Bipolar disorder and medical comorbidities: A Portuguese population-based observational retrospective study (2008-2015)

BACKGROUND

This study aimed to use the Charlson Comorbidity Index (CCI) to assess the prevalence of medical comorbidities among hospitalization episodes with a primary Bipolar Disorder (BD) diagnosis, and to analyze its association with hospitalization outcomes.

METHODS

A population-based observational retrospective study was conducted using a Portuguese administrative database containing all mainland public hospitalizations. From 2008-2015, hospitalization episodes with a primary diagnosis of BD were analysed. Outcomes included: length of stay (LoS), in-hospital mortality and discharge destination.

RESULTS

Overall, 20807 hospitalization episodes were analysed. Mean±standard deviation age at admission was 47.9±14.3 years, and these episodes mostly refer to women's admissions (66.6%). Median (1st quartile; 3rd quartile) LoS was 16.0 (9.0; 25.0) days. A total of 2145 (10.3%) episodes had ≥1 CCI comorbidities registered, being diabetes the most prevalent. LoS was significantly higher for episodes with secondary diagnoses of congestive heart failure, cerebrovascular disease, dementia, diabetes, renal disease and malignancy (all p<0.05). Episodes with a registry of myocardial infarction, peripheral vascular disease, malignancy and renal disease diagnoses had higher in-hospital mortality.

LIMITATIONS

Limitations include the use of data registered for administrative reasons rather than research purposes, and the analysis of hospitalization episodes, instead of patients.

CONCLUSIONS

In this Portuguese nationwide study, greater comorbidity had a measurable impact on BD hospitalization outcomes. During the study period the prevalence of CCI comorbidities rose from 8.1% to 17.4%, which may reflect the overall increasing quality of hospital-coded data in Portugal throughout the years. The detection and timely management of medical comorbid conditions will likely prevent the high BD medical burden.

Update on use of enteral and parenteral nutrition in hospitalized patients with a diagnosis of malnutrition in the United States

BACKGROUND

Malnutrition continues to be associated with outcomes in hospitalized patients.

METHODS

An updated review of national data in patients with a coded diagnosis of malnutrition (CDM) and the use of nutrition support (enteral nutrition [EN] and parenteral nutrition [PN]) was conducted using the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project and Medicare Claims data.

RESULTS

Results demonstrated a growing trend in CDM accompanied by continued low utilization of PN and EN.

CONCLUSION

Underutilization of nutrition support may be due to product shortages, reluctance of clinicians to use these therapies, undercoding of nutrition support, strict adherence to published guidelines, and other factors.

How Malignant Mesothelioma Was Coded in Mortality Data in Taiwan During Years When the Specific ICD Code Was Not Available?

Purpose

Malignant mesothelioma (MM) is associated with past exposure to asbestos and the latency period ranged from 20 to 40 years. Asbestos consumption reached a peak in the 1980s in Taiwan, and the MM mortality is expected to increase since 2000s. However, no specific code for MM was available before the International Classification of Disease, Tenth Revision (ICD-10), which was launched in 2008 in Taiwan. We examined how MM was coded in mortality data in Taiwan during the years when the ICD, Ninth Revision (ICD-9) was used.

Patients and Methods

Double-coded mortality data (each death coded according to both ICD-10 and ICD-9 codes) for the period 2002–2008 were obtained for analysis. Detection rates (similar to sensitivity) and confirmation rates (similar to positive predictive value) for various potential proxy ICD-9 codes for MM were calculated.

Results

For 113 deaths, for which the underlying cause of death was ICD-10 code C45 (MM), 14 corresponding ICD-9 codes were used. Four ICD-9 codes constituted 77% (87/113) of all MM deaths. The detection rate for code 199 (malignant neoplasm [MN] without specification of site) was 37% (42/113), that for code 163 (MN of pleura) was 18% (20/113), that for code 162 (MN of trachea, bronchus, and lung) was 12% (14/113), and that for code 173 (other MN of skin) was 10% (11/113). The confirmation rates for codes 199, 163, 162, and 173 were 0.9% (42/4759), 14.3% (20/140), 0.03% (14/51,778), and 1.5% (11/717), respectively.

Conclusion

ICD-9 codes 199, 163, 162, and 173 were most commonly used for MM deaths in Taiwan during the years before the ICD-10 introduction. However, when we used only ICD-9 code 163, which was most commonly used as a surrogate measure of MM in mortality studies during the ICD-9 era, we could detect only one-fifth of MM deaths in Taiwan.

Validation of International Classification of Diseases criteria to identify severe influenza hospitalizations

In this cohort study of hospitalized patients with linked medical record data, we developed International Classification of Diseases (ICD) criteria that accurately identified laboratory‐confirmed, severe influenza hospitalizations (positive predictive value [PPV] 80%, 95% confidence interval [CI] 71–87%), which we validated through medical record documentation. These criteria identify patients with clinically important influenza illness outcomes to inform evaluation of preventive and therapeutic interventions and public health policy recommendations.

Allergy in the World Health Organization's International Classification of Diseases (ICD)-11

The International Classification of Diseases (ICD) provides a common language for use worldwide as a diagnostic and classification tool for epidemiology, clinical purposes, and health management. The change in the hierarchy in ICD-11 permitted the construction of the pioneer section addressed to allergic and hypersensitivity conditions (A/H), which may result in more accurate mortality and morbidity statistics, including more accurate accounting for mortality due to anaphylaxis, strengthen classification, terminology, and definitions. The ICD-11 was presented and adopted by the 72nd World Health Assembly in May 2019, and the implementation is ongoing worldwide. The Montpellier World Health Organization (WHO) Collaborating Centre on Classification Scientific Support was designated in 2018 and is responsible for supporting the WHO through representing A/H in the international classifications and quality care of patients from the public health perspective.

Pitfalls of International Classification of Diseases - Perinatal mortality in analysing stillbirths

OBJECTIVES

This study aimed to evaluate the trend of stillbirth from 2009 to 2018. The causes of stillbirth were classified using the International Classification of Diseases - Perinatal Mortality (ICD-PM).

STUDY DESIGN AND METHODS

A retrospective chart review was performed on 135 stillbirths from 2009 to 2018 in a tertiary university teaching hospital. The annual stillbirth rate was calculated, and the trend was evaluated. The cause of death was reclassified using ICD-PM.

RESULTS

The stillbirth rate was 3.70 per 1000 total births, and it remained stable over the studied period (P = 0.238). Most of the stillbirth (97.8%) were antepartum deaths. The proportion of unexplained stillbirth was reduced from 57% to 18.5% after reclassified by ICD-PM coding. Another major cause of antepartum stillbirths was disorders related to fetal growth, which consisted of mothers with medical and surgical conditions (11%, n = 15, ICD-PM code A5, M4) or mothers with complications of placenta, cord and membranes (8.9%, n = 12, ICD-PM code A5, M1).

CONCLUSION

The use of ICD-PM was useful in reducing the proportion of unexplained stillbirths. ICD-PM has the advantages of coding related to the timing of stillbirth and associated maternal conditions. Pitfalls including the unclear use of the code A3-'antepartum hypoxia,' guidance on coding of well-controlled maternal medical conditions and placental pathology and the importance of subcategorisation need to be addressed.

What are the functions of psychiatric classifications?

Faced with clinical, methodological, conceptual and modeling challenges, psychiatric nosology turned forty years ago towards a descriptive approach, with the third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III). More recently, the discipline has sought answers in precision medicine and biomarkers, e.g., with the Research Domain Criteria project of the National Institute for Mental Health, towards statistical and dimensional approaches, e.g., the Hierarchical Taxonomy of Psychopathology, or towards dynamic (e.g., staging models) and computational approaches (e.g., symptom network in psychopathology). However, despite these attempts to guardedly move away from a descriptive perspective, the functions of classifications have remained the same in psychiatry over time. These functions could be seen as the guarantee of a stable ground for psychiatry, and it seems necessary that they be made explicit from methodological and deontological points of view.

Comparing reliability of ICD-10-based COVID-19 comorbidity data to manual chart review, a retrospective cross-sectional study

International Statistical Classification of Disease and Related Health Problems, 10th Revision codes (ICD‐10) are used to characterize cohort comorbidities. Recent literature does not demonstrate standardized extraction methods. Objective: Compare COVID‐19 cohort manual‐chart‐review and ICD‐10‐based comorbidity data; characterize the accuracy of different methods of extracting ICD‐10‐code‐based comorbidity, including the temporal accuracy with respect to critical time points such as day of admission. Design: Retrospective cross‐sectional study. Measurements: ICD‐10‐based‐data performance characteristics relative to manual‐chart‐review. Results: Discharge billing diagnoses had a sensitivity of 0.82 (95% confidence interval [CI]: 0.79–0.85; comorbidity range: 0.35–0.96). The past medical history table had a sensitivity of 0.72 (95% CI: 0.69–0.76; range: 0.44–0.87). The active problem list had a sensitivity of 0.67 (95% CI: 0.63–0.71; range: 0.47–0.71). On day of admission, the active problem list had a sensitivity of 0.58 (95% CI: 0.54–0.63; range: 0.30–0.68)and past medical history table had a sensitivity of 0.48 (95% CI: 0.43–0.53; range: 0.30–0.56). Conclusions and Relevance: ICD‐10‐based comorbidity data performance varies depending on comorbidity, data source, and time of retrieval; there are notable opportunities for improvement. Future researchers should clearly outline comorbidity data source and validate against manual‐chart‐review.

ICD-11 Classification of Pediatric Chronic Pain Referrals in Ireland, with Secondary Analysis of Primary vs Secondary Pain Conditions

Objective

To classify pediatric chronic pain referrals in Ireland according to the classification system of the 11^th version of the International Classification of Diseases (ICD-11). In addition, differences between primary and secondary pain groups were assessed.

Methods

Retrospective review of complex pain assessment forms completed at the time of initial attendance at pediatric chronic pain clinics in Dublin, Ireland. Patients were classified as having a chronic primary (CPP) or chronic secondary (CSP) pain condition as per ICD-11 classification. Secondary analysis of between-group and within-group differences between primary and secondary pain conditions was undertaken.

Results

Of 285 patients coded, 123 patients were designated as having a CPP condition (77% of whom were assigned an adjunct parent code) and 162 patients as having a CSP condition (61% of whom were assigned an adjunct parent code). Between-group comparisons found that the lowest reported pain scores were higher in CPP than in CSP conditions. There were stronger correlations between parental pain catastrophizing and pain intensity, school attendance, and pain interference with social activities in the CSP group than in the CPP group.

Conclusions

The majority of children with both CPP and CSP were assigned multiple parent codes. There appears to be a gradient in the differences in biopsychosocial profile between CPP and CSP conditions. Additional field testing of the ICD-11 classification in pediatric chronic pain will be required.

ICD-11 extension codes support detailed clinical abstraction and comprehensive classification

Background

The new International Classification of Diseases—11th revision (ICD-11) succeeds ICD-10. In the three decades since ICD-10 was released, demands for detailed information on the clinical history of a morbid patient have increased.

Methods

ICD-11 has now implemented an addendum chapter X called “Extension Codes”. This chapter contains numerous codes containing information on concepts including disease stage, severity, histopathology, medicaments, and anatomical details. When linked to a stem code representing a clinical state, the extension codes add significant detail and allow for multidimensional coding.

Results

This paper discusses the purposes and uses of extension codes and presents three examples of how extension codes can be used in coding clinical detail.

Conclusion

ICD-11 with its extension codes implemented has the potential to improve precision and evidence based health care worldwide.

Explainable ICD multi-label classification of EHRs in Spanish with convolutional attention

BACKGROUND

This work deals with Natural Language Processing applied to Electronic Health Records (EHRs). EHRs are coded following the International Classification of Diseases (ICD) leading to a multi-label classification problem. Previously proposed approaches act as black-boxes without giving further insights. Explainable Artificial Intelligence (XAI) helps to clarify what brought the model to make the predictions.

GOAL

This work aims to obtain explainable predictions of the diseases and procedures contained in EHRs. As an application, we show visualizations of the attention stored and propose a prototype of a Decision Support System (DSS) that highlights the text that motivated the choice of each of the proposed ICD codes.

METHODS

Convolutional Neural Networks (CNNs) with attention mechanisms were used. Attention mechanisms allow to detect which part of the input (EHRs) motivate the output (medical codes), producing explainable predictions.

RESULTS

We successfully applied methods in a Spanish corpus getting challenging results. Finally, we presented the idea of extracting the chronological order of the ICDs in a given EHR by anchoring the codes to different stages of the clinical admission.

CONCLUSIONS

We found that explainable deep learning models applied to predict medical codes store helpful information that could be used to assist medical experts while reaching a solid performance. In particular, we show that the information stored in the attention mechanisms enables DSS and a shallow chronology of diagnoses.

Effect of Adjusting for Baseline Stroke Severity in the National Inpatient Sample

[Figure: see text].

Preliminary study of patient safety and quality use cases for ICD-11 MMS

OBJECTIVE

This study investigated how well-suited the International Classification of Diseases, 11th Revision, for Mortality and Morbidity Statistics, (ICD-11 MMS) is for 2 morbidity use cases, patient safety and quality, examining the level of detail captured, and evaluating the necessity for the development of a US clinical modification (CM).

MATERIALS AND METHODS

Utilizing the 5 NCVHS-specified perspectives plus the consumer perspective, a framework was created of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) use cases. Analysis yielded candidate source criteria for use in case evaluation. Patient safety and quality were chosen because they are relevant across all perspectives.Granularity differences and content coverage of ICD-11 MMS entities were assessed pre- and post-coordination to determine suitability for the 2 use cases. Pressure ulcers, a common condition across 3 patient safety applications, became the focus for comparing ICD-10-CM codes to ICD-11 MMS codes. For 3 electronic clinical quality measures (eCQMs), the evaluation centered on specified value sets for ischemic stroke, hypertension, and diabetes.

RESULTS

For pressure ulcers, the ICD-11 MMS was found to exceed ICD-10-CM capabilities via post-coordinated extension codes. For the 3 eCQM value sets explored, the ICD-11 MMS fully represented the disease concepts when post-coordinated code clusters were used.

CONCLUSIONS

The examples from the patient safety and quality use cases evaluated in this study are appropriate for ICD-11 MMS. It captures greater detail than ICD-10-CM, and ICD-11 MMS specificity would benefit both use cases. The authors believe this preliminary study indicates the US should invest resources to explore adopting the WHO ICD-11 MMS and tooling and guidelines to implement post-coordination.

Impact of the ICD-9-CM to ICD-10-CM transition on the incidence of severe maternal morbidity among delivery hospitalizations in the United States

BACKGROUND

Surveillance of maternal mortality and severe maternal morbidity is important to identify temporal trends, evaluate the impact of clinical practice changes or interventions, and monitor quality of care. A common source for severe maternal morbidity surveillance is hospital discharge data. On October 1, 2015, all hospitals in the United States transitioned from the International Classification of Diseases, Ninth Revision, Clinical Modification to the International Classification of Diseases, Tenth Revision, Clinical Modification coding for diagnoses and procedures.

OBJECTIVE

This study aimed to evaluate the impact of the transition from the International Classification of Diseases, Ninth Revision, Clinical Modification to the International Classification of Diseases, Tenth Revision, Clinical Modification coding systems on the incidence of severe maternal morbidity in the United States in hospital discharge data.

STUDY DESIGN

Using data from the National Inpatient Sample, obstetrical deliveries between January 1, 2012, and December 31, 2017, were identified using a validated case definition. Severe maternal morbidity was defined using the International Classification of Diseases, Ninth Revision, Clinical Modification (January 1, 2012, to September 30, 2015) and the International Classification of Diseases, Tenth Revision, Clinical Modification (October 1, 2015, to December 31, 2017) codes provided by the Centers for Disease Control and Prevention. An interrupted time series and segmented regression analysis was used to assess the impact of the transition from the International Classification of Diseases, Ninth Revision, Clinical Modification to the International Classification of Diseases, Tenth Revision, Clinical Modification coding on the incidence of severe maternal morbidity per 1000 obstetrical deliveries.

RESULTS

From 22,751,941 deliveries, the incidence of severe maternal morbidity in the International Classification of Diseases, Ninth Revision, Clinical Modification coding era was 19.04 per 1000 obstetrical deliveries and decreased to 17.39 per 1000 obstetrical deliveries in the International Classification of Diseases, Tenth Revision, Clinical Modification coding era (P<.001). The transition to International Classification of Diseases, Tenth Revision, Clinical Modification coding led to an immediate decrease in the incidence of severe maternal morbidity (-2.26 cases of 1000 obstetrical deliveries) (P<.001). When blood products transfusion was removed from the case definition, the magnitude of the decrease in the incidence of SMM was much smaller (-0.60 cases/1000 obstetric deliveries), but still significant (P<.001).

CONCLUSION

After the transition to the International Classification of Diseases, Tenth Revision, Clinical Modification coding for health diagnoses and procedures in the United States, there was an abrupt statistically significant and clinically meaningful decrease in the incidence of severe maternal morbidity in hospital discharge data. Changes in the underlying health of the obstetrical population are unlikely to explain the sudden change in severe maternal morbidity. Although much work has been done to validate the International Classification of Diseases, Ninth Revision, Clinical Modification codes for severe maternal morbidity, it is critical that validation studies be undertaken to validate the International Classification of Diseases, Tenth Revision, Clinical Modification codes for severe maternal morbidity to permit ongoing surveillance, quality improvement, and research activities that rely on hospital discharge data.

Training and experience of coding with the World Health Organization's International Classification of Diseases, Eleventh Revision

BACKGROUND

The new International Classification of Diseases, Eleventh Revision for Mortality and Morbidity Statistics (ICD-11) was developed and released by the World Health Organization (WHO) in June 2018. Because ICD-11 incorporates new codes and features, training materials for coding with ICD-11 are urgently needed prior to its implementation.

OBJECTIVE

This study outlines the development of ICD-11 training materials, training processes and experiences of clinical coders while learning to code using ICD-11.

METHOD

Six certified clinical coders were recruited to code inpatient charts using ICD-11. Training materials were developed with input from experts from the Canadian Institute for Health Information and the WHO, and the clinical coders were trained to use the new classification. Monthly team meetings were conducted to enable discussions on coding issues and to select the correct ICD-11 codes. The training experience was evaluated using qualitative interviews, a questionnaire and a coding quiz.

RESULTS

total of 3011 charts were coded using ICD-11. In general, clinical coders provided positive feedback regarding the training program. The average score for the coding quiz (multiple choice, True/False) was 84%, suggesting that the training program was effective. Feedback from the coders enabled the ICD-11 code content, electronic tooling and terminologies to be updated.

CONCLUSION

This study provides a detailed account of the processes involved with training clinical coders to use ICD-11. Important findings from the interviews were reported at the annual WHO conferences, and these findings helped improve the ICD-11 browser and reference guide.

Comparison of routine blood alcohol tests and ICD-10-AM coding of alcohol involvement for major trauma patients

BACKGROUND

Alcohol use is a key preventable risk factor for serious injury. To effectively prevent alcohol-related injuries, we rely on the accurate surveillance of alcohol involvement in injury events. This often involves the use of administrative data, such as International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) coding.

OBJECTIVE

To evaluate the completeness and accuracy of using administrative coding for the surveillance of alcohol involvement in major trauma injury events by comparing patient blood alcohol concentration (BAC) with ICD-10-AM coding.

METHOD

This retrospective cohort study examined 2918 injury patients aged ≥18 years who presented to a major trauma centre in Victoria, Australia, over a 2-year period, of which 78% (n = 2286) had BAC data available.

RESULTS

While 15% of patients had a non-zero BAC, only 4% had an ICD-10-AM code suggesting acute alcohol involvement. The agreement between blood alcohol test results and ICD-10-AM coding of acute alcohol involvement was fair (κ = 0.33, 95% confidence interval: 0.27-0.38). Of the 341 patients with a non-zero BAC, 82 (24.0%) had ICD-10-AM codes related to acute alcohol involvement. Supplementary factors Y90 Evidence of alcohol involvement determined by blood alcohol level codes, which specifically describe patient BAC, were assigned to just 29% of eligible patients with a non-zero BAC.

CONCLUSION

ICD-10-AM coding underestimated the proportion of alcohol-related injuries compared to patient BAC.

IMPLICATIONS

Given the current role of administrative data in the surveillance of alcohol-related injuries, these findings may have significant implications for the implementation of cost-effective strategies for preventing alcohol-related injuries.

Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

Background

The International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning– and natural language processing–related approaches have been studied to assist disease coders.

Objective

This paper aims at constructing a deep learning model for ICD-10 coding, where the model is meant to automatically determine the corresponding diagnosis and procedure codes based solely on free-text medical notes to improve accuracy and reduce human effort.

Methods

We used diagnosis records of the National Taiwan University Hospital as resources and apply natural language processing techniques, including global vectors, word to vectors, embeddings from language models, bidirectional encoder representations from transformers, and single head attention recurrent neural network, on the deep neural network architecture to implement ICD-10 auto-coding. Besides, we introduced the attention mechanism into the classification model to extract the keywords from diagnoses and visualize the coding reference for training freshmen in ICD-10. Sixty discharge notes were randomly selected to examine the change in the F₁-score and the coding time by coders before and after using our model.

Results

In experiments on the medical data set of National Taiwan University Hospital, our prediction results revealed F₁-scores of 0.715 and 0.618 for the ICD-10 Clinical Modification code and Procedure Coding System code, respectively, with a bidirectional encoder representations from transformers embedding approach in the Gated Recurrent Unit classification model. The well-trained models were applied on the ICD-10 web service for coding and training to ICD-10 users. With this service, coders can code with the F₁-score significantly increased from a median of 0.832 to 0.922 (P<.05), but not in a reduced interval.

Conclusions

The proposed model significantly improved the F₁-score but did not decrease the time consumed in coding by disease coders.

[Old age and the new ICD-11: Position of the Latin American Academy of Medicine for Older PersonsA velhice e a nova CID-11: posição da Academia Latino-americana de Medicina do Idoso]

Desde 1948, la Organización Mundial de la Salud ha venido publicando un sistema codificado de causas de enfermedad y muerte bajo el nombre genérico de Clasificación Estadística Internacional de Enfermedades (CIE), con revisiones en profundidad cada 10-15 años. En su última versión, CIE-11, se utiliza una terminología para caracterizar la vejez como “períodos geriátricos inicial y final”, lo que implica una medicalización de esta etapa de la vida que ha generado confusión y polémica. En este trabajo se discute la nueva terminología propuesta a la luz del conocimiento actual en torno a la vejez y el proceso de envejecimiento, y su definición más aceptada. La CIE no solo clasifica las enfermedades sino también los períodos de la vida y los “problemas relacionados con la salud”, y la vejez por sí sola no representa un problema relacionado con la salud para muchos de quienes se encuentran en esta etapa de la vida. Desde esta perspectiva, es imprescindible cambiar o matizar el epígrafe “vejez” de la CIE-11 para que no se perciba como síntoma, signo o resultado clínico anómalo, e introducir términos que reflejen mucho mejor el estado de envejecimiento patológico. Entre los términos que gozan de un creciente soporte experimental y bibliográfico están “fragilidad” y “pérdida de la capacidad intrínseca”, que aportan mucha mayor precisión a la hora de definir la condición de la persona que no goza de un envejecimiento saludable.

The frequency of severe Fetal Alcohol Spectrum Disorders in the neonatal period using data from the French hospital discharge database between 2006 and 2013

BACKGROUNDS

At birth, only complete Fetal Alcohol Syndrome (FAS) can be properly diagnosed. However, other Consequences of prenatal Alcohol Exposure (CAE) can also be recorded. Our objective was to describe the frequency of diagnoses highly suggestive of "potential Fetal Alcohol Syndrome Disorder" (pFASD, i.e., FAS and CAE) among hospitalized neonates, during the neonatal period, in France, between 2006 and 2013.

METHODS

We used the French national hospital discharge database to identify the Q86.0 (FAS) and P04.3 (CAE) ICD-10 codes in hospital stays occurring in the first 28 days of life. FAS, CAE and pFASD rates were estimated per 1000 live births at the national level for the 2009-2013 period. We compared the 2006-2009 and 2010-2013 rates. The pFASD rates were also estimated at the regional level.

RESULTS

Overall, 3,207 cases of pFASD were diagnosed during the neonatal period (i.e., 0.48 cases per 1000 live births, including 0.07 cases of FAS per 1000). Between 2006-2009 and 2010-2013, pFASD remained stable, despite a moderate decrease in reported FAS (0.08 vs 0.06 cases per 1000, p < 0.001). At the regional level, pFASD rates varied between 0.13 and 1.22 cases per 1000.

CONCLUSIONS

This study provides the first national estimate of neonatal diagnosis of FAS, and more broadly pFASD, in France. Although our data certainly underestimate the real prevalence of FASD, they provide a minimal estimate of the burden of alcohol use during pregnancy. Observed variations deserve to be analyzed in the light of concomitant prevention and public information campaigns.

Conceptual and historical evolution of psychiatric nosology

Psychiatric taxonomies exist within conceptual frameworks which presuppose certain conceptions of psychiatric distress and offer guiding principles. This article provides an overview of the historical development of psychiatric classifications with an emphasis on their methodological assumptions. After identifying roots of scientific psychiatric classifications in the works of Sydenham and Linnaeus and discussing early classification systems, our survey focuses on the Kahlbaum-Hecker-Kraepelin paradigm (with its emphasis on longitudinal course of illness), the Wernicke-Kleist-Leonhard tradition (with its emphasis on neural systems), the development of the ICD and the DSM classifications (with their roots in medical statistics, their pragmatic nature, and their emphasis on descriptive and operationalized criteria), psychodynamic and idiographic perspectives (e.g. the Psychodynamic Diagnostic Manual), and transdiagnostic approaches (e.g. Research Domain Criteria). The central philosophical questions of nosology (descriptive vs aetiological, symptoms vs course of illness, idiographic vs nomothetic, categorical vs dimensional, etc.) have appeared and reappeared throughout this evolution. Ongoing controversies reflect the epistemological and ontological difficulties inherent in defining and classifying mental illness. It may be that no single taxonomy can satisfy all clinical, research, and administrative needs, and that, echoing the ideas of Aubrey Lewis, multiple systems may be required to serve different needs.

Changes from ICD-10 to ICD-11 and future directions in psychiatric classification

This article provides a brief overview of the changes from ICD-10 to ICD-11 regarding the classification of mental, behavioral, or neurodevelopmental disorders. These changes include a new chapter structure, new diagnostic categories, changes in diagnostic criteria, and steps towards dimensionality. Additionally, we review evaluative field studies of ICD-11, which provide preliminary evidence for higher reliability and clinical utility of ICD-11 compared with ICD-10. Despite the extensive revision process, changes from ICD-10 to ICD-11 were relatively modest in that both systems are categorical, classifying mental phenomena based on self-reported or clinically observable symptoms. Other recent approaches to psychiatric nosology and classification (eg, neurobiology-based or hierarchical) are discussed. To meet the needs of different user groups, we propose expanding the stepwise approach to diagnosis introduced for some diagnostic categories in ICD-11, which includes categorical and dimensional elements.


Effect of ICD-9-CM to ICD-10-CM coding system transition on identification of common conditions: An interrupted time series analysis

PURPOSE

To evaluate the effect of diagnostic coding system transition on the identification of common conditions recorded in Taiwan's national claims database.

METHODS

Using the National Health Insurance Research Database, we estimated the 3-month prevalence of recorded diagnosis of 32 conditions based on the ICD-9-CM codes in 2014-2015 and the ICD-10-CM codes in 2016-2017. Two algorithms were assessed for ICD-10-CM: validated ICD-10 codes in the literature and codes translated from ICD-9-CM using an established mapping algorithm. We used segmented regression analysis on time-series data to examine changes in the 3-month prevalence (both level and trend) before and after the ICD-10-CM implementation.

RESULTS

Significant changes in the level were found in 19 and 11 conditions when using the ICD-10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD-10 codes from the literature or mapping algorithm.

CONCLUSIONS

Less than half of the 32 conditions studied had a smooth transition between the ICD-9-CM and ICD-10-CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.

[Algorithms to identify chronic inflammatory rheumatism and psoriasis in medico-administrative databases: A review of the literature]

BACKGROUND

We aimed to describe and discuss the algorithms used to identify chronic inflammatory rheumatisms and psoriasis in medico-administrative databases.

METHODS

We performed a literature review on the Medline database of articles published up to 31 January 2018. Our inclusion criteria were: original articles using medico-administrative databases in accordance with the International Classification of Diseases, version 10 (ICD-10) and concerning rheumatoid arthritis (RA) or ankylosing spondylitis (AS) or psoriatic arthritis (PsoA) or Psoriasis (Pso). Our exclusion criteria were: letters to the editor, commentaries on published articles, studies using codes other than those of the ICD or a previous version.

RESULTS

Out of the 590 articles identified, 37 studies were included. Concerning RA (n=10), all studies used the M05 code, associated with the M06 code in six studies. The remaining four studies specifically targeted codes M06.0, M06.2, M06.3, M06.8, M06.9, and two of them also used code M12.3. For AS (n=8), 7 studies used the M45 code, while only one study used M45.9, M46.1 or M46.8. For Pso (n=17), all studies used the L40 code and/or at least two dispensations of vitamin D. Concerning PsoA (n=13), all studies used the same codes: M07.0, M07.1, M07.2, M07.3.

CONCLUSION

We recommend using codes M05 and M06 rather than M06.1 and M06.4 for RA, M45 for AS, the algorithm L40 and/or two dispensations of topical vitamin D for psoriasis, and codes M070 to M073 to identify PsoA patients in medico-administrative databases.

Rate and characteristics of infection after transrectal prostate biopsy: a retrospective observational study

OBJECTIVES

The aim of this study was to assess the incidence of infection after transrectal prostate biopsy (TRbx). Secondary objectives were to describe infection characteristics, antibiotic resistance patterns, ICD-10 coding, and costs.

METHODS

TRbx carried out at the hospitals of Ängelholm and Helsingborg, Scania, Sweden, between October 2017 and March 2019, were identified based on the NOMESCO Classification of Surgical Procedures code for TRbx, TKE00. All patients received per oral antibiotic prophylaxis, usually 750 mg ciprofloxacin at biopsy. Other preventative measures were not used. Medical care within 30 days of the biopsy was evaluated through a manual retrospective medical chart review. Data on patient and infection characteristics were collected. The costs of infections causing hospitalization were estimated.

RESULTS

After 36 (5.4%) of 670 biopsies, the patient developed post-biopsy infection within 30 days after TRbx. Twenty-six patients (3.9%) required hospitalization for an average of 6 days, at an estimated direct cost of USD 9174 (EUR 8031) per patient. Nine patients (1.3%) had a complicated infection leading to intensive care, multiple hospitalizations or emergency department visits. The inpatient care episodes for the 26 hospitalized patients were categorized with 15 different ICD-codes. In 6 episodes no ICD-code related to infection was used.

CONCLUSIONS

In this study, we found an infection rate of 5.4% after TRbx; 3.9% of the patients were hospitalized for a post-TRbx infection and 1.3% had complicated infections. A specific ICD code for post-TRbx infections would facilitate evaluation and monitoring of this common, costly, and sometimes serious complication.

Ten-Year Trend in Age, Sex, and Racial Disparity in tPA (Alteplase) and Thrombectomy Use Following Stroke in the United States

[Figure: see text].