Dataset Information

Validity of diagnostic codes to identify cases of severe acute liver injury in the US Food and Drug Administration's Mini-Sentinel Distributed Database.

ABSTRACT: The validity of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to identify diagnoses of severe acute liver injury (SALI) is not well known. We examined the positive predictive values (PPVs) of hospital ICD-9-CM diagnoses in identifying SALI among health plan members in the Mini-Sentinel Distributed Database (MSDD) for patients without liver/biliary disease and for those with chronic liver disease (CLD).We selected random samples of members (149 without liver/biliary disease; 75 with CLD) with a principal hospital diagnosis suggestive of SALI (ICD-9-CM 570, 572.2, 572.4, 572.8, 573.3, 573.8, or V42.7) in the MSDD (2009-2010). Medical records were reviewed by hepatologists to confirm SALI events. PPVs of codes and code combinations for confirmed SALI were determined by CLD status.Among 105 members with available records and no liver/biliary disease, SALI was confirmed in 26 (PPV, 24.7%; 95%CI, 16.9-34.1%). Combined hospital diagnoses of acute hepatic necrosis (570) and liver disease sequelae (572.8) had high PPV (100%; 95%CI, 59.0-100%) and identified 7/26 (26.9%) events. Among 46 CLD members with available records, SALI was confirmed in 19 (PPV, 41.3%; 95%CI, 27.0-56.8%). Acute hepatic necrosis (570) or hepatorenal syndrome (572.4) plus any other SALI code had a PPV of 83.3% (95%CI, 51.6-97.9%) and identified 10/19 (52.6%) events.Most individual hospital ICD-9-CM diagnoses had low PPV for confirmed SALI events. Select code combinations had high PPV but did not capture all events.

SUBMITTER: Lo Re V

PROVIDER: S-EPMC4409951 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Validity of diagnostic codes to identify cases of severe acute liver injury in the US Food and Drug Administration's Mini-Sentinel Distributed Database.

Lo Re Vincent V Haynes Kevin K Goldberg David D Forde Kimberly A KA Carbonari Dena M DM Leidl Kimberly B F KB Hennessy Sean S Reddy K Rajender KR Pawloski Pamala A PA Daniel Gregory W GW Cheetham T Craig TC Iyer Aarthi A Coughlin Kara O KO Toh Sengwee S Boudreau Denise M DM Selvam Nandini N Cooper William O WO Selvan Mano S MS VanWormer Jeffrey J JJ Avigan Mark I MI Houstoun Monika M Zornberg Gwen L GL Racoosin Judith A JA Shoaibi Azadeh A

Pharmacoepidemiology and drug safety 20130625 8

<h4>Purpose</h4>The validity of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to identify diagnoses of severe acute liver injury (SALI) is not well known. We examined the positive predictive values (PPVs) of hospital ICD-9-CM diagnoses in identifying SALI among health plan members in the Mini-Sentinel Distributed Database (MSDD) for patients without liver/biliary disease and for those with chronic liver disease (CLD).<h4>Methods</h4>We selected ...[more]

PMID: 23801638

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Project description:Background and purposeThe transition from International Classification of Diseases, 9th revision, clinical modification (ICD-9-CM) to ICD-10-CM poses a challenge to epidemiologic studies that use diagnostic codes to identify health outcomes and covariates. We evaluated coding trends in health outcomes in the US Food and Drug Administration's Sentinel System during the transition.MethodsWe reviewed all health outcomes coding trends reports on the Sentinel website through November 30, 2019 and analyzed trends in incidence and prevalence across the ICD-9-CM and ICD-10-CM eras by visual inspection.ResultsWe identified 78 unique health outcomes (22 acute, 32 chronic, and 24 acute or chronic) and 140 time-series graphs of incidence and prevalence. The reports also included code lists and code mapping methods used. Of the 140 graphs reviewed, 81 (57.9%) showed consistent trends across the ICD-9-CM and ICD-10-CM eras, while 51 (36.4%) and 8 (5.7%) graphs showed inconsistent and uncertain trends, respectively. Chronic HOIs and acute/chronic HOIs had higher proportions of consistent trends in prevalence definitions (83.9% and 78.3%, respectively) than acute HOIs (28.6%). For incidence, 55.6% of acute HOIs showed consistent trends, while 41.2% of chronic HOIs and 39.3% of acute/chronic HOIs showed consistency.ConclusionsResearchers using ICD-10-CM algorithms obtained by standardized mappings from ICD-9-CM algorithms should assess the mapping performance before use. The Sentinel reports provide a valuable resource for researchers who need to develop and assess mapping strategies. The reports could benefit from additional information about the algorithm selection process and additional details on monthly incidence and prevalence rates.Key pointsWe reviewed health outcomes coding trends reports on the US FDA Sentinel website through November 30, 2019 and analyzed trends in incidence and prevalence across the International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) and ICD-10-CM eras by code mapping method and the type of health outcomes of interest (acute, chronic, acute or chronic). More than a third of the 140 time-series graphs of incidence and prevalence of health outcomes showed inconsistent or uncertain trends. Consistency in trends varied by code mapping method, type of health outcomes of interest, and whether the measurement was incidence or prevalence. Studies using ICD-9-CM-based algorithms mapped to ICD-10-CM codes need to assess the performance of the mappings and conduct manual refinement of the algorithms as needed before using them.

Project description:Mushroom poisoning contributes significantly to global foodborne diseases and related fatalities. Amanita mushrooms frequently cause such poisonings; however, identifying these toxic species is challenging due to the unavailability of fresh and intact samples. It is often necessary to analyze residues, vomitus, or stomach extracts to obtain DNA sequences for the identification of species responsible for causing food poisoning. This usually proves challenging to obtain usable DNA sequences that can be analyzed using conventional molecular biology techniques. Therefore, this study aimed to develop a DNA mini-barcoding method for the identification of Amanita species. Following the evaluation and optimization of universal primers for DNA mini-barcoding in Amanita mushrooms, we found that the internal transcribed spacer (ITS) gene sequence primer ITS-a was the most suitable DNA barcode primer for identifying Amanita species. Forty-three Amanita samples were subsequently amplified and sequenced. The sequences obtained were analyzed for intra- and inter-species genetic distances, and a phylogenetic tree was constructed. The findings indicated that the designed primers had strong universality among the Amanita samples and could accurately identify the target gene fragment with a length of 290 bp. Notably, the DNA mini-barcode accurately identified the 43 Amanita samples, demonstrating high consistency with the conventional DNA barcode. Furthermore, it effectively identified DNA from digested samples. In summary, this DNA mini-barcode is a promising tool for detecting accidental ingestion of toxic Amanita mushrooms. It may be used as an optimal barcode for species identification and traceability in events of Amanita-induced mushroom poisoning. KEY POINTS: • Development of a DNA mini-barcoding method for Amanita species identification without fresh samples. • The ITS-a primer set was optimized for robust universality in Amanita samples. • The mini-barcode is suitable for screening toxic mushroom species in mushroom poisoning cases.

Project description:The visual cortex's hierarchical, multi-level organization is captured in many biologically inspired computational vision models, the general idea being that progressively larger scale (spatially/temporally) and more complex visual features are represented in progressively higher areas. However, most earlier models use localist representations (codes) in each representational field (which we equate with the cortical macrocolumn, "mac"), at each level. In localism, each represented feature/concept/event (hereinafter "item") is coded by a single unit. The model we describe, Sparsey, is hierarchical as well but crucially, it uses sparse distributed coding (SDC) in every mac in all levels. In SDC, each represented item is coded by a small subset of the mac's units. The SDCs of different items can overlap and the size of overlap between items can be used to represent their similarity. The difference between localism and SDC is crucial because SDC allows the two essential operations of associative memory, storing a new item and retrieving the best-matching stored item, to be done in fixed time for the life of the model. Since the model's core algorithm, which does both storage and retrieval (inference), makes a single pass over all macs on each time step, the overall model's storage/retrieval operation is also fixed-time, a criterion we consider essential for scalability to the huge ("Big Data") problems. A 2010 paper described a nonhierarchical version of this model in the context of purely spatial pattern processing. Here, we elaborate a fully hierarchical model (arbitrary numbers of levels and macs per level), describing novel model principles like progressive critical periods, dynamic modulation of principal cells' activation functions based on a mac-level familiarity measure, representation of multiple simultaneously active hypotheses, a novel method of time warp invariant recognition, and we report results showing learning/recognition of spatiotemporal patterns.

Dataset Information

Validity of diagnostic codes to identify cases of severe acute liver injury in the US Food and Drug Administration's Mini-Sentinel Distributed Database.

Publications

Validity of diagnostic codes to identify cases of severe acute liver injury in the US Food and Drug Administration's Mini-Sentinel Distributed Database.

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