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ICD-9-CM and ICD-10 Codes to Define Co-morbidity Among Patients Referred for Sleep Diagnostic Testing

ICD-9-CM and ICD-10 Codes to Define Co-morbidity Among Patients Referred for Sleep Diagnostic Testing

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Observational outcome studies of patients with obstructive sleep apnea (OSA) require adjustment for co-morbidity to produce valid results. The aim of this study was to evaluate whether the combination of administrative data and self-reported data provided a more complete estimate of co-morbidity among patients referred for sleep diagnostic testing....

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... conditions were identified within the Alberta Health and Wellness administrative databases using the International Classification of Diseases (ICD-9-CM and ICD-10) definitions for the nine specific co-morbidities. When available, validated algorithms were used to define each co-morbid condition (Table 1) [43][44][45][46][47][48]. These algo- rithms were further supplemented by the ICD-10 coding scheme developed by Quan et al. [49]. ...

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Although obstructive sleep apnoea (OSA) has been linked to insulin resistance and glucose intolerance, it is unclear whether there is an independent association between OSA and diabetes mellitus (DM) and whether all patients with OSA are at risk. The objective of this study was to determine the association between OSA and DM in a large cohort of pa...

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... However, studies have assessed concordance between two of these three groups. Six studies have analysed comorbidity concordance between patient self-reports and administrative data [16][17][18][19][20][21].. As with our study, most found high level of concordance for diabetes (four of six studies had kappa scores ranging from 0.70 and 0.83) [16][17][18]20]. ...
... Six studies have analysed comorbidity concordance between patient self-reports and administrative data [16][17][18][19][20][21].. As with our study, most found high level of concordance for diabetes (four of six studies had kappa scores ranging from 0.70 and 0.83) [16][17][18]20]. There was a variable level of agreement found for both myocardial infarct (kappa values between 0.14 and 0.75) [17][18][19] and asthma (kappa values between 0.11 and 0.66) [17,18,20]. ...
... As with our study, most found high level of concordance for diabetes (four of six studies had kappa scores ranging from 0.70 and 0.83) [16][17][18]20]. There was a variable level of agreement found for both myocardial infarct (kappa values between 0.14 and 0.75) [17][18][19] and asthma (kappa values between 0.11 and 0.66) [17,18,20]. Two out of the six studies which compared concordance of heart disease demonstrated only fair concordance (kappa values of 0.36 and 0.38) [16,20]. ...
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Background: Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close are these to the "truth". We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10th edition [ICD-10 AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy. Methods: We included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. Eligible patients from the PCOR-Vic underwent a radical prostatectomy between January 2017 and April 2018.Health Information Manager's in each hospital, provided each patient's associated administrative ICD-10 AM comorbidity codes. Medical charts were reviewed to extract comorbidity data. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men. Results: The percentage agreement between the administrative data, medical charts and self-reports ranged from 92 to 99% in the 122 patients from the 217 eligible participants who responded to the questionnaire. The presence of comorbidities showed a poor level of agreement between data sources. Conclusion: Relying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient's disease profile. There does not appear to be a 'gold-standard' data source for the collection of data on comorbidities.
... Patient surveys are often used in epidemiology to collect health data. However, the reliability and accuracy of patient-reported data, including patients' own accounts of whether or not they have been diagnosed with a particular chronic disease, have been questioned [1]. Administrative datahospital data collected for a range of administrative purposes including managing payments to the healthcare providers for every hospital admission and procedureoffer an alternative source of data [2]. ...
... Few studies have assessed the consistency of patient-reported chronic diseases with chronic diseases derived from administrative data [5][6][7]. The studies that did were predominantly cohort studies with relatively small sample sizes that reported single measures of agreement, such as the kappa statistic [1,8]. A few larger scale studies investigated the agreement of a small number of patient-reported chronic diseases, with the most common being high blood pressure, stroke, heart disease and diabetes [5][6][7]. ...
... A few larger scale studies investigated the agreement of a small number of patient-reported chronic diseases, with the most common being high blood pressure, stroke, heart disease and diabetes [5][6][7]. These studies found results for the agreement between patient-reported chronic diseases and hospital administrative data to vary significantly [1,[9][10][11]. ...
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Background This study examined the agreement between patient-reported chronic diseases and hospital administrative records in hip or knee arthroplasty patients in England. Methods Survey data reported by 676,428 patients for the English Patient Reported Outcome Measures (PROMs) programme was linked to hospital administrative data. Sensitivity and specificity of 11 patient-reported chronic diseases were estimated with hospital administrative data as reference standard. Results Specificity was high (> 90%) for all 11 chronic diseases. However, sensitivity varied by disease with the highest found for ‘diabetes’ (87.5%) and ‘high blood pressure’ (74.3%) and lowest for ‘kidney disease’ (18.8%) and ‘leg pain due to poor circulation’ (26.1%). Sensitivity was increased for diseases that were given as specific examples in the questionnaire (e.g. ‘parkinson’s disease’ (65.6%) and ‘multiple sclerosis’ (69.5%), compared to ‘diseases of the nervous system’ (20.9%)). Conclusions Patients can give information about the presence of chronic diseases that is consistent with chronic diseases derived from hospital administrative data if the description in the patient questionnaire is precise and if the disease is familiar to most patients and has significant impact on their life. Such patient questionnaires need to be validated before they are used for research and service evaluation projects. Electronic supplementary material The online version of this article (10.1186/s12874-019-0729-5) contains supplementary material, which is available to authorized users.
... Particularly for RA, patients overestimate RA as they do not differentiate RA from other arthritic conditions (60). There is variable concordance between self-report and administrative databases ranging from similar to poor (26,28,61,62). RDCI and CCI have been used with self-report data. ...
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Introduction Comorbidities influence the prognosis, clinical outcomes, disease activity, and treatment response in rheumatoid arthritis (RA). RA patients have a high-comorbidity burden necessitating their study. Comorbidity indices are used to measure comorbidities and to study their impacts on different outcomes. A large number of such indices are used in clinical research. Some indices have been specifically developed in RA patients. Aim This review aims to provide an overview of generic and specific comorbidity indices commonly used in RA research. Methods We performed a critical literature review of comorbidity indices in RA using the PubMed database. Results/discussion This non-systematic literature review provides an overview of generic and specific comorbidity indices commonly used in RA studies. Some of the older but commonly used comorbidity indices like the Charlson comorbidity index and the Elixhauser comorbidity measure were primarily developed to estimate mortality risk from comorbid diseases. They were not specifically developed for RA patients but have been widely used in rheumatology comorbidity measurement. Of the many comorbidity indices available, only the rheumatic disease comorbidity index (RDCI) and the multimorbidity index have been specifically developed in RA patients. The functional comorbidity index was developed to look at functional disability and has been used in RA patients considering that morbidity is more important than mortality in such patients. While there is limited data comparing these indices, available evidence seems to favor the use of RDCI as it predicts mortality, hospitalization, disability, and healthcare utilization. The choice of the index, however, depends on several factors such as the population under study, outcome of interest, and sources of data. More research is needed to study the RA-specific comorbidity measures to make evidence-based recommendations for the choice of a comorbidity measure.
... Co-morbidities were determined by chart review, and enhanced [18] from hospital discharge abstracts to determine comorbidities contained in the Charlson index [19]. The diagnosis of COPD was determined by spirometry using the Global initiative for chronic Obstructive Lung disease (GOLD) criteria. ...
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Background: Obese hypoxemic patients have a high prevalence of sleep disordered breathing (SDB). It is unclear to what extent treatment of SDB can improve daytime hypoxemia. Methods: We performed a retrospective cohort study of obese hypoxemic individuals, all of whom underwent polysomnography, arterial blood gas analysis, and subsequent initiation of positive airway pressure (PAP) therapy for SDB. Patients were followed for one year for change in partial pressure of arterial oxygen and the need for supplemental oxygen. Results: One hundred and seventeen patients were treated with nocturnal PAP and had follow-up available. Adherence to PAP was satisfactory in 60%, and was associated with a significant improvement in daytime hypoxemia and hypercapnea; 56% of these patients were able to discontinue supplemental oxygen. Adherence to PAP therapy and the baseline severity of OSA predicted improvement in hypoxemia, but only adherence to PAP therapy predicted liberation from supplemental oxygen. Conclusions: The identification and treatment of SDB in obese hypoxemic patients improves daytime hypoxemia. It is important to identify SDB in these patients, since supplemental oxygen can frequently be discontinued following treatment with PAP therapy.
... The index was revised by combining the score from inpatient and outpatient data and was further evaluated, compared with the original [44,45] found that self-reported data and administrative data adaptations had similar ability to predict various outcomes. Ronksley et al. [46] found that selfreport of comorbid conditions had varying levels of agreement with those derived from administrative data, ranging from poor to substantial agreement depending on the comorbid condition (k 5 0.14e0.79). ...
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... Patients with obesity or cardiovascular disease are at increased risk. 19 The severity of obstructive sleep apnea is usually graded using the apnea-hypopnea index (the mean number of apneas and hypopneas per hour of sleep) as follows: mild (5)(6)(7)(8)(9)(10)(11)(12)(13)(14), moderate (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) and severe (≥ 30). 18,20 Other, less common types of sleep-disordered breathing include upper airway resistance syndrome, obesity hyperventilation syndrome, central sleep apnea, and nocturnal hypoventilation/ hypoxemia secondary to cardiopulmonary or neuromuscular disease. ...
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Background: Greater awareness of sleep-disordered breathing and rising obesity rates have fueled demand for sleep studies. Sleep testing using level 3 portable devices may expedite diagnosis and reduce the costs associated with level 1 in-laboratory polysomnography. We sought to assess the diagnostic accuracy of level 3 testing compared with level 1 testing and to identify the appropriate patient population for each test. Methods: We conducted a systematic review and meta-analysis of comparative studies of level 3 versus level 1 sleep tests in adults with suspected sleep-disordered breathing. We searched 3 research databases and grey literature sources for studies that reported on diagnostic accuracy parameters or disease management after diagnosis. Two reviewers screened the search results, selected potentially relevant studies and extracted data. We used a bivariate mixed-effects binary regression model to estimate summary diagnostic accuracy parameters. Results: We included 59 studies involving a total of 5026 evaluable patients (mostly patients suspected of having obstructive sleep apnea). Of these, 19 studies were included in the meta-analysis. The estimated area under the receiver operating characteristics curve was high, ranging between 0.85 and 0.99 across different levels of disease severity. Summary sensitivity ranged between 0.79 and 0.97, and summary specificity ranged between 0.60 and 0.93 across different apnea-hypopnea cut-offs. We saw no significant difference in the clinical management parameters between patients who underwent either test to receive their diagnosis. Interpretation: Level 3 portable devices showed good diagnostic performance compared with level 1 sleep tests in adult patients with a high pretest probability of moderate to severe obstructive sleep apnea and no unstable comorbidities. For patients suspected of having other types of sleep-disordered breathing or sleep disorders not related to breathing, level 1 testing remains the reference standard.
... Using either self-report or administrative data, an enhanced measure of comorbidity was developed. This method has been shown to have face validity and provide clinically meaningful trends in the prevalence of comorbidity among this population [33]. ...
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Although obstructive sleep apnea (OSA) is more common in patients with kidney disease, whether nocturnal hypoxia affects kidney function is unknown. We studied all adult subjects referred for diagnostic testing of sleep apnea between July 2005 and December 31 2007 who had serial measurement of their kidney function. Nocturnal hypoxia was defined as oxygen saturation (SaO2) below 90% for ≥12% of the nocturnal monitoring time. The primary outcome, accelerated loss of kidney function, was defined as a decline in estimated glomerular filtration rate (eGFR) ≥4 ml/min/1.73 m(2) per year. 858 participants were included and followed for a mean study period of 2.1 years. Overall 374 (44%) had nocturnal hypoxia, and 49 (5.7%) had accelerated loss of kidney function. Compared to controls without hypoxia, patients with nocturnal hypoxia had a significant increase in the adjusted risk of accelerated kidney function loss (odds ratio (OR) 2.89, 95% confidence interval [CI] 1.25, 6.67). Nocturnal hypoxia was independently associated with an increased risk of accelerated kidney function loss. Further studies are required to determine whether treatment and correction of nocturnal hypoxia reduces loss of kidney function.
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Excessive daytime sleepiness is an important public health concern associated with increased morbidity and mortality. However, in the absence of sleep diagnostic testing, it is difficult to separate the independent effects of sleepiness from those of intrinsic sleep disorders such as obstructive sleep apnea (OSA). The objective of this study was to determine if excessive daytime sleepiness was independently associated with increased health care utilization among patients referred for assessment of OSA. Cross-sectional study. 2149 adults referred for sleep diagnostic testing between July 2005 and August 2007. N/A. Subjective daytime sleepiness was defined as an Epworth Sleepiness Scale score ≥10. Health care use (outpatient physician visits, all-cause hospitalizations, and emergency department visits) was determined from Alberta Health and Wellness administrative databases for the 18-month period preceding their sleep study. Rates of health resource use were analyzed using negative binomial regression, with predictors of increased health care use determined using logistic regression. excessive daytime sleepiness was associated with an increased rate of outpatient physician visits after adjustment for demographic variables, sleep medication use, hypertension, diabetes, depression, and OSA severity (rate ratio [RR]: 1.09 (95% confidence interval [CI]: 1.01, 1.18, P = 0.02) compared to non-sleepy subjects. There was an interaction between severe OSA and sleepiness (RR: 1.22 [95% CI: 1.06, 1.41]), although OSA was not an independent predictor of health care use. Also, sleepy patients with treated depression had a lower likelihood of outpatient visits (RR: 0.95 [95% CI: 0.86, 1.05]). Finally, sleepiness was an independent predictor of increased health care use for outpatient physician visits (odds ratio [OR]: 1.25 [95% CI: 1.00, 1.57; P = 0.048]) and all-cause hospitalizations (OR: 3.94 [95% CI: 1.03, 15.04; P = 0.046]). Excessive daytime sleepiness is associated with increased health care utilization among patients referred for assessment of OSA. Further investigation is required to determine whether the findings are related to direct effects of sleepiness, or in part, to interactions with other comorbidity such as OSA.