pre-dialysis stage) of 135 cases. Some 35% of the CVCs validated (n = 49) at 7/12 units were not categorized as temporary or permanent.

CONCLUSION

The SRR provides a reliable resource on current vascular access care.

Potentials, challenges, and limitations of the analysis of administrative data on vascular limb amputations in health care

Although more and more data on lower limb amputations are becoming available by leveraging the widening access to health care administrative databases, the applicability of these data for public health decisions is still limited. Problems can be traced back to methodological issues, how data are generated and to conceptual issues, namely, how data are interpreted in a multidimensional environment. The present review summarised all of the steps from converting the claims data of administrative databases into the analytical data and reviewed the wide array of sources of potential biases in the analysis of such data. The origins of uncertainty of administrative data analysis include uncontrolled confounding due to a lack of clinical data, the left- and right-censored nature of data collection, the non-standardized diagnosis/procedure-based data extraction methods (i.e., numerator/denominator problems) and additional methodological problems associated with temporal and spatial analyses. The existence of these methodological challenges in the administrative data-based analysis should not deter the analysts from using these data as a powerful tool in the armamentarium of clinical research. However, it must be done with caution and a thorough understanding and respect of the methodological limitations. In addition to this requirement, there is a profound need for pursuing further research on methodology and widening the search for other indicators (structural, process or outcome) that allow a deeper insight how the quality of vascular care may be assessed. Effective research using administrative data is based on strong collaboration in three domains, namely expertise in claims data handling and processing, the clinical field, and statistical analysis. The final interpretations of results and the countermeasures on the level of vascular care ought to be grounded on the integrity of research, open discussions and institutionalized mechanisms of science arbitration and honest brokering.