Explore the vast range of titles available.

Background The World Health Organization (WHO) International Classification of Diseases and Related Health Problems (ICD) is used globally by 194 WHO member nations. It is used for assigning clinical diagnoses, providing the framework for reporting public health data, and to inform the organization and reimbursement of health services. Guided by overarching principles of increasing clinical utility and global applicability, the 11th revision of the ICD proposes major changes that incorporate empirical advances since the previous revision in 1992. To test recommended changes in the Mental, Behavioral, and Neurodevelopmental Disorders chapter, multiple vignette-based case-controlled field studies have been conducted which examine clinicians’ ability to accurately and consistently use the new guidelines and assess their overall clinical utility. This manuscript reports on the results from the study of the proposed ICD-11 guidelines for feeding and eating disorders (FEDs). Method Participants were 2288 mental health professionals registered with WHO’s Global Clinical Practice Network. The study was conducted in Chinese, English, French, Japanese, and Spanish. Clinicians were randomly assigned to apply either the ICD-11 or ICD-10 diagnostic guidelines for FEDs to a pair of case vignettes designed to test specific clinical questions. Clinicians selected the diagnosis they thought was correct for each vignette, evaluated the presence of each essential feature of the selected diagnosis, and the clinical utility of the diagnostic guidelines. Results The proposed ICD-11 diagnostic guidelines significantly improved accuracy for all FEDs tested relative to ICD-10 and attained higher clinical utility ratings; similar results were obtained across all five languages. The inclusion of binge eating disorder and avoidant-restrictive food intake disorder reduced the use of residual diagnoses. Areas needing further refinement were identified. Conclusions The proposed ICD-11 diagnostic guidelines consistently outperformed ICD-10 in distinguishing cases of eating disorders and showed global applicability and appropriate clinical utility. These results suggest that the proposed ICD-11 guidelines for FEDs will help increase accuracy of public health data, improve clinical diagnosis, and enhance health service organization and provision. This is the first time in the revision of the ICD that data from large-scale, empirical research examining proposed guidelines is completed in time to inform the final diagnostic guidelines.

An update of the chapter on Mental, Behavioral and Neurodevelopmental Disorders in the International Classification of Diseases and Related Health Problems (ICD) is of great interest around the world. The recent approval of the 11th Revision of the ICD (ICD-11) by the World Health Organization (WHO) raises broad questions about the status of nosology of mental disorders as a whole as well as more focused questions regarding changes to the diagnostic guidelines for specific conditions and the implications of these changes for practice and research. This Forum brings together a broad range of experts to reflect on key changes and controversies in the ICD-11 classification of mental disorders. Taken together, there is consensus that the WHO’s focus on global applicability and clinical utility in developing the diagnostic guidelines for this chapter will maximize the likelihood that it will be adopted by mental health professionals and administrators. This focus is also expected to enhance the application of the guidelines in non-specialist settings and their usefulness for scaling up evidence-based interventions. The new mental disorders classification in ICD-11 and its accompanying diagnostic guidelines therefore represent an important, albeit iterative, advance for the field.

OBJECTIVES:To validate and compare performance of an International Classification of Diseases, tenth revision (ICD-10) version of a combined comorbidity index merging conditions of Charlson and Elixhauser measures against individual measures in the prediction of 30-day mortality. To select a weight derivation method providing optimal performance across ICD-9 and ICD-10 coding systems. RESEARCH DESIGN:Using 2 adult population-based cohorts of patients with hospital admissions in ICD-9 (2005, n=337,367) and ICD-10 (2011, n=348,820), we validated a combined comorbidity index by predicting 30-day mortality with logistic regression. To appreciate performance of the Combined index and both individual measures, factors impacting indices performance such as population characteristics and weight derivation methods were accounted for. We applied 3 scoring methods (Van Walraven, Schneeweiss, and Charlson) and determined which provides best predictive values. RESULTS:Combined index [c-statistics0.853 (95% confidence intervalCI, 0.848–0.856)] performed better than original Charlson [0.841 (95% CI, 0.835–0.844)] or Elixhauser [0.841 (95% CI, 0.837–0.844)] measures on ICD-10 cohort. All weight derivation methods provided close high discrimination results for the Combined index (Van Walraven0.852, Schneeweiss0.851, Charlson0.849). Results were consistent across both coding systems. CONCLUSIONS:The Combined index remains valid with both ICD-9 and ICD-10 coding systems and the 3 weight derivation methods evaluated provided consistent high performance across those coding systems.

Long-term follow-up of population-based prospective studies is often achieved through linkages to coded regional or national health care data. Our knowledge of the accuracy of such data is incomplete. To inform methods for identifying stroke cases in UK Biobank (a prospective study of 503,000 UK adults recruited in middle-age), we systematically evaluated the accuracy of these data for stroke and its main pathological types (ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage), determining the optimum codes for case identification. We sought studies published from 1990-November 2013, which compared coded data from death certificates, hospital admissions or primary care with a reference standard for stroke or its pathological types. We extracted information on a range of study characteristics and assessed study quality with the Quality Assessment of Diagnostic Studies tool (QUADAS-2). To assess accuracy, we extracted data on positive predictive values (PPV) and-where available-on sensitivity, specificity, and negative predictive values (NPV). 37 of 39 eligible studies assessed accuracy of International Classification of Diseases (ICD)-coded hospital or death certificate data. They varied widely in their settings, methods, reporting, quality, and in the choice and accuracy of codes. Although PPVs for stroke and its pathological types ranged from 6-97%, appropriately selected, stroke-specific codes (rather than broad cerebrovascular codes) consistently produced PPVs >70%, and in several studies >90%. The few studies with data on sensitivity, specificity and NPV showed higher sensitivity of hospital versus death certificate data for stroke, with specificity and NPV consistently >96%. Few studies assessed either primary care data or combinations of data sources. Particular stroke-specific codes can yield high PPVs (>90%) for stroke/stroke types. Inclusion of primary care data and combining data sources should improve accuracy in large epidemiological studies, but there is limited published information about these strategies.

The Diabetes Complications Severity Index (DCSI) converts diagnostic codes and laboratory results into a 14-level metric quantifying the long-term effects of diabetes on seven body systems. Adoption of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) necessitates translation from ICD-9-CM and creates refinement opportunities. ICD-9 codes for secondary and primary diabetes plus all five ICD-10 diabetes categories were incorporated into an updated tool. Additional modifications were made to improve the accuracy of severity assignments. The tools were tested in a Medicare Advantage population. In the type 2 subpopulation, prevalence steadily declined with increasing score according to the updated DCSI tool, whereas the original tool resulted in an aberrant local prevalence peak at DCSI = 2. In the type 1 subpopulation, score prevalence was greater in type 1 versus type 2 subpopulations (3 versus 0) according to both instruments. Both instruments predicted current-year inpatient admissions risk and near-future mortality, using either purely ICD-9 data or a mix of ICD-9 and ICD-10 data. While the performance of the tool with purely ICD-10 data has yet to be evaluated, this updated tool makes assessment of diabetes patient severity and complications possible in the interim.

Background Because of their individual rarity, genetic diseases and other types of rare diseases are under-represented in healthcare coding systems; this contributes to a lack of ascertainment and recognition of their importance for healthcare planning and resource allocation, and prevents clinical research from being performed. Methods Orphanet was given the task to develop an inventory of rare diseases and a classification system which could serve as a template to update International terminologies. When the World Health Organization (WHO) launched the revision process of the International Classification of Diseases (ICD), a Topic Advisory Group for rare diseases was established, managed by Orphanet and funded by the European Commission. Results So far 5,400 rare diseases listed in the Orphanet database have an endorsed representation in the foundation layer of ICD-11, and are thus provided with a unique identifier in the Beta version of ICD-11, which is 10 times more than in ICD10. A rare disease linearization is also planned. The current beta version is open for public consultation and comments, and to be used for field testing. The adoption by the World Health Assembly is planned for 2017. Conclusions The overall revision process was carried out with very limited means considering its scope, ambition and strategic significance, and experienced significant hurdles and setbacks. The lack of funding impacted the level of professionalism that could be attained. The contrast between the initially declared goals and the currently foreseen final product is disappointing. In the context of uncertainty around the outcome of the field testing and the potential willingness of countries to adopt this new version, the European Commission Expert Group on Rare Diseases adopted in November 2014 a recommendation for health care coding systems to consider using ORPHA codes in addition to ICD10 codes for rare diseases having no specific ICD10 codes. The Orphanet terminology, classifications and mappings with other terminologies are freely available at www.orphadata.org .

Intentional and unintentional injuries are a leading cause of death and disability globally. International Classification of Diseases (ICD), Tenth Revision (ICD-10) codes are used to classify injuries in administrative health data and are widely used for health care planning and delivery, research, and policy. However, a systematic review of their overall validity and reliability has not yet been done. To conduct a systematic review of the validity and reliability of external cause injury ICD-10 codes. MEDLINE, EMBASE, COCHRANE, and SCOPUS were searched (inception to April 2023) for validity and/or reliability studies of ICD-10 external cause injury codes in all countries for all ages. We examined all available data for external cause injuries and injuries related to specific body regions. Validity was defined by sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Reliability was defined by inter-rater reliability (IRR), measured by Krippendorff's alpha, Cohen's Kappa, and/or Fleiss' kappa. Twenty-seven published studies from 2006 to 2023 were included. Across all injuries, the mean outcome values and ranges were sensitivity: 61.6% (35.5%-96.0%), specificity: 91.6% (85.8%-100%), PPV: 74.9% (58.6%-96.5%), NPV: 80.2% (44.6%-94.4%), Cohen's kappa: 0.672 (0.480-0.928), Krippendorff's alpha: 0.453, and Fleiss' kappa: 0.630. Poisoning and hand and wrist injuries had higher mean sensitivity (84.4% and 96.0%, respectively), while self-harm and spinal cord injuries were lower (35.5% and 36.4%, respectively). Transport and pedestrian injuries and hand and wrist injuries had high PPVs (96.5% and 92.0%, respectively). Specificity and NPV were generally high, except for abuse (NPV 44.6%). The validity and reliability of ICD-10 external cause injury codes vary based on the injury types coded and the outcomes examined, and overall, they only perform moderately well. Future work, potentially utilizing artificial intelligence, may improve the validity and reliability of ICD codes used to document injuries.

INTRODUCTION US Medicare claims can be used to identify dementia cases for research. Our objective was to evaluate the performance of International Classification of Diseases, 10th Revision (ICD‐10) code definitions versus research‐based dementia ascertainment. METHODS Participants of five Rush Alzheimer's Disease Center (RADC) cohorts with study visits between October 2015 and December 2019 and fee‐for‐service Medicare contributed observations. For each observation, we compared research‐based dementia status to dementia status based on six ICD‐10 code definitions. RESULTS A total of 1869 participants contributed 5309 observations (mean age 82.9 years, 21.0% Black, 9.3% met research‐based dementia criteria). The accuracy of ICD‐10 code definitions was high (87%–90%); five of six code definitions favored specificity over sensitivity. All ICD‐10 code definitions were less accurate among subgroups defined by older age, minoritized race, increased depressive symptoms, and history of stroke. DISCUSSION Performance of ICD‐10 code definitions mirrored that of ICD‐9 code definitions. Awareness of differential performance by participant characteristics can improve the robustness of research. Highlights We report the performance of the International Classification of Diseases, 10th Revision (ICD‐10) code versus research‐based dementia ascertainment. ICD‐10 performed worse with age, depressive symptoms, minoritized race, and stroke. Awareness of accuracy and differential performance can improve research robustness.

Background The International Classification of Functioning, Disability and Health (ICF) was introduced by the World Health Organization as a common taxonomy to describe the burden of health conditions. This study focuses on the development of a scale for staging basic mobility and walking functions based on the ICF. Methods Thirty-three ICF codes were selected to test their fit to the Rasch model and their location. Of these ICF items, four were used to develop a Guttman- type scale of “basic mobility” and another four to develop a“walking” scale to stage functional performance in the elderly. The content validity and differential item functioning of the scales were assessed. The participants, chosen at random, were Japanese over 65 years old using the services of public long-term care insurance, and whose functional assessments were used for scale development and scale validation. Results There were 1164 elderly persons who were eligible for scale development. To stage the functional performance of elderly persons, two Guttman-type scales of “basic mobility” and “walking” were constructed. The order of item difficulty was validated using 3260 elderly persons. There is no differential item functioning about study location, sex and age-group in the newly developed scales. These results suggested the newly developed scales have content validity. Conclusions These scales divided functional performance into five stages according to four ICF codes, making the measurements simple and less time-consuming and enable clear descriptions of elderly functioning level. This was achieved by hierarchically rearranging the ICF items and constructing Guttman-type scales according to item difficulty using the Rasch model. In addition, each functional level might require similar resources and therefore enable standardization of care and rehabilitation. Illustrations facilitate the sharing of patient images among health care providers. By using the ICF as a common taxonomy, these scales could be used internationally as assessment scales in geriatric care settings. However these scales require further validity and reliability studies for international application.

Background This study assessed the accuracy of three International Classification of Diseases (ICD) codes methods derived from Global Burden of Disease (GBD) sepsis study (modified GBD method) in identifying sepsis, compared to the Angus method. Sources of errors in these methods were also reported. Methods Prospective multicentre, observational, study. Emergency Department patients aged ≥ 16 years with high sepsis risk from nine hospitals in NSW, Australia were screened for clinical sepsis using Sepsis 3 criteria and coded as having sepsis or not using the modified GBD and Angus methods. The three modified GBD methods were: Explicit —sepsis-specific ICD code recorded; Implicit —sepsis-specific code or infection as primary ICD code plus organ dysfunction code; Implicit plus —as for Implicit but infection as primary or secondary ICD code. Agreement between clinical sepsis and ICD coding methods was assessed using Cronbach alpha (α). For false positive cases (ICD-coded sepsis but not clinically diagnosed), the ICD codes leading to those errors were documented. For false negatives (clinically diagnosed sepsis but ICD-coded), uncoded sources of infection and organ dysfunction were documented. Results Of 6869 screened patients, 450 (median age 72.4 years, 48.9% females) met inclusion criteria. Clinical sepsis was diagnosed in 215/450 (47.8%). The explicit, implicit, implicit plus and Angus methods identified sepsis in 108/450 (24.0%), 175/450 (38.9%), 222/450 (49.3%) and 170/450 (37.8%), respectively. Sensitivity was 41.4%, 58.1%, 67.4% and 55.8%, and specificity 91.9%, 78.7%, 67.2% and 79.1%, respectively. Agreement between clinical sepsis and all ICD coding methods was low (α = 0.52–0.56). False positives were 19, 50, and 77, while false negatives were 126, 90, and 70 for the explicit, implicit, and implicit plus methods, respectively. For false positive cases, unspecified urinary tract infection, hypotension and acute kidney failure were commonly assigned infection and organ dysfunction codes. About half (44.3%-55.6%) of the false negative cases didn’t have a pathogen documented. Conclusion The modified GBD method demonstrated low accuracy in identifying sepsis; with the implicit plus method being the most accurate. Errors in identifying sepsis using ICD codes arise mostly from coding for unspecified urinary infections and associated organ dysfunction. Trial registration The study was registered at the ANZCTR (ACTRN12621000333819) on 24 March 2021.