Measuring cardiopulmonary complications of carfilzomib treatment and associated risk factors using the SEER-Medicare database

BACKGROUND

Carfilzomib improves survival in patients with recurrent myeloma. Given the strict eligibility criteria in clinical trials, the actual frequency of cardiac adverse events (CAEs) and pulmonary adverse events (PAEs) and the risk factors associated with these AEs in the general population need to be established.

METHODS

The authors extracted myeloma cases in the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database from 2000 through 2013 and corresponding claims through 2014. They then identified patients who received carfilzomib during their disease course. Subsequently, the International Classification of Diseases, Ninth Revision (ICD-9) was used to identify all the codes for CAEs, PAEs, and respiratory infections associated with carfilzomib use. Preexisting diagnoses corresponding to the CAEs and PAEs of interest were excluded to distinguish toxicity from comorbidity. Multivariate Cox regression was performed to determine those variables independently associated with the development of CAEs and PAEs.

RESULTS

Of the 635 patients analyzed, the median age was 72 years (range, 36-94 years); 55% of the patients were male and 79% were white. The median duration of carfilzomib treatment was 58 days (range, 1-716 days). Overall, approximately 66% of the patients had codes for either CAEs or PAEs. In terms of CAEs, approximately 22% of patients developed hypertension, 15% developed peripheral edema, and 14% experienced heart failure. With regard to PAEs, approximately 28% of patients developed dyspnea, 15% developed cough, and 15% developed pneumonia. Only chronic obstructive pulmonary disease (COPD) was found to be independently associated with the development of CAEs. Patients with preexisting COPD were found to have a 40% increase in their hazard of developing CAEs (adjusted hazard ratio, 1.40; 95% CI, 1.03-1.90).

CONCLUSIONS

In older adults with myeloma who are undergoing treatment with carfilzomib, new cardiac and pulmonary diagnoses were common. Patients with preexisting COPD were found to be at an increased risk of developing CAEs.

Mental, behavioral and neurodevelopmental disorders in the ICD-11: an international perspective on key changes and controversies

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.

Electronic Health Record Phenotypes for Identifying Patients with Late-Stage Disease: a Method for Research and Clinical Application

BACKGROUND

Systematic identification of patients allows researchers and clinicians to test new models of care delivery. EHR phenotypes-structured algorithms based on clinical indicators from EHRs-can aid in such identification.

OBJECTIVE

To develop EHR phenotypes to identify decedents with stage 4 solid-tumor cancer or stage 4-5 chronic kidney disease (CKD).

DESIGN

We developed two EHR phenotypes. Each phenotype included International Classification of Diseases (ICD)-9 and ICD-10 codes. We used natural language processing (NLP) to further specify stage 4 cancer, and lab values for CKD.

SUBJECTS

Decedents with cancer or CKD who had been admitted to an academic medical center in the last 6 months of life and died August 26, 2017-December 31, 2017.

MAIN MEASURE

We calculated positive predictive values (PPV), false discovery rates (FDR), false negative rates (FNR), and sensitivity. Phenotypes were validated by a comparison with manual chart review. We also compared the EHR phenotype results to those admitted to the oncology and nephrology inpatient services.

KEY RESULTS

The EHR phenotypes identified 271 decedents with cancer, of whom 186 had stage 4 disease; of 192 decedents with CKD, 89 had stage 4-5 disease. The EHR phenotype for stage 4 cancer had a PPV of 68.6%, FDR of 31.4%, FNR of 0.5%, and 99.5% sensitivity. The EHR phenotype for stage 4-5 CKD had a PPV of 46.4%, FDR of 53.7%, FNR of 0.0%, and 100% sensitivity.

CONCLUSIONS

EHR phenotypes efficiently identified patients who died with late-stage cancer or CKD. Future EHR phenotypes can prioritize specificity over sensitivity, and incorporate stratification of high- and low-palliative care need. EHR phenotypes are a promising method for identifying patients for research and clinical purposes, including equitable distribution of specialty palliative care.

IMP-ICDX: an injury mortality prediction based on ICD-10-CM codes

Background

The International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) Injury Severity Score (ICISS) is a risk adjustment model when injuries are recorded using ICD-9-CM coding. The trauma mortality prediction model (TMPM-ICD9) provides better calibration and discrimination compared with ICISS and injury severity score (ISS). Though TMPM-ICD9 is statistically rigorous, it is not precise enough mathematically and has the tendency to overestimate injury severity. The purpose of this study is to develop a new ICD-10-CM injury model which estimates injury severities for every injury in the ICD-10-CM lexicon by a combination of rigorous statistical probit models and mathematical properties and improves the prediction accuracy.

Methods

We developed an injury mortality prediction (IMP-ICDX) using data of 794,098 patients admitted to 738 hospitals in the National Trauma Data Bank from 2015 to 2016. Empiric measures of severity for each of the trauma ICD-10-CM codes were estimated using a weighted median death probability (WMDP) measurement and then used as the basis for IMP-ICDX. ISS (version 2005) and the single worst injury (SWI) model were re-estimated. The performance of each of these models was compared by using the area under the receiver operating characteristic (AUC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion statistic.

Results

IMP-ICDX exhibits significantly better discrimination (AUCIMP-ICDX, 0.893, and 95% confidence interval (CI), 0.887 to 0.898; AUCISS, 0.853, and 95% CI, 0.846 to 0.860; and AUCSWI, 0.886, and 95% CI, 0.881 to 0.892) and calibration (HLIMP-ICDX, 68, and 95% CI, 36 to 98; HLISS, 252, and 95% CI, 191 to 310; and HLSWI, 92, and 95% CI, 53 to 128) compared with ISS and SWI. All models were improved after the extension of age, gender, and injury mechanism, but the augmented IMP-ICDX still dominated ISS and SWI by every performance.

Conclusions

The IMP-ICDX has a better discrimination and calibration compared to ISS. Therefore, we believe that IMP-ICDX could be a new viable trauma research assessment method.

Standardization of State Definitions for Neonatal Abstinence Syndrome Surveillance and the Opioid Crisis

Rates of neonatal abstinence syndrome (NAS) have increased fivefold in the past decade. To address this expanding and complex issue, state public health agencies have addressed the opioid crisis affecting newborns in diverse ways, leading to a variety of methods to quantify the burden of NAS.In an effort to understand this variability, we summarized clinical case and surveillance definitions used across jurisdictions in the United States. We confirmed that the rapid progression of the nation's opioid crisis resulted in heterogeneous processes for identifying NAS. Current clinical case definitions use different combinations of clinician-observed signs of withdrawal and evidence of perinatal substance exposure. Similarly, there is discordance in diagnosis codes used in surveillance definitions. This variability makes it difficult to produce comparable estimates across jurisdictions, which are needed to effectively guide public health strategies and interventions.Although standardization is complicated, consistent NAS definitions would increase comparability of NAS estimates across the nation and would better guide prevention and treatment efforts for women and their infants.

An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes

BACKGROUND AND OBJECTIVE

Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective, and it requires medical coders with extensive training. This study aims to evaluate the performance of deep-learning-based systems to automatically map clinical notes to ICD-9 medical codes.

METHODS

The evaluations of this research are focused on end-to-end learning methods without manually defined rules. Traditional machine learning algorithms, as well as state-of-the-art deep learning methods such as Recurrent Neural Networks and Convolution Neural Networks, were applied to the Medical Information Mart for Intensive Care (MIMIC-III) dataset. An extensive number of experiments was applied to different settings of the tested algorithm.

RESULTS

Findings showed that the deep learning-based methods outperformed other conventional machine learning methods. From our assessment, the best models could predict the top 10 ICD-9 codes with 0.6957 F₁ and 0.8967 accuracy and could estimate the top 10 ICD-9 categories with 0.7233 F₁ and 0.8588 accuracy. Our implementation also outperformed existing work under certain evaluation metrics.

CONCLUSION

A set of standard metrics was utilized in assessing the performance of ICD-9 code assignment on MIMIC-III dataset. All the developed evaluation tools and resources are available online, which can be used as a baseline for further research.

Identifying mental health and substance use disorders using emergency department and hospital records: a population-based retrospective cohort study of diagnostic concordance and disease attribution

OBJECTIVES

Administrative data are increasingly being used for surveillance and monitoring of mental health and substance use disorders (MHSUD) across Canada. However, the validity of the diagnostic codes specific to MHSUD is unknown in emergency departments (EDs). Our objective was to determine the concordance, and individual-level and hospital-level factors associated with concordance, between diagnosis codes assigned in ED and at discharge from hospital for MHSUD-related conditions.

DESIGN

Population-based retrospective cohort study.

SETTING

EDs and hospitals within Vancouver Coastal Health Authority (VCH), British Columbia, Canada.

PARTICIPANTS

16 926 individuals who were admitted into a VCH hospital following an ED visit from 1 April 2009 to 31 March 2017, contributing to 48 116 pairs of ED and hospital discharge diagnoses.

PRIMARY AND SECONDARY OUTCOME MEASURES

We examined concordance in identifying MHSUD between the primary discharge diagnosis codes based on the International Statistical Classification of Diseases, 9th and 10th Revisions (Canada) assigned in the ED and those assigned in the hospital among all ED visits resulting in a hospital admission. We calculated the percent overall agreement, positive agreement, negative agreement and Cohen's kappa coefficient. We performed multiple regression analyses to identify factors independently associated with discordance.

RESULTS

We found a high level of concordance for broad categories of MH conditions (overall agreement=0.89, positive agreement=0.74 and kappa=0.67), and a fair level of concordance for SUDs (overall agreement=0.89, positive agreement=0.31 and kappa=0.27). SUDs were less likely to be indicated as the primary cause in ED as opposed to in hospital (3.8% vs 11.7%). In multiple regression analyses, ED visits occurring during holidays, weekends and overnight (21:00-8:59 hours) were associated with increased odds of discordance in identifying MH conditions (adjusted OR 1.47, 95% CI 1.11 to 1.93; 1.27, 95% CI 1.16 to 1.40; 1.30, 95% CI 1.19 to 1.42, respectively).

CONCLUSIONS

ED data could be used to improve surveillance and monitoring of MHSUD. Future efforts are needed to improve screening for individuals with MHSUD and subsequently connect them to treatment and follow-up care.

An Updated International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) Surveillance Case Definition for Injury Hospitalizations

The National Center for Health Statistics (NCHS) and National Center for Injury Prevention and Control (NCIPC) have routinely collaborated with injury epidemiology partners to develop standard injury surveillance case definitions based on the International Classification of Diseases (ICD). With the transition in October 2015 to the use of the ICD, 10th Revision, Clinical Modification (ICD-10-CM) for reporting medical information in administrative claims data, NCHS and NCIPC proposed an ICD-10-CM surveillance case definition for injury hospitalizations. At the time, ICD-10-CM coded data were not readily available, and the proposed surveillance definition could not be tested using real data. As ICD-10-CM coded data became available, NCHS and NCIPC collaborated with the Council of State and Territorial Epidemiologists, injury epidemiologists from state and local health departments, and the Agency for Healthcare Research and Quality to test the proposed definition. This report summarizes the findings from the testing process and describes how the findings were used to update the proposed case definition. In the updated ICD-10-CM surveillance case definition, injury hospitalizations are identified as hospitalization records with a principal diagnosis of select ICD-10-CM S, T, O, and M codes. The codes must indicate an initial encounter for active treatment of an injury or be missing encounter type information. The selection criteria exclude hospitalization records with an injury as a secondary or subsequent diagnosis (not the principal diagnosis) or that have an external cause-of-injury code but do not have an injury code as the principal diagnosis. The updated ICD-10-CM surveillance case definition for injury hospitalizations provides standardized selection criteria for monitoring differences in hospitalization rates among populations and over time.

Towards controlled terminology for reporting germline cancer susceptibility variants: an ENIGMA report

The vocabulary currently used to describe genetic variants and their consequences reflects many years of studying and discovering monogenic disease with high penetrance. With the recent rapid expansion of genetic testing brought about by wide availability of high-throughput massively parallel sequencing platforms, accurate variant interpretation has become a major issue. The vocabulary used to describe single genetic variants in silico, in vitro, in vivo and as a contributor to human disease uses terms in common, but the meaning is not necessarily shared across all these contexts. In the setting of cancer genetic tests, the added dimension of using data from genetic sequencing of tumour DNA to direct treatment is an additional source of confusion to those who are not experienced in cancer genetics. The language used to describe variants identified in cancer susceptibility genetic testing typically still reflects an outdated paradigm of Mendelian inheritance with dichotomous outcomes. Cancer is a common disease with complex genetic architecture; an improved lexicon is required to better communicate among scientists, clinicians and patients, the risks and implications of genetic variants detected. This review arises from a recognition of, and discussion about, inconsistencies in vocabulary usage by members of the ENIGMA international multidisciplinary consortium focused on variant classification in breast-ovarian cancer susceptibility genes. It sets out the vocabulary commonly used in genetic variant interpretation and reporting, and suggests a framework for a common vocabulary that may facilitate understanding and clarity in clinical reporting of germline genetic tests for cancer susceptibility.

Evaluating the assessment of the ICD-11 personality disorder diagnostic system

Proposed for the ICD-11 is a dimensional model of personality disorder that, if approved, would be a paradigm shift in the conceptualization of personality disorder. The proposal consists of a general severity rating, 5 maladaptive personality trait domains, and a borderline pattern qualifier. The general severity rating can be assessed by the Standardized Assessment of Severity of Personality Disorder (SASPD), the trait domains by the Personality Inventory for ICD-11 (PiCD), and the borderline pattern by the Borderline Pattern Scale (BPS), which is developed in the present study. To date, no study has examined the relations among all 3 components, due in part to the absence of direct measures for each component (until recently). The current study develops and provides initial validation evidence for the BPS, and examines the relations among the BPS, SASPD, and PiCD. Also considered is their relationship with the 5-factor model of general personality as well as with 2 other measures of personality disorder severity (including the DSM-5 Level of Personality Functioning Scale [LPFS]). Further, an alternative trait-based coding of the DSM-5 LPFS is examined (modeled after the ICD-11 SASPD), suggesting that its coverage of diverse maladaptivity may not be because it assesses the core of personality disorder, but rather because it has items specific to the different domains of personality. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Developing behavioural indicators for intellectual functioning and adaptive behaviour for ICD-11 disorders of intellectual development

BACKGROUND

We present the work conducted to arrive at deriving behavioural indicators that could be used to guide clinical judgement in determining the presence and severity of deficits in intellectual functioning and adaptive behaviour for the purpose of making a diagnosis of disorders of intellectual development.

METHODS

An interdisciplinary expert panel provided guidance in developing behavioural indicators for intellectual functioning. A national dataset of adaptive behaviour on a sample of individuals with a diagnosis of intellectual disability was used to develop the behavioural indicators for the adaptive behaviour. The adaptive behaviour data were analysed using a cluster analysis procedure to define the different severity groupings by chronological age groups.

RESULTS

We present a series of tables containing behavioural indicators across the lifespan for intellectual functioning and adaptive behaviour, including conceptual, social and practical skills. These tables of behavioural indicators have been proposed for use in the clinical version of the 11th revision of the International Classification of Diseases and Related Health Problems (ICD-11) to be published by the World Health Organization.

CONCLUSIONS

The proposed behavioural indicators for disorders of ID described in the present article and to be included in the ICD-11 Clinical Descriptions and Diagnostic Guidelines are put forth to assist professionals in making an informed clinical decision regarding an individual's level of intellectual functioning and adaptive behaviour for the purpose of making a determination about the presence and severity of disorders of ID.

Challenges of Using ICD-9-CM and ICD-10-CM Codes for Soft-Tissue Sarcoma in Databases for Health Services Research

OBJECTIVES

Soft-tissue sarcoma (STS) is a heterogeneous group of rare solid tumors that arise from various soft tissues in the body, such as muscle, fat, nerves, and blood vessels. Current International Classification of Diseases (ICD) coding systems include a set of nonspecific codes for malignancies of connective and soft tissue (ICD-9-CM code 171 and ICD-10-CM code C49). The goal of this study was to evaluate the use of these codes for health services research involving patients with a diagnosis of this rare malignancy.

METHODS

Two databases were utilized to explore ICD coding for STS: claims data from Truven MarketScan and electronic medical records (EMRs) from Flatiron Health. Eligible patients from claims data were those with at least two ICD-9-CM codes of 171.x on two different days between July 1, 2004, and March 30, 2014. The treatment patterns of these cases were evaluated for consistency with known therapeutic approaches for STS. Eligible patients from the Flatiron EMR system were those who received olaratumab (a drug indicated only for use in patients diagnosed with STS) after its US Food and Drug Administration approval in October 2016 through the end of the data set (November 2017). ICD-10-CM codes were evaluated for this known STS cohort.

RESULTS

In claims data, 4,159 patients were eligible for inclusion. Although national treatment guidelines include only a limited number of drugs used to treat STS, 98 unique anticancer drugs were identified as being used to treat patients in a claims data cohort. Only 7.7 percent of patients had claims for doxorubicin-based therapy and 3.8 percent had claims for ifosfamide-based therapy as initial treatment for STS, despite these being a standard of care. In the EMR data, 350 patients were eligible; only 170 patients (48.6 percent) had any evidence in the database of a connective or soft-tissue ICD-10-CM malignancy code within 60 days before or after initiation of olaratumab.

CONCLUSIONS

ICD coding for STS using the "Malignant neoplasm of connective and soft tissue" code is not reliable as a method to identify patients diagnosed with STS. Although codes reflecting the primary site of disease may have clinical relevance, lack of consistency in ICD coding for the diagnosis and treatment of this disease is a limiting factor in the ability to conduct real-world observational research of this rare disease. In the absence of consistent use of this code, an algorithm needs to be developed and validated to accurately identify patients with STS in these databases.

Analyzing the ICD-10-CM Transition and Post-implementation Stages: A Public Health Institution Case Study

On October 1, 2015, the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) was incorporated into the US public health system. Because of significant opposition and reservations expressed by stakeholders, while the proposed rule for ICD-10-CM adoption was issued in 2009, the transition did not occur until October 2015. The purpose of this study was to identify conversion initiatives used by a public health institution during the initial and subsequent stages of ICD-10-CM implementation, to help similar institutions address future unfunded healthcare data infrastructure mandates. The data collection for this study occurred from 2015 to 2018, encompassing 20 semistructured interviews with 13 department heads, managers, physicians, and coders. Research findings from this study identified several trends, disruptions, challenges, and lessons learned that might support the industry with strategies to foster success for the transition to future coding revisions (i.e., ICD-11).

The Standardization of Diagnostic Criteria for Fetal Alcohol Spectrum Disorder (FASD): Implications for Research, Clinical Practice and Population Health

OBJECTIVE

Fetal Alcohol Spectrum Disorder (FASD) is a preventable disorder caused by maternal alcohol consumption and marked by a range of physical and mental disabilities. Although recognized by the scientific and medical community as a clinical disorder, no internationally standardized diagnostic tool yet exists for FASD.

METHODS AND RESULTS

This review seeks to analyse the discrepancies in existing diagnostic tools for FASD, and the repercussions these differences have on research, public health, and government policy.

CONCLUSIONS

Disagreement on the adoption of a standardised tool is reflective of existing gaps in research on the conditions and factors that influence fetal vulnerability to damage from exposure. This discordance has led to variability in research findings, inconsistencies in government messaging, and misdiagnoses or missed diagnoses. The objective measurement of the timing and level of prenatal alcohol exposure is key to bridging these gaps; however, there is conflicting or limited evidence to support the use of existing measures.

[An online dynamic knowledge base in multiple languages on general medicine and primary care]

INTRODUCTION

The International Classification of Primary Care, Second version (ICPC-2) aligned with the 10th Revision of the International Classification of Disease (ICD-10) is a standard for primary care epidemiology compendium. ICPC-2 has been also intended to identify the clinical topics in family medicine. Contextual field-specific knowledge in family medicine and primary care such as health structures, management, categories of patients, research methods, ethical or environmental features are not standardized and reflect, more often, the views of experts.

METHODS

A qualitative research method, applied to the analysis of several Family Medicine congresses, has helped identify, in addition to clinical items, a spectrum of contextual concepts addressed by family doctors during their exchanges at the congresses. Assembled in a hierarchical manner, these concepts were given expression, together with ICPC-2, under the name of Q-codes Version 2.5, in the multilingual multi-terminology semantic server of the Department of Information and medical informatics (D2Im) at the University of Rouen, France. The two classifications are edited under the acronym 3 CGP for Core Content classification of General Practice. This free access server allows you to consult the ICPC-2 in 22 languages and the Q-codes in ten languages.

RESULTS

The result of the joint use of these two classifications, as descriptors in congress to identify the concepts in texts or index the gray literature for family medicine and primary care is presented here in its various pilot uses. The validity and generalizability of 3CGP appears to be good in the light of the translations already carried out by colleagues around the world and of the applicability of the method in the two sides of the Atlantic. However the reproducibility and the inter-coder variations still remain to be tested for Q-codes. Maintenance remains an issue.

CONCLUSION

This method highlights the conceptual extension, the complexity and the dynamics of the role of general practitioner and family doctor as well as of primary care physician.

The rendering of human phenotype and rare diseases in ICD-11

ICD-11 (International Classification of Diseases, 11th Revision) is the next major revision of the ICD by the World Health Organization (WHO). ICD-11 differs dramatically from historical versions, as it is based on an underlying semantic network of terms and meaning, called the Foundation. To function as a mutually exclusive and exhaustive statistical classification, ICD-11 creates derivative linearizations from the network that is a monohierarchy with residual categories such as Not Elsewhere Classified. ICD-11 also introduces the widespread post-coordination of terms, which allows for highly expressive representation of detailed patient descriptions. Phenotyping features are included in many subchapters or the signs and symptoms chapter. Composite phenotype descriptions of specific presentations or syndromes can be represented though post-coordination. Rare diseases are well represented in the Foundation, though not all appear in the relatively shallow linearization hierarchies.

Assessing the accuracy of using diagnostic codes from administrative data to infer antidepressant treatment indications: a validation study

PURPOSE

To assess the accuracy of using diagnostic codes from administrative data to infer treatment indications for antidepressants prescribed in primary care.

METHODS

Validation study of administrative diagnostic codes for 13 plausible indications for antidepressants compared with physician-documented treatment indications from an indication-based electronic prescribing system in Quebec, Canada. The analysis included all antidepressant prescriptions written by primary care physicians between January 1, 2003 and December 31, 2012 using the electronic prescribing system. Patient prescribed antidepressants were linked to physician claims and hospitalization data to obtain all diagnoses recorded in the past year.

RESULTS

Diagnostic codes had poor sensitivity for all treatment indications, ranging from a high of only 31.2% (95% CI, 26.8%-35.9%) for anxiety/stress disorders to as low as 1.3% (95% CI, 0.0%-5.2%) for sexual dysfunction. Sensitivity was notably worse among older patients and patients with more chronic comorbidities. Physician claims data were a better source of diagnostic codes for antidepressant treatment indications than hospitalization data.

CONCLUSIONS

Administrative diagnostic codes are poor proxies for antidepressant treatment indications. Future work should determine whether the use of other variables in administrative data besides diagnostic codes can improve the ability to predict antidepressant treatment indications.

Sensitivity of Claims-Based Algorithms to Ascertain Smoking Status More Than Doubled with Meaningful Use

BACKGROUND

The "meaningful use of certified electronic health record" policy requires eligible professionals to record smoking status for more than 50% of all individuals aged 13 years or older in 2011 to 2012.

OBJECTIVES

To explore whether the coding to document smoking behavior has increased over time and to assess the accuracy of smoking-related diagnosis and procedure codes in identifying previous and current smokers.

METHODS

We conducted an observational study with 5,423,880 enrollees from the year 2009 to 2014 in the Truven Health Analytics database. Temporal trends of smoking coding, sensitivity, specificity, positive predictive value, and negative predictive value were measured.

RESULTS

The rate of coding of smoking behavior improved significantly by the end of the study period. The proportion of patients in the claims data recorded as current smokers increased 2.3-fold and the proportion of patients recorded as previous smokers increased 4-fold during the 6-year period. The sensitivity of each International Classification of Diseases, Ninth Revision, Clinical Modification code was generally less than 10%. The diagnosis code of tobacco use disorder (305.1X) was the most sensitive code (9.3%) for identifying smokers. The specificities of these codes and the Current Procedural Terminology codes were all more than 98%.

CONCLUSIONS

A large improvement in the coding of current and previous smoking behavior has occurred since the inception of the meaningful use policy. Nevertheless, the use of diagnosis and procedure codes to identify smoking behavior in administrative data is still unreliable. This suggests that quality improvements toward medical coding on smoking behavior are needed to enhance the capability of claims data for smoking-related outcomes research.

Chart validation of inpatient International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) administrative diagnosis codes for venous thromboembolism (VTE) among intravenous immune globulin (IGIV) users in the Sentinel Distributed Database

The Sentinel Distributed Database (SDD) is a database of patient administrative healthcare records, derived from insurance claims and electronic health records, sponsored by the US Food and Drug Administration for evaluation of medical product outcomes. There is limited information on the validity of diagnosis codes for acute venous thromboembolism (VTE) in the SDD and administrative healthcare data more generally.In this chart validation study, we report on the positive predictive value (PPV) of inpatient administrative diagnosis codes for acute VTE-pulmonary embolism (PE) or lower-extremity or site-unspecified deep vein thrombosis (DVT)-within the SDD. As part of an assessment of thromboembolic adverse event risk following treatment with intravenous immune globulin (IGIV), charts were obtained for 75 potential VTE cases, abstracted, and physician-adjudicated.VTE status was determined for 62 potential cases. PPVs for lower-extremity DVT and/or PE were 90% (95% CI: 73-98%) for principal-position diagnoses, 80% (95% CI: 28-99%) for secondary diagnoses, and 26% (95% CI: 11-46%) for position-unspecified diagnoses (originating from physician claims associated with an inpatient stay). Average symptom onset was 1.5 days prior to hospital admission (range: 19 days prior to 4 days after admission).PPVs for principal and secondary VTE discharge diagnoses were similar to prior study estimates. Position-unspecified diagnoses were less likely to represent true acute VTE cases.

Validation of intellectual disability coding through hospital morbidity records using an intellectual disability population-based database in Western Australia

OBJECTIVES

To investigate how well intellectual disability (ID) can be ascertained using hospital morbidity data compared with a population-based data source.

DESIGN, SETTING AND PARTICIPANTS

All children born in 1983-2010 with a hospital admission in the Western Australian Hospital Morbidity Data System (HMDS) were linked with the Western Australian Intellectual Disability Exploring Answers (IDEA) database. The International Classification of Diseases hospital codes consistent with ID were also identified.

MAIN OUTCOME MEASURES

The characteristics of those children identified with ID through either or both sources were investigated.

RESULTS

Of the 488 905 individuals in the study, 10 218 (2.1%) were identified with ID in either IDEA or HMDS with 1435 (14.0%) individuals identified in both databases, 8305 (81.3%) unique to the IDEA database and 478 (4.7%) unique to the HMDS dataset only. Of those unique to the HMDS dataset, about a quarter (n=124) had died before 1 year of age and most of these (75%) before 1 month. Children with ID who were also coded as such in the HMDS data were more likely to be aged under 1 year, female, non-Aboriginal and have a severe level of ID, compared with those not coded in the HMDS data. The sensitivity of using HMDS to identify ID was 14.7%, whereas the specificity was much higher at 99.9%.

CONCLUSION

Hospital morbidity data are not a reliable source for identifying ID within a population, and epidemiological researchers need to take these findings into account in their study design.

A Comprehensive Approach to Convert a Radiology Department From Coding Based on International Classification of Diseases, Ninth Revision, to Coding Based on International Classification of Diseases, Tenth Revision

PURPOSE

The purpose of this study was to adapt our radiology reports to provide the documentation required for specific International Classification of Diseases, tenth rev (ICD-10) diagnosis coding.

MATERIALS AND METHODS

Baseline data were analyzed to identify the reports with the greatest number of unspecified ICD-10 codes assigned by computer-assisted coding software. A two-part quality improvement initiative was subsequently implemented. The first component involved improving clinical histories by utilizing technologists to obtain information directly from the patients or caregivers, which was then imported into the radiologist's report within the speech recognition software. The second component involved standardization of report terminology and creation of four different structured report templates to determine which yielded the fewest reports with an unspecified ICD-10 code assigned by an automated coding engine.

RESULTS

In all, 12,077 reports were included in the baseline analysis. Of these, 5,151 (43%) had an unspecified ICD-10 code. The majority of deficient reports were for radiographs (n = 3,197; 62%). Inadequacies included insufficient clinical history provided and lack of detailed fracture descriptions. Therefore, the focus was standardizing terminology and testing different structured reports for radiographs obtained for fractures. At baseline, 58% of radiography reports contained a complete clinical history with improvement to >95% 8 months later. The total number of reports that contained an unspecified ICD-10 code improved from 43% at baseline to 27% at completion of this study (P < .0001).

CONCLUSION

The number of radiology studies with a specific ICD-10 code can be improved through quality improvement methodology, specifically through the use of technologist-acquired clinical histories and structured reporting.

Evaluating the Impact of Incorrect Diabetes Coding on the Performance of Multivariable Prediction Models

The use of electronic health records for risk prediction models requires a sufficient quality of input data to ensure patient safety. The aim of our study was to evaluate the influence of incorrect administrative diabetes coding on the performance of a risk prediction model for delirium, as diabetes is known to be one of the most relevant variables for delirium prediction. We used four data sets varying in their correctness and completeness of diabetes coding as input for different machine learning algorithms. Although there was a higher prevalence of diabetes in delirium patients, the model performance parameters did not vary between the data sets. Hence, there was no significant impact of incorrect diabetes coding on the performance for our model predicting delirium.