Dataset Information

Temporal trends in the accuracy of hospital diagnostic coding for identifying acute stroke: A population-based study.

ABSTRACT:

Introduction

Administrative hospital diagnostic coding data are increasingly being used in identifying incident and prevalent stroke cases, for outcome audit and for 'big data' research. Validity of administrative coding has varied in previous studies, but little is known about the temporal trends of coding accuracy, which could bias analyses.

Patients and methods

Using all incident and recurrent strokes in a population-based cohort (Oxford Vascular Study/OXVASC) with multiple sources of ascertainment as the reference, we determined the temporal trends in sensitivity and positive predictive value of hospital diagnostic codes for identifying acute stroke from 2002 to 2017.

Results

Of 1883 hospitalised strokes, 1341 (71.2%) were correctly identified by coding. Sensitivity of coding improved over time for all strokes (p_trend?=?0.005) and for incident cases (p_trend?=?0.002). Of 1995 apparent stroke admissions identified by International Classification of Disease-10 stroke codes (I60-I68), 1588 (79.6%) used the stroke-specific codes (I60-I61/I63-I64). Positive predictive value was higher with the use of specific codes (83.2% vs. 69.2% for all codes) and highest if combined with the first admission only (88.5%), particularly during more recent time periods (2014-2017?=?90.3%). Of 2254 OXVASC incident strokes, 833 (37.0%) were not hospitalised. Sensitivity of coding increased over time for non-disabling stroke (p_trend?=?0.001), but not for disabling/fatal stroke (p_trend?=?0.40).

Conclusions

Although accuracy of hospital diagnostic coding for identifying acute strokes improved over the last 15?years, residual insensitivity supports linkage to other sources in large epidemiological studies. Moreover, differences in the time trends of coding sensitivity in relation to stroke severity might bias studies of trends in stroke outcome if only administrative coding is used.

SUBMITTER: Li L

PROVIDER: S-EPMC7092737 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Temporal trends in the accuracy of hospital diagnostic coding for identifying acute stroke: A population-based study.

Li Linxin L Binney Lucy E LE Luengo-Fernandez Ramon R Silver Louise E LE Rothwell Peter M PM

European stroke journal 20191014 1

<h4>Introduction</h4>Administrative hospital diagnostic coding data are increasingly being used in identifying incident and prevalent stroke cases, for outcome audit and for 'big data' research. Validity of administrative coding has varied in previous studies, but little is known about the temporal trends of coding accuracy, which could bias analyses.<h4>Patients and methods</h4>Using all incident and recurrent strokes in a population-based cohort (Oxford Vascular Study/OXVASC) with multiple sou ...[more]

PMID: 32232167

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Project description:BackgroundAssessing the accuracy of diagnostic coding is essential to ensure the validity and reliability of administrative coded data. The aim of the study was to evaluate the accuracy of assigned International Classification of Diseases version 10-Australian Modification (ICD-10-AM) codes for influenza by comparing with patients' results of their polymerase chain reaction (PCR)-based laboratory tests.MethodA retrospective study was conducted across seven public hospitals in New South Wales, Australia. A total of 16,439 patients who were admitted and tested by either cartridge-based rapid PCR or batched multiplex PCR between January 2016 and December 2017 met the inclusion criteria. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of ICD-10-AM coding using laboratory results as a gold standard. Separate analyses were conducted to determine whether the availability of test results at the time of hospital discharge influenced diagnostic coding accuracy.ResultsLaboratory results revealed 2759 positive influenza cases, while ICD-10-AM coding identified 2527 patients. Overall, 13.7% (n = 378) of test positive patients were not assigned an ICD-10-AM code for influenza. A further 5.8% (n = 146) patients with negative test results were incorrectly assigned an ICD-10-AM code for influenza. The sensitivity, specificity, PPV and NPV of ICD-10-AM coding were 93.1%; 98.9%; 94.5% and 98.6% respectively when test results were received before discharge and 32.7%; 99.2%; 87.8% and 89.8% respectively when test results were not available at discharge. The sensitivity of ICD-10-AM coding varied significantly across hospitals. The use of rapid PCR or hospitalisation during the influenza season were associated with greater coding accuracy.ConclusionAlthough ICD-10-AM coding for influenza demonstrated high accuracy when laboratory results were received before discharge, its sensitivity was substantially lower for patients whose test results were not available at discharge. The timely availability of laboratory test results during the episode of care could contribute to improved coding accuracy.

Project description:INTRODUCTION:Recanalisation therapy in acute ischaemic stroke is highly time-sensitive, and requires early identification of eligible patients to ensure better outcomes. Thus, a number of clinical assessment tools have been developed and this review examines their diagnostic capabilities. METHODS:Diagnostic performance of currently available clinical tools for identification of acute ischaemic and haemorrhagic strokes and stroke mimicking conditions was reviewed. A systematic search of the literature published in 2015-2018 was conducted using PubMed, EMBASE, Scopus and The Cochrane Library. Prehospital and in-hospital studies with a minimum sample size of 300 patients reporting diagnostic accuracy were selected. RESULTS:Twenty-five articles were included. Cortical signs (gaze deviation, aphasia and neglect) were shown to be significant indicators of large vessel occlusion (LVO). Sensitivity values for selecting subjects with LVO ranged from 23 to 99% whereas specificity was 24 to 97%. Clinical tools, such as FAST-ED, NIHSS, and RACE incorporating cortical signs as well as motor dysfunction demonstrated the best diagnostic accuracy. Tools for identification of stroke mimics showed sensitivity varying from 44 to 91%, and specificity of 27 to 98% with the best diagnostic performance demonstrated by FABS (90% sensitivity, 91% specificity). Hypertension and younger age predicted intracerebral haemorrhage whereas history of atrial fibrillation and diabetes were associated with ischaemia. There was a variation in approach used to establish the definitive diagnosis. Blinding of the index test assessment was not specified in about 50% of included studies. CONCLUSIONS:A wide range of clinical assessment tools for selecting subjects with acute stroke has been developed in recent years. Assessment of both cortical and motor function using RACE, FAST-ED and NIHSS showed the best diagnostic accuracy values for selecting subjects with LVO. There were limited data on clinical tools that can be used to differentiate between acute ischaemia and haemorrhage. Diagnostic accuracy appeared to be modest for distinguishing between acute stroke and stroke mimics with optimal diagnostic performance demonstrated by the FABS tool. Further prehospital research is required to improve the diagnostic utility of clinical assessments with possible application of a two-step clinical assessment or involvement of simple brain imaging, such as transcranial ultrasonography.

Project description:OBJECTIVES:A growing number of patients survive sepsis hospitalizations each year and are at high risk for readmission. However, little is known about temporal trends in hospital-based acute care (emergency department treat-and-release visits and hospital readmission) after sepsis. Our primary objective was to measure temporal trends in sepsis survivorship and hospital-based acute care use in sepsis survivors. In addition, because readmissions after pneumonia are subject to penalty under the national readmission reduction program, we examined whether readmission rates declined after sepsis hospitalizations related to pneumonia. DESIGN AND SETTING:Retrospective, observational cohort study conducted within an academic healthcare system from 2010 to 2015. PATIENTS:We used three validated, claims-based approaches to identify 17,256 sepsis or severe sepsis hospitalizations to examine trends in hospital-based acute care after sepsis. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:From 2010 to 2015, sepsis as a proportion of medical and surgical admissions increased from 3.9% to 9.4%, whereas in-hospital mortality rate for sepsis hospitalizations declined from 24.1% to 14.8%. As a result, the proportion of medical and surgical discharges at-risk for hospital readmission after sepsis increased from 2.7% to 7.8%. Over 6 years, 30-day hospital readmission rates declined modestly, from 26.4% in 2010 to 23.1% in 2015, driven largely by a decline in readmission rates among survivors of nonsevere sepsis, and nonpneumonia sepsis specifically, as the readmission rate of severe sepsis survivors was stable. The modest decline in 30-day readmission rates was offset by an increase in emergency department treat-and-release visits, from 2.8% in 2010 to a peak of 5.4% in 2014. CONCLUSIONS:Owing to increasing incidence and declining mortality, the number of sepsis survivors at risk for hospital readmission rose significantly between 2010 and 2015. The 30-day hospital readmission rates for sepsis declined modestly but were offset by a rise in emergency department treat-and-release visits.

Project description:Temporal trends and factors associated with the withdrawal of life-sustaining therapy (WLST) after acute stroke are not well determined.DesignObservational study (2008-2021).SettingFlorida Stroke Registry (152 hospitals).PatientsAcute ischemic stroke (AIS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH) patients.InterventionsNone.Measurements and main resultsImportance plots were performed to generate the most predictive factors of WLST. Area under the curve (AUC) for the receiver operating curve were generated for the performance of logistic regression (LR) and random forest (RF) models. Regression analysis was applied to evaluate temporal trends. Among 309,393 AIS patients, 47,485 ICH patients, and 16,694 SAH patients; 9%, 28%, and 19% subsequently had WLST. Patients who had WLST were older (77 vs 70 yr), more women (57% vs 49%), White (76% vs 67%), with greater stroke severity on the National Institutes of Health Stroke Scale greater than or equal to 5 (29% vs 19%), more likely hospitalized in comprehensive stroke centers (52% vs 44%), had Medicare insurance (53% vs 44%), and more likely to have impaired level of consciousness (38% vs 12%). Most predictors associated with the decision to WLST in AIS were age, stroke severity, region, insurance status, center type, race, and level of consciousness (RF AUC of 0.93 and LR AUC of 0.85). Predictors in ICH included age, impaired level of consciousness, region, race, insurance status, center type, and prestroke ambulation status (RF AUC of 0.76 and LR AUC of 0.71). Factors in SAH included age, impaired level of consciousness, region, insurance status, race, and stroke center type (RF AUC of 0.82 and LR AUC of 0.72). Despite a decrease in the rates of early WLST (< 2 d) and mortality, the overall rates of WLST remained stable.ConclusionsIn acute hospitalized stroke patients in Florida, factors other than brain injury alone contribute to the decision to WLST. Potential predictors not measured in this study include education, culture, faith and beliefs, and patient/family and physician preferences. The overall rates of WLST have not changed in the last 2 decades.

Project description:BackgroundAcute stroke impairments often result in poor long-term outcome for stroke survivors. The aim of this study was to estimate the trends over time in the prevalence of these acute stroke impairments.Methods and findingsAll first-ever stroke patients recorded in the South London Stroke Register (SLSR) between 2001 and 2018 were included in this cohort study. Multivariable Poisson regression models with robust error variance were used to estimate the adjusted prevalence of 8 acute impairments, across six 3-year time cohorts. Prevalence ratios comparing impairments over time were also calculated, stratified by age, sex, ethnicity, and aetiological classification (Trial of Org 10172 in Acute Stroke Treatment [TOAST]). A total of 4,683 patients had a stroke between 2001 and 2018. Mean age was 68.9 years, 48% were female, and 64% were White. After adjustment for demographic factors, pre-stroke risk factors, and stroke subtype, the prevalence of 3 out of the 8 acute impairments declined during the 18-year period, including limb motor deficit (from 77% [95% CI 74%-81%] to 62% [56%-68%], p < 0.001), dysphagia (37% [33%-41%] to 15% [12%-20%], p < 0.001), and urinary incontinence (43% [39%-47%) to 29% [24%-35%], p < 0.001). Declines in limb impairment over time were 2 times greater in men than women (prevalence ratio 0.73 [95% CI 0.64-0.84] and 0.87 [95% CI 0.77-0.98], respectively). Declines also tended to be greater in younger patients. Stratified by TOAST classification, the prevalence of all impairments was high for large artery atherosclerosis (LAA), cardioembolism (CE), and stroke of undetermined aetiology. Conversely, small vessel occlusions (SVOs) had low levels of all impairments except for limb motor impairment and dysarthria. While we have assessed 8 key acute stroke impairments, this study is limited by a focus on physical impairments, although cognitive impairments are equally important to understand. In addition, this is an inner-city cohort, which has unique characteristics compared to other populations.ConclusionsIn this study, we found that stroke patients in the SLSR had a complexity of acute impairments, of which limb motor deficit, dysphagia, and incontinence have declined between 2001 and 2018. These reductions have not been uniform across all patient groups, with women and the older population, in particular, seeing fewer reductions.

Dataset Information

Temporal trends in the accuracy of hospital diagnostic coding for identifying acute stroke: A population-based study.

Introduction

Patients and methods

Results

Conclusions

Publications

Temporal trends in the accuracy of hospital diagnostic coding for identifying acute stroke: A population-based study.

Similar Datasets

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