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Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients.

ABSTRACT: BACKGROUND:Invasive fungal infection (IFI) detection requires application of complex case definitions by trained staff. Administrative coding data (ICD-10-AM) may provide a simplified method for IFI surveillance, but accuracy of case ascertainment in children with cancer is unknown. OBJECTIVE:To determine the classification performance of ICD-10-AM codes for detecting IFI using a gold-standard dataset (r-TERIFIC) of confirmed IFIs in paediatric cancer patients at a quaternary referral centre (Royal Children's Hospital) in Victoria, Australia from 1st April 2004 to 31st December 2013. METHODS:ICD-10-AM codes denoting IFI in paediatric patients (<18-years) with haematologic or solid tumour malignancies were extracted from the Victorian Admitted Episodes Dataset and linked to the r-TERIFIC dataset. Sensitivity, positive predictive value (PPV) and the F1 scores of the ICD-10-AM codes were calculated. RESULTS:Of 1,671 evaluable patients, 113 (6.76%) had confirmed IFI diagnoses according to gold-standard criteria, while 114 (6.82%) cases were identified using the codes. Of the clinical IFI cases, 68 were in receipt of ?1 ICD-10-AM code(s) for IFI, corresponding to an overall sensitivity, PPV and F1 score of 60%, respectively. Sensitivity was highest for proven IFI (77% [95% CI: 58-90]; F1 = 47%) and invasive candidiasis (83% [95% CI: 61-95]; F1 = 76%) and lowest for other/unspecified IFI (20% [95% CI: 5.05-72%]; F1 = 5.00%). The most frequent misclassification was coding of invasive aspergillosis as invasive candidiasis. CONCLUSION:ICD-10-AM codes demonstrate moderate sensitivity and PPV to detect IFI in children with cancer. However, specific subsets of proven IFI and invasive candidiasis (codes B37.x) are more accurately coded.

SUBMITTER: Valentine JC

PROVIDER: S-EPMC7480858 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients.

Valentine Jake C JC Worth Leon J LJ Verspoor Karin M KM Hall Lisa L Yeoh Daniel K DK Thursky Karin A KA Clark Julia E JE Haeusler Gabrielle M GM

PloS one 20200909 9

<h4>Background</h4>Invasive fungal infection (IFI) detection requires application of complex case definitions by trained staff. Administrative coding data (ICD-10-AM) may provide a simplified method for IFI surveillance, but accuracy of case ascertainment in children with cancer is unknown.<h4>Objective</h4>To determine the classification performance of ICD-10-AM codes for detecting IFI using a gold-standard dataset (r-TERIFIC) of confirmed IFIs in paediatric cancer patients at a quaternary refe ...[more]

PMID: 32903280

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Project description:Objectives To determine the accuracy of coding of admissions for stroke on weekdays versus weekends and any impact on apparent outcome.Design Prospective population based stroke incidence study and a scoping review of previous studies of weekend effects in stroke.Setting Primary and secondary care of all individuals registered with nine general practices in Oxfordshire, United Kingdom (OXVASC, the Oxford Vascular Study).Participants All patients with clinically confirmed acute stroke in OXVASC identified with multiple overlapping methods of ascertainment in 2002-14 versus all acute stroke admissions identified by hospital diagnostic and mortality coding alone during the same period.Main outcomes measures Accuracy of administrative coding data for all patients with confirmed stroke admitted to hospital in OXVASC. Difference between rates of "false positive" or "false negative" coding for weekday and weekend admissions. Impact of inaccurate coding on apparent case fatality at 30 days in weekday versus weekend admissions. Weekend effects on outcomes in patients with confirmed stroke admitted to hospital in OXVASC and impacts of other potential biases compared with those in the scoping review.Results Among 92?728 study population, 2373 episodes of acute stroke were ascertained in OXVASC, of which 826 (34.8%) mainly minor events were managed without hospital admission, 60 (2.5%) occurred out of the area or abroad, and 195 (8.2%) occurred in hospital during an admission for a different reason. Of 1292 local hospital admissions for acute stroke, 973 (75.3%) were correctly identified by administrative coding. There was no bias in distribution of weekend versus weekday admission of the 319 strokes missed by coding. Of 1693 admissions for stroke identified by coding, 1055 (62.3%) were confirmed to be acute strokes after case adjudication. Among the 638 false positive coded cases, patients were more likely to be admitted on weekdays than at weekends (536 (41.0%) v 102 (26.5%); P<0.001), partly because of weekday elective admissions after previous stroke being miscoded as new stroke episodes (267 (49.8%) v 26 (25.5%); P<0.001). The 30 day case fatality after these elective admissions was lower than after confirmed acute stroke admissions (11 (3.8%) v 233 (22.1%); P<0.001). Consequently, relative 30 day case fatality for weekend versus weekday admissions differed (P<0.001) between correctly coded acute stroke admissions and false positive coding cases. Results were consistent when only the 1327 emergency cases identified by "admission method" from coding were included, with more false positive cases with low case fatality (35 (14.7%)) being included for weekday versus weekend admissions (190 (19.5%) v 48 (13.7%), P<0.02). Among all acute stroke admissions in OXVASC, there was no imbalance in baseline stroke severity for weekends versus weekdays and no difference in case fatality at 30 days (adjusted odds ratio 0.85, 95% confidence interval 0.63 to 1.15; P=0.30) or any adverse "weekend effect" on modified Rankin score at 30 days (0.78, 0.61 to 0.99; P=0.04) or one year (0.76, 0.59 to 0.98; P=0.03) among incident strokes.Conclusion Retrospective studies of UK administrative hospital coding data to determine "weekend effects" on outcome in acute medical conditions, such as stroke, can be undermined by inaccurate coding, which can introduce biases that cannot be reliably dealt with by adjustment for case mix.

Project description:BackgroundPneumonia has traditionally been classified into two subtypes: community-acquired pneumonia (CAP) and nosocomial pneumonia (NP). Recently, a new entity has been defined, called healthcare-associated pneumonia (HCAP). Few studies have investigated the potential of population-based, electronic, healthcare databases to identify the incidences of these three subtypes of pneumonia. The aim of this study was to estimate the burden of the three subtypes of pneumonia in elderly patients (aged 65+ years) in a large region of central Italy.MethodsA retrospective cohort study was performed using linked regional Hospital Information System and Mortality Register. All episodes of pneumonia in elderly patients, who were discharged from the hospital in 2006-2008, were selected for the study. Following a validated ICD-9-coding algorithm, incidents of pneumonia events were classified into three groups (HCAP; probable nosocomial pneumonia, PNP; and CAP). Hospitalisation rates were calculated by age group (65-79, 80+), gender, and year, using the population from the Institute of Statistics (ISTAT) census estimates as denominators.ResultsA total of 26,239 pneumonia events occurred in 24,338 patients residing in the Lazio region, aged 65+ years: 2257 HCAP, 6775 PNP, and 17,107 CAP. For all subtypes, the proportion of males was greater than females. Comorbidity status was more severe in HCAP than in the other categories. In-hospital mortality, 30-day mortality, and length of hospital stay were twice higher in HCAP than in CAP episodes. The annual incidence rates were 0.7, 2.1, and 5.4 episodes per 1000 residents for HCAP, PNP, and CAP, respectively. From 2006 to 2008, incidence rates slightly increased for all three subtypes.ConclusionHealth care databases can be used to give a timely and inexpensive picture of the epidemiology of pneumonia. HCAP represents a distinct category of pneumonia, with the longest stay, highest mortality, and the greatest comorbidity.

Dataset Information

Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients.

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Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients.

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