Misclassification of incident hospitalized and outpatient heart failure in administrative claims data: the Atherosclerosis Risk in Communities (ARIC) study

Camplain, Ricky; Kucharska‐Newton, Anna; Cuthbertson, Carmen C.; Wright, Jacqueline D.; Alonso, Álvaro; Heiss, Gerardo

doi:10.1002/pds.4162

Cited by 16 publications

(20 citation statements)

References 15 publications

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“…For the current study, from those 3,504,732 US veterans in RCAV, we identified 224,858 patients with a hospitalization or two outpatient encounters with ICD-9 code 428 for HF during follow-up through August 2013 (S1 Fig). [18–20] Of these, 46,206 cases were considered prevalent cases (HF diagnosis in the first year from cohort entry) and were excluded. [21] Of the remaining 178,652 patients, we further excluded 36,565 veterans without any outpatient potassium measurements six months prior to HF diagnosis, information on covariates, or linkage to all-cause mortality, leaving 142,087 patients with newly diagnosed HF for this study.…”

Section: Methodsmentioning

confidence: 99%

Dyskalemia, its patterns, and prognosis among patients with incident heart failure: A nationwide study of US veterans

et al. 2019

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Background Although hypokalemia has been viewed as a significant concern among patients with heart failure (HF), recent advances in HF management tend to increase the risk of hyperkalemia. Objective To characterize contemporary data regarding correlates and prognostic values of dyskalemia in patients with HF. Design, setting, and participants In cross-sectional and longitudinal analyses, we studied 142,087 patients with newly diagnosed HF in US nationwide Veterans Administration database from 2005 through 2013. Exposures Demographic characteristics, laboratory variables, comorbidities, and medication use for the analysis of correlates of dyskalemia as well as potassium level in the analysis of mortality. Main Outcomes and Measures Dyskalemia and mortality. Results Hypokalemia (<3.5 mmol/L) at baseline was observed in 3.0% of the population, whereas hyperkalemia (≥5.5 mmol/L) was seen in 0.9%. An additional 20.4% and 5.7% had mild hypokalemia (3.5–3.9 mmol/L) and mild hyperkalemia (5.0–5.4 mmol/L). Key correlates were black race, higher blood pressure, and use of potassium-wasting diuretics for hypokalemia, and lower kidney function for hyperkalemia. Baseline potassium levels showed a U-shaped association with mortality, with the lowest risk between 4.0–4.5 mmol/L. With respect to potassium levels over a year after HF diagnosis, persistent (>50% of measurements), intermittent (>1 occurrence but ≤50%), and transient (1 occurrence) hypo- and hyperkalemia were also related to increased mortality in a graded fashion regardless of the aforementioned thresholds for dyskalemia. These dyskalemic patterns were also related to other clinical actions and demands such as emergency room visit. Conclusions Potassium levels below 4 mmol/L and above 5 mmol/L at and after HF diagnosis were associated with poor prognosis and the clinical actions. HF patients (particularly with risk factors for dyskalemia like black race and kidney dysfunction) may require special attention for both hypo- and hyperkalemia.

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Section: Methodsmentioning

confidence: 99%

Dyskalemia, its patterns, and prognosis among patients with incident heart failure: A nationwide study of US veterans

et al. 2019

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“…For this, the investigator examined the electronic medical record for the 2 years preceding the study hospitalisation to determine if the patient had a prior diagnosis of or hospitalisation for HF. 16 …”

Section: Methodsmentioning

confidence: 99%

Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration

et al. 2018

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ObjectivesWe aimed to validate an algorithm using both primary discharge diagnosis (International Classification of Diseases Ninth Revision (ICD-9)) and diagnosis-related group (DRG) codes to identify hospitalisations due to decompensated heart failure (HF) in a population of patients with diabetes within the Veterans Health Administration (VHA) system.DesignValidation study.SettingVeterans Health Administration—Tennessee Valley Healthcare SystemParticipantsWe identified and reviewed a stratified, random sample of hospitalisations between 2001 and 2012 within a single VHA healthcare system of adults who received regular VHA care and were initiated on an antidiabetic medication between 2001 and 2008. We sampled 500 hospitalisations; 400 hospitalisations that fulfilled algorithm criteria, 100 that did not. Of these, 497 had adequate information for inclusion. The mean patient age was 66.1 years (SD 11.4). Majority of patients were male (98.8%); 75% were white and 20% were black.Primary and secondary outcome measuresTo determine if a hospitalisation was due to HF, we performed chart abstraction using Framingham criteria as the referent standard. We calculated the positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for the overall algorithm and each component (primary diagnosis code (ICD-9), DRG code or both).ResultsThe algorithm had a PPV of 89.7% (95% CI 86.8 to 92.7), NPV of 93.9% (89.1 to 98.6), sensitivity of 45.1% (25.1 to 65.1) and specificity of 99.4% (99.2 to 99.6). The PPV was highest for hospitalisations that fulfilled both the ICD-9 and DRG algorithm criteria (92.1% (89.1 to 95.1)) and lowest for hospitalisations that fulfilled only DRG algorithm criteria (62.5% (28.4 to 96.6)).ConclusionsOur algorithm, which included primary discharge diagnosis and DRG codes, demonstrated excellent PPV for identification of hospitalisations due to decompensated HF among patients with diabetes in the VHA system.

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“…flawed measurement, non-compliance with treatment, inappropriate use of time windows)[2, 3, 16, 40, 41, 54, 55, 58, 87, 91, 93, 94, 96, 101, 110, 119, 121, 130, 138, 140, 146, 147, 152, 154, 156, 158, 159, 164]23.9 Misclassification of outcomeError in the diagnosis (e.g. clinical ambiguity, non-uniform coding)[2, 3, 6, 7, 16, 40, 41, 54, 58, 87, 91, 93, 94, 96, 101, 110, 112, 114, 121, 125, 135–137, 141, 143, 149, 153, 155, 157, 160–163]28.2Time-related biasFollow-up time and exposure status are inadequately taken into account in the study-design or analysis stages[2, 7, 40, 41, 57, 68–75, 77, 83, 86, 87, 90, 99, 101, 105–107, 111, 114, 118, 128, 129, 133, 142, 165–170…”

Section: Resultsmentioning

confidence: 99%

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

Prada-Ramallal

Takkouche

Figueiras

2019

BMC Med Res Methodol

111

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BackgroundThe availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases.MethodsScoping review of the scientific literature published during the period 2000–2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases.ResultsA total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and “immortal time” bias (25.6%) were the subcategories most frequently mentioned.ConclusionsSuboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.Electronic supplementary materialThe online version of this article (10.1186/s12874-019-0695-y) contains supplementary material, which is available to authorized users.

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Misclassification of incident hospitalized and outpatient heart failure in administrative claims data: the Atherosclerosis Risk in Communities (ARIC) study

Cited by 16 publications

References 15 publications

Dyskalemia, its patterns, and prognosis among patients with incident heart failure: A nationwide study of US veterans

Dyskalemia, its patterns, and prognosis among patients with incident heart failure: A nationwide study of US veterans

Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

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