scholarly journals Identifying Cases of Sleep Disorders through International Classification of Diseases (ICD) Codes in Administrative Data

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Rachel J Jolley ◽  
Zhiying Liang ◽  
Mingkai Peng ◽  
Sachin R Pendharkar ◽  
Willis Tsai ◽  
...  
Keyword(s):  
Sleep Disorders ◽  
Ambulatory Care ◽  
Sleep Disorder ◽  
Administrative Data ◽  
Medical Records ◽  
Claims Data ◽  
Clinic Visit ◽  
Reference Standard ◽  
Case Definitions ◽  
Icd Codes

Objectives Prevalence, and associated morbidity and mortality of chronic sleep disorders have been limited to small cohort studies, however, administrative data may be used to provide representation of larger population estimates of disease. With no guidelines to inform the identification of cases of sleep disorders in administrative data, the objective of this study was to develop and validate a set of ICD-codes used to define sleep disorders including narcolepsy, insomnia, and obstructive sleep apnea (OSA) in administrative data. Methods A cohort of adult patients, with medical records reviewed by two independent board-certified sleep physicians from a sleep clinic in Calgary, Alberta between January 1, 2009 and December 31, 2011, was used as the reference standard. We developed a general ICD-coded case definition for sleep disorders which included conditions of narcolepsy, insomnia, and OSA using: 1) physician claims data, 2) inpatient visit data, 3) emergency department (ED) and ambulatory care data. We linked the reference standard data and administrative data to examine the validity of different case definitions, calculating estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).  Results From a total of 1186 patients from the sleep clinic, 1045 (88.1%) were classified as sleep disorder positive, with 606 (51.1%) diagnosed with OSA, 407 (34.4%) with insomnia, and 59 (5.0%) with narcolepsy. The most frequently used ICD-9 codes were general codes of 307.4 (Nonorganic sleep disorder, unspecified), 780.5 (unspecified sleep disturbance) and ICD-10 codes of G47.8 (other sleep disorders), G47.9 (sleep disorder, unspecified). The best definition for identifying a sleep disorder was an ICD code (from physician claims) 2 years prior and 1 year post sleep clinic visit: sensitivity 79.2%, specificity 28.4%, PPV 89.1%, and NPV 15.6%. ICD codes from ED/ambulatory care data provided similar diagnostic performance when at least 2 codes appeared in a time period of 2 years prior and 1 year post sleep clinic visit: sensitivity 71.9%, specificity 54.6%, PPV 92.1%, and NPV 20.8%. The inpatient data yielded poor results in all tested ICD code combinations. Conclusion Sleep disorders in administrative data can be identified mainly through physician claims data and with some being determined through outpatient/ambulatory care data ICD codes, however these are poorly coded within inpatient data sources. This may be a function of how sleep disorders are diagnosed and/or reported by physicians in inpatient and outpatient settings within medical records. Future work to optimize administrative data case definitions through data linkage are needed.

Abstract 18870: Adjudicated versus “Administrative” Heart Failure With Preserved Ejection Fraction

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Andreas P Kalogeropoulos ◽  
Akash Patel ◽  
Song Li ◽  
Gregory Burkman ◽  
Lampros Papadimitriou ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Administrative Data ◽  
Medical Records ◽  
Patient Characteristics ◽  
Preserved Ejection Fraction ◽  
Icd Codes ◽  
And Gender ◽  
Special Causes

Introduction: The proportion of heart failure (HF) with preserved ejection fraction (HFpEF) is reported to be as high as 40%-60% based on administrative data, but these estimates have not been clinically validated. Methods: We evaluated 1752 consecutive patients who received outpatient care during the first quarter of 2012 for an encounter ICD-9 code of 402.X1, 404.X1, 404.X3, or 428.XX. Medical records were reviewed for HF symptoms, signs, and treatment; last reported ejection fraction (EF); all previous EF documentations; and special causes of HF (congenital heart disease or specific cardiomyopathies). We classified confirmed HF cases not due to special causes into 3 mutually exclusive categories: (1) HFpEF: current EF >40% without any previous EF ≤40%; (2) HF with recovered EF (HFrecEF): current EF >40% but previous EF ≤40%; and (3) HF with reduced EF (HFrEF): current EF ≤40%. Results: HF was confirmed in 1652 cases (94.3%). Among these, 321 had HFpEF (19.4%; 95%CI 17.6-21.4); 268 had HFrecEF (16.2%; 95%CI 14.5-18.1); and 992 had HFrEF (60.0%; 95%CI 57.7-62.4); the remaining 71 cases (4.3%) had HF due to special causes. In comparison, the proportion of HFpEF on the basis of ICD codes and last EF without further adjudication would have been 39.0%. Patient characteristics are summarized in Table 1. After 2 years of follow up, age- and gender- adjusted mortality was 10.2% in HFrEF, 8.6% in HFpEF, and 4.4% in HFrecEF patients (stratified log-rank P=0.005), Fig. 1 . Conclusions: The proportion of clinically verified HFpEF is considerably lower compared to estimates from administrative data. Many patients with preserved EF actually represent HFrecEF, which has a more favorable prognosis.

scholarly journals Validation of case definitions of depression derived from administrative data against the CIDI-SF as reference standard: results from the PROspective Québec (PROQ) study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ana Paula Bruno Pena-Gralle ◽  
Denis Talbot ◽  
Xavier Trudel ◽  
Karine Aubé ◽  
Alain Lesage ◽  
...  
Keyword(s):  
Risk Factors ◽  
Sensitivity And Specificity ◽  
Administrative Data ◽  
Short Form ◽  
High Specificity ◽  
Composite International Diagnostic Interview ◽  
Epidemiological Studies ◽  
Correct Identification ◽  
Reference Standard ◽  
Case Definitions

Abstract Background Administrative data have several advantages over questionnaire and interview data to identify cases of depression: they are usually inexpensive, available for a long period of time and are less subject to recall bias and differential classification errors. However, the validity of administrative data in the correct identification of depression has not yet been studied in general populations. The present study aimed to 1) evaluate the sensitivity and specificity of administrative cases of depression using the validated Composite International Diagnostic Interview – Short Form (CIDI-SF) as reference standard and 2) compare the known-groups validity between administrative and CIDI-SF cases of depression. Methods The 5487 participants seen at the last wave (2015–2018) of the PROQ cohort had CIDI-SF questionnaire data linked to hospitalization and medical reimbursement data provided by the provincial universal healthcare provider and coded using the International Classification of Disease. We analyzed the sensitivity and specificity of several case definitions of depression from this administrative data. Their association with known predictors of depression was estimated using robust Poisson regression models. Results Administrative cases of depression showed high specificity (≥ 96%), low sensitivity (19–32%), and rather low agreement (Cohen’s kappa of 0.21–0.25) compared with the CIDI-SF. These results were consistent over strata of sex, age and education level and with varying case definitions. In known-groups analysis, the administrative cases of depression were comparable to that of CIDI-SF cases (RR for sex: 1.80 vs 2.03 respectively, age: 1.53 vs 1.40, education: 1.52 vs 1.28, psychological distress: 2.21 vs 2.65). Conclusion The results obtained in this large sample of a general population suggest that the dimensions of depression captured by administrative data and by the CIDI-SF are partially distinct. However, their known-groups validity in relation to risk factors for depression was similar to that of CIDI-SF cases. We suggest that neither of these data sources is superior to the other in the context of large epidemiological studies aiming to identify and quantify risk factors for depression.

Rising prevalence of vascular comorbidities in multiple sclerosis: validation of administrative definitions for diabetes, hypertension, and hyperlipidemia

2012 ◽  
Vol 18 (9) ◽  
pp. 1310-1319 ◽  
Author(s):  
Ruth Ann Marrie ◽  
Bo Nancy Yu ◽  
Stella Leung ◽  
Lawrence Elliott ◽  
Patricia Caetano ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
General Population ◽  
Administrative Data ◽  
Medical Records ◽  
Temporal Trends ◽  
Prevalence Ratio ◽  
Hospital Physician ◽  
Case Definitions ◽  
General Population Cohort ◽  
General Populations

Background: Despite the importance of comorbidity in multiple sclerosis (MS), methods for comorbidity assessment in MS are poorly developed. Objective: We validated and applied administrative case definitions for diabetes, hypertension, and hyperlipidemia in MS. Methods: Using provincial administrative data we identified persons with MS and a matched general population cohort. Case definitions for diabetes, hypertension, and hyperlipidemia were derived using hospital, physician, and prescription claims, and validated in 430 persons with MS. We examined temporal trends in the age-adjusted prevalence of these conditions from 1984–2006. Results: Agreement between various case definitions and medical records ranged from kappa (κ) =0.51–0.69 for diabetes, κ =0.21–0.71 for hyperlipidemia, and κ =0.52–0.75 for hypertension. The 2005 age-adjusted prevalence of diabetes was similar in the MS (7.62%) and general populations (8.31%; prevalence ratio [PR] 0.91; 0.81–1.03). The age-adjusted prevalence did not differ for hypertension (MS: 20.8% versus general: 22.5% [PR 0.91; 0.78–1.06]), or hyperlipidemia (MS: 13.8% versus general: 15.2% [PR 0.90; 0.67–1.22]). The prevalence of all conditions rose in both populations over the study period. Conclusion: Administrative data are a valid means of tracking diabetes, hypertension, and hyperlipidemia in MS. The prevalence of these comorbidities is similar in the MS and general populations.

Severe Maternal Morbidity: A Comparison of Definitions and Data Sources

2021 ◽  
Author(s):  
Jonathan M Snowden ◽  
Audrey Lyndon ◽  
Peiyi Kan ◽  
Alison El Ayadi ◽  
Elliott Main ◽  
...  
Keyword(s):  
Administrative Data ◽  
Maternal Morbidity ◽  
Claims Data ◽  
Birth Certificate ◽  
Population Level ◽  
Kappa Statistic ◽  
Data Sources ◽  
Severe Maternal Morbidity ◽  
Fine Grained ◽  
Control And Prevention

Abstract Severe maternal morbidity (SMM) is a composite outcome measure that indicates serious, potentially life-threatening maternal health problems. There is great interest in defining SMM using administrative data for surveillance and research. In the US, one common way of defining SMM at the population level is an index developed by the Centers for Disease Control and Prevention. Modifications have been proposed to this index (e.g., excluding maternal transfusion); some research defines SMM using an index introduced by Bateman et al. Birth certificate data are also increasingly being used to define SMM. We compared commonly used US definitions of SMM to each other among all California births, 2007-2012, using the Kappa statistic and other measures. We also evaluated agreement between maternal morbidity fields on the birth certificate compared to claims data. Concordance was generally low between the 7 definitions of SMM analyzed (i.e., κ < 0.4 for 13 of 21 two-way comparisons), Low concordance was particularly driven by presence/absence of transfusion and claims data versus birth certificate definitions. Low agreement between administrative data-based definitions of SMM highlights that results can be expected to differ between them. Further research is needed on validity of SMM definitions, using more fine-grained data sources.

scholarly journals Concordance in diagnosis of diabetes between electronic medical records and claims data

2013 ◽  
Vol 16 (3) ◽  
pp. A24
Author(s):  
Z. Cao ◽  
A. Farr ◽  
W.M. Johnson ◽  
D.M. Smith
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Claims Data

Validation of diabetes case definitions using administrative claims data

2011 ◽  
Vol 28 (4) ◽  
pp. 424-427 ◽  
Author(s):  
S. Amed ◽  
S. E. Vanderloo ◽  
D. Metzger ◽  
J.-P. Collet ◽  
K. Reimer ◽  
...  
Keyword(s):  
Claims Data ◽  
Administrative Claims ◽  
Case Definitions ◽  
Administrative Claims Data ◽  
Diabetes Case

Sleep Disorders Research From 1945 to 2020: A Bibliometric Analysis

2021 ◽  
Vol 20 ◽  
Author(s):  
Waseem Hassan ◽  
Mehreen Zafar ◽  
Hamsa Noreen ◽  
Amina Ara ◽  
Antonia Eliene Duarte ◽  
...  
Keyword(s):  
Growth Rate ◽  
Sleep Disorders ◽  
Sleep Disorder ◽  
Bibliometric Analysis ◽  
Critical Analysis ◽  
Present Review ◽  
Productivity Index ◽  
Scopus Database ◽  
Number Of Publications

Background: The objective of the present review is to perform the 1st bibliometric analysis of sleep disorders research. Methods: The data was retrieved from Scopus in July, 2020 for detail analysis. Results: The 1st precise document about the sleep disorder was published in 1945. Till 15th July 2020, total 69657 documents were found in Scopus database. Approximately eighty two percent (57013/81.87%) documents are published in the last twenty years (from 2001-2020). We calculated the per year growth rate (GR) of publications (from 2000-onwards). The highest number of documents are published in 2019 (4337/7.90% of 57013) followed by 2018 (4249/7.74% of 57013) and 2017 (3974/7.24% of 57013). Infact the productivity index (PI) for 1950-1960 and 2011-2020 era was found to be 100.21. We also provided the details of the top 50 countries with maximum number of publications (from 1945 to July 2020). The top three (3) countries are USA with 24262 publications (34.83%), followed by UK (5566/8.0%) and Germany (4791/6.87%). We also performed the co-words analysis. Infact total 956643 (0.95 million) keywords were retrieved from 69657 published documents. After critical analysis we categorized them in different groups to show the trend in various domains. In the next phase of the study, only those documents were analyzed which contained the phrase “sleep disorder” in the titles of the publications. Total 3626 documents were found. We calculated the per year growth rate (GR). The continental distribution, the list of top twenty authors, sources/journals, departments or institutes, countries and research documents with highest citations are provided. By VOSviewer analysis, 6752, 36511 and 11473 terms in titles of the manuscripts, abstracts and keywords were recorded, respectively. This may help in describing the overall trend in these publications. Conclusions: The present study provides a detail list of top authors, departments, countries, sources and top 20 most cited documents. The co-words analysis may help in describing the trends in the field of sleep disorders.

Status Epilepticus in Children With Epilepsy: The Role of Antiepileptic Drug Levels in Prevention

PEDIATRICS ◽  
1996 ◽  
Vol 98 (6) ◽  
pp. 1119-1121
Author(s):  
Joseph Maytal ◽  
Gerald Novak ◽  
Catherine Ascher ◽  
Robert Bienkowski
Keyword(s):  
Status Epilepticus ◽  
Antiepileptic Drug ◽  
Medical Records ◽  
Clinic Visit ◽  
Drug Levels ◽  
Progressive Encephalopathy ◽  
History Of ◽  
The Mean ◽  
Subtherapeutic Level

Objectives. To determine the association between subtherapeutic antiepileptic drug (AED) levels or AED withdrawal and status epilepticus (SE) in children with epilepsy. Methods. We studied the AED levels at the time of SE in 51 consecutive children with epilepsy. Information about prior AED levels, possible etiology of seizures, and acute precipitants was extracted from medical records. Results. The mean age at the time of SE was 5.7 years (range, 3 months through 18 years). Forty-three patients had history of remote insult, five had history of progressive encephalopathy, and three patients were classified as idiopathic. At the time of SE all AED levels were therapeutic in 34 (66%) patients and at least one level was therapeutic in 42 (82%) patients. All levels were subtherapeutic in 9 (18%) patients. Four patients had their AED reduced or discontinued less than 1 week before SE. Twelve patients with therapeutic AED levels on their most recent clinic visit had at least one subtherapeutic level at the time of SE. Eight (16%) patients were febrile and one was hyponatremic. Of the 51 patients, 31 (61%) had no obvious explanation for the development of SE, as all known AEDs were therapeutic and there were no known acute insults. Conclusions. Neurologically abnormal children with preexisting epilepsy are at high risk for development of SE despite having therapeutic AED levels at that time. Acute precipitants of SE, such as fever or AED withdrawal, may play a role in inducing SE only in a minority of patients.

Abstract P250: A Strategy for Reliable Identification of Ischemic Stroke, Thrombolytics, and Thrombectomy in Large Administrative Databases

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Kori S Zachrison ◽  
Sijia Li ◽  
Mathew J Reeves ◽  
Opeolu M Adeoye ◽  
Carlos A Camargo ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Administrative Data ◽  
Administrative Databases ◽  
Stroke Patients ◽  
Inpatient Hospitalization ◽  
Accurate Identification ◽  
Stroke Research ◽  
Diagnosis Codes ◽  
Icd 10 ◽  
Icd Codes

Background: Administrative data are frequently used in stroke research. Ensuring accurate identification of ischemic stroke patients, and those receiving thrombolysis and endovascular thrombectomy (EVT) is critical to ensure representativeness and generalizability. We examined differences in patient samples based on different modes of identification, and propose a strategy for future patient and procedure identification in large administrative databases. Methods: We used nonpublic administrative data from the state of California to identify all ischemic stroke patients discharged from an emergency department or inpatient hospitalization from 2010-2017 based on ICD-9 (2010-2015), ICD-10 (2015-2017), and MS-DRG discharge codes. We identified patients with interhospital transfers, patients receiving thrombolytics, and patients treated with EVT based on ICD, CPT and MS-DRG codes. We determined what proportion of these transfers and procedures would have been identified with ICD versus MS-DRG discharge codes. Results: Of 365,099 ischemic stroke encounters, most (87.7%) had both a stroke-related ICD-9 or ICD-10 code and stroke-related MS-DRG code; 12.3% had only an ICD-9 or ICD-10 code, and 0.02% had only a MS-DRG code. Nearly all transfers (99.9%) were identified using ICD codes. We identified32,433 thrombolytic-treated patients (8.9% of total) using ICD, CPT, and MS-DRG codes; the combination of ICD and CPT codes identified nearly all (98%). We identified 7,691 patients treated with EVT (2.1% of total) using ICD and MS-DRG codes; both MS-DRG and ICD-9/-10 codes were necessary because ICD codes alone missed 13.2% of EVTs. CPT codes only pertain to outpatient/ED patients and are not useful for EVT identification. Conclusions: ICD-9/-10 diagnosis codes capture nearly all ischemic stroke encounters and transfers, while the combination of ICD-9/-10 and CPT codes are adequate for identifying thrombolytic treatment in administrative datasets. However, MS-DRG codes are necessary in addition to ICD codes for identifying EVT, likely due to favorable reimbursement for EVT-related MS-DRG codes incentivizing accurate coding.

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