A predictive model to identify Parkinson disease from administrative claims data

Nielsen, Susan Searles; Warden, Mark N.; Camacho‐Soto, Alejandra; Willis, Allison W.; Wright, Brenton A.; Racette, Brad A.

doi:10.1212/wnl.0000000000004536

Cited by 46 publications

(105 citation statements)

References 30 publications

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“…For this population-based study, Medicare base files and comprehensive (inpatient, outpatient, physician/supplier Part B, durable medical equipment, and home health care) claims data from 2004–2009 were used as detailed previously 17 . Briefly, to be eligible for this study, individuals had to be age 66–90 years old, enrolled in Medicare Part A and/or B, and living in the United States (U.S.) in 2009.…”

Section: Subjects/materials and Methodsmentioning

confidence: 99%

“…Controls were randomly selected from all remaining Medicare beneficiaries who met the same study eligibility criteria. Controls were not matched to PD patients on demographic factors so that we could include these demographic factors in risk factor analyses 17 , and this allowed the baseline hazard rate function for TBI to be representative of the study population. PD patients and controls who had a personal history of TBI using the ICD-9 diagnosis V code (V15.52) or other TBI code prior to the beginning of the study period (817 cases, 497 controls) were excluded to ensure an event was correctly identified as the first TBI.…”

Section: Subjects/materials and Methodsmentioning

confidence: 99%

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Traumatic brain injury in the prodromal period of Parkinson's disease: A large epidemiological study using medicare data

Camacho‐Soto

Warden

Nielsen

et al. 2017

Annals of Neurology

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Objective Studies suggest a greater risk of Parkinson disease (PD) after traumatic brain injury (TBI), but it is possible that the risk of TBI is greater in the prodromal period of PD. We aimed to examine the time-to-TBI in PD patients in their prodromal period compared to population-based controls. Methods We identified 89,790 incident PD cases and 118,095 comparable controls > 65 years of age in 2009 using Medicare claims data. Using data from the preceding five years, we compared time-to-TBI in PD patients in their prodromal period to controls. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for TBI in a Cox regression, while adjusting for age, sex, race/ethnicity, modified Charlson comorbidity index, smoking, and alcohol use. Results Risk of TBI was greater in PD patients in their prodromal period across all age and sex groups, with HRs consistently increasing with proximity to PD diagnosis. HRs ranged from 1.64 (95% CI 1.52, 1.77) five years prior to diagnosis to 3.93 (95% CI 3.74, 4.13) in the year prior. The interaction between PD, TBI, and time was primarily observed for TBI attributed to falls. Motor dysfunction and cognitive impairment, suggested by corresponding ICD-9 codes, partially mediated the PD-TBI association. Interpretation There is a strong association between PD and a recent TBI in the prodromal period of PD. This association strengthens as PD diagnosis approaches and may be a result of undetected non-motor and motor symptoms, but confirmation will be required.

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Section: Subjects/materials and Methodsmentioning

confidence: 99%

Section: Subjects/materials and Methodsmentioning

confidence: 99%

Traumatic brain injury in the prodromal period of Parkinson's disease: A large epidemiological study using medicare data

Camacho‐Soto

Warden

Nielsen

et al. 2017

Annals of Neurology

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“…Nearly all individuals ≥65 years old in the U.S. utilize Medicare, thus allowing for the construction of large, nationwide, population-based studies in this age group. In the present work, we started with all controls from our original PD case-control study [17], who represented a population-based sample of beneficiaries age eligible for Medicare and without PD in 2009 ( Figure 1). We then restricted to those who survived through the end of 2009 and followed this cohort for up to five years.…”

Section: Studymentioning

confidence: 99%

“…Method. We identified PD as ≥1 ICD-9 332 or 332.0 code, without Lewy body dementia (ICD-9 331.82), other extrapyramidal disease and abnormal movement disorders (ICD-9 333 or 333.0), or typographic error (nonpyogenic meningitis (ICD-9 322 or 322.0) without diagnostic lumbar puncture (CPT 62270)) [17]. We considered the date of the first code as the date of diagnosis.…”

Section: Pd Ascertainmentmentioning

confidence: 99%

“…Early identification of PD might be facilitated by known strong associations between PD and age, sex, race/ethnicity [8], smoking [9,10], and several medical conditions including anosmia/hyposmia [11], constipation [12,13], REM sleep behavior disorder [14], and depression [15,16]. ese are typically included as core predictors in algorithms for identifying PD, and we recently identified numerous other predictors in a cross-validated model based only on Medicare data using a population-based sample of beneficiaries with and without incident PD [17]. Within this same case-control sample, this model performed well, with an area under the receiver-operating characteristic curve (AUC) of 85.7%.…”

Section: Introductionmentioning

confidence: 99%

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Validation of a Parkinson Disease Predictive Model in a Population-Based Study

Faust

Racette

Nielsen

2020

Parkinson's Disease

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Parkinson disease (PD) has a relatively long prodromal period that may permit early identification to reduce diagnostic testing for other conditions when patients are simply presenting with early PD symptoms, as well as to reduce morbidity from fall-related trauma. Earlier identification also could prove critical to the development of neuroprotective therapies. We previously developed a PD predictive model using demographic and Medicare claims data in a population-based case-control study. e area under the receiver-operating characteristic curve (AUC) indicated good performance. We sought to further validate this PD predictive model. In a randomly selected, population-based cohort of 115,492 Medicare beneficiaries aged 66-90 and without PD in 2009, we applied the predictive model to claims data from the prior five years to estimate the probability of future PD diagnosis. During five years of follow-up, we used 2010-2014 Medicare data to determine PD and vital status and then Cox regression to investigate whether PD probability at baseline was associated with time to PD diagnosis. Within a nested case-control sample, we calculated the AUC, sensitivity, and specificity. A total of 2,326 beneficiaries developed PD. Probability of PD was associated with time to PD diagnosis (p < 0.001, hazard ratio � 13.5, 95% confidence interval (CI) 10.6-17.3 for the highest vs. lowest decile of probability). e AUC was 83.3% (95% CI 82.5%-84.1%). At the cut point that balanced sensitivity and specificity, sensitivity was 76.7% and specificity was 76.2%. In an independent sample of additional Medicare beneficiaries, we again applied the model and observed good performance (AUC � 82.2%, 95% CI 81.1%-83.3%). Administrative claims data can facilitate PD identification within Medicare and Medicare-aged samples.

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Air Pollution and Parkinson Disease in a Population-Based Study

Krzyzanowski,

Mullan,

Turcano

et al. 2024

JAMA Netw Open

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ImportanceThe role of air pollution in risk and progression of Parkinson disease (PD) is unclear.ObjectiveTo assess whether air pollution is associated with increased risk of PD and clinical characteristics of PD.Design, Setting, and ParticipantsThis population-based case-control study included patients with PD and matched controls from the Rochester Epidemiology Project from 1998 to 2015. Data were analyzed from January to June 2024.ExposuresMean annual exposure to particulate matter with a diameter of 2.5 µm or less (PM2.5) from 1998 to 2015 and mean annual exposure to nitrogen dioxide (NO2) from 2000 to 2014.Main Outcomes and MeasuresOutcomes of interest were PD risk, all-cause mortality, presence of tremor-predominant vs akinetic rigid PD, and development of dyskinesia. Models were adjusted for age, sex, race and ethnicity, year of index, and urban vs rural residence.ResultsA total of 346 patients with PD (median [IQR] age 72 [65-80] years; 216 [62.4%] male) were identified and matched on age and sex with 4813 controls (median [IQR] age, 72 [65-79] years, 2946 [61.2%] male). Greater PM2.5 exposure was associated with increased PD risk, and this risk was greatest after restricting to populations within metropolitan cores (odds ratio [OR], 1.23; 95% CI, 1.11-1.35) for the top quintile of PM2.5 exposure compared with the bottom quintile. Greater NO2 exposure was also associated with increased PD risk when comparing the top quintile with the bottom quintile (OR, 1.13; 95% CI, 1.07-1.19). Air pollution was associated with a 36% increased risk of akinetic rigid presentation (OR per each 1-μg/m3 increase in PM2.5, 1.36; 95% CI, 1.02-1.80). In analyses among patients with PD only, higher PM2.5 exposure was associated with greater risk for developing dyskinesia (HR per 1-μg/m3 increase in PM2.5, 1.42; 95% CI, 1.17-1.73), as was increased NO2 exposure (HR per 1 μg/m3 increase in NO2, 1.13; 95% CI, 1.06-1.19). There was no association between PM2.5 and all-cause mortality among patients with PD.Conclusions and RelevanceIn this case-control study of air pollution and PD, higher levels of PM2.5 and NO2 exposure were associated with increased risk of PD; also, higher levels of PM2.5 exposure were associated with increased risk of developing akinetic rigid PD and dyskinesia compared with patients with PD exposed to lower levels. These findings suggest that reducing air pollution may reduce risk of PD, modify the PD phenotype, and reduce risk of dyskinesia.

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A predictive model to identify Parkinson disease from administrative claims data

Abstract: Using only demographic data and selected diagnosis and procedure codes readily available in administrative claims data, it is possible to identify individuals with a high probability of eventually being diagnosed with PD.

Cited by 46 publications

References 30 publications

Traumatic brain injury in the prodromal period of Parkinson's disease: A large epidemiological study using medicare data

Traumatic brain injury in the prodromal period of Parkinson's disease: A large epidemiological study using medicare data

Validation of a Parkinson Disease Predictive Model in a Population-Based Study

Air Pollution and Parkinson Disease in a Population-Based Study

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