ICD-10: History and Context

Hirsch, Joshua A; Nicola, Gregory N.; McGinty, Geraldine; Liu, R.W.; Barr, Robert M.; Chittle, Melissa D; Manchikanti, Laxmaiah

doi:10.3174/ajnr.a4696

Cited by 130 publications

(81 citation statements)

References 13 publications

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“…Most validation studies for liver-related administrative health data [5][6][7][8] originate from the USA, where ICD-9 codes were used until 2015, unlike the Nordic countries where ICD-10 was introduced already in the 1990s [9,10]. The healthcare system and the financing of care in the USA are also different from that in most European countries where publicly funded health care is more common.…”

Section: Introductionmentioning

confidence: 99%

Validity of administrative codes associated with cirrhosis in Sweden

Bengtsson

Askling

Ludvigsson

et al. 2020

Scandinavian Journal of Gastroenterology

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Objectives: Although cirrhosisis a major cause of liver-related mortality globally, validation studies of the administrative coding for diagnoses associated with cirrhosis are scarce. We aimed to determine the validity of the International Classification of Diseases, 10th revision (ICD-10) codes corresponding to cirrhosis and its complications in the Swedish National Patient Register (NPR). Methods: We randomly selected 750 patients with ICD codes for either alcohol-related cirrhosis (K70.3), unspecified cirrhosis (K74.6) oesophageal varices (I85.0/I85.9), hepatocellular carcinoma (HCC, C22.0) or ascites (R18.9) registered in the NPR from 72 healthcare centres in 2000-2016. Hospitalisation events and outpatient visits in specialised care were included. Positive predictive values (PPVs) were calculated using the information in the patient charts as the gold standard. Results: Complete data were obtained for 630 (of 750) patients (84%). For alcohol-related cirrhosis, 126/136 cases were correctly coded, corresponding to a PPV of 93% (95% confidence interval, 95%CI: 87-96). The PPV for cirrhosis with unspecified aetiology was 91% (121/133, 95%CI: 85-95) and 96% for oesophageal varices (118/123, 95%CI: 91-99). The PPV was lower for HCC, 84% (91/109, 95%CI: 75-90). The PPV for liver-related ascites was low, 43% (56/129, 95%CI: 35-52), as this category often consisted of non-hepatic ascites. When combining the ascites code with a code for chronic liver disease, the PPV for liver-related ascites increased to 93% (50/54, 95%CI: 82-98). Conclusions: The validity of ICD-10 codes for cirrhosis, oesophageal varices and HCC is high. However, coding for ascites should be combined with a code of chronic liver disease to have an acceptable validity.

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Section: Introductionmentioning

confidence: 99%

Validity of administrative codes associated with cirrhosis in Sweden

Bengtsson

Askling

Ludvigsson

et al. 2020

Scandinavian Journal of Gastroenterology

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“…ICD 1955ICD , 1965ICD , 1975'teki revizyonlarını takiben son revizyonu olan ICD-10 1990'da yayımlanmıştır. (2,3) Dünya Sağlık Örgütü (DSÖ) 1978'de Alma Ata'daki Temel Sağlık Hizmetleri Konferansında "2000 Yılında Herkese Sağlık" hedefi için birinci basamak sağlık hizmetlerinin temel olmasına karar vermiştir. Bu tarihten sonra birinci basamak sağlık hizmetinin yeni bir sınıflandırmaya ihtiyacı olduğunu düşünülerek "Karşılaşma Nedenlerinin Sınıflaması" (RFEC) geliştirilmiştir.…”

Section: Discussionunclassified

The effect of using ICD-10 or ICPC-2-R coding systems on primary health care service planning

Aypak¹,

Akbıyık²,

Suvak³

et al. 2018

tahd

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“…In addition to structured EHR data, in this study, we also extracted physician entered diagnoses from clinical notes of both Mayo and REP sources leveraging natural language processing (NLP) techniques. We updated the algorithm to incorporate ICD 10 codes which have been adopted in late 2015 in the United States (Hirsch et al, 2016). To collect more complete data, we also added medication extracted by NLP from clinical notes in both Mayo and REP sources.…”

Section: Phenotyping Algorithmsmentioning

confidence: 99%

Impact of Diverse Data Sources on Computational Phenotyping

et al. 2020

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Electronic health records (EHRs) are widely adopted with a great potential to serve as a rich, integrated source of phenotype information. Computational phenotyping, which extracts phenotypes from EHR data automatically, can accelerate the adoption and utilization of phenotype-driven efforts to advance scientific discovery and improve healthcare delivery. A list of computational phenotyping algorithms has been published but data fragmentation, i.e., incomplete data within one single data source, has been raised as an inherent limitation of computational phenotyping. In this study, we investigated the impact of diverse data sources on two published computational phenotyping algorithms, rheumatoid arthritis (RA) and type 2 diabetes mellitus (T2DM), using Mayo EHRs and Rochester Epidemiology Project (REP) which links medical records from multiple health care systems. Results showed that both RA (less prevalent) and T2DM (more prevalent) case selections were markedly impacted by data fragmentation, with positive predictive value (PPV) of 91.4 and 92.4%, false-negative rate (FNR) of 26.6 and 14% in Mayo data, respectively, PPV of 97.2 and 98.3%, FNR of 5.2 and 3.3% in REP. T2DM controls also contain biases, with PPV of 91.2% and FNR of 1.2% for Mayo. We further elaborated underlying reasons impacting the performance.

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ICD-10: History and Context

Cited by 130 publications

References 13 publications

Validity of administrative codes associated with cirrhosis in Sweden

Validity of administrative codes associated with cirrhosis in Sweden

The effect of using ICD-10 or ICPC-2-R coding systems on primary health care service planning

Impact of Diverse Data Sources on Computational Phenotyping

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