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

Al-Jaishi, Ahmed A.; Moist, Louise; Oliver, Matthew J.; Nash, Danielle M.; Fleet, Jamie L.; Garg, Amit X.

doi:10.1177/1129729818762008

Cited by 4 publications

(3 citation statements)

References 17 publications

(20 reference statements)

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“…For example, Al-Jaishi demonstrated 86% validity in an administrative database on vascular access in a Canadian audit. 7 The reporting rate differed between counties, from 55% (Jämtland) to 93% (Örebro) (Figure 4). The observed disparity may be explained by several factors such as training in data recording, organization, and leadership at the units allowing time for registrations and education.…”

Section: Discussionmentioning

confidence: 99%

Validating vascular access data in the Swedish Renal Registry SRR

Welander

Sigvant

2020

J Vasc Access

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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.

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Section: Discussionmentioning

confidence: 99%

Validating vascular access data in the Swedish Renal Registry SRR

Welander

Sigvant

2020

J Vasc Access

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“…We ascertained center-and patient-level characteristics using records from linked healthcare databases in Ontario, Canada (Additional file 1: Appendix 1) [25][26][27][28][29][30][31][32][33][34][35][36][37][38]. These datasets were linked using unique encoded identifiers and analyzed at ICES [39].…”

Section: Data Sourcesmentioning

confidence: 99%

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

Al-Jaishi

Dixon

McArthur³

et al. 2021

Trials

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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.

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“…We ascertained center-and patient-level characteristics using records from linked healthcare databases in Ontario, Canada (Appendix 1). [25][26][27][28][29][30][31][32][33][34][35][36][37][38] These datasets were linked using unique encoded identi ers and analyzed at ICES. 39…”

Section: Data Sourcesmentioning

confidence: 99%

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

Al-Jaishi

Dixon

McArthur

et al. 2021

Preprint

Self Cite

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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 three-year cluster randomized trial, with no active intervention, that started April 1st, 2014, and ended March 31st, 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 up to April 1st, 2013. Initially, we generated 1,000 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. We then randomly sampled 1,000 schemes from the 30,000 best allocations. We summarized our results with each randomization approach as the median (25th, 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). The median number of balanced baseline characteristics using the two covariate-constrained randomizations were statistically different from simple randomization (p-value < 0.0001). 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.

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

Cited by 4 publications

References 17 publications

Validating vascular access data in the Swedish Renal Registry SRR

Validating vascular access data in the Swedish Renal Registry SRR

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

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

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