[ICD-11: New revision and impact of this classification in psychiatry]

OBJECTIVES

After more than 20 years of work, the World Health Organization's efforts have culminated in the adoption of the 11th revision of the International Classification of Diseases (ICD-11). The process has been guided by the principles of global applicability, scientific validity, and clinical utility. The update of the chapter on mental, behavioral, and neurodevelopmental disorders (MBND) within the ICD-11 has generated widespread interest worldwide. This has raised various questions about the status of mental disorder nosology, changes to diagnostic guidelines, and the potential implications for clinical practice.

METHODS

We conducted a narrative analysis of the literature in four different languages to track the various stages of the ICD-11 revision and to highlight the major changes. We searched databases such as PubMed, EMBASE, MEDLINE, and Google Scholar, and consulted the official websites of the WHO, APA, and UNICEF. A total of 79 articles from 40 different editorials and websites were analyzed and included in this study.

RESULTS

The new chapters on mental disorders in the ICD-11 include 21 groups, as opposed to the 11 in the ICD-10. The changes aim to align the diagnoses with those of the DSM-5 and introduce a new chapter structure, new diagnostic categories, modifications to diagnostic criteria, and advancements in dimensionality. For the first time in the history of the ICD, sleep and wakefulness disorders, as well as disorders related to sexual health, have been addressed in separate chapters of the international classification. Four new diagnoses have been added: complex post-traumatic stress disorder (PTSD), gaming disorder, prolonged grief disorder, and compulsive sexual behavior which replaces "excessive sexual activity" in the ICD-10. Moreover, the ICD-11 revision has brought about a fundamental change in the clinical conceptualization of addictive behaviors, introducing a distinction between substance use disorders and addictive behaviors. The criteria for many existing conditions have been revised, particularly those related to bipolar disorders, eating disorders, and gender identity disorders.

CONCLUSIONS

The revision process for mental, behavioral, and neurodevelopmental disorders in the ICD-11 has witnessed unprecedented participation in the history of mental disorder classification. These changes could have a significant impact on clinical practice in psychiatry. However, it is crucial to examine the advantages and limitations of this new classification compared to previous versions.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study

BACKGROUND

Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear.

OBJECTIVE

This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions.

METHODS

Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic.

RESULTS

Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems.

CONCLUSIONS

This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Short report: Transition to International Classification of Diseases, 10th Revision and the prevalence of autism in a cohort of healthcare systems

LAY ABSTRACT

Currently, the prevalence of autism spectrum disorder (henceforth "autism") is 1 in 36, an increasing trend from previous estimates. In 2015, the United States adopted a new version (International Classification of Diseases, 10th Revision) of the World Health Organization coding system, a standard for classifying medical conditions. Our goal was to examine how the transition to this new coding system impacted autism diagnoses in 10 healthcare systems. We obtained information from electronic medical records and insurance claims data from July 2014 through December 2016 for each healthcare system. We used member enrollment data for 30 consecutive months to observe changes 15 months before and after adoption of the new coding system. Overall, the rates of autism per 1000 enrolled members was increasing for 0- to 5-year-olds before transition to International Classification of Diseases, 10th Revision and did not substantively change after the new coding was in place. There was variation observed in autism diagnoses before and after transition to International Classification of Diseases, 10th Revision for other age groups. The change to the new coding system did not meaningfully affect autism rates at the participating healthcare systems. The increase observed among 0- to 5-year-olds is likely indicative of an ongoing trend related to increases in screening for autism rather than a shift associated with the new coding.

National Institutes of Health Stroke Scale Reporting in Medicare Claims Data: Reporting in the First 3 Years

BACKGROUND

Since 2016, hospitals have been able to document International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for the National Institutes of Health Stroke Scale (NIHSS). As of 2023, the Centers for Medicare & Medicaid Services uses NIHSS as a risk adjustment variable. We assessed associations between patient- and hospital-level variables and contemporary NIHSS reporting.

METHODS

We performed a retrospective cross-sectional analysis of 2019 acute ischemic stroke admissions using deidentified, national 100% inpatient Medicare Fee-For-Service data sets. We identified index acute ischemic stroke admissions using the ICD-10-CM code I63.x and abstracted demographic information, medical comorbidities, hospital characteristics, and NIHSS. We linked Medicare and Mount Sinai Health System (New York, NY) registry data from 2016 to 2019. We calculated NIHSS documentation at the patient and hospital levels, predictors of documentation, change over time, and concordance with local data.

RESULTS

There were 231 383 index acute ischemic stroke admissions in 2019. NIHSS was documented in 44.4% of admissions and by 66.5% of hospitals. Hospitals that documented ≥1 NIHSS were more commonly teaching hospitals (39.0% versus 5.5%; standardized mean difference score, 0.88), stroke certified (37.2% versus 8.0%; standardized mean difference score, 0.75), higher volume (mean, 80.8 [SD, 92.6] versus 6.33 [SD, 14.1]; standardized mean difference score, 1.12), and had intensive care unit availability (84.9% versus 23.2%; standardized mean difference score, 1.57). Adjusted odds of documentation were lower for patients with inpatient mortality (odds ratio, 0.64 [95% CI, 0.61-0.68]; P<0.0001), in nonmetropolitan areas (odds ratio, 0.49 [95% CI, 0.40-0.61]; P<0.0001), and male sex (odds ratio, 0.95 [95% CI, 0.93-0.97]; P<0.0001). NIHSS was documented for 52.9% of Medicare cases versus 93.1% of registry cases, and 74.7% of Medicare NIHSS scores equaled registry admission NIHSS.

CONCLUSIONS

Missing ICD-10-CM NIHSS data remain widespread 3 years after the introduction of the ICD-10-CM NIHSS code, and there are systematic differences in reporting at the patient and hospital levels. These findings support continued assessment of NIHSS reporting and caution in its application to risk adjustment models.

Validity of ICD-10 diagnosis codes for placenta accreta spectrum disorders

BACKGROUND

The 10th revision of the International Classification of Diseases, Clinical Modification (ICD-10) includes diagnosis codes for placenta accreta spectrum for the first time. These codes could enable valuable research and surveillance of placenta accreta spectrum, a life-threatening pregnancy complication that is increasing in incidence.

OBJECTIVE

We sought to evaluate the validity of placenta accreta spectrum diagnosis codes that were introduced in ICD-10 and assess contributing factors to incorrect code assignments.

METHODS

We calculated sensitivity, specificity, positive predictive value and negative predictive value of the ICD-10 placenta accreta spectrum code assignments after reviewing medical records from October 2015 to March 2020 at a quaternary obstetric centre. Histopathologic diagnosis was considered the gold standard.

RESULTS

Among 22,345 patients, 104 (0.46%) had an ICD-10 code for placenta accreta spectrum and 51 (0.23%) had a histopathologic diagnosis. ICD-10 codes had a sensitivity of 0.71 (95% CI 0.56, 0.83), specificity of 0.98 (95% CI 0.93, 1.00), positive predictive value of 0.61 (95% CI 0.48, 0.72) and negative predictive value of 1.00 (95% CI 0.96, 1.00). The sensitivities of the ICD-10 codes for placenta accreta spectrum subtypes- accreta, increta and percreta-were 0.55 (95% CI 0.31, 0.78), 0.33 (95% CI 0.12, 0.62) and 0.56 (95% CI 0.31, 0.78), respectively. Cases with incorrect code assignment were less morbid than cases with correct code assignment, with a lower incidence of hysterectomy at delivery (17% vs 100%), blood transfusion (26% vs 75%) and admission to the intensive care unit (0% vs 53%). Primary reasons for code misassignment included code assigned to cases of occult placenta accreta (35%) or to cases with clinical evidence of placental adherence without histopatholic diagnostic (35%) features.

CONCLUSION

These findings from a quaternary obstetric centre suggest that ICD-10 codes may be useful for research and surveillance of placenta accreta spectrum, but researchers should be aware of likely substantial false positive cases.

Improvements in Neoplasm Classification in the International Classification of Diseases, Eleventh Revision: Systematic Comparative Study With the Chinese Clinical Modification of the International Classification of Diseases, Tenth Revision

BACKGROUND

The International Classification of Diseases, Eleventh Revision (ICD-11) improved neoplasm classification.

OBJECTIVE

We aimed to study the alterations in the ICD-11 compared to the Chinese Clinical Modification of the International Classification of Diseases, Tenth Revision (ICD-10-CCM) for neoplasm classification and to provide evidence supporting the transition to the ICD-11.

METHODS

We downloaded public data files from the World Health Organization and the National Health Commission of the People's Republic of China. The ICD-10-CCM neoplasm codes were manually recoded with the ICD-11 coding tool, and an ICD-10-CCM/ICD-11 mapping table was generated. The existing files and the ICD-10-CCM/ICD-11 mapping table were used to compare the coding, classification, and expression features of neoplasms between the ICD-10-CCM and ICD-11.

RESULTS

The ICD-11 coding structure for neoplasms has dramatically changed. It provides advantages in coding granularity, coding capacity, and expression flexibility. In total, 27.4% (207/755) of ICD-10 codes and 38% (1359/3576) of ICD-10-CCM codes underwent grouping changes, which was a significantly different change (χ21=30.3; P<.001). Notably, 67.8% (2424/3576) of ICD-10-CCM codes could be fully represented by ICD-11 codes. Another 7% (252/3576) could be fully described by uniform resource identifiers. The ICD-11 had a significant difference in expression ability among the 4 ICD-10-CCM groups (χ23=93.7; P<.001), as well as a considerable difference between the changed and unchanged groups (χ21=74.7; P<.001). Expression ability negatively correlated with grouping changes (r=-.144; P<.001). In the ICD-10-CCM/ICD-11 mapping table, 60.5% (2164/3576) of codes were postcoordinated. The top 3 postcoordinated results were specific anatomy (1907/3576, 53.3%), histopathology (201/3576, 5.6%), and alternative severity 2 (70/3576, 2%). The expression ability of postcoordination was not fully reflected.

CONCLUSIONS

The ICD-11 includes many improvements in neoplasm classification, especially the new coding system, improved expression ability, and good semantic interoperability. The transition to the ICD-11 will inevitably bring challenges for clinicians, coders, policy makers and IT technicians, and many preparations will be necessary.

Positive Predictive Value of Cirrhosis and Chronic Pancreatitis Diagnoses in Individuals with Alcohol Use Disorder: A Single-Center Study

BACKGROUND

Although accuracy of diagnosis codes for cirrhosis and chronic pancreatitis (CP) has been evaluated in multiple studies, none have focused on patients with alcohol use disorders (AUD). We evaluated the positive predictive value (PPV) for a verified diagnosis of cirrhosis and CP in AUD patients treated at a tertiary care center.

METHODS

We performed a detailed review of electronic health records for AUD patients assigned ICD-9 or 10 codes for alcoholic cirrhosis (ALC) (n = 199), CP (n = 200), or both (n = 200). We calculated PPV for a verified diagnosis of cirrhosis and CP and performed multivariable regression analysis to assess the impact of relevant factors on PPV for a verified diagnosis.

RESULTS

PPV of cirrhosis was 81.2% (95% CI 77.0 to 84.9%) which increased to 87.5% (95% CI 83.8 to 90.6%) if the definition was relaxed to include alcohol-related hepatitis. PPV of CP was 54.5% (95% CI 49.5 to 59.5%) which increased to 78% (95% CI 73.6 to 82.0%) when recurrent acute pancreatitis was included in the definition. In multivariable analyses, the odds of a verified diagnosis were significantly higher in individuals aged 65+ years for both cirrhosis (OR 12.23, 95% CI 2.19 to 68.42) and CP (OR 8.84, 95% CI 2.7 to 28.93) and in ever smokers for CP (OR 1.95, 95% CI 1.05 to 3.65).

CONCLUSION

PPV for diagnosis codes in AUD patients is high for a verified diagnosis of cirrhosis but only modest for CP. While administrative datasets can provide reliable information for cirrhosis, future studies should focus on ways to boost the diagnostic validity of administrative datasets for CP.

Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities

BACKGROUND AND AIMS

International Classification of Diseases (ICD) diagnosis codes are often used in research to identify patients with opioid use disorder (OUD), but their accuracy for this purpose is not fully evaluated. This study describes application of ICD-10 diagnosis codes for opioid use, dependence and abuse from an electronic health record (EHR) data extraction using data from the clinics' OUD patient registries and clinician/staff EHR entries.

DESIGN

Cross-sectional observational study.

SETTING

Four rural primary care clinics in Washington and Idaho, USA.

PARTICIPANTS

307 patients.

MEASUREMENTS

This study used three data sources from each clinic: (1) a limited dataset extracted from the EHR, (2) a clinic-based registry of patients with OUD and (3) the clinician/staff interface of the EHR (e.g. progress notes, problem list). Data source one included records with six commonly applied ICD-10 codes for opioid use, dependence and abuse: F11.10 (opioid abuse, uncomplicated), F11.20 (opioid dependence, uncomplicated), F11.21 (opioid dependence, in remission), F11.23 (opioid dependence with withdrawal), F11.90 (opioid use, unspecified, uncomplicated) and F11.99 (opioid use, unspecified with unspecified opioid-induced disorder). Care coordinators used data sources two and three to categorize each patient identified in data source one: (1) confirmed OUD diagnosis, (2) may have OUD but no confirmed OUD diagnosis, (3) chronic pain with no evidence of OUD and (4) no evidence for OUD or chronic pain.

FINDINGS

F11.10, F11.21 and F11.99 were applied most frequently to patients who had clinical diagnoses of OUD (64%, 89% and 79%, respectively). F11.20, F11.23 and F11.90 were applied to patients who had a diagnostic mix of OUD and chronic pain without OUD. The four clinics applied codes inconsistently.

CONCLUSIONS

Lack of uniform application of ICD diagnosis codes make it challenging to use diagnosis code data from EHR to identify a research population of persons with opioid use disorder.

Using a severity threshold to improve occupational injury surveillance: Assessment of a severe traumatic injury-based occupational health indicator across the International Classification of Diseases lexicon transition

BACKGROUND

Traumatic injury is a leading cause of death and disability among US workers. Severe injuries are less subject to systematic ascertainment bias related to factors such as reporting barriers, inpatient admission criteria, and workers' compensation coverage. A state-based occupational health indicator (OHI #22) was initiated in 2012 to track work-related severe traumatic injury hospitalizations. After 2015, OHI #22 was reformulated to account for the transition from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) to ICD-10-CM. This study describes rates and trends in OHI #22, alongside corresponding metrics for all work-related hospitalizations.

METHODS

Seventeen states used hospital discharge data to calculate estimates for calendar years 2012-2019. State-panel fixed-effects regression was used to model linear trends in annual work-related hospitalization rates, OHI #22 rates, and the proportion of work-related hospitalizations resulting from severe injuries. Models included calendar year and pre- to post-ICD-10-CM transition.

RESULTS

Work-related hospitalization rates showed a decreasing monotonic trend, with no significant change associated with the ICD-10-CM transition. In contrast, OHI #22 rates showed a monotonic increasing trend from 2012 to 2014, then a significant 50% drop, returning to a near-monotonic increasing trend from 2016 to 2019. On average, OHI #22 accounted for 12.9% of work-related hospitalizations before the ICD-10-CM transition, versus 9.1% post-transition.

CONCLUSIONS

Although hospital discharge data suggest decreasing work-related hospitalizations over time, work-related severe traumatic injury hospitalizations are apparently increasing. OHI #22 contributes meaningfully to state occupational health surveillance efforts by reducing the impact of factors that differentially obscure minor injuries; however, OHI #22 trend estimates must account for the ICD-10-CM transition-associated structural break in 2015.

Incidents and Sudden Patient Deteriorations Occurring During Their Rehabilitation Sessions in an Acute Care Hospital: A Retrospective Cohort Study

Objective

To investigate the occurrence of incidents and sudden deteriorations during rehabilitation in an acute care setting by disease category based on the International Classification of Diseases and Related Health Problems, 10th Revision.

Design

Retrospective cohort study.

Setting

University hospital in Japan with 1376 beds.

Participants

A total of 49,927 patients who were admitted to the acute care wards and underwent rehabilitation over 8 years, from April 1, 2013, to March 31, 2021.

Interventions

Rehabilitation in an acute care setting.

Main Outcome Measures

Incidents and sudden deteriorations reported in medical charts.

Results

Among 49,927 admissions, 455 incidents and 683 sudden deteriorations occurred during rehabilitation. The incidents and sudden deteriorations occurred at rates of 0.009/person (0.50 case/1000 h) and 0.012/person (0.75 case/1000 h), respectively. The 3 most frequent incidents were "route-related incidents" (178 cases, 39.1%), followed by "bleeding/abrasions" (131 cases, 28.8%) and "falls" (125 cases, 27.5%). Among 12 disease categories with over 500 admissions and 10,000 rehabilitation hours, the highest incident rate occurred in "certain infectious and parasitic diseases" (0.81 case/1000 h), followed by "diseases of the musculoskeletal system and connective tissue" (0.67 case/1000 h) and "diseases of the genitourinary system" (0.66 case/1000 h). The commonest sudden deterioration was "vomiting" (460 cases, 67.3%), followed by "decreased level of consciousness (with reduced blood pressure)" (42 cases, 6.1%) and "seizure" (39 cases, 5.7%). Furthermore, the highest sudden deterioration rate was in the "endocrine, nutritional, and metabolic diseases" (1.19 case/1000 h) category, followed by "neoplasms" (1.04 case/1000 h) and "certain infectious and parasitic diseases" (0.99 case/1000 h).

Conclusions

An incident and sudden deterioration occurred every 2000 and 1333 h, respectively, during rehabilitation. Therefore, understanding the actual occurrence of incidents and sudden deteriorations during rehabilitation may provide valuable insights into preventing incidents and emergencies.

Interpreting and coding causal relationships for quality and safety using ICD-11

Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10. An essential part of the causal relationship interpretation relies on the presence of "connecting terms," key elements in assessing the level of certainty regarding a potential relationship and how to proceed in coding a causal relationship using the new ICD-11 coding convention of postcoordination (i.e., clustering of codes). In addition, determining causation involves using documentation from healthcare providers, which is the foundation for coding health information. The coding guidelines and examples (taken from the quality and patient safety domain) presented in this article underline how new ICD-11 features and coding rules will enhance future health information systems and healthcare.

Validation of an ICD-Based Algorithm to Identify Sepsis: A Retrospective Study

Background

Sepsis surveillance was important for resources allocation, prevention, and development of health policy.

Objective

The aim of the study was to validate a modified International Classification of Diseases (ICD)-10 based algorithm for identifying hospitalized patients with sepsis.

Methods

We retrospectively analyzed a prospective, single-center cohort of adult patients who were consecutively admitted to one medical ICU ward and ten non-ICU wards with suspected or confirmed infections during a 6-month period. A modified ICD-10 based algorithm was validated against a reference standard of Sequential Organ Failure Assessment (SOFA) score based on Sepsis-3. Sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and areas under the receiver operating characteristic curves (AUROCs) were calculated for modified ICD-10 criteria, eSOFA criteria, Martin's criteria, and Angus's criteria.

Results

Of the 547 patients in the cohort, 332 (61%) patients met Sepsis-3 criteria and 274 (50%) met modified ICD-10 criteria. In the ICU setting, modified ICD-10 criteria had SE (84.47%), SP (88.57%), PPV (95.60), and NPV (65.96). In non-ICU settings, modified ICD-10 had SE (64.19%), SP (80.00%), PPV (80.33), and NPV (63.72). In the whole cohort, the AUROCs of modified ICD-10 criteria, eSOFA, Angus's criteria, and Martin's criteria were 0.76, 0.75, 0.62, and 0.62, respectively.

Conclusion

This study demonstrated that modified ICD-10 criteria had higher validity compared with Angus's criteria and Martin's criteria. Validity of the modified ICD-10 criteria was similar to eSOFA criteria. Modified ICD-10 algorithm can be used to provide an accurate estimate of population-based sepsis burden of China.

Validation of diagnosis codes for chorioamnionitis in medical encounter data, 2013-2018

PURPOSE

Chorioamnionitis refers to intrauterine infection/inflammation that can be diagnosed clinically or from laboratory testing. This study aimed to validate chorioamnionitis International Classification of Diseases (ICD) codes using reference standards for clinical and histologic cases.

METHODS

Department of Defense Birth and Infant Health Research program data identified a cohort of live deliveries at two United States military hospitals from 2013 to 2018. Deliveries were screened for chorioamnionitis using ICD codes from maternal delivery records; a sample of screen positive and negative deliveries was selected for chart review. Primary analyses validated deliveries using a reference standard for clinical chorioamnionitis; secondary analyses employed a reference standard that also included histologic cases, but were limited by temporal differences in availability of laboratory data. Sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were calculated with 95% confidence intervals (CIs).

RESULTS

Overall, 1857 deliveries (465 screen positive, 1392 screen negative) were eligible for analysis and 336 met the reference standard for clinical chorioamnionitis, yielding a PPV of 0.68 (95% CI 0.63, 0.72) and sensitivity of 0.76 (95% CI 0.72, 0.81). In secondary analyses, 390 deliveries met the reference standard for clinical or histologic chorioamnionitis, resulting in an overall PPV of 0.75 (95% CI 0.71, 0.79); in 2018, when more laboratory results were available, the PPV was 0.91 (95% CI 0.84, 0.97). NPV and specificity were ≥0.97 across reference standards.

CONCLUSIONS

Chorioamnionitis ICD codes exhibited moderate correlation with clinical disease, suggesting challenges in using medical encounter data to isolate clinical cases from those only identified through laboratory testing.

Error codes at autopsy to study potential biases in diagnostic error

OBJECTIVES

Current autopsy practice guidelines do not provide a mechanism to identify potential causes of diagnostic error (DE). We used our autopsy data registry to ask if gender or race were related to the frequency of diagnostic error found at autopsy.

METHODS

Our autopsy reports include International Classification of Diseases (ICD) 9 or ICD 10 diagnostic codes for major diagnoses as well as codes that identify types of error. From 2012 to mid-2015 only 2 codes were used: UNDOC (major undocumented diagnoses) and UNCON (major unconfirmed diagnoses). Major diagnoses contributed to death or would have been treated if known. Since mid-2015, codes included specific diagnoses, i.e. undiagnosed or unconfirmed myocardial infarction, infection, pulmonary thromboembolism, malignancy, or other diagnosis as well as cause of death. Adult autopsy cases from 2012 to 2019 were assessed for DE associated with reported sex or race (nonwhite or white). 528 cases were evaluated between 2012 and 2015 and 699 between 2015 and 2019.

RESULTS

Major DEs were identified at autopsy in 65.9 % of cases from 2012 to 2015 and in 72.1 % from 2015 to 2019. From 2012 to 2015, female autopsy cases showed a greater frequency in 4 parameters of DE, i.e., in the total number of cases with any error (p=0.0001), in the number of cases with UNDOC errors (p=0.0038) or UNCON errors (p=0.0006), and in the relative proportions of total numbers of errors (p=0.0001). From 2015 to 2019 undocumented malignancy was greater among males (p=0.0065); no other sex-related error was identified. In the same period some DE parameters were greater among nonwhite than among white subjects, including unconfirmed cause of death (p=0.035), and proportion of total error diagnoses (p=0.0003), UNCON diagnoses (p=0.0093), and UNDOC diagnoses (p=0.035).

CONCLUSIONS

Coding for DE at autopsy can identify potential effects of biases on diagnostic error.

Evaluation of Neural Machine translation for conversion of International Classification of disease codes to the Abbreviated injury Scale

The Abbreviated Injury Scale (AIS) is an essential tool for injury research since it allows for comparisons of injury severity among patients, however, the International Classification of Diseases (ICD) is more widely used to capture medical information. The problem of conversion between these two medical coding systems has similarities to the challenges encountered in language translation. We therefore hypothesize that neural machine translation (NMT), a deep learning technique which is commonly used for human language translation, could be used to convert ICD codes to AIS. The objective of this study was to compare the accuracy of a NMT model for determining injury severity compared to two established methods of conversion. The injury severity classifications used for this study were Injury Severity Score (ISS) ≥ 16, Maximum AIS severity (MAIS) ≥ 3, and MAIS ≥ 2. Data from a US national trauma registry, which has patient injuries coded in both AIS and ICD, was used to train a NMT model. Testing data from a separate year was used to determine the accuracy of the NMT model predictions against the actual ISS recorded in the registry. The prediction accuracy of the NMT model was compared to that of the official Association for the Advancement of Automotive Medicine (AAAM) ICD-AIS map and the R package 'ICD Program for Injury Categorization in R' (ICDPIC-R). The results show that the NMT model was the most accurate across all injury severity classifications, followed by the ICD-AIS map and then ICDPIC-R package. The NMT model also showed the highest correlation between the predicted and observe ISS scores. Overall, NMT appears to be a promising method for predicting injury severity from ICD codes, however, validation in external databases is needed.

A practical strategy to use the ICD-11 for morbidity coding in the United States without a clinical modification

OBJECTIVE

The aim of this study was to derive and evaluate a practical strategy of replacing ICD-10-CM codes by ICD-11 for morbidity coding in the United States, without the creation of a Clinical Modification.

MATERIALS AND METHODS

A stepwise strategy is described, using first the ICD-11 stem codes from the Mortality and Morbidity Statistics (MMS) linearization, followed by exposing Foundation entities, then adding postcoordination (with existing codes and adding new stem codes if necessary), with creating new stem codes as the last resort. The strategy was evaluated by recoding 2 samples of ICD-10-CM codes comprised of frequently used codes and all codes from the digestive diseases chapter.

RESULTS

Among the 1725 ICD-10-CM codes examined, the cumulative coverage at the stem code, Foundation, and postcoordination levels are 35.2%, 46.5% and 89.4% respectively. 7.1% of codes require new extension codes and 3.5% require new stem codes. Among the new extension codes, severity scale values and anatomy are the most common categories. 5.5% of codes are not one-to-one matches (1 ICD-10-CM code matched to 1 ICD-11 stem code or Foundation entity) which could be potentially challenging.

CONCLUSION

Existing ICD-11 content can achieve full representation of almost 90% of ICD-10-CM codes, provided that postcoordination can be used and the coding guidelines and hierarchical structures of ICD-10-CM and ICD-11 can be harmonized. The various options examined in this study should be carefully considered before embarking on the traditional approach of a full-fledged ICD-11-CM.

A comparison of two registry-based systems for the surveillance of persons hospitalised with COVID-19 in Norway, February 2020 to May 2022

BackgroundThe surveillance of persons hospitalised with COVID-19 has been essential to ensure timely and appropriate public health response. Ideally, surveillance systems should distinguish persons hospitalised with COVID-19 from those hospitalised due to COVID-19.AimWe compared data in two national electronic health registries in Norway to critically appraise and inform the further development of the surveillance of persons hospitalised with COVID-19.MethodWe included hospitalised COVID-19 patients registered in the Norwegian Patient Registry (NPR) or the Norwegian Pandemic Registry (NoPaR) with admission dates between 17 February 2020 and 1 May 2022. We linked patients, identified overlapping hospitalisation periods and described the overlap between the registries. We described the prevalence of International Classification of Diseases (ICD-10) diagnosis codes and their combinations by main cause of admission (clinically assessed as COVID-19 or other), age and time.ResultsIn the study period, 19,486 admissions with laboratory-confirmed COVID-19 were registered in NoPaR and 21,035 with the corresponding ICD-10 code U07.1 in NPR. Up to late 2021, there was a 90-100% overlap between the registries, which thereafter decreased to < 75%. The prevalence of ICD-10 codes varied by reported main cause, age and time.ConclusionChanges in patient cohorts, virus characteristics and the management of COVID-19 patients from late 2021 impacted the registration of patients and coding practices in the registries. Using ICD-10 codes for the surveillance of persons hospitalised due to COVID-19 requires age- and time-specific definitions and ongoing validation to consider temporal changes in patient cohorts and virus characteristics.

Epidemiology Trend of Urogenital System Disorders of Hospitalized Patients in an Eastern Chinese City from 2017 to 2019

OBJECTIVE

To evaluate trends in the prevalence and clinical characteristics of urogenital diseases in hospitalized patients of secondary and tertiary hospitals in Ningbo, an east coast city in China, from 2017 to 2019.

METHODS

We collected the data on hospitalized patients in Ningbo secondary and tertiary hospitals from January 1, 2017 to December 31, 2019. The data included age, sex, and diagnosis identified using the International Classification of Diseases (ICD) codes, which were obtained from Ningbo National Health Information Platform. We quantified the epidemiology (age/sex-specific) trend of urogenital system disorders.

RESULTS

From January 2017 to December 2019, there were 256750 hospitalized patients with urogenital system disorders. These hospitalized patients comprised more women than men (1.45:1.00). The number of hospitalized patients with these diseases significantly increased over the 3 years (77505, 89167, and 90078, respectively; Z = 20.03, p < 0.001). The highest prevalence of these diseases was in the 40- to 64-year-old age group (47.37%), followed by the 18- to 39-year-old age group (23.94%). Over the 3 years, the five most common diseases in hospitalized male patients were male reproductive organ disorders, urolithiasis, tubulointerstitial disease, renal failure, and glomerular disease; Whereas the five most common diseases in hospitalized female patients were non-inflammatory disorders of the female genital tract, benign or dynamic undetermined tumors of the female reproductive organs, disorders of breast (according to ICD-10, disorders of breast (N60-N64) were involved in urogenital system diseases (N00-N99)), inflammatory diseases of female pelvic organs, and renal tubulointerstitial disease. In addition, the number of inpatients with renal tubulointerstitial disease significantly increased from 5952 to 9616 over the 3 years (rank increased from 6 to 3).

CONCLUSIONS

Patients with urogenital system disorders significantly increased over the 3 years, occurring more often in women and most commonly in young and middle-aged people, which warrants more attention in clinical practice.

Comparison of the accuracy of inpatient morbidity coding with ICD-11 and ICD-10

BACKGROUND

One of the challenges when transitioning from International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) to International Classification of Diseases, 11th Revision (ICD-11) is to ensure clinical coding accuracy.

OBJECTIVE

To determine the accuracy of clinical coding with ICD-11 in comparison with ICD-10 and identify causes of coding errors in real clinical coding environments.

METHOD

The study was conducted prospectively in two general hospitals. Medical records of discharged inpatients were coded by hospital clinical coders with both ICD-11 and ICD-10 on different days. These medical records were recoded by five mentors. Codes assigned by mentors were used as the gold standard for the evaluation of accuracy.

RESULTS

The accuracy of ICD-10 and ICD-11 coding for 1578 and 2168 codes was evaluated. Coding accuracy was 89.1% and 74.2% for ICD-10 and ICD-11. In ICD-11, the lowest accuracy was observed in chapters 22 (injuries), 10 (ear) and 11 (circulatory) (51.1%, 53.8% and 62.7%, respectively). In both ICD-10 and ICD-11, the most important cause of the coding errors was clinical coders' mistakes (79.5% and 81.8% for ICD-10 and ICD-11, respectively).

CONCLUSION

Accuracy of clinical coding with ICD-11 was lower relative to ICD-10. Hence, it is essential to carry out initial preparations, particularly the training of clinical coders based on their needs, as well as the necessary interventions to enhance the documentation of medical records according to ICD-11 before or simultaneous with the country-wide implementation.

IMPLICATIONS

Clinical coders need complete training, especially in using extension codes and post-coordination coding. Local ICD-11 guidelines based on the needs of local users and reporting policies should be developed. Furthermore, documentation guidelines based on ICD-11 requirements should be developed.

Operational definitions and measurement of externalizing behavior problems: An integrative review including research models and clinical diagnostic systems

Measurement of externalizing disorders such as antisocial disorders, attention-deficit/hyperactivity disorder or borderline disorder have relevant implications for the daily lives of people with these disorders. While the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD) have provided the diagnostic framework for decades, recent dimensional frameworks question the categorical approach of psychopathology, inherent in traditional nosotaxies. Tests and instruments develop under the DSM or ICD framework preferentially adopt this categorical approach, providing diagnostic labels. In contrast, dimensional measurement instruments provide an individualized profile for the domains that comprise the externalizing spectrum, but are less widely used in practice. Current paper aims to review the operational definitions of externalizing disorders defined under these different frameworks, revise the different measurement alternatives existing, and provide an integrative operational definition. First, an analysis of the operational definition of externalizing disorders among the DSM/ICD diagnostic systems and the recent Hierarchical Taxonomy of Psychopathology (HiTOP) model is carried out. Then, in order to analyze the coverage of operational definitions found, a description of measurement instruments among each conceptualization is provided. Three phases in the development of the ICD and DSM diagnosis systems can be observed with direct implications for measurement. ICD and DSM versions have progressively introduced systematicity, providing more detailed descriptions of diagnostic criteria and categories that ease the measurement instrument development. However, it is questioned whether the DSM/ICD systems adequately modelize externalizing disorders, and therefore their measurement. More recent theoretical approaches, such as the HiTOP model seek to overcome some of the criticism raised towards the classification systems. Nevertheless, several issues concerning this model raise mesasurement challenges. A revision of the instruments underneath each approach shows incomplete coverage of externalizing disorders among the existing instruments. Efforts to bring nosotaxies together with other theoretical models of psychopathology and personality are still needed. The integrative operational definition of externalizing disorders provided may help to gather clinical practice and research.

Sensitivity of ICD coding for sepsis in children-a population-based study

Background

International Classification of Diseases 10th edition (ICD-10) is widely used to describe the burden of disease.

Aim

To describe how well ICD-10 coding captures sepsis in children admitted to the hospital with blood culture-proven bacterial or fungal infection and systemic inflammatory response syndrome.

Methods

Secondary analysis of a population-based, multicenter, prospective cohort study on children with blood culture-proven sepsis of nine tertiary pediatric hospitals in Switzerland. We compared the agreement of validated study data on sepsis criteria with ICD-10 coding abstraction obtained at the participating hospitals.

Results

We analyzed 998 hospital admissions of children with blood culture-proven sepsis. The sensitivity of ICD-10 coding abstraction was 60% (95%-CI 57-63) for sepsis; 35% (95%-CI 31-39) for sepsis with organ dysfunction, using an explicit abstraction strategy; and 65% (95%-CI 61-69) using an implicit abstraction strategy. For septic shock, the sensitivity of ICD-10 coding abstraction was 43% (95%-CI 37-50). Agreement of ICD-10 coding abstraction with validated study data varied by the underlying infection type and disease severity (p < 0.05). The estimated national incidence of sepsis, inferred from ICD-10 coding abstraction, was 12.5 per 100,000 children (95%-CI 11.7-13.5) and 21.0 per 100,000 children (95%-CI 19.8-22.2) using validated study data.

Conclusions

In this population-based study, we found a poor representation of sepsis and sepsis with organ dysfunction by ICD-10 coding abstraction in children with blood culture-proven sepsis when compared against a prospective validated research dataset. Sepsis estimates in children based on ICD-10 coding may thus severely underestimate the true prevalence of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44253-023-00006-1.

Development of a computable phenotype using electronic health records for venous thromboembolism in medical inpatients: the Medical Inpatient Thrombosis and Hemostasis study

Background

Accurate and efficient methods to identify venous thromboembolism (VTE) events in hospitalized people are needed to support large-scale studies. Validated computable phenotypes using a specific combination of discrete, searchable elements in electronic health records to identify VTE and distinguish between hospital-acquired (HA)-VTE and present-on-admission (POA)-VTE would greatly facilitate the study of VTE, obviating the need for chart review.

Objectives

To develop and validate computable phenotypes for POA- and HA-VTE in adults hospitalized for medical reasons.

Methods

The population included admissions to medical services from 2010 to 2019 at an academic medical center. POA-VTE was defined as VTE diagnosed within 24 hours of admission, and HA-VTE as VTE identified more than 24 hours after admission. Using discharge diagnosis codes, present-on-admission flags, imaging procedures, and medication administration records, we iteratively developed computable phenotypes for POA-VTE and HA-VTE. We assessed the performance of the phenotypes using manual chart review and survey methodology.

Results

Among 62,468 admissions, 2693 had any VTE diagnosis code. Using survey methodology, 230 records were reviewed to validate the computable phenotypes. Based on the computable phenotypes, the incidence of POA-VTE was 29.4 per 1000 admissions and that of HA-VTE was 3.6 per 1000 admissions. The POA-VTE computable phenotype had positive predictive value and sensitivity of 88.8% (95% CI, 79.8%-94.0%) and 99.1% (95% CI, 94.0%- 99.8%), respectively. Corresponding values for the HA-VTE computable phenotype were 84.2% (95% CI, 60.8%-94.8%) and 72.3% (95% CI, 40.9%-90.8%).

Conclusion

We developed computable phenotypes for HA-VTE and POA-VTE with adequate positive predictive value and sensitivity. This phenotype can be used in electronic health record data-based research.

[Aspects of psychosomatics and psychotraumatology in refugees : Challenges for internal medicine]

Many people seek protection from war and violence in Germany. Flight and displacement often have an impact on mental health. The situation in the country of arrival, separation, grief and loss, and worries about the future can be additional burdens. The barriers to adequate healthcare are high, a visit to the doctor for somatoform complaints is sometimes less alien than talking about fears and hopelessness. The medical internistic encounter thus plays an important key role in recognizing and adequately assessing psychological symptoms without pathologizing. A brief overview of innovations in the International Classification of Diseases and Related Health Problems 11th edition (ICD-11) on stress-related disorders aims to facilitate the assignment. Information on screening, trauma-informed interviewing and interpreter-assisted communication complement the recommendations of the current guidelines.